Online Course

Intro to Apache Kafka

Apache Kafka has become the central nervous system of modern data engineering, powering the real-time movement of data across applications, analytics systems, and increasingly, AI infrastructure.

This course gives you a strong conceptual and practical foundation in Apache Kafka and event streaming. It is designed for graduate students, early-career engineers, and professionals who want to understand not only how Kafka works, but also why it has become such an important part of modern data and AI architectures.

You’ll start with Kafka’s core use cases and learn how it differs from traditional messaging systems and databases. From there, you’ll explore the architecture behind Kafka, including clusters, brokers, the KRaft control plane, topics, partitions, replication, fault tolerance, storage, and message ordering.

Next, you’ll examine how data actually moves through Kafka. You’ll learn how producers write events, how consumers process them, how offsets track progress, and how consumer groups enable horizontal scaling. You’ll also develop an understanding of exactly-once semantics and the mechanisms Kafka uses to support reliable data processing.

The course then expands into the broader Kafka ecosystem. You’ll explore Kafka Connect, stream processing, Schema Registry, data contracts, and Change Data Capture (CDC), building a clearer picture of how Kafka integrates with modern data platforms.

A dedicated section focuses on Kafka for AI applications. You’ll see how event streaming can help solve the data freshness problem in Retrieval-Augmented Generation (RAG), support real-time feature engineering for machine learning, enable event-driven AI agents, and form part of the infrastructure behind modern AI systems.

Finally, you’ll bring everything together in a guided capstone project where you build a real-time transaction monitoring pipeline. You’ll launch a Kafka cluster, create a producer and consumer, implement processing logic, verify the complete pipeline, and complete a coding exercise focused on transaction anomaly filtering.

By the end of the course, you’ll understand Kafka’s architecture, mechanics, and design trade-offs—and be able to explain where it fits within modern data engineering, machine learning, RAG, and agentic AI systems.