All language subtitles for 36 - Exercise OLTP vs OLAP English

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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,800 --> 00:00:08,390 ‫I thought it would be fun to complement this theoretical course with a small exercise around OLAP versus 2 00:00:08,390 --> 00:00:15,700 ‫old TP because they are kind of abstract concepts, so let's dive into it exercise. 3 00:00:16,160 --> 00:00:23,720 ‫So the format here is that we are going to be guessing which ones are all Tepes and which ones are all 4 00:00:23,720 --> 00:00:24,250 ‫apeace. 5 00:00:24,860 --> 00:00:34,610 ‫So a database is being used to log orders and customers to a database is being used to figure out what 6 00:00:34,610 --> 00:00:36,020 ‫new products we should offer. 7 00:00:37,350 --> 00:00:45,780 ‫Three, a database is being used to derive statistics for reporting to the executives and for a database 8 00:00:45,780 --> 00:00:48,600 ‫is being used to keep track of logged in users. 9 00:00:49,110 --> 00:00:50,740 ‫So pause the video right here. 10 00:00:50,940 --> 00:00:51,690 ‫Think about it. 11 00:00:51,870 --> 00:00:53,450 ‫Write down one, two, three, four. 12 00:00:53,460 --> 00:00:57,650 ‫And what are you think one is an all tip or an oil AP and two and three and four. 13 00:00:57,990 --> 00:00:59,850 ‫And when you come back, we'll run through them. 14 00:01:00,660 --> 00:01:01,250 ‫All right. 15 00:01:01,290 --> 00:01:02,060 ‫So we're back. 16 00:01:02,550 --> 00:01:06,540 ‫One was a database is being used to log orders and customers. 17 00:01:06,570 --> 00:01:07,530 ‫So let's go look at that. 18 00:01:08,100 --> 00:01:09,780 ‫Well, that's an old TPE. 19 00:01:10,560 --> 00:01:12,510 ‫So it's a transactional database. 20 00:01:12,690 --> 00:01:13,620 ‫An old. 21 00:01:15,210 --> 00:01:16,720 ‫Why is it a transactional database? 22 00:01:16,740 --> 00:01:17,640 ‫Why is it old? 23 00:01:18,510 --> 00:01:23,250 ‫Well, it is all because it's being used to drive the day to day of a business. 24 00:01:24,480 --> 00:01:29,880 ‫All right, let's take a look at two, a database is being used to figure out what new products we should 25 00:01:29,880 --> 00:01:30,180 ‫offer. 26 00:01:30,540 --> 00:01:31,650 ‫Let's go take a quick look. 27 00:01:32,790 --> 00:01:34,480 ‫O l a p. 28 00:01:35,280 --> 00:01:39,810 ‫All right, so too was OLP, why is it an analytical platform? 29 00:01:40,080 --> 00:01:45,270 ‫Well, as I said, a database that's being used to figure out what new products we should offer to look 30 00:01:45,270 --> 00:01:51,300 ‫to the future to do kind of like an analysis on where we are and what we're doing that's typically used 31 00:01:51,300 --> 00:01:52,380 ‫for analysis. 32 00:01:52,680 --> 00:01:56,190 ‫So it's being used to figure out the day to day driving of the business. 33 00:01:56,940 --> 00:02:01,560 ‫Three, a database is being used to derive statistics for reporting to executives. 34 00:02:03,100 --> 00:02:11,250 ‫That is well, again, we're using a database to derive statistics for reporting to the executives, 35 00:02:11,470 --> 00:02:15,730 ‫so we're trying to figure out, OK, what's going on here, what is in the company, what data, what 36 00:02:15,730 --> 00:02:18,190 ‫statistics do we need to run in order to figure it out? 37 00:02:18,190 --> 00:02:23,110 ‫So we're pumping out data from a database and we're trying to figure out some stuff on top of it. 38 00:02:23,950 --> 00:02:28,000 ‫And then for a database is being used to keep track of the logged in users. 39 00:02:28,180 --> 00:02:33,490 ‫Now, this one was tricky because to keep track of the logged in users, you may think, oh, I can 40 00:02:33,490 --> 00:02:34,750 ‫use that for analytics. 41 00:02:35,680 --> 00:02:42,610 ‫But in essence, it is all TPE because it's being used to drive the day of the business, you're basically 42 00:02:42,610 --> 00:02:45,850 ‫using it to keep track of, OK, this many users are logged in. 43 00:02:45,850 --> 00:02:47,500 ‫This is how many sessions we have. 44 00:02:47,830 --> 00:02:54,280 ‫Eventually, it could be fed into a system that could use it to derive analytics to, for instance, 45 00:02:54,280 --> 00:02:57,040 ‫figure out, OK, this is our daily active user. 46 00:02:57,400 --> 00:03:03,370 ‫But in essence, that database that's keeping track of that stuff at that given point in time is being 47 00:03:03,370 --> 00:03:08,230 ‫used as a transactional database because someone's logging in the record is going in saying I'm logged 48 00:03:08,230 --> 00:03:10,330 ‫in them when they log out at a log out of time. 49 00:03:10,600 --> 00:03:17,170 ‫And by doing that, by that definition, it is all TPE, but it could be used in an overlap way. 50 00:03:17,770 --> 00:03:18,310 ‫All right. 51 00:03:18,310 --> 00:03:20,020 ‫So let's move on to the next video. 5058

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