Certified Hadoop Developer

Certified Hadoop Developer

Why should you take this Certification?

This certification will make you Internationally Certified and will help in growing your career.

This certification will help you to get Job & Freelance opportunities from thousands of companies.

Average salary given to a Certified Hadoop Professional is around $60,000 per annum.

Exam Cost: USD 30.00 5 out of 5 based on 5625 ratings.

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What Is Hadoop?

Apache Hadoop is a set of open-source software tools for solving problems involving large volumes of data and processing using a network of many computers. It's a MapReduce programming model-based software framework for distributed storage and processing of massive data. Hadoop was created with the intention of being used in computer clusters made of commodity hardware, which is still the case today. It has since been used on higher-end hardware clusters. All of Hadoop's modules are built on the idea that hardware failures would occur frequently and should be handled automatically by the framework.

Apache Hadoop is made up of two parts: a storage system called Hadoop Distributed File System and a processing system called MapReduce. Hadoop divides files into big chunks and distributes them among cluster nodes. The bundled code is subsequently transferred to nodes, which process the data in parallel. This method makes use of data locality, which allows nodes to manipulate the data they have access to. This allows the information to be processed more quickly and efficiently than in a more traditional supercomputer architecture that depends on a parallel file system and distributes computation and data over high-speed networking.

The following modules make up the Apache Hadoop framework's foundation:

Hadoop Common - includes libraries and utilities that are required by other Hadoop modules.

Hadoop Distributed File System (HDFS) — a distributed file system that stores data on commodity devices and provides extremely high aggregate bandwidth throughout the cluster;

Hadoop YARN is a platform for managing computing resources in clusters and using them to schedule programs for users.

Hadoop MapReduce is a large-scale data processing implementation of the MapReduce programming concept.

Hadoop Ozone is a Hadoop object store.

 

Salary Range of An Hadoop Professional

Depending on the experience level and the demographic area, the salary of a Hadoop Professional varies widely.

The following is the average Hadoop Professional Salary in USA:

Best Minds In Hadoop$100,000
Senior Hadoop Professionals$ 85,000
Intermediate Hadoop Professionals$ 65,000
Hadoop Freshers$ 50,000

 

The following is the average Hadoop Professional Salary in India:

Best Minds In HadoopINR 120,000
Senior Hadoop ProfessionalsINR 90,000
Intermediate Hadoop ProfessionalsINR 70,000
Hadoop FreshersINR 50,000

 

What Is Hadoop Certification?

Hadoop Certification assesses a person's knowledge of hadoop as well as their understanding of digital concepts. A variety of certifying authorities, ranging from government agencies to commercial enterprises and organisations, offer the Hadoop certification. Certifications are normally obtained by the completion of an online or offline exam.

All certificates have their own set of benefits, such as international recognition, career opportunities, freelancing, and so on. So, Hadoop certification is an online exam that evaluates a Professional's skills and knowledge in order to match them with the suitable opportunities.

 

Why should you take this Online Hadoop Certification?

The online Hadoop certification from Loopskill will assist you in becoming a certified Professional. You can take this exam and by scoring 70% you will become an internationally certified Hadoop Professional. This certification will help you in three different ways:

  • You can demonstrate your Hadoop certification to potential employers and can stand out of the crowd.
  • You can apply for great jobs using loopskill website or app; moreover, our partners companies will contact you directly for full-time or part-time opportunities depending on your skills & requirements.
  • Loopskill is not just a platform to get certified or to find full time jobs; here being a certified Professional you can also do freelancing for the clients around the globe. You will be approached by the clients who need your help in building some web based platform or some app based platform.

The loopskill’s online Hadoop certification is created to help people in exploring and achieving their full potential so they can get connected to the best opportunities around the globe.

 

Hadoop Application

1. The financial industry

Hadoop is used by financial institutions to detect and prevent fraud. Apache Hadoop is used to reduce risk, identify rogue traders, and analyze fraud tendencies. Hadoop assists them in fine-tuning their marketing efforts based on consumer segmentation.

2. Law Enforcement and Security

Hadoop is used by the US National Security Agency to prevent terrorist attacks and detect and prevent cyber-attacks. Police departments use Big Data techniques to apprehend criminals and even predict criminal activities. Hadoop is employed in a variety of government sectors, including defense, intelligence, research, and cybersecurity.

