Big Data Hadoop Certification Training
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Simplilearn Americas LLC
Course Summary
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.
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Course Description
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:Introduction to Big Data and Hadoop
Big Data and Hadoop Administrator Certification Training
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:Data Scientist
Business Analytics Expert
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Course Syllabus
Course preview
Big Data Hadoop and Spark Developers
Lesson 00 - Course Introduction 04:10
0.1 Introduction 04:10
Lesson 01 - Introduction to Big data and Hadoop Ecosystem 15:43
1.1 Introduction 00:38
1.2 Overview to Big Data and Hadoop 05:13
1.3 Pop Quiz
1.4 Hadoop Ecosystem 08:57
1.5 Quiz
1.6 Key Takeaways 00:55
Lesson 02 - HDFS and YARN 47:08
2.1 Introduction 06:10
2.2 HDFS Architecture and Components 08:59
2.3 Pop Quiz
2.4 Block Replication Architecture 09:53
2.5 YARN Introduction 21:25
2.6 Quiz
2.7 Key Takeaways 00:41
2.8 Hands-on Exercise
Lesson 03 - MapReduce and Sqoop 57:00
3.1 Introduction 00:41
3.2 Why Mapreduce 11:57
3.3 Small Data and Big Data 15:53
3.4 Pop Quiz
3.5 Data Types in Hadoop 04:23
3.6 Joins in MapReduce 04:43
3.7 What is Sqoop 18:21
3.8 Quiz
3.9 Key Takeaways 01:02
3.10 Hands-on Exercise
Lesson 04 - Basics of Hive and Impala 19:00
4.1 Introduction 04:07
4.2 Pop Quiz
4.3 Interacting with Hive and Impala 14:07
4.4 Quiz
4.5 Key Takeaways 00:46
Lesson 05 - Working with Hive and Impala 28:36
5.1 Working with Hive and Impala 07:08
5.2 Pop Quiz
5.3 Data Types in Hive 07:47
5.4 Validation of Data 07:47
5.5 What is Hcatalog and Its Uses 05:25
5.6 Quiz
5.7 Key Takeaways 00:29
5.8 Hands-on Exercise
Lesson 06 - Types of Data Formats 14:35
6.1 Introduction 00:44
6.2 Types of File Format 02:35
6.3 Pop Quiz
6.4 Data Serialization 03:11
6.5 Importing MySql and Creating hivetb 04:32
6.6 Parquet With Sqoop 02:37
6.7 Quiz
6.8 Key Takeaways 00:56
6.9 Hands-on Exercise
Lesson 07 - Advanced Hive Concept and Data File Partitioning 17:00
7.1 Introduction 07:41
7.2 Pop Quiz
7.3 Overview of the Hive Query Language 08:18
7.4 Quiz
7.5 Key Takeaways 01:01
7.6 Hands-on Exercise
Lesson 08 - Apache Flume and HBase 28:06
8.1 Introduction 12:29
8.2 Pop Quiz
8.3 Introduction to HBase 14:40
8.4 Quiz
8.5 Key Takeaways 00:57
8.6 Hands-on Exercise
Lesson 09 - Pig 18:08
9.1 Introduction 10:45
9.2 Pop Quiz
9.3 Getting Datasets for Pig Development 06:45
9.4 Quiz
9.5 Key Takeaways 00:38
9.6 Hands-on Exercise
Lesson 10 - Basics of Apache Spark 39:54
10.1 Introduction 16:04
10.2 Spark - Architecture, Execution, and Related Concepts 07:10
10.3 Pop Quiz
10.4 RDD Operations 10:39
10.5 Functional Programming in Spark 05:34
10.6 Quiz
10.7 Key Takeaways 00:27
10.8 Hands-on Exercise
Lesson 11 - RDDs in Spark 16:09
11.1 Introduction 00:46
11.2 RDD Data Types and RDD Creation 10:14
11.3 Pop Quiz
11.4 Operations in RDDs 04:35
11.5 Quiz
11.6 Key Takeaways 00:34
11.7 Hands-on Exercise
Lesson 12 - Implementation of Spark Applications 13:54
12.1 Introduction 03:57
12.2 Running Spark on YARN 01:27
12.3 Pop Quiz
12.4 Running a Spark Application 01:47
12.5 Dynamic Resource Allocation 01:06
12.6 Configuring Your Spark Application 04:24
12.7 Quiz
12.8 Key Takeaways 01:13
Lesson 13 - Spark Parallel Processing 08:40
