As an increasing number of companies are using real-time big data to gain insights and make data-driven decisions, the requirement for a resilient tool to process this data in real-time is also increasing.
Apache Kafka is a tool used in big data systems because of its ability to handle high throughput and real-time processing of large amounts of data.
What is Apache Kafka
Apache Kafka is an open-source software that enables storing and processing data streams over a distributed streaming platform. It provides various interfaces for writing data to Kafka clusters and reading, importing, and exporting data to and from third-party systems.
Apache Kafka was initially developed as a LinkedIn message queue. As a project of the Apache Software Foundation, the open-source software has developed into a robust streaming platform with a wide range of functions.
The system is based on a distributed architecture centered around a cluster containing multiple topics, optimized for processing large data streams in real time as shown in the picture below:
With the help of Kafka, data streams can be stored and processed. It makes Kafka suitable for large amounts of data and applications in the big data environment.
Loading data streams from third-party systems or exporting them to these systems via the interfaces provided is possible. The core component of the system is a distributed commit or transaction log.
Kafka: Basic Function
Kafka solves the problems that arise when data sources and data receivers are connected directly.
For example, when the systems are connected directly, it is impossible to buffer data if the recipient is unavailable. In addition, a sender can overload the receiver if it sends data faster than the receiver accepts and processes it.
Kafka acts as a messaging system between the sender and the receiver. Thanks to its distributed transaction log, the system can store data and make it available with high availability. The data can be processed at high speed as soon as the data arrives. Data can be aggregated in real-time.
Kafka Architecture
Kafka’s architecture consists of a cluster computer network. In this network of computers, so-called brokers store messages with a time stamp. This information is called topics. The stored information is replicated and distributed in the cluster.
Producers are applications that write messages or data to a Kafka cluster. Consumers are applications that read data from the Kafka cluster.
In addition, a Java library called Kafka Streams reads data from the cluster, processes it, and writes the results back to the cluster.
Kafka distinguishes between “Normal Topics” and “Compacted Topics.” Normal topics are stored for a certain period and must not exceed a defined storage size. If the period of storage limit is exceeded, Kafka may delete old messages. Compacted topics are subject to neither a time limit nor a storage space limit.
A topic is divided into partitions. The number of partitions is set when the topic is created, and it determines how the topic scales. The messages of a topic are distributed to the partitions. The offset is per partition. Partitions are the fundamental mechanism through which both scaling and replication work.
Writing to or reading from a topic always refers to a partition. Each partition is sorted by its offset. If you write a message on a topic, you have the option of specifying a key.
The hash of this key ensures that all messages with the same key end up in the same partition. Adherence to the order of the incoming messages is guaranteed within a partition.
Kafka Interfaces
Overall, Kafka offers these four main interfaces (APIs – Application Programming Interfaces):
- Producer API
- Consumer API
- Stream’s API
- ConnectAPI
The Producer API allows applications to write data or messages to a Kafka cluster. The data of a Kafka cluster can be read out via the Consumer API. Producer and Consumer API use the Kafka message protocol. It is a binary protocol. In principle, developing producer and consumer clients is possible in any programming language.
The Streams API is a Java library. It can process data streams in a stateful and fault-tolerant manner. Filtering, grouping, and assignment of data are possible via provided operators. In addition, you can integrate your operators into the API.
The Streams API supports tables, joins, and time windows. The reliable storage of application states is ensured by logging all state changes in Kafka Topics. If a failure occurs, the application state can be restored by reading the state changes from the topic.
The Kafka Connect API provides the interfaces for loading and exporting data from or into third-party systems. It is based on the Producer and Consumer APIs.
Special connectors handle communication with third-party systems. Numerous commercial or free connectors connect third-party systems from different manufacturers to Kafka.
Features of Kafka
Kafka is a valuable tool for organizations looking to build real-time data systems. Some of its major features are:
High Throughput
Kafka is a distributed system that can run on multiple machines and is designed to handle a high data throughput, making it an ideal choice for handling large amounts of data in real-time.
Durability and Low Latency
Kafka stores all the published data, which means that even if a consumer is offline, it can still consume the data once it comes back online. Moreover, Kafka is designed to have low latency, so it can process data quickly and in real-time.
High Scalability
Kafka can handle an increasing amount of data in real-time with little or no degradation in performance, making it suitable for use in large-scale, high-throughput data processing applications.
Fault tolerance
Fault tolerance is also built into Kafka’s design as it replicates data across multiple nodes, so if one node fails, it is still available on other nodes. Kafka ensures that the data is always available, even in the event of a failure.
