Overview

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In a data-driven world, real-time processing is no longer optional—it's required. This Apache Kafka Fundamentals is a beginner-friendly but in-depth course for developers, data engineers, system administrators, and IT professionals who want to understand the fundamentals of Apache Kafka and its role in creating modern, high-performance data pipelines. You will begin by studying the growth of data systems and the growing significance of big data, analytics, and messaging platforms. Then explore Apache Kafka's architecture, including its key components (partitions, producers, consumers, and brokers), as well as how it handles distributed messaging at scale. You'll learn how to install and set up Kafka and Zookeeper, as well as how to create and operate producers and consumers. You'll also work with crucial features including serializers, deserializers, offset management, and consumer group rebalancing. Hands-on demos, videos, and graded assignments will provide you with practical experience with Kafka in real-world streaming applications. By the end, you'll be able to design and build scalable streaming applications using Apache Kafka. Enroll today and take the first step toward the future of real-time data.

Syllabus

Big Data and Messaging Fundamentals

This module introduces the evolution of data systems, Big Data concepts, analytics, and messaging systems. It builds the foundational understanding required to work with distributed streaming platforms like Kafka.

Introduction to Apache Kafka

This module covers Kafka architecture, components, clusters, terminologies, and real-world use cases. It helps you understand how Kafka works as a distributed messaging system.

Kafka Setup and Cluster Configuration

This module provides hands-on experience with installing Zookeeper and Kafka and setting up single-node and multi-node clusters. It builds practical skills in configuring Kafka environments.

Kafka Producer: Architecture and Configuration

This module explains Kafka producer architecture, configuration options, and message publishing workflows. It focuses on how data is sent into Kafka topics.

Kafka Producer: Serialization and Partitioning

This module covers data serialization techniques, custom serializers, Apache Avro, and partitioning concepts. It explains how Kafka efficiently structures and distributes data.

Kafka Consumer: Architecture and Configuration

This module introduces consumer architecture, consumer groups, polling mechanisms, and offset basics. It explains how data is read and processed from Kafka topics.

Kafka Consumer : Offset Management and Deserialization

This module focuses on commit strategies, offset management, rebalancing, and custom deserializers. It completes the full Kafka data consumption lifecycle.