3. Companies use Hadoop to learn about their consumers' needs.

Hadoop's most essential use is deciphering customer requirements. Various businesses, such as finance and telecommunications, use Hadoop to determine a customer's needs by analyzing large amounts of data and extracting meaningful information from it. Organizations can enhance their sales by better understanding client behavior.

4. Applications of Hadoop in the Retail Industry

Hadoop is used by both online and offline retailers to increase sales. Hadoop is used by many e-commerce sites to keep track of the things that customers buy together. When a customer is seeking to buy one of the appropriate products from that group, they are given suggestions to buy the other product based on this. When a customer attempts to purchase a mobile phone, for example, it proposes that the customer purchase a mobile back cover and screen protection.

5. Customer data analysis in real time

Hadoop is capable of real-time analysis of client data. It can track clickstream data since it was designed to store and interpret large amounts of clickstream data. When a visitor visits a website, Hadoop can gather information like as the visitor's origin before arriving at the page, as well as the search performed to arrive at the website.

6. Applications of Hadoop in the Public Sector

Hadoop is used by the government to analyze massive volumes of data for the development of the country, states, and cities. They utilize Hadoop, for example, to manage traffic on city streets, to construct smart cities, and to improve municipal transportation.

7. Advertisement Targeting Platforms Using Hadoop

Hadoop is used by advertising targeting platforms to capture and analyze clickstream, video, transaction, and social media data. They evaluate data collected by many social media platforms such as Facebook, Twitter, Instagram, and others before focusing on their target audience.

8. Hadoop is used by businesses for sentiment analysis.

Hadoop is capable of capturing and analyzing sentiment data. Sentiment data are unstructured bits of information like attitudes, opinions, and emotions that are commonly found on social networking platforms, blogs, customer service exchanges, and online product evaluations, among other places.

9. Financial Trading and Forecasting Applications of Hadoop

Hadoop is also used in the trading industry. It features a number of complicated algorithms that monitor markets for trading opportunities using predetermined parameters and criteria. It can function without the need for human intervention. There is no need for a human to keep an eye on things. In high-frequency trading, Apache Hadoop is employed. The majority of trading choices are made solely by algorithms.

10. The use of Hadoop to improve personal quantification

Hadoop can be put to good use in one's personal life. It offers numerous ways to improve our everyday lives by tracking the entire daily routine, sleep pattern, and diet plan of healthy people.

11. Health-care-related industries

In the health-care industry, Hadoop plays a critical role in improving public health. It analyzes massive amounts of data from medical devices, test findings, doctor's notes, clinical data, imaging reports, and other sources to help health-care organizations treat patients more effectively. Hadoop deduces truths that can be employed in medicine to improve public health based on this analysis.

12. Improving the efficiency of machines

Hadoop is used to improve the performance of machines. Self-driving automobiles are being developed in mechanical disciplines. These self-driving automobiles have advanced sensors, GPS, cameras, and other features. There is no need for a driver in these self-driving cars. In this field, Apache Hadoop plays a critical role.

 

Important Topics to Learn & Master in Hadoop

INTRODUCTION TO BIG DATA

  • What is RDBMS?
  • What is Big Data?
  • Problems with the RDBMS and other existing systems
  • Requirement for the new approach
  • Solution to the problem with huge
  • Difference between relational databases and NoSQL type databases
  • Need of NoSQL type databases
  • Problems in processing of Big Data with the traditional systems
  • How to process and store Big Data?
  • Where to use Hadoop?

HADOOP BASIC CONCEPTS

  • What is Hadoop?
  • Why to use Hadoop?
  • Architecture of Hadoop
  • Difference between Hadoop 1.x and Hadoop 2.x
  • What is YARN?
  • Advantage of Hadoop 2.x over Hadoop 1.x
  • Use cases for using Hadoop
  • Components of Hadoop
  • Hadoop Distributed File System (HDFS)
  • Map Reduce

HADOOP DISTRIBUTED FILE SYSTEM

  • Components of HDFS
  • What was the need of HDFS?
  • Data Node, Name Node, Secondary name Node
  • High Availability and Fault Tolerance
  • Command Line interface
  • Data Ingestion
  • Hadoop Commands