Publish-subscribe model
In Kafka producers write data to topics, and consumers read from topics. This allows for a high degree of decoupling between the data producers and consumers, making it a great option for creating event-driven architectures.
Simple API
Kafka provides a simple, easy-to-use API for producing and consuming data, making it accessible to a wide range of developers.
Compression
Kafka supports data compression, which can help reduce the amount of storage space required and increase data transfer speed.
Real-time Stream Processing
Kafka can be used for real-time stream processing, enabling organizations to process data in real-time as it is generated.
Uses Cases of Kafka
Kafka offers a wide range of possible uses. Typical areas of application are:
Real-time website activity tracking
Kafka can collect, process, and analyze website activity data in real-time, enabling businesses to gain insights and make decisions based on user behavior.
Real-time financial data analysis
Kafka allows you to process and analyze financial data in real-time, allowing faster identification of trends and potential breakouts.
Monitoring of distributed applications
Kafka can collect and process log data from distributed applications, enabling organizations to monitor their performance and quickly identify and troubleshoot issues.
Aggregation of log files from different sources
Kafka can aggregate them from different sources and make them available in a centralized location for analysis and monitoring.
Synchronization of data in distributed systems
Kafka allows you to synchronize data across multiple systems, ensuring that all systems have the same information and can work together effectively. This is why it is used by retail stores like Walmart.
Another important area of application for Kafka is machine learning. Kafka supports machine learning, among other things:
Training of models in real-time
Apache Kafka can stream data in real-time to train machine learning models, allowing for more accurate and up-to-date predictions.
Derivation of analytical models in real-time
Kafka can process and analyze data to derive analytical models, providing insights and predictions that can be used to make decisions and take action.
Examples of machine learning applications are fraud detection by linking real-time payment information with historical data and patterns, cross-selling through tailor-made, customer-specific offers based on current, historical, or location-based data, or predictive maintenance through machine data analysis.
Kafka Learning Resources
Now that we have talked about what Kafka is and what its use cases following are some resources that will help in learning and using Kafka in the real world:
#1. Apache Kafka Series – Learn Apache Kafka for Beginners v3
Learn Apache Kafka for beginners is an introductory course offered by Stephane Maarek on Udemy. The course aims to provide a comprehensive introduction to Kafka for individuals who are new to this technology but have some prior understanding of Java and Linux CLI.
It covers all the fundamental concepts and provides practical examples along with a real-world project that helps you better understand how Kafka works.
#2. Apache Kafka Series – Kafka Streams
Kafka Streams for data processing is another course offered by Stephane Maarek aimed at providing an in-depth understanding of Kafka Streams.
The course covers topics such as Kafka Streams architecture, Kafka Streams API, Kafka Streams, Kafka Connect, Kafka Streams, and KSQL, and includes some real-world use cases and how to implement them using Kafka Streams. The course is designed to be accessible to those with prior experience with Kafka.
#3. Apache Kafka for Absolute Beginner
Kafka for absolute beginners is a newbie-friendly course that covers the basics of Kafka, including its architecture, core concepts, and features. It also covers setting up and configuring a Kafka cluster, producing and consuming messages, and a micro project.
#4. The Complete Apache Kafka Practical Guide
Kafka practical guide aims to provide hands-on experience working with Kafka. It also covers the fundamental Kafka concepts and a practical guide on creating clusters, multiple brokers, and writing custom producers and consoles. This course does not require any prerequisites.
#5. Building Data Streaming Applications with Apache Kafka
Building Data Streaming Applications with Apache Kafka is a guide for developers and architects who want to learn how to build data streaming applications using Apache Kafka.
The book covers the key concepts and architecture of Kafka and explains how to use Kafka to build real-time data pipelines and streaming applications.
It covers topics such as setting up a Kafka cluster, sending and receiving messages, and integrating Kafka with other systems and tools. Additionally, the book provides best practices to help readers build high-performance and scalable data streaming applications.
#6. Apache Kafka Quick Start Guide
Kafka Quick Start Guide covers the basics of Kafka, including its architecture, key concepts, and basic operations. It also provides step-by-step instructions for setting up a simple Kafka cluster and using it to send and receive messages.
Additionally, the guide provides an overview of more advanced features such as replication, partitioning, and fault tolerance. This guide is intended for developers, architects, and data engineers who are new to Kafka and want to get up and running with the platform quickly.
Conclusion
Apache Kafka is a distributed streaming platform that builds real-time data pipelines and streaming applications. Kafka plays a key role in big data systems by providing a fast, reliable, and scalable way to collect and process large amounts of data in real-time.
It enables companies to gain insights, make data-driven decisions, and improve their operations and overall performance.
You may also explore data processing with Kafka and Spark.
