Power Query in Power BI

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Power Query in Power BI  Take your data manipulation skills to the next level with our in-depth exploration of the Power Query Editor in Power BI. This playlist is designed for professionals, data enthusiasts, and Power BI users eager to fully understand the capabilities of the Power Query Editor. In this course, we dive deep into the various tools and techniques available for transforming and shaping your data, allowing you to create more accurate and efficient data models.  Whether you’re dealing with simple datasets or complex data transformations, this course will equip you with the knowledge to make the most of your data.  What You’ll Learn: • Advanced data transformation techniques using Power Query Editor • How to clean, filter, and reshape data for better analysis • Strategies for optimizing data models through effective data preparation • Best practices for handling large datasets and improving performance  Ideal For: • Data Analysts and Business Intelligence Professionals • Power BI users looking to master the Power Query Editor • Anyone interested in enhancing their data preparation and transformation skills  Subscribe to stay updated with the latest software tutorials and deepen your expertise in Power Query Editor and data transformation.  Power Query tutorial for beginners Advanced Power Query techniques in Power BI How to use Power Query in Power BI Cleaning data with Power Query Power Query best practices in Power BI Transforming data with Power Query Introduction to Power Query in Power BI Merging queries in Power Query Data modeling with Power Query in Power BI Power Query functions and formulas  #PowerBI #PowerQuery #BusinessIntelligence #DataVisualization #DataModeling #DataAnalytics #DataTransformation #ETLProcess #DataAnalysis #PowerBITutorial #ExcelPowerQuery #MicrosoftPowerBI #PowerQueryFunctions #DataPreparation #PowerBITips #PowerBITricks #PowerQueryTutorial #LearnPowerBI #BITools #PowerQueryExamples #DataAnalyst #DataCleaning #PowerBIProject #AdvancedPowerQuery #DataInsights #Reporting #Data #Analytics #Self-ServiceBI

Transcript

00:00Hey friends, welcome back.

00:07Now, in the last video, we imported one of the most important tables in Power BI,

00:12which is the dates table. Now, we have seen already a few transformations.

00:17Remember what we did when we appended the three sales tables, but of course,

00:22there are much more transformations inside the Power Query editor, which you can see under home,

00:27all these different kinds of transformations, as well as under transform, under add column, and so on.

00:32And now, let's explore a few of them, so you better understand what is available for you in this project,

00:37but also for all your future projects. Now, close and apply, we already know that

00:42this allows us to apply the steps we have created and go back to our Power BI report.

00:47On a new source, we have seen that this would be an option to import additional tables.

00:52So, you cannot only import tables directly from here by clicking on the Excel option,

00:57for instance, here in the ribbon bar, but also, as long as you have the Power Query editor open,

01:02there is still an option to import data from here.

01:07So, for instance, I could go here to Excel workbook, and now I could choose additional tables, which I like to load.

01:12The difference is that if I do it from here, then those files I select are directly showing up here,

01:18and I can also first transform them.

01:23Under recent sources, I would see all my recent sources, and if I want to enter data manually,

01:28I could go here and simply click on, in this case, enter data, and then in here, I could create a sample data here.

01:33I could say, this is, for instance, here, column one, and then I type here one and two,

01:38and here, I go in here, column two, and three, and four, and I can name this table.

01:43That's my, for instance, manual TVL, right, like that.

01:48I can click OK, and now this table would also appear here, like here, right, and I could use it in my model.

01:53I don't need this. It was just an example that you can also insert data manually.

01:58Or Ctrl C, Ctrl V to paste. I think that's also possible.

02:03I don't know for sure right now, but if that's something you would like to try, feel free to do that.

02:08I can say I'd like to delete this because this was just an example. Delete it, and yes, I'd like to delete it.

02:13Make sure, yes, I am sure, and now this table is gone.

02:18What else do we have under home? Well, let me select maybe one of the tables first, and now we can see that those options are no longer grayed out.

02:26Well, more interesting, of course, are maybe starting from here, right?

