Batch of 13 All Batches
  • Batch 1 (Weekend Batch)

    Feb 13 - Mar 12 (9 Days)
    • Feb
    • Sat 13
    • Sun 14
    • Sat 20
    • Sun 21
    • Sat 27
    • Sun 28
    • Mar
    • Sat 05
    • Sun 06
    • Sat 12

    Time (MST)07:00 - 11:00

  • Batch 2

    Feb 14 - Feb 29 (12 Days)
    • Feb
    • Sun 14
    • Mon 15
    • Tue 16
    • Wed 17
    • Thu 18
    • Sun 21
    • Feb
    • Mon 22
    • Tue 23
    • Wed 24
    • Thu 25
    • Sun 28
    • Mon 29

    Time (MST)17:30 - 20:30

  • Batch 3

    Feb 19 - Mar 18 (9 Days)
    • Feb
    • Fri 19
    • Sat 20
    • Fri 26
    • Sat 27
    • Mar
    • Fri 04
    • Sat 05
    • Fri 11
    • Sat 12
    • Fri 18

    Time (MST)20:30 - 01:30

  • Batch 4

    Feb 22 - Mar 08 (12 Days)
    • Feb
    • Mon 22
    • Tue 23
    • Wed 24
    • Thu 25
    • Fri 26
    • Mon 29
    • Mar
    • Tue 01
    • Wed 02
    • Thu 03
    • Fri 04
    • Mon 07
    • Tue 08

    Time (MST)07:30 - 10:30

  • Batch 5 (Weekend Batch)

    Feb 27 - Mar 26 (9 Days)
    • Feb
    • Sat 27
    • Sun 28
    • Mar
    • Sat 05
    • Sun 06
    • Sat 12
    • Sun 13
    • Sat 19
    • Sun 20
    • Mar
    • Sat 26

    Time (MST)07:00 - 12:00

  • Batch 6

    Mar 04 - Apr 01 (9 Days)
    • Mar
    • Fri 04
    • Sat 05
    • Fri 11
    • Sat 12
    • Fri 18
    • Sat 19
    • Mar
    • Fri 25
    • Sat 26
    • Apr
    • Fri 01

    Time (MST)20:30 - 01:30

  • Batch 7

    Mar 07 - Mar 22 (12 Days)
    • Mar
    • Mon 07
    • Tue 08
    • Wed 09
    • Thu 10
    • Fri 11
    • Mon 14
    • Mar
    • Tue 15
    • Wed 16
    • Thu 17
    • Fri 18
    • Mon 21
    • Tue 22

    Time (MST)07:30 - 11:30

  • Batch 8 (Weekend Batch)

    Mar 12 - Apr 09 (9 Days)
    • Mar
    • Sat 12
    • Sun 13
    • Sat 19
    • Sun 20
    • Sat 26
    • Sun 27
    • Apr
    • Sat 02
    • Sun 03
    • Sat 09

    Time (MST)07:00 - 12:00

  • Batch 9

    Mar 13 - Mar 28 (12 Days)
    • Mar
    • Sun 13
    • Mon 14
    • Tue 15
    • Wed 16
    • Thu 17
    • Sun 20
    • Mar
    • Mon 21
    • Tue 22
    • Wed 23
    • Thu 24
    • Sun 27
    • Mon 28

    Time (MST)18:30 - 21:30

  • Batch 10

    Mar 18 - Apr 15 (9 Days)
    • Mar
    • Fri 18
    • Sat 19
    • Fri 25
    • Sat 26
    • Apr
    • Fri 01
    • Sat 02
    • Fri 08
    • Sat 09
    • Fri 15

    Time (MST)21:30 - 01:30

  • Batch 11

    Mar 21 - Apr 05 (12 Days)
    • Mar
    • Mon 21
    • Tue 22
    • Wed 23
    • Thu 24
    • Fri 25
    • Mon 28
    • Mar
    • Tue 29
    • Wed 30
    • Thu 31
    • Apr
    • Fri 01
    • Mon 04
    • Tue 05

    Time (MST)08:30 - 11:30

  • Batch 12

    Mar 25 - Apr 22 (9 Days)
    • Mar
    • Fri 25
    • Sat 26
    • Apr
    • Fri 01
    • Sat 02
    • Fri 08
    • Sat 09
    • Fri 15
    • Sat 16
    • Apr
    • Fri 22

    Time (MST)21:30 - 01:30

  • Batch 13 (Weekend Batch)

    Mar 26 - Apr 23 (9 Days)
    • Mar
    • Sat 26
    • Sun 27
    • Apr
    • Sat 02
    • Sun 03
    • Sat 09
    • Sun 10
    • Sat 16
    • Sun 17
    • Apr
    • Sat 23

    Time (MST)08:00 - 12:00

  • To view info on all the batches scheduled for the course in next 90 days,
    please Download Full Schedule

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Key Features

MONEY BACK GUARANTEE

How this works :

For all refunds, please raise a refund request through the Help and Support section of our website. The mode of reimbursement will be same as the mode of payment used for the enrolment fees.

