Category: Amazon DynamoDB

In this post, we show you how to use AWS Fault Injection Service (AWS FIS) to validate that your application handles regional disruptions the way you expect, by running controlled experiments against your DynamoDB global tables. We cover both multi-Region strong consistency (MRSC) and multi-Region eventually consistent (MREC) global tables, because AWS FIS works differently with each.

In this post we cover the two primary failover strategies for DynamoDB global tables, the tradeoffs between them, and the operational considerations that you must be aware of during and after a failover.

This is Part 1 of a series on best practices for DynamoDB global tables. In this post, we focus on preparation: understanding how replication works, what your resilience posture looks like, and the operational groundwork that separates a controlled failover from a scramble.

Today, we are announcing ExtendDB, an open source Amazon DynamoDB-compatible adapter with pluggable storage backends, released under the Apache 2.0 License. ExtendDB implements the DynamoDB wire protocol and ships with PostgreSQL as its first backend, so any AWS SDK, CLI, or tool that works with DynamoDB works with ExtendDB unchanged. In this post, we introduce ExtendDB, walk through getting started, and explain the architecture. This is a v0.1 release for development, testing, and experimentation.

In this post, we show you how to use the new cdk orphan command to safely migrate a DynamoDB table from the Table construct to TableV2 with zero downtime. Your data stays intact, streams keep flowing, and your application remains available throughout the process.

In this post, we show how you can load (import) an Amazon DynamoDB full or incremental table export into a second DynamoDB table with precise control over what gets loaded, at what write rate, and with the ability to observe the progress. This technique helps drive large-scale data migrations and synchronizations where you want maximum control.

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.