02:31So, refresh preview just means that if, right now, we are still in the Power Query editor,

02:36maybe the underlying data has changed because one of our colleagues has added additional data to the sales 2023,

02:43which are brand new, and then, of course, we could simply click on refresh preview, and we would see this change reflect.

02:49Also, of course, in here, let's say we have a new salesperson hired, then he or she would now also appear here.

02:55If the data, underlying data, is updated, and we click on refresh preview, then, of course, the new name would also appear here.

03:01That's what this is for, just to get the latest data, just in case something has changed while you are actually in here and transforming the data.

03:10We also have the advanced editor. We already talked about this, that you can click on it just to see what kind of transformation steps in the mQuery language have been applied for us.

03:21So, let me just close this for now. What else do we have?

03:26We have the option to manage. This could also be very interesting. You go in here.

03:32You have the option, of course, to delete a specific table from here. Be careful with that.

03:38We don't want to do this, but there's also an option to duplicate and reference.

03:44Now, if I click on duplicate, let me just click on it, you'll see that now we have salespeople tables 2, right?

03:50A second one, which is exactly a copy of the first one.

03:56We have different kinds of transformations on the second one, right? So, then, this could make sense to have this in here.

04:02The other one, the other option here is reference. I could also go here and say reference.

04:08Now, I have a salespeople 3, which talks exactly as a salespeople 2.

04:14The difference is between the duplication, which we've done, the manage here, duplicate and reference.

04:20The difference is that duplicate means the table salespeople and salespeople 2, which is a duplication.

04:26They are different from each other. Meaning, if I would now change something in salespeople 1, in this one here, the original one,

04:33this change would not be reflected in salespeople 2, because it's just a duplication.

04:38It is a complete separate table, just a copy, but a complete separate copy.

04:43For the reference, on the other hand, this is different. The reference always reflects the changes which have been done in the first one.

04:49So, for instance, because salespeople 3 is a reference of salespeople 2, if I would go in here and say, for instance, right-click here and say remove,

04:59remove salesperson name, you see that in salespeople 3, because it is a reference, also this column has been removed.

05:07So, a reference compared to the duplication is a reference always reflects the reference table and those transformations.

05:16The duplicate, on the other hand, you can see here, there's only one column. In the original one, we still have both columns.

05:23The duplication is completely independent from each other. That's the difference.

05:26So, there are use cases for both of them, but it's very crucial and important that you understand the difference.

05:32So, for now, let me just select the tables. We don't want them. So, right-click here and click on delete.

05:39And, yes, I'd like to delete them. It was just an example. So, here we go. Got our original model back.

05:46Also, by the way, as I said before, Power BI is kind of redundant. That means you can do the same transformations in several ways.

05:56So, if I right-click on my salespeople, I also have here the option to create a duplicate or a reference.

06:02So, you don't need to use the ribbon bar. You can also do it directly from here.

06:05So, let's select this one more time. So, that's under manage. This is regarding the query.

06:11Beside this, we have the option to manage the columns. We can choose columns or remove columns.

06:17So, maybe let's go to a table like sales table here. We have a little bit more columns, so it's easier to see from here.

06:24So, under choose columns, you can click on this, and then you can say I want to choose columns, and then you can choose the columns you want to keep.

06:31So, for instance, I only want to keep, let's say here, the salesperson, the custom ID, the order date, and the price.

06:38Select those, click OK, and then you see that all the other columns have been removed, which again has then been also applied in here in the applied steps window.

06:48So, that's maybe an easier way to do it directly from the choose columns.

06:52Instead of selecting the columns from here, of course, you can also hold your control key, select the columns, right click, and also remove columns that will also work.

07:01But maybe if you have a lot of columns, so maybe let's say 100 different columns, it's easier to do it directly from the choose columns option.

07:09Let me just remove this step. Again, that's the great thing that the applied steps here are recorded, so you can always go back by simply removing the step, and we can see our original data.

