AWS Database Blog

In this post, we show you how to save costs on Amazon ElastiCache by upgrading your cluster engine to ElastiCache for Valkey. If you’re currently using ElastiCache for Redis OSS, you can achieve up to 60% cost savings by upgrading to Valkey.

In this post, we explore the support for AWS bare metal instances on Amazon EC2 bare metal Instances for Amazon RDS for Oracle and RDS Custom for Oracle.

In this post, we show how to use Amazon Timestream for InfluxDB read replicas to scale your read operations by adding additional read replicas while maintaining a single write endpoint. Built in partnership with InfluxData, our read replica add-on offers InfluxDB open source customers the ability to horizontally scale their read capacity.

In this post, we walk through a solution that automates the notification of Amazon RDS and Aurora recommendations through email using AWS Lambda, Amazon EventBridge and Amazon Simple Email Service (Amazon SES).

AWS recently announced virtual target mode in AWS Database Migration Service (AWS DMS) Schema Conversion. This feature helps you start migration planning without provisioning target databases. In this post, we show you how to get started using virtual target mode in AWS DMS Schema Conversion.

In this post we are announcing the strengthening of the partnership between AWS and InfluxData as Timestream adopts InfluxDB as the main purpose-built time series database.

Amazon DynamoDB delivers consistent single-digit millisecond performance at any scale, making it ideal for mission-critical workloads. However, as with any distributed system, a small percentage of requests may experience significantly longer response times than the average. This phenomenon, known as tail latency, refers to these slower outliers that can be seen by looking at metrics such as the 99th or 99.9th percentile of response times. In this post, we explore how Global Payments Inc. (GPN) reduced their tail latency by 30% using request hedging. We review the technical details and challenges they faced, providing insights into how you can optimize your own latency-sensitive applications. In a next post we’ll share detailed implementation examples.

Amazon Neptune version 1.4.5 introduces engine improvements and support for AWS Graviton-based r8g instances. In this post, we show you how these updates can improve your graph database performance and reduce costs. We walk you through the benchmark results for Gremlin and openCypher comparing Neptune v1.4.5 on r8g instances against previous versions. You’ll see performance improvements of up to 4.7x for write throughput and 3.7x for read throughput, along with the cost implications.

In this post, we show how to capture and retain failed stream events for later analysis or replay using Amazon S3 as a durable destination. We compare this approach with the traditional Amazon SQS dead-letter queue (DLQ) pattern, and explain when and why Amazon S3 is a preferred option.

In this post, we dive deeper into database optimization for your Amazon Relational Database Service (Amazon RDS), exploring how you can use AWS Compute Optimizer recommendations to make cost-aware resource configuration decisions for your MySQL and PostgreSQL databases.