Copyright TechTarget - All rights reserved https://cyber.law.harvard.edu/rss/rss.html Techtarget Feed Generator en Thu, 19 Feb 2026 05:19:59 GMT https://www.techtarget.com/searchdatamanagement editor@techtarget.com <p>Uncertainty is no longer episodic but a persistent feature of the operating environment for businesses, as market volatility, geopolitical risk, regulatory changes, demographic shifts and climate pressures interact in unexpected, non-linear ways.</p> <p>These forces expose the blind spots of traditional forecasting approaches, which might be data-driven but rely primarily on extrapolating from the past, requiring executives to enhance their forecasting and analytical techniques.</p> <p>Synthetic data and simulation forecasting can bridge this gap by overcoming structural data limitations such as data silos, privacy regulations, data bias, and the high cost and long lead times of data acquisition.</p> <section class="section main-article-chapter" data-menu-title="How synthetic data and simulation forecasting work"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How synthetic data and simulation forecasting work</h2> <p>Synthetic data is <a href="https://www.techtarget.com/searchenterpriseai/tip/How-and-why-to-create-synthetic-data-with-generative-AI">artificially generated data</a> that mirrors the statistical properties and constraints of real-world data without reproducing actual individuals, transactions or events. Unlike anonymized data, it reflects macro-level patterns while being privacy-safe at the individual level. Enterprises generate synthetic data using rule-based models and generative techniques, validating outputs against business rules and consistency checks so datasets remain realistic.</p> <p>Simulation forecasting uses computational models to test how complex systems behave under different assumptions, inputs and shocks. Techniques <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Types-of-simulation-models-used-in-data-analytics">such as Monte Carlo methods</a>, agent-based modeling, discrete-event simulation and system dynamics enable enterprises to evaluate outcomes across thousands of possible scenarios rather than relying on a single projection.</p> <p>Together, synthetic data expands the range of conditions available for analysis, while simulations model how those conditions affect performance, risk and strategic outcomes.</p> </section> <section class="section main-article-chapter" data-menu-title="Why synthetic data and simulation matter for executives"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Why synthetic data and simulation matter for executives</h2> <p>From an executive standpoint, synthetic data and simulation forecasting add a critical analytic layer. Real data often underrepresents rare events and extreme outcomes, while <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Synthetic-data-vs-real-data-for-predictive-analytics">synthetic data allows executives to rebalance forecasts</a> by design, reflecting a wider spectrum of scenarios rather than an extension of the past.</p> <p>Synthetic data also reduces regulatory friction and bottlenecks associated with sensitive user data while also enabling richer scenario planning. Because simulation forecasts produce distributions rather than single-number estimates, they bring tail risks and downside exposure that might otherwise remain invisible.</p> <p>Working in tandem, synthetic data and simulation enable enterprises to explore low-probability, high-stakes scenarios in a virtual environment and plan proactively for crisis conditions, functioning as a form of strategic business continuity planning and a good example of AI-augmented executive decision-making.</p> </section> <section class="section main-article-chapter" data-menu-title="Four enterprises benefits of synthetic data and simulation forecasting"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Four enterprises benefits of synthetic data and simulation forecasting</h2> <p>With real data as an anchor, enterprises use synthetic data to expand analytic coverage and simulation models to test system behavior under uncertainty.</p> <p>Enterprise strategy teams establish baseline realism using available data, then enrich it by correcting imbalances, filling gaps and injecting extreme scenarios. Simulation models evaluate how outcomes respond to differing assumptions, policies and shocks. Because new markets, products and regulatory shifts often lack precedents, executives must reason forward and make higher-quality decisions rather than rely solely on historical patterns.</p> <h3>Improved enterprise resiliency</h3> <p>Forecast accuracy improves when models are exposed to the full range of conditions they might encounter rather than only the most common ones. Synthetic data expands the training and testing space beyond what real data naturally provides.</p> <p>Instead of relying on a narrow <a href="https://www.techtarget.com/whatis/definition/validation-set">validation set</a>, simulation runs models across thousands of possible scenarios, revealing where forecasts are robust and where they fail. This enables teams to address weaknesses before deployment and reduces catastrophic forecast failures, even if average accuracy improves only slightly.</p> <h3>Reduced data costs</h3> <p>Real data collection is expensive and time-consuming, whereas synthetic data scales at near-zero marginal cost once generation pipelines are established. It also reduces time-to-insight by enabling faster iteration, enabling organizations to shorten model development timelines and work around data-access constraints.</p> <h3>Enhanced decision quality under uncertainty</h3> <p>Executives can draw on rehearsed playbooks rather than improvising during a crisis. Leaders gain clarity on tipping points, such as the demand threshold before profitability disappears, the operating conditions that trigger liquidity stress, or the inventory buffer required for a demand surge. Decision-making becomes more calibrated under uncertainty.</p> <h3>Better risk management</h3> <p>Synthetic data enables stress scenarios that go beyond historical crises to include correlated failures, simultaneous shocks and extreme but plausible conditions that have never occurred together in reality. Simulation propagates these shocks through financial, operational and behavioral systems to expose systemic weakness. Stress testing becomes a strategic risk mitigation instrument rather than a compliance exercise.</p> </section> <section class="section main-article-chapter" data-menu-title="Use cases of synthetic data and simulations"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Use cases of synthetic data and simulations</h2> <p>Synthetic data and simulation forecasting are already embedded in enterprise strategy across highly regulated and operationally complex industries. The following examples illustrate how organizations apply these techniques to strengthen forecasting, risk management and planning.</p> <h3>Finance</h3> <p>In financial services, synthetic transaction data trains fraud and anti-money-laundering models without exposing sensitive customer information, and improving detection rates while reducing false positives by oversampling rare fraud patterns.</p> <p>Synthetic borrower populations enable credit risk testing for systematic bias and fairness checks across demographic groups and economic conditions without relying on real individuals' data, supporting both regulatory compliance and ethical risk management.</p> <p>Simulation forecasting underpins liquidity and capital stress testing. Banks use scenario-based simulations to model balance sheet resilience under extreme conditions, including correlated defaults and rapid withdrawals. These simulations are required by regulators, but they also inform internal capital allocation decisions.</p> <h3>Healthcare</h3> <p>Healthcare faces stringent <a href="https://www.techtarget.com/healthtechanalytics/feature/Weighing-the-pros-and-cons-of-synthetic-healthcare-data-use">privacy regulations and ethical constraints</a> that limit access to patient data. Synthetic electronic health records <a href="https://www.techtarget.com/healthtechanalytics/feature/High-value-use-cases-for-synthetic-data-in-healthcare">enable analytics and AI development</a> while protecting confidentiality, enabling hospitals and researchers to train predictive models and explore rare disease trajectories. Rebalancing underrepresented populations in synthetic datasets improves model equity across demographic groups.</p> <p>Simulation forecasting also supports operational planning through patient-flow modeling, helping hospitals anticipate capacity bottlenecks, staffing needs and surge response for seasonal outbreaks or pandemics.</p> <h3>Logistics and supply chain management</h3> <p>In logistics and supply chains, small disruptions can propagate rapidly across networks. &nbsp;Synthetic demand data enables forecasting models to learn from volatility that has not yet occurred, such as sudden spikes, collapses or regional shifts.</p> <p>Simulation is extensively used to model transportation networks, warehouse operations and inventory policies. This enables enterprises to simulate disruptions such as port closures, supplier failures or labor shortages to understand recovery dynamics and identify choke points. These insights guide resilience investments, such as multi-sourcing and strategic inventory buffers.</p> <p>Another important use case is digital twins, which combine real-time operational data, synthetic scenario generation and simulation forecasting. This enables real-time planning, since potential disruptions are identified and addressed before they occur.</p> </section> <section class="section main-article-chapter" data-menu-title="Risks, limitations and governance considerations"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Risks, limitations and governance considerations</h2> <p>Synthetic data and simulation are powerful, but they also have their limitations. If synthetic data is repeatedly generated without recalibrating against real-world data, it can gradually <a href="https://www.techtarget.com/healthtechanalytics/news/366590158/Predictive-Models-May-Negatively-Impact-the-Performance-of-Future-Tools">diverge from reality</a>. This fidelity drift introduces subtle distortions that can compound over time, leading models to appear accurate in testing but underperform in production.</p> <p>Simulation models are vulnerable to misspecification because they encode assumptions about system behavior. Omitted variables or incorrect causal relationships can produce misleading results, and this risk increases as models grow more complex and harder to validate.</p> <p>Because synthetic data and simulation influence high-stakes decisions, governance must be rigorous and continuous. Synthetic datasets should be evaluated for statistical fidelity, semantic coherence, and privacy leakage before deployment. Simulation models should undergo calibration tests, sensitivity analysis and periodic review to ensure assumptions remain valid as the business environment changes. These are not static assets; they require regular updates as underlying real-world conditions continuously evolve.</p> </section> Synthetic data and simulation forecasting help executives overcome data constraints, test scenarios and strengthen strategic decision-making under uncertainty. https://cdn.ttgtmedia.com/rms/onlineimages/arvr_a305628750.jpg https://www.techtarget.com/searchbusinessanalytics/feature/Improving-business-forecasting-with-synthetic-data-and-simulation-modeling Wed, 18 Feb 2026 13:19:00 GMT <p>With its latest Analyst Studio update, ThoughtSpot continues its progress toward becoming an agentic platform.</p> <p>First <a href="https://www.techtarget.com/searchbusinessanalytics/news/366617947/ThoughtSpot-adds-data-preparation-with-Analyst-Studio-launch">released in January 2025</a>, Analyst Studio is ThoughtSpot's <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Top-data-preparation-challenges-and-how-to-overcome-them">data preparation</a> suite. Initial features included connectors that enable analysts and engineers to combine data from disparate sources, an AI-assisted <a href="https://www.techtarget.com/searchdatamanagement/definition/SQL">SQL</a>-based development environment, and capabilities aimed at helping customers control data management costs.</p> <p>Among other tools, the update, released on Wednesday, adds SpotCache, a caching capability that builds on Analyst Studio's pre-existing cost management capabilities, a data preparation agent that enables users to perform tasks using natural language and a native spreadsheet interface for scaling data preparation workloads.</p> <p>Coming just over two months after ThoughtSpot unveiled plans <a href="https://www.techtarget.com/searchbusinessanalytics/news/366636078/ThoughtSpot-automates-full-platform-with-new-Spotter-agents">automate its analytics capabilities</a> with agents, the new version of Analyst Studio represents ThoughtSpot's progression beyond agentic analytics toward agentic data management as well, according to Donald Farmer, founder and principal of TreeHive Strategy.</p> <p>"I don't think this release is a big deal in itself, but it steadily moves ThoughtSpot forward on the path to an agentic data platform," he said. "With each release, the workflow is less dashboard-centric."</p> <p>Michael Ni, an analyst at Constellation Research, similarly noted that the Analyst Studio is significant because it shows ThoughtSpot moving beyond its roots as an analytics specialist toward becoming a more broad-based data and analytics provider.</p> <p>"Thoughtspot addresses a key pain by reducing prep friction, improving cost predictability and tightening governance," he said. "At the same time, it's strategic for ThoughtSpot. The expansion upstream into data prep and cost control -- areas traditionally owned by hyperscalers and transformation tools -- moves [ThoughtSpot] toward becoming an AI workload optimizer. That's where enterprise dollars are moving."</p> <p>Based in Mountain View, Calif., ThoughtSpot provided an AI-powered analytics platform from its inception in 2012. Now, just as peers such as <a href="https://www.techtarget.com/searchbusinessanalytics/news/252493644/Data-prep-in-browser-highlights-Tableau-BI-platform-update">Tableau</a> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366626961/Qlik-adds-trust-score-to-aid-data-prep-for-AI-development">Qlik</a> did before, ThoughtSpot is expanding beyond its roots to provide a wider array of data, analytics and AI development capabilities.</p> <section class="section main-article-chapter" data-menu-title="Predictable data prep"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Predictable data prep</h2> <p>While some enterprises are <a target="_blank" href="https://www.ey.com/en_us/newsroom/2025/07/ai-investments-surge-but-agentic-ai-understanding-and-adoption-lag-behind" rel="noopener">investing heavily</a> in building AI tools that make employees better informed and operations more efficient, the expense required to develop and maintain agents, chatbots and other AI applications has <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">proven prohibitive</a> for many others.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The expansion upstream into data prep and cost control -- areas traditionally owned by hyperscalers and transformation tools -- moves [ThoughtSpot] toward becoming an AI workload optimizer. That's where enterprise dollars are moving. </figure> <figcaption> <strong>Michael Ni</strong>Analyst, Constellation Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>AWS recently made cost control one of the focal points of <a href="https://www.techtarget.com/searchdatamanagement/news/366635663/Latest-AWS-data-management-features-target-cost-control">the data management capabilities</a> it introduced during its annual re:Invent conference. In addition, numerous database vendors have made performance a priority so that customers can run more efficient workloads.</p> <p>With SpotCache now available in Analyst Studio, ThoughtSpot is similarly taking aim at helping customers reduce spending on part of the development process.</p> <p>Caching is the process of storing data in a temporary storage area -- a cache -- to enable fast access that improves the performance of applications and other systems. Using SpotCache, developers and analysts can create representations of data that can be queried an unlimited number of times in ThoughtSpot, which lowers costs by reducing the frequency data must be accessed in <a href="https://www.techtarget.com/searchdatamanagement/feature/Evaluate-cloud-data-warehouses-based-on-data-outcomes">cloud data warehouses</a>.&nbsp;</p> <p>Given that SpotCache addresses one of the problems enterprises encounter when trying to develop cutting-edge AI tools, it is perhaps the most valuable new feature in Analyst Studio, according to Ni.</p> <p>"SpotCache is the sleeper hit in their announcement," he said. "While agentic data prep is powerful, cost certainty is what unlocks enterprise scale. If leaders know they can run unlimited AI-driven queries without blowing up their warehouse bill, adoption accelerates. That's what makes this the most strategically significant feature."</p> <p>Farmer likewise highlighted SpotCache, noting that cloud cost control -- or lack thereof -- has been <a href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage">an ongoing problem</a>.</p> <p>"I like that they are tackling the problem of scaling with predictable cost management," he said. "That has been a barrier to broader adoption for some time. So, SpotCache stands out as arguably the most valuable new feature here."</p> <p>Beyond SpotCache, ThoughtSpot's Analyst Studio update includes the following features:</p> <ul type="disc" class="default-list"> <li>A governed spreadsheet interface so that users familiar with Excel worksheets can perform data preparation tasks such as advanced manipulations in a familiar environment without having to leave Analyst Studio.</li> <li>A data prep agent that enables analysts to profile datasets, generate queries and troubleshoot schemas using natural language.</li> <li>Unified Data Mashup, a feature that enables data teams to deliver a unified, <a href="https://www.techtarget.com/searchdatamanagement/opinion/Trusted-data-is-the-foundation-of-data-driven-decisions-GenAI">trusted view</a> of their organization's business by blending data across cloud data warehouses, business applications and files such as Google Sheets and Microsoft Excel spreadsheets within Analyst Studio's SQL-based development environment.</li> <li>Flexibility to choose live connections for real-time needs or cached snapshots through SpotCache.</li> </ul> <p>Like Farmer and Ni, Anjali Kumari, ThoughtSpot's vice president of product management, named SpotCache the most valuable of Analyst Studio's new features. Meanwhile, she noted that the impetus for designing the new capabilities came from observing how agentic AI and generative AI have <a href="https://www.techtarget.com/searchbusinessanalytics/news/366618249/Trusted-data-at-the-core-of-successful-GenAI-adoption">intensified the need for data</a>.</p> <p>"Getting data ready for AI has become the most important step in how organizations use, benefit and see valuable return on investment," Kumari said. "We understand the pressures that are placed on data analysts, and these tools are designed to streamline their role by addressing their top concerns -- speed, efficiency and cost."</p> <p>While ThoughtSpot's expansion beyond business intelligence into data preparation with Analyst Studio is beneficial for the vendor's users, pairing analytics and data management is not unique. Not only do hyperscale cloud providers such as AWS, Google and Microsoft offer an array of data management, application development and analytics tools, but so do <a href="https://www.techtarget.com/searchbusinessanalytics/news/366637892/Domo-adds-App-Catalyst-to-platform-to-aid-AI-development">Domo</a>, Qlik, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366553555/Sisense-unveils-composable-toolkit-for-app-development">Sisense</a>, Tableau and other one-time analytics specialists.</p> <p>However, the launch of a data prep agent is unique, according to Farmer.</p> <p>"The biggest differentiator from other prep tools, such as Tableau or [Microsoft's] Power Query, is that ThoughtSpot offers a natural language data prep agent whereas AI in other tools is mostly limited to 'smart suggestions' or separate AI copilots," he said.</p> <p>In addition, by integrating data preparation, data modeling and analytics in a single workflow designed for consumption via AI applications, ThoughtSpot is doing something different than its <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Top-cloud-based-analytics-tools-for-enterprise-use">closest competition</a>, according to Ni.</p> <p>"Data prep is consolidating into platforms, and every business intelligence vendor has some version of it. What's interesting here is that ThoughtSpot is making data prep part of an agentic operating model," he said. "Instead of separate tooling for prep, modeling and analysis, they're collapsing it into one workflow designed for AI readiness."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With the Analyst Studio update generally available, ThoughtSpot is focused on turning its platform into an enabler of autonomous action, according to Kumari.</p> <p>Spotter is <a href="https://www.techtarget.com/searchbusinessanalytics/news/366615693/ThoughtSpot-AI-agent-Spotter-enables-conversational-BI">ThoughtSpot's agent-powered interface</a>, and the vendor provides Spotter agents for specific tasks such as building dashboards and embedding intelligence. Together, with Spotter as the central orchestrator, ThoughtSpot aims to automate analysis and data preparation to deliver insights within user workflows.</p> <p>"To power this, we are investing heavily in data readiness and expanding our semantic and modeling capabilities, ensuring these agents operate on a robust, context-aware foundation," Kumari said.</p> <p>As ThoughtSpot expands beyond its roots, Farmer advised the vendor to add <a href="https://www.techtarget.com/searchcustomerexperience/news/366636690/Agentic-orchestration-the-next-AI-issue-for-CIOs-to-tackle">multi-agent coordination capabilities</a>. ThoughtSpot's <a target="_blank" href="https://modelcontextprotocol.io/docs/getting-started/intro" rel="noopener">Model Context Protocol</a> server enables agents to securely interact with data sources. Soon, as more enterprises deploy agents, the agents will need similar secure connections to each other to become fully autonomous.</p> <p>"Multi-agent coordination [is] moving from a single prep agent to a 'hive' where a prep agent automatically communicates with a security agent to apply row-level permissions during the transformation process," Farmer said.</p> <p>In addition, ThoughtSpot could add <a href="https://www.techtarget.com/whatis/definition/write-back">write-back</a> capabilities and add to its burgeoning support for unstructured data by integrating unstructured data processing directly into Analyst Studio.</p> <p>Ni, meanwhile, suggested that ThoughtSpot move beyond descriptive BI to incorporate more forward-looking <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Descriptive-vs-prescriptive-vs-predictive-analytics-explained">predictive and prescriptive capabilities</a>.</p> <p>"Descriptive BI is yesterday, diagnostic AI is today, and predictive and prescriptive intelligence that tell me what could happen and where I should focus is tomorrow," he said "ThoughtSpot is strong at explaining the past and present. … Their next leap is forecasting impact and prioritizing what matters next."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> A data prep agent and caching capabilities aimed at helping users control spending help the vendor stand out from its peers as it evolves toward becoming an agentic data platform. https://cdn.ttgtmedia.com/rms/onlineimages/disaster_recovery_a379640336.jpg https://www.techtarget.com/searchbusinessanalytics/news/366639258/ThoughtSpot-boosts-agentic-push-with-Analyst-Studio-update Wed, 18 Feb 2026 09:00:00 GMT <p>Streaming data specialist Redpanda on Wednesday launched new features in its Agentic Data Plane aimed at enabling customers to create a unified governance layer for managing connections between agents and data sources.</p> <p>Redpanda <a href="https://www.techtarget.com/searchdatamanagement/news/366633563/Streaming-vendor-Redpanda-buys-SQL-engine-unveils-AI-suite">first launched the Agentic Data Plane</a> (ADP) in October 2025 featuring capabilities that enabled connectivity between agents and streaming data sources, including support for <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">the Model Context Protocol</a>&nbsp;(MCP) and&nbsp;<a href="https://www.techtarget.com/searchenterpriseai/news/366622027/Google-intros-tools-for-building-agents-and-a-new-protocol">Agent2Agent Protocol&nbsp;</a>(A2A) frameworks.</p> <p>However, capabilities that govern those connections were not yet ready.</p> <p>Now, the vendor is adding features such as AI Gateway to provide users a centralized governance pane, AI observability via the <a target="_blank" href="https://opentelemetry.io/docs/specs/otel/protocol/" rel="noopener">OpenTelemetry Protocol</a> (OTLP) to inspect and monitor agent behavior and new security controls.</p> <p>Given that agents and multi-agent systems require proper governance frameworks to ensure that they act in accordance with an enterprise's policies and meet regulatory requirements, the new ADP features are significant for Redpanda customers, according to William McKnight, president of McKnight Consulting.</p> <p>"The ADP has the potential to transform Redpanda from a simple streaming engine into a centralized governance layer for enterprise AI," he said. "The update addresses critical barriers by providing unified security and operational control over AI costs and token budgets. This update enables 'glass box' visibility and framework flexibility, allowing users to move … from risky experimentation to secure production."</p> <p>Based in San Francisco, Redpanda provides <a href="https://www.techtarget.com/searchdatamanagement/news/366574612/Redpanda-serverless-streaming-option-targets-cost-control">a streaming data platform</a> that enables users to capture and process data to fuel real-time analysis. Like many data management providers, the vendor has responded to <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">increasing interest in AI development</a> and added tools that let customers connect data to agents and other AI applications in addition to traditional data products such as dashboards and reports.</p> <section class="section main-article-chapter" data-menu-title="Keeping control"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Keeping control</h2> <p>Agents need governance.