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Business Business Analytics & Intelligence Hadoop

Hands-on HADOOP Masterclass - Tame the Big Data!

Big Data, Hadoop, MapReduce, HDFS, HIVE, PIG, Mahout, NoSQL, Oozie, Flume, Storm, Avro, Spark, Sqoop, Cloudera and more
Rating: 3.9 out of 53.9 (416 ratings)
20,876 students
Created by EDU CBA
Last updated 9/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Learn the concepts of Hadoop and Big Data
  • Learn in details the concepts of MapReduce, HDFS, HIVE, PIG
  • Learn Mahout, NoSQL, Oozie, Flume, Storm, Avro, Spark, Sqoop, Cloudera and more
  • Perform Data Analytics using Hadoop
  • Master the concepts of Hadoop framework
  • Get experience on different configurations of Hadoop cluster
  • Work with real-time projects using Hadoop

Requirements

  • Basic Computer Knowledge
  • Basic knowledge of Java and SQL will serve as an added advantage
  • No prior knowledge is required. The course starts from scratch and will go to an advanced level with different projects
  • There are no special skills needed to take this Hadoop course

Description

Learn from well crafted study materials on Big Data, Hadoop, MapReduce, HDFS, HIVE, PIG, Mahout, NoSQL, Oozie, Flume, Storm, Avro, Spark, Sqoop, Cloudera, Data Analysis, Survey Analysis, Data Management, Sales Analysis, salary Analysis, Traffic Analysis, Loan Analysis, Log Data Analysis, Youtube Data Analysis, Sensor Data Analysis. Learn by doing. Learn from hands-on examples of analyzing big data. Turn your Crafting ability which can be a mixed bag ranging from developers to data scientists using procedural languages in the Hadoop space. Discover and learn the fundamentals of Hadoop. Be a person comfortable in managing the development and deployment of Hadoop applications.

What is Big Data

Big data is a collection of large datasets which cannot be processed using the traditional techniques. Big data uses various tools and techniques to collect and process the data. Big data deals with all types of data including structured, semi-structured and unstructured data. Big data is used in various fields data like

  • Black box data

  • Social media data

  • Stock exchange data

  • Power Grid Data

  • Transport Data

  • Search Engine Data


Benefits of Big Data

Big data has become very important and it is emerging as one of the crucial technologies in today’s world. The benefits of big data are listed below

Big data can be used by the companies to know the effectiveness of their marketing campaigns, promotions and other advertising media

Big data helps the companies to plan their production

Using the information provided through Big data companies can deliver better and quick service to their customers

Big data helps in better decision making in the companies which will increase the operational efficiencies and reduces the risk of the business

Big data handles huge volume of data in real time and thus enables data privacy and security to a great extent


Challenges faced by Big Data

The major challenges of big data are as follows

  • Curation

  • Storage

  • Searching

  • Transfer

  • Analysis

  • Presentation


What is Hadoop

Hadoop is an open source software framework which is used for storing data of any type. It also helps in running applications on group of hardware. Hadoop has huge processing power and it can handle more number of tasks. Open source software here means it is free to download and use. But there are also commercial versions of Hadoop which is becoming available in the market. There are four basic components of Hadoop – Hadoop Common, Hadoop Distributed File System (HDFS), MapReduce and Yet Another Resource Negotiator (YARN).


Benefits of Hadoop Course

Hadoop is used by most of the organizations because of its ability to store and process huge amount of any type of data. The other benefits of Hadoop includes

  • Computing Power

  • Flexibility

  • Fault Tolerance

  • Low Cost

  • Scalability


Uses of Hadoop

Hadoop is used by many of the organization’s today because of its following uses

Low cost storage and active data archive

Staging area for a data warehouse and analytics store

Data lake

Sandbox for discovery and analysis

Recommendation Systems

Who this course is for:

  • Software developers and Architects
  • Analytics Professionals
  • Anyone who is interested in pursuing his career in Big data analytics
  • Data Management Professionals
  • Business Intelligence Professionals
  • Testing Professionals
  • Data Scientists
  • Data analysts/developers
  • Anyone who wants to learn about application of Hadoop

