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“Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. With our online Hadoop training, you’ll learn how the components of the Hadoop ecosystem, such as Hadoop 2.7, Yarn, MapReduce, HDFS, Pig, Impala, HBase, Flume, Apache Spark, etc. fit in with the Big Data processing lifecycle. Implement real life projects in banking, telecommunication, social media, insurance, and e-commerce
on CloudLab.

Course Advisor

Ronald van Loon
Top 10 Big Data & Data Science Influencer, Director – Adversitement

Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author for a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. He also regularly speaks at renowned events.

  Accredited by 

Key Features 

40 hours of instructor-led training (for Live Virtual Classroom)

24 hours of self-paced video

5 real-life industry projects using Hadoop and Spark

Hands-on practice on CloudLab

Training on Yarn, MapReduce, Pig, Hive, Impala, HBase, and Apache Spark

Aligned to Cloudera CCA175 certification exam

Mode of learning

Online self paced learning:


  • 180 days of access to high-quality, self-paced learning content designed by industry experts

 

USD 499

Live virtual classroom:


  • 90 days of access to 14+ instructor-led online training classes
  • 180 days of access to high-quality, self-paced learning content designed by industry experts
  • Flexible weekend class weekly

 

USD 999

Description

The Big Data Hadoop Certification course is designed to give you in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion with our big data training.

You will master real-time data processing using Spark, including functional programming in Spark, implementing Spark applications, understanding parallel processing in Spark, and using Spark RDD optimization techniques. With our big data course, you will also learn the various interactive algorithms in Spark and use Spark SQL for creating, transforming, and querying data forms.

As a part of the big data course, you will be required to execute real-life industry-based projects using CloudLab in the domains of banking, telecommunication, social media, insurance, and e-commerce. This Big Data Hadoop training course will prepare you for the Cloudera CCA175 big data certification.

Big Data Hadoop training will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment. You will learn to:

  • Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark with this Hadoop course.
  • Understand Hadoop Distributed File System (HDFS) and YARN architecture, and learn how to work with them for storage and resource management
  • Understand MapReduce and its characteristics and assimilate advanced MapReduce concepts
  • Ingest data using Sqoop and Flume
  • Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
  • Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
  • Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
  • Understand and work with HBase, its architecture and data storage, and learn the difference between HBase and RDBMS
  • Gain a working knowledge of Pig and its components
  • Do functional programming in Spark, and implement and build Spark applications
  • Understand resilient distribution datasets (RDD) in detail
  • Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
  • Understand the common use cases of Spark and various interactive algorithms
  • Learn Spark SQL, creating, transforming, and querying data frames
  • Prepare for Cloudera CCA175 Big Data certification

Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology in Big Data architecture. Big Data training is best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data, including:

  • Software Developers and Architects
  • Analytics Professionals
  • Senior IT professionals
  • Testing and Mainframe Professionals
  • Data Management Professionals
  • Business Intelligence Professionals
  • Project Managers
  • Aspiring Data Scientists
  • Graduates looking to build a career in Big Data Analytics

The Hadoop Training course includes five real-life, industry-based projects on CloudLab. Successful evaluation of one of the following two projects is a part of the certification eligibility criteria.

  • Project 1
    Domain- Banking
    Description: A Portuguese banking institution ran a marketing campaign to convince potential customers to invest in a bank term deposit. Their marketing campaigns were conducted through phone calls, and sometimes the same customer was contacted more than once. Your job is to analyze the data collected from the marketing campaign.
  • Project 2
    Domain- Telecommunication
    Description: A mobile phone service provider has launched a new Open Network campaign. The company has invited users to raise complaints about the towers in their locality if they face issues with their mobile network. The company has collected the dataset of users who raised a complaint. The fourth and the fifth field of the dataset has a latitude and longitude of users, which is important information for the company. You must find this latitude and longitude information on the basis of the available dataset and create three clusters of users with a k-means algorithm.

For additional practice, we have three more projects to help you start your Hadoop and Spark journey.