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The ADP has the potential to transform Redpanda from a simple streaming engine into a centralized governance layer for enterprise AI. </figure> <figcaption> <strong>William McKnight</strong>President, McKnight Consulting </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Unlike chatbots and other AI applications, agents are <a href="https://www.techtarget.com/searchenterpriseai/tip/A-technical-guide-to-agentic-AI-workflows">capable of autonomous behavior</a>. Rather than requiring user prompts before taking action, they can be trained to work independently.</p> <p>For example, they can constantly analyze an enterprise's data estate to surface insights that a human analyst might never have discovered. They can take on repetitive, menial work so that employees can be more efficient and spend time doing more meaningful work. And they can work together to optimize complex processes such as managing supply chains.</p> <p>Because they can make entire organizations better informed and more efficient, agents have been <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Agents-semantic-layers-among-top-data-analytics-trends">the dominant trend</a> in AI development over the past two years. But because just one agent taking the wrong action can <a target="_blank" href="https://hackernoon.com/22-examples-of-incompetent-ai-agents" rel="noopener">cause significant harm</a>, strict policies and procedures must be &nbsp;in place to ensure that agents can be trusted when put into production.</p> <p>With most AI initiatives <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">never making it past the pilot stage</a>, a mix of customer feedback and Redpanda's own experiences developing agents provided the impetus for adding new ADP features designed to engender trust that agents will act as intended once deployed, according to Tyler Akidau, the streaming data vendor's chief technology officer.</p> <p>"Our roadmap has been developed in response to direct customer feedback, our own experiences developing and deploying agents internally and our vision for what is needed to unlock agentic AI in the enterprise," he said.</p> <p>AI Gateway acts as a unified access layer for connecting agents with AI models and MCP servers by centralizing routing data, enforcing organizational policies, limiting <a href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage">spending that can spiral</a> when cloud usage isn't controlled and enabling observability of AI systems.</p> <p>Observability includes automatically generated metrics, traces, logs and transcripts using the OTLP Protocol so that users can check agent behavior in their Redpanda console and take appropriate action such as debugging when necessary.</p> <p>In addition, the ADP now includes security through <a href="https://www.techtarget.com/searchsecurity/feature/How-to-use-OpenID-Connect-for-authentication">the OpenID Connect standard</a> and fine-grained authorization policies so that every interaction with an agent, whether by a human user or another agent, is properly checked and governed.</p> <p>Meanwhile, the ADP is designed to work in conjunction with <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms">any agentic framework</a> so that customers can easily run and govern agents fueled by Redpanda's streaming data capabilities.</p> <p>Collectively, the new ADP features are valuable for Redpanda customers given that they address numerous concerns and that they add governance to data that fuels real-time analysis, according to Kevin Petrie, an analyst at BARC U.S.</p> <p>"This announcement … is comprehensive," he said." Redpanda's platform addresses data and AI governance, observability and even FinOps objectives. That's a broader set of capabilities than most platforms have. The announcement [also] stands out because Redpanda is building these capabilities onto a data streaming platform rather than a standard data-at-rest platform."&nbsp;</p> <p>Perhaps the most significant of the new ADP features is AI Gateway, according to McKnight. Meanwhile, from a competitive standpoint, Redpanda's new governance capabilities could help differentiate the vendor's capabilities from competing streaming data platforms <a href="https://www.techtarget.com/searchdatamanagement/news/366633567/Streaming-specialist-Confluent-unveils-AI-development-suite">such as Confluent</a>, which is the industry standard for commercial Apache Kafka and is now under agreement to be <a href="https://www.techtarget.com/searchdatamanagement/news/366636098/IBM-acquiring-Confluent-to-boost-AI-development-capabilities">acquired by IBM</a>, he continued.</p> <p>Redpanda's streaming capabilities are comparable to Confluent's, outperforming Confluent in some benchmark testing, McKnight noted. But AI governance is where Redpanda could truly stand apart.</p> <p>"Redpanda is starting to differentiate by shifting from 'data piping' to a dedicated AI governance infrastructure," McKnight said. "Unlike standard tools that require fragmented security at every source, its AI Gateway will provide a centralized control plane for managing policies, token budgets and Model Context Protocol servers."</p> <p>Petrie similarly suggested that the breadth of the new ADP features help Redpanda distinguish itself from competitors, which include AWS, Google Cloud and Microsoft, beyond <a href="https://www.techtarget.com/searchbusinessanalytics/news/252529637/Data-streaming-platforms-fuel-for-agile-decision-making">streaming data specialists</a>.</p> <p>"This announcement strengthens its competitive standing by integrating so many capabilities into one streaming solution," he said. "To get the same features from the larger vendors, you would need to buy multiple products. Redpanda also has the advantage of platform neutrality -- it operates across sources, systems and clouds."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>Following the initial launch of the ADP last October, Redpanda's new governance features represent the second phase of the streaming data vendor's ADP rollout. With an overarching goal of helping customers deploy agentic AI across their organization, more features that enable users to <a href="https://www.techtarget.com/searchenterpriseai/opinion/Building-governance-for-machine-speed-The-path-to-trusted-AI-autonomy">trust and deploy agents</a> are a prominent part of Redpanda's roadmap, according to Akidau.</p> <p>"We'll be delivering additional features in our AI and MCP gateways, rolling out more agent evaluation functionality, delivering manual and automatic agent kill switches, connecting agents to more data sources, and rolling out [a SQL engine] for federated query support," he said.</p> <p>One way that Redpanda could better serve its current users and perhaps attract new ones would be to market AI Gateway as a tool for <a href="https://www.techtarget.com/searcherp/feature/Predictability-emerging-as-enterprise-ITs-new-north-star">financial governance</a> in addition to technical governance, according to McKnight.</p> <p>"They could also solidify the 'real-time' advantage by demonstrating that [its] low-latency foundation creates better agents, not just faster data," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> New Agentic Data Plane features enable users to create a governance layer for agents and could help the vendor differentiate itself from its closest competitors. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a373894778.jpg https://www.techtarget.com/searchdatamanagement/news/366639150/Streaming-specialist-Redpanda-adds-governance-to-AI-suite Wed, 18 Feb 2026 09:00:00 GMT <p>The AI industry has spent years pushing larger models, assuming scale alone would unlock breakthrough capabilities. The real competitive advantage in AI isn't who can build the largest model; it's who has the best data.</p> <p>This <a href="https://www.techtarget.com/searchenterpriseai/tip/Beyond-algorithms-The-rise-of-data-centric-AI">shift toward data-centric AI</a> represents a fundamental change in how organizations approach AI investments. Instead of focusing primarily on model architectures and parameter counts, the focus is shifting to what actually makes AI systems work: <a href="https://www.techtarget.com/searchdatamanagement/opinion/Data-quality-fuels-analytics-AI">high-quality, well-organized and representative data</a>.</p> <p>Many organizations have been slow to adopt this perspective, but it's becoming harder to ignore.</p> <section class="section main-article-chapter" data-menu-title="Model scale is not the source of AI's advantage"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Model scale is not the source of AI's advantage</h2> <p>The reason is straightforward. Poor data quality produces unreliable AI, regardless of how sophisticated the underlying model may be. This pattern appears repeatedly in systems that perform well in testing but struggle with real-world scenarios. These failures often trace back to data issues, including incomplete datasets, inconsistent labeling or training data that doesn't reflect actual use cases.</p> <p>This matters particularly in <a href="https://www.techtarget.com/healthtechanalytics/feature/How-healthcare-organizations-can-prioritize-AI-governance">high-stakes applications</a>. In healthcare, financial services and other regulated industries, AI errors aren't just inconvenient; they carry material consequences. As AI moves from experimental projects to core business processes, tolerance for unreliable systems continues to shrink. Regulators are scrutinizing not only AI outputs but also the data and processes behind them. Organizations that treat data quality as an afterthought are increasing their exposure to regulatory and operational risk.</p> <p>The limitations of model-centric thinking are becoming clearer. Large language models generate impressive text, but also produce hallucinations and perpetuate biases present in their training data. Recommendation systems drive engagement yet may amplify problematic content when the underlying data is poorly curated. As AI becomes embedded in the decision-making process, demand for explainability and accountability increases -- and that requires rigorous oversight of data.</p> </section> <section class="section main-article-chapter" data-menu-title="Enterprise risk and competitive implications"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Enterprise risk and competitive implications</h2> <p>From a strategic standpoint, data-centric AI offers a more sustainable path forward. Building ever-larger models requires enormous computational resources that few organizations can afford. Improving data quality, by contrast, is <a href="https://www.techtarget.com/searchdatamanagement/feature/Proactive-practices-for-data-quality-improvement">achievable for organizations of any size</a>. It requires discipline and investment, but not access to massive compute infrastructure.</p> <p>Organizations embracing this approach are seeing tangible results. Better data leads to more accurate models, faster development cycles and AI systems that deliver value in production. It also creates a defensible competitive advantage as proprietary, high-quality datasets are difficult for competitors to replicate.</p> </section> <section class="section main-article-chapter" data-menu-title="Operationalizing a data-centric strategy"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Operationalizing a data-centric strategy</h2> <p>The path forward requires rethinking priorities. Organizations should begin by auditing existing data. Where are the gaps? What biases exist? How well does training data reflect real-world scenarios? These questions often reveal uncomfortable truths, but they're essential starting points.</p> <p>Data infrastructure investment warrants the same attention traditionally shown to model development. This includes tools for data labeling and validation, development of data products, formal processes to <a href="https://www.techtarget.com/searchdatamanagement/tip/6-dimensions-of-data-quality-boost-data-performance">maintain data quality</a> over time and teams with both technical and domain-specific knowledge of the data.</p> <p>Most importantly, it requires cultural change. Data work needs to be treated as a strategic function rather than a preprocessing task. Organizations that recognize data as a core asset and invest accordingly will be positioned to build AI systems that are more capable, trustworthy and sustainable over the long term.</p> <p><em>Stephen Catanzano is a senior analyst at Omdia where he covers data management and analytics.</em></p> <p>&nbsp;</p> <p><em>Omdia is a division of&nbsp;Informa TechTarget.&nbsp;Its analysts have business relationships with technology vendors.</em></p> </section> AI's competitive advantage is shifting from model scale to data quality. Organizations that invest in governance and infrastructure build more reliable, defensible systems. https://cdn.ttgtmedia.com/visuals/digdeeper/2.jpg https://www.techtarget.com/searchdatamanagement/opinion/The-future-of-AI-depends-on-better-data-not-bigger-models Tue, 17 Feb 2026 14:38:00 GMT <p>With retrieval-augmented generation pipelines struggling to deliver the relevant data that agents and other AI applications need to deliver trustworthy outputs, Graphwise launched GraphRAG to provide customers with an alternative designed to enable more successful AI development.</p> <p>Retrieval-augmented generation (<a href="https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation">RAG</a>) is a framework for connecting applications such as agents and chatbots with data sources. However, with most AI initiatives failing to make it past the pilot stage and into production, standard RAG pipelines haven't proven good enough on their own to enable enterprises to deliver usable, trustworthy AI tools.</p> <p>In January, <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">Databricks launched Instructed Retriever</a>, an alternative to RAG that adds more context to data retrieval such as user instructions and previous examples. &nbsp;Traditional RAG systems only use a user's query.</p> <p>Graphwise's GraphRAG, which was released on Feb. 16, unites agents and other applications with a <a href="https://www.techtarget.com/searchenterpriseai/definition/knowledge-graph-in-ML">knowledge graph</a> that acts as a semantic layer and is similarly aimed at improving on standard RAG pipelines.</p> <p>Alan Morrison, an independent analyst, noted that while knowledge graphs can be traced back to the 1960s, they are taking on greater importance because agents need the context knowledge graphs provide to perform to enterprise standards. As a result, GraphRAG is a significant addition for Graphwise users.</p> <p>"Graphwise can bring all enterprise data, content and knowledge together using standard-based graph description logic that's been around for decades, but only now becoming indispensable because the agent paradigm is here, and agents desperately need reliable context," he said. "With GraphRAG, tapping the power of that contextualized data becomes simpler."</p> <p>Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget, likewise noted that GraphRAG is valuable for Graphwise users given that it combines graph technology and RAG.</p> <p>"Bringing them together is powerful," he said. "GraphRAG is a significant addition for Graphwise customers as it enables them to leverage knowledge graphs as a semantic backbone, ensuring AI responses are grounded in verifiable enterprise facts and complex relationships. This is something standard RAG systems struggle to achieve."</p> <p>With a North American headquarters in New York City and a European headquarters in Sofia, Bulgaria, Graphwise is a graph technology vendor formed in 2024 when Ontotext merged with Semantic Web Company. Competitors include specialists such as <a href="https://www.techtarget.com/searchdatamanagement/news/366630145/Neo4js-latest-targets-graph-database-performance-at-scale">Neo4j</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366618412/TigerGraph-launches-Savanna-to-aid-AI-development">TigerGraph</a> as well as broader-based database providers featuring graph database capabilities including AWS, Google Cloud and Microsoft.</p> <section class="section main-article-chapter" data-menu-title="Improving AI development"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Improving AI development</h2> <p>Problems related to data aren't the sole reason <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">most AI initiatives fail</a> before making it into production. Unrealistic expectations, lack of a clear business case and difficulties integrating applications into real-world workflows are among the other reasons an estimated 80% of all AI projects fail.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> GraphRAG is a significant addition for Graphwise customers as it enables them to leverage knowledge graphs as a semantic backbone, ensuring AI responses are grounded in verifiable enterprise facts and complex relationships. This is something standard RAG systems struggle to achieve. </figure> <figcaption> <strong>Stephen Catanzano</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>But issues with data -- including the lack of sufficient high-quality, relevant data -- are among the main ones.</p> <p>Graphwise's new feature targets the discovery of <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Talend-CEO-discusses-importance-of-mining-relevant-data">relevant data</a>. Agents and other applications are built for specific tasks. For example, many enterprises are building agents that autonomously handle customer service. If the pipelines that feed those agents don't deliver the specific data relevant to an individual customer and their problem, the agent won't be effective.</p> <p>Semantic modeling -- ensuring that metadata is consistently and clearly classified whenever it is ingested or transformed -- is one means of improving search relevance and is <a href="https://www.techtarget.com/searchdatamanagement/news/366631576/New-consortium-to-aid-AI-by-standardizing-semantic-modeling">gaining popularity</a> as enterprises <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">invest more heavily in AI initiatives</a>. Graphwise's knowledge graph serves as a semantic layer, adding context to data and finding relationships between data points to make them easily discoverable.</p> <p>GraphRAG unites large language models, an enterprise's data, a structured knowledge graph and multiple search methods such as similarity search and keyword search to deliver appropriate data to agents and other applications to provide accurate outputs at a much higher rate than standard RAG.</p> <p>"The development of GraphRAG was driven by a specific structural failure in the market we call the 'Prototype Plateau,'" said Andreas Blumauer, founder of Semantic Web Company and Graphwise's senior vice president of growth. "While customers were indeed requesting better accuracy, the primary motivation came from observing enterprises stuck in a cycle of failed pilots."</p> <p>In particular, <a href="https://www.techtarget.com/searchenterpriseai/tip/RAG-best-practices-for-enterprise-AI-teams">RAG systems</a> didn't provide enough context to retrieving data, he continued.</p> <p>"Our motivation was to transform RAG from a simple associative engine into a reasoning engine," Blumauer said. "By injecting a 'Semantic Backbone', we moved beyond probability-based guesses to explicit, logic-based relationships."</p> <p>Specific GraphRAG features include the following:</p> <ul type="disc" class="default-list"> <li>Semantic Metadata Control Plane, a semantic model designed to substantially improve the accuracy of AI outputs, including reducing the likelihood of <a href="https://www.techtarget.com/searchenterpriseai/tip/Why-does-AI-hallucinate-and-can-we-prevent-it">AI hallucinations</a>, by grounding responses in an enterprise's consistent metadata.</li> <li>Explainability and Provenance Panels that display how AI responses are generated, enabling users to check for accuracy and supporting regulatory compliance by providing transparency.</li> <li>Visual debugging and monitoring capabilities that allow developers and engineers to trace an error path and drastically reduce the amount of time previously needed to troubleshoot.</li> <li>A low-code interface that enables business users to adjust AI logic without involving <a href="https://www.techtarget.com/whatis/definition/Python">Python</a> code experts.</li> <li>Built-in templates that provide governance and enable query expansion that would otherwise require extensive research and development and technical support.</li> <li><a target="_blank" href="https://www.w3.org/2004/02/skos/" rel="noopener">Simple Knowledge Organization System</a> (SKOS)-like enrichment to capture domain-specific intelligence so that AI tools can understand an enterprise's unique terminology and ensure that users get accurate responses regardless of how they phrase a query.</li> </ul> <p>Most valuable are the Semantic Metadata Control Plane and SKOS-style enrichment, according to Morrison, who noted that the control plane is where enterprises can make data accessible and discoverable across their entire data estate while SKOS-style enrichment allows non-technical users to work with data.</p> <p>Catanzano likewise highlighted the Semantic Metadata Control Plane. In addition, he noted the value of the <a href="https://www.techtarget.com/searchenterpriseai/tip/Low-code-no-code-tools-simplify-AI-customization-for-engineers">low-code interface</a>. Meanwhile, capabilities such as GraphRAG help Graphwise differentiate from competing graph technology vendors by integrating knowledge graphs with AI, Catanzano continued.</p> <p>"Its capabilities, such as explainability, provenance, and domain-specific intelligence, position it as a leader in making generative AI reliable and scalable, surpassing the limitations of traditional graph database vendors," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With GraphRAG now available, Graphwise's product development plans include adding AI-assisted automation capabilities and improving the memory of its platform, according to Blumauer.</p> <p>Memory initiatives include moving beyond session-based interactions to retaining user preferences and context to provide more personalization. AI-assisted automation plans include tools that augment text <a href="https://www.techtarget.com/searchdatamanagement/tip/How-to-make-a-metadata-management-framework">with metadata</a> to make it discoverable and generating <a href="https://www.techtarget.com/searchdatamanagement/definition/schema">schemas</a> to aid data modeling.</p> <p>"A major theme is reducing manual effort through AI-assisted automation," Blumauer said.</p> <p>Morrison advised that Graphwise to develop a multi-layer <a target="_blank" href="https://www.linkedin.com/pulse/context-graphs-capturing-why-age-ai-dharmesh-shah-oyyze/" rel="noopener">context graph</a> to expand on the context GraphRAG currently provides.</p> <p>Catanzano, meanwhile, suggested that Graphwise develop new integrations with data and AI providers to <a href="https://www.techtarget.com/searchbusinessanalytics/news/252515720/Gartner-Augmented-analytics-ecosystem-for-BI-now-key">expand its ecosystem</a> and create prebuilt templates that simplify its platform for enterprises in certain industries.</p> <p>"Industry-specific templates … would not only deepen its value for current users but also attract new customers seeking tailored, ready-to-deploy solutions," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> With standard RAG pipelines proving unreliable, the vendor's new feature uses knowledge graphs to add needed context to the data retrieval process that fuels AI outputs. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1304896250.jpg https://www.techtarget.com/searchdatamanagement/news/366639196/Graphwise-aims-to-boost-AI-accuracy-with-GraphRAG-launch Tue, 17 Feb 2026 14:34:00 GMT <p>Tuesday was a day of doubles for SurrealDB.</p> <p>The startup <a href="https://www.techtarget.com/searchdatamanagement/feature/The-rise-of-multi-model-databases-to-support-data-variety">multimodel database</a> vendor secured $23 million in venture capital funding, an extension of its Series A round that nearly doubles its total funding to $44 million. In addition, SurrealDB launched version 3.0 of its platform.</p> <p>The $23 million brings SurrealDB's Series A round to $38 million, with Chalfen Ventures and Begin Capital joining previous investors FirstMark and Georgian in their investment in the database specialist. As part of the deal, Mike Chalfen, founder of Chalfen Ventures, joins SurrealDB as a director</p> <p>Meanwhile, SurrealDB 3.0 includes new features such as a new control layer and improved vector storage and indexing capabilities, and is designed to help customers unify multiple data models -- relational, document, <a href="https://www.techtarget.com/searchdatamanagement/news/366593101/Graph-technology-helps-battle-election-misinformation">graph</a>, time-series, vector, <a href="https://www.techtarget.com/searchbusinessanalytics/news/252523998/Geospatial-data-a-key-means-of-combating-climate-change">geospatial</a> and key-value -- to fuel AI development.</p> <p>Both the new funding and platform update are significant for SurrealDB users, with one providing the cash that will enable SurrealDB to grow its multimodel capabilities and the other demonstrating those capabilities, according to Kevin Petrie, an analyst at BARC U.S.</p> <p>"This funding announcement reflects the compelling pain that so many enterprises feel as they adopt AI," he said. "They struggle to integrate disparate data sources to provide agents with the business context they need to make trustworthy decisions and actions. This level of funding can help SurrealDB deepen its product capabilities and get more serious go-to-market activities."</p> <p>Based in London, SurrealDB provides a platform that supports various data types so that users can integrate data to inform decisions based on more than just one type of data. Other vendors providing multimodel capabilities include <a href="https://www.techtarget.com/searchdatamanagement/news/366636081/Couchbase-adds-agentic-AI-development-suite-to-Capella-DBaaS">Couchbase</a>, Redis and the <a href="https://www.theserverside.com/tip/MySQL-vs-PostgreSQL-Compare-popular-open-source-databases">open-source PostgreSQL platform</a>, while hyperscale cloud providers AWS, Google Cloud and Microsoft also offer multimodel databases.</p> <section class="section main-article-chapter" data-menu-title="Cash infusion"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Cash infusion</h2> <p>SurrealDB's latest funding comes at a time when venture capital investments in data management vendors are few and far between.</p> <p>Throughout the 2010s and into the early 2020s, funding poured into the data and analytics market. In 2021 alone, vendors such as Aiven, Confluent, Databricks, Reltio, SnapLogic, ThoughtSpot and TigerGraph raised $100 million or more with Databricks' funding round reaching $1 billion. In early 2022, Aiven raised another $210 million and Sigma secured $300 million.