Hadoop Admin Training Institutes in Bangalore

Hadoop Admin Training in Marathahalli & Hadoop Admin Training Institutes in Bangalore

Hadoop Admin Training in Bangalore
MNP Technologies located in Marathalli, Bangalore is a leading training institute providing real-time and placement oriented Hadoop Admin Training Courses in Bangalore. Our Hadoop Admin training course includes basic to advanced levels. we have a team of certified trainers who are working professionals with hands on real time Hadoop Admin projects knowledge which will provide you an edge over other training institutes.

Our Hadoop Admin training center is well equipped with lab facilities and excellent infrastructure for providing you real time training experience. We also provide certification training programs in Hadoop Admin Training.  We have successfully trained and provided placement for many of our students in major MNC Companies, after successful completion of the course. We provide placement support for our students.

Our team of experts at MNP Technologies Training Institute, Bangalore have designed our Hadoop Admin Training course content and syllabus based on students requirements to achieve everyone's career goal.  Our Hadoop Admin Training course fee is economical and tailor-made based on training requirement.

We Provide regular training classes(day time classes), weekend training classes, and fast track training classes for Hadoop Admin Training in our centers located across Bangalore. We also provide Online Training Classes for Hadoop Admin Training Course.

Contact us today to schedule a free demo and complete course details on Hadoop Admin Training Course.


Hadoop Admin Training Syllabus in Bangalore

Hadoop Course Content

  • Hadoop Overview, Architecture Considerations, Infrastructure, Platforms and Automation
  • Use case walkthrough
  • ETL
  • Log Analytics
  • Real Time Analytics

Hbase for Developers

  • NoSQL Introduction
  • Traditional RDBMS approach
  • NoSQL introduction
  • Hadoop & Hbase positioning
  • Hbase Introduction
  • What it is, what it is not, its history and common use-cases
  • Hbase Client – Shell, exercise
  • Hbase Architecture
  • Building Components
  • Storage, B+ tree, Log Structured Merge Trees
  • Region Lifecycle
  • Read/Write Path
  • Hbase Schema Design
  • Introduction to hbase schema
  • Column Family, Rows, Cells, Cell timestamp
  • Deletes
  • Exercise - build a schema, load data, query data
  • Hbase Java API – Exercises
  • Connection
  • CRUD API
  • Scan API
  • Filters
  • Counters
  • Hbase MapReduce
  • Hbase Bulk load
  • Hbase Operations, cluster management
  • Performance Tuning
  • Advanced Features
  • Exercise
  • Recap and Q&A

MapReduce for Developers

  • Introduction
  • Traditional Systems / Why Big Data / Why Hadoop
  • Hadoop Basic Concepts/Fundamentals
  • Hadoop in the Enterprise
  • Where Hadoop Fits in the Enterprise
  • Review Use Cases
  • Architecture
  • Hadoop Architecture & Building Blocks
  • HDFS and MapReduce
  • Hadoop CLI
  • Walkthrough
  • Exercise
  • MapReduce Programming
  • Fundamentals
  • Anatomy of MapReduce Job Run
  • Job Monitoring, Scheduling
  • Sample Code Walk Through
  • Hadoop API Walk Through
  • Exercise
  • MapReduce Formats
  • Input Formats, Exercise
  • Output Formats, Exercise

Hadoop File Formats

  • MapReduce Design Considerations
  • MapReduce Algorithms
  • Walkthrough of 2-3 Algorithms
  • MapReduce Features
  • Counters, Exercise
  • Map Side Join, Exercise
  • Reduce Side Join, Exercise
  • Sorting, Exercise
  • Use Case A (Long Exercise)
  • Input Formats, Exercise
  • Output Formats, Exercise
  • MapReduce Testing
  • Hadoop Ecosystem
  • Oozie
  • Flume
  • Sqoop
  • Exercise 1 (Sqoop)
  • Streaming API
  • Exercise 2 (Streaming API)
  • Hcatalog
  • Zookeeper
  • HBase Introduction
  • Introduction
  • HBase Architecture

MapReduce Performance Tuning
Development Best Practice and Debugging
Apache Hadoop for Administrators

  • Hadoop Fundamentals and Architecture
  • Why Hadoop, Hadoop Basics and Hadoop Architecture
  • HDFS and Map Reduce
  • Hadoop Ecosystems Overview
  • Hive
  • Hbase
  • ZooKeeper
  • Pig
  • Mahout
  • Flume
  • Sqoop
  • Oozie
  • Hardware and Software requirements
  • Hardware, Operating System and Other Software
  • Management Console
  • Deploy Hadoop ecosystem services
  • Hive
  • ZooKeeper
  • HBase
  • Administration
  • Pig
  • Mahout
  • Mysql
  • Setup Security
  • Enable Security Configure Users, Groups, Secure HDFS, MapReduce, HBase and Hive
  • Configuring User and Groups
  • Configuring Secure HDFS
  • Configuring Secure MapReduce
  • Configuring Secure HBase and Hive

