Big Data & Hadoop

This course will enable an Analyst to work on Big Data and Hadoop which takes into consideration the burgeoning demands of the industry to process and analyze data at high speeds.
This Training Course will give you the right skills to deploy various tools and techniques to be a Hadoop Analyst working with Big Data.

This course will enable an Analyst to work on Big Data and Hadoop which takes into consideration the burgeoning demands of the industry to process and analyze data at high speeds.
This Training Course will give you the right skills to deploy various tools and techniques to be a Hadoop Analyst working with Big Data.

  • Hadoop Architecture and Ecosystem
  • Learn about Apache Hive, Pig, YARN
  • Complex data processing techniques
  • Come up with Hadoop real-time queries using Impala
  • Integrate HBase with MapReduce
  • Deploy MapReduce advanced Indexing
  • ETL connectivity with Hadoop ecosystem
  • Real-time analysis on large data sets
  • Business Professionals, Data and System Analysts
  • ETL and Data warehousing Professionals, Project Managers and Business Intelligence experts
  • Anyone wants to learn Big data and Hadoop and doesn’t have programming experience
  • 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

Trainer Name:

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Course Features

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ON, Canada.

         

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