Category: Advanced (300)

In this post, we show you how to use Amazon CloudWatch Database Insights for lock analysis in Amazon Aurora PostgreSQL. You learn how to enable the feature, interpret lock tree visualizations, resolve common lock-related issues, and maintain optimal database performance. This lock tree analysis feature also applies to Amazon RDS for PostgreSQL.

Managing massive data volumes at scale presents significant operational challenges. At Similarweb we faced these challenges with Apache HBase and found a solution in Amazon DynamoDB. Similarweb is a digital intelligence platform that provides AI-powered insights into website traffic, app usage, and market trends to help businesses benchmark competitors and optimize growth strategies. We faced growing scalability and operational complexity issues with our existing Apache HBase infrastructure, which prompted us to explore more flexible and efficient alternatives. This post walks you through our journey migrating our data storage from Apache HBase to DynamoDB. We discuss the technical challenges, migration approach, data modeling strategies, cost optimization techniques, and key benefits achieved along the way.

In this post, we show you how to use EXPLAIN plans to diagnose and improve query performance in Amazon Aurora DSQL. We introduce a three-layer filter model as a practical framework for understanding where your predicates are evaluated, and walk through the architecture differences that make Aurora DSQL plans unique, the anatomy of an EXPLAIN output, access method selection, and a step-by-step query improvement workflow.

In this post, we show you how to provision key components of the Oracle Database@AWS offering, including the ODB network, Oracle Exadata infrastructure, Exadata VM clusters and Autonomous VM clusters using Terraform.

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 will learn how to integrate Okta with AWS IAM Identity Center and implement Amazon Relational Database Service (Amazon RDS) AWS Identity and Access Management (AWS IAM) authentication to create a unified authentication flow. You configure attribute-based access control (ABAC) that automatically maps user identities from your IdP to database permissions, supporting interactive user sessions and helping you avoid shared accounts. By the end, you have a working system where database authentication works exactly like your application authentication.

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 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.

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.