Complete PySpark – Big Data Processing with Apache Spark

Description

✅ What You Will Learn

📌 Module 1 – Introduction to PySpark

• Overview of Apache Spark & Big Data Ecosystem

• Installing and Setting Up PySpark

• Spark Architecture & Execution Model

📌 Module 2 – RDD (Resilient Distributed Dataset) Operations

• Creating & Transforming RDDs

• Actions vs. Transformations

• Persistence & Caching Techniques

📌 Module 3 – DataFrames & Spark SQL

• Creating & Querying DataFrames

• Spark SQL for Data Analysis

• Schema Definition, Casting, and Optimization

📌 Module 4 – Data Transformation & Cleaning

• Filtering, Aggregations, and Joins

• Handling Missing Data & Null Values

• Complex Data Types (Arrays, Structs, Maps)

📌 Module 5 – Advanced PySpark Operations

• User-Defined Functions (UDFs) & Pandas UDFs

• Window Functions & Analytical Queries

• Performance Tuning & Partitioning Strategies

📌 Module 6 – Streaming & Real-Time Data Processing

• Introduction to Spark Structured Streaming

• Streaming Data Sources & Sinks

• Real-Time Analytics Pipelines

📌 Module 7 – Machine Learning with PySpark MLlib

• Feature Engineering in PySpark

• Building & Training Machine Learning Models

• Model Evaluation & Hyperparameter Tuning

📌 Module 8 – Deployment & Integration

• Running PySpark on AWS, Azure, and Databricks

• Cluster Management with YARN & Kubernetes

• Packaging PySpark Applications for Production

👤 Who Is This Course For?

• Data Engineers & Data Scientists working with large datasets

• Developers transitioning into big data frameworks

• Business Intelligence Professionals looking to scale analytics

• Anyone preparing for PySpark job interviews or certifications