Is Apache Kafka Right for You? Core Features, Stream Processing, and Use Cases

In recent years Apache Kafka’s functionality and coverage have expanded dramatically, with one‑third of Fortune‑500 companies using it, including 7 of the world’s top‑10 banks, 8 of the top‑10 insurers, and 9 of the top‑10 telecom firms in the United States.

First, let’s look at Kafka’s two core capabilities:

(1) Messaging System

Messages are used in two ways:

Queue

In a queue, consumers belong to a worker group, and each message is processed by only one worker, efficiently distributing work. Queues are well‑suited for fault tolerance and scaling.

Publish/Subscribe

Subscribers operate independently; each receives every message, similar to a broadcast system. The publish/subscribe model helps decouple system components.

(2) Stream Processing

With a robust, scalable messaging system, it becomes valuable to process message streams easily. Kafka’s Stream API provides this capability; it is a Java client library offering a higher‑level abstraction than plain producers and consumers.

The Stream API can readily implement:

Stateless operations, such as filtering and transforming information streams.

Stateful operations, such as joins and aggregations within a time window.

Stream Processing Example Code

Read text from an input stream, count each word, and write the results to an output stream.

Typical Kafka Use Cases

For a travel website where hotel and flight prices constantly change, components such as price alerts and analytics need these updates. Changes are sent to a Kafka topic, and interested components subscribe to receive notifications.

Kafka can track and analyze website behavior (page views, searches, etc.). This was Kafka’s original use case at LinkedIn. Different behavior types are sent to separate topics for real‑time analysis, supporting engagement metrics and operational strategies.

When handling large volumes of location data in real time—e.g., tracking vehicle paths—devices publish location data to Kafka, and the Stream API processes it, such as extracting a user’s positions within a specific time window.

When Not to Use Kafka?

If your services cannot or do not want to use Java/Scala, you should avoid Kafka, as the Stream API would be unavailable.

If you only need a simple task queue, consider RabbitMQ instead.

If you process only a few thousand messages per day, Kafka may be overkill; its deployment and maintenance costs are justified for large‑scale stream processing.

Source translated and compiled from:

https://medium.freecodecamp.org/how-to-know-if-apache-kafka-is-right-for-you-1b2e468d52b9