Apache Kafka is an open-source platform for real-time data handling – primarily through a data stream-processing engine and a distributed event store – to support low-latency, high-volume data relaying tasks. This article explains the meaning of Kafka, its functionalities and architecture, and the primary use cases where Kafka is leveraged.
Table of Contents
What Is Apache Kafka?
Apache Kafka is defined as an open-source platform for real-time data handling – primarily through a data stream-processing engine and a distributed event store – to support low-latency, high-volume data relaying tasks.
Apache Kafka is a distributed data storage system for real-time streaming data processing requirements. Streaming data is information that is constantly produced by hundreds of data sources, most of which provide records of data concurrently. This continuous inflow of data requires a streaming system that can process it sequentially and progressively. Therefore, Kafka has the following major tasks: posting and subscribing to data streams and processing record dataflows in real-time, while storing them in the order in which they were produced.
Kafka is often used to build real-time data streams and applications. Combining communications, storage, and stream processing enables the collection and analysis of real-time and historical data. It is a Scala and Java application frequently used for big data analytics and real-time event stream processing. Kafka enables asynchronous data flow between processes, applications, and servers, much like other message broker systems.
Before the development of event streaming systems like Apache Kafka, data processing was traditionally carried out through recurring batch tasks, where raw data is initially stored and then processed at random intervals. Kafka captures precisely what occurred and when using streaming data. This file is known as an immutable commit log. It may be added to but cannot be altered, making it immutable. From there, you can access the data by subscribing to the log and publishing it from various systems and streaming real-time apps.
Kafka, however, has a reasonably low overhead compared to other messaging systems since it does not monitor user activity or remove messages that have been read. On the other hand, it keeps all messages for a predetermined period and leaves it up to the user to track which messages have been read. Each node in a Kafka cluster is referred to as a broker, and the Kafka software operates on one or more servers.
The broker’s responsibility is to assist producer apps in writing data to topics and consumer applications in reading from topics. Kafka utilizes an open-source server called Apache ZooKeeper to manage clusters. To make topics more manageable, they are separated into partitions, and Kafka ensures strong ordering for every partition. In 2011, Kafka (initially created at LinkedIn) was made publicly available.Â
Kafka was jointly created by Jun Rao, Jay Kreps, and Neha Narkhede. Jay Kreps enjoyed Kafka’s writing, and because the framework is “a system geared for writing,†he was inspired by the notable author, Franz Kafka, when naming it. Currently, Jay Rao is the CEO and co-founder of Confluent; he is also the primary creator of several notable projects, including Apache Samza, Voldemort, and Azkaban. Kafka was created as the ingestion engine behind data writing use cases.
See More: What Is a Data Catalog? Definition, Examples, and Best Practices
How Does Kafka Work?
Kafka integrates publish-subscribe and queuing messaging technologies to give users the key advantages of each. Queuing is very scalable because it enables the distribution of data processing across numerous consumer instances. Traditional queues, however, are not multi-subscriber compatible. Even though the publish-subscribe method can have more than one subscriber, one cannot use it to divide work between different worker processes. This is because every message is sent to every subscriber.
Kafka combines these two ideas using a partitioned log model. A log is an organized collection of records, and these logs are divided into partitions that represent various subscribers. This enables greater scalability allowing several subscribers to the same subject, each of whom is given a partition. Replayability is another part of the Kafka ecosystem. It lets multiple independent applications that read from data streams work independently and at their own speed.
Applications (also known as producers) send data records (i.e., messages) to a Kafka node (broker), where they are processed by other applications (also known as consumers). Apache gathers readable data from a vast array of data sources and groups it into “topics.â€
For consumers to get fresh communications, they must subscribe to the topic, and once they do so, the messages mentioned earlier will be saved automatically. Because of their potential to become rather extensive, topics are broken down into more manageable subtopics to enhance their performance and scalability.
For example, if you were storing user login attempts, you could sort them by the first character of the user’s username. Kafka guarantees that all communications within a partition are organized in chronological order. You can identify a communication by studying its offset, similar to a standard array index. This offset is a sequence number that is increased for each new message in a partition.
The Kafka Streams application programming interface (API) may function as a stream processor to generate an outgoing data stream to one or more topics. This operates by consuming data streams from the topics and allowing the API to construct an outgoing stream from it.Â
Additionally, you may create reusable producer or consumer connections that join Kafka topics to already-running programs. There are already hundreds of connectors available, some of which connect to essential services like Dataproc, BigQuery, Structured Query Language (SQL), and others.
Apache Kafka offers durable storage. Kafka may act as a “source of truth†because of its ability to distribute data across multiple nodes for a high availability deployment inside a single data center or in various availability zones. A Kafka broker does not track the number of data packets that each consumer has ingested. Consumers must track the data they have used.
