I beforehand defined in a weblog submit what skinny reviews are and why we should always care about them. I additionally defined Report Stage Measures in one other weblog submit. On this submit, I attempt to elevate some real-world challenges we face when creating skinny reviews. I additionally present an answer to these challenges.
Report Stage Measure Associated Challenges
Creating and utilizing Report Stage Measures is comparatively straightforward, however there are some challenges that we face now and again, reminiscent of:
- Distinguishing Report Stage Measures from Dataset Stage Measures
- Report Stage Measure dependencies
Figuring out Report Stage Measures from Dataset Stage Measures
One of many challenges that Energy BI Builders face is creating many report stage measures. Sadly, Energy BI Desktop presently makes use of the identical iconography for each sorts of measures, making it laborious to differentiate the precise measures created inside the dataset from the report stage measures. It will get much more difficult if we have to write technical documentation for an current skinny report. We’ve got to open the PBIX file of the skinny report within the Energy BI Desktop and click on each single measure. If the expression bar seems, the chosen measure is a report stage measure; in any other case, it’s a dataset stage measure.
So except we use third-party instruments, which I clarify on this submit, we should undergo the handbook course of.
Report Stage Measure dependencies
One other ache level associated to the earlier problem is discovering the dependencies between the report stage measures. It’s essential to pay attention to the interdependencies when doing impression evaluation. We have to perceive how a change in a report stage measure impacts different report stage measures. Once more, Energy BI Desktop doesn’t presently have any choices supporting that, so we’ve got to click on each measure and browse by the DAX expressions to determine the dependencies or use the third-party instruments to save lots of growth time.
Dataset and Skinny Studies Dependency Challenges
The opposite challenges are much more tough to beat relate to interdependencies between datasets and skinny reviews. Energy BI Service gives a lineage view that reveals the dependencies between a dataset and its linked skinny reviews. However the challenges can get extra complicated to beat manually. The next are some real-world examples of extra complicated conditions:
- What if we have to analyse the impression of modifications in a dataset measure on all report stage measures of the linked skinny reviews?
- How will we analyse the impression of modifications on a dataset measure on all linked skinny reviews, together with the visuals, filters, and so forth…?
- What if we have to tune the efficiency and we need to discover a checklist of all unused tables or unused fields?
As you possibly can see, the state of affairs can get fairly complicated, so handbook operations are nearly not possible.
However there’s a third social gathering software we will use which gives heaps of capabilities with a few clicks.
Introducing A Third Occasion Software That Can Assist
Fortuitously, there’s a third social gathering software that may assist to resolve all of the above challenges. The Information Vizioner group, myself included, labored laborious to implement an add-on for Energy BI Documenter that helps skinny reviews. Let’s get to it and see the way it works.
Getting a Record of Report Stage Measures and Their DAX Expressions utilizing Energy BI Documenter
We are able to presently use the out-of-box function to get all report stage measures and their DAX expressions within the Energy BI Documenter with out activating any add-ons. All you should do is create an account when you haven’t already completed so. As chances are you’ll know, Energy BI Documenter presently accepts Energy BI Template information (PBIT); so you should open the skinny report in Energy BI Desktop and export it to PBIT, then observe these steps:
- Login to Energy BI Documenter
- Click on the Add PBIT button
- Click on Browse and choose the PBIT file to add
- The Documenter detects the report kind is a skinny report
- Click on the skinny report and navigate to the Mannequin tab
- Increase the Report Stage Measures part
- Click on the Obtain as CSV file button
As proven within the previous picture, you possibly can see the report stage measures, their DAX expressions, and the visuals utilizing them.
However wait, what concerning the different challenges we simply mentioned, the dataset to all skinny reviews dependencies, used and unused fields, and so forth?
Allow us to see how Energy BI Documenter might help with these.
Skinny Report Add-on for Energy BI Documenter
As talked about, we labored laborious at Information Vizioner to arrange an add-on for Energy BI Documenter. After activating the add-on in your Energy BI Documenter account, a brand new Analyse button seems on the highest proper of the Information web page.
Allow us to add a number of skinny reviews and their associated dataset information (PBIT) within the Documenter and see how straightforward it’s to get all of the dependencies in a few clicks:
- Click on the Add PBIT file button
- Click on Browse
- Choose all required PBIT information, together with the PBIT containing the dataset and all associated skinny reviews
- Click on Open
After the information are uploaded into the documented, the documented mechanically detects the file kind as under:
Now, allow us to choose the dataset and all associated skinny reviews:
- Click on the ellipsis button on the specified file
- Click on the Choose associated reviews from the context menu
- Now that every one associated reviews and their dataset are chosen, click on the Analyse button
- Choose the specified choice from the menu, the Documenter presently helps the next 4 choices:
- Unused tables: downloads a CSV file containing a listing of the tables from the dataset that none of their fields is used wherever throughout the dataset itself and all chosen skinny reviews
- Unused fields: downloads a CSV file containing a listing of all unused fields together with columns, calculated columns, measures, and report stage measures
- Used tables: downloads a CSV file containing a listing of the tables that at the very least one in every of their fields is used someplace inside the dataset itself or any of the chosen skinny reviews
- Used fields: downloads a CSV file containing a listing of the fields which can be used someplace both inside the dataset or any of the chosen skinny reviews or their report stage measures
There you go! You’ve got it. Within the subsequent part, we clarify what the CSV information give us.
