Big Data: Revolutionizing the Future

Big Data

Revolutionizing the Future

Big Data

Revolutionizing the Future

Parvati Mishra

Big Data: Revolutionizing the Future

Parvati Mishra

ISBN - 9789361522178

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Preface

Big data is a book written by defining what it means and why it is important for? Big data is written for business managers and people who want to know more about big data analytics. Big data and big data analytics are well known in the information technology or data analytics sector.

Each chapter in this book is well explained and supported with the necessary illustrations. This book explains the scope of the subject and also explains the philosophy of the subject. We have worked out big data analytics and have written this book with solved problems and examples. Whatever it may be, it explains the analytics of big data. This book presents various novel approaches to big data analytics. By analyzing this book, we know it aims to provide a stepping stone for acknowledging the work done up to date to store current approaches. Big data has won the presence in every section of the digital economy and the social media sector. After examining the big data, the data has been launched as big data analytics. And this big data analytics have become everywhere that it was hard to web search and visit the websites.

Big data produces a huge amount of data every day, whether we have known about it or not. And with this huge amount of data, it only makes sense for companies to use this data to better understand their customers and behavior. The era of big data analytics has been developing gradually from the expectations of the traditional features, which are measurable by adding more functionalities achieved from re-use, sustainable development, etc.

Finally, the overview of the book is to understand the basic technology of big data analytics in society. Here the data alone does not make a Big Data revolution. And I hope that this book will clarify for us the distinctive perspective and high impact that researchers have in this area. And at the end of the book, the glossary will help you know the knowledge explored in the vast industry field. And every part of the big data is covered in this book.

Happy reading!!!

