hi everyone and welcome to the Microsoft fabric breaking news party yeah welcome we've brought balloons we brought of course the new logo logo we brought us we bought topics and of course we will talk about this excellent new tool that that we have but yeah this is the breaking news sessions about Microsoft fabric at data Revolution yes my name is Brian you want matis I'm Matthias and uh we got two things this week we both got fabric yes for Microsoft and it's really it's like a Christmas yeah I also brought one other thing actually oh did we get something else from Microsoft yesterday we got this from Microsoft we got announced partner of the year in Denmark it's fulfillment so um You probably don't care about it but but we do and we're pretty proud of it so of course we couldn't help but share that that other excellent news exactly but but Matthias I would actually like to ask you what is Fabric and and and how do we get started what what are the news the news is fabric I mean fabric is Microsoft fabric is a game changer I I I used to say I think this is the biggest thing in bi since power bi but I but I actually I don't think that anymore I think it's the biggest thing in bi since forever I think this is this is going to be a revolution in how we do we do data so and and today we'll cover that more or less within the next four sessions within the next question 45 minutes we'll cover the basics of what is Microsoft Fabric and throughout the day we will have as brand zest we will have four sessions in total and each of them will go down into specific topics so we'll talk about in depth what is data warehousing in Microsoft purpose one of the the key new areas we will talk about um what are the news and what are the the we will explore what is new for power bi in Fabric and how that how it is going to play together in the future and finally we'll talk about something that is to me very important and that is how we as organizations should think about Microsoft fabric beyond the technology and the tools and the new cool gadgets so what should we actually think about for our Organization for culture for governance and all these uh okay great very important things so a full day of fabric with a bunch of sessions so I'm looking very much forward to it you mentioned power bi I mentioned and I have seen the portal for path paprik and that portal looks a lot like the power bi user experience so what will happen to power bi it's a fabric I'm glad you asked and I brought a little slide to show that to show what will happen to power bi and that is of course a truth of of a bit of at least a bit of modification because yes we're getting some new icons there's a brush drop UI but the power by bi we know and trust and uh and enjoy is still there and we can still use it so anything we already built will still work will still function as as it already I can still use my power but there's some deploy it to the workspace and and leverage the power bi experience as I could last week yes and all your skills competences know-how you will be able to continue using this because the data set isn't going anywhere the data flows isn't going anywhere and the report is isn't going anywhere okay a matter of fact it's just going to be um expanded upon okay so it's not nothing it is if we go to the slide it is nothing initially because our current Solutions will stay a disease and as it is but what will happen is that we're expanding our toolbox but so suddenly but you're closing the gaps that we have before with power bi we never was really able to do a full end-to-end data platform in power bi without having gaps of features and functionalities so we get we we we fill out those gaps now we have a bunch of more Tools in our toolbox so we can do more than we ever could and all of that that is of course wrapped up into what is Microsoft fabric but so what you're telling me here is that the services I was used to use uh for Azure synapse for a SQL server for integration with data Factory and and as a data Explorer and and all those services that I as a data engineer or data architect or me working with data I used to work with is now a part of fabric yeah fabric so there is actually perhaps I'm pushing the limit here but there is nothing I know there are great news but but the the services inside fabric are services that I'm if I'm a data engineer used to work with yes okay so there are a few things here there where there are some some net new features to all of these uh these areas but in the general terms um the features will now be available in a software as a service platform within Microsoft fabric meaning that you will be able to do the same thing you always did there's not really much news for you either so if we look at the at the complete stack of what we had to do with with Azure synapse to cover all these areas beforehand we had to make sure we got the core Azure working we had a service management layer to make sure we had the right Landing zones and policies and everything around that basically good Cloud uh Cloud processes and governance and infrastructure an infrastructure we needed to make sure we had no proper networking private links endpoints VPN all these things and once we had all of that done we could start thinking about the actual data solution so so there was a bunch of overhead that we needed to to have to think about and we still need to to think about but with Microsoft fabric all of these are bundled up in one manage software as a service solution where where the solution itself will cover and offer as a best practice