Country

Watch Introduction Video

Classroom Training in Woodbridge Change city

Select a convenient batch & Register

Online Classroom

Online classroom

An Instructor led Online Classroom is a live Online classroom where you can interact with your trainer and peers. It is as simple as attending a web meeting.

Watch a Sample Session

Sep 04 - Oct 02
Batch Schedule Dates

Sep

  • Fri
    04
  • Sat
    05
  • Fri
    11
  • Sat
    12
  • Fri
    18
  • Sat
    19
  • Fri
    25
  • Sat
    26

Oct

  • Fri
    02
 
Time
22:30 - 02:30
 
Location
Online Classroom
 
Price
$ 999
 
Sep 12 - Oct 10
{Weekend Batch}
Batch Schedule Dates

Sep

  • Sat
    12
  • Sun
    13
  • Sat
    19
  • Sun
    20
  • Sat
    26
  • Sun
    27

Oct

  • Sat
    03
  • Sun
    04
  • Sat
    10
 
Time
09:00 - 13:00
 
Location
Online Classroom
 
Price
$ 999
 
Sep 14 - Sep 25
Batch Schedule Dates

Sep

  • Mon
    14
  • Tue
    15
  • Wed
    16
  • Thu
    17
  • Fri
    18
  • Mon
    21
  • Tue
    22
  • Wed
    23
  • Thu
    24
  • Fri
    25
 
Time
09:30 - 12:45
 
Location
Online Classroom
 
Price
$ 1199
 
Sep 14 - Sep 29
Batch Schedule Dates

Sep

  • Mon
    14
  • Tue
    15
  • Wed
    16
  • Thu
    17
  • Fri
    18
  • Mon
    21
  • Tue
    22
  • Wed
    23
  • Thu
    24
  • Fri
    25
  • Mon
    28
  • Tue
    29
 
Time
09:30 - 12:30
 
Location
Online Classroom
 
Price
$ 999
 
Sep 20 - Oct 01
Batch Schedule Dates

Sep

  • Sun
    20
  • Mon
    21
  • Tue
    22
  • Wed
    23
  • Thu
    24
  • Sun
    27
  • Mon
    28
  • Tue
    29
  • Wed
    30

Oct

  • Thu
    01
 
Time
19:30 - 22:30
 
Location
Online Classroom
 
Price
$ 1199
 
Oct 09 - Nov 06
Batch Schedule Dates

Oct

  • Fri
    09
  • Sat
    10
  • Fri
    16
  • Sat
    17
  • Fri
    23
  • Sat
    24
  • Fri
    30
  • Sat
    31

Nov

  • Fri
    06
 
Time
22:30 - 02:30
 
Location
Online Classroom
 
Price
$ 999
 
Oct 12 - Oct 27
Batch Schedule Dates

Oct

  • Mon
    12
  • Tue
    13
  • Wed
    14
  • Thu
    15
  • Fri
    16
  • Mon
    19
  • Tue
    20
  • Wed
    21
  • Thu
    22
  • Fri
    23
  • Mon
    26
  • Tue
    27
 
Time
09:30 - 12:30
 
Location
Online Classroom
 
Price
$ 999
 
Oct 17 - Nov 14
{Weekend Batch}
Batch Schedule Dates

Oct

  • Sat
    17
  • Sun
    18
  • Sat
    24
  • Sun
    25
  • Sat
    31

Nov

  • Sun
    01
  • Sat
    07
  • Sun
    08
  • Sat
    14
 
Time
09:00 - 13:00
 
Location
Online Classroom
 
Price
$ 999
 
Oct 18 - Oct 29
Batch Schedule Dates

Oct

  • Sun
    18
  • Mon
    19
  • Tue
    20
  • Wed
    21
  • Thu
    22
  • Sun
    25
  • Mon
    26
  • Tue
    27
  • Wed
    28
  • Thu
    29
 
Time
19:30 - 22:30
 
Location
Online Classroom
 
Price
$ 1199
 
Nov 06 - Dec 04
Batch Schedule Dates

Nov

  • Fri
    06
  • Sat
    07
  • Fri
    13
  • Sat
    14
  • Fri
    20
  • Sat
    21
  • Fri
    27
  • Sat
    28

Dec

  • Fri
    04
 
Time
21:30 - 02:30
 
Location
Online Classroom
 
Price
$ 999
 
Nov 09 - Nov 24
Batch Schedule Dates

Nov

  • Mon
    09
  • Tue
    10
  • Wed
    11
  • Thu
    12
  • Fri
    13
  • Mon
    16
  • Tue
    17
  • Wed
    18
  • Thu
    19
  • Fri
    20
  • Mon
    23
  • Tue
    24
 
Time
08:30 - 12:30
 
Location
Online Classroom
 
Price
$ 999
 
Nov 14 - Dec 12
{Weekend Batch}
Batch Schedule Dates

Nov

  • Sat
    14
  • Sun
    15
  • Sat
    21
  • Sun
    22
  • Sat
    28
  • Sun
    29

Dec

  • Sat
    05
  • Sun
    06
  • Sat
    12
 
Time
08:00 - 13:00
 
Location
Online Classroom
 
Price
$ 999
 
Nov 15 - Nov 30
Batch Schedule Dates

Nov

  • Sun
    15
  • Mon
    16
  • Tue
    17
  • Wed
    18
  • Thu
    19
  • Sun
    22
  • Mon
    23
  • Tue
    24
  • Wed
    25
  • Thu
    26
  • Sun
    29
  • Mon
    30
 
Time
18:30 - 22:30
 
Location
Online Classroom
 
Price
$ 999
 

View more batches View less batches

View more batches View less batches

View more batches View less batches

Can't find convenient schedule? Let us know

 
Online Classroom Season Pass

- Be a sure Shot Big Data & Hadoop Developer

- Access 9+ Big Data Hadoop Online classrooms over 90 Days

$ 10,999

$ 1,499

Online self learning

3 DAYS MONEY BACK GUARANTEE

How this works:

For refund, write to support@simplilearn.com within 3 days of purchase

The mode of reimbursement will be same as the mode of payment used during enrollment fees. For example: If a participant has paid through credit cards, we will reimburse through credit card

Note: Money back guarantee is void if the participant has accessed more than 50% of the course content.


Start Anytime! Anywhere in the world
Access days:
180 Days
$299

 

Key Features

  • 40 Hours of Lab Exercises with Proprietary VM
  • 25 Hours of High Quality E-learning Content
  • 13 Chapter-end Quizzes
  • 2 Big Data & Hadoop Simulation Exams
  • 60 Hours Of Real Time Industry-based Projects
  • 30 PDUs Offered
  • 5 Projects with 11 Unique Data Sets Included
  • Java Essentials for Hadoop Included
  • Downloadable eBook Included
  • Industry Specific Projects on Top 3 Sectors - Retail, Telecom, & Insurance
  • Additional Free Online Course: Certified Data Scientist - R Language
  • Big Data and Hadoop Developer Certificate
  • 4 Days Classroom or 32 hours of Instructor led Training
  • 40 Hrs of Lab Exercises with proprietary VM
  • 25 Hrs of High Quality e-Learning content
  • Free 90 Days e-Learning access
  • 13 Chapter-end Quizzes
  • 2 Big Data & Hadoop Simulation Exams
  • 60 Hrs of Real Time Industry based Projects
  • Downloadable e-Book Included
  • Java Essentials for Hadoop Included
  • 5 Projects with 11 Unique Data Sets Included
  • Industry Specific Projects on Top 3 Sectors - Retail, Telecom & Insurance
  • 45 PDUs Offered
  • Additional Free Online course: Certified Data Scientist with R Language
  • Big Data and Hadoop Developer Certificate
  • 40 Hours of Lab Exercises with Proprietary VM
  • 25 Hours of High Quality E-learning Content
  • 13 Chapter-end Quizzes
  • 2 Big Data & Hadoop Simulation Exams
  • 60 Hours Of Real Time Industry-based Projects
  • 30 PDUs Offered
  • 5 Projects with 11 Unique Data Sets Included
  • Java Essentials for Hadoop Included
  • Downloadable eBook Included
  • Industry Specific Projects on Top 3 Sectors - Retail, Telecom, & Insurance
  • Additional Free Online Course: Certified Data Scientist - R Language
  • Big Data and Hadoop Developer Certificate
Specials Offer(s) Available

Flat 30% Off on All Online courses + details Flat 30% Off on All Online courses
Use Coupon: OSL30 valid till 4th Sep
Use Coupon:OSL30

Get 1 course free :Certified Data Scientist with R Language + details Pay for 1 course and learn 1 more course(s): Certified Data Scientist with R Language for FREE.

