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Apache Kafka Certification Training (Self-Paced Learning)

> Kafka is very popular because of its performance, availability and scalability

> Many Fortune 500 companies like Yahoo, Twitter,  Netflix, Uber, Goldman Sachs,PayPal, Airbnb, LinkedIn use Kafka

> As per Payscale.com, the average salary of a Software Engineer with Apache Kafka skill is $ 109,922

USD 299 USD 399

Course Overview

Apache Kafka Certification Training provide trainees with the understanding of the concepts about Kafka Architecture, Kafka Cluster, Kafka Consumer. This course provide insights into Integration of Kafka with Hadoop, Spark and Storm and implementation of Twitter Streaming with Kafka.

Key Highlights

  • 30 Hours of Online Self-Paced Training
  • Real-life Case Studies
  • Assignments
  • Lifetime access to Learning Management System (LMS)
  • 24 x 7 Expert Support
  • Certification
  • Community forum for all our Learners
  • No exam included

What You'll Learn

  • Learning about Kafka Cluster and its components
  • Learning to construct a Kafka Producer
  • Learning to set up an end to end Kafka cluster along with Hadoop and YARN cluster
  • Learning to integrate Kafka with Storm and Spark
  • Learning to create and configure Kafka consumer
  • Learning to use Kafka to produce and consume messages from streaming sources like Twitter
  • Understanding Kafka Stream APIs
  • Learning to implement Twitter Streaming with Kafka, Flume, Hadoop & Storm

Career Benefits

  • Growing market
  • Better career prospects
  • Lucrative pay packages

Who Can Attend

  • Developers
  • Big Data Architects
  • Testing Professionals
  • Project Managers, 
  • Admins

Exam Formats

No Exam Included.

Course Delivery

This course is available in the following formats:

  • Self-Paced Learning Duration: 30 Hrs

Related Courses

Course Syllabus

Introduction to Big Data and Apache Kafka

Goal: In this module, you will understand where Kafka fits in the Big Data space, and Kafka Architecture. In addition, you will learn about Kafka Cluster, its Components, and how to Configure a Cluster




  • Kafka Concepts
  • Kafka Installation
  • Configuring Kafka Cluster


Objectives: At the end of this module, you should be able to: 

  • Explain what is Big Data
  • Understand why Big Data Analytics is important
  • Describe the need of Kafka
  • Know the role of each Kafka Components
  • Understand the role of ZooKeeper
  • Install ZooKeeper and Kafka 
  • Classify different type of Kafka Clusters
  • Work with Single Node-Single Broker Cluster



  • Introduction to Big Data
  • Big Data Analytics
  • Need for Kafka
  • What is Kafka? 
  • Kafka Features
  • Kafka Concepts
  • Kafka Architecture
  • Kafka Components 
  • ZooKeeper
  • Where is Kafka Used?
  • Kafka Installation
  • Kafka Cluster 
  • Types of Kafka Clusters
  • Configuring Single Node Single Broker Cluster


Hands on:

  • Kafka Installation
  • Implementing Single Node-Single Broker Cluster

Kafka Producer

GoalKafka Producers send records to topics. The records are sometimes referred to as Messages. In this Module, you will work with different Kafka Producer APIs.



  • Configure Kafka Producer
  • Constructing Kafka Producer
  • Kafka Producer APIs
  • Handling Partitions



At the end of this module, you should be able to:

  • Construct a Kafka Producer
  • Send messages to Kafka
  • Send messages Synchronously & Asynchronously
  • Configure Producers
  • Serialize Using Apache Avro
  • Create & handle Partitions



  • Configuring Single Node Multi Broker Cluster
  • Constructing a Kafka Producer
  • Sending a Message to Kafka
  • Producing Keyed and Non-Keyed Messages 
  • Sending a Message Synchronously & Asynchronously
  • Configuring Producers
  • Serializers
  • Serializing Using Apache Avro
  • Partitions


