Kafka is a popular distributed streaming platform that can be used for a variety of use cases. Here are some of the most common use cases for Kafka that you are likely to run into as a Data Engineer:

Website Activity Tracking 🔍

Kafka can be used to track website activity by collecting events like page views, clicks, and conversions. These events can then be processed and analyzed to gain insights into user behavior.

Database Replication 🗂️

Kafka can be used to replicate databases by copying the commit log from one database to another. This ensures that both databases have the same data at all times.

Logging and Metrics Aggregation 📊

Kafka can be used to collect and aggregate logs and metrics from applications and systems. This data can then be used to monitor performance, identify problems, and improve the overall health of your systems.

Stream Processing 🌊

Kafka can be used to process streaming data in real time. This data can then be used to generate alerts, make predictions, and automate workflows.

Messaging 📨

Kafka can be used as a messaging broker to decouple applications and systems. This can improve performance and scalability by allowing applications to send and receive messages without having to be directly connected to each other.

These are just a few of the many use cases for Kafka. By understanding these use cases, you can start to see how Kafka can be used to solve real-world problems.

❗️Note: It is important to consider the complexity of Kafka before using it for a particular use case. In some cases, it may be better to use a more traditional framework.