About Course

Course Preview

    • Getting started with Big-Data and Hadoop Developer 28:0
      • Getting started with Big-Data and Hadoop Developer 14:0
    • Lesson 00 - Course Introduction 8:22
      • 0.1 Welcome 1:8
      • 0.2 Course Introduction 1:17
      • 0.3 Course Objectives 1:27
      • 0.4 Course Overview 1:48
      • 0.5 Course Overview(contd.) 1:54
      • 0.6 Value to Professionals 1:50
      • 0.7 Lessons Covered 1:9
      • 0.8 Thank You 1:8
    • Lesson 01 - Introduction to Big Data and Hadoop 29:44
      • 1.1 Introduction to Big Data and Hadoop 1:18
      • 1.2 Objectives 1:17
      • 1.3 Data Explosion 2:4
      • 1.4 Types of Data 1:43
      • 1.5 Need for Big Data 2:19
      • 1.6 Data - The Most Valuable Resource 1:35
      • 1.7 Big Data and Its Sources 1:43
      • 1.8 Three Characteristics of Big Data 0:0
      • 1.9 Characteristics of Big Data Technology 2:31
      • 1.10 Appeal of Big Data Technology 1:38
      • 1.11 Leveraging Multiple Sources of Data 0:0
      • 1.12 Traditional IT Analytics Approach 2:1
      • 1.13 Big Data Technology - Platform for Discovery and Exploration 1:54
      • 1.14 Big Data Technology - Capabilities 1:26
      • 1.15 Big Data - Use Cases 1:43
      • 1.16 Handling Limitations of Big Data 1:37
      • 1.17 Introduction to Hadoop 1:45
      • 1.18 History and Milestones of Hadoop 2:27
      • 1.19 Organizations Using Hadoop 1:28
      • 1.20 Quiz 0:0
      • 1.21 Summary 1:48
      • 1.22 Thank You 1:5
    • Lesson 02 - Getting Started with Hadoop update 26:52
      • 2.1 Getting Started with Hadoop 1:19
      • 2.2 Objectives 1:29
      • 2.3 VMware Player - Introduction 1:38
      • 2.4 VMware Player - Hardware Requirements 1:37
      • 2.5 Steps to Install VMware Player 1:58
      • 2.6 Install VMware Player - Step 1 1:22
      • 2.7 Install VMware Player - Step 2 1:20
      • 2.8 Install VMware Player - Step 3 1:26
      • 2.9 Install VMware Player - Step 4 1:21
      • 2.10 Install VMware Player - Step 5 1:19
      • 2.11 Install VMware Player - Step 6 1:17
      • 2.12 Install VMware Player - Step 7 1:16
      • 2.13 Install VMware Player - Step 8 1:16
      • 2.14 Install VMware Player - Step 9 1:51
      • 2.15 Steps to Create a VM in VMware Player 1:49
      • 2.16 Create a VM in a VMware Player - Step 1 1:17
      • 2.17 Create a VM in a VMware Player - Step 2 1:12
      • 2.18 Create a VM in a VMware Player - Step 3 1:15
      • 2.19 Create a VM in a VMware Player - Step 4 1:14
      • 2.20 Create a VM in a VMware Player - Step 5 1:16
      • 2.21 Create a VM in a VMware Player - Step 6 1:15
      • 2.22 Open a VM in VMware Player - Step1 1:22
      • 2.23 Open a VM in VMware Player - Step2 1:20
      • 2.24 Oracle VirtualBox to Open a VM 1:35
      • 2.25 Open a VM using Oracle VirtualBox - Step 1 1:26
      • 2.26 Open a VM using Oracle VirtualBox - Step 2 1:13
      • 2.27 Open a VM using Oracle VirtualBox - Step 3 1:14
      • 2.28 Open a VM using Oracle VirtualBox - Step 4 1:11
      • 2.29 Business Scenario 1:57
      • 2.30 Demo 0:0
      • 2.31 Demo Summary 1:12
      • 2.32 Summary 1:32
      • 2.33 Thank You 1:7
    • Lesson 03 - Hadoop Architecture 44:44
      • 3.1 Hadoop Architecture 1:17
      • 3.2 Objectives 1:28
      • 3.3 Key Terms 1:34
      • 3.4 Hadoop Cluster Using Commodity Hardware 1:42
      • 3.5 Hadoop Configuration 0:0
      • 3.6 Hadoop Core Services 1:30
      • 3.7 Apache Hadoop Core Components 1:26
      • 3.8 Hadoop Core Components - HDFS 1:57
      • 3.9 Hadoop Core Components - MapReduce 1:41
      • 3.10 Regular File System vs. HDFS 1:48
      • 3.11 HDFS - Characteristics 2:40
      • 3.12 HDFS - Key Features 1:55
      • 3.13 HDFS Architecture 2:4
      • 3.14 HDFS - Operation Principle 3:25
      • 3.15 HDFS 2:11
      • 3.16 File System Namespace 1:31
      • 3.17 NameNode Operation 3:20
      • 3.18 Data Block Split 1:57
      • 3.19 Benefits of Data Block Approach 1:17
      • 3.20 HDFS - Block Replication Architecture 1:57
      • 3.21 Replication Method 1:47
      • 3.22 Data Replication Topology 1:27
      • 3.23 Data Replication Representation 2:7
      • 3.24 HDFS Access 1:31
      • 3.25 Business Scenario 1:28
      • 3.26 Demo 0:0
      • 3.27 Demo Summary 1:11
      • 3.28 Quiz 0:0
      • 3.29 Summary 1:36
      • 3.30 Thank You 1:5
    • Lesson 04 - Hadoop Deployment 52:44
      • 4.1 Hadoop Deployment 1:21
      • 4.2 Objectives 1:25
      • 4.3 Ubuntu Server - Introduction 1:49
      • 4.4 Installation of Ubuntu Server 12.04 1:50
      • 4.5 Business Scenario 1:56
      • 4.6 Demo 1 0:0
      • 4.7 Demo Summary 1:46
      • 4.8 Hadoop Installation - Prerequisites 1:24
      • 4.9 Hadoop Installation 2:48
      • 4.10 Hadoop Installation - Step 1 1:32
      • 4.11 Hadoop Installation - Step 2 2:6
      • 4.12 Hadoop Installation - Step 3 1:22
      • 4.13 Hadoop Installation - Step 4 1:23
      • 4.14 Hadoop Installation - Step 5 1:14
      • 4.15 Hadoop Installation - Step 6 1:17
      • 4.16 Hadoop Installation - Step 7 1:21
      • 4.17 Hadoop Installation - Step 7 (contd.) 1:21
      • 4.18 Hadoop Installation - Step 8 1:27
      • 4.19 Hadoop Installation - Step 8 (contd.) 1:15
      • 4.20 Hadoop Installation - Step 8 (contd.) 1:13
      • 4.21 Hadoop Installation - Step 9 1:23
      • 4.22 Hadoop Installation - Step 9 (contd.) 1:35
      • 4.23 Hadoop Installation - Step 10 1:20
      • 4.24 Hadoop Installation - Step 10 (contd.) 1:20
      • 4.25 Hadoop Installation - Step 11 1:36
      • 4.26 Hadoop Installation - Step 12 1:38
      • 4.27 Hadoop Installation - Step 12 (contd.) 1:31
      • 4.28 Demo 2 0:0
      • 4.29 Demo Summary 2:16
      • 4.30 Hadoop Multi-Node Installation - Prerequisites 1:26
      • 4.31 Steps for Hadoop Multi-Node Installation 1:43
