Master Distributed Computing and Big Data Processing
Duration
8 Weeks
Level
Advanced
Price
Rs.18,000
Batch Size
20 Students
Course Overview
Apache Spark is the industry standard for big data processing. PySpark
is the Python API for Spark, enabling you to write scalable
distributed data processing applications. This advanced course covers
Spark's architecture, RDDs, DataFrames, and real-world big data
scenarios.
Perfect for data engineers and data scientists who need to process
massive datasets and build production-ready data pipelines.
Course Syllabus
Module 1: Big Data Fundamentals & Spark Basics
Introduction to Big Data and Spark
Spark architecture and components
Installation and setup
Spark ecosystem overview
Module 2: RDDs and Core Concepts
Resilient Distributed Datasets (RDDs)
Creating and transforming RDDs
RDD operations and lazy evaluation
Performance optimization with RDDs
Module 3: DataFrames and SQL
Creating DataFrames
DataFrame operations and transformations
Spark SQL and queries
Working with different data sources
Module 4: Advanced Data Processing
Window functions and complex aggregations
User-defined functions (UDFs)
Data partitioning and bucketing
Join operations at scale
Module 5: Machine Learning with MLlib
ML fundamentals with Spark MLlib
Classification and regression
Clustering algorithms
Feature engineering at scale
Module 6: Production & Performance Tuning
Debugging and monitoring Spark applications
Performance tuning techniques
Production deployment strategies
Handling real-world challenges
What You'll Learn
Understand Spark architecture and distributed computing
Work with RDDs and DataFrames at scale
Write efficient PySpark applications
Query big data using Spark SQL
Build machine learning pipelines
Optimize and debug Spark applications
Deploy Spark in production environments
Ready to Master PySpark?
Begin your journey into big data processing today!