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  • Big Data and Hadoop Certification Course

    • About Course
    • Course Overview
    • Key Features
    • Agenda
    • FAQ
    • TRAINING CALENDAR

    What is Big Data Hadoop?

    Big data is a collection of the large volumes of data that can’t be processed using the traditional Database management systems. This huge amount of data is coming from various sources like smartphones, twitters, facebook and other sources. According to various survey’s 90% of the world’s data is generated in the last two years.

    To address these issues, google labs came up with an algorithm to split their large amount of data into smaller chunks and map them to many computers and when calculations were done, bring back the results to consolidate. This software framework for storing and processing big data is known as Hadoop. Hadoop framework has many components such as HDFS, MapReduce, HBase, Hive, Pig,sqoop, zookeeper to analyze structured and unstructured data using commodity hardware. This is an industry recognized training course that is a combination of the training courses in Hadoop developer, Hadoop administrator, Hadoop testing, and big data analytics. This Cloudera Hadoop training will prepare you to clear big data certification.

    Big Data Analytics using Hadoop

    Hadoop provides platform to store large volumes of data on distributed file system which is reliable, flexible, economical and scalable solution. There are multiple solutions available to analyse this huge data like Mapredue, Hive and Pig to uncover correlations and patterns that provides insights on making better business decisions.

    Big data and Hadoop classroom training covers all aspects of Data Analyst training as detailed out in Cloudera Certification Training.

    Pre-requisites for Online Big Data Hadoop Certification Course:

    * There are no pre-requisites to learn Big Data Hadoop Training Course. Basic knowledge of Core Java SQL will be beneficial, but certainly not mandatory.

    * As part of Big Data and Hadoop Certification course, IT Skills Training Services can provide a complementary self-paced course on core java.

    Audience for Hadoop Certification Training:

    * Software developers/Engineers

    * Project leads, Architects and Project Managers

    * Analysts, Data analysts, Java Architects, DBA, and Database related professionals

    * Graduates and Professionals aspiring for making a career in Big data and Hadoop

    IT Skills’s Big Data Hadoop Certification Course has helped thousands of Big Data Hadoop professionals around the globe to bag top jobs in the industry. Our Big Data Hadoop Training Course includes lifetime access, 24X7 support and class recordings.

    Course Overview

    In this Big Data Hadoop Certification Course, trainees will gain a practical skill set on Hadoop in detail, including its fundamental and latest modules, like HDFS, Map Reduce, Hive, HBase, Sqoop, Flume, Oozie, Zoopkeeper, Spark and Storm. At end of the program, aspirants are awarded with Big Data & Hadoop Certification. You will also work on a project as part of your training which would prepare to take up assignments on Big data

    Objectives of the Course

    After completion of the Big Data and Hadoop Course from IT Skills, you will be able to:

    * Completely understanding Apache Hadoop Framework

    * Understanding of HDFS, learn how MapReduce processes the data

    * Hadoop development and implementation

    * Understand how YARN engages in managing to compute resources into clusters

    * Design, build, install, configuring the applications involving Big Data and Hadoop Ecosystem

    * Maintain security and data privacy

    Who can become a Big Data and Hadoop Professional?

    There are no predefined or stringent prerequisites to learn Hadoop, but comprehensive Hadoop Certification Training can help you get a Big data Hadoop job if you have the readiness to build a career in Big Data Domain.

    It’s a wrong belief that only professionals with familiarity in Java programming background are suitable for learning Big Data Hadoop or joining a career in this domain. An elementary knowledge of any programming language like Java, C++ or Python, and Linux is always an additional advantage. The following individuals are able to become a BigData Hadoop Professional, Software developers, Architects, Analysts, DBA, Data Analysts, Business Analysts, Big Data professionals, or anyone who is considering to building a career in Big Data and Hadoop is ideal applicants for the Big Data and Hadoop training.

    Key Features

    • 32 hours of high quality training
    • Trainers are Industry experts & working professionals
    • Comprehensive up-to date contents
    • Exercises & Hands-on assignments
    • 100% Money back guarantee
    • Course completion certificate

    How are the classes conducted?

