AWS Database Blog

In this post, we look at two capabilities in Amazon Bedrock Knowledge Bases that make it easier to build RAG workflows with Amazon Aurora Serverless v2 as the vector store. The first capability helps you easily create an Aurora Serverless v2 knowledge base to use with Amazon Bedrock and the second capability enables you to automate deploying your RAG workflow across environments.

In this post, we introduce postgres_get_av_diag(), a new function available in RDS for PostgreSQL to monitor aggressive autovacuum blockers. By using this function, you can identify and address performance and availability risks through actionable insights provided by postgres_get_av_diag().

Valkey is an open-source, distributed, in-memory key-value data store that offers high-performance data retrieval and storage capabilities, making it an ideal choice for scalable, low-latency modern application development. Originating as a fork of Redis OSS following recent licensing changes, Valkey maintains full compatibility with its predecessor while providing high performance alternative for its developers. Valkey […]

Amazon Relational Database Service (Amazon RDS) for MySQL currently supports a variety of Community MySQL major versions including 5.7, 8.0, and 8.4 which present many different features and bug fixes. Upgrading from one major version to another requires careful consideration and planning. For a complete list of compatible major versions, see Supported MySQL major versions […]

In this post, we dive deep into concurrency control, providing valuable insights into crafting efficient transaction patterns and presenting examples that demonstrate effective solutions to common concurrency challenges. We also include a sample code that illustrates how to implement retry patterns for seamlessly managing concurrency control exceptions in Amazon Aurora DSQL (DSQL).

Today, we’re excited to inform you about a new generative AI feature in DMS SC. You can now use advanced language models to streamline and enhance your migration workflow. In this post, we discuss the key capabilities of DMS SC with generative AI and how to enable it to offer you additional recommendations to reduce manual conversion effort and time.

Today, we introduce Amazon Aurora DSQL, the fastest serverless distributed SQL database for always available applications. It offers virtually unlimited scale, highest availability, and zero infrastructure management. It can scale to meet any workload demand without database sharding or instance upgrades. In this post, we discuss the benefits of Aurora DSQL and how to get started.

In this post, we show you how to use CodePipeline to streamline your Aurora database deployments. We dive into a detailed architecture and steps for using CodePipeline in conjunction with AWS CodeBuild and AWS Secrets Manager. By the end of this post, you’ll have a clear understanding of how to set up a robust, automated pipeline for your database changes, allowing you to focus on what really matters—delivering value to your customers through innovative features and optimized performance.

We are excited to announce Amazon Timestream for LiveAnalytics as a newly supported target endpoint for AWS Database Migration Service (AWS DMS). This addition allows you to move time-series data from an AWS DMS supported source database to Timestream. In this post, we show you how to use Timestream as a target for an example PostgreSQL source endpoint in AWS DMS.

In this post, we demonstrate how to set up an event-driven workflow to run stored procedures for Amazon RDS for PostgreSQL with AWS Lambda to bridge this gap by securely connecting to an Aurora PostgreSQL database using AWS Secrets Manager, making sure that stored procedures can be managed in the cloud. We explore the step-by-step process, discuss the advantages of this approach, and address the limitations of invoking stored procedures from Lambda functions.