</p> <p>But then <a href="https://www.techtarget.com/searchbusinessanalytics/news/252520740/Tech-stock-sell-off-signals-tough-times-for-data-vendors">tech stock prices plummeted</a> in mid-2022, and the funding for data and analytics vendors evaporated.</p> <p>Since then, while vendors such as <u>Aerospike</u> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366585775/Differentiation-key-as-Sigma-Computing-raises-200M">Sigma</a> have attracted investments, few data and analytics vendors have raised capital. The common theme among the data and analytics vendors that continue to attract funding is their enablement of AI development.</p> <p>Databricks, for example, has focused heavily on AI, and continues to <a href="https://www.techtarget.com/searchdatamanagement/news/366636532/Databricks-adds-4B-funding-round-IPO-could-be-next">attract massive amounts of investment capital</a>.</p> <p>SurrealDB aims to be a layer in the AI development process. It's that focus on enabling customers to develop agents and other AI applications that helped the vendor raise funding, according to Tobie Morgan Hitchcock, SurrealDB's co-founder and CEO.</p> <p>"Raising now signals we're not just another database vendor, but increasingly an enabling layer for enterprise AI workflows," he said. "SurrealDB is being used as part of enterprise AI deployments, including agentic workflows that depend on fast, consistent data access. … Investors are leaning into infrastructure that makes AI systems production-grade, which is exactly what we are building."</p> <p>Matt Aslett, an analyst at ISG Software Research, similarly noted that SurrealDB's ability to attract venture capital funding reflects its focus on helping enterprises <a target="_blank" href="https://www.reuters.com/business/ai-venture-funding-continued-surge-third-quarter-data-shows-2025-10-06/" rel="noopener">build AI applications</a>.</p> <p>"While many VCs are chasing potential returns from investment in AI specialists, there is always investor interest in startups with the potential to make an impact in the lucrative database market, especially providers that are responding to the need for innovation to support AI initiatives," he said.</p> <p>SurrealDB plans to use the added $23 million to accelerate product engineering and improve go-to-market efforts, according to Hitchcock.</p> <p>"It lets us scale the team and the platform in parallel, shipping more capability, hardening reliability and security, and supporting larger deployments," he said. "In short, it accelerates our path from rapid adoption to durable, global scale."</p> <p>Given that SurrealDB, which was founded in 2021, is a relatively new database vendor compared to peers such as <a href="https://www.techtarget.com/searchdatamanagement/news/252525824/ArangoDB-expands-scope-of-graph-database-platform">ArangoDB</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/252514648/Redis-launches-JSON-database-capabilities-with-RedisJSON-20">Redis</a>, improving its platform and increasing its profile are wise areas of focus, according to Aslett.</p> <p>"SurrealDB is in the early stages, and its new funding round will help the company accelerate the development of both its core database and its platform capabilities, as well as expanding investment in support and services resources as well as raising its profile in a crowded market," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Platform update"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Platform update</h2> <p>While the new funding will be used, in part, to fuel future product development, SurrealDB 3.0 represents the vendor's current product development.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This funding announcement reflects the compelling pain that so many enterprises feel as they adopt AI. They struggle to integrate disparate data sources to provide agents with the business context they need to make trustworthy decisions and actions. </figure> <figcaption> <strong>Kevin Petrie</strong>Analyst, BARC U.S. </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>SurrealDB is designed to provide agents and other applications with unified data -- disparate data types integrated as one -- to give them proper context and memory so that they remember facts even as <a target="_blank" href="https://explodingtopics.com/blog/data-generated-per-day" rel="noopener">data volume and complexity increase</a>. To provide that proper context and memory, SurrealDB positions context graphs in its database next to the data.</p> <p>With that focus on enabling customers to build intelligence applications that feature contextual awareness and the memory to recall and learn from previous interactions, SurrealDB, though a startup competing against more established vendors for market share, has an opportunity to play the role of disruptor, according to Aslett.</p> <p>"The evolving requirements for operational databases are to support the development of intelligent applications infused with contextually relevant recommendations, predictions and forecasting driven by machine learning, generative AI and agentic AI," he said. "These evolving requirements are providing opportunities for emerging database providers to disrupt established incumbents."</p> <p>ISG predicts that within the next two years, around three-quarters of all enterprises will have adopted operational databases specifically designed to support the <a href="https://www.techtarget.com/whatis/definition/What-is-AI-inference">AI inferencing</a> capabilities that intelligent applications require, Aslett added.</p> <p>SurrealDB 3.0 builds on previous platform capabilities by adding the following:</p> <ul type="disc" class="default-list"> <li>Surrealism, a layer that enables developers and administrators to customize <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> -- the rules, processes and operations that determine how an enterprise uses SurrealDB -- including access controls and version controls.</li> <li>Improved vector search and indexing to enable the discovery and use of unstructured data such as text and images.</li> <li>Support for both <a href="https://www.techtarget.com/whatis/feature/Structured-vs-unstructured-data-The-key-differences">structured and unstructured data</a>.</li> <li>Architectural changes that add stability and improve SurrealDB's performance such as separating data values from data expressions.</li> <li>An improved developer experience, including a refined model that enables users to define custom API endpoints directly within the database, among other functions.</li> </ul> <p>A combination of customer feedback and market observations provided SurrealDB with the impetus for developing the individual features that comprise its update, according to Hitchcock.</p> <p>"The focus is on removing friction -- making the platform easier to adopt, operate, and scale -- while expanding the capabilities teams need in production," he said. "The goal is to keep the developer experience simple as use cases become more demanding."</p> </section> <section class="section main-article-chapter" data-menu-title="Competitive standing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Competitive standing</h2> <p>Looking ahead, SurrealDB is focused on three main initiatives, according to Hitchcock: maturing its platform to meet the needs of enterprises at scale, expanding its capabilities so customers don't have to integrate it with as many other tools to build AI applications and continuing to enhance <a href="https://www.techtarget.com/searchsoftwarequality/feature/6-key-ways-to-improve-developer-productivity">the developer experience</a>.</p> <p>"As more customers deploy AI in production, we're investing in capabilities that make it easier to deploy and scale AI-powered applications and agentic workflows," Hitchcock said. "Our focus is broad. … It's all about simplification."</p> <p>Despite still prioritizing some foundational capabilities such enabling enterprise-scale workloads and adding features that allow users to simplify their AI development stacks, SurrealDB is establishing itself as a viable alternative to other database vendors, according to Aslett.</p> <p>All database providers are similarly adding capabilities that foster AI development, such <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">as vector storage and indexing</a>. But Aslett noted that SurrealDB's support for various data types and the quality if its vector storage and indexing capabilities stand out.</p> <p>"SurrealDB is ahead of many established providers in terms of delivering differentiating capabilities, including enhanced vector search and indexing as well as native agent memory and context graphs, … an intuitive visual user interface and support for relational, document, graph, time-series, vector, search, geospatial and key-value data types in a single database."</p> <p>Petrie similarly noted that SurrealDB is staking out a place for itself in <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-NoSQL-database-types-in-the-cloud">a competitive market</a> with the variety of its multimodel capabilities.</p> <p>"I'm impressed with the range of data types that SurrealDB already supports as a Series A startup," he said. "This really simplifies your agentic AI architecture. The more you can consolidate diverse data and models onto a single platform, with real-time performance and memory, the better you can streamline your projects and reduce time to production."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> Though funding isn't flowing as freely into data management as it once was, the startup is attracting interest by focusing on enabling customers to unify data for AI development. https://cdn.ttgtmedia.com/rms/onlineimages/money_g1250581414.jpg https://www.techtarget.com/searchdatamanagement/news/366639042/SurrealDB-raises-23M-launches-update-to-fuel-agentic-AI Tue, 17 Feb 2026 09:51:00 GMT <p>For decades, data management professionals have argued that their work should be seen as a strategic issue rather than a technical housekeeping task. Once dismissed as old-fashioned, that argument has resurfaced at the executive level in 2026, with the analyst firm BARC identifying data quality -- closely followed by security – as a top priority.</p> <p>An accompanying <a href="https://barc.com/data-quality-beats-ai-hype/" target="_blank" rel="noopener">article</a> from BARC CEO Carsten Bange observes, "What began as excitement over a breakthrough promising more automation, productivity, and intelligence has matured into a more balanced discussion -- one that carefully weighs costs against benefits."</p> <p>As a result, data quality is no longer viewed solely as an IT responsibility but as an enterprise-wide governance commitment. The stakes have risen since <a href="https://www.techtarget.com/whatis/feature/How-companies-are-tackling-AI-hallucinations">AI systems can hallucinate or reproduce unwanted bias</a>, enabling errors to spread much faster than with earlier technologies. Executives are relearning the lesson of garbage in, garbage out, with generative AI amplifying failures at scale.</p> <p>To manage this risk, executives expect data quality to be measured, monitored and audited reliably and consistently, and they require governance frameworks that keep pace with AI adoption. This increases scrutiny on the tools and platforms that support data governance, analytics, AI deployment and performance management.</p> <section class="section main-article-chapter" data-menu-title="From backroom to boardroom"> <h2 class="section-title"><i class="icon" data-icon="1"></i>From backroom to boardroom</h2> <p>Data quality was once the province of database administrators and IT ops teams. As practitioners, they generally worked with six dimensions: accuracy, validity, completeness, uniqueness, consistency and timeliness. These measures assumed that data recorded objective business facts and that quality meant ensuring those records were complete and consistent.</p> <p>These dimensions remain important as technical measures, but machine learning introduces a different kind of dependency. AI models don't simply store data; they learn patterns from it, which can encode bias or assumptions as facts. As a result, executives concerned with <a href="https://www.techtarget.com/searchenterpriseai/feature/Top-resources-to-build-an-ethical-AI-framework">deploying AI ethically and responsibly</a> also must consider whether data sets are transparent, fair, secure and representative.</p> <p>This shift explains why data quality has moved from a technical concern to an executive-level concern. Quality now reflects not only correctness, but what organizations permit systems to learn and reproduce. Governance, therefore, extends beyond technical policies and controls to include accountability for outcomes.</p> <p>At the same time, executive evaluation remains grounded in business value. Ethical risk and governance maturity increasingly factor into platform decisions, alongside ROI and operational performance.</p> </section> <section class="section main-article-chapter" data-menu-title="ROI and total cost of ownership"> <h2 class="section-title"><i class="icon" data-icon="1"></i>ROI and total cost of ownership</h2> <p>Before approving any substantial platform investments, executives demand quantifiable justification. The evaluation question has shifted from platform price to exposure risk: What does poor data quality cost today, and how much of that risk does the platform reduce?</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The platforms that win budget approval demonstrate how this investment protects the reliability of decision-making and the organization's reputation. </figure> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>The costs of investing in data quality platforms are visible and upfront, including licensing fees, implementation effort and training. But the costs of poor data quality are distributed across the business, making them harder to trace. Customer attrition might rise if support teams rely on outdated or incorrect records, but that might not surface as a data quality problem. Similarly, budgets and forecasts built on flawed data might lead to poor spend management, which is rarely traced back to data.</p> <p>The benefits of data quality are often incremental and preventive. Slightly better decisions across thousands of interactions, or problems avoided because they were caught early, are difficult to quantify, but central to executive evaluation.</p> <p>As a result, many organizations do not tackle data quality strategically until a crisis arises, which forces the issue. Instead, organizations typically adopt one of three postures toward data quality:</p> <ul class="default-list"> <li>Do nothing and treat poor quality as a cost of doing business and accept the inherent risk.</li> <li>Rely on reactive remediation, correcting errors only after problems surface. This reactivity is often too late to prevent damage.</li> <li>Use <a href="https://www.techtarget.com/searchdatamanagement/feature/Proactive-practices-for-data-quality-improvement">proactive remediation</a>, which focuses on identifying the data records most critical to the business, defining quality rules for those records, and monitoring compliance to find issues early. Executives today increasingly favor this approach.</li> </ul> <p>Proactive approaches require upfront investment in tools and processes, but do not require universal coverage of every data element. Targeted monitoring of high-value datasets can reduce cascading errors across analytics and AI pipelines, shifting effort from manual cleanup to prevention.</p> <p>When executives evaluate the total cost of ownership of platforms, they weigh identifiable platform costs against the potential costs of inaction. Platforms that support this evaluation by making incidents, remediation efforts and governance exposure visible are more likely to receive approval. This is a valid approach, but it requires discipline. It requires tracking the rate of data quality incidents, remediation time and compliance violations, and estimating the cost of those incidents. Most organizations don't do this systematically.</p> </section> <section class="section main-article-chapter" data-menu-title="Interoperability and observability"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Interoperability and observability</h2> <p>The selection of a data quality platform depends on how well it integrates with existing systems across complex cloud and hybrid environments. Executives evaluating platforms look for interoperability that reduces fragmentation rather than adding another isolated tool to the stack.</p> <p>Open standards offer one practical response to this sprawl of tools across the network. For example, OpenTelemetry provides a <a href="https://opentelemetry.io/" target="_blank" rel="noopener">vendor-neutral framework</a> for instrumentation. Organizations can use a <a href="https://www.techtarget.com/searchapparchitecture/tip/APM-vs-distributed-tracing-How-they-differ">distributed tracing</a> architecture to consolidate logs, metrics, traces and quality monitoring into a single view, making it easier for operations teams and executives to oversee coherent reporting.</p> <p>The key question is whether a platform consolidates observability across the data landscape or if it introduces additional complexity. Platforms that improve coherence across monitoring, reporting and governance are more likely to align with executive expectations.</p> </section> <section class="section main-article-chapter" data-menu-title="The vendor landscape and selection criteria"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The vendor landscape and selection criteria</h2> <p>Traditional vendor rankings by analysts often fail to reflect the complexity of the data quality landscape. As a result, CDOs and other executives tend to structure their comparisons around categories of capabilities rather than vendor position.</p> <p>Rather than comparing products directly, leaders assess whether a platform supports core governance, quality and observability functions across the data lifecycle. The emphasis is on how capabilities align with existing architectures and executive priorities, not on category labels. &nbsp;</p> <p>In practice, executive evaluation criteria tend to cluster around four questions:</p> <ul type="disc" class="default-list"> <li><b>Structural fit</b>. How well does the platform integrate with existing infrastructure, including ERP or CRM systems, data warehouses, and other business applications, including legacy systems?</li> <li><b>Scalability</b>. Cloud-based platforms offer flexible resources without large upfront infrastructure investments. Can the platform adapt to changing data volumes and usage patterns without introducing operational friction or unexpected costs?</li> <li><b>Strategic alignment</b>. Does the platform support the company's current business priorities, analytics strategies and governance objectives?</li> <li><b>Regulatory readiness.</b> Can the platform demonstrate compliance with evolving requirements such as the <a href="https://www.techtarget.com/searchsecurity/tip/State-of-data-privacy-laws">EU AI Act, GDPR, CCPA</a>, and industry-specific standards?</li> </ul> <p>Executives who have committed to major cloud vendors such as Microsoft, Amazon, Google or Oracle must also consider whether to follow a single-vendor approach for data quality and observability, or to choose best-of-breed tools from independent vendors. The right choice depends on organizational context and existing investments.</p> </section> <section class="section main-article-chapter" data-menu-title="Quality assurance and data trust metrics"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Quality assurance and data trust metrics</h2> <p>Enterprise data quality frameworks still rely on a set of well-established technical measures. These six dimensions of data quality provide a baseline for evaluating platform capabilities and monitoring operational consistency:</p> <ul type="disc" class="default-list"> <li><b>Accuracy. </b>The degree to which data reflects real-world conditions</li> <li><b>Validity. </b>Conformance to defined formats and rules.</li> <li><b>Completeness.</b> Presence of required values.</li> <li><b>Uniqueness. </b>Absence of duplicate records.</li> <li><b>Consistency. </b>Alignment across systems and sources.</li> <li><b>Timeliness. </b>Currency of data relative to business needs.</li> </ul> <p>While these dimensions are easy to define and measure, executive evaluation increasingly requires data trust. Data trust, however, is not established solely through metrics. A single high-profile failure can destroy years of credibility because people remember failures more than successes. If analysts routinely doubt their data, they will build workarounds, often using spreadsheets that undermine governance.</p> <p>As a result, executives increasingly look for qualitative signals of trust alongside formal metrics. How do people feel about the data? Are they confident? Do they rely on the official reports, or do they maintain those shadow spreadsheets? Qualitative surveys aim to make visible what accounting systems miss by acknowledging that formal metrics can be misleading and that users' informal sentiment is critical.</p> </section> <section class="section main-article-chapter" data-menu-title="Conclusion"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Conclusion</h2> <p>Good data <a href="https://www.techtarget.com/searchdatamanagement/tip/Why-businesses-should-know-the-importance-of-data-quality">quality is a business advantage</a>. Organizations with clean, reliable data make decisions with greater confidence. Tying measures, reports and dashboards to quality outcomes, such as improvements in decision-making or reductions in customer complaints traceable to errors in data, translates the abstract goals of governance into business language that boards understand.</p> <p>Research from BARC reinforces this point. Leading organizations that balance governance and innovation invest in quality, literacy, and accountability as foundations to scale AI responsibly. Laggards remain tightly focused on compliance and operations, missing the bigger picture and the opportunity to turn data into business value.</p> <p>Executive evaluation of data quality platforms reflects these priorities. Measurable returns, alignment with regulations and verifiable trust drive many decisions about data architecture. The platforms that win budget approval demonstrate how this investment protects the reliability of decision-making and the organization's reputation. Management boards increasingly hold executives accountable for proving that case.</p> <p><em>Donald Farmer is a data strategist with 30+ years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.</em></p> </section> Data quality strategy now functions as a governance and risk discipline, with executives weighing metrics, ROI accountability and data trust as indicators of enterprise reliability. https://cdn.ttgtmedia.com/rms/onlineimages/folder-files13.jpg https://www.techtarget.com/searchdatamanagement/feature/What-executives-look-for-in-a-data-quality-platform Tue, 10 Feb 2026 14:37:00 GMT <p>Aerospike on Monday launched native Dynamic Data Masking, a new feature that customers can easily apply to protect personally identifiable information.</p> <p>Personally identifiable information (PII) is information about an individual that could reveal their identity which, if exposed, could violate regulations such as the <a href="https://www.techtarget.com/searchsecurity/tip/Use-this-CCPA-compliance-checklist-to-get-up-to-speed">California Consumer Privacy Act</a> and the European Union's <a href="https://www.techtarget.com/searchdatabackup/feature/Principles-of-the-GDPR-explained">General Data Protection Regulation</a>.</p> <p>While it's imperative that enterprises protect PII when running data workloads, many databases don't have native features that protect PII. Instead, they require system administrators to create masked views, aggregation pipelines and other configurations that require ongoing, complex oversight to hide individual indicators.</p> <p>Popular databases such as Microsoft SQL Server and Oracle Database do come with native dynamic data masking. However, the feature is less common with <a href="https://www.techtarget.com/searchdatamanagement/definition/NoSQL-Not-Only-SQL">NoSQL</a> databases such as Aerospike, Amazon Dynamo DB, Couchbase, MongoDB and Redis.</p> <p>Now, Aerospike, a NoSQL database vendor based in Mountain View, Calif., is aiming to stand apart from <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-NoSQL-database-types-in-the-cloud">its database competitors</a> by simplifying the protection of PII in a move Holger Mueller, an analyst at Constellation Research, called significant for the vendor's customers.</p> <p>"It makes things easier -- in case you need to mask -- which is a clumsy and sensitive process you have to do manually otherwise," he said.</p> <p>Aerospike's native Dynamic Data Masking is available as part of the vendor's latest Aerospike Database 8 update, which also includes support for <a href="https://www.techtarget.com/searchitoperations/definition/YAML-YAML-Aint-Markup-Language">YAML</a>-based server configuration and improved data recovery capabilities, among other new features. System administrators can deploy native Dynamic Data Masking by applying a rule to mask data for all users and machines other than those granted specific privileges to view and work with PII.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> It makes things easier -- in case you need to mask -- which is a clumsy and sensitive process you have to do manually otherwise. </figure> <figcaption> <strong>Holger Mueller</strong>Analyst, Constellation Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Once the rule is applied, PII protection is automatically enforced at the database layer so that developers and engineers can build analytics and <a href="https://www.techtarget.com/searchcustomerexperience/news/366636690/Agentic-orchestration-the-next-AI-issue-for-CIOs-to-tackle">AI tools</a> without having to configure PII protection at the application level.</p> <p>Meanwhile, the impetus for developing native Dynamic Data Masking came from customer feedback, according to Srini Srinivasan, Aerospike's co-founder and chief technology officer.</p> <p>"Aerospike has many fintech and banking customers who have always cared deeply about protecting PII, and for those customers, it's now even easier for them with native support," he said. "But now, almost all applications have a PII or payment component. Everything is digital. Dynamic data masking applies in far more places, and it needs to be easier to deploy and manage centrally."</p> <p>Beyond its value for users, native Dynamic Data Masking could help Aerospike stand apart from other NoSQL database providers, according to Mueller.