Hadoop Training 2018-02-13T07:41:36+00:00

Hadoop Training

By Our Industry Expert

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Course Type: OnlineClassroom

Hadoop training by Sathya Technologies helps you build application and solution for Big Data. Hadoop is the back bone for processing the Big Data applications. With digital marketing and social media marketing taking the forefront, there is a constant demand from industries to analyse, process and get analytical data for better business planning. Hadoop and Big Data application are used extensively across industry verticals to carry out these activities.

Why This Course?

  • Average salaries for Hadoop professionals are about Rs. 12 to 16 lacs PA.
  • With Big Data in constant demand for managing very large unstructured data, Hadoop will be in constant use.
  • Use of Big Data applications are increasing year-on- year across industry verticals ensuring a constant demand for Hadoop developers.

Upcoming Batches

06
JAN
Saturday
7:00 AM IST
06
JAN
Saturday
7:00 AM IST
06
JAN
Saturday
7:00 AM IST
06
JAN
Saturday
7:00 AM IST

About Course

There is a lot of Data that keeps flooding from various social network sites, public information sites , Internet Archives etc .To manage such large amounts of data we have Big Data. Hadoop is the backbone for Big Data. Hadoop is a set of programs and procedures used extensively when we learn about BigData. It helps in distributed storage and processing of data of Big Data. Understanding Hadoop is a highly valuable skill for anyone working with large amounts of data.It is a programming model which involves large scale processing of data within reasonable time framework.

At Sathya Technologies , we provide a detailed understanding of the concepts of Hadoop and practical usage of the technology. The training starts with introduction of the scope of Hadoop and understanding the scenarios in which it can be applied. Proceeding further , the training focuses on learning the Pillars of Hadoop which is HAdoop Distributed File System and Map Reduce. The remaining part of the taining program consists of learning the various concepts that build the Hadoop ecosystem like HIVE, PIG,HBASE,SQOOP,NOSQL,FLUME.

Course Objective

  • Master the Hadoop Distributed File System
  • Learn Map Reduce and Architecture and understanding its Programming model.
  • Working with Hive Query Language and learn more about the Hive Architecture

How the program will be conducted

Sathya Technologies with its start-of- art class rooms and Lab infrastructure at Ameerpet Hyderabad offer the best and most conducive learning environment, with a team of highly skilled trainers having years of industry experience.  Classroom trainings will be conducted on a daily basis. Practical exercises are provided for the topics conducted on daily basis to be worked upon during the lab session.  Online session conducted through the virtual classroom also have the same program flow with theory and practical sessions.  Our Labs can be accessed online from across the world allowing our online training student to make the best use of the infrastructure from the comfort of their home.

Career Opportunities in Hadoop

With the popularity of Big Data increasing exponentially, opportunities as Hadoop administrators/consultants/analytics has been growing in all major industry sectors like Financial application, Enterprise processing, Business Service sector etc.  Training programs on Hadoop technology by Sathya Technologies focuses on empowering the students with the latest concepts and industry specific topics.  Our well experienced trainer and well planned course materials ensures for 100% success in interviews.

Who can learn?

Targeted Audience

  • Java consultants
  • DBA consultants
  • SQL Experts
  • College Freshers with Programming background
  • ETL Professionals

Prerequisite to learn the course

Having basic knowledge on LINUX would be helpful in learning Hadoop. Also knowing the basic programing principles of Java would be an added advantage. Knowledge in SQL will improve the overall learning experience.

Hadoop Course Syllabus

Duration: 40 Hours

  • Map Reduce Architecture
  • Map Reduce Programing Model
  • Map Reduce Program structure
  • Hadoop streaming
  • Executing Java – Map Reduce Job
  • Understanding of Java Map Reduce Classes
  • Configuration
  • Path
  • Job
  • Mapper
  • Reducer
  • Text
  • Intwritables
  • Long writables
  • File Input Format
  • File Output Format
  • Generic Options Pavser

  • Map Joins
  • reduce Joins

  • Python Map Reduce
  • Unit Testing Mapeduce Jobs
  • Hadoop Pipelining
  • Creating Input and Output Formats in Map Reduce Jobs
  • Text Input Format
  • Key Value Input Format
  • Sequence File Input Format
  • Data Localization in Map Reduce
  • Examples

  • Introduction
  • Hive Architecture
  • Hive Metastore
  • Hive Query Launguage
  • Difference between HQL and SQL
  • Hive Built in Functions
  • Hive UDF (user defined functions)
  • Hive UDAF (user defined Aggregated functions)
  • Hive UDTF (user defined table Generated functions)
  • Hive Serde?
  • Hive & Hbase Integration
  • Hive Working with unstructured data
  • Hive Working With Xml Data
  • Hive Working With Json Data
  • Hive Working With Urls And Weblog Data
  • Hive – Json – Serde
  • Loading Data From Local Files To Hive Tables
  • Loading Data From Hdfs Files To Hive Tables
  • Tables Types
  • Inner Tables
  • External Tables
  • Partitioned Tables
  • Non – Partitioned Tables
  • Dynamic Partitions In Hive
  • Concept Of Bucketing
  • Hive Views
  • Hive Unions
  • Hive Joins
  • Multi Table / File Inserts
  • Inserting Into Local Files
  • Inserting Into Hdfs Files
  • Array Operations In Hive

