Hadoop Tutorial

Today, enterprise data is generated at a rapid rate, and how we make use of this data for the development of a company matters a lot.  Hadoop is evolving to new heights with its enormous support in big data storage and analytics. Companies across the globe started transferring their data to Hadoop to join with the early adopters of this technology, and to gain maximum benefits from the data they possess.

If you are looking to learn what is Hadoop and how it works, then you are in the right place. In this tutorial, we are going to discuss essential topics of Big data Hadoop & its features. We are going to cover all the topics right from the basic to the advanced level. Now, let’s get into the subject without waiting. 

In this Hadoop tutorial article, we will be covering the following topics:

Table of Contents - Hadoop Tutorial

What is Data Where data is being generated What is Big Data What is Hadoop? Hadoop features Hadoop Core Components Hadoop Installation on Linux server Hadoop operation modes Hadoop & Big data Use cases

What is Data?

Data is a distinct piece of information collected and stored for the purpose of future reference. This information lies in different formats such as text, video, audio, or software programs. 

Where data is being generated?

There are multiple sources from which the data is getting generated on a regular basis. It was very limited in the past. But, the sources of data generation have been increasing over the years due to technological advancements, and the easy availability of the internet. The data is produced from different sources such as social media, cameras, microphones, radio-frequency identification readers, business transactions, and information from sensors, etc. 

Today, the advanced developments in IOT (Internet of Things) and social media have made the roots for huge data creation. There are lakhs of IoT devices and social media users who are generating data relentlessly.     

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What is Big Data

Big data is a term that is used to indicate the large volumes of data that may be structured or unstructured. Business organizations process this data to get the hidden insight out of it, and it helps them in taking instant & valid decisions. Big data is associated with different challenges such as the collection of data, storing, transferring, analysis, visualization, querying, etc.

Organizations process extensive data sets by using relational database management systems, and software packages to visualize this data, but these traditional tools failed to do that due to increased data volumes.  To solve this problem, we need high computational power systems that can process the data parallelly on thousands of servers.   

How much data an organization owns is not essential but how efficiently they can utilize it matters most. If an organization can make the maximum of big data, then it will have better growth in the future. There are many benefits of big data such as cost-saving, time reduction, new product development, understanding market trends, etc. 

The traditional approach for processing data

In the conventional method, an enterprise typically has a system to process and store big data. Here, data will be stored in RDBMS, such as MS SQL servers, Oracle database, and advanced software that can be written to integrate with the database and process the required data to present it to the user for decision making. 

But, when it comes to handling the massive amount of data using traditional processors it was tedious work as well as these processors were unable to cope up with increased volumes of data. To overcome this hindrance, there was an urgent need for the development of software that can tackle the data processing problem. That made the roots for the development of a software framework known as Hadoop. 

What is Hadoop?

Hadoop is an open-source software framework that is designed to store enormous volumes of data sets in a distributed way on large clusters of the commodity. Hadoop software has been designed on a paper released by Google on MapReduce,  and it applies concepts of functional programming.  Hadoop was developed in Java programming language, and it was designed by Doug Cutting and Michael J. Cafarella and licensed under the Apache V2 license. 

Hadoop features

In this section, let’s discuss all the elements of Hadoop and how they can help  Hadoop in standing out from all the other software. Hadoop is capable of processing large volumes of data with its enormous computational power. As we know, it is open-source software that we can customize according to our organizational needs. It is a cost-effective and fast processor compared to traditional processors. 

Below explained are the unique features of Hadoop.

1. Flexibility in the data  processing

In the past, organizations faced a problem in processing data. They did not have the technology to process the data. They used to process only structured data which is a small portion of the entire data. They ignored data that was unstructured or semi-structured. Due to this, they lost the value they could have acquired by processing unstructured data.

But, Hadoop has come up with solutions to all these data-related problems. It can process all kinds of data, whether it may be structured, unstructured, or semi-structured. Hadoop filters Big data and brings the hidden value into the light,  which helps organizations in taking quick & valid decisions that work in real-time. 

2. Easily Scalable

This feature made Hadoop more popular. It’s an open-source platform that can run on any industry-standard hardware. It makes  Hadoop an exceptionally scalable platform where one can easily add nodes to the system when required without making any alterations to the existing system or programs.  