For Self Placed Learning :

Raise refund request within 7 days of purchase of course. Money back guarantee is void if the participant has accessed more than 50% content.

For Instructor Led Training :

Raise refund request within 7 days of commencement of the first batch you are eligible to attend. Money back guarantee is void if the participant has accessed more than 50% content of an e-learning course or has attended Online Classrooms for more than 1 day.

  • 36 hours of Instructor led Training
  • 24 hours of High Quality e-learning
  • 60 hours of industry projects with 3.5 Bn. data points
  • Hands-on projects execution with CloudLab
  • Access to On Demand support
  • Get experience certificate in Hadoop 2.7

About Course

  • What is this course about?

    Big Data and Hadoop Certification Training from Simplilearn is designed to ensure that you are job ready to take up an assignment in Big Data. This training not just equips you with essential skills of Hadoop 2.7, but also gives you the required work experience in Big Data Hadoop via implementation of real life industry projects spanned across 3 months.

    Course gives you a unique offering of executing all the hand-on project work of Hadoop 2.7 with CloudLab – a cloud based Hadoop environment lab.

  • What are the course objectives?

    By the end of Simplilearn’s training in Big Data & Hadoop, you will be able to:
    • Master the concepts of Hadoop 2.7 framework and its deployment in a cluster environment
    • Learn to write complex MapReduce programs
    • Perform Data Analytics using  Pig & Hive
    • Acquire in-depth understanding of Hadoop Ecosystem including Flume, Apache Oozie workflow scheduler, etc.
    • Master advance concepts of Hadoop 2.7 : Hbase, Zookeeper, and Sqoop
    • Get hands-on experience in setting up different configurations of Hadoop cluster
    • Work on real-life industry based projects using Hadoop 2.7

  • What is CloudLab feature offered by Simplilearn?

    CloudLab is a cloud based Hadoop environment lab to ensure hassle free execution of all the hand-on project work with Hadoop 2.7.

    With CloudLab, you will not require to install Hadoop using a virtual machine. Instead, you will be able to access already set up Hadoop environment lab using CloudLab. And hence you will not have to face following challenges related with Hadoop installation using virtual machine
    • Installation & system compatibility issues
    • Difficulties in configuring systems
    • Issues with Rights & permissions
    • Network slowdown & failure
    You will be able to access CloudLab from Simplilearn LMS (Learning Management System). A video on introduction and how to use CloudLab is provided in Simplilearn LMS. You can also access this video from here- Video link. You will have access to CloudLab, throughout the timespan you have the Online Self Learning (OSL) access for the Big Data Hadoop Developer course.

  • What is an On Demand support as offered by Simplilearn?

    With the On-Demand support, you will receive support from experts in resolving following queries while you are completing the Big Data Hadoop Developer course.
    • Technical support: Queries related to technical, installation, administration issues in Big Data Hadoop Developer course. In case of critical issues, support will be rendered through a remote desktop.
    • Project support: Queries related to solving & completing Projects, case-studies which are part of Big Data Hadoop developer course offered by Simplilearn     
    • Hadoop Programming: Queries related to Hadoop programming while solving & completing Projects, case-studies which are part of Big Data Hadoop developer course offered by Simplilearn     
    • CloudLab Support: Queries related to CloudLab while you are using CloudLab to execute projects, case studies and exercises of Big Data Hadoop Developer course offered by Simplilearn
    How to avail On-demand support?
    To avail On-demand Support, submit a query to Simplilearn through any of following channels of Simplilearn’s Help & Support team.
    On-demand Support will get in touch with you to assist with query resolution within 48 hours.

  • Who should do this course?

    With the number of Big Data career opportunities on the rise, Hadoop is fast becoming a must-know technology for the following professionals:
    • Software Developers and Architects 
    • Analytics Professionals
    • Data Management Professionals
    • Business Intelligence Professionals
    • Project Managers
    • Aspiring Data Scientists
    • Anyone with a genuine interest in Big Data Analytics
    • Graduates looking to build a career in Big Data Analytics  
    Prerequisite: Knowledge of Java is needed for this course. Hence, we are providing complimentary access to “Java Essentials for Hadoop” along with this course.

  • How would this Certification help me building a career in Big Data Hadoop?

    BDH Developer certification provides a solid foundation for starting a career in Big Data Hadoop Data Architect career path.
    After completion of this foundation course we would recommend you to enhance your Hadoop expertize by acquiring skills with following Big Data Hadoop Certifications from Simplilearn.
    • NoSQL Database Technologies
      • MongoDB Developer and Administrator Certification Training
      • Apache Cassandra Certification Training
    • Real time processing and real time analytics with Big Data
      • Apache Spark and Scala Certification Training
      • Apache Storm Certification Training
      • Apache Kafka Certification Training
    • Real time interactive analysis of the Big data via a native SQL environment
      • Impala - An Open Source SQL Engine for Hadoop Training
    These certifications would certainly make you proficient with skillsets required for building a career path from Big Data Hadoop developer to Big Data Hadoop Architect.

