Hadoop Training

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Croma Campus Provides Big Data Hadoop Training in Noida stands for data sets that are so large or complex that traditional data processing applications are insufficient to handle. It includes both structured and unstructured data that every application deals with. Industry defines Big Data as the three Vs: “Volume”, “Velocity” & “Variety”.

Now a days Big Data is becoming a problem as data is increasing second by second. So Hadoop is the solution for all the problems. Hadoop is an framework used for storing data and running all domain application on multi node clusters. It provides huge storage for all kind of data with huge processing power and the ability to handle limitless concurrent tasks or jobs.

The primary goal of Hadoop course in Croma Campus is to prepare the students for certification exams(Cloudera & Horton works) & current inustry requirement. This course is designed to provide professionals /graduates with an in-depth understanding of Bigdata/Hadoop concepts. In addition, this course provides sample Hadoop certification exam questions and an opportunity to assess your overall knowledge of Hadoop concepts.

Backed by experienced faculty, Croma Campus is proficiently providing Hadoop Training in Noida to the students having interest in making their career in the field of Hadoop data management. We provide best techniques to solve the complex queries in less possible time, efficiently. To assure the best result, we provide complete knowledge of entire syllabus in a sequential manner for the easy understanding of students.

For providing this future oriented course or training, we have hired talented faculty members. Our expert tutors are recruited on the basis of their academic qualification and experience in their respective domain. They have attained their education from the reputed institutes and universities, and put their best efforts when it comes to provide best Hadoop Training in Noida to our students. All our previously trained students have given their tremendous feedback for our well-professional faculty.

Besides providing the thorough knowledge of syllabus, we also work on the personality development of our students to make them best choice of employers. Moving ahead, being a most reputed Big Data Hadoop Training Institute in Noida, Delhi & NCR, we have kept our fee structure very affordable so that our courses and training programs can reach to the maximum number of students.

Key features of Bigdata/Hadoop Training in Croma Campus:

  • Ene-to-end concepts of Bigdata/Hadoop
  • Trainer having 10+ years Industrial Experience.
  • Covering latest Eco Systems
  • Hands on Practise
  • Job-Oriented Course Curriculum.
  • Post Training Support will helps the associate to implement the knowledge on client Projects.
  • Training for Cloudera & Horton Works Certification

Croma Campus Bigdata/Hadoop Training Map

Bigdata/Hadoop Training Program
Core Java OOP’s Concepts, String, Exception Handling, Collection, Threading, IO
Hadoop Concepts Development
Ecosystems backend with any database.
Hot Topics architecture or 2 Tier architecture.
*For B.Tech/MCA Industrial Training: Project Synopsis/Project for College Submission/Industrial Training Certificate.

Fundamental: Introduction to BIG Data

Introduction: Apache Hadoop

  • Why Hadoop?
  • Core Hadoop Components
  • Fundamental Concepts

Hadoop Installation and Initial Configuration

  • Deployment Types
  • Installing Hadoop
  • Specifying the Hadoop Configuration
  • Performing Initial HDFS Configuration
  • Performing Initial YARN and MapReduce Configuration
  • Hadoop Logging

Hadoop Security

  • Why Hadoop Security Is Important
  • Hadoop Security System Concepts
  • What Kerberos Is and How it Works
  • Securing a Hadoop Cluster with Kerberos

HDFS

  • HDFS Features
  • Writing and Reading Files
  • NameNode Memory Considerations
  • Overview of HDFS Security
  • Using the NameNode Web UI
  • Using the Hadoop File Shell

Fundamentals: Introduction to Hadoop and its Ecosystem

Installing and Configuring Hive, Impala and Pig

  • Hive
  • Impala
  • Pig

Managing and Scheduling Jobs

  • Managing Running Jobs
  • Scheduling Hadoop Jobs
  • Configuring the FairScheduler
  • Impala Query Scheduling

Getting Data into HDFS

  • Ingesting Data from External Sources with Flume
  • Ingesting Data from Relational Databases with Sqoop
  • REST Interfaces
  • Best Practices for Importing Data

Hadoop Clients

  • What is a Hadoop Client?
  • Installing and Configuring Hadoop Clients
  • Installing and Configuring Hue
  • Authentication and Authorization

Cluster Maintenance

  • Checking HDFS Status
  • Copying Data between Clusters
  • Adding and Removing Cluster Nodes
  • Rebalancing the Cluster
  • Cluster Upgrading.