3. Fault Tolerance 

This feature of Hadoop assures users free from fear of losing data. In Hadoop, when a user stores the data in HDFS, then the data gets automatically replicated into two other locations. So, even in the case where one system collapses, the data will still be available in two other locations. 

Its fault tolerance system makes Hadoop an extremely reliable data warehouse system.  When anything goes wrong, or if a node loses its functionality, then the system automatically assigns the work to another location of data, and it works continuously to process the data without any stoppage in between due to node failovers.  

4.  High-speed data processing

Our traditional data processors used to take a long time to process the data. Sometimes, it may take hours, or maybe days, or even weeks as well to load the data. The demand to analyze real-time data has been increasing day by day. 

Hadoop is highly effective and fast at high volume batch processing because of its parallel processing ability. Hadoop processes data are ten times faster than on a mainframe or on a single thread server.   

5. Data Locality

It works on the data locality formula which states that, move computation to data rather than moving data to computation. Whenever a user submits the algorithm, it directly goes into the data instead of bringing data to the location where the algorithm is applied and processes. 

Hadoop Core Components

Hadoop mainly comprises four components, and they are explained below.   

1. Hadoop common

It is considered as one of the Hadoop core components because it serves as a medium or a SharePoint for all other Hadoop components. Hadoop common consists of a set of libraries or common utilities that support other Hadoop modules. Let's consider an example: If HBase or Hive wants access to HDFS, first they have to make use of Java archives (JAR files) which are presented in the Hadoop Common. 

2. Hadoop Distributed File System (HDFS)

It is a default data storage for Hadoop and the data is stored in HDFS until the user needs it for processing. In HDFS, the data is split into multiple units called blocks and gets distributed in the cluster. It creates several replicas of data blocks and spread them all over the clusters for reliable and easy access. 

Related Article: HDFS Hadoop

HDFS consists of three other main components which are Namenode, Data Node, and secondary Name node.  It operates on the Master-Slave architecture model. In this architecture, Namenode acts as a master node to keep track of the storage system, and the Data node works as a slave node, to sum up, various systems in the Hadoop cluster.

Related Article: HDFS Commands

Below are some unique features of the Hadoop Distributed File System (HDFS).

• Designed with the anticipation of Hardware failure. 
• Built for large data, it comes with a default block size of 128 kb.
• Developed for sequential operations
• Supports heterogeneous clusters. 

3. YARN (Yet Another Resource Negotiator) 

The main idea behind the YARN is to segregate the functionalities of resource management and job scheduling into different daemons. YARN is responsible for assigning resources to various applications that run on the Hadoop cluster. 

YARN consists of two main components which are Resource manager and Node manager. These 2 components together create the data computation framework. The resource manager has the authority to delegate the work among all applications in the system whereas the node manager is responsible for containers, and monitors their resource utilization (CPU, disk, memory, network), and transmits the same information to the Resource manager. 

YARN components : (Yet Another Resource Negotiator) 

Hadoop YARN decentralizes the work between Its components and makes them responsible for completing the assigned task. Below explained are the tasks assigned to different Core components of YARN.    

• A global Resource manager takes responsibility to accept job submissions from users, and it schedules these jobs by allocating resources to them.
• A Node manager acts as a Reporter to the Resource manager. A node manager is installed in each Node and reports back each node functionality to the Resource manager. 
• An ApplicationMaster is created for each application to smoothen the process of Resource allocation, and it helps the Node Manager in executing and monitoring tasks. 
• Another component of YARN is the Resource container which is managed by Node managers and assigned with the system resources allocated to individual applications.

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4. MapReduce

MapReduce is a major component of Apache Hadoop. It enables developers in writing applications to process enormous volumes of data.  MapReduce is written in Java and is able to compute large sets of data.  Its primary task is to split the data into small independent chunks that are easy to process in a parallel way.  

MapReduce algorithm consists of two core components which are Map and Reduce. Reduce function starts once the Map function finishes its task. The map takes specific data and transforms this data into tuples.  Reduce function takes the output of the Map function and combines them to create another set of tuples. The parallel processing feature of MapReduce plays a critical role in Hadoop. It allows multiple machines in the same cluster to perform big data analysis. 

