Navigating architectural choices for a lakehouse using Amazon SageMaker

This article guides organizations on choosing the right lakehouse architecture pattern using Amazon SageMaker, comparing data lake-centric, data warehouse-centric, and hybrid approaches.

• Lakehouse architecture combines data lake flexibility with data warehouse performance and ACID compliance
• Three data ingestion patterns: Traditional ETL for complex transformations, Zero-ETL for near real-time replication, Data federation for query-in-place access
• Storage options include general purpose S3, S3 Tables with automated optimization, and Redshift Managed Storage for high-concurrency BI
• SageMaker lakehouse uses Apache Iceberg, AWS Glue Data Catalog, and Lake Formation for unified governance and access control
• Federated catalogs enable querying existing Redshift warehouses without data movement or migration
• Self-managed Iceberg on S3 offers maximum control; S3 Tables provides simplified operations with automated maintenance

Organizations can build modern, scalable data platforms by strategically combining data lakes and warehouses rather than choosing between them, optimizing for both flexibility and performance.