Kafka can handle many more consumers with little impact on throughput because it doesn’t keep track of data acknowledgments and messages from each consumer application. Many applications even adopt a batch user-style in production, when a consumer receives all the messages in a queue at regular intervals.
Kafka acts as the “central nervous system†via which data is passed through input and capture apps, data processing engines, and storage lakes when its components are combined with the other standard pieces of an extensive data analytics framework.
See More: What Is Data Governance? Definition, Importance, and Best Practices
Understanding the Architecture of Kafka
To gain more profound knowledge of Apache Kafka for distributed streaming, let’s take a closer look at its architecture and the interactions between its many architectural elements. The Kafka architecture comprises fundamental aspects like topics, partitions, producers, consumers, etc.
1. Kafka API architecture
Producer, Consumer, Streams, and Connector are the four primary APIs of the Apache Kafka Architecture. Let’s go over each one individually:
- Producer API: An application may submit a data stream to one or many Kafka topics using the Producer API.Â
- Consumer API: This one allows applications to manage the stream of data sent to them and subscribe to one or more topics.
- Streams API: An application gets the ability to perform the duties of a stream processor thanks to the Streams API. The application receives an input stream from either one or some of the topics, processes it, and then sends the completed stream to the output topics.Â
- Connector API: The Connector API enables the creation and operation of reusable producers and consumers that link Kafka topics to apps or information systems. For example, links to relational databases may preserve a record of every modification made to tables.Â
2. Kafka cluster architecture
Let’s now examine some of the primary structural elements of a Kafka cluster in more detail:
- Kafka Brokers: A server participating in a Kafka cluster is known as a broker. The Kafka cluster is typically created by numerous brokers cooperating to enable load balancing, trustworthy redundancy, and failover. For cluster administration and coordination, brokers use Apache ZooKeeper.
Each broker instance can handle read and write volumes of up to tens of thousands per second without affecting performance. Each broker has a unique ID and is capable of managing divisions of one or more topic logs. The brokers also use ZooKeeper for a process known as leader elections, where a broker is chosen to take the lead in handling client requests for a particular topic’s unique partition.
- Kafka ZooKeeper: The Kafka cluster is managed and coordinated by Kafka brokers using ZooKeeper. When a Kafka cluster changes, ZooKeeper notifies all nodes. For instance, when a new broker enters the cluster or a broker fails, ZooKeeper notifies the cluster.
Moreover, ZooKeeper facilitates leadership elections among broker and topic partition pairings. It also aids in determining which broker will serve as the lead for each partition and which brokers have identical copies of the data.
- Kafka Producers: The idea of a producer is the same in Apache Kafka as it is in most messaging systems. A data producer specifies the subject on which one should broadcast a particular record or message. A producer can also determine which partition a specific record or message is published to because partitions are utilized to offer further scalability. Producers do not need to define any particular partition; consequently, it is possible to load balance topics in a round-robin fashion.
- Kafka Consumers: The Kafka partition is offset because Kafka brokers are stateless. The consumer keeps track of how many messages have been consumed. Additionally, once the consumer recognizes a specific message offset, you can be sure that they have consumed all previous messages. The consumer sends asynchronous pull requests to the broker to provide a buffer of bytes ready for consumption. Users can fast-forward to any point in a partition by simply providing an offset value. Consumers are also informed of the offset value via ZooKeeper.
See More: What Is Enterprise Data Management (EDM)? Definition, Importance, and Best Practices
Concepts of the Basic Kafka Architecture
The following ideas serve as the basis for understanding Kafka’s architecture:
1. Kafka topics
A Kafka topic describes a channel for streaming data, a logical channel for producers to publish messages, and for consumers to receive messages. Consumers hear messages from the topics they subscribe to once producers submit messages to those subjects.Â
Certain types of communications are published to particular topics, and topics organize messages. There is no limitation on the quanta of subjects that one can generate within a Kafka cluster. Topics or themes/subjects are given distinct names.Â
2. Kafka partitions
Topics are separated into partitions inside the Kafka cluster, and the partitions are replicated among brokers. A topic can be read in parallel by many consumers from each partition. Producers may also attach a key to a message, which directs all messages with an identical key to that division. Messages carrying keys are round-robin transferred to partitions, but messages sans keys are progressively added and stored inside partitions. Using keys, you may enforce the processing order of communications in Kafka that contain the same key.Â
3. Topic replication factor
Topic replication is needed to make reliable Kafka deployments and offer higher availability. When one broker fails, topic replicas on others remain available. This ensures that data remains accessible and the Kafka implementation does not encounter any issues or unexpected downtime. The replication factor will determine the number of copies of a subject stored in the Kafka cluster. This happens at the partition level and is set up at the topic level. Notably, the number of brokers in the cluster cannot be more than the replication factor.