Manage and Monitor your cluster
Command Line Interface
Troubleshooting your cluster
Introduction to Big Data and Hadoop

  • Hadoop Overview
  • Why Hadoop
  • Hadoop Basic Concepts
  • Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout
  • Where Hadoop fits in the Enterprise
  • Review use cases
  • Apache Hive & Pig for Developers
  • Overview of Hadoop
  • Big Data and the Distributed File System
  • MapReduce
  • Hive Introduction
  • Why Hive?
  • Compare vs SQL
  • Use Cases
  • Hive Architecture – Building Blocks
  • Hive CLI and Language (Exercise)
  • HDFS Shell
  • Hive CLI
  • Data Types
  • Hive Cheat-Sheet
  • Data Definition Statements
  • Data Manipulation Statements
  • Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
  • Built-in Functions
  • Union, Sub Queries, Sampling, Explain
  • Hive Usecase implementation - (Exercise)
  • Use Case 1
  • Use Case 2
  • Best Practices
  • Advance Features
  • Transform and Map-Reduce Scripts
  • Custom UDF
  • UDTF
  • SerDe
  • Recap and Q&A
  • Pig Introduction
  • Position Pig in Hadoop ecosystem
  • Why Pig and not MapReduce
  • Simple example (slides) comparing Pig and MapReduce
  • Who is using Pig now and what are the main use cases
  • Pig Architecture
  • Discuss high level components of Pig
  • Pig Grunt - How to Start and Use
  • Pig Latin Programming
  • Data Types
  • Cheat sheet
  • Schema
  • Expressions
  • Commands and Exercise
  • Load, Store, Dump, Relational Operations,Foreach, Filter, Group, Order By, Distinct, Join, Cogroup,Union, Cross, Limit, Sample, Parallel
  • Use Cases (working exercise)
  • Use Case 1
  • Use Case 2
  • Use Case 3 (compare pig and hive)

Advanced Features, UDFs

  • Best Practices and common pitfalls
  • Mahout & Machine Learning
  • Mahout Overview
  • Mahout Installation
  • Introduction to the Math Library
  • Vector implementation and Operations (Hands-on exercise)
  • Matrix Implementation and Operations (Hands-on exercise)
  • Anatomy of a Machine Learning Application
  • Classification
  • Introduction to Classification
  • Classification Workflow
  • Feature Extraction
  • Classification Techniques (Hands-on exercise)
  • Evaluation (Hands-on exercise)
  • Clustering
  • Use Cases
  • Clustering algorithms in Mahout
  • K-means clustering (Hands-on exercise)
  • Canopy clustering (Hands-on exercise)
  • Clustering
  • Mixture Models
  • Probabilistic Clustering – Dirichlet (Hands-on exercise)
  • Latent Dirichlet Model (Hands-on exercise)
  • Evaluating and Improving Clustering quality (Hands-on exercise)
  • Distance Measures (Hands-on exercise)
  • Recommendation Systems
  • Overview of Recommendation Systems
  • Use cases
  • Types of Recommendation Systems
  • Collaborative Filtering (Hands-on exercise)
  • Recommendation System Evaluation (Hands-on exercise)
  • Similarity Measures
  • Architecture of Recommendation Systems
  • Wrap Up

Our Hadoop Admin Trainers


  • More than 10 Years of experience in Hadoop Admin Technologies
  • Has worked on multiple realtime Hadoop Admin projects
  • Working in a top MNC company in Bangalore
  • Trained 2000+ Students so far.
  • Strong Theoretical & Practical Knowledge
  • Certified Professionals

Our Hadoop Admin Placement Team


  • More than 2000+ students Trained in Hadoop Admin
  • 92% percent Placement Record
  • 1000+ Interviews Organized

Our Hadoop Admin Training centers


  • Bangalore Center
  • Marathalli Center
  • White Field Center
  • K R Puram Center
  • BTM Center
  • Cunningham Road Center
  • J P Nagar Center
  • Kammanahalli Center
  • Ganga Nagar Center
  • Koramangala Center
  • Vijaya Nagar Center
  • Malleswaram Center
  • Ramamurthy Nagar Center
  • Indira Nagar Center
  • Jaya Nagar Center
  • Richmond Road Center
  • Rajaji Road Center
  • Mathikere Center

Hadoop Admin Regular Batch ( Morning, Day time & Evening)


  • Seats Available : 8 (maximum)

Hadoop Admin Weekend Training Batch (Saturday, Sunday & Holidays)


  • Seats Available : 8 (maximum)

Hadoop Admin Fast Track batch


  • Seats Available : 5 (maximum)

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MNP Technologies

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based on 3197 ratings.