aws
A few years back, when I was working as an Associate Software Engineer, we worked on a batch processing system that handled large amounts of data through Kinesis streams and stored processed records in DynamoDB. At that time, I didn’t know much about retry strategies or distributed system failure
aws
One thing I learned while working with DynamoDB single table design is that it should not start only from entities and relationships like traditional relational databases. In single table design, multiple entities are usually stored in the same table, and the structure is mainly optimized around how the application reads
aws
Serverless First, let’s understand what serverless actually means. In the traditional way of building applications, you build your application and host it inside a server. But when doing that, writing the application itself is not the only thing you need to care about. You also need to think about
nosql
So when coming to the NoSQL world, it’s a bit different compared to SQL. When I first worked with DynamoDB, I thought, “what a wonderful behavior it has.” Everything felt fast, simple, almost like the database was doing less complaining and more working. No strict schemas, no heavy joins,
aws
Amazon Kinesis is a powerful service for streaming real-time data, and partitioning plays a big role in ensuring your streams are scalable and perform well. Let’s take a closer look at partitioning and how you can use it effectively. What’s a Shard in Amazon Kinesis? In Kinesis, a
aws
Amazon DynamoDB is a fully managed NoSQL database designed for high availability, scalability, and low-latency performance. If you’re working with DynamoDB, understanding partitioning is key to making your database fast and efficient. In this guide, we’ll break down how partition keys work, common challenges like hot partitions, and