Hadoop Admin - MNP Technologies

Hadoop Admin Course

Hadoop Admin Course Overview

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.

 

Book a Free Demo






    MNP Advantage

    Training Modes

    Clasroom Training

    Online Training

    Corporate Training

    Our Happy Students!

    5/5
    4.8

    200+ Reviews

    4.6

    400+ Reviews

    4.8

    100+ Reviews

    4.7

    200+ Reviews

    Hadoop Admin Course Syllabus

    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

    Frequently Asked Questions

    Trainer

    Our Hadoop Admin Trainers
    • Have 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 more than 2000+ Students so far.
    • Strong Theoretical & Practical Knowledge
    • Certified Professionals
    Our Hadoop Admin Placement Team
    • Has Trained & Placed 2000+ Students
    • 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)
    Hadoop Admin Online batch
    • Seats Available : 5 (maximum)

    Course

    Our Hadoop Admin Course Duration is
    • Approximately 40 Hrs, Interactive & Practical Training Sessions
    • Assignments & Projects will be Provided 
    Hadoop Admin Course is Suitable for
    • Windows administrators
    • Software developers
    • Unix system administrators
    • Database administrators
    • Solutions architects
    • Technical support executives
    • And anyone who is interested in Hadoop Admin.

    Both are great but are suited for different categories of people. So, those who are time- pressed, are employed, or are busy otherwise, can benefit from online training. It will save time, and trainees will be able to utilize their idle time by taking lessons. There are other benefits too. For instance, they don’t need to be physically present at their training Centre, which not just saves time but also money. This is also helpful when there is no training Centre nearby to enroll for classroom training. Having said that, classroom training has its own upsides too, which we all know of. So, go for whatever suits you best.

    • We have excellent trainers for Hadoop Admin with rich experience in industry.
    • 100% student satisfaction rate in Hadoop Admin training
    • More than 1000 students completed training in Hadoop Admin
    • Excellent Lab facility for Hadoop Admin Training
    • We have excellent rating till date, overall 4.9 Rating in Google & Facebook.