Master Real-Time Streaming with Kafka & Spark

Overview

Learners will build, analyze, and implement real-time data pipelines; produce and consume streaming data; and apply aggregation logic to identify top trending songs using Apache Kafka and Apache Spark. This hands-on course equips learners with practical skills in streaming architecture, data ingestion, transformation logic, and end-to-end pipeline execution. Throughout the course, learners benefit from a structured, project-based approach that mirrors real industry workflows. They will set up a complete development environment, design a scalable project structure, implement streaming logic in Scala, and write processed data back to Kafka topics. By completing the project, learners gain confidence in working with real-time event data—one of the most in-demand capabilities in today’s data engineering roles. What makes this course unique is its clear focus on a real-world use case: computing top trending songs from continuous data streams. This contextual approach ensures that learners not only understand Kafka and Spark concepts but also apply them in a meaningful, production-style pipeline. Ideal for aspiring data engineers, developers, and technology professionals seeking practical, industry-relevant streaming expertise.

Syllabus

Building the Foundation for Real-Time Streaming

This module introduces learners to the fundamentals of real-time data streaming using Apache Kafka and Apache Spark. It covers project setup, environment configuration, IDE preparation, and the foundational Scala structures needed to build a streaming analytics pipeline. Learners gain clarity on how data flows through the system and begin constructing the core components required for identifying top trending songs.

Implementing and Executing the Kafka Streaming Pipeline

This module guides learners through implementing, testing, and executing the complete Kafka–Spark streaming workflow. It covers producing real-time data, validating streaming output, writing processed results back to Kafka, and applying practical execution tips to ensure pipeline reliability. By the end, learners will be able to operationalize a fully functional data-streaming solution for ranking top trending songs.