    • Class Room Training
    • Instructor-Led online Training
    • Online Self learning

    Money back Guarantee

    • If you don't like the training, inform us after 1st session. 100% money will be refunded with no questions asked

    Group Discount

    • 10% discount for 3 or more registration 

    Agenda

    Module 1: Introduction to Big Data Hadoop Spark Developers

    • What is Big Data?
    • The Rise of Bytes
    • Data Explosion and its Sources
    • Types of Data – Structured, Semi-structured, Unstructured data
    • Why did Big Data suddenly become so prominent
    • Data – The most valuable resource
    • Characteristics of Big Data – IBM’s Definition
    • Limitations of Traditional Large-Scale Systems
    • Various Use Cases for Big Data
    • Challenges of Big Data
    • Hadoop Introduction - What is Hadoop? Why Hadoop?
    • Is Hadoop a fad or here to stay? - Hadoop Job Trends
    • History and Milestones of Hadoop
    • Hadoop Core Components – MapReduce & HDFS
    • Why HDFS?
    • Comparing SQL Database with Hadoop
    • Understanding the big picture - Hadoop Eco-Systems
    • Commercial Distribution of Hadoop – Cloudera, Hortonworks, MapR, IBM BigInsight, Cloud Computing - Amazon Web Services, Microsoft Azure HDInsight
    • Supported Operating Systems
    • Organizations using Hadoop
    • Hands on with Linux File System
    • Hadoop Documentation and Resources

    Module 2: Getting Started with Hadoop Setup

    • Deployment Modes – Standalone, Pseudo-Distributed Single node, Multinode
    • Demo Pseudo-Distributed Virtual Machine Setup on Windows
    • Virtual Box - Introduction
    • Install Virtual Box
    • Open a VM in Virtual Box
    • Hadoop Configuration overview
    • Configuration parameters and values
    • HDFS parameters
    • MapReduce parameters
    • YARN parameters
    • Hadoop environment setup
    • Environment variables
    • Hadoop Core Services – Daemon Process Status using JPS
    • Overview of Hadoop WebUI
    • Firefox Bookmarks
    • Web Ports
    • Eclipse development environment setup

    Module 3: Hadoop Architecture and HDFS

    • Introduction to Hadoop Distributed File System
    • Regular File System v/s HDFS
    • HDFS Architecture
    • Components of HDFS - NameNode, DataNode, Secondary NameNode
    • HDFS Features - Fault Tolerance, Horizontal Scaling
    • Data Replication, Rack Awareness
    • Setting up HDFS Block Size
    • HDFS2.0 - High Availability, Federation
    • Hands on with Hadoop HDFS,WebUI and Linux Terminal Commands
    • HDFS File System Operations
    • Name Node Metadata, File System Namespace, NameNode Operation,
    • Data Block Split, Benefits of Data Block Approach, HDFS - Block Replication Architecture, Block placement, Replication Method, Data Replication Topology, Network Topology, Data Replication Representation
    • Anatomy of Read and Write data on HDFS
    • Failure and Recovery in Read/Write Operation
    • Hadoop Component failures and recoveries
    • HDFS Programming Basics – Java API
    • Java API Introduction
    • Hadoop Configuration API
    • HDFS API Overview
    • Accessing HDFS Programmatically

    Module 4: MapReduce Framework

    • What is MapReduce and Why it is popular
    • MapReduce Framework– Introduction, Driver, Mapper, Reducer, Combiner, Split, Shuffle & Sort
    • Example: Word Count the Hello World of MapReduce
    • Use cases of MapReduce
    • MapReduce Logical Data Flow – with multiple/single reduce task
    • MapReduce Framework revisited
    • Steps to write a MapReduce Program
    • Packaging MapReduce Jobs in a JAR
    • MapReduce CLASSPATH
    • Different ways of running MapReduce job
    • Run on Eclipse – local v/s HDFS
    • Run M/R job using YARN
    • Writing and Viewing Log Files and Web UI
    • Input Splits in MapReduce
    • Relation between Input Splits and HDFS Blocks
    • Hands on with Map Reduce Programming

    Module 5: MapReduce Advanced

    • Map Reduce Architecture
    • Responsibility of JobTracker, TaskTracker in classic MapReduce v1
    • Anatomy of MapReduce Jobs Execution in classic MRv1(JT, TT)
    • Hadoop 2.0, YARN, MRv2
    • Hadoop 1.0 Limitations
    • MapReduce Limitations
    • YARN Architecture
    • Classic vs YARN
    • YARN multitenancy
    • MapReduce and YARN command line tools
    • Anatomy of MapReduce Jobs Execution MRv2 - YARN(RM, AM, NM)
    • How status updates in MRv1 and YARN
    • Speculative Execution
    • Data Locality
    • Reducer – Shuffle, Sort and Partitioner
    • How Partitioners and Reducers Work Together
    • Determining the optimal number of Reducers for a Job
    • Setting Mapper Counts and Reducer Counts
    • Writing Customer Partitioners
    • Strategies for Debugging MapReduce Code
    • Counters - Retrieving Job Information
    • Logging in Hadooop
    • MapReduce unit tests with JUnit and MRUnitframkework
    • MapReduce I/O Format
    • Understanding Data Types of Keys and Values
    • Understanding Input/output Format, Sequence Input/output format
    • Creating Custom Writable and WritableComparable Implementations
    • Implementing Custom InputFormats and OutputFormats
    • Saving Binary Data Using SequenceFile and Avro Data Files
    • Map-Side Join, Reduce-Side Join, Cartesian Product
    • Creating Map-Only MapReduce Jobs Example: DistCp command

