A petabyte is a unit of digital information storage representing one quadrillion bytes <a href="#book-info_2433384">(Thomas M. Siebel et al., 2019)</a>. It is equivalent to 1,000 terabytes or 1,000,000,000,000,000 bytes <a href="#book-info_1809911">(Hunter Whitney et al., 2012)</a>. As computing capacity has expanded exponentially from the era of kilobytes and megabytes, the petabyte has become an increasingly relevant metric for measuring the vast data sets managed by modern corporations and cloud services <a href="#book-info_4314007">(J. Adam Carter et al., 2024)</a>.

The petabyte sits within a hierarchy of digital measurement starting from the bit, the most basic unit of information <a href="#book-info_2433384">(Thomas M. Siebel et al., 2019)</a>. While a single byte consists of eight bits, a petabyte represents 10 to the power of 15 bytes <a href="#book-info_1809911">(Hunter Whitney et al., 2012)</a>. To provide perspective, the entire U.S. Library of Congress contains approximately 15 terabytes of information, meaning a single petabyte could hold many times that volume of data <a href="#book-info_2433384">(Thomas M. Siebel et al., 2019)</a>.

Petabyte-scale storage is typically housed in data centers or data warehouses, which utilize rows of high-performance servers stacked in racks <a href="#book-info_1809911">(Hunter Whitney et al., 2012)</a>. While early personal computers like the 1984 Macintosh had only 128 kilobytes of memory, modern tech giants like Google and Amazon now manage scores of petabytes and even exabytes of data <a href="#book-info_2433384">(Thomas M. Siebel et al., 2019)</a><a href="#book-info_4314007">(J. Adam Carter et al., 2024)</a>. This shift marks the Petabyte Age, where global data generation is projected to reach 175 zettabytes annually by 2025 <a href="#book-info_4314007">(J. Adam Carter et al., 2024)</a>.

5. Big Data

The ASCII encoding system, developed from telegraph code in the 1960s, enables the representation of any character or word as a sequence of zeros and ones. As information theory developed and we began to amass increasingly large data sets, a language was developed to describe this phenomenon. The essential unit of information is a bit. A string of eight bits in a sequence is a byte. We measure computer storage capacity as multiples of bytes as follows: One byte is 8 bits. One thousand (1000) bytes is a kilobyte. One million (1000 2) bytes is a megabyte. One billion (1000 3) bytes is a gigabyte. One trillion (1000 4) bytes is a terabyte. One quadrillion (1000 5) bytes is a petabyte. One quintillion (1000 6) bytes is an exabyte. One sextillion (1000 7) bytes is a zettabyte. One septillion (1000 8) bytes is a yottabyte. To put this in perspective, all the information contained in the U.S. Library of Congress is on the order of 15 terabytes. 1 It is not uncommon for large corporations today to house scores of petabytes of data. Google, Facebook, Amazon, and Microsoft collectively house on the order of an exabyte of data. 2 As we think about big data in today’s computer world, we are commonly addressing petabyte- and exabyte-scale problems. There are three essential constraints on computing capacity and the resulting complexity of the problem a computer can address. These relate to (1) the amount of available storage, (2) the size of the binary number the central processing unit (CPU) can add, and (3) the rate at which the CPU can execute addition. Over the past 70 years, the capacity of each has increased dramatically. As storage technology advanced from punch cards, in common use as recently as the 1970s, to today’s solid-state drive (SSD) non-volatile memory storage devices, the cost of storage has plummeted, and the capacity has expanded exponentially. A computer punch card can store 960 bits of information

6 A digital epistemology of Big Data

6 A digital epistemology of Big Data DOI: 10.4324/9781003098966-6 6.1 The Petabyte Age: an introduction When Steve Jobs launched the original Apple Macintosh computer in 1984, it was thought to be a revolutionary computer. (Remember the famous Ridley Scott 1984 video?) But this revolutionary computer contained just 128 kilobytes of memory storage. That is about enough space to hold about 1/15th of the book you are reading now, which is around 2MB. In 1986, the next version of the Macintosh – the Macintosh 512K – was released, and it more than tripled the storage space of the 1984 model. Although still not enough to hold this book’s 2MB, that might have still seemed impressive at the time! But just imagine if we’d told folks in 1986 that just 15 years later, in 2001, the first iPod would ship with 5,000,000 kilobytes (i.e., 5 gigabytes) of memory storage, the equivalent of about 40,000 1984 Macintoshes that you could put in your pocket! We are now 20+ years past the first iPod, and not only does 5 gigabytes of storage not sound impressive anymore, but just as we once made the transition from conceptualising information in kilobytes, and then to megabytes and gigabytes, we are now becoming more accustomed to thinking (as far as computer storage goes) in terabytes, which themselves are just a small fraction of (increasingly relevant) petabytes, exabytes and zettabytes. Figure 6.1 offers a perspective on these unit sizes. Figure 6.1 Source: My Nasa Data mynasadata.larc.nasa.gov The International Data Corporation (IDC) estimates, in their Data Age 2025 report, that 175 zettabytes of data will be generated annually worldwide by 2025

