Introduction to Big Data and Hadoop

Module 1: Understanding Big Data

⏳ ~1 hour

• Topics: Big Data definition, characteristics (3/4 V’s), data types.

• Hands‑on: Reflect on real-world examples and quiz circuits for foundational understanding.

Module 2: Hadoop Architecture

⏳ ~2 hours

• Topics: HDFS structure (NameNode/DataNode), YARN resource management, replication, fault tolerance.

• Hands‑on: Navigate cluster architecture and configure fault tolerance scenarios.

Module 3: MapReduce Basics

⏳ ~2 hours

• Topics: MapReduce cycle, job lifecycle, shuffle and sort, and distributed computation concepts.

• Hands‑on: Build MapReduce logic and analyze with quizzes.

Module 4: Working with HDFS

⏳ ~1 hour

• Topics: Filesystem commands, data storage, block replication and data locality.

• Hands‑on: Execute HDFS commands and experiment with replication.

Module 5: Interacting with Hadoop Clusters

⏳ ~1.5 hours

• Topics: Cluster setup, configuration, hands-on terminal interaction.

• Hands‑on: Connect to live clusters, traverse directories, and analyze configs.

Module 6: Spark Overview

⏳ ~1 hour

• Topics: Spark basics, RDDs/DataFrames, Spark vs MapReduce, cluster integration.

• Hands‑on: Run simple Spark jobs to consolidate learning.

Module 7: Ecosystem Tools Introduction

⏳ ~1 hour

• Topics: Overview of Hive, Pig, HBase, Flume, Sqoop and their use with Hadoop.

• Hands‑on: Quiz-based walkthrough using sample queries.

Module 8: Best Practices & Review

⏳ ~30 minutes

• Topics: Fault tolerance strategies, performance tuning, real-world use cases.

• Hands‑on: Final summary quiz covering all modules.

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