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Big Data Hadoop Online Training & certification

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Chairman, Bluewater Corp.

Big Data Hadoop Online Training

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative. Hadooptrainingonline.com Trainings Provides Best Hadoop Online Training By Real-time Experts

Big Data Hadoop Online Course

  • Scenario Oriented Training
  • Materials and Certification Guidance
  • Access For Hands-On
  • Live-Support During Sessions Hours

Our Trainers

  • More than 8 Years of experience in Hadoop Technologies
  • Has worked on multiple realtime BigData projects
  • Working in a top MNC company
  • Trained 2000+ Students so far.
  • Strong Theoretical & Practical Knowledge
  • Industry certified Professionals

Big Data Hadoop Process Management Info

Big Data Hadoop Online Course Structure

  • Big-Data and Hadoop
    • Introduction to big data and Hadoop
    • Hadoop Architecture
    • Installing Ubuntu with Java 1.8 on VM Workstation 11
    • Hadoop Versioning and Configuration
    • Single Node Hadoop 1.2.1 installation on Ubuntu 14.4.1
    • Multi Node Hadoop 1.2.1 installation on Ubuntu 14.4.1
    • Linux commands and Hadoop commands
    • Cluster architecture and block placement
    • Modes in Hadoop
      • Local Mode
      • Pseudo Distributed Mode
      • Fully Distributed Mode
    • Hadoop Daemon
      • Master Daemons(Name Node, Secondary Name Node, Job Tracker)
      • Slave Daemons(Job tracker, Task tracker)
    • Task Instance
    • Hadoop HDFS Commands
    • Accessing HDFS
      • CLI Approach
      • Java Approach
  • Map-Reduce
    • Understanding Map Reduce Framework
    • Inspiration to Word-Count Example
    • Developing Map-Reduce Program using Eclipse Luna
    • HDFS Read-Write Process
    • Map-Reduce Life Cycle Method
    • Serialization(Java)
    • Datatypes
    • Comparator and Comparable(Java)
    • Custom Output File
    • Analysing Temperature dataset using Map-Reduce
    • Custom Partitioner & Combiner
    • Running Map-Reduce in Local and Pseudo Distributed Mode.
  • Advanced Map-Reduce
    • Enum(Java)
    • Custom and Dynamic Counters
    • Running Map-Reduce in Multi-node Hadoop Cluster
    • Custom Writable
    • Site Data Distribution
      • Using Configuration
      • Using DistributedCache
      • Using stringifie
    • Input Formatters
      • NLine Input Formatter
      • XML Input Formatter
    • Sorting
      • Reverse Sorting
      • Secondary Sorting
    • Compression Technique
    • Working with Sequence File Format
    • Working with AVRO File Format
    • Testing MapReduce with MR Unit
    • Working with NYSE DataSets
    • Working with Million Song DataSets
    • Running Map-Reduce in Cloudera Box
  • HIVE
    • Hive Introduction & Installation
    • Data Types in Hive
    • Commands in Hive
    • ExploringInternal and External Table
    • Partitions
    • Complex data types
    • UDF in Hive
      • Built-in UDF
      • Custom UDF
    • Thrift Server
    • Java to Hive Connection
    • Joins in Hive
    • Working with HWI
    • Bucket Map-side Join
    • More commands
      • View
      • SortBy
      • Distribute By
      • Lateral View
    • Running Hive in Cloudera
  • SQOOP
    • Sqoop Installations and Basics
    • Importing Data from Oracle to HDFS
    • Advance Imports
    • Real Time UseCase
    • Exporting Data from HDFS to Oracle
    • Running Sqoop in Cloudera
  • PIG
    • Installation and Introduction
    • WordCount in Pig
    • NYSE in Pig
    • Working With Complex Datatypes
    • Pig Schema
    • Miscellaneous Command
      • Group
      • Filter
      • Order
      • Distinct
      • Join
      • Flatten
      • Co-group
      • Union
      • Illustrate
      • Explain
    • UDFs in Pig
    • Parameter Substitution and DryRun
    • Pig Macros
    • Running Pig in Cloudera
  • OOZIE
    • Installing Oozie
    • Running Map-Reduce with Oozie
    • Running Pig and Sqoop with Oozie
  • Big Data Hadoop Optimizations
  • Hbase
    • What is HBase?
    • HBase Architecture in Hadoop
    • Hadoop HBase API
    • Managing large data sets with HBase
    • Using HBase in Hadoop applications.
  • Hadoop Zookeeper
  • Summary
  • Sample Applications
  • References of training.

Prerequisites

  • All attendees should have a basic knowledge of Java.

Duration

Online

  • It is a 12 days program and extends up to 2hrs each.
  • The format is 20% theory, 80% Hands-on.
  • Instructor-Led Regular Online (Limited Persons Per Group) Training.
  • Instructor-Led Online On Demand Training ( 1-1 or Corporate Training ).

Corporate

  • It is a 3 days program and extends up to 8hrs each.
  • The format is 20% theory, 80% Hands-on.

Classroom

  • Private Classroom arranged on request and minimum attendees for batch is 4.

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