07:20Also, there's another option if you click on this little down arrow here, which is the go to column.

07:27This is, at least from my perspective, also very helpful if you have a table with, again, let's say 100 different columns, and you need to search for a specific column you want to select it.

07:37Then, instead of using the scroll bar here at the bottom, you could simply go to column, and then select the column you want to go to.

07:43Here, for instance, I would like to go to, let's say, the quantity column, select it, click OK, and then you see that this column is selected directly, and you can do your modifications in here.

07:53So, as I said, it's kind of easier than just scroll here through 100 columns from left to right.

08:00Removing columns, I think that's quite clear, right?

08:03You just can remove the columns, either the selected one, currently quantity selected, so if you click on remove columns, that will remove the quantity column.

08:13You just go back.

08:14The other option would be if you select the quantity column, or maybe you hold your control key and select a few columns, and then you go here, and you remove other columns, then only the selected one will be kept.

08:26So, also quite easy to just specify here, maybe certain kind of columns you want to keep, and which are relevant to your model.

08:34Even though, always, I like to always point this out, remember, only import the data you need.

08:40So, for instance, if you don't need a table at all, don't import it, but here you can, of course, further make the table even smaller, which is also best practice here inside the Power Query editor, where you can then, again, make less columns or less rows.

08:55You can filter this data down, and the applied filters, which we do here.

08:59So, remember when I told you that if you are in the data view, and you do, for instance, a filtering of columns and so on, this, of course, is not applied in the report.

09:09But if you do any kind of transformation here, like cleaning, removing anything inside the query editor, this gets applied to your original data.

09:17So, there, really, this has an impact.

09:21So, let me just cross this out, so we have our original data back.

09:24So, keep rows, remove rows, works exactly the same, but instead of four columns, it is four rows.

09:29So, just to show you that, and a quick example, yeah, keep top rows, keep bottom rows, keep range of rows, and so on.

09:36So, for instance, if I say, keep top rows, I can simply type in, I only want to keep the top 10 rows.

09:41I go in here, and just type 10, click OK, and you see the step gets applied, and I only kept the rows from 1 to 10.

09:52And everything else gets removed.

09:55So, quite easy, really, to filter the data, to keep specific rows or columns in here, simply by selecting it from here.

10:03Let's just remove this, and go to the next one, sorting.

10:07I think that's clear, you can either sort from A to Z, or Z to A, quite similar way as in Excel.

10:12Split columns, this could be interesting, for instance, for, let's say, the customer here.

10:17The customer has a surname, full name is in here, so you could select the column here, and then you could go under split column,

10:27and there are various options, split by delimiter, by number of characters, by positions, lower to uppercase, digit to non-digit, and so on.

10:35So, a lot of options in here, but for us now, it's simply by delimiter, and then I could say, there's a space, that's true,

10:41there's a space between the two names, so I can split, and then I can specify, do I only want to split by the first space, by the last space,

10:50or by all the spaces, if I say each occurrence in here, and click OK, that's all.

10:56Click OK here, then you would see that now I have two columns, which here, one name for the customer, and the second name of the client.

11:04And of course, I could double click here, and then just rename the columns, right, that surname, and so on.

11:08So, that's how easy it is to split columns into several columns.

11:13So, let me just click on the cross symbol here, also click on the split by delimiter, because you can see there have been two steps applied,

11:21even though we only took the option here, but this has automatically created two steps in here.

11:28So, that's a splitting here.

11:31Grouping, this is just, let's go inside the sales, so you understand that.

11:35Grouping just means we could group the data.

11:38So, for instance, in here, I have a row for each of my transactions, but let's just say I would like to group my data,

11:44let's say by the location, I could select the location, I could say I want to group my data, group by,

11:50and then I say inside here, yes, I'd like to group by the location ID,

11:54and I simply want to know how many, or how many transactions have been, well, created for each of the locations.

12:01So, I click OK.

