In my consulting expertise working with prospects utilizing Energy BI, many challenges that Energy BI builders face are because of negligence to information sorts. Listed here are some frequent challenges which can be the direct or oblique outcomes of inappropriate information sorts and information sort conversion:

• Getting incorrect outcomes whereas all calculations in your information mannequin are right.
• Poor performing information mannequin.
• Bloated mannequin dimension.
• Difficulties in configuring user-defined aggregations (agg consciousness).
• Difficulties in organising incremental information refresh.
• Getting clean visuals after the primary information refresh in Energy BI service.

On this blogpost, I clarify the frequent pitfalls to forestall future challenges that may be time-consuming to determine and repair.

Background

Earlier than we dive into the subject of this weblog submit, I wish to begin with a little bit of background. Everyone knows that Energy BI is just not solely a reporting device. It’s certainly a knowledge platform supporting numerous points of enterprise intelligence, information engineering, and information science. There are two languages we should be taught to have the ability to work with Energy BI: Energy Question (M) and DAX. The aim of the 2 languages is sort of totally different. We use Energy Question for information transformation and information preparation, whereas DAX is used for information evaluation within the Tabular information mannequin. Right here is the purpose, the 2 languages in Energy BI have totally different information sorts.

The commonest Energy BI growth eventualities begin with connecting to the info supply(s). Energy BI helps tons of of information sources. Most information supply connections occur in Energy Question (the info preparation layer in a Energy BI answer) until we join reside to a semantic layer reminiscent of an SSAS occasion or a Energy BI dataset. Many supported information sources have their very own information sorts, and a few don’t. As an illustration, SQL Server has its personal information sorts, however CSV doesn’t. When the info supply has information sorts, the mashup engine tries to determine information sorts to the closest information sort out there in Energy Question. Although the supply system has information sorts, the info sorts won’t be suitable with Energy Question information sorts. For the info sources that don’t help information sorts, the matchup engine tries to detect the info sorts based mostly on the pattern information loaded into the info preview pane within the Energy Question Editor window. However, there is no such thing as a assure that the detected information sorts are right. So, it’s best apply to validate the detected information sorts anyway.

Energy BI makes use of the Tabular mannequin information sorts when it masses the info into the info mannequin. The info sorts within the information mannequin could or will not be suitable with the info sorts outlined in Energy Question. As an illustration, Energy Question has a Binary information sort, however the Tabular mannequin doesn’t.

The next desk exhibits Energy Question’s datatypes, their representations within the Energy Question Editor’s UI, their mapping information sorts within the information mannequin (DAX), and the inner information sorts within the xVelocity (Tabular mannequin) engine:

Because the above desk exhibits, in Energy Question’s UI, Complete Quantity, Decimal, Mounted Decimal and Share are all in sort quantity within the Energy Question engine. The sort names within the Energy BI UI additionally differ from their equivalents within the xVelocity engine. Allow us to dig deeper.

Information Sorts in Energy Question

As talked about earlier, in Energy Question, we have now just one numeric datatype: quantity whereas within the Energy Question Editor’s UI, within the Rework tab, there’s a Information Sort drop-down button exhibiting 4 numeric datatypes, as the next picture exhibits:

In Energy Question method language, we specify a numeric information sort as sort quantity or Quantity.Sort. Allow us to have a look at an instance to see what this implies.

The next expression creates a desk with totally different values:

#desk({"Worth"}
	, {
		{100}
		, {65565}
		, {-100000}
		, {-999.9999}
		, {0.001}
		, {10000000.0000001}
		, {999999999999999999.999999999999999999}
		, {#datetimezone(2023,1,1,11,45,54,+12,0)}
		, {#datetime(2023,1,1,11,45,54)}
		, {#date(2023,1,1)}
		, {#time(11,45,54)}
		, {true}
		, {#length(11,45,54,22)}
		, {"It is a textual content"}
	})

The outcomes are proven within the following picture:

Now we add a brand new column that exhibits the info sort of the values. To take action, use the Worth.Sort([Value]) perform returns the kind of every worth of the Worth column. The outcomes are proven within the following picture:

To see the precise sort, we need to click on on every cell (not the values) of the Worth Sort column, as proven within the following picture:

With this technique, we have now to click on every cell in to see the info varieties of the values that isn’t supreme. However there’s presently no perform out there in Energy Question to transform a Sort worth to Textual content. So, to indicate every sort’s worth as textual content in a desk, we use a easy trick. There’s a perform in Energy Question returning the desk’s metadata: Desk.Schema(desk as desk). The perform leads to a desk revealing helpful details about the desk used within the perform, together with column Identify, TypeName, Type, and so forth. We need to present TypeName of the Worth Sort column. So, we solely want to show every worth right into a desk utilizing the Desk.FromValue(worth as any) perform. We then get the values of the Type column from the output of the Desk.Schema() perform.