for these things so so I have a question yeah um if I would like to to enable Fabric in my organization um I no longer no Let me refresh if I would like to start a data platform in my in my organization yes prior I had to contact my infrastructure team my my my I.T department and and perhaps other people inside my organization to get the entire data platform up and running before I could start coding and in just data exactly um how is that done now with fabric how am I enabling this not not infrastructure but SAS platform is it easy or you just log in I mean you go to the website it's AKA dot Ms slash try Dash fabric the link will probably be posted somewhere but you go to this link you should be able to Google it as well then you sign up for a trial and everything is there for you to use right off the right of the Box you don't need to configure anything you don't need to set up anything you don't need to spin up any Azure resources you you just need to log in so I could be up running with in within five minutes or I would argue and say you could be I've been running it in in one minute in a minute of seconds you might have seconds even and and and given that then perhaps it is a matter of contacting my power bi administrator and have them enabling tried no sorry fabric inside my power bi um workspace absolutely so enable Fabric and you'll be ready to get going so yeah grab that power bi administrator which will probably become a fabric administrator in the future or something like that yeah okay um I want to go back for you because you had a good point yeah you said but we already did this in the in the cloud and we had a bit more control there we could fine-tune things and we still can do that so of course if we want to need that fine tuning if we need to do it in the cloud we can still do that Microsoft fabric is also a public preview product it's from today so you can all try it today but it's not supported it's not General availability we should not use it for production okay it's yet but the beauty is that because all the tools in fabric are known and familiar we will just be able to kind of take what we build in Azure and should be able to build it in a similar manner in fabric so if you wanted to get started today or in a month and we need something in production in maybe three months for a month I don't know we probably just should just start in synapse as we always would have okay and then we can always think about porting it uh it later so it's it actually creates a lot of flexibility that it's the same no known tools so what you're saying is if I've developed a data pipeline or a or a data Factory orchestration that would be in in perhaps not not with a click of a button but it will be Deployable or approachable to the fabric is the same similar functionality yes okay and we had we will have critics today and probably one of you watching is also skeptical about this and skepticism is is yeah it's fine it's natural but we also have skepticism when Cloud started becoming a thing that said but I want the server in my basement so I can fine tune here and fine tune there and now we have Cloud where a lot of these things are managed for us but still a lot of things need to be done manually in the cloud yeah and with the software as a service solution all of that complexity is is cut off okay and we just can use it for Solutions so we we can think about just the tools how to use the tools and how to use them in a best practice manner because of course we can also use these news tools in a wrong way sure yeah as always and and so I I think we need to empathize here that the fabric fast solution for Microsoft is still in public preview and if you get errors if you see things that are not working you cannot get support from Microsoft because they are still building tweaking optimizing the the experience and the SAS platform for us and they will do so until it goes generally available so Microsoft support is of course a little pedal thing I mean yes you will not have official support they will not take responsibility for something that you may have lost the server may be down one day and they don't have slas and stuff like that it's not production ready but I mean if you need help and if you have a problem if you spot bucks and errors Microsoft is Morehead and open for trying to help you work these things out uh give you I mean post it on a firm I'm pretty sure they will be helpful and I think they're very interested in also getting your feedback so it's not about being left to yourself it's about that formal who takes the responsibility if something goes wrong they they they assume that few things will go wrong there will be a box here and there yeah and then and from my point of view I would still encourage you as the viewer today to go ahead and try it out because you can get a 60 days trial for free yep with the compute yes yeah great yep so Microsoft fabric it it has a lot of of of uh of consolidated services inside the the SAS platform and one new one one new one um and we see it on on the slide here we have data Factory a a new subset of agitator Factory not comparable to CNS pipelines but it is still the service that we need to leverage in order to to move our data to transform it and to orchestrate the the execution of those pipelines yes it's important experience we have data we have so in try in in fabric we have a lot of different uh experiences we have data Factory similar to as you say what we know from from synapse and ADF we have synapse data engineering we have synapse data warehouse we