Amazon Aurora PostgreSQL introduces native support for querying Apache Iceberg and Parquet formatted data directly from data lakes alongside live transactional data. This innovation removes the need for complex ETL workflows and enables unified analytics using familiar PostgreSQL syntax.
- Queries join live Aurora data with Iceberg and Parquet data in S3 using PostgreSQL syntax.
- Reduces operational complexity by eliminating ETL duplication and synchronization overhead.
- Optimizations include predicate pushdown, caching, and catalog federation for efficient queries.
Infrastructure signal
Amazon Aurora PostgreSQL now embeds DuckDB to enable direct access to data lake content stored in Apache Iceberg and Parquet formats without requiring ETL pipelines. This reduces the need for data duplication and separate batch processes that traditionally synchronize operational and archival datasets.
The integration supports querying Iceberg tables registered through AWS Glue Data Catalog and external federated catalogs, allowing seamless joins between live operational tables and historical data across multiple catalogs. Performance improvements include predicate pushdown to filter data early and caching of frequently accessed files in Aurora for faster repeat queries.
Developer impact
Developers benefit from simplified workflows by querying both transactional and large-scale historical datasets using standard PostgreSQL syntax and tools. This unified query interface eliminates the need to maintain separate ETL pipelines and data replication, reducing errors and accelerating feature delivery.
The setup involves enabling the aurora_analytics extension and assigning appropriate IAM roles for secure S3 and Glue Data Catalog access, all configured via standard PostgreSQL clients or AWS consoles. Monitoring query metrics such as cache hits and data scanned is exposed through aurora_analytics_stat_statements(), enhancing transparency into query efficiency.
What teams should watch
Cloud infrastructure and data platform teams should plan for potential cost savings by reducing duplicated storage and operational overhead related to data syncing pipelines. Observability teams can leverage new query performance metrics to optimize resource usage and troubleshoot data lake access.
Application teams embedding AI or analytics that require wide data access should explore this unified querying for more dynamic insights without upfront ETL design. Database and API architects must consider how federated catalogs and cross-source join capabilities impact data modeling and service boundaries.