This repository provides an introduction to PySpark, a Python library for distributed data processing with Apache Spark. PySpark enables efficient and scalable data processing by leveraging the power of distributed computing. With PySpark, you can perform various data manipulation and analysis tasks on large datasets, making it a valuable tool for big data processing.
Key Features:
PySpark Basics: Learn the fundamentals of PySpark, including SparkContext initialization, SparkSession creation, and RDD (Resilient Distributed Dataset) operations.
Data Manipulation: Explore how to load, transform, and filter data using PySpark's DataFrame API. Perform operations like selecting columns, applying filters, grouping data, and aggregating results.
Data Processing with SQL: Learn how to use PySpark's SQL module to query and manipulate data using SQL-like syntax. Utilize SQL functions, perform joins, and create temporary views for efficient data processing.
Machine Learning with PySpark: Discover how to leverage PySpark's MLlib library to build and train machine learning models. Understand the workflow for data preparation, feature engineering, model training, and evaluation.