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Apache Kafka Fundamentals Course

Course Level: Beginner
Duration: 2 Hrs 13 Min
Total Videos: 28 On-demand Videos

Dive deep into the world of distributed systems and real-time data streaming with our comprehensive Apache Kafka Fundamentals course. Perfect for IT professionals, data engineers, and software developers, this course provides the necessary knowledge and skills to design, deploy, and manage efficient event-driven systems using Apache Kafka.

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Learning Objectives

01

Understand the fundamentals and history of Apache Kafka, including its common use cases and APIs.

02

Gain a comprehensive understanding of Kafka’s core concepts, such as event data streams and messaging.

03

Learn about Kafka’s architecture, including topics, partitions, segments, and the role of brokers and clusters.

04

Understand the function and importance of Kafka Streams, patterns, Zookeeper, and Kraft in the Kafka ecosystem.

05

Learn how to install and deploy Kafka, considering various installation options and requirements.

06

Gain hands-on experience in starting, managing, and terminating a Kafka environment.

07

Understand how to connect and process events in Kafka, and explore additional resources for further learning.

08

Review and integrate all course learnings to effectively use Apache Kafka in various use cases and environments.

Course Description

Whether you’re a data engineer, software developer, or IT professional, this Apache Kafka Fundamentals course is designed to help you master real-time data streaming and event-driven architectures from the ground up. By the end, you’ll confidently design, deploy, and manage robust stream-based solutions using Apache Kafka, translating complex concepts into practical, production-ready capabilities.

In this practical program, you’ll gain hands-on experience with distributed systems and learn how Kafka fits into modern data pipelines. You’ll explore real-time data streaming use cases, understand the messaging model, and master the essentials of deploying and operating Kafka in a scalable way. The course blends theory with demonstrations, whiteboard discussions, and guided exercises to reinforce your learning and help you apply what you’ve learned to real-world problems.

What you’ll master includes core concepts of Kafka, installation and setup, topics, partitions, brokers and zookeepers, and how to run reliable stream processing with Kafka streams. You’ll investigate operational aspects like monitoring, troubleshooting, and managing Kafka environments, ensuring you can keep data flowing smoothly in production. Whether you’re building data pipelines, analytics workflows, or real-time dashboards, this course equips you with the practical know-how you need.

Strong outcomes you can expect include the ability to design event-driven systems, implement reliable data ingestion, and optimize performance for high-throughput streaming. You’ll gain proficiency in deployment patterns, fault tolerance, and scalable architecture, along with hands-on skills that translate directly to enterprise environments.

  • Implement real-time data streaming and event-driven architectures using Apache Kafka
  • Deploy and operate Kafka clusters with confidence, including topic partitioning and fault tolerance
  • Develop familiarity with Kafka streams basics and común messaging patterns
  • Monitor, troubleshoot, and manage production Kafka environments for reliability
  • Apply practical workflows to data pipelines, analytics, and streaming integrations

Enroll now to build a solid foundation in Apache Kafka fundamentals online, at your own pace. Stay ahead in the evolving field of event streaming systems and empower yourself with the skills that can transform how your organization collects, processes, and analyzes data in real time.

Who Benefits From This Course

  • Software developers keen on mastering real-time data processing
  • Data scientists aiming to improve their data pipeline management skills
  • IT professionals looking to leverage real-time data for decision making
  • System administrators tasked with deploying and managing data streaming platforms
  • Software architects designing systems around real-time data streams
  • Database administrators seeking to enhance their knowledge of data streaming services
  • Technical managers wanting to understand the capabilities and use cases of event data streams

