The importance of managing data is growing every day. Many companies are switching from monolithic applications to microservices and an event-driven architecture. Monitoring and logging are also receiving an increasing amount of attention over the past years.

As a logical consequence of those headlines, Apache Kafka will experience a significant rise in popularity as well. But why exactly is this? What is Kafka, and what problems does it solve? Why is it worth learning in the year 2026?

This article takes a look at the use cases of Kafka and gives a general outlook on why Kafka is a great nice-to-have for aspiring DevOps engineers.

Event-driven Architecture

Kafka provides a neat option for a unique approach to a loosely coupled software architecture. Traditionally, microservices communicate over API requests with other microservices. This makes managing and debugging the infrastructure pretty complex as time goes on.

With Kafka, we can create topics for each API request, which provides the benefits of a distributed log. This makes it much easier to debug an application, as we don’t have to step through the debugger. Kafka serves as the log, and when an error occurs, we can pinpoint the exact point of failure in the software.

Backup and Restore

In its core, Kafka is no more than a distributed log. Kafka saves the data in binary format, which means it processes requests with remarkable speed. And because of automatic replication to the other nodes in the cluster, the data is safe even in the case that one node in the cluster goes down.

These characteristics are perfect for backing up logging data. Kafka can save business-critical logging data and persist it in the case of hardware failure, significantly enhancing the durability of the data.

You could back up all kinds of data, of course. But because logs are often located only on the server where the application resides, this is the perfect use case for Kafka.

Stream Processing

Another interesting facet of Kafka is the ability to transform your data inside Kafka itself. With Kafka Streams, you can aggregate your data, enrich it, and transform it to your needs. Let’s take a look at an example of how Kafka Streams works.

Imagine you have a log producer sending data to Kafka. But you don’t like how Kafka formats the logs, as your company has strict requirements on how to save logs across the whole project. Your job is to transform the logging data from an application into a uniform log format for all microservices.

This is where Kafka Streams comes in. With Kafka Streams, you can easily ingest your logging data with a normal producer in the language of your choice. Kafka takes that data, transforms it internally using Kafka Streams, and then sends it to the consumer. All the consumer has to do then is save the data, as usual.