Shared Datasets have been round for fairly some time now. In June 2019, Microsoft introduced a brand new function referred to as Shared and Licensed Datasets with the mindset of supporting enterprise-grade BI inside the Energy BI ecosystem. In essence, the shared dataset function permits organisations to have a single supply of reality 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 Reside connectivity mode. So, we mainly have a number of reviews linked to a single dataset. Now that we all know what a skinny report is, let’s see why it’s best apply to observe 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, equivalent to:

• Having many disparate islands of information as a substitute of a single supply of reality.
• 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 shouldn’t be a multi-user utility.
• The reviews have been strictly linked to the underlying dataset so it’s so arduous, if not completely inconceivable, to decouple a report from a dataset and join it to a special dataset. This was fairly restrictive for the builders to observe the Dev/Take a look at/Prod strategy.
• If we had a pretty big report with many pages, say greater than 20 pages, then once more, it was nearly inconceivable to interrupt the report down into some smaller and extra business-centric reviews.
• Placing an excessive amount of load on the information sources linked to many disparate datasets. The scenario will get even worst after we schedule a number of refreshes a day. In some instances the information refresh course of put unique locks on the the supply system that may probably trigger many points down the highway.
• Having many datasets and reviews made it tougher and costlier to keep up the answer.

In my earlier weblog, I defined the completely different parts of a Enterprise Intelligence resolution 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 resolution. 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 linked 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 linked skinny reviews makes lots of sense. This strategy covers all of the disadvantages of the earlier growth 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 considerations.

At this level, you could assume why I say having some shared datasets as a substitute of getting a single dataset masking all points of the enterprise. That is really a really fascinating level. Our purpose is to have a single supply of reality accessible to everybody throughout the organisation, which interprets to a single dataset. However there are some situations through which having a single dataset doesn’t fulfil all enterprise necessities. A standard instance is when the enterprise has strict safety necessities {that a} particular group of customers and the report writers can’t entry or see some delicate information. In that state of affairs, it’s best to create a totally separate dataset and host it on a separate Workspace in Energy BI Service.

Choices for Creating Skinny Studies

We presently 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 presently the predominant growth instrument accessible with many capabilities that aren’t accessible in Energy BI Service equivalent to the flexibility 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.

Create Skinny Studies with Energy BI Desktop

Creating a skinny report within the Energy BI Desktop may be very straightforward. Observe the steps under to construct one:

1. On the Energy BI Desktop, click on the Energy BI Dataset from the Information part on the Residence ribbon
2. Choose any desired shared dataset to connect with
3. Click on the Create button

4. Create the report as normal

5. Final however not least, we Publish the report back to the Energy BI Service

As you might have seen, we’re linked stay from the Energy BI Desktop to an current dataset on the Energy BI Service. As you may see the Information 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.

Create Skinny Studies on Energy BI Service

Creating skinny reviews on the Energy BI Service can be straightforward, however it isn’t as versatile as Energy BI Desktop is. As an illustration, we presently can’t see the underlying information mannequin on the service. The next steps clarify tips on how to construct a brand new skinny report straight from the Energy BI Service:

1. On the Energy BI Service, navigate to any desired Workspace the place you want to create your report and click on the New button
2. Click on Report

3. Click on Choose a broadcast dataset

4. Choose the specified dataset
5. Click on the Create button

6. Create the report as normal

7. Click on the File menu
8. Click on Save to avoid wasting the report

Obtain Skinny Report from a Revealed Full Report from Energy BI Service

We are able to obtain a skinny report model of an already revealed report from Energy BI Service. Because of certainly one of my weblog readers, Leslie Welch, for bringing it to my consideration. I used this new function whereas engaged on a undertaking in Dec 2022, however I forgot to replace this weblog publish until I noticed Leslie’s remark.

Anyhow… Right here is how we do it. Let’s say I’ve a full report, and I wish to cut up the skinny report from the dataset. The one factor I must do is to publish the report back to Energy BI Service if I haven’t executed it already and undergo the following couple of steps:

1. Open a report from the specified Workspace and click on the File menu
2. Choose the Obtain this file choice from the menu
3. Choose the A replica of your report with a stay connection to information on-line (.pbix) choice
4. Click on Obtain

That is it. You’ve gotten it. In case you have any feedback, ideas or suggestions please share them with me within the feedback part under.