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Apr 28 - May 01
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Price
$ 999
 
May 15 - Jun 06
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22:30 - 02:30
 
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May 16 - Jun 07
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May 17 - May 28
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May 23 - May 31
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    23
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Location
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Price
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Early Bird
May 26 - May 29
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May

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    27
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Time
10:00 - 18:00
 
Location
Seattle
 
Price
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Key Features

  • 4 Days Classroom 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
  • Excellence in Hadoop Certificate
  • Industry Specific Projects on Top 3 Sectors - Retail, Telecom & Insurance
  • 45 PDUs Offered
  • Additional Free Online course: Business Analytics Professional - R Language
  • 40 Hrs of Lab Exercises with proprietary VM
  • 25 Hrs of High Quality e-Learning content
  • 13 Chapter-end Quizzes
  • 2 Big Data & Hadoop Simulation Exams
  • 60 Hrs of Real Time Industry based Projects
  • Java Essentials for Hadoop Included
  • Downloadable e-Book Included
  • Hadoop Installation Procedure Included
  • Hadoop Deployment and Maintenance Tips
  • 5 Projects with 11 Unique Data Sets Included
  • Excellence in Hadoop Certificate
  • Industry Specific Projects on Top 3 Sectors - Retail, Telecom & Insurance
  • 30 PDUs Offered
  • Certified Big Data and Hadoop Developer
  • Additional Free Online course: Business Analytics Professional - R Language
  • 40 Hrs of Lab Exercises with proprietary VM
  • 25 Hrs of High Quality e-Learning content
  • 13 Chapter-end Quizzes
  • 2 Big Data & Hadoop Simulation Exams
  • 60 Hrs of Real Time Industry based Projects
  • Java Essentials for Hadoop Included
  • Downloadable e-Book Included
  • 5 Projects with 11 Unique Data Sets Included
  • Excellence in Hadoop Certificate
  • Industry Specific Projects on Top 3 Sectors - Retail, Telecom & Insurance
  • 30 PDUs Offered
  • Special offer: Free Business Analytics Professional - R, 9 hr online course
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About Course

Course Preview

    • Getting started with Big-Data and Hadoop Developer 14:0
      • Getting started with Big-Data and Hadoop Developer 14:0
    • Lesson 00 - Course Introduction 4:41
      • 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 15:22
      • 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 13:56
      • 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 22:52
      • 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 26:52
      • 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 28:0
      • 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 21:6
      • 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 23:44
      • 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 25:22
      • 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 16:51
      • 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 13:11
      • 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 29:27
      • 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 12:49
      • 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 20:29
      • 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 6:34
      • Final Words 6:34
    • Lesson 01 - Essentials of Java for Hadoop 32:10
      • 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 22:31
      • 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 29:37
      • 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 7:0
      • 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 15:24
      • 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 70:15
      • 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 111:51
      • 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 199:11
      • 4.1 Predictive Modelling Techniques 6:45
      • 4.2 Linear Regression 3:22
      • 4.3 Regression analysis and types of regression models 5:59
      • 4.4 Build a simple linear regression model 40:8
      • 4.5 Logistic Regression 21:30
      • 4.6 Cluster Analysis 31:42
      • 4.7 Time series 3:39
      • 4.8 Cyclical versus seasonal analysis 14:53
      • 4.9 Decomposing Non-Seasonal Time Series 3:26
      • 4.10 Exponential smoothing 8:44
      • 4.11 Advantages and disadvantages of Exponential Smoothing 17:37
      • 4.12 White noise 6:24
      • 4.13 Auto-Regressive (AR) Models 8:57
      • 4.14 Forecasting methods - Summarized table 37:55
      • 4.15 Summary 2:10
      • 4.16 Quiz 0:0
  • 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. Big Data certification is one of the most recognized credentials of today.

    Along with this course, get 9 hours of  Business Analytics Professional - R Language Online Self Learning Course free

  • Why is the certification most sought-after?

    As the Big data buzz is getting louder with Volume, Variety and Velocity, certified hadoopers equipped with the right skills to process the Big data through Hadoop are the ‘most wanted’ in Fortune 500 companies worldwide. This has greatly increased the career scope for certified hadoopers in comparison to their non-certified peers. Below are well known facts as to why one should opt for the Big Data & Hadoop Certification:

    • 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 “Big Data, Big Pay – Average salary can reach up to $154,250”

  • 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 makes you ride the Big data wave, enhances your analytics skills and helps you to land in job roles like Data Scientist, Hadoop Developer, Hadoop Architect, 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.
    • Certified Big data professionals with Hands-on exposure to industry relevant tools have a growing career graph.

  • Who should do this course?