have synapse data science synapse real-time analytics we have the old and known then we have a data activator coming soon data activated data activated and then we have on below everything we have one leg tying everything neatly together into a unified experience and I think one leg is the the big unifier of of Epic one like in my ears sounds a bit like OneDrive yeah it's also it's it's one thing one drive for your data one drive for more data so what is one leg if I see all of these seven services on on on on top what does one leg do with all these seven services so let's let's talk about the scenario in this scenario you'll be our data analyst yes you want to make a report yes and I'm the data scientist who wants to do some machine learning so I work in public right you're working power bis you approach me and say hey Matthias I want I would like I have this data and we would like a model to to predict this this something else okay we have some inventory data we would like a model to predict uh what should be the maximum and minimum values for your inventory okay and as data scientist I say That's great so back in the days I would have to ask you can you prepare some CSV files and send them to me and you would say yes I can export to excel send the files and I will create a model and everything would look good on paper I could find some good predictions and stuff like that and then I want to put it into production then I have to turn on turn on a new bespoke service put it in production yes contact you and again I say yes I got your data but now I need a way to get your data every day to retrain this model or every month on a recurring way how do we handle that and suddenly because we don't know any better we may have making a schedule where you have to produce this data for me every week or every day and I send it to you and send it to me manually and everything and nobody wants that we know the struggle and we know the struggle so today you as say I have this data can you make a model I just say just say yes we all work in one Lake where is the data and you say well it's placed this place in in this folder in this folder in this one like I said sure make sure I have access I can access the files I can even put a shortcut into my own my my own part of fabric my own my own lake house or something like that shortcut into your data now I can use it for my experiments I can use it for creating my actual models and I can write the data from my models directly back into your data so not only do you have direct access to my result without us having to do all this bunch of extra work but you also have access to my results the second that they created and automatically propagate it into all your reports without you having to do anything or me having to do anything so so one leg as I hear you say it is is is a consolidated capacity or or storage for for leveraging storing sharing working with data throughout the organization one point of entry for data sharing and one point of injury for for data manipulation right absolutely okay it's it's it's a big platform for making of enabling us to collaborate across all data roles so data warehouse developers data Engineers data administrators data scientists and data analysts and probably a hundred of other data roles we can come up with will now with with one leg be able to collaborate seamlessly easy and in one unified platform and never have to think about schedules of sending CSV files back and forth and missing some and and stuff like that one platform so and if I need to share data between my my organizational juice units or perhaps outside the organization do we then need to agree on a on a on a on a data format or or is Microsoft helping us in in some ways to to say okay we as Microsoft are now giving you this service we are doing this for you yes so the only way that the whole idea of one lake is possible is that we are Microsoft is unifying on one format one file format the Delta uh the Delta parquet format which is an open source open format um that will be used across all the experiences and all of One lake so we can use it with power bi we can use it with our machine learning modeling we can use it in notebooks and our data warehouse we can even do SQL or SQL on that data but it's all stored in the Del Sur K format so all of these seven services will all of the same will be able to read and write standardized standardize and Delta format be able to read and write and and and everything and then I also know that if I upload a CSV file to my one leg it is a very very easy perhaps we will see that in a later demo I don't know but but or else we we can share it later it is very easy to click on that file convert it to a Delta file and then the one leg or the services inside fabric will convert that CSV file to a Delta parquet file and store it where it is needed to be stored and to be shared for other services or outside the organization absolutely great absolutely and I said before it was an open format yeah and that's that sometimes can yeah we just that that sounds good and all but this actually means that if you have a different product if you have a different service outside of Microsoft fabric that you just need to make your your organization work you need to improve your business it's just a critical tool it just needs to be able to read Delta packet which is an open open source very very much commonly used format it's used between all the cloud providers it's used in everything data laser likewise they just need to be able to use that format then they'll be