</p> <p>Among NoSQL databases, Mueller noted that only Microsoft's <a href="https://www.techtarget.com/searchbusinessanalytics/news/366624233/New-Microsoft-database-analytics-tools-target-agentic-AI">Azure Cosmos DB</a> offers native dynamic data masking capabilities. Those capabilities, introduced in November 2025, are in preview rather than generally available.</p> <p>"It is a new feature [among NoSQL databases]," he said, noting that otherwise "it is a manual coding effort, so it's much better to have this as a product feature."</p> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With the latest Aerospike Database 8 update now available, <a href="https://www.techtarget.com/searchdatamanagement/news/366578850/Aerospike-raises-100M-to-fuel-database-innovation-for-GenAI">the vendor's product development plans</a> are focused on adding capabilities that enable customers to develop AI applications, according to Srinivasan.</p> <p>"There's a lot of work and features coming in Aerospike in this and other areas to make it even faster to create a proof of concept, see the value, and go from start to scale on Aerospike," he said, noting that more than half of Aerospike workloads are now related to AI and machine learning.</p> <p>In addition, <a href="https://www.techtarget.com/searchdatamanagement/news/366629235/Aerospike-update-aims-to-improve-database-performance">database performance</a>, scale, stability and cost optimization are also points of emphasis.</p> <p>Aerospike's focus on AI is appropriate, according to Mueller.</p> <p>While Aerospike does provide vector search and storage capabilities, with enterprises continuing to <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">invest heavily in developing AI tools</a>, he suggested that the vendor add more capabilities to its database that support AI development. In addition, he noted that Aerospike could provide AI capabilities of its own to make its database easier and faster to use.</p> <p>"It is AI time, so [Aerospike could add] out of the box guardrails, [improved] vector support and use AI to run the database administration tasks," Mueller said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> By adding native protection of personally identifiable information, the vendor is simplifying system administration while simultaneously pushing the NoSQL market. https://cdn.ttgtmedia.com/rms/onlineimages/security_g1192070289.jpg https://www.techtarget.com/searchdatamanagement/news/366638953/PII-masking-a-differentiator-for-Aerospikes-NoSQL-database Mon, 09 Feb 2026 09:22:00 GMT <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{178}" paraid="485118795"><span xml:lang="EN-US" data-contrast="auto">Databricks Lakebase is now generally available, eight months after the PostgreSQL database purposed for AI development was first unveiled in public preview.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{184}" paraid="1686772772"><span xml:lang="EN-US" data-contrast="auto">Lakebase, which was launched on AWS on Feb. 3, is the result of Databricks' $1 billion </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366623864/Databricks-adds-Postgres-database-with-1B-Neon-acquisition"><span xml:lang="EN-US" data-contrast="none">acquisition of Neon</span></a><span xml:lang="EN-US" data-contrast="auto">, a cloud-based database vendor providing a platform built on the open-source PostgreSQL format, in May 2025. Databricks has since rebranded Neon's capabilities, and now Databricks has integrated them with </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366560094/Databricks-puts-AI-at-core-of-new-Data-Intelligence-Platform"><span xml:lang="EN-US" data-contrast="none">its Data Intelligence Platform</span></a><span xml:lang="EN-US" data-contrast="auto"> to provide customers with an operational database in conjunction with its data lakehouse.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{200}" paraid="560365120"><span xml:lang="EN-US" data-contrast="auto">Beyond integration with Databricks' broader platform, Lakebase fosters AI development by separating compute from storage, unlike many PostgreSQL databases that couple them together. By separating the processing power for queries from the power needed for storage, Lakebase eliminates competition between the two for memory resources and the resulting resource management tasks that can slow development initiatives.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{206}" paraid="1946560941"><span xml:lang="EN-US" data-contrast="auto">In addition, Lakebase features autoscaling to help users </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchcloudcomputing/tip/Implement-AI-driven-cloud-cost-optimization-to-reduce-waste"><span xml:lang="EN-US" data-contrast="none">control the cost</span></a><span xml:lang="EN-US" data-contrast="auto"> of building agents and other AI applications, and unified governance through Databricks' Unity Catalog, among other capabilities.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{225}" paraid="729396782"><span xml:lang="EN-US" data-contrast="auto">Given that Lakebase better integrates PostgreSQL workloads with the broader Databricks platform, it is a significant addition for the vendor's customers, according to Devin Pratt, an analyst at IDC.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{231}" paraid="2001649299"><span xml:lang="EN-US" data-contrast="auto">"The opportunity is to reduce friction between operational and analytical data so real-time applications and AI agents can work from governed data that stays current, with less ETL and duplication," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{237}" paraid="586289825"><span xml:lang="EN-US" data-contrast="auto">William McKnight, president of McKnight Consulting, similarly noted that Lakebase's value lies in its integration with other Databricks capabilities, reducing the need for </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/definition/data-egress"><span xml:lang="EN-US" data-contrast="none">data egress</span></a><span xml:lang="EN-US" data-contrast="auto"> pipelines between the database and other tools.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{1}" paraid="1002527863"><span xml:lang="EN-US" data-contrast="auto">"This architectural shift minimizes fragile pipelines by co-locating transactional workloads with heavy analytics under a single governance model," he said. "It effectively removes the 'architectural tax' that has historically separated live apps from data lakes."</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <h2>Prowess of PostgreSQL</h2> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{13}" paraid="1406101758"><span xml:lang="EN-US" data-contrast="auto">Based in San Francisco and one of the pioneers of the data lakehouse architecture for storing data, Databricks, like many data management vendors, has added AI development capabilities over the past few years in response to </span><a rel="noreferrer noopener" target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai"><span xml:lang="EN-US" data-contrast="none">rising customer interest</span></a><span xml:lang="EN-US" data-contrast="auto"> in building AI tools that call on an enterprise's proprietary data to understand its unique operations.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The opportunity is to reduce friction between operational and analytical data so real-time applications and AI agents can work from governed data that stays current, with less ETL and duplication. </figure> <figcaption> <strong>Devin Pratt </strong>Analyst, IDC </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{24}" paraid="1459340638"><span xml:lang="EN-US" data-contrast="auto">Because PostgreSQL databases are more flexible than many other databases, PostgreSQL is now the most popular database format, according to the </span><a rel="noreferrer noopener" target="_blank" href="https://survey.stackoverflow.co/2024/technology"><span xml:lang="EN-US" data-contrast="none">2024 Developer Survey</span></a><span xml:lang="EN-US" data-contrast="auto"> by Stack Overflow.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{35}" paraid="332370681"><span xml:lang="EN-US" data-contrast="auto">Versatility -- handling geospatial, time series, JSON and vector database workloads -- and flexibility are two of the main reasons PostgreSQL databases are now more popular than fellow open-source MySQL databases and databases provided by vendors such as Microsoft, MongoDB and Redis.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{41}" paraid="491134092"><span xml:lang="EN-US" data-contrast="auto">With PostgreSQL so popular, and its adaptability enabling users to run workloads that aid AI development, hyperscale cloud vendors AWS, Google, IBM, Microsoft and Oracle all offer PostgreSQL databases that can be used with their AI development tools. Now, more specialized data management vendors are doing the same.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{49}" paraid="633463368"><span xml:lang="EN-US" data-contrast="auto">Three weeks after Databricks acquired Neon, rival </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366625068/Snowflake-acquisition-of-Crunchy-Data-adds-Postgres-database"><span xml:lang="EN-US" data-contrast="none">Snowflake purchased Crunchy Data</span></a><span xml:lang="EN-US" data-contrast="auto"> to add a PostgreSQL database. Then in October 2025, </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366633563/Streaming-vendor-Redpanda-buys-SQL-engine-unveils-AI-suite"><span xml:lang="EN-US" data-contrast="none">Redpanda acquired Oxla</span></a><span xml:lang="EN-US" data-contrast="auto"> to likewise add a PostgreSQL database.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{65}" paraid="437426485"><span xml:lang="EN-US" data-contrast="auto">"PostgreSQL has evolved into the great consolidator of the modern data stack by transforming from a traditional relational database into a unified, multi-model engine capable of powering the agentic AI era," McKnight said. "By natively integrating vector search with structured business data, it eliminates the need for fragmented point solutions, reducing development complexity."</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{71}" paraid="1452036302"><span xml:lang="EN-US" data-contrast="auto">In addition, pricing is a factor in PostgreSQL's growing popularity, McKnight continued, noting that PostgreSQL databases often cost less than databases from </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchcloudcomputing/definition/hyperscale-cloud"><span xml:lang="EN-US" data-contrast="none">hyperscale cloud</span></a><span xml:lang="EN-US" data-contrast="auto"> vendors.&nbsp;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{82}" paraid="1610933445"><span xml:lang="EN-US" data-contrast="auto">"As enterprises pivot toward Sovereign AI to maintain data gravity and avoid public cloud lock-in, PostgreSQL has become the strategic foundation for organizations that want a secure, high-performance platform to manage the transactions and vectors required for modern AI at scale," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{88}" paraid="919164422"><span xml:lang="EN-US" data-contrast="auto">Although PostgreSQL databases are gaining popularity as more enterprises invest in AI development, Databricks' Lakebase and </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366638535/Snowflake-launches-new-AI-tools-unveils-OpenAI-partnership"><span xml:lang="EN-US" data-contrast="none">Snowflake Postgres</span></a><span xml:lang="EN-US" data-contrast="auto"> are differentiated from standalone PostgreSQL databases by their integration with broader data management and AI development platforms, according to Pratt.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{99}" paraid="1693840052"><span xml:lang="EN-US" data-contrast="auto">Both reduce the need to move data between systems, which can increase development costs and potentially expose data to breaches, and both enable hybrid transactional and analytical workflows that are relevant for AI and real-time analytics workloads.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{109}" paraid="1248643844"><span xml:lang="EN-US" data-contrast="auto">But whether one proves more effective than the other remains to be seen.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{115}" paraid="1253390927"><span xml:lang="EN-US" data-contrast="auto">"Both are pushing PostgreSQL closer to analytics and AI, and the real differences will come down to platform integration and day-to-day operational experience," Pratt said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{121}" paraid="629852949"><span xml:lang="EN-US" data-contrast="auto">In addition to separation of compute and storage, key features of Lakebase include the following:</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{127}" paraid="1486727951"><span xml:lang="EN-US" data-contrast="auto">Serverless autoscaling that automatically adjusts compute resources to match workload demands, including shutting off when no workloads are running to </span><a rel="noreferrer noopener" target="_blank" href="https://www.computerweekly.com/news/366599472/How-to-stop-AI-costs-from-soaring"><span xml:lang="EN-US" data-contrast="none">eliminate wasted spending</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="2" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{138}" paraid="867718339"><span xml:lang="EN-US" data-contrast="auto">Unified governance through the Databricks Unity Catalog, enabling users to manage and secure data across their entire data estate.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="3" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{144}" paraid="1100517225"><span xml:lang="EN-US" data-contrast="auto">Instant database branching so users can quickly create isolated clones of production data to conduct risk-free testing and development work.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="4" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{150}" paraid="348295022"><span xml:lang="EN-US" data-contrast="auto">Point-in-time recovery, a feature that protects against accidental deletions or </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchsoftwarequality/definition/bug"><span xml:lang="EN-US" data-contrast="none">bugs</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="5" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{161}" paraid="117781018"><span xml:lang="EN-US" data-contrast="auto">Sync tables to automatically synchronize operational data and historical lakehouse context without having to build and manage complex pipelines.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{167}" paraid="1834989908"><span xml:lang="EN-US" data-contrast="auto">Collectively, the features that comprise Lakebase are designed to let users run governed, secure operational data workloads directly on Databricks without having to configure connections between their PostgreSQL database and AI development pipeline or move data between systems, according to a Databricks spokesperson.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{179}" paraid="206944759"><span xml:lang="EN-US" data-contrast="auto">Meanwhile, instant database branching stands out as perhaps Lakebase's most significant feature, according to Pratt.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{185}" paraid="1338761809"><span xml:lang="EN-US" data-contrast="auto">"Instant branching improves developer productivity by making it easier to test on production-like data without putting production systems at risk," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{191}" paraid="1538658303"><span xml:lang="EN-US" data-contrast="auto">McKnight, however, highlighted </span><a rel="noreferrer noopener" target="_blank" href="https://www.linkedin.com/pulse/decoupled-storage-compute-paradigm-shift-building-modern-kamdar/"><span xml:lang="EN-US" data-contrast="none">decoupled compute and storage</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{202}" paraid="1922075115"><span xml:lang="EN-US" data-contrast="auto">"This fundamental shift directly addresses the long-standing 'architectural bottleneck' by facilitating serverless autoscaling and limiting resource contention between demanding analytical workloads and live operational applications," he said.&nbsp;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <h2>Looking ahead</h2> </div> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{214}" paraid="683076"><span xml:lang="EN-US" data-contrast="auto">With Lakebase now generally available, one of Databricks' focal points is to make it easy to operate a large number of databases at scale, according to the spokesperson.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{220}" paraid="719851116"><span xml:lang="EN-US" data-contrast="auto">Ease-of-use is a wise focus for Databricks, according to McKnight.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{226}" paraid="595465481"><span xml:lang="EN-US" data-contrast="auto">Databricks has historically appealed to technical experts while rival Snowflake has targeted business users. To broaden its appeal, McKnight advised Databricks to improve Databricks Serverless, a fully managed service that removes infrastructure management tasks, and its Databricks One user interface.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{232}" paraid="922340491"><span xml:lang="EN-US" data-contrast="auto">"By evolving its Serverless and Databricks One initiatives into a true zero-administration environment, Databricks can appeal to business analysts who want the architectural efficiency of a lakehouse without the traditional engineering overhead," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{238}" paraid="673756392"><span xml:lang="EN-US" data-contrast="auto">An additional area of focus could be </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage"><span xml:lang="EN-US" data-contrast="none">cost control</span></a><span xml:lang="EN-US" data-contrast="auto">, McKnight continued.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{249}" paraid="1018231783"><span xml:lang="EN-US" data-contrast="auto">"To neutralize Snowflake, Databricks must … prove that it can provide a lower total cost of ownership while bridging the AI return on investment gap with production-ready, operational templates," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{255}" paraid="1824299946"><span xml:lang="EN-US" data-contrast="auto">Pratt, meanwhile, suggested that Databricks expand efforts to converge operational and analytical workloads to fuel AI initiatives, including providing practical guidance and reference architectures that help customers </span><a rel="noreferrer noopener" target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail"><span xml:lang="EN-US" data-contrast="none">move from pilots to enterprise-wide production</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{1caa322e-0772-464b-9c46-3f256cf96d10}{11}" paraid="447530315"><span xml:lang="EN-US" data-contrast="auto">"The next chapter is adoption, helping customers turn convergence into production applications that deliver real-time decisions," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{1caa322e-0772-464b-9c46-3f256cf96d10}{17}" paraid="939591930"><span xml:lang="EN-US" data-contrast="auto">Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:210,&quot;335559740&quot;:276}">&nbsp;</span></p> </div> Resulting from the $1B acquisition of Neon, the database built for AI workloads -- including separate compute and storage -- is now integrated with the vendor's broader platform. https://cdn.ttgtmedia.com/rms/onlineimages/disaster_recovery_a379640336.jpg https://www.techtarget.com/searchdatamanagement/news/366638723/Databricks-launches-PostgreSQL-Lakebase-to-aid-AI-developers Thu, 05 Feb 2026 11:35:00 GMT <p>Pentaho on Wednesday launched its latest platform update, featuring a browser-based version of Pipeline Designer to simplify data integration workloads and a new semantic modeling tool to help customers consistently organize data across their organization.</p> <p>In addition, Version 11 of Pentaho Data Integration and Business Analytics includes new project profiling capabilities to simplify deployments, improved <a href="https://www.techtarget.com/searchdatamanagement/tip/Data-governance-roles-and-responsibilities-Whats-needed">governance and security controls</a> and a modernized user interface.</p> <p>Collectively, while none of the new features represent cutting-edge innovation, Pentaho's platform update addresses customer needs and is therefore valuable, according to Kevin Petrie, an analyst at BARC U.S.</p> <p>"This is an incremental improvement in some critical areas, most notably ease of use, governance and performance," he said. "Market demands are rising in all three areas as enterprises adopt AI to democratize data consumption and streamline or enhance business processes without incurring significant risk. Pentaho is responding to the right customer priorities."</p> <p>Steven Catanzano, an analyst at Omdia -- a division of Informa TechTarget -- similarly noted that Pentaho's platform update is significant because it targets the growing need for faster, easier and more secure data integration and <a href="https://www.techtarget.com/searchbusinessanalytics/feature/The-future-of-business-intelligence-Top-trends">analytics workflows</a>.</p> <p>"Pentaho Version 11 enables organizations to become more data-driven by simplifying complex processes, reducing operational risks and providing a modern user interface that supports AI readiness," he said. "These enhancements make it easier for enterprises to extract value from their data while meeting the demands of an AI-driven future."</p> <p>Based in Santa Clara, Calif., Pentaho is an independent business unit of <a href="https://www.techtarget.com/searchdatacenter/definition/Hitachi-Vantara-formerly-Hitachi-Data-Systems-or-HDS">Hitachi Vantara</a> that provides <a href="https://www.techtarget.com/searchdatamanagement/news/252467598/Hitachi-Vantara-updates-Pentaho-83-to-expand-DataOps-vision">a platform</a> for data integration and analytics. Competitors include fellow data integration vendors such as Alteryx, Fivetran and Informatica, as well as analytics specialists such as Qlik and Tableau.</p> <section class="section main-article-chapter" data-menu-title="Speed, simplification and security"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Speed, simplification and security</h2> <p>Many enterprises have <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">made AI the focus</a> of their application development initiatives since OpenAI's November 2022 launch of ChatGPT marked significant improvement in generative AI technology. AI applications, however, require far more data than traditional analytics reports and dashboards to be accurate.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This is an incremental improvement in some critical areas, most notably ease of use, governance and performance. Market demands are rising in all three areas as enterprises adopt AI to democratize data consumption and streamline or enhance business processes without incurring significant risk. </figure> <figcaption> <strong>Kevin Petrie</strong>Analyst, BARC U.S. </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>As a result, the volume and complexity of data workloads is increasing.</p> <p>The capabilities that comprise Pentaho's platform update are designed to help users better manage larger and more elaborate data workloads that provide <a href="https://www.techtarget.com/searchdatamanagement/opinion/Trusted-data-is-the-foundation-of-data-driven-decisions-GenAI">a foundation for AI development</a>, and were prioritized based on customer feedback, according to Sandeep Prakash, the vendor's vice president of product management.</p> <p>"Version 11 has a good balance of features based on customer requests and elements we know customers will benefit from as they manage heavier data workloads," he said.</p> <p>For example, the new user interface was developed in response to user feedback while the browser-based version of Pipeline Designer is a feature aimed at easing burdens on <a href="https://www.techtarget.com/searchdatamanagement/tip/essential-data-engineer-skills-for-modern-data-environments">data engineering teams</a>, Prakash continued.</p> <p>Pipeline Designer is part of Pentaho Data Integration and is a feature that enables users to create pipelines for tasks such as <a href="https://www.techtarget.com/searchdatamanagement/definition/Extract-Load-Transform-ELT">extract, load and transform</a> (ELT) workflows. The browser-based version simplifies pipeline development by removing local installation requirements -- configurations that need to be set up on local systems -- and includes a new interface for creating jobs to make it more accessible to business users.</p> <p>Project Profile likewise addresses pipeline development. But rather than simplify individual data integration jobs, it enables Pentaho users to group related jobs, transformations and configuration files into <a href="https://www.techtarget.com/searchitoperations/definition/container-containerization-or-container-based-virtualization">containers</a> to reduce deployment complexity and better enable collaboration.</p> <p>While Pipeline Designer and Project Profile simplify building and managing pipelines, Semantic Model Editor is aimed at making it easier to model data. The new tool replaces Schema Workbench and Data Source Wizard with a modernized means of <a target="_blank" href="https://tdwi.org/articles/2023/07/13/arch-all-importance-of-the-universal-semantic-layer-in-modern-data-analytics-and-bi.aspx" rel="noopener">creating and managing semantic models</a> that standardize defining data's characteristics -- its metadata -- to make it easier to discover and operationalize relevant data for a given initiative.</p> <p>Meanwhile, new authentication that integrates with identity providers such as Azure, Google and Okta, and redesigned permission controls both address governance and security.</p> <p>Perhaps the browser-based Pipeline Designer and Semantic Model Editor are the highlight features of Pentaho's platform update given that each simplify complex processes, according to Catanzano.</p> <p>"Pipeline Designer removes the need for local installations and offers a streamlined, intuitive interface, making it easier for distributed teams to collaborate and accelerate pipeline development," he said. "The Semantic Model Editor modernizes the analytics experience by replacing older tools with a cleaner, web-based workflow, ensuring a smoother transition for users while enhancing usability and governance."</p> <p>Petrie, meanwhile, highlighted Project Profile because it helps enterprises standardize data consumption across environments such as <a href="https://www.techtarget.com/searchnetworking/tip/How-multi-cloud-networking-can-ensure-reliability">multiple clouds</a> and on-premises systems.