Big Data Hadoop Certification Training

Practice Hadoop to manage processing and storage for big data applications

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Key Highlights

  • 30 hours of Instructor-led online live sessions
  • 10 sessions of 3 hours each (Weekend)
  • 15 sessions of 2 hours each (Weekday)
  • Complimentary Self-paced course of 'Java Essentials for Hadoop'
  • Case Studies on Real-life Scenarios
  • Lifetime Access to Learning Management System
  • Practice Assignments
  • 24X7 Expert Support
  • Online Forum for Discussions

Course Price Range

$99.00 - $449.00 $200.00 - $900.00

Contact Us

1866-216-7898

(Toll Free)

Available Courses Delivery

This course is available in the following formats:

Self Paced (On-Demand)

24x7 access to instructor-led videos and practical activities
Convenient training that syncs with your schedule
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$200  $99

Virtual Live

Access live online training from anywhere taught by expert instructors
Search and study from listed class recordings and materials
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Upcoming Batches


Nov 24th
Filling Fast
Delivery: Online
Access: Lifetime
Sun - Thu (15 Days)
Timings - 08:30 PM to 10:30 PM (EST)

Weekdays Batch (Evening)
$900  $449
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Nov 30th
Delivery: Online
Access: Lifetime
Sat - Sun (5 Weeks)
Timings - 10:00 AM to 01:00 PM (EST)

Weekend Batch (Morning)
$900  $449
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Course Overview

Big Data Hadoop certification introduces learners to the various tools and techniques and makes an impact in the Big Data Analytics industry. It covers all the topics, from basic to advanced. It aims to develop proficiency in Hadoop Architecture, HDFS, Hadoop MapReduce Framework, Apache Pig, Apache Hive, Apache HBase, and Oozie.

Course Objectives

  • Teach methodologies of Big Data Analytics
  • Equip learners with the various tools of Hadoop Ecosystem
  • Make learners adept at fundamentals of Hadoop
  • Teach about Hadoop administration activities like monitoring, administration, troubleshooting, and cluster managing
  • Educate learners about the configuration of ETL tools
  • Train learners in Hadoop testing applications

Career Benefits

  • Opportunity to work in the most promising field of technology
  • Be considered a significant link between technology and business
  • Great chances of developing future Big Data Systems
  • Scope of large-scale development of Hadoop applications
  • Chance to work with big brands
  • High paycheck

Prerequisites

  • Basic knowledge of Core Java and SQL will be an added advantage

Who should take up?

  • Software Developers
  • Project Managers
  • Software Architects
  • ETL Professionals
  • Data Warehousing Professionals
  • Data Engineers
  • Data Analysts
  • Business Intelligence Professionals
  • DBAs and DB Professionals
  • Senior IT Professionals
  • Testing Professionals
  • Mainframe Professionals
  • Professionals who want to build a career in Big Data

Course Content

  • Introduction to Big Data & Big Data Challenges
  • Limitations & Solutions of Big Data Architecture
  • Hadoop & its Features
  • Hadoop Ecosystem
  • Hadoop 2.x Core Components
  • Hadoop Storage: HDFS (Hadoop Distributed File System)
  • Hadoop Processing: MapReduce Framework
  • Different Hadoop Distributions
  • Hadoop 2.x Cluster Architecture
  • Federation and High Availability Architecture
  • Typical Production Hadoop Cluster
  • Hadoop Cluster Modes
  • Common Hadoop Shell Commands
  • Hadoop 2.x Configuration Files
  • Single Node Cluster & Multi-Node Cluster set up
  • Basic Hadoop Administration
  • Traditional way vs MapReduce way
  • Why MapReduce
  • YARN Components
  • YARN Architecture
  • YARN MapReduce Application Execution Flow
  • YARN Workflow
  • Anatomy of MapReduce Program
  • Input Splits, Relation between Input Splits and HDFS Blocks
  • MapReduce: Combiner & Partitioner
  • Demo of Health Care Dataset
  • Demo of Weather Dataset
  • Counters
  • Distributed Cache
  • MRunit
  • Reduce Join
  • Custom Input Format
  • Sequence Input Format
  • XML file Parsing using MapReduce
  • Introduction to Apache Pig
  • MapReduce vs Pig
  • Pig Components & Pig Execution
  • Pig Data Types & Data Models in Pig
  • Pig Latin Programs
  • Shell and Utility Commands
  • Pig UDF & Pig Streaming
  • Testing Pig scripts with Punit
  • Aviation use-case in PIG
  • Pig Demo of Healthcare Dataset
  • Introduction to Apache Hive
  • Hive vs Pig
  • Hive Architecture and Components
  • Hive Metastore
  • Limitations of Hive
  • Comparison with Traditional Database
  • Hive Data Types and Data Models
  • Hive Partition
  • Hive Bucketing
  • Hive Tables (Managed Tables and External Tables)
  • Importing Data
  • Querying Data & Managing Outputs
  • Hive Script & Hive UDF
  • Retail use case in Hive
  • Hive Demo on Healthcare Dataset
  • Hive QL: Joining Tables, Dynamic
  • Custom MapReduce Scripts Partitioning
  • Hive Indexes and views
  • Hive Query Optimizers
  • Hive Thrift Server
  • Hive UDF
  • Apache HBase: Introduction to NoSQL Databases and HBase
  • HBase v/s RDBMS
  • HBase Components
  • HBase Architecture
  • HBase Run Modes
  • HBase Configuration
  • HBase Cluster Deployment
  • HBase Data Model
  • HBase Shell
  • HBase Client API
  • Hive Data Loading Techniques
  • Apache Zookeeper Introduction
  • ZooKeeper Data Model Zookeeper Service
  • HBase Bulk Loading
  • Getting and Inserting Data
  • HBase Filters
  • What is Spark
  • Spark Ecosystem
  • Spark Components
  • What is Scala
  • Why Scala
  • SparkContext
  • Spark RDD
  • Oozie
  • Oozie Components
  • Oozie Workflow
  • Scheduling Jobs with Oozie Scheduler
  • Demo of Oozie Workflow
  • Oozie Coordinator
  • Oozie Commands
  • Oozie Web Console
  • Oozie for MapReduce
  • Combining flow of MapReduce Jobs
  • Hive in Oozie
  • Hadoop Project Demo
  • Hadoop Talend Integration

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