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These are the user uploaded subtitles that are being translated: 1 00:00:00,000 --> 00:00:02,000 Instructor: All right, so we just walked through 2 00:00:02,000 --> 00:00:04,000 how to stack some tables together 3 00:00:04,000 --> 00:00:06,000 using the Query Editor append tools 4 00:00:06,000 --> 00:00:09,000 and we took these three individual sales data files 5 00:00:09,000 --> 00:00:12,000 from 2022, '21, and '20 and combined them 6 00:00:12,000 --> 00:00:15,000 into a new appended data set, right, 7 00:00:15,000 --> 00:00:18,000 where we stacked all three of them on top of each other. 8 00:00:18,000 --> 00:00:19,000 And that worked pretty well. 9 00:00:19,000 --> 00:00:20,000 But remember the downside, 10 00:00:20,000 --> 00:00:23,000 this new table we created is now dependent 11 00:00:23,000 --> 00:00:26,000 on three individual component tables. 12 00:00:26,000 --> 00:00:29,000 So we can't delete any of these individual tables 13 00:00:29,000 --> 00:00:33,000 or if we need to add in new years worth of data, 14 00:00:33,000 --> 00:00:35,000 like, we'll have to reconfigure the append 15 00:00:35,000 --> 00:00:35,000 to make that work. 16 00:00:35,000 --> 00:00:38,000 So, again, it's just, it works decently 17 00:00:38,000 --> 00:00:40,000 but it doesn't scale very well. 18 00:00:40,000 --> 00:00:42,000 So what we're gonna do here 19 00:00:42,000 --> 00:00:45,000 is we're gonna actually delete these four sales queries 20 00:00:45,000 --> 00:00:47,000 and then we're gonna use Power BI 21 00:00:47,000 --> 00:00:50,000 and connect to a folder instead, right? 22 00:00:50,000 --> 00:00:53,000 So first things first, we can delete this append query 23 00:00:55,000 --> 00:00:58,000 and then once that's removed, we can actually start removing 24 00:00:58,000 --> 00:01:01,000 our individual sales data files, right? 25 00:01:01,000 --> 00:01:05,000 Because now, they're not referencing any other queries. 26 00:01:05,000 --> 00:01:07,000 And delete 2021. 27 00:01:09,000 --> 00:01:14,000 And then our last file here for 2022, we'll delete that. 28 00:01:15,000 --> 00:01:18,000 All right, and we're good to go here. 29 00:01:18,000 --> 00:01:19,000 So now that we've done that, 30 00:01:19,000 --> 00:01:22,000 we're left with our six kind of main lookup tables 31 00:01:22,000 --> 00:01:25,000 that we're gonna be using to build out our data model. 32 00:01:25,000 --> 00:01:26,000 And from here, what we want to do 33 00:01:26,000 --> 00:01:28,000 is we actually want to go in 34 00:01:28,000 --> 00:01:31,000 and connect to a new source, right? 35 00:01:31,000 --> 00:01:32,000 That's gonna be that folder. 36 00:01:32,000 --> 00:01:34,000 And I quickly want to show you 37 00:01:34,000 --> 00:01:36,000 what's contained within that folder. 38 00:01:36,000 --> 00:01:39,000 So I'm gonna navigate to that folder on my desktop. 39 00:01:40,000 --> 00:01:41,000 All right, so this is the folder 40 00:01:41,000 --> 00:01:44,000 where we've got all of the AdventureWorks data 41 00:01:44,000 --> 00:01:47,000 and I have this sales data folder here. 42 00:01:47,000 --> 00:01:48,000 I double click into this, 43 00:01:48,000 --> 00:01:51,000 you'll see that we've got our three sales tables 44 00:01:51,000 --> 00:01:54,000 for 2022, '21, and '20 within here. 45 00:01:54,000 --> 00:01:56,000 And this is what we're gonna be connecting to 46 00:01:56,000 --> 00:01:59,000 from the Power BI folder data source. 