Category: MySQL compatible

Today, we announce the Amazon Aurora MySQL power for Kiro. The power connects Kiro’s AI agent to Aurora MySQL and pairs live database access with curated best-practice guidance. You describe what you need in natural language. The agent generates the API calls, SQL, and configuration for you to review and run. In this post, we walk through how the power guides a production migration from Amazon Relational Database Service (Amazon RDS) for MySQL 8.0 to Aurora MySQL through four phases: assessment, replica creation, promotion, and post-cutover validation.

Today, we are excited to announce the general availability of Amazon Aurora MySQL 8.4, our latest major version, compatible with community MySQL 8.4.7. This release marks an important milestone for Aurora MySQL customers, introducing a simplified versioning model aligned directly with community MySQL, along with a streamlined patch version experience, and the full set of community MySQL 8.4 enhancements. In this post, we discuss the customer challenges that this release addresses, introduce Aurora MySQL 8.4, walk through the new versioning approach and its benefits for customers, cover the key capabilities delivered in Aurora MySQL 8.4, and show you how to get started.

In this post, we show you how to optimize full-text search (FTS) performance in Amazon RDS for MySQL and Amazon Aurora MySQL-Compatible Edition through proper maintenance and monitoring. We discuss why FTS indexes require regular maintenance, common issues that can arise, and best practices for keeping your FTS-enabled databases running smoothly.

In this post, we demonstrate how to migrate from Cloud SQL for MySQL 8+ to Amazon RDS for MySQL 8+ or Amazon Aurora MySQL–Compatible using AWS DMS over an AWS Site-to-Site VPN. We cover preparing the source and target environments, exemplifying cross-cloud connectivity, and setting up DMS tasks.

Amazon RDS for MySQL and Amazon Aurora MySQL provide built-in audit logging capabilities, but customers might need to export and store these logs for long-term retention and analysis. Amazon S3 offers an ideal destination, providing durability, cost-effectiveness, and integration with various analytics tools. In this post, we explore two approaches for exporting MySQL audit logs to Amazon S3: either using batching with a native export to Amazon S3 or processing logs in real time with Amazon Data Firehose.

In this post, we discuss methods to effectively monitor parallel replication performance and tune its related parameters for Amazon Aurora MySQL and Amazon Relational Database Service for MySQL and MariaDB.

In this first post, we dive into the world of MySQL replication, with a special focus on parallel replication techniques. We start with a quick overview of how MySQL replication works, then explore the intricacies of multithreaded replication. We discuss key configuration options and best practices for optimization.

In this post, we explore how zero-ETL integration works, the key benefits it delivers for data-driven teams, and how it aligns with the broader zero-ETL strategy in AWS services. You’ll learn how this integration can enhance your data workflows, whether you’re building predictive models, entering interactive SQL queries, or visualizing business trends. By eliminating traditional extract, transform, and load (ETL) processes, this solution enables real-time intelligence securely and at scale to help you make faster, data-driven decisions.

Data masking is an important technique in cybersecurity, allowing organizations to safeguard personally identifiable information (PII) and other confidential data, while maintaining its utility for development, testing, and analytics purposes. Data masking involves replacing original sensitive data with false, yet realistic information. This process helps ensure that the masked version preserves the format and characteristics […]

In this post, we explore how you can use Vercel’s v0 generative UI to build applications with a modern UI for AWS purpose-built databases such as Amazon Aurora, Amazon DynamoDB, Amazon Neptune, and Amazon ElastiCache.