</p> <p>"It gives data and DevOps engineers modular, containerized pipelines that they can reuse on various platforms to speed data readiness and reduce governance risk," he said. "This helps simplify data consumption across hybrid and multi-cloud environments, which is to say most data environments."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With Version 11 of its Data Integration and Business Analytics platform update now available, Pentaho's product development roadmap is focused on helping customers build <a href="https://www.techtarget.com/searchbusinessanalytics/news/366618249/Trusted-data-at-the-core-of-successful-GenAI-adoption">a trusted data foundation</a> for AI initiatives and providing customers with automation and natural language processing capabilities to improve productivity, according to Prakash.</p> <p>"We see our roadmap aligning with customer needs that fall into two categories -- data for AI, and AI for data," he said. "Over the coming quarters, you'll see us deliver capabilities around AI-enabled discovery, semantic search for data [and] building … agentic workflows."</p> <p>Petrie noted that Pentaho's data integration and analytics capabilities are generally in line with those of its competitors. However, one way the vendor differentiates itself is with data optimization capabilities that help customers identify and archive less-used datasets to <a href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage">reduce costs</a>. Creating messaging that emphasizes Pentaho's unique capabilities would be wise, Petrie advised.</p> <p>"I'd be interested to see Pentaho play this up more in their sales and marketing efforts," he said.</p> <p>Catanzano, meanwhile, suggested that Pentaho could continue serving its current users and perhaps attract new ones by adding features and integrations that better enable customers to <a href="https://www.techtarget.com/searchapparchitecture/opinion/A-hands-on-look-at-AI-agents">develop AI tools</a>.</p> <p>"To continue evolving, Pentaho could expand its AI and machine learning capabilities by integrating with popular AI frameworks or offering pre-built, industry-specific AI models," he said. "This would not only enhance its value for existing users but also attract new customers seeking to accelerate their AI adoption."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> The vendor's latest platform update aims to speed, simplify and better govern workloads to help customers build a trusted foundation for AI development. https://cdn.ttgtmedia.com/rms/onlineimages/storage_g1226966455.jpg https://www.techtarget.com/searchdatamanagement/news/366638792/Pentaho-update-aids-data-integration-semantic-modeling Wed, 04 Feb 2026 08:30:00 GMT <p>As winter's chill blankets much of the U.S., Snowflake continues to drop new capabilities that simplify developing agents and other advanced applications.</p> <p>On Tuesday during Snowflake Build London, a user event in the United Kingdom, the vendor launched Cortex Code, Semantic View Autopilot and the native integration of <a href="https://www.techtarget.com/searchdatamanagement/news/366625068/Snowflake-acquisition-of-Crunchy-Data-adds-Postgres-database">Snowflake Postgres</a> in its AI Data Cloud, among a spate of other capabilities.</p> <p>Cortex Code is an agent that enables users to generate code for building pipelines and applications while applying an enterprise's security and governance controls. Semantic View Autopilot is an AI-powered service that automates creating and governing the semantic views that give agents proper context. And Snowflake Postgres is a PostgreSQL database that Snowflake acquired in June 2025.</p> <p>In addition to the new features, Snowflake on Feb. 2 unveiled a $200 million partnership with OpenAI that makes <a href="https://www.techtarget.com/searchenterpriseai/news/366622996/Whats-new-and-not-new-with-OpenAIs-latest-reasoning-models">OpenAI models</a> natively available within Snowflake's Cortex AI development environment. In addition, it includes plans for collaborating to build and deploy customized AI capabilities.</p> <p>Collectively, the partnership and new capabilities are important advances for Snowflake, according to William McKnight, president of McKnight Consulting. In particular, he noted the value of eliminating costly and complex data pipelines by natively embedding Snowflake Postgres in the vendor's AI Data Cloud and Cortex Code's understanding of an enterprise's data environment.</p> <p>"Snowflake, in this trove of announcements, wins&nbsp;the&nbsp;year in data so far and [furthers] its transition from a specialized data warehouse to a comprehensive AI and application platform," McKnight said.</p> <p>Sanjeev Mohan, founder and principal of analyst firm SanjMo, similarly called Snowflake's latest slew of features significant for <a href="https://www.techtarget.com/searchdatamanagement/feature/Whoop-pushing-AI-limits-powered-by-Snowflake">the vendor's customers</a>.</p> <p>"Snowflake already innovates fast, and the pace has picked up," he said. "Collectively, they give customers more optionality. And there was a big emphasis on skills, helping users codify their complex processes. That's a big benefit."</p> <p>Based in Bozeman, Mont., but with no central headquarters, Snowflake is a data management vendor that has added AI development capabilities over the past few years in response to surging interest from customers in AI. Build London marks the third event in the last eight months at which Snowflake has unveiled a multitude of new AI capabilities, following <a href="https://www.techtarget.com/searchdatamanagement/news/366625010/AI-tools-highlight-latest-swath-of-Snowflake-capabilities">Summit last June</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366634007/Snowflake-delivers-slew-of-AI-tools-introduces-new-ones">Build last November</a>.</p> <section class="section main-article-chapter" data-menu-title="Driving development"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Driving development</h2> <p>Snowflake's aim is to enable customers to create a connected data estate that can be trusted as a foundation for building and deploying AI and analytics applications, according to Christian Kleinerman, the vendor's executive vice president of product who spoke during a virtual press conference on Jan. 28.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Snowflake, in this trove of announcements, wins the year in data so far and [furthers] its transition from a specialized data warehouse to a comprehensive AI and application platform. </figure> <figcaption> <strong>William McKnight</strong>President, McKnight Consulting </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Despite enterprises <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">increasing their investments in AI development</a> and vendors such as Snowflake and rival Databricks attempting to simplify building cutting-edge tools by providing development frameworks, most AI initiatives <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">never make it past experimentation</a> and into production.</p> <p>Poor data foundations and improper alignment with governance policies are among the main reasons that the failure rate remains so high.</p> <p>Each of the three main capabilities Snowflake unveiled on Tuesday are designed to help customers create a connected, trusted data foundation for AI and analytics.</p> <p>AI-powered code generation capabilities are not uncommon. However, tools that align natural language-generated code with governance and security policies from the outset of the development process are uncommon. And when enterprise-grade governance and security policies are applied to code late in development, the AI-generated code often doesn't align with an enterprise's governance and security standards, and the project never makes it past the pilot stage.</p> <p>Domo recently <a href="https://www.techtarget.com/searchbusinessanalytics/news/366637892/Domo-adds-App-Catalyst-to-platform-to-aid-AI-development">launched App Catalyst</a>, an AI-powered code generator that integrates governance and security from the outset of a project. Now, Snowflake is doing something similar with the release of Cortex Code.</p> <p>Cortex Code, now part of Snowflake's Cortex AI development suite, understands user data, governance and operational semantics to give it context for creating code, and maintains an enterprise's governance and security standards to ensure that the code is enterprise-grade. Using the tool, data and AI teams can create production-ready applications far more efficiently than when they write code on their own.</p> <p>"The most significant announcement we're making at Build is we're introducing Cortex Code," Kleinerman said.</p> <p>While Cortex Code aids AI development by simplifying code generation, Semantic View Autopilot automates the creation of <a target="_blank" href="https://tdwi.org/articles/2023/07/13/arch-all-importance-of-the-universal-semantic-layer-in-modern-data-analytics-and-bi.aspx" rel="noopener">a semantic layer</a> so that <a href="https://www.techtarget.com/whatis/definition/metadata">metadata</a> and metrics are consistent across an organization and data can be discovered and trusted to inform analytics and AI applications. Similarly, running Snowflake Postgres natively within Snowflake's AI Data Cloud rather than externally through an integration advances development by simplifying access to unified transactional and analytical data that informs applications.</p> <p>"I like Semantic View Autopilot," Mohan said. "For non-technical users to create agents with Snowflake Intelligence -- Cortex Code is for techies -- really well, there has to a robust semantic layer. That, to me, is the most important of the new items."</p> <p>McKnight, meanwhile, called out integrating Snowflake Postgres into <a href="https://www.techtarget.com/searchdatamanagement/news/366625218/Snowflake-continues-to-add-AI-boost-Cortex-capabilities">the AI Data Cloud</a> as perhaps the most valuable of the new features.</p> <p>"Snowflake Postgres [transforms] Snowflake from a purely analytical data warehouse into a transactional and analytical platform," he said. "Snowflake is not the first to bridge this gap, but it's significant because … it opens entirely new use cases, removes the cost and complexity of [extract, transform and load] pipelines, and enables zero-code migration."&nbsp;</p> </section> <section class="section main-article-chapter" data-menu-title="Beyond the big three"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Beyond the big three</h2> <p>In addition to the launches of Cortex Code and Semantic View Autopilot, and the native integration of Snowflake Postgres in the AI Data Cloud, Snowflake's new partnership with OpenAI is a significant move, according to McKnight.</p> <p>Cortex AI enables users to access numerous AI models, including those from AI21 Labs, Anthropic, DeepSeek, Google Cloud, Meta and Mistral AI.</p> <p>Even OpenAI models were available to users before the new partnership between the AI developer and Snowflake. However, they were only available through <a href="https://www.techtarget.com/searchdatamanagement/news/366619812/Snowflake-adds-OpenAI-models-with-Microsoft-integration">Snowflake's integration with Microsoft</a>.</p> <p>Native availability is a direct integration between a model and the architecture of an AI development environment such as Cortex AI, including its access to data and enforcement of governance and security policies. Unlike other methods of connecting models with development environments such API integrations or plug-ins, no complex configurations are required.</p> <p>In addition to OpenAI's models, models from <a href="https://www.techtarget.com/searchdatamanagement/news/366635815/Snowflake-Anthropic-boost-partnership-with-200M-commitment">Anthropic</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366637132/Snowflake-boosts-Google-partnership-integrates-Gemini-3">Google Cloud</a>, Meta and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366572276/Snowflake-signals-AI-commitment-with-Mistral-AI-partnership">Mistral AI</a> are natively available in Cortex AI.</p> <p>"Moving OpenAI models natively into Snowflake is a game-changer because it keeps sensitive data entirely within the Snowflake security perimeter, effectively removing the complex governance and data egress obstacles that kill enterprise AI projects," McKnight said. "Instead of complicated engineering, analysts can trigger GPT using simple SQL functions, democratizing high-level AI across the organization."</p> <p>Additional new features Snowflake unveiled during Build London include the following:</p> <ul type="disc" class="default-list"> <li>Expansion of the Snowflake Horizon Catalog to include the open-source Polaris Catalog, which lets customers securely access data in <a target="_blank" href="https://iceberg.apache.org/" rel="noopener">Apache Iceberg</a> tables as well as create, update and manage data stored in Iceberg tables.</li> <li>Open Format Data Sharing to extend Snowflake's zero-ETL capabilities to open table formats Apache Iceberg and <a href="https://www.techtarget.com/searchdatamanagement/news/366542953/Databricks-introduces-Delta-Lake-30-to-help-unify-data">Delta Lake</a>.</li> <li>Snowflake Backups to protect business-critical data from ransomware or disruptions.</li> <li>Updates to Snowflake Notebooks, including an integration with Cortex Code and Experiment Tracking, to make it easy for teams to compare testing results and reproduce top-performing models.</li> <li>Cortex Agent Evaluations so users can trace, measure and audit agent behavior.</li> <li>An integration with Vercel that enables <a href="https://www.techtarget.com/searchcio/feature/Vibe-coding-What-IT-leaders-need-to-know">vibe coding</a> -- AI-assisted code generation using natural language prompts -- to build applications that can be deployed in Snowflake through Snowpark Container Services.</li> <li>An integration with the Brave Search API so users can integrate real-time information from the internet into Snowflake Intelligence, Cortex Code and Cortex Agents to augment an enterprise's proprietary data.</li> <li>New features in Workspaces, Snowflake Notebooks and OpenID Connect aimed at better enabling <a href="https://www.techtarget.com/searchsoftwarequality/tip/Improving-DevOps-collaboration-Challenges-and-tips">collaborative development</a>.</li> </ul> <p>"Some of [the new features] are improvement on existing technologies, but in all instances it's customer-driven innovation," Kleinerman said regarding Snowflake's impetus for developing the capabilities introduced at Build London.</p> </section> <section class="section main-article-chapter" data-menu-title="Competitive standing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Competitive standing</h2> <p>Two years after making AI development its main priority, Snowflake might have finally caught up to Databricks and other data and AI platform vendors, according to Mohan.</p> <p>After OpenAI's November 2022 launch of ChatGPT significantly improved generative AI technology, Databricks and hyperscale cloud vendors AWS, Google Cloud and Microsoft all quickly reacted. They created environments for customers to build AI tools, including development frameworks and integrations with AI providers such as OpenAI.</p> <p>Snowflake was slower to react, and only fully committed to enabling AI development in February 2024 when <a href="https://www.techtarget.com/searchbusinessanalytics/news/366571855/Snowflake-CEO-Slootman-steps-down-Ramaswamy-takes-over">Sridhar Ramaswamy was named CEO</a>.</p> <p>"They've caught up," Mohan said, noting that Google Cloud similarly had to catch up after being viewed as an innovator of machine learning capabilities with its 2017 release of the Transformer <a href="https://www.techtarget.com/searchenterpriseai/definition/neural-network">neural network</a> architecture.</p> <p>"Google invented the Transformer and then watched the whole world take off with not only OpenAI but Meta with Llama and others," Mohan continued. "But look where Google is now with Gemini. So, it is too early to call winners in AI, and Snowflake has demonstrated that it has caught up after a late start."</p> <p>McKnight likewise noted that with the release of its latest set of features -- particularly the integration of its Polaris and Horizon <a href="https://www.techtarget.com/searchbusinessanalytics/news/252510804/Data-catalogs-fuel-increased-efficiency-speed-to-insight">data catalogs</a> -- Snowflake has fully caught up with its peers.</p> <p>"Snowflake is now arguably ahead of Databricks in its ability to unify transactional applications and analytics, while having simultaneously neutralized the 'lock-in' argument," he said. "By embedding Apache Polaris directly into the Horizon Catalog, Snowflake now offers the same open governance as Databricks' Unity Catalog."</p> <p>Looking ahead to what Snowflake could do next to continue serving its users and perhaps even attract new ones, McKnight named adding <a href="https://www.computerweekly.com/opinion/Better-governance-is-required-for-AI-agents">agent governance capabilities</a> and more cost transparency.</p> <p>"In its highly competitive market, it needs to address agent governance with a layer that governs intent and action, and application-centric costing where instead of seeing costs by warehouse, there is a 'Product View' that bundles the costs of the Postgres instance, the Snowpark Container Services and the Cortex API."</p> <p>Mohan, meanwhile, suggested that Snowflake take steps to unify transactional processing and observational data such as <a href="https://www.techtarget.com/searchcustomerexperience/feature/To-predict-customer-buying-behavior-stop-look-listen-analyze">customer behavior</a>.</p> <p>"I would like them to show how I as a developer can access all my data in a unified manner through a catalog," he said. "Horizon doesn't handle observe data, and I'd like to see all data in one place."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with three decades of experience. He covers analytics and data management.</i></p> </section> New features such as an agent-powered code generator and automated semantic modeling simplify developing cutting-edge applications and improve the vendor's competitive standing. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1287248739.jpg https://www.techtarget.com/searchdatamanagement/news/366638535/Snowflake-launches-new-AI-tools-unveils-OpenAI-partnership Tue, 03 Feb 2026 03:00:00 GMT <p>Alteryx on Wednesday unveiled an expanded partnership with Google Cloud that includes the launch of Live Query for BigQuery, a new feature that enables joint customers to build and run Alteryx workflows in BigQuery.</p> <p>Previously, to use <a href="https://www.techtarget.com/searchdatamanagement/news/366615682/Alteryx-adds-tools-for-cloud-hybrid-analytics-deployments">Alteryx's data integration and preparation capabilities</a>, Alteryx users that store data in BigQuery first had to move data out of BigQuery and into Alteryx. Doing so, they incurred both the cost of <a href="https://www.techtarget.com/searchdatamanagement/definition/data-egress">data egress</a> and the inherent risk of exposure anytime data passes from one platform to another.</p> <p>Live Query for BigQuery eliminates those costs and risks.</p> <p>Beyond security and efficiency, it enables joint Alteryx and Google Cloud customers to better scale workloads given that BigQuery's servers have more capacity than Alteryx's servers, according to Donald Farmer, founder and principal of TreeHive Strategy. As a result, he noted that Live Query for BigQuery is a significant new feature for Alteryx customers using Google Cloud for storing their data.</p> <p>"Live Query for BigQuery is good in many ways because it enables BigQuery-scale analytics, which is way beyond the capacity of any Alteryx server, and Google Cloud customers will like it because it keeps data in-place, secured and managed by Google," Farmer said.</p> <p>In addition to Live Query for BigQuery, Alteryx's expanded partnership includes developing Alteryx One: Google Edition, a version of Alteryx's platform purpose-built for <a href="https://www.computerweekly.com/news/366546394/Pentland-Brands-marshals-Google-BigQuery-to-improve-customer-insight">Google Cloud customers</a> that will be available through the Google Cloud Marketplace.</p> <section class="section main-article-chapter" data-menu-title="Combined capabilities"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Combined capabilities</h2> <p>Based in Irvine, Calif., Alteryx is a longtime data management vendor providing a platform for integrating data from disparate sources and preparing it for analytics and AI. In recent years, Alteryx has made automation a key feature of its various tools, and in May 2025 <a href="https://www.techtarget.com/searchdatamanagement/news/366623973/Alteryx-One-launch-aims-to-unify-simplify-vendors-platform">launched Alteryx One</a> to unify previously disparate capabilities and make its suite easier to navigate.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Live Query for BigQuery is good in many ways because it enables BigQuery-scale analytics, which is way beyond the capacity of any Alteryx server, and Google Cloud customers will like it because it keeps data in-place, secured and managed by Google. </figure> <figcaption> <strong>Donald Farmer</strong>Founder and principal, TreeHive Strategy </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>BigQuery, meanwhile, is a fully managed data warehouse and AI platform purpose-built to enable fast <a href="https://www.techtarget.com/searchdatamanagement/definition/SQL">SQL</a> queries and data analysis across massive datasets.</p> <p>The impetus for developing Live Query for BigQuery came from a shared realization between Alteryx and Google Cloud that joint customers want to use Alteryx and BigQuery in conjunction with one another, but that doing so can be too complex for some data teams, according to Ben Canning, Alteryx's chief product officer.</p> <p>Specifically, he noted that users want the power, security and governance of <a href="https://www.techtarget.com/searchbusinessanalytics/news/366599575/Google-Clouds-BigQuery-gets-AI-injection-Looker-to-follow">a centralized platform like BigQuery</a> along with the data preparation and integration capabilities of a vendor such as Alteryx.</p> <p>"Business users clearly see the value of these platforms, but they're often too complex for non-technical teams to fully take advantage of on their own," Canning said. "That means a lot of the business-critical work -- things like data prep, calculations and logic -- still happens outside the platform. … Live Query for BigQuery brings those together."</p> <p>The result is stronger governance and security for <a href="https://www.techtarget.com/searchdatamanagement/feature/Effective-integration-key-to-creating-trusted-data">data integration</a> and preparation workloads with reduced complexity, he continued.</p> <p>"That shared customer need is what really drove this partnership forward," Canning said.&nbsp;</p> <p>Specific benefits of Live Query for BigQuery include the following, according to Alteryx:</p> <ul type="disc" class="default-list"> <li>Preparing data and applying business logic to data using Alteryx directly in BigQuery.</li> <li>Executing and automating governed data integration and preparation workflows at data warehouse scale.</li> <li>Maintaining the <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Data-governance-framework-key-to-analytics-success">centralized governance</a>, security, and performance of Google Cloud.</li> <li>Enabling faster insight generation for data, analytics and AI teams.</li> </ul> <p>Matt Aslett, an analyst at ISG Software Research, noted that the widespread adoption of cloud data platforms such as AWS, Google Cloud, Microsoft, Snowflake and Databricks has led to close integration between analytics and data preparation specialists such as Alteryx and <a href="https://www.techtarget.com/searchcloudcomputing/definition/hyperscale-cloud">hyperscale cloud</a> providers. As a result, Alteryx's alignment with Google Cloud is not the first such partnership to produce capabilities that reduce the need to move data between platforms.</p> <p>In fact, Alteryx offers Live Query capabilities for Databricks and Snowflake. However, Live Query for BigQuery is nevertheless significant given that it expands Alteryx's Live Query offerings to those customers that also use BigQuery for their <a href="https://www.techtarget.com/searchdatamanagement/feature/Evaluate-cloud-data-warehouses-based-on-data-outcomes">data warehouse needs</a>, according to Aslett.</p> <p>"Alteryx Live Query for BigQuery will enable Alteryx users with Google BigQuery to … potentially improve performance and reduce cost and complexity," he said.</p> <p>Farmer similarly noted that Live Query for BigQuery isn't unique in the sense that it enables users to push their analytics and data preparation workloads down into their data warehouse. However, the new tool has characteristics such as a drag-and-drop experience for direct queries and optimization for BigQuery that somewhat differentiate it from similar offerings, Farmer added.</p> <p>"I would say only that it is unique in its specifics, rather than its concepts," he said.</p> <p>Meanwhile, despite Live Query for BigQuery's potential value to joint Alteryx and Google Cloud customers, not all users might like the feature, Farmer cautioned.</p> <p>Live Query for BigQuery changes the way users work with data stored in BigQuery, removing it from <a href="https://www.techtarget.com/searchbusinessanalytics/news/366617637/New-Alteryx-CEO-sees-platform-as-the-canvas-for-AI-prep">the Alteryx environment</a> some users might prefer. However, for large workloads, the benefits of working with data where it resides rather than moving it between systems likely outweighs other concerns.</p> <p>"Alteryx had the best user experience to support an iterative workflow of preparation, analysis, re-preparation when something did not look quite right, and re-analysis," Farmer said. "This becomes much more difficult with live queries. But at this scale, it becomes somewhat impractical anyway."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>Just as Live Query for Big Query speeds and simplifies data workflows, Alteryx's product development plans for 2026 focus on further enabling customers to manage and analyze data more efficiently, according to Canning.</p> <p>"That means continuing to bring analytics and AI workflows closer to where trusted data lives, expanding in-place execution, and making business logic a governed, reusable asset rather than something buried in spreadsheets or code," he said.</p> <p>In addition, Alteryx is focused on helping customers build AI models and applications using clean, well-prepared data <a href="https://www.techtarget.com/searchdatamanagement/opinion/Generative-AI-shines-spotlight-on-data-governance-and-trust">that can be trusted</a>, Canning continued.