HADOOP DEVELOPER TRAINING

HADOOP DEVELOPER TRAINING

MyPadhai provides the best Hadoop Developer Training in India. MyPadhai is a standout amongst the most Hadoop Developer Training platforms on Pan India level offering hands-on handy learning and full employment help with fundamental and in addition propelled level Hadoop instructional classes. At MyPadhai, Hadoop Developer Training is led by subject pro-corporate experts with 9+ years of involvement in overseeing continuous Hadoop ventures. MyPadhai executes a mix of a Hadoop learning and viable sessions to give the understudy ideal presentation that guides in the change of guileless understudies into intensive experts that are effortlessly enlisted inside the business.

Hadoop Developer instructional class incorporates “Learning by Experiments” methodology to get Hadoop Developer Training and performing continuous practices and ongoing balance. These additional standard practices with live condition involvement in Hadoop Developer Training guarantees that you are prepared to apply your Hadoop information in huge enterprises after the Hadoop Developer training is finished.

On the off chance that we discussed arrangement situations, at that point, MyPadhai is one and just the best Hadoop Developer training platform in India. We have set many contenders to enormous MNCs till now. Hadoop Developer Training is overseen amid Week Days Classes from 9:00 AM to 6:00 PM, Weekend Classes in the meantime. We have additionally game plan if any competitor needs to learn best Hadoop Developer training in less time term.

Hadoop Developer brings fitness to cheaply prepare a lot of information, paying little mind to its development. By substantial, we show from 10-100 gigabytes or more. A student gets the probability to take in every single specialized detail with MyPadhai and turn into power quickly. MyPadhai has arranged an assortment of showing programs relying upon well-known needs and time. This course is unique is organized such that it finishes the total training inside a brief timeframe and spares cash and important time for individuals.

It can be exceptionally useful for individuals who are at this point working. The training staffs of MyPadhai put stock in building a fledgling from the base and making a specialist of them. Different types of training are directed; test, taunt undertakings and useful issue tackling lessons are embraced. The sensible based training modules are fundamentally arranged by MyPadhai to draw a pro out of all.

Requirements

This course is suitable for engineer’s will identity composing, keeping up as well as improving Hadoop occupations. Members ought to have a programming background; learning Java is exceedingly suggested. Comprehension of regular software engineering ideas is an or more. Earlier learning of Hadoop is not required.

Hands-On Exercises

Throughout the course, understudies compose Hadoop code and perform different hands-on activities to cement their comprehension of the ideas being exhibited.

Discretionary Certification Exam

Following effective consummation of the instructional course, participants can get a Cloudera Certified Developer for Apache Hadoop (CCDH) hone test. MyPadhai Training and the practice test together give the best assets to get ready for the accreditation exam. A voucher for the preparation can be gained in the mix with the preparation.

Target Group

This session is suitable for designers will identity composing, keeping up or streamlining

Hadoop employments

Members ought to have programming knowledge, ideally with Java. Comprehension of calculations and other software engineering points is an or more.

IT Skills Training Services is leading 4 days Big-Data and Hadoop Developer accreditation preparing, conveyed by guaranteed and exceptionally experienced coaches. We IT Skills Training Services are one of the best Big-Data and Hadoop Developer Training organizations. This Big-Data and Hadoop Developer course incorporates intelligent Big-Data and Hadoop Developer classes, Hands-on Sessions, Java Introduction, free access to web-based preparing, rehearse tests, and Hadoop Ecosystems Included.

AZ-300 Microsoft Azure training Course Fees & Duration

TRACK	Week Days	Weekend	Fast Track
Course Duration	75 Days	8 Weekends	15 Days
Hours	2 Hours Per Day	3 Hours Per Day	6+ Hours Per Day
Training Mode	Classroom/ Online	Classroom/ Online	Classroom/ Online
Indian Candidate		Overseas Candidate

Get Certification in Big Data and Hadoop Development from MyPadhai. The preparation program is stuffed with the Latest and Advanced modules like YARN, Flume, Oozie, Mahout, and Chukwa.

• 1 Days Instructor-Led Training
• 1 Year eLearning Access
• Virtual Machine with Built-in Data Sets
• 2 Simulated Projects
• Receive Certification on Successful Submission Of Project
• 45 PMI PDU Certificate
• 100% Money-Back Guarantee

Other Related Courses:

• Hadoop Developer Training in Delhi
• Hadoop Developer Training in Gurgaon

Career Benefits of Big Data/Hadoop Developer

• Career growth.
• The pay package increases.
• Job Opportunities will increases.

Key Features of Big Data & Hadoop 2.5.0 Development Training are:

• Design POC (Proof of Concept): This process is used to ensure the feasibility of the client application.
• Video Recording of every session will be provided to candidates.
• Live Project Based Training.
• Job-Oriented Course Curriculum.
• Course Curriculum is approved by Hiring Professionals of our client.
• Post Training Support will help the associate to implement the knowledge on client Projects.
• Certification Based Training is designed by Certified Professionals from the relevant industries focusing on the needs of the market & certification requirement.
• Interview calls till placement.

