• 1,1 Course Introduction
• 1.2 Learning Objectives
• 1.3 Introduction
• 1.4 Working of Java program
• 1.5 Object Oriented Programming
• 1.6 Install and Work with Eclipse
• 1.7 Basic Elements of Java
• 1.8 Unicode Characters
• 1.9 Variables
• 1.10 Data Types
• 1.11 Operators
• 1.12 Operator (Logical Operator)
• 1.13 Operators Precedence
• 1.14 Type Casting or Type Conversion
• 1.15 Conditional Statements
• 1.16 Conditional Statement (Nested if)
• 1.17 Loops
• 1.18 for vs while vs do while
• 1.19 Access Specifiers
• 1.20 Java Eleven
• 1.21 Null, this, and instance of Operators
• 1.22 Destructors
• 1.23 Code Refactoring
• 1.24 Garbage Collector
• 1.25 Static Code Analysis
• 1.26 String
• 1.27 Arrays Part One
• 1.28 Arrays Part Two
• 1.29 For – Each Loop
• 1.30 Method Overloading
• 1.31 Command Line Arguments
• 1.32 Parameter Passing Techniques
• 1.33 Types of Parameters
• 1.34 Variable Arguments
• 1.35 Initializer
• 1.36 Demo - Basic Java Program
• 1.37 Demo - Displaying Content
• 1.38 Demo - String Functions Program
• 1.39 Demo - Quiz Program
• 1.40 Demo - Student Record and Displaying by Registration Number Program
• 1.41 Summary

• 2.1 Learning Objectives
• 2.2 Packages in Java
• 2.3 Inheritance in Java
• 2.4 Object Type Casting in Java
• 2.5 Methоd Оverriding in Java
• 2.6 Lambda Expression in Java
• 2.7 Static Variables and Methods
• 2.8 Abstract Classes
• 2.9 Interface in Java
• 2.10 Jаvа Set Interfасe
• 2.11 Marker Interfaces in Java
• 2.12 Inner Class
• 2.13 Exception Handling in Java
• 2.14 Java Memory Management
• 2.15 Demo - Utility Packages Program
• 2.16 Demo - Bank Account Statement using Inheritance
• 2.17 Demo - House Architecture using Polymorphism Program
• 2.18 Demo - Creating Errors and Catching the Exception Program
• 2.19 Summary

• 3.1 Learning Objectives
• 3.2 Multithreading
• 3.3 Introduction to Threads
• 3.4 Thread Life Cycle
• 3.5 Thread Priority
• 3.6 Deamon Thread in Java
• 3.7 Thread Scheduling and Sleeping
• 3.8 Thread Synchronization
• 3.9 Wrapper Classes
• 3.10 Autoboxing and Unboxing
• 3.11 java.util and java.lang Classes
• 3.12 java.lang - String Class
• 3.13 java.util - StringBuilder and StringTokenizer Class
• 3.14 java.lang - Math Class
• 3.15 java.util - Locale Class
• 3.16 Jаvа Generics
• 3.17 Collections Framework in Java
• 3.18 Set Interface in Collection
• 3.19 Hashcode() in Collection
• 3.20 List in Collections
• 3.21 Queue in Collections
• 3.22 Соmраrаtоr Interfасe in Collections
• 3.23 Deque in Collections
• 3.24 Map in Collections
• 3.25 For - Each Method in Java
• 3.26 Differentiate Collections and Array Class
• 3.27 Input or Output Stream
• 3.28 Java.io.file Class
• 3.29 Byte Stream Hierarchy
• 3.30 CharacterStream Classes
• 3.31 Serialization
• 3.32 JUnit 
• 3.33 Logger - log4j
• 3.34 Demo - Creating and Sorting Students Regno using Arrays
• 3.35 Demo - Stack Queue and Linked List Programs
• 3.36 Demo - Multithreading Program
• 3.37 Summary

