Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconBig Databreadcumb forward arrow iconApache Kafka: Architecture, Concepts, Features & Applications

Apache Kafka: Architecture, Concepts, Features & Applications

Last updated:
9th Mar, 2021
Views
Read Time
7 Mins
share image icon
In this article
Chevron in toc
View All
Apache Kafka: Architecture, Concepts, Features & Applications

Kafka was launched in 2011, all thanks to LinkedIn. Since then, it has witnessed incredible growth to the point that most companies listed in Fortune 500 now use it. It is a highly scalable, durable and high-throughput product that can handle large amounts of streaming data. But is that the only reason behind its tremendous popularity? Well, no. We haven’t even got started on its features, the quality it produces, and the ease it provides to users.

We will dive into that later. Let’s first understand what Kafka is and where it is used. 

What is Apache Kafka?

Apache Kafka is a open-source stream-processing software that aims to deliver high-throughput and low-latency while managing real-time data. Written in Java and Scala, Kafka provides durability via in-memory microservices and has an integral role to play in maintaining supply events to Complex Event Streaming Services, otherwise known as CEP or Automation Systems. 

It is an exceptionally versatile, fault-proof distributed system, which enables companies like Uber to manage passenger and driver matching. It also provides real-time data and proactive maintenance for British Gas’ smart home products apart from helping LinkedIn in tracking multiple real-time services. 

Ads of upGrad blog

Often employed in real-time streaming data architecture to deliver real-time analytics, Kafka is a swift, sturdy, scalable, and publish-subscribe messaging system. Apache Kafka can be used as a substitute for traditional MOM because of its excellent compatibility and flexible architecture that allows it to track service calls or IoT sensor data. 

Kafka works brilliantly with Apache Flume/Flafka, Apache Spark Streaming, Apache Storm, HBase, Apache Flink, and Apache Spark for real-time ingestion, research, analysis, and processing streaming data. Kafka intermediaries also facilitate low-latency follow-up reports in Hadoop or Spark. Kafka also has a subsidiary project named Kafka Stream that works as an effective tool for real-time analysis. 

Explore our Popular Software Engineering Courses

Kafka Architecture and Components

Kafka is used for streaming real-time data to multiple recipient systems. Kafka works as a central layer for decoupling real-time data pipelines. It doesn’t find much use in direct computations. It is most compatible with fast lane feeding systems, real-time or operational data-based, to stream a significant amount of data for batch data analysis.

Storm, Flink, Spark, and CEP frameworks are a few data systems that Kafka works with to accomplish real-time analytics, creating backups, audits, and more. It can also be integrated with big data platforms or database systems like RDBMS, and Cassandra, Spark, etc, for data science crunching, reporting, etc. 

The diagram below illustrates the Kafka Ecosystem:

Source

Explore Our Software Development Free Courses

Here are the various components of the Kafka ecosystem as illustrated in the Kafka architecture diagram:

1. Kafka Broker

Kafka emulates a cluster that comprises multiple servers, each known as a “broker.” Any communication among clients and servers adheres to a high-performance TCP protocol. It comprises more than one stateless broker to handle heavy loading. A single Kafka broker is capable of managing several lacs of reads and writes every second without compromising on the performance. They use ZooKeeper to maintain clusters and elect the broker leader. 

2. Kafka ZooKeeper

As mentioned above, ZooKeeper is in charge of managing Kafka brokers. Any new addition or failure of a broker in the Kafka ecosystem is brought to a producer or consumer’s notice via the ZooKeeper. 

3. Kafka Producers

They are responsible for sending data to brokers. Producers do not rely on brokers to acknowledge the receipt of a message. Instead, they determine how much a broker can handle and send messages accordingly.

4. Kafka Consumers

It is the responsibility of Kafka consumers to keep a record of the number of messages consumed by the partition offset. Acknowledging a message indicates that the messages sent before they have been consumed. To ensure that the broker has a buffer of bytes ready to send to the consumer, the consumer initiates an asynchronous pull request. The ZooKeeper has a role to play in maintaining the offset value of skipping or rewinding a message. 

Kafka’s mechanism involves sending messages between applications in distributed systems. Kafka employs a commit log, which when subscribed to publishes the data present to a variety of streaming applications. The sender sends messages to Kafka, while the recipient receives messages from the stream distributed by Kafka. 

Messages are assembled into topics — an effective deliberation by Kafka. A given topic represents organized steam of data based on a specific type or classification. The producer writes messages for consumers to read which are based on a topic.

Every topic is given a unique name. Any message from a given topic sent by a sender is received by all users who are tuning in to that topic. Once published, the data in a topic cannot be updated or modified. 

In-Demand Software Development Skills

Features of Kafka

  1. Kafka consists of a perpetual commit log that allows you to subscribe to it, and subsequently publish data to multiple systems or real-time applications. 
  2. It gives applications the ability to control that data as it comes. The Streams API in Apache Kafka is a powerful, light-weight library that facilitates on-the-fly batch data processing. 
  3. It is a Java application that allows you to regulate your workflow and significantly reduces any requirement of maintenance. 
  4. Kafka functions as a “storage of truth” distributing data to multiple nodes by enabling data deployment via multiple data systems. 
  5. Kafka’s commit log makes it a reliable storage system. Kafka creates replicas/backups of a partition which help prevent data loss (the right configurations can result in zero data loss). This also prevents server failure and enhances the durability of Kafka.
  6. Topics in Kafka have thousands of partitions, making it capable of handling an arbitrary amount of data and heavy loading.
  7. Kafka depends on the OS kernel to move data around at a fast pace. These clusters of information are end-to-end encrypted, producer to file system to end consumer.
  8. Batching in Kafka makes data compression efficiency and decreases I/O latency.