Fundamental: Introduction to BIG Data

Introduction to BIG Data

• Introduction
• BIG Data: Insight
• What do we mean by BIG Data?
• Understanding BIG Data: Summary
• Few Examples of BIG Data
• Why BIG data is a BUZZ?

BIG Data Analytics and why it is a Need Now?

• What is BIG data Analytics?
• Why BIG Data Analytics is a need now?
• BIG Data: The Solution
• Implementing BIG Data Analytics Different Approaches

Traditional Analytics vs. BIG Data Analytics

• The Traditional Approach: Business Requirement Drives Solution Design
• The Big Data Approach: Information Sources drive Creative Discovery
• Traditional and BIG Data Approaches
• BIG Data Complements Traditional Enterprise Data Warehouse
• Traditional Analytics Platform v/s BIG Data Analytics Platform.

Real-Time Case Studies

• BIG Data Analytics Use Cases
• BIG Data to predict your Customers Behaviors
• When to consider for BIG Data Solution?
• BIG Data Real-Time Case Study

Technologies within BIG Data Eco System

• BIG Data Landscape
• BIG Data Key Components
• Hadoop at a Glance

Fundamentals: Introduction to Apache Hadoop and its Ecosystem

The Motivation for Hadoop

• Traditional Large Scale Computation
• Distributed Systems: Problems
• Distributed Systems: Data Storage
• The Data-Driven World
• Data Becomes the Bottleneck
• Partial Failure Support
• Data Recoverability
• Component Recovery
• Consistency
• Scalability
• Hadoop History
• Core Hadoop Concepts
• Hadoop Very High/Level Overview

Hadoop: Concepts and Architecture

• Hadoop Components
• Hadoop Components: HDFS
• Hadoop Components: MapReduce
• HDFS Basic Concepts
• How Files Are Stored?
• How Files Are Stored. Example
• More on the HDFS NameNode
• HDFS: Points To Note
• Accessing HDFS
• Hadoop fs Examples
• The Training Virtual Machine
• Demonstration: Uploading Files and new data into HDFS
• Demonstration: Exploring Hadoop Distributed File System
• What is MapReduce?
• Features of MapReduce?
• Giant Data: MapReduce and Hadoop
• MapReduce: Automatically Distributed
• MapReduce Framework
• MapReduce: Map Phase
• MapReduce Programming Example: Search Engine
• Schematic process of a map-reduce computation
• The use of a combiner
• MapReduce: The Big Picture
• The Five Hadoop Daemons
• Basic Cluster Combination
• Submitting A job
• MapReduce: The JobTracker
• MapReduce: Terminology
• MapReduce: Terminology Speculative Execution
• MapReduce: The Mapper
• Example Mapper: Upper Case Mapper
• Example Mapper: Explode Mapper
• Example Mapper: Filter Mapper
• Example Mapper: Changing Keyspaces
• MapReduce: The Reducer
• Example Reducer: Sum Reducer
• Example Reducer: Identify Reducer
• MapReduce Example: Word Count
• MapReduce: Data Locality
• MapReduce: Is Shuffle and Sort a Bottleneck?
• MapReduce: Is a Slow Mapper a Bottleneck?
• Demonstration: Running a MapReduce Job

Hadoop and the Data Warehouse

• Hadoop and the Data Warehouse
• Hadoop Differentiators
• Data Warehouse Differentiators
• When and Where to Use Which

Introducing Hadoop Ecosystem components

• Other Ecosystem Projects: Introduction
• Hive
• Pig
• Flume
• Sqoop
• Oozie
• HBase
• Hbase vs Traditional RDBMSs

Advance: Basic Programming with the Hadoop Core API

Writing MapReduce Program

• A Sample MapReduce Program: Introduction
• Map Reduce: List Processing
• MapReduce Data Flow
• The MapReduce Flow: Introduction
• Basic MapReduce API Concepts
• Putting Mapper & Reducer together in MapReduce
• Our MapReduce Program: WordCount
• Getting Data to the Mapper
• Keys and Values are Objects
• What is WritableComparable?
• Writing MapReduce application in Java
• The Driver
• The Driver: Complete Code
• The Driver: Import Statements
• The Driver: Main Code
• The Driver Class: Main Method
• Sanity Checking The Jobs Invocation
• Configuring The Job With JobConf
• Creating a New Job Conf Object
• Naming The Job
• Specifying Input and Output Directories
• Specifying the InputFormat
• Determining Which Files To Read
• Specifying Final Output With OutputFormat
• Specify The Classes for Mapper and Reducer
• Specify The Intermediate Data Types
• Specify The Final Output Data Types
• Running the Job
• Reprise: Driver Code
• The Mapper
• The Mapper: Complete Code
• The Mapper: import Statements
• The Mapper: Main Code
• The Map Method
• The map Method: Processing The Line
• Reprise: The Map Method
• The Reducer
• The Reducer: Complete Code
• The Reducer: Import Statements
• The Reducer: Main Code
• The reduce Method
• Processing The Values
• Writing The Final Output
• Reprise: The Reduce Method
• Speeding up Hadoop development by using Eclipse
• Integrated Development Environments
• Using Eclipse
• Demonstration: Writing a MapReduce program

Introduction to Combiner

• The Combiner
• MapReduce Example: Word Count
• Word Count with Combiner
• Specifying a Combiner
• Demonstration: Writing and Implementing a Combiner

Introduction to Partitioners

• What Does the Partitioner Do?
• Custom Partitioners
• Creating a Custom Partitioner
• Demonstration: Writing and implementing a Partitioner

Advance: Problem Solving with MapReduce

Sorting & searching large data sets

• Introduction
• Sorting
• Sorting as a Speed Test of Hadoop
• Shuffle and Sort in MapReduce
• Searching

Performing a secondary sort

• Secondary Sort: Motivation
• Implementing the Secondary Sort
• Secondary Sort: Example

Indexing data and inverted Index

• Indexing
• Inverted Index Algorithm
• Inverted Index: DataFlow
• Aside: Word Count

Term Frequency – Inverse Document Frequency (TF- IDF)

• Term Frequency Inverse Document Frequency (TF-IDF)
• TF-IDF: Motivation
• TF-IDF: Data Mining Example
• TF-IDF Formally Defined
• Computing TF-IDF

Calculating Word co-occurrences

• Word Co-Occurrence: Motivation
• Word Co-Occurrence: Algorithm

Eco System: Integrating Hadoop into the Enterprise Workflow

Augmenting Enterprise Data Warehouse

• Introduction
• RDBMS Strengths
• RDBMS Weaknesses
• Typical RDBMS Scenario
• OLAP Database Limitations
• Using Hadoop to Augment Existing Databases
• Benefits of Hadoop
• Hadoop Tradeoffs

Introduction, usage and Basic Syntax of Sqoop

• Importing Data from an RDBMS to HDFS
• Sqoop: SQL to Hadoop
• Custom Sqoop Connectors
• Sqoop: Basic Syntax
• Connecting to a Database Server
• Selecting the Data to Import
• Free-form Query Imports
• Examples of Sqoop
• Sqoop: Other Options
• Demonstration: Importing Data With Sqoop

Eco System: Machine Learning & Mahout

Basics of Machine Learning

• Machine Learning: Introduction
• Machine Learning – Concept
• What is Machine Learning?
• The Three Cs
• Collaborative Filtering
• Clustering
• Clustering – Unsupervised learning
• Approaches to unsupervised learning
• Classification
• Lesson 2: Basics of Mahout
• Mahout: A Machine Learning Library
• Demonstration: Using a Mahout Recommender

Eco System: Hadoop Eco System Projects

HIVE

• Hive & Pig: Motivation
• Hive: Introduction
• Hive: Features
• The Hive Data Model
• Hive Data Types
• Timestamps data type
• The Hive Metastore
• Hive Data: Physical Layout
• Hive Basics: Creating Table
• Loading Data into Hive
• Using Sqoop to import data into HIVE tables
• Basic Select Queries
• Joining Tables
• Storing Output Results
• Creating User-Defined Functions
• Hive Limitations

PIG

• Pig: Introduction
• Pig Latin
• Pig Concepts
• Pig Features
• A Sample Pig Script
• More PigLatin
• More PigLatin: Grouping
• More PigLatin: FOREACH
• Pig Vs SQL

Oozie

• Purpose of Oozie
• The Motivation for Oozie
• What is Oozie
• HPDL
• Working with Oozie
• Oozie workflow Basics
• Workflow Nodes
• Control flow Node – Start Node
• Control flow Node – End Node
• Control flow Node – Kill Node
• Control flow Node – Decision Node
• Control flow Node – Fork and Join Node
• Oozie: Example
• Oozie Workflow: Overview
• Simple Oozie Example
• Oozie Workflow Action Nodes
• Submitting an Oozie Workflow
• More on Oozie

Flume

• Flume: Basics | Flume’s high-level architecture
• Flow in Flume | Flume: Features
• Flume Agent Characteristics | Flume Design Goals: Reliability
• Flume Design Goals: Scalability | Flume Design Goals: Manageability
• Flume Design Goals: Extensibility | Flume: Usage Patterns

This excellent Hadoop Developer Training is developed to offer the best support to the students who want to grow their career in this field. The course content is prepared by the experts in this field which will be easy to understand for all the students. We are prepared with a professional training course to make the students aware of each strategy used while integrating the Hadoop in the real-time industry. As a leading Hadoop Developer Training Platform, we will also offer you a certification and the placement assistance too. In this way, we become the best platform in this domain.