Introduction to Big Data

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Introduction to Big Data

In this section we are going to introduce you with the Big Data.

In this section we are going to introduce you with the Big Data.

Introduction to Big Data - Big Data technologies for processing tremendous amount of data

In this section we are going to discuss about Big Data and understand its importance in today's world to manage tremendous amount of data. You will learn how what is Big Data and how it's important in today's world? We will also discuss its implementation in real-world for storing and processing large data sets to get real value of data. Data processing technologies are used to process vast amount of data and come out with actionable insight.

This is the first article to learn Big Data and we will discuss Big Data in very big detail. You will learn the fundamentals of Big Data, different types of Big Data and the technologies used for working with it. We will also discuss the lifecycle and various activities involved to work with Big Data.

If you see any job portal you will find many job in various Big Data and Data Science. There is huge demand of skilled Big Data professional in the IT field but enough resources are not available to handle highly technical work in this field. Companies are looking for skilled professionals in various job roles in the Big Data field to work on huge projects involving small to large clusters. So, if you are planning to make you career in Big Data then you should learn all the necessary kills in Big Data. Our Free Big Data Training and Certification will help you in getting started with Big Data and Hadoop for free.

Let's see Big Data in more details.

What is Big Data?

Big Data is a term which is used generally for 'Huge Data' which can't be handled and processed on a single computer or traditional means of processing. Facebook Data, Google search index Data, Banking Data, Data generated by Air plane sensors, Data generated by space telescopes etc.. are few example of 'Huge Data' which can't be stored and processed by single or combination of few computers. So, here Big Data comes into picture which provides technologies for gathering, storing, processing/cleansing and analyzing such a huge collection of data to get more details about data.

So, Big Data is a blanket term for the activities which is necessary for handling large data sets. The major activities involved are project planning, data collection, processing, programming, analyzing and visualizing. All these activities are very complex because the data formats are varying from industry to industry and their processing requirements are altogether different.

Big Data generally means:

  • Huge Datasets that can't be managed with traditional means of computing and
  • The technologies, processes, software and hardware system for managing such a huge data sets

These days Big Data technologies are used by almost all the industries for developing modern applications. Companies are investing money in Big Data platform to develop applications for storing and analyzing their data for better managing their business. Research organizations and universities are using Big Data for processing, analyzing with the help of latest machine learning and deep learning models. In coming days there will be huge demand of skilled professionals to work on various projects.

What are the characteristics of Big Data?

As discussed earlier Big Data refers to the very large amount of data, now we discuss the characteristics of these data. There are many ways and at different speed data is generated by different sources in today's world. For example in case of Social Networking applications, data is generated by the users of these application and it includes text, voice, images, videos; along with the application logs. The well-designed Big Data System captures all these data for better user experience. Log files are used to see the application performance, application and much more details.

In case of Industrials sensors data are generated at very high speed and the data volume is very large. For example Air Plane (Boeing 787s) sensor generates half terabyte of data per flight. In case smart cities sensors are also generating large volume of data. So, in Big Data we have to process different data types and at very different speed for different use case.

These characteristics of Big Data are known as 5V's. The 5V's of Big Data is as follows:

  • Velocity - The Velocity feature refers to the speed at which data is being generated, collected, stored and analyzed by Big Data system. Every minute large quantity of data is generated by source applications/devices and it varies from industry to industry. For designing the Big Data system velocity of data is considered so that the data is collected efficiently for further analysis.
     
  • Volume - The Volume refers to the amount of data generated from the source which is to be handled by Big Data system. From industry to industry data amount is different. Social networking site like Facebook, Twitter is generating huge amount of text, images, audio and video data. So, Big Data storage system should be designed in a such a way that it can handle growing data storage requirement.
     
  •  Value - The Value is most important component while considering deploying a Big Data system. It actually means the value of the data begin collected, extracted, stored and analyzed. The data collected should add value to the business and it should be more the investment made on the total Big Data projects. There should be better ROI for any Big Data project for a company venturing into it.
     
  • Variety - The Variety refers to the different kinds of data generated by source such as structured data (RDBMS data), semi structured (JSON, XML), unstructured (text), binary files and audio/visual files. The 80% data generated in Big Data are images, audio and videos. Big Data solution should be designed to process all such files by the system at optimal speed. Variety of Big Data technologies are used to make solutions to meet company's data processing requirements.
      
  • Veracity - The Veracity is used for the quality or the trustworthiness of data. If the data quality is not good then system should be designed to make it more accurate so that business decisions can be taken. The reliability and data quality is very important in Big Data system as based on the data business decisions are taken.

There are more details about Big Data on our website and you check it at What do you understand by Big Data?

Which Data is called Big Data?

In simple terms Big Data is amount of data which can't fit into single computer and requires huge data storage on the distributed computer. One or few computers can't process data generated by today's data source such as industrial sensor, social network data, mobile operator's data and so on. In such cases we need a system which can store and process data on distributed machines. Here parallel computing plays major role as processing is done over the serves in the cluster.