    Module 6: Data Warehousing - Pig

    • Pig Data Flow Language – MapReduce using Scripting 
    • Challenges Of MapReduce Development Using Java 
    • Need for High Level Languages - Pig 
    • PIG vs MapReduce 
    • What is/n’t PIG, PigLatin, Grunt Shell 
    • Where to/not to use Pig? 
    • Pig Installation and Configuration  
    • Architecture: The Big Picture, Pig Components 
    • Execution Environments - Local, Mapreduce 
    • Different ways of Invoking Pig – Interactive, Batch 
    • Pig Example: Data Analysis in Pig Latin 
    • Quickstart and Interoperability 
    • Data Model 
    • Expression in Pig Latin 
    • Pig Data Types, 
    • Nulls in Pig Latin 
    • Pig Operation 
    • Core Relational Operators – Load, Store, Filter, Transform, Join, Group, CoGroup, Union, Foreach, Sort/Order, Combine/Split, Distinct, Filter, Limit, Describe, Explain, Illustrate 
    • Group v/s CoGroup v/s Join 
    • PIG Latin: File Loaders & in built UDF(Python, Java) usage 
    • PIG v/s SQL 
    • Implementation & Usage of Pig UDF

    Module 7 : Data Warehousing - Hive and HiveQL

    • Limitations of MapReduce
    •  Need for High Level Languages 
    • Analytical OLAP - Datawarehousing with Apache Hive and Apache Pig 
    • HiveQL- SQL like interface for MapReduce 
    • What is Hive, Background, Hive QL 
    • Where to use Hive? Why use Hive when Pig is here?  
    • Pig v/s Hive 
    • Hive Installation, Configuration Files 
    • Hive Components, Architecture and Metastore
    • Metastore – configuration 
    • Driver, Query Compiler, Optimizer and Execution Engine 
    • Hive Server and Client components
    • Hive Data Types 
    • Hive Data Mode 
    • File Formats 
    • Hive Example 
    • Hive DDL  
    • CREATE,ALTER,DROP,TRUNCATE  
    • Create/Show Database  
    • Create/Show/Drop Tables 
    • Hive DML 
    • SELECT, INSERT, OVERWRITE, EXPLAIN 
    • Load Files & Insert Data into Tables 
    • Managed Tables v/s External Tables – Loading Data
    • Hive QL - Select, Filter, Join, Group By, Having, Cubes-Fact/Dimension(Star Schema)  
    • Implementation & Usage of Hive UDF, UDTF and SerDe
    • Partitioned Table - loading data 
    • Clustered Table – loading data in Clustered Table Views 
    • Bucketing 
    • Multi-Table Inserts  
    • Joins  
    • Hands on with Hive – CRUD - Get,Put,Delete,Scan
    • Limitations of Hive  
    • SQLv/s Hive

    Module 8: NoSQL Databases - HBase

    • NoSQL Introduction 
    • RDBMS (SQL) v/s HBase (NoSQL) 
    • RDBMS – Benefits, ACID, Demarits
    • CAP Theorem and Eventual consistency  
    • Row Oriented v/s Column Oriented Storage 
    • NoSQL: ColumnDB(HBase,Cassandra),Document(MongoDB,CouchDB, MarkLogic),GraphDB(Neo4J),
    • KeyValue(Memcached, Riak, Redis, DynamoDB) 
    • What is HBase? 
    • Synopsis of how typical RDBMS scaling story runs 
    • HBase comes as a rescue
    • HBase – Hadoop Database 
    • HBase Introduction, Installation, Configuration 
    • HBase Overview: part of Hadoop Ecosystem 
    • Problems with Batch Processing like MR 
    • HBase v/s HDFS 
    • Batch vs. Real Time Data Processing 
    • Use-cases for Real Time Data Read/Write 
    • HBase Storage Architecture 
    • Write Path, Read Path 
    • HBase components - HMaster, HRegionServer
    • ZooKeeper
    • Replication 
    • HBase Data Model 
    • Column Families 
    • Column Value & Key Pair 
    • HBase Operation - Memstore / HFile / WAL 
    • HBase Client - HBase Shell 
    • CRUD Operations 
    • Create via Put method 
    • Read via Get method 
    • Update via Put method
    • Delete via Delete method 
    • Creating table, table properties, versioning, compression 
    • Bulk Loading HBase
    • Accessing HBase using Java Client v/s Admin API 
    • Introduction to Java API 
    • Read / Write path
    • CRUD Operations – Create, Read, Update, Delete 
    • Drop 
    • Scans
    • Scan Caching 
    • Batch Caching 
    • MapReduce Integration 
    • Filters
    • Counters
    • Co-processors
    • Secondary Index
    • Compaction – major, minor 
    • Splits
    • Bloom Filters
    • Caches 
    • Apache Phoenix
    • When Would I Use Apache HBase?
    • Companies Using HBase
    • When/Why to use HBase/Cassandra/MongoDB/Neo4J?