The Definition of Used and Unused
Because the previous picture reveals, we analyse the information into the next 4 classes:
- Unused tables
- Unused fields
- Used tables
- Used fields
To grasp these classes we’ve got to have a definition for used objects the place the objects are Tabular mannequin objects. We presently do not issue the Energy Question objects and their interdependencies within the evaluation. So, whereas we’ve got confidence within the output, it is necessary for the customers to know that they should sense test earlier than deleting the unused objects from their mannequin.
The Definition of Used Fields’ definition will change as we add further capabilities, so all the time test for the newest definition.
The Definition of Used Fields
A area, from a Tabular object mannequin perspective, contains columns, calculated columns, and measures. A used area is a area that seems in any of the next throughout the dataset and all skinny reviews chosen by the person:
- Dataset stage dependencies
- Relationships
- Tabular object dependencies in DAX
- Calculated column expressions
- Measure expressions
- Calculated desk expressions
- Calculation teams
- Safety
- Row Stage Safety (RLS)
- Object Stage Safety (OLS)
- Kind by column
- Report stage dependencies
- Filters
- Report filters
- Web page filters
- Visible filters
- Wherever on Visuals together with however not restricted to
- Axis or values
- Conditional formatting
- Dynamic conditional formatting
- Tooltips
- Report stage measures
- Report stage measure’s dependencies
- Dependency on different report stage measures
- Dependency on dataset fields
- Filters
The Definition of Unused Fields
By having the definition of the used fields readily available, the unused ones are these fields that don’t seem within the checklist of used fields.
The Definition of Used and Unused Tables
A used desk is a desk with at the very least one area showing within the checklist of used fields. Conversely, an unused desk is a desk with no fields showing within the used fields’ checklist.
Understanding the CSV Output
As you might have already famous, figuring out the dependencies between dataset objects and all linked skinny reviews is a fancy course of. So the dimensions of generated CSV file varies relying on the dataset measurement, its complexity, the variety of linked skinny reviews, and their complexity. We’re additionally conscious that CSV isn’t the simplest format to know and interpret the knowledge, so we goal to arrange a user-friendly UI sooner or later. However for now, let’s decide one choice and see what we get within the CSV file and find out how to interpret the information.
In my pattern, I chosen a dataset and 11 skinny reviews. The next picture reveals the leads to the downloaded CSV file for Used Fields appears just like the under when opened in Excel:
We are able to filter the title to reply many questions reminiscent of the next:
What report stage measures do we’ve got in all skinny reviews?
To reply this query we simply must filter the CSV when the Kind column is REPORT_MEASURE. The next picture reveals the outcomes:
The place the Date column from the Date desk is used throughout the dataset and skinny reviews?
To reply this query we have to filter the CSV when each the Desk and Kind columns’ worth is Date. The next picture reveals the outcomes:
What’s the impression of adjusting the Transport Value, a dataset measure, on report stage measures?
To reply this query we simply must filter the CSV as follows:
- Filter the Discipline Title column to Transport Value
- Filter the Kind column to Measure
- Filter the Dependent Report column and exclude Blanks
- Filter the Dependent Discipline Expression column and exclude Blanks
The next picture reveals the outcomes:
These are only some examples of questions we will reply utilizing the CSV output of the Skinny Report add-on within the Energy BI Documenter as you possibly can think about. For extra details about how the Skinny Report add-on works watch the next brief video:
Do you want what you see? In case your reply is sure, proceed studying.
Enabling Skinny Report Add-on in Energy BI Documenter
Because the title of this function implies it’s an add-on you could allow in your Energy BI Documenter account. We presently allow this add-on solely by way of request. I hear you ask Why? As talked about earlier, the method of figuring out all interdependencies between the dataset objects and all skinny report objects is fairly resource-intensive that may value us some huge cash. So we can’t allow it for hundreds of customers. You don’t need to see us bankrupted, do you? So I encourage you to specific your curiosity by filling out the next type and we get again to you as quickly as we course of your request:
As all the time, I’d love to listen to your ideas. So please go away your message within the feedback part under.
