AWS Database Blog

This post is for database architects, developers, and DBAs who must evaluate Amazon Aurora DSQL or work with PostgreSQL workloads on a distributed database. Knowing exactly where Amazon Aurora DSQL aligns with standard PostgreSQL and where it diverges helps you to reduce risk and design schemas that perform well from day one. You might find that most existing PostgreSQL applications work with minimal changes.

In this post, we show you how Kajabi navigated complex Aurora PostgreSQL database upgrades and achieved an 80.53% cost reduction through strategic planning and technical execution. You’ll discover their hybrid approach combining Amazon Aurora blue/green deployments with PostgreSQL native replication. You’ll also learn about their implementation of Aurora I/O-Optimized storage and the key lessons from their journey. Whether you’re managing large-scale databases or planning your own upgrade path, Kajabi’s experience offers valuable insights. You’ll see how to balance performance requirements with cost optimization while maintaining continuous availability.

In this post, we walk through the available options for sharing Oracle Database@AWS (ODB@AWS) resources across AWS accounts. We also cover common cross-account architecture patterns, along with best practices and key considerations. This helps you design your ODB@AWS architecture across your AWS accounts efficiently.

This post addresses recovery challenges in multi-database architectures, focusing on both low-consistency and mission-critical scenarios. We explore practical strategies for implementing resilient recovery mechanisms across Amazon DynamoDB, Amazon Aurora, Amazon Neptune, Amazon OpenSearch Service, and other AWS database services.

In this post, we share Ring’s billion-scale semantic video search on Amazon RDS for PostgreSQL with pgvector architectural decisions vs alternatives, cost-performance-scale challenges, key lessons, and future directions. The Ring team designed for global scale their vector search architecture to support millions of customers with vector embeddings, the key technology for numerical representations of visual content generated by an AI model. By converting video frames into vectors-arrays of numbers that capture what’s happening (visual content) in each frame – Ring can store these representations in a database and search them using similarity search. When you type “package delivery,” the system converts that text into a vector and finds the video frames whose vectors are most similar-delivering relevant results in under 2 seconds.

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.

In this post, we explore Oracle Database@AWS high performance networking capabilities and provide a step-by-step guide to help you configure and deploy this feature.

In this post, we show you how to build a multi-Region Kerberos authentication system that matches your Aurora Global Database’s resilience using AWS Directory Service for Microsoft Active Directory (AWS Managed Microsoft AD) with multi-Region replication and a one-way forest trust to your on-premises Active Directory, so your Linux clients can authenticate without joining the AD domain.

As SQL Server workloads grow on Amazon Relational Database Service (Amazon RDS) for Db2, the 64 TiB storage limit can force architectural issues that constrain business growth and create performance bottlenecks when transaction logs compete with data for I/O resources. The additional storage volumes feature in Amazon RDS for SQL Server solves these challenges. You can use Amazon RDS for SQL Server to attach additional storage volumes beyond the root volume, with each volume having different storage classes and performance characteristics. In this post, you will learn how to use the additional storage volumes feature in Amazon RDS for SQL Server to address these common challenges.

In this post, we walk through the steps to set up the custom migration assistant agent and migrate a PostgreSQL database to Aurora DSQL. We demonstrate how to use natural language prompts to analyze database schemas, generate compatibility reports, apply converted schemas, and manage data replication through AWS DMS. As of this writing, AWS DMS does not support Aurora DSQL as target endpoint. To address this, our solution uses Amazon Simple Storage Service (Amazon S3) and AWS Lambda functions as a bridge to load data into Aurora DSQL.