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  • Big Data Hadoop helps in storing huge data which is beyond storage capacity and processing power. It also assures managing virtually limitless concurrent jobs. Croma Campus offers the best Big Data Hadoop Training in Gurgaon to students looking to gain knowledge in the discipline and get a secured job in a leading MNC or a corporate giant. In fact, you will find Big Data Hadoop Training being especially designed to offer you an in-depth knowledge of the framework utilizing Hadoop and Spark. Here, you will receive a hands-on Hadoop training along with real industry-based projects using Integrated Lab.
  • By acquiring detailed information about Big Data Hadoop, you will get the chance to know about the exceptional and newest features of this specific technology. If you are anytime planning to establish your career in this field, getting started with Big Data Hadoop Training in Gurgaon will be an ideal move for your career.

Big Data Hadoop Training in Gurgaon

About-Us-Course

  • Hadoop Big Data is one of the most demanding courses belonging to the IT domain. For beginners, Big Data Hadoop Training in Gurgaon 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 Gurgaon, you will come across the exact topics and sub-topics in an explained manner.
    • Right at the beginning of the course, our trainers will help you know its basic fundamentals.

      You will also receive sessions concerning how to start working with real-life industry use cases.

      You will get the chance to analyze Hadoop features like HDFS, YARN, Hive, MapReduce, Pig, Spark, HBase, Sqoop, Flume, Oozie, Hive, etc.

      You will also get the chance to choose from roles like Developer, Administrator, Data Analyst, Tester, and Solution Architect.

      In fact, you will end up passing the certifications ensures deep learning of various big data concepts.

  • Whereas salary structure is concerned, then it's genuinely one of the well-paid fields. By acquiring a licit accreditation of Big Data Hadoop Training in Gurgaon in hand, you will end up grabbing a decent salary package.
    • Right at the beginning of your career, you will earn around Rs. 3.6 Lakh, which is quite good for freshers.

      On the other hand, an experienced Big Data Hadoop Developer earns Rs. 11.5 Lakh annually.

      Further, by acquiring more work experience along with the latest skills, 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 field that provides various opportunities to develop and grow your career. Hadoop is genuinely one of the most valuable skills to learn today that can assist you in acquiring a rewarding job. If your interest lies in this direction, approaching this direction will be suitable for your career in numerous ways.
    • By opting 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.

      Knowing 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.

  • Big Data Hadoop developers are responsible for Building and coding Hadoop applications. As mentioned earlier, Big Data Hadoop is an open-source framework that handles and accumulates big data applications that execute within-cluster systems. So, in a way, essentially a Hadoop Developer creates applications to manage and maintain an organization's big data. Well, by getting in touch with decent Big Data Hadoop Training Institute in Gurgaon, you will be able to analyze each role in a much-detailed way.
    • Your foremost duty will be to meet with the development team to assess the organization’s big data infrastructure.

      You will also have to design and code Hadoop applications to examine data collections.

      Creating data processing frameworks, extracting data and isolating data clusters will also be counted as your main responsibility.

      You will also have to do the testing scripts and analyzing results.

  • In recent times, Hadoop Big Data has genuinely become a mandatory skill as industries are expanding, the aim is to assemble information and find hidden facts behind the data. To be precise, Data defines how industries can improvise their activity and affair. A large number of industries are evolving across the data, and there is a large amount of data that has been gathered and examined through various processes with various tools. So, if your interest lies in this process, enrolling with Big Data Hadoop Training in Gurgaon will eventually be a good decision for your career.
    • 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 Gurgaon.

      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 Gurgaon 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 Gurgaon will be a suitable move for your career. So, getting associated with a decent Big Data Hadoop Training Institute in Gurgaon will be beneficial for you to secure a higher position.
    • UST, Octro.com, Impetus, Crisp Analytics, etc. are some of the well-known companies hiring skilled candidates.

      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.

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CURRICULUM & PROJECTS

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
    • HDFS
    • YARN
    • 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
    • Partitions
    • 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
    • Bucketing
    • 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
    • Collections
    • 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
    • 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: 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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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.
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Our strong associations with top organizations like HCL, Wipro, Dell, Birlasoft, TechMahindra, TCS, IBM etc. makes us capable to place our students in top MNCs across the globe. 100 % free personality development classes which includes Spoken English, Group Discussions, Mock Job interviews & Presentation skills.

The need of It professionals are increasing so Big data hadoop is one of the better choice for career growth and enough income. Apache Hadoop provides you the better package.

Join Croma Campus and complete your training with free demo class provided by the institute before joining.

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