Fabric RTI 101: Apache and Confluent Kafka

Apache Kafka is one of the most widely used event streaming systems in the world, and for good reason. At its core, Kafka is a distributed, open-source platform that makes it possible to capture, process, and deliver millions of events per second with high reliability.

Kafka organizes data into topics, which you can think of as named channels. A producer writes events into a topic — this could be an application logging user activity, or a payment system recording transactions.

On the other side, consumers subscribe to those topics and read the events, often at their own pace and in parallel. This means producers and consumers don’t need to be tightly coupled — they operate independently, which makes the system very flexible.

One of Kafka’s greatest strengths is that it’s built for scale. It’s designed to run across clusters of servers, which means you can handle extremely high volumes of data while distributing the load. It’s also fault-tolerant by design: events are replicated across multiple nodes, so if one server fails, your data isn’t lost. That reliability makes Kafka a natural fit for mission-critical workloads.

Kafka has become the backbone of streaming pipelines for thousands of organizations. Whether it’s financial institutions monitoring trades, e-commerce sites tracking user behavior, or IoT platforms capturing telemetry from millions of devices, Kafka often sits at the center of the architecture, moving data between systems in real time.

Confluent Kafka

Now, in addition to the open-source Apache Kafka, there’s also Confluent Kafka, which is a commercial distribution from the company founded by Kafka’s creators. There are also other distributions of Kafka, and even Azure Event Hubs provides a Kafka-compatible endpoint.

Confluent provides additional tools, management features, and cloud services that make running Kafka at enterprise scale easier. Many organizations start with Apache Kafka and later adopt Confluent to reduce operational overhead.

Analogy

You can think of Kafka like a massive train station. Each topic is like a train line. Producers are loading events — like passengers — onto trains. Consumers can hop on at different times, ride at different speeds, and even rewind to catch earlier trains if they missed something. The key is that the station keeps everything organized, reliable, and always running, no matter how many trains or passengers are flowing through.

Kafka, whether Apache or Confluent, is the de facto standard for real-time event streaming. It’s scalable, reliable, and trusted worldwide as the foundation for modern streaming pipelines.
 

Learn more about Fabric RTI

If you really want to learn about RTI right now, we have an online on-demand course that you can enrol in, right now. You’ll find it at Mastering Microsoft Fabric Real-Time Intelligence

2026-04-29