4. Consumer group
Consumers who share a task or are related comprise a Kafka consumer group. Kafka distributes messages from topic partitions to group members that are consumers. Each section is read just once by a single consumer in the group at the moment it is read. A consumer group can run numerous processes or instances concurrently and has a distinct group-id. One user can read from a singular partition for each consumer group with several partitions.Â
See More: Top 8 Big Data Security Best Practices for 2021
Top 6 Uses of Kafka
Real-time data streaming plays a vital role in the digital world. This makes Apache Kafka highly relevant to most modern applications – let us examine Kafka’s top use cases in detail:
1. Activity monitoring
This was Kafka’s original use case. LinkedIn’s user activity tracking process had to be rebuilt as a series of real-time publish-subscribe streams. Since each user page view creates several activity messages, such as user clicks, registrations, likes, time spent on particular pages, orders, environmental changes, and so on, activity tracking is frequently at a high volume.
Users may print these occasions on specific Kafka-related topics. Each feed can be loaded into a data lake or data warehouse for offline processing and reporting, among various other use cases. The data is then processed as needed by other applications that have subscribed to the topics.
2. As a messaging broker
As an alternative to a more conventional message broker, Kafka performs admirably. There are several uses for messages. Kafka is a good choice for large-scale message processing use cases. This is due to its faster throughput, built-in segmentation, increased replication, and fault tolerance compared to other message systems.
Messaging applications frequently require low end-to-end latency, but they are also frequently dependent on the robust durability assurances that Kafka offers. This is often the case for analytics in edge computing use cases, where fault tolerance and low latency are must-haves.
3. Use in text streaming platforms
Kafka users often process data by using multi-stage processing pipelines. In these processes, raw input data from Kafka topics is merged, augmented, or otherwise transformed before being placed in new topics for additional usage or processing.Â
For example, a processing workflow that proposes news articles may scrape news article material from RSS feeds and submit it to an “articles†topic. In the next processing stage, this content might be standardized or removed from duplicates, and the users might post the cleaned-up content to a new topic. In the last stage of processing, one might suggest this content to users. These processing pipelines make graphs of real-time data flows based on different themes.
4. To centralize raw log data
For raw log data, Kafka makes a suitable transport layer. Even though it is advisable to use a centralized site for log storage, Kafka is useful when you need to spread data for various needs.Â
Imagine you’re unsatisfied with your existing log aggregation solution and want to switch to something else. However, you can also plan for upcoming changes rather than just scripting the switch. Apps may use Kafka as a data transit point to move data to Kafka topics, making it an excellent match for cybersecurity logging and monitoring and security information and event management (SIEM). After that, you can select how to use the information. Create a consumer that automates notifications and real-time data aggregation.
5. Data Analysis for Internet of Things (IoT)
Another possibility is to use Kafka as the central hub for sending and receiving data from Internet of Things (IoT) devices. You may have several IoT devices feeding Kafka data, like how consumers visit your website. You may set Kafka up to scale out or scale in to handle peak traffic levels as your fleet of devices expands. Consider installing an IoT device on each train in a city. Information regarding the train will be sent from each IoT device.Â
However, there is a tiny problem. You must have a small code footprint for IoT devices. Because libraries are typically large, using a traditional Kafka client won’t work. Because of this, these devices publish messages using the message queuing telemetry transport.
6. Processing data in real-time
Many systems need data to be handled whenever it is made available. With very low latency, Kafka delivers data from producers to consumers. Financial institutions can use this to collect and process payments and financial transactions in real-time, stop fraudulent activities as soon as they are discovered, or update dashboards with current market pricing.Â
Predictive maintenance for IoT implementations, in which models continuously examine streams of measurements from out-of-the-box equipment and sound alerts as soon as they spot deviations that might be signs of impending breakdown. Finally, real-time data processing is necessary for autonomous mobile devices to navigate a physical environment.
See More: What Is Deepfake? Meaning, Types of Frauds, Examples, and Prevention Best Practices for 2022
Takeaway
Kafka is an essential tool for those working with data applications, mainly due to the rise of real-time data streaming. Confluent’s 2022 “Business Impact of Data Streaming: State of Data in Motion Report†found that 97% of companies today tap into real-time data streams; for 80%, it is critical to building business processes and customer experience.Â
Kafka involves a bit of a learning curve, but it is flexible, powerful, and accessible to all users, from small startups and independent developers to large companies. This, combined with Kafka’s interoperability with most technology systems, makes it foundational to modern IT infrastructure.
Did this article help you understand how Kafka works? Tell us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!Â