Features of the lakehouse architecture
Page topics
General
Open allOpen data access with Apache Iceberg REST Catalog APIs
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 on a single copy of data
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.
Fully ACID-compliant storage
With Apache Iceberg compatibility, all data is fully ACID (Atomic, Consistent, Isolated, Durable) compliant for high-performance SQL analytics.
Federated data queries
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.
Access to Amazon Redshift storage features
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.
Zero-ETL integration for near real-time analytics
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.
Integrated access controls
Define fine-grained permissions once and have them enforced across all your data in all analytic tools and engines.
Did you find what you were looking for today?
Let us know so we can improve the quality of the content on our pages