I beforehand defined in a weblog publish what skinny experiences are and why we should always care about them. I additionally defined Report Degree Measures in one other weblog publish. On this publish, I attempt to elevate some real-world challenges we face when creating skinny experiences. I additionally present an answer to these challenges.
Report Degree Measure Associated Challenges
Creating and utilizing Report Degree Measures is comparatively straightforward, however there are some challenges that we face every now and then, equivalent to:
- Distinguishing Report Degree Measures from Dataset Degree Measures
- Report Degree Measure dependencies
Figuring out Report Degree Measures from Dataset Degree Measures
One of many challenges that Energy BI Builders face is creating many report degree measures. Sadly, Energy BI Desktop presently makes use of the identical iconography for each kinds of measures, making it exhausting to tell apart the precise measures created inside the dataset from the report degree measures. It will get much more difficult if we have to write technical documentation for an present skinny report. Now we have 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 degree measure; in any other case, it’s a dataset degree measure.
So except we use third-party instruments, which I clarify on this publish, we should undergo the handbook course of.
Report Degree Measure dependencies
One other ache level associated to the earlier problem is discovering the dependencies between the report degree measures. It’s essential to pay attention to the interdependencies when doing impression evaluation. We have to perceive how a change in a report degree measure impacts different report degree measures. Once more, Energy BI Desktop doesn’t presently have any choices supporting that, so we’ve to click on each measure and browse by means of the DAX expressions to determine the dependencies or use the third-party instruments to save lots of growth time.
Dataset and Skinny Stories Dependency Challenges
The opposite challenges are much more tough to beat relate to interdependencies between datasets and skinny experiences. Energy BI Service offers a lineage view that reveals the dependencies between a dataset and its linked skinny experiences. However the challenges can get extra advanced to beat manually. The next are some real-world examples of extra advanced conditions:
- What if we have to analyse the impression of modifications in a dataset measure on all report degree measures of the linked skinny experiences?
- How can we analyse the impression of modifications on a dataset measure on all linked skinny experiences, together with the visuals, filters, and so on…?
- What if we have to tune the efficiency and we need to discover a record of all unused tables or unused fields?
As you may see, the scenario can get fairly advanced, so handbook operations are nearly inconceivable.
However there’s a third celebration device we are able to use which offers heaps of capabilities with a few clicks.
Introducing A Third Social gathering Instrument That Can Assist
Luckily, there’s a third celebration device that may assist to resolve all of the above challenges. The Knowledge Vizioner workforce, myself included, labored exhausting to implement an add-on for Energy BI Documenter that helps skinny experiences. Let’s get to it and see the way it works.
Getting a Record of Report Degree Measures and Their DAX Expressions utilizing Energy BI Documenter
We will presently use the out-of-box function to get all report degree measures and their DAX expressions within the Energy BI Documenter with out activating any add-ons. All you have to do is create an account when you haven’t already completed so. As it’s possible you’ll know, Energy BI Documenter presently accepts Energy BI Template recordsdata (PBIT); so you have to open the skinny report in Energy BI Desktop and export it to PBIT, then comply with 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 Degree Measures part
- Click on the Obtain as CSV file button
As proven within the previous picture, you may see the report degree measures, their DAX expressions, and the visuals utilizing them.
However wait, what concerning the different challenges we simply mentioned, the dataset to all skinny experiences dependencies, used and unused fields, and so on?
Allow us to see how Energy BI Documenter may help with these.
Skinny Report Add-on for Energy BI Documenter
As talked about, we labored exhausting at Knowledge 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 experiences and their associated dataset recordsdata (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 recordsdata, together with the PBIT containing the dataset and all associated skinny experiences
- Click on Open
After the recordsdata 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 experiences:
- Click on the ellipsis button on the specified file
- Click on the Choose associated experiences from the context menu
- Now that each one associated experiences 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 an inventory of the tables from the dataset that none of their fields is used anyplace throughout the dataset itself and all chosen skinny experiences
- Unused fields: downloads a CSV file containing an inventory of all unused fields together with columns, calculated columns, measures, and report degree measures
- Used tables: downloads a CSV file containing an inventory of the tables that at the very least one in all their fields is used someplace inside the dataset itself or any of the chosen skinny experiences
- Used fields: downloads a CSV file containing an inventory of the fields which might be used someplace both inside the dataset or any of the chosen skinny experiences or their report degree measures
There you go! You’ve it. Within the subsequent part, we clarify what the CSV recordsdata give us.
The Definition of Used and Unused
Because the previous picture reveals, we analyse the info into the next 4 classes:
- Unused tables
- Unused fields
- Used tables
- Used fields
To grasp these classes we’ve 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 confidence within the output, it will be significant for the customers to grasp 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 most recent definition.
The Definition of Used Fields
A subject, from a Tabular object mannequin perspective, consists of columns, calculated columns, and measures. A used subject is a subject that seems in any of the next throughout the dataset and all skinny experiences chosen by the consumer:
- Dataset degree dependencies
- Tabular object dependencies in DAX
- Calculated column expressions
- Measure expressions
- Calculated desk expressions
- Calculation teams
- Row Degree Safety (RLS)
- Object Degree Safety (OLS)
- Type by column
- Report degree dependencies
- Report filters
- Web page filters
- Visible filters
- Anyplace on Visuals together with however not restricted to
- Axis or values
- Conditional formatting
- Dynamic conditional formatting
- Report degree measures
- Report degree measure’s dependencies
- Dependency on different report degree measures
- Dependency on dataset fields
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 record of used fields.
The Definition of Used and Unused Tables
A used desk is a desk with at the very least one subject showing within the record of used fields. Conversely, an unused desk is a desk with no fields showing within the used fields’ record.
Understanding the CSV Output
As you could have already famous, figuring out the dependencies between dataset objects and all linked skinny experiences 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 experiences, and their complexity. We’re additionally conscious that CSV isn’t the best format to grasp and interpret the knowledge, so we purpose to arrange a user-friendly UI sooner or later. However for now, let’s choose one choice and see what we get within the CSV file and the right way to interpret the info.
In my pattern, I chosen a dataset and 11 skinny experiences. The next picture reveals the ends in the downloaded CSV file for Used Fields appears to be like just like the under when opened in Excel:
We will filter the title to reply many questions equivalent to the next:
What report degree measures do we’ve in all skinny experiences?
To reply this query we simply have to filter the CSV when the Sort 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 experiences?
To reply this query we have to filter the CSV when each the Desk and Sort columns’ worth is Date. The next picture reveals the outcomes:
What’s the impression of adjusting the Transport Price, a dataset measure, on report degree measures?
To reply this query we simply have to filter the CSV as follows:
- Filter the Discipline Title column to Transport Price
- Filter the Sort 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 are able to reply utilizing the CSV output of the Skinny Report add-on within the Energy BI Documenter as you may think about. For extra details about how the Skinny Report add-on works watch the next quick 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 identify 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 not allow it for 1000’s 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 kind and we get again to you as quickly as we course of your request:
As all the time, I might love to listen to your ideas. So please depart your message within the feedback part under.