Category: Learning Levels

Amazon DynamoDB has announced support for up to 8 attributes in composite keys for Global Secondary Indexes (GSIs). Now, you can specify up to four partition keys and four sort keys to identify items as part of a GSI, allowing you to query data at scale across multiple dimensions. In this post we show you how to design similar data models more efficiently using Global Secondary Indexes with the additional attribute support in composite keys and provide examples of DynamoDB data models with reduced complexity.

Today, we’re introducing the Amazon DynamoDB Data Model Validation Tool, a new component of the MCP server that closes the loop between generation, evaluation, and execution. The validation tool automatically tests generated data models against Amazon DynamoDB local, refining them iteratively until every access pattern behaves as intended.

In this post, we walk through the steps of integrating Oracle Database@AWS (ODB@AWS) with Amazon Bedrock for by creating a RAG assistant application using an Amazon Titan embedding model in Amazon Bedrock and vectors stored in Oracle AI Database 26ai.

In this post, we show how you can use the Database Modernizer workshop to accelerate your database modernization journey from MySQL to Amazon DynamoDB. Traditional approaches to migrating from relational databases to NoSQL solutions like DynamoDB can take several months, requiring extensive expertise in data modeling, application refactoring, and migration strategies. The Database Modernizer workshop, which can be scheduled by your account team as part of the Amazon DynamoDB Immersion Day program, uses AI to help you complete database modernization projects in days instead of months.

In this post, we show an algorithm to efficiently compare two Amazon DynamoDB tables and find the differences between their items. We provide an example where two tables, each containing approximately half a billion items, are compared in less than 7 minutes, for less than $10.

Today, we’re introducing Amazon RDS Blue/Green support for Aurora Global Database, enabling database upgrades and modifications with minimal downtime. With just a few steps, you can create a blue/green deployment that establishes a fully managed staging (green) environment mirroring the existing production (blue) environment, including the primary and its associated secondary regions of the Global Database.

Part 1 of this series showed how to rate-limit calls to Amazon DynamoDB by using Python Boto3 event hooks. In this post, I expand on the concept and show how to rate-limit calls in a distributed environment, where you want a maximum allowed rate across the full set of clients but can’t use direct client-to-client communication.

In this post, I present a technique where a Python script making calls to Amazon DynamoDB can rate limit its consumption of read and write capacity units. The technique uses Boto3 event hooks to apply the rate limiting without having to modify the client code performing the read and write calls.

In this post, we explore the use cases for delayed replication, the recovery procedures, and best practices for managing delayed replicas to help ensure your database recovery strategy is both robust and efficient.

On Oct 28, 2025, Amazon DocumentDB (with MongoDB compatibility) introduced a new query planner (NQP) to improve database performance and stability. The redesigned architecture uses improved cost estimation techniques and optimized algorithms for smarter query plan selection.