AWS Database Blog

Amazon ElastiCache now supports Valkey 9.1, bringing the latest community-driven innovations from the Valkey open source project to customers running latency-sensitive, high-throughput, and operationally demanding in-memory workloads on ElastiCache. In this post, we discuss how Valkey 9.1 helps you get more throughput and memory efficiency from demanding workloads while providing stronger isolation for multi-tenant and shared-cluster deployments. We also cover new commands that simplify common application and operational workflows, new observability features that give operators better visibility into engine behavior, and how ElastiCache continues to deliver the latest Valkey open source innovations in a fully managed service.

In this post, we show how Amazon’s Finance Technology (FinTech) team uses Amazon Neptune to model complex corporate treasury structures as a property graph. These structures include the legal entity relationships, intercompany agreements, and bank account associations that govern payment routing and cash management.

In this post, we share how Securonix migrated hundreds of Amazon ElastiCache clusters from Redis OSS to Valkey, achieving a 20% reduction in caching costs. This amounts to over $100,000 in annualized savings. The migration also improved CPU utilization and overall throughput across Securonix’s global SIEM platform, which processes hundreds of terabyte data volumes daily for enterprise security teams worldwide.

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 a previous post, Implement network connectivity patterns for Oracle Database@AWS, we covered three connectivity patterns. These are direct peering between an application VPC and the Oracle Database@AWS network, single-Region connectivity using AWS Transit Gateway, and multi-Region connectivity using AWS Cloud WAN. This post walks you through two centralized inspection patterns that route traffic through a dedicated inspection VPC before it reaches its destination: one using AWS Transit Gateway and another using AWS Cloud WAN with service insertion.

In this post, you learn how to build a Spring Boot REST API that integrates with Aurora DSQL. You’ll configure the Aurora DSQL JDBC Connector for IAM authentication, implement optimistic concurrency control, and run the application across two regional nodes to observe active-active behavior.

Keeping non-production environments current with production data is a common operational need. In this post, you learn how to automate cross-account environment refresh for Amazon Relational Database Service (Amazon RDS) Multi-AZ DB clusters (available for PostgreSQL and MySQL) using a serverless pipeline that runs with a single trigger.

This is Part 1 of a two-part series covering the key features in PostgreSQL 18. In this post, we focus on performance enhancements: skip scan optimization for multicolumn indexes, enhanced EXPLAIN output, automatic removal of unnecessary self-joins, and several vacuum and autovacuum improvements that help keep your database running efficiently.

In Part 1 of this series, we explored the performance enhancements in PostgreSQL 18, including skip scan optimization, enhanced EXPLAIN output, automatic self-join removal, and vacuum/autovacuum improvements. In this second part, we focus on security, monitoring, developer productivity, and logical replication enhancements that improve operational efficiency and the overall developer experience.

In this post, we show you how to use Amazon CloudWatch Database Insights for lock analysis in Amazon Aurora PostgreSQL. You learn how to enable the feature, interpret lock tree visualizations, resolve common lock-related issues, and maintain optimal database performance. This lock tree analysis feature also applies to Amazon RDS for PostgreSQL.