Course content

31 sections • 525 lectures • 67h 30m total length

  • Preview09:45
  • Preview10:04
  • Write Anatomy
    06:38
  • Continuation os Write Anatomy
    06:43
  • Read Anatomy
    06:40
  • Continuation os Read Anatomy
    06:15
  • Word Count in Hadoop
    11:14
  • Running Hadoop Application
    04:09
  • Continuation Hadoop Application
    04:06
  • Working on Sample Program
    08:30
  • Creating Method Map
    08:06
  • Iterable Values
    07:51
  • Output Path
    05:36
  • Scary Catch Box
    03:03

  • Introduction to Hadoop Admin
    11:16
  • Limitations of Existing System
    11:24
  • Hadoop Key Characteristics
    10:14
  • Hadoop Distributed File System
    10:05
  • Storage Layer of Hadoop
    10:41
  • Hadoop 1.0 Core Components
    10:59
  • FS Images
    11:12
  • Secondary Name Node
    10:48
  • HDFC Architecture
    11:10
  • Block Placement Policy
    12:08
  • Assignments
    11:51
  • Hadoop Architecture Cluster Setup
    04:29
  • Installation of Hadoop in Vmware Workstation
    07:50
  • Hadoop Package Installation
    05:03
  • Configuration of Host Name and Gateway
    07:28
  • Copying of ISO File to Centos
    05:32
  • Installation of SSH File Using Yum
    07:09
  • Copy the Public Key to Authorized Key in SSH
    11:57
  • Setup for Block Size and Mapped
    05:17
  • Create SSH -keygen for HD User
    07:08
  • Start the Map Reduce in Hadoop
    05:21
  • Creating a Clone for Hadoop
    07:20
  • Changing the Hostname
    11:33
  • Configuring Hadoop Site
    06:30
  • Slave File Configuration
    06:19
  • Creating Name node and Data Node In Hadoop
    07:36
  • Understanding HDFS
    05:38
  • Hadoop Core Config Files
    06:50
  • Hadoop Cluster and Password less SSH
    06:43
  • Configuring Rack Awareness
    07:38
  • Configuring Rack Awareness Continues
    04:55
  • Running DFS Admin Report
    05:09
  • Hadoop Map Reduce
    08:39
  • Running Hadoop NameNode
    05:01
  • Executing Hadoop Command
    07:35
  • Writing File in Hadoop Cluster
    05:43
  • Understanding FS Command
    08:48
  • Directories of Data
    04:59
  • Fie System Check
    07:25
  • Writing Data in HDFS
    06:17
  • Checkpointing Node
    06:38
  • Merging the Metadata
    06:43
  • Cluster in Safe Mode
    07:08
  • Cluster in Maintainance Mode
    07:08
  • Commissioning of Data Nodes
    07:50
  • Name Node
    05:35
  • Validating the Data Node
    06:56
  • Storage Considerations
    06:33

  • Secondary Sort Hadoop
    08:42
  • Creating Composite Key
    08:29
  • Continue on Composite Key
    09:19
  • Word Count Group
    06:32
  • Importance of Partition
    11:16
  • Hadoop FS - LS
    04:55
  • Joins in Hadoop
    07:28
  • Creating Configuration Object
    06:20
  • Setup Method
    07:19
  • Map Side Join Mapper
    07:50
  • Hadoop Commands
    06:43
  • Combiner in Hadoop
    06:10
  • Continue on Combiner in Hadoop
    08:57
  • Uploading Combiner Jar
    04:27
  • Introduction to Real World
    10:08
  • Ratings Mapper
    07:26
  • Movie and Ratings Runner
    08:47
  • Movie and Rating Calc Jar
    04:09
  • Total Ratings By A User
    08:15
  • User Rating Reducer
    11:19
  • User Rating Class
    04:57
  • Yarn Basic Tutorial
    10:04
  • Node Manager
    09:35

