Learn how to design, analyze, and optimize distributed data processing workloads for scalable performance and cost efficiency. This course is designed for data engineers, data professionals, and learners interested in distributed computing, Apache Spark, and modern compute architectures.

You'll explore distributed processing fundamentals, Spark's in-memory computing model, compute architecture, tasks, stages, and data partitioning. Understand how these components support efficient execution of large-scale data workloads. Discover how operator fusion, shuffle mechanics, and serialization affect performance. Examine data skew, stragglers, speculative execution, and Adaptive Query Execution to identify bottlenecks and evaluate optimization strategies. You'll also explore Bloom filters, resource isolation, micro-batch and continuous processing, and compute-storage separation. Learn how distributed compute FinOps, data governance, and lineage support cost control and operational visibility. By the end of the course, you'll be able to analyze distributed architectures, diagnose performance challenges, evaluate optimization techniques, and make informed scalability and cost decisions. What makes this course distinctive is its integrated approach to distributed execution, performance optimization, and cost-aware compute management, helping you build practical knowledge for managing scalable data workloads.