Category: Database

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.

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 […]