PySpark for Data Engineers Full Course 2026 | Basics to Advanced

Video Description

🔥PySpark is the backbone of modern big data and data engineering — but most people only learn enough to “make Spark work,” not enough to use it correctly, efficiently, and at scale. This 6-hour PySpark for Data Engineers masterclass is designed to change that. In this full-length course, you’ll learn PySpark the way real data engineers use it in production — from core Spark fundamentals to advanced transformations, performance tuning, and pipeline-ready patterns. This is not a surface-level Spark tutorial. This is an end-to-end PySpark course focused on real data engineering workflows, not toy examples. Everything is explained clearly, practically, and with intent — so you understand why things work, not just how to write Spark code. No fluff. No shortcuts. Just PySpark done properly. 🚀 What You’ll Learn in This PySpark Masterclass In this complete PySpark for Data Engineers course, you’ll learn: • How Spark works internally (architecture & execution model) • Working with DataFrames, schemas, and Spark SQL • Reading and writing large-scale data (CSV, JSON, Parquet) • Transformations, actions, and lazy evaluation • Handling messy, real-world data in Spark • Joins, aggregations, window functions, and analytics • Performance tuning, partitioning, and optimization • Writing production-ready PySpark pipelines • How PySpark fits into real data engineering systems All concepts are demonstrated with real datasets and practical examples, not classroom-style demos. 🧰 Tech Stack & Tools Used Docker Desktop: https://www.docker.com/products/docker-desktop/ PySpark Apache Spark Python Spark SQL Parquet / CSV / JSON VS Code: https://code.visualstudio.com/ 📊 Course Highlights • Full PySpark for Data Engineers course in one video (6 hours) • Beginner-friendly explanations with real-world depth • Practical, pipeline-oriented examples • Clear focus on performance and scalability • Designed for interviews, real projects, and job readiness • Strong foundation for Airflow, Databricks, and big data roles 📘 Structured Study Material: https://1drv.ms/w/c/528673ccd24bac3c/IQAySK5HXXhLS793-2VNwBsbAfqqGwHtLEtdCUMD05ktRBw?e=hJqrys 📂 Resources & Code GitHub Repository: https://github.com/Jay61616/pyspark-masterclass Python Masterclass: https://youtu.be/XMmfJFS_MFQ 🕒 Timestamps PHASE 0 — FOUNDATIONS 0:32 — 0.1 Spark Problem Statement 7:52 — 0.2 Batch vs Distributed Batch 13:03 — 0.3 Spark vs MapReduce PHASE 1 — SPARK ARCHITECTURE & EXECUTION MODEL 15:54 — 1.1 Spark Architecture 25:19 — 1.2 Jobs, Stages & Tasks 1:12:41 — 1.3 Lazy Evaluation 1:36:21 — 1.4 Spark DAG PHASE 2 — SPARK SETUP USING DOCKER 1:54:26 — 2.1 Why Docker 1:58:25 — 2.2 Spark Setup PHASE 3 — DATA INGESTION WITH PYSPARK 2:17:29 — 3.1 Data Sources & Reader API 2:45:29 — 3.2 CSV vs JSON vs Parquet 3:13:58 — 3.3 Schema Inference vs Explicit 3:35:03 — 3.4 StructType vs DDL Schema PHASE 4 — DATAFRAME TRANSFORMATIONS 3:53:46 — 4.1 Narrow vs Wide Ops 4:03:59 — 4.2 Basic Transformations 4:20:42 — 4.3 Actions & Execution PHASE 5 — INTERMEDIATE TRANSFORMATIONS & SHUFFLES 4:25:15 — 5.1 groupBy & agg Risks 4:30:45 — 5.2 Spark Join Strategies 4:47:52 — 5.3 repartition vs coalesce PHASE 6 — ADVANCED FUNCTIONS & ANALYTICS 5:06:37 — 6.1 Window Functions 5:24:22 — 6.2 Data Skew Handling PHASE 7 — UDFS & PERFORMANCE TRAPS 5:37:59 — 7.1 UDF Internals 5:47:23 — 7.2 When to Use UDFs PHASE 8 — DATA WRITING & STORAGE 5:49:14 — 8.1 Data Writer API 5:58:02 — 8.2 Write Modes & Failures PHASE 9 — TABLES, METASTORE & SQL 6:11:53 — 9.1 Managed vs External Tables 6:22:18 — 9.2 Spark SQL Execution PHASE 10 — SPARK STREAMING 6:32:30 — 10.1 Structured Streaming Model 📣 Let’s Connect LinkedIn: https://linkedin.com/in/jayachandrakadiveti GitHub: https://github.com/Jay61616 Website: https://jay.unaux.com 🎯 Hashtags #PySpark #PySparkForDataEngineering #DataEngineering #BigData #ApacheSpark #SparkSQL #ETLPipelines #DataPipelines #Databricks #AnalyticsEngineering