  • Project 3
    Domain- Social Media
    Description: As part of a recruiting exercise, a major social media company asked candidates to analyze a dataset from Stack Exchange. You will be using the dataset to arrive at certain key insights.
  • Project 4
    Domain- Website providing movie-related information
    Description: IMDB is an online database of movie-related information. IMDB users rate movies on a scale of 1 to 5 — 1 being the worst and 5 being the best — and provide reviews. The dataset also has additional information, such as the release year of the movie. You are tasked to analyze the data collected.
  • Project 5
    Domain- Insurance
    Description: A US-based insurance provider has decided to launch a new medical insurance program targeting various customers. To help a customer understand the the market better, you must perform a series of data analyses using Hadoop.

Big data is one of the fastest growing and most promising fields in technology for applying large volumes of data to meet business objectives. This Big Data Hadoop training will help you kickstart your career by arming you with the most in-demand professional skills in big data and analytics.

The field of big data and analytics is a dynamic one, adapting rapidly as technology evolves over time. Those professionals who take the initiative and excel in big data and analytics are well-positioned to keep pace with changes in the technology space and fill growing job opportunities. Some trends in big data include:

  • Global Hadoop Market to Reach $84.6 Billion by 2021 – Allied Market Research
  • Shortage of 1.4 -1.9 million Hadoop Data Analysts in the US alone by 2018– McKinsey
  • Hadoop Administrators in the US receive salaries of up to $123,000 – indeed.com

The world is getting increasingly digital, and this means big data is here to stay. In fact, the importance of big data and data analytics is going to continue growing in the coming years. Choosing a career in the field of big data and analytics might just be the type of role that you have been trying to find to meet your career expectations.

Professionals who are working in this field can expect an impressive salary, with the median salary for data scientists being $116,000. Even those who are at the entry level will find high salaries, with average earnings of $92,000. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. Close to 80% of data scientists say there is currently a shortage of professionals working in the field.

There are no prerequisites for learning this course. However, knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. If you wish to brush up your Core-Java skills, Simplilearn offers a complimentary self-paced course “Java essentials for Hadoop” when you enroll for this course. For Spark, this course uses Python and Scala, and an e-book is provided to support your learning.

You will use CloudLab to complete projects.

CloudLab is a cloud-based Hadoop and Spark environment lab that we offer with the Hadoop Training course to ensure a hassle-free execution of your hands-on projects. There is no need to install and maintain Hadoop or Spark on a virtual machine. Instead, you’ll be able to access a preconfigured environment on CloudLab via your browser. This environment is very similar to what companies are using today to optimize Hadoop installation scalability and availability.

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

No, we do not enforce an order for our Hadoop Training course completion. Our Master’s Program recommends the ideal path for becoming a Big Data Expert, but it is learner’s preference to complete the courses in any order.

Course Curriculum

No curriculum found !

