The Enterprise Intelligence Platform for AI and Analytics
Access and query data across systems with a unified context layer. No pipelines or data movement required.
Starburst by the numbers
3.2x
Faster analytics
7.3x
Cost savings
50+
Connectors
Starburst is trusted by:
*Make Decisions,*,, Not Dashboards.
Ask. Use natural language to query and explore data across your systems.
Understand. Run ad hoc analysis and generate visualizations without waiting on BI.
Trust. Get answers grounded in governed data, shared definitions, and policy-enforced access, including from external agents via MCP.
Unify Context,, *Across Every System.*
Connect. Access distributed data across cloud, lake, warehouse, and SaaS sources, with shared context applied across catalogs and systems.
Organize. Layer business definitions, policy, and metadata across domains so every query uses the same trusted context.
Reuse. Create governed, reusable data products and manage context, access, and logic as code across teams.
*Faster Queries.*,, Lower Bills.
Accelerate. Run high-concurrency analytics across distributed data with fast, predictable performance at scale.
Ingest. Combine batch and streaming data with managed pipelines and automated table maintenance.
Modernize. Adopt Iceberg and open formats incrementally, without replatforming your entire stack.
Run Anywhere.,, *Stay in Control.*
Scale. Run reliably across cloud, hybrid, and on-premises environments at enterprise scale.
Govern. Enforce security, access controls, and policy across all data and queries.
Optimize. Control compute, cost, and performance with intelligent query and resource management.
Bring AI to Your Data
Powered by Trino. Built for enterprise reality.
Founded by Trino’s creators, Starburst delivers an enterprise-ready, fully managed version of the fastest open query engine for federated analytics. By adding workload isolation and Iceberg optimization to Trino’s core, we provide elite performance and scale without the operational overhead of running it yourself.
Frequently Asked Questions
Wherever you need us, we’re there. We’re always on hand to answer questions and engage in thoughtful conversations with the Starburst community.
What is a data lakehouse?
A data lakehouse combines the flexibility and scale of a data lake with the performance, governance, and reliability traditionally associated with a data warehouse. It allows teams to run analytics and AI workloads on open data, often using table formats like Apache Iceberg, without locking the data into a single proprietary platform.
How can teams query data without moving or copying it?
Teams can query data in place using a federated query engine, which connects to data across warehouses, lakes, databases, and SaaS applications. This reduces the need for duplicate pipelines, lowers data movement costs, and gives analysts and AI applications faster access to distributed enterprise data.
Starburst uses a high-performance Trino engine to query data across systems without forcing centralization.
What is federated query, and when should you use it?
Federated query lets users run a single query across multiple data sources, even when the data lives in different systems. It is useful when data is distributed across clouds, regions, warehouses, lakes, or operational databases, and moving everything into one platform would be slow, expensive, or impractical.
Starburst adds governance, workload management, and performance optimization for federated analytics at enterprise scale.
What is Apache Iceberg, and why does it matter for analytics?
Apache Iceberg is an open table format for large analytic datasets. It helps teams improve reliability, performance, schema evolution, and interoperability across lakehouse environments. Iceberg is especially valuable for organizations that want open, high-performance analytics without being locked into one vendor’s storage or compute platform.
Starburst optimizes Trino-based analytics for Iceberg and supports production capabilities through Icehouse.
How can AI agents safely query enterprise data and data products?
AI agents need governed, reliable access to trusted enterprise data so they can answer questions, trigger workflows, and support analysis without bypassing security controls. Data products help make that possible by packaging data with the right context, ownership, access policies, and quality expectations.
Starburst enables teams to expose governed data products across distributed data sources, giving both human analysts and AI agents controlled access to live enterprise data through workload isolation, query governance, and an MCP query endpoint.
When should I use Starburst instead of Snowflake or Databricks?
Starburst is a strong fit when data is distributed across multiple platforms and teams need fast, governed analytics without moving everything into a single warehouse or lakehouse. Snowflake and Databricks are often used as centralized data platforms; Starburst is designed to query data across existing systems, optimize Iceberg workloads, and reduce unnecessary data movement.
Explore how we compare
What data sources and connectors does Starburst support?
Starburst connects to 50+ data sources across cloud storage, data warehouses, databases, and SaaS platforms, including Amazon S3, Snowflake, Redshift, BigQuery, Databricks, PostgreSQL, and more. Teams can query these sources in place while applying consistent governance and access controls.
How does Starburst pricing work?
Starburst Galaxy uses usage-based pricing, so costs depend on how much compute is used for analytics workloads. Organizations can reduce total analytics costs by improving query efficiency, isolating workloads, and avoiding unnecessary data movement or infrastructure management.