able to reach the one Lake folders and use the files from the get-go so not only are we standardizing our tools within Microsoft Microsoft fabric is actually standardizing and unifying and creating this kind of a beating heart in our data platform that unifies all of the services even outside of their own ecosystem okay great but okay so we create something in Fabric and we can use it everywhere we create something someplace else and we can add it to fabric and when you say that the first thing that comes to my mind is what about security what about if I share my one leg will I then share the entire entity of one leg with all the data and and how do I lock that down in order not to be compromised and this is one of the questions that will also go a bit more in depth with in our fourth session of today where we talk about um where we talk about the data driven organization the intern and end-to-end pipelines and everything around security and governance and stuff like that the short answer is um we don't know for sure yet right now it's a preview so there are some limitations in in these areas but I'm pretty sure that Microsoft is working hard on I mean of course to work as a unified platform the security will be unified as well it needs to be there it needs to be there sure okay gotcha gotcha so should we uh should we see what each of these seven Services consist of yes yeah I talked briefly about the data Factory Service um move transform store orchestrate your data movement on a daily basis and execute the um um the the data movement on on a daily basis right gotcha and um but the data Factory is is consisting of of of the uh of the data pipeline where we which we know from data Factory and from synapse pipelines if you have seen that one and it will also consist of the a new optimized or reconfigured Rewritten data flow yes called data flow Gen 2 yes and data flow is data flow from my understand is is is the power query slash m engine that we uh use to work with inside power bi and inside the power bi portal for data flows is that the same thing here so so to go back a bit if we had announced if Microsoft this year at build had announced only and it's such a small component but only data flow is Gen 2. I would already with that be up in the air happy about what we're getting okay because it sounds interesting data flow Gen 2 solves one big limitation in the current form of data flow it is we are able to do all these Transformations we were able to clean our data do quality checks stuff like that using power query using power query or yeah an M code but with dataflow Gen 2 we get one important added ability and that's the ability to write that data back to a SQL Server a SQL database or a um the one lake house in one Lake the one Lake okay and even so data flows does not yes it can still write data to it with the data Lake as we used to yeah but it will also now be able to write to us even so you say one thing yeah so you say right there is a today's league as we used to but that's kind of a hack I mean that's I know you need to set up a bunch of elaborate settings in your workspace and then you could sync your data flows to a data Lake but the truth is that for 9 out of 10 organizations this is not something that is part of the toolbox they don't know or they don't utilize this so for most of them they just were never able to save that data to any storage oh um of course they could if they found someone who knew these in a way they knew the way around but even that is cumbersome and complex now it's just a click you add the data destination you add where it should be saved what database what table or what the what the lake house and what folder and it's just safe there and you can use it for everywhere else which means that data flows goes from being a tool that pulls in data to be used for data sets to now being a tool that pulls in to be used for anything so it is no longer no longer no longer locked down when you use data flow you it data flow is just an engine you can use to transform and move your data into storage elsewhere yes yes great so all in itself very amazing okay so that is amazing and it sounds so and but we still have the data pipelines we still have the ability to execute the code the pipelines the Transformations and all the stuff and that's the data little flows we we know and then on top of that we also got the um we also got the data pipelines yeah our data pipelines are really really good when we talk about large-scale data that needs to be moved a complex processing of that data or complex data sources like rest API with with exotic pagination rules and stuff like that okay all of that in a very Dynamic manner can we do with pipelines so we can go create a pipeline we can even tell tell it I think this is this is one of the most dynamic things I've made with a pipeline it even go make a pipeline have it scan what are all the databases of our entire entire SQL Server yeah then go through each of the databases and scan what are all the tables of that database and then within one single pipeline we can go through all databases even if it's hundreds thousands and in all databases all the tables and copy that to our destination dynamically dynamically so we add new new databases they are automatic part of that ETL process okay um I I think there are some people out out there in on online thinking the data Factory in fabric has more or less the same name as a data Factory yeah and it still have the Pipelines um can I compare Azure data Factory with that with data Factory in in fabric because