Master the key concepts of Hadoop and Big data. Join and learn from a Big Data/Hadoop expert.

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Big Data & Hadoop


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  • Hadoop is an important Big Data operating system. In fact, it is evaluated for parallel processing utilizing structured and unstructured data, using low hardware costs. Whereas Hadoop’s work is concerned, it's done in batch, not in real-time, duplicating the data through the network respectively.
  • By deep-delving into its topic, you will surely get to know how Big Data Hadoop helps in storing a huge mass of data efficiently. So, if you are looking for any sort of professional training, then Croma Campus is the genuinely the best place for you to have a detailed insight of Big Data Hadoop Training in Delhi. Here, you will not only gain theoretical information, but will also receive placement assistance.

Big Data Hadoop Training in Delhi


  • Big Data Hadoop is one of the most demanding courses belonging to the IT domain. For beginners, Big Data Hadoop Training in Delhi might seem a bit difficult one's, but with adequate guidance, you will surely end up understanding every part of this course. By getting in touch with Big Data Hadoop Training Institute in Delhi, you will come across the exact topics and sub-topics in an explained manner.
    • At the initial level of the course, our trainers will help you know its basic fundamentals.

      Further, you will also receive sessions concerning Hadoop Architecture Distributed Storage (HDFS) and YARN, What Is HDFS, Need for HDFS, Need for HDFS, etc.

      Our trainers will also help you know the whole Data Replication Topology, HDFS Command Line, command line, Data Ingestion into Big Data Systems and ETL, distributed processing, Hive SQL over Hadoop MapReduce, basics of Functional Programming and Scala, etc.

      Here, you will also get a chance to imbibe new skills concerning this course.

      In a nutshell, we can say, you will end up imbibing every detail of this course in a detailed manner.

  • Whereas salary package is concerned, then it's eventually one of the well-paid fields. By obtaining a legitimate accreditation of Big Data Hadoop Training in hand, you will end up obtaining a decent salary package.
    • At the initial level, you will earn around Rs. 3 Lakh to Rs. 3.5 Lakh, which is quite good for freshers.

      Likewise, an experienced Big Data Hadoop Developer earns Rs. 11.5 Lakh annually.

      Moreover, by obtaining more work experience along with the latest skills, and trends your salary structure will expand.

      By taking projects as a freelancer, you will make some good additional income also.

  • To be precise, Hadoop is one sort of technology that offers numerous opportunities to build and grow your career. In fact, Hadoop is genuinely one of the most valuable skills to imbibe today that can help you obtain a rewarding job. If your interest lies in this direction, choosing this field will be suitable for your career in numerous ways.
    • By obtaining for its legit training from a reputed educational foundation, you will turn into a knowledgeable Big Data Hadoop Developer.

      Well, withholding a proper accreditation of Big Data Hadoop Developer, you will be offered an excellent salary package right from the beginning of your career.

      Getting to know each side of Big Data Hadoop Developer will also push you forward to come up with innovative applications.

      Knowing this skill will eventually enhance your resume.

      You will always have numerous jobs offers in hand.

  • A skilled Big Data Hadoop Developer is responsible for executing a lot of tasks, and to smoothly execute those tasks, you will have to imbibe those skills adequately as well. By getting in touch with a decent Big Data Hadoop Training Institute in Delhi, you will get the chance to understand each role in quite an explained manner.
  • Some of the major roles are mentioned below.
    • Firstly, you will have to build, imply and maintain Big Data solutions.

      In fact, you will also have to execute database modeling and development, data mining, and warehousing.

      You will also have to design and deploy data platforms across numerous domains assuring operability.

      Our trainers will also help you to experience the Hadoop ecosystem (HDFS, MapReduce, Oozie, Hive, Impala, Spark, Kerberos, KAFKA, etc.).

      At the same time, you will also have to transform data for meaningful analyses and enhance data efficiency.

      Often you will have to execute the testing scripts and analyze the results.

  • If you will deep delve into its topic, then you will find various reasons to get started with Big Data Hadoop Training in Delhi. One of the significant one's its ability to handle huge data.
    • By getting started with this specific course, you will end up strengthening your base knowledge.

      You will know the various features and offerings of this technology by getting in touch with a well-established Big Data Hadoop Training Institute in Delhi.

      You will also know about building a new sort of application and implying some latest features.

      You will end up obtaining some untold, and hidden facts from the Big Data Hadoop Training in Delhi respectively.

  • At the moment, you will find Big Data Hadoop Developers extensively in demand, and yet the grant is low. If you are also planning to construct your career in this field, getting started with Big Data Hadoop Training in Delhi will be a suitable move for your career. So, getting associated with a decent Big Data Hadoop Training Institute in Delhi will be beneficial for you to secure a higher position.
    • By joining Croma Campus, you will get the opportunity to get placed in your choice of companies post enrolling with the Big Data Hadoop course.

