Course

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.

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Part of the Data Engineer (Python) path.

4.8 (359 reviews)

8 learners enrolled in this course.

Intermediate friendly
4 hours
Self paced
3 lessons

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Otávio Silveira

Data Analyst @ Hortifruti

PySpark for Data Engineering

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Course overview

Building PySpark notebooks is one thing. Building production pipelines that integrate with your company’s cloud infrastructure is another. This course teaches you to write PySpark code that runs reliably every day in real environments. You’ll start by building a complete ETL pipeline that cleans messy CSV data with inconsistent formats and quality issues. Then you’ll learn systematic performance optimization, taking a slow pipeline and making it 10x faster by reading the Spark UI and applying targeted fixes. Finally, you’ll explore the big data ecosystem—understanding managed Spark platforms like Databricks and how to integrate PySpark with cloud storage (AWS S3) and data catalogs (AWS Glue). By the end, you’ll know how to build pipelines that work at scale, diagnose performance problems, and deploy on the platforms that companies actually use.

Key skills

Building production-ready ETL pipelines with proper structure, error handling, and logging
Handling messy real-world data with inconsistent formats and quality issues
Deploying PySpark jobs that run reliably on production schedules
Reading the Spark UI to identify performance bottlenecks in job execution
Applying optimization techniques to make pipelines 10x faster without changing hardware
Understanding when to optimize and which techniques to apply for specific bottlenecks
Comparing managed Spark platforms and choosing the right one for different use cases
Connecting PySpark to cloud storage and data catalogs for complete data lake integration

Course outline

PySpark for Data Engineering [3 lessons]

Build Your First ETL Pipeline with PySpark 2h

Lesson Objectives

Extract data defensively from messy CSV files
Transform mixed-format data using PySpark DataFrame operations
Implement data quality checks and validation patterns
Structure ETL projects with separation of concerns
Orchestrate complete pipelines with error handling and logging

PySpark Performance Tuning and Optimization 2h

Lesson Objectives

Diagnose PySpark bottlenecks using Spark UI metrics
Eliminate redundant operations like excessive count() calls
Optimize partitioning with coalesce() for proper data sizing
Implement strategic caching for reused DataFrames efficiently
Apply predicate pushdown by filtering early in pipelines
Combine multiple aggregations into single-pass operations

Integrating PySpark with Big Data Ecosystem 2h

Lesson Objectives

Identify where PySpark runs in production environments
Compare managed Spark platforms: Databricks, EMR, Dataproc
Explain storage format tradeoffs: Parquet, Delta, Iceberg
Adapt local PySpark code for cloud deployment
Understand PySpark's role in modern data architecture

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