The performance of a data warehouse. The scale of a data lake.
CHANGE THE ECONOMICS OF YOUR DATA ARCHITECTURE
Fast
Query petabyte-scale data lakes, fast with a multi-parallel processing query engine
Efficient
Reduce costs while adding elasticity to your architecture by separating storage and compute
Reliable
Bring the simplicity of SQL tables to big data with the Apache Iceberg table format
How it works
Land
Starburst works directly with your cloud object storage. Start by choosing where you want to store your data – Amazon S3, Azure Data Lake Storage, or Google Cloud Storage. Gather and land that data in your object storage location of choice.
Structure
Connect Starburst Galaxy to your object storage and transform your raw data files into Iceberg tables. Or, leverage Galaxy’s built-in ingestion solution to automatically stream data into your data lake and land it in Iceberg format.
Consume
Curate gold-standard tables that are ready to be queried using SQL or Python data pipelines. Add proper data governance with built-in access controls to ensure proper read-only access to your data sets.
Optimize
Automatically run common data lake management tasks like compaction, vacuuming, and retention with Starburst Galaxy to ensure optimal performance.
Core Platform Capabilities
Ingest data in near real-time
Continuously ingesting data from a Kafka-compliant topic into your data lake and land it in Iceberg format with Galaxy’s fully-managed streaming ingest.
Automate lakehouse management
Schedule routine maintenance operations on your data lakehouse, like data compaction, vacuuming, and data retention, directly from the Galaxy UI.
Unify on a single, powerful platform
Build your lakehouse in Galaxy to leverage powerful platform capabilities like built-in access controls, data products, and more.
Available on all three major clouds
Deploy Starburst Galaxy across all three major clouds and leverage cross-cloud connectivity to unify your data.
“The move to Starburst and Iceberg has resulted in a 12x reduction in compute costs versus our previous data warehouse. This efficiency allows us to focus our attention on using analytics for revenue-generating opportunities.”
– Peter Lim, Sr. Data Engineer, Yello
“We moved from a monolithic Snowflake approach to a decentralized approach with Starburst and Iceberg. Now we can skip the data warehouse step completely, and complete analytics on the data right where it sits.”
– Lutz Künneke, Director of Engineering, BestSecret
“The combination of Starburst Galaxy and Apache Iceberg offers exceptional value, delivering far more for the same investment. It’s a clear win for efficiency and productivity in our data-driven environment.”
– Johni Michels, Data Team Lead, Kovi
“The time it took from reporting Trino + Iceberg bugs to deployed fixes was very fast and demonstrated the commitment Trino has to being a leader in Iceberg adoption across the various compute engines.”
— Marc Laforet, Senior Software Engineer, Shopify