  • Running a MapReduce Program
    12:09
  • Running a MapReduce Program Continues
    11:03
  • HDFS File System
    10:02
  • Combination of Word Count Functionality
    09:29
  • Word Count With Tools
    10:27
  • Log Processor
    11:07
  • Advanced MapReduce and PIG
    10:11
  • More on Advanced MapReduce
    08:56
  • Executing Similar Program
    08:29
  • HDI Data and Export Data
    12:41
  • Creating New Java Class
    12:00
  • Text Out Inverted Indexer
    12:52
  • Introduction to MapReduce on Hadoop
    09:52
  • Java Build Path
    10:11
  • Local MapReduce
    04:29
  • Using MapReduce
    08:54
  • Sequence file Format
    11:17
  • Parse Weblogs
    10:43
  • Page View Mapper
    09:25
  • Analytics Program
    08:52
  • Analytics Program Continue
    11:51
  • Inverted Index Map Reduce
    11:24
  • Friend Sofa Friend
    07:39
  • Cloud era Local Host
    07:28
  • Cloud era Local Host Output
    10:42
  • Final Module MapReduce Program
    11:06
  • Strands
    09:20
  • File Path Filter
    09:14
  • Example
    08:57
  • Example Continue
    09:30

  • Introduction to HIVE
    10:45
  • HIVE Data Base
    10:21
  • Load Data Command
    05:37
  • How to Replace Column
    04:17
  • External Table
    06:26
  • HIVE Metastore
    03:25
  • What is Hive Partition
    07:29
  • Creating Partition Table
    08:30
  • Insert Overwrite Table
    03:55
  • Dynamic Partition True
    01:57
  • Hive Bucketing
    05:24
  • Decomposing Data Sets
    05:30
  • Hive Joins
    08:51
  • Hive Joins Continue
    09:45
  • Skew Join
    02:54
  • What is Serde
    07:29
  • Serde in Hive
    08:55
  • Hive UDF
    09:46
  • Hive UDF Continues
    07:28
  • More Hive UDF
    06:58
  • Maxcale Function
    03:01
  • Hive Example Use Case
    12:04

  • Introduction to Hive Concepts and Hands-on Demonstration
    05:59
  • Internal Table and External Table
    06:19
  • Inserting Data Into Tables
    07:25
  • Date and Mathematical Functions
    09:00
  • Conditional Statements
    06:40
  • Explode and Lateral View
    07:59
  • Sorting
    06:18
  • Join
    08:44
  • Map Join
    02:10
  • Static and Dynamic Partitioning
    07:17
  • More on Dynamic Partitioning
    06:59
  • Alter Command
    06:15
  • MSCK Command
    08:44
  • Bucketing
    08:08
  • Table Sampling
    03:05
  • Archiving
    02:44
  • Ranks
    08:44
  • Creating Views
    08:39
  • Advantages of views and Altering Views
    06:50
  • What is Indexing
    Processing..
  • Compact and Bitmap Index Running Time
    05:25
  • Hive Commands in Bash Shell
    05:24
  • Hive Variables - Hiveconf
    04:10
  • Hive Variables -Hiveconf in Bash Shell
    05:08
  • Configuring a Hive Var Variable
    08:57
  • Variable Substitution
    02:14
  • Word Count
    05:47
  • Hive Architecture
    03:14
  • Parallelism in Hive
    06:14
  • Table Properties in Hive
    06:06
  • Null Format Properties
    05:31
  • Null Format Properties Continues
    03:39
  • Purge Commands in Hives
    04:41
  • Slowing Changing Dimension
    06:56
  • Implement the SCD
    08:57
  • Example of the SCD
    04:02
  • How to Load XML Data in Hive
    05:11
  • How to Load XML Data in Hive Continue
    08:48
  • No Drop and Offline in Hive
    08:09
  • Immutable Table
    09:09
  • How to Create Hive RC File
    08:38
  • Multiple Tables
    06:25
  • Merging Hive Created Files and Function rLike
    05:32
  • Various Configuration Settings in Hive
    09:07
  • Various Configuration Settings in Hive Continues
    03:12
  • Compressing Various Files in Hive
    05:45
  • Different Modes in Hive
    03:54
  • File Compression in Hive
    05:30
  • Type of Mode in Hive
    03:56
  • Comparison of Internal and External Table
    08:19