12:03For the aggregation count was fine, and now I can see here in my dataset for the location, for instance,

12:09I have a unique list of locations in here, and in total, I had 154 rows, so transactions, for this specific location in my dataset.

12:18That's what grouping does.

12:20It simply creates here an aggregation, unique list here of location IDs, and then, in this case, a count,

12:26because that's what the aggregation type we chose, simply for the underlying data, which was sales.

12:32So, that, for instance, could be interesting, but if I would like to have a table like that,

12:36then I would, prior to this aggregation, this grouping, I would right-click, and then I would reference the table,

12:42in this case, right, because I do not want to have my origin table modified like that.

12:48So, that would be, for instance, a use case.

12:50If you have, or if you need this table like that, if you have a requirement,

12:54then I would here use a duplicate or a reference, depending on what you need.

12:58So, let me just remove this for now.

13:00I'd like to have my original sales table.

13:02So, that's the grouping.

13:04The data type is just changing the data type to a different, well, data type.

13:09Currently, it's text, as you can see here.

13:11I could change it to a different type, but this can also be done by clicking on a little icon here, so ABC.

13:17I can also do it here, and I could also right-click on a column,

13:21and there's also, on a change type, the option to change data type.

13:25So, it doesn't matter where you do it.

13:27It's all the same.

13:29Besides this, we have the option to transform the, in this case, the first row as header,

13:35or if you click on the down arrow, this is actually true for most of those.

13:41So, oftentimes, there's the default option by simply clicking on it,

13:45and there's this little down arrow.

13:47So, I encourage you always, instead of clicking on the default one,

13:50click on the down arrow to see all the options which are available.

13:53And there's an option to use the first row as headers,

13:56or also the headers as first row.

13:59For instance, if I would say that order ID is not actually the header,

14:04I could simply use header as first row,

14:06and now you see that the order ID is here now the first row in our dataset,

14:10and it says here the columns 1, 2, and so on are now the headers.

14:13And, of course, I could also, if I have my data like that,

14:17for instance, when you import the data,

14:19and Power BI has not recognized that this is actually your header,

14:22then you could go in here and say you would like to use the first row as header,

14:26click on it, and then actually the first row of the table in here

14:32would be the header, like that.

14:34So, that's where this could be useful.

14:36In our case, now we have done it twice,

14:38so let's actually get rid of that, get rid of that,

14:41get rid of that, and get rid of that, right?

14:43Because it was just example, because we first demoted the headers,

14:47and then we promoted the headers.

14:49And because Power BI already read the data correctly,

14:52that's not required. It was just an example.

14:54So, replace values.

14:56This could be interesting if you would like to replace specific values

14:59inside one of the columns.

15:01So, just to show this to you, if you go to customer, for instance,

15:05and you say that, for instance, Thomas should be Tom,

15:09should not be Thomas Duncan, it should be Tom Duncan, right?

15:13This is something you cannot do like in Excel,

15:16simply clicking here and double click and change anything.

15:18That doesn't work in Power BI.

15:20But what you could do is you could go select the column first,

15:24then you could say replace values,

15:26and then you say I'm searching here for Thomas Duncan,

15:29that's already filled out because I clicked in it,

15:31and I want to replace this value with, let's say, Tom, right?

15:35Tom Duncan.

15:37And if I do that, click on OK,

15:39then you see that now Thomas is called Tom.

15:43Tom, sorry.

15:45So, that's how you can use this replace value option.

15:48And this would also be possible if you right click here

15:53and say here, replace values, there it's also available, right?

15:57So, a lot of transformations from here,

15:59from the top ribbon, are also in here.

16:01They are exactly the same,

16:03so I don't think it's necessary to explain them here the same way.

16:06So, now we have these transform options here,

16:11and the append queries, we already talked about this.

16:14We have seen it actually in practice

16:15when we combined the sales numbers here together,

16:19the merge queries.

16:21The only difference here is merging means

16:23not stacking the tables on top of each other,

16:26but horizontally.

16:28That means if we have a common field,

16:30we can combine the tables based on the common field

16:33to one giant table, right?

