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About Big Data Hadoop Training In Gurgaon

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About Big Data Hadoop Training In Gurgaon

Duration: 2 Month(s)

Organizations around the globe today discover it progressively hard to compose and oversee extensive volumes of information. Hadoop has risen as the most proficient information stage for organizations working with huge information, and is a fundamental piece of putting away, dealing with and recovering huge measures of information in an assortment of uses. Hadoop runs profound examination which can't be adequately dealt with by a database motor. 

Huge ventures the world over have observed Hadoop to be a distinct advantage in their Big Data administration, and as more organizations grasp this intense innovation the interest for Hadoop Developers is additionally developing. By figuring out how to outfit the influence of Hadoop 2.0 to control, break down and perform calculations on Big Data, you will be making ready for an advancing and fiscally remunerating profession as a specialist Hadoop designer. 

Hadoop 2.0 Developer preparing at AP Edusoft will show you the specialized parts of Apache Hadoop, and you will acquire a more profound comprehension of the energy of Hadoop. Our accomplished coaches will handhold you through the improvement of uses and investigations of Big Data, and you will have the capacity to appreciate the key ideas required to make vigorous enormous information handling applications. Effective hopefuls will gain the certification of Hadoop Professional, and will be equipped for taking care of and breaking down Terabyte size of information effectively utilizing MapReduce.

Apache Hadoop is an open-source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware. Hadoop is an Apache top-level project being built and used by a global community of contributors and users. The Apache Hadoop software library is a framework that allows distributed processing of large data sets across clusters of computers using simple programming models. Hadoop gives the ability to cheaply process large amounts of data, regardless of its structure by large, we mean from 10-100 gigabytes and above.

Hadoop Professionals can work under the following Profiles:-

o Hadoop Developer

o Hadoop Architect

o Java Hadoop Developer

o Big Data Hadoop Engineer

o Hadoop Lead

o Hadoop Lead Software Engineer

o Hadoop Tester

o Data Scientist

Prerequisite: 

All students must be comfortable with the Java programming language (since all programming exercises are in Java), familiar with Linux commands.

Eligibility:
The personal that are pursuing or have completed MCA, BCA, B.Sc(IT),M.Sc(IT), B.Sc, B.Tech or B.E(Any Branch),O Level, A Level can enroll in this program.

Big Data & Hadoop with Spark Course Content:

Introduction to Big Data and Hadoop
o What is Big Data?
o Diff Sources of Big Data?
o 3V’s of Big Data
o What are the traditional technologies support for big data and their limitations?
o What are the challenges associated with big data?
o What is Hadoop and its Architechture with core components?
o Hadoop features and Complete cycle of hadoop big data capture, storage,
analysis and visulize
o Why Hadoop and its Real time Use cases
o Hadoop implementation over Cloud
o Our Role with Hadoop in Industry
o History of Hadoop
o Diff Platform for Hadoop
o Diff vendors (Hadoop Distribution) and certifications of Hadoop
o Diff Modes and Versions of Hadoop
o Diff Scopes of Hadoop in Industry
o Hadoop ecosystem (Diff subsoftwares use on the top of Hadoop)
o Architectural Diff between Hadoop 1.x and 2.x (Yarn)
o Batch Vs Real-Time Processing (OLTP and OLAP)
o Data Analytics Real Time Use Cases
o Big data life cycle (storage, processing and visualization)

Linux Flavour: Ubuntu Installation:
o Dual Boot up Installation Ubuntu With Windows
o Types of Operating Systems
o Linux Origins
o Why Linux/Unix fit for Hadoop?
o Diff between Unix and Linux Flavours
o Advantages and Disadvantages of Unix/Linux
o Architecture and File System
o Commands, Utilities and shell script
o Admin, Group and User Account Management

Hadoop Cluster Setup (Single and Multi Nodes):
o Installing Java with eclipse
o Installing OpenSSh Server and Client
o Installing Apache Hadoop on Linux Operating System and Vmware
o Installing, Configuring and tuning Hadoop 1.x and 2.x
o Using Cloudera Hadoop Distribution 5.x Version on Vmware
o Using HortonWorks Hadoop Distribution 2.x Version on Virtual Box
o Using Hadoop Distribution over Microsoft Cloud
o Creating Cluster Single and Multi Nodes
o Increasing & Decreasing the Cluster size
o Monitoring the Cluster Health
o Starting and Stopping the Hadoop Daemons
o Show and tuning cluster using UI Mode
o Common errors when running hadoop cluster and solutions

