Big Data Hadoop Training in Nepal

Big Data Hadoop Training in Kathmandu, Nepal

Career: Big Data Developer
Training Mode: Both, Physical & Live Online Classes
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Big Data and Hadoop Training in Nepal

Broadway Infosys Nepal 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 and Hadoop Training in Nepal

Big data and Hadoop training offers several benefits to IT professionals willing to enhance their career in data analysis and management. Some of the key benefits include:

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

Our Big Data and Hadoop Training is ideal for those preparing for certification in Hadoop, aspiring data scientists, business intelligence professionals and anyone interested in a career in Big Data analytics.

Benefits of Big Data and Hadoop Training 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 discount for needy/deserving students.
  • Internship and job placement opportunities as data analyst.

Big Data Hadoop Training in Nepal - Outlines
  • Introduction to Hadoop and Big Data: 3Hrs

    • 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
  • Using Basic Linux commands: 6Hrs

    HDFS: Hadoop Distributed File System: 12 Hrs

    – Significance of HDFS in Hadoop

    • Features of HDFS
    • 5 daemons of Hadoop
      1. Name Node and its functionality
      2. Data Node and its functionality
      3. Secondary Name Node and its functionality
      4. Job Tracker and its functionality
      5. Task Tracker and its functionality
    • Data Storage in HDFS
      1. Introduction about Blocks
      2. Data replication
    • Accessing HDFS
      1. CLI (Command Line Interface) and admin commands
      2. Java Based Approach
    • Fault tolerance
    • Download Hadoop
    • Installation and set-up of Hadoop
      1. Start-up & Shut down process
    • HDFS Federation
  • Map Reduce: 12Hrs

    • Map Reduce history
    • Architecture of Map Reduce
    • Working mechanism
    • Developing Map Reduce
    • Map Reduce Programming Model
      1. Different phases of Map Reduce Algorithm.
      2. Different Data types in Map Reduce.
      3. Writing a basic Map Reduce Program.
      4. Driver Code
      5. Mappers
      6. Reducer
    • Creating Input and Output Formats in Map Reduce Jobs
      1. Text Input Format
      2. Key Value Input Format
      3. Sequence File Input Format
      4. Data localization in Map Reduce
      5. Combiner (Mini Reducer) and Partitioner
      6. Hadoop I/O
      7. Distributed cache
  • PIG: 6Hrs

    • 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
  • HIVE: 8Hrs

    • 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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