Content

01. Overview Of Big Data - Exploring The Big Data

1.1 Know about Big Data 1

1.2 Engaging Big data 2

1.3 Big data analytics 11

1.4 Big data impacts 25

1.5 Positive and negative impacts 30

1.6 Summary 31

1.7 Inquiries 31

02. The Public Relations- Big Data Revolution

2.1 The conceptual framework 34

2.2 Unleashing the power of Big data in PR 36

2.3 What’s PR data mean? 36

2.4 The big data generation for PR 37

2.5 Big Data Analytics for PR 37

2.6 Providing the greatest value 38

2.7 Moving beyond social media 39

2.8 Summary 39

2.8 Inquiries 40

03. Big Data Sources

3.1 Top 5 sources in Big data 42

3.2 Big data sources: Internal and External 44

3.3 Hidden Big data sources 45

3.4 Data source in Big data 46

3.5 World’s biggest data source 48

3.6 Summary 49

3.7 Inquiries 49

04. Structure Of Data

4.1 Structured data 52

4.2 Semi-Structured data 52

4.3 Unstructured data 54

4.4 Quasi- Structured data 56

4.5 Summary 56

4.5 Inquiries 56

05. Why Big data?

5.1 Why Big data is a great deal? 58

5.2 Why is it so popular? 60

5.3 Where did it come from? 62

5.4 Why is it important in today’s era? 63

5.5 Summary 66

5.6 Inquiries 67

06. Types of tools used in Big data

6.1 Software framework 70

6.2 Big data analytics application 71

6.3 Risks of big data 76

6.4 Is big data dangerous? 78

6.5 Understanding the risks 81

6.6 Recognising the risks from every angle and Preventing them 83

6.7 Summary 83

6.6 Inquiries 84

07. Big Data In Health Care

7.1 Management and future prospects 87

7.2 Four areas of transforming healthcare 90

7.3 A systematic review 92

7.4 Importance 94

7.5 Main sources of Big data in health care 97

7.6 Summary 99

7.7 Inquiries 100

08. Components Of Big Data

8.1 Machine Learning 103

8.2 Why AI Machine Learning algorithms are important in Big Data analytics 105

8.3 NLP ( Natural Language Processing) 107

8.4 Business intelligence 110

8.5 Cloud computing 118

8.6 Big Data Ingestion 131

8.7 Batch and streaming 141

8.8 Summary 145

8.9 Inquiries 146

09. Big Data Carrier Opportunities

9.1 Paying big data careers 150

9.2 Top big data careers 152

9.3 Data architect 161

9.4 Big data engineer 163

9.5 Database manager 165

9.6 Database warehouse manager 170

9.7 Database developer 180

9.8 Summary 185

9.9 Inquiries 187

10. Big Data Documentation

10.1 Data sets 191

10.2 Datasets for big data tasks 193

10.3 Public data sets for data Visualization initiatives 195

10.4 Pivot tables 200

10.5 Inquiries 205

Glossary 207

Index214

Chapter 1. Overview Of Big Data - Exploring The Big Data

Abstract

This chapter focuses on the advantages of using big data. What are the impacts of big data on different sectors and types of big data, characteristics of big data, etc.? Mainly the Big data was originally associated with three key concepts: volume, variety, and velocity. The current usage of big data refers to predictive analytics, behavior analytics, and any other advanced data analytics that removes the value from the big data. The term big data has been used since the year 1930s, which was popularized by John Mashey. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, manage, and process data within a tolerable elapsed time. It was mostly used in the data science, IT sectors. This book gives an overview of big data and highlights the technological challenges faced in handling Big Data.

1.1 Know about Big Data

There’s a lot of concepts to know about Big data. Basically, Big data was a field that treats how to analyze and collect information from the application software. As it was a broad subject based on the IT sector, to learn it, a lot of knowledge is required in all sectors. In this smarter world, a lot of information has been uploaded through the internet, and this data often holds many useful insights, such as for businesses hiding information on data trends. Nowadays, big data is used in many fields, such as social media, marketing, the internet, and websites. The goal of Big data is to generate these insights that translate to visible business and marketing benefits. It was the largest thing in the marketplace as we know that the technology was creating more and lots of job opportunities globally. Therefore, the businesses considered this technology a challenge and more opportunities for the developers and experts.

1.2 Engaging Big data

Big Data Marketing reveals patterns in your customers’ behavior and proven ways to elevate customer experiences. Big data analytics is the use of advanced techniques that includes enormous data sets. Big data can come from a variety of sources such as learning management systems, websites, marketing, and social media. These data streams can ultimately be combined with administrative data to provide a variety of sources. Big data has changed the world completely through this online marketing as we can connect with others or to customers and communicate with them in an online mode, record every data that has been going through the web, marketers identify the customer’s behavior and tailor their message based on their behavior, can listen to all public conservations, can watch every step in the store. The big data collects all the data with a detailed analysis.

1.2.1 Types of Big data

There are many types of big data that produce data at a quick rate. And these data sources are available throughout the world, mainly in the social media sector and online marketing. We can utilize many of the terms in the social media sector such as Facebook, Twitter, WhatsApp, Messenger….as an illustration because it creates access to 500 terabytes of the data consistently and the consisted data incorporates messages photo’s, videos. Some of the types of big data are given below.

� Network data

� Event data

� Time series data

� Natural language data

� Structured data

� Unstructured data

� Semi-structured data

� Geographic data

� Real-time data

� Linked data

These types of big data can be accessed, processed, and stored in a fixed format. The domain of big data analytics is based on the shoulders of monsters: the capability of data analyzing and harvesting down has been known for quite a long time, if not hundreds of years.

Fig 1.1 Data types

Network data:

In this network data, information transfers from one network access point to another through system controls, transmission lines, and data switching. The network data consists of communication systems such as packet switches and circuit switches to transfer this data.

Event data:

The data which was measured about the event is known as event data. As nowadays, more devices are connected to the internet. So it becomes easier to collect the data of the event.

Time series data:

The collection of observations is obtained by the repeatedly measured time, and it’s used for Tracking the weather data, tracking network logs, tracking changes in application performances.

Natural language data:

This data is helpful to extract the information about the people, places, events and better understand social media customer conversations. To reveal the structure and meaning of the text, the natural language uses machine learning.

Structured data:

It was data that conforms to a data model and follows a proper order and can be easily accessed and used by a computer program. Moreover, it has a well-defined structure. To manage the structured data stored in a database, it uses SQL( Structured query language).

Unstructured data:

Here in this data doesn’t conform to a data model and cannot be easily identifiable. It cannot be used by the computer program easily compared to Structured data. Hence it is not suitable for a mainstream relational database.

Semi-structured data:

The data cannot be organized in relational databases. This is because it doesn’t have a strict structural framework. The semi-structured data includes text organized by the subject, as the open-ended text has no structure.

Geographic data:

It helps in describing the location and the attributes of the things along with their shapes and representations. This data is processed with the geographic Information system software, which can produce maps. This data describes how our world allows for city planning, emergency service routing, and many applications.