I know um there is there is a a subset of features in synapse pipelines it is not a full feature set from data Factory in Azure what about the data Factory in in in in fabric the the factory in fabric is a is it similar experience but it's a new new set of features it's it's the same it's the same way of working it's the same pipeline it's the same copy activity it's it's the same known and and and tried features but uh but for a feature parity it is a new new uh new own set of features there there may be new things in in data Factory in fabric that are not in in in synapse or in ADF and one of them are there already today you can store your data to a lake house in your one leg yeah this is a net new thing in in in this Factory in Fabric and they're probably already also something you cannot do in in database in fabric data Factory that you cannot can do incentive pipelines yeah okay one of the examples is in there's a factory in fabric we use we don't use linked Services anymore as we do in the Azure version yep we use the data sources that we know from from Power bi so this is a new terminology but also a few new tweaks here and there but I can still so import data from an external Source right yeah it's this it doesn't have to be a part of fabric the same use cases that it solves it's a new new but very similar experience I think it's the closest thing to to call it great that was Data Factory that was Data Factory then we have the synapse data warehouse It's is is that the is not that old but is it the cinef data warehouse just in fabric is it the synapse that way no okay it's not it's not but so I have new features no but but just think about it for a second with synapse data warehouse I get the name confusing this is the laser Warehouse in fabric okay um it's just Microsoft fabrics and updated Warehouse guys this is the experience but the data warehouse imagine this we can now make a data warehouse in our online software as a service the the service that before was power bi now it's now it's Fabric in that service we can now make a data warehouse we don't need server in our basement we don't even you need an Azure SQL Cloud sorry but we don't don't need a synapse Cloud we don't need and database to create a data warehouse because we now got all these SQL SQL capabilities inside of fabric inside the data warehouse storage artifact great and and to mind our knowledge then the scene of data warehouse is more or less automatically applied to my one leg and I can I can more with a few clicks of of what I have tested I can access my one leg I can transform my my data and then at the top right corner I think it is right now in the UI I can switch to my SQL endpoint and then I'm actually diverted into my scene of data warehouse with the actual tables I have made in Delta parquet format are now attached as tables on the SQL Server it's kind of it's kind of magic it's a little bit of magic because what we have another magic later but it is it's kind of kind of magic because what we have is that it has everything we need and want from a SQL database yeah but it's it's actually not a SQL database it's still living it's living on one Lake yeah so it's a tool on top of data Lake files working with them on them as if they were SQL tables so I can it it is just a well I'm simplifying too much but but it is a matter of enabling me to write SQL code to boards my testified yes but more than that to run stored procedures to create views to do everything you want in an actual the SQL based data warehouse you can do this in the in the new fabric data warehouse um but you still get all the advantages of the common file format of the um of the the data Lake storage and and the flexibility with that great yep so to sum it up we can actually build an actual data warehouse in I want to say in power bi but in in fabric yeah okay great um and this is something we we as Architects and consultants and Engineers has been working on when synapse with for a lot of time trying to make a solution where we can store it in the data Lake but query it with SQL we had tools to help us with that but doing it with one click and then you have an object that just does it for us and that's the magic magic part if you ask me okay but but no data warehouse without data engineering no there's a warehouse how do I move my data now oh um we still will move it with pipelines you can move it in with pipelines you can even use the data flow we talked about before then you have your data ready yeah you have your staging layer as we will call it and you can transform it like you would in a data warehouse stored procedures okay you can make a ViewHouse if you if you want that yeah yeah but stuff you has is a logical database based on YouTube you have logically the warehouse with views and Views and Views and Views sometimes that is efficient so that's the way to go very very much not efficient but yeah you can as we would do sometimes on our synapse serverless yeah okay so we could do a review on your view and view Warehouse or we could do start procedures to actually change the data um we could even cheat a little bit and use a data flow in the middle of a warehouse to pull in the data and then save it back in and do some Transformations so again it's a unified platform so we can we we don't have to choose one tool and then use that we can always be flexible around this works and then the next service on our list is the uh the data science data engineering engineering data engineering let's say engineering yeah so data engineering is is what the not old but