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Big Data Hadoop Training in Pune with Real Time Classes

    Technogeeks is biggest in BigData Hadoop, Spark, NoSQL, Cloud Computing Tools Training and Placement We have placed more than 800 people in IT Industry in 2017 and count is increasing including IT Working profssionals and Freshers Candidates!! Technogeeks provides not only Trainings but also Placement callss guarantee without any limitations factros So, Come To Lean and Go To Lead!!

    Why Join Hadoop+Spark+NoSQL+Hadoop on Cloud in Technogeeks:

    Duration: 60 hours classroom program
    10 Weekends
    70+ Assignments in classroom
    4 POCs , 2 Real time Projects
    Note: We Implement Project in Classroom itself!!
    We Provide Proper Guidance on Resume Building
    We also Train on Hadoop On Cloud using AWS Cluster
    100% Placements Calls Guarantee!!

    Introduction To Hadoop Ecosystem

    • Why we need Hadoop
    • Why Hadoop is in demand in market now a days
    • Key points , Why Hadoop is leading tool in current IT Market
    • Definition of BigData
    • Hadoop nodes
    • Introduction to Hadoop
    • Hadoop Daemons in Hadoop Release-1.x
    • Hadoop Daemons in Hadoop Release-2.x
    • Hadoop Release-3.x
    • Hadoop Cluster and Racks
    • Hadoop Cluster Demo
    • Types of Projects in Hadoop in Different Domains
    • Clients want POC and migration of Existing tools and Technologies on Hadoop Technology
    • How Open Source tool (HADOOP) is capable to run jobs in lesser time which take longer time in
    • Why we need Spark and NOSQL DataBases with Hadoop
    • HDFS (Hadoop Distributed file system)
    • Hadoop Processing Frameworks Map Reduce And YARN
    • Distributed warehouse Hive and Impala
    • Most demanding tools which can run on the top of Hadoop as part of Hadoop Ecosystem
    • Data import/Export tools

    Hadoop Installation and Basic Hands on Cluster

    • Hadoop installation
    • Introduction to Hadoop FS and Processing Environment’s UIs
    • How to read and write files
    • Basic Unix commands for Hadoop
    • Putty-based access
    • Tools for Data Transmission
    • Hadoop FileSystem Shell Commands
    • Hadoop Releases Hands-on
    • Hadoop daemons
    • How to Copy Data Across Clusters
    • Data Sets for Basic Operations in Hadoop File System using CLI

    Introduction to Pig (ETL Tool)

    • Pig Introduction
    • Why Pig if Map Reduce is there?
    • How Pig is different from Programming languages
    • Pig Data flow Introduction
    • Why Schema is optional in Pig
    • Pig Data types
    • Pig Commands – 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
    • Files Data processing in Pig
    • Semi-structured data processing in Pig
    • Pig Advanced Assignment
    • Real time scenarios on Pig
    • When we should use Pig
    • When we shouldn’t use Pig
    • Live examples of Pig Use cases

    Introduction To Hive and Impala (DataWarehouse)

    • Meta storage in Hive
    • Introduction to Derby Database
    • 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

    Advanced concepts in Hive

    • Hive Releases
    • Hive OLP Applications
    • Partitioning
    • Bucketing
    • Different File Formats in Hive
    • Performance optimization in Hive
    • Load based Teting in Hive
    • Hive on Cloud
    • Hive logs analysis
    • How to run Hive in differnt modes like MapReduce and Spark Mode
    • Why we use Parquet File Formats in Most of The Projects
    • Application of External Tables in Hive
    • UDF and UDAF in Hive
    • HQL and HPL Languages
    • How to Capture Inserts, Updates and Deletes in Hive
    • How to Query in Data using Hive and Implala
    • How to Convert SQL in HQL Logic
    • Hive Architecture
    • Thrift Server
    • Hue Interface for Hive
    • How to analyze data using Hive script
    • Differentiation between Hive and Impala
    • 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
    • Application of Hive in Reporting
    • Hive and Database Connectors

