Hadoop Training Big Data Hadoop

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Best Hadoop Training in Noida & Hadoop Training Institute in Noida

Croma Campus is delivering the best Hadoop training in Noida which uses its industrial expert trainer strength to educate candidate with 100% placement assistance. Croma Campus is one of the best Hadoop training institutes in Noida offering real-time project practice in our campus. We’ve designed our Hadoop Course Content according to Hadoop Organization recommended because of they need perfection in Salesforce cloud application. We provide most learning environment for major technical Hadoop at affordable price. At Croma Campus, Hadoop training is provided by highly experienced corporate trainers who have expertise in the core field of Hadoop.

Croma Campus is the absolute best place where anybody confident to learn big information Hadoop can be at. Our showing offers the unpredictable knowledge in such a mode, to the point that anybody can take in the advantage and trouble and be a specialist. The “Enormous information Hadoop” makes open-source programming for reliable, adaptable, spread registering. Big data Hadoop has been the dynamic drive behind the extension of the huge information creation. Hadoop brings the bent to cheaply process a lot of information, paying little mind to its development.

Big Data and Hadoop Training is fundamental to comprehend the energy of Big Data. The preparation presents about Hadoop, Map Reduce, and Hadoop Distributed File framework (HDFS). It will drive you through the way toward creating conveyed handling of expansive informational collections crosswise over bunches of PCs and directing Hadoop. The members will figure out how to deal with heterogeneous information originating from various sources.

Croma Campus Noida offers big data Training with decision of different preparing areas crosswise over noida. Our ibm huge information hadoop Training focuses are outfitted with lab offices and fantastic framework. We likewise give ibm huge information hadoop accreditation preparing way for our understudies in noida. Through our related enormous information preparing focuses, we have prepared more than 132 major information understudies and gave 86 percent situation. Our Big data hadoop course charge is an incentive for cash and tailor-made course expense in light of the every understudy’s preparation necessities. Big data preparing in noida led on day time classes, end of the week instructional courses, evening cluster classes and quick track instructional courses.

Hadoop Training & Placement in Noida

Huge information implies an accumulation of vast data sets that can’t be prepared utilizing customary figuring frameworks. Huge information is not only an information; rather it has turned into an entire subject, which incorporates different devices, strategies and systems.

Associations are discovering that essential estimates can be made by sorting through and breaking down Big Data. As over 75% of this information seems to be “unstructured”, it must be designed in a way that makes it reasonable for information mining and further examination.

Croma Campus Noida is one of the best Hadoop Training institute in Noida with 100% condition bolster. Croma Campus has all around characterized course modules and instructional courses for understudies.

Key features of Big Data/Hadoop Training in Croma Campus:

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

Croma Campus Big Data/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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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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