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Hadoop Course

Hadoop Course Overview

Hadoop Training in Bangalore

An open-source framework for data storage, HADOOP gives you infinite opportunities for use with various web applications. You can also run clusters of applications and process any size of data using HADOOP. Big Data is the biggest thing happening in the virtual world now. The boom of the ecommerce industry and the necessity for storing and managing huge amounts of data has created a need for a software program that can ensure safety of data without compromising on its performance. All this has led to the increased use of HADOOP in various online-based industries. If you too want to be an integral part of such a developing company, then you should take up a course in one of the best HADOOP training institutes in Bangalore.
 There are ample jobs available for those who have expertise in HADOOP. Apart from software engineer, there are loads of other job profiles you can apply for if you have HADOOP certified. Some of the job descriptions you can apply for are Big Data architect, technical lead, software developer, HADOOP administrator, data scientist, Machine learning, software developer, Java application architect, HADOOP manager, and technical product manager. College learning will help you understand the basis of this computing language. If you want to learn the advanced concepts and get practical on-the-floor training, then you should go to one of the leading HADOOP training institutes in Bangalore.
 The need for HADOOP experts is on the rise because it has such versatile applications. Packed with immense computing power, HADOOP enables you to sort, classify, and analyze huge amounts of data in a short span of time. Plus, it has fault tolerance. Even if there is a failure in the hardware, the nodes with all your important information will be secure. Its flexibility and scalability enable deployment in varied types of applications. Above all, the cost of using this open frame network for data management is relatively less. With so many benefits, it is no wonder that many companies are adopting HADOOP for big data management.
 MNP is one of the leading HADOOP training institutes in Bangalore. Our team of trainers are industry experts with ample knowledge and experience in this computing language. Moreover, MNP Technologies offers advanced course programs that can be customized according to the student’s preferences and requirements. Plus, we offer 24/7 support for students. MNP Technologies also has a strong placement assistance cell and can help you get a good job after completing the training program.

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    Hadoop Course Syllabus

    • 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:
    • How to create XSD,XML
    • y Coupled language)
    • What is simple type ,complex type
    • What is XPATH
    • What is Xquery
    • What is XSLT
    Introduction to OSB and OSB Architecture:
    • Understand OSB & Weblogic Console, Eclipse
    • OSB Key Architecture Concepts
    • Binding Layer
    • Transport Layer
    • Proxy and Business Services
    OSB Key Concepts:
    • Message Context
    • Message Flows
    • Understand OSB & Weblogic Console, Eclipse
    • OSB Message Patterns
    • OSB Design Time Components
    • Development of Proxy Service using Eclipse /Web logic console
    • What is proxy?
    • What is Business Service?

    Frequently Asked Questions


    Our Hadoop Trainers
    • Have more than 10 Years of experience in Hadoop Technologies
    • Has worked on multiple realtime Hadoop projects
    • Working in a top MNC company in Bangalore
    • Trained more than 2000+ Students so far.
    • Strong Theoretical & Practical Knowledge
    • Certified Professionals
    Our Hadoop Placement Team
    • Has Trained & Placed 2000+ Students
    • 92% percent Placement Record
    • 1000+ Interviews Organized
    Our Hadoop 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 Regular Batch ( Morning, Day time & Evening)
    • Seats Available : 8 (maximum)
    Hadoop Weekend Training Batch (Saturday, Sunday & Holidays)
    • Seats Available : 8 (maximum)
    Hadoop Fast Track batch
    • Seats Available : 5 (maximum)
    Hadoop Online batch
    • Seats Available : 5 (maximum)


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

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