13.1 Introduction 05:41
13.2 Pop Quiz
13.3 Parallel Operations on Partitions 02:28
13.4 Quiz
13.5 Key Takeaways 00:31
13.6 Hands-on Exercise
Lesson 14 - Spark RDD Optimization Techniques 14:23
14.1 Introduction 04:40
14.2 Pop Quiz
14.3 RDD Persistence 08:59
14.4 Quiz
14.5 Key Takeaways 00:44
14.6 Hands-on Exercise
Lesson 15 - Spark Algorithm 27:09
15.1 Introduction 00:49
15.2 Spark: An Iterative Algorithm 03:13
15.3 Introduction To Graph Parallel System 02:34
15.4 Pop Quiz
15.5 Introduction To Machine Learning 10:27
15.6 Introduction To Three C's 08:07
15.7 Quiz
15.8 Key Takeaways 01:59
What’s next? 05:28
The Next Step 05:28
Lesson 16 - Spark SQL 13:21
16.1 Introduction 06:36
16.2 Pop Quiz
16.3 Interoperating with RDDs 06:08
16.4 Quiz
16.5 Key Takeaways 00:37
16.6 Hands-on Exercise
Projects
Project For Submission
Projects with solutions
Simulation Test Paper Instructions 00:20
Instructions 00:20
Course Feedback
Course Feedback
Free Course Apache Kafka
Lesson 00 - Course introduction 01:35
0.1 Course Introduction 00:11
0.2 Course Objectives 00:20
0.3 Course Overview 00:18
0.4 Target Audience 00:17
0.5 Prerequisites 00:14
0.6 Lessons Covered 00:08
0.7 Conclusion 00:07
Lesson 01 - Big Data Overview 18:21
1.1 Lesson 1—Big Data Overview 00:08
1.2 Objectives 00:21
1.3 Big Data—Introduction 00:25
1.4 The Three Vs of Big Data 00:14
1.5 Data Volume 00:34
1.6 Data Sizes 00:28
1.7 Data Velocity 00:49
1.8 Data Variety 00:38
1.9 Data Evolution 00:54
1.10 Features of Big data 00:50
1.11 Industry Examples 01:42
1.12 Big Data Analysis 00:39
1.13 Technology Comparison 01:05
1.14 Stream 00:50
1.15 Apache Hadoop 00:55
1.16 Hadoop Distributed File System 00:58
1.17 MapReduce 00:43
1.18 Real-Time Big Data Tools 00:13
1.19 Apache Kafka 00:19
1.20 Apache Storm 00:26
1.21 Apache Spark 00:56
1.22 Apache Cassandra 00:55
1.23 Apache Hbase 00:22
1.24 Real-Time Big Data Tools—Uses 00:26
1.25 Real-Time Big Data—Use Cases 01:32
1.26 Quiz
1.27 Summary 00:53
1.28 Conclusion 00:06
Lesson 02 - Introduction to Zookeeper 24:27
2.1 Introduction to ZooKeeper 00:10
2.2 Objectives 00:26
2.3 ZooKeeper—Introduction 00:30
2.4 Distributed Applications 01:06
2.5 Challenges of Distributed Applications 00:17
2.6 Partial Failures 00:41
2.7 Race Conditions 00:40
2.8 Deadlocks 00:41
2.9 Inconsistencies 00:48
2.10 ZooKeeper Characteristics 00:53
2.11 ZooKeeper Data Model 00:42
2.12 Types of Znodes 00:38
2.13 Sequential Znodes 00:32
2.14 VMware 00:29
2.15 Simplilearn Virtual Machine 00:23
2.16 PuTTY 00:22
2.17 WinSCP 00:19
2.18 Demo—Install and Setup VM 00:06
2.19 Demo—Install and Setup VM 08:12
2.20 ZooKeeper Installation 00:20
2.21 ZooKeeper Configuration 00:18
2.22 ZooKeeper Command Line Interface 00:27
2.23 ZooKeeper Command Line Interface Commands 01:07
2.24 ZooKeeper Client APIs 00:30
2.25 ZooKeeper Recipe 1: Handling Partial Failures 00:58
2.26 ZooKeeper Recipe 2: Leader Election 02:09
2.27 Quiz
2.28 Summary 00:35
2.29 Conclusion 00:08
Lesson 03 - Introduction to Kafka 16:01
3.1 Lesson 3 Introduction to Kafka 00:09
3.2 Objectives 00:19
3.3 Apache Kafka—Introduction 00:23
3.4 Kafka History 00:30
3.5 Kafka Use Cases 00:48
3.6 Aggregating User Activity Using Kafka—Example 00:43
3.7 Kafka Data Model 01:27
3.8 Topics 01:15
3.9 Partitions 00:36
3.10 Partition Distribution 00:48
3.11 Producers 00:48
3.12 Consumers 00:46
3.13 Kafka Architecture 01:10
