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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

    • As knowledge of Java is necessary for this course, we are providing a complimentary access to the “Java Essentials for Hadoop” course
    • For Spark, we use Python and Scala. An e-book is provided for support.
    • Knowledge of an operating system such as Linux is useful for this course

  • What is CloudLab?

    CloudLab is a cloud-based Hadoop and Spark environment lab that Simplilearn offers with the 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.


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

    The 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.

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 Options00:57
      • 5.7 Creating a Message00:15
      • 5.8 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 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 - Essentials of Java for Hadoop 31:10
      • 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
    • Lesson 02 - Java Constructors 21:31
      • 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 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
    • Lesson 03 - Essential Classes and Exceptions in Java 28:37
      • 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
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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%


To keep me updated with the new technologies and developments in IT world I joined the Big Data Hadoop and Spark Developer from Simplilearn. The course helped me to enhance my career from Tech Support to a Senior position with a salary hike.

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Simplilearn is one website where you can avail amazing courses which are specifically tailored for the industry by skilled people. I have personally taken the Big Data and Spark Developer course. I feel confident after knowing what tools and skills are actually used in the Big data production environment. The technical support staff is brilliant. It is definitely better than classroom teaching for specialized skills.

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I have recently completed the Big Data Hadoop and Spark Developer certification course from Simplilearn. The course was pretty informative and covered many useful topics in good depth. Besides the course content, the customer support from Simplilearn was one of the great features in my experience. My queries were resolved timely which really helped me in completing the course smoothly.

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The overall experience has been great with Simplilearn. The course content is very thorough and easy to understand. Online classes are held on a regular basis with the terrific instructor who makes it much easier for us to understand the concepts. Real time examples make the content relatable for even a layman. Faster query resolution is provided. They have support ticketing system through which you can create a support ticket for any kind of queries to get a quick reply. Live chat sessions are also there to get real-time help. The course videos are designed in a very interactive way and the duration of videos is minimal to keep you interested in the topic. The cloud lab feature is also great for people who are not able to install the software on their own machines. The cloud lab allows you to complete your assignments and practice work there itself. In-depth information is provided about each topic and other course materials are also provided to help with complete learning. All in all, I will suggest to try out the courses provided by Simplilearn as they are excellent according to different certifications which are great for the professional career.

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I had enrolled in Simplilearn for the BigData and Hadoop courses. I had taken similar courses in the market, also had been reading a lot on the subject. Must say that Simplilearn has exceeded its expectations. The course materials were very well organized and enhanced the learning experience. My overall experience with Simplilearn was excellent. I highly recommend Simplilearn. The support service provided by the team is impressive. I had a great and happy learning experience.

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The training was conducted well, the instructor explained the concepts from scratch, made us think beyond our imagination and this has made me more confident. I learnt different solutions used in Hadoop designing. I recommend the course.

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I am extremely happy to have Simplilearn as my online education provider. With outstanding education content materials, project mentoring sessions, Cloud labs access and excellent customer support I see Simplilearn as the topmost online education provider worldwide.

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Excellent learning center for management and technical trainings. Simplilearn's training would be the first step for a career change. Materials and online training videos are great - you can upgrade your skills without attending face to face session. Good work from the company. I have been attending the training from 2014. I feel Simplilearn is my place to upgrade my career.

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There is no question that the Big Data revolution sweeping through the world of business has made its impact on companies big and small. Multinational giants and startups alike are going out of their way to incorporate Big Data capabilities. Simplilearn’s Big Data and Hadoop Developer Certification Training offers you an opportunity to start your dream career in big data from scratch. Being a .Net developer and having little knowledge of Java, I enrolled in Flexi-pass program of Simplilearn’s Big Data and Hadoop Developer Course.This was my first experience of e-learning. The pre-recorded video tutorials have nice and clear video & voice clarity. The instructor’s accent is easy to understand and interpret. Difficult topics are explained using interesting real life examples. Support staff, back office, teaching staff are quite responsive and approachable.This course is very feasible to the beginners and the course contents are downloadable on the go. I recommend this course to all aspiring big data candidates. This is a good starting point for your journey. Apart from the quality of the offerings, which is exceptional, the price point of the course also makes it value-for-money.

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Simplilearn courses are well structured to meet the market requirements. They constantly update the content to make sure that the candidates keep up with the latest market trends. I have taken a bunch of courses from Simplilearn and have gained a lot from them.

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It's a great learning with SimpliLearn. I spent a lot of time on the internet, read many blogs to identify the best training platform, and then I found SimpliLearn. I got in touch with the SimpliLearn Support team and got the program I was looking for. I have completed my course and believe me, I have had a great experience, I learnt a new technology in a matter of few weeks. Trainers are really knowledgeable and have provided the best training sessions. I was able to attend different trainings conducted by different trainers as well. One of the best platforms to learn any new technology I must say.

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I enrolled for Big Data and Hadoop Developer Course and its amazing. Not just the content but the expertise of coaching and the perks offered like access to the cloudlab is impressive. Thanks Simplilearn for the incredible experience! I will definitely recommend Simplilearn for Data Science.

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Excellent elaboration on fsimage and edit logs, I have been trying to get a grasp of these topics from a long time. The trainer is very good in explaining the concepts through analogies.

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Real time hands-on experience! The concepts were delivered very neatly, clearly and in an understandable way.

Everything is perfect and awesome. Thank you. The content and the planning of the batches makes learning awesome.

Course advisor

Sina Jamshidi Big Data Lead at Bell Labs

Sina has over 10 years of experience in the technology field as a Big Data Architect at Bell Labs and a Platinum level trainer. Sina is a very passionate about building a Big Data education ecosystem and has been a contributor in a number of public and journal publications.


  • 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.

Big Data and Hadoop Developer Certification Training in Lucknow, India

Lucknow is the capital city of the Indian state of Uttar Pradesh. The economy of Lucknow is powered by manufacturing and trading industries. Companies like TCS, Singsys and Sahara call this city home and give people plenty of opportunities to succeed in their careers. Apache Hadoop is a platform that allows you to process and analyze large datasets stored for analysis. After taking Simplilearn’s Hadoop course in Lucknow, 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 about 19 lakhs a year in India and are in demand throughout the world. Simplilearn is powered by the Apache Software Foundation and offers a Hadoop certification course in Lucknow. 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.