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These are the user uploaded subtitles that are being translated: 1 00:00:00,110 --> 00:00:05,700 In this section, we're going to start learning the most fundamental sequel commands and clauses that 2 00:00:05,700 --> 00:00:07,770 we can write in the bakery interface. 3 00:00:08,310 --> 00:00:13,620 And we're also going to upload our first dataset and create a table out of it. 4 00:00:14,500 --> 00:00:21,180 Now, if you recall, in Section one, we were exploring the Chicago taxi file and table. 5 00:00:21,600 --> 00:00:26,780 And what I've done is because there were a lot of records, I've created a sample data set and downloaded 6 00:00:26,790 --> 00:00:28,580 that in a CSB format. 7 00:00:28,860 --> 00:00:32,340 And so we're gonna take that CSB format and basically upload it here. 8 00:00:33,630 --> 00:00:36,480 Now, the first thing that you can see here is the left hand side. 9 00:00:36,480 --> 00:00:40,800 We have our project, big course demo and nothing within it. 10 00:00:41,460 --> 00:00:47,700 So I step one, we have to create a dataset so you can simply click create data set here. 11 00:00:48,700 --> 00:00:50,320 And it's fairly simple to do. 12 00:00:50,560 --> 00:00:55,510 We just have to put a data set I.D. that would be taxi. 13 00:00:57,890 --> 00:00:59,210 Data set. 14 00:01:00,080 --> 00:01:01,460 And you have to use underscores. 15 00:01:01,490 --> 00:01:04,240 You cannot have spaces when you're creating those ideas. 16 00:01:04,460 --> 00:01:06,530 That's just good, good practice. 17 00:01:08,030 --> 00:01:12,650 The default table expiration because we're using the sandbox account, it defaults to 60 days. 18 00:01:13,010 --> 00:01:16,610 So we're going to keep that there and create a data set. 19 00:01:16,910 --> 00:01:21,530 And almost instantly, you'll see that the data set has been created by expand here. 20 00:01:21,890 --> 00:01:28,760 We have our data set and we can click through in the data set and now we have the option to create a 21 00:01:28,760 --> 00:01:32,210 table table is what hosts the data. 22 00:01:32,810 --> 00:01:37,250 So we're going to create a table here and we will have a couple of options. 23 00:01:37,460 --> 00:01:39,940 So this menu pops up a create table. 24 00:01:39,950 --> 00:01:44,240 If I click create table from here, there are a few options. 25 00:01:45,200 --> 00:01:47,210 There is the drive. 26 00:01:47,240 --> 00:01:52,610 So you could upload a Google Drive, Google Sheets, a file. 27 00:01:52,670 --> 00:01:59,480 You could use Google cloud storage or in our case, you can also upload a C as V, so click, upload 28 00:02:00,050 --> 00:02:02,780 and I'll click select file here. 29 00:02:03,050 --> 00:02:08,720 Now I've you would have this file available in your resource section so you can download it on your 30 00:02:08,720 --> 00:02:13,400 local machine and you should be able to follow through this section. 31 00:02:14,180 --> 00:02:22,160 So you will simply click, browse and wherever you store that file, you will find it and click on Chicago 32 00:02:22,160 --> 00:02:22,470 trips. 33 00:02:22,610 --> 00:02:24,160 Ten thousand records sample. 34 00:02:25,130 --> 00:02:31,410 And what that does is one site click it immediately to file format changes to see ASV. 35 00:02:31,700 --> 00:02:34,190 Google actually recognized that that's a CSB file. 36 00:02:35,630 --> 00:02:39,800 And the destination here project, we've already have that data set. 37 00:02:39,830 --> 00:02:46,270 We simply created that the table here would be taxi trips. 38 00:02:47,450 --> 00:02:53,450 And I will put sample because all I've done is from the million and million records of that public data 39 00:02:53,450 --> 00:02:53,780 set. 40 00:02:54,650 --> 00:02:59,810 I've taken that and I've just picked ten thousand records that are now, you know, randomized. 41 00:03:01,720 --> 00:03:05,200 Once we have the table ready, then we go to the schema. 42 00:03:05,320 --> 00:03:07,240 So we we talked a lot about the schema. 43 00:03:07,870 --> 00:03:10,270 And there's a couple of options here when you're uploading. 44 00:03:10,690 --> 00:03:14,200 Now, the easy one that we're going to use here is auto detect. 