Data is powerful: Used right, companies, scientists, research institutions, or even the average person can make informed choices based on data discoveries. With enough information parsed through the right tools, medical professionals can track trends in the spread of illnesses, companies can predict customer needs and wants, or environmental scientists can discover new correlations between human actions and environmental effects — the possibilities may well be endless.
However, to achieve these goals, the data sets processed need to contain a vast amount of data — not just terabytes of information, but peta- or exabytes; the sort of data that is usually too unwieldy for traditional relational databases and data processing systems to handle.
It is just not a matter of databases, either: When handling massive amounts of information, concerns are not limited to the storage of it but also its analysis, visualization, searching, sharing, and more. These issues are condensed into the “Three Vs” of managing big data: Volume, velocity, and variety.
The Three Vs of Big Data
Volume dictates the amount of data being processed; this is the petabytes or exabytes of information that need to be parsed to make the data usable. As mentioned, the problem with this level of data that it is often too much for a traditional relational database to handle, which means sharing the workload across multiple servers via cluster computing is ideal for managing big data.
The amount of data itself is not just integral to big data because of its sheer size, however. The more data, the more accurate the conclusions generated from that data. Think of a scientific study: A study with of 100,000 participants is always more precise than a study using only ten. Similarly, big data allows for the use of more overall factors; instead of working with three or four points of data, hundreds can be used to generate predictions. The same idea drives big data — the more information processed, the more accurate any conclusions derived. It is not just looking at a user’s gender, occupation, or purchased items anymore.
We all know that the transfer of information between two servers can be slow at times — now imagine transferring an entire petabyte! The speed in which data can be sent, shared, and processed is a constant concern in managing big data. Beyond this, the rapidity at which new data is generated is also a concern. It is not just about parsing 10 petabytes of static data; it is working with data that moves, changes and grows as reports come in. The quicker this data gets from reporting service to a company’s data processing solution, the more timely and on-point actions taken based on the data can be. A user might only be looking for car parts until their car is fixed, after all — what good will sending a newsletter about cars do for them two weeks later?
Big data is not about basic information. Data can draw from images, audio, video, and other sources, as well as text. This returns to the concept of having a variety of factors to pull from; only we are not limited to text-based strings. Big data solutions need to be able to process unstructured data and find structured, analytical meaning. Solutions also need to be intuitive and handle data that may not always follow the same patterns — reports from Firefox are not the same as reports from Chrome. When someone refers to “Washington,” is it the state or district? These differences matter in determining the accuracy of the analyzed data.
Other Vs are often added by companies, marketers, big data experts, and laymen, as needed, but at its core, the three originally supplied by research company Gartner are most applicable for all big data uses, especially for businesses or persons first stepping into the realm.
Look for it! Not enough big data for you? Linux Academy will be releasing more big data offerings shortly. Check back for announcements and more blogs on data solutions.