Hadoop Essentials: HDFS & MapReduce Simplified

Categories: BIG DATA
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About Course

Hadoop Essentials: HDFS & MapReduce Simplified is an entry-level course designed to demystify the core building blocks of Apache Hadoop  HDFS and MapReduce. You’ll learn how large datasets are split, stored, and processed across multiple machines using distributed computing principles.

The course includes real-world analogies, architecture walkthroughs, and hands-on exercises to help you understand the underlying concepts without complex programming. Whether you’re an aspiring data engineer, analyst, or cloud professional, this course sets the stage for mastering the broader Big Data ecosystem.

What Will You Learn?

  • Introduction to Apache Hadoop and its role in Big Data
  • How HDFS stores massive datasets across a cluster
  • Understanding NameNodes, DataNodes, and data blocks
  • How MapReduce performs distributed batch processing
  • Writing and running simple MapReduce programs
  • The importance of fault tolerance and replication in HDFS
  • Real-world use cases of Hadoop in companies like Facebook, Yahoo, and LinkedIn
  • Basics of setting up a local Hadoop environment for practice

Course Content

Module 1: Hadoop & Big Data Overview
What is Hadoop? Evolution from traditional to distributed systems

Module 2: HDFS Architecture Deep Dive
Role of NameNode and DataNode Block storage, replication factor, and fault tolerance HDFS commands and file system navigation

Module 3: MapReduce Basics
Map and Reduce functions explained with examples JobTracker, TaskTracker (legacy) and YARN introduction Writing a basic WordCount MapReduce job

Module 4: Hands-on and Simulation
Run sample jobs on a pseudo-distributed setup Use real-world datasets in sample MapReduce jobs Troubleshooting and interpreting logs

Module 5: Case Studies & Career Insights
Industry applications of Hadoop Where Hadoop fits in the modern Big Data architecture Transition paths to Spark and other modern tools

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