Hands On:

  • Working with Single Node Multi Broker Cluster
  • Creating a Kafka Producer
  • Configuring a Kafka Producer
  • Sending a Message Synchronously & Asynchronously

Kafka Consumer

Goal: Applications that need to read data from Kafka use a Kafka Consumer to subscribe to Kafka topics and receive messages from these topics. In this module, you will learn to construct Kafka Consumer, process messages from Kafka with Consumer, run Kafka Consumer and subscribe to Topics


  • Configure Kafka Consumer
  • Kafka Consumer API
  • Constructing Kafka Consumer

Objectives: At the end of this module, you should be able to:

  • Perform Operations on Kafka
  • Define Kafka Consumer and Consumer Groups
  • Explain how Partition Rebalance occurs 
  • Describe how Partitions are assigned to Kafka Broker
  • Configure Kafka Consumer
  • Create a Kafka consumer and subscribe to Topics
  • Describe & implement different Types of Commit
  • Deserialize the received messages


  • Consumers and Consumer Groups
  • Standalone Consumer
  • Consumer Groups and Partition Rebalance
  • Creating a Kafka Consumer
  • Subscribing to Topics
  • The Poll Loop
  • Configuring Consumers
  • Commits and Offsets
  • Rebalance Listeners
  • Consuming Records with Specific Offsets
  • Deserializers



  • Creating a Kafka Consumer
  • Configuring a Kafka Consumer
  • Working with Offsets

Kafka Internals

Goal: Apache Kafka provides a unified, high-throughput, low-latency platform for handling real-time data feeds. Learn more about tuning Kafka to meet your high-performance needs.



  • Kafka APIs
  • Kafka Storage 
  • Configure Broker



At the end of this module, you should be able to:

  • Understand Kafka Internals
  • Explain how Replication works in Kafka
  • Differentiate between In-sync and Out-off-sync Replicas
  • Understand the Partition Allocation
  • Classify and Describe Requests in Kafka
  • Configure Broker, Producer, and Consumer for a Reliable System
  • Validate System Reliabilities
  • Configure Kafka for Performance Tuning



  • Cluster Membership
  • The Controller
  • Replication
  • Request Processing
  • Physical Storage
  • Reliability 
  • Broker Configuration
  • Using Producers in a Reliable System
  • Using Consumers in a Reliable System
  • Validating System Reliability
  • Performance Tuning in Kafka


Hands On:

  • Create topic with partition & replication factor 3 and execute it on multi-broker cluster
  • Show fault tolerance by shutting down 1 Broker and serving its partition from another broker

Kafka Cluster Architectures & Administering Kafka

Goal:  Kafka Cluster typically consists of multiple brokers to maintain load balance. ZooKeeper is used for managing and coordinating Kafka broker. Learn about Kafka Multi-Cluster Architectures, Kafka Brokers, Topic, Partitions, Consumer Group, Mirroring, and ZooKeeper Coordination in this module.



  • Administer Kafka



At the end of this module, you should be able to

  • Understand Use Cases of Cross-Cluster Mirroring
  • Learn Multi-cluster Architectures
  • Explain Apache Kafka’s MirrorMaker
  • Perform Topic Operations
  • Understand Consumer Groups
  • Describe Dynamic Configuration Changes
  • Learn Partition Management
  • Understand Consuming and Producing
  • Explain Unsafe Operations



  • Use Cases - Cross-Cluster Mirroring
  • Multi-Cluster Architectures
  • Apache Kafka’s MirrorMaker
  • Other Cross-Cluster Mirroring Solutions
  • Topic Operations
  • Consumer Groups
  • Dynamic Configuration Changes
  • Partition Management
  • Consuming and Producing
  • Unsafe Operations


Hands on:

  • Topic Operations
  • Consumer Group Operations
  • Partition Operations

Kafka Monitoring and Kafka Connect

Goal: Learn about the Kafka Connect API and Kafka Monitoring. Kafka Connect is a scalable tool for reliably streaming data between Apache Kafka and other systems.