Curriculum

0.1 Introduction04:10

1.1 Introduction00:38

1.2 Overview to Big Data and Hadoop05:13

1.3 Pop Quiz

1.4 Hadoop Ecosystem08:57

1.5 Quiz

1.6 Key Takeaways00:55

2.1 Introduction06:10

2.2 HDFS Architecture and Components08:59

2.3 Pop Quiz

2.4 Block Replication Architecture09:53

2.5 YARN Introduction21:25

2.6 Quiz

2.7 Key Takeaways00:41

2.8 Hands-on Exercise

3.1 Introduction00:41

3.2 Why Mapreduce11:57

3.3 Small Data and Big Data15:53

3.4 Pop Quiz

3.5 Data Types in Hadoop04:23

3.6 Joins in MapReduce04:43

3.7 What is Sqoop18:21

3.8 Quiz

3.9 Key Takeaways01:02

3.10 Hands-on Exercise

4.1 Introduction04:07

4.2 Pop Quiz

4.3 Interacting with Hive and Impala14:07

4.4 Quiz

4.5 Key Takeaways00:46

5.1 Working with Hive and Impala07:08

5.2 Pop Quiz

5.3 Data Types in Hive07:47

5.4 Validation of Data07:47

5.5 What is Hcatalog and Its Uses05:25

5.6 Quiz

5.7 Key Takeaways00:29

5.8 Hands-on Exercise

6.1 Introduction00:44

6.2 Types of File Format02:35

6.3 Pop Quiz

6.4 Data Serialization03:11

6.5 Importing MySql and Creating hivetb04:32

6.6 Parquet With Sqoop02:37

6.7 Quiz

6.8 Key Takeaways00:56

6.9 Hands-on Exercise

7.1 Introduction07:41

7.2 Pop Quiz

7.3 Overview of the Hive Query Language08:18

7.4 Quiz

7.5 Key Takeaways01:01

7.6 Hands-on Exercise

8.1 Introduction12:29

8.2 Pop Quiz

8.3 Introduction to HBase14:40

8.4 Quiz

8.5 Key Takeaways00:57

8.6 Hands-on Exercise

9.1 Introduction10:45

9.2 Pop Quiz

9.3 Getting Datasets for Pig Development06:45

9.4 Quiz

9.5 Key Takeaways00:38

9.6 Hands-on Exercise

10.1 Introduction16:04

10.2 Spark – Architecture, Execution, and Related Concepts07:10

10.3 Pop Quiz

10.4 RDD Operations10:39

10.5 Functional Programming in Spark05:34

10.6 Quiz

10.7 Key Takeaways00:27

10.8 Hands-on Exercise

11.1 Introduction00:46

11.2 RDD Data Types and RDD Creation10:14

11.3 Pop Quiz

11.4 Operations in RDDs04:35

11.5 Quiz

11.6 Key Takeaways00:34

11.7 Hands-on Exercise

12.1 Introduction03:57

12.2 Running Spark on YARN01:27

12.3 Pop Quiz

12.4 Running a Spark Application01:47

12.5 Dynamic Resource Allocation01:06

12.6 Configuring Your Spark Application04:24

12.7 Quiz

12.8 Key Takeaways01:13

13.1 Introduction05:41

13.2 Pop Quiz

13.3 Parallel Operations on Partitions02:28

13.4 Quiz

13.5 Key Takeaways00:31

13.6 Hands-on Exercise

14.1 Introduction04:40

14.2 Pop Quiz

14.3 RDD Persistence08:59

14.4 Quiz

14.5 Key Takeaways00:44

14.6 Hands-on Exercise

15.1 Introduction00:49

15.2 Spark: An Iterative Algorithm03:13

15.3 Introduction To Graph Parallel System02:34

15.4 Pop Quiz

15.5 Introduction To Machine Learning10:27

15.6 Introduction To Three C’s08:07

15.7 Quiz

15.8 Key Takeaways01:59

What’s next?05:28

The Next Step05:28

16.1 Introduction06:36

16.2 Pop Quiz

16.3 Interoperating with RDDs06:08

16.4 Quiz

16.5 Key Takeaways00:37

16.6 Hands-on Exercise

Project For Submission

Projects with solutions

Instructions00:20

Course Feedback

 