      • 4.32 Hadoop Multi-Node Installation - Steps 1 and 2 1:18
      • 4.33 Hadoop Multi-Node Installation - Step 3 1:23
      • 4.34 Hadoop Multi-Node Installation - Step 3 (contd.) 1:43
      • 4.35 Hadoop Multi-Node Installation - Step 4 1:52
      • 4.36 Hadoop Multi-Node Installation - Step 4( contd.) 1:20
      • 4.37 Hadoop Multi-Node Installation - Step 4 (contd.) 1:28
      • 4.38 Single-Node Cluster vs. Multi-Node Cluster 1:43
      • 4.39 Demo 3 0:0
      • 4.40 Demo Summary 1:16
      • 4.41 Demo 4 0:0
      • 4.42 Demo Summary 3:1
      • 4.43 Demo 5 0:0
      • 4.44 Demo Summary 3:2
      • 4.45 Quiz 0:0
      • 4.46 Summary 2:2
      • 4.47 Thank You 1:6
    • Lesson 05 - Introduction to MapReduce 56:0
      • 5.1 Introduction to MapReduce 1:18
      • 5.2 Objectives 1:20
      • 5.3 MapReduce - Introduction 2:3
      • 5.4 MapReduce - Analogy 2:1
      • 5.5 MapReduce - Analogy (contd.) 1:49
      • 5.6 MapReduce - Example 2:54
      • 5.7 Map Execution 0:0
      • 5.8 Map Execution - Distributed Two Node Environment 1:0
      • 5.9 MapReduce Essentials 2:13
      • 5.10 MapReduce Jobs 2:6
      • 5.11 MapReduce Engine 1:49
      • 5.12 MapReduce and Associated Tasks 1:59
      • 5.13 MapReduce Association with HDFS 1:39
      • 5.14 Hadoop Job Work Interaction 0:0
      • 5.15 Characteristics of MapReduce 1:51
      • 5.16 Real-Time Uses of MapReduce 1:56
      • 5.17 Prerequisites for Hadoop Installation in Ubuntu Desktop 12.04 1:21
      • 5.18 Steps to Install Hadoop 1:52
      • 5.19 Business Scenario 2:4
      • 5.20 Set up Environment for MapReduce Development 1:25
      • 5.21 Small Data and Big Data 1:35
      • 5.22 Uploading Small Data and Big Data 1:30
      • 5.23 Demo 1 0:0
      • 5.24 Demo Summary 1:12
      • 5.25 Build MapReduce Program 1:52
      • 5.26 Hadoop MapReduce Requirements 1:59
      • 5.27 Hadoop MapReduce - Features 1:49
      • 5.28 Hadoop MapReduce - Processes 1:44
      • 5.29 Steps of Hadoop MapReduce 2:16
      • 5.30 MapReduce - Responsibilities 1:53
      • 5.31 MapReduce Java Programming in Eclipse 1:25
      • 5.32 Create a New Project: Step 1 1:24
      • 5.33 Create a New Project: Step 2 1:11
      • 5.34 Create a New Project: Step 3 1:12
      • 5.35 Create a New Project: Step 4 1:13
      • 5.36 Create a New Project: Step 5 1:20
      • 5.37 Demo 2 0:0
      • 5.38 Demo Summary 1:12
      • 5.39 Demo 3 0:0
      • 5.40 Demo Summary 1:23
      • 5.41 Checking Hadoop Environment for MapReduce 1:39
      • 5.42 Demo 4 0:0
      • 5.43 Demo Summary 1:15
      • 5.44 Demo 5 0:0
      • 5.45 Demo Summary 1:36
      • 5.46 Demo 6 0:0
      • 5.47 Demo Summary 1:54
      • 5.48 MapReduce v 2.0 1:8
      • 5.49 Quiz 0:0
      • 5.50 Summary 1:32
      • 5.51 Thank You 1:6
    • Lesson 06 - Advanced HDFS and MapReduce 41:12
      • 6.1 Advanced HDFS and MapReduce 1:20
      • 6.2 Objectives 1:24
      • 6.3 Advanced HDFS - Introduction 1:47
      • 6.4 HDFS Benchmarking 1:32
      • 6.5 HDFS Benchmarking (contd.) 1:19
      • 6.6 Setting Up HDFS Block Size 1:45
      • 6.7 Setting Up HDFS Block Size - Step 1 1:13
      • 6.8 Setting Up HDFS Block Size - Step 2 1:40
      • 6.9 Decommissioning a DataNode 1:49
      • 6.10 Decommissioning a DataNode - Step 1 1:14
      • 6.11 Decommissioning a DataNode - Step 2 1:14
      • 6.12 Decommissioning a DataNode - Step 3 and 4 1:21
      • 6.13 Business Scenario 1:34
      • 6.14 Demo 1 0:0
      • 6.15 Demo Summary 1:30
      • 6.16 Advanced MapReduce 1:48
      • 6.17 Interfaces 0:0
      • 6.18 Data Types in Hadoop 2:19
      • 6.19 InputFormats in MapReduce 2:19
      • 6.20 OutputFormats in MapReduce 2:44
      • 6.21 Distributed Cache 2:6
      • 6.22 Using Distributed Cache - Step 1 1:20
      • 6.23 Using Distributed Cache - Step 2 1:16
      • 6.24 Using Distributed Cache - Step 3 1:24
      • 6.25 Joins in MapReduce 0:0
      • 6.26 Reduce Side Join 1:38
      • 6.27 Reduce Side Join (contd.) 1:36
      • 6.28 Replicated Join 1:30
      • 6.29 Replicated Join (contd.) 1:41
      • 6.30 Composite Join 1:34
      • 6.31 Composite Join (contd.) 1:28
      • 6.32 Cartesian Product 1:38
      • 6.33 Cartesian Product (contd.) 1:25
      • 6.34 Demo 2 0:0
      • 6.35 Demo Summary 1:48
      • 6.36 Quiz 0:0
      • 6.37 Summary 1:44
      • 6.38 Thank You 1:6
    • Lesson 07 - Pig 46:28
      • 7.1 Pig 1:16
      • 7.2 Objectives 1:21
      • 7.3 Challenges Of MapReduce Development Using Java 1:54
      • 7.4 Introduction To Pig 1:36
      • 7.5 Components Of Pig 1:51
      • 7.6 How Pig Works 1:47
      • 7.7 Data Model 1:40
      • 7.8 Data Model (contd.) 2:37
      • 7.9 Nested Data Model 1:28
      • 7.10 Pig Execution Modes 1:29
      • 7.11 Pig Interactive Modes 1:30
      • 7.12 Salient Features 1:32
      • 7.13 Pig vs. SQL 1:55
      • 7.14 Pig vs. SQL - Example 2:12
      • 7.15 Installing Pig Engine 1:24
      • 7.16 Steps To Installing Pig Engine 1:32
      • 7.17 Installing Pig Engine - Step 1 1:16
      • 7.18 Installing Pig Engine - Step 2 1:33
      • 7.19 Installing Pig Engine - Step 3 1:20
      • 7.20 Installing Pig Engine - Step 4 1:9
      • 7.21 Installing Pig Engine - Step 5 1:10
      • 7.22 Run A Sample Program To Test Pig 1:36
      • 7.23 Getting Datasets For Pig Development 1:22
      • 7.24 Prerequisites To Set The Environment For Pig Latin 1:21
      • 7.25 Prerequisites To Set The Environment For Pig Latin - Step 1 1:17
      • 7.26 Prerequisites To Set The Environment For Pig Latin - Step 2 1:12
      • 7.27 Prerequisites To Set The Environment For Pig Latin - Step 3 1:13
      • 7.28 Loading And Storing Methods - Step 1 1:32
      • 7.29 Loading and Storing Methods - Step 2 1:24
      • 7.30 Script Interpretation 1:44
      • 7.31 Filtering and Transforming 1:28
      • 7.32 Grouping and Sorting 1:21
