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Big Data Hadoop Training

Big Data Hadoop Training in Nepal

Career Option: Big Data Developer

Updated on: 18th Sep, 2018

Big Data and Hadoop Training in Nepal

Broadway Infosys Nepal has designed Hadoop training course after analyzing the global need of handling large volume of data in multiple businesses. Thus, the primary objective of introducing Big Data and Hadoop training in Nepal is to produce the data analysts or Hadoop experts capable of big data processing in banking, telecommunication and social media and so on. This training makes the students completely familiar with the open source data storage and processing framework, Hadoop and its increasing implications in big data management.

Course Highlights

  • Introduction to Big Data and Hadoop framework
  • Understanding basics of Hadoop framework
  • Understand Hadoop Distributed File Systems and MapReduce
  • Learning fundamentals of data warehousing in Hadoop
  • Learn to Install Cloudera Hadoop cluster in the cloud
  • Exploring the implications of Hadoop knowledge in real world scenario
  • Regular lab exercises
  • Project work in big data related topics

Benefits of Big Data and Hadoop Training at Broadway Infosys Nepal

  • Well designed and industry standard courses
  • Big Data Experts and experienced data scientists as instructors
  • Sufficient training materials
  • Regular practical classes with real world data sets
  • Mockup tests to prepare trainees for Hadoop certification exam
  • Internship and job placement opportunity in data analyst positions
  • Opportunity to get involved in Hadoop community
  • Affordable cost of training with special discount facilities for needy students

The target audience for this training course includes the aspiring data scientists, business intelligence professionals and anyone looking to start career in Big Data Analytics. However, it will be relatively easier for students with prior knowledge of Java. Should you be interested to join the upcoming Hadoop training session please reach out to us to secure your seat at the earliest. Our Hadoop experts are excited to hear from you.

Courses Outline :- Big Data Hadoop Training in Nepal
  • 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
    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 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
  • 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




Flume 10 Hrs


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