  • What projects will you be working on?

    You will be working on 4 live industry-based projects covering around 3.5 Billion Data Points.

    Project 1
    Domain: Insurance
    A US-based insurance provider has 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 provided pointers to the data set you can use.

    Project 2
    Domain: Retail
    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 also provided pointers to the data set you can use.

    Project 3
    Domain: Social Media
    As part of a recruiting exercise of the biggest social media company, they asked candidates to analyze data set from Stack Exchange. We will be using similar data set to arrive at certain key insights.

    Project 4
    Domain: Education
    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.

Course Preview

    • Lesson 00 - Course Introduction 15:11
      • 0.1 Course Introduction 1:10
      • 0.2 Why Big Data 1:56
      • 0.3 What is Big Data 1:42
      • 0.4 What is Big Data (contd.) 1:36
      • 0.5 Facts about Big Data 2:36
      • 0.6 Evolution of Big Data 1:47
      • 0.7 Case StudyNetflix and the House of Cards 2:49
      • 0.8 Market Trends 1:47
      • 0.9 Course Objectives 2:21
      • 0.10 Course Details 2:37
      • 0.11 Project Submission and Certification 2:21
      • 0.12 On Demand Support 2:15
      • 0.13 Key Features 2:5
      • 0.14 Conclusion 1:9
    • Lesson 01 - Introduction to Big Data and Hadoop 18:24
      • 1.1 Introduction to Big Data and Hadoop 1:17
      • 1.2 Objectives 1:19
      • 1.3 Data Explosion 2:3
      • 1.4 Types of Data 1:36
      • 1.5 Need for Big Data 1:59
      • 1.6 Big Data and Its Sources 1:31
      • 1.7 Characteristics of Big Data 2:32
      • 1.8 Characteristics of Big Data Technology 2:36
      • 1.9 Knowledge Check 0:0
      • 1.10 Leveraging Multiple Data Sources 1:35
      • 1.11 Traditional IT Analytics Approach 1:25
      • 1.12 Traditional IT Analytics Approach (contd.) 1:22
      • 1.13 Big Data TechnologyPlatform for Discovery and Exploration 1:28
      • 1.14 Big Data TechnologyPlatform for Discovery and Exploration (contd.) 1:27
      • 1.15 Big Data TechnologyCapabilities 1:18
      • 1.16 Big DataUse Cases 1:35
      • 1.17 Handling Limitations of Big Data 1:32
      • 1.18 Introduction to Hadoop 1:50
      • 1.19 History and Milestones of Hadoop 3:6
      • 1.20 Organizations Using Hadoop 1:17
      • 1.21 VMware PlayerIntroduction 1:17
      • 1.22 VMware PlayerHardware Requirements 1:25
      • 1.23 Oracle VirtualBox to Open a VM 0:0
      • 1.24 Installing VM using Oracle VirtualBox Demo 01 1:5
      • 1.25 Opening a VM using Oracle VirtualBox Demo 02 2:55
      • 1.26 Quiz 0:0
      • 1.27 Summary 1:46
      • 1.28 Conclusion 1:8
    • Lesson 02 - Hadoop Architecture 26:22
      • 2.1 Hadoop Architecture 1:11
      • 2.2 Objectives 1:17
      • 2.3 Key Terms 1:23
      • 2.4 Hadoop Cluster Using Commodity Hardware 1:34
      • 2.5 Hadoop Configuration 0:0
      • 2.6 Hadoop Core Services 1:24
      • 2.7 Apache Hadoop Core Components 1:18
      • 2.8 Why HDFS 2:31
      • 2.9 What is HDFS 1:16
      • 2.10 HDFSReal-life Connect 1:24
      • 2.11 Regular File System vs. HDFS 1:37
      • 2.12 HDFSCharacteristics 2:25
      • 2.13 HDFSKey Features 1:40
      • 2.14 HDFS Architecture 1:46
      • 2.15 NameNode in HA mode 2:11
      • 2.16 NameNode HA Architecture 2:44
      • 2.17 HDFS Operation Principle 3:16
      • 2.18 File System Namespace 1:31
      • 2.19 NameNode Operation 2:27
      • 2.20 Data Block Split 1:46
      • 2.21 Benefits of Data Block Approach 1:10
      • 2.22 HDFSBlock Replication Architecture 1:38
      • 2.23 Replication Method 1:38
      • 2.24 Data Replication Topology 1:16
      • 2.25 Data Replication Representation 1:49
      • 2.26 HDFS Access 1:22
      • 2.27 Business Scenario 1:21
      • 2.28 Create a new Directory in HDFS Demo 2:1