      • 7.33 Combining and Splitting 1:22
      • 7.34 Pig Commands 2:8
      • 7.35 Business Scenario 1:52
      • 7.36 Demo 1 0:0
      • 7.37 Demo Summary 1:23
      • 7.38 Demo 2 0:0
      • 7.39 Demo Summary 1:26
      • 7.40 Demo 3 0:0
      • 7.41 Demo Summary 1:26
      • 7.42 Demo 4 0:0
      • 7.43 Demo Summary 1:25
      • 7.44 Demo 5 0:0
      • 7.45 Demo Summary 1:41
      • 7.46 Demo 6 0:0
      • 7.47 Demo Summary 1:16
      • 7.48 Quiz 0:0
      • 7.49 Summary 1:38
      • 7.50 Thank You 1:5
    • Lesson 08 - Hive 49:44
      • 8.1 Hive 1:14
      • 8.2 Objectives 1:23
      • 8.3 Need for Additional Data Warehousing System 1:47
      • 8.4 Hive - Introduction 1:46
      • 8.5 Hive - Characteristics 2:7
      • 8.6 System Architecture and Components of Hive 1:20
      • 8.7 Metastore 1:29
      • 8.8 Metastore Configuration 1:34
      • 8.9 Driver 1:26
      • 8.10 Query Compiler 1:21
      • 8.11 Query Optimizer 1:30
      • 8.12 Execution Engine 1:21
      • 8.13 Hive Server 1:32
      • 8.14 Client Components 1:41
      • 8.15 Basics of The Hive Query Language 1:37
      • 8.16 Data Model - Tables 1:37
      • 8.17 Data Model - External Tables 2:4
      • 8.18 Data Types in Hive 0:0
      • 8.19 Data Model - Partitions 1:42
      • 8.20 Serialization and Deserialization 2:5
      • 8.21 Hive File Formats 1:34
      • 8.22 Hive Query Language - Select 1:26
      • 8.23 Hive Query Language - JOIN and INSERT 1:13
      • 8.24 Hive Installation - Step 1 1:25
      • 8.25 Hive Installation - Step 2 1:20
      • 8.26 Hive Installation - Step 3 1:18
      • 8.27 Hive Installation - Step 4 1:23
      • 8.28 Running Hive 1:25
      • 8.29 Programming in Hive 1:20
      • 8.30 Programming in Hive (contd.) 1:17
      • 8.31 Programming in Hive (contd.) 1:31
      • 8.32 Programming in Hive (contd.) 1:16
      • 8.33 Programming in Hive (contd.) 1:11
      • 8.34 Programming in Hive (contd.) 1:15
      • 8.35 Programming in Hive (contd.) 1:11
      • 8.36 Programming in Hive (contd.) 1:13
      • 8.37 Programming in Hive (contd.) 1:13
      • 8.38 Hive Query Language - Extensibility 1:21
      • 8.39 User-Defined Function 1:41
      • 8.40 Built-In Functions 1:26
      • 8.41 Other Functions in Hive 2:24
      • 8.42 MapReduce Scripts 1:56
      • 8.43 UDF and UDAF vs. MapReduce Scripts 1:29
      • 8.44 Business Scenario 1:41
      • 8.45 Demo 1 0:0
      • 8.46 Demo Summary 1:29
      • 8.47 Demo 2 0:0
      • 8.48 Demo Summary 1:17
      • 8.49 Demo 3 0:0
      • 8.50 Demo Summary 1:16
      • 8.51 Demo 4 0:0
      • 8.52 Demo Summary 1:29
      • 8.53 Quiz 0:0
      • 8.54 Summary 1:42
      • 8.55 Thank You 1:4
    • Lesson 09 - HBase 32:42
      • 9.1 HBase 1:14
      • 9.2 Objectives 1:24
      • 9.3 HBase - Introduction 2:8
      • 9.4 Characteristics of HBase 1:37
      • 9.5 Companies Using HBase 1:13
      • 9.6 HBase Architecture 1:50
      • 9.7 HBase Architecture (contd.) 1:48
      • 9.8 Storage Model of HBase 1:59
      • 9.9 Row Distribution of Data between RegionServers 1:25
      • 9.10 Data Storage in HBase 1:41
      • 9.11 Data Model 1:0
      • 9.12 When to Use HBase 1:38
      • 9.13 HBase vs. RDBMS 1:59
      • 9.14 Installation of HBase 1:41
      • 9.15 Installation of HBase - Step 1 1:13
      • 9.16 Installation of HBase - Steps 2 and 3 1:23
      • 9.17 Installation of HBase - Steps 4 and 5 1:23
      • 9.18 Installation of HBase - Steps 6 and 7 1:19
      • 9.19 Installation of HBase - Step 8 1:13
      • 9.20 Configuration of HBase 1:9
      • 9.21 Configuration of HBase - Step 1 1:19
      • 9.22 Configuration of HBase - Step 2 1:16
      • 9.23 Configuration of HBase - Steps 3 and 4 1:23
      • 9.24 Business Scenario 1:25
      • 9.25 Demo 0:0
      • 9.26 Demo Summary 1:47
      • 9.27 Connecting to HBase 1:46
      • 9.28 HBase Shell Commands 1:28
      • 9.29 HBase Shell Commands (contd.) 1:27
      • 9.30 Quiz 0:0
      • 9.31 Summary 1:37
      • 9.32 Thank you 1:6
    • Lesson 10 - Commercial Distribution of Hadoop 25:22
      • 10.1 Commercial Distribution of Hadoop 1:18
      • 10.2 Objectives 1:27
      • 10.3 Cloudera - Introduction 1:39
      • 10.4 Cloudera CDH 1:59
      • 10.5 Downloading The Cloudera QuickStart Virtual Machine 1:22
      • 10.6 Starting The Cloudera VM 1:47
      • 10.7 Starting The Cloudera VM - Steps 1 and 2 1:21
      • 10.8 Starting The Cloudera VM - Steps 3 and 4 1:20
      • 10.9 Starting The Cloudera VM - Step 5 1:25
      • 10.10 Starting The Cloudera VM - Step 6 1:15
      • 10.11 Logging Into Hue 1:23
      • 10.12 Logging Into Hue (contd.) 1:21
      • 10.13 Logging Into Hue (contd.) 1:22
      • 10.14 Cloudera Manager 1:30
      • 10.15 Logging Into Cloudera Manager 0:0
      • 10.16 Business Scenario 1:50
      • 10.17 Demo 1 0:0
      • 10.18 Demo Summary 1:23
      • 10.19 Demo 2 0:0
      • 10.20 Demo Summary 1:35
      • 10.21 Hortonworks Data Platform 1:38
      • 10.22 MapR Data Platform 1:40
      • 10.23 Pivotal HD 1:55
      • 10.24 IBM InfoSphere BigInsights 1:44
      • 10.25 IBM InfoSphere BigInsights (contd.) 0:0
      • 10.26 Quiz 0:0
      • 10.27 Summary 1:49
      • 10.28 Thank You 1:8
    • Lesson 11 - ZooKeeper Sqoop and Flume 57:54
      • 11.1 ZooKeeper, Sqoop and Flume 1:18
      • 11.2 Objectives 1:30
      • 11.3 Introduction to ZooKeeper 1:21
      • 11.4 Features of ZooKeeper 2:3
      • 11.5 Challenges Faced in Distributed Applications 1:39
      • 11.6 Coordination 2:3
      • 11.7 Goals of ZooKeeper 1:39
      • 11.8 Uses of ZooKeeper 1:41
      • 11.9 ZooKeeper Entities 1:50
      • 11.10 ZooKeeper Data Model 1:40
      • 11.11 ZooKeeper Services 1:35
      • 11.12 ZooKeeper Services (contd.) 1:49
      • 11.13 Client API Functions 2:3
      • 11.14 Recipe 1: Cluster Management 1:46
