Introduction
Kafka is a distributed streaming platform built using the publish/subscribe model to help companies receive large amounts of real-time data and process it. Here’s what you need to know.Apache Kafka is a distributed streaming platform designed to handle large volumes of real-time data. It’s an open-source system used for stream processing, real-time data pipelines and data integration. LinkedIn originally developed Kafka in 2011 to handle real-time data feeds. It was built on the concept of a publish/subscribe model and provides high throughput, reliability and fault tolerance. It can handle over a million messages per second, or trillions of messages per day. Kafka is a critical tool for modern data feeds. As data continues to grow every day, we need tools to handle massive amounts of data. This introduces two challenges: First, how to collect a large amount of data, and second, how to analyze the collected data. To overcome these challenges, we need a messaging system.
KAFKA EXPLAINED
Apache Kafka is an open-sourced distributed streaming platform designed to handle large volumes of real-time data. It’s become a critical tool for modern data feeds as it helps them transfer data between applications and analyze the data to decide how to share it.
A messaging system helps to transfer data between applications. It helps applications to concentrate on data and the messaging system decides how to share the data.
Let’s take the data pipeline below. We have a source system and a target system, and we exchange the data between them. It looks pretty simply, right?
The source system can be any system such as an app, email, financial data, streaming data etc. The target system can also be any system such as a database, email or analytics, etc. We’ll call them the source and target systems in this article for easy illustration.
What happens if we have multiple sources and target systems, and they all have to exchange data with one another? For example, let’s assume we have five sources and four target systems as below.
To exchange the data, each source system has to connect with the target system, which results in multiple integrations across the source and target systems. Each integration also comes with various difficulties.
Let’s take our earlier example and integrate it through Apache Kafka.
We can see from the image above that Apache Kafka helps us to decouple the source and target system. Source systems are called producers, which can send multiple streams of data to the Kafka brokers. Target systems are called consumers, where clients can read the data from the brokers and process it. Multiple consumers can read the same data; it’s not limited to one single destination. Source and target systems are completely decoupled, avoiding complex integrations.
There are two types of messaging systems companies can use: Point-to-point and publish-subscribe messaging systems. In a point-to-point system, producers persist data in a queue and only one application can read the data from the queue. The message gets removed from the queue once this system reads the data.
In the publish-subscribe messaging system, consumers can subscribe to multiple topics in the message queue and receive specific messages relevant to their application. Apache Kafka is based on a publish-subscribe messaging system.
KAFKA USED FOR
Apache Kafka is used by a wide range of companies and organizations across various industries that need to build real-time data pipelines or streaming applications.
Developers with a strong understanding of distributed systems, data streaming techniques and good programming skills should take the time to become familiar with Apache Kafka. It’s written in Java, and it provides client libraries for other languages, such as C/C++, Python, Go, Node.js and Ruby. Primarily, software engineers, data engineers, machine learning engineers and data scientists work on Apache Kafka in the organization.