• 4.1 Learning Objectives
• 4.2 Java Debugging Techniques
• 4.3 Tracing and Logging Analysis
• 4.4 Log Levels and Log Analysis
• 4.5 Stack Trace
• 4.6 Logging using log4j
• 4.7 Best Practices of log4j Part - One
• 4.8 Best Practices of log4j Part - Two
• 4.9 log4j Levels
• 4.10 Eclipse Debugging Support
• 4.11 Setting Breаkроints
• 4.12 Stepping Through or Variable Inspection
• 4.13 Demo - Analysis of Reports with Logging
• 4.14 Summary

• 5.1 Learning Objectives
• 5.2 Introduction
• 5.3 Unit Testing
• 5.4 JUnit Test Framework
• 5.5 JUnit Test Framework - Annotations
• 5.6 JUnit Test Framework - Assert Class
• 5.7 JUnit Test Framework - Test Suite
• 5.8 JUnit Test Framework - Exceptions Test
• 5.9 Demo - Generating Report using JUnit
• 5.10 Demo - Testing Student Mark System with JUnit
• 5.11 Summary

• 6.1 Learning Objectives
• 6.2 Cryptography
• 6.3 Two Types of Authenticators
• 6.4 CHACHA20 Stream Cipher and Poly1305 Authenticator
• 6.5 Example Program
• 6.6 Demo - Cryptographic Program
• 6.7 Summary

• 7.1 Learning Objectives
• 7.2 Introduction of Design Pattern
• 7.3 Types of Design Patterns
• 7.4 Creational Patterns
• 7.5 Fасtоry Method Раttern
• 7.6 Singletоn Design Раttern
• 7.7 Builder Pattern
• 7.8 Struсturаl Раtterns
• 7.9 Adарter Раttern
• 7.10 Bridge Раttern
• 7.11 Fасаde Раttern
• 7.12 Flyweight Design Раttern
• 7.13 Behаviоrаl Design Раtterns
• 7.14 Strategy Design Pattern
• 7.15 Сhаin оf Resроnsibility Раttern
• 7.16 Command Design Pattern
• 7.17 Interрreter Design Раttern
• 7.18 Iterаtоr Design Раttern
• 7.19 Mediаtоr Design Pаttern
• 7.20 Memento Design Раttern
• 7.21 Null Object Design Pattern
• 7.22 Observer Design Pattern
• 7.23 State Design Pattern
• 7.24 Template Method Design Pattern
• 7.25 Visitor Design Pattern
• 7.26 JEE or J2EE Design Patterns
• 7.27 Demo - Loan Approval Process using One of Behavioural Design Pattern
• 7.28 Demo - Creating Family of Objects using Factory Design Pattern
• 7.29 Demo - State Design Pattern Program
• 7.30 Summary

• 1.1 Course Introduction

• 2.1 Introduction
• 2.2 Linux
• 2.3 Linux vs. Windows
• 2.4 Linux vs Unix
• 2.5 Open Source
• 2.6 Multiple Distributions of Linux
• 2.7 Key Takeaways
• Knowledge Check
• Exploration of Operating System

• 3.1 Introduction
• 3.2 Ubuntu Distribution
• 3.3 Ubuntu Installation
• 3.4 Ubuntu Login
• 3.5 Terminal and Console
• 3.6 Kernel Architecture
• 3.7 Key Takeaways
• Knowledge Check
• Installation of Ubuntu

• 4.1 Introduction
• 4.2 Gnome Desktop Interface
• 4.3 Firefox Web Browser
• 4.4 Home Folder
• 4.5 LibreOffice Writer
• 4.6 Ubuntu Software Center
• 4.7 System Settings
• 4.8 Workspaces
• 4.9 Network Manager
• 4.10 Key Takeaways
• Knowledge Check
• Exploration of the Gnome Desktop and Customization of Display

• 5.1 Introduction
• 5.2 File System Organization
• 5.3 Important Directories and Their Functions
• 5.4 Mount and Unmount
• 5.5 Configuration Files in Linux (Ubuntu)
• 5.6 Permissions for Files and Directories
• 5.7 User Administration
• 5.8 Key Takeaways
• Knowledge Check
• Navigation through File Systems