      • 11.15 Recipe 2: Leader Election 1:41
      • 11.16 Recipe 3: Distributed Exclusive Lock 1:54
      • 11.17 Business Scenario 1:35
      • 11.18 Demo 1 0:0
      • 11.19 Demo Summary 1:16
      • 11.20 Why Sqoop 2:13
      • 11.21 Why Sqoop (contd.) 1:56
      • 11.22 Benefits of Sqoop 1:38
      • 11.23 Sqoop Processing 1:37
      • 11.24 Sqoop Under The Hood 1:30
      • 11.25 Importing Data Using Sqoop 1:22
      • 11.26 Sqoop Import - Process 0:0
      • 11.27 Sqoop Import - Process (contd.) 1:55
      • 11.28 Importing Data To Hive 0:0
      • 11.29 Importing Data To HBase 1:30
      • 11.30 Importing Data To HBase (contd.) 0:0
      • 11.31 Exporting Data From Hadoop Using Sqoop 1:13
      • 11.32 Exporting Data From Hadoop Using Sqoop (contd.) 1:37
      • 11.33 Sqoop Connectors 1:48
      • 11.34 Sample Sqoop Commands 1:0
      • 11.35 Business Scenario 1:41
      • 11.36 Demo 2 0:0
      • 11.37 Demo Summary 1:48
      • 11.38 Demo 3 0:0
      • 11.39 Demo Summary 1:36
      • 11.40 Why Flume 1:30
      • 11.41 Apache Flume - Introduction 1:32
      • 11.42 Flume Model 1:36
      • 11.43 Flume - Goals 1:42
      • 11.44 Scalability In Flume 1:33
      • 11.45 Flume - Sample Use Cases 1:34
      • 11.46 Business Scenario 1:28
      • 11.47 Demo 4 0:0
      • 11.48 Demo Summary 1:37
      • 11.49 Quiz 0:0
      • 11.50 Summary 2:1
      • 11.51 Thank You 1:7
    • Lesson 12 - Ecosystem and its Components 24:38
      • 12.1 Ecosystem and Its Components 1:19
      • 12.2 Objectives 1:18
      • 12.3 Apache Hadoop Ecosystem 0:0
      • 12.4 Apache Oozie 1:45
      • 12.5 Apache Oozie Workflow 1:51
      • 12.6 Apache Oozie Workflow (contd.) 1:47
      • 12.7 Introduction to Mahout 0:0
      • 12.8 Why Mahout 1:30
      • 12.9 Features of Mahout 1:35
      • 12.10 Usage of Mahout 1:27
      • 12.11 Usage of Mahout (contd.) 1:32
      • 12.12 Apache Cassandra 1:51
      • 12.13 Why Apache Cassandra 1:38
      • 12.14 Apache Spark 2:17
      • 12.15 Apache Spark Tools 2:9
      • 12.16 Key Concepts Related to Apache Spark 1:0
      • 12.17 Apache Spark - Example 1:12
      • 12.18 Hadoop Integration 1:38
      • 12.19 Quiz 0:0
      • 12.20 Summary 1:51
      • 12.21 Thank You 1:9
    • Lesson 13 - Hadoop Administration, Troubleshooting, and Security 39:58
      • 13.1 Hadoop Administration, Troubleshooting and Security 1:19
      • 13.2 Objectives 1:23
      • 13.3 Typical Hadoop Core Cluster 1:33
      • 13.4 Load Balancer 1:30
      • 13.5 Commands Used in Hadoop Programming 1:55
      • 13.6 Different Configuration Files of Hadoop Cluster 2:7
      • 13.7 Properties of hadoop default.xml 2:8
      • 13.8 Different Configurations for Hadoop Cluster 1:42
      • 13.9 Different Configurations for Hadoop Cluster (contd.) 2:32
      • 13.10 Port Numbers for Individual Hadoop Services 2:31
      • 13.11 Performance Monitoring 1:42
      • 13.12 Performance Tuning 1:25
      • 13.13 Parameters of Performance Tuning 2:27
      • 13.14 Troubleshooting and Log Observation 1:43
      • 13.15 Apache Ambari 1:31
      • 13.16 Key Features of Apache Ambari 1:47
      • 13.17 Business Scenario 1:0
      • 13.18 Demo 1 0:0
      • 13.19 Demo Summary 1:24
      • 13.20 Demo 2 0:0
      • 13.21 Demo Summary 1:45
      • 13.22 Hadoop Security - Kerberos 1:58
      • 13.23 Kerberos - Authentication Mechanism 0:0
      • 13.24 Kerberos Configuration 2:8
      • 13.25 Data Confidentiality 2:7
      • 13.26 Quiz 0:0
      • 13.27 Summary 1:35
      • 13.28 Thank You 1:17
    • Final Words 12:8
      • Final Words 6:34
    • Lesson 01 - Essentials of Java for Hadoop 63:20
      • 1.1 Essentials of Java for Hadoop 1:19
      • 1.2 Lesson Objectives 1:24
      • 1.3 Java Definition 1:27
      • 1.4 Java Virtual Machine (JVM) 1:34
      • 1.5 Working of Java 2:1
      • 1.6 Running a Basic Java Program 1:56
      • 1.7 Running a Basic Java Program (contd.) 2:15
      • 1.8 Running a Basic Java Program in NetBeans IDE 1:11
      • 1.9 BASIC JAVA SYNTAX 1:12
      • 1.10 Data Types in Java 1:26
      • 1.11 Variables in Java 2:31
      • 1.12 Naming Conventionsof Variables 2:21
      • 1.13 Type Casting. 2:5
      • 1.14 Operators 1:30
      • 1.15 Mathematical Operators 1:28
      • 1.16 Unary Operators. 1:15
      • 1.17 Relational Operators 1:19
      • 1.18 Logical or Conditional Operators 1:19
      • 1.19 Bitwise Operators 2:21
      • 1.20 Static Versus Non Static Variables 1:54
      • 1.21 Static Versus Non Static Variables (contd.) 1:17
      • 1.22 Statements and Blocks of Code 2:21
      • 1.23 Flow Control 1:47
      • 1.24 If Statement 1:40
      • 1.25 Variants of if Statement 2:7
      • 1.26 Nested If Statement 1:40
      • 1.27 Switch Statement 1:36
      • 1.28 Switch Statement (contd.) 1:34
      • 1.29 Loop Statements 2:19
      • 1.30 Loop Statements (contd.) 1:49
      • 1.31 Break and Continue Statements 1:44
      • 1.32 Basic Java Constructs 2:9
      • 1.33 Arrays 2:16
      • 1.34 Arrays (contd.) 2:7
      • 1.35 JAVA CLASSES AND METHODS 1:9
      • 1.36 Classes 1:46
      • 1.37 Objects 2:21
      • 1.38 Methods 2:1
      • 1.39 Access Modifiers 1:49
      • 1.40 Summary 1:41
      • 1.41 Thank You 1:9
    • Lesson 02 - Java Constructors 44:2
      • 2.1 Java Constructors 1:22
      • 2.2 Objectives 1:42
      • 2.3 Features of Java 2:8
      • 2.4 Classes Objects and Constructors 2:19
      • 2.5 Constructors 1:34
      • 2.6 Constructor Overloading 2:8
      • 2.7 Constructor Overloading (contd.) 1:28
      • 2.8 PACKAGES 1:9
      • 2.9 Definition of Packages 2:12
      • 2.10 Advantages of Packages 1:29
      • 2.11 Naming Conventions of Packages 1:28
      • 2.12 INHERITANCE 1:9
      • 2.13 Definition of Inheritance 2:7
      • 2.14 Multilevel Inheritance 2:15
      • 2.15 Hierarchical Inheritance 1:23
