AWS Database Blog

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.

In Part 1 of this two-part blog post series, we focused on understanding how certain Amazon Aurora Serverless v2 database parameters influence the scaling of Aurora capacity units (ACUs) to its minimum and maximum amounts. This post is Part 2, and it focuses on understanding how the minimum and maximum configuration of ACUs impacts scaling behavior in Aurora Serverless v2 and how fast scaling occurs after it starts.

The unit of measure for Aurora Serverless v2 is the Aurora capacity unit (ACU). Each workload has unique minimum and maximum ACU requirements. Finding the right ACU configuration and understanding factors influencing Aurora Serverless v2 scaling is essential. This post is Part 1 of a two-part blog post series and focuses on understanding how certain database parameters impact Aurora Serverless v2 scaling behavior for PostgreSQL-compatible DB instances. This post considers minimum ACU to be 0.5 or higher and does not include the new automatic pause feature.

Amazon Web Services (AWS) users frequently use multiple accounts, organizing them efficiently with AWS Organizations. This system structures the accounts hierarchically and groups them into Organizational Units (OUs). However, this setup can sometimes add complexity, especially for teams that support the entire organization. Consider the following example of a database operations team’s predicament. Their task […]

Today, we are adding support for Valkey 8.0 on Amazon ElastiCache. ElastiCache version 8.0 for Valkey brings faster scaling for ElastiCache Serverless and memory optimizations for node-based clusters. In this post, we discuss these improvements and how you can benefit from them.

Today, Amazon RDS has announced support for MySQL version 8.4, which is the latest Long-Term Support (LTS) major version from the MySQL community. With that, Amazon RDS now supports MySQL Community Edition versions 8.0 and 8.4. In addition to the two community-supported LTS releases, Amazon RDS also offers MySQL 5.7 under RDS Extended Support, where RDS provides critical patches and bug fixes for the engine. For any of these versions, you can bring your existing MySQL code, applications, and tools to Amazon RDS. With MySQL 8.4, the MySQL community has introduced, as well as retired, multiple features, which are listed in the MySQL 8.4 reference manual. In this post, we explore some of these features, list known breaking changes, and provide recommendations to ease the migration of your workloads to this version.

Amazon Aurora Serverless v2 now supports scaling capacity down to 0 ACUs, enabling you to optimize costs during periods of database inactivity. Aurora Serverless is an on-demand, auto scaling configuration of Aurora that automatically adjusts your database capacity based on your workload requirements. Aurora Serverless measures database capacity in Aurora Capacity Units (ACUs) billed per second. 1 […]

Migrating an Oracle application and its underlying database to the cloud can be inherently complex. Complexity is significantly amplified by various factors, including operating system compatibility, database and application version, software availability, database storage technologies such as Automatic Storage Management (ASM), and stringent business downtime requirements. AWS Application Migration Service accelerates the migration of applications to Amazon Web Services (AWS) by automatically replicating entire servers at the block level. In this post, we show you the process of migrating Oracle E-Business Suite to AWS using Application Migration Service.