Chapter 1. From Terabytes to Insights

A single bit has two possible values, commonly notated as “1” and “0.” Think of a single bit like a light switch that can be either “off” or “on.” One bit isn’t a lot of storage, but it can be used to store things like the value of a “yes/no” statement (see Figure 1.19). 8 bits = 1 byte 1000 bytes = 1 kilobyte 1,000,000 bytes = 1 megabyte 1,000,000,000 bytes = 1 gigabyte 1,000,000,000,000 bytes = 1 terabyte 1,000,000,000,000,000 bytes = 1 petabyte 1,000,000,000,000,000,000 bytes =1 exabyte FIGURE 1.19 Kerplunk! On their own, these numbers seem fairly meaningless. Although it’s possible to picture in your mind’s eye having five of something (five jelly beans easily fit in the palm of your hand) or even 100 of something (the number of tiles in a Scrabble ® game), we can’t picture a trillion of something. The flow of data The process by which data is collected, transmitted, and stored varies widely between types of data and storage methods used, but the basic process is always the same. Digital data is collected through electronic sensors or through human input. This data is then transmitted across some distance that can be as short as the few centimeters between the keyboard on a laptop and its hard drive or as far away as the Voyager 1 Spacecraft, more than 11 billion miles from Earth. Once the data reaches its destination, it is stored on magnetic, optical, or solid-state media. “Big Data” collections are stored in data centers or data warehouses. A data center is a special room or building with rows and rows of computers stacked on top of each other in a rack. The computers used in data centers, servers, are generally much more powerful than standard desktop or laptop computers, with significantly more storage space, processing power, and random-access memory (RAM). The servers talk to each other on a high-performance network that allows data to be transferred at very high speeds

The legendary Silicon Valley entrepreneur examines how both business and government organizations can harness the power of disruptive technologies.   
  
 
 
 Tom Siebel, the billionaire technologist and founder of Siebel Systems, discusses how four technologies—elastic cloud computing, big data, artificial intelligence, and the internet of things—are fundamentally changing how business and government will operate in the 21st century. While this profound and fast-moving transformation can appear daunting to some, Siebel shows how organizations can not only survive, but thrive in the new digital landscape.
 
 
  
 
 
 In this authoritative yet accessible book, Siebel guides readers through the technologies driving digital transformation, and demonstrates how they can strategically exploit their powerful capabilities. He shows how leading enterprises such as Enel, 3M, Royal Dutch Shell, the U.S. Department of Defense, and others are applying AI and IoT with stunning results.

Survive and Thrive in an Era of Mass Extinction

Digital Transformation

Information we use to structure our lives is increasingly stored digitally, rather than in biomemory. (Just think: if your online calendar went down, would you know where you are supposed to be and at what time next week?) Likewise, with breakthroughs such as those from Google DeepMind and OpenAI, discoveries at the frontiers of knowledge are increasingly due to machine learning (often, applied to massive datasets, extracted from a fast-growing datasphere) rather than to brainbound cognition. It's hard to deny that knowledge retention and production are becoming increasingly – in various ways – digitised.Digital Knowledge: A Philosophical Investigation is the first book to squarely and rigorously investigate digital knowledge: what it is, how to make sense of it in connection with received theories of knowledge, and where it is going. Key questions J. Adam Carter examines along the way are the following:<ul> <li>How is mere digital information converted into reliable digital knowledge?</li> <li>To what extent can digital knowledge be vindicated against sceptical challenges, and in what ways might digital knowledge stand distinctively subject to defeat?</li> <li>What is the epistemically optimal way for us to decide which tasks to outsource entirely to intelligent machines, and to what extent is further outsourcing appropriate (or not) to verify the results of that same outsourced cognition?</li> <li>Are there any ways in which the expansion of the datasphere threatens to make knowledge less, rather than more, easy to come by? If so, are there any promising ways to safeguard, epistemically, against such threats?</li> </ul>Using fascinating examples throughout, such as the recent chess match between Stockfish and Google's AlphaZero, smartphones and personalisation, Digital Knowledge: A Philosophical Investigation is ideal for researchers investigating this fascinating area of research at the intersection of traditional mainstream epistemology, the philosophy of cognitive science, the philosophy of technology, and computer science.

A Philosophical Investigation

Digital Knowledge

Data Insights: New Ways to Visualize and Make Sense of Data offers thought-provoking insights into how visualization can foster a clearer and more comprehensive understanding of data. The book offers perspectives from people with different backgrounds, including data scientists, statisticians, painters, and writers. It argues that all data is useless, or misleading, if we do not know what it means.Organized into seven chapters, the book explores some of the ways that data visualization and other emerging approaches can make data meaningful and therefore useful. It also discusses some fundamental ideas and basic questions in the data lifecycle; the process of interactions between people, data, and displays that lead to better questions and more useful answers; and the fundamentals, origins, and purposes of the basic building blocks that are used in data visualization. The reader is introduced to tried and true approaches to understanding users in the context of user interface design, how communications can get distorted, and how data visualization is related to thinking machines. Finally, the book looks at the future of data visualization by assessing its strengths and weaknesses. Case studies from business analytics, healthcare, network monitoring, security, and games, among others, as well as illustrations, thought-provoking quotes, and real-world examples are included.This book will prove useful to computer professionals, technical marketing professionals, content strategists, Web and product designers, and researchers.
- Demonstrates, with a variety of case studies, how visualizations can foster a clearer and more comprehensive understanding of data

- Answers the question, "How can data visualization help me?" with discussions of how it fits into a wide array of purposes and situations

- Makes the case that data visualization is not just about technology; it also involves a deeply human process

Petabyte

What Is a Petabyte?

Scale and Composition of a Petabyte

Your digital library for Petabyte and Computer Science

Operational Context and Big Data

Related key terms