Live Online Apache Hadoop Course for Data Engineering

Live Course Module: Apache Hadoop Course for Data Engineering

Total Duration: 40 Hours (5 Weeks)

---

Week 1: Big Data Fundamentals and Hadoop Ecosystem Overview

Total Time: 8 hours

1. Introduction to Big Data (1 hr)

   • What is Big Data?

   • 3Vs of Big Data (Volume, Velocity, Variety)

   • Role of Data Engineering

2. Overview of Hadoop Ecosystem (1.5 hrs)

   • Hadoop history and evolution

   • Core components: HDFS, YARN, MapReduce

   • Ecosystem tools: Hive, Pig, Sqoop, Flume, Oozie

3. Hadoop Architecture (2 hrs)

   • Namenode, Datanode, Secondary Namenode

   • Hadoop Cluster topology and setup

   • Block storage mechanism

4. Setting up Hadoop Environment (1.5 hrs – Lab)

   • Single-node cluster setup using local VM or Docker

   • Basic Hadoop commands

5. Hands-On & Assignment (2 hrs)

   • Explore HDFS shell commands

   • Upload and retrieve files from HDFS

   • Assignment: Simulate HDFS data flow

---

Week 2: Hadoop Distributed File System (HDFS) and Data Management

Total Time: 8 hours

1. HDFS Deep Dive (1.5 hrs)

   • Architecture and components

   • Read/Write operations

   • Fault tolerance and replication

2. HDFS Commands and API (2 hrs – Lab)

   • File operations with CLI and Java API

   • Permissions, quotas, and configuration

3. Data Ingestion into HDFS (1.5 hrs)

   • Tools: Flume, Sqoop basics

   • Importing data from relational sources

4. Hands-On & Assignment (3 hrs)

   • Load data using Sqoop and Flume

   • Validate replication and data recovery

   • Assignment: Design a data ingestion workflow

---

Week 3: MapReduce for Data Engineering

Total Time: 8 hours

1. Introduction to MapReduce (1.5 hrs)

   • Programming model: Mapper, Reducer, Combiner

   • InputFormat and OutputFormat

2. Developing MapReduce Programs (2 hrs – Lab)

   • Writing MapReduce jobs in Java and Python

   • Running jobs on a Hadoop cluster

3. Advanced MapReduce Concepts (2 hrs)

   • Custom InputFormat and Partitioner

   • Counters, DistributedCache, and job optimization

4. Hands-On & Assignment (2.5 hrs)

   • WordCount and Log Analysis projects

   • Assignment: Build and optimize a MapReduce ETL job

---

Week 4: Hive, Pig, and Data Processing Tools

Total Time: 8 hours

1. Apache Hive for Data Warehousing (2 hrs)

   • Hive architecture and metastore

   • Creating databases, tables, and partitions

   • Writing HiveQL queries

2. Apache Pig for Data Flow Processing (1.5 hrs)

   • Pig architecture and execution modes

   • Pig Latin scripts for data transformation

3. Integrating Hive and Pig with HDFS (1.5 hrs – Lab)

   • Loading HDFS data into Hive and Pig

   • Using SerDe and UDFs

4. Hands-On & Assignment (3 hrs)

   • ETL pipeline using Hive and Pig

   • Assignment: Transform raw log data into analytics-ready tables

---

Week 5: Hadoop Ecosystem, Workflow, and Project Implementation

Total Time: 8 hours

1. Workflow Management and Orchestration (1.5 hrs)

   • Introduction to Oozie

   • Building workflows for Hadoop jobs

2. Hadoop Integration with Other Tools (1.5 hrs)

   • Connecting Hadoop with Spark, Kafka, and HBase

   • Hadoop on Cloud: AWS EMR, GCP Dataproc

3. Performance Tuning and Troubleshooting (2 hrs)

   • Cluster monitoring and resource optimization

   • Log analysis and debugging

4. Capstone Project (3 hrs)

   • Build a complete data engineering pipeline using Hadoop tools

   • Ingest → Process → Store → Analyze

   • Example: Retail or IoT data pipeline

---

🧩 Final Deliverables

• Mini Projects: 3 (HDFS, MapReduce, Hive)

• Capstone Project: 1 End-to-End Data Engineering Workflow

• Assessments: Weekly quizzes + final project review

• Tools Covered: HDFS, YARN, MapReduce, Hive, Pig, Sqoop, Flume, Oozie