## Introduction

Suppose your customer wants to calculate the weight of the components that makes up your product (Bill of Material aka the BOM). This blogpost is a description about summarizing and calculating of quantities and the weight of products and sales.

For this blogpost I've used the explanantion of Exceleratorbi.com.

## The Data

So, I've created a small example of the problem. There are three tables in the sample workbook and they are a Material (Product), BOM and a Sales facttable. In order to make the example more interesting I've created some data like car, bicycle and others, together with the parts that made them up like wheels, frame, steering wheel. Lastly a fact with the sales data is created where a quantity tells how many was sold on a day.

So. I'm interested in knowing the weight of the materials and also knowing the weight of the parts of the Material (Product).

## The data model

In the diagram I've plotted the datamodel. The keys of the DimMaterial table are propagated in different directions and not in one direction we 'normally' use in dimensional modelling.

## The DAX expressions

For the initial implementation of the problem in the workbook the following DAX expressions are used. Pay attention to the TotalMaterialWeight DAX expression calculation. This DAX expression uses a SUMX DAX function. It calculates the weight on a row-by-row manner. SUMX is an interator and is less efficient that the SUM function. Normally, I don't use the SUMX function very often, but in this case it is needed because you need to multiply the rows first and then sum it up.

```       ```
TotalQuantity:=SUM(FactSales[Quantity])

TotalBOMWeight:=SUM(DimBom[BOMWeight])

TotalMaterialWeight:=SUMX(DimMaterial;[TotalQuantity]*[TotalBOMWeight])
``````

The first DAX expression calculates the quantity sold in the FactSales, the TotalBOMWeight expression calculates the weight of the individual BOM parts and the TotalMaterialWeight calcualtes the summarization of the multiplication of the TotalQuantity and TotalBOMWeight.

## The outcome

Below a first impression of the Pivottable based on the PowerPivot model and here you can see that the TotalQuantity, the TotalMaterialWeight are calcualted correctly. Next to the pivottable I've included a manual calculation and it is the same as the automated calculation

Below a screenshot of the weights of the BOM parts and that calculation is also correct.

## How to improve the model

Now there is something wrong with the TotalBOMWeight. It is 1225 on every row and that doesn't seem right. The problem is that the relation towards the DimBom is not enhanced in a standard manner. In PowerBI Desktop you can set the relation to be bi directional in order to make it work. Now, in order to change this behavior in PowerPivot you have to add some black magic to the DAX expression.

```       ```
```TotalSalesQuantity:=CALCULATE(SUM(FactSales[Quantity]);DimBom)

TotalBOMWeight:=Calculate(SUM(DimBom[BOMWeight]);FactSales)

TotalMaterialWeight:=SUMX(DimMaterial;[TotalSalesQuantity]*[TotalBOMWeight])
```

The sums of TotalSalesQuantity and TotalBOMWeight are now wrapped with a CALCULATE function and this handles the calculation of the TotalBOMWeight better.

For a full explanation of this solution, see the blogpost of Exceleratorbi.com.

## Conclusion

This blogpost is about the calculation of measures that is not a standard pattern. The method descibed handles a calculation of the total weight of Materials (products) sold and their BOM parts (components). We also explored the problem of bidirectional relations.

Greetz,

Hennie

## Introduction

Currently, developing a Tabular Model in SSDT and somehow I managed to let my measures disappear. What? Yep! This happened me a couple of times now. This blogpost is about the problem and how to solve it.

## Situation described

Now it occured when I was entering some measures in SSDT.

Even though I don't saw the errors in the formulas, a blue mark is show in the tab. A bit annoying and you can't see why it's giving me the error. No measure with error  is shown.

## Solution option 1

In the forums of Microsoft someone suggested to delete the layout file in order to get the error measures back. So I did..

When I do that, the erroneous measures are shown again but all the measures are now shown on two rows and it is completely reformatted.

Not what I want! I have to copy and paste manually the formulas to a format that I appreciate. This is a bit labor intensive when you have a lot of measures.

## Solution option 2

Now after investigating the files more closely I opened the bim file. Always make a copy of the file!

And I removed the following snippet (although the warning says that you don't want to change it manually;-) )

```       ```
```
<Command>

<Text>----------------------------------------------------------

-- PowerPivot measures command (do not modify manually) --

----------------------------------------------------------

CREATE MEASURE 'FactSales'[Copy of PYSalesNetRevenue]=CALCULATE([TotalSalesNetRevenue], FILTER(SAMEPERIODLASTYEAR(DimCalendar[Date]), DimCalendar[Date] &lt;= LASTNONBLANK(DimCalendar[Date], [TotalSalesNetRevenue])));

</Text>

<Annotations>

<Annotation>

<Name>FullName</Name>

<Value>Copy of PYSalesNetRevenue</Value>

</Annotation>

<Annotation>

<Name>Table</Name>

<Value>FactSales</Value>

</Annotation>

</Annotations>

</Command>
```

That solved my problem. The layout of my measures stayed the same and the wrong measures were removed from the model.

