AWS Database Blog

Amazon Aurora PostgreSQL-Compatible Edition supports managed blue/green deployments to help reduce downtime and minimize risk during updates. Even with thorough planning and testing in non-production environments, unexpected issues can emerge after a version upgrade. In these cases, having a rollback plan is essential to quickly restore service stability. While the managed Blue/Green deployment feature doesn’t currently include built-in rollback functionality, you can implement alternative solutions for version management. In this post, we show how you can manually set up a rollback cluster using self-managed logical replication to maintain synchronization with the newer version after an Amazon RDS Blue/Green deployment switchover.

In this post, we explore how a leading AWS customer in the learning services industry successfully modernized its legacy SAP ASE environment by migrating to Amazon Aurora PostgreSQL-Compatible Edition. Partnering with AWS, the customer engineered a comprehensive migration strategy to transition from a proprietary system to an open source database while providing high availability, performance optimization, and cost-efficiency.

Database outages, whether planned or unexpected, pose significant challenges to applications. Planned outages for maintenance can be scheduled but still impact users. Unplanned outages are more disruptive and can happen at critical times. Even the most robust and resilient databases will inevitably experience outages, making application resiliency a critical consideration in modern system design. In […]

In this post, we explore SQL Server replication implementation on Amazon RDS Custom. You’ll learn about different replication types supported on RDS Custom SQL Server, including snapshot, transactional, and merge replication, along with their specific use cases. Finally, we provide a step-by-step guide to setting up replication, from configuring the distributor to creating publications and managing subscriptions.

Row-level security (RLS) is a security mechanism that enhances data protection in scalable applications by controlling access at the individual row level. It enables organizations to implement fine-grained access controls based on user attributes, so users can only view and modify data they’re authorized to access. This post focuses on implementing a cost-effective custom RLS solution using native MySQL features, making it suitable for a wide range of use cases without requiring additional software dependencies. This solution is applicable for both Amazon Relational Database Service (Amazon RDS) for MySQL and Amazon Aurora MySQL-Compatible Edition, providing flexibility for users of either service.

When using AWS DMS, replication lags, task stalls, or resource bottlenecks can occur—and identifying the root cause quickly can become critical. The enhanced monitoring dashboard is a comprehensive monitoring tool that provides visibility into critical metrics for database migration tasks and replication instances. In this post, we discuss some use cases showcasing how you can use the enhanced monitoring dashboard.

Connecting to an Amazon RDS for Db2 instance has traditionally required spinning up an Amazon EC2 bastion host or running Db2 clients locally. With the new AWS CloudShell VPC integrated environments, you can now securely connect—with no Amazon EC2 required, no local installs, and no cost beyond normal Amazon RDS and AWS networking. In this post, we show you how to connect to Amazon RDS for Db2 using CloudShell.

Organizations running SQL Server workloads on Amazon RDS sometimes need to migrate their databases to different AWS accounts. This migration becomes more complex when mission-critical data requires column-level encryption to meet compliance requirements. In this post, we demonstrate how you can migrate your symmetric key-encrypted database on Amazon RDS for SQL Server to another AWS account without compromising security. The solution we present can also help you implement symmetric key encryption on a new database in Amazon RDS for SQL Server.

Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse allows you to run analytics workloads on your DynamoDB data without having to set up and manage extract, transform, and load (ETL) pipelines. In this post we cover setting up Amazon SageMaker Unified Studio, followed by running data analysis to showcase its capabilities. We illustrate our solution walkthrough with an example of a credit card company that wants to analyze its customer behavior and spending trends.

Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse allows you to run analytics workloads on your DynamoDB data without having to set up and manage extract, transform, and load (ETL) pipelines. In this two-part series, we first walk through the prerequisites and initial setup for the zero-ETL integration. In Part 2, we cover setting up Amazon SageMaker Unified Studio, followed by running data analysis to showcase its capabilities. We illustrate our solution walkthrough with an example of a credit card company that wants to analyze its customer behavior and spending trends.