FREE COURSE

Apache Kafka

0.1 Course Introduction00:11

0.2 Course Objectives00:20

0.3 Course Overview00:18

0.4 Target Audience00:17

0.5 Prerequisites00:14

0.7 Conclusion00:07

1.1 Lesson 1Big Data Overview 00:08

1.2 Objectives00:21

1.3 Big DataIntroduction00:25

1.4 The Three Vs of Big Data00:14

1.5 Data Volume00:34

1.6 Data Sizes00:28

1.7 Data Velocity00:49

1.8 Data Variety00:38

1.9 Data Evolution00:54

1.10 Features of Big data00:50

1.11 Industry Examples01:42

1.12 Big Data Analysis00:39

1.13 Technology Comparison01:05

1.14 Stream00:50

1.15 Apache Hadoop00:55

1.16 Hadoop Distributed File System00:58

1.17 MapReduce00:43

1.18 Real-Time Big Data Tools00:13

1.19 Apache Kafka00:19

1.20 Apache Storm00:26

1.21 Apache Spark00:56

1.22 Apache Cassandra00:55

1.23 Apache Hbase00:22

1.24 Real-Time Big Data ToolsUses00:26

1.25 Real-Time Big DataUse Cases01:32

1.26 Quiz

1.27 Summary00:53

1.28 Conclusion00:06

2.1 Introduction to ZooKeeper00:10

2.2 Objectives00:26

2.3 ZooKeeperIntroduction00:30

2.4 Distributed Applications01:06

2.5 Challenges of Distributed Applications00:17

2.6 Partial Failures00:41

2.7 Race Conditions00:40

2.8 Deadlocks00:41

2.9 Inconsistencies00:48

2.10 ZooKeeper Characteristics00:53

2.11 ZooKeeper Data Model00:42

2.12 Types of Znodes00:38

2.13 Sequential Znodes00:32

2.14 VMware00:29

2.15 Simplilearn Virtual Machine00:23

2.16 PuTTY00:22

2.17 WinSCP00:19

2.18 DemoInstall and Setup VM00:06

2.19 DemoInstall and Setup VM08:12

2.20 ZooKeeper Installation00:20

2.21 ZooKeeper Configuration00:18

2.22 ZooKeeper Command Line Interface00:27

2.23 ZooKeeper Command Line Interface Commands01:07

2.24 ZooKeeper Client APIs00:30

2.25 ZooKeeper Recipe 1: Handling Partial Failures00:58

2.26 ZooKeeper Recipe 2: Leader Election02:09

2.27 Quiz

2.28 Summary00:35

2.29 Conclusion00:08

3.2 Objectives00:19

3.3 Apache KafkaIntroduction00:23

3.4 Kafka History00:30

3.5 Kafka Use Cases00:48

3.6 Aggregating User Activity Using KafkaExample00:43

3.7 Kafka Data Model01:27

3.8 Topics01:15

3.9 Partitions00:36

3.10 Partition Distribution00:48

3.11 Producers00:48

3.12 Consumers00:46

3.13 Kafka Architecture01:10

3.14 Types of Messaging Systems00:42

3.15 Queue SystemExample00:37

3.16 Publish-Subscribe SystemExample00:34

3.17 Brokers00:24

3.18 Kafka Guarantees00:58

3.19 Kafka at LinkedIn00:54

3.20 Replication in Kafka00:44

3.21 Persistence in Kafka00:41

3.22 Quiz

3.23 Summary00:38

3.24 Conclusion00:07

4.2 Objectives00:22

4.3 Kafka Versions00:49

4.4 OS Selection00:19

4.5 Machine Selection00:34

4.6 Preparing for Installation00:19

4.7 Demo 1Kafka Installation and Configuration00:05

4.8 Demo 1Kafka Installation and Configuration00:05

4.9 Demo 2Creating and Sending Messages00:05

4.10 Demo 2Creating and Sending Messages00:05

4.11 Stop the Kafka Server00:40

4.12 Setting up Multi-Node Kafka ClusterStep 100:24

4.13 Setting up Multi-Node Kafka ClusterStep 200:59

4.14 Setting up Multi-Node Kafka ClusterStep 301:04

4.15 Setting up Multi-Node Kafka ClusterStep 400:36

4.16 Setting up Multi-Node Kafka ClusterStep 500:29

4.17 Setting up Multi-Node Kafka ClusterStep 601:08

4.18 Quiz

4.19 Summary00:33

4.20 Conclusion00:07

5.1 Lesson 5Kafka Interfaces 00:09

5.2 Objectives00:18

5.3 Kafka InterfacesIntroduction00:21

5.4 Creating a Topic01:23

5.5 Modifying a Topic00:36

5.6 kafka-topics.sh Options00:57

5.7 Creating a Message00:15

5.8 kafka-console-producer.sh Options01:48

5.9 Creating a MessageExample 101:01

5.10 Creating a MessageExample 200:39

5.11 Reading a Message00:21

5.12 kafka-console-consumer.sh Options01:32

5.13 Reading a MessageExample00:44

5.14 Java Interface to Kafka00:18

5.15 Producer Side API00:42

5.16 Producer Side API ExampleStep 100:32

5.17 Producer Side API ExampleStep 200:15

5.18 Producer Side API ExampleStep 300:21

5.19 Producer Side API ExampleStep 400:21

5.20 Producer Side API ExampleStep 500:17

5.21 Consumer Side API00:37

5.22 Consumer Side API ExampleStep 100:21

5.23 Consumer Side API ExampleStep 200:15

5.24 Consumer Side API ExampleStep 300:20

5.25 Consumer Side API ExampleStep 400:25

5.26 Consumer Side API ExampleStep 500:25

5.27 Compiling a Java Program00:29

5.28 Running the Java Program00:18

5.29 Java Interface Observations00:39

5.30 Exercise 1Tasks00:05

5.31 Exercise 1Tasks (contd.)00:05

5.32 Exercise 1Solutions00:05

5.33 Exercise 1Solutions (contd.)00:05

5.34 Exercise 1Solutions (contd.)00:05

5.35 Exercise 2Tasks00:05

5.36 Exercise 2Tasks (contd.)00:05

5.37 Exercise 2Solutions00:05

5.38 Exercise 2Solutions (contd.)00:05

5.39 Exercise 2Solutions (contd.)00:05

5.40 Exercise 2Solutions (contd.)00:05

5.41 Exercise 2Solutions (contd.)00:05

5.42 Quiz

5.43 Summary00:30

5.44 Thank You00:08

 

