Introduction
One of the biggest challenges that is associated with big data is, analyzing the data. But before we get to that part, the data has to be first collected, and also for a system to process impeccably it should be able to grasp and make the data available to users. This is where Apache Kafka comes in handy.
Let’s briefly understand how Kafka came into existence? It was developed by a team from LinkedIn in 2011, to solve the low-latency ingestion of large amounts of event data from their website and to handle real-time event processing systems. Later, it was donated to the Apache Software Foundation.
Apache Kafka has the ability to handle trillions of events occurring in a day. Kafka was initially developed for a messaging queue. A message queuing system helps in transferring data between applications so that the applications can just concentrate on the data rather than on how the data can be transferred and shared.
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Apache Kafka is the most popular distributed messaging and streaming data platform in the IT world these days. In this blog, we will learn what Kafka is and why it has become one of the most in-demand technologies among big firms and organizations.
About Apache Kafka
Apache Kafka is an open-source, distributed, and publish–subscribe messaging system that manages and maintains the real-time stream of data from different applications, websites, etc.
It enables the communication between producers and consumers using message-based topics. It designs a platform for high-end new-generation distributed applications. Kafka permits a large number of permanent or ad-hoc consumers.
Why Kafka is Used?
The ability to give higher throughput, reliability, and replication has made this technology replace conventional message brokers.
Kafka can work with Apache HBase, Flink, Flafka, and Spark for the analysis and processing of real-time data, which makes Kafka scalable as it allows us to build distributed applications. Kafka can be used to direct data for Hadoop Big Data lakes.
Kafka relies on the filesystem for storage and caching purposes, thus it’s fast. It prevents data loss and is fault-tolerant. Kafka is a publish–subscribe messaging system, and it is used in use cases where JMS, RabbitMQ, and AMQP may not even be considered due to volume and responsiveness.
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| Apache Flume | Apache Kafka |
| A Special-purpose tool for specific applications | A General-purpose tool for multiple producers and consumers |
| Data flow: push | Data flow: pull |
| Does not replicate events | Replicates the events using ingest pipelines |
| High velocity | Higher velocity |
| Apache Kafka | RabbitMQ |
| Kafka is distributed. Data is shared and replicated with guaranteed durability and availability. | RabbitMQ provides relatively less support for these features. |
| High-performance rate to the tune of 100,000 messages/second | The performance rate is around 20,000 messages/per second |
| Kafka also comes with consumer frameworks that allow reliable log-distributed processing. There is stream processing semantics built into the Kafka Streams. | Consumer in RabbitMQ is just FIFO based, reading from the HEAD and processing one by one. |
How does Apache Kafka differ from other queuing systems?
Apache Kafka is a new-generation message streaming service incepted by LinkedIn. Let’s see how it differs when compared to traditional queuing systems.
| Traditional Queuing Systems | Apache Kafka |
| Most traditional queuing systems remove messages after they have been processed, typically from the end of the queue. | In Kafka, the messages persist after being processed, and they don’t get removed as consumers receive them. |
| Similar to imperative programming where the originating system decides if a system should take an action that occurs in a downstream system. | Reactive programming is employed here in which there is a reactive publish–subscribe architecture. |
| It is not possible to process logic, based on similar messages or events. | Kafka lets us process logic, based on similar events. |
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Apache Kafka Architecture
Kafka is usually integrated with Apache Storm, Apache HBase, and Apache Spark in order to process real-time streaming data. It is capable of delivering massive message streams to the Hadoop cluster regardless of the industry or use case. Its process flow can be better understood if we take a close look into its ecosystem.
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Kafka is deployed as a cluster implemented on one or more servers. The cluster is capable of storing topics that consist of streams of ‘records’ or ‘messages’. Every message holds details like a key and a value. Brokers are abstractions used to manage the persistence and replication of the message.
Basically, it has four core APIs:
- Producer API: This API permits applications to publish a stream of records to one or more topics.
- Consumer API: Consumer API lets applications to subscribe to one or more topics and process the produced stream of records.
- Streams API: This API takes the input from one or more topics and produces the output to one or more topics by converting the input streams to the output ones.
- Connector API: This API is responsible for producing and executing reusable producers and consumers who are able to link topics to the existing applications.
Advantages of Apache Kafka
- Tracking web activities: Apache Kafka keeps a track of web activities by storing or sending events for real-time processes.
- Highly scalable: Apache Kafka shows high scalability, without any downtime.
- Standard format: It transforms data of different formats into a standard format so that there is no ambiguity.
- Continuous streaming: Apache Kafka keeps up the continuous processing of streaming data.
- High throughput: Kafka is able to handle a high volume of data even at a high velocity. It also supports high message throughput, i.e., it can handle thousands of messages every second.
- Low latency: Apache Kafka decreases the latency, making it possible for you to deliver data in mere milliseconds in real-time.
- Fault-tolerant: Kafka is highly available and resilient to node failures and supports automatic recovery.
- Durable: Apache Kafka lets us replicate messages, and the messages persist as fast as possible on the disk which makes Kafka durable as well.
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Apache Kafka: Career Scope
LinkedIn has deployed one of the biggest clusters of Kafka and has reported that ‘Back in 2011, it was ingesting more than 1 billion events a day. Recently, it has reported ingestion rates of 1 trillion messages a day.’
There are many tech leaders who have implemented Apache Kafka and have benefited from it. Some of them are:
- Twitter
- LinkedIn
- Netflix
- Mozilla
- Oracle
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‘The partnership with popular streaming systems like Spark has resulted in the consistent growth of active users on the Kafka user’s mailing list, which is just over 260% since July 2014′ – Fintan Ryan, Redmonk Analyst
Job Roles in Apache Kafka
- Kafka Developers
- Kafka Testing Professionals
- Big Data Architects in Kafka
- Kafka Project Managers
This powerful technology has created a lot of buzz since its emergence due to its special features that distinguish it from other similar tools. Its ability to provide a unique design makes it suitable for various software architectural challenges.
Who is the right audience for Apache Kafka?
Apache Kafka is the best-suited course for aspirants willing to make their career as Big Data Analysts, Big Data Hadoop Developers, Architects, Testing Professionals, Project Managers, and Messaging and Queuing System Professionals.
Thorough knowledge of Java, Scala, Distributed Messaging Systems, and Linux is recommended for aspirants who want to make it big in the Big Data domain.
With this, we have come to the end of the blog on Apache Kafka. From the facts and figures mentioned in this blog, we can definitely assess the extent to which tech giants are craving Kafka professionals. One thing is clear, Kafka has already created a solid impact on the leading market players and is anticipated to grow in the near future. Thus, mastering it will definitely give you a sure-shot success. Though there are many technologies in the market which address similar issues, it has created a niche for itself by delivering high-end services to companies wanting to process streaming data in real-time. The range of qualities it offers is broad, and hence it is being widely accepted by all major technology leaders. The growing popularity of this technology has created a huge demand for its professionals offering high-paying jobs and a lucrative careers.
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