Read our Popular Articles related to Software Development

Applications of Kafka

Plenty of companies who deal with large amounts of data daily use Kafka. 

  1. LinkedIn uses Kafka to track user activity and performance metrics. Twitter combines it with Storm to enable a stream-processing framework. 
  2. Square uses Kafka to facilitate the movement of all system events to other Square data centres. This includes logs, custom events, and metrics.
  3. Other popular companies that avail the benefits of Kafka include Netflix, Spotify, Uber, Tumblr, CloudFlare, and PayPal.

Why Should you Learn Apache Kafka?

Kafka is an excellent event streaming platform that can efficiently handle, track and monitor real-time data. Its fault-tolerant and scalable architecture allow low-latency data integration resulting in a high throughput of streaming events. Kafka significantly reduces the “time-to-value” for data.

It works as the foundational system producing information to organizations by eliminating “logs” around data. This allows data scientists and specialists to easily access information at any point in time. 

Ads of upGrad blog

For these reasons, it is the top streaming platform of choice for many top companies and therefore, candidates with a qualification in Apache Kafka are highly-sought after.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

Check our other Software Engineering Courses at upGrad.

Profile

Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.
Get Free Consultation

Select Coursecaret down icon
Selectcaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Big Data Course

Frequently Asked Questions (FAQs)

1Why is Apache Kafka so famous?

Apache Kafka has established itself as the industry standard for real-time data analytics. This remarkable technology has generated a lot of attention since its introduction, owing to the unique characteristics that set it apart from other similar technologies. Furthermore, its one-of-a-kind design makes it suitable for a variety of software architecture difficulties. Many tech companies have actively integrated Kafka into their data analytics platforms, including Twitter, LinkedIn, and Netflix. LinkedIn has installed one of the largest Kafka clusters, which has become well-known. Furthermore, Kafka is used by the majority of Fortune 500 firms.

2Why are replicas created in Kafka?

Kafka emphasizes the need to create topic replicas. These are used to build Kafka deployments that are both durable and highly available. Whenever a broker fails, the topic copies on other brokers remain operational, ensuring that information is not erased and Kafka deployment is not affected. Replication guarantees that the messages that have been published do not go missing. It provides the number of copies of a subject that are stored across the Kafka cluster. It occurs at the partition level and is controlled by a person. The replication factor cannot exceed the entire number of brokers in the cluster.

3Who can learn Kafka?

Kafka is a must-have skill for people interested in learning Kafka techniques and is highly recommended for professionals looking to further their careers in the technology field. Kafka can be learned not just by freshmen but also by seasoned and working professionals. Developers that desire to advance their careers as Kafka Big Data Developers can choose this option. It can also assist testing specialists working on Queuing and Messaging systems in progressing their careers. Kafka may also be learned by Big Data Architects, as many of them like to incorporate Kafka into their environment. Learning Kafka is also valuable for project managers working on messaging system initiatives.

Explore Free Courses

Suggested Blogs

13 Ultimate Big Data Project Ideas & Topics for Beginners [2023]
91249
Big Data Project Ideas Big Data is an exciting subject. It helps you find patterns and results you wouldn’t have noticed otherwise. This skill
Read More

by upGrad

07 Sep 2023

Big Data Architects Salary in India: For Freshers & Experienced [2023]
898883
Big Data – the name indicates voluminous data, which can be both structured and unstructured. Many companies collect, curate, and store data, but how
Read More

by Rohit Sharma

04 Sep 2023

12 Exciting Spark Project Ideas & Topics For Beginners [2023]
29098
What is Spark? Spark is an essential instrument in advanced analytics as it can swiftly handle all sorts of data, independent of quantity or complexi
Read More

by Rohit Sharma

29 Aug 2023

35 Must Know Big Data Interview Questions and Answers 2023: For Freshers & Experienced
2145
Introduction The demand for potential candidates is increasing rapidly in the big data technologies field. There are plenty of opportunities in this
Read More

by Mohit Soni

29 Aug 2023

Top 5 Big Data Use Cases in Healthcare
5833
Thanks to improved healthcare services, today, the average human lifespan has increased to a great extent. While this is a commendable milestone for h
Read More

by upGrad

28 Aug 2023

Big Data Career Opportunities: Ultimate Guide [2023]
5277
Big data is the term used for the data, which is either too big, changes with a speed that is hard to keep track of, or the nature of which is just to
Read More

by Rohit Sharma

22 Aug 2023

Apache Spark Dataframes: Features, RDD & Comparison
5340
Have you ever wondered about the concept behind spark dataframes? The spark dataframes are the extension version of the Resilient Distributed Dataset,
Read More

by Rohit Sharma

21 Aug 2023

Big Data Tutorial for Beginners: All You Need to Know
8298
Big Data, as a concept, has been evoked in almost every conversation about digital innovations, the Internet of Things (IoT), and data science researc
Read More

by Mohit Soni

28 Jun 2023

Cassandra Vs Hadoop: Difference Between Cassandra and Hadoop
5592
Big Data is thriving, and so are the technologies associated with it. Cassandra and Hadoop are a few of the popular technologies, which are used for,
Read More

by Rohan Vats

28 Jun 2023

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon