Hadoop Developer

4 Star Rating: Very Good 4.48 out of 5 based on 241 ratings.

Hadoop Developer Training in Noida & Best Hadoop Developer Training Center in Noida

Croma campus offers Hadoop Developer Training in Noida understudies a chance to make strong information about Hadoop Developer Training in Noida handling applications utilizing Apache Hadoop Training institute in Noida. In the wake of finishing this course, understudies will have the capacity to understand work process execution and working with APIs by executing joins and composing Map Reduce code.

This course will offer the most amazing practice condition for this present reality issues confronted by Hadoop designers. With Big Data being the popular expression, Hadoop Training institute in Noida accreditation and aptitudes are being looked for by organizations all over the globe. Enormous Data Analytics is the need for some vast associations, and it causes them enhance execution. In this manner, experts with Big Data Hadoop ability are required by the business on the loose.

This four-day instructional class is for engineers who need to figure out how to utilize Apache Hadoop to assemble capable information handling applications.

Requirements

This course is suitable for engineer’s will identity composing, keeping up as well as improving Hadoop occupations. Members ought to have programming background; learning of 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. Croma campus 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 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 and then some.

Get Certification in Big Data and Hadoop Development from Croma campus. 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

Career Benefits of Big Data/Hadoop Developer

  • Career growth.
  • 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 helps the associate to implement the knowledge on client Projects.
  • Certification Based Training are 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 its 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 Eco system 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
  • Cloudera Certified Developer for Hadoop

    (CCDH) Exam Code: CCD-410

    hadoop_Certification

    Cloudera Certified Developer for Apache Hadoop Exam:
    • Number of Questions: 50 - 55 live questions
    • Item Types: multiple-choice & short-answer questions
    • Exam time: 90 Mins.
    • Passing score: 70%
    • Price: $295 USD

    Syllabus Cloudera Develpoer Certification Exam

    Infrastructure Objectives 25%
    • Recognize and identify Apache Hadoop daemons and how they function both in data storage and processing.
    • Understand how Apache Hadoop exploits data locality.
    • Identify the role and use of both MapReduce v1 (MRv1) and MapReduce v2 (MRv2 / YARN) daemons.
    • Analyze the benefits and challenges of the HDFS architecture.
    • Analyze how HDFS implements file sizes, block sizes, and block abstraction.
    • Understand default replication values and storage requirements for replication.
    • Determine how HDFS stores, reads, and writes files.
    • Identify the role of Apache Hadoop Classes, Interfaces, and Methods.
    • Understand how Hadoop Streaming might apply to a job workflow
    Data Management Objectives 30%
    • Import a database table into Hive using Sqoop.
    • Create a table using Hive (during Sqoop import).Successfully use key and value types to write functional MapReduce jobs.
    • Given a MapReduce job, determine the lifecycle of a Mapper and the lifecycle of a Reducer.
    • Analyze and determine the relationship of input keys to output keys in terms of both type and number, the sorting of keys, and the sorting of values.
    • Given sample input data, identify the number, type, and value of emitted keys and values from the Mappers as well as the emitted data from each Reducer and the number and contents of the output file(s).
    • Understand implementation and limitations and strategies for joining datasets in MapReduce.
    • Understand how partitioners and combiners function, and recognize appropriate use cases for each.
    • Recognize the processes and role of the the sort and shuffle process.
    • Understand common key and value types in the MapReduce framework and the interfaces they implement.
    • Use key and value types to write functional MapReduce jobs.
    Job Mechanics Objectives 25%
    • Construct proper job configuration parameters and the commands used in job submission.
    • Analyze a MapReduce job and determine how input and output data paths are handled.
    • Given a sample job, analyze and determine the correct InputFormat and OutputFormat to select based on job requirements.
    • Analyze the order of operations in a MapReduce job.
    • Understand the role of the RecordReader, and of sequence files and compression.
    • Use the distributed cache to distribute data to MapReduce job tasks. Build and orchestrate a workflow with Oozie.
    Querying Objectives 20%
    • Write a MapReduce job to implement a HiveQL statement.
    • Write a MapReduce job to query data stored in HDFS.

    View Details

Please write to us at info@cromacampus.com for the course price, schedule & location.

Enquire Now

Frequently Asked Questions:

All training courses offered by us are through IT Professional with 10+ years of experience. Freshers/College Students/Professionals(IT & Non-IT) can spot the quality of training by attending one lecture. Hence, we provide one free demo class to all our trainees so that they can judge on their own.

No, you don’t have to pay anything to attend the demo class. You are required to pay the training fee after free demo only if you are fully satisfied and want to continue the training.

To register for free demo, visit our campus or call our counsellors on the numbers given on contact us page.

Yes, all the trainees shall work on live projects provided by Croma Campus after completing their training part.

You will never lose any lecture. You can choose either of the two options:
View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.

Please note, access to the course material will be available for lifetime once you have enrolled into the course.

Yes, Training certificate & Project completion will be issued by Croma Campus(ISO 9001-2000 Certified Training Center)

Yes, Croma Campus conduct special training programs on week end for college students throughout the year.

Croma Campus is the largest education company and lots of recruitment firms contacts us for our students profiles from time to time. Since there is a big demand for this skill, we help our certified students get connected to prospective employers. We also help our customers prepare their resumes, work on real life projects and provide assistance for interview preparation. Having said that, please understand that we don’t guarantee any placements however if you go through the course diligently and complete the project you will have a very good hands on experience to work on a Live project.

Yes, Course Fee can be paid in two equal installments with prior Approval.

Yes, Croma Campus offer various group or special discounts.

No, Lab is open from 8 A.M. to 8 P.M. seven days a week. This time can be extended upto 11 PM if need arises.

Yes, students can take breaks during their exams and can resume it later without paying any fee. Apart from this, Students can attend batches for revision even after completion of their courses.

Batch strength differ from technology to technology. Minimum batch strength at Croma Campus is 10 and maximum batch strength is 30.

Drop us a query

Course Features

Get Practical and Well focused training from Top IT Industry experts.

Get Routine assignments based on learning from previous classes.

Live project, during or after the completion of the syllabus.

Lifetime access to the learning management system including Class recordings, presentations, sample code and projects

Lifetime access to the support team (available 24/7) in resolving queries during and after the course completion

Get certification after the course completion.

Scholarship Exam +91-9711526942 whatsapp

Testimonials