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Let’s discuss each function in detail.

Map Stage:

The mapper function is used to convert the input data. The data may be in different formats such as files or directories which are stored in HDFS. The entire data is passed into the Map Function in a sequential manner, and it converts the data into tuples.  

Reduce stage:

In this stage, the data is shuffled and reduced to some extent. It executes the data processing function with the output of the Map function.  After completion of the reduce function, it produces a new output which automatically gets stored in the Hadoop Distributed File System. 

Job opportunities and salary structures of Hadoop developers

Big data is growing at a rapid speed, and organizations also started to depend on this data to harness the hidden insights out of it. IDC estimates that the growth of the organizations depends on how effectively they utilize Big data. To process these data organizations requires skilled human resource.  

According to Forbes, the recent study shows massive growth in the percentage of industries, looking for candidates who have excellent analytical skills to drive insights from big data. Technical services, manufacturing, IT, Retail, and Finance industries are among the top in hiring Big data professionals. The requirement in these industries may vary according to their level of usage of big data. 

The median advertised salary for a professional with big data expertise is $1,24,000 per annum.  There are different jobs available in this category which are Big Data Platform Engineer, Information System developer, Software Engineer, Data Quality director, and many other roles also there.

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Hadoop Installation

Hadoop can be installed on GNU/Linux and its flavors. To set up a Hadoop environment, we need to install the Linux operating system on our system.  If you are using any  OS other than Linux you can still install VirtualBox software and can have Linux inside of the VirtualBox. 

Pre-installation Setup 

Before installing Hadoop, we need to create a Linux environment by using Secure Shell.  Below mentioned are the steps to follow to create the Linux environment.

Creating User

At the start, it is recommended to create a  user for Hadoop separating to segregate the Hadoop file system from the Unix file system. 

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SSH Setup and key generation

SSH key helps in Performing different operations, which are starting, stopping, distributed node operations, etc. To connect with different users of Hadoop, we need a pair of public/private keys, and it should be shared with all other users of Hadoop.

Install Java

Java is the main prerequisite for running Hadoop. Check the java version of your system, and if java is not installed in your system, then install it, and set up the configurations required to run  Hadoop. 

Related Article: Java to Hadoop

The final step is to download Hadoop and Extract 2.4.1 from the Apache software foundation. For more information on the installation process, click here 

Hadoop operation modes

After downloading and setting up the Hadoop, we will be having three ways to use it, which are the Local mode or standalone mode, Pseudo-distributed mode, and Fully distributed mode. Let's have some idea about each mode.

1. Local Mode or Standalone Mode

Standalone mode is the default mode used to run Hadoop. It works faster than the two other modes because it uses the local file system for all the input and output data.  Standalone mode helps in debugging purposes where we don’t use HDFS. 

2. Pseudo-distributed Mode 

It is also known as a single node cluster because the Namenode and Data node resides on the same machine. In pseudo-distributed mode, all the master and slave daemons will run on the same node. This mode is mainly used for testing purposes and helps the development process. 

3. Fully-Distributed Mode (Multi-Node Cluster)

It is the production model of the Hadoop with multiple nodes running on two or more machines on the same cluster. In a multi-node cluster, the data will be distributed on each node and processing will also be done on each node.  

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Hadoop & Big data Use cases

Let’s consider two use cases to know exactly how it works in real time: 

Hadoop in the Healthcare sector

Healthcare is one of the main industries which has got benefited a lot from big data & Hadoop. It has leveraged big data for curing diseases, recording patient health data, reducing medical costs, predicting and creating a solution for epidemics, and maintaining the quality of human life by tracking records of large-scale health indices and metrics.

In this section, let's discuss how Hadoop can help the Healthcare sector by using big data. 

The data generated in the Healthcare sector is very vast because of the transactions that happen every day.  McKinsey forecasted that the implementation of big data and Hadoop in the healthcare sector can reduce data Warehouse expenses by $300-$500 billion dollars per annum globally. It is complicated to handle massive electronic health data with traditional database management systems.