Real-time data:

Real-time data is often mainly used for Tracking and navigation. It was not the same as dynamic data. There is no delay in the timeliness of the information provided. With real-time data tools, you’ll know the minute your business has been mentioned by a blogger or other online publication and the implication that mention may have in terms of traffic and potential reciprocal reactions.

Linked data:

The linked data is the method of publishing the structured data using the vocabularies such as schema.org that can be connected. These are interrupted by machines. By using this linked data, the statements encoded in the triples can spread across all the websites on the internet.

1.2.2 7 V’s of Big data

The 7V’s of the big data are:

� Volume

� Velocity

� Variety

� Variability

� Veracity

� Visualization

� Value

Volume:

The volume is the base of the big data, and it was related to the size, which is enormous. The size of the data plays an important role in determining the value of the data. If the volume of data is large, it is considered Big Data. Big data is dependent on the volume of data. Hence it’s necessary to consider the volume of the data while dealing with big data. Therefore, the volume of big data is projected to change significantly in the coming years.

Velocity:

The speed of the data is considered to be a velocity (measured in gigabytes, terabytes, megabytes). In Big Data, velocity flows in the sources like machines, networks, social media, mobile phones, etc. Sampling data can help deal with an issue like ‘velocity’ as it is one of the important characteristics of big data. It has more impact on all businesses as it speeds up the decision-making process in the marketplace. Hence the speed of the data was measured by the velocity of big data. Therefore, the speeding of data processing depends upon the velocity.

Variety:

It was one of the biggest challenges of big data. It includes many different types of data, from XML to video to SMS. Ensuring consistency is crucial when accessing your data from different sources, specifically data lakes(typically unstructured), data warehouses(typically structured), and data lakehouse. It refers to all the structured and unstructured data that can be created either by humans or by machines. The most commonly added data are – structured data, tweets, pictures, posts, and videos, whereas unstructured data like hand-written texts, emails, recordings, ECG reading are the important elements in variety. Variety in Big data is used in classifying the incoming data into various categories.

Variability:

The variability is different from variety. The data which keeps on changing constantly refers to variability. It mainly focuses on understanding the raw data. And it was one of the unfortunate characteristics of Big data. It refers to the inconsistent speed at which big data is loaded with the database.

Veracity:

The quality of data that was being analyzed is referred to as veracity. The high veracity data has many records that are valuable to analyze and that contribute in a meaningful way to the overall results. The second side of data veracity entails ensuring the processing method of the actual data makes sense based on business needs, and the output is pertinent to objectives. Unfortunately, many organizations can’t spend all the time needed to truly discern whether a big data source and processing method uphold a high level of veracity.

Visualization:

Big data visualization is the process of displaying the data in the form of charts, maps, graphs, and other visual forms. It is used to help people easily understand their data at a glance and to clearly show trends and patterns that arise from this data. It not only makes understanding and interrupting the data faster and easier but also identifies and highlights observations that might not be noticed when viewing a list of numbers. It contains more variables and parameters.

Value:

Finally, the value was at the top of the big data pyramid. And it was the ability to transform the data into the business, and its potential is huge. The value of big data lies in the rigorous analysis of accurate data and the information and insights this provides.

1.2.3 Growth

The companies that use big data have a bright future. Based on all the categories, mainly the non-relational analytic data is the fastest growing technology in big data. Sales, Marketing, Research & Development, Supply Chain Management (SCM), distribution, and Workplace Operations are where advanced analytics, including Big Data, make the greatest contributions to revenue growth today. As a result, worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48%. As part of this forecast, Wikibon estimates the worldwide Big Data market is growing at an 11.4% CAGR between 2017 and 2027, growing from $35B to $103B.

Fig 1.2 Big data market size revenue forecast worldwide.

According to the new Vantage venture partners, the big data delivered the most value to the enterprises by decreasing the expenses and creating innovations(44.3%). Big data delivers the most measurable results by discovering new opportunities to reduce expenses by combining advanced big data analytics. Further leading to this category, 69.4% has started using big data to create a new data-driven culture with 27.9% results. The success rate of big data analytics is shown below:

Fig 1.3 Big data initiatives and success rate.

The Hadoop and the big market were projected to grow from $17.1B in 2017 to $99.31B in 2022 by attaining a CAGR of 28.5%. This was the greatest period of the projected growth in 2017 to 2022 when the market is projected to be $30B.

Fig 1.4 Size of Hadoop and big data market worldwide

Big data analytics and its applications are projected to grow from $16.5B to $21.3B from 2018 to 2026 by attaining