Real-Time Data Processing with Apache Kafka

Real-Time Data Processing with Apache Kafka:  Everything You Need To Know 

Apache Kafka is an open source platform for real-time message brokers. This platform allows both large-scale data streaming and real-time processing of this data . For several years, Kafka has established itself as the benchmark for streaming and processing hundreds of GB of data on a large scale, while ensuring high availability of services. 

In this article, zcoderz will detail the main components to know everything about the architecture of Apache Kafka , and why this solution is so popular among many developers. 

What is Apache Kafka? 

Developed in Java and Scala , Apache Kafka has quickly become the go-to solution for large-scale, real-time streaming processing, and has numerous implementations in most languages. 

Apache Kafka was originally developed by LinkedIn in 2011, historically to centralize every log that systems and services produced. Indeed, the data streams were so large that LinkedIn developers sought to develop a Big Data platform that could handle millions of events per minute . A few months later, the project was made open source and incubated by the Apache Software Foundation. 

Kafka works in clusters : to ensure high availability (the least possible service interruption), several brokers are used , i.e. several machines that run Kafka. It is therefore a distributed and scalable platform , while ensuring the lowest possible latency . 

This distributed cluster architecture allows brokers to be added or removed depending on demand: this ensures that the system is always operational, manages peaks in demand, and only uses the necessary computing power (cost optimization). 

Apache Kafka Concepts 

A key topic in Kafka is the notion of a topic . A topic is a category where messages (called records ) are produced, similar to a messaging system where each email would arrive in the order it was sent. Each message contains a piece of data sent to Kafka, and can be seen as an atomic unit relative to a context, such as 

One of the major differences between Kafka and other tools is the way messages are stored. Each topic is made up of partitions , which represent stacks (in the storage sense) of messages. Since they are stacks, new messages are always added to the partitions, and an identifier (called an offset ) is assigned to them based on their position in the stack. 

This notion of stack is important because it allows the ordering of messages and therefore their processing priority. Each partition is hosted on a different Kafka broker , allowing to ensure load balancing for a topic. For example, in a cluster with 4 brokers, rather than having 100% of the traffic of a topic on a single broker, with 5 partitions, each broker will support 25% of the traffic of the topic. 

Offset Management 

Offset management is a crucial issue in a Kafka cluster, because it is what allows you to manage the order in which records appear. Since each partition manages its own offset system, it is therefore necessary to coordinate the different partitions with each other. 

The applications that will act on the topics are producers and consumers , coded mainly in Java or Python. This representation is based on the publish-subscribe

model : applications will send data to topics (publish), and others will consume this data from topics (subscribe), without having any direct coupling on either side. 

Advantages of Kafka’s Model 

Coupling is weak between applications, making it easier to add, modify or remove applications, without having to adapt everything for each modification. 

It allows for significant scaling , because without loose coupling, adding a new application can be done as smoothly as possible. 

Producers will produce messages on one or more topics, where the choice of the partition is left to them. These producers can be web servers, IoT devices or any script running on a machine. For example, on a web platform, each user event (page viewed, button clicked, etc.) can be intercepted and sent to a Kafka topic. Consumers will then take over. 

Creating a producer is much easier than a consumer. Indeed, it is a similar operation to that of a messaging system: the producer sends events, and waits for a response from the tool for each of these events. 

Consumers will consume each message in one or more topics: these are multi-subscribers and can consume several topics at the same time. These can be applications written in Java, Python or Scala, which will for example perform processing and send the result to a target database (Data Lake or Data Warehouse). 

In practice, consuming records is a difficult subject . For example, how do you know that a message that has been processed will not be processed a second time by another consumer? What to do if a consumer crashes while processing a message? 

Many internal Kafka applications will provide solutions to each of these questions. 

In case there are many messages to process, one can use frameworks like Apache Spark or Apache Flink to parallelize the calculations. 

Each consumer can belong to a consumer group : the main idea is that each consumer group manages by itself the offset of each topic to which consumers have subscribed. In other words, each consumer group will read all messages of a topic from the beginning. 

Finally, to be fault tolerant in the event that a Kafka broker stops working, it is advisable to create topic replicas . By defining a replica factor , the topic and its partitions will be duplicated in as many brokers as specified. 

This allows constant operation at a just-in-time rate with effective message consumption 100% of the time, where no message would be lost and processing would be ensured. 

When to use Apache Kafka? 

This tool should be used in situations where the following three needs are met . 

It is therefore not surprising that LinkedIn, where Kafka was created, uses many Kafka clusters. With this publish-subscribe model , Kafka is the preferred solution. From their point of view, consumption is crucial because the associated operations have a real impact on the platform. 

We therefore find Kafka as a message distribution system in situations where the volume of data is significant and where latency is critical. 

To Wrap Things Up 

Apache Kafka is a powerful solution for real-time data streaming, offering scalability, fault tolerance, and low-latency processing. Its distributed architecture makes it ideal for industries handling high-volume data, from finance to IoT. As real-time analytics grow in importance, Kafka remains a key tool for efficient and reliable data processing.