  • Hive UDF by Java
  • Hive UDF by Python

  • Introduction to pig
  • Pig Latin Script
  • Pig Console / Grunt Shell
  • Execting Pig Latin Script
  • Pig Relations, Bags, Tuples, Fields
  • Data Types
  • Nulls
  • Constants
  • Expressions
  • Schemas
  • Parameter Substitution
  • Arithmetic Operators
  • Comparison Operators
  • Null Operators
  • Boolean Operators
  • Defence Operators
  • Sign Operators
  • Flatten Operators
  • Caster Operators

  • Ico group
  • Cross
  • Distinct
  • Filter
  • Foreach
  • Group
  • Join (Inner)
  • Join (Outer)
  • Limit
  • Load
  • Order
  • Sample
  • Spilt
  • Store
  • Union

  • Describe
  • Dump
  • Explain
  • Illustrate

  • Avg
  • Concat
  • Count
  • Coni-star
  • Diff
  • Is Empty
  • Max
  • Min
  • Size
  • Sum
  • Tokenize
  • writing Custom UDFS in Pig
  • Using Java
  • Using Python

  • Introduction to Sqoop
  • SQOOP Import
  • SQOOP Export
  • Importing Data From RDBMS to HDFS
  • Importing Data From RDBMS to HIVE
  • Importing Data From RDBMS to HBASE
  • Exporting From HASE to RDBMS
  • Exporting From HBASE to RDBMS
  • Exporting From HIVE to RDBMS
  • Exporting From HDFS to RDBMS
  • Transformations While Importing / Exporting
  • Defining SQOOP Jobs

  • What is “Not only SQL”
  • NOSQL Advantages
  • What is problem with RDBMS for Large
  • Data Scaling Systems
  • Types of NOSQL & Purposes
  • Key Value Store
  • Columer Store
  • Document Store
  • Graph Store
  • Introduction to ricsk – NOSQL Database
  • Introduction to cassandra – NOSQL Database
  • Introduction to MangoDB and CouchDB Database
  • Introduction to Neo4j – NOSQL Database
  • Intergration of NOSQL Databases with Hadoop

  • Introduction to big table
  • What is NOSQL and colummer store Database
  • HBASE Introduction
  • Hbase use cases
  • Hbase basics
  • Column families
  • Scans
  • Hbase Architecture
  • Clients
  • Rest
  • Thrift
  • Java
  • Hive
  • Map Reduce Integration
  • Map Reduce Over Hbase
  • Hbase data Modeling
  • Hbase Schema design
  • Hbase CRUD operators
  • Hive & Hbase interagation
  • Hbase storage handles

  • Introduction to OOZIE
  • OOZIE as a seheduler
  • OOZIE as a Workflow designer
  • Seheduling jobs (OOZIE CODE)
  • Defining Dependences between jobs
  • (OOZIE Code Examples)
  • Conditionally controling jobs
  • (OOZIE Code Examples)
  • Defining parallel jobs (OOZIE Code Examples)

  • Introduction to FLUME
  • What is the streaming File
  • FLUME Architecture
  • FLUME Nodes & FLUME Manager
  • FLUME Local & Physical Node
  • FLUME Agents & FLUME Collector

  • Introduction to ZOOKEEPER
  • ZOOKEEPER Architecture
  • Controlling Connection of Distbrited Apps
  • HBASE & ZOOKEEPER
  • Flume & ZOOKEEPER
  • A Sample Code

  • Introduction and Tools
  • Purpose of Visualfoce
  • MVC Architecture

Certification

Sathya technologies offer certification programs for BlockChain technology. Certificates are issues on successful completion of the course and the assessment examination. Students are requested to participate in the real-time project program to get first-hand experience on the usage and application of the BlockChain technology. The real-time projects are designed by our team of industry experts to help students get best possible exposure to the BlockChain technology and its applications.

FAQS

In the event of missing out of attending any session the student can

  • Attend the same in another batch for classroom training or online training
  • Get access to the recording for the missed session

With over 2 decades of experience in training and consulting Sathya technologies have a large client base and we constantly get requirements for providing good resources.  On successful completion of the course and completion of real-time project, students are requested to submit the updated profile with Sathya Technologies.  Our placement team will notify the students as and when suitable opportunities are available.

Free demo sessions are conducted before the start of every batch which allows the students to get a firsthand interaction with the trainer. These demo session also help the student to understand the training methodology followed by Sathya Technologies and also help visit and review the classroom and lab infrastructure and facilities.

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This Sliding Bar can be switched on or off in theme options, and can take any widget you throw at it or even fill it with your custom HTML Code. Its perfect for grabbing the attention of your viewers. Choose between 1, 2, 3 or 4 columns, set the background color, widget divider color, activate transparency, a top border or fully disable it on desktop and mobile.
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