Batch of 14 All Batches
  • Batch 1

    Sep 02 - Sep 30 (9 Days)
    • Sep
    • Fri 02
    • Sat 03
    • Fri 09
    • Sat 10
    • Fri 16
    • Sat 17
    • Sep
    • Fri 23
    • Sat 24
    • Fri 30

    Time (CDT)22:30 - 02:30

  • Batch 2 (Weekend Batch)

    Sep 10 - Oct 08 (9 Days)
    • Sep
    • Sat 10
    • Sun 11
    • Sat 17
    • Sun 18
    • Sat 24
    • Sun 25
    • Oct
    • Sat 01
    • Sun 02
    • Sat 08

    Time (CDT)09:00 - 13:00

  • Batch 3

    Sep 16 - Oct 14 (9 Days)
    • Sep
    • Fri 16
    • Sat 17
    • Fri 23
    • Sat 24
    • Fri 30
    • Oct
    • Sat 01
    • Fri 07
    • Sat 08
    • Fri 14

    Time (CDT)22:30 - 02:30

  • Batch 4

    Sep 19 - Oct 04 (12 Days)
    • Sep
    • Mon 19
    • Tue 20
    • Wed 21
    • Thu 22
    • Fri 23
    • Mon 26
    • Sep
    • Tue 27
    • Wed 28
    • Thu 29
    • Fri 30
    • Oct
    • Mon 03
    • Tue 04

    Time (CDT)09:30 - 12:30

  • Batch 5

    Sep 23 - Oct 21 (9 Days)
    • Sep
    • Fri 23
    • Sat 24
    • Fri 30
    • Oct
    • Sat 01
    • Fri 07
    • Sat 08
    • Fri 14
    • Sat 15
    • Fri 21

    Time (CDT)22:30 - 02:30

  • Batch 6 (Weekend Batch)

    Sep 24 - Oct 22 (9 Days)
    • Sep
    • Sat 24
    • Sun 25
    • Oct
    • Sat 01
    • Sun 02
    • Sat 08
    • Sun 09
    • Sat 15
    • Sun 16
    • Oct
    • Sat 22

    Time (CDT)09:00 - 13:00

  • Batch 7

    Sep 25 - Oct 10 (12 Days)
    • Sep
    • Sun 25
    • Mon 26
    • Tue 27
    • Wed 28
    • Thu 29
    • Oct
    • Sun 02
    • Mon 03
    • Tue 04
    • Wed 05
    • Thu 06
    • Sun 09
    • Oct
    • Mon 10

    Time (CDT)19:30 - 22:30

  • Batch 8

    Sep 30 - Oct 28 (9 Days)
    • Sep
    • Fri 30
    • Oct
    • Sat 01
    • Fri 07
    • Sat 08
    • Fri 14
    • Sat 15
    • Fri 21
    • Oct
    • Sat 22
    • Fri 28

    Time (CDT)22:30 - 02:30

  • Batch 9 (Weekend Batch)

    Oct 08 - Nov 05 (9 Days)
    • Oct
    • Sat 08
    • Sun 09
    • Sat 15
    • Sun 16
    • Sat 22
    • Sun 23
    • Oct
    • Sat 29
    • Sun 30
    • Nov
    • Sat 05

    Time (CDT)09:00 - 13:00

  • Batch 10

    Oct 14 - Nov 11 (9 Days)
    • Oct
    • Fri 14
    • Sat 15
    • Fri 21
    • Sat 22
    • Fri 28
    • Sat 29
    • Nov
    • Fri 04
    • Sat 05
    • Fri 11

    Time (CDT)22:30 - 01:30

  • Batch 11

    Oct 17 - Nov 01 (12 Days)
    • Oct
    • Mon 17
    • Tue 18
    • Wed 19
    • Thu 20
    • Fri 21
    • Mon 24
    • Oct
    • Tue 25
    • Wed 26
    • Thu 27
    • Fri 28
    • Mon 31
    • Nov
    • Tue 01

    Time (CDT)09:30 - 12:30

  • Batch 12

    Oct 21 - Nov 18 (9 Days)
    • Oct
    • Fri 21
    • Sat 22
    • Fri 28
    • Sat 29
    • Nov
    • Fri 04
    • Sat 05
    • Fri 11
    • Sat 12
    • Fri 18

    Time (CDT)22:30 - 01:30

  • Batch 13 (Weekend Batch)

    Oct 22 - Nov 19 (9 Days)
    • Oct
    • Sat 22
    • Sun 23
    • Sat 29
    • Sun 30
    • Nov
    • Sat 05
    • Sun 06
    • Sat 12
    • Sun 13
    • Sat 19

    Time (CDT)09:00 - 12:00

  • Batch 14

    Oct 30 - Nov 14 (12 Days)
    • Oct
    • Sun 30
    • Mon 31
    • Nov
    • Tue 01
    • Wed 02
    • Thu 03
    • Sun 06
    • Mon 07
    • Tue 08
    • Nov
    • Wed 09
    • Thu 10
    • Sun 13
    • Mon 14

    Time (CDT)19:30 - 21:30

  • 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 25% 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 25% 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 Elearning
  • 60 hours of industry projects with 3.5 billion data points
  • Hands-on project execution with CloudLab
  • Expert Assistant Premium Support
  • Earn Hadoop 2.7 experience certificate

Course Description

  • What’s the focus of the course?