    Map Reduce Framework and APIs

    • MapReduce Introduction
    • Mapper and Reducer Classes and Methods in these Classes
    • Shuffler, Partitioner and Combiner
    • MapReduce Hands-on
    • MapReduce, Pig and Scala Comparison
    • 1. X MapReduce APIs
    • 2. X MapReduce APIs
    • Input Split Concept and HDFS Block mechanism
    • Writables Data types in Hadoop
    • Binary Data Processing in MapReduce
    • Usage of Reduce
    • Output Collector, Reporter and Context
    • Types of Joins in MapReduce
    • How to Set Number of Map and Reduce Tasks
    • Relationship between number of Reducer and returns in Partitioner class
    • Performance Optimization Techniques
    • Counters
    • Distributed Cache
    • MRunit
    • Reduce Side Join,Replicated Join
    • Composite Join,Cartesian Product
    • File Input Format
    • File Output Format
    • Sub-classes of File Input Format and FIle Output Format
    • Sequence Input Format
    • Different Parsers in MapReduce
    • MapReduce Implementation with ore library
    • MapReduce and Java Library Integration
    • POC based on Pig, Hive, HDFS, MR

    Java for MapReduce

    • Introduction to Java Process
    • Byte code generation and Execution with the help of JVM
    • Variables Declaration in Java
    • Java Data types
    • Collections
    • OOPs Concepts
    • Constructors and Methods
    • Method Overloading and Overriding
    • Garbage collector
    • Nested classes
    • Final and Static Keywords
    • Abstract classes and Interfaces
    • Java Security Classes
    • Java library with core packages

    NOSQL Databases and Introduction to HBase

    • 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

    Advanced Map Reduce and HBase

    • 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’t 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

    Zookeeper and SQOOP

    • Introduction to Zookeeper
    • How Zookeeper helps in Hadoop Ecosystem
    • 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

    Flume , Oozie (Job Scheduling Tool) and YARN Framework

    • 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

    Hue, Hadoop Releases comparison, Hadoop Real time scenarios

    SPARK and Scala Basics

      • 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
      • 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

      SPARK and Scala Advanced

      Additional Benefits

      Hadoop on Cloud

      Additional Benefits

        • Introduction to Spark
        • Introduction to scala
        • Basics Features of SPARK and Scala available in Hue
        • 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
        • Spark RDDs Transformations and Actions
        • Spark SQL, DataSets and Data Frames
        • Spark Streaming
        • Real time project use cases examples based on Spark and Scala
        • How we can reduce
        • 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 from Healthcare , Financial , Automotive ,Insurance , Banking , Retail etc , which will be given to our students as per their requirements .
        • Introduction to Cloud Computing
        • AWS SaaS, Paas and IaaS
        • Introduction to EC2 Instance for Processing
        • Introduction to S3 Buckets
        • Hadoop Tools commands on the Cloud
        • EMR Instances
        • Hands-on experinece on Hadoop Ecosystem on AWS
        • How to Work in real time project on Hadoop in Cloud
        • We have one-o-one Batch facility also
        • Limited candidates in a batch
        • Fee access of material and classroom training for one year
        • Certification preparation also included in Training
        • Real time project will be implemented in class
        • Interview Preparation and CV updates
Hadoop, Big Data, Spark, Scala, NoSQL based training.
We Cover Cloudera and Hortonworks Certification Track also in same training 
This course is designed to provide you training with Real time project Implementation in your system and after training you will also get assitance abut placement in MNCs. We provide Placement Assistance Guarantee because we have tie ups with Many Companies and HRs to provide skilled candidates to them in time.
You can also come and join the Free demo session and meet our experts on multiple skill sets because Technogeeks is the platform where Techies and Candidates can meet and can get the proper guidance about the career path and Technology track free of cost.
Hadoop BigData is one of the demanding technology in IT Industry and New Era of Hadoop Big Data is Spark and Data Science.
Now Hadoop with Spark and Data Science is the best combination for the clients to manage historical data in warehouse repository. Technogeeks is one of the leading Institute in Pune that Provides the Training and Project Combination by Real time IT Experts from different MNCs.

In Technogeeks more than 400 Candidates placed in past one year with package mor than 8 LPA and majority of the candidates got the job on Hadoop Big Data and Analytics fields.

Technogeeks provides Training by only IT working professionals. You can chek about our trainers on our google page.

We Start new batch of Hadoop Big Data including Spark, Scala and NoSQL Every Saturday.
Please reach us at contact@technogeekscs.co.in for more datils.

We Cover below mentioed projects as part of the Training:
Banking Domain Project
Healthcare Domain Project for Data Analytics

Enquiry Now !