Dive into Microsoft Fabric's Power BI Direct Lake 

Awesome demo! Quick and right on the spot.

Thanks for this insightful video. Do you have any performance/capability metrix for comparison between Import, Direct query and Direct Lake , pls share. Thanks in advance

Great demo. I've been using direct lake in a current project and absolutely love it

Great video, Patrick!

I have several sematic models that are alike. Do I need to have a fabric capacity to merge them and treat the data like I do for SQL? Thanks a lot.

Super post Patrick , as usual

How to create dax column in direct lake dataset

Not using it currently but I can see some potential benefits to it. Will have to look into it

How costing will work for direct lake mode?

If my Delta Lake is in Azure Data Lake Stoage can I still use Direct Lake?

Q I love the ease of creating new semantic models but I keep coming across the problem whereby I have to give somebody access to the whole lake house in order to give them access to a segmented part of that data but I only want them to see via a semantic model. Is there any way that I can create a gold lake house in one workspace then create multiple semantic models in other workspaces and only give users access to those?

Do you know when Direct Lake will be available to use in Power BI Desktop?

THIS IS BANANAS!!!!!!!! WOOOHOOOO Is there still a row limit? (On data that you can actually bring into Power BI) I seem to remember hitting an upper limit on rows using SQL Endpoint / Direct Query.. I had this MONSTER data set of about 14m rows that the stakeholder insisted he needed all of the data.

Patrick, thanks 👍

Is direct lake using Delta Live Tables? It seems like it's the same technology.

Awesome 🙏

I watch Patrick

I’m up

Does this mean the massive 25GB model I have that holds too many years of data because the "business" needs it, even though they only look at a few years, can be removed and then only the partitions of data that is needed will be held into memory? Lowing the memory used on the capacity and the amount of data and CPU needed to crunch all the measures? Can I recreate the model using direct lake against our ADLS gen2 databricks parquet files which are already the fact tables we pull in. Do you need to setup partitions in the onelake or does it automatically do it for you? This does seem to remove the query folding performance gains, so it seems like the parquet files will need to be rewrote to be better optimized and only include the data that is needed in the model. Also is that python library to refresh a dataset available outside of onelake? aka would love an easy way to refresh a PBI model from an Azure databricks notebook versus adf xmla call

Does Fablic available for developers for free?