Data lake analytics
Analyze data in Apache Iceberg and open formats from one engine, with the speed you expect from a warehouse
One engine for your warehouse and data lake
Run familiar SQL across data in your data lakes that are stored in Apache Iceberg and other open formats. No data movement. No duplication. No new engine to manage. Join data across your data warehouse and data lake for deeper insights. Redshift brings warehouse-grade performance to your open data lake, so every team can query the same trusted data with the tools they already use.
Benefits
Redshift supports reading and writing to your data lake using open formats like Apache Iceberg. Any Iceberg-compatible engine can read it.
Redshift delivers fast query performance on open table formats. Your data lake queries run with the same speed and concurrency you expect from a dedicated warehouse.
Query your data warehouse and data lake from the same compute. There's no separate engine to provision, manage, or pay for. A vectorized query engine built directly into the Redshift processor handles Apache Iceberg and other open formats natively alongside your warehouse workloads.
Query data in Apache Iceberg tables and other open formats directly in Amazon S3. Your data stays where it is.
Data points
- more Apache Iceberg data scanned with Amazon Redshift in 2026 than 2025
- 19X
- Apache Iceberg workloads run up to 2.4X faster on RG than RA3
- 2.4X
- Apache Parquet workloads run up to 1.5X faster on RG than RA3
- 1.5X
A first-class engine for Apache Iceberg
Read and write access for Apache Iceberg v3 tables in your data lake, with support for default column values, row lineage, and deletion vectors.
Advanced pruning, vectorized scans, an intelligent I/O and caching framework, and automatic just-in-time (JIT) statistics for Iceberg tables.
Precompute expensive joins and aggregations once in Amazon Redshift, store the results as open Apache Iceberg tables, and query them from any Iceberg-compatible engine.
Join Redshift tables with external Iceberg tables using SQL.
Customer quotes
Monks
"Amazon Redshift on Apache Iceberg gives Monks an open lakehouse with warehouse-class performance. Our near real-time sports social trends analytics tool uses Redshift to transform data across bronze, silver, and gold tiers in Amazon S3 Tables, governed by AWS Lake Formation, all from a single streaming source. By redesigning our Iceberg tables around an insert-only write pattern, we cut Redshift processing time by more than 60%, so trends from our neuro-symbolic classification pipeline reach broadcast producers faster."
Benjamin Moody, Senior Technical Architect - Director, Monks
Resources
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