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Welcome to episode 336 of the
Microsoft Cloud IT Pro podcast

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recorded live on May 31st, 2023.

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This is a show about Microsoft 365 and
Azure from the perspective of it pros and

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end users where we discuss the topic or
recent news and how it relates to you.

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Microsoft build has come and gone and we
have some updates to discuss this week

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regarding announcements made at
the conference. As can be expected,

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there was a bunch of AI related news
around co-pilot chat G P T and OpenAI

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and then finally a new SaaS-based
data product, Microsoft Fabric.

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Are you gonna catch me up because I was
on vacation last week and you would be

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proud of me Scott.

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I turned on my computer I
think once the entire week cuz

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I actually had to do some podcast stuff.

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I have tried to catch up on blog
posts and news and all of that,

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but I have not had the time I wanted
to to dive in to build stuff and I saw

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you put stuff and I've flagged
some articles I saw but again,

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I have not gone into depth
on anything in particular.

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So what shiny build stuff
should we talk about? So let.

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Me catch you up on some
Microsoft build stuff.

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So Microsoft Build 2023
happened last week, two days.

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You can absolutely go out and watch
the on-demand sessions at this point.

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It's all out there ready to go.
All the blog posts and things,

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we'll have links in the show
notes and all that good stuff.

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So I highly recommend
everyone go watch the keynotes

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even though keynotes are largely useless.

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I think it's very interesting to just
see the kind of tone and approach

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around things like ai. I was,

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I was kind of joking around
as the keynote was going on.

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Like my big takeaway from the keynote
was that you're going to be able to build

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co-pilots for your co-pilots
to pilot your co-pilots,

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to get you another co-pilot
to do something for you. And,

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and I was very confused by this
because I had been introduced and

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I think most of us has been introduced
to co-pilots as marketing terms

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and not as a,

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a feature like it's a product versus
a feature like GitHub co-pilot

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is, you know, GitHub's
ability to do AI generated,

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ML generated code for you M
365 co-pilot like copilot for

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Word is the same thing,

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it's a large language model that can
respond to prompts in Microsoft Word

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and Microsoft kind of came out
and they said sure we have copilot

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and it's a marketing term so it's,

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it's a product and a feature and now
we're gonna let you go build your own

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co-pilots as well. So not only
are there the existing co-pilots,

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but theoretically you could build
a co-pilot for another co-pilot.

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Like say you wanted to, I don't know,

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you wanted to build a co-pilot for
Word that interacted with Microsoft's

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co-pilot. Like that would
theoretically be possible.

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Like you could do some own pro,

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some of your own prompt engineering
and prompt hacking and inject on

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top of their L L M <laugh> with
your L L M and and potential

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responses or context of your business
and and things like that on top of it.

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So that was kind of interesting
to go through and see.

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I also recommend that everybody watch
the keynote that happened right after

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that, which I believe
was with Kevin Scott,

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he's the recently crowned
VP of AI or something

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like that at Microsoft. I
forget his ex exact title,

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but he kind of goes into more of that
ecosystem and everything that's going on.

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So a couple things, uh,
co-pilots for everybody.

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Like absolutely you can go out
and build your own co-pilots.

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There's a service inside
of Azure called Azure AI

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Studio where you can go and train
these models and design your

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co-pilots and release them out there.

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Some of the demoware I saw was kind of,

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it was a kin to the old like chatbot
in five minute kind of demos that you

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used to be able to do.

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I think it's a little richer than that
just based on kind of the language models

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and and what they have access to
today and just kind of how far prompt

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hacking has has come along or I guess
prompt engineering to get things to where

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they need to be.

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The other interesting thing
that was announced was

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that plugins are coming to
chat G P T integration in

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Bing. So today bing kind of runs on the,

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I think the G P T three five
turbo model and it is coming over

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to G P T four and when it does that,

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it will have access to the same exact
plugins that you have access to if

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you are a premium
subscriber open AI today.

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So if you're already paying open AI,

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the monthly fee for access to
the paid version of that product,

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you can already flip over to
the G P T four model and with

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G P T four you get plugins.

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So plugins will let you do things
like have more context around

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kind of these outside data ecosystems.

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So some of the examples that they
brought up cuz it's, it's bing,

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it's kind of consumer grade
and and consumer wear. Yep.

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Where things like open table Zillow,

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you know like hey I want you to
go out and build me a table of

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houses that are within five mile
radius of this address that meet these

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criteria and put them in a table
and rank them by price and number of

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bedrooms, number of
bathrooms, things like that.

