AWS Database Blog

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 discuss handling large collections that are approaching 32 TiB for Amazon DocumentDB. We demonstrate solutions for transitioning from uncompressed to compressed collections using AWS DMS. This migration not only accommodates larger uncompressed data volumes, but also significantly reduces storage, compute costs associated with Amazon DocumentDB and improves performance.

Last week, AWS announced Amazon Keyspaces change data capture (CDC) streams, a new feature that captures real-time data changes in your Amazon Keyspaces tables. In this post, we discuss the architecture of Amazon Keyspaces CDC streams, explore its use cases and benefits, and provide an example demonstrating how to set up CDC streams, stream data, and capture the streamed records.

Aqua Security is a leading provider of cloud-based security solutions, trusted by global enterprises to secure their applications from development to production. In this post, we explore how Aqua Security automates the use of Amazon Aurora fast clones to support read-heavy operations at scale, simplify their data workflows, and maintain operational efficiency.

For years, TalentNeuron, a leader in talent intelligence and workforce planning, has been empowering organizations with data-driven insights by collecting and processing vast amounts of job board data. In this post, we share three key benefits that TalentNeuron realized by using Amazon Aurora I/O-Optimized as part of their new data platform: reduced monthly database costs by 29%, improved data validation performance, and accelerated innovation through modernization.

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.

AWS DMS customers might choose not to use the data validation feature provided by the AWS DMS service due to the time it takes to complete validation after a load, a large dataset transfer or a data reload, where business requires rapid availability of data in the target environment. As a result, you might opt to perform validation manually or use a single pass full load only validation, which requires additional effort and time. In this post, we walk you through how to build a custom AWS DMS data validation solution with AWS serverless services.