  • Kafka Connect
  • Metrics Concepts
  • Monitoring Kafka


Objectives: At the end of this module, you should be able to:

  • Explain the Metrics of Kafka Monitoring
  • Understand Kafka Connect
  • Build Data pipelines using Kafka Connect
  • Understand when to use Kafka Connect vs Producer/Consumer API 
  • Perform File source and sink using Kafka Connect


  • Topics:
  • Considerations When Building Data Pipelines
  • Metric Basics
  • Kafka Broker Metrics
  • Client Monitoring
  • Lag Monitoring
  • End-to-End Monitoring
  • Kafka Connect
  • When to Use Kafka Connect?
  • Kafka Connect Properties


Hands on:

  • Kafka Connect

Kafka Stream Processing

Goal: Learn about the Kafka Streams API in this module. Kafka Streams is a client library for building mission-critical real-time applications and microservices, where the input and/or output data is stored in Kafka Clusters.



  • Stream Processing using Kafka



  • At the end of this module, you should be able to,
  • Describe What is Stream Processing
  • Learn Different types of Programming Paradigm
  • Describe Stream Processing Design Patterns
  • Explain Kafka Streams & Kafka Streams API


  • Stream Processing
  • Stream-Processing Concepts
  • Stream-Processing Design Patterns
  • Kafka Streams by Example
  • Kafka Streams: Architecture Overview


Hands on:

  • Kafka Streams
  • Word Count Stream Processing

Integration of Kafka With Hadoop, Storm and Spark

Goal: In this module, you will learn about Apache Hadoop, Hadoop Architecture, Apache Storm, Storm Configuration, and Spark Ecosystem. In addition, you will configure Spark Cluster, Integrate Kafka with Hadoop, Storm, and Spark.



  • Kafka Integration with Hadoop
  • Kafka Integration with Storm
  • Kafka Integration with Spark



At the end of this module, you will be able to:

  • Understand What is Hadoop
  • Explain Hadoop 2.x Core Components
  • Integrate Kafka with Hadoop
  • Understand What is Apache Storm
  • Explain Storm Components
  • Integrate Kafka with Storm
  • Understand What is Spark
  • Describe RDDs
  • Explain Spark Components
  • Integrate Kafka with Spark



  • Apache Hadoop Basics
  • Hadoop Configuration
  • Kafka Integration with Hadoop
  • Apache Storm Basics
  • Configuration of Storm 
  • Integration of Kafka with Storm
  • Apache Spark Basics
  • Spark Configuration
  • Kafka Integration with Spark


Hands On:

  • Kafka integration with Hadoop
  • Kafka integration with Storm
  • Kafka integration with Spark

Integration of Kafka With Talend and Cassandra

Goal: Learn how to integrate Kafka with Flume, Cassandra and Talend.



  • Kafka Integration with Flume
  • Kafka Integration with Cassandra
  • Kafka Integration with Talend



At the end of this module, you should be able to,

  • Understand Flume
  • Explain Flume Architecture and its Components
  • Setup a Flume Agent
  • Integrate Kafka with Flume
  • Understand Cassandra
  • Learn Cassandra Database Elements
  • Create a Keyspace in Cassandra
  • Integrate Kafka with Cassandra
  • Understand Talend
  • Create Talend Jobs
  • Integrate Kafka with Talend


  • Flume Basics
  • Integration of Kafka with Flume
  • Cassandra Basics such as and KeySpace and Table Creation
  • Integration of Kafka with Cassandra
  • Talend Basics
  • Integration of Kafka with Talend


Hands On:

  • Kafka demo with Flume
  • Kafka demo with Cassandra
  • Kafka demo with Talend

Kafka In-Class Project

Goal: In this module, you will work on a project, which will be gathering messages from multiple 




In E-commerce industry, you must have seen how catalog changes frequently. Most deadly problem they face is “How to make their inventory and price



There are various places where price reflects on Amazon, Flipkart or Snapdeal. If you will visit Search page, Product Description page or any ads on Facebook/google. You will find there are some mismatch in price and availability. If we see user point of view that’s very disappointing because he spends more time to find better products and at last if he doesn’t purchase just because of consistency.