Free Course

Java Essentials for Hadoop

1.1 Essentials of Java for Hadoop00:19

1.2 Lesson Objectives00:24

1.3 Java Definition00:27

1.4 Java Virtual Machine (JVM)00:34

1.5 Working of Java01:01

1.6 Running a Basic Java Program00:56

1.7 Running a Basic Java Program (contd.)01:15

1.8 Running a Basic Java Program in NetBeans IDE00:11

1.9 BASIC JAVA SYNTAX00:12

1.10 Data Types in Java00:26

1.11 Variables in Java01:31

1.12 Naming Conventionsof Variables01:21

1.13 Type Casting.01:05

1.14 Operators00:30

1.15 Mathematical Operators00:28

1.16 Unary Operators.00:15

1.17 Relational Operators00:19

1.18 Logical or Conditional Operators00:19

1.19 Bitwise Operators01:21

1.20 Static Versus Non Static Variables00:54

1.21 Static Versus Non Static Variables (contd.)00:17

1.22 Statements and Blocks of Code01:21

1.23 Flow Control00:47

1.24 If Statement00:40

1.25 Variants of if Statement01:07

1.26 Nested If Statement00:40

1.27 Switch Statement00:36

1.28 Switch Statement (contd.)00:34

1.29 Loop Statements01:19

1.30 Loop Statements (contd.)00:49

1.31 Break and Continue Statements00:44

1.32 Basic Java Constructs01:09

1.33 Arrays01:16

1.34 Arrays (contd.)01:07

1.35 JAVA CLASSES AND METHODS00:09

1.36 Classes00:46

1.37 Objects01:21

1.38 Methods01:01

1.39 Access Modifiers00:49

1.40 Summary00:41

1.41 Thank You00:09

2.1 Java Constructors00:22

2.2 Objectives00:42

2.3 Features of Java01:08

2.4 Classes Objects and Constructors01:19

2.5 Constructors00:34

2.6 Constructor Overloading01:08

2.7 Constructor Overloading (contd.)00:28

2.8 PACKAGES00:09

2.9 Definition of Packages01:12

2.10 Advantages of Packages00:29

2.11 Naming Conventions of Packages00:28

2.12 INHERITANCE00:09

2.13 Definition of Inheritance01:07

2.14 Multilevel Inheritance01:15

2.15 Hierarchical Inheritance00:23

2.16 Method Overriding00:55

2.17 Method Overriding(contd.)00:35

2.18 Method Overriding(contd.)00:15

2.19 ABSTRACT CLASSES00:10

2.20 Definition of Abstract Classes00:41

2.21 Usage of Abstract Classes00:36

2.22 INTERFACES00:08

2.23 Features of Interfaces01:03

2.24 Syntax for Creating Interfaces00:24

2.25 Implementing an Interface00:23

2.26 Implementing an Interface(contd.)00:13

2.27 INPUT AND OUTPUT00:14

2.28 Features of Input and Output00:49

2.29 System.in.read() Method00:20

2.30 Reading Input from the Console00:31

2.31 Stream Objects00:21

2.32 String Tokenizer Class00:43

2.33 Scanner Class00:32

2.34 Writing Output to the Console00:28

2.35 Summary01:03

2.36 Thank You00:14

3.1 Essential Classes and Exceptions in Java00:18

3.2 Objectives00:31

3.3 The Enums in Java01:00

3.4 Program Using Enum00:44

3.5 ArrayList00:41

3.6 ArrayList Constructors00:38

3.7 Methods of ArrayList01:02

3.8 ArrayList Insertion00:47

3.9 ArrayList Insertion (contd.)00:38

3.10 Iterator00:39

3.11 Iterator (contd.)00:33

3.12 ListIterator00:46

3.13 ListIterator (contd.)01:00

3.14 Displaying Items Using ListIterator00:32

3.15 For-Each Loop00:35

3.16 For-Each Loop (contd.)00:23

3.17 Enumeration00:30

3.18 Enumeration (contd.)00:25

3.19 HASHMAPS00:15

3.20 Features of Hashmaps00:56

3.21 Hashmap Constructors01:36

3.22 Hashmap Methods00:58

3.23 Hashmap Insertion00:44

3.24 HASHTABLE CLASS00:21

3.25 Hashtable Class an Constructors01:25

3.26 Hashtable Methods00:41

3.27 Hashtable Methods00:48

3.28 Hashtable Insertion and Display00:29

3.29 Hashtable Insertion and Display (contd.)00:22

3.30 EXCEPTIONS00:22

3.31 Exception Handling01:06

3.32 Exception Classes00:26

3.33 User-Defined Exceptions01:04

