Starburst chews into the fruits of agentic
Bostonian data platform company Starburst has updated its flagship software services, known as Starburst Enterprise Platform and Starburst Galaxy, with the additional boost that every other software vendor on the planet is aligning to.
No prizes for guessing, yes, it’s agentic AI.
The new Starburst AI Agent and new AI Workflows are designed to accelerate enterprise AI initiatives and support the transition to a future-ready data architecture built on data lakehouse technologies.
By bringing distributed, hybrid data to a “lakeside state” to power AI, apps and analytics, Starburst claims it can help enable faster, secure collaborative data access.
With native AI tooling, the Starburst Data Catalogue (Americanized as Catalog) and advancements to data ingestion, table maintenance and governance, enterprises can use a modern data lakehouse.
Connect the data dots
Starburst AI Workflows is set of capabilities that help AI experimentation to production for enterprises. The company says that AI Workflows “connect the dots” between vector-native search, metadata-driven context and robust governance, all on an open data lakehouse architecture.
Starburst is also launching Starburst AI Agent, an out-of-the-box natural language interface for Starburst’s data platform that can be built and deployed by data analysts and application-layer AI agents to bring faster insights to business stakeholders. With AI Workflows and the Starburst AI Agent, enterprises can build and scale AI applications with compliance and control.
“AI is raising the bar for enterprise data platforms, but most architectures aren’t ready,” said Justin Borgman, CEO and co-founder of Starburst. “At the end of the day, your AI is only as powerful as the data it can access. Starburst is removing the friction between data and AI by bringing distributed, hybrid data lakeside, enabling enterprise data teams to rapidly build AI, apps, agents, and analytics on a single, governed foundation.”
Matt Fuller, VP of AI/ML products at Starburst concurs with his boss and says that the company is “turning the data lakehouse into an enterprise-grade platform for AI agents and applications” now. This means that it is designed to support air-gapped environments without compromising on flexibility.
“Whether deployed in a secure on-premise environment or cloud-enabled ecosystem, Starburst delivers federated, governed access, real-time context, and high-performance query processing,” Fuller.
George Karapalidis, director of data at Checkatrade thinks that user role-based routing in Galaxy made it simple to direct queries to the right cluster based on team roles. He says that it’s intuitive, seamlessly integrated into the Galaxy UX and it helps his team optimise for both performance and cost without adding operational overhead.
Ice ice, maybe?
According to Ricardo Cardante, staff engineer at TalkDesk, “Before Starburst, maintaining our Iceberg tables was a manual, error-prone process that only covered a fraction of our data. With Automated Table Maintenance, we applied compaction and cleanup across the board, going from 16% table maintenance coverage to 100%.”
He says that this enhancement led to a 66% reduction in S3 storage costs for the firm’s data platform buckets and contributed to an overall 20% decrease in S3 storage expenses across the company.
“It’s a game changer for scaling Iceberg performance with minimal overhead,” he enthused.
Starburst CEO Borgman concludes by explaining that his company’s mission is to meet the data challenges faced by complex, global institutions. He suggests that Starburst continues to expand its reach into high-demand, regulated industries where AI is becoming a cornerstone of transformation.
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