To take action, we add a brand new column to get textual values from the Type column. We named the brand new column Datatypes. The next expression caters to that:

Desk.Schema(
      Desk.FromValue([Value])
      )[Kind]{0}

The next picture exhibits the outcomes:

Because the outcomes present, all numeric values are of sort quantity and the way in which they’re represented within the Energy Question Editor’s UI doesn’t have an effect on how the Energy Question engine treats these sorts. The info sort representations within the Energy Question UI are by some means aligned with the kind sides in Energy Question. A aspect is used so as to add particulars to a sort form. As an illustration, we are able to use sides to a textual content sort if we need to have a textual content sort that doesn’t settle for null. We will outline the worth’s sorts utilizing sort sides utilizing Aspect.Sort syntax, reminiscent of utilizing In64.Sort for a 64-bit integer quantity or utilizing Share.Sort to indicate a quantity in share. Nonetheless, to outline the worth’s sort, we use the sort typename syntax reminiscent of defining quantity utilizing sort quantity or a textual content utilizing sort textual content. The next desk exhibits the Energy Question sorts and the syntax to make use of to outline them:

Sadly, the Energy Question Language Specification documentation doesn’t embody sides and there aren’t many on-line sources or books that I can reference right here apart from Ben Gribaudo’s weblog who completely defined sides intimately which I strongly suggest studying.

Whereas Energy Question engine treats the values based mostly on their sorts not their sides, utilizing sides is beneficial as they have an effect on the info when it’s being loaded into the info mannequin which raises a query: what occurs after we load the info into the info mannequin? which brings us to the subsequent part of this weblog submit.

Information sorts in Energy BI information mannequin

Energy BI makes use of the xVelocity in-memory information processing engine to course of the info. The xVelocity engine makes use of columnstore indexing know-how that compresses the info based mostly on the cardinality of the column, which brings us to a crucial level: though the Energy Question engine treats all of the numeric values as the kind quantity, they get compressed otherwise relying on their column cardinality after loading the values within the Energy BI mannequin. Due to this fact, setting the proper sort aspect for every column is vital.

The numeric values are one of the frequent datatypes utilized in Energy BI. Right here is one other instance exhibiting the variations between the 4 quantity sides. Run the next expression in a brand new clean question within the Energy Question Editor:

// Decimal Numbers with 6 Decimal Digits
let
    Supply = Record.Generate(()=> 0.000001, every _ <= 10, every _ + 0.000001 ),
    #"Transformed to Desk" = Desk.FromList(Supply, Splitter.SplitByNothing(), null, null, ExtraValues.Error),
    #"Renamed Columns" = Desk.RenameColumns(#"Transformed to Desk",{{"Column1", "Supply"}}),
    #"Duplicated Supply Column as Decimal" = Desk.DuplicateColumn(#"Renamed Columns", "Supply", "Decimal", Decimal.Sort),
    #"Duplicated Supply Column as Mounted Decimal" = Desk.DuplicateColumn(#"Duplicated Supply Column as Decimal", "Supply", "Mounted Decimal", Forex.Sort),
    #"Duplicated Supply Column as Share" = Desk.DuplicateColumn(#"Duplicated Supply Column as Mounted Decimal", "Supply", "Share", Share.Sort)
in
    #"Duplicated Supply Column as Share"

The above expressions create 10 million rows of decimal values between 0 and 10. The ensuing desk has 4 columns containing the identical information with totally different sides. The primary column, Supply, comprises the values of sort any, which interprets to sort textual content. The remaining three columns are duplicated from the Supply column with totally different sort sides, as follows:

• Decimal
• Mounted decimal
• Share

The next screenshot exhibits the ensuing pattern information of our expression within the Energy Question Editor:

Now click on Shut & Apply from the House tab of the Energy Question Editor to import the info into the info mannequin. At this level, we have to use a third-party neighborhood device, DAX Studio, which could be downloaded from right here.

After downloading and putting in, DAX Studio registers itself as an Exterior Software within the Energy BI Desktop as the next picture exhibits:

Click on the DAX Studio from the Exterior Instruments tab which routinely connects it to the present Energy BI Desktop mannequin, and comply with these steps:

1. Click on the Superior tab
2. Click on the View Metrics button
3. Click on Columns from the VertiPaq Analyzer part
4. Take a look at the Cardinality, Col Dimension, and % Desk columns

The next picture exhibits the previous steps:

The outcomes present that the Decimal column and Share consumed essentially the most vital a part of the desk’s quantity. Their cardinality can be a lot greater than the Mounted Decimal column. So right here it’s now extra apparent that utilizing the Mounted Decimal datatype (aspect) for numeric values can assist with information compression, lowering the info mannequin dimension and growing the efficiency. Due to this fact, it’s clever to all the time use Mounted Decimal for decimal values. Because the Mounted Decimal values translate to the Forex datatype in DAX, we should change the columns’ format if Forex is unsuitable. Because the title suggests, Mounted Decimal has fastened 4 decimal factors. Due to this fact, if the unique worth has extra decimal digits after conversion to the Mounted Decimal, the digits after the fourth decimal level shall be truncated.

That’s the reason the Cardinality column within the VertiPaq Analyzer in DAX Studio exhibits a lot decrease cardinality for the Mounted Decimal column (the column values solely hold as much as 4 decimal factors, no more).

Obtain the pattern file from right here.

So, the message is right here to all the time use the datatype that is sensible to the enterprise and is environment friendly within the information mannequin. Utilizing the VertiPaq Analyzer in DAX Studio is nice for understanding the varied points of the info mannequin, together with the column datatypes. As a knowledge modeler, it’s important to grasp how the Energy Question sorts and sides translate to DAX datatypes. As we noticed on this weblog submit, information sort conversion can have an effect on the info mannequin’s compression charge and efficiency.