Here you have to build a system which should be consistent in nature. For example, if you are getting product feeds either through flat file or any event

stream you have to make sure you don’t lose any events related to product specially inventory and price.


If we talk about price and availability it should always be consistent because there might be possibility that the product is sold or the seller doesn’t want to sell it anymore or any other reason. However, attributes like Name, description doesn’t make that much noise if not updated on time.


Problem Statement

You have given set of sample products. You have to consume and push products to Cassandra/MySQL once we get products in the consumer. You have to save below-mentioned fields in Cassandra.

1. PogId

2. Supc

3. Brand

4. Description

5. Size

6. Category

7. Sub Category

8. Country

9. Seller Code


In MySQL, you have to store

1. PogId

2. Supc

3. Price

4. Quantity

Certification Project

This Project enables you to gain Hands-On experience on the concepts that you have learned as part of this Course. 


You can email the solution to our Support team within 2 weeks from the Course Completion Date. Edureka will evaluate the solution and award a Certificate with a Performance-based Grading.


Problem Statement:

You are working for a website techreview.com that provides reviews for different technologies. The company has decided to include a new feature in the website which will allow users to compare the popularity or trend of multiple technologies based on twitter feeds. They want this comparison to happen in real time. So, as a big data developer of the company, you have been task to implement following things:


• Near Real Time Streaming of the data from Twitter for displaying last minute's count of people tweeting about a particular technology.


• Store the twitter count data into Cassandra.


What if I miss a class?

You will never miss a lecture at Upskill Yourself! You can choose either of the two options:

  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch.

Can I attend a demo session before enrollment?

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Why learn Apache Kafka?

Apache Kafka is one of the most popular publish subscribe messaging systems which is used to build real-time streaming data pipelines that are robust, reliable, fault tolerant & distributed across a cluster of nodes. Kafka supports a variety of use-cases which commonly include Website activity tracking, messaging, log aggregation, Commit log & stream processing. These are reasons why many giants such as Airbnb, PayPal, Oracle, Netflix, Mozilla, Uber, Cisco, Coursera, Spotify, Twitter, Tumblr are looking for professionals with Kafka skills. Getting Kafka certified will help you land your dream job.

What is the best way to learn Apache kafka?

 Apache Kafka Certification Training is curated by industry experts and it covers in-depth knowledge on Kafka Producer & Consumer, Kafka Internals, Kafka Cluster Architecture, Kafka Administration, Kafka Connect & Kafka Streams. Throughout this online instructor-led Kafka Training you will be working on real-world Kafka use-cases belonging to finance, marketing and e-commerce domain, etc.

What is the career progression and opportunities in Apache Kafka?

Technology Giants & MNCs such as Airbnb, PayPal, Oracle, Netflix, Mozilla, Uber, Cisco, Coursera, Spotify, Twitter & Tumblr are looking for Kafka certified professionals. Not only this, SMEs are also using Apache Kafka to build real-time streaming data pipelines. This will also lead to exponential growth in number of Kafka jobs available in the market.

What are the skills needed to master Apache Kafka?

To master Apache Kafka, you need to learn all the concepts related to Apache Kafka – Kafka Architecture, Kafka Producer & Consumer, Configuring Kafka Cluster, Kafka Monitoring, Kafka Connect & Kafka Streams. Knowledge of Kafka integration with other Big Data tools such as Hadoop, Flume, Talend, Cassandra, Storm and Spark will be a plus point.

Mike Williams, Direct Consultant