      • 2.16 Method Overriding 1:55
      • 2.17 Method Overriding(contd.) 1:35
      • 2.18 Method Overriding(contd.) 1:15
      • 2.19 ABSTRACT CLASSES 1:10
      • 2.20 Definition of Abstract Classes 1:41
      • 2.21 Usage of Abstract Classes 1:36
      • 2.22 INTERFACES 1:8
      • 2.23 Features of Interfaces 2:3
      • 2.24 Syntax for Creating Interfaces 1:24
      • 2.25 Implementing an Interface 1:23
      • 2.26 Implementing an Interface(contd.) 1:13
      • 2.27 INPUT AND OUTPUT 1:14
      • 2.28 Features of Input and Output 1:49
      • 2.29 System.in.read() Method 1:20
      • 2.30 Reading Input from the Console 1:31
      • 2.31 Stream Objects 1:21
      • 2.32 String Tokenizer Class 1:43
      • 2.33 Scanner Class 1:32
      • 2.34 Writing Output to the Console 1:28
      • 2.35 Summary 2:3
      • 2.36 Thank You 1:14
    • Lesson 03 - Essential Classes and Exceptions in Java 58:14
      • 3.1 Essential Classes and Exceptions in Java 1:18
      • 3.2 Objectives 1:31
      • 3.3 The Enums in Java 1:0
      • 3.4 Program Using Enum 1:44
      • 3.5 ArrayList 1:41
      • 3.6 ArrayList Constructors 1:38
      • 3.7 Methods of ArrayList 2:2
      • 3.8 ArrayList Insertion 1:47
      • 3.9 ArrayList Insertion (contd.) 1:38
      • 3.10 Iterator 1:39
      • 3.11 Iterator (contd.) 1:33
      • 3.12 ListIterator 1:46
      • 3.13 ListIterator (contd.) 1:0
      • 3.14 Displaying Items Using ListIterator 1:32
      • 3.15 For-Each Loop 1:35
      • 3.16 For-Each Loop (contd.) 1:23
      • 3.17 Enumeration 1:30
      • 3.18 Enumeration (contd.) 1:25
      • 3.19 HASHMAPS 1:15
      • 3.20 Features of Hashmaps 1:56
      • 3.21 Hashmap Constructors 2:36
      • 3.22 Hashmap Methods 1:58
      • 3.23 Hashmap Insertion 1:44
      • 3.24 HASHTABLE CLASS 1:21
      • 3.25 Hashtable Class an Constructors 2:25
      • 3.26 Hashtable Methods 1:41
      • 3.27 Hashtable Methods 1:48
      • 3.28 Hashtable Insertion and Display 1:29
      • 3.29 Hashtable Insertion and Display (contd.) 1:22
      • 3.30 EXCEPTIONS 1:22
      • 3.31 Exception Handling 2:6
      • 3.32 Exception Classes 1:26
      • 3.33 User-Defined Exceptions 2:4
      • 3.34 Types of Exceptions 1:44
      • 3.35 Exception Handling Mechanisms 1:54
      • 3.36 Try-Catch Block 1:15
      • 3.37 Multiple Catch Blocks 1:40
      • 3.38 Throw Statement 1:33
      • 3.39 Throw Statement (contd.) 1:25
      • 3.40 User-Defined Exceptions 1:11
      • 3.41 Advantages of Using Exceptions 1:25
      • 3.42 Error Handling and finally block 1:30
      • 3.43 Summary 1:41
      • 3.44 Thank You 1:4
    • Lesson 00 - Business Analytics Foundation With R Tools 14:0
      • 0.1 Business Analytics Foundation With R Tools 1:10
      • 0.2 Objectives 1:34
      • 0.3 Analytics 1:57
      • 0.4 Places Where Analytics is Applied 2:19
      • 0.5 Topics Covered 2:25
      • 0.6 Topics Covered (contd.) 2:11
      • 0.7 Career Path 2:7
      • 0.8 Thank You 1:17
    • Lesson 01 - Introduction to Analytics 29:48
      • 1.1 Introduction to Analyics 1:45
      • 1.2 analytics vs analysis 1:47
      • 1.3 What is Analytics 3:17
      • 1.4 Popular Tools 1:30
      • 1.5 Role of a Data Scientist 1:58
      • 1.6 Data Analytics Methodology 1:53
      • 1.7 Problem Definition 3:28
      • 1.8 Summarizing Data 2:21
      • 1.9 Data collection 2:45
      • 1.10 Data Dictionary 1:45
      • 1.11 Outlier Treatment 2:55
      • 1.12 Quiz 0:0
    • Lesson 02 - Statistical Concepts And Their Application In Business 139:30
      • 2.1 Statistical Concepts And Their Application In Business 10:12
      • 2.2 Descriptive Statistics 10:51
      • 2.3 Probability Theory 22:38
      • 2.4 Tests of Significance 22:23
      • 2.5 Non-parametric Testing 8:11
      • 2.6 Quiz 0:0
    • Lesson 03 - Basic Analytic Techniques - Using R 222:42
      • 3.1 Introduction 6:16
      • 3.2 Data Exploration 24:50
      • 3.3 Data Visualization 2:59
      • 3.4 Pie Charts 25:4
      • 3.5 Correlation 8:29
      • 3.6 Analysis of variance 11:13
      • 3.7 Chi-squared test 9:50
      • 3.8 T-test 29:15
      • 3.9 Summary 1:55
      • 3.10 Quiz 0:0
    • Lesson 04 - Predictive Modelling Techniques 383:18
      • 4.1 Predictive Modelling Techniques 2:37
      • 4.2 Regression Analysis and Types of regression models 3:48
      • 4.3 Linear Regression 7:21
      • 4.4 Coefficient of determination R 2:14
      • 4.5 How good is the model 2:9
      • 4.6 How to find Liner regression equation 3:49
      • 4.7 Commands to perform linear regression 4:45
      • 4.8 Linear regression to predict sales 8:14
      • 4.9 Case Study - Linear Regression 8:17
      • 4.10 Case Study - Classification 11:49
      • 4.11 Logistic regression 5:39
      • 4.12 Example - Logistic regression in R 8:2
      • 4.13 Logistic Regression Predicting recurrent visits to a web site 9:51
      • 4.14 Cluster Analysis 8:20
      • 4.15 Command to perform clustering in R 6:16
      • 4.16 Hierarchical Clustering 7:28
      • 4.17 Case Study - Implement K means and Hierarchical Clustering 12:41
      • 4.18 Time Series 3:39
      • 4.19 Cyclical versus seasonal analysis 2:38
      • 4.20 Decomposition of Time Series 4:24
      • 4.21 Caes Study- Time Series Analysis 9:54
      • 4.22 Decomposing Non-Seasonal Time Series 3:26
      • 4.23 Exponential Smoothing 8:45
      • 4.24 Advantaged and Diadavantages of Exponential Smoothing 1:51
      • 4.25 Exponential smoothing and forecasting in R 2:50
      • 4.26 Example - Holt Winters 14:58
      • 4.27 White Noise 2:2
      • 4.28 Correlogram Analysis 2:38
      • 4.29 Box-Jenkins forecasting Models 12:22
      • 4.30 Case Study - Time Series Data using ARMA 17:28
      • 4.31 Business Case 20:50
      • 4.32 Summary 1:52
      • 4.33 Thank You 1:12
    • {{childObj.title}}
      • {{lesson.title}}