      Our trainers will also help you in building an impressive resume.

      They will also suggest you some effective tips to pass the interviews.

Why you should get started with the Big Data Hadoop Course?

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Plenary for Big Data Hadoop Training

Track Week Days Weekends Fast Track
Course Duration 40-45 Days 7 Weekends 8 Days
Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
Training Mode Classroom/Online Classroom/Online Classroom/Online
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Big Data Hadoop Training

  • Croma Campus offers the best Hadoop development Training in Noida with most experienced professionals. Our Instructors are working in Big Data Space and related technologies for years in MNC's.
  • We aware of industry needs and we are offering Hadoop development Training in more practical way. Our team of Hadoop trainers offers the in-classroom training with best industry practices.
  • We framed our syllabus to match with the real-world requirements for beginner level to advanced level. Our training will be handled in either weekday or weekends programme depends on participants requirements.
  • In this program you will learn:
    • Introduction to Big Data & Hadoop



      Managing and Scheduling Jobs

      Apache Sqoop

      Apache Flume

      Getting Data into HDFS

      Apache Kafka

      Hadoop Clients

      Cluster Maintenance

      Cloudera Manager

      Cluster Monitoring and Troubleshooting

      Planning Your Hadoop Cluster

      Advanced Cluster Configuration

      MapReduce Framework

      Apache PIG

      Apache HIVE

      No SQL Databases HBase

      Functional Programming using Scala

      Apache Spark

      Hadoop Datawarehouse

      Writing MapReduce Program

      Introduction to Combiner

      Problem-solving with MapReduce

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  • Introduction to Big Data
    • Overview of Course

      What is Big Data

      Big Data Analytics

      Challenges of Traditional System

      Distributed Systems

  • Introduction to Hadoop
    • Components of Hadoop Ecosystem

      Commercial Hadoop Distributions

      Why Hadoop

      Fundamental Concepts in Hadoop

  • Security in Hadoop
    • Why Hadoop Security Is Important

      Hadoop’s Security System Concepts

      What Kerberos Is and How it Works

      Securing a Hadoop Cluster with Kerberos

  • Initial Setup and Configuration
    • Deployment Types

      Installing Hadoop

      Specifying the Hadoop Configuration

      Performing Initial HDFS Configuration

      Performing Initial YARN and MapReduce Configuration

      Hadoop Logging

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  • HDFS
    • What is HDFS

      Need for HDFS

      Regular File System vs HDFS

      Characteristics of HDFS

      HDFS Architecture and Components

      High Availability Cluster Implementations

      HDFS Component File System Namespace

      Data Block Split

      Data Replication Topology

      HDFS Command Line

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  • YARN
    • Yarn Introduction

      Yarn Use Case

      Yarn and its Architecture

      Resource Manager

      How Resource Manager Operates

      Application Master

      How Yarn Runs an Application

      Tools for Yarn Developers

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  • Managing and Scheduling Jobs
    • Managing Running Jobs

      Scheduling Hadoop Jobs

      Configuring the Fair Scheduler

      Impala Query Scheduling

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  • Apache Sqoop
    • Apache Sqoop

      Sqoop and Its Uses

      Sqoop Processing

      Sqoop Import Process

      Sqoop Connectors

      Importing and Exporting Data from MySQL to HDFS

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  • Apache Sqoop
    • Apache Flume

      Flume Model

      Scalability in Flume

      Components in Flume’s Architecture

      Configuring Flume Components

      Ingest Twitter Data

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  • Getting Data into HDFS
    • Data Ingestion Overview

      Ingesting Data from External Sources with Flume

      Ingesting Data from Relational Databases with Sqoop

      REST Interfaces

      Best Practices for Importing Data

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  • Apache Kafka
    • Apache Kafka

      Aggregating User Activity Using Kafka

      Kafka Data Model


      Apache Kafka Architecture

      Setup Kafka Cluster

      Producer Side API Example

      Consumer Side API

      Consumer Side API Example

      Kafka Connect

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  • Hadoop Clients
    • What is a Hadoop Client

      Installing and Configuring Hadoop Clients

      Installing and Configuring Hue

      Hue Authentication and Authorization

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  • Cluster Maintenance
    • Checking HDFS Status

      Copying Data between Clusters

      Adding and Removing Cluster Nodes

      Rebalancing the Cluster

      Cluster Upgrading

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  • Cloudera Manager
    • The Motivation for Cloudera Manager