Frequently Asked Questions

What is Apache Kafka and why is it important in the IT industry?
Apache Kafka is an open-source distributed event streaming platform that is used for building real-time data streaming and processing applications. It's designed to handle large volumes of data and is capable of processing millions of messages per second. This makes it an essential tool in the IT industry, especially in environments where real-time data processing is crucial. Apache Kafka enables companies to handle their big data needs, facilitate real-time analytics, and integrate multiple systems and applications. The technology's ability to handle high-throughput and fault-tolerant messaging systems is why it's used by many large enterprises worldwide.
What are some common uses of Apache Kafka?
Apache Kafka is used for various purposes in the IT industry, including:
  • Real-time streaming of data: Kafka can process and analyze data in real-time, making it ideal for applications that require immediate response, such as fraud detection or live analytics.
  • Integration with big data tools: Kafka can be integrated with various big data tools like Hadoop, Spark, and Storm for real-time data processing and analysis.
  • Event sourcing: Kafka can be used as a source of events which can be processed and reprocessed as needed.
  • Log aggregation: Kafka allows gathering logs from different sources and sending them to a centralized system for processing and analysis.
What is the architecture of Apache Kafka?
Apache Kafka has a distributed architecture that is based on the publish-subscribe model. It consists of three main components:
  • Producers: They publish (write) data to topics, which are similar to message queues.
  • Brokers: These are servers that store published data. A Kafka cluster may consist of multiple brokers to ensure fault-tolerance.
  • Consumers: They subscribe (read) to topics and process the data.
Kafka also has a distributed commit log service where all data is stored and can be processed in real-time. This architecture ensures that Kafka is highly scalable and fault-tolerant.
What are the operational strategies of Apache Kafka?
Operational strategies in Apache Kafka involve managing and maintaining Kafka clusters to ensure optimal performance and reliability. These include:
  • Monitoring: This involves tracking the performance and health of Kafka clusters, including message rates, latency, and error rates.
  • Scaling: Kafka clusters can be scaled up or down based on the volume of data. This involves adding or removing brokers and redistributing data.
  • Fault-tolerance: Kafka provides replication features to ensure data is not lost when a broker fails. This involves maintaining multiple replicas of data and designating new leaders if a broker fails.
  • Security: Kafka provides features for authentication, authorization, and encryption to protect data.
What skills are required to work with Apache Kafka?
To work with Apache Kafka, one needs to have a solid foundation in several areas:
  • Understanding of distributed systems: Since Kafka is a distributed system, you need to understand concepts like data replication, fault-tolerance, and consensus algorithms.
  • Programming skills: To write producers and consumers, you need to be proficient in a programming language supported by Kafka, such as Java, Python, or Scala.
  • Data streaming: Understanding how data streaming works, including concepts like real-time processing, stream processing, and batch processing.
  • System design: You should be able to design systems that can handle large volumes of data and integrate with other systems.
  • Operations: You need to know how to monitor, manage, and troubleshoot Kafka clusters.

Included In This Course

Apache Kafka - Introduction

  •    Course Introduction
  •    Instructor Introduction

Module 1: Overview of Apache Kafka and Common Use Cases

  •    1.1 Overview and Common Use Cases
  •    1.2 What is Kafka
  •    1.3 Kafka History
  •    1.4 Kafka Use Cases
  •    1.5 Kafka APIs
  •    1.6 Kafka Pub Sub Patterns
  •    1.7 Whiteboard Discussion- Use Case

Module 2: Kafka Core Concepts

  •    2.1 Kafka Core Concepts
  •    2.2 The Importance of Event Data Streams
  •    2.3 Kafka Messaging, Topics, Partitions and Segments
  •    2.4 Whiteboard - Kafka Components
  •    2.5 Whiteboard - Brokers and Clusters
  •    2.6 Kafka Streams and Patterns
  •    2.7 Whiteboard - Zookeeper and Kraft
  •    2.8 Demonstration - Kafka Connect
  •    2.9 Whiteboard- - Architecture Deep Dive
  •    2.10 Whiteboard - Kafka Design Patterns

Module 3: Installing and Deploying Kafka

  •    3.1 Installing and Deploying Kafka
  •    3.2 Demonstration - Kafka Resources and Licensing
  •    3.3 Demonstration - Kafka Installation Options, Considerations and Requirements
  •    3.4 Demonstration - Deployment and Environment
  •    3.5 Demonstration - Starting Kafka
  •    3.6 Demonstration - Terminating Kafka Environment
  •    3.7 Whiteboard - Connections and Processing Events
  •    3.8 Additional Resources
  •    3.9 Putting it all together - Course Review