    Module 9: Import/Export Data - Sqoop, Flume

    • Setup MySQL RDBMS
    • Sqoop - Import/Export Structured Data to/from HDFS from/to RDBMS 
    • Introduction to Sqoop
    • Installing Sqoop, Configuration 
    • Why Sqoop
    • Benefits of Sqoop
    • Sqoop Processing  
    • How Sqoop works 
    • Sqoop Architecture 
    • Importing Data – to HDFS, Hive, HBase
    • Exporting Data – to MySQL 
    • Sqoop Connectors  
    • Sqoop Commands 
    • Flume – Import Semi-Structured (Ex. Log message) Data to HDFS 
    • Why Flume 
    • Flume - Introduction  
    • Flume Model 
    • Scalability In Flume
    • How Flume works 
    • Flume Complex Flow - Multiplexing  
    • Hands on with Sqoop, Flume

    Module 10: Workflows using Oozie

    • MapReduce Workflows
    • Workflows Introduction
    • Oozie - Simple/Complex MapReduce Workflow
    • Introduction to Oozie
    • Oozie Workflows
    • Oozie Service/Scheduler
    • Oozie use-cases

    Module 11: Administering Hadoop

    • Oracle VirtualBox to Open a VM
    • Open a VM using Oracle 
    • Hadoop Cluster Configuration overview 
    • Configuration parameters and values 
    • HDFS parameters 
    • MapReduce parameters 
    • Hadoop environment setup 
    • Include and Exclude configuration files
    • Site v/s Default conf files
    • Environment Variables 
    • Scripts
    • Hadoop Multi-node Installation
    • Passwordless SSH setup  
    • Configuration Files of Hadoop Cluster 
    • hadoop default.xml 
    • Safe Mode 
    • DfsAdmin
    • Hadoop Ports 
    • Hadoop Quotas  
    • Security - Kerberos 
    • ZooKeeper
    • What is Zookeeper 
    • Introduction to ZooKeeper
    • Challenges Faced in Distributed Applications 
    • ZooKeeper Coordination, Architecture,  
    • Hue, Cloudera Manager 
    • Hadoop Cluster Performance Management 
    • Important Hadoop tuning parameters 
    • Hadoop Cluster Benchmarking Jobs – How to run the jobs
    • Counters 
    • HDFS Benchmarking 
    • Debugging, Troubleshooting 
    • Reference

    Module 12: Apache Spark

    • Spark Concepts, Installation and Architecture
    • Spark Modes
    • Spark web UI 
    • Spark shell
    • RDD Operations / transformations 
    • Key-Value pair RDDs
    • MapReduce on RDD 
    • Submitting the first program to Spark
    • Spark SQL

    FAQ

    Who are the instructors?

    We believe in quality & follow a rigorous process in selecting our trainers. All our trainers are industry experts/ professionals with an experience in delivering trainings.

    Whom do I contact, if I have further clarifications?

    You can call us on +1-888-828-1956 (USA Tollfree Number), India - 91-9108460933 or email at email@it-skillstraining.com.

    What is Online Classroom training?

    Online Classroom training for ITIL is a live training conducted via online live streaming of a class. This is often surpass ITIL certified trainer with over 10 years of labour expertise within the domain and coaching.

    What if I miss the online class?

    You will get the recording of the session and also you can attend the session from the next batch.

    What are the pre-requisites for the course?

    • Some experience in IT industry or education in IT
    • Some experience in system administration is preferred
    • Basic understanding of virtualization is preferred

    Do I get certification?

    After the completion of the training, you will be awarded the course completion certificate from IT Skills Training Services.

     

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    • Web Analytics

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    • Mobile Marketing

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    • PPC Marketing

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    • Big Data and Hadoop Adminstrator

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    • Apache Spark & Scala

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    • MongoDB Developer & Administrator

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    • Apache Cassandra

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      INR. 5000
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      INR. 6000
      INR. 5000 (Inclusive of 14.5% Tax)
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      USD 500
      USD 400
      USD 600
      USD 500
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    Classroom Training: 32 hours

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    Project: 20 hours

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