      • 2.29 Spot the Error 0:0
      • 2.30 Quiz 0:0
      • 2.31 Case Study 0:0
      • 2.32 Case Study - Demo 5:50
      • 2.33 Summary 1:30
      • 2.34 Conclusion 1:6
    • Lesson 03 - Hadoop Deployment 6:34
      • 3.1 Hadoop Deployment 1:10
      • 3.2 Objectives 1:21
      • 3.3 Ubuntu ServerIntroduction 1:34
      • 3.4 Installation of Ubuntu Server 14.04 0:0
      • 3.5 Business Scenario 1:27
      • 3.6 Installing Ubuntu Server 14.04 Demo 01 1:7
      • 3.7 Hadoop InstallationPrerequisites 1:17
      • 3.8 Hadoop Installation 1:5
      • 3.9 Installing Hadoop 2.7 Demo 02 1:7
      • 3.10 Hadoop Multi-Node InstallationPrerequisites 1:20
      • 3.11 Steps for Hadoop Multi-Node Installation 0:0
      • 3.12 Single-Node Cluster vs. Multi-Node Cluster 1:33
      • 3.13 Creating a Clone of Hadoop VM Demo 03 1:5
      • 3.14 Performing Clustering of the Hadoop Environment Demo 04 1:5
      • 3.15 Spot the Error 0:0
      • 3.16 Quiz 0:0
      • 3.17 Case Study 0:0
      • 3.18 Case Study - Demo 2:15
      • 3.19 Summary 1:34
      • 3.20 Conclusion 1:34
    • Lesson 04 - Introduction to MapReduce 53:32
      • 4.1 Introduction to YARN and MapReduce 1:15
      • 4.2 Objectives 1:16
      • 4.3 Why YARN 1:48
      • 4.4 What is YARN 1:19
      • 4.5 YARNReal Life Connect 1:53
      • 4.6 YARN Infrastructure 1:45
      • 4.7 YARN Infrastructure (contd.) 2:24
      • 4.8 ResourceManager 2:49
      • 4.9 Other ResourceManager Components 2:14
      • 4.10 ResourceManager in HA Mode 2:12
      • 4.11 ApplicationMaster 2:7
      • 4.12 NodeManager 1:53
      • 4.13 Container 1:57
      • 4.14 Applications Running on YARN 1:43
      • 4.15 Application Startup in YARN 3:49
      • 4.16 Application Startup in YARN (contd.) 1:19
      • 4.17 Role of AppMaster in Application Startup 1:40
      • 4.18 Why MapReduce 1:51
      • 4.19 What is MapReduce 1:18
      • 4.20 MapReduceReal-life Connect 1:21
      • 4.21 MapReduceAnalogy 1:44
      • 4.22 MapReduceAnalogy (contd.) 1:35
      • 4.23 MapReduceExample 2:37
      • 4.24 Map Execution 0:0
      • 4.25 Map ExecutionDistributed Two Node Environment 1:38
      • 4.26 MapReduce Essentials 1:58
      • 4.27 MapReduce Jobs 1:0
      • 4.28 MapReduce and Associated Tasks 1:31
      • 4.29 Hadoop Job Work Interaction 1:38
      • 4.30 Characteristics of MapReduce 1:36
      • 4.31 Real-time Uses of MapReduce 1:31
      • 4.32 Prerequisites for Hadoop Installation in Ubuntu Desktop 14.04 1:13
      • 4.33 Steps to Install Hadoop 1:34
      • 4.34 Business Scenario 1:38
      • 4.35 Set up Environment for MapReduce Development 1:16
      • 4.36 Small Data and Big Data 0:0
      • 4.37 Uploading Small Data and Big Data 1:17
      • 4.38 Installing Ubuntu Desktop OS Demo 1 2:24
      • 4.39 Build MapReduce Program 1:40
      • 4.40 Build a MapReduce Program Demo 2 2:8
      • 4.41 Hadoop MapReduce Requirements 1:46
      • 4.42 Steps of Hadoop MapReduce 2:5
      • 4.43 MapReduceResponsibilities 1:35
      • 4.44 MapReduce Java Programming in Eclipse 1:15
      • 4.45 Create a New Project 1:46
      • 4.46 Checking Hadoop Environment for MapReduce 1:23
      • 4.47 Build a MapReduce Application using Eclipse and Run in Hadoop Cl Demo 3 9:19
      • 4.48 MapReduce v 2.7 1:6
      • 4.49 Spot the Error 0:0
      • 4.50 Quiz 0:0
      • 4.51 Case Study 0:0
      • 4.52 Case Study - Demo 9:35
      • 4.53 Summary 1:43
      • 4.54 Conclusion 1:8
    • Lesson 05 - Advanced HDFS and MapReduce 26:19
      • 5.1 Advanced HDFS and MapReduce 1:9
      • 5.2 Objectives 1:16
      • 5.3 Advanced HDFSIntroduction 1:34
      • 5.4 HDFS Benchmarking 1:29
      • 5.5 Setting Up HDFS Block Size 1:0
      • 5.6 Decommissioning a DataNode 1:30
      • 5.7 Business Scenario 1:18
      • 5.8 HDFS Demo 01 5:47
      • 5.9 Setting HDFS block size in Hadoop 2.7.1 Demo 02 3:13
