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  • 40 hours of instructor-led training
  • 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

Course description

  • What are the objectives of our Big Data Hadoop Online Course?

    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.

  • What skills will you learn with our Big Data Hadoop Certification Training?

    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

  • Who should take this Big Data Hadoop Training Course?

    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

  • What projects are included in this Big Data Hadoop Online Training Course?

    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.

  • Why you should go for Big Data Hadoop Online Training?

    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.

  • How will Big Data Training help your career?

    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

  • Why Learn Big Data and Hadoop?

    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.

  • What are the pre-requisites for this Hadoop Training Course?

    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.

  • How will I execute projects in this Hadoop training course?

    You will use Simplilearn’s CloudLab to complete projects.

  • What is CloudLab?

    CloudLab is a cloud-based Hadoop and Spark environment lab that Simplilearn offers 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 the Simplilearn LMS (Learning Management System) for the duration of the course. You can learn more about CloudLab by viewing our CloudLab video.

Course preview

    • Lesson 00 - Course Introduction 04:10
      • 0.1 Introduction04:10
    • Lesson 01 - Introduction to Big data and Hadoop Ecosystem 15:43
      • 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
    • Lesson 02 - HDFS and YARN 47:08
      • 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
    • Lesson 03 - MapReduce and Sqoop 57:00
      • 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
    • Lesson 04 - Basics of Hive and Impala 19:00
      • 4.1 Introduction04:07
      • 4.2 Pop Quiz
      • 4.3 Interacting with Hive and Impala14:07
      • 4.4 Quiz
      • 4.5 Key Takeaways00:46
    • Lesson 05 - Working with Hive and Impala 28:36
      • 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
    • Lesson 06 - Types of Data Formats 14:35
      • 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
    • Lesson 07 - Advanced Hive Concept and Data File Partitioning 17:00
      • 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
    • Lesson 08 - Apache Flume and HBase 28:06
      • 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
    • Lesson 09 - Pig 18:08
      • 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
    • Lesson 10 - Basics of Apache Spark 39:54
      • 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
    • Lesson 11 - RDDs in Spark 16:09
      • 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
    • Lesson 12 - Implementation of Spark Applications 13:54
      • 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
    • Lesson 13 - Spark Parallel Processing 08:40
      • 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
    • Lesson 14 - Spark RDD Optimization Techniques 14:23
      • 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
    • Lesson 15 - Spark Algorithm 27:09
      • 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
    • Lesson 16 - Spark SQL 13:21
      • 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
    • Projects
      • Project For Submission
      • Projects with solutions
    • Simulation Test Paper Instructions 00:20
      • Instructions00:20
    • Course Feedback
      • Course Feedback
    • Lesson 00 - Course introduction 01:35
      • 0.1 Course Introduction00:11
      • 0.2 Course Objectives00:20
      • 0.3 Course Overview00:18
      • 0.4 Target Audience00:17
      • 0.5 Prerequisites00:14
      • 0.6 Lessons Covered00:08
      • 0.7 Conclusion00:07
    • Lesson 01 - Big Data Overview 18:21
      • 1.1 Lesson 1—Big Data Overview00:08
      • 1.2 Objectives00:21
      • 1.3 Big Data—Introduction00:25
      • 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 Tools—Uses00:26
      • 1.25 Real-Time Big Data—Use Cases01:32
      • 1.26 Quiz
      • 1.27 Summary00:53
      • 1.28 Conclusion00:06
    • Lesson 02 - Introduction to Zookeeper 24:27
      • 2.1 Introduction to ZooKeeper00:10
      • 2.2 Objectives00:26
      • 2.3 ZooKeeper—Introduction00:30
      • 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 Demo—Install and Setup VM00:06
      • 2.19 Demo—Install 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
    • Lesson 03 - Introduction to Kafka 16:01
      • 3.1 Lesson 3 Introduction to Kafka00:09
      • 3.2 Objectives00:19
      • 3.3 Apache Kafka—Introduction00:23
      • 3.4 Kafka History00:30
      • 3.5 Kafka Use Cases00:48
      • 3.6 Aggregating User Activity Using Kafka—Example00:43
      • 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 System—Example00:37
      • 3.16 Publish-Subscribe System—Example00:34