Hadoop helped in solving this problem with the capability to process complex data types on its distributed file systems.   Using Hadoop to process these large data sets can help the Health sector in taking instant real-time decisions, finding clinical solutions for epidemics, fault tolerance, and data querying. 

Hadoop allows storing multiple structures of data in its native way. It can process massive amounts of healthcare data with ease with its parallel data processing, fault tolerance system, and storage capacity for a large number of data sets. The Hadoop system has made data processing simple and clear. 

Big data and Hadoop play crucial role in the development of the healthcare insurance business. It uses Healthcare intelligence applications to process data on the distributed database system and assists hospitals, beneficiaries, and medical insurance companies to enhance their product value by creating smart business plans. 

Let's consider an example here: If a medical insurance company can get to know the data or amount of people who are non-victims of any diseases under a specific age, then it is effortless for the company to create a product that is least priced and can yield high benefits for both the parties.

To develop such kinds of policies, it needs to process large volumes of data of different types such as geographic regions, diseases, patient care records, and medication records of patients, etc.  Hadoop is the only tool that can process these many varieties of data at a very economical price.         

The primary motive behind the implementation of big data and Hadoop in healthcare is to store and analyze healthcare data which can be leveraged to spot the health trends of billions of populations across the world and to create treatment plans for patients according to their requirements. 

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Hadoop in the Retail sector

The retail industry is one of the fast-growing segments in today's business world. This sector relies largely on data to make appropriate decisions, to promote existing products, new product development,  to make investment decisions, etc. They capture the vast amounts of data delivered from point-of-sale transactions from various sources.

They process this data to analyze market trends and consumer behavior. With this information, they will be able to predict the future demand for their products and services. 

Retail analytics is one of the primary users of the data warehouse industry and is responsible for the development and innovation of the retail sector. It is responsible for collecting and storing data about various transactions of consumers and their purchasing behavior. The retail segment uses its previous sales data along with Hadoop and  MapReduce to analyze the data and to increase sales.

The data that the retail stores generate today is no longer like it used to be previously.  Today, to process these new varieties of data, we need advanced processing mechanisms such as language processing, sentiment analysis, pattern recognition, etc.  Traditional database management systems are no longer able to process and store the complex data meant for such analysis.  

Hadoop stepped into the situation to solve the data processing problem in the retail segment. Dump all historical Sale Point data into the Hadoop cluster, and after that, you can build analytics applications using MapReduce, Hive, and Apache Spark.  It provides us with a system to analyze the massive amount of data with low latency and at a very reasonable price. 

Several retail companies out there have benefited from the usage of Hadoop technology. Let’s consider the real-time scenarios of two companies Etsy and Sears. Etsy runs its business via online stores, whereas  Sears operates online and Offline.  The ultimate goal of these two companies is to analyze the data for multiple uses such as sales management, inventory management, marketing campaigns, etc. 

These companies have used Amazon Elastic MapReduce services to create a Hadoop Cluster. Etsy and Sears used this cluster to store and Analyse data, and after that, to estimate product promotion, targeted marketing, inventory management, product placement, search recommendation, consumer behavior, etc. 

The following are the main areas in the retail industry where big data and Hadoop are being used. 

• Retail analytics for creating a fair price for their products.
• Retail analytics for creating an effective supply chain management system. 
• To forecast losses and to prevent them in advance. 
• Retail Analytics for designing creative and cost-effective marketing campaigns. 
• To develop new products based on the increased needs of the customers. 
• Retail analytics for maintaining inventory according to the market demand.

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The number of users of social media and retail channels has been increasing over the years.  Before making any purchase decision, customers are doing the right amount of market research. With this kind of behavior, customers are quickly shifting from one retailer to another. 

It is essential for retail companies to have awareness of customer behavior and the strategies of other companies to stay in the market. It is necessary for companies to make use of Big data and Hadoop to understand consumer behavior and in creating products and services, to sustain the business in the long run. 

Conclusion

So, till now, we have analyzed what big data is, what is Hadoop, and what are all components are associated with it.  I hope you have got an idea from the beginning of data generation to its processing with the help of Hadoop. Happy Learning! 

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