</p> <p>Aslett noted that Alteryx has substantially expanded its product portfolio since <a href="https://www.techtarget.com/searchbusinessanalytics/news/366563665/Alteryx-to-be-acquired-by-private-equity-firms-for-44-billion">it was acquired</a> by Clearlake Capital and Insight Partners. In particular, the vendor has improved its offerings for cloud and hybrid environments and added AI-powered tools for data preparation and analytics.</p> <p>Regarding potential next steps, Aslett suggested that after expanding its partnership with Google Cloud, Alteryx do the same with other hyperscale cloud providers.</p> <p>"By offering tight integration with Google Cloud and ease of adoption through the Google Cloud Marketplace, Alteryx lowers barriers to adoption for Google Cloud customers," he said. "As such, I would anticipate similar editions of Alteryx One purpose-built for the likes of AWS, Microsoft Azure, Databricks and Snowflake."</p> <p>Farmer, meanwhile, suggested that Alteryx do more to help customers control costs. According to <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2024-11-05-gartner-says-cios-need-to-overcome-four-emerging-challenges-to-deliver-value-with-artificial-intelligence#:~:text=The%20Cost%20of%20AI%20Can,risk%20as%20security%20or%20hallucinations." rel="noopener">a 2024 Gartner survey</a> of more than 300 CIOs, cost is a prohibitive factor in developing advanced applications. Spending on cloud-native capabilities such as Live Query for BigQuery can quickly add up, so providing a tool that enables customers to predict expenses would be beneficial.</p> <p>"They need deeper cost governance, especially with this Live Query feature because as users run more of these queries cloud costs can spike," Farmer said. "They need a cost estimator that tells a user how much a workflow will cost in BigQuery credits before they hit 'run'. … Alteryx could do something interesting here."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more three decades of experience. He covers analytics and data management.</i></p> </section> Live Query for BigQuery eliminates the need to move data between systems, saving customers from spending on data egress and reducing the risk of security leaks. https://cdn.ttgtmedia.com/rms/onlineimages/storage_g1226966455.jpg https://www.techtarget.com/searchdatamanagement/news/366637939/Alteryx-launches-in-warehouse-data-prep-tool-for-BigQuery Wed, 28 Jan 2026 15:33:00 GMT <p>Domo, once an analytics specialist, is evolving with market trends to add capabilities to its platform that simplify developing agents and other cutting-edge AI applications.</p> <p>Toward that end, the vendor on Wednesday launched App Catalyst, a new tool within its AI and Data Products Platform that enables developers and other application builders to use natural language prompts when creating <a href="https://www.techtarget.com/searchitchannel/post/What-low-code-no-code-and-pro-code-mean-for-providers">pro-code tools</a> informed by their Domo data.</p> <p>However, unlike natural language interfaces such as <a href="https://www.techtarget.com/searchcio/feature/Vibe-coding-What-IT-leaders-need-to-know">vibe coding tools</a> that are aimed at quickly generating raw code to fuel AI pilots, App Catalyst helps data and AI teams generate code while also applying data access, security and operational standards at the outset of AI projects. The intent is to give AI initiatives a better chance of having a business impact than when access to data, governance frameworks and security restrictions are applied later in the development process.</p> <p>Given that App Catalyst combines AI-powered code generation with the governance and security enterprises require to use an application, its addition to Domo's platform is significant, according to Mike Leone, an analyst at Omdia, a division of Informa TechTarget.</p> <p>"We're seeing a shift where the hard part is no longer writing code, but more managing it," he said. "For Domo customers, this announcement matters because it … allows customers to move from a rough idea and experimentation to a legitimate, compliant business app without getting bogged down by the usual deployment hurdles."</p> <p>Based in American Fork, Utah, Domo is a cloud-based analytics vendor that, like peers including Qlik and Strategy, has added AI development capabilities since OpenAI's November 2022 launch of ChatGPT and sparked <a target="_blank" href="https://www.ey.com/en_us/newsroom/2025/07/ai-investments-surge-but-agentic-ai-understanding-and-adoption-lag-behind" rel="noopener">surging interest building AI tools</a>.</p> <section class="section main-article-chapter" data-menu-title="Striving for success"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Striving for success</h2> <p>Despite the emphasis enterprises and platform providers alike are placing on AI development, most AI pilots <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">never make it into production</a>.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> For Domo customers, this announcement matters because it … allows customers to move from a rough idea and experimentation to a legitimate, compliant business app without getting bogged down by the usual deployment hurdles. </figure> <figcaption> <strong>Mike Leone</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Recently, vendors including <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">Databricks</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development">MongoDB</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366637641/Teradatas-AgentStack-aims-to-simplify-building-managing-AI">Teradata</a> have all introduced capabilities that are designed to improve aspects of the AI development cycle in an attempt to help customers more successfully build AI tools. With App Catalyst now part of its platform, Domo is similarly trying to help customers more successfully build AI tools.</p> <p>Vibe coding, which is AI-assisted development using natural language prompts to create code, has enabled data and AI teams to quickly build prototypes. But it's not meant for creating enterprise-grade AI applications that can be trusted to generate insights and autonomously execute business processes.</p> <p>Fragile code and lack of alignment with enterprise data governance and security policies are among <a target="_blank" href="https://www.linkedin.com/pulse/vibe-coding-creates-nightmare-computer-scientists-fix-yerramsetti-b5pjc/" rel="noopener">vibe coding's shortcomings</a>.</p> <p>App Catalyst is designed to offer the same simplicity as vibe coding, according to Ben Schein, Domo's chief analytics officer and SVP of product development. But instead of solely simplifying coding, App Catalyst integrates code from the outset with enterprise-grade capabilities.</p> <p>"Vibe coding was all over the place, and our team was using it for fun side projects at home," Schein said. "But Domo always had the ability to create pro-code app components for use in low-code apps and dashboards. … So, it was natural to think we could lower that barrier."</p> <p>Key benefits of App Catalyst's addition to the Domo AI and Data Products Platform include quick ideation through natural language prompts, self-service development of pro-code applications, rapid prototyping that enables projects to move go ideas to production and automatic integrations with <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Data-governance-framework-key-to-analytics-success">governance and security policies</a>.</p> <p>Perhaps most valuable to Domo users is the automatic integration with governance and security frameworks, according to Leone, who noted that innovation stalls when applications aren't built with consistent governance standards that can be applied across an organization.</p> <p>"It's about more than just security permissions," he said. "By baking governance in up front, Domo is helping customers solve the compliance headache that typically kills projects before they start."</p> <p>Like Leone, David Menninger, an analyst at ISG Software Research, named integration with governance and security frameworks App Catalyst's most valuable feature, while calling a tool that enables users to create enterprise-grade applications with natural language "significant."</p> <p>"Generative AI has enabled all types of natural language processing and assistance in software products, but natural language app creation is probably one of the most significant improvements," he said. "It's not bounded the way other assistants are bounded. Users are only bounded by their imagination -- and the availability of the data."</p> <p>Regarding the value of <a href="https://www.techtarget.com/searchdatabackup/tip/Enterprise-data-governance-Frameworks-and-best-practices">a governance foundation</a> from the outset, Menninger added that attempting to add governance after an application is built often causes substantial delays.</p> <p>"While it may not be sexy, the governance foundation inherent in the Domo platform is one of the benefits enterprises will appreciate the most," he said.</p> <p>While beneficial for <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Domo-platform-a-difference-maker-for-check-guarantee-vendor">Domo users</a>, App Catalyst's AI-powered code generation capabilities are not unique, Leone noted. Many vendors provide tools that perform the same task, including hyperscale cloud vendors AWS, Google Cloud and Microsoft. Integration with governance, security and curated data from the outset, however, is more unique, Leone continued.</p> <p>"Most other options require you to stitch together a database, a separate App Catalyst and a governance layer," he said. "Domo is differentiating by offering that entire stack in one place. … That end-to-end flow is a bit harder to find."</p> <p>Menninger likewise noted that App Catalyst adds something unique to Domo's platform, but predicted&nbsp; that competing vendors will be able to provide similar capabilities.</p> <p>"Nearly all have generative AI assistants that can create dashboards and answer natural language queries," he said. "Some have code generation within notebooks, but few have full app development capabilities."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>Just as App Catalyst's addition to Domo's platform adds an AI-powered tool for fueling application development, the vendor's product development plans over the first half of 2026 focus on <a href="https://www.techtarget.com/searchbusinessanalytics/news/366621048/Domo-unveils-agentic-AI-toolkit-to-simplify-development">providing more AI capabilities</a>, according to Schein.</p> <p>"Cloud Integrations and AI remain a strong focus," he said.</p> <p>AI initiatives include continuing to make it easier for customers to develop customized AI-powered agents and chatbots and expanding <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">its Model Context Protocol server</a> to provide users with a framework for connecting agents developed in Domo with other agents and AI platforms, Schein continued.</p> <p>One feature Domo and its peers would be wise to add is <a href="https://www.techtarget.com/searchbusinessanalytics/news/252523947/Scenario-planning-fertile-ground-for-analytics-vendors">a scenario planning tool</a>, according to Menninger. Despite analytics vendors expanding beyond their traditional roots over the past few years, few have added capabilities that enable users to evaluate potential outcomes.</p> <p>"Humans and agents need to evaluate alternative scenarios and that requires planning capabilities," Menninger said. "There are very few vendors that have combined planning with analytics, but I expect we will see more."</p> <p>Leone, meanwhile, suggested that Domo advance App Catalyst beyond being an interactive tool and turn it into <a href="https://www.techtarget.com/searchenterpriseai/news/366617713/Autonomous-AI-agents-on-the-rise">an autonomous agent</a>. He noted that, in general, Domo has proven to be resiliently innovative over the past few years by adding AI development and AI-powered capabilities to its platform. Advancing App Catalyst would further demonstrate Domo's adaptability.</p> <p>"They're already simplifying the build process," Leone said. "There's a massive need for apps that can execute tasks without constant human input. If they can make these AI-generated apps smart enough to trigger their own workflows based on real-time data changes, that will be a gamechanger."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> By combining natural language code generation with enterprise-grade security and governance, the vendor aims to help customers more successfully build cutting-edge applications. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1289411982.jpg https://www.techtarget.com/searchbusinessanalytics/news/366637892/Domo-adds-App-Catalyst-to-platform-to-aid-AI-development Wed, 28 Jan 2026 09:51:00 GMT <p>A fundamental tension is emerging in enterprise AI that few organizations are prepared to address. I call it the "trust-at-speed paradox," and it's quietly becoming the biggest obstacle between AI pilots and production deployments.</p> <p>Organizations are racing to deploy agentic AI systems capable of autonomous decision-making. These systems promise unprecedented efficiency, operating at machine speed to analyze data, make decisions and take actions with minimal human intervention. But the faster these systems operate, the faster they propagate errors, act on compromised data or create cascading compliance violations.</p> <p>When a human analyst makes a mistake, it's usually contained. They catch it, correct it and move on. When an AI agent acts on bad data, it can make hundreds of downstream decisions before anyone notices something is wrong. A single flaw in data lineage or quality doesn't create one problem; it triggers untraceable, cascading risk events that ripple through business processes at machine speed.</p> <section class="section main-article-chapter" data-menu-title="The governance gap"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The governance gap</h2> <p>Traditional data governance was designed for a different era. It assumed human-paced decision-making, including approval workflows in which someone reviews access requests, quarterly or annual audits, and manual oversight of data quality issues as they arise.</p> <p>Agentic AI breaks this model entirely. A human cannot approve every data access request when an agent is making thousands of decisions per hour. The math doesn't work, and the resulting latency would defeat the purpose. Without that oversight, <a href="https://www.techtarget.com/searchenterpriseai/news/366622900/AI-companies-claim-existing-rules-can-govern-agentic-AI">organizations are effectively letting autonomous systems loose</a> on their most sensitive data assets using governance frameworks that were never designed to monitor, control or audit machine-speed operations.</p> <p>This isn't a theoretical concern. Many organizations have AI pilots successfully running in controlled environments, but far fewer have moved to production at scale. In most cases, progress stalls due to <a href="https://www.techtarget.com/searchdatamanagement/opinion/The-future-of-AI-depends-on-better-data-not-bigger-models">mistrust in data quality and data governance</a>, which fail to meet compliance and data security requirements.</p> </section> <section class="section main-article-chapter" data-menu-title="The c-suite blind spot"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The c-suite blind spot</h2> <p>This governance gap is creating new risk exposure that boards and executives don't yet fully understand. When AI agents act autonomously, <a href="https://www.techtarget.com/searchenterpriseai/feature/Ethical-considerations-of-agentic-AI-and-how-to-navigate-them">fundamental questions become surprisingly difficult to answer</a>: Who is accountable when an agent makes a poor decision? How do you trace a business outcome back through an agent's decision chain? What data did it access, and was that data accurate at the moment of access?</p> <p>Fragmented governance creates visibility gaps at the executive level precisely when they need more visibility, not less. The irony is that organizations are <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-prepare-your-business-for-agentic-AI-adoption">deploying AI to gain a competitive advantage</a> through speed and efficiency while simultaneously creating blind spots that could result in regulatory penalties, reputational damage and operational failures.</p> </section> <section class="section main-article-chapter" data-menu-title="Data quality is now existential"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Data quality is now existential</h2> <p>For traditional analytics, poor data quality led to inaccurate reports -- annoying, but manageable. For agentic AI, poor data quality leads to autonomous systems taking real-world actions based on flawed information. The stakes are fundamentally different.</p> <p>Without <a href="https://www.techtarget.com/searchenterpriseai/tip/The-role-of-trusted-data-in-building-reliable-effective-AI">reliable data</a>, even the most advanced AI models will produce flawed and potentially harmful results. This is no longer about model accuracy. It's about agents executing transactions, making recommendations to customers or adjusting operational parameters based on data that may be incomplete, outdated or simply wrong.</p> <p>The organizations that will successfully scale agentic AI aren't necessarily the ones with the most sophisticated models. They're the ones who recognize that governed autonomy requires a completely new approach to data governance, designed for machine speed from the ground up.</p> <p>The good news is that this can be addressed by recognizing how AI operates on enterprise data and rethinking where and how governance is automated and applied.</p> <p><em>Stephen Catanzano is a senior analyst at Omdia where he covers data management and analytics.</em></p> <p>&nbsp;</p> <p><em>Omdia is a division of&nbsp;Informa TechTarget.&nbsp;Its analysts have business relationships with technology vendors.</em></p> </section> Agentic AI operates autonomously, exposing gaps in governance, data quality and accountability. Executives must address these limits to manage risk and move AI into production. https://cdn.ttgtmedia.com/visuals/digdeeper/2.jpg https://www.techtarget.com/searchdatamanagement/opinion/Most-data-governance-wasnt-built-for-AI Tue, 27 Jan 2026 12:28:00 GMT <p>Teradata's product development priority is to create an agentic AI development suite that enables customers to move pilots into production.</p> <p>In September, <a href="https://www.techtarget.com/searchdatamanagement/news/366631543/Teradata-unveils-AgentBuilder-to-aid-agentic-AI-development">the vendor introduced AgentBuilder</a>, a set of tools aimed at simplifying developing agents. AgentBuilder includes a <a target="_blank" href="https://modelcontextprotocol.io/docs/getting-started/intro" rel="noopener">Model Context Protocol</a> server for connecting AI applications with data sources, integration with Teradata's data management and analytics platforms, and development templates.</p> <p>On Tuesday, Teradata unveiled Enterprise AgentStack, a feature set scheduled for general availability by midyear that is designed to not only simplify the process of building agents but also make it easier to deploy and govern them. Enterprise AgentStack includes AgentBuilder for development and adds AgentEngine for deployment and AgentOps for governance.&nbsp;</p> <p>Given that Enterprise AgentStack furthers Teradata's evolution from managing and analyzing historical data toward enabling AI-driven decisions, the suite will be a significant addition for Teradata customers once generally available, according to William McKnight, president of McKnight Consulting.</p> <p>"Enterprise AgentStack could mark an important shift in how Teradata is used," he said. "It provides a path to operationalize AI agents at scale, allowing them to collaborate, reason over governed enterprise data and produce higher-order outputs like strategic plans rather than isolated query responses."</p> <p>Based in San Diego, Teradata is a longtime data management and analytics vendor providing <a href="https://www.techtarget.com/searchbusinessanalytics/news/366610972/Teradata-ClearScape-Analytics-update-targets-ROI-on-AI-ML">ClearScape Analytics</a> for data analysis and <a href="https://www.techtarget.com/searchbusinessanalytics/news/365532103/New-integration-links-Teradatas-VantageCloud-with-Azure-ML">VantageCloud</a> for storing and preparing data.</p> <p>Like peers such as Databricks and Snowflake, Teradata expanded beyond its roots into AI development after OpenAI's November 2022 launch of ChatGPT marked significant improvement in generative AI (GenAI) technology and sparked <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">surging interest in building AI tools</a>.</p> <section class="section main-article-chapter" data-menu-title="Simplifying AI development, management"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Simplifying AI development, management</h2> <p>Despite heightened interest in AI development, <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">the vast majority</a> of all AI projects never make it into production.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Enterprise AgentStack could mark an important shift in how Teradata is used. It provides a path to operationalize AI agents at scale, allowing them to collaborate, reason over governed enterprise data and produce higher-order outputs like strategic plans rather than isolated query responses. </figure> <figcaption> <strong>William McKnight</strong>President, McKnight Consulting </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Fragmented infrastructure systems, disorganized data estates and the sheer complexity of building applications that can perform as or more intelligently than humans are among the many problems that stall AI projects. In response, some data management vendors are trying to help customers by providing capabilities that better enable them to discover and operationalize relevant data and simplify developing agents and other AI tools.</p> <p>For example, Databricks recently <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">launched Instructed Retriever</a>, a new feature in its AI development environment aimed at improving the data discovery and retrieval processes to make AI applications more accurate. MongoDB, meanwhile, launched <a href="https://www.techtarget.com/searchdatamanagement/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development">new vector embedding and ranking models</a> that similarly address data retrieval to make AI applications more effective.</p> <p>Teradata's Enterprise AgentStack is a feature set designed to make the vendor's platform not just a system of knowledge, but one that also enables customers to derive AI-powered insights, according to Sumeet Arora, Teradata's chief product officer.</p> <p>"We see an intelligence revolution sweeping enterprises," he said. "Customers are increasingly focused on deriving outcomes from their enterprise data and knowledge. In conjunction with Teradata's analytics and customer intelligence applications, AgentStack enables agentic AI-driven outcomes for customers in a secure and private fashion, within the environment where their data and knowledge reside."</p> <p>Each of the components that comprise Enterprise AgentStack serves a specific purpose.</p> <p>AgentBuilder is a framework designed to make it faster and easier for customers to develop agents than it would be if they had to put the process together on their own. Included are integrated no-code and pro-code options, Teradata's data management and analytics capabilities to give agents contextual awareness, and industry-specific data models.</p> <p>In addition, AgentBuilder features integrations with cloud service providers, <a href="https://www.techtarget.com/searchenterpriseai/podcast/Exploring-Nvidias-approach-to-AI-factories">Nvidia</a> and large language model APIs, prebuilt agents for system monitoring, and <a href="https://www.techtarget.com/searchdatamanagement/definition/SQL">SQL</a> optimization, among others.</p> <p>Similarly, Enterprise MCP, a Model Context Protocol server that enables agents to autonomously interact with data sources -- including both structured and unstructured data -- is intended to simplify and speed up the development process.</p> <p>AgentStack, meanwhile, is a secure and scalable environment for deploying both individual agents as well as multi-agent systems across cloud, on premises and hybrid Teradata infrastructures. Lastly, AgentOps is a centralized interface for monitoring and managing agents, ensuring <a href="https://www.techtarget.com/searchenterpriseai/tip/Agentic-AI-governance-strategies-A-complete-guide">security and governance</a> by enforcing an enterprise's policies and compliance checks.</p> <p>By including different components that address the agentic AI lifecycle, Enterprise AgentStack appears to be a well-constructed feature set for building and managing agents, according to McKnight.</p> <p>"It consolidates the entire agent lifecycle -- building, deploying and managing -- into a unified platform that runs directly where mission-critical data resides, solving key security and latency hurdles," he said. "Its most valuable components for this transition are AgentEngine, which enables secure deployment … and AgentOps, which mitigates economic and regulatory risks."</p> <p>Donald Farmer, founder and principal of TreeHive Strategy, likewise noted that Enterprise AgentStack appears well-constructed. He pointed out that the suite does not include support of <a href="https://www.techtarget.com/searchenterpriseai/news/366622027/Google-intros-tools-for-building-agents-and-a-new-protocol">Agent2Agent Protocol</a> (A2A), a framework that enables agents to interact with one another. But by enabling agents to share memory and data structures, Teradata is providing similar capabilities.</p> <p>"Instead of agents just talking to each other -- which can lead to a game of 'Telephone' [and produce] errors -- they are operating in a shared workspace," Farmer said. "This actually may be more robust for enterprise data than simple chat-based A2A. … A shared state is much more efficient."</p> <p>However, Farmer cautioned that while shared memory may enable more efficient interactions between agents in an enterprise's Teradata's environment, it may not enable the same <a href="https://www.techtarget.com/searchcustomerexperience/news/366636690/Agentic-orchestration-the-next-AI-issue-for-CIOs-to-tackle">openness as A2A</a>, which facilitates agentic interaction across environments.</p> <p>"Shared memory often implies a shared brain or controller," he said. "This is great for a single-vendor multi-agent system, but it’s the definition of a walled garden. For example, it's very hard for a Microsoft agent to step into a Teradata shared memory space."