This Hadoop Fundamentals training is designed to help participants understand and use the Hadoop ecosystem for distributed storage and big data processing. The course covers Hadoop architecture, HDFS, YARN, MapReduce fundamentals, and integration with modern data processing tools. Participants will gain hands-on experience to store, manage, and process large datasets using Hadoop for enterprise big data environments.

• Understand the architecture, ecosystem, and core components of Apache Hadoop for big data processing.
• Work with HDFS for distributed storage, file management, and large-scale data handling.
• Understand MapReduce concepts and execute distributed data processing jobs.
• Configure and manage Hadoop clusters, resource allocation, and job scheduling.
• Integrate Hadoop with ecosystem tools for data ingestion, processing, and analytics.
• Build scalable big data solutions using Hadoop best practices and enterprise workflows.

 • Big data developers and engineers
 • Data engineers and analytics engineers
 • IT professionals supporting big data platforms
 • Data analysts working with large datasets
 • Professionals starting with Hadoop ecosystem

 • Basic understanding of data concepts and databases
 • Familiarity with Linux commands is helpful
 • Interest in big data and distributed systems

 • Understanding Hadoop architecture and components
 • Working with HDFS for distributed storage
 • Managing data using HDFS commands
 • Understanding YARN resource management
 • Running basic MapReduce jobs
 • Integrating Hadoop with analytics tools
 • Managing Hadoop clusters basics
 • Applying Hadoop best practices

By the end of this training, participants will be able to understand and operate Hadoop for distributed storage and big data processing. Learners will gain strong fundamentals in HDFS, YARN, and MapReduce, enabling them to support enterprise big data environments and integrate Hadoop with modern analytics tools.

Introduction to Hadoop & Big Data Concepts
 • What is Hadoop and where it is used
 • Big data challenges and Hadoop solutions
 • Hadoop ecosystem overview
 • Hadoop cluster architecture

HDFS Fundamentals
 • HDFS architecture and components
 • NameNode and DataNode roles
 • HDFS file operations
 • Data replication and fault tolerance

Hadoop Installation & Cluster Setup Basics
 • Hadoop installation overview
 • Pseudo-distributed mode setup
 • Configuration files basics
 • Verifying Hadoop setup

YARN & Resource Management
 • YARN architecture
 • ResourceManager and NodeManager
 • Scheduling and queues
 • Monitoring cluster resources

MapReduce Fundamentals
 • MapReduce programming model
 • Writing basic MapReduce jobs
 • Input and output formats
 • Running and monitoring MapReduce jobs

Data Ingestion Tools Overview
 • Introduction to Sqoop
 • Introduction to Flume
 • Data ingestion use cases
 • Integrating external data sources

Hadoop Ecosystem Components
 • Hive for SQL on Hadoop
 • Pig basics
 • HBase overview
 • Oozie workflow scheduler basics

Integration with Spark & Modern Tools
 • Using Spark on Hadoop
 • Hadoop and lakehouse integration concepts
 • Migrating workloads from MapReduce to Spark
 • Modern big data architecture overview

Cluster Administration & Monitoring Basics
 • Hadoop cluster monitoring
 • Log management
 • Basic troubleshooting
 • Backup and recovery basics

Security & Governance Overview
 • Authentication and authorization basics
 • Kerberos overview
 • Data governance concepts
 • Compliance considerations

Hadoop Project Workshop & Best Practices
 • Building a complete Hadoop data processing workflow
 • Managing data in HDFS
 • Running analytics jobs
 • Final project review and optimization

• Hadoop Setup & Architecture Assessment
• HDFS & Distributed Storage Assessment
• MapReduce Programming Assessment
• YARN & Resource Management Assessment
• Big Data Processing Mini Project Assessment

Participants will be evaluated through hands-on Hadoop labs, practical HDFS and MapReduce exercises, instructor-led reviews, and a final project-based assessment focused on building and managing a Hadoop data processing workflow.

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Upon successful completion of the training, participants will receive an AcadNXT Certificate of Completion for Hadoop Fundamentals. This digital, verifiable certification validates practical Hadoop distributed storage, big data processing, and Hadoop ecosystem fundamentals and can be shared on LinkedIn and included in professional profiles to enhance big data and data engineering career credibility.