Shared Datasets have been round for fairly some time now. In June 2019, Microsoft introduced a brand new characteristic known as Shared and Licensed Datasets with the mindset of supporting enterprise-grade BI throughout the Energy BI ecosystem. In essence, the shared dataset characteristic permits organisations to have a single supply of fact throughout the organisation serving many reviews.
A Skinny Report is a report that connects to an current dataset on Energy BI Service utilizing the Join Dwell connectivity mode. So, we mainly have a number of reviews related to a single dataset. Now that we all know what a skinny report is, let’s see why it’s best follow to comply with this strategy.
Previous to the Shared and Licensed Datasets announcement, we used to create separate reviews in Energy BI Desktop and publish these reviews into Energy BI Service. This strategy had many disadvantages, comparable to:
- Having many disparate islands of knowledge as an alternative of a single supply of fact.
- Consuming extra storage on Energy BI Service by having repetitive desk throughout many datasets
- Decreasing collaboration between information modellers and report creators (contributors) as Energy BI Desktop isn’t a multi-user software.
- The reviews have been strictly related to the underlying dataset so it’s so laborious, if not completely unattainable, to decouple a report from a dataset and join it to a special dataset. This was fairly restrictive for the builders to comply with the Dev/Check/Prod strategy.
- If we had a pretty big report with many pages, say greater than 20 pages, then once more, it was nearly unattainable to interrupt the report down into some smaller and extra business-centric reviews.
- Placing an excessive amount of load on the info sources related to many disparate datasets. The state of affairs will get even worst after we schedule a number of refreshes a day. In some circumstances the info refresh course of put unique locks on the the supply system that may doubtlessly trigger many points down the street.
- Having many datasets and reviews made it tougher and dearer to keep up the answer.
In my earlier weblog, I defined the totally different elements of a Enterprise Intelligence answer and the way they map to the Energy BI ecosystem. In that publish, I discussed that the Energy BI Service Datasets map to a Semantic Layer in a Enterprise Intelligence answer. So, after we create a Energy BI report with Energy BI Desktop and publish the report back to the Energy BI Service, we create a semantic layer with a report related to it altogether. By creating many disparate reviews in Energy BI Desktop and publishing them to the Energy BI Service, we’re certainly creating many semantic layers with many repeated tables on prime of our information which doesn’t make a lot sense.
Then again, having some shared datasets with many related skinny reviews makes lots of sense. This strategy covers all of the disadvantages of the earlier improvement methodology; as well as, it decreases the confusion for report writers across the datasets they’re connecting to, it helps with storage administration in Energy BI Service, and it’s simpler to adjust to safety and privateness issues.
At this level, you might assume why I say having some shared datasets as an alternative of getting a single dataset protecting all elements of the enterprise. That is really a really fascinating level. Our goal is to have a single supply of fact accessible to everybody throughout the organisation, which interprets to a single dataset. However there are some situations during which having a single dataset doesn’t fulfil all enterprise necessities. A typical instance is when the enterprise has strict safety necessities {that a} particular group of customers and the report writers can not entry or see some delicate information. In that state of affairs, it’s best to create a very separate dataset and host it on a separate Workspace in Energy BI Service.
Choices for Creating Skinny Stories
We at the moment have two choices to implement skinny reviews:
- Utilizing Energy BI Desktop
- Utilizing Energy BI Service
As all the time, the primary choice is the popular methodology as Energy BI Desktop is at the moment the predominant improvement device accessible with many capabilities that aren’t accessible in Energy BI Service comparable to the power to see the underlying information mannequin, create report degree measures and create composite fashions, simply to call some. With that, let’s shortly see how we are able to create a skinny report on prime of an current dataset in each choices.
Creating Skinny Stories with Energy BI Desktop
Creating a skinny report within the Energy BI Desktop may be very straightforward. Comply with the steps under to construct one:
- On the Energy BI Desktop, click on the Energy BI Dataset from the Knowledge part on the Residence ribbon
- Choose any desired shared dataset to connect with
- Click on the Create button
- Create the report as standard
- Final however not least, we Publish the report back to the Energy BI Service
As you will have seen, we’re related stay from the Energy BI Desktop to an current dataset on the Energy BI Service. As you may see the Knowledge view tab disappeared, however we are able to see the underlying information mannequin by clicking the Mannequin view as proven on the next screenshot:
Now, allow us to take a look on the different choice for creating skinny reviews.
Creating Skinny Stories on Energy BI Service
Creating skinny reviews on the Energy BI Service can be straightforward, however it’s not as versatile as Energy BI Desktop is. As an example, we at the moment can not see the underlying information mannequin on the service. The next steps clarify the right way to construct a brand new skinny report straight from the Energy BI Service:
- On the Energy BI Service, navigate to any desired Workspace the place you want to create your report and click on the New button
- Click on Report
- Click on Decide a printed dataset
- Choose the specified dataset
- Click on the Create button
- Create the report as standard
- Click on the File menu
- Click on Save to save lots of the report
That is it. You have got it. In case you have any feedback, ideas or suggestions please share them with me within the feedback part under.
