Overview of Course

Learn Hadoop, the open-source software framework, with our Hadoop Training course. Gain in-depth knowledge of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, HBase, and Spark. Our course is designed to teach you how to use Hadoop to handle big data and perform data analytics.

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Course Highlights

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Comprehensive course covering Hadoop and its ecosystem

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Hands-on experience with Hadoop cluster setup and data processing

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Real-world case studies and projects for practical experience<br /><br />

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    Real-world Projects

    We work with experts to curate real business scenarios as training projects

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Skills You’ll Learn


Understand the fundamentals of Hadoop and its ecosystem


Set up a Hadoop cluster and configure HDFS and MapReduce


Perform data processing using Pig and Hive


Use HBase and Spark for real-time data processing


Analyze big data and extract insights using Hadoop

Training Options

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1-on-1 Training

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  • Option Item Access to live online classes
  • Option Item Flexible schedule including weekends
  • Option Item Hands-on exercises with virtual labs
  • Option Item Session recordings and learning courseware included
  • Option Item 24X7 learner support and assistance
  • Option Item Book a free demo before you commit!
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Corporate Training

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  • Option Item Everything in 1-on-1 Training plus
  • Option Item Custom Curriculum
  • Option Item Extended access to virtual labs
  • Option Item Detailed reporting of every candidate
  • Option Item Projects and assessments
  • Option Item Consulting Support
  • Option Item Training aligned to business outcomes
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Course Reviews


  • Introduction to Big Data
  • Limitations and Solutions of existing Data Analytics Architecture
  • Introduction to Hadoop
  • Hadoop Features
  • Hadoop Ecosystem
  • Hadoop 2.x core components
  • Hadoop Storage: HDFS
  • Hadoop Processing: MapReduce Framework
  • Hadoop Different Distributions. 

  • YARN (Yet another Resource Negotiator) – Next Gen.
  • Map Reduce
  • What is YARN?
  • Difference between Map Reduce & amp; YARN
  • YARN Architecture
  • Resource Manager Application Master Node Manager.

  • Hadoop 2.x Cluster Architecture - Federation and High Availability
  • A Typical Production Hadoop Cluster
  • Hadoop Cluster Modes
  • Common Hadoop Shell Commands
  • Single node cluster and Multi node cluster set up Hadoop Administration

  • MapReduce Use Cases
  • Why MapReduce 
  • Hadoop 2.x MapReduce Architecture
  • Hadoop 2.x MapReduce Components 
  • YARN MR Application Execution Flow
  • YARN Workflow
  • Demo on MapReduce
  • Input Splits
  • Relation between Input Splits and HDFS Blocks 
  • MapReduce: Combiner & Partitioner
  • Sequence Input Format
  • Xml file Parsing using MapReduce

  • Introduction to Pig
  • MapReduce Vs Pig
  • Pig Use Cases
  • Programming Structure in Pig
  • Pig Running Modes
  • Pig components
  • Pig Execution
  • Pig Latin Program
  • Data Models inPig
  • Pig Data Types
  • Shell and Utility Commands

  • Hive Background
  • Hive Use Case
  • About Hive
  • Hive Vs Pig
  • Hive Architecture and Components
  • Metastore in Hive
  • Limitations of Hive
  • Comparison with Traditional Database
  • Hive Data Types and Data Models
  • Partitions and Buckets
  • Hive Tables(Managed Tables and External Tables)
  • Importing Data 
  • Querying Data

  • Hive QL: Joining Tables
  • Dynamic Partitioning
  • Hive Indexes and views Hive query optimizers
  • Hive: Thrift Server
  • User Defined Functions
  • HBase: Introduction to NoSQL Databases and HBase
  • HBase v/s RDBMS
  • HBase Components
  • HBase Architecture
  • Run Modes & Configuration
  • HBase Cluster Deployment

  • HBase Data Model
  • HBase Shell
  • Data Loading Techniques
  • ZooKeeper Data Model
  • Zookeeper Service
  • Zookeeper 
  • Demos on Bulk Loading
  • Getting and Inserting Data
  • Filters in HBase

  • Sqoop Architecture 
  • Sqoop Installation 
  • Sqoop Commands(Import, Hive-Import, EVal, Hbase Import, Import All tables,Export) 
  • Connectors to Existing DBs and DW 
  • Hands on Exercise  

  • Flume Introduction
  • Flume Architecture
  • Flume Master
  • Flume Collector and Flume Agent
  • Flume Configurations
  • Real Time Use Case using Apache Flume 

  • Need of NoSQL Databases
  • Relational VS Non-Relational Databases
  • Introduction to MongoDB
  • Features of MongoDB
  • Installation of MongoDB
  • Mongo DB Basic operations
  • REAL Time Use Cases on Hadoop &amp;
  • MongoDB Use Case 

  • Introduction to Apache Spark 
  • Role of Spark in Big data 
  • Who is using Spark 
  • Installation of SparkShell and StandAlone Cluster 
  • Configuration 
  • RDD Operations (Transformations and actions) 

  • A demo project using all the components of the above topics 
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Target Audience:

  • Data analysts and engineers
  • Software developers
  • IT professionals
  • Big data enthusiasts
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  • Basic understanding of programming and Linux commands
  • Familiarity with SQL is a plus
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Benefits of the course:

  • Gain expertise in Hadoop and its ecosystem
  • Expand your career opportunities with big data skills
  • Hands-on experience with real-world projects
  • Industry-recognized certification upon completion
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Exam details to pass the course:

  • The course is assessed through practical projects and assignments.
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Certification path:

  • Upon successful completion of the Hadoop Training course, you will be awarded the Skillzcafe Hadoop certification.
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Career options:

  • Big data engineer
  • Hadoop developer
  • Data analyst
  • Data scientist

Why should you take this course from Skillzcafe:

Why should you take this course from Skillzcafe:
  • Bullet Icon Comprehensive course with in-depth coverage of Hadoop and its ecosystem
  • Bullet Icon Hands-on experience with real-world projects and case studies
  • Bullet Icon Industry experts as trainers with years of experience in Hadoop
  • Bullet Icon Flexible learning options with self-paced and instructor-led modes
  • Bullet Icon Industry-recognized certification upon completion


Hadoop is an open-source software framework used for storing and processing big data.

Basic programming and Linux command knowledge is required. Familiarity with SQL is a plus.

You can pursue a career as a big data engineer, Hadoop developer, data analyst, or data scientist.

The course duration is 60 hours.

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