a given domain, the domain's accessibility content volumes from CDN. To the extent that content filtering or shaping is done, such as with "Open DNS" [Open16], per user per endpoint visibility can also be collected. Finally, application data are important if they can be acquired as they provide full visibility for a given application-which devices from which networks, from which locations are accessing a given application. A simple example of this comes from weblogs, although more diverse application- specific data can be envisioned. 8.2 BIG DATA Big data is a term that attempts to define data that are SO large that traditional approaches toward analyzing or processing them are impractical. Nevertheless, as computers and applications become more powerful, exactly how large a data set needs to be to challenge traditional processing is a moving target. Beyond considering only the size of the data as the defining characteristic for big data, Doug Laney from Gartner [Lane01] has offered some further attributes of big data by intro- ducing the "3Vs" of volume, variety, and velocity. Since then, others [Livi13, Data14, Wikibig] have extended the 3Vs to 5Vs or 7Vs to include other characteristics such as variability, validity/veracity, visibility/visualization, and value. These attributes describe a set of challenges when dealing with big data as described in the next section. 8.2.1 DEALING WITH BIG DATA'S 7Vs 1. Volume: As noted above, the most fundamental element of big data is its size, or vol- ume. The challenge for many businesses is the amount of data that is possible to store or process, which is overwhelming. To illustrate the size of today's digital data universe, it has been estimated that in 2015 the total volume of world's data was 7.9 zettabytes (1 zettabyte = 10^21 bytes) [Impa16]. Today, it is not uncommon for many businesses to store tens (or higher) PB of data. Storing and processing this amount of data cost effec- tively has led to new approaches such as the Hadoop ecosystem [Hado], described in sub- s