Big Data Hadoop Training in Nepal

Big Data Hadoop

Course Overview
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Broadway Infosys is proud to be the pioneer of Big Data and Hadoop training in Nepal.

Big Data is best described as any voluminous amount of unstructured, structured or semi structured data with the potential to be mined. And, Hadoop manages storage and data processing for big data apps.

We have designed Big Data and Hadoop training course in Nepal keeping in mind the demand for Hadoop experts/data analysts for big data processing in banking, online businesses, telecommunication and other sectors in Nepal and the international market.  

Benefits of Big Data Hadoop

Big data and Hadoop training offer several benefits to IT professionals seeking to enhance their careers in data analysis and management. Some of the key benefits include:

  • Big career opportunity in the IT field.
  • High-paying jobs in Data analysis, storage, and processing.
  • There is a high demand for skilled professionals in big data and Hadoop.
  • Drastically improves the portfolio of IT students and professionals.

Benefits of Big Data Hadoop at Broadway Infosys

  • Experienced Big Data and Hadoop professionals as instructors.
  • Well-equipped labs for training classes.
  • We use real-world data sets for our regular practical classes.
  • Mockup Hadoop certification tests to prepare trainees for the real one.
  • Cost-effective prices and special discounts for needy or deserving students.
  • Internship and job placement opportunities as a data analyst.

Our graduates are hired by 350+ companies in Nepal

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Lesson 1:

  • What is Big Data?
  • Challenges for processing big data?
  • Technologies support big data?
  • What is Hadoop?
  • Why Hadoop?
  • Hadoop History
  • Use cases of Hadoop
  • RDBMS vs Hadoop
  • When to use and when not to use Hadoop
  • Hadoop Ecosystem
  • Vendor comparison
  • Hardware Recommendations & Statistics

HDFS: Hadoop Distributed File System: 12 Hrs

– Significance of HDFS in Hadoop

  • Features of HDFS
  • 5 daemons of Hadoop
    • Name Node and its functionality
    • Data Node and its functionality
    • Secondary Name Node and its functionality
    • Job Tracker and its functionality
    • Task Tracker and its functionality
  • Data Storage in HDFS
    • Introduction about Blocks
    • Data replication
  • Accessing HDFS
    • CLI (Command Line Interface) and admin commands
    • Java Based Approach
  • Fault tolerance
  • Download Hadoop
  • Installation and set-up of Hadoop
    • Start-up & Shut down process
  • HDFS Federation

  • Map Reduce history
  • Architecture of Map Reduce
  • Working mechanism
  • Developing Map Reduce
  • Map Reduce Programming Model
    • Different phases of Map Reduce Algorithm.
    • Different Data types in Map Reduce.
    • Writing a basic Map Reduce Program.
    • Driver Code
    • Mappers
    • Reducer
  • Creating Input and Output Formats in Map Reduce Jobs
    • Text Input Format
    • Key Value Input Format
    • Sequence File Input Format
    • Data localization in Map Reduce
    • Combiner (Mini Reducer) and Partitioner
    • Hadoop I/O
    • Distributed cache

  • Introduction to Apache Pig
  • Map Reduce Vs. Apache Pig
  • SQL vs. Apache Pig
  • Different data types in Pig
  • Modes of Execution in Pig
  • Grunt shell
  • Loading data
  • Exploring Pig
  • Latin commands

  • Architecture and schema design
  • HBase vs. RDBMS
  • HMaster and Region Servers
  • Column Families and Regions
  • Write pipeline
  • Read pipeline
  • HBase commands

 

OOZIE 9Hrs

SQOOP 8Hrs

Flume 10 Hrs

 

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