Big Data & Cloud Computing - Complete Course Guide

HOW TO START THIS COURSE

Welcome! New to Big Data?

1. Read Course Overview → Course Outline (5 minutes)
2. Check Prerequisites → See Prerequisites below
3. Follow Learning Path → See Step-by-Step Guide below
4. Budget Time → Allow 4-6 weeks for complete course (2-3 hours/day)

Prerequisites

Required Before Starting:

• Basic Programming Knowledge (any language - Python/Java preferred)
• Command Line Basics (navigate directories, run commands)
• Computer with 8GB+ RAM for installations
• Stable Internet Connection for downloads

System Requirements:

• Linux/Mac: Preferred (native support)
• Windows: Use WSL2 or Docker for best experience
• Available Storage: 10GB+ free space
• Admin Access: Required for installations

Nice to Have (but not required):

• Basic understanding of databases
• Familiarity with distributed systems concepts

STEP-BY-STEP LEARNING PATH

WEEK 1: Foundation (Days 1-7)

Day 1-2: Core Concepts (2-3 hours)

• What you'll learn: Big Data definition, 5 V's, real-world examples
• Time: 1 hour
• Success criteria: Can explain what Big Data is in your own words

• What you'll learn: Overview of all technologies (Hadoop, Spark, etc.)
• Time: 1-2 hours
• Success criteria: Understand how different tools connect

Day 3-4: Engineering Basics (2-3 hours)

• What you'll learn: ETL, data pipelines, data engineering roles
• Time: 2 hours
• Success criteria: Understand data engineering vs data science

• What you'll learn: Essential background knowledge
• Time: 1 hour

Day 5-7: System Skills (3-4 hours)

• What you'll learn: Essential Linux commands for Big Data
• Time: 2-3 hours
• Success criteria: Comfortable with terminal, file operations
• CHECKPOINT: Take notes, practice commands

WEEK 2: Setup & Installation (Days 8-14)

Day 8-10: Choose Your Installation Path (3-5 hours)

1. Hadoop Installation (for Mac) - 2 hours
2. S3 + Spark (for MAC) - 1-2 hours

1. Hadoop Installation - 2-3 hours

1. Hadoop Via Docker - 1-2 hours

Day 11-14: Hadoop Fundamentals (4-6 hours)

• Prerequisites: Completed installation above
• What you'll learn: Hadoop components, architecture
• Time: 2 hours

• What you'll learn: How Hadoop is organized
• Time: 1 hour

• What you'll learn: Distributed file system concepts
• Time: 1-2 hours
• CHECKPOINT: Understand the difference between local and distributed storage

WEEK 3: Hadoop Mastery (Days 15-21)

Day 15-17: HDFS Hands-On (3-4 hours)

• Prerequisites: HDFS Overview completed, Hadoop installed
• What you'll learn: Practical HDFS operations
• Time: 2-3 hours
• Success criteria: Can navigate HDFS, upload/download files

Day 18-21: MapReduce Deep Dive (5-7 hours)

• What you'll learn: Distributed processing paradigm
• Time: 2-3 hours

• What you'll learn: Advanced MapReduce concepts
• Time: 1-2 hours

• What you'll learn: Hands-on MapReduce programming
• Time: 2-3 hours
• CHECKPOINT: Successfully run a MapReduce job

WEEK 4: Spark & Advanced Topics (Days 22-28)

Day 22-24: Spark Foundation (4-5 hours)

• Prerequisites: Hadoop working, comfortable with command line
• Time: 2 hours

• What you'll learn: Spark vs Hadoop, RDDs, DataFrames
• Time: 2-3 hours

Day 25-26: Spark Practical (3-4 hours)

• Prerequisites: Spark setup complete
• What you'll learn: Real-world Spark applications
• Time: 3-4 hours
• Success criteria: Build and run a Spark application

Day 27-28: Monitoring & Cloud (3-4 hours)

• What you'll learn: Monitoring setup for Big Data systems
• Time: 1-2 hours

• What you'll learn: Visualizing Spark metrics
• Time: 1-2 hours

• What you'll learn: Cloud storage integration
• Time: 1-2 hours

FINAL VALIDATION: Practice & Assessment

Test Your Knowledge (2-3 hours)

• Purpose: Test fundamental concepts
• Time: 1-1.5 hours

• Purpose: Test advanced concepts
• Time: 1-1.5 hours

HELP & TROUBLESHOOTING

Stuck? Check These First:

1. Installation Issues: Revisit Scripts/readme for automated setup
2. Command Errors: Double-check Linux Basics
3. Concept Confusion: Re-read Pre Topics

Getting Help:

• Re-read prerequisites for each section
• Practice basic commands before advanced topics
• Take breaks - Big Data concepts need time to sink in

PROGRESS TRACKING

Completion Checklist:

• Week 1: Foundation (Can explain Big Data concepts)
• Week 2: Setup Complete (Hadoop running successfully)
• Week 3: Hadoop Expert (Can use HDFS and MapReduce)
• Week 4: Spark Master (Built Spark applications)
• Final: Validated (Passed practice questions)

Key Milestones:

QUICK REFERENCE

All Course Materials by Section

Fundamentals

• What is Big Data
• Big Data Ecosystem
• Data Engineering Fundamentals
• Pre Topics
• Linux Basics

Installation & Setup

• Hadoop Installation
• Hadoop Installation (for Mac)
• Hadoop Via Docker
• S3 + Spark (for MAC)

Hadoop Ecosystem

• Hadoop Ecosystem
• Hadoop Structure
• HDFS Overview
• Hadoop Essential Commands (HDFS)
• MapReduce Overview
• MapReduce Things
• MapReduce Mini Practice

Spark

• Spark Setup
• Spark Overview
• Spark Something Practical

Monitoring & Visualization

• Grafana Installation
• Spark + Grafana Integration

Cloud Computing

• AWS Setup + S3

Practice & Assessment

• Practice Questions Set 1
• Practice Questions Set 2

Scripts & Automation

• Scripts/readme

SUCCESS TIPS

Best Practices:

1. Don't Rush: Each week builds on previous knowledge
2. Practice Daily: 2-3 hours of focused study
3. Take Notes: Document your learning journey
4. Ask Questions: Re-read if concepts aren't clear
5. Test Installations: Don't proceed with broken setups

Common Mistakes to Avoid:

• Skipping fundamental concepts
• Rushing through installations
• Not practicing commands
• Ignoring error messages
• Jumping ahead without mastering basics

Study Strategy:

• Morning: Theory and concepts
• Afternoon: Hands-on practice
• Evening: Review and note-taking

FOLDER STRUCTURE