    Simplilearn’s Big Data and Hadoop Certification course is designed to prepare you for a job assignment in the Big Data world. The course provides you not only with Hadoop 2.7 essential skills, but also gives you practical work experience in Big Data Hadoop by completing long-term, real-world projects. You’ll use Hadoop 2.7 with CloudLab—a cloud-based Hadoop environment lab—to complete your hands-on project work.

  • What are the course objectives?

    After completing the Big Data and Hadoop course, you will:

    • Master Hadoop 2.7 framework’s concepts, along with its deployment in a cluster environment
    • Learn to write complex MapReduce programs
    • Perform Data Analytics using Pig and Hive Hadoop Components
    • Acquire an in-depth understanding of the Hadoop ecosystem including Flume, Apachie Oozie workflow scheduler, and more
    • Master advanced Hadoop 2.7 concepts including Hbase, Zookeeper, and Sqoop
    • Get hands-on experience in setting up different Hadoop cluster configurations
    • Work on real-life, industry-based projects using Hadoop 2.7

  • What is CloudLab?

    CloudLab is a cloud-based Hadoop environment lab Simplilearn offers along with the course to ensure a hassle-free execution of the hands-on project work you’ll complete in the Hadoop 2.7 course.

    With CloudLab, you won’t have to install Hadoop on a virtual machine. Instead, you’ll be able to access a preconfigured environment—on CloudLab.

    You’ll have access to CloudLab from the Simplilearn LMS (Learning Management System) for the duration of the course. You can learn more about CloudLab by viewing our CloudLab video.

  • What is Expert Assistant Premium Support?

    We provide expert support and assistance to everyone who takes this course. It includes:
    • Mentoring Sessions: Live Interaction with a subject matter expert to help participants with questions regarding project implementation as well as the course in general
    • Course Forum: Industry experts respond to participant questions on the course forum regarding technical concepts, projects, and case-studies
    • Project Assistance: Help with solving and completing projects and case studies
    • Technical Assistance: Help with technical, installation, and administration issues
    • Hadoop Programming: Help with programming while solving and completing projects
    • CloudLab Support: Help with using CloudLab to execute projects, case studies, and exercises
    How to avail the Support?
    When you need support, you can use any of the following methods to contact Simplilearn’s Help and Support Team. A Teaching Assistant will get in touch with you to help answer your questions within 48 hours. In case of critical issues, support will be rendered through remote desktop.

  • Who should take this course?

    Big Data career opportunities are on the rise, and Hadoop is quickly 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
    • Graduates looking to build a career in Big Data Analytics
    • Anyone interested in Big Data Analytics
    Prerequisite: Knowledge of Java is necessary for this course, so we are providing complimentary access to “Java Essentials for Hadoop” along with the 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 kinds of projects will I complete during the course?

    As part of the course work, you will complete four live industry-based projects covering approximately 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 and has provided pointers to the data set for you to use. To help this company understand the current realities of the marketplace, you will perform a series of data analytics tasks using Hadoop.

    Project#2
    Domain: Retail
    A US-based online retailer plans to launch a new product category and wants to understand the potential growth areas as well as the areas that have stagnated over a period of time, and has provided pointers to the data set for you to use. It wants to leverage data to ensure its product focus is aligned to opportunities that will grow over the next 5 – 7 years.