      • 5.10 Advanced MapReduce 1:38
      • 5.11 Interfaces 1:31
      • 5.12 Data Types in Hadoop 1:35
      • 5.13 Data Types in Hadoop (contd.) 1:9
      • 5.14 InputFormats in MapReduce 1:57
      • 5.15 OutputFormats in MapReduce 2:15
      • 5.16 Distributed Cache 1:49
      • 5.17 Using Distributed CacheStep 1 1:5
      • 5.18 Using Distributed CacheStep 2 1:5
      • 5.19 Using Distributed CacheStep 3 1:5
      • 5.20 Joins in MapReduce 2:1
      • 5.21 Reduce Side Join 1:24
      • 5.22 Reduce Side Join (contd.) 1:28
      • 5.23 Replicated Join 1:20
      • 5.24 Replicated Join (contd.) 1:33
      • 5.25 Composite Join 1:26
      • 5.26 Composite Join (contd.) 1:20
      • 5.27 Cartesian Product 1:28
      • 5.28 Cartesian Product (contd.) 1:21
      • 5.29 MapReduce program for Writable classes Demo 03 4:13
      • 5.30 Spot the Error 0:0
      • 5.31 Quiz 0:0
      • 5.32 Case Study 0:0
      • 5.33 Case Study - Demo 2:36
      • 5.34 Summary 1:39
      • 5.35 Conclusion 1:5
    • Lesson 06 - Pig 51:40
      • 6.1 Pig 1:7
      • 6.2 Objectives 1:12
      • 6.3 Why Pig 1:45
      • 6.4 What is Pig 1:22
      • 6.5 PigReal-life Connect 1:22
      • 6.6 Components of Pig 1:38
      • 6.7 How Pig Works 1:40
      • 6.8 Data Model 2:9
      • 6.9 Nested Data Model 1:19
      • 6.10 Pig Execution Modes 1:19
      • 6.11 Pig Interactive Modes 1:19
      • 6.12 Salient Features 1:22
      • 6.13 Pig vs. SQL 1:44
      • 6.14 Pig vs. SQLExample 2:5
      • 6.15 Additional Libraries for Pig 1:41
      • 6.16 Installing Pig Engine 1:17
      • 6.17 Steps to Installing Pig Engine 1:20
      • 6.18 Business Scenario 1:25
      • 6.19 Installing Pig in Ubuntu Server 14.04 LTS Demo 01 6:33
      • 6.20 Steps to Run a Sample Program to Test Pig 1:31
      • 6.21 Getting Datasets for Pig Development 1:5
      • 6.22 Prerequisites to Set the Environment for Pig Latin 1:22
      • 6.23 Loading and Storing Methods 1:35
      • 6.24 Script Interpretation 1:31
      • 6.25 Various Relations 0:0
      • 6.26 Various Pig Command 0:0
      • 6.27 Convert Unstructured Data into Equivalent Words Demo 02 6:18
      • 6.28 Loading Files into Relations Demo 03 3:15
      • 6.29 Finding the Number of Occurrences of a particular Word Demo 04 4:20
      • 6.30 Performing Combining, Splitting, and Joining relations Demo 05 5:49
      • 6.31 Performing Transforming and Shaping Relations Demo 06 3:7
      • 6.32 Spot the Error 0:0
      • 6.33 Quiz 0:0
      • 6.34 Case Study 0:0
      • 6.35 Case Study - Demo 16:26
      • 6.36 Summary 1:37
      • 6.37 Conclusion 1:5
    • Lesson 07 - Hive 28:29
      • 7.1 Hive 1:8
      • 7.2 Objectives 1:15
      • 7.3 Why Hive 1:18
      • 7.4 What is Hive 1:56
      • 7.5 HiveCharacteristics 1:38
      • 7.6 HiveArchitecture and Components 1:17
      • 7.7 Metastore 0:0
      • 7.8 Driver 2:3
      • 7.9 Hive Thrift Server 1:21
      • 7.10 Client Components 1:33
      • 7.11 Basics of Hive Query Language 1:26
      • 7.12 Data ModelTables 1:39
      • 7.13 Data ModelExternal Tables 1:35
      • 7.14 Data Types in Hive 1:29
      • 7.15 Data ModelPartitions 1:21
      • 7.16 Bucketing in Hive 1:40
      • 7.17 Serialization and Deserialization 1:55
      • 7.18 Hive File Formats 1:24
      • 7.19 Hive Query Language 0:0
      • 7.20 Running Hive 1:17
      • 7.21 Programming in Hive 2:33
      • 7.22 Hive Query LanguageExtensibility 1:15
      • 7.23 User-Defined Function 1:34
      • 7.24 Built-In Functions 1:12
      • 7.25 Other Functions in Hive 2:7
      • 7.26 MapReduce Scripts 1:41
      • 7.27 UDF/ UDAF vs. MapReduce Scripts 1:21
      • 7.28 New Features supported in Hive 2:26
      • 7.29 Business Scenario 1:28
      • 7.30 Installing Hive in Ubuntu Server 14.04 LTS Demo 01 1:28
      • 7.31 Advanced Data Analytics Demo 02 4:8
      • 7.32 Determining Word Count Demo 03 3:49
