Data Stream Engineering with Spark, Kafka, and Spring Boot

In today's fast-paced digital landscape, the ability to process data as it flows is a critical skill for modern developers and architects. This course provides a clear path for beginners to master the core technologies used to build scalable, high-speed data pipelines. You will progress from understanding basic event-driven concepts to implementing complex streaming workflows that handle massive volumes of events per second. By reading through these lessons, you will learn how to design and maintain resilient systems that transform raw data into actionable insights in real time. What you'll learn: - Understand the core principles of stream processing, including windowing, watermarking, and stateful operations. - Configure Kafka as a distributed message broker for reliable event storage and retrieval. - Utilize Spark Structured Streaming to perform real-time data transformations and large-scale analytics. - Build event-driven microservices using Spring Boot and modern functional programming models. - Implement schema management and data serialization patterns for robust communication between services. - Apply modern best practices for monitoring, scaling, and maintaining stream processing applications. The course begins with essential terminology and the theory of distributed systems before moving into the practical implementation of each component in the pipeline. It is designed for software developers and aspiring data engineers who want to build a strong foundation in stream processing through clear, written explanations and code examples. Begin your journey into the world of high-speed data engineering today.