16:35That would be merging,

16:37or if you have some kind of experience,

16:39it is normally a simple join, what you do here, right?

16:42So, next to that, under these options,

16:46we have the AI insights, which is text analytics,

16:49vision, and Azure machine learning.

16:51However, these tools are only available

16:53if you have a premium account in Power BI.

16:56So, you cannot use them if you do not have Power BI premium.

16:59That's why we will skip it for now,

17:01but I just want to mention that those are also available

17:03and what they can do for you,

17:05just as an example, the text analytics

17:07can give you, for instance, the sentiment

17:08of a specific text you have in one of the columns,

17:12or it could also extract the language

17:14in which this specific text is written,

17:16anything like that.

17:18This, for instance, could be done in text analytics.

17:21So, under transform,

17:24we would see that there are a few transformations,

17:27which you can find again,

17:29which are already under home.

17:31Again, I mentioned it a couple of times already,

17:34but Power BI provides this what I call redundancy,

17:36which I personally don't think is bad,

17:38but I just want to mention it is,

17:40in Power BI that way,

17:42that you have several ways to do the same transformation.

17:44So, on several options,

17:46like right-clicking on a column,

17:48or under transform, or under home.

17:50Here again, we have the group by.

17:52We have seen this before.

17:54Use first row as headers,

17:56or if you click on the little down arrow,

17:58use headers as first row.

18:00This is also available.

18:02Also, you have transpose option.

18:04You're probably familiar from this

18:06with Excel, right?

18:08You can transpose your data,

18:10so put the columns into the rows.

18:12If you want to do that, you can reverse rows.

18:14You can also count the rows.

18:16So, for instance, I have here my sales table.

18:18I would like to know how many rows are those.

18:20I can simply click on count rows,

18:22and it gives me here simply the number,

18:24which is 10,889.

18:26That's actually the amount of transactions

18:28I have in the sales table, right?

18:30I can simply do this or get this by doing that.

18:33Let me just revert it back to my original table,

18:36and that's what's simply inside here.

18:38So, quite easy to do.

18:40Simply click on it.

18:42I can rename my table.

18:44Actually, the column in here, I can rename it,

18:46but this also could be done

18:48by simply double clicking on a column, right?

18:50That would also work to rename it.

18:52So, what else do we have?

18:54We can detect the data type.

18:56So, if Power BI has not yet recognized data type,

18:58you can click on this detect data type,

19:00and Power BI will try to guess

19:02what is the right data type for a column.

19:04Of course, you could also simply go here

19:06and type in the data type.

19:08So, under replace values, we talked about this.

19:12Fill, the fill option, fill up or down.

19:15This could be helpful if you have missing,

19:17well, missing entries in your data.

19:19So, for instance, you could say that

19:21if I go to, what do I have?

19:23Customer, let's just check that.

19:25For instance, here, or let's say location,

19:27maybe location.

19:29Here, for instance, we have California, right?

19:31So, California is here as one entry.

19:33And let's say that, for instance,

19:34California is only in the first row,

19:36or the first two rows, and so on,

19:38but it is missing in some of the rows.

19:40In that case, you can simply go in here,

19:42select the row, the column,

19:44and then you could go to fill,

19:46and it allows you to fill down or fill up.

19:48And what happens then is just,

19:50for fill down, for instance,

19:52then Power BI is just copying the prior value

19:54to the rows below.

19:56That's all what happens.

19:58That's just fill.

20:00Sometimes it's helpful, depending on

20:02how you have structured your data,

20:04how you have stored it.

20:06You can pivot and unpivot your data.

20:08It's also kind of similar to transposing,

20:10even though it's not the same.

20:14So, other ones in here,

20:16which might be interesting,

20:18split column, we have seen that.

20:20We can extract something on a transform.

20:22So, if I go in here,

20:24I could extract the length, for instance,

20:26of this country.

20:28I think it's called county.

20:30Sorry for that.

