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Choreography-based Saga for Microservices and Serverless Applications

choreography

How do you take care of transactions in a microservices or serverless architecture? We’ll talk about choreography-based saga to solve this.

Understanding Data Mesh: A Paradigm Shift in Data Management

Data Mesh is a decentralized approach to data management that treats data as a product and assigns ownership to domain-specific teams. By breaking away from centralized architectures like data warehouses and lakes, it ensures scalability, agility, and improved data quality. Emphasizing principles like domain-oriented ownership, self-serve infrastructure, and federated governance, Data Mesh enables faster insights, fosters collaboration, and breaks down silos. With applications across industries like e-commerce, healthcare, and finance, it transforms how organizations leverage data while addressing challenges like governance complexity and cultural shifts.

Redundancy in a distributed system

redundancy in storage

We don’t think of introducting redundancy in our systems early enough. This is bad. Let’s see why with an example, and how to fix it.

Getting started with Apache Kafka Streams

apache_kafka_streams

In the age of big data and data science, stream processing is very significant. So it's not at all surprising that every major organisation has at least one stream processing service. Apache has a few too, but today we're going to look at Apache's Kafka Streams. Kafka is a very popular pub-sub service. And if you've worked with Kafka before, Kafka Streams is going to be very easy to understand. And if you haven't got any idea of Kafka, you don't have to worry, because most of the underlying technology has been abstracted in Kafka Streams so that you don't have to deal with consumers, producers, partitions, offsets, and the such. In this post, we'll look that a few concepts of

How you can improve your backend services’ performance using Apache Kafka

apache_kafka_streams

In most real world applications, we have a RESTful API service facing various client applications and a collection of backend services which process the data coming from those clients. Depending on the application, the architecture might have various services spread across multiple clusters of servers, and some form of queue or messaging service gluing them together. Today, we're going to talk about one such messaging service - Apache Kafka - and how it can improve the performance of your services. We're going to assume that we have at least two microservices, one for the APIs that are exposed to the world, and one which processes the requests coming in from the API microservice, but in an async fashion. Because this is

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