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Big Data Hadoop In Bangalore

Learn Big Data Hadoop from start for beginner

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    Big Data Hadoop
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Course Summary

Want to Become Hadoop Developer, Hadoop Administrator? Big Data Hadoop training include the basic primitives of UNIX, Big Data, Hadoop Core Concepts, Hadoop Distributed File System (HDFS), Understanding - Map-Reduce Basics and Map-Reduce Types and Formats, Developing Map Reduce Programs, Understanding ToolRunner, and Hadoop Eco-Components (Flume, Sqoop, Hive).

  1. Origin of Unix, Overview of System Administration
  2. Unix basic commands.
  3. Working with editors – vi and sed.
  4. Linking files with symbolic link and hard link.
  5. Understanding job processes.
  6. Killing processes in different ways.
  1. Understanding Data & Hadoop: Basic Concepts.
  2. What is BigData.
  3. Characteristics of BigData.
  4. Challenges with Traditional Systems.
  5. Problems with BigData.
  6. Handling BigData.

  1. Problems with Existing Distributed Systems to deal Big Data.
  2. Why Hadoop and An Overview and History of Hadoop.
  3. Requirements of New Approach.
  4. The Hadoop Project and Hadoop Components.
  1. What is HDFS, Why it is required for running Map-Reduce. .
  2. How it differs from other distributed file systems.
  3. Design of HDFS & Concepts.
  4. Command Line Interface, Hadoop File Systems, Java Interface.
  5. Anatomy of a File Read, Anatomy of a File Write.
  6. Hadoop Archives.
  7. Hands-on Exercise on HDFS.
  1. Describe Map-Reduce framework works & Flow.
  2. Why Map-Reduce is tightly coupled with HDFS.
  3. What are the different types of Input and Output formats and why they are required?.
  4. Architecture of map reduce framework.
  5. Hadoop Data Types.
  6. Concept of Mappers & Reducers.
  7. Concept of Partitioners & Combiners.
  8. Secondary sort.
  9. Input Formats (Input Splits and Records, Text Input, SequentialFile Input, Multiple Inputs, KeyvalueTextInput, Database Input and Output).
  10. Output Formats (TextOutput, BinaryOutPut, Multiple Outputs, Databaseoutput).
  11. Hands-on Exercise.

  1. Setting up Eclipse Development Environment.
  2. Eclipse integration with HADOOP for Rapid Application Development.
  3. Understanding HADOOP API.
  4. Creating Map Reduce Projects.
  5. Writing MapReduce Drivers, Mappers and Reducers in Java.
  6. Driver Code.
  7. Mapper Code.
  8. Reducer Code.
  9. Map Reduce Code.
  10. Differences Between the Old and New MapReduce APIs.
  11. Hands-on Exercise.
  12. Working around with directories.

  1. More about ToolRunner.
  2. Combiner.
  3. Reducer.
  4. Configure and close methods.
  5. Hands-on Exercise.

  1. Extract records from the third party resources using Hadoop API.
  2. Process the records using Sentiment analysis.
  3. Integrating with netcat server and pulling out the data.

  1. Importing data to and from RDBMS to Hadoop.
  2. Exporting data to RDBMS.
  3. Creating sqoop job and implementing updates.

  1. Getting Data into Hive.
  2. Manipulating Data with Hive.
  3. Partitioning and Bucketing Data.

Big Data Hadoop Solution Architect Trainer

A dynamic and self - motivated Trainer and System Administrator. Aspiring for a Bright and challenging career in the field of Training and Networking Technology, which could enable me to upgrade myself with emerging trends and technologies to benefits of the professional growth and accomplishment of organizational goals.

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Course Features

  • Duration : 50 Hours
Price : ₹ 21240

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