• 6.1 Introduction
• 6.2 Starting Up the Terminal
• 6.3 Running Commands as Superuser
• 6.4 Finding Help
• 6.5 Manual Sections
• 6.6 Manual Captions
• 6.7 Man K Command
• 6.8 Find Command
• 6.9 Moving Around the File System
• 6.10 Manipulating Files and Folders
• 6.11 Creating Files and Directories
• 6.12 Copying Files and Directories
• 6.13 Renaming Files and Directories
• 6.14 Moving Files and Directories
• 6.15 Removing Files and Directories
• 6.16 System Information Commands
• 6.17 Free Command
• 6.18 Top Command
• 6.19 Uname Command
• 6.20 Lsb Release Command
• 6.21 IP Command
• 6.22 Lspci Command
• 6.23 Lsusb Command
• 6.24 Key Takeaways
• Knowledge Check
• Exploration of Manual Pages

• 7.1 Introduction
• 7.2 Introduction to vi Editor
• 7.3 Create Files Using vi Editor
• 7.4 Copy and Cut Data
• 7.5 Apply File Operations Using vi Editor
• 7.6 Search Word and Character
• 7.7 Jump and Join Line
• 7.8 grep and egrep Command
• 7.9 Key Takeaways
• Knowledge Check
• Copy and Search Data

• 8.1 Introduction
• 8.2 Repository
• 8.3 Repository Access
• 8.4 Introduction to apt get Command
• 8.5 Update vs. Upgrade
• 8.6 Introduction to PPA
• 8.7 Key Takeaways
• Knowledge Check
• Check for Updates

• Ubuntu Installation

Big data refers to a collection of extensive data sets, including structured, unstructured, and semi-structured data coming from various data sources and having different formats. These data sets are so complex and broad that they can't be processed using traditional techniques. When you combine big data with analytics, you can use it to solve business problems and make better decisions. 

Hadoop is an open-source framework that allows organizations to store and process big data in a parallel and distributed environment. It is used to store and combine data, and it scales up from one server to thousands of machines, each offering low-cost storage and local computation.

Spark is an open-source framework that provides several interconnected platforms, systems, and standards for big data projects. Spark is considered by many to be a more advanced product than Hadoop.

There are basically three concepts associated with Big Data - Volume, Variety, and Velocity. The volume refers to the amount of data we generate which is over 2.5 quintillion bytes per day, much larger than what we generated a decade ago. Velocity refers to the speed with which we receive data, be it real-time or in batches. Variety refers to the different formats of data like images, text, or videos.

Hadoop is one of the leading technological frameworks being widely used to leverage big data in an organization. Taking your first step toward big data is really challenging. Therefore, we believe it’s important to learn the basics about the technology before you pursue your certification. Simplilearn provides free resource articles, tutorials, and YouTube videos to help you to understand the Hadoop ecosystem and cover your basics. Our extensive course on Big Data Hadoop certification training will get you started with big data.

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, Simplilearn’s Big Data Hadoop training and course materials are very much effective and will help you pass the CCA175 Hadoop certification exam.

Online classroom training for the Big Data Hadoop certification course is conducted via online live streaming of each class. The classes are conducted by a Big Data Hadoop certified trainer with more than 15 years of work and training experience.

If you enroll for self-paced e-learning, you will have access to pre-recorded videos. If you enroll for the online classroom Flexi Pass, you will have access to live Big Data Hadoop training conducted online as well as the pre-recorded videos.

All of our highly qualified Hadoop certification trainers are industry Big Data 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.

You can enroll for this Big Data Hadoop certification training on our website and make an online payment using any of the following options:

• Visa Credit or Debit Card
• MasterCard
• American Express
• Diner’s Club
• PayPal

Once payment is received you will automatically receive a payment receipt and access information via email.

The tools you’ll need to attend Big Data Hadoop 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 instructions 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.

We offer this training in the following modes:

• Live Virtual Classroom or Online Classroom: Attend the Big Data 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 enrollment 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 Simplilearn website, or select the Live Chat link. Our customer service representatives can provide more details.

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.

You can either enroll in our Big Data Engineer certification training or if you are looking to get the University certificate, you can enroll in the Post Graduate Program in Data Engineering.

