Big Data Volume
Big Data Volume refers to the vast amount of data generated and collected by organizations, often exceeding the capacity of traditional data processing systems. This includes structured and unstructured data from various sources such as social media, sensors, and business transactions. Managing and analyzing big data volumes requires specialized tools and technologies to derive valuable insights and make informed decisions.
7 Key excerpts on "Big Data Volume"
eBook - ePubCreating Smart Enterprises
Leveraging Cloud, Big Data, Web, Social Media, Mobile and IoT Technologies
- Vivek Kale(Author)
- 2017(Publication Date)
- Auerbach Publications(Publisher)
...The answer to these challenges is a scalable, integrated computer systems hardware and software architecture designed for parallel processing of Big Data computing applications. This chapter explores the challenges of Big Data computing. 7.1.1 What Is Big Data? Big Data can be defined as volumes of data available in varying degrees of complexity, generated at different velocities and varying degrees of ambiguity that cannot be processed using traditional technologies, processing methods, algorithms, or any commercial off-the-shelf solutions. Data defined as Big Data includes weather, geospatial, and geographic information system (GIS) data; consumer-driven data from social media; enterprise-generated data from legal, sales, marketing, procurement, finance and human-resources departments; and device-generated data from sensor networks, nuclear plants, X-ray and scanning devices, and airplane engines (Figures 7.1 and 7.2). Figure 7.1 4V characteristics of Big Data. Figure 7.2 Use cases for Big Data computing. 7.1.1.1 Data Volume The most interesting data for any organization to tap into today is social media data. The amount of data generated by consumers every minute provides extremely important insights into choices, opinions, influences, connections, brand loyalty, brand management, and much more. Social media sites not only provide consumer perspectives but also competitive positioning, trends, and access to communities formed by common interest. Organizations today leverage the social media pages to personalize marketing of products and services to each customer. Many additional applications are being developed and are slowly becoming a reality...
eBook - ePubApplication of Big Data for National Security
A Practitioner's Guide to Emerging Technologies
- Babak Akhgar, Gregory B. Saathoff, Hamid R Arabnia, Richard Hill, Andrew Staniforth, Petra Saskia Bayerl(Authors)
- 2015(Publication Date)
- Butterworth-Heinemann(Publisher)
...The characteristics of Big Data—too big, too fast, and too hard—increase the complexity for existing tools and techniques to process them (Courtney, 2012a ; Dong and Srivatsava, 2013). The core concept of Big Data theory is to extract the significant value out of the raw datasets to drive meaningful decision making. Because we see more and more data generated every day and the data pile is increasing, it has become essential to introduce the veracity nature of the data in Big Data processing, which determines the dependability level of the processed value. Volume Among the five V’s, volume is the most dominant character of Big Data, pushing new strategies in storing, accessing, and processing Big Data. We live in a society in which almost all of our activities are turning out to be a data generation event. This means that enterprises tend to swim in an enormous pool of data. The data are ever-growing at a rate governed by Moore’s law, which states that the rate at which the data are generated is doubling approximately in a period of just less than every 2 years. The more devices generate data, the more the data pile up in databases. The data volume is measured more in terms of bandwidth than its scale. A quick revolution of data generation has driven data management to deal with terabytes instead of petabytes, and inevitably to move to zettabytes in no time. This exponential generation of data reflects the fact that the volume of tomorrow’s data will always be higher than what we are facing today. Social media sites such as Facebook and Twitter generate text and image data through uploads in the range of terabytes every day. A survey report of the Guardian (Murdoch, Monday May 20, 2013) says that Facebook and Yahoo carry out analysis on individual pieces of data that would not fit on a laptop or a desktop machine...