HADOOP CLUSTER

  • Installation of Hadoop
  • Understanding the Configuration of Hadoop
  • Starting the Hadoop related Processes
  • Visualization of Hadoop in UI
  • Writing the files to the HDFS
  • Reading the files from the Hadoop Cluster
  • Work flow of the JoB

HBASE

  • What is HBASE?
  • Why HBASE is needed?
  • HBASE Architecture and Schema Design
  • Column Oriented and Row Oriented Databases
  • HBASE Vs RDBMS

MAP REDUCE PROGRAMMING

  • Overview of the Map Reduce
  • History of Map Reduce
  • Flow of Map Reduce
  • Working of Map Reduce with simple example
  • Difference Between Map phase and Reduce phase
  • Concept of Partition and Combiner phase in Map Reduce
  • Submission of a Map Reduce job in Hadoop cluster and it’s completion
  • File support in Hadoop
  • Achieving different goals using Map Reduce programs

SQOOP

  • What is Sqoop ?
  • Use Case for Sqoop?
  • Configuring Sqoop
  • Importing and Exporting Data using Sqoop
  • Importing data into Hive using Sqoop
  • Code Generation using sqoop
  • Using Map Reduce with the Sqoop

PIG

  • Introduction to Apache Pig
  • Architecture of Apache Pig
  • Why Pig?
  • RDBMS Vs Apache PIG
  • Loading data using PIG
  • Different Modes of execution of PIG Commands
  • PIG Vs Map Reduce coding
  • Diagnostic operations in Pig
  • Combining and Filtering Operations in Pig

FLUME

  • What is Flume?
  • Architecture of Flume
  • Why we need Flume?
  • Problem with traditional export method
  • Configuring Flume
  • Different Channels in Flume
  • Importing data using Flume
  • Using Map Reduce with the Flume

HIVE

  • Introduction to HIVE
  • Architecture of HIVE
  • Why HIVE?
  • RDBMS Vs HIVE
  • Introduction to HiveQL
  • Loading data using HIVE
  • HIVE Vs Map Reduce Coding
  • Different functions supported in HIVE
  • Partitioning, Bucketing in HIVE
  • Hive Built-In Operators and Functions
  • Why do we need Partitioning and Bucketing in HIVE?

MONGODB

  • What is MongoDB?
  • Difference between MongoDB and RDBMS
  • Advantages of MongoDB over RDBMS
  • Installing MongoDB
  • What are Collections and Documents?
  • Creating Databases and Collections.
  • Working with Databases and Collections

ANALYSIS USING R LANGUAGE

  • Introduction to R Language
  • Introduction to R Studio
  • Why to use R?
  • R Vs Other Languages
  • Using R to analyze the data extracted using Map Reduce
  • Introduction to ggplot package
  • Plotting the graphs of the extracted data from Map Reduce using R

 

Future of Hadoop

Hadoop is a technology that will be used in the future, notably by large businesses. The amount of data being generated is only going to grow, and the demand for this software is only going to grow. The global Big Data and business analytics market was valued at US$ 169 billion in 2018, and it is expected to reach US$ 274 billion by 2022. Furthermore, according to a PwC report, there will be roughly 2.7 million employment openings in Data Science and Analytics in the United States alone by 2020.

Because of one critical limitation, the engineers who can satisfy that need will be in short supply. MapReduce is a computational methodology that is used to create Hadoop applications. If you ask one of your batchmates if they know how to write in MapReduce, you'll get a blank except for the name. It would also be difficult to find skilled engineers in the Analytics section. Despite this, the market continues to grow, as shown in the graph below:

One of the following profiles is available to you.

Hadoop Developer: The primary responsibility would be to create Hadoop technologies using Java, HQL, and scripting languages.

Hadoop Architect: The person who plans and creates the Big Data architecture is known as a Hadoop architect. He or she leads the project and oversees the development and deployment of Hadoop applications.

Hadoop Tester: When the program is finished, the tester checks it for problems and patches bugs, broken code snippets, and other issues.

Hadoop Administrator: S/he installs and monitors Hadoop clusters using monitoring technologies such as Nagios, Ganglia, and others.

Data Scientist: A data scientist solves business challenges using big data technologies and statistical approaches, and plays a critical role in deciding the organization's path.

 

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Certified Hadoop Developer Certified Hadoop Developer
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