Category: PostgreSQL compatible

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.

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.

In this post, we discuss configuring AWS DMS tasks to migrate HierarchyID columns from SQL Server to Aurora PostgreSQL-Compatible efficiently.

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.

In this post, we discuss how the Shared Plan Cache feature of the Amazon Aurora PostgreSQL-Compatible Edition can significantly reduce memory consumption of generic SQL plans in high-concurrency environments.

Correlated subqueries can cause performance challenges in Amazon Aurora PostgreSQL which can cause applications to experience reduced performance as data volumes grow. In this post, we explore the advanced optimization configurations available in Aurora PostgreSQL that can transform these performance challenges into efficient operations without requiring you to modify a single line of SQL code.

In this post, we demonstrate how your workloads can benefit from upgrading Graviton2-based R6g and R6gd instances to Graviton4-based R8gd instances with Aurora PostgreSQL 17.5 on Aurora I/O-Optimized using an Optimized Reads-enabled tiered cache.

With the Letta Developer Platform, you can create stateful agents with built-in context management (compaction, context rewriting, and context offloading) and persistence. Using the Letta API, you can create agents that are long-lived or achieve complex tasks without worrying about context overflow or model lock-in. In this post, we guide you through setting up Amazon Aurora Serverless as a database repository for storing Letta long-term memory. We show how to create an Aurora cluster in the cloud, configure Letta to connect to it, and deploy agents that persist their memory to Aurora. We also explore how to query the database directly to view agent state.