Batch of 20 All Batches
  • Batch 1

    May 06 - Jun 03 (9 Days)
    • May
    • Fri 06
    • Sat 07
    • Fri 13
    • Sat 14
    • Fri 20
    • Sat 21
    • May
    • Fri 27
    • Sat 28
    • Jun
    • Fri 03

    Time (CDT)22:30 - 02:30

  • Batch 2

    May 13 - Jun 10 (9 Days)
    • May
    • Fri 13
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    • Sat 21
    • Fri 27
    • Sat 28
    • Jun
    • Fri 03
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    Time (CDT)22:30 - 02:30

  • Batch 3 (Weekend Batch)

    May 14 - Jun 11 (9 Days)
    • May
    • Sat 14
    • Sun 15
    • Sat 21
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    • Sat 28
    • Sun 29
    • Jun
    • Sat 04
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    • Sat 11

    Time (CDT)09:00 - 13:00

  • Batch 4

    May 15 - May 30 (12 Days)
    • May
    • Sun 15
    • Mon 16
    • Tue 17
    • Wed 18
    • Thu 19
    • Sun 22
    • May
    • Mon 23
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    • Sun 29
    • Mon 30

    Time (CDT)19:30 - 22:30

  • Batch 5

    May 27 - Jun 24 (9 Days)
    • May
    • Fri 27
    • Sat 28
    • Jun
    • Fri 03
    • Sat 04
    • Fri 10
    • Sat 11
    • Fri 17
    • Sat 18
    • Jun
    • Fri 24

    Time (CDT)22:30 - 02:30

  • Batch 6 (Weekend Batch)

    May 28 - Jun 25 (9 Days)
    • May
    • Sat 28
    • Sun 29
    • Jun
    • Sat 04
    • Sun 05
    • Sat 11
    • Sun 12
    • Sat 18
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    • Jun
    • Sat 25

    Time (CDT)09:00 - 13:00

  • Batch 7

    Jun 03 - Jul 01 (9 Days)
    • Jun
    • Fri 03
    • Sat 04
    • Fri 10
    • Sat 11
    • Fri 17
    • Sat 18
    • Jun
    • Fri 24
    • Sat 25
    • Jul
    • Fri 01

    Time (CDT)22:30 - 02:30

  • Batch 8

    Jun 10 - Jul 08 (9 Days)
    • Jun
    • Fri 10
    • Sat 11
    • Fri 17
    • Sat 18
    • Fri 24
    • Sat 25
    • Jul
    • Fri 01
    • Sat 02
    • Fri 08

    Time (CDT)22:30 - 02:30

  • Batch 9 (Weekend Batch)

    Jun 11 - Jul 09 (9 Days)
    • Jun
    • Sat 11
    • Sun 12
    • Sat 18
    • Sun 19
    • Sat 25
    • Sun 26
    • Jul
    • Sat 02
    • Sun 03
    • Sat 09

    Time (CDT)09:00 - 13:00

  • Batch 10

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

    Time (CDT)09:30 - 12:30

  • Batch 11

    Jun 19 - Jul 04 (12 Days)
    • Jun
    • Sun 19
    • Mon 20
    • Tue 21
    • Wed 22
    • Thu 23
    • Sun 26
    • Jun
    • Mon 27
    • Tue 28
    • Wed 29
    • Thu 30
    • Jul
    • Sun 03
    • Mon 04

    Time (CDT)19:30 - 22:30

  • Batch 12

    Jun 24 - Jul 22 (9 Days)
    • Jun
    • Fri 24
    • Sat 25
    • Jul
    • Fri 01
    • Sat 02
    • Fri 08
    • Sat 09
    • Fri 15
    • Sat 16
    • Jul
    • Fri 22

    Time (CDT)22:30 - 02:30

  • Batch 13 (Weekend Batch)

    Jun 25 - Jul 23 (9 Days)
    • Jun
    • Sat 25
    • Sun 26
    • Jul
    • Sat 02
    • Sun 03
    • Sat 09
    • Sun 10
    • Sat 16
    • Sun 17
    • Jul
    • Sat 23

    Time (CDT)09:00 - 13:00

  • Batch 14

    Jul 01 - Jul 29 (9 Days)
    • Jul
    • Fri 01
    • Sat 02
    • Fri 08
    • Sat 09
    • Fri 15
    • Sat 16
    • Jul
    • Fri 22
    • Sat 23
    • Fri 29

    Time (CDT)22:30 - 02:30

  • Batch 15

    Jul 08 - Aug 05 (9 Days)
    • Jul
    • Fri 08
    • Sat 09
    • Fri 15
    • Sat 16
    • Fri 22
    • Sat 23
    • Jul
    • Fri 29
    • Sat 30
    • Aug
    • Fri 05

    Time (CDT)22:30 - 02:30

  • 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 :

At Simplilearn, we greatly value the trust of our patrons. Our courses were designed to deliver an effective learning experience, and have helped over half a million find their professional calling. But if you feel your course is not to your liking, we offer a 7-day money-back guarantee. Just send us a refund request within 7 days of purchase, and we will refund 100% of your payment, no questions asked!

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
  • Expert Assistant Premium 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 the Expert Assistant Premium Support provided by Simplilearn ?

        Expert Assistance:
    • Mentoring Sessions: Live Interaction with a subject matter expert to help participants with queries regarding project implementation and the course in general
    • Guidance on forum: Industry experts to respond to participant queries on forum regarding technical concepts, projects and case-studies
     
     
        Teaching Assistance:
    • Project Assistance: Queries related to solving & completing Projects, case-studies which are part of Big Data Hadoop developer course offered by Simplilearn     
    • Technical Assistance: 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.
    • 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 the Support?
    To avail the Support, submit a query to Simplilearn through any of following channels of Simplilearn’s Help & Support team. A Teaching Assistant 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 Syllabus

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

Great approach for the core understanding of Hadoop. Concepts are repeated from different points of view, responding to audience. At the end of the class you understand it.

Read more Read less

The course is very informative and interactive and that is the best part of this training.

Very informative and active sessions. Trainer is easy going and very interactive.

I am enjoying this class as well as the feedback of other students.

The content is well designed and the instructor was excellent.

The trainer really went the extra mile to help me work along. Thanks

Excellent learning experience. The training was superb! Thanks Simplilearn for arranging such wonderful sessions.

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.

FAQs

  • 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 offer group discounts for our online training programs. Get in touch with us over the Drop us a Query/Request a Callback/Live Chat channels to find out more about our group discount packages.

  • 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

  • I want to know more about the training program. Whom do I contact?

    Please join our Live Chat for instant support, call us, or Request a Call Back to have your query resolved.

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