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Big Data Hadoop Certification

This is course is designed for aspiring Hadoop Developers. We will start from scratch and at the end of the course you should be confident enough for any type of Hadoop interview or project.


Curriculum:

Module 1- Introduction to Hadoop

  • What is Big Data?
  • What is Hadoop?
  • The relation between Big Data and Hadoop.
  • What is the need for going ahead with Hadoop?
  • Scenarios to apt Hadoop Technology in REAL TIME Projects
  • Challenges with Big Data
    • Storage
    Processing
  • How Hadoop is addressing Big Data Changes
  • Comparison with Other Technologies
    • RDBMS
    Data Warehouse
    Teradata
  • Different Components of Hadoop Echo System
    • Storage Components
    Processing Components
Free Demo

Module 2- Introduction to HDFS (Hadoop Distributed File System)

  • What is a Cluster Environment?
  • Cluster Vs Hadoop Cluster.
  • The significance of HDFS in Hadoop
  • Features of HDFS
  • Storages aspects of HDFS
    • Block
    How to Configure block size
    Default Vs Configurable Block size
    Why HDFS Block size so large?
    Design Principles of Block Size
Free Demo

Module 3 – HDFS Architecture – 5 Daemons of Hadoop

  • Name Node and its functionality
  • Data Node and its functionality
  • Job Tracker and its functionality
  • Task Track and its functionality
  • Secondary Name Node and its functionality.

Module 4 – Data Storage in Data Nodes – Fail Over Mechanism

  • Data Storage in Data Nodes
  • Fail Over Mechanism in Hadoop – Replication
  • Replication Configuration
  • Custom Replication
  • Design Constraints with Replication Factor

Module 5 – Accessing HDFS

  • CLI(Command Line Interface) and HDFS Commands
  • Java Based Approach

Module 6- Map Reduce

  • Why is Map Reduce essential in Hadoop?
  • Processing Daemons of Hadoop
  • Job Tracker
    • Roles Of Job Tracker
    Drawbacks w.r.to Job Tracker failure in Hadoop Cluster
    How to Configure Job Tracker in the Hadoop cluster
  • Task Tracker
    • Roles of Task Tracker
    Drawbacks w.r.to Job Tracker Failure in Hadoop Cluster

Module 7- Input Split

  • Input Split
  • Need of Input Split in Map Reduce
  • Input Split Size
  • Input Split Size Vs Block Size
  • Input Split Vs Mappers

Module 8 – Map Reduce Life Cycle

  • Communication Mechanism of Job Tracker & Task Tracker
  • Input Format Class
  • Record Reader Class
  • Success Case Scenarios
  • Failure Case Scenarios
  • Retry Mechanism in Map Reduce

Module 9 - Map Reduce Programming Model

  • Different phases of Map Reduce Algorithm
  • Different Data Types in Map Reduce
    • Primitive Data Types Vs Map Reduce Data Types
  • How to write a basic Map Reduce Program
    • Driver Code
    Mapper Code
    Reducer Code
  • Driver Code
    • Importance of Driver Code in a Map-Reduce Program
    How to Identify the Driver Code in Map Reduce Program
    Different sections of Driver code
  • Mapper Code
    • Importance of Mapper Phase in Map Reduce
    How to Write a Mapper Class?
    Methods in Mapper Class
  • Reducer Code
    • Importance of Reduce phase in Map Reduce
    How to Write Reducer Class?
    Methods in Reducer Class
    Identify Mapper and Reducer

Module 10 – Input format in Map reduce

  • Text Input Format
  • Key Value Text Input Format
  • Nine Input Format
  • DB Input Format
  • Sequence File Input Format.
  • How to Use the specific Output format in Map Reduce

Module 11 – Output Format’s in Map Reduce

  • Text Output Format
  • Key Value Text Input Format
  • Nine Input Format
  • DB Input Format
  • Sequence File Input Format.
  • How to Use the specific Output format in Map Reduce

Module 12- Combiner and Practitioner in Map Reduce

  • Is combiner mandate in Map Reduce
  • How to Use the Combiner class in Map Reduce
  • Performance tradeoffs w.r.to Combiner
  • Importance of Practitioner class in Map Reduce
  • How to use the Partitioner class in Map Reduce
  • Hash Partitioner Functionality
  • How to write a custom Partitioner

Module 13- Compression techniques in Map Reduce

  • Importance of Compression in Map Reduce
  • What is CODEC
  • Compression Types
  • Gzip Codec
  • Bzip Codec
  • LZO Codec
  • Snappy Codec
  • Configurations w.r.to Compression Techniques
  • How to customize the Compression per one job Vs all the job.

Module 14- Joins and Data Localization in Map Reduce

  • Joins – in Map Reduce
    • Map Side Join
    Reduce Side Join
    Distributed cache
  • How to debug MapReduce jobs in Local and Pseudo cluster Mode
  • Data Localization in Map Reduce

Module 15- Introduction to Apache Pig

  • Introduction to Apache Pig
  • SQL Vs Apache Pig
  • Different data types in Pig
  • Modes of Execution in Pig
    • Local Mode
    Map Reduce OR Distributed Mode

Module 16 – Scripting in PIG

  • Execution Mechanism
    • Grunt Shell
    Script

Module 17 – Develop Complex Pig Script

  • Embedded
  • Transformations in Pig
  • How to develop the Complex Pig Script

Module 18 – Bags, Tuples and UDFs in PIG

  • Bags, Tuples, and fields in PIG
  • UDF’s in Pig
    • Need for using UDF’s in PIG
    How to use UDF’s
    REGISTER keyword in PIG
  • When to use Map Reduce & Apache PIG in REAL TIME Projects

