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Best Hadoop Training and Class in Pune- Big Data Training Institute Pune
    Hadoop with Spark is the Best combination now to start / Switch your career in IT Industry because of two major Reasons: 1.) Hadoop is Open Source and cost saving solution for the client 2.) Hadoop is designed as BigData solution for Batch Processing and Its combination with Spark Scala enable Hadoop to work on TBs of data in real time as well. Almost all the big clients who are handling TBs or more data in real time are using Hadoop and Data Science as solution We provide the combination of Hadoop with Spark by IT working professionals who are working on Hadoop BigData and Data Science in real time And they not only teach by also share there challenges and complex use cases with candidates to get most out of it. You can Attend Free demo session and experience the knowledge provided by our trainer.

    Duration: 45 hours classroom program
    9 Weekends
    70+ Assignments in classroom
    4 POCs , 1 Real time Project
    Cluster Based Training

    Introduction To Hadoop Ecosystem

    Hadoop Installation and Hands-on on Live Machine

    Introduction to Pig (ETL Tool) Basics and Advanced

    Hive Datawarehouse Basics and Advanced

    Map Reduce Framework and APIs

    NOSQL Databases and Introduction to HBase and Zookeeper

    Flume , Oozie (Job Scheduling Tool) and YARN Framework

    Project Implementation Phase-1

    Spark and Scala Introduction based on Spark 1.x and Spark 2.x

    Spark SQL DataSet and DataFrame

    Major benefits of Training

    • Why we need Hadoop
    • Why Hadoop is in demand in market
    • Key points , Why Hadoop is leading tool in current IT Trend
    • Definition of BigData
    • Hadoop nodes
    • Introduction to Hadoop Releases
    • Hadoop Daemons
    • Hadoop Cluster and Racks
    • Hadoop Cluster Demo
    • Types of projects in Hadoop
    • Why Clients want POCs and migration of Existing tools and Technologies on Hadoop Ecosystem
    • How Open Source tool (HADOOP) is capable to run jobs in lesser time
    • Hadoop Storage HDFS (Hadoop Distributed file system)
    • Hadoop Processing Framework (Map Reduce AND YARN)
    • Alternates of Map Reduce
    • Need of Spark and Scala with Hadoop
    • In Memory Cluster Computing
    • Why NOSQL is in much demand
    • Distributed warehouse for HDFS (Hive)
    • Most demanding tools which can run on the top of Hadoop Ecosystem for specific requirements in specific scenarios

    • Data import/Export tools (SQOOP, Flume, Kafka)
    • Hadoop installation
    • Introduction to Hadoop FS and Processing Environments
    • Basics of Unix
    • Unix commands
    • Unix File System Stoage
    • Introduction to Hadoop File System Shell
    • Basic commands of HDFS Shell
    • How to read and write files in HDFS
    • Hadoop releases Hands on
    • Hadoop daemons Hands on
    • Pig Introduction
    • Why Pig if Map Reduce is available?
    • How Pig is different from Programming languages
    • Pig Data flow Introduction
    • How Schema is optional in Pig
    • Pig Data types
    • Pig Commands like Load, Store , Describe , Dump
    • Map Reduce job started by Pig Commands
    • Execution plan
    • Pig- UDFs
    • Pig Use cases
    • Pig Assignment
    • Complex Use cases on Pig
    • XML Data Processing in Pig
    • Structured Data processing in Pig
    • Semi-structured data processing in Pig
    • Pig Advanced Assignment
    • Real time scenarios on Pig
    • When we should use Pig
    • Live examples of Pig Use cases
    • Hive Introduction
    • Meta storage and meta store
    • Introduction to Derby and MySQL Databases
    • Hive Data types
    • HQL
    • DDL, DML and sub languages of Hive
    • Managed , External and Temp tables in Hive
    • Differentiation between SQL based Datawarehouse and Hive
    • Hive releases
    • Why Hive is not best solution for OLTP
    • Hive as OLAP
    • Partitioning
    • Bucketing
    • Hive Architecture
    • Hue Interface for Hive
    • How to analyze data using Hive script
    • Differentiation between Hive and Impala
    • UDFs in Hive
    • Complex Use cases in Hive
    • Hive Advanced Assignment
    • Real time scenarios of Hive
    • POC on Pig and Hive , With real time data sets and problem statements
    • How Map Reduce works as Processing Framework
    • End to End execution flow of Map Reduce job
    • Different tasks in Map Reduce job
    • Why Reducer is optional while Mapper is mandatory?
    • Introduction to Combiner
    • Introduction to Partitioner
    • Programming languages for Map Reduce
    • Why Java is preferred for Map Reduce programming
    • POC based on Pig, Hive, HDFS, MR
    • Introduction to NOSQL
    • Why NOSQL if SQL is in market since several years
    • Databases in market based on NOSQL
    • CAP Theorem
    • ACID Vs. CAP
    • OLTP Solutions with different capabilities
    • Which Nosql based solution is capable to handle specific requirements
    • Examples of companies like Google, Facebook, Amazon, and other clients who are using NOSQL based databases
    • HBase Architecture of column families
    • How to work on Map Reduce in real time
    • Map Reduce complex scenarios
    • Introduction to HBase
    • Introduction to other NOSQL based data models
    • Drawbacks of Hadoop
    • Why Hadoop can not work for real time processing
    • How HBase or other NOSQL based tools made real time processing possible on the top of Hadoop
    • HBase table and column family structure
    • HBase versioning concept
    • HBase flexible schema
    • HBase Advanced
    • Introduction to Zookeeper
    • How Zookeeper helps in Hadoop Ecosystem
    • SQOOP Data Exchange Tool and Revision of All Covered Topics

