Live Online Apache Kafka Course for Data Analytics

Live Course Module: Apache Kafka Course for Data Analytics

Total Duration: 40 Hours (4 Weeks)

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Week 1: Introduction to Apache Kafka and Core Concepts (6 Hours)

1. Overview of Kafka and its role in real-time data analytics

2. Understanding event streaming and distributed systems

3. Kafka ecosystem components: Broker, Topic, Producer, Consumer, Zookeeper

4. Kafka architecture and data flow mechanism

5. Installing and configuring Kafka on local and cloud environments

6. Hands-on: Creating and managing Kafka topics, producers, and consumers

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Week 2: Kafka Data Pipelines and Integration (6 Hours)

1. Kafka message serialization (Avro, JSON, String)

2. Kafka Producers: configuration, acknowledgments, and delivery semantics

3. Kafka Consumers: consumer groups, offsets, and partitions

4. Building a reliable streaming data pipeline

5. Integrating Kafka with external systems (Python, Java, Spark, and Hadoop)

6. Hands-on: Developing a simple real-time data ingestion pipeline

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Week 3: Stream Processing and Advanced Kafka Features (6 Hours)

1. Introduction to Kafka Streams API and KSQL

2. Stream processing concepts: transformations, joins, aggregations

3. Windowing operations and stateful stream processing

4. Error handling, retries, and message ordering

5. Working with Kafka Connect for data integration

6. Hands-on: Real-time analytics using Kafka Streams and KSQL

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Week 4: Kafka in Production and Analytics Project (6 Hours)

1. Kafka cluster management, scaling, and performance tuning

2. Monitoring and logging using Kafka Manager and Prometheus

3. Security in Kafka: authentication, authorization, and encryption

4. Real-time analytics with Kafka + Spark/Flink integration

5. Capstone Project: End-to-end real-time data analytics pipeline using Kafka

6. Final review, project presentation, and Q&A

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🧩 Mini Project Ideas (Week 4 Hands-on)

Learners will implement an end-to-end data streaming pipeline, such as:

1. Project 1: Real-Time Log Processing and Visualization using Kafka + Spark

2. Project 2: Streaming E-commerce Transactions Analytics using Kafka + Flink

3. Project 3: IoT Sensor Data Stream Processing using Kafka Connect + HDFS

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🧑‍🏫 Teaching Methodology

• Live Demonstrations and code walkthroughs

• Hands-on Labs after each core topic

• Assignments & Mini Challenges to reinforce learning

• Interactive Q&A and Discussion Sessions

• Capstone Project Presentation in the final week

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🏁 Final Deliverables

• Certificate of Completion

• Fully Functional Kafka Data Pipeline Project

• Proficiency in Kafka for Real-Time Data Analytics

Course Outcomes:

By the end of this course, learners will be able to:

• Understand Kafka’s architecture and core components.

• Build producers, consumers, and topics for real-time data streaming.

• Design scalable and fault-tolerant data pipelines.

• Integrate Kafka with data analytics tools like Spark, Flink, and Hadoop.

• Deploy Kafka clusters and manage streaming data for analytics use cases.