      Cloudera Manager Features

      Express and Enterprise Versions

      Cloudera Manager Topology

      Installing Cloudera Manager

      Installing Hadoop Using Cloudera Manager

      Performing Basic Administration Tasks using Cloudera Manager

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  • Cluster Monitoring and Troubleshooting
    • General System Monitoring

      Monitoring Hadoop Clusters

      Common Troubleshooting Hadoop Clusters

      Common Misconfigurations

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  • Planning Your Hadoop Cluster
    • General Planning Considerations

      Choosing the Right Hardware

      Network Considerations

      Configuring Nodes

      Planning for Cluster Management

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  • Advanced Cluster Configuration
    • Advanced Configuration Parameters

      Configuring Hadoop Ports

      Explicitly Including and Excluding Hosts

      Configuring HDFS for Rack Awareness

      Configuring HDFS High Availability

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  • MapReduce Framework
    • What is MapReduce

      Basic MapReduce Concepts

      Distributed Processing in MapReduce

      Word Count Example

      Map Execution Phases

      Map Execution Distributed Two Node Environment

      MapReduce Jobs

      Hadoop MapReduce Job Work Interaction

      Setting Up the Environment for MapReduce Development

      Set of Classes

      Creating a New Project

      Advanced MapReduce

      Data Types in Hadoop

      Output formats in MapReduce

      Using Distributed Cache

      Joins in MapReduce

      Replicated Join

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  • Apache PIG
    • Introduction to Pig

      Components of Pig

      Pig Data Model

      Pig Interactive Modes

      Pig Operations

      Various Relations Performed by Developers

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  • Apache HIVE
    • Introduction to Apache Hive

      Hive SQL over Hadoop MapReduce

      Hive Architecture

      Interfaces to Run Hive Queries

      Running Beeline from Command Line

      Hive Meta Store

      Hive DDL and DML

      Creating New Table

      Data Types

      Validation of Data

      File Format Types

      Data Serialization

      Hive Table and Avro Schema

      Hive Optimization Partitioning Bucketing and Sampling

      Non-Partitioned Table

      Data Insertion

      Dynamic Partitioning in Hive


      What Do Buckets Do

      Hive Analytics UDF and UDAF

      Other Functions of Hive

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  • No SQL Databases HBase
    • NoSQL Databases HBase

      NoSQL Introduction

      HBase Overview

      HBase Architecture

      Data Model

      Connecting to HBase

      HBase Shell

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  • Functional Programming using Scala
    • Basics of Functional Programming and Scala

      Introduction to Scala

      Scala Installation

      Functional Programming

      Programming with Scala

      Basic Literals and Arithmetic Programming

      Logical Operators

      Type Inference Classes Objects and Functions in Scala

      Type Inference Functions Anonymous Function and Class


      Types of Collections

      Operations on List

      Scala REPL

      Features of Scala REPL

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  • Apache Spark
    • Apache Spark Next-Generation Big Data Framework

      History of Spark

      Limitations of MapReduce in Hadoop

      Introduction to Apache Spark

      Components of Spark

      Application of In-memory Processing

      Hadoop Ecosystem vs Spark

      Advantages of Spark

      Spark Architecture

      Spark Cluster in Real World

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  • Data warehouse in Hadoop
    • Hadoop and the Data Warehouse

      Hadoop Differentiators

      Data Warehouse Differentiators

      When and Where to Use Which

  • 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 Trade-offs

  • Advance Programming in Hadoop
    • Advance Programming in Hadoop

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  • 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: Word Count

      Getting Data to the Mapper

      Keys and Values are Objects

      What is Writable Comparable

      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 Job’s Invocation

      Configuring the Job with Job Conf

      Creating a New Job Conf Object

      Naming the Job

      Specifying Input and Output Directories

      Specifying the Input Format

      Determining Which Files to Read

      Specifying Final Output with Output Format

      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

      Writing a MapReduce program

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  • Introduction to Combiner
    • The Combiner

      MapReduce Example: Word Count

      Word Count with Combiner

      Specifying a Combiner

      Demonstration: Writing and Implementing a Combiner

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  • Sorting & Searching large data sets
    • Introduction


      Sorting as a Speed Test of Hadoop

      Shuffle and Sort in MapReduce


  • 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: Data Flow

      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

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+ More Lessons

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Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
How Croma Campus Mock Interview Works?


Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards. Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.
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This specific course will hardly take 45-50 days to completely understand its functions.

Yes, they are hugely in demand. So, choosing this field will be beneficial for your career.

Yes, there are two ways of training, online/offline.

Yes, along with study material, you will also get access to our LMS Portal which will help you review your sessions.

Career Assistancecareer assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
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