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So plugins are coming and and rapidly
coming and I'm kind of looking forward

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to those. I think they'll be
nifty especially once you know,

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some more stuff comes out there like I
would love to see a plugin for something

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like Reddit like that's built around
the Reddit API and not just bing

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scraping Reddit and it has context
of things like upvotes and kind

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of hot articles and and things like that.

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I think it's gonna make data
mining with some of the LLMs just a

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lot smoother at the end of the day.

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How do these plug-ins work then?
Where do they live? Is this again,

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I haven't read up on this at all.

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Is this plug-ins to connect to Bing two
services or plug-ins that you can put

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like in Bing for instance you
said build me a table of houses

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on Zillow. Does that return,

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is that a plugin for Bing to go out to
Zillow and then it just returns on like

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the Bing chat UI or how,

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I guess how do those plugins work and
where do they sit? Does that make sense?

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Yes, so the plugins, my
understanding of them,

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and I haven't written one of these so
don't like hold my feet to the fire.

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My understanding is
plugins are executed within

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the context of chat G P T
but then they basically have

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connectors to go out and be able
to interact with those other

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services.

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So you can almost think about it as
like a plugin is exposing another API

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to chat G P T so that it can go out and
interact with that data and then that

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data's returned in a format and in
a manner that is all up friendly

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to chat G P T and and being able to
have everything just kind of come back

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and be able to be massaged into
the right format. So you're likely,

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it's not like,

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I don't think it's like Bing has
gone out and indexed all of Zillow,

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it's more like it's calling Zillow
with a pre-engineered prompt and then

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Zillow has an a p I endpoint that's
able to respond to that prompt based on

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either a data set that they've built out
or maybe they even call into another L

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L M on their side to get some data back
from it and then they return all that to

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the client munt together and
it's all kind of ready to go.

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Okay. I think that's making
sense and I'm still trying, it's.

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Basically like APIs, like
it is just API interaction.

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I think the interesting thing is it's
almost like APIs talking to APIs just

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because of the human language nature
of L L M prompts and kind of how,

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how they're constructed, right? It's it's,

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it's not like you are writing a database
statement and you're saying you know,

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set options strict on,

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you're going in just human language and
you're saying hey large language model,

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I need you to imagine that you are a CEO

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at blah blah blah blah company and you
always act in this manner and you're

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gonna be kind and you're gonna respond
in this way and you're gonna be held to

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this set of standards in
your response. You know,

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where I think we're used to more like
just arbitrary like binary on off kinds of

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things as we're, you know,

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maybe scripting or or interacting with
programs today or or writing our own.

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So it's a little more free flowing
than that. So I, I don't know,

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we'll see what comes out of it. Like.

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Developers are writing these that and
essentially submitting these two yes G P

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T or Bing and saying hey here's my plugin.

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I want to bring in this additional data
or add this additional functionality

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to G P T. So is this
adding it to chat G P T?

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Is this adding it to open ai? Is
it adding it like this is also,

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I was starting to look through this,

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this is getting all kind of confusing
because everybody kind of uses chat G P T

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as a default but it looks like
open AI is now using Bing as the

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search experience for chat G P
T but I don't think Bing Chat is

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necessarily chat G P T and trying to
keep all the terminology straight in my

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head too. So.

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Open AI is the company, they're the big
broad company. They build a bunch of.

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Models and the learning models
and all of that. Yep, yep.

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They build a bunch of products.

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One of those products is called
Chat G P T and then within

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chat G P T you have
various flavors of language

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models that are available to you
basically like what's the switch?

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I wanna flip and chat G P T for the types
of responses that I wanna give back.

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Those are all the G P T
models you hear about.

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So you hear about like G P T three
five, G P T three five Turbo,

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G P T four, those are
kind of the features.

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Right? Built by open ai right?

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And then chat G P T is sitting on top
of those different flavors of G P T

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3 3 54 chat.

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G P T is just branding like
chat G P T is a website.

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It's the thing you go to to interact with
a set of features that you turn on or

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off and the models are the features that
you go back and forth and you turn on

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and off that way.

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So you could go in like if you're a
paid subscriber to chat G P T today,

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you can go into chat G P T and you
can say okay I wanna work with the

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G P T three five Turbo model and
the G P T three five turbo model has

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a certain set of constructs.

