equent sections. 2. Velocity: Perhaps even more daunting than volume, is how fast the volume of data is grow- ing, which is known as it's velocity. Indeed, data are growing SO fast that 90% of the world's data has been produced in the last 2 years. In addition, while the world has 7.9 zettabytes of data today, by 2020 the total digital data universe is expected to grow to 44 zettabytes, with each person generating 1.7 megabyte every second [Entr16]. At some point, the data become SO large for a business that it becomes necessary to typically either limit its' reten- tion or only keep summarized older data. Recently, new near real-time data processing technologies in the Hadoop ecosystem also provide the ability to process data in real time or micro-batches to extract the most important features of the data, which lessens the need to store the raw data at all. Organizationally, some [Entr16] have noted that the speed of data also suggests that it is impractical to have any single "data insight team" in a company, and that data analyses and processing needs to have some decentralization to keep up with the ever changing and new data. 3. Variety: Data also comes in different varieties. Some data are structured; having a known format such as might be produced from IOT devices emitting a known set of data elements in a predefined format. Some of it is unstructured, as might come from the content of a Twitter feed, social media, texting data, etc. Big data analyses often combine both types Network Data and Optimization of data to get the full picture of whatever is being analyzed. For instance, to better solve a customer's problem, a customer care big data application may utilize structured data hav- ing the account number, name, tenure, product type information, as well as unstructured data containing the "verbatim of the customer's problem description," any "error log files produced from the product" and any previous customer care "chat logs." Often, however, unstructured data are processed with machine learning (ML)