Category: Amazon DynamoDB

In this post, we show how to capture and retain failed stream events for later analysis or replay using Amazon S3 as a durable destination. We compare this approach with the traditional Amazon SQS dead-letter queue (DLQ) pattern, and explain when and why Amazon S3 is a preferred option.

In this post, we explore how you can use Vercel’s v0 generative UI to build applications with a modern UI for AWS purpose-built databases such as Amazon Aurora, Amazon DynamoDB, Amazon Neptune, and Amazon ElastiCache.

Today, we’re announcing improved observability for throttled requests in Amazon DynamoDB. These enhancements provide developers with enriched exception messages, detailed Amazon CloudWatch metrics, and a new, more cost-effective mode for CloudWatch Contributor Insights. Together, these improvements make it straightforward to understand, monitor, and optimize your DynamoDB applications’ performance. In this post, we explore how these […]

To help you move faster with greater confidence, we’re introducing a new DynamoDB data modeling tool, available as part of our DynamoDB Model Context Protocol (MCP) server. The DynamoDB MCP data modeling tool integrates with AI assistants that support MCP, providing a structured, natural-language-driven workflow to translate application requirements into DynamoDB data models. In this post, we show you how to generate a data model in minutes using this new data modeling tool.

In this post, we show you how to evolve your DynamoDB table’s data model to meet changing application requirements while maintaining zero downtime in production systems. We explore two main techniques with examples that you can apply to your own applications: Adding new attributes and Creating new entities.

In this post, we demonstrate how to evaluate throughput utilization for DynamoDB tables in provisioned mode. Understanding this metrics helps you determine whether switching to on-demand mode is the right choice. Moving to on-demand mode, where you pay-per-request for throughput, can optimize costs, eliminate capacity planning, minimize operational overhead, and enhance overall user experience for your applications.

The transition from SQL-based access patterns to a DynamoDB API-driven approach presents opportunities to optimize how your application interacts with its data layer. This final part of our series focuses on implementing an effective abstraction layer and handling various data access patterns in DynamoDB.

In this post, we explore strategies for designing DynamoDB data models, including entity identification, table design decisions, and relationship modeling approaches. We examine practical scenarios comparing different modeling strategies, helping you make informed decisions for your specific use case.

This is the first part of a series exploring how to effectively migrate from SQL to DynamoDB. We will examine how to analyze existing database structures and access patterns to prepare for migration, focusing on schema analysis, query patterns, and usage metrics that inform DynamoDB data model design.

Amazon Aurora, Amazon DynamoDB, and Amazon ElastiCache are popular choices for developers powering critical workloads, including global commerce platforms, financial systems, and real-time analytics applications. To enhance productivity, developers are supplementing everyday tasks with AI-assisted tools that understand context, suggest improvements, and help reason through system configurations. Model Context Protocol (MCP) is at the helm of this revolution, rapidly transforming how developers integrate AI assistants into their development pipelines. In this post, we explore the core concepts behind MCP and demonstrate how new AWS MCP servers can accelerate your database development through natural language prompts.