but what the data engineers in the future should work towards so we have we have the notebooks with the Jupiter notebooks that we know and we have the data pipelines that we also know yeah and and and then we have these um we have these possibilities to join the new one leg and build the lake house with notebooks with Pipelines um and and and and get our data engineering work done and executed yeah so what we can do with data engineering that we may not be able to with just a data warehouse is we can do actual a code first programming without data great so in a lot of in a lot of other areas around power bi or fabric we may prefer low code no code tools because they do enable us to use less tick uh Tech trained people to do best to enable to enable business people to actually do what they need to get done and not be relying on technical uh technical Manpower or I.T yeah Department okay um but there is a lot of things that we can do better and faster if we can do actual code so if if we're able to which we are able to with notebooks we can use Python we can use uh even SQL still um but but just being able to use python in itself open up so many possibilities because now we can actually code with our data we can start thinking about making data quality unit tests we can start thinking about doing act doing very systematic data cleaning and it it's just so many use cases it's even hard to find out what to mention it's just we just open up the whole world of programming we get access to libraries so other very technical very very uh clever people around the world already made great libraries to use in our python code well we can just tap into that and with one line of code code we can install that library and use it with our data pipelines and with the one leg experience I can now drag and drop my artifacts into my notebook yep and then the fabric service will begin to write not write the code for me but give me a starting point to to access that those files yes absolutely that data okay great so we have all the tools to be able to build a professional lake house with the with the with some clicks and then we can start doing our coding so even for a code first pro developer it's not about reducing their capabilities or reducing their ability to do what they want it's about even the cumbersome task that they don't want to be concerned with to yeah make them easy with a few clicks and they're started and then they can get right into doing their actual programming not setting up a bunch of environments and stuff around it and with notebooks we can tie this all over to our data science part yeah because the data science part also contains our notebooks that we're used to in in Python and it also now leverages the possibility for us to build experiments to to play with the data to to to build a data node and an AI model a machine learning model that perhaps could help the business and when we find those models within the the experiment we can then deploy the model as a Machinery model to the fabric service absolutely great and and I want to say notebooks in data engineering and notebooks in data science it's the same notebooks so it's just a matter of how we decide to use dates notebooks yeah um yeah so so we enable this power to use machine learning around our data in fabric with synapse data science which I think is really cool because a lot of companies and organizations want to do something with machine learning and they have a huge potential to do something with machine learning but the the overhead of getting started and onboarding new programs to do that new softwares is well a bit limiting so being able to do it from a platform you're already working at working in is such a big benefit okay yeah and and the example you gave me before where I was the data analyst and you were the data scientist then now we can begin to share data exactly on these data science projects yep okay absolutely great yeah so we can do data science more easily but more importantly we can share that work and put it in I want to say we can move from an ID to actual impact much quicker yeah and much more easily across these different data roles yes okay and then we have something I know that you are very excited about this is my new let me call it my new baby sorry for I don't have other words but real-time analytics real-time analytics tell me why real-time analytics is the coolest thing ever well real-time analytics is is a new word for for Azure data Explorer or CNF data Explorer actually the same engine it is it is the custom engine beneath it and the real-time analytics inside fabric is now leveraging the possibility for the for the business to to have a one point of entry for the RG devices for their Telemetry for their uh it has a it has another name called immutable data the the data that cannot be changed historically and it is just flowing in from an iot device like a temperature sensor or a pressure sensor or the weather forecast or something like that and then based on those incoming data we can now very quickly once it has been stored in the kql database the custom database we can then begin to build insights on let me call it near real-time analytics because what is real time we will also we will always have a small lack of of when data comes in and then it needs to be stored and then we can work with it but the real-time analytics is now leveraging the adx as a data Explorer slash synapse data Explorer experience within the fabric service and we no longer have to spin up at least two virtual machines to start our our Crystal