47 00:01:59,000 --> 00:02:02,000 So we'll jump back to the Query Editor 48 00:02:02,000 --> 00:02:05,000 and we'll come up here to a new source, 49 00:02:05,000 --> 00:02:08,000 and I'm gonna click more for more options. 50 00:02:08,000 --> 00:02:10,000 All right, and from the Get Data window, 51 00:02:10,000 --> 00:02:12,000 you actually see we've got our folder option right here. 52 00:02:12,000 --> 00:02:14,000 We can click connect 53 00:02:14,000 --> 00:02:18,000 and the Query Editor prompts us with the folder path. 54 00:02:18,000 --> 00:02:21,000 Click browse to browse your file structure. 55 00:02:22,000 --> 00:02:25,000 Right, desktop, Adventure Works Raw Data, 56 00:02:25,000 --> 00:02:27,000 and then our sales data, right? 57 00:02:27,000 --> 00:02:30,000 So this is the file path that we want. 58 00:02:30,000 --> 00:02:31,000 Click okay. 59 00:02:31,000 --> 00:02:33,000 And again, this is gonna follow that same process 60 00:02:33,000 --> 00:02:35,000 where it's gonna bring up a data preview window. 61 00:02:35,000 --> 00:02:38,000 We'll be able to check out what that data looks like. 62 00:02:38,000 --> 00:02:40,000 Now, what's interesting here is this preview window 63 00:02:40,000 --> 00:02:42,000 looks a little bit different, right? 64 00:02:42,000 --> 00:02:45,000 It's got this content column that says binary. 65 00:02:45,000 --> 00:02:48,000 It's got file names, and extensions, 66 00:02:48,000 --> 00:02:50,000 and date accessed, and modified. 67 00:02:51,000 --> 00:02:53,000 It's got the folder path string. 68 00:02:53,000 --> 00:02:55,000 So rather than actually previewing the data, 69 00:02:55,000 --> 00:02:58,000 the actual rows and the columns, 70 00:02:58,000 --> 00:03:01,000 what we're actually previewing are the files that exist 71 00:03:01,000 --> 00:03:03,000 within the folder path, right? 72 00:03:03,000 --> 00:03:05,000 So it's a little bit different here 73 00:03:05,000 --> 00:03:06,000 but a similar process, right? 74 00:03:06,000 --> 00:03:09,000 And we've got two options as well. 75 00:03:09,000 --> 00:03:11,000 We can combine and transform 76 00:03:11,000 --> 00:03:13,000 or we can just transform the data. 77 00:03:13,000 --> 00:03:14,000 Either one of these options 78 00:03:14,000 --> 00:03:17,000 is gonna kind of put us into the same spot, 79 00:03:17,000 --> 00:03:19,000 but I'm gonna click transform data 80 00:03:19,000 --> 00:03:21,000 so we can walk through each of these steps. 81 00:03:22,000 --> 00:03:24,000 So here we go, we pressed edit, 82 00:03:24,000 --> 00:03:26,000 we're back in our Query Editor, 83 00:03:26,000 --> 00:03:29,000 and we see that similar kind of view 84 00:03:29,000 --> 00:03:30,000 that we got in preview, right? 85 00:03:30,000 --> 00:03:33,000 We got content and the name, 86 00:03:33,000 --> 00:03:35,000 our file extension, date modified, 87 00:03:35,000 --> 00:03:39,000 created our folder path, and all that kind of stuff. 88 00:03:39,000 --> 00:03:41,000 So again, we're still not looking at data points here. 89 00:03:41,000 --> 00:03:44,000 We're still looking at the attributes of the files 90 00:03:44,000 --> 00:03:46,000 that exist within that folder that we've linked to. 91 00:03:47,000 --> 00:03:50,000 The key here is the first column. 