20:32I can extract the first character,

20:34the text before delimiter,

20:36then I need to specify the delimiter,

20:38and so on.

20:40So, if you want to extract something

20:42from a name, from a location,

20:44so for any kind of text, for instance,

20:46then this extract could be helpful.

20:48And on a parse,

20:50parse is for XML and JSON.

20:52It's something we do not deal

20:54in here right now,

20:56but if you have a use case for that,

20:58then you might want to have a look

21:00at parse here, extract rows and columns

21:02from XML or JSON formatted text.

21:04So, if I want to extract something

21:06from a web query,

21:08then this might be helpful.

21:10Statistics and so on.

21:12So, this could be helpful

21:14if you refer to, in this case,

21:16a numerical column.

21:18So, for instance,

21:20if I scroll to the right,

21:22I have here my, where is it,

21:24in this case the quantity,

21:26for instance, I can select this one,

21:28and there you can see,

21:30you can now do some kind of statistics.

21:32You can do standard transformations,

21:34to the number I have in here.

21:36Could be interesting if you say,

21:38for instance, these are the old prices

21:40we have, and now we have a price

21:42increase of 10%.

21:44Just use multiply here,

21:46the multiply option,

21:48select the column first,

21:50go to standard, then go to multiply,

21:52and simply multiply each of those

21:54entries by 1.1, right?

21:56That could be a use case for that.

21:58So, quite easy, some kind of basic

22:00math transformations, by the way.

22:02So, that's it for that.

22:04You need to first select the date

22:06column. So, this one, for instance.

22:08And also, the column needs to be

22:10treated as a date. So, you need to

22:12see this calendar icon. Only then,

22:14those options here are available,

22:16and time and duration is still

22:18grayed out, because this is a date

22:20column. It's not a time column,

22:22right? It doesn't contain any

22:24timestamp. So, I could go in here,

22:26and under date, I could extract now,

22:28for instance, I only would select

22:30to see the year, or the start of

22:32the year, or the end of the year,

22:34depending on a specific date.

22:36For instance, here, if I have the

22:386th of January, 2023, if I use the

22:40start of the year, I would get the

22:421st of January, 2023, for this

22:44entry, right? So, that's what I

22:46could do in here. And finally, in

22:48here, I could also run R and

22:50Python scripts. So, of course, you

22:52would need to have installed R

22:54and Python, but it's quite

22:56interesting, especially for data

22:58scientists, for instance, or anyone

23:00who is familiar with programming,

23:02that you could also run R and

23:04Python scripts. So, that's it,

23:06actually, under transform. And

23:08under add column, there are also

23:10a few things, like, for instance,

23:12we could add here additional

23:14columns, a custom column where we

23:16write our own M code, or we can

23:18invoke a function. Remember, a

23:20function could be, in this case,

23:22anything like, for instance, the

23:24function which we use in order to

23:26create our dates table. So, if

23:28you have a function, and you want

23:30to invoke the function for each

23:32row of a table, you can click on

23:34this invoke function, custom

23:36function. We could also add

23:38conditional columns in here. So,

23:40as an example, to show you how

23:42that works, I could say I have a

23:44price in here, right, this price

23:46column. So, let me just select

23:48this, and if I go for my price

23:50here, and I create a conditional,

23:52go to conditional column in here,

23:54you see that now you have this

23:56one here, and then you can

23:58specify a name for the new column,

24:00and then you simply can check

24:02here. For instance, you could say

24:04is greater than, and then you

24:06can insert a value here, then you

24:08would like to have output, right.

24:10For instance, let's just say,

24:12let's call it expensive, expensive,

24:14expensive. So, you can see that

24:16really how that works, and you

24:18can see there are numbers here

24:20and somehow greater than 2,000.

