Category: Amazon Aurora

In this post, I dive deep into fundamental concepts that are important to comprehend the benefits of Aurora DSQL, its feature set, and its underlying components.

Today, we are launching dynamic data masking feature for Amazon Aurora PostgreSQL-Compatible Edition. In this post we show how dynamic data masking can help you meet data privacy requirements. We discuss how this feature is implemented and demonstrate how it works with PostgreSQL role hierarchy.

Today, we’re introducing Amazon RDS Blue/Green support for Aurora Global Database, enabling database upgrades and modifications with minimal downtime. With just a few steps, you can create a blue/green deployment that establishes a fully managed staging (green) environment mirroring the existing production (blue) environment, including the primary and its associated secondary regions of the Global Database.

This post shows you how to use PostgreSQL to store and search JSON data effectively. You’ll learn when to use JSON versus JSONB, how to create the right indexes, and how to write queries that perform well at scale.

In this post, we explore an artificial intelligence and machine learning (AI/ML)-powered database monitoring tool for PostgreSQL, using a self-managed or managed database service such as Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL.

Database migrations from Oracle to PostgreSQL are becoming increasingly common as organizations seek to optimize database costs while leveraging the benefits of open-source database solutions. However, these migrations present specific challenges, particularly when dealing with an Oracle-specific feature such as reference partitioning, which doesn’t have a direct equivalent in PostgreSQL. In this post, we show you how to migrate Oracle reference-partitioned tables to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL-Compatible Edition using AWS DMS.

In this post, we demonstrate how you can use Amazon CloudWatch Database Insights to analyze your SQL execution plan to troubleshoot and optimize your SQL query performance in an Aurora PostgreSQL cluster.

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.