Course Overview Of Hadoop Online Training : 

you ready to dive into the world of Big Data? Our Hadoop Online Training is designed to equip you with the skills needed to process, manage, and analyze massive datasets using the most in-demand open-source
framework — Apache Hadoop.

Are Whether you’re a student, developer, or IT professional, this course offers end-to-end Hadoop training with hands-on labs, real-time projects, and certification guidance.

What You’ll Learn in Hadoop Online Training

✅ Introduction to Big Data and Hadoop
✅ Hadoop Distributed File System (HDFS)
✅ MapReduce Programming Model
✅ Hive, Pig, Sqoop, Flume & HBase
✅ YARN – Yet Another Resource Negotiator
✅ Data ingestion and transformation
✅ Hands-on project with real-world scenarios
✅ Hadoop Certification (Cloudera/Hortonworks) preparation

            EnrollNow And Become Hadoop Expert

Prerequisites for Hadoop Training :

1. Basic Knowledge of Programming

Familiarity with programming languages like Java, Python, or SQL is helpful — especially for writing  MapReduce code or scripts.

2. Understanding of Linux/Unix

Basic knowledge of Linux commands is recommended, as Hadoop runs on Linux environments.

3. Basic Database & SQL Concepts

Understanding how databases work and writing basic SQL queries will help when working with Hive and Pig.

4. Concept of Distributed Systems (Optional but Useful)

 Having an idea about distributed computing or client-server models helps grasp Hadoop’s architecture   faster.

5. No Big Data Experience Needed

 You don’t need prior Big Data experience — we start from the basics and gradually move to advanced   topics.

Why Choose Our Hadoop Online Training?

Certified & Experienced Trainers:
Get mentored by real-world Big Data professionals.

100% Practical Training:
Hands-on projects that simulate real-time Hadoop environments.

Certification Assistance:
We guide you step-by-step toward clearing industry-recognized Hadoop certifications.

Career Support & Interview Prep:
Resume building, mock interviews, and job referrals.

Course Content Hadoop:

Module 1: Introduction to Big Data & Hadoop

• What is Big Data? Characteristics and challenges
• Traditional systems vs. Big Data systems
• Introduction to Hadoop ecosystem
• Benefits and use cases of Hadoop
• Hadoop architecture overview

Module 2: Hadoop Distributed File System (HDFS)

• HDFS architecture and block storage
• NameNode and DataNode roles
• HDFS commands and operations
• Replication and fault tolerance
• File read/write process in HDFS

 Module 3: MapReduce Programming

• What is MapReduce?
• MapReduce job lifecycle
• Writing your first MapReduce program (Java/Python)
• InputSplit, RecordReader, Mapper, Reducer
• Combiner and Partitioner
• Optimization and performance tuning

 Module 4: Hive – Data Warehousing on Hadoop

• Introduction to Apache Hive
• Hive architecture & components
• HiveQL vs SQL
• Creating tables, partitions, and buckets
• Data loading and querying
• UDFs and Joins in Hive

 Module 5: Apache Pig

• What is Pig? Why use Pig?
• Pig Latin syntax and operators
• Writing scripts with Pig
• Pig vs Hive
• UDFs in Pig

 Module 6: Data Ingestion Tools (Sqoop & Flume)

• Introduction to Sqoop
• Import/export from RDBMS to HDFS
• Sqoop commands and connectors
• Introduction to Flume
• Architecture and agents
• Collecting real-time streaming data

 Module 7: NoSQL with HBase

• Overview of NoSQL and HBase
• HBase architecture and components
• Column families, tables, and regions
• HBase vs RDBMS
• CRUD operations with HBase Shell and Java API

 Module 8: YARN (Yet Another Resource Negotiator)

• What is YARN?
• YARN architecture and components
• ResourceManager and NodeManager
• Scheduling and managing jobs on Hadoop

 Module 9: Introduction to Apache Spark (BONUS Module)

• Basics of Spark architecture
• Spark vs MapReduce
• RDDs and transformations
• Introduction to PySpark

 Module 10: Hadoop Cluster Setup & Administration

• Installing Hadoop in pseudo-distributed mode
• Configuring HDFS and MapReduce
• Multi-node cluster setup (local/cloud)