24:22So, let's say if the price is

24:24greater than 2,000, then the

24:26output should be expensive,

24:28expensive. Another one, it's,

24:30let's call it cheap, okay,

24:32cheap, for instance. So, like

24:34this, okay, and you see that

24:36now we have a new column in here,

24:38which is simply cheap or

24:40expensive, depending on the price

24:42number we have here. That's how

24:44that works. Quite easy to do,

24:46but maybe something you're going

24:48to use in your reports, and easy

24:50to create in here in Power Query.

24:52So, let me just remove it, so

24:54you see how that works. Okay.

24:56Then, again, we find various

24:58transformations in here, which

25:00we have already seen, so they

25:02are also available under

25:03Power Query. Very important,

25:05just in this case, the

25:07transformation at column is not

25:09the same. That is very

25:11important. I'd like to mention

25:13that at the end of this video.

25:15The difference is, if you use

25:17options from transforming here,

25:19so, for instance, if I say here,

25:21I go to date for my date column,

25:23and then I parse something, then

25:25this modifies the column I have

25:27currently selected, right? So,

25:29if I select my order date here,

25:31and then I play around with

25:33the difference, that means that

25:35I modify the current column I

25:37have selected. When I go to

25:39add column, and use any of the

25:41date transformations from here,

25:43for instance, here I say I would

25:45like to have only the year, click

25:47on year, you see that my order

25:49column is still the way it was

25:51before, but a new column has

25:53created, which does the

25:55transformation, which I want to

25:57use. So, the difference under

25:59add column always means you

26:01create a new column, but you

26:03modify your original column.

26:05That is the difference, okay?

26:07So, in this case, between those

26:09two tabs, this is not redundancy,

26:11this is really a clear difference

26:13between creating something new

26:15with add columns and transforming

26:17something you already have with

26:19transform. Okay. So, that is

26:21that, and the other options under

26:24view here, important is, I think,

26:26the formula bar, if you untick

26:28them, then the formula, you

26:30wouldn't see it, so I always like

26:31to keep it, and also here,

26:33column profile and distribution,

26:35this is something, for instance,

26:37you can see here the green bars,

26:39if you uncheck this option, you

26:41wouldn't see it, and also column

26:43profile, this is if you select

26:45one of the columns, you see here

26:47distribution, if you don't want

26:49this, uncheck this column profile

26:51and you don't see it by

26:53selecting the column, right?

26:55So, it's up to you whether you

26:57want to do that or also add the

26:59column quality, which tells you

27:01what's in the column, if you

27:03want to see this by default, then

27:05you just need to check here the

27:07options you have in here, right?

27:09So, it's up to you whether you

27:11want to do that or not. Under

27:13tools, also this is not very

27:15important for us now, this is

27:17more about if you got some kind

27:19of issues inside the program

27:21itself regarding performance and

27:23other things, you need, want to

27:25start a diagnostic and see what

27:27the issue could be, but for now

27:29this is not very important for

27:31the software, so the current

27:33Power BI version and so on. So,

27:35if you want to dive into that,

27:37but I think that's quite clear

27:39and nothing very important when

27:41it comes to the Power BI editor.

27:43And that's it actually for

27:45exploring the editor. So, the

27:47last thing we need to do now is

27:49we've seen our transformations.

27:51In this case, we are done with

27:53the model, we have our dimension

27:55tables, which are the term

27:57dimension just referring to all

27:59the tables which are not the

28:01specific, well, term which you

28:03use to describe it, but we have

28:05all our tables which we're going

28:07to use, including our dates

28:09table, and now if we're done, we

28:11simply click on close and apply

28:13and then we can actually start

28:15with our model itself. So, that's

28:17it for this video. Thanks a lot

28:19for watching. Again, it was quite

28:21long, I know that, but hopefully

28:23seeing all the examples really

28:25hands on and maybe doing it

28:27yourself, you see how powerful

28:29the Power BI editor can be and

28:31not only in your current project,

28:33but also in all your future

28:35projects regarding your data. So,

28:37that's it. Thanks a lot for

28:39watching and also for

28:41participating, working with me,

28:43and I can't wait to see you in

28:45the next video. Until then,

28:47best guys.

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