      How to load data from Relational storage in Hadoop
    • Sqoop basics
    • Sqoop practical implementation
    • Sqoop alternative
    • Sqoop connector
    • Quick revision of previous classes to fill the gap in your understanding and correct understandings
    • How to load data in Hadoop that is coming from web server or other storage without fixed schema
    • How to load unstructured and semi structured data in Hadoop
    • Introduction to Flume
    • Hands-on on Flume
    • How to load Twitter data in HDFS using Hadoop
    • Introduction to Oozie
    • How to schedule jobs using Oozie
    • What kind of jobs can be scheduled using Oozie
    • How to schedule jobs which are time based
    • Hadoop releases
    • From where to get Hadoop and other components to install
    • Introduction to YARN
    • Significance of YARN
    • Introduction to Hue
    • How Hue is used in real time
    • Hue Use cases
    • Real time Hadoop usage
    • Real time cluster introduction
    • Hadoop Release 1 vs Hadoop Release 2 in real time
    • Hadoop Real time project Based on RDBMS, File System and Hadoop Ecosystem
    • Major POC based on combination of several tools of Hadoop Ecosystem
    • Comparison between Pig and Hive real time scenarios
    • Real time problems and frequently faced errors with solution
    • Introduction to Spark
    • Introduction to scala
    • Basics Features of SPARK and Scala available in Termoinal Based UI
    • Spark and Scala Advanced

      Why Spark demand is increasing in market
    • How can we use Spark with Hadoop Eco System
    • Datasets for practice purpose
    • Spark use cases with real time scenarios
    • Introducion to Spark SQL
    • Code in elipse on Spark SQL
    • Spark DataFrame and limitations in Spark 1.x
    • Spark DataFrame and Improvement in Spark 2.x
    • 3 Codes comparison in Spark IDE
    • How to design Custom function in Spark
    • This training program contains 5 POCs and Two real time projects with problem statements and data sets
    • This training is based on multi node Hadoop Cluster machines
    • We provide you several data sets which you can use for further practices on Hadoop
    • 42 Hours Classroom Section, 30 Hours of assignments, 25 hours for One Project and 50 Hrs for 2 Project, 350+ Interview Questions
    • Administration and Manual Installation of Hadoop with other Domain based projects will be done on regular basis apart from our normal batch schedule .We do have projects on Healthcare , Financial , Automotive ,Insurance , Banking , Retail etc , which will be given to our students as per their requirements
Cloudera Certification
Hortonworks Certification
Training + Certification by It working Professionals
We Provide BigData Hadoop+ Spark Training by IT Working Professionals
We Provide Training by IT Working Professionals only. All of our Trainer are working  in IT and have Real time Project and Domain Knowledge

Please contact us @ 860-099-8107 for batch details!

Banking , healthcare and Retail Domain

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