Here are the list of data which we can call Big Data:

  • Social Networking platform data - Data generated by social networking platform such as Facebook, Twitter, Instagram etc... falls into the Big Data category.
     
  • Air Plane data - Data generated by the sensors of Air Plane comes under Big Data.
     
  • Medical Science and Genomic data - These are very large data sets and its processing can be done over the Big Data cluster only.
     
  • Banking Data - Millions of customers around the world is performing transactions every second and this data is very huge which can be handled by well-designed Big Data system.
     
  • Symantec Web and Knowledge Graph data - There are large pool of data over the internet in the form of text, audio and videos. These data can be studied through machine learning to create a knowledge graph. Such data is also comes under Big Data.
     
  • Manufacturing data - Data generated by thousands of machineries uses in industries is also comes under the Big Data category.

These are the important industries generating Big Data; apart from this many industries such as retail, transportations, navigation, security/firewall and many more are the source of Big Data in today's world. So, so should be aware of these industries and think about the innovative solutions for them.

What is life cycle of Big Data?

The life cycle of Big Data starts from the data source such as industrial sensors in case of IIoT (Industrial IoT). In case of social networking platform end users are the source of data. Once the data is generated it is collected by the Big Data system with process call ingestion. During data is pre-processed and save into the Big Data storage system. After storage it is further cleansed and saved as good data for further analysis. Finally data is analyzed through various means and business report is generated for stack-holders.

Here is the life cycle of Big Data:

  • Data Generation
  • Data ingestion
  • Data pre-processing
  • Data cleansing
  • Data analysis and Machine learning
  • Final report generation

In the future tutorials we will learn each step in great details.

Distributed storage and Cluster computing in Big Data

The Big Data system is using distributed storage solution which splits and distributes the data on the data nodes in the cluster. The master machine keeps the details about data being stored on the cluster. Multiple copies of data is saved on the different nodes in the cluster so that if any or machine crashes data can be recovered automatically. Big Data software system is designed in a way that new nodes can be added dynamically without cluster shutdown or any down time.

Computing over data stored in the cluster is also an important part of Big Data cluster and the computing is done on the nodes where data resides. For example if you submits a job for processing so data then the processing logic is sent to the nodes where data resides and finally result is retrieved from those nodes.

Big Data system is designed to distribute job among nodes in the clusters so that job can be finished fast.

Big Data cluster provides following features:

  • Resource Pooling
  • High Availability
  • Easy Scalability
  • Distributed computing
  • Fault tolerance
  • High scalability
  • Security

Big Data Software Platforms

The Big Data Software Platforms are specialized software package that can be installed on multiple commodity serves to make a Big Data cluster consisting of few to thousands of nodes. Big Data platform provides distributed storage, distributing computing, fault tolerance and security. It comes with the API and software tools to save, update, search and delete the data stored in the Big Data environment.

Big Data platform provides software packages for executing batch and real-time jobs over nodes in the cluster. These software packages provide fault-tolerance for these jobs. Here are the lists of top Big Data Platforms:

  • Apache Hadoop
  • Cloudera
  • Hortonworks
  • MapR
  • IBM Open Platform
  • Microsoft HDInsight

The Cloudera, Hortonworks and Microsoft HDInsight are Apache Hadoop based Big Data platform. You can find complete details of these at What is Big Data Platform?

Hadoop as Big Data Platform

Apache Hadoop is top big data platform and it comes with many software components such as Spark, Hive, Sqoop, HBase etc.. for handing various kind of jobs in Big Data environment. Apache Hadoop is open source distributed storage and computing engine for building Big Data cluster. Hadoop is available for Ubuntu, Redhat and Centos operating system. So, if you are planning for Big Data cluster then you have to use any of this Linux operating system and install Hadoop in your cluster.

Hadoop comes with the distributed storage system called HDFS (Hadoop File System) which is used to save files in Big Data environment. You can store any of the file type on the HDFS. Hadoop comes with Hive to store tabular format data. It also provides HBase NoSQL database system for storing columnar data over HDFS.

As a developer or administrator you will have understand Hadoop, HDFS and its other component software in very detail. 

How to learn Big Data Technologies?

Now we have the final question "How to learn Big Data Technologies?". Yes, there are many technologies in Big Data and its impossible to learn all these technologies quickly. Also these technologies are changing fast. So, how to learn Big Data and Hadoop technologies?

Well one should consider the basics programming skills and data management skills fist. You should learn any one programming language such as Java or Python and then learn SQL concepts. Finally after learning these you can start learning Hadoop and Big Data technologies.

There are many Big Data platform but you should star learning with Hadoop.

You can join our Free Big Data Training and Certification course to learn Big Data with Hadoop.

If you can check out tutorial Hadoop Learning Path - Quick start your career in IT industry to see the topics you should learn.

Big Data Visualization open source tools to learn

There are many open source Big Data visualization tools that you must learn to give better performance on your work. There are many visualization tools but you should learn D3js, ELK stack (Elasticsearch, Log stash and Kibana) and any other tool selected for your project.

In this section we gave you detailed introduction to Big Data and its technologies.

You can check more tutorials at: Hadoop Tutorials page.

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