eBook - ePubInnovating Analytics
How the Next Generation of Net Promoter Can Increase Sales and Drive Business Results
- Larry Freed(Author)
- 2013(Publication Date)
- Wiley(Publisher)
...Remember microfiche? Remember stacks of old, yellowing newspapers and magazines in libraries? No more. I bet my sons have never even used microfiche. With the amount of digital data doubling every three years, as of 2013 less than 2 percent of all stored information is nondigital. An extraordinary change. So what is a workable definition of big data? For me, it is the explosion of structured and unstructured data about people caused by the digitization and networking of everything: computers, smartphones, GPS devices, embedded microprocessors, and sensors, all connected by the mobile Internet that is generating data about people at an exponential rate. Big data is driven by the three Vs: an increasing Volume of data with a wide range of Variety and gathered and processed at a higher Velocity. Big Data Volume The increase in volume provides us a bigger set of data to manipulate. This provides higher accuracy, a lower margin of error, and the ability to analyze the data into many more discrete segments. As entrepreneur and former director of the MIT Media Lab Frank Moss explains in an interview on MSN 1 : Every time we perform a search, tweet, send an e-mail, post a blog, comment on one, use a cell phone, shop online, update our profile on a social networking site, use a credit card, or even go to the gym, we leave behind a mountain of data, a digital footprint, that provides a treasure trove of information about our lifestyles, financial activities, health habits, social interactions, and much more. He adds that this trend has been “accelerated by the spectacular success of social networks like Facebook, Twitter, Foursquare, and LinkedIn and video- or picture-sharing services like YouTube and Flickr. When acting together, these services generate exponential rates of growth of data about people in astonishingly short periods of time.” More statistics show the scope of big data...
eBook - ePubRecent Trends in Communication and Electronics
Proceedings of the International Conference on Recent Trends in Communication and Electronics (ICCE-2020), Ghaziabad, India, 28-29 November, 2020
- Sanjay Sharma, Astik Biswas, Brajesh Kumar Kaushik, Vibhav Sachan, Sanjay Sharma, Astik Biswas, Brajesh Kumar Kaushik, Vibhav Sachan(Authors)
- 2021(Publication Date)
- CRC Press(Publisher)
...A database stores the data in the form of tables with rows and columns, for example: relational data. Unstructured data is unorganized data or the data which does not have a predefined data model, for example: Word, Text, PDF,Media logs. Semi-structured data does not follow any formal data model, but does contain some markers or tags that can separate the elements into various hierarchies. An example of such data is JSON (thestructure that DataAccess uses by default),.csv files, XML, tab delimited files etc. Various studies and research in the field of Big Data have shown that if we can find a way to manage and process the data in an effective way then Big Data has the capacity to save time and money,boost efficiency and improve decision making in the fields of fraud control, weather forecasting, health and medicines, national security, business areas, education and traffic control. 2 The 4 V'S Of Big Data The whole theory of Big Data revolves around the 3 V’s, namely, volume, variety and velocity. A fourth V has also been now introduced which expands to veracity. Figure 1. The four V's of big data. Below is the detailed description of these V’s which form the building block for Big Data: 2.1 Volume It is the huge amount of data that exists today which is the reason for the discovery of the term “Big Data”. Therefore, volume of the data is the core feature of “Big Data”. Today, data generation is increasing exponentially which can be quantified not in terms of terabytes but zettabytes and brontobytes. Today, the data generated every minute is the same amount which was generated between the oldest date and 2008. Hence, the available conventional means are of no use in the management of such a massive amount of data...
