Course Details
This Spark and Hadoop for Big Data Analytics course introduces learners to the foundational components of modern Big Data systems. The course is designed for anyone looking to understand distributed computing using Hadoop and Apache Spark. Through interactive lessons and hands-on labs, learners will build real-world skills in big data storage, processing, and analytics.
Tools & Technologies You’ll Master:
- Hadoop – Distributed storage (HDFS) and batch processing (MapReduce)
- Apache Spark – Fast, in-memory data processing engine for big data workloads
- Spark SQL – Query structured data with SQL support
- Spark Streaming – Handle real-time streaming data
- Hive & HBase – Querying and NoSQL storage in the Hadoop ecosystem
- Kubernetes – Container orchestration for Spark deployment
You’ll gain hands-on experience through labs and projects simulating real-world big data scenarios including recommendation systems, log analysis, and fraud detection.
Course Content
Requirements
This course is designed to be beginner-friendly and is open to anyone, regardless of prior experience or background.
Course Description
This Spark and Hadoop for Big Data Analytics course offers a comprehensive understanding of the Big Data ecosystem and its real-world applications. Learners begin by exploring the fundamentals of Big Data, including its characteristics and the need for distributed computing. The course dives into the Hadoop architecture—covering HDFS, YARN, and MapReduce—and transitions to Apache Spark, where learners explore RDDs, DataFrames, SparkSQL, and Spark Streaming. Real-world analytics use cases and hands-on projects round out the experience, giving learners the confidence to process and analyze massive datasets with modern tools.
Course Content
This course is divided into 7 modules with quizzes, labs, and a capstone project:
- Introduction to Big Data & Objectives
- Parallel Processing & Scalability
- Big Data Tools and Ecosystem
- Big Data Use Cases
- Practice Quiz & Graded Quiz (9 Questions)
- Hadoop Core Concepts: HDFS, YARN
- MapReduce Programming Model
- Intro to Hive and HBase
- Hands-on Labs: Hive, MapReduce
- Practice Quiz & Graded Quiz (8 Questions)
- Why Apache Spark?
- Functional Programming Basics
- RDDs and Parallel Programming
- Hands-on Lab: Spark with Python
- Practice Quiz & Graded Quiz (7 Questions)
- Working with DataFrames & Datasets
- Catalyst Optimizer & Tungsten Engine
- ETL Pipelines using Spark SQL
- Hands-on Labs: DataFrames & Spark SQL
- Practice Quiz & Graded Quiz (7 Questions)
- Understanding Spark Architecture & Cluster Modes
- Submitting Spark Applications
- Spark on IBM Cloud & Kubernetes
- Hands-on Labs: Submit & Run Apps
- Practice Quiz & Graded Quiz (7 Questions)
- Using Spark UI for Monitoring
- Debugging Spark Applications
- Memory and CPU Tuning Techniques
- Hands-on Lab: Performance Tuning
- Practice Quiz & Graded Quiz (7 Questions)
- Final Project: Data Processing with Spark
- Final Exam (20 Questions)
- Course Rating and Wrap-up
- Certificate & Badge
