PySpark Tutorials – From Basics to Advanced Data Engineering
🚀 PySpark Tutorials
Welcome to the PySpark Tutorials hub.
This section is designed to take you from PySpark fundamentals to advanced, production-ready data engineering concepts used in real companies.
The tutorials are structured to reflect how PySpark is used in batch, streaming, and analytics pipelines.
🧱 PySpark Introduction & Basics
Get started with PySpark and understand its core architecture.
- PySpark Introduction
- PySpark Architecture
- PySpark Installation
- RDD vs DataFrame
- SparkSession vs SparkContext
- Your First PySpark Job
👉 Start here if you are new to PySpark.
🔗 PySpark RDDs
Learn the low-level RDD APIs and transformations.
👉 Helps you understand how Spark works internally.
📊 PySpark DataFrames Basics
Work with structured data using the DataFrame API.
- Creating DataFrames from CSV
- DataFrame API Overview
- DataFrame Joins
- Aggregations
- Window Functions
- Handling Missing Data
- Working with Dates & Timestamps
👉 Most commonly used APIs in real-world projects.
🧠 PySpark SQL
Query data using Spark SQL for analytics and reporting.
👉 Widely used in BI and analytics workloads.
⚙️ PySpark Advanced Transformations
Advanced transformations for complex data processing.
- Explode & Lateral View
- Pivot & Unpivot
- Join Optimization
- Sorting, Sampling & Limit
- Partitioning & Bucketing
👉 Important for large-scale datasets.
⚡ PySpark Performance & Optimization
Learn how to debug and optimize Spark jobs.
- Caching & Persisting
- Shuffle, Narrow & Wide Transformations
- Spark UI & Debugging
- Catalyst & Tungsten
- Partition Tuning
👉 Essential for interviews and production workloads.
🌊 PySpark Streaming
Process real-time data using Structured Streaming.
👉 Used for real-time pipelines.
🤖 PySpark Machine Learning
Apply machine learning using Spark MLlib.
- ML Pipelines, Transformers & Estimators
- Regression & Classification
- Linear Regression - Explained Simply
- Linear Regression — Manual Math Breakdown
- Predicting House Price from Size Using Linear Regression
- Logistic Regression - Explained Simply
- Logistic Regression - Practical handson
- Logistic Regression Mini Project — Forecasting Customer Churn
- Clustering & Recommendation
👉 Suitable for large-scale ML workloads.
🧩 Integrations & Real-World Scenarios
Use PySpark in real-world data engineering pipelines.
- PySpark Integrations (Snowflake, Databricks, Hive)
- Processing Semi-Structured Data
- End-to-End PySpark ETL Project
👉 Bridges theory and real-world practice.
🎯 Pyspark Interview Questions & Answers
Master Pyspark concepts with structured, real-world interview questions—covering fundamentals to advanced scenarios.
- Essential PySpark Interview Question & Answer(Explained Through Real-World Stories) – Part 1
- Essential PySpark Interview Question & Answer(Explained Through Real-World Stories) – Part 2
- Essential PySpark Interview Question & Answer(Explained Through Real-World Stories) – Part 3
- Essential PySpark Interview Question & Answer(Explained Through Real-World Stories) – Part 4
- Essential PySpark Interview Question & Answer(Explained Through Real-World Stories) – Part 5
- Essential PySpark Interview Question & Answer(Explained Through Real-World Stories) – Part 6
👉 Ideal for cracking Pyspark interviews at product companies & top MNCs.
🎯 Pyspark Quizzes"
Master Pyspark concepts with structured quizzes—covering fundamentals to Advanced topics.
- PySpark Quiz — Basics
- PySpark Quiz — Intermediate
- PySpark Quiz — Advanced
- PySpark Quiz — Expert (Streaming & ML)
- PySpark Quiz — Expert Level 2
- PySpark Quiz — Structured Streaming & Production Pipelines
- PySpark Quiz — MLlib Advanced & Production Pipelines
👉 Ideal for testing your knowledge and preparing for real-world Pyspark scenarios and top-tier Quizzes.
📌 How to Use This Section
- Follow sections top-down if learning
- Jump directly to Performance & Streaming for interviews
- Use this hub as a daily PySpark reference