Module 19 – Introduction to Apache Hive

  • Hive Introduction
  • Need of Apache HIVE in Hadoop
  • Hive Architecture
    • Driver
    Compiler
    Executor(Semantic Analyzer)
  • Meta Store in Hive
    • Importance of Hive Meta Store
    Embedded meta store configuration
    External meta store configuration
    Communication mechanics with Metastore

Module 20- Hive Query Language Scripting

  • Hive Integration with Hadoop
  • Hive Query Language (Hive QL)
  • Configuring Hive With MySQL Metastore
  • SQL Vs Hive QL

Module 21 – Data Slicing Mechanisms and Data Types in Hive

  • Data Slicing Mechanisms
    • Partitions in Hive
    Buckets In Hive
    Partitioning Vs Bucketing
    Real-Time Use Cases
  • Collection Data Types in HIVE
    • Array
    Struct
    Map

Module 22- UDFS in HIVE

  • User Defined Functions(UDFs) in HIVE
    • UDFs
    UDAFs
    UDTFs
    Need of UDFs in HIVE
  • Hive Serializer/De-serializer – SerDe
  • HIVE – HBase Integration

Module 23 –Apache SQOOP and it’s commands

  • Introduction to Sqoop.
  • MySQL client and Server Installation
  • How to connect to Relational Database using Sqoop
  • Different Sqoop Commands
    • Different Flavors of Imports
    Export
    Hive-Imports

Module 24- Apache HBase and Map reduce Integration

  • HBase Introduction
  • HDFS Vs HBase
  • HBase Use cases
  • HBase basics
    • Column Families
    Scans
  • HBase Architecture
  • Clients
    • REST
    Thrift
    Java Based
    Avro
  • Map Reduce Integration
  • Map Reduce over HBase
  • HBase Admin
    • Scheme Definition
    Basic CRUD Operations

Module 25- APACHE FLUME

  • Flume Introduction
  • Flume Architecture
  • Flume Master, Flume Collector, and Flume Agent
  • Flume Configurations
  • Real-Time Use Case using Apache Flume

Module 26 – Apache Oozie

  • Oozie Introduction
  • Oozie Architecture
  • Oozie Configuration Files
  • Oozie Job Submission
    • Workflow.xml
    Coordinators.xml
    Job.coordinator.properties

Module 27 - YARN (Yet Another Resource Negotiator)-Next Gen.Map Reduce

  • What is YARN?
  • YARN Architecture
    • Resource Manager
    Application Master
    Node Manager
  • When should we go ahead with YARN
  • Classic Map Reduce Vs YARN Map Reduce, Different Configuration Files for YARN

Module 28 - MongoDB (As part of NoSQL Databases)

  • The need for NoSQL Database
  • Relational Vs Non-Relational Databases
  • Introduction to MongoDB
  • Installation of MongoDB
  • Mongo DB Basic operations

Module 29 – Apache Spark

  • Spark Architecture
  • Spark Processing with Use cases
  • Spark with SCALA
  • Spark With SQL

Module 30 - Hadoop Administration

  • Hadoop Single Node Cluster Set Up(Hands on Installation on Laptops)
    • Operating System Installation
    JDK Installation
    SSH Configuration
    Dedicated Group & User Creation
    Hadoop Installation
    Different Configuration Files Setting
    Name node format
    Starting the Hadoop Daemons

Module 31 – PIG, SQOOP, HIVE and HBase installation

  • PIG Installation (Hands on Installation on Laptops)
    • Local Mode
    Clustered Mode
    Bashrc file configuration
  • SQOOP Installation (Hand on Installation on Laptops)
    • Sqoop installation with MySQL Client
  • HIVE Installation(Hands on Installation on Laptops)
    • Local Mode
    Clustered Mode
  • HBase Installation(Hands on Installation on Laptops)
    • Local Mode
    Clustered Mode

Module 32– Take Away from the Training

  • Provided 2 POC’s to work with Hadoop and Its Components
  • Provided All the Materials Soft copy with Use cases
  • Provided Certification Assistance
  • Provided Project Exposure and Discussion

Course Highlights


        No. of hours: 50 Hours

        Star Rating: (5)

       Trainer:




Fee:

Online Virtual Class Room

Enroll in any above batch and attend live class at scheduled time

14999      




Upcoming Batches:

2024-10-19
10:00 AM IST
Weekend Batch (2 Hours Sat & Sun)
 ₹14999 ₹13499.1 (10% OFF)
Offer Ends in 10 d: 12h : : 35s
2024-10-07
8:00 AM IST
Weekdays Batch
 14999
2024-10-14
8:00 PM IST
Weekdays Batch
 ₹14999 ₹13499.1 (10% OFF)
Offer Ends in 5 d: 12h : : 35s


FAQ:

1. Who can learn this course?
Anyone who is interested and having knowledge in SQL server can easily learn this program.
Anyone who is interested and having knowledge in SQL server can easily learn this program.
Java can be good to have if you know but it is not mandatory to know
3. What are career opportunities from this course?
Hadoop is being used by most of top MNCs like google, yahoo, Facebook, Bank of America, United Health Group. You can get good opportunity after completing this course.
4. How long this course will take?
This course will take 50 hours practical live class. After that you can practice as much as you can.
5. Will you provide soft copy material?
Yes, we will share the soft copy material and you will also get recording of our live classes.
6. Do you provide projects to work on?
Yes, after completion of course you will to work on 2 or 3 projects to work. It will hand on experience to clear the interviews confidently.


Reviews:



Jun-03-2024

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