eBook - ePub- Brian C. Castellani, Rajeev Rajaram(Authors)
- 2022(Publication Date)
- SAGE Publications Ltd(Publisher)
...For example, in the Forbes article we mentioned earlier about how big data is changing the airline industry, its author explained that ‘today, through thousands of sensors and sophisticated digitised systems, the newest generation of jets collects exponentially more, with each flight generating more than 30 times the amount of data the previous generation of wide-bodied jets produced. . . . By 2026, annual data generation should reach 98 billion gigabytes, or 98 million terabytes, according to a 2016 estimate by Oliver Wyman.’ 3 Variety: Big data today is also generated through a wide array of types and formats: structured and unstructured, relational, transactional, longitudinal, discrete, dynamic, visual, textual, numeric, audio, geospatial, physical, ecological, biological, psychological, social, economic, cultural, political and so on and so forth. Velocity: In our big data world, the issue is not just the speed at which massive amounts of data are being generated but also the speed at which they often need to be acquired and processed. Also, there is a significant amount of big data that remains important for very short moments of time: for example, delayed flight schedules, ticket price fluctuations or sudden interruptions in travel that an airport has to respond to quickly. And then there are the complex ways this increased data velocity, in turn, speeds up the decision-making process – forcing decisions, often times, into a matter on nanoseconds rather than days, weeks or months; all of these present major challenges to the hardware and software of companies and users – not to mention the ‘knockoff’ effects on social life that come from this increased speed in decision-making. Variability: While the velocity and volume of big data appear constant, in actuality they are rather variable, with inconsistencies in their flow, as in the case of a sudden Twitter trend or online searches in response to a disease outbreak...
eBook - ePubBig Data Analytics
Turning Big Data into Big Money
- Frank J. Ohlhorst(Author)
- 2012(Publication Date)
- Wiley(Publisher)
...Chapter 1 What Is Big Data? What exactly is Big Data ? At first glance, the term seems rather vague, referring to something that is large and full of information. That description does indeed fit the bill, yet it provides no information on what Big Data really is. Big Data is often described as extremely large data sets that have grown beyond the ability to manage and analyze them with traditional data processing tools. Searching the Web for clues reveals an almost universal definition, shared by the majority of those promoting the ideology of Big Data, that can be condensed into something like this: Big Data defines a situation in which data sets have grown to such enormous sizes that conventional information technologies can no longer effectively handle either the size of the data set or the scale and growth of the data set. In other words, the data set has grown so large that it is difficult to manage and even harder to garner value out of it. The primary difficulties are the acquisition, storage, searching, sharing, analytics, and visualization of data. There is much more to be said about what Big Data actually is. The concept has evolved to include not only the size of the data set but also the processes involved in leveraging the data. Big Data has even become synonymous with other business concepts, such as business intelligence, analytics, and data mining. Paradoxically, Big Data is not that new. Although massive data sets have been created in just the last two years, Big Data has its roots in the scientific and medical communities, where the complex analysis of massive amounts of data has been done for drug development, physics modeling, and other forms of research, all of which involve large data sets...
eBook - ePubIt's All Analytics!
The Foundations of Al, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government
- Scott Burk, Gary D. Miner(Authors)
- 2020(Publication Date)
- Productivity Press(Publisher)
...Examples might be color, breed of dog, state of residence and phone brand. Quantitative data can be measured on numeric scales such as the number of readmissions per year, per member per month (PMPM) insurance rates, Gross Domestic Product (GDP) and revenue per year. What Is Big Data? There are various reports of who officially coined the term “Big Data” and of where it actually started. Part of the confusion revolves around the question, “Is Big Data a descriptive term or a technology?” We cover both in this section. We like the following as a descriptive term: Big Data – a massive volume of data that is so large it is difficult to process using traditional technology (as of about 2005). In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. From a technology basis, the following are some (there are others) of the technologies created to support big data: Data Lakes High-Performance Relational Database Technologies (Massive Parallel Processing (MPP)) Hadoop, HDFS, and MapReduce (see following gray box, “Quick Note on Apache, Hadoop and Spark”) Data Hubs Cloud Data Warehouses Data Virtualization (DV) These technologies are sometimes defined as big data, but they support big data rather than describing what big data is. Additionally, the description of big data may include the 3 V’s or 5 V’s. Initially, there were three: 1) Data Volume – the sheer amount of data 2) Data Variety – disparate types, different structures, and formats of data 3) Data Velocity – how fast data is being added to systems, refreshed Then two more qualities were added to make it the 5 V’s of Big Data. 4) Value – What is the return on investment for sourcing this data? 5) Veracity – What is the quality, reliability, and trustworthiness of the data? Quick Note on Apache, Hadoop and Spark The Apache Software Foundation (www.apache.org) was incorporated in 1999 as an American nonprofit corporation...