Our Big Data Hadoop certification training course allows you to learn Hadoop's frameworks, Big data tools, and technologies for your career as a big data developer. The course completion certification from Simplilearn will validate your new big data and on-the-job expertise. The Hadoop certification trains you on Hadoop Ecosystem tools such as HDFS, MapReduce, Flume, Kafka, Hive, HBase, and many more to be a Data Engineering expert.

Hadoop is an open-source software environment that stores data and runs on commodity hardware clusters. It offers a large amount of storage, a huge processing capacity, and the ability to conduct nearly unlimited concurrent tasks or jobs. Hadoop course is meant to make you a certified big data practitioner by offering you extensive practical training in the Hadoop Ecosystem.

No, Big Data Hadoop isn't difficult to learn. Apache Hadoop is a significant ecosystem with several technologies ranging from Apache Hive to Hbase, MapReduce, HDFS, and Apache Pig. So you should know these technologies to understand Hadoop. Use the integrated lab to carry out real-life, business-based projects with Simplilearn's hands-on Hadoop course.

ReactJS developers are open to high demand and even diversified jobs, such as UI engineers, full-stack developers, or any web development domain. Get mastery of React and earn React certification to become a successful Web Developer to remain at the top of the competition.

Hadoop is the leading technological framework used by a company for leveraging big data. It is incredibly challenging to take your first step towards big data. Therefore, before you obtain your certification, it is vital to grasp the basics of technology. To help you understand the Hadoop environment and cover your essential information, Simplilearn offers free resource articles, tutorials, and YouTube video clipboards. You will get started with big data from our extensive Big Data Hadoop training program.

There is a need for Hadoop skills - this is evident! There is now an urgent need for IT professionals to stay up with Hadoop and Big Data technologies. Our Hadoop training gives you the means to boost your profession and offers you the following benefits:

• Accelerated career progress
• Increased pay package because of Hadoop skill

In Big Data, you will also discover numerous profiles to build on your career in distinct Big Data profiles, like Hadoop Developer, Hadoop Admin, Hadoop Architect, and Big Data Analyst, along with their tasks and responsibilities, skills, and experience. Hadoop certification will help you land in these roles for a promising career.

Hadoop developers are responsible for the development and coding of applications. Hadoop is an open-source environment for managing and storing big data systems applications running within-cluster systems. A Hadoop developer essentially designs programs to manage and maintain big data for a firm. The Hadoop certification provides you with detailed knowledge of Hadoop and Spark's Big Data infrastructure.

Professionals enrolling for Hadoop certification training should have a basic knowledge of Core Java and SQL. Simplilearn offers a self-paced course of Java essentials for Hadoop in the course curriculum if you want to boost your Core Java skills.

Not only are Hadoop jobs offered by IT companies, but various sorts of companies use highly paid Hadoop candidates, including financial firms, retail, bank, and healthcare. The Hadoop course can help you carve out your career in the big data business and take top Hadoop jobs.

Top firms, namely Oracle, Cisco, Apple, Google, EMC Corporation, IBM, Facebook, Hortonworks, and Microsoft, have several Hadoop job titles with various positions in almost all cities of India. With Hadoop certification, the candidates are validated with high-level knowledge, skills, and an in-depth understanding of Hadoop tools and concepts.

Joining Hadoop training is a quick resource to learn Hadoop. You can ensure that you get in no time what is required and the basics of powerful Hadoop technology. The second-best approach to learn Hadoop is to understand the most fantastic books, and here are some books to get started.

• Hadoop Beginner's Guide (by Garry Turkington)
• Hadoop, the Definitive Guide - 3rd edition (by Tom White)
• Hadoop for Dummies (by Dirk Deroos)
• Big Data and Analytics (by Seema Acharya & Subhashini Chellappan)
• Hadoop In Action (by Chuck Lan)

Coming to the big data analytics salary, in most locations and nations, big data specialists' pay and compensation trends are improving continually over and above the profiles of other software engineering industries. Suppose you want a big leap in your career. In that case, this is the most significant moment to gain Hadoop certification to master big data skills. The average median salary of Big data Hadoop professionals across the world as per PayScale are:

• India: 900k
• US: $87,321
• Canada: C$93k
• UK: £50k
• Singapore: S$81k