Apache Kafka is a real-time streaming data processing platform. Discover everything there is to know to master Kafka.
Streaming data processing offers numerous advantages, particularly in establishing a more efficient Data Engineering architecture. However, additional technologies are required. One of these technologies is Apache Kafka.
What is Apache Kafka ?
Apache Kafka is an open-source data streaming platform. Originally, it was developed in-house by LinkedIn as a messaging queue. However, this tool has evolved significantly, and its use cases have multiplied.
This platform is written in Scala and Java but is compatible with a wide variety of programming languages.
Unlike traditional messaging queues like RabbitMQ, Kafka retains messages after they have been consumed for a certain period of time. Messages are not immediately deleted upon receipt confirmation.
Furthermore, messaging queues are typically designed to scale vertically by adding more power to a single machine. In contrast, Kafka scales horizontally by adding additional nodes to the server cluster.
It’s important to note that Kafka is distributed, meaning its capabilities are elastic. You can simply add nodes, meaning servers, to a cluster to expand it.
Another notable feature of Kafka is its low latency. This means it can handle the processing of a large volume of real-time data efficiently.
The main Apache Kafka concepts
To understand how Apache Kafka works, it’s essential to grasp several concepts. First, an “event” is an atomic piece of data. An event, for example, is created when a user registers on a system.
An event can also be seen as a message containing data. This message can be processed and stored somewhere if needed. Using the example of user registration, the event would be a message containing information such as the username, email address, or password. Thus, Kafka is a platform for working with event streams.
Events are written by “producers,” another key concept. There are different types of producers: web servers, application components, IoT devices, and more—all write events and send them to Kafka. For instance, a connected thermometer will produce “events” every hour containing information about temperature, humidity, or wind speed.
On the other hand, a “consumer” is an entity that uses the data events. It receives the data written by the producer and makes use of it. Examples of consumers include databases, Data Lakes, or analytical applications. An entity can be both a producer and a consumer, such as applications or application components.
Producers publish events to Kafka “topics.” Consumers can subscribe to access the data they need. Topics are sequences of events, and each can serve data to multiple consumers. This is why producers are sometimes called “publishers,” and consumers are called “subscribers.”
In reality, Kafka acts as an intermediary between applications generating data and applications consuming data. A Kafka cluster consists of multiple servers called “nodes.”
The “brokers” are software components running on a node. Data is distributed among several brokers in a Kafka cluster, making it a distributed solution.
There are multiple copies of data within the same cluster, and these copies are called “replicas.” This mechanism makes Kafka more stable, fault-tolerant, and reliable. Information is not lost in case of an issue with a broker; another one takes over.
Finally, partitions are used to replicate data among brokers. Each Kafka topic is divided into multiple partitions, and each partition can be placed on a separate node.
What are Apache Kafka's use cases?
Apache Kafka has numerous use cases, particularly in real-time data processing. Many modern systems require data to be processed as soon as it becomes available.
For example, in the financial sector, it’s essential to immediately block fraudulent transactions. Similarly, for predictive maintenance, data streams from equipment need continuous monitoring to trigger alerts when issues are detected.
IoT devices also require real-time data processing. In this context, Kafka is highly valuable as it enables streaming data transfer and processing.
Originally, Kafka was created by LinkedIn for application activity tracking, which remains one of its primary use cases. Every event occurring within an application can be published to the corresponding Kafka topic.
User clicks, registrations, likes, time spent on a page—these are all events that can be sent to Kafka topics. Consumer applications can subscribe to these topics and process the data for various purposes, including monitoring, analysis, reporting, news feeds, and personalization.
Furthermore, Apache Kafka is used for logging and monitoring systems. Logs can be published to Kafka topics, and these logs can be stored on a Kafka cluster for a certain period. They can then be aggregated and processed.
It’s possible to build pipelines consisting of multiple producers and consumers where logs are transformed in a certain way. Afterward, logs can be saved on traditional solutions.
If a system has a dedicated monitoring component, this component can read data from Kafka topics, making it valuable for real-time monitoring.
What are the advantages of Kafka?
The use of Apache Kafka brings several major advantages to businesses. This tool is designed to address three specific needs: providing a publish/subscribe messaging model for data distribution and consumption, enabling long-term data storage, and allowing access to and processing of real-time data.
It’s in these three areas that Kafka excels. While less versatile than other messaging systems, this solution focuses on distribution and a publish/subscribe model that is compatible with stream processing.
Furthermore, Apache Kafka shines through its data persistence, fault tolerance, and repeatability capabilities. Data is replicated across the cluster, and elasticity allows for data sharing across partitions to handle increased workloads and data volumes. Topics and partitions also simplify data access.
Designed as a communication layer for real-time log processing, Apache Kafka naturally suits real-time stream processing applications. This tool is ideally suited for applications that leverage a communication infrastructure capable of distributing high volumes of data in real-time.
By combining messaging and streaming features, Kafka delivers a unique capability to publish, subscribe, store, and process records in real-time. Persistent data storage on a cluster enables fault tolerance.
Moreover, this platform allows for the efficient and rapid movement of data in the form of records, messages, or streams. This is key to interconnectivity and enables the inspection, transformation, and exploitation of data in real-time.
Finally, the Connector API enables the integration of numerous third-party solutions, other messaging systems, or legacy applications through connectors or open-source tools. Different connectors are available depending on the application’s needs.
What are Kafka's limits?
However, Kafka is not suitable for all situations. This tool is not adapted for processing a small volume of daily messages. It is designed for handling large volumes. For up to a few thousand messages per day, traditional message queues like RabbitMQ may be more appropriate.
Furthermore, Kafka does not easily allow for on-the-fly data transformation. It requires building a complex interaction pipeline between producers and consumers and maintaining the entire system. This demands a lot of time and effort. Therefore, it’s advisable to avoid using this solution for ETL tasks, especially when real-time processing is required.
Finally, it’s not relevant to use Kafka as a replacement for a database. This platform is not suitable for long-term storage. Data can be retained for a specified period, but this period should not be too long. Additionally, Kafka keeps copies of data, increasing storage costs. It’s better to opt for a database optimized for data storage, compatible with various query languages, and allowing for data insertion and retrieval.
How do I learn to use Apache Kafka? DataScientest training courses
Mastering Kafka is a highly sought-after skill in the business world, as many organizations today have real-time data processing needs. Therefore, learning to handle this tool can open up many opportunities.
To acquire this mastery, you can consider DataScientest’s training programs. Apache Kafka is at the core of the “Big Data Vitesse” module in our Data Engineer training alongside Spark Streaming.
This training program enables you to acquire all the skills and knowledge required to become a data engineer. Other modules cover programming, databases, Big Data Volume, and automation.
This training is accessible to individuals with a Bachelor’s degree or equivalent. It can be completed in nine months as Continuous Education or in 11 weeks as a Bootcamp. Like all our programs, the Blended Learning approach combines in-person and online learning to offer the best of both worlds.
Upon completion of the program, learners receive a diploma certified by the University of Sorbonne. Among our alumni, 93% find employment immediately after completing the training.
Don’t wait any longer; discover our Data Engineer curriculum today!