    View More

    View Less

  • What is this course about?

    Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand, the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

    Simplilearn’s training in Big Data & Hadoop is an ideal training package for every aspiring professional who wants to make a career in Big Data Analytics using Hadoop Framework. The course equips the participant to work on the Hadoop environment with ease and get hands-on experience with various modules such as YARN, Flume, Oozie, Mahout, & Chukwa.

    Along with this course, get 9 hours of Certified Data Scientist - R Language Online Self Learning Course free.

  • Why is the certification most sought-after?

    As the Big Data buzz is getting bigger with volume, variety, and velocity, certified hadoopers equipped with the right skills to process the Big Data through Hadoop are becoming the ‘most wanted’ employees in Fortune 500 companies. This incredible surge has greatly increased the career scope for certified hadoopers in comparison to their non-certified peers. This certification is one among the most sought-after certifications for the reasons given below:
    • According to Gartner – “Big Data & Analytics is one of the top 10 strategic technologies for businesses and there would be 4.4 Million Big Data jobs by 2015”
    • Top companies like Microsoft, Software AG, IBM, Oracle, HP, SAP, EMC2, and Dell have invested a huge $15 billion on Data Management and Analytics
    • According to IDC – “Big Data market would grow up to $16.1 billion”
    • According to Indeed.com – “Certified Big Data analysts start earning $117,000 in comparison to their non-certified peers”
    • According to Robert Half Technology’s ‘2015 Salary guide for Technology professionals’- “Big Data is one of the key drivers for Technology hiring in 2015.”

  • What learning benefits do you get from Simplilearn’s training?

    At the end of Simplilearn’s training in Big Data & Hadoop, participants will be able to:
    • Master the concepts of Hadoop framework and its deployment in a cluster environment
    • Learn to write complex MapReduce programs in both MRv1 & MRv2 (Yarn)
    • Learn high-level scripting frameworks Pig & Hive and perform data analytics using high level scripting language under Pig & Hive
    • Have a good understanding of Ecosystem and its advance components like Flume, Apache Oozie workflow scheduler etc.
    • Understand advance concepts Hadoop 2.0 : Hbase, Zookeeper, and Sqoop
    • Get hands-on experience in different configurations of Hadoop cluster, its optimization & troubleshooting
    • Understand Hadoop Architecture by understanding Hadoop Distribution File System operations principles (vHDFS 1.0 & vHDFS 2.0)
    • Understand advance concepts of parallel processing in MapReduce1.0 (or MRv1) & MapReduce2.0 (or MRv2)
    • Process Big Data sets (around 3.5 Billion Data Points covering in 5 Projects) with high efficiency and can derive a logical conclusion, which is helpful in live industrial scenarios

  • What are the career benefits in-store for you?

    • The certification make participants ride the Big Data wave, enhance their analytics skills, and help them to land in job roles like Data Scientist, Hadoop Developer, Hadoop Architect, and Hadoop Tester.
    • Top companies like Microsoft, Software AG, IBM, Oracle, HP, SAP, EMC2, and Dell have invested a huge $15 billion on data management and analytics, thereby increasing the number of opportunities for Big data & Hadoop certified professionals.
    • Certified analysts earn $117,000 in comparison to their non-certified peers. (Source-www.payscale.com)
    • Certified Big Data professionals with hands-on exposure to industry relevant tools have a growing career graph.
    • Possessing this coveted skill makes it easier for an aspirant to switch to his/her industry or function of choice.

  • Who should do this course?

    Java developers, Architects, Big Data professionals, or anyone who is looking to build a career in Big Data and Hadoop are ideal participants for the Big Data and Hadoop training. Additionally, it is suitable for participants who are:
    • Data Management Professionals
    • Data Warehousing Professionals
    • Business Intelligence Professionals
    • Graduates

  • What is the benefit of learning R along with Hadoop ?

    R is the most popular open source language for data analysis. It is the most popular language for data science and an essential tool for Finance and analytics-driven companies. R has been well established to communicate efficiently with Hadoop and is the new favorite of Big Data scientists. Organizations are looking for professionals who can handle humongous data to draw actionable insights. Hence, professionals who are skilled in both Hadoop and R Language are in high demand in the industry.

    Learning Benefits of Certified Data Scientist - R Language:
    • Work efficiently on data exploration, data visualization, and predictive modeling techniques with ease
    • Gain fundamental knowledge on Analytics and how it assists in decision making
    • Understand and work on statistical concepts like linear & logistic regression, cluster analysis, and forecasting
    Key features of Certified Data Scientist -R Language:
    • 9 hours of High-Quality E-learning Content
    • 5+ Real life Case Studies
    • 4 Data Science Simulation Exams

  • Why choose Simplilearn for your training?

    Simplilearn’s Big Data & Hadoop training is the first of its kind, which provides comprehensive training and is suitable for professionals. It is the best in terms of time and money invested. We stand out because participants:-
    • Have the flexibility to choose from 3 different modes of learning
    • Get hands-on lab exercises
    • Work on 5 real life industry-based projects covering 3.5 Billion Data Points spanned across 11 Data Sets. All Projects are based upon real life industrial scenarios.
    Take a look at how Simplilearn’s training stands above other training providers:
     
    Simplilearn Other Training Providers
    160 Hrs of Total learning  (2x more as compared to any other Training Provider) 90 Hrs or less
    60 Hrs of Real Time Industry Based Projects - 3.5 Bn Datapoints to work upon (3x more as compared to any other Training Provider) 20 Hrs or less
    25 Hrs of High Quality e-Learning content Not Available
    5 Hrs of Doubt Clarification Classes Not Available
    Flexibility to choose training type from 3 available training modes Only single training mode
    Chance to work on 5 Real Life industry based Projects 1 or No Project
    Industry Specific Projects on Top 3 Sectors - Retail, Telecom & Insurance Not Available
    11 Unique Data Sets to work upon 7 or less
    Free 90 Days e-Learning access worth $252 with every Instructor led training Not Available
    Free Java Essentials for Hadoop Not Available

    In addition, Simplilearn’s training is the best because:      
    • Simplilearn is the World’s Largest Certification Training Provider, with over 400,000+ professionals trained globally.
    • Trusted by the Fortune 500 companies as their learning provider for career growth and training.
    • 2,000+ certified and experienced trainers conduct training for various courses across the globe.
    • All our Courses are designed and developed under a tried and tested Unique Learning Framework that is proven to deliver 98.6% clearance rate in first attempt.
    • Accredited, Approved, and Recognized as a training organization, partner, education provider, and examination center by globally renowned names like Project Management Institute of USA, APMG, CFA Institute, GARP, ASTQB, IIBA, and others.

Exam & Certification

  • How do I become Certified Big Data & Hadoop Developer?

