Kafka 101: Essential Core Concepts for Building Resilient Event-Driven Architectures

Apache Kafka is an open-source stream-processing software platform developed by the Apache Software Foundation, written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for real-time data feeds. It is horizontally scalable, fault-tolerant, and speedy.

The core concepts of Kafka are:

1. Topics: A stream of messages belonging to a particular category is called a topic.
2. Producers: Producers are the client applications that publish messages to a Kafka topic.
3. Consumers: Consumers are the client applications that read messages from a Kafka topic.
4. Brokers: A Kafka cluster is composed of multiple brokers. Each broker is a separate process that runs on a different machine.
5. Zookeeper: Apache Zookeeper is used to manage the Kafka cluster. It is responsible for maintaining the brokers, topics, and partitions list.

Some limitations of Kafka include the following:

1. It does not support random access to messages.
2. It does not have built-in support for data transformation.
3. It does not have a user-friendly interface for monitoring and managing the cluster.

Some competitors of Kafka include:

1. RabbitMQ
2. Amazon Kinesis
3. Google Cloud Pub/Sub
4. Apache Flume

Some advantages of Kafka include the following:

1. High throughput: Kafka can handle high throughput with low latency.
2. Scalability: Kafka is horizontally scalable, which can drive increased traffic by adding more brokers to the cluster.
3. Durability: Kafka persists all published messages to disk, providing durability in server failures.
4. Fault tolerance: Kafka is designed to be fault-tolerant, meaning that it can continue to operate even when some of its servers fail.

In this session, we will cover the following things.

• Producer
• Consumer
• Broker
• Cluster
• Topic
• Partitions
• Offset
• Consumer groups

We also cover a high-level example of the Kafka use case.

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