This repository contains a PySpark notebook developed and executed in Google Colab, designed for learning and building scalable data engineering workflows without requiring a local Spark setup.
📌 Overview
The notebook demonstrates how to:
Set up a PySpark environment in Google Colab Initialize a Spark session Perform data ingestion and transformation Apply common PySpark operations (filter, join, aggregation, etc.) Work with structured data using DataFrames
This approach is ideal for beginners and professionals who want to experiment with PySpark in a lightweight, cloud-based environment.
🚧 Use Cases Data Engineering practice ETL pipeline prototyping Learning Spark transformations Interview preparation for PySpark roles
📌 Future Improvements Add real-world datasets Integrate with cloud storage (GCS/AWS S3) Include advanced transformations and optimizations Convert notebook into production-grade pipeline