Category: Amazon DynamoDB

In this series of posts, we walk through the process of creating a DynamoDB data model using an example multi-tenant application, a customer issue tracking service. The goal of this series is to explore areas that are important for decision-making and provide insights into the influences to help you plan your data model for a multi-tenant application. In this post, we define the access patterns and decide on the table design.

In this post, we explore a streamlined solution that uses AWS Lambda and Python to read and ingest CSV data into an existing Amazon DynamoDB table. This approach adheres to organizational security restrictions, supports infrastructure as code (IaC) for table management, and provides an event-driven process for ingesting CSV datasets into DynamoDB.

When getting started with DynamoDB, one of the first decisions you will make is choosing between two throughput modes: on-demand and provisioned. On-demand mode is the default and recommended throughput option because it simplifies building modern, serverless applications that can start small and scale to millions of requests per second. However, choosing the right throughput strategy requires evaluating your operational needs, development velocity, and application characteristics, with cost being a key consideration. In this post, we examine both throughput modes in detail, exploring their characteristics, strengths, and ideal use cases.

In this post, we discuss how the Amazon Finance Automation team used AWS purpose built databases, such as Amazon DynamoDB, Amazon OpenSearch Service, and Amazon Neptune together coupled with serverless compute like AWS Lambda to build an Operational Data Store (ODS) to store financial transactional data and support FinOps applications with millisecond latency. This data is the key enabler for FinOps business.

In this post, we examine the realities behind common myths about DynamoDB on-demand capacity mode across three key areas: cost implications and efficiency, operational overhead and management, and performance considerations. We provide practical guidance to help you make informed decisions about throughput management.

In this post we demonstrate how to enforce client-side timestamp-based write sequence order on Amazon DynamoDB. The goal is to ensure items with lower timestamps don’t overwrite items with higher timestamps, even if the requests are received out of order by the database.

2024 marked a significant year for Amazon DynamoDB, with advancements in security, performance, cost-effectiveness, and integration capabilities. This year-in-review post highlights key developments that have enhanced the DynamoDB experience for our customers. Whether you’re a long-time DynamoDB user or just getting started, this post will guide you through the most impactful changes of 2024 and how they can help you build reliable, faster, and more secure applications. We’ve sorted the post by alphabetical feature areas, listing releases in reverse chronological order.

Amazon DynamoDB enables you to back up your table data continuously by using point-in-time recovery (PITR). When you enable PITR, DynamoDB backs up your table data automatically with per-second granularity. PITR helps protect you against accidental writes and deletes. For example, if a test script accidentally writes to a production DynamoDB table, or someone mistakenly […]

In the world of air travel, baggage handling isn’t just about keeping track of baggage, but a seamless orchestration of different processes to improve the passenger baggage experience. A key component to make this happen is a strong database management strategy. In this post, we discuss how AWS Partner IBM Consulting developed an initiative to modernize a traditional baggage database architecture using Amazon DynamoDB and other Amazon Web Services (AWS) managed services, addressing the evolving needs of the airline industry.

For the Amazon DynamoDB team, AWS re:Invent 2024 was an incredible experience to connect and reconnect with our customers. The key themes this year were “better together” integrations, data modeling, and building globally resilient, scalable applications on DynamoDB. In case you missed some of these sessions, or you wanted to get caught up on why customers like Klarna, Krafton, Vanguard, Fidelity, and JPMorgan Chase are building on DynamoDB, you can read this helpful summary of some of the DynamoDB highlights from re:Invent 2024.