Best Hadoop Online Training in India

About The Course

Hadoop Online coaching in ameerpet Hyderabad and marathahalli Bangalore, India providing professional course in Hadoop Technology.
The growing, data vows cannot be met by conventional technologies and need some really organized and automated technology.
Big data and Hadoop are the two kinds of the promising technologies that can analyze, curate, and manage the data.
The course on Hadoop and Big data is to provide enhanced knowledge and technical skills needed to become an efficient developer in Hadoop technology.
Along with learning, there is virtual implementation using the core concepts of the subject upon live industry based applications.
With the simple programming modules, large clusters of data can be managed into simpler versions for ease of accessibility and management.
Best hadoop Online Training India has the best expertise to handle the Hadoop Online training in India. hadoop-online-training-in-india-why-to-learn-hadoop-to-get-job

Course Objectives

The Course goes with the aim to understand key concepts about:

  • HDFS and MapReduce Framework
  • Architecture of Hadoop 2.x
  • To write Complex MapReduce Programs and Set Up Hadoop Cluster
  • Making Data Analytics by using Pig, Hive and Yarn
  • Sqoop and Flume for learning Data Loading Techniques
  • Implementation of integration by HBase and MapReduce
  • To implement Indexing and Advanced Usage
  • To Schedule jobs with the use of Oozie application
  • To implement best practices for Hadoop Development Program
  • Working on Real Life Projects basing on Big Data Analytics

Course Content

During this course, you will learn

  • Introduction to Big Data and Analytics
  • Introduction to Hadoop
  • Hadoop ecosystem – Concepts
  • Hadoop Map-reduce concepts and features
  • Developing the map-reduce Applications
  • Pig concepts
  • Hive concepts
  • Sqoop concepts
  • Flume Concepts
  • Oozie workflow concepts
  • Impala Concepts
  • Hue Concepts
  • HBASE Concepts
  • ZooKeeper Concepts
  • Real Life Use Cases
  • Reporting Tool
  1. Virtualbox/VM Ware Basics
  2. Linux Basics
  3. Hadoop Why Hadoop?
    Distributed Framework
    Hadoop v/s RDBMS
    Brief history of hadoop
  4. Setup hadoop Pseudo mode
    Cluster mode
    Installation of java, hadoop
    Configurations of hadoop
    Hadoop Processes ( NN, SNN, JT, DN, TT)
    Temporary directory
    Common errors when running hadoop cluster, solutions
  5. HDFS- Hadoop distributed File System HDFS Design and Architecture
    HDFS Concepts
    Interacting HDFS using command line
    Interacting HDFS using Java APIs
  6. Hadoop Processes Name node
    Secondary name node
    Job tracker
    Task tracker
    Data node
  7. Map Reduce Developing Map Reduce Application
    Phases in Map Reduce Framework
    Map Reduce Input and Output Formats
    Advanced Concepts
    Sample Applications
  8. Joining datasets in Mapreduce jobs Map-side join
    Reduce-Side join
  9. Map reduce – customization Custom Input format class
    Hash Partitioner
    Custom Partitioner
    Sorting techniques
    Custom Output format class
  10. Hadoop Programming Languages I.HIVE
    Installation and Configuration
    Interacting HDFS using HIVE
    Map Reduce Programs through HIVE
    HIVE Commands
    Loading, Filtering, Grouping….
    Data types, Operators…..
    Joins, Groups….
    Sample programs in HIVE

    II. PIG
    Installation and Configurations

  11. Introduction
  12. The Motivation for Hadoop Problems with traditional large-scale systems
    Requirements for a new approach
  13. Hadoop: Basic Concepts An Overview of Hadoop
    The Hadoop Distributed File System
    Hands-On Exercise
    How MapReduce Works
    Hands-On Exercise
    Anatomy of a Hadoop Cluster
    Other Hadoop Ecosystem Components
  14. Writing a MapReduce Program The MapReduce Flow
    Examining a Sample MapReduce Program
    Basic MapReduce API Concepts
    The Driver Code
    The Mapper
    The Reducer
    Hadoop’s Streaming API
    Using Eclipse for Rapid Development
    Hands-on exercise
    The New MapReduce API
  15. Common MapReduce Algorithms Sorting and Searching
    Machine Learning With Mahout
    Term Frequency – Inverse Document Frequency
    Word Co-Occurrence
    Hands-On Exercise
  16. PIG Concepts.. Data loading in PIG
    Data Extraction in PIG
    Data Transformation in PIG
    Hands on exercise on PIG
  17. Hive Concepts. Hive Query Language
    Alter and Delete in Hive
    Partition in Hive
    Joins in Hive.Unions in hive
    Industry specific configuration of hive parameters
    Authentication & Authorization
    Statistics with Hive
    Archiving in Hive
    Hands-on exercise
  18. Working with Sqoop Introduction
    Import Data
    Export Data
    Sqoop Syntaxs
    Databases connection
    Hands-on exercise
  19. Working with Flume Introduction
    Configuration and Setup
    Flume Sink with example
    Flume Source with example
    Complex flume architecture
  20. OOZIE Concepts
  21. IMPALA Concepts
  22. HUE Concepts
  23. HBASE Concepts
  24. ZooKeeper Concepts