45 00:03:14,440 --> 00:03:20,270 So relieving it up to big query to detect each each value that is within that data set. 46 00:03:20,680 --> 00:03:23,020 What type of what is the format? 47 00:03:24,340 --> 00:03:28,650 And it's using, you know, what are the input parameters. 48 00:03:29,140 --> 00:03:30,860 And that's all happening automatic. 49 00:03:31,690 --> 00:03:35,290 Now, you could do that manually either by editing this text. 50 00:03:35,350 --> 00:03:42,670 So if I click edit this text, I could do this whole process manually, or I can add the fields here 51 00:03:43,060 --> 00:03:45,730 simply by typing the name of the column. 52 00:03:46,450 --> 00:03:52,120 Choosing the type and you have string, integer, float and etc. and in choosing the mode. 53 00:03:52,750 --> 00:03:57,010 But we're not gonna do this in this in this case because we can auto detect. 54 00:03:57,820 --> 00:03:59,000 So we'll click on Detect. 55 00:03:59,800 --> 00:04:01,840 And we're going to move on to partitioning. 56 00:04:02,020 --> 00:04:03,040 So partitioning. 57 00:04:03,910 --> 00:04:06,500 We're gonna explore that in our first project. 58 00:04:06,610 --> 00:04:10,870 But it has to do with how much data do you want to load? 59 00:04:11,260 --> 00:04:16,810 Because when you create tables, you don't really want to query the whole table where there's millions 60 00:04:16,810 --> 00:04:17,680 and millions of records. 61 00:04:17,710 --> 00:04:22,150 If you just want to look at a subset, you can what is called a partition, that subset of data. 62 00:04:22,510 --> 00:04:25,210 But we're gonna see this in action in our project. 63 00:04:26,410 --> 00:04:28,900 Now, last but not least, advanced options here. 64 00:04:29,320 --> 00:04:33,070 When you click, then you'll expanded writing preference. 65 00:04:33,520 --> 00:04:38,020 Now, there's three options here because we're creating a brand new table. 66 00:04:39,070 --> 00:04:39,400 Right. 67 00:04:39,430 --> 00:04:40,120 If empty. 68 00:04:40,330 --> 00:04:43,750 So all the data that you're uploading will be written into the table. 69 00:04:43,780 --> 00:04:47,050 And there's going to be no problem because that table is anyways empty. 70 00:04:47,950 --> 00:04:49,990 Now, you can also choose a pen to table. 71 00:04:50,830 --> 00:04:57,460 And this could be used in a scenario where you have all the January data file and you want to upload 72 00:04:57,460 --> 00:04:58,270 the February data. 73 00:04:58,750 --> 00:05:03,220 So if you have a separate file, if you upload, then file in your keeping the same schema. 74 00:05:03,280 --> 00:05:04,330 And that's very important. 75 00:05:04,360 --> 00:05:09,010 If you're keeping the same schema, you can click append to table and all that will be appended as opposed 76 00:05:09,010 --> 00:05:12,010 to overwritten in some way. 77 00:05:12,790 --> 00:05:14,980 And that obviously brings us to option three. 78 00:05:15,100 --> 00:05:20,390 If we do want to override completely and change the entire table with new data. 79 00:05:20,620 --> 00:05:24,100 We can simply override would our new file. 80 00:05:24,340 --> 00:05:31,180 So if you've actually consolidated your, let's say, data for January and February already in a S.A.C. 81 00:05:31,180 --> 00:05:36,460 file and you just want to override the main table, you could do that by picking the overwrite table 82 00:05:36,460 --> 00:05:36,790 option. 83 00:05:37,570 --> 00:05:40,690 But in our case, right of empty because it's a brand new table. 84 00:05:41,820 --> 00:05:43,130 So we're all set here. 85 00:05:43,590 --> 00:05:45,150 We're going to click create table. 86 00:05:47,180 --> 00:05:53,230 It's going to take a little bit of time to compute the table, and that would show you can see Loja 87 00:05:53,360 --> 00:05:59,330 job created and immediately says taxi trips, sample, create it and you can see it reflected here. 88 00:05:59,920 --> 00:06:00,770 So taxi trips. 89 00:06:00,770 --> 00:06:09,740 And if I click on this, what we're going to see is our familiar type mode and the entire data schema. 90 00:06:09,890 --> 00:06:13,410 So it becomes very similar to what you saw in the public dataset. 91 00:06:14,450 --> 00:06:19,190 Now, in the next part, we're actually going to dive a little bit deeper into this and write our very 92 00:06:19,190 --> 00:06:20,030 first query. 9330