Scheduling and Running PySpark Jobs for Scalable Pipelines

Managing data workflows at scale requires more than just writing code; it demands robust automation, monitoring, and execution strategies. This course teaches you how to transition from local PySpark scripts to fully automated, resilient data pipelines that run reliably in production environments. You will learn to orchestrate your data workflows, manage dependencies, and handle failures gracefully. By completing this course, you will be able to design, schedule, and monitor production-ready PySpark pipelines using industry-standard tools and cloud environments. What you'll learn: - Understand the core architecture of PySpark jobs and how they execute in distributed environments. - Configure and write Airflow DAGs to orchestrate complex PySpark data workflows. - Deploy and run PySpark jobs on cloud platforms and managed Spark services. - Implement modern logging, monitoring, and alerting practices to catch pipeline failures early. - Apply best practices for resource allocation, data partitioning, and performance tuning in production. This course begins with foundational concepts, establishing key terminology before moving into practical pipeline orchestration, scheduling patterns, and cloud deployment strategies. You will progress from basic job execution to managing multi-stage dependency workflows. This course is designed for beginner data engineers, analysts, and developers who have a basic understanding of Python and Spark and want to learn how to automate and scale their data processing in production. No prior experience with orchestration tools is required. Start building resilient, automated data pipelines today.