Fundamental: Introduction to BIG Data

YARN and MapReduce

  • What Is MapReduce?
  • Basic MapReduce Concepts
  • YARN Cluster Architecture
  • Resource Allocation
  • Failure Recovery
  • Using the YARN Web UI
  • MapReduce Version 1

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

Cluster Monitoring and Troubleshooting

  • General System Monitoring
  • Monitoring Hadoop Clusters
  • Common Troubleshooting Hadoop Clusters
  • Common Misconfigurations

Planning Your Hadoop Cluster

  • General Planning Considerations
  • Choosing the Right Hardware
  • Network Considerations
  • Configuring Nodes
  • Planning for Cluster Management

Advanced Cluster Configuration

  • Advanced Configuration Parameters
  • Configuring Hadoop Ports
  • Explicitly Including and Excluding Hosts
  • Configuring HDFS for Rack Awareness
  • Configuring HDFS High Availability.

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’s 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 Customer 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

Fundamental: Introduction to 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 Job Invocation
  • Configuring The Job With JobConf
  • Creating a New JobConf 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 Administrator for Hadoop

    (CCAH) Exam Code: CCA-410

    hadoop_Certification

    Cloudera Certified Administrator for Apache Hadoop Exam :
    • Number of Questions: 60
    • Item Types: multiple-choice & short-answer questions
    • Exam time: 90 Mins.
    • Passing score: 70%
    • Price: $295 USD

    Syllabus Cloudera Administrator Certification Exam

    HDFS 38%
    • Describe the function of all Hadoop Daemons
    • Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing.
    • Identify current features of computing systems that motivate a system like Apache Hadoop.
    • Classify major goals of HDFS Design
    • Given a scenario, identify appropriate use case for HDFS Federation
    • Identify components and daemon of an HDFS HA-Quorum cluster
    • Analyze the role of HDFS security (Kerberos)
    • Determine the best data serialization choice for a given scenario
    • Describe file read and write paths
    • Identify the commands to manipulate files in the Hadoop File System Shell.
    MapReduce 10%
    • Understand how to deploy MapReduce MapReduce v1 (MRv1)
    • Understand how to deploy MapReduce v2 (MRv2 / YARN)
    • Understand basic design strategy for MapReduce v2 (MRv2)
    Hadoop Cluster Planning 12%
    • Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster.
    • Analyze the choices in selecting an OS
    • Understand kernel tuning and disk swapping
    • Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
    • Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
    • Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
    • Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
    Hadoop Cluster Installation and Administration 17%
    • Given a scenario, identify how the cluster will handle disk and machine failures.
    • Analyze a logging configuration and logging configuration file format.
    • Understand the basics of Hadoop metrics and cluster health monitoring.
    • Identify the function and purpose of available tools for cluster monitoring.
    • Identify the function and purpose of available tools for managing the Apache Hadoop file system.
    Resource Management 06%
    • Understand the overall design goals of each of Hadoop schedulers.
    • Given a scenario, determine how the FIFO Scheduler allocates cluster resources.
    • Given a scenario, determine how the Fair Scheduler allocates cluster resources.
    • Given a scenario, determine how the Capacity Scheduler allocates cluster resources
    Monitoring and Logging 12%
    • Understand the functions and features of Hadoop’s metric collection abilities
    • Analyze the NameNode and JobTracker Web UIs
    • Interpret a log4j configuration
    • Understand how to monitor the Hadoop Daemons
    • Identify and monitor CPU usage on master nodes
    • Describe how to monitor swap and memory allocation on all nodes
    • Identify how to view and manage Hadoop’s log files
    • Interpret a log file
    The Hadoop Ecosystem 05%
    • Understand Ecosystem projects and what you need to do to deploy them on a cluster.

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  • 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.

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

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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.

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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.

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