In this post I’ll introduce DynamoDB, a very powerful fully managed NoSQL wide-column data store in AWS. We will talk about data structures, the APIs to read and write data, indexes, as well as performance and cost considerations. In the end you will gain a solid understanding of the basics, which will serve as a starting point for further research.
DynamoDB streams help you respond to changes in your tables, which is commonly used to create aggregations or trigger other workflows once data is updated. Getting a near-real-time view into these Streams can also be helpful during developing or debugging
Testing is one of the most critical activities in software development and using third-party APIs like DynamoDB in your code comes with challenges when writing tests. Today, I’ll show you how you can start writing tests for code that accesses Dynamo
I will show you how to implement pessimistic locking using Python with DynamoDB as our backend. Before we start, we’ll review the basics and discuss some of the design criteria we’re looking for. In an earlier post, I outlined to you how to im
Concurrent access to the same items in DynamoDB can lead to consistency problems. In this post I explain why that is and introduce optimistic locking as a technique to combat this issue.
DynamoDB supports complex data types like lists. In this post we take a look at different ways to interact with lists. We will use Python to write code that may be used in a data access layer to manipulate items with list attributes.