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Starburst Data’s lakehouse model supports AI models by using data where it already lives without needing to copy it into a centralized repository and without requiring external data pipelines

May 21, 2025 //  by Finnovate

Starburst Data  is unveiling a suite of enhancements intended to make it easier for enterprises to develop and apply artificial intelligence models. Starburst’s updates are focused on enabling what it calls an AI “lakeside,” in which companies can use data where it already lives without needing to copy it into a centralized repository. Starburst defines a lakeside as a staging ground for AI, or an area adjacent to the data lakehouse where data is the most complete, cost-efficient and governed. The company’s new Lakeside AI architecture combines AI-ready tools with an open data lakehouse model. It allows companies to experiment with, train and deploy AI systems while keeping sensitive or regulated data in place. Starburst AI Workflows accelerates AI application development by making it easier to transform unstructured data into vector embeddings, a machine learning technique that turns data into numerical representations that capture the meaning and relationships between different data points without requiring explicit keywords. Workflows manage prompts and models with SQL and enforce governance policies. Starburst said these capabilities are fully contained within its platform and require no external data pipelines. Data is stored on Apache Iceberg tables with connectors available for a variety of third-party vector databases. Basically, this means users can build AI features that rely on unstructured or semi-structured sources like emails, documents and logs without having to move data or stitch together multiple tools. The Starburst AI Agent is a built-in natural language interface that allows users to talk to their data using natural language. It automatically scans for sensitive data such as names, email addresses and other personally identifiable information at the column level and tags it so access policies can be applied. That reduces the need for manual checks and helps organizations enforce privacy rules more consistently. A new Starburst data catalog replaces the aging Hive metastore and provides better support for the Iceberg data format that is rapidly becoming the standard for cloud data lakes. The new catalog supports both legacy Hive data and Iceberg tables. To improve performance across large-scale deployments, Starburst is also introducing a native ODBC Driver that improves connection speed and reliability with business intelligence tools such as Salesforce Inc.’s Tableau and Microsoft Corp.’s Power BI.

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Category: Data Economy & Privacy, Innovation Topics

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