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MySQL is an open-source relational database management system (RDBMS) used by many web applications and websites. It is a popular choice of database for use in web applications, and is widely used in both small and large scale websites. The latest articles, news, and videos about MySQL provide information about the latest developments, trends, and features in the database. They also provide tutorials, tips, and advice for developers who are new to MySQL or want to learn more about the platform. From the latest features to best practices, these resources provide valuable insight into the world of MySQL.
Managing large volumes of data is a challenge every organization faces as applications grow. For MySQL users, this often means deciding what to do with historical or less frequently accessed data: keep it in production tables at the cost of performance, or archive it and lose the ability to query it efficiently. Traditionally, archiving has
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.
In 2023, AWS introduced the AWS advanced JDBC wrapper, enhancing the capabilities of existing JDBC drivers with additional functionality. This wrapper enables support of AWS and Amazon Aurora functions on top of an existing PostgreSQL, MySQL, or MariaDB JDBC driver of your choice. This wrapper supports a variety of plugins, including the Aurora connection tracker plugin, the limitless connection plugin, and the read-write splitting plugin. In this post, we discuss the benefits, use cases, and implementation details for two popular AWS Advanced JDBC Wrapper Driver plugins: the Aurora Initial Connection Strategy and Failover v2 plugins.
We are now announcing support for Lake Formation tag-based access control (LF-TBAC) to federated catalogs of S3 Tables, Redshift data warehouses, and federated data sources such as Amazon DynamoDB, MySQL, PostgreSQL, SQL Server, Oracle, Amazon DocumentDB, Google BigQuery, and Snowflake. In this post, we illustrate how to manage S3 Tables and Redshift tables in the lakehouse using a single fine-grained access control mechanism of LF-TBAC. We also show how to access these lakehouse tables using your choice of analytics services, such as Athena, Redshift, and Apache Spark in Amazon EMR Serverless.