3.14 Types of Messaging Systems 00:42
3.15 Queue System—Example 00:37
3.16 Publish-Subscribe System—Example 00:34
3.17 Brokers 00:24
3.18 Kafka Guarantees 00:58
3.19 Kafka at LinkedIn 00:54
3.20 Replication in Kafka 00:44
3.21 Persistence in Kafka 00:41
3.22 Quiz
3.23 Summary 00:38
3.24 Conclusion 00:07
Lesson 04 - Installation and Configuration 08:53
4.1 Lesson 4—Installation and Configuration 00:10
4.2 Objectives 00:22
4.3 Kafka Versions 00:49
4.4 OS Selection 00:19
4.5 Machine Selection 00:34
4.6 Preparing for Installation 00:19
4.7 Demo 1—Kafka Installation and Configuration 00:05
4.8 Demo 1—Kafka Installation and Configuration 00:05
4.9 Demo 2—Creating and Sending Messages 00:05
4.10 Demo 2—Creating and Sending Messages 00:05
4.11 Stop the Kafka Server 00:40
4.12 Setting up Multi-Node Kafka Cluster—Step 1 00:24
4.13 Setting up Multi-Node Kafka Cluster—Step 2 00:59
4.14 Setting up Multi-Node Kafka Cluster—Step 3 01:04
4.15 Setting up Multi-Node Kafka Cluster—Step 4 00:36
4.16 Setting up Multi-Node Kafka Cluster—Step 5 00:29
4.17 Setting up Multi-Node Kafka Cluster—Step 6 01:08
4.18 Quiz
4.19 Summary 00:33
4.20 Conclusion 00:07
Lesson 05 - Kafka Interfaces 18:17
5.1 Lesson 5—Kafka Interfaces 00:09
5.2 Objectives 00:18
5.3 Kafka Interfaces—Introduction 00:21
5.4 Creating a Topic 01:23
5.5 Modifying a Topic 00:36
5.6 kafka-topics.sh Options 00:57
5.7 Creating a Message 00:15
5.8 kafka-console-producer.sh Options 01:48
5.9 Creating a Message—Example 1 01:01
5.10 Creating a Message—Example 2 00:39
5.11 Reading a Message 00:21
5.12 kafka-console-consumer.sh Options 01:32
5.13 Reading a Message—Example 00:44
5.14 Java Interface to Kafka 00:18
5.15 Producer Side API 00:42
5.16 Producer Side API Example—Step 1 00:32
5.17 Producer Side API Example—Step 2 00:15
5.18 Producer Side API Example—Step 3 00:21
5.19 Producer Side API Example—Step 4 00:21
5.20 Producer Side API Example—Step 5 00:17
5.21 Consumer Side API 00:37
5.22 Consumer Side API Example—Step 1 00:21
5.23 Consumer Side API Example—Step 2 00:15
5.24 Consumer Side API Example—Step 3 00:20
5.25 Consumer Side API Example—Step 4 00:25
5.26 Consumer Side API Example—Step 5 00:25
5.27 Compiling a Java Program 00:29
5.28 Running the Java Program 00:18
5.29 Java Interface Observations 00:39
5.30 Exercise 1—Tasks 00:05
5.31 Exercise 1—Tasks (contd.) 00:05
5.32 Exercise 1—Solutions 00:05
5.33 Exercise 1—Solutions (contd.) 00:05
5.34 Exercise 1—Solutions (contd.) 00:05
5.35 Exercise 2—Tasks 00:05
5.36 Exercise 2—Tasks (contd.) 00:05
5.37 Exercise 2—Solutions 00:05
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 Summary 00:30
5.44 Thank You 00:08
Free Course Java Essentials for Hadoop
Lesson 01 - Essentials of Java for Hadoop 31:10
1.1 Essentials of Java for Hadoop 00:19
1.2 Lesson Objectives 00:24
1.3 Java Definition 00:27
1.4 Java Virtual Machine (JVM) 00:34
1.5 Working of Java 01:01
1.6 Running a Basic Java Program 00:56
1.7 Running a Basic Java Program (contd.) 01:15
1.8 Running a Basic Java Program in NetBeans IDE 00:11
1.9 BASIC JAVA SYNTAX 00:12
1.10 Data Types in Java 00:26
1.11 Variables in Java 01:31
1.12 Naming Conventionsof Variables 01:21
1.13 Type Casting. 01:05
1.14 Operators 00:30
1.15 Mathematical Operators 00:28
1.16 Unary Operators. 00:15
1.17 Relational Operators 00:19
1.18 Logical or Conditional Operators 00:19
1.19 Bitwise Operators 01:21