      • 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
    • Lesson 04 - Installation and Configuration 08:53
      • 4.1 Lesson 4—Installation and Configuration00:10
      • 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 1—Kafka Installation and Configuration00:05
      • 4.8 Demo 1—Kafka Installation and Configuration00:05
      • 4.9 Demo 2—Creating and Sending Messages00:05
      • 4.10 Demo 2—Creating and Sending Messages00:05
      • 4.11 Stop the Kafka Server00:40
      • 4.12 Setting up Multi-Node Kafka Cluster—Step 100:24
      • 4.13 Setting up Multi-Node Kafka Cluster—Step 200:59
      • 4.14 Setting up Multi-Node Kafka Cluster—Step 301:04
      • 4.15 Setting up Multi-Node Kafka Cluster—Step 400:36
      • 4.16 Setting up Multi-Node Kafka Cluster—Step 500:29
      • 4.17 Setting up Multi-Node Kafka Cluster—Step 601:08
      • 4.18 Quiz
      • 4.19 Summary00:33
      • 4.20 Conclusion00:07
    • Lesson 05 - Kafka Interfaces 18:17
      • 5.1 Lesson 5—Kafka Interfaces00:09
      • 5.2 Objectives00:18
      • 5.3 Kafka Interfaces—Introduction00:21
      • 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 Message—Example 101:01
      • 5.10 Creating a Message—Example 200:39
      • 5.11 Reading a Message00:21
      • 5.12 kafka-console-consumer.sh Options01:32
      • 5.13 Reading a Message—Example00:44
      • 5.14 Java Interface to Kafka00:18
      • 5.15 Producer Side API00:42
      • 5.16 Producer Side API Example—Step 100:32
      • 5.17 Producer Side API Example—Step 200:15
      • 5.18 Producer Side API Example—Step 300:21
      • 5.19 Producer Side API Example—Step 400:21
      • 5.20 Producer Side API Example—Step 500:17
      • 5.21 Consumer Side API00:37
      • 5.22 Consumer Side API Example—Step 100:21
      • 5.23 Consumer Side API Example—Step 200:15
      • 5.24 Consumer Side API Example—Step 300:20
      • 5.25 Consumer Side API Example—Step 400:25
      • 5.26 Consumer Side API Example—Step 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 1—Tasks00:05
      • 5.31 Exercise 1—Tasks (contd.)00:05
      • 5.32 Exercise 1—Solutions00:05
      • 5.33 Exercise 1—Solutions (contd.)00:05
      • 5.34 Exercise 1—Solutions (contd.)00:05
      • 5.35 Exercise 2—Tasks00:05
      • 5.36 Exercise 2—Tasks (contd.)00:05
      • 5.37 Exercise 2—Solutions00:05
      • 5.38 Exercise 2—Solutions (contd.)00:05
      • 5.39 Exercise 2—Solutions (contd.)00:05
      • 5.40 Exercise 2—Solutions (contd.)00:05
      • 5.41 Exercise 2—Solutions (contd.)00:05
      • 5.42 Quiz
      • 5.43 Summary00:30
      • 5.44 Thank You00:08
    • Lesson 01 - Java Introduction 1:18:27
      • 1.1 Introduction to Java25:37
      • 1.2 Features of Java811:41
      • 1.3 Object Oriented Programming (OOP)23:00
      • 1.4 Fundamentals of Java18:09
      • Quiz
    • Lesson 02 - Working with Java Variables 36:00
      • 2.1 Declaring and Initializing Variables11:47
      • 2.2 Primitive Data Types06:50
      • 2.3 Read and Write Java Object Fields10:27
      • 2.4 Object Lifecycle06:56
      • Quiz
    • Lesson 03 - Java Operators and Decision Constructs 15:01
      • 3.1 Java Operators and Decision Constructs15:01
      • Quiz
    • Lesson 04 - Using Loop Constructs in Java 17:42
      • 4.1 Using Loop Constructs in Java17:42
      • Quiz
    • Lesson 05 - Creating and Using Array 36:16
      • 5.1 Creating and Using One-dimensional Array26:53
      • 5.2 Creating and Using Multi-dimensional Array09:23
      • Quiz
    • Lesson 06 - Methods and Encapsulation 35:55
      • 6.1 Java Method04:36
      • 6.2 Static and Final Keyword15:16
      • 6.3 Constructors and Access Modifiers in Java07:04
      • 6.4 Encapsulation08:59
      • Quiz
    • Lesson 07 - Inheritance 40:32
      • 7.1 Polymorphism Casting and Super23:46
      • 7.2 Abstract Class and Interfaces16:46
      • Quiz
    • Lesson 08 - Exception Handling 36:17
      • 8.1 Types of Exceptions and Try-catch Statement18:48
      • 8.2 Throws Statement and Finally Block11:27
      • 8.3 Exception Classes06:02
      • Quiz
    • Lesson 09 - Work with Selected classes from the Java API 1:01:06
      • 9.1 String28:16
      • 9.2 Working with StringBuffer05:44
      • 9.3 Create and Manipulate Calendar Data13:03
      • 9.4 Declare and Use of Arraylist14:03
      • Quiz
    • Lesson 10 - Additional Topics 45:03
      • 10.1 Inner classes Inner Interfaces and Thread16:51
      • 10.2 Collection Framework05:05
      • 10.3 Comparable Comparator and Iterator10:19
      • 10.4 File Handling and Serialization12:48
      • Quiz
    • Lesson 11 - JDBC 47:54
      • 11.1 JDBC and its Architecture08:50
      • 11.2 Drivers in JDBC03:09
      • 11.3 JDBC API and Examples24:44
      • 11.4 Transaction Management in JDBC11:11
      • Quiz
    • Lesson 12 - Miscellaneous and Unit Testing 19:24
      • 12.1 Unit Testing19:24
      • Quiz
    • Lesson 13 - Introduction to Java 8 18:53
      • 13.1 Introduction to Java 818:53
      • Quiz
    • Lesson 14 - Lambda Expression 14:39
      • 14.1 Lambda Expression14:39
      • Quiz
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Exam & certification FREE PRACTICE TEST

