PySpark Essential Training: Introduction to Building Data Pipelines

Introduction

• Course overview
• Prerequisites
• Using GitHub repo

1. Introduction to Spark and PySpark

• Introduction to Apache Spark: The foundation of PySpark
• The Apache Spark ecosystem
• Spark vs. PySpark

2. Setting Up PySpark

• Google Colab notebook setup
• Downloading a dataset

3. Working with PySpark DataFrames

• Introduction to PySpark DataFrames
• Data formats and loading data
• Schema and data types
• Basic querying (select, filter, and sort)
• Challenge: Querying a DataFrame
• Solution: Querying a DataFrame

4. Essential PySpark Data Manipulation

• Handling missing data
• Creating new columns
• Unions and joins
• Aggregating
• Writing data
• Challenge: Essential data manipulation
• Solution: Essential data manipulation

5. PySpark SQL

• What is PySpark SQL?
• Creating temporary views
• Using SQL queries
• Challenge: PySpark SQL
• Solution: PySpark SQL

6. PySpark in a Production Environment

• Production environment requirements
• Example production environment setup
• A typical PySpark production workflow
• Cloud services

Conclusion

• Recap of key concepts and next steps