</p> <p>Customers in risk averse industries such as banking and healthcare may prefer to prevent agents in one system from interacting with agents in another, Farmer continued. But with competitors including <a href="https://www.techtarget.com/searchcustomerexperience/news/366632736/Salesforce-to-release-Agentforce-360-Slack-agents">Salesforce</a> and <a href="https://www.techtarget.com/searchunifiedcommunications/news/366635194/Microsoft-bets-on-human-agent-team-collaboration">Microsoft</a> touting agent ecosystems that enable agents to constantly interact, Teradata's approach comes with risk.</p> <p>"Teradata is likely betting that its customers don't want autonomous negotiation yet," Farmer said. "However, if Teradata's agents can only talk to other Teradata agents within the same stack, they miss out on the broader AI economy. If the rest of the world moves toward an open Agent Exchange, Teradata risks becoming a very secure, very expensive vault that no one has the key to."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>Once Enterprise AgentStack is released, it will bring Teradata's agentic AI development capabilities in line with other vendors' development suites, according to McKnight. And with its focus on keeping agentic interactions within Teradata -- as Farmer noted -- the suite could particularly appeal to highly regulated enterprises.</p> <p>"Where Teradata really has an edge is in running those agents safely in the real world -- especially across hybrid setups or fully air-gapped environments -- which is something most cloud-first players have a hard time supporting," McKnight said. "That matters a lot for regulated industries, and this is where AgentStack stands out."</p> <p>Beyond the specific launch of Enterprise AgentStack, Teradata's product development plans center on providing customers with a platform prepared for <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms">agentic AI ecosystems</a>, according to Arora.</p> <p>"In the coming months, Teradata&nbsp;is&nbsp;focused on its vision of enabling the autonomous enterprise by leaning hard into the concept of an autonomous AI and knowledge platform ready for agentic workloads," he said.&nbsp;</p> <p>That focus is wise, according to McKnight, both for Teradata as well as competing vendors that now emphasize agentic AI development and deployment.</p> <p>Specifically, he suggested that Teradata and its peers add features such as automated testing, cross-vendor orchestration standards, risk-based autonomy controls, <a href="https://www.techtarget.com/searchsecurity/definition/sandbox">sandboxing</a>, versioning, rollback, <a target="_blank" href="https://tdwi.org/articles/2023/07/13/arch-all-importance-of-the-universal-semantic-layer-in-modern-data-analytics-and-bi.aspx" rel="noopener">semantic standards</a> and human-in-the-loop oversight to enable customers to trust agents in production.</p> <p>"Teradata and others need to move beyond enabling agent creation and treat AI agents like production software, with strong safety, governance and interoperability baked in," he said. "I would also encourage all autonomous agentic AI platform builders to [reinforce] the message that enterprise success will come from disciplined engineering, not AI novelty."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.</i></p> </section> Featuring capabilities for developing, deploying and governing agents, the vendor's new suite addresses many of the problems enterprises face when trying to launch agentic systems. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1297696209.jpg https://www.techtarget.com/searchdatamanagement/news/366637641/Teradatas-AgentStack-aims-to-simplify-building-managing-AI Tue, 27 Jan 2026 09:00:00 GMT <p>Snowflake on Tuesday introduced Energy Solutions, a suite of capabilities aimed at making it easier for enterprises in the energy sector to manage data and develop AI applications than when using the vendor's general-purpose platform.</p> <p>In addition to features available to all Snowflake users such as <a href="https://www.techtarget.com/searchdatamanagement/news/366634007/Snowflake-delivers-slew-of-AI-tools-introduces-new-ones">Snowflake Intelligence</a> -- an agent that enables data exploration and analysis using natural langue -- and the vendor's <a href="https://www.techtarget.com/searchdatamanagement/news/366625218/Snowflake-continues-to-add-AI-boost-Cortex-capabilities">Cortex AI development environment</a>, Energy Solutions includes data and AI governance capabilities, more than 30 partner-developed applications that users can adopt, and industry-specific datasets.</p> <p>Snowflake first launched industry-specific capabilities in 2021 when it released the <a href="https://www.techtarget.com/searchdatamanagement/news/252506699/Snowflake-aims-at-financial-services-with-data-cloud">Financial Services Data Cloud</a>. Since then, it has added eight more industry-specific offerings, including targeted suites for the financial services, manufacturing and technology sectors, among others.</p> <p>Given that packaged capabilities geared toward specific industries make it easier for enterprises to manage their data and develop data-informed applications than general-purpose platforms, Snowflake's Energy Solutions is significant, according to Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget.</p> <p>"Industry-specific platforms are just better at solving the exact problems a sector faces," he said. "There may be a trend building for AI industry-specific stories. [Energy Solutions] comes with the right tools, integrations and data already built in, so companies don't have to spend as much time customizing things. It's faster, easier, and more effective than trying to make a general-purpose platform fit."</p> <p>Based in Bozeman, Mont., but with no central headquarters, Snowflake is a data management vendor that, like many of its peers, has added AI development capabilities over the past few years. Recently, the vendor <a href="https://www.techtarget.com/searchdatamanagement/news/366637132/Snowflake-boosts-Google-partnership-integrates-Gemini-3">made Google's Gemini 3 model available</a> in Cortex AI to provide users with a new large language model <a target="_blank" href="https://www.linkedin.com/pulse/how-choose-right-large-language-model-your-business-kgy7f/" rel="noopener">to choose from when building AI tools</a>.</p> <section class="section main-article-chapter" data-menu-title="Energy boost"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Energy boost</h2> <p>As enterprises <a href="https://www.ey.com/en_us/newsroom/2025/07/ai-investments-surge-but-agentic-ai-understanding-and-adoption-lag-behind">increasingly invest in developing agents</a> and other AI tools, a strong data foundation is critical. Without discoverable, relevant, high-quality data, AI initiatives are doomed.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Industry-specific platforms are just better at solving the exact problems a sector faces. There may be a trend building for AI industry-specific stories. [Energy Solutions] comes with the right tools, integrations and data already built in, so companies don't have to spend as much time customizing things. </figure> <figcaption> <strong>Stephen Catanzano</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>While Snowflake's general-purpose platform is designed to help organizations effectively manage their data and build data-informed applications, data management and AI development are still complex processes. Industry-specific offerings, while not eliminating complexity, ease some of the difficulties enterprises face when trying to organize billions of data points and develop AI-powered applications that need to be accurate to be trusted.</p> <p>Snowflake is not the only data management vendor to provide targeted versions of their platforms. Rival Databricks similarly provides users in industries such as <a href="https://www.techtarget.com/searchdatamanagement/news/366566672/Databricks-launches-platform-for-the-telecom-industry">telecom</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/252514358/Databricks-extends-data-lakehouse-platform-to-healthcare">healthcare</a> with prepackaged tools geared toward particular sectors. In addition, SAS and SAP are among the vendors offering industry-specific capabilities.</p> <p>While Snowflake has been providing specialized offerings geared toward different sectors for five years, the impetus for developing Energy Solutions came from customer feedback, according to Fred Cohagan, the vendor's global head of energy.</p> <p>"As supply and demand conditions change more frequently and unpredictably, customers tell us they need a secure, governed data foundation that supports real operational workflows -- not just analytics or AI pilots -- so teams can make faster, more reliable decisions," he said. "These new solutions are designed to … create greater value over time as customers expand use cases."</p> <p>Regarding the reason Snowflake chose the energy sector for its latest <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Domain-specific-BI-vendors-gain-foothold-with-expertise">targeted offering</a>, the data-intensive nature of the energy industry made it a good candidate, Cohagan continued.</p> <p>"Energy is one of the most data-intensive and mission-critical industries in the world -- it underpins nearly every economy and sector," he said. "There is decades of operational, engineering, and business data spread across systems, which makes energy a natural fit for Snowflake's ability to bring that data together in a governed way so energy companies can apply AI reliably and responsibly at scale."</p> <p>Collectively, the capabilities that comprise Energy Solutions are designed to enable Snowflake customers in the oil and gas, power, and utilities industries to build data foundations by securely connecting data across IT, operational technology and IoT systems. Once integrated and organized, the data foundations can be used to develop trustworthy AI tools, including those aimed at progressing toward more reliable energy solutions and <a target="_blank" href="https://www.bbvacib.com/insights/news/energy-sector-challenges-and-opportunities-in-the-transition-to-sustainability/" rel="noopener">a lower-carbon future</a>.</p> <p>Because industry-specific offerings simplify data management and AI development, Kevin Petrie, an analyst at BARC U.S., like Catanzano lauded their value.</p> <p>"To gain real competitive advantage with AI, organizations must apply advanced models to their proprietary business processes and datasets," he said. "Industry-specific solutions help achieve this by streamlining integration work and enabling AI adopters to get into production faster."</p> <p>Regarding a targeted offering's value to the energy sector, Petrie noted that enterprises in the industry tend to be slower to adopt cutting-edge technologies than those in some other industries. As a result, Energy Solutions will serve as an AI accelerator for Snowflake customers.</p> <p>"The energy sector is not typically an early adopter of new technologies such as AI," Petrie said. "This solution will reduce the level of in-house expertise they need to make AI projects feasible and to reach production."</p> <p>While serving to accelerate AI development in a sector slower than some others to evolve, the individual component of Energy Solutions that will perhaps be most significant toward that end will be its <a href="https://www.techtarget.com/searchdatamanagement/feature/Effective-integration-key-to-creating-trusted-data">consolidation of complex data</a>, according to Catanzano.</p> <p>"The biggest win is how it brings all kinds of data like IT, OT and IoT, into one place and makes it easy to analyze," he said. "That means companies can use AI and advanced tools to make smarter decisions, save money, and improve reliability."</p> <p>Regarding potential differentiation, Catanzano added that, while Energy Solutions is a valuable addition for Snowflake users, other vendors also offer suites targeted at the energy sector. However, the <a href="https://www.techtarget.com/searchdatamanagement/feature/5-data-integration-challenges-and-how-to-overcome-them">data unification capabilities</a> and inclusion of partner-built applications could help Snowflake stand out.</p> <p>"What makes Snowflake different is how it focuses on unifying all the data types and its strong ecosystem of partners that bring extra functionality," Catanzano said. "I haven't seen as much of this with the others."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>As Snowflake makes <a href="https://www.techtarget.com/searchdatamanagement/feature/Customers-pleased-with-Snowflake-plans-for-AI">its product development plans</a>, helping customers reliably operationalize data and AI is the vendor's guiding principle, according to Cohagan.</p> <p>"That includes continued platform innovation and working closely with partners to support real-world operational use cases across industries," he said.</p> <p>Beyond continuing to add more industry-specific offerings and refining those it already provides, Snowflake could serve the needs of current users and potentially attract news ones by providing a broader array of advanced AI tools, according to Catanzano.</p> <p>"Adding more advanced AI tools or tighter integrations with operational systems like SCADA would be smart moves," he said. "Partnering with more startups and industry leaders could also help them stay ahead of the game."</p> <p>Petrie similarly suggested that Snowflake partner with third parties to <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms">add AI expertise</a> that can be passed on to customers.</p> <p>"BARC research shows that organizations are looking outside for AI expertise," he said. "In fact, they are more satisfied with the AI contributions of vendors and consultants than they are with their own IT departments. Given this, I'd recommend that Snowflake deepen and extend its partnerships with consulting firms."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> Energy Solutions joins the vendor's spate of industry-specific offerings and includes partner-built applications and pre-modeled datasets that accelerate data-driven initiatives. https://cdn.ttgtmedia.com/rms/onlineimages/toolGearArrow_g473747386.jpg https://www.techtarget.com/searchdatamanagement/news/366637773/New-Snowflake-suite-simplifies-data-AI-for-energy-sector Tue, 27 Jan 2026 09:00:00 GMT <p>Business leaders often frame the role of data architectures around competitiveness, scalability and efficiency. But those benefits no longer capture its most critical role: Risk management. Today, data architecture decisions function as the core mechanism for managing enterprise risk.</p> <p>Rather than treating data architecture assessment primarily as a technical decision, business leaders increasingly view architecture design as a control mechanism for overall enterprise stability – one that limits risk exposure, supports business continuity, enables faster recovery and minimizes the financial consequences of disruption.<a name="_rnn76ysvut2m"></a><a name="_tml8vqxi9o4p"></a></p> <section class="section main-article-chapter" data-menu-title="The data modernization trend"> <h2 class="section-title"><i class="icon" data-icon="1"></i><a name="_vkwvhr2k3fq8"></a>The data modernization trend</h2> <p>Data architecture is the high-level strategy an organization uses to store, process, secure, integrate, transform, analyze and discard its data. At the highest level, architectures are distinguished by whether they use centralized, decentralized or hybrid architecture.</p> <p>With the exception of young organizations that have <a href="https://www.techtarget.com/searchdatamanagement/feature/Building-a-strong-data-analytics-platform-architecture">built data architectures</a> from scratch using modern designs, data architectures are often more aspirational than fully realized. In practice, most businesses operate with default data architectures that emerged incrementally and don't align well with any single model.</p> <p>Data modernization addresses this gap by updating architectures to reflect more effective approaches and technologies. As Gartner notes, data modernization offers a way to "<a href="https://www.gartner.com/en/data-analytics/topics/data-management">increase value and reduce costs</a>" and to "deliver innovative digital transformation products and competitive IT services at optimum cost," in IDC's assessment.</p> </section> <section class="section main-article-chapter" data-menu-title="Data modernization's role in enterprise risk management"> <h2 class="section-title"><i class="icon" data-icon="1"></i><a name="_yje9zzedxhqs"></a>Data modernization's role in enterprise risk management</h2> <p>For businesses with inefficient or unscalable architectures, data modernization offers a path forward that extends beyond faster processing or lower data management costs.</p> <p>The most important reason to modernize data architecture is to supercharge <a href="https://www.techtarget.com/searchcio/feature/8-top-enterprise-risk-management-trends">enterprise risk management</a>. Data has become the foundation for virtually everything in a typical organization. By extension, data architecture now plays a vital role in risk management across industries.</p> <h3><a name="_qnow52w4r296"></a>Cybersecurity</h3> <p>An organization's ability to minimize <a href="https://www.techtarget.com/searchsecurity/tip/Cybersecurity-risk-management-Best-practices-and-frameworks">exposure to cybersecurity vulnerabilities</a> and breaches hinges on the data architecture it chooses.</p> <p>Architectures with weak governance controls make it harder to manage access to sensitive information. In contrast, architectures that enforce clear access boundaries – ensuring data is available only to authorized users, when needed and in the appropriate form -- face an overall lower risk of data exfiltration, breaches and phishing attacks.</p> <p>From a risk perspective, the question to consider isn't whether data is centralized or distributed, structured or unstructured, but whether consistent security and access policies are enforced wherever data resides.</p> <h3><a name="_meybuwcn7xym"></a>Resilience</h3> <p>Business resilience -- the ability to avoid operational disruption and recover quickly when disruptions occur -- also depends heavily on data architecture.</p> <p>Decentralized or distributed data architectures, <a href="https://www.techtarget.com/searchdatamanagement/feature/The-differences-between-a-data-warehouse-vs-data-mart">such as data marts</a>, tend to improve resilience by spreading data across the organization and reducing single points of failure that could bring all business operations to a halt.</p> <p>However, decentralization strengthens resilience only when supported by efficient processes and policies that ensure timely access to required data. Otherwise, operations may slow down or fail because teams can't access the data they need to work efficiently.</p> <h3>Governance</h3> <p>Governance refers to the practices and rules organizations use to manage assets and operations, and while it extends beyond data, data provides a practical foundation for establishing a strong culture of governance.</p> <p>Modern data architectures with clear, consistently enforced governance policies and controls help set the foundation for governance across all facets of the business.</p> <p>No single <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-lake-vs-data-warehouse-Key-differences-explained">data architecture is inherently better than others</a>. The key is to ensure that your data platforms and infrastructure include reliable governance standards that define who can access data, who can modify it and how long it should be stored.</p> <h3>Compliance</h3> <p>Compliance, like governance, extends beyond data, but a strong compliance posture often starts with data compliance.</p> <p>Data compliance involves <a href="https://www.techtarget.com/searchdatamanagement/tip/3-considerations-for-a-data-compliance-management-strategy">identifying relevant regulations</a> and ensuring that your data architecture enforces their requirements. For example, data sovereignty rules might require certain types of data to remain within specific jurisdictions, while other regulations might require encryption by default.</p> <p>By embedding compliance requirements directly into data architecture, organizations <a href="https://www.techtarget.com/searchsecurity/post/The-business-benefits-of-data-compliance">can reduce exposure to regulatory gaps</a> that may lead to compliance shortcomings and establish a solid foundation for other compliance practices beyond the realm of data.</p> <h3>Financial risk management</h3> <p>Mitigating financial risks also becomes easier with an effective data architecture in place. This is partly because failures in areas such as cybersecurity and compliance often carry direct financial consequences when they are not managed properly. It also reflects the role architecture plays in supporting operational efficiency, faster time-to-market and stronger margins.</p> <p>In practice, organizations that manage their data securely and efficiently are less prone to financial waste and disruption.</p> </section> <section class="section main-article-chapter" data-menu-title="A practical approach to modernizing data and reducing risk"> <h2 class="section-title"><i class="icon" data-icon="1"></i>A practical approach to modernizing data and reducing risk</h2> <p>Data modernization as the foundation for enterprise risk mitigation is far from theoretical. Given the complexity of most organizations' data assets, data processes and data management needs, achieving data modernization can be challenging. However, there are actionable steps business leaders can take to streamline the process:</p> <ul class="default-list"> <li><b>Assess the existing data architecture.</b> Leaders should comprehensively analyze the data architecture they currently have in place, including where data originates, how it's processed and who can access it.</li> <li><b>Survey stakeholders about data challenges.</b> Asking employees about challenges they face in accessing, transforming or processing data can go far identifying ways in which data architectures can be improved. For instance, a survey might reveal that data access is currently too centralized and restricted, and that a more flexible approach could improve availability.</li> <li><b>Build a risk map.</b> Risk maps are visual representations of where risks lie within an organization, and can help leaders understand which risks they are struggling to mitigate. And while it's unlikely that data architecture changes alone will solve all risks, a risk map is likely to highlight areas where changes to data infrastructure and processes can improve risk outcomes.</li> <li><b>Establish an architecture governance board.</b> Architecture governance boards, also known as architecture review boards, bring together stakeholders from across the organization to review proposed infrastructure or process changes. Regular assessment helps make data modernization a standard, ongoing practice rather than a one-off initiative requiring direct executive involvement. Forrester has suggested that <a href="https://www.forrester.com/blogs/the-augmented-architect-real-time-enterprise-architecture-in-the-age-of-ai/">AI agents can assist in the architecture review</a> process to streamline operations and turn boards into decision accelerators.</li> </ul> <p><em>Chris Tozzi is a freelance writer, research adviser, and professor of IT and society who has previously worked as a journalist and Linux systems administrator.</em></p> </section> As organizations modernize their data systems, architecture choices will determine how risk is governed, disruptions are absorbed, and regulatory obligations are managed over time. https://cdn.ttgtmedia.com/rms/onlineimages/container_g95769788.jpg https://www.techtarget.com/searchdatamanagement/feature/Modern-data-architectures-as-a-risk-management-strategy Wed, 21 Jan 2026 16:00:00 GMT <p>ScyllaDB on Tuesday launched vector search and storage capabilities in its X Cloud database platform.</p> <p>Vector search and storage are now <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">fundamental to many AI pipelines</a> because they enable enterprises to operationalize unstructured data as well as make traditional structured data more easily discoverable.</p> <p>Vector embeddings are algorithmically assigned numerical representations of data that symbolize data's characteristics such as <a href="https://tdwi.org/articles/2023/07/13/arch-all-importance-of-the-universal-semantic-layer-in-modern-data-analytics-and-bi.aspx">semantic definitions</a> and relationships to other data. As a result, they foster similarity searches that help developers and engineers find more relevant data to train AI tools -- which require far greater volumes of data than traditional analytics tools to be accurate and reliable -- than they can with other search types including exact matches.</p> <p>Given their vital role in AI development, coupled with many enterprises substantially <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">increasing their investments in building AI tools</a> since OpenAI's November 2022 launch of ChatGPT marked significant improvement in generative AI (GenAI) capabilities, data management vendors en masse have added vector search and storage capabilities.</p> <p>For example, hyperscalers <a href="https://www.techtarget.com/searchdatamanagement/news/366577632/Vector-search-and-storage-key-to-AWS-database-strategy">AWS</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366583139/Oracle-adds-vector-search-capabilities-to-database-platform">Oracle</a> made vector search and storage key components of their database platforms while more specialized providers such as <a href="https://www.techtarget.com/searchbusinessanalytics/news/366583835/Databricks-adds-vector-search-new-LLM-support-to-AI-suite">Databricks</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366553301/MongoDB-reveals-new-generative-AI-vector-search-tools">MongoDB</a> did so as well. In addition, vendors such as <a href="https://www.techtarget.com/searchdatamanagement/news/366609193/Pinecone-launches-serverless-vector-database-on-Azure-GCP">Pinecone</a>, Milvus and Chroma are vector database specialists.</p> <p>Now, ScyllaDB is making vector search and storage part of its X Cloud platform in a move that brings the vendor's fully managed database in line with those of competitors, according to Devin Pratt, an analyst at IDC; the vendor launched vector search and storage capabilities in its self-managed database on Jan 8.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Vector search is becoming standard for AI applications, and ScyllaDB's approach is to deliver it at scale inside the operational data platform teams already run. </figure> <figcaption> <strong>Devin Pratt</strong>Analyst, IDC </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>"Vector search is becoming standard for AI applications, and ScyllaDB's approach is to deliver it at scale inside the operational data platform teams already run," he said.