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

    Project#4
    Domain: Education
    Your company has recently landed a large assignment from a US-based customer that provides 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 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 Study Netflix 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 Technology Platform for Discovery and Exploration 00:28
      • 1.14 Big Data Technology Platform for Discovery and Exploration (contd.) 00:27
      • 1.15 Big Data Technology Capabilities 00:18
      • 1.16 Big Data Use 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 Player Introduction 00:17
      • 1.22 VMware Player Hardware 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 HDFS—Real-life Connect 00:24
      • 2.11 Regular File System vs. HDFS 00:37
      • 2.12 HDFS Characteristics 01:25
      • 2.13 HDFS Key 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 HDFS Block 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 Server Introduction 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 Installation Prerequisites 00:17
      • 3.8 Hadoop Installation 00:05
      • 3.9 Installing Hadoop 2.7 Demo 02 00:07
      • 3.10 Hadoop Multi-Node Installation Prerequisites 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 YARN Real Life Connect 00:53
      • 4.6 YARN Infrastructure 00:45
      • 4.7 YARN Infrastructure (contd.) 01:24
      • 4.8 Resource Manager 01:49
      • 4.9 Other Resource Manager Components 01:14
      • 4.10 Resource Manager in HA Mode 01:12
      • 4.11 Application Master 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 MapReduce Real-life Connect 00:21
      • 4.21 MapReduce Analogy 00:44
      • 4.22 MapReduce Analogy (contd.) 00:35
      • 4.23 MapReduce Example 01:37
      • 4.24 Map Execution 00:00
      • 4.25 Map Execution Distributed 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 MapReduce Responsibilities 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 HDFS Introduction 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 Input Formats in MapReduce 00:57
      • 5.15 Output Formats in MapReduce 01:15
      • 5.16 Distributed Cache 00:49
      • 5.17 Using Distributed Cache–Step 1 00:05
      • 5.18 Using Distributed Cache–Step 2 00:05
      • 5.19 Using Distributed Cache–Step 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 Pig Real-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. SQL Example 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 Hive Characteristics 00:38
      • 7.6 Hive Architecture 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 Model—Tables 00:39
      • 7.13 Data Model External Tables 00:35
      • 7.14 Data Types in Hive 00:29
      • 7.15 Data Model Partitions 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 Language Extensibility 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 HBase Real-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 Cloudera Introduction 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 Big Insights 00:00
      • 9.18 IBM InfoSphere Big Insights (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 1 Cluster Management 00:33
      • 10.14 Recipe 2 Leader Election 00:35
      • 10.15 Recipe 3 Distributed Exclusive Lock 00:41
      • 10.16 Business Scenario 00:26
      • 10.17 View ZooKeeper Nodes Using C LI Demo 1 01:25
      • 10.18 Why Sqoop 00:49
      • 10.19 What is Sqoop 00:26
      • 10.20 Sqoop Real-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 Execution Process 00:23
      • 10.26 Importing Data Using Sqoop 00:12
      • 10.27 Sqoop Import Process 00:20
      • 10.28 Sqoop Import Process (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 Flume Introduction 01:15
      • 10.39 Flume Model 00:26
      • 10.40 Flume Goals 00:32
      • 10.41 Scalability in Flume 00:21
      • 10.42 Flume Sample 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 Study ZooKeeper 00:00
      • 10.48 Case Study ZooKeeper Demo 07:54
      • 10.49 Case Study Sqoop 00:00
      • 10.50 Case Study Sqoop Demo 08:51
      • 10.51 Case Study Flume 00:00
      • 10.52 Case Study Flume Demo 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 Spark Example 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 Cluster Critical Parameters 00:42
      • 12.9 Hadoop DFS Operation Critical 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 Security Kerberos 00:51
      • 12.21 Kerberos Authentication Mechanism 00:35
      • 12.22 Kerberos Configuration Steps 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 can I become a Certified Big Data & Hadoop Developer?

      To become a Certified Big Data & Hadoop Developer, you must fulfill both the following criteria:

      Complete any one project out of the four projects given by Simplilearn, within the maximum time allotted for the Big Data Hadoop developer course. When you have completed your project, you can submit it through LMS.

      Passing the online examination with a minimum score of 80%. If you don’t pass the exam the first time, you can re-attempt the exam one more time.

      When you have completed the course, you will receive an experience certificate stating that you have 3 months experience in implementing Big Data and Hadoop Projects.

      Note: You must fulfill both the criteria (completion of any one Project and passing the online exam with minimum score of 80%) to become a Certified Big Data & Hadoop Developer.

    Reviews

    I really like the content of the course and the way trainer relates it with real-life examples.

    Dedication of the trainer towards answering each & every question of the trainees makes us feel great and the online session as real as a classroom session.

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    The trainer was knowledgeable and patient in explaining things. Many things were significantly easier to grasp with a live interactive instructor. I also like that he went out of his way to send additional information and solutions after the class via email.

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    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.

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    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.

    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.

    FAQs

    • How will the Labs be conducted?

      You’ll use CloudLab to execute all the hands-on project work using Hadoop 2.7. CloudLab is a cloud-based Hadoop environment lab offered by Simplilearn to its participants. CloudLab itself as well as video instruction on its use are accessible from within the Simplilearn LMS (Learning Management System).

    • Who are the trainers?

      The instructors for this course are highly qualified and certified, with industry relevant experience.

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

      We offer this training in two formats:

      1. Live Virtual Classroom or Online Classroom: With 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.
      2. Online Self-Learning: In this mode, you will use the lecture videos and you can complete the course at your own pace.

    • What if I miss a class?

      We record the class sessions and provide them to participants after the session is conducted. If you miss a class, you can view the recording before the next class session.

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

      Yes, you can cancel your enrolment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

    • How will I receive my course completion certificate?

      Your course completion certificate will be auto generated in the LMS once you meet all of the criteria below:
      • Completion of at least 85% of the eLearning course
      • Submission of project per course requirements
      • Successfully meeting the project evaluation criteria set by our experts

    • Do you offer group discounts for classroom training?

      Yes, we have group discount options for our online training programs. Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

    • What payment options are available?

      Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.
      • Visa Credit or Debit card
      • MasterCard
      • American Express
      • Diner’s Club
      • PayPal

    • I’d like to learn more about this training program. Who should I contact?

      Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

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