## Conclusion

It's possible to let measures disappear from SSDT but with editing the .bim file you can remove the measures.

Greetz,

Hennie

## Introduction

This blogpost is about processing a tabular model in Analysis Services in SQL Server 2014. At first glance, the number of processing options is a bit overwhelming but when you have a certain understanding about the options you get the idea behind it. Although, I've gathered this information for SQL Server 2014, most of it is applicable for other versions as well.

I've read a couple of blogposts about processing a tabular model and most of this blogpost is based on the following blogs:
So all the credits goes the people who wrote these blogs.

There are two stages of loading data in a tabular model. First, The xVelocity engine takes each column in each table and builds a dictionary from it.
Secondly, data recalculation takes place, meaning that the engine populates any calculated columns, hierarchies, relationships and indexes for the database.

## Processing options

As said before, SSAS Tabular model comes with a couple of processing options that can be used differently depending on the object you want to process.

With the processing option Process Add it is possible to implement incremental loading of the cube. By defining the rows that are new to the cube, incremental loading of the cube is possible. As Marco Russo is writing in his blog, when executing the Process Add option, the SSAS engine will create a new partition and this is merged with the existing partition.

An effect of the Process Add is that automatically recalculates the affected objects.

## Process Clear

Process Clear drops all the data in a database, table, or partition. Is there more to say;-)

## Process Data

Process Data loads data into a partition or a table. It reads the data and it builds dictionaries, but doesn't process any calculated columns, indexes, relationships or hierarchies. Therefore it's required to perform a Process Recalc after all tables' dictionaries have been updated using Process Data.

## Process Default

Process Default loads data into unprocessed partitions or tables. Any affected calculated columns, relationships, user hierarchies, or internal engine structures (except table dictionaries) are recalculated. It recalculates the affected objects (and only the affected objects).

## Process Defrag

Process Defrag optimizes the table dictionary (an internal engine structure) for a given table or for all tables in the database. This operation removes all dictionary entries that no longer exist in the data set and rebuilds the partition data based on the new dictionaries.

If you often add or remove partitions from a table, or if you often reprocess individual partitions in a table, you should Process Defrag regularly but carefully.

Process Defrag may be an expensive operation for a large table.

If you Process Data on a table level, it is never necessary to do a Process Defrag

Run Process Recalc after running Process Defrag.

## Process Full

Process Full loads data into all selected partitions or tables. Any affected calculated columns, relationships, user hierarchies, or internal engine structures (except table dictionaries) are recalculated. It recalculates the affected objects. Process Full runs a Process Data on the table or partition, followed by a Process Recalc.

## Process Recalc

For all tables in the database, recalculates calculated columns,  Process Recalc rebuilds relationships. rebuilds user hierarchies, and rebuilds other internal engine structures. Table dictionaries are not affected.

Process Recalc is always the last thing you do, and you can batch up as many processing operations as you want before issuing that last Process Recalc.

Keep in mind that you can only issue Process Recalc at the database level. This means that after a Process Data operation on a dimension, you may be stuck recalculating a large fact table when it was not impacted by the initial processing operation.

## Processing scenarios

With Process Full you can perform a full load of the database, partition or a table. It will drop all of the data and then it will process the object. This can become handy for an initial loading of the cube or in case of a structural change of an object in the cube or the cube itself. In that case, you need to redeploy the cube and perform a Process Full of the cube.

2) Incremental processing
Two options:
• 1) Create a new partition and then use Process Data to populate it. This new partition can then be merged with an existing partition if you like.
• 2) Use Process Add to add data to the existing partition. If used make sure that you don’t add duplicate rows.
Two options:
• Change the partition definition to the query that returns the set of rows you want returned by the incremental processing operation.
• If your partition definition is based on a view, make sure that the view on the original data source now returns just the set of rows you want returned by the incremental processing operation.

3) Semi-incremental processing
Yet another option is to create a partition for a year or a month in a table and process that with a Process Data. This will limit the data loading significantly but it 's simple but effective.

No matter how you do incremental processing, you must Process Recalc on the database afterwards before moving on with your life. If you do this enough, you’ll want to Process Defrag too.

Greetz,

Hennie