Big data has been around since the late 1990s, when companies needed new and different ways to manage higher volumes of data than they\u2019d ever dealt with before. Offline processing used big data to run search engines; web crawlers processed data and then updated search results in early search engines. But, as our tastes evolved, we wanted instant access to the latest information\u2014news happening this minute, reflected on our screen. We sought to shorten the time between data arrival and the presentation of information at our fingertips. <\/span><\/p>\n\n\n\n Which brings us to fast data, which sounds like big data\u2019s smooth-talking cousin. You know, the one who owns his own rip-off used car dealership? Like any smooth-talking, swindling cousin, fast data is really, really good at what it does. Unlike the cousin, however, fast data is actually an improvement on big data, and its speedy title is offered as a compliment, not an insult. <\/span><\/p>\n\n\n\n Defining Fast Data<\/b><\/p>\n\n\n\n Image source: Pixabay<\/span><\/em><\/p>\n\n\n\n When comparing big data and fast data, it is easy to get caught in the phrasing. On the surface, fast data sounds like the less desirable of the two, but that isn\u2019t always the case. Identifying the difference isn\u2019t hard. In fact, it’s in the name. <\/span><\/p>\n\n\n\n The simple explanation of the two is that big data is data at rest\u2014it\u2019s an enormous collection of data points. On the other hand, fast data is data that is constantly moving; it\u2019s sifting through that big data to find the most valuable information. In big data, a physician examines a history of patient information to recommend treatment. With fast data, a doctor receives information in real time and makes a recommendation for treatment based on what\u2019s being provided to them in the moment. In essence, the doctor is able to process logs upon logs of patient data to find commonalities and patterns, resulting in an even better, more precise diagnosis.<\/span><\/p>\n\n\n\n As a patient, fast data provides an experience that typically results in better treatment. If the medical community\u2019s job is to serve patients, it is a worthwhile exploration. <\/span><\/p>\n\n\n\n Edge computing is fast data–the process of taking data as it is available from where it is created rather than sending it away to a data center for processing. Edge computing allows for the rapid interpretation of information, which is infinitely useful in situations where time is important. Medicine is a great example of this, because it clearly differentiates between big data and fast data. <\/span><\/p>\n\n\n\n So What is Big Data?<\/b><\/p>\n\n\n\n Image source: Pixabay<\/span><\/em><\/p>\n\n\n\n Broadly, big data refers to the application of sets of data that are, unsurprisingly, big. It is voluminous and varied, structured, unstructured, and somewhere in between. Big data is designed to be stored and eventually interpreted and explored, but it isn\u2019t just big. The data sets are so large that traditional software applications are unable to deal with them in a productive way. This is good and bad. Certainly, it adds an additional step, but for people who work as analysts, this means job security. <\/span><\/p>\n\n\n\n![\"\"](\"https:\/\/www.autodesk.com\/products\/fusion-360\/blog\/wp-content\/uploads\/2023\/04\/Screen-Shot-2019-01-03-at-8.43.21-AM-1024x730.jpg\")
<\/figure>\n\n\n\n![\"\"](\"https:\/\/www.autodesk.com\/products\/fusion-360\/blog\/wp-content\/uploads\/2019\/01\/Screen-Shot-2019-01-03-at-8.43.31-AM-1024x679.jpg\")
<\/figure>\n\n\n\n
![\"\"](\"https:\/\/www.autodesk.com\/products\/fusion-360\/blog\/wp-content\/uploads\/2023\/04\/Screen-Shot-2019-01-03-at-8.43.38-AM-1024x574.jpg\")
<\/figure>\n\n\n\n