    Java developers, Architects, Big Data professionals, anyone who is looking forward towards building a career in Bigdata and Hadoop are ideal participants for the Big Data and Hadoop training. Additionally, it is suitable for participants who are

    • Aspiring to be in fast growing career
    • Looking for a more challenging position
    • Aiming to get into a more skillful role

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

    R is the most popular open source language for data analysis. It is most widely used to perform statistical modeling and more sophisticated data modeling techniques. R has been well establish to communicate efficiently with Hadoop. Being open source with a strong community, it 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 Business Analytics Professional -  R Language:

    • Work on data exploration, data visualization and predictive modeling techniques with ease
    • Gain fundamental knowledge on Analytics and how it assists in decision making
    • Work with confidence in R language
    • Understand and work on statistical concepts like linear & logistic regression, cluster analysis and forecasting

    Key features of Business Analytics Professional -  R Language:
    • 9 hrs of High-Quality e-Learning content
    • 20+ Case studies and tools videos
    • 4 Business Analytics simulation exams

  • Why Simplilearn?

    Why choose Simplilearn for your training?
    Simplilearn’s Big Data & Hadoop training is the first of its kind providing comprehensive training and is ideal for professionals. It is the best in terms of time & 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 Bn Data Points spanned across 11 Data Sets. All Projects are based upon real life industrial scenario
     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 200,000 professionals trained globally
    • Trusted by the Fortune 500 companies as their learning provider for career growth and training
    • 2000+ certified and experienced trainers conduct trainings 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% pass 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?

    Participants get certified in Big Data & Hadoop by:

    • 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 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 Bn Data points. This adds immense domain knowledge and real life industry experience to 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, 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 dataset 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 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 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 as decided to launch a new medical insurance program targeting various customers. To help this customer understand the current realities and the market better, you have to perform a series of data analytics tasks using Hadoop. The customer has been provided data set that they can use.

  • What are the prerequisites for the certification?

    Anyone who has knowledge on Java, basic UNIX and basic SQL can opt for the Big Data and Hadoop training course.

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 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. Through PayPal

  • Where will be the training held?

    Venue is finalized few weeks before 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. 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?

    You will have free access to the online Big Data and CompTia Cloud e-learning, Java Introduction and real life scenario based projects along with the training.

  • 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 you complete refund after deducting the administration fee. To know more please go through our Refund Policy.

  • Do you provide money back guarantee for the training programs?

    Yes, we do provide 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. No such governing body administers Big Data and Hadoop exam. However, many training providers conduct exam to evaluate a candidate’s skills on Big Data and Hadoop training.

  • Do you provide any course completion certificate?

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

  • What are System Requirements?

    To run Hadoop participant's system needs to fulfill following requirements:

    • 64-bit Operating System
    • 4GB Ram

  • How do I enroll for the online training?

    You can enroll for the training online. Payments can be made using any of the following options and 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. Through PayPal

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

    Yes, you can cancel your enrollment. We provide you complete refund after deducting the administration fee. To know more please go through our 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 the distributes 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 System Requirements?

    To run Hadoop participant's system needs to fulfill following requirements:

    • 64-bit Operating System
    • 4GB Ram

Reviews

The course is good to have an overall understanding of Big Data Hadoop development at high level.

The training was good. It was very informative. All basic details were covered.

Very interactive and helpful.

Very good training by Prashant. Good experience shown in resolving the issues.

The training was extensive to understand the concepts of big data hadoop and gave the broad pattern of the information in todays market.

Training course meet expectations. Training was interacting and get all answers of all the queries. Thank you Mukund.

Good course content with good knowledge and it conpletely helps one to have knowledge of upcoming trend i.e hadoop big data in IT.

Good course with detailed explanation of tools related with the hadoop. Hands on experience with the practice on the technology helped in clearing the doubts.

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

Clear and understandable.

It provided me both theoritical as well as practical knowledge.

Excellent ambience. True to its USP. Your pace your place on all aspects.

This training provided much needed base to move forward as I was not familiar with this area. Received high level exposure of HDFS, mapreduce, pig, hive etc.

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It is new area of learning for me. Lots of info on technologies. Hope to build a career in big data space.

The hadoop training session is more in-depth.

This training session is awesome. It gave me good matertial on hadoop developer. I am very thankful to Simplilearn.

Simply loved the Simplilearn big data course. I would highly recommend this to anyone who want to start up there career in big data.

I thanks Simplilearn for providing me a good insight on what big data is. Special thanks to my trainer Sahoo for giving me a good knowledge and helping me work on the content.

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Venue

Executive Hotel Pacific Seattle,400 Spring Street, Seattle, WA,98104

Note: This is an indicative location only. The actual venue will be communicated one week before the training begins.

About Seattle

Seattle is the capital of the Washington in the west coast of USA. Seattle is the most vibrant city in the country as it is known for aircraft manufacturing, city closest to Alaska, famous cultural and art programme like Symphony Orchestra and rock music. Seattle also has huge ports that connect tourism and trade with Asia. Manufacturing, Retail, Tourism, IT, Tele Communications etc. are the most popular sectors in the city. This garners more scope for professionals who are certified in various professional certification courses like PMP, Agile Certification, Six Sigma, ITIL, Cloud Computing and CISSP.

Note: This is an indicative location only. The actual venue will be communicated one week before the training begins.

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