Category: Announcements

The new aws_dynamodb_global_secondary_index resource treats each GSI as an independent resource with its own lifecycle management. You can use this feature to make capacity adjustments for GSI and tables outside of Terraform. In this post, I demonstrate how to use Terraform’s new aws_dynamodb_global_secondary_index resource to manage GSI drift selectively. I walk you through the limitations of current approaches and guide you through implementing the solution.

Today, we’re announcing multi-account global tables for Amazon DynamoDB, which let you replicate DynamoDB table data across multiple AWS accounts and AWS Regions. This feature adds account-level isolation to global tables, so you can replicate DynamoDB table data across multiple AWS accounts and Regions for stronger isolation and resiliency. In this post, we show you how to create and configure a multi-account global table, and introduce use cases highlighting the value of using this feature.

Amazon Keyspaces now supports the pre-warming feature to provide you with proactive throughput management. With pre-warming, you can set minimum warm throughput values that your table can handle instantly, avoiding the cold start delays that occur during dynamic partition splits. In this post, we discuss the Amazon Keyspaces pre-warming feature capabilities and demonstrate how it can enhance your throughput performance.

In this post, we show you how to create and deploy SQL Server Developer Edition on Amazon RDS.

With the introduction of the additional storage volume feature, you can now attach up to three additional storage volumes to your Amazon RDS for SQL Server instances. By using this feature, you can distribute your data and log files across multiple volumes. This enhancement offers more granular control over storage configuration and performance optimization. In this post, you will learn about the following scenarios: Adding a new storage volume, Scaling an existing storage volume, Restoring a database on an additional storage volume, and Deleting a storage volume.

In this post, we show how you can turn your ideas into full-stack applications with Kiro powers for Aurora. We explore how a new innovation, Kiro powers, can help you use Amazon Aurora best practices built into your development workflow, automatically implementing configurations and optimizations that make sure your database layer is production-ready from day one.

Amazon RDS for SQL Server now supports the Optimize CPU feature. With the Optimize CPU feature you can define the number of vCPUs when you launch new instances or when modifying existing database instances. This feature also provides a detailed billing breakdown of RDS infrastructure costs, and licensing costs for SQL Server and Windows OS. It is available starting from the 7th Generation instance class. In this post, we explore how to use the Optimize CPU feature with Amazon RDS for SQL Server.

Today, we’re announcing a new integration between Mem0 Open Source, Amazon ElastiCache for Valkey, and Amazon Neptune Analytics to provide persistent memory capabilities to agentic AI applications. This integration solves a critical challenge when building agentic AI applications: without persistent memory, agents forget everything between conversations, making it impossible to deliver personalized experiences or complete multi-step tasks effectively. In this post, we show how you can use this new Mem0 integration.

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.

You can now use resource governor with Amazon RDS for SQL Server to optimize your database performance by controlling how compute resources are allocated across different workloads. This post shows you how to optimize your database performance using resource governor on Amazon RDS for SQL Server. We walk you through the step-by-step process of enabling and configuring the feature, including how to set up resource pools, create workload groups, and implement classifier functions for effective resource management.