  • What do I need to do to unlock my Simplilearn certificate?

    Online 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%

Reviews

Malek ِAl Haddad

Simplilearn is an excellent online platform for online trainings with flexible hours of training and well-planned course content with great depth and case studies. The most interesting part which differentiates Simplilearn from other online vendors is the quality of the customer service - 24 / 7. I would strongly recommend Simplilearn to all who are looking for a change in career. Enroll for the courses and get the experience to become a professional.

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Ludovick Jacob

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

Puviarasan Sivanantham

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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Richard Kershner

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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Aaron Whigham

Very knowledgeable trainer, appreciate the time slot as well… Loved everything so far. I am very excited…

Rudolf Schier

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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Kinshuk Srivastava

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

Priyanka Garg

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

Peter Dao

The content is well designed and the instructor was excellent.

Anil Prakash Singh

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

Dipto Mukherjee

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

Amit Mutreja

This course has provided me both theoretical and practical knowledge.

Soumya Mukhopadhyay

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

Gaurav Pandya

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

Nagesh SS

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

FAQs

  • What are the System Requirements?

    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.

  • Who are the trainers?

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

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

    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.

  • 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.

  • Are there any group discounts for classroom training programs?

    Yes, we have group discount options for our 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 can provide 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. Whom 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.

  • Who are our faculties and how are they selected?

    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.

  • What is Global Teaching Assistance?

    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.

  • What is covered under the 24/7 Support promise?

    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.

  • If I am not from a Programming Background but have a basic knowledge of Programming, can I still learn Hadoop?

    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.

  • Can I switch from Self-Paced Training To Online Instructor-Led Training?

    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.

  • What if I miss a class?

    • Simplilearn has 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, Simplilearn gives you access to as many as 15 sessions for 90 days

  • What are the other top Big Data Certification Courses Simplilearn is offering?

    Keeping up with the Big Data & Analytics boom, Simplilearn has tailored very comprehensive Big Data certification programs which ensures a complete development as a Big Data professional.

    Few of the courses offered around Big Data are:

    In addition to the above, Simpliearn has created Big Data Hadoop Architect Masters Program on Big Data which follows a curated learning path.

    Simplilearn also offers the following Masters program with respect to Data Science and Business Intelligence:

    Big Data Hadoop Certification Training in New York City, United States

    New York is considered the business capital of the world. New York is a hub for industries of various sectors. With the continuous economic growth in New York, most professionals can enjoy excellent career opportunities. Professional certification courses like an AWS course can help New Yorkers improve their career and enhance their quality of life. Apache Hadoop is a platform that allows you to process and analyze large datasets stored for analysis. After taking Simplilearn’s Hadoop course in New York City, you will be able to master real-time data processing using tools like Spark. Becoming an expert in Hadoop will enable you to fully understand data in order to be more intelligent about your business. Data Scientists have the potential to earn up to $140,000 in America and are in demand throughout the world. Simplilearn is powered by the Apache Software Foundation and offers a Hadoop certification course in New York City. If you’re interested in becoming a data scientist, Simplilearn’s learning delivery model combines the personal motivation of live virtual classroom instruction with the reinforcement of relevant practical projects. Our experts will ensure that you understand various aspects of the Hadoop Distributed File System (HDFS). If you ever have any questions about your training, the support of our 24/7 live teaching assistants is another benefit of learning with Simplilearn. Programmers interested in becoming a data scientist need to be proficient in Hadoop. Let Simplilearn help you make the most of your current or future data analysis career with an intensive training course for Hadoop certification.

    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.