Live Online Apache Hadoop Course for Data Analytics

Live Course Module: Apache Hadoop Course for Data Analytics

Total Duration: 40 Hours (6 Weeks)

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Week 1: Introduction to Big Data and Hadoop Ecosystem (6 Hours)

1. Overview of Big Data and its role in analytics

2. Limitations of traditional data systems and need for Hadoop

3. Understanding Hadoop Ecosystem and its components

4. Hadoop architecture – HDFS, YARN, and MapReduce overview

5. Hadoop cluster setup and environment configuration

6. Hands-on: Installing Hadoop and exploring HDFS commands

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Week 2: HDFS and Data Management in Hadoop (6 Hours)

1. Hadoop Distributed File System (HDFS) architecture

2. Data blocks, replication, and fault tolerance mechanism

3. HDFS operations – read, write, copy, delete, move data

4. Using Hadoop shell commands and Web UI for monitoring

5. Data ingestion tools – Sqoop, Flume, and File-based ingestion

6. Hands-on: Uploading, retrieving, and managing data in HDFS

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Week 3: MapReduce and Data Processing Framework (6 Hours)

1. Introduction to MapReduce programming model

2. Anatomy of MapReduce – Mapper, Reducer, and Combiner

3. Writing and executing MapReduce jobs using Java / Python

4. Understanding input/output formats and partitioners

5. Performance tuning and optimization techniques in MapReduce

6. Hands-on: Word count, log analysis, and summarization examples

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Week 4: Hadoop Ecosystem Tools for Data Analytics (6 Hours)

1. Introduction to Hive – data warehousing on Hadoop

2. HiveQL for querying and analyzing large datasets

3. Pig for data transformation and pipeline scripting

4. Comparison of Pig, Hive, and MapReduce use cases

5. Using HCatalog for metadata management

6. Hands-on: Data analysis using Hive and Pig scripts

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Week 5: Advanced Data Analytics with Hadoop Integration (6 Hours)

1. Integrating Hadoop with Apache Spark for faster analytics

2. Using Hadoop with NoSQL databases (HBase, Cassandra)

3. Workflow scheduling and orchestration using Oozie

4. Data ingestion from real-time sources using Kafka and Flume

5. Security and authentication in Hadoop (Kerberos, Ranger)

6. Hands-on: Building an ETL pipeline using Sqoop, Hive, and Spark

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Week 6: Hadoop Administration, Optimization & Capstone Project (6 Hours)

1. Hadoop cluster monitoring and resource management

2. Scaling and performance optimization techniques

3. Data governance and fault recovery strategies

4. Deploying Hadoop on cloud (AWS EMR, Azure HDInsight, GCP Dataproc)

5. Capstone Project: End-to-End Big Data Analytics Pipeline using Hadoop

6. Final review, assessment, and certification presentation

Mini Project Ideas (Week 6 Hands-on)

Learners will implement an end-to-end data pipeline:

1. Project 1: Log Analysis using Hadoop & Hive

2. Project 2: Retail Sales Data Processing using Pig and HBase

3. Project 3: Social Media Data Ingestion using Flume and Visualization in Power BI

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🧑‍🏫 Teaching Methodology

• Live Demonstrations for setup and execution

• Hands-on Labs for each major concept

• Assignments after each module

• Interactive Q&A Sessions

• Mini Project Presentation in final week

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🏁 Final Deliverables

• Certificate of Completion

• Completed Mini Project

• Practical experience with Hadoop ecosystem for real-world analytics

Course Outcome:

By the end of the course, learners will be able to:

• Understand Hadoop architecture and ecosystem components.

• Install and configure Hadoop in pseudo and cluster modes.

• Process and analyze large datasets using HDFS and MapReduce.

• Work with Hive, Pig, and HBase for analytical data processing.

• Integrate Hadoop with modern data analytics tools.