      • 7.33 Partitioning with Hive Demo 04 4:12
      • 7.34 Spot the Error 0:0
      • 7.35 Quiz 0:0
      • 7.36 Case Study 0:0
      • 7.37 Case Study - Demo 2:15
      • 7.38 Summary 1:40
      • 7.39 Conclusion 1:5
    • Lesson 08 - HBase 21:57
      • 8.1 Hbase 1:8
      • 8.2 Objectives 1:14
      • 8.3 Why HBase 1:53
      • 8.4 What is HBase 1:27
      • 8.5 HBaseReal-life Connect 1:35
      • 8.6 Characteristics of HBase 1:29
      • 8.7 Companies Using HBase 1:7
      • 8.8 HBase Architecture 1:40
      • 8.9 HBase Components 1:40
      • 8.10 Storage Model of HBase 1:49
      • 8.11 Row Distribution of Data between RegionServers 1:17
      • 8.12 Data Storage in HBase 1:34
      • 8.13 Data Model 1:50
      • 8.14 When to Use HBase 1:27
      • 8.15 HBase vs. RDBMS 1:50
      • 8.16 Installation of HBase 1:28
      • 8.17 Configuration of HBase 1:5
      • 8.18 Business Scenario 1:17
      • 8.19 Installing and configuring HBase Demo 01 6:5
      • 8.20 Connecting to HBase 1:36
      • 8.21 HBase Shell Commands 1:38
      • 8.22 Spot the Error 0:0
      • 8.23 Quiz 0:0
      • 8.24 Case Study 0:0
      • 8.25 Case Study - Demo 6:8
      • 8.26 Summary 1:34
      • 8.27 Conclusion 1:6
    • Lesson 09 - Commercial Distribution of Hadoop 6:21
      • 9.1 Commercial Distribution of Hadoop 1:8
      • 9.2 Objectives 1:16
      • 9.3 ClouderaIntroduction 1:27
      • 9.4 Cloudera CDH 1:39
      • 9.5 Downloading the Cloudera VM 0:0
      • 9.6 Starting the Cloudera VM 1:37
      • 9.7 Logging into Hue 1:41
      • 9.8 Cloudera Manager 1:18
      • 9.9 Logging into Cloudera Manager 0:0
      • 9.10 Business Scenario 1:25
      • 9.11 Download,start and Work with Cloudera VM Demo 01 1:5
      • 9.12 Eclipse with MapReduce in Cloudera's Quickstart VM Demo 02 1:6
      • 9.13 Hortonworks Data Platform 0:0
      • 9.14 MapR Data Platform 0:0
      • 9.15 Pivotal HD 0:0
      • 9.16 Pivotal HD (contd.) 1:21
      • 9.17 IBM InfoSphere BigInsights 0:0
      • 9.18 IBM InfoSphere BigInsights (contd.) 1:37
      • 9.19 Quiz 0:0
      • 9.20 Summary 1:34
      • 9.21 Conclusion 1:7
    • Lesson 10 - ZooKeeper, Sqoop, and Flume 63:14
      • 10.1 ZooKeeper, Sqoop, and Flume 1:10
      • 10.2 Objectives 1:20
      • 10.3 Why ZooKeeper 1:44
      • 10.4 What is ZooKeeper 1:31
      • 10.5 Features of ZooKeeper 1:51
      • 10.6 Challenges Faced in Distributed Applications 1:26
      • 10.7 Coordination 1:54
      • 10.8 Goals and Uses of ZooKeeper 0:0
      • 10.9 ZooKeeper Entities 1:40
      • 10.10 ZooKeeper Data Model 1:42
      • 10.11 Znode 2:8
      • 10.12 Client API Functions 1:46
      • 10.13 Recipe 1Cluster Management 1:33
      • 10.14 Recipe 2Leader Election 1:35
      • 10.15 Recipe 3Distributed Exclusive Lock 1:41
      • 10.16 Business Scenario 1:26
      • 10.17 View ZooKeeper Nodes Using CLI Demo 1 2:25
      • 10.18 Why Sqoop 1:49
      • 10.19 What is Sqoop 1:26
      • 10.20 SqoopReal-life Connect 1:27
      • 10.21 Sqoop and Its Uses 2:1
      • 10.22 Sqoop and Its Uses (contd.) 1:55
      • 10.23 Benefits of Sqoop 1:27
      • 10.24 Sqoop Processing 1:27
      • 10.25 Sqoop ExecutionProcess 1:23
      • 10.26 Importing Data Using Sqoop 1:12
      • 10.27 Sqoop ImportProcess 1:20
      • 10.28 Sqoop ImportProcess (contd.) 1:45
      • 10.29 Importing Data to Hive and HBase 0:0
      • 10.30 Exporting Data from Hadoop Using Sqoop 1:35
      • 10.31 Sqoop Connectors 1:36
      • 10.32 Sample Sqoop Commands 1:53
      • 10.33 Business Scenario 1:30
      • 10.34 Install Sqoop Demo 2 7:6
      • 10.35 Import Data on Sqoop Using MySQL Database Demo 3 4:16
      • 10.36 Export Data Using Sqoop from Hadoop Demo 4 4:13
      • 10.37 Why Flume 1:52
      • 10.38 Apache FlumeIntroduction 2:15
      • 10.39 Flume Model 1:26
      • 10.40 FlumeGoals 1:32
      • 10.41 Scalability in Flume 1:21
