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Big Data Hadoop
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Big Data Hadoop

Big Data Hadoop is the next gen highly demanding technology today. It has very high capacity and potential to sustain in the market need. Simply put, it is the technology behind all the big social sites, search engines and advertisements. With this framework we can process huge data in terms of Velocity, Volume and Variery, it is set to change the IT industries in a big way that data is stored and processed. The employees and candidates who are not upgrading in the Big Data skills will be laid off in huge number in coming years. We are a focused enterprise that strongly believes in empowering minds with multi skills and enlightening them for current and future need. It making us best big data hadoop training institute in Pimpri Chinchwad.

Requirements
  • Basic knowledge of DBMS/SQL concepts.
  • Basic knowledge of any Programming Language/Core Java/Oops concepts.
  • Minimum i3 processor machine with 8GB RAM.
  • Windows 7/8/10 Or Ubuntu/Linux any stable OS.
FAQ
Can I just enroll in a single course?

Yes, You can enroll for any single course. Prior you have to send us an email or enquiry for the same.

Can I enroll for this course without any IT background?

Yes. You can enroll but you will have to work hard for learning from the scratch.

What is the refund policy?

Yes. 70% amount can be refunded in case of enrollment cancellation is done in between 1-15 days from starting of course.

What background knowledge is necessary?

You must have basic knowledge of any programing language and dbms/sql.

Do i need to take the courses in a specific order?

Yes, we always recommend that course has to be taken from topics/subjects order to undestand better.

Big Data Hadoop

Hadoop is an open source distributed processing framework and technology that manages huge data processing and storage for applications running in clustere.

Big data Concepts
    Distributed network and computation
    Challenges in data management and control
    Introduction to Big Data
    Types of data in detail
    Sources of Big Data
    Concept of Streaming data
    Batch and Streaming data processing  
    Big data Hadoop future opportunities

Hadoop Overview                                      
    Need of Hadoop technology
    Overview of Data centers and Cluster
    Hadoop Cluster and Racks in detail
    Learning Ubuntu for Hadoop
    Overview of Hadoop tools
    Overview of Map Reduce
    Big data Concepts
    Distributed network and computation
    Challenges in data management and control
    Introduction to Big Data
    Types of data in detail
    Sources of Big Data
    Concept of Streaming data
    Batch and Streaming data processing  
    Big data Hadoop future opportunities

Hadoop Overview                                      
    Need of Hadoop technology
    Overview of Data centers and Cluster
    Hadoop Cluster and Racks in detail
    Learning Ubuntu for Hadoop
    Overview of Hadoop tools
    Overview of Map Reduce
    Understanding the Hadoop Installation and Configuration
    5 daemons of Hadoop
    Name Node and its functionality
    Data Node and its functionality
    Secondary Name Node and its functionality
    Job Tracker and its functionality
    Task Tracker and its functionality

HDFS
   HDFS Daemons
   Introduction about Blocks
   Data replication
   Hadoop DFS and Processing 
   Fault Tolerance in Hadoop        
   Files operations in Hadoop
   FS shell commands in use
 
YARN (Yet Another Resource Negotiator)
   Introduction to YARN 
   YARN Daemons
   Job assignment and Execution flow
   Map Reduce Programming Model
   Word count program demonstration

Apache Pig
   Introduction to Apache Pig
   Apache pig Architecture
   Advantage of Pig over MapReduce programming
   Pig Latin and Grunt Shell
   Pig Latin basics
   Operators in Pig
   Group, Join
   Split and Combine
   Built in functions
   Load and Store function
   Date, Math and String functions
   Schema and Schema-less data in Pig
   Data processing in Pig
   Pig UDFs writing
   HCatalog
   Pig and Hive comparison

Apache Hive
   Data warehouse basics
   OLTP and OLAP Concepts
   Introduction to Hive 
   Hive Architecture in detail
   Metastore DB and Metastore Service
   Hive Query Language (HQL)
   Types of table in hive
   Partitioning and Bucketing
   Built in Functions
   Case writing
   Views and Indexes
   Joins and Group by
   Sorting, Distribute by
   Query Optimization methods
   JDBC , ODBC connection to Hive
   Hive Transactions
   Hive UDFs and UDAFs
   Working with file formats

Sqoop
   Sqoop basic commands
   Sqoop practical implementation
   Sqoop Architecture
   Database operations
   Importing RDBMS data to HDFS
   Importing RDBMS data to Hive
   Exporting data to RDBMS
   Sqoop connectors
   Importing into Hive

Flume
  Flume Architecture
  Flume Environment
  Data transfer and data flow
  Flume Configuration
  Configuration of Source, Channel and Sink
  Loading from web server or other storage data
  Loading from raw/flow data in HDFS using flume

Oozie
  Introduction to Oozie
  Designing workflow jobs
  Job scheduling using Oozie
  Time based job scheduling
  Oozie Conf file
  Crontab use and implementation

Hands on HUE (Hadoop User Interface) 
  HUE usage
  User management
  Using Pig, Hive, Impala

Impala
  Impala overview
  Impala Architecture
  Impala Vs Hive
  Impala Built in functions
  Connecting to impala
  Java for impala JDBC
  Output creation

HBASE Basics
  Architecture and schema design
  HBase vs. RDBMS
  HMaster and Region Servers
  Column Families and Regions
  Write pipeline
  Read pipeline
  HBase commands

Domain Based Project With Real Time Data
100 Assignments

Course Review
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