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Big Data Hadoop Training in Nepal
Big Data Hadoop Training in Kathmandu, Nepal
Thousands of students have started their careers after getting certified by Broadway Infosys
Updated On: 26/04/2026
Created On: 25/10/2017
Course Overview
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.
Why Big Data Hadoop?
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.
Success Stories From our Graduates
Hear from graduates who have completed our courses.
College/Faculty: Nava Kshitiz College / BCA
Working At: Mandolly Tech
Position: Online Technical Support
College/Faculty: Bhairahawa Multiple Campus / Bachelor of Computer Science and Information Technology
Working At: Parasi Glorious Secondary School
Position: IT Officer
College/Faculty: Bhaktapur Multiple Campus / BIT
Working At: Codesc Nepal Pvt. Ltd
Position: Devops Engineer
College/Faculty: Patan Multiple Campus / BBS
Working At: Hi-Tech Engineering Pvt. Ltd.
Position: Graphic Designer
Our graduates are hired by 470+ companies in Nepal
Time for you to be the next hire. With our advanced and industry relevant courses, you are on the right stage to start your dream career.
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Our syllabus outlines are only the headlines of the major modules. To ensure a complete understanding of the course, we offer free counseling. Also, if you have specific modules in mind, you can customize the course. Send your inquiry today!
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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