Starburst was founded in 2017 by Justin Borgman along with Trino (then PrestoSQL) creators Martin Traverso, Dain Sundstrom, and David Phillips. The company raised over $400 million in funding, reaching a $3.35 billion valuation at its peak.
The core product is Starburst Enterprise, an enterprise distribution of Trino with additional security, performance, and management features. Think of the relationship similarly to how Red Hat relates to Linux or Databricks to Spark — commercial support and enterprise features built on top of an open-source foundation.
Starburst Galaxy is the fully managed SaaS offering. You connect your data sources, and Galaxy handles the infrastructure — provisioning clusters, scaling compute, and managing upgrades. Queries federate across your data wherever it lives, whether that’s S3, Snowflake, PostgreSQL, or dozens of other supported sources.
The data products feature lets teams publish curated, governed datasets that other teams can discover and query. This addresses the data mesh concept where domain teams own and share their data as products, with Starburst providing the query fabric that connects everything.
Access control in Starburst goes beyond standard SQL permissions. Role-based access control, column masking, row filtering, and audit logging provide the governance features that enterprise security teams require. Data stays in place — Starburst queries it where it lives rather than requiring you to copy it into a central warehouse.
The company positions itself as the “analytics anywhere” platform, competing with approaches that require consolidating all data into a single warehouse. For organizations with data distributed across cloud providers, on-premises systems, and SaaS platforms, Starburst argues that federation is more practical than migration.
Starburst’s biggest challenge is that most organizations are already invested in Snowflake, BigQuery, or Databricks. Convincing them that query federation is a better approach than data consolidation requires shifting architectural assumptions.
