Smart Data Engineering: SQL, Spark, Kafka, and GenAI Pipelines

Modern organizations require data pipelines that do more than just move data; they need to process it in real-time and enrich it using artificial intelligence. Understanding how to connect traditional data engineering tools with generative AI is now a critical skill for aspiring data professionals. This text-based course guides you from data engineering fundamentals to constructing intelligent pipelines. You will read comprehensive explanations, study clean code snippets, and complete written exercises to understand how to ingest, transform, and enrich data at scale. What you'll learn: * Understand foundational data engineering concepts, pipeline architectures, and relational SQL operations. * Process large-scale datasets efficiently using Spark's structured APIs and modern dataframe optimizations. * Stream real-time event data continuously with Kafka topic configurations and consumer patterns. * Integrate generative AI models and vector databases into data pipelines for intelligent data enrichment. * Apply pipeline observability and monitoring practices to ensure data quality and system reliability. The course begins with core definitions and SQL fundamentals before moving into distributed processing with Spark and real-time streaming with Kafka. Finally, you will explore how to incorporate modern AI workflows, such as retrieval-augmented generation (RAG) and vector storage, into your pipeline designs. Designed for beginners eager to enter the data engineering space, this course requires no prior pipeline experience, though a basic familiarity with programming concepts is helpful. Start reading today to build the foundation for modern, AI-driven data systems.