    To become a Certified Big Data & Hadoop Developer, it is mandatory that the participant fulfills both the following criteria:
    • Completing any one project out of the five projects given by Simplilearn. The outcome of the project should be verified by the lead trainer and the candidate is evaluated thereafter. Necessary screenshots of the outputs of the project should be mailed to support@simplilearn.com.
    • Clearing the online examination with a minimum score of 80%. Note: It is mandatory that a participant fulfills both the criteria i.e. completion of 1 Project and clearing the online exam with minimum score of 80% to become a Certified Big Data & Hadoop Developer.

  • What are the projects covered to get certified and their benefits?

    The exceptionality of Simplilearn’s Big Data & Hadoop training is the opportunity for participants to work on 5 live industry-based projects spanning across 11 Unique Data Sets and covering around 3.5 Billion Data Points. This adds immense domain knowledge and real life industry experience to a participant’s Curriculum Vitae.

    Project 1: Analyzing a series of Data sets for a US-based customer to arrive at a prudent product mix, product positioning and marketing strategy.

    Through this project, participants will get a chance to work as a Hadoop developer and the responsibility for completing all the subprojects within the defined timeframe.

    Scenario: Your Company has recently bagged a large assignment from a US-based customer that is into training and development. The larger outcome of this project, deals with launching a suite of educational and skill development programs to consumers across the globe. As part of the project, the customer wants your company to analyze a series of data sets to arrive at a prudent product mix, product positioning, and marketing strategy that will be applicable for at least a decade.

    The whole project is divided into 7 subprojects, each involving its own data set.
    Subproject 1: Identify motivators for continuous adult education.
    Subproject 2: Identify occupations poised for growth & decline over the next 10 years.
    Subproject 3: Identify regions that have potential for growth across potential industries.
    Subproject 4: Categorize financial capacity of consumers across regions and demographics.
    Subproject 5: Identify major gender and geographic attributes for education.
    Subproject 6: Analyze the education expenditure and related parameters across the globe.
    Subproject 7: Analyze the strength of the financial sector in target markets and participation of the population in the financial sectors.

    Project 2: Analyze and perform page ranking for Twitter data set

    Scenario: As a Hadoop developer, your task is to perform page ranking of the Twitter data based on the data set provided.

    Project 3: Analyze Monthly retail report for the US Market - Retail Industry

    Scenario: A US - based online retailer wants to launch a new product category and wants to understand the potential growth areas and areas that have stagnated over a period of time. It wants to use this information to ensure its product focus is aligned to opportunities that will grow over the next 5–7 years. The customer has been provided the data set that they can use.

    Project 4: Analyze Mobile connectivity report for the UK Market - Telecom Industry

    Scenario: A UK - based customer wants to launch 3G devices in regions where their penetration is low and you have been allocated the task of performing this analysis using Hadoop. The customer has been provided the data set that they can use.

    Project 5: Analyze health reports across years for the US Market – Insurance Industry

    Scenario: A US – based insurance provider has decided to launch a new medical insurance program targeting various customers. To give this customer, a better understanding of the current realities and the market, you have to perform a series of data analytics tasks using Hadoop. The customer has been provided the data set that they can use.

  • What are the prerequisites for the certification?

    Aspirants with fundamental programming skills are eligible for this certification. However, a working knowledge of Java, UNIX, and SQL would be an added advantage.

FAQs

  • Who will be the trainer for the classroom training?

    Highly qualified and certified instructors with industry relevant experience deliver classroom trainings.

  • How do I enroll for the classroom training?

    You can enroll for this classroom training online. Payments can be made using any of the following options and a receipt of the same will be issued to the candidate automatically via email.
    1. Visa Debit/credit Card
    2. American Express and Diners Club Card
    3. Master Card, Or
    4. PayPal

  • Where will be the training held?

    The venue is finalized a few weeks prior to the training and you will be informed via email. You can get in touch with our 24/7 Support Team for more details. Email us at support@simplilearn.com and we will promptly answer all your queries. If you are looking for an instant support, you can chat with us too.

  • Do you provide transportation and refreshments along with the training?

    We do not provide transportation or refreshments along with the training.

  • What will I get along with this training?

    In this training, you will have free access to the online Big Data and Hadoop e-learning content, Java Introduction, and real life scenario-based projects.

  • Can I change the city, place, and date after enrolling for any classroom training?

    Yes, you can change the city, place, and date for any classroom training. However, a rescheduling fee is charged. For more information, please go through our Rescheduling Policy.

  • Can I cancel my enrollment? Do I get a refund?

    Yes, you can cancel your enrollment. We provide a complete refund after deducting the administration fee. To know more please go through our Refund Policy.

  • Do you provide a Money Back Guarantee for the training programs?

    Yes, we do provide a Money Back Guarantee for some of our training programs. You can contact support@simplilearn.com for more information.

  • Do you provide assistance for the exam?

    Yes, we do provide guidance and assistance for some of our certification exams.

  • Who provides the certification?

    Big Data and Hadoop training is ideal for Java developers and architects. There is no governing body that administers Big Data and Hadoop exams. However, many training providers conduct exams to evaluate a candidate’s skills on Big Data and Hadoop training.

  • Do you provide any course completion certificate?

    Yes. We offer a course completion certificate after you successfully complete the training program.

  • Do you provide any group discounts for classroom training programs?

    Yes, we have group discount packages for classroom training programs. Contact support@simplilearn.com to know more about the group discounts.

  • What is Big Data?

    Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications.

  • What is Apache Hadoop?

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

  • Do you provide PDUs after completing the training? How many hours of PDU certificate do I get after attending the training?

    Yes, we offer PDU certificates to candidates after successfully completing the training. You can earn 45 hours of PDUs after attending the training.

  • What are the System Requirements?

    To run Hadoop, the participant's system needs to fulfill the following requirements:
    • 64-bit Operating System
    • 4GB Ram

  • How do I enroll for the online training?

    You can enroll for this online training through our website. Payments can be made using any of the following options and a receipt of the same will be issued to the candidate automatically via email.
    1. Visa Debit/Credit Card
    2. American Express and Diners Club Card
    3. Master Card
    4. PayPal

  • Can I cancel my enrollment? Do I get a refund?

    Yes, you can cancel your enrollment. We provide a complete refund after deducting the administration fee. To know more please go through our Refund Policy.

    Hyperlink to Refund Policy: http://www.simplilearn.com/terms-and-conditions#/refund-policy

  • Can I extend the access period?

    Yes, you can extend the access period by paying an additional fee. Contact support@simplilearn.com for more information.

  • I am not able to access the online course. Whom should I contact for a solution?

    Please send an email to support@simplilearn.com. You can also chat with us to get an instant solution.

  • What is Apache Hadoop?

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

  • What is Big Data?

    Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications.

  • What are the System Requirements?

    To run Hadoop, the participant's system needs to fulfill the following requirements:
    • 64-bit Operating System
    • 4GB Ram

Reviews

Very good course and a must for who wants to have a career in Quant.

Great course and very easy to grasp the concept.

Good Experience. Very interactive course. Covered the basic topics in Hadoop in the most efficient way.

Training course meets expectations. Training was interactive and got all answers for all the queries. Thank you Mukund.

Overall I found the course content is good. You will get a better idea about hadoop.

Drop us a Query
Name *
Email *
Your Query *
Looking for a training for
Myself My team/organization
I agree to be contacted over email
1800-232-5454(9am-7pm)
We are looking into your query.
Our consultants will get in touch with you soon.
 
Group Buy

corporatesales@simplilearn.com

Knowledge Bank

Request for a custom quote

Please fill in the details and our inhouse support team will get back to you within 1 business day

Name*

Email*

Phone*

Course*
Company
Looking for*
Online license
training
Onsite
training
Online Virtual
training
Please select one of the above
Your Query
I agree to be contacted over mail
Please accept to proceed
/index/hidden/ - Never remove this line
  • Live Chat

  • Query