How to Build an End-to-End Data Engineering and Machine Learning Pipeline with Apache Spark and PySpark

This tutorial demonstrates how to utilize Apache Spark's capabilities through PySpark within Google Colab, enabling scalable data processing and machine learning workflows in a single-node environment. It guides users through setting up a Spark session, performing data transformations, executing SQL queries, and applying window functions, illustrating Sparks versatility for analytics tasks even without a distributed cluster. A key innovation is the integration of Sparks distributed data processing with machine learning, exemplified by building and evaluating a logistic regression model to predict user subscription types. The tutorial also covers practical aspects such as saving and reloading data in Parquet format, showcasing how