Beginner Kafka Tutorial: Get Started with Distributed Systems
Explore the fundamentals of Apache Kafka and distributed systems. Understand Kafka's architecture, key features, including topics, partitions, brokers, producers, and consumers. Learn how Kafka supports scalable, fault-tolerant real-time data streaming and event-driven applications. This lesson prepares you for advanced Kafka topics and hands-on data pipeline development.
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Distributed systems are collections of computers that work together to form a single computer for end-users. They allow us to scale at exponential rates, and they can handle billions of requests and upgrades without downtime. Apache Kafka has become one of the most widely used distributed systems on the market today.
According to the official Kafka site, Apache Kafka is an “open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.” Kafka is used by many Fortune 100 companies, including big tech names like LinkedIn, Netflix, and Microsoft.
In this Apache Kafka tutorial, we’ll discuss the uses, key features, and architectural components of the distributed streaming platform. Let’s get started!
What is Kafka?
Apache Kafka is an open-source software platform written primarily in Java and Scala programming languages. Kafka started in 2011 as a messaging system for LinkedIn but has since grown to become a popular distributed event streaming platform. The platform is capable of handling trillions of records per day.
Kafka is a distributed system comprised of servers and clients that communicate through a TCP network protocol. The system allows us to read, write, store, and process events. We can think of an event as an independent piece of information that needs to be relayed from a producer to a consumer. Some relevant examples of this include Amazon payment transactions, iPhone location updates, FedEx shipping orders, and much more. Kafka is primarily used for building data pipelines and implementing streaming solutions.
Kafka allows us to build apps that can constantly and accurately consume and process multiple streams at very high speeds. It works with streaming data from thousands of different data sources. With Kafka, we can:
Process records as they occur
Store records accurately and consistently
Publish or subscribe to data or event streams ...