[MUSIC] 0:00 In this stage, we'll take a more specific look into where big data is being used, 0:04 as well as how companies or organizations are making use of its power. 0:09 The most important point to remember throughout these examples is that you 0:14 don't have to be a massive company to leverage data you have available 0:17 on a large scale. 0:21 Although it's helpful, 0:23 you don't need to have a data scientist on staff to get started. 0:24 Now oftentimes, you can tackle the data problems you have right now with only 0:27 a technical engineer that can write code in languages like Python, Java, or Scala. 0:31 This is especially important to remember for startups, where in this case, 0:35 any advantage over your competitors could mean the difference between 0:39 succeeding or failing. 0:42 Again, with just a few hundred megabytes or gigabytes of data on hand, you can 0:44 begin to see the benefits from using modern tools and frameworks in big data. 0:49 It does help, though, to take a look at more notable use cases of big data at 0:54 the companies we all know and use on a regular basis. 0:58 Google processes 40,000 search queries every second, on average, 1:02 which is 3.5 billion per day, and 1.2 trillion every year, worldwide. 1:07 Snapchat averages 2.5 billion pictures taken per day that have to be processed, 1:13 delivered, and stored. 1:19 Facebook averages 8 billion video views daily. 1:21 All of those videos have to have stats collected about them for 1:25 each user, as well as being cached effectively worldwide. 1:28 With these examples in mind, let's discuss the types of data that you may encounter 1:33 in the wild, and then we'll run through the three major domains of big data, 1:37 storage, computations, and infrastructure. 1:42 Let's kick it off by discussing what types of data exist, 1:45 right after this quick break. 1:48