      • 10.42 FlumeSample Use Cases 1:22
      • 10.43 Business Scenario 1:19
      • 10.44 Configure and Run Flume Agents Demo 5 3:44
      • 10.45 Spot the Error 0:0
      • 10.46 Quiz 0:0
      • 10.47 Case StudyZooKeeper 0:0
      • 10.48 Case StudyZooKeeperDemo 8:54
      • 10.49 Case StudySqoop 0:0
      • 10.50 Case StudySqoopDemo 9:51
      • 10.51 Case StudyFlume 0:0
      • 10.52 Case StudyFlumeDemo 6:24
      • 10.53 Summary 1:54
      • 10.54 Conclusion 1:7
    • Lesson 11 - Ecosystem and Its Components 21:59
      • 11.1 Ecosystem and Its Components 1:9
      • 11.2 Objectives 1:9
      • 11.3 Apache Hadoop Ecosystem 1:35
      • 11.4 File System Component 1:17
      • 11.5 Data Store Components 1:21
      • 11.6 Serialization Components 1:22
      • 11.7 Job Execution Components 1:34
      • 11.8 Work Management, Operations, and Development Components 2:44
      • 11.9 Security Components 1:22
      • 11.10 Data Transfer Components 1:43
      • 11.11 Data Interactions Components 0:0
      • 11.12 Data Interactions Components (contd.) 0:0
      • 11.13 Analytics and Intelligence Components 1:39
      • 11.14 Search Frameworks Components 1:24
      • 11.15 Graph-Processing Framework Components 1:20
      • 11.16 Apache Oozie 1:30
      • 11.17 Apache Oozie Workflow 1:38
      • 11.18 Apache Oozie Workflow (contd.) 1:37
      • 11.19 Introduction to Mahout 1:30
      • 11.20 Schedule workflow with Apache Oozie Demo 01 3:43
      • 11.21 Introduction to Mahout (contd.) 1:19
      • 11.22 Features of Mahout 1:24
      • 11.23 Usage of Mahout 1:19
      • 11.24 Usage of Mahout (contd.) 1:21
      • 11.25 Apache Cassandra 1:41
      • 11.26 Characteristics of Apache Cassandra 1:31
      • 11.27 Apache Spark 2:3
      • 11.28 Apache Spark Tools 1:57
      • 11.29 Key Concepts of Apache Spark 1:42
      • 11.30 Apache SparkExample 1:5
      • 11.31 Building a program using Apache Spark Demo 02 2:47
      • 11.32 Hadoop Integration 1:30
      • 11.33 Spot the Error 0:0
      • 11.34 Quiz 0:0
      • 11.35 Case Study 0:0
      • 11.36 Case Study - Demo 1:49
      • 11.35 Summary 1:44
      • 11.36 Conclusion 1:10
    • Lesson 12 - Hadoop Administration, Troubleshooting, and Security 73:3
      • 12.1 Hadoop Administration, Troubleshooting, and Security 1:11
      • 12.2 Objectives 1:18
      • 12.3 Typical Hadoop Core Cluster 1:24
      • 12.4 Load Balancer 1:20
      • 12.5 Commands Used in Hadoop Programming 1:42
      • 12.6 Different Configuration Files of Hadoop Cluster 1:45
      • 12.7 Properties of hadoop-default.xml 0:0
      • 12.8 Hadoop ClusterCritical Parameters 1:42
      • 12.9 Hadoop DFS OperationCritical Parameters 2:11
      • 12.10 Port Numbers for Individual Hadoop Services 1:12
      • 12.11 Performance Monitoring 1:30
      • 12.12 Performance Tuning 1:17
      • 12.13 Parameters of Performance Tuning 2:6
      • 12.14 Troubleshooting and Log Observation 1:35
      • 12.15 Apache Ambari 1:12
      • 12.16 Key Features of Apache Ambari 1:35
      • 12.17 Business Scenario 1:33
      • 12.18 Troubleshooting a Missing DataNode Issue Demo 01 1:5
      • 12.19 Optimizing a Hadoop Cluster Demo 02 1:5
      • 12.20 Hadoop SecurityKerberos 1:51
      • 12.21 KerberosAuthentication Mechanism 1:35
      • 12.22 Kerberos ConfigurationSteps 1:53
      • 12.23 Data Confidentiality 0:0
      • 12.24 Spot the Error 0:0
      • 12.25 Quiz 0:0
      • 12.26 Case Study 0:0
      • 12.27 Case Study - Demo 61:5
      • 12.28 Summary 1:33
      • 12.29 Thank you 1:6
      • 12.30 Usage of Trademarks 1:17
    • 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 192:9
      • 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 Advantages 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
    • 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
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Exam & Certification

