AWS Database Blog

Category: Compute

Automating Amazon RDS and Amazon Aurora recommendations via notification with AWS Lambda, Amazon EventBridge, and Amazon SES

In this post, we walk through a solution that automates the notification of Amazon RDS and Aurora recommendations through email using AWS Lambda, Amazon EventBridge and Amazon Simple Email Service (Amazon SES).

Gracefully handle failed AWS Lambda events from Amazon DynamoDB Streams

In this post, we show how to capture and retain failed stream events for later analysis or replay using Amazon S3 as a durable destination. We compare this approach with the traditional Amazon SQS dead-letter queue (DLQ) pattern, and explain when and why Amazon S3 is a preferred option.

How to optimize Amazon RDS and Amazon Aurora database costs/performance with AWS Compute Optimizer

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.

Leveling up Amazon RDS with AWS Graviton4: Benchmarks

In November 2024, AWS introduced the latest evolution of its custom-designed ARM-based processors with Graviton4, delivering significant performance and efficiency improvements for Amazon RDS for PostgreSQL, MySQL, and MariaDB and Amazon Aurora. In this post, we focus on Amazon RDS for PostgreSQL and compare the performance of the new Graviton4 instances to both Graviton3 and Graviton2. Using benchmarks, we evaluate throughput, latency, and price-performance, showcasing the advantages of Graviton4 for modern database workloads.

Use AWS FIS to test the resilience of self-managed Cassandra

Database outages can have devastating effects on your applications and business operations. For teams running self-managed Apache Cassandra clusters, unexpected node failures or memory issues can lead to service degradation, data inconsistency, or even complete system outages. AWS Fault Injection Service (AWS FIS) is a managed service that you can use to perform fault injection experiments on your AWS workloads. In this post, we review how you can use AWS FIS to craft a chaos experiment to test the resilience of your self-managed Cassandra clusters running on Amazon EC2. This can help you understand your application’s ability to reestablish a connection to a healthy node.

Automate Amazon RDS for PostgreSQL major or minor version upgrade using AWS Systems Manager and Amazon EC2

In this post, we guide you through setting up automation for pre-upgrade checks and upgrading a fleet of Amazon RDS for PostgreSQL instances. In this solution, we use AWS Systems Manager to automate the Amazon RDS upgrade job.

Run SQL Server post-migration activities using Cloud Migration Factory on AWS

In this post, we show you essential post-migration tasks to perform after migrating your SQL Server database to Amazon EC2 and how to automate this activity by using Cloud Migration Factory on AWS (CMF), such as validating database status, configuring performance settings, and running consistency checks. Additionally, we explore how the CMF solution can automate these essential tasks, providing efficiency, scalability, and heightened visibility to simplify and expedite your migration process.

Achieve up to 1.7 times higher write throughput and 1.38 times better price performance with Amazon Aurora PostgreSQL on AWS Graviton4-based R8g instances

In this post, we demonstrate how upgrading to Graviton4-based R8g instances with Aurora PostgreSQL-Compatible 17.4 on Aurora I/O-Optimized cluster configuration can deliver significant price-performance gains – delivering up to 1.7 times higher write throughput, 1.38 times better price-performance and reducing commit latency by up to 46% on r8g.16xlarge instances and 38% on r8g.2xlarge instances as compared to Graviton2-based R6g instances.

Ingest CSV data to Amazon DynamoDB using AWS Lambda

In this post, we explore a streamlined solution that uses AWS Lambda and Python to read and ingest CSV data into an existing Amazon DynamoDB table. This approach adheres to organizational security restrictions, supports infrastructure as code (IaC) for table management, and provides an event-driven process for ingesting CSV datasets into DynamoDB.