92 00:03:50,000 --> 00:03:52,000 And this column is kind of similar to the column 93 00:03:52,000 --> 00:03:54,000 that was created when we merged tables 94 00:03:54,000 --> 00:03:56,000 where we had to take one extra step 95 00:03:56,000 --> 00:04:00,000 to actually split out the columns from the table itself. 96 00:04:00,000 --> 00:04:02,000 It's the same idea here. 97 00:04:02,000 --> 00:04:05,000 So you can see when I hover, it says, "Combine Files." 98 00:04:05,000 --> 00:04:07,000 And when we press this, 99 00:04:07,000 --> 00:04:11,000 it runs through a different append process. 100 00:04:11,000 --> 00:04:15,000 So once Power BI finishes that behind the scenes process, 101 00:04:15,000 --> 00:04:17,000 we're brought to this new window 102 00:04:17,000 --> 00:04:19,000 with this combine files header. 103 00:04:19,000 --> 00:04:21,000 And this looks like the data preview that we were seeing 104 00:04:21,000 --> 00:04:25,000 when we brought in our individual sales data tables, right? 105 00:04:25,000 --> 00:04:26,000 Our order and stock dates, order number, 106 00:04:26,000 --> 00:04:29,000 product key, customer key, you know, 107 00:04:29,000 --> 00:04:31,000 order line items, order quantity, right? 108 00:04:31,000 --> 00:04:34,000 So this all looks very familiar. 109 00:04:34,000 --> 00:04:35,000 The other thing that's interesting here too 110 00:04:35,000 --> 00:04:37,000 is if you click on the sample file, 111 00:04:37,000 --> 00:04:39,000 the default is the first file 112 00:04:39,000 --> 00:04:40,000 that you've connected to, right? 113 00:04:40,000 --> 00:04:41,000 So 2020. 114 00:04:41,000 --> 00:04:44,000 But you can actually click through and view previews 115 00:04:44,000 --> 00:04:46,000 based on the other data tables 116 00:04:46,000 --> 00:04:48,000 that are contained within that folder, right? 117 00:04:48,000 --> 00:04:51,000 So this is just another great way to kind of go in 118 00:04:51,000 --> 00:04:54,000 and check to make sure that column structures 119 00:04:54,000 --> 00:04:57,000 and all of that stuff is the exact same. 120 00:04:57,000 --> 00:04:58,000 All right, so everything looks good here, 121 00:04:58,000 --> 00:04:59,000 kind of checks out. 122 00:04:59,000 --> 00:05:01,000 We've got 2021 for our order date. 123 00:05:01,000 --> 00:05:03,000 From here, we're gonna click okay. 124 00:05:05,000 --> 00:05:07,000 All right, we've got a whole bunch 125 00:05:07,000 --> 00:05:08,000 of different applied steps here 126 00:05:08,000 --> 00:05:10,000 that were automatically generated. 127 00:05:10,000 --> 00:05:14,000 If we check out our order date column here, we can load more 128 00:05:16,000 --> 00:05:20,000 and we can actually see, so we've got 2020, 2021, 2022, 129 00:05:20,000 --> 00:05:22,000 all the way through June, right? 130 00:05:22,000 --> 00:05:24,000 So everything looks good there. 131 00:05:24,000 --> 00:05:27,000 Our column structure is all the exact same 132 00:05:27,000 --> 00:05:30,000 with the exception of this new source name column. 133 00:05:30,000 --> 00:05:33,000 And what this column is doing is it just tells us 134 00:05:33,000 --> 00:05:37,000 which file from within the folder that the data came from. 135 00:05:37,000 --> 00:05:39,000 So it's potentially a helpful attribute 136 00:05:39,000 --> 00:05:40,000 if you do want to keep it. 137 00:05:40,000 --> 00:05:43,000 In this case, we really don't need it 138 00:05:43,000 --> 00:05:46,000 so let's go ahead and we can remove this column 139 00:05:46,000 --> 00:05:47,000 from our sales data table. 