Course · Intermediate
PySpark for Data Engineering
You know PySpark basics, but production data engineering is different. Real pipelines handle messy CSV files with inconsistent formats, run reliably on schedules, and need to process growing datasets without crashing or taking hours. They also need to integrate with cloud storage, data catalogs, and managed Spark platforms. This course bridges the gap between notebook experiments and production systems. You'll build complete ETL pipelines that handle the chaos of real data, learn to diagnose and fix performance bottlenecks systematically, and understand how to deploy PySpark jobs on cloud platforms like Databricks and AWS. Whether you're dealing with pipelines that can't finish before the next run starts or figuring out how to connect PySpark to your company's data lake, you'll learn the practical techniques that data engineers use daily at companies processing terabytes of data.
- Intermediate friendly
- 6 hrs
- 3 lessons
- Premium
Course overview
Move beyond notebooks to build production-grade PySpark ETL pipelines that handle messy data, scale efficiently, and run reliably in the cloud.
What's inside
3 lessons
- 01 120 minBuild Your First ETL Pipeline with PySpark
Learn to build a real-world ETL pipeline with PySpark. Handle messy data, clean and transform it, and automate reliable daily workflows.
- 02 120 minPySpark Performance Tuning and Optimization
Learn how to diagnose and fix slow PySpark pipelines by removing bottlenecks, tuning partitions, caching smartly, and cutting runtimes.
- 03 120 minIntegrating PySpark with Big Data Ecosystem
Learn how PySpark transitions from local development to production: understand managed platforms (Databricks, EMR, Dataproc), storage formats (Parquet, Delta Lake, Iceberg), and what actually changes in your code when deploying to cloud environments like AWS, GCP, or Azure.
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