Category: Amazon Aurora

Managing Aurora PostgreSQL-Compatible Edition upgrades across multiple database clusters can be time-consuming and error-prone when done manually. In this post, we show you how to automate Amazon Aurora PostgreSQL upgrades across your entire database fleet through consistent, repeatable procedures.

In this post, you learn how to use AWS DMS Schema Conversion to migrate Oracle schemas to PostgreSQL. AWS DMS Schema Conversion converts database schemas and code objects to formats compatible with your target database. You also learn how to use AWS DMS to migrate data to Amazon Aurora PostgreSQL-Compatible Edition.

In this post, you’ll build a working veterinary clinic CLI application that demonstrates production-ready patterns for connecting Drizzle ORM to Aurora DSQL. By the end, you’ll have a running app with one-to-many and many-to-many relationships, and the patterns you learn (UUID primary keys, application-level relationships, and a custom migration runner) work with other TypeScript ORMs on Aurora DSQL too.

In this post, you learn three pagination techniques for Aurora DSQL: OFFSET/LIMIT, cursor-based (keyset), and temporal. You implement keyset pagination in SQL and Python, build it into an API layer, optimize with composite indexes, handle batch processing within the 3,000-row transaction limit, and avoid five common anti-patterns. By the end, you can choose the right pagination method for your workload and implement it with confidence.

In this post, you learn how to build a generative AI–powered migration assistant that helps automate portions of the last mile of code conversion. Using Anthropic’s Claude Sonnet 4.6 on Amazon Bedrock, the Strands Agents framework, and the AWS Knowledge MCP Server, you can automate the conversion and validation of PL/SQL objects against Amazon Aurora PostgreSQL-Compatible Edition. The assistant reads the AWS DMS SC assessment CSV, fetches live PL/SQL source from Oracle, converts each object, deploys the result to Aurora PostgreSQL through AWS Lambda, and runs automated tests, in a single pipeline.

In this post, you’ll build a working veterinary clinic command line interface (CLI) application that demonstrates production-ready patterns for connecting SQLAlchemy to Aurora DSQL. The patterns you implement (UUID primary keys, application-level relationships, and AUTOCOMMIT engine configuration) apply to other Python ORMs on Aurora DSQL.

In this post, we dive deep into the Aurora backup architecture, how it differs from Amazon RDS backups, and the Amazon CloudWatch metrics available to monitor your backup storage usage. Through detailed scenarios and visualizations, we demonstrate how workload patterns and retention periods impact backup costs. We also explore cross-Region backup options and share recommended practices to optimize your backup storage consumption.

In Part 1, Part 2, and Part 3 of this series, we explored PostgreSQL’s native indexes (B-tree, GIN, GiST, HASH, BRIN) and specialized extension-based index types (SP-GiST, btree_gin, btree_gist). In this post, we dive into three additional extensions: Bloom (for space-efficient multi-column equality filtering), pg_trgm (for fuzzy text matching and similarity searches), and pg_bigm (for full-text search optimized for Asian languages)

In this post, the third in the series, we dive into three extension-based index types: SP-GiST, btree_gin, and btree_gist. These are available in Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service (Amazon RDS) for PostgreSQL. PostgreSQL’s index infrastructure is extensible. Operator classes define how indexes behave for specific data types and operations. The SP-GiST, btree_gin, and btree_gist extensions take advantage of this extensibility to give you additional indexing strategies beyond the native options. We walk through when to use each extension, the data types they support, and practical examples that demonstrate their performance benefits.

This post walks through migrating data from an Oracle source to Amazon Aurora DSQL, using AWS DMS, Amazon S3, AWS Glue, and AWS Step Functions to create an automated, cost-effective migration pipeline suitable for enterprise-scale deployments.