cluster cool yeah that is pretty cool and in addition to that we get a brand new service as I see it called event stream nice the event stream service is a I would say no code solution yeah for streaming data from several sources handle them centrally and then store them where you would like to store them perhaps one place or several places with the same data you can store it in acoustic database and then you have the data live to do custom query on it yeah and you can store it in the lake house directly with one click that sounds really powerful and really fancy and really complex so what what kind of skills do I need to to build this in event stream you need to to be able to click the mouse with your with your pointy finger and and then just know what you need to do okay because defining a source is is very very easy trans transforming the data is a matter of leveraging the UI the UI helps you a lot you can do aggregations you can do sorting you can do merging you can do filtering and then defining a new destination is a matter of on the slide we just saw before hitting the plus sign on both left and right left is new source right is new destination and then you get guided towards where would you like to store your data wow so what if I have some solar panels on my house just as a just for me as a private person sure could could could I do I do I need programming do I need something so I can just go and tap into that data yes you can tap into that aggregate it you can save it in your one leg or you can pipe it directly to your custom database how about you want an alert if suddenly the it's it stops working or something can also get an alert with this event yes yeah and and but I think when you get the alert perhaps you would look into the data activator which is coming later because that that does what I've heard way more things that we can do in in in the event stream cool so yeah and then we have the last last bit here we also have we talked about all these um synapse experience so what we used to be able to do in synapse we're now doing fabric so what about power bi what's new what's new in power bi I think it's an interesting thing to talk about so in a few hours three hours from no two hours from now we will have a guest from Microsoft who will helpbox explore exactly that water is new within power and it's not just any guest it's not what just any one from the last senior program manager from the cat team the customer advisory team so if you're a really really big really really big organization you will really need the big guns in anything related to power bias is who you call yeah yeah yeah and he will tell us about um this concept called direct Lake which very very briefly is a way for even power bi to connect directly to that one lake so we're now going into more details even power bi itself will now be part of this unified data so a big Cliffhanger there big Cliffhanger there join us one one uh one o'clock one at one o'clock in Central European Time yeah we will go live with a session with Microsoft talking about this topic exploring power bi in this new world of direct Lake I would like to add to power bi yeah within most of the services that we have gone through today there is a new button called give me a data model or create a Power report if I click that button then the fabric service will actually provide me with a either a data model or a a power bi report which I can build on with with one click so you say if I spin up a lake house or a warehouse that's automatically a data set on top of that warehouse or that lake house yes so immediately just from importing the data I can start using it for analytical and Reporting purposes yes and you can start to drag and drop into your visuals in power bi and then deploy the report for the inducer amazing amazing so everything we know from Power bi is still there yeah we can still use it so when we say that you can connect to the the one Lake files the one lake lake tables that doesn't mean that you still cannot just import your data into a data set sure whatever we can do today we can still do in the future we should not be worried about losing functionality here which only gaining new capabilities which is why we are so happy and excited because yeah exactly so we are running a bit out of time so a quick word on data activator yeah so these are data actually which is coming soon which is we'll talk about it's a no code way of trigger actions based on our data so if we get that input from our solar patterns or input from our power bi meshes or reports or kql or event hubs or even a SQL data warehouse or something like that we can get those data in and we can look through the patterns of that code and make some complex Advanced rules that will give us actions based on that so those actions could be emails it could be alerts it could be power to make flows it could be a bunch of anything thanks yeah yeah so it's an advanced way to activate our data go from the inside to actually generate actions based on our data really we will see more on that later this year right absolutely should we rev it up yeah and so with that we wrap it up and say we're happy with with fabric announced this week and we are still head deep in exploring all the quirks and possibilities with this we've been doing this for some time we're happy to help out someone who is just starting out and tune in in 15 minutes tune in in 15 minutes where we will have Andy curler Microsoft data platform MVP talk about the data warehousing experience in Microsoft fabric see you later see you bye