Starburst Data

Definition

Starburst is a data platform company built on Trino, the distributed SQL query engine, that lets analysts and applications run a single SQL query across data sitting in many different systems, such as a data lake, a warehouse, and a relational database, without first copying all of that data into one place. Its core pitch is querying data where it already lives rather than requiring a centralized data-movement project before analysis can begin.

Overview

Starburst addresses the common situation where an organization's data is scattered across a data lake, several relational databases, a cloud warehouse, and assorted other stores, and getting a single answer that spans all of them traditionally requires either building extract-transform-load pipelines to consolidate everything into one warehouse first, or manually stitching together results from separate queries run against each system by hand. Mechanically, Starburst is built on top of Trino, an open-source distributed SQL query engine originally developed at Facebook as Presto, which uses a federated architecture: a single SQL query submitted to Starburst is parsed and broken into sub-queries that are pushed down to each underlying data source's own connector, and the partial results are then combined and returned as one unified result set, all without the underlying data ever being copied into Starburst itself. This lets a query join a table in a cloud object store with a table in a traditional relational database in one statement, something a single-source query engine cannot do natively. Among distributed query engines, Starburst's central differentiator from a traditional data warehouse is that it is a query layer, not a storage system: the actual data stays in its original location and format, and Starburst's connectors handle translating a standard SQL query into each source's native access pattern. That contrasts with warehouse-first architectures that require loading data into the warehouse before it becomes queryable, trading some raw performance for the flexibility of never having to move or duplicate data across systems. In practice, data platform teams deploy Starburst to give analysts one SQL endpoint that can query across a data lake and several operational databases without waiting for a data-engineering team to build ETL pipelines connecting them all, and organizations doing incremental data-lake migrations use it to keep querying legacy systems and newly migrated lake tables side by side during the transition period. Business intelligence tools connect to Starburst the same way they would connect to a conventional warehouse, since it exposes a standard SQL interface. The main trade-off is query performance: pushing computation down to and pulling partial results back from several heterogeneous systems in one federated query typically runs slower than querying a single, purpose-built warehouse holding pre-loaded, optimized data, so workloads with strict latency requirements or very high query concurrency often still benefit from a dedicated warehouse rather than a federated query layer alone. Many organizations resolve this by using Starburst for exploratory and cross-system questions while keeping their highest-traffic, latency-sensitive reporting on a dedicated warehouse built for that narrower purpose.

Key Features

• Federated SQL queries spanning data lakes, warehouses, and databases
• Built on Trino, the open-source distributed SQL query engine
• Query pushdown to native connectors for each underlying data source
• No requirement to copy or centralize data before querying it
• Standard SQL interface compatible with existing BI tool connections
• Support for incremental data-lake migrations alongside legacy systems
• Unified access layer reducing dependency on new ETL pipeline builds
• Governance and access-control features layered on the query engine

Use Cases

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Frequently Asked Questions

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