Machine Learning

Why is Kafka used for building real-time data analytics?

Analyzing data in real-time and deriving business intelligence insights from the processed data has become a popular trend in today’s data world. Real-time data analytics helps in taking meaningful decisions at the right time. In this blog post, we will explore why Kafka used for developing real-time streaming data analytics.

What is Kafka?

Let us have a look at the Apache Kafka architecture to understand how Kafka as a message broker helps in real-time data streaming.


Apache Kafka works as a cluster which stores messages from one or more servers called producers.The data is partitioned into different partitions called topics. Each topic is indexed and stored with a timestamp. It processes the real-time and streaming data along with Apache Storm, Apache HBase, and Apache Spark. There are four major APIs in Kafka, namely:

  1. Producer API – allows the application to publish the stream of data to one or more Kafka topics
  2. Consumer API – allows the application to subscribe to one or more topics and process the stream of records
  3. Streams API – It converts input stream to output and produce result
  4. Connector API – allows building and running reusable producers or consumers
 Real-time streaming architecture using Kafka

The image below shows a simple illustration of how Kafka is integrated with Spark Streaming.


Producer, It can be an individual web host or web server which publishes the data. In Kafka data is partitioned into topics. Producer publishes data to a topic. Consumers or spark streaming listen to the topic and reliably consumes the data. Spark streaming is directly connected to Kafka, and the processed data is stored in MySQL. Cassandra can also be used for storing data.

Enterprises widely use Kafka for developing real-time data pipelines as it can extract high-velocity high volume data. This high-velocity data is passed through a real-time pipeline of Kafka. The published data is subscribed using any streaming platforms like Spark or using any Kafka connectors like Node Rdkafka, Java Kafka connectors. The data which is subscribed is then pushed to the dashboard using APIs.

Advantages of using Kafka
  1. Kafka can handle large volumes of data & is a highly reliable system, fault tolerant, scalable.
  2. Kafka is a distributed publish-subscribe messaging system(The publish-subscribe messaging system in Kafka is called brokers) which makes it better than other message brokers like JMS, RabbitMQ, and AMQP.
  3. Kafka can handle high-velocity real-time data unlike JMS, RabbitMQ, and AMQP message brokers.
  4. Kafka is a highly durable system as the data is persistent and cannot be replicated
  5. Kafka can handle messages with very low latency

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