Hadoop Admin Training in Bangalore

Learn Hadoop Admin Course from Expert Oracle Trainers who are Real-time Working Professionals.
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Best Hadoop Admin Training in BTM Layout Bangalore

Based on Google and other third-party reviews, Softgen Infotech is recognized as one of the Hadoop Admin Training in BTM Layout Bangalore. We have completed over 2000 HADOOP training sessions with a 100% placement rate, assisting college students. For the past 5 years, the team at Softgen Infotech has been dedicated to providing High-Quality Hadoop Admin Training in BTM Layout Bangalore.

Softgen Infotech’s trainers come from the HADOOP industry and have at least 5 years of experience implementing HADOOP. All HADOOP trainers are real-time specialists who provide hands-on experience with practicals.

Our Hadoop Admin Training in BTM Layout Bangalore is well equipped with labs and good infrastructure to provide you with hands-on training. We also offer HADOOP certification training programmes. We have successfully trained and placed the majority of our students in large MNC firms. Softgen provides HADOOP programmes with 100% placement assistance for our students.

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    Why Choose Softgen Infotech

    Softgen Infotech is a leading Training Institute in Bangalore providing Quality Training for Multiple IT Courses with Placement Assistance.

    Live & Interactive Sessions

    Real Time Training

    Practical Classes

    Interview & Placement Assistance

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    Worried or Confused whether this is the right course for you? Feel free to reach our Expert Consultant for all your Queries !

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    Course Material & Syllabus

    Get all Course Content and Syllabus. Get Trained like a  Professional. We Provide Course Material, Prepare for Interviews, Arrange Mock Interviews and 100 % Placement Assistance

    Hadoop training course content and 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

    Hadoop Admin Training Course Duration & Batch Timings

    Duration : 30 Hours Version : latest Version
    Regular : 1 Hour per day Fast Track : 2 - 3 Hours per day: 10 days
    Weekdays : Monday - Friday Weekend : Saturday and Sunday
    Online Training : Available Class Room Training : Available
    Course Fee : Talk to our Customer Support Mode of Payment : Talk to our Customer Support

    Frequently Asked Questions for Hadoop Admin Training Course

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