AWS Database Blog
Category: PostgreSQL compatible
Migrating Amazon RDS for PostgreSQL to Amazon Aurora using seeded logical replication
In this post, we show you how to migrate from an Amazon RDS for PostgreSQL to Amazon Aurora PostgreSQL-Compatible Edition using seeded logical replication. For live migrations with minimal downtime, AWS provides several approaches, including Aurora read replicas, the snapshot/restore method combined with ongoing replication, and AWS DMS.
Query billion-scale vectors with SQL: Integrating Amazon S3 Vectors and Aurora PostgreSQL
In this post, you’ll learn how to query Amazon S3 Vectors from Amazon Aurora PostgreSQL-Compatible Edition using standard SQL, and how to combine vector similarity results with relational filters in a single query, for example, finding the most semantically similar products and then filtering by price, stock status, or tenant in one SQL statement.
How Kajabi optimized costs with Amazon Aurora upgrades
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.
Ring’s Billion-Scale Semantic Video Search with Amazon RDS for PostgreSQL and pgvector
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.
Connecting .NET Lambda to Amazon Aurora PostgreSQL via RDS Proxy
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.
How to build unified JSON search solutions in AWS
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.
PostgreSQL logical replication: How to replicate only the data that you need
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.
Replicate spatial data using AWS DMS and Amazon RDS for PostgreSQL
In this post, we show you how to migrate spatial (geospatial) data from self-managed PostgreSQL, Amazon RDS for PostgreSQL, or Amazon Aurora PostgreSQL-Compatible Edition to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL using AWS DMS. Spatial data is useful for applications such as mapping, routing, asset tracking, and geographic visualization. We walk through setting up your environment, configuring AWS DMS, and validating the successful migration of spatial datasets.
Build a custom solution to migrate SQL Server HierarchyID to PostgreSQL LTREE with AWS DMS
In this post, we discuss configuring AWS DMS tasks to migrate HierarchyID columns from SQL Server to Aurora PostgreSQL-Compatible efficiently.
Strategies for upgrading Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL from version 13
In this post, we help you plan your upgrade from PostgreSQL version 13 before standard support ends on February 28, 2026. We discuss the key benefits of upgrading, breaking changes to consider, and multiple upgrade strategies to choose from.









