5 Kafka in Real World Use Cases

 

This chapter covers

  • Key scenarios where Kafka excels
  • Identifying situations where Kafka may not be the ideal choice
  • Comparing Kafka with other technologies that serve similar purposes.

Now that you understand the basics of how Kafka works, it’s time to put that knowledge to work. Turning theory into practice: you’ll learn to turn uncertain “should we use Kafka?” conversations into confident, defensible decisions grounded in clear patterns and trade-offs. We walk through high-impact use cases—notifications, external data integration, real-time analytics, and log aggregation—showing when Kafka’s guarantees help and what they cost. We call out anti-patterns and edge cases, then offer side-by-side alternatives (RabbitMQ, Pulsar, and managed cloud services) so you can pick the right tool for each job. By the end, you’ll have a practical checklist for evaluating Kafka, a solid mental model of its limits, and implementation pointers for both Kafka-first and Kafka-free designs.

5.1 Field notes: When to choose Kafka—and when not to

Gathered in their meeting room once again, Max Sellington, Eva Catalyst, and Rob Routine discuss the use cases for Kafka and its alternatives.

Max: Alright team, let's talk about the use cases for Kafka. Are there any alternatives we should consider? Maybe we want to test something else alongside Kafka?

5.2 Navigating real-world implementation

5.2.1 Event-driven microservices

5.2.2 Data integration

5.2.3 Collecting logs

5.2.4 Real-time data processing

5.3 Differences with other messaging platforms

5.3.1 Publish-subscribe model

5.3.2 Partitioned data

5.3.3 Lack of broker-side logic

5.3.4 Sequential data access

5.3.5 Message persistence

5.3.6 Limitations in handling large messages

5.3.7 Scalability and high throughput

5.3.8 Fault tolerance

5.3.9 Batch processing

5.3.10 Lack of global ordering

5.4 Kafka Alternatives

5.4.1 RabbitMQ

5.4.2 Apache Pulsar

5.4.3 Solutions from cloud providers

5.5 Online Resources

5.6 Summary