Category: Amazon DynamoDB

This post addresses recovery challenges in multi-database architectures, focusing on both low-consistency and mission-critical scenarios. We explore practical strategies for implementing resilient recovery mechanisms across Amazon DynamoDB, Amazon Aurora, Amazon Neptune, Amazon OpenSearch Service, and other AWS database services.

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, I introduce a framework for managing atomicity, consistency, isolation, and durability (ACID) compliant transactions in Amazon DynamoDB using C#, featuring support for nested transactions. This capability allows you to implement sophisticated logic with finer control over data consistency and error handling within your .NET applications. With this nested transaction framework, you can isolate issues, allow for partial rollbacks, and build maintainable, modular workflows on top of the built-in transactional capabilities of 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.