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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 Data Skills: Learn how to handle, process, and analyze large and complex datasets using Hadoop technologies.
Hadoop Ecosystem: Understand key tools such as HDFS, MapReduce, Hive, and Pig, as well as other components used in big data processing.
Distributed Data Processing: Learn how data is stored and processed across multiple systems for better scalability and performance.
Industry Applications: Understand how big data tools are used for data management, analytics, and large-scale processing.
Career-Ready Skills: Build a foundation for opportunities in big data, Hadoop administration, data engineering, and analytics.
College/Faculty: Mega National College / BBS
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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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