Category: Announcements

This technical deep dive into Amazon Timestream for InfluxDB 3 explores the architectural decisions, features, and capabilities that make this release a significant evolution in time series database technology. This next-generation time series database represents is an architectural redesign from the previous engine version; built from the ground up with modern technologies including Rust for core performance, Apache Arrow for columnar data processing, Apache Parquet for efficient storage, and Apache Arrow Flight SQL for high-performance querying.

Amazon Aurora Serverless is an on-demand, auto scaling configuration for Aurora that scales up to support your most demanding workloads and down to zero when you don’t need it. The latest improvements deliver up to 30% better performance and enhanced scaling that understands your workload. These enhancements are available at no additional cost for a better price-performance ratio. In this post, we’ll share recent performance and scaling improvements with benchmark results, showing how Aurora Serverless can now scale up to 45.0% faster with a 32.9% faster workload completion time.

Amazon Aurora DSQL now supports PostgreSQL-compatible identity columns and sequence objects, so developers can generate unique integer identifiers with configurable performance characteristics optimized for distributed workloads. In distributed database environments, generating unique, sequential identifiers is a fundamental challenge: coordinating across multiple nodes creates performance bottlenecks, especially under high concurrency workloads. In this post, we show you how to create and manage identity columns for auto-incrementing IDs, selecting between identity columns and standalone sequence objects, and improving cache settings while choosing between UUIDs and integer sequences for your workload requirements.

Amazon DocumentDB 8.0 brings in support for MongoDB 8.0 API driver compatibility while maintaining support for applications built using MongoDB API versions 6.0 and 7.0. This post explores the new features in Amazon DocumentDB 8.0 and demonstrates how they improve performance and cost efficiency.

In this post, you learn how Amazon Aurora now provides encryption at rest by default for all new database clusters using AWS owned keys. You’ll see how to verify encryption status using the new StorageEncryptionType field, understand the impact on new and existing clusters, and explore migration options for unencrypted databases.

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.