3.34 Types of Exceptions00:44

3.35 Exception Handling Mechanisms00:54

3.36 Try-Catch Block00:15

3.37 Multiple Catch Blocks00:40

3.38 Throw Statement00:33

3.39 Throw Statement (contd.)00:25

3.40 User-Defined Exceptions00:11

3.41 Advantages of Using Exceptions00:25

3.42 Error Handling and finally block00:30

3.43 Summary00:41

3.44 Thank You00:04

Exam & Certification

FREE PRACTICE TEST

Live Virtual Classroom:

  • You need to attend one complete batch.
  • Complete one project and one simulation test with a minimum score of 80%

Online Self-Learning:

  • Complete 85% of the course.
  • Complete one project and one simulation test with a minimum score of 80%

FAQ

You can enroll for the training online. Upon successful payment you will receive an email from Yan Academy with an activation link to access the SimpliLearn online learning platform where all learnings are conducted. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.

  • Visa debit/credit card
  • American express and Diners club card
  • Master Card, or
  • PayPal

The tools you’ll need to attend training are:

Windows: Windows XP SP3 or higher

Mac: OSX 10.6 or higher

Internet speed: Preferably 512 Kbps or higher

Headset, speakers and microphone: You’ll need headphones or speakers to hear instruction clearly, as well as a microphone to talk to others. You can use a headset with a built-in microphone, or separate speakers and microphone.

The trainings are delivered by highly qualified and certified instructors with relevant industry experience.

We offer this training in the following modes:

Live Virtual Classroom or Online Classroom: Attend the course remotely from your desktop via video conferencing to increase productivity and reduce the time spent away from work or home.

Online Self-Learning: In this mode, you will access the video training and go through the course at your own convenience.

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.

Yes, we have group discount options for our training programs. Contact us using the form on the right of any page on the our website, or select the Live Chat link. Our customer service representatives can provide more details.

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

All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience in Big Data Hadoop. Each of them has gone through a rigorous selection process which includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating continue to train for us.

Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours for this Big Data Hadoop training course.

We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us to discuss Big Data and Hadoop topics.

Yes, you can learn Hadoop without being from a software background. We provide complimentary courses in Java and Linux so that you can brush up on your programming skills. This will help you in learning Hadoop technologies better and faster.

Yes, if you would want to upgrade from the self-paced training to instructor-led training then you can easily do so by paying the difference of the fees amount and joining the next batch of classes which shall be separately notified to you.

We have Flexi-pass that lets you attend classes to blend in with your busy schedule and gives you an advantage of being trained by world-class faculty with decades of industry experience combining the best of online classroom training and self-paced learning

With Flexi-pass, we give you access to as many as 15 sessions for 90 days

 

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