Amazon Aurora PostgreSQL now lets you run SQL against Apache Iceberg and Parquet files stored in Amazon S3 without moving the data into the database. The feature embeds DuckDB inside Aurora, exposing an aurora_analytics extension that creates foreign tables pointing at lake files and lets you join them with live transactional tables using ordinary PostgreSQL syntax.
How the feature works
Support is available on Aurora PostgreSQL major versions 17 (from 17.11) and 18 (from 18.6). To enable it you provision an Aurora cluster, attach an IAM role that includes the AuroraAnalytics permission, and run CREATE EXTENSION aurora_analytics;. The role authorises Aurora to read objects in S3 and to query the AWS Glue Data Catalog, which can also federate external IRC‑compatible catalogs. After the extension is active you define a foreign table that references a lake object; Aurora reads the file metadata to infer the schema, so you do not need to list columns manually.
CREATE EXTENSION aurora_analytics;CREATE FOREIGN TABLE transaction_history () SERVER aurora_analytics_server OPTIONS ( location 's3://my-bucket/finance/transaction_history.parquet', format 'parquet' );
Once the foreign table exists you can write a single query that joins recent_transactions (a regular Aurora table) with transaction_history (the Parquet file). The query runs entirely inside Aurora, avoiding extra network hops or ETL pipelines.
Why this matters for AI, cloud, and operations teams
AI agents that need both current state and historical context no longer require a separate data‑replication layer. Engineers can keep a single PostgreSQL endpoint for dashboards, transaction enrichment, or model feature extraction, reducing infrastructure cost and operational overhead. The embedded DuckDB engine also brings familiar PostgreSQL tooling to lake data, meaning existing CI/CD pipelines, monitoring, and alerting can stay unchanged.
Architectural and operational considerations
- Data locality and performance: Aurora pushes predicates and prunes columns before reading S3 objects, and frequently accessed lake data is cached locally. This mitigates the latency of remote reads but still depends on S3 throughput and object size.
- IAM role scope: The attached role must grant Aurora read access to the specific bucket and Glue catalog resources. Over‑permissive policies could expose unrelated lake data to the database.
- Catalog management: Iceberg tables can be registered in the Glue Data Catalog or in external IRC‑compatible catalogs that are federated through Glue. Each catalog registration is a separate step; changes to catalog metadata are reflected automatically when queries run.
- Observability: The
aurora_analytics_stat_statements()function reports rows scanned, bytes read from S3, and cache hits per query, giving operators a way to monitor lake‑query cost. - Version compatibility: Only Aurora PostgreSQL 17.11+ and 18.6+ include the extension. Clusters on earlier versions must be upgraded before they can use the feature.
Security implications
The extension does not introduce new network paths; all processing stays inside the Aurora instance. However, the IAM role that enables S3 and Glue access becomes a critical trust boundary. Practitioners should audit the role’s policies, enforce least‑privilege permissions, and consider using resource‑based policies or bucket policies to limit exposure. Because the engine can read uncommitted writes, any SQL user with permission to query the foreign table can see live transactional data combined with lake data, so role‑based access control on the database side remains essential.
Related CloudNinjas coverage: AWS.
What This Means For Practitioners
Adopt the aurora_analytics extension when you need a single SQL surface for both operational and historical data, especially for AI feature pipelines or real‑time analytics. Start by provisioning a compatible Aurora version, attach a tightly scoped IAM role, and enable the extension in a test environment. Use aurora_analytics_stat_statements() to benchmark query cost and adjust S3 object layout or partitioning if needed. Finally, review IAM policies and database permissions to ensure that only authorized roles can query lake data, keeping the expanded data surface under control.