New Starburst platform extends AI to distributed data
The vendor's approach to developing agents and other cutting-edge applications eliminates the time, cost and risks of moving data and could be a competitive differentiator.
With the launch of Aida and the introduction of the Starburst Enterprise Intelligence Platform, Starburst is extending AI-fueled query and analysis capabilities beyond its data management environment and into the workflows and applications where business users do their jobs.
First introduced in April, Aida -- which stands for AI data assistant -- is an AI-powered assistant that lets users explore and analyze data using natural language and is the main interface for Starburst's Enterprise Intelligence Platform.
However, unlike assistants featuring basic text-to-SQL translation capabilities that enable only rudimentary questions and answers, Aida is built on a framework that allows it to access an organization's data products, such as governed datasets that include semantically related tables, so it can reason, act and observe based on proper context. In addition, rather than accessing only centralized data or data duplicated and imported into Starburst, Aida works across distributed data environments such as multiple clouds, different data lakes and warehouses, and disparate systems and applications.
By working across distributed data estates, Aida enables organizations to save the time it takes to extract, transform and load data, and avoid the cost and risk of exposure associated with moving data. Additionally, it allows organizations to keep their existing data infrastructures rather than build new ones for AI.
As a result, the Starburst Intelligence Platform is a valuable addition for the vendor's users, according to Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget.
"The Starburst Enterprise Intelligence Platform represents a significant addition for existing users because it transforms their data infrastructure from a query engine into a comprehensive AI enablement layer without requiring them to rethink their architecture or move their data," Catanzano said. "Users can now … effectively shorten the path from data access to AI-driven business value."
Based in Boston, Starburst provides a data lakehouse platform geared toward connecting data generated and stored in otherwise disparate systems. The new features were unveiled during AI & Datanova, Starburst's user conference in Miami.
Enabling AI
Difficulty accessing relevant data, along with other data issues such as poor quality, are among the problems preventing many enterprises from building agents and other AI tools trustworthy enough to move into production. As a result, data management and analytics vendors such as Databricks and Tableau have introduced new capabilities over the past few months designed to help customers better access the contextually relevant data that AI tools need to deliver trusted outcomes.
The Starburst Enterprise Intelligence Platform represents a significant addition for existing users because it transforms their data infrastructure from a query engine into a comprehensive AI enablement layer without requiring them to rethink their architecture or move their data.
Stephen CatanzanoAnalyst, Omdia
Now, Starburst is doing the same, but with its own approach.
Rather than import data to AI, the Starburst Enterprise Intelligence Platform connects and governs distributed data so AI can run directly on that data, underpinned by the context provided by trusted data products.
A combination of customer feedback and market trends pushed Starburst to build the new platform, according to Jitender Aswani, the vendor's senior vice president and global head of engineering and security.
Customers were attempting to build agents and other AI tools on top of Starburst's query engine but struggling with data that wasn't AI-ready, he noted. Simultaneously, Starburst recognized a similar problem in the broader market with agents failing because of problems related to their underlying data.
"These two signals confirmed that our investment should focus on bridging that gap," Aswani said. "The Enterprise Intelligence Platform is … a natural evolution of Starburst's foundation in federated, governed data."
Catanzano, meanwhile, noted that Starburst's extension of AI to data distributed to AI-powered analysis could distinguish the vendor from competitors such as Databricks, Dremio -- which is being acquired by SAP -- and Snowflake.
"Starburst's approach is differentiated in its commitment to true in-place data processing without requiring consolidation or re-platforming, combined with its focus on bringing AI directly into existing workflows," he said.
However, whether Starburst's unique approach proves more effective remains to be seen, Catanzano continued.
"The ultimate differentiation will depend on execution quality and how well these capabilities perform at enterprise scale compared to competitors," he said.
Like Catanzano, Kevin Petrie, an analyst at BARC U.S., noted that a decentralized approach to AI-powered analysis has advantages.
Data consolidation is a struggle -- and ultimately a mirage -- for many companies, according to Petrie. Although they migrate and centralize certain data on one platform for an initiative, inevitably other units within the business migrate and centralize certain data on another platform for a different initiative. Meanwhile, still other data needs to remain in place due to migration complexity or data sovereignty concerns.
"All this means that the data inputs needed for AI projects -- especially unstructured objects like documents or emails -- often reside in multiple locations," Petrie said.
Starburst's Enterprise Intelligence Platform, therefore, could be an important new option for many enterprises, he continued, adding that BARC research shows that companies with data products as part of a foundation for AI are three times more likely to put agents in production than those that don't use data products for AI initiatives.
"AI adopters need governed, enterprise-wide programs in order to get agentic analytics safely into production," Petrie said. "These new capabilities from Starburst help organizations meet those requirements by federating metadata, enforcing consistent rules, and aligning outputs on standard business definitions. Packaging these features with data products is critical."
Beyond the Starburst Enterprise Intelligence Platform, Starburst unveiled the following during its user conference:
An engine that improves the performance of the open source Trino query engine that powers Starburst Enterprise and Starburst Galaxy.
Support for Model Context Protocol (MCP) to connect Aida with external tools and third-party systems.
Resilience capabilities that keep AI systems, including agents, operating without disruption when infrastructures fail.
Icehouse Ingest -- data loading capabilities that combine the Trino engine with Apache Iceberg tables -- to load batch and streaming files
Icehouse LakeOps to automatically optimize Iceberg tables, observe the health of data tables and tune queries.
A BYOC deployment option.
Collectively, Starburst's new features, including those that comprise its Enterprise Intelligence Platform, are seemingly aligned to enable running AI on distributed data, according to Catanzano. However, tools that add model observability and explainability capabilities would improve the platform, he continued.
"Explicit capabilities around AI model observability and explainability help users understand not just what their AI agents are doing but why specific recommendations or actions were generated, which becomes increasingly critical as these systems move to autonomous decision-making in production environments," Catanzano said.
Next steps
As Starburst plans product development, focal points include building agents that don't merely respond to questions, but instead proactively surface insights, schedule data workflows and flag data anomalies, according to Aswani.
In addition, adding depth to its ecosystem by connecting to more sources through MCP and federating context and governance across distributed data estates are priorities, he continued.
"The goal across all three is the same: helping customers move from AI pilots to production without forcing them to move their data, rebuild their stack or compromise on governance," Aswani said.
Catanzano, meanwhile, suggested that Starburst could continue serving the needs of users by adding industry-specific capabilities designed to speed and simplify building and deploying agents. In addition, he noted that a broader partnership ecosystem and cost control capabilities would be beneficial.
"Enhanced cost optimization tools that provide visibility into the economics of distributed AI workloads would address margin pressure concerns and position Starburst as not just an enabler of AI but a protector of AI ROI," Catanzano said.
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.
