Category: Amazon Aurora

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 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 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.

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.

In this post, we review the options for changing the AWS KMS key on your Amazon RDS database instances and on your Amazon RDS and Aurora clusters. We start with the most common approach, which is the snapshot method, and then we include additional options to consider when performing this change on production instances and clusters that can mitigate downtime. Each of the approaches mentioned in this post can be used for cross-account or cross-Region sharing of the instance’s data while migrating it to a new AWS KMS key.

In this post, I show you how to connect Lambda functions to Aurora PostgreSQL using Amazon RDS Proxy. We cover how to configure AWS Secrets Manager, set up RDS Proxy, and create a C# Lambda function with secure credential caching. I provide a GitHub repository which contains a YAML-format AWS CloudFormation template to provision the key components demonstrated, a C# sample function. I also walk through the Lambda function deployment step by step.

Using a movie streaming reference architecture, this post shows how to implement and sync operational, analytical, and search JSON workloads across AWS services. This pattern provides a scalable blueprint for any use case requiring multi-modal JSON data capabilities.

In this post, we show how logical replication with fine-grained filtering works in PostgreSQL, when to use it, and how to implement it using a realistic healthcare compliance scenario. Whether you’re running Amazon RDS for PostgreSQL, Amazon Aurora PostgreSQL, or a self-managed PostgreSQL database on an Amazon EC2 instance, the approach is the same.