Features of the lakehouse architecture

Gain the flexibility to access and query your data in-place, with any Apache Iceberg–compatible tools and engines of your choice.

Run analytics and ML workloads - including Apache Spark jobs, SQL dashboards, ML models, and generative AI applications - on a single copy of data, storing it in the format best suited for your workloads.

With Apache Iceberg compatibility, all data is fully ACID (Atomic, Consistent, Isolated, Durable) compliant for high-performance SQL analytics.

Run federated queries on data stored across multiple third-party sources such as Google BigQuery, SQL Server, and Snowflake to access and query your data in-place.

Get the flexibility of a data lake and performance of a data warehouse, without changing your existing data architecture. Access highly optimized Amazon Redshift storage and secondary data structures, such as materialized views, to speed up SQL analytics in your data lakes.

Bring data from your operational databases such as Amazon DynamoDB, Amazon Aurora MySQL, Amazon Aurora PostgreSQL, Amazon RDS for MySQL and applications including Salesforce, ServiceNow, and Zendesk to the lakehouse using zero-ETL integrations for near real-time analytics.

Define fine-grained permissions once and have them enforced across all your data in all analytic tools and engines.