The amount of information being processed and stored is rapidly increasing as technology advances present an ever-increasing ability to generate and store data. This data is commonly stored in computer-based systems in structured data stores. For example, one common type of data store is a so-called “flat” file such as a spreadsheet, plain-text document, or XML document. Another common type of data store is a relational database comprising one or more tables. Other examples of data stores that comprise structured data include, without limitation, files systems, object collections, record collections, arrays, hierarchical trees, linked lists, stacks, and combinations thereof.
Numerous organizations, including industry, retail, and government entities, recognize that important information and decisions can be drawn if massive data sets can be analyzed to determine useful information. Collecting, classifying, and processing large sets of data can allow these entities to make more informed decisions. The manner in which these organizations collect and classify information, however, has become antiquated over time and do not take advantage of the higher processing speeds associated with the “Big Data” era. With advances in multi-tenant environments (e.g., the “cloud”), new techniques previously unthought-of are being designed to efficiently sift through billions of data points to draw new and useful information.