Hadoop Core Components HDFS & MapReduce

HDFS (Hadoop Distributed File System)
o Introduction to HDFS
o Features of HDFS
o Hadoop Cluster Environment
o Diff between HDFS and traditional RDBMS
o Hdfs internal mechanism to store and manage datasets in distribution and Scaling
Manner
o Hdfs Storage Aspects
o Hdfs memory block
o How to configure block size
o Default Vs Configurable block size
o Why Hdfs block size so large?
o Design Principles of block size
o Hdfs Architecture – 5 Daemons of Hadoop
o Name Node and its functionality
o Data Node and its functionality
o Secondary Name Node and its functionality
o Task Tracker and its functionality
o Job Tracker and its functionality
o Data Replication in Hadoop
o Data Storage in Data Nodes
o Failover Mechanism in Hadoop-Replication
o Replication Configuration
o Custom Replication
o Design Constraints with Replication Factor
o Diff Modes of Accessing HDFS
o Command Line Interface (CLI) and HDFS Commands
o Java Programming based Approach
o Metadata, FS image, Edit log, Secondary Name Node and Safe Mode
o Node Commissioning and Decommissioning
o Hadoop Administration
o Start and Stop Nodes
o Exploring the HDFS Web UI
o How to verify dead node and how to start again
o Data Locality, Data Integrity and Rack Awareness
o Nodes Heatbeat Meachnism
o Difference between Hadoop 1.x and Hadoop 2.x versions
o Introduction to Name node federation
o Introduction to Name node High Availability

MAP REDUCE (Processing):
o Introduction to Map Reduce
o Map Reduce architecture
o Functional Programming Basics.
o Map and Reduce Basics
o How Map Reduce Works
o Anatomy of a Map Reduce Job Run
o Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task
Execution, Progress and Status Updates
o Job Completion, Failures
o Shuffling and Sorting
o Splits, Record reader, Partition, Types of partitions & Combiner
o Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots.
o Types of Schedulers and Counters.
o Comparisons between Old and New API at code and Architecture Level.
o Getting the data from RDBMS into HDFS using Custom data types.
o Distributed Cache and Hadoop Streaming (Python).
o YARN.
o Sequential Files and Map Files.
o Enabling Compression Codec’s.
o Map side Join with distributed Cache.
o Types of I/O Formats: Multiple outputs, NLINEinputformat.
o Handling small files using CombineFileInputFormat.
o Exploring Map Reduce web Ui

Map/Reduce Programming – Java Programming:
o Hands on “Word Count” in Map/Reduce in standalone and Pseudo distribution
Mode.
o Sorting files using Hadoop Configuration API discussion
o Emulating “grep” for searching inside a file in Hadoop
o Diff Input/Output File Formats Supported by MR
o Job Dependency API discussion
o Input Format API discussion
o Input Split API discussion
o Custom Data type creation in Hadoop.

Hadoop 2.x YARN (Yet Another Resource Negotiator):
o Yarn Introduction
o Yarn Architechture
o Resource Manager
o Application Master
o Node Manager
o When we should go ahead with Yarn
o Classic Map Reduce Vs Yarn Map Reduce
o Different Configuration Files for Yarn

Apache HIVE:
o Introduction
o Hive Architecture
o Hive Service, Shell, Server and Web Interface
o OLTP Vs OLAP
o Hive MetaStore
o Hive Query Language
o Diff b/w Hql and Sql
o Hive Built in Functions
o Hive UDF (User Defined Function)
o Hive UDAF (User Defined Aggregated Function)
o Hive UDTF (User Defined Table Generated Function)
o Hive Serde?
o Hive & Hbase Integration
o Hive working with unstructured data
o Hive working with xml data
o Hive working with Json data
o Hive working with url and weblog data
o Hive – Json – Serde
o Loading Data from Local files to Hive tables
o Loading Data from Hdfs files to Hive tables
o Tables Types
o Inner Tables
o External Tables
o Partitioned Tables
o Non-Partitioned Tables
o Dynamic Partitions in Hive
o Concept of Bucketing
o Hive Views
o Hive Unions
o Hive Joins
o Multi Table / File Inserts
o Inserting into Local Files
o Inserting into Hdfs Files
o Array Operations in Hive
o Word Count Example

Apache PIG:
o Introduction to Apache PIG
o Introduction to PIG Data Flow Engine
o MapReduce vs. PIG in detail
o When should PIG use?
o Data Types in PIG
o Basic PIG programming
o Modes of Execution in PIG
o Local Mode and MapReduce Mode
o Execution Mechanisms
o Grunt Shell
o Script
o Embedded
o Operators/Transformations in PIG
o PIG UDF’s with Program
o Word Count Example in PIG
o The difference between the MapReduce and PIG

Apache SQOOP:
o Introduction to Sqoop
o SQOOP Import
o SQOOP Export
o Mysql client and Server Installation
o Importing Data from RDBMS to HDFS
o Importing Data from RDBMS to HIVE
o Importing Data from RDBMS to HBase
o Exporting Data HBase to RDBMS
o Exporting Data HIVE to RDBMS
o Exporting Data HDFS to RDBMS
o Transformation while importing / exporting
o Defining SQOOP Jobs

Apache FLUME:
o Introduction to flume
o What is the Streaming file
o Flume Architechture
o Flume Nodes and Manager
o Flume Local and Phisical Node
o Flume agent and collector

