Overview

By completing this course, learners will be able to identify Big Data challenges, explain Hadoop’s architecture, configure HDFS for distributed storage, execute MapReduce programs, and apply advanced cluster management techniques. Participants will also develop the ability to validate system health, implement fault tolerance, and integrate Java applications with Hadoop for real-world use cases. This comprehensive program takes a structured approach by starting with Big Data foundations and gradually progressing to advanced Hadoop operations. Learners will gain both theoretical knowledge and practical skills through topics such as write/read anatomy, Word Count implementation, Hadoop administration, shell commands, rack awareness, checkpointing, safe mode, and DataNode commissioning. What makes this course unique is its integration of three training tracks—Big Data Hadoop, Hadoop Architecture & HDFS, and Hadoop on Cloudera—into a single, well-sequenced learning journey. Unlike standalone tutorials, this course blends fundamentals with hands-on administration and system maintenance, preparing learners for both development and operational roles. By the end of the course, learners will be equipped with industry-ready skills to manage Hadoop clusters, process massive datasets, and ensure system reliability in enterprise environments.

Syllabus

Big Data Foundations & Hadoop Basics

This module introduces learners to the fundamentals of Big Data and the Hadoop ecosystem. It covers the challenges posed by massive datasets, explains the flow of data through HDFS write and read processes, and demonstrates MapReduce basics with the Word Count example to establish a solid foundation.

Hands-On with Hadoop Fundamentals

This module dives deeper into Hadoop fundamentals by connecting Big Data concepts to practical scenarios. Learners explore the MapReduce model, gain familiarity with the Cloudera distribution, and use interfaces like HDFS Web UI, HUE, and shell commands for hands-on understanding.

Applied HDFS and Java Integration

This module emphasizes hands-on management of HDFS and integrates Java for real-world applications. Learners practice advanced shell operations, understand the responsibilities of Hadoop administrators, and explore critical HDFS components like FS Image and Secondary NameNode.

Mastering Hadoop Architecture

This module focuses on Hadoop architecture and system setup. It guides learners through HDFS architecture, block placement policy, installation processes, and critical cluster configuration steps such as hostnames, gateways, SSH, and password-less authentication.

Configurations and Cluster Management

This module builds expertise in managing Hadoop clusters through detailed configuration tasks. It covers Hadoop’s core files, slave file setup, rack awareness, DFS admin tools, and essential commands for executing and managing MapReduce jobs.

Advanced HDFS Operations & Cluster Maintenance

This module equips learners with advanced operational skills for managing and maintaining Hadoop clusters. It covers HDFS file operations, checkpointing, safe and maintenance modes, DataNode commissioning, validation, and storage planning considerations.