  • Introduction to Pig
    04:56
  • Features of Apache Pig
    08:08
  • Pig Vs Hive
    10:10
  • Apache Pig Local and MR Modes
    04:30
  • Launching Local Modes
    05:59
  • Data Types in Pig
    08:58
  • Pig Commands - Store and Load
    08:38
  • Load Command
    06:02
  • Pig Commands - Group
    06:12
  • CoGroup Operator
    06:19
  • Join and Cross operators in Pig
    07:24
  • Join and Cross operators in Pig Continues
    07:19
  • Union and Split Operators in Pig
    05:19
  • More on Split Operators
    07:41
  • Filter Distinct and For each
    10:47
  • Pig Functions
    04:32
  • Pig Functions Continues
    08:10
  • Input Data Size
    07:39

  • Getting Started with PIG
    04:58
  • Installation Process
    10:09
  • PIG Latin
    07:32
  • Uploading the File in HDFS
    10:25
  • PIG Script
    10:02
  • PIG Latin Basics
    07:31
  • Up and Running with Pig
    08:20
  • Loading and Storage
    07:23
  • Loading and Storage Continue
    08:07
  • Debugging
    10:35
  • Grunt Shell
    08:27
  • UDFs and Piggy Bank
    10:45

  • A Brief History of NoSQL
    08:40
  • Schema Agnostic
    06:55
  • Nonrelational
    06:01
  • Enterprise NoSQL
    09:17
  • Recent Trends in IT
    07:36
  • NoSQL Benefits and Precautions
    09:34
  • Managing Different Data Types
    07:43
  • Triple and Graph Store
    08:05
  • Hybrid NoSQL Databases
    07:53
  • Applying Consistency Method
    07:08
  • Choosing ACID or BASE?
    09:49
  • Developing Application on NoSQL
    04:58
  • Semantics
    08:39
  • Public Cloud
    07:05
  • Managing Availability
    06:25
  • Versioning Data
    06:25

  • What is Mahout
    06:55
  • Mahout Architecture
    08:51
  • Subversion Installation
    06:44
  • Item Based Recommendation
    07:15
  • Example- CBayes Classifier
    08:19
  • Command Line Options
    10:40
  • Canopy Clustering
    10:52
  • Basic Recommender
    10:38
  • Practical Examples
    08:19
  • Mahout Seqdumper Command
    06:44
  • Running Code through Eclipse
    06:27
  • Reading from Code
    06:17
  • Introduction to Apache Mahout Deep Dive
    09:22
  • Use Cases
    09:09
  • Recommendation
    11:00
  • Example - Tanimoto Distance
    07:08
  • How to Use Mahout?
    09:42
  • Exercise
    07:05
  • Example - Evaluation
    07:13
  • Deep Dive Canopy Clustering
    10:15
  • Classification
    10:18
  • Vector File
    08:10
  • Naïve Bayes Classifier from Code
    11:27
  • KMeans Clustering
    05:51
  • Logistic Regression
    08:17

Instructor

EDU CBA
Learn real world skills online
EDU CBA
  • 3.6 Instructor Rating
  • 5,930 Reviews
  • 183,729 Students
  • 29 Courses

EDUCBA is a leading global provider of skill based education addressing the needs of members across 100+ Countries. We are the LARGEST edu-tech firm in Asia with a portfolio of 5498+ online courses, 205+ Learning Paths, 150+ Job Oriented Programs (JOPs) and 50+ Career based Course Bundles prepared by top notch professionals from the Industry. Our training programs are Job oriented skill based programs demanded by the Industry across Finance, Technology, Business, Design, Data and new and upcoming technology.

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