Skinny Stories, What Are They, Why Ought to I Care and How Can I Create Them?

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 inside the Energy BI ecosystem. In essence, the shared dataset characteristic permits organisations to have a single supply of reality throughout the organisation serving many stories.

A Skinny Report is a report that connects to an present dataset on Energy BI Service utilizing the Join Dwell connectivity mode. So, we mainly have a number of stories linked 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 method.

Previous to the Shared and Licensed Datasets announcement, we used to create separate stories in Energy BI Desktop and publish these stories into Energy BI Service. This method had many disadvantages, reminiscent of:

• Having many disparate islands of information as an alternative of a single supply of reality.
• Consuming extra storage on Energy BI Service by having repetitive desk throughout many datasets
• Lowering collaboration between information modellers and report creators (contributors) as Energy BI Desktop will not be a multi-user software.
• The stories had been strictly linked to the underlying dataset so it’s so arduous, if not completely not possible, to decouple a report from a dataset and join it to a distinct dataset. This was fairly restrictive for the builders to comply with the Dev/Check/Prod method.
• If we had a reasonably large report with many pages, say greater than 20 pages, then once more, it was virtually not possible to interrupt the report down into some smaller and extra business-centric stories.
• Placing an excessive amount of load on the info sources linked to many disparate datasets. The state of affairs will get even worst once we schedule a number of refreshes a day. In some instances the info refresh course of put unique locks on the the supply system that may probably trigger many points down the street.
• Having many datasets and stories made it more durable and dearer to take care of the answer.

In my earlier weblog, I defined the completely different elements 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, once 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 stories in Energy BI Desktop and publishing them to the Energy BI Service, we’re certainly creating many semantic layers with many repeated tables on high of our information which doesn’t make a lot sense.

Alternatively, having some shared datasets with many linked skinny stories makes lots of sense. This method 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 suppose why I say having some shared datasets as an alternative of getting a single dataset masking all elements of the enterprise. That is truly a really attention-grabbing level. Our goal is to have a single supply of reality obtainable 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 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 present have two choices to implement skinny stories:

• Utilizing Energy BI Desktop
• Utilizing Energy BI Service

As at all times, the primary possibility is the popular methodology as Energy BI Desktop is at present the predominant growth software obtainable with many capabilities that aren’t obtainable in Energy BI Service reminiscent of the power to see the underlying information mannequin, create report degree measures and create composite fashions, simply to call some. With that, let’s rapidly see how we are able to create a skinny report on high of an present dataset in each choices.

Creating Skinny Stories with Energy BI Desktop

Creating a skinny report within the Energy BI Desktop could be very straightforward. Comply with the steps beneath to construct one:

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

4. Create the report as traditional

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

As you’ll have observed, we’re linked reside from the Energy BI Desktop to an present dataset on the Energy BI Service. As you possibly can 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 possibility for creating skinny stories.

Creating Skinny Stories on Energy BI Service

Creating skinny stories on the Energy BI Service can be straightforward, however it isn’t as versatile as Energy BI Desktop is. As an example, we at present can not see the underlying information mannequin on the service. The next steps clarify how you can construct a brand new skinny report instantly 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 Decide a broadcast dataset

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

6. Create the report as traditional

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

That is it. You could have it. If in case you have any feedback, ideas or suggestions please share them with me within the feedback part beneath.