1.20 Static Versus Non Static Variables 00:54
1.21 Static Versus Non Static Variables (contd.) 00:17
1.22 Statements and Blocks of Code 01:21
1.23 Flow Control 00:47
1.24 If Statement 00:40
1.25 Variants of if Statement 01:07
1.26 Nested If Statement 00:40
1.27 Switch Statement 00:36
1.28 Switch Statement (contd.) 00:34
1.29 Loop Statements 01:19
1.30 Loop Statements (contd.) 00:49
1.31 Break and Continue Statements 00:44
1.32 Basic Java Constructs 01:09
1.33 Arrays 01:16
1.34 Arrays (contd.) 01:07
1.35 JAVA CLASSES AND METHODS 00:09
1.36 Classes 00:46
1.37 Objects 01:21
1.38 Methods 01:01
1.39 Access Modifiers 00:49
1.40 Summary 00:41
1.41 Thank You 00:09
Lesson 02 - Java Constructors 21:31
2.1 Java Constructors 00:22
2.2 Objectives 00:42
2.3 Features of Java 01:08
2.4 Classes Objects and Constructors 01:19
2.5 Constructors 00:34
2.6 Constructor Overloading 01:08
2.7 Constructor Overloading (contd.) 00:28
2.8 PACKAGES 00:09
2.9 Definition of Packages 01:12
2.10 Advantages of Packages 00:29
2.11 Naming Conventions of Packages 00:28
2.12 INHERITANCE 00:09
2.13 Definition of Inheritance 01:07
2.14 Multilevel Inheritance 01:15
2.15 Hierarchical Inheritance 00:23
2.16 Method Overriding 00:55
2.17 Method Overriding(contd.) 00:35
2.18 Method Overriding(contd.) 00:15
2.19 ABSTRACT CLASSES 00:10
2.20 Definition of Abstract Classes 00:41
2.21 Usage of Abstract Classes 00:36
2.22 INTERFACES 00:08
2.23 Features of Interfaces 01:03
2.24 Syntax for Creating Interfaces 00:24
2.25 Implementing an Interface 00:23
2.26 Implementing an Interface(contd.) 00:13
2.27 INPUT AND OUTPUT 00:14
2.28 Features of Input and Output 00:49
2.29 System.in.read() Method 00:20
2.30 Reading Input from the Console 00:31
2.31 Stream Objects 00:21
2.32 String Tokenizer Class 00:43
2.33 Scanner Class 00:32
2.34 Writing Output to the Console 00:28
2.35 Summary 01:03
2.36 Thank You 00:14
Lesson 03 - Essential Classes and Exceptions in Java 28:37
3.1 Essential Classes and Exceptions in Java 00:18
3.2 Objectives 00:31
3.3 The Enums in Java 01:00
3.4 Program Using Enum 00:44
3.5 ArrayList 00:41
3.6 ArrayList Constructors 00:38
3.7 Methods of ArrayList 01:02
3.8 ArrayList Insertion 00:47
3.9 ArrayList Insertion (contd.) 00:38
3.10 Iterator 00:39
3.11 Iterator (contd.) 00:33
3.12 ListIterator 00:46
3.13 ListIterator (contd.) 01:00
3.14 Displaying Items Using ListIterator 00:32
3.15 For-Each Loop 00:35
3.16 For-Each Loop (contd.) 00:23
3.17 Enumeration 00:30
3.18 Enumeration (contd.) 00:25
3.19 HASHMAPS 00:15
3.20 Features of Hashmaps 00:56
3.21 Hashmap Constructors 01:36
3.22 Hashmap Methods 00:58
3.23 Hashmap Insertion 00:44
3.24 HASHTABLE CLASS 00:21
3.25 Hashtable Class an Constructors 01:25
3.26 Hashtable Methods 00:41
3.27 Hashtable Methods 00:48
3.28 Hashtable Insertion and Display 00:29
3.29 Hashtable Insertion and Display (contd.) 00:22
3.30 EXCEPTIONS 00:22
3.31 Exception Handling 01:06
3.32 Exception Classes 00:26
3.33 User-Defined Exceptions 01:04
3.34 Types of Exceptions 00:44
3.35 Exception Handling Mechanisms 00:54
3.36 Try-Catch Block 00:15
3.37 Multiple Catch Blocks 00:40
3.38 Throw Statement 00:33
3.39 Throw Statement (contd.) 00:25
3.40 User-Defined Exceptions 00:11
3.41 Advantages of Using Exceptions 00:25
3.42 Error Handling and finally block 00:30
3.43 Summary 00:41
3.44 Thank You 00:04
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