PySpark Training

Course Overview

This PySpark course goes beyond basics to give you the deep understanding needed to build, optimise and maintain large-scale Spark applications. From understanding Spark's execution model to writing production-grade streaming jobs.

You'll work with real datasets, build streaming pipelines, tune Spark applications for performance, and integrate Spark with Delta Lake and modern data stacks.

✅ What You Will Learn

✓ Spark Architecture & Execution Model

✓ RDDs, DataFrames & Datasets

✓ Spark SQL & Query Optimisation

✓ Structured Streaming

✓ Spark MLlib

✓ Spark Performance Tuning

✓ Spark + Delta Lake Integration

✓ Spark on AWS EMR & Databricks

Course Curriculum

Module 1: Python & Spark Fundamentals▼

Python for Spark — collections, functions, comprehensions
Spark architecture — Driver, Executors, DAG
Setting up PySpark locally & on cloud
SparkContext & SparkSession

Module 2: RDDs & DataFrames▼

RDD creation, transformations & actions
DataFrame API — read/write, transformations
Spark SQL — views, joins, window functions
Catalyst optimizer & Tungsten engine

Module 3: Structured Streaming▼

Streaming sources — Kafka, files, sockets
Output modes — append, complete, update
Watermarking & late data handling
Stateful streaming operations

Module 4: Performance & Projects▼

Partitioning, caching & broadcast joins
Spark UI & performance debugging
Project 1 – Batch ETL with PySpark
Project 2 – Real-Time Kafka Streaming
Project 3 – PySpark + Delta Lake Pipeline
Project 4 – MLlib Recommendation System

₹15,000 / course

EMI: 2 months @ ₹7,500/month

🎓 Enroll Now 📅 Book Free Demo

✅ 50 hours live training
✅ 4 real-world projects
✅ Spark cluster lab access
✅ Lifetime recording access
✅ Placement support
✅ Course certificate

Course Overview

✅ What You Will Learn

Course Curriculum

Master PySpark from Scratch