Category: PostgreSQL compatible

In this post, we show how to rebuild large indexes on Amazon Aurora PostgreSQL by combining Amazon Aurora Blue/Green Deployments with Aurora Optimized Reads. By performing the reindex on the green (staging) environment with a Non-Volatile Memory express (NVMe)-backed instance class, the sort phase uses fast local storage instead of Amazon EBS over the network, and you avoid impacting production workloads.

In this post, we demonstrate how to use the PostgreSQL 18 logical replication improvements on RDS for PostgreSQL: replicating STORED generated columns with the publish_generated_columns parameter, monitoring conflicts through the new counters in pg_stat_subscription_stats, verifying that parallel streaming is enabled by default, toggling two-phase commit on a running subscription, and configuring idle_replication_slot_timeout for automatic slot cleanup. These features are available on RDS for PostgreSQL 18.0 and later and Aurora PostgreSQL.

In this post, we show you how to deploy an automated pipeline that extracts PostgreSQL audit logs from CloudWatch Logs, converts them into structured comma-separated values (CSV) format, and stores them in Amazon S3 for long-term analysis. The solution processes log entries in near real time after generation.

Running pgvector on Amazon Aurora PostgreSQL gives you a production-grade vector store on a database you already know, backed by the operational tooling, high availability, and scaling behaviour of Amazon Aurora. Production traffic does introduce a predictable set of operational considerations: query latency as the corpus grows, recall on filtered vector searches, memory headroom during index builds, and connection behaviour under load. This post is scoped to the database operations that keep the RAG retrieval layer healthy. In this post, we cover the operational practices that keep a pgvector workload healthy once you depend on it: choosing the right index and distance function, scaling with quantization and partitioning, managing Hierarchical Navigable Small World (HNSW) churn, sizing for memory-resident operation, and the observability signals that catch problems early.

In this post, you learn how to use AWS DMS Schema Conversion to migrate Oracle schemas to PostgreSQL. AWS DMS Schema Conversion converts database schemas and code objects to formats compatible with your target database. You also learn how to use AWS DMS to migrate data to Amazon Aurora PostgreSQL-Compatible Edition.

In this post, you learn how to build a generative AI–powered migration assistant that helps automate portions of the last mile of code conversion. Using Anthropic’s Claude Sonnet 4.6 on Amazon Bedrock, the Strands Agents framework, and the AWS Knowledge MCP Server, you can automate the conversion and validation of PL/SQL objects against Amazon Aurora PostgreSQL-Compatible Edition. The assistant reads the AWS DMS SC assessment CSV, fetches live PL/SQL source from Oracle, converts each object, deploys the result to Aurora PostgreSQL through AWS Lambda, and runs automated tests, in a single pipeline.

In this post, we demonstrate how to implement a production-ready CDC solution by using Amazon Aurora for PostgreSQL, Debezium connectors, and Amazon Managed Streaming for Apache Kafka (Amazon MSK). This solution captures database changes in real time and streams them to Kafka topics so that downstream consumers can process the same data for different business purposes.

In this post, you learn how Amazon Aurora PostgreSQL-Compatible Edition accelerates developer productivity in the agentic AI era. We explore three core design convictions: meet developers where they work, absorb workload variability, and grow with the application from prototype to global scale.

In this post, we show you how to migrate from an Amazon RDS for PostgreSQL to Amazon Aurora PostgreSQL-Compatible Edition using seeded logical replication. For live migrations with minimal downtime, AWS provides several approaches, including Aurora read replicas, the snapshot/restore method combined with ongoing replication, and AWS DMS.

In this post, you’ll learn how to query Amazon S3 Vectors from Amazon Aurora PostgreSQL-Compatible Edition using standard SQL, and how to combine vector similarity results with relational filters in a single query, for example, finding the most semantically similar products and then filtering by price, stock status, or tenant in one SQL statement.