In this article, we are going to discuss Microservices Data Management in order to understand data considerations for microservices. As you know that we learned practices and patterns about Microservices Data Design patterns and add them into our design toolbox. And we will use these pattern and practices when designing e-commerce microservice architecture. By the end of the article, you will learn how to manage data in Microservices Architectures with applying Microservices Data Design patterns and principles.
The microservices era has been good for software architecture. I remember when the idea of multiple databases was punishable by death. But, the over-focus on micro has detracted from the true benefits of microservices which are about improving the quality and speed of development. Over the past couple of years, I’ve seen organizations referring to microservices as Domain
Monorepository is a hot topic at the table. Though the concept first appeared about a decade ago, it took so many years for this tool to evolve at a large scale. You would be amazed to know that Google was among the very first companies that embraced this approach along with all its downsides. A
Nowadays, Microservices is one of the most popular buzz-words in the field of software architecture. There are quite a lot of learning materials on the fundamentals and the benefits of microservices, but there are very few resources on how you can use microservices in the real world enterprise scenarios. In this post, I’m planning to
The Geode pattern involves deploying a collection of backend services into a set of geographical nodes, each of which can service any request for any client in any region. This pattern allows serving requests in an active-active style, improving latency and increasing availability by distributing request processing around the globe. Context and problem Many large-scale services have specific