</p> <p>Of particular value to ScyllaDB customers is that vector search and storage within X Cloud eliminates the need to run a separate vector database, Pratt added.</p> <p>Matt Aslett, an analyst at ISG Software Research, similarly noted that ScyllaDB's addition of vector search and storage provides users with capabilities that have become critical <a href="https://www.techtarget.com/searchdatamanagement/tip/Top-vector-database-options-for-similarity-searches">components of AI development pipelines</a>.</p> <p>"Vector search and retrieval have quickly become a table stakes requirement for data platform providers to support use&nbsp;cases involving generative AI," he said.</p> <p>ScyllaDB's vector search and storage capabilities are built on the vendor's shard per-core architecture, a configuration common to distributed databases such as ScyllaDB that optimizes resource allocation and fosters scalability by dividing workloads into small, independent units called <a href="https://www.techtarget.com/searchoracle/definition/sharding">shards</a>.</p> <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/traditional_search_vs_vector_search-f.png" alt="A graphic shows the differences between keyword search and vector search.">Informa TechTarget </div> <p>ScyllaDB's Vector Store service automatically updates vector embeddings through change data capture capabilities and builds approximate-nearest-neighbor indexes within its main memory.</p> <p>The move to add vector search and storage to X Cloud closely follows <a href="https://www.techtarget.com/searchdatamanagement/news/366637326/ScyllaDB-X-Cloud-update-addresses-database-cost-performance">ScyllaDB's Jan. 15 X Cloud update</a> and was motivated by a combination of customer feedback and broad market trends, according to Dor Laor, the vendor's co-founder and CEO.</p> <p>"Customers have been asking for vector&nbsp;search to be added to ScyllaDB -- no one wants to run five different databases," he said. "In addition, AI is clearly taking the world by storm, and we have a unique solution for real-time AI at scale."</p> <p>Based in Sunnyvale, Calif., ScyllaDB provides a <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-NoSQL-database-types-in-the-cloud">NoSQL database</a> platform compatible with <a href="https://www.techtarget.com/searchdatamanagement/news/252528398/Apache-Cassandra-41-extends-open-source-NoSQL-database">Apache Cassandra</a> and Amazon DynamoDB. To date, the vendor has raised just over $100 million in venture capital funding since being founded in 2012. Competitors include Aerospike, Couchbase, <a href="https://www.techtarget.com/searchdatamanagement/news/366599526/New-features-make-Googles-Spanner-a-database-for-AI">Google Cloud Spanner</a>, MongoDB and Redis along with Cassandra and DynamoDB.</p> <p><i>Eric Avidon is a senior news writer for&nbsp;Informa&nbsp;TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> The feature's direct integration in the X Cloud platform is aimed at simplifying AI development by eliminating the need for customers to add specialized databases. https://cdn.ttgtmedia.com/rms/onlineimages/health%20analytics_g1450914513.jpg https://www.techtarget.com/searchdatamanagement/news/366637573/ScyllaDB-adds-vector-search-to-managed-database-platform Tue, 20 Jan 2026 12:42:00 GMT <p>ScyllaDB on Thursday launched its latest X Cloud update, a new version of its fully managed database platform featuring tools aimed at lowering usage costs and optimizing performance.</p> <p>Unlike some databases that only run table-sized workloads, ScyllaDB is a <a href="https://www.techtarget.com/searchoracle/definition/distributed-database">distributed database</a> that -- like peers such as <a href="https://www.techtarget.com/searchdatamanagement/news/252522172/Yugabyte-distributed-SQL-database-adds-migration-service">YugabyteDB</a>, Amazon DynamoDB and <a href="https://www.techtarget.com/searchdatamanagement/news/366599526/New-features-make-Googles-Spanner-a-database-for-AI">Google's Cloud Spanner</a> -- breaks workloads in tablets, which are portions of tables. Benefits include more efficient storage that enables workloads to scale across multiple nodes rather than loading a single node.</p> <p>Among ScyllaDB X Cloud's new capabilities are tablet-based autoscaling so that customers only pay for the compute power needed to execute a workload, advanced compression to reduce storage and the extension of tablet-based elasticity to new use cases such as change data capture (CDC).</p> <p>Collectively, the new features are valuable for ScyllaDB users, according to Matt Aslett, an analyst at ISG Software Research.</p> <p>"The general availability of ScyllaDB X Cloud is a significant milestone for the company in delivering a new architecture that delivers enhanced elasticity and flexibility," he said. "This better enables customers to use the database service for variable and unpredictable workloads [when] compared to its existing cluster type."</p> <p>Based in Sunnyvale, Calif., ScyllaDB is a <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-NoSQL-database-types-in-the-cloud">NoSQL database</a> vendor that was designed to be compatible with Apache Cassandra and Amazon DynamoDB, which enables users of those platforms to switch to ScyllaDB if they choose to without having to re-architect their database systems.</p> <section class="section main-article-chapter" data-menu-title="Prioritizing price and performance"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Prioritizing price and performance</h2> <p>Many enterprises have struggled with the cost of cloud computing dating back to the last decade. Economic uncertainty following the COVID-19 pandemic and rising inflation exacerbated concerns over cloud spending in the early 2020s. And over the past few years, expenses related to exploding interest in developing AI tools -- which require massive volumes of high-quality data to properly perform -- have proven <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2024-11-05-gartner-says-cios-need-to-overcome-four-emerging-challenges-to-deliver-value-with-artificial-intelligence#:~:text=The%20Cost%20of%20AI%20Can,risk%20as%20security%20or%20hallucinations." rel="noopener">prohibitive for some organizations</a>.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The general availability of ScyllaDB X Cloud is a significant milestone for the company in delivering a new architecture that delivers enhanced elasticity and flexibility. </figure> <figcaption> <strong>Matt Aslett</strong>Analyst, ISG Software Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>In response, some vendors have prioritized cost certainty and performance efficiency to help customers properly plan for their data management and AI development initiatives.</p> <p>For example, cost control was at the core of numerous <a href="https://www.techtarget.com/searchdatamanagement/news/366635663/Latest-AWS-data-management-features-target-cost-control">AWS platform updates</a> unveiled during the tech giant's re:Invent conference in December 2025, including a new pricing model for its databases and performance improvements aimed at cutting costs in half. Similarly, database vendors <a href="https://www.techtarget.com/searchdatamanagement/news/366629235/Aerospike-update-aims-to-improve-database-performance">Aerospike</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366630145/Neo4js-latest-targets-graph-database-performance-at-scale">Neo4j</a> made performance the focus of recent releases.</p> <p>Now, driven by customer feedback, according to co-founder and CEO Dor Laor, ScyllaDB's X Cloud update includes cost control measures and capabilities aimed at improving performance.</p> <p>"[Cost control] was high on the list of customer feature requests," he said. "The key reason is that our customers really appreciated the price [and] performance of the previous generation, but its scaling speed was unpredictable. Adding nodes one at a time was particularly slow for, say, clusters that span five regions with three zones each."</p> <p>Tablet-based autoscaling increases or decreases the resources needed to run workloads depending on demand, helping customers reduce wasteful spending on compute power that is not needed. Advanced <a href="https://www.techtarget.com/searchstorage/definition/compression">data compression</a>, meanwhile, dramatically lowers the amount of storage required to house a given amount of data, which also lowers expenses. Finally, improved storage utilization reduces the number of servers required to store data to lower infrastructure costs.</p> <p>In addition, ScyllaDB now guarantees that the cost of using X Cloud is 50% or lower than the cost of using Amazon DynamoDB for the same storage and workloads.</p> <p>Given that the X Cloud update emphasizes a managed approach to scaling database workloads and extends tablet-based elasticity to use cases including CDC, it is significant for ScyllaDB customers, according to Devin Pratt, an analyst at IDC.</p> <p>"ScyllaDB X Cloud aims to make scaling feel routine, with predictable performance even as capacity changes," he said.</p> <p>Beyond features targeting cost control and performance, new ScyllaDB X Cloud capabilities include <a href="https://www.techtarget.com/searchbusinessanalytics/news/252529637/Data-streaming-platforms-fuel-for-agile-decision-making">faster data streaming</a>, mixed node sizes within clusters to simplify operations and improved <a href="https://www.techtarget.com/searchnetworking/definition/throughput">throughput</a> that enables ScyllaDB to process more data in a given period of time.</p> <p>Improved storage utilization -- when used in conjunction with autoscaling -- is perhaps the standout new feature given that it addresses infrastructure costs, according to Aslett.</p> <p>"Improved resource utilization should enable enterprises to lower infrastructure costs by avoiding over-provisioning, especially with the autoscaling capability that enables users to define utilization targets against which scaling to be automatically triggered and optimized," he said.</p> <p>Pratt, meanwhile, highlighted extending tablet-based elasticity to new workload types and improved storage utilization.</p> <p>"ScyllaDB's message here is operational predictability, scaling and efficiency that are consistent enough to plan and budget around," he said.</p> <p>From a competitive standpoint, the new features align ScyllaDB with competitors such as <a href="https://www.techtarget.com/searchdatamanagement/news/252528398/Apache-Cassandra-41-extends-open-source-NoSQL-database">Apache Cassandra</a>, DynamoDB and DataStax -- <a href="https://www.techtarget.com/searchdatamanagement/news/366619511/IBM-to-buy-open-source-data-platform-and-AI-vendor-DataStax">now part of IBM</a> -- Pratt continued.</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>ScyllaDB has always taken a <a href="https://www.computerweekly.com/blog/CW-Developer-Network/What-is-close-to-the-metal">close-to-the-metal</a> approach to designing its platform that prioritizes direct interaction between the database system and its underlying hardware while using the <a href="https://www.techtarget.com/searchdatamanagement/definition/C">C++</a> programming language to build its architecture, according to Laor.</p> <p>With the latest X Cloud update now generally available, he said that ScyllaSB's next initiatives are to improve the consistency of the platform's data plane and to add tiered storage such as "hot storage" that enables low-latency access to frequently used data, "warm storage" with medium performance for less frequently used data and "cold storage" at a low cost for rarely used data.</p> <p>Something else ScyllaDB could focus on is adding prowess to its <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">vector search and storage capabilities</a>, according to Aslett.</p> <p>Beyond vector database specialists such as Pinecone and ChromaDB, many data management vendors including Databricks and Snowflake added vector search and storage in 2023 and 2024 to help customers <a target="_blank" href="https://www.ey.com/en_us/newsroom/2025/07/ai-investments-surge-but-agentic-ai-understanding-and-adoption-lag-behind" rel="noopener">suddenly interested in developing AI</a> applications following OpenAI's late 2022 launch of ChatGPT.</p> <p>ScyllaDB, however, only added such capabilities as part of its ScyllaDB 2025.4 release on Jan. 8; ScyllaDB 2025.4 is the self-managed version of the vendor's platform while ScyllaDB X Cloud is its fully managed version.</p> <p>"Data platform providers seeking to differentiate in relation to vector storage and retrieval can still do so by focusing on vector indexing as a means of improving the performance and accuracy of similar search results, as well as implementing hybrid search capabilities," Aslett said.</p> <p>Pratt, meanwhile, suggested ScyllaDB simplify migration workflows to make <a href="https://www.techtarget.com/searchdatamanagement/news/252513244/ScyllaDB-50-set-to-advance-NoSQL-database-capabilities">its database</a> easier for new customers to adopt.</p> <p>"ScyllaDB is moving in the right direction by combining operational simplicity with modern capabilities, and the next step is making adoption even easier for more teams," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.</i></p> </section> Features such as autoscaling and advanced compression are designed to help customers reduce spending on their data management, analytics and AI initiatives. https://cdn.ttgtmedia.com/rms/onlineimages/security_a386211215.jpg https://www.techtarget.com/searchdatamanagement/news/366637326/ScyllaDB-X-Cloud-update-addresses-database-cost-performance Thu, 15 Jan 2026 12:33:00 GMT <p>MongoDB continues to make AI development a priority.</p> <p>Four months <a href="https://www.techtarget.com/searchdatamanagement/news/366631455/MongoDB-adds-MCP-server-expands-AI-development-capabilities">after launching</a> a Model Context Protocol server and an AI-powered service that helps customers modernize their data infrastructures, the vendor on Thursday released five Voyage AI embedding and reranking models to help users build the data layer that informs agents and other AI tools.</p> <p>Voyage AI, now MongoDB's suite for embedding and reranking models, was an AI startup that <a target="_blank" href="https://www.prnewswire.com/news-releases/mongodb-announces-acquisition-of-voyage-ai-to-enable-organizations-to-build-trustworthy-ai-applications-302382979.html" rel="noopener">MongoDB acquired in February 2025</a>. General availability of the latest Voyage models in MongoDB is aimed at improving the accuracy of vector search while simplifying the infrastructure needed to build analytics and AI applications by eliminating the need to move data between systems.</p> <p>In addition, among other new capabilities, MongoDB made an AI-powered assistant generally available for MongoDB Compass and Atlas Data Explorer and added a feature in MongoDB Vector Search that automatically generates vector embeddings whenever data is ingested, updated or queried.</p> <p>Collectively, the new capabilities are significant because the models, when integrated with operational data and vector search, form a unified data intelligence layer for analytics and AI, according to William McKnight, president of McKnight Consulting.</p> <p>"This matters because it attacks the biggest practical failure point in AI initiatives, which is the gap between promising demos and systems that actually run the business," he said.</p> <p>In addition, the new features potentially strengthen MongoDB's position relative to competitors such as Amazon DynamoDB, Apache Cassandra, Couchbase, Snowflake and Redis, McKnight continued.</p> <p>"Its cloud-based service, MongoDB Atlas, is still gaining traction, but the new capabilities likely strengthen its position against competitors, especially in handling large-scale data and providing a more streamlined experience," he said. "They should accelerate the adoption of MongoDB for AI-driven applications."</p> <p>Based in New York City, MongoDB is a <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-NoSQL-database-types-in-the-cloud">NoSQL database</a> vendor providing a platform designed to handle the increasing scale of enterprise data and AI workloads. In addition, like many data management vendors, MongoDB now provides capabilities aimed at enabling customers to <a href="https://www.techtarget.com/searchdatamanagement/news/366583234/MongoDB-launches-tools-for-developing-generative-AI-apps">build and manage AI applications</a>.</p> <section class="section main-article-chapter" data-menu-title="Modeling for success"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Modeling for success</h2> <p>Most AI initiatives still <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">fail to make it past the pilot stage</a> despite the emphasis many enterprises have placed on AI development over the past few years, coupled with attempts by data management and AI vendors to create environments within their platforms that make it easy to build AI tools.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This matters because it attacks the biggest practical failure point in AI initiatives, which is the gap between promising demos and systems that actually run the business. </figure> <figcaption> <strong>William McKnight</strong>President, McKnight Consulting </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Fragmented systems and disorganized data estates that make relevant data difficult to discover are among the myriad problems many enterprises face when trying to develop agents and other applications.</p> <p>Databricks recently <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">launched Instructed Retriever</a>, an alternative to <a href="https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation">retrieval-augmented generation</a> pipelines that augments user queries with additional specifications to better enable users to discover relevant information.</p> <p>MongoDB is similarly attempting to improve data retrieval. However, rather than develop a new method of retrieving data, the vendor is working to improve the models that categorize data to make it discoverable. The move was driven by conversations with customers, according to Ben Cefalo, senior vice president and head of core products at MongoDB.</p> <p>"Over the past few months, we've spent time with countless customers to understand where things break as AI moves from prototype to production," he said during a virtual press conference on Jan. 12. "Those conversations start with AI models. … As AI moves from demos to production-grade applications, we saw the same pattern again and again -- retrieval was fragmented and accuracy suffered."</p> <p>The Voyage 4 series of models represent MongoDB's attempt to <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Talend-CEO-discusses-importance-of-mining-relevant-data">improve retrieval</a> to foster more successful AI development. Meanwhile, natively integrating the Voyage models is aimed at providing <a href="https://www.techtarget.com/searchdatamanagement/opinion/The-race-to-build-the-ultimate-data-platform">a unified data platform</a> that enables AI development without forcing customers to piece together pipelines, Cefalo continued.</p> <p>"In an AI world, a database is not enough," he said.</p> <p>Vector search and storage quickly <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">became an essential element</a> of AI pipelines after OpenAI's November 2022 launch of ChatGPT sparked <a target="_blank" href="https://www.ey.com/en_us/newsroom/2025/07/ai-investments-surge-but-agentic-ai-understanding-and-adoption-lag-behind" rel="noopener">surging interest in generative AI</a> (GenAI).</p> <p>Vector embeddings are numerical representations of data -- including unstructured data such as text and images -- that make it easily discovered. In addition, because the numerical representations symbolize data's essential characteristics, vector embeddings enable similarity searches to improve the relevancy of search responses.</p> <p>Embedding models automatically assign vector embeddings. Reranking models, meanwhile, refine and reorder lists of items such as search results to make AI outputs more relevant.</p> <p>MongoDB's new Voyage 4 models each include embedding and retrieval capabilities aimed at improving retrieval accuracy in AI pipelines, but with nuances. The general-purpose voyage-4 model balances retrieval accuracy, cost and <a href="https://www.techtarget.com/whatis/definition/latency">latency</a>; the voyage-4-large model delivers the highest retrieval accuracy; voyage-4-lite is optimized for cost and latency; and voyage-4-nano is designed for local development and testing.</p> <p>In addition, MongoDB launched <a href="https://www.techtarget.com/searchdatamanagement/news/366629096/MongoDB-unveils-enterprise-focused-AI-models">voyage-multimodal-3.5</a>, which enables users to assign vector embeddings to video in addition to the text and images supported by voyage-multimodal-3.</p> <p>Given that the models aim to improve retrieval accuracy, they are significant for MongoDB users, according to Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget.</p> <p>"The launch of the Voyage 4 series of embedding models enables users to achieve state-of-the-art retrieval accuracy while optimizing for cost and latency," he said. "These models also introduce … video processing, which expands the scope of applications developers can build without requiring extensive architectural changes."</p> <p>McKnight similarly noted that beyond forming a data intelligence layer in conjunction with operational data and vector search, the Voyage 4 models are valuable because they <a href="https://www.techtarget.com/searchenterpriseai/tip/RAG-best-practices-for-enterprise-AI-teams">improve retrieval accuracy</a> and process video content in addition to text and images. In addition, they are important additions because allow users to switch models without having to rewrite code, he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Additional capabilities"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Additional capabilities</h2> <p>Beyond the new Voyage models, new MongoDB capabilities include the following:</p> <ul type="disc" class="default-list"> <li>Automatically generated vector embeddings in MongoDB Vector Search, eliminating the need for separate embedding pipelines; a similar feature is in public preview for the MongoDB Community Edition.</li> <li>An AI assistant in MongoDB Compass and Atlas Data Explorer that enables users to interact with their systems using natural language.</li> <li>Lexical Prefilters for Vector Search, which provides advanced filtering capabilities for developers building <a target="_blank" href="https://tdwi.org/articles/2023/07/13/arch-all-importance-of-the-universal-semantic-layer-in-modern-data-analytics-and-bi.aspx" rel="noopener">semantic search interfaces</a>.</li> <li>A new unified web interface for Atlas Data Explorer that enables users to develop sophisticated queries with AI assistance across all MongoDB Atlas <a href="https://www.techtarget.com/whatis/definition/cluster">clusters</a>.</li> <li>An AI skills certification to help data and AI teams scale their data strategies and accelerate development cycles.</li> </ul> <p>Beyond the Voyage AI models, automatically generated vector embeddings and the AI-powered assistant are MongoDB's highlight new features, according to McKnight.</p> <p>"Together, these capabilities drastically lower operational overhead, allowing teams to transition from prototype to mission-critical production with a much simpler architecture," he said.</p> <p>Catanzano likewise named automatically generated vector embeddings and the intelligent assistant the most significant new features.</p> <p>"The former eliminates the need for external embedding pipelines, simplifying architecture, while the latter provides tailored, in-app AI guidance, reducing friction for developers working on complex data operations," he said.</p> <p>Regarding the overall release, Catanzano termed the new capabilities "significant" because they address critical challenges related to AI deployment. In addition, he noted that <a href="https://www.techtarget.com/searchdatamanagement/feature/Evaluating-the-different-types-of-DBMS-products">from a competitive standpoint</a>, they help differentiate MongoDB by unifying operational data and retrieval in a single system.</p> <p>"MongoDB is reducing latency and complexity compared to fragmented solutions offered by competitors," Catanzano said. "We have seen a strong push by enterprises to use data platforms with all the AI capabilities they need. MongoDB provides many of the capabilities along with a solid ecosystem of parts to fill in any gaps."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>While the new features address some of the difficulties enterprises experience when trying to build AI tools, there is more MongoDB should do to improve <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-NoSQL-database-types-in-the-cloud">its competitive position</a>, according to McKnight.</p> <p>"MongoDB must improve large-scale <a href="https://www.theserverside.com/definition/JSON-Javascript-Object-Notation">JSON</a> insertion operations that currently perform slower than unified platforms and enhance JSON query performance that lags behind modern unified engines," he said.</p> <p>In addition, MongoDB would be wise to improve compatibility with third-party platforms to make it easier for customers to configure customized data and AI stacks, provide more guidance to users integrating MongoDB with other systems, and add integrations with <a href="https://www.computerweekly.com/news/366575034/The-evolution-of-DevOps-Why-platform-engineering-is-gaining-momentum">developer platforms</a>, McKnight continued.</p> <p>"These improvements would directly address MongoDB's documented weaknesses in extreme-scale scenarios while maintaining its core strengths as a flexible, developer-friendly document database for general-purpose workloads," he said.</p> <p>Catanzano likewise suggested that MongoDB add integrations with third-party <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms">AI frameworks</a> and popular developer platforms.</p> <p>"Additionally, focusing on real-time analytics and predictive modeling capabilities could attract new users and solidify its position as a leader in AI-driven data platforms," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.</i></p> </section> With many enterprises struggling to build advanced applications, new embedding and reranking models improve data retrieval to make agents and other tools more effective. https://cdn.ttgtmedia.com/rms/onlineimages/arvr_g1200559136.jpg https://www.techtarget.com/searchdatamanagement/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development Thu, 15 Jan 2026 10:00:00 GMT 60 webmaster@techtarget.com