![]() ![]() With Direct Lake mode no further refresh is needed, so each semantic model reuses the same copy of each dimension table and fact table and shows exactly the same data, saving time and compute and making them all consistent with each other. How much time and CPU is spent refreshing each of these tables? How many different versions of these tables are there, each one refreshed at different times? In Fabric you can maintain a single physical copy of your shared dimension tables and fact tables in Delta format in a Lakehouse, load data into them once, then reuse them in as many semantic models as you want via shortcuts. Think about the number of Import mode semantic models you have in your organisation: each one will have a Date dimension table for sure, and there will certainly be a lot of dimension tables and probably a few fact tables duplicated across them. What’s more if Power BI chose one “right” way of doing things it might exclude other “right” ways doing things, which would alienate the adherents of those other ways and be commercially damaging.įabric provides several new opportunities for reuse, with shortcuts and Direct Lake mode as the most obvious examples. If Power BI was more prescriptive and made you to do the “right” thing up front then I doubt the company discussed on Reddit in the link above would be more successful instead it would add so many barriers to getting started they probably wouldn’t be using Power BI in the first place, they would be using Excel or some other tool in an equally inefficient way. I believe a good BI tool gives you the flexibility to use it however you like so long as it can be used in the right way if you want – where “right” will mean different things for different organisations. ![]() Would Power BI be better if it forced all developers to build their semantic layer upfront? No, I don’t think so. Creating the minimum number of semantic models necessary and using them as the source for your reports has always been a best practice in Power BI and the new name will, I hope, prompt developers to think about doing this more. We all know that Bad Things happen to companies like the one discussed here on Reddit which create one semantic model per report: source systems are overloaded by the number of refreshes, the burden of maintenance becomes overwhelming and there are multiple versions of the truth. However you define the term “semantic layer”, reusability of data and business logic is a key feature. A lot of things (not just names) have changed in the world of Microsoft BI since I wrote that post which, in my opinion, only strengthen my arguments. Even if it isn’t immediately apparent, Power BI is a semantic layer, a semantic layer made up of one or more semantic models. Power BI as a semantic layer is certainly one of my favourite topics: I wrote a very popular post on it last year. …and when someone as senior as Amir Netz asks me to do something, it’s probably a good idea for me to oblige □: While I don’t want you to read too much into the name change (Christian Wade does a good job of explaining how and why the name “semantic model” was chosen at the start of this Ignite session) and it’s always a mistake to think that we at Microsoft have some elaborate secret master plan for our products’ future development, people are nevertheless asking what the name “semantic model” signifies: ![]() What else is there to say?Ī name is often not just a name, it’s a statement of intent. Kurt Buhler has just written a very detailed post on what semantic models are. The term “dataset” is too generic and too confusing for new developers “semantic model” is a lot more specific and descriptive. When Marco approves of a change the whole Fabric team breathes a sigh of relief. But there was a general consensus that the change was the right thing to do: ![]() Of course it’s very disruptive – trust me, I know, I have around 500 blog posts that I need to do a search-and-replace on at some point – so I have a lot of sympathy for people with books or training courses that need updating or who are getting calls from confused end users who are wondering where their datasets have gone. The name change proved to be surprisingly uncontroversial. You can read the announcement blog post here and see the change in the Fabric/Power BI UI already. Last week it was announced that Power BI datasets have been renamed: they are now semantic models. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |