Category: Amazon DynamoDB

In this post, we show you how to implement search on Amazon DynamoDB data using the zero-ETL integration with Amazon OpenSearch Service. You will learn how to add full-text search, fuzzy matching, and complex search queries to your application without building and maintaining data pipelines.

In this post, we explore how to use resource-based policies with DynamoDB Streams to enable cross-account Lambda consumption. We focus on a common pattern where application workloads live in isolated accounts, and stream processing happens in a centralized or analytics account.

The new aws_dynamodb_global_secondary_index resource treats each GSI as an independent resource with its own lifecycle management. You can use this feature to make capacity adjustments for GSI and tables outside of Terraform. In this post, I demonstrate how to use Terraform’s new aws_dynamodb_global_secondary_index resource to manage GSI drift selectively. I walk you through the limitations of current approaches and guide you through implementing the solution.

Today, we’re announcing multi-account global tables for Amazon DynamoDB, which let you replicate DynamoDB table data across multiple AWS accounts and AWS Regions. This feature adds account-level isolation to global tables, so you can replicate DynamoDB table data across multiple AWS accounts and Regions for stronger isolation and resiliency. In this post, we show you how to create and configure a multi-account global table, and introduce use cases highlighting the value of using this feature.

In this post, we talk about the benefits of caching in generative AI applications. We also elaborated on a few implementation strategies that can help you create and maintain an effective cache for your application.

In this post we show you how to build production-ready AI agents with durable state management using Amazon DynamoDB and LangGraph with the new DynamoDBSaver connector, a LangGraph checkpoint library maintained by AWS for Amazon DynamoDB.

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 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.