1 00:00:00,620 --> 00:00:01,740 - [Instructor] Next up let's talk about 2 00:00:01,740 --> 00:00:03,750 NoSQL document databases, 3 00:00:03,750 --> 00:00:07,330 which are focused on storing semi-structured data, 4 00:00:07,330 --> 00:00:08,680 typically in the form 5 00:00:08,680 --> 00:00:11,280 of JavaScript object notation documents 6 00:00:11,280 --> 00:00:13,660 or XML documents. 7 00:00:13,660 --> 00:00:17,150 MongoDB is the most popular of the document databases 8 00:00:17,150 --> 00:00:21,450 and its name actually comes from the word humongous, 9 00:00:21,450 --> 00:00:22,340 which as you can see, 10 00:00:22,340 --> 00:00:24,940 we put the word mongo in bold 11 00:00:24,940 --> 00:00:27,280 right in the middle of the word. 12 00:00:27,280 --> 00:00:30,450 Now because we're going to be talking about MongoDB 13 00:00:30,450 --> 00:00:33,940 in a bit more detail a few videos from now, 14 00:00:33,940 --> 00:00:36,760 we'll simply talk about the example that we're going to do 15 00:00:36,760 --> 00:00:39,520 which is we're going to harvest, if you will, 16 00:00:39,520 --> 00:00:44,070 10,000 live tweets and we'll store them as they're arriving 17 00:00:44,070 --> 00:00:46,600 directly into a MongoDB database, 18 00:00:46,600 --> 00:00:49,340 not as the individual tweet texts 19 00:00:49,340 --> 00:00:53,100 but as the full JavaScript object notation objects 20 00:00:53,100 --> 00:00:57,870 that we get back from the Twitter streaming API. 21 00:00:57,870 --> 00:01:00,760 So we'll take 10,000 tweets on a specific topic, 22 00:01:00,760 --> 00:01:02,860 store them into the database, 23 00:01:02,860 --> 00:01:05,650 and then we'll analyze those tweets, 24 00:01:05,650 --> 00:01:07,340 and we're going to do some plotting 25 00:01:07,340 --> 00:01:10,230 on an interactive map as well 26 00:01:10,230 --> 00:01:12,030 as part of that example. 27 00:01:12,030 --> 00:01:14,620 Now some of the other popular document databases 28 00:01:14,620 --> 00:01:16,470 as I mentioned in the preceding video, 29 00:01:16,470 --> 00:01:19,910 Amazon DynamoDB is a document database 30 00:01:19,910 --> 00:01:22,960 as well as being a key-value database. 31 00:01:22,960 --> 00:01:27,180 Microsoft's Azure Cosmos DB and Apache's Couch DB 32 00:01:27,180 --> 00:01:29,410 are also document databases. 33 00:01:29,410 --> 00:01:31,020 And these are not all of the ones, 34 00:01:31,020 --> 00:01:33,200 these are just some of the most popular ones 35 00:01:33,200 --> 00:01:34,150 that are out there.