  • How to become a Certified Big Data & Hadoop Developer?

    To become a Certified Big Data & Hadoop Developer, it is mandatory to fulfill both the following criteria:
    • Completing any one project out of the four projects given by Simplilearn, within the OSL access period of the Big Data Hadoop developer course. The project is evaluated by the lead trainer. Screenshots of the final output and the source code used should be mailed to projectsubmission@simplilearn.com within the Online Self Learning (OSL) access period of the course. In case, you have any queries or difficulties while solving projects then you can get assistance from On Demand support to clarify such queries & difficulties. For Live Virtual Classroom Training, in case you have doubts in implementing the project, you may attend any of the ongoing batches of Big Data Hadoop to get help in Project work.
    • Clearing the online examination with a minimum score of 80%. In case, you don’t clear the online exam in the first attempt, you can re-attempt the exam one more time.
    At the end of the course, you will receive an experience certificate stating that you have 3 months experience in implementing Big Data and Hadoop Projects.

    Note: It is mandatory that you fulfill both the criteria i.e. completion of any one Project and clearing the online exam with minimum score of 80%, to become a Certified Big Data & Hadoop Developer.

Reviews

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

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

This course has provided me both theoretical and practical knowledge.

The training was good in terms of explanation and clearing the concepts theoretically. The fundamentals were covered.

The Big Data course content was elaborate and the training was great.

The entire Big Data and Hadoop course content was completed and covered in-depth in 4 days. The training was good.

Great course and very easy to grasp the concept.

It was an amazing experience to train on BIG DATA with Simplilearn. This course is very feasible to the beginners and the course contents are downloadable on the go.

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The training provided an insight into Big Data Hadoop. The classes were good and the pace was moderate. The trainer was good and was able to give good theoretical concepts on the Hadoop framework.

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The training was good. It helped me to configure all the Hadoop components. The training covered most of the topics in Hadoop.

Very good introduction to Big data Hadoop. Clearly organized and even a non-technical person can go through the course in a very organized manner.

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Thumbs up for training material quality, training methodology & experienced trainer for segment.

A well planned course on Hadoop. The training was good and to the point.

Extremely satisfied with the course. Got the basic knowledge of big data and hadoop and all the related components.

The training sufficiently covered cloud fundamentals. Good outline.

FAQs

  • What are the System Requirements?

    • 1 GHz or faster Processor
    • 32-bit (x86) or 64-bit (x64) processor
    • 1 GB RAM (32-bit) or 2 GB RAM (64-bit)
    • Minimum 512kbps Internet Speed

  • How will the Labs be conducted?

    You will be using CloudLab - A cloud based Hadoop environment lab, a unique offering by Simplilearn to execute all the hand-on project work with Hadoop 2.7.

    CloudLab will be accessible from Simplilearn LMS. A video on introduction and how to use CloudLab is provided in Learning Management System.

  • Who are the trainers?

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

  • What are the modes of training offered for this course?

    We offer this training in the following modes:

    • Live Virtual Classroom or Online Classroom: In online classroom training, you have the option to attend the course remotely from your desktop via video conferencing. This format saves productivity challenges and decreases your time spent away from work or home.
    • Online Self-Learning: In this mode, you will get the lecture videos and you can go through the course as per your comfort level.

  • What if I miss a class?

    We provide the recordings of the class after the session is conducted. So, if you miss a class, you can go through the recordings before the next session.

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

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

  • Who provides the certification?

    At the end of the training, you will work on a real life industry-based project which will be evaluated by our expert. Subject to satisfactory evaluation of the project and the score of the online exam (minimum 80%), you will get a certificate from Simplilearn stating that you have 3 months experience in Big Data and Hadoop.

  • Are there any group discounts for classroom training programs?

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

  • What are the payment options?

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

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