Event Streaming: Real-Time Data in Motion

Learn to architect, process, and react to real-time data events using modern tools like Apache Kafka, Flink, and stream-first design principles.

Course Objectives

• Understand the core concepts of event-driven architecture and stream processing.
• Compare message brokers, queues, and event streaming platforms.
• Design scalable data pipelines using Kafka and Flink.
• Implement producers, consumers, and stream transformations in code.
• Evaluate use cases such as fraud detection, system monitoring, and IoT telemetry.

Course Overview

Event streaming is changing how organizations interact with data—from batch processing to continuous, real-time responsiveness. This course introduces the principles of stream-first architecture and the tools that make it possible, including Apache Kafka and Flink. Whether you’re building fraud detection systems or responsive user analytics, this course delivers the patterns and skills to move from static reporting to real-time insight.

Sample Module: Event Streaming Architecture 101

This module lays the foundation for understanding how producers, brokers, consumers, and stream processors work together in a modern streaming ecosystem. You’ll learn how to design fault-tolerant and scalable pipelines for high-volume data flow.

Lesson: Building a Kafka Topic and Real-Time Consumer

In this lesson, learners will configure a Kafka topic, implement a producer to publish events, and create a real-time consumer to process incoming messages. Key concepts include partitions, offsets, and delivery semantics.