140 00:05:48,000 --> 00:05:49,000 And there you have it. 141 00:05:49,000 --> 00:05:51,000 We have our appended table 142 00:05:51,000 --> 00:05:53,000 containing all of our sales data in one place. 143 00:05:53,000 --> 00:05:55,000 And as you can probably see here, 144 00:05:55,000 --> 00:05:58,000 it gave us an automatic table name 145 00:05:58,000 --> 00:06:00,000 that's based on the folder, right? 146 00:06:00,000 --> 00:06:02,000 So our folder name was Sales Data, right? 147 00:06:02,000 --> 00:06:05,000 So we could update this if we wanted to. 148 00:06:05,000 --> 00:06:07,000 Honestly, I think that this is a great name for it 149 00:06:07,000 --> 00:06:09,000 so we're gonna leave it as is. 150 00:06:09,000 --> 00:06:10,000 One last thing to note here, 151 00:06:10,000 --> 00:06:12,000 over on the left hand side of the screen 152 00:06:12,000 --> 00:06:13,000 where the queries are, 153 00:06:13,000 --> 00:06:15,000 is we've got a whole bunch of other detail 154 00:06:15,000 --> 00:06:19,000 and information that was automatically generated 155 00:06:19,000 --> 00:06:20,000 by the Query Editor. 156 00:06:20,000 --> 00:06:21,000 And basically, what this is 157 00:06:21,000 --> 00:06:24,000 is this is all of the behind the scenes stuff 158 00:06:24,000 --> 00:06:27,000 that Power BI needed to do in order to append 159 00:06:27,000 --> 00:06:30,000 those three files together from that folder. 160 00:06:30,000 --> 00:06:32,000 So typically, what I'll do here 161 00:06:32,000 --> 00:06:34,000 is I'll just collapse these queries, right? 162 00:06:34,000 --> 00:06:36,000 And just kind of hide them. 163 00:06:36,000 --> 00:06:37,000 And that way, I can kind of stay focused 164 00:06:37,000 --> 00:06:40,000 on my other queries that I have here. 165 00:06:40,000 --> 00:06:42,000 We are ending up with that same end result 166 00:06:42,000 --> 00:06:46,000 as appending those three individuals tables together, 167 00:06:46,000 --> 00:06:49,000 except now, we've got one nice clean table 168 00:06:49,000 --> 00:06:53,000 without all of those extra tables and dependencies. 169 00:06:53,000 --> 00:06:54,000 And even more importantly, 170 00:06:54,000 --> 00:06:58,000 if we ever needed to pull in additional years of sales data, 171 00:06:58,000 --> 00:07:02,000 we could simply drop those CSV files right into that folder, 172 00:07:02,000 --> 00:07:04,000 press refresh right here inside the Query Editor 173 00:07:04,000 --> 00:07:07,000 and all of that data would be automatically pulled in. 174 00:07:07,000 --> 00:07:10,000 So that's just about do it here for this demo 175 00:07:10,000 --> 00:07:14,000 on connecting to data that's located in a folder. 176 00:07:14,000 --> 00:07:16,000 Let's come up here, we'll click close and apply. 177 00:07:16,000 --> 00:07:19,000 And Power BI is working on updating all of those changes 178 00:07:19,000 --> 00:07:22,000 that we made within the Query Editor. 179 00:07:22,000 --> 00:07:23,000 All right, so once this wraps up, 180 00:07:23,000 --> 00:07:26,000 we should see that sales data 2022 table disappear 181 00:07:26,000 --> 00:07:29,000 and just be updated with the sales data table. 182 00:07:29,000 --> 00:07:31,000 All right, so it looks like we're good to go here. 183 00:07:31,000 --> 00:07:33,000 And that's gonna wrap up your pro tip 184 00:07:33,000 --> 00:07:36,000 on connecting to data that's located in a folder. 14519

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