Kafka (Stream tool)
Apache Storm (Real time processing)

NOSQL Database:
o What is “Not only SQL”
o NOSQL Advantages
o What is problem with RDBMS for Large Data Scaling Systems
o Catgories of NoSQL and Purposes
o Key-Value Database
o Document DataBase
o Column Family DataBase
o Graph DataBase
o Introduction to ricsk – NOSQL Database
o Introduction to Cassandra – NOSQL Database
o Integration of NOSQL Databases with Hadoop

HBASE (Nosql Database):
o Introduction to Big Table
o What is NOSQL and Columner store Database
o HBase Introduction
o HBase use cases
o HBase Basics
o Column Families
o Scans
o HBase Architecture
o Clients
o Rest
o Thrift
o Java Approach
o Hive
o Map Reduce Integration
o Map Reduce Over HBase
o HBase Data Modeling
o HBase Schema Design
o HBase CURD operators and Java Approach
o Hive and HBase Integration
o HBase storage handles
o Web Based UI

Zookeeper:
o Inroduction to ZOOKEPER
o ZOOKEPER Architecture
o Controlling Connection
o HBase and ZOOKEPER
o FLUME and ZOOKEPER
o A sample code

MongoDB (NoSQL Database):
o What is MongoDB?
o Where to Use?
o Configuration On Windows
o Inserting the data into MongoDB?
o Reading the MongoDB data

Cassandra (No Sql DB)

Apache OOZIE:
o Introduction to OOZIE
o Use of OOZIE
o Where to use?

Apache Mahout (Machine Learining):
o Introduction to Machine Learning (ML) Language
o Types f Machine Learning
o Introduction to Apache Mahout
o Real Time Use case using Classifier Algorithm of Mahout

Advance technologies Architectural Disscussion:
o Storm and Flink (Real time data streaming)
o Cassandra (NOSQL database)
o Solr (Search engine)
o Nutch (Web Crawler)
o Lucene (Indexing data)
o Hue, Cloudera Manager, Ambari, Ganglia, Nagios (Cluster Monitoring and
Management tools)
o Cloudera, Hortonworks, MapR, Microsoft, Amazon EMR (Distributions)

Introduction to Microsoft Power BI and Tableau (BI & Reporting tool)
Introduction to R
Introduction to Scala (Object Oriented and Functional Prog. Language)
Introduction to Python

Apache Spark (Batch and Near Real time):
o Batch Versus Real-Time Data Processing
o Spark Vs Hadoop
o Architecture of Spark
o Coding Spark Jobs in Scala
o Exploring the Spark Shell
o RDD Programing
o Opreations on RDD
o Configuration and Running the Spark Cluster
o Cluster Management
o Submitting the Spark jobs and running in the clustered mode
o Tuning and Debugging Spark

Apache Flink (Batch and Real Time)

Tableau (Reporting Tool to creare dashboard)

Hadoop Administration:
o Linux Ubuntu and CentOs Installation (Hands on Installation on Laptops)
o Dual boot Ubuntu with Windows. Also Virtual Machine installation.
o Hadoop Single Node Cluster Set Up (Hands on installation on Laptops)
  - Operating System Installation (Linux: Ubuntu and CentOs)
  - JDK installation
  - SSH (Secure Shell) Configuration
  - Dedicated Group & User Creation
  - Hadoop Installation (Hadoop 1.x and 2.x)
  - Different Configuration Files Setting
  - Name Node format
  - Starting the Hadoop Daemons

o Multi Node Hadoop Cluster Set Up (Hands on Installation on Laptops)
  - Network Related Settings
  - Host Configuration
  - Password less SSH Communication
  - Hadoop Instalation (Pseudo Mode)
  - Configuration File Setting
  - Name Node Format
  - Starting the Hadoop Daemons
o Pig Installation (Hands on Installation on Laptops)
Local Mode Cluster Mode Bashrc file Configuration

o Sqoop Installation (Hands on Installation on Laptops)
Sqoop Installation with MySQL Client

o Hive Installation (Hands on Installation on Laptops)
Local Mode Clustered Mode

o Hbase Installation (Hands on Installation on Laptops)
Local Mode Clustered Mode

o OOZIE Installation (Hands on Installation on Laptops)
o MongoDB Installation (Hands on Installation on Laptops)
o Apache Spark with Scala Installation (Hands on Installation on Laptops)

Offerings From AP Edusoft:
o Complete Softwares: Hadoop, Linux Ubuntu and CentOs.
o Detailed Assistance in RESUME Prepration with Real Time Projects based on your
technical background.
o All the Interview Questions will be provided
o Discussing the new happenings in Hadoop.
o Discussing Cloudera Distribution Certification Related topics on daily basis.
o Written Exams will be conducted during the course with Real Time Scenarios.
o Cluster setup (20 Nodes cluster) knowledge sharing with setup document.

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