Category: Amazon DynamoDB

In this post, we show how you can use the Database Modernizer workshop to accelerate your database modernization journey from MySQL to Amazon DynamoDB. Traditional approaches to migrating from relational databases to NoSQL solutions like DynamoDB can take several months, requiring extensive expertise in data modeling, application refactoring, and migration strategies. The Database Modernizer workshop, which can be scheduled by your account team as part of the Amazon DynamoDB Immersion Day program, uses AI to help you complete database modernization projects in days instead of months.

In this post, we show an algorithm to efficiently compare two Amazon DynamoDB tables and find the differences between their items. We provide an example where two tables, each containing approximately half a billion items, are compared in less than 7 minutes, for less than $10.

Part 1 of this series showed how to rate-limit calls to Amazon DynamoDB by using Python Boto3 event hooks. In this post, I expand on the concept and show how to rate-limit calls in a distributed environment, where you want a maximum allowed rate across the full set of clients but can’t use direct client-to-client communication.

In this post, I present a technique where a Python script making calls to Amazon DynamoDB can rate limit its consumption of read and write capacity units. The technique uses Boto3 event hooks to apply the rate limiting without having to modify the client code performing the read and write calls.

In this post, we describe how Zepto transformed its data infrastructure from a centralized relational database to a distributed system for select use cases. We discuss the challenges encountered with Zepto’s original architecture to support the business scale, the shift towards using key-value storage for cases where eventual consistency was acceptable, and Zepto’s adoption of Amazon DynamoDB.

In this post, we explore how Elliptic uses Amazon DynamoDB to build a crypto-intelligence platform that scales to over 10 billion wallets globally and supports real-time risk detection across the fast-evolving digital asset ecosystem. We discuss the data model design, indexing strategies, and operational setup that Elliptic uses to power real-time risk analysis and complex investigations at scale.

In this post, I show you how to build a serverless workflow orchestration engine that uses Amazon DynamoDB and AWS Lambda. The complete implementation is available in a GitHub repository, which includes two fully functional examples that you can deploy and run immediately to see the orchestration engine in action.

In this three-part series, we explore approaches to implement enhanced event-driven patterns for DynamoDB-backed applications. Throughout this series, we’ve examined various strategies for managing data within DynamoDB. This post shifts the focus to an event-driven pattern that reliably schedules future downstream actions using EventBridge Scheduler.

In this three-part series, we explore approaches to implement enhanced event-driven patterns for DynamoDB-backed applications. In this post (Part 2), we explore another method which uses global secondary indexes (GSIs) to handle fine-grained Time to Live (TTL) requirements.

In this three-part series, we explore approaches to implement enhanced event-driven patterns for DynamoDB-backed applications. In this post (Part 1), we focus on improving DynamoDB’s native TTL functionality by implementing near real-time data eviction using EventBridge Scheduler, reducing the typical time to delete expired items from within a few days to less than one minute.