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It was built on top of one
Lang large language model.

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Or you can say I wanna do G P T four And
probably the biggest difference between

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like some of the older models
and G P T four is that G P

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T four is the one that has the plugins
and it can do like real live internet

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searches.

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So remember like when you chat G P T
initially came out and they said okay we

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have this model right that you can
interact with and don't worry about it.

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One of the big constraints of it was
that model was only trained on a data set

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that was current up to late 2021. Yeah.

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I remember there was a date there, I
couldn't remember if it was 2020 and 2021,

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but I do remember that where you'd go
ask it stuff and it'd be like my data's

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limited to data up to this
point in time because it,

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it couldn't go grab recent news.

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Recent articles didn't really
touch the internet. Correct.

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So G P T four opens that up,

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lets it go and do more realtime
contextual searches and

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the Bing part of it is
Bing is using <laugh>,

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the G P T four model or transitioning to
it cuz they will be as they bring like

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plugin integrations and
things like that. Yep.

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So Bing is using a large language
model in the background and then the

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background of GP chat G P T, they're
also using Bing as their search engine.

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So what they've said is like G P T four
when you're using that model then it

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needs to reach out to the internet and
it needs to do a search in real time.

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It's gonna use Bing as the search
engine of choice to do that.

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Got it.

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So that's how they're kind of connecting
G P T four and to be able to search

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some of the internet as leveraging Bing.

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But then Bing is using G P T
four in the background as well.

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It's a.

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Little weird. So Bing would be the
backend search engine for chat G P T. Yep.

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And chat G P T would be the backend
large language model for Bing. Okay.

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You could almost like draw like a weird
circle and put some double arrows on it

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and it, it would make for you know, a
pretty funky physio diagram. That's.

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What I was trying to kind of wrap my head
around as we were talking through this

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is how all of those were tied together
but what you just explained makes sense

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and then they're using all these same
standards then for plug-ins that if you

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write a plug-in for open AI
because of the weird circle model,

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all those plug-ins will work in chat
G P T and they'll work in Bing and

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they'll work in co-pilot and all of
that because it's all just one big happy

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circular family. Correct.

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Like theoretically that's, yeah
that's, that's the way it all goes.

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So we'll see if it manifests
that way but <laugh>.

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And then you can go build your own
co-pilot on top of all that using those

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add-ons and yeah.

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You can.

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So you can go out into Azure AI studio
today and you can do that another place

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that co-pilot is coming in
the Microsoft ecosystem that I

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thought was kind of interesting as I spend
more and more time there is Power bi.

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So there's gonna be a Power
BI desktop developer mode

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that's going to enable a
workflow for interacting with,

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well I guess a couple things.

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So you're gonna get co-pilot integration
on Power BI and then you're also going

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00:14:07,860 --> 00:14:11,320
to get integration and some other things
coming to Power BI that I thought were

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00:14:11,350 --> 00:14:14,560
kind of exciting. Like I, I would
totally use that all the time.

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00:14:14,620 --> 00:14:18,560
I'm tired of sending the same P B I X
file to 30 people and then wondering where

227
00:14:18,560 --> 00:14:19,393
they've uploaded it to.

228
00:14:22,900 --> 00:14:26,840
Do you feel overwhelmed by trying to
manage your Office 365 environment?

229
00:14:26,940 --> 00:14:30,600
Are you facing unexpected issues that
disrupt your company's productivity?

230
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Intelligent is here to help much like you
take your car to the mechanic that has

231
00:14:34,560 --> 00:14:38,680
specialized knowledge on how to best keep
your car running Intelligent helps you

232
00:14:38,680 --> 00:14:42,200
with your Microsoft Cloud environment
because that's their expertise.

233
00:14:42,310 --> 00:14:46,280
Intelligent keeps up with
the latest updates on the
Microsoft Cloud to help keep

234
00:14:46,280 --> 00:14:48,680
your business running smoothly
and ahead of the curve.

235
00:14:48,710 --> 00:14:52,480
Whether you are a small organization
with just just a few users up to an

236
00:14:52,480 --> 00:14:56,640
organization of several thousand
employees they want to partner with you to

237
00:14:56,640 --> 00:14:59,800
implement and administer your
Microsoft Cloud technology,

238
00:15:00,410 --> 00:15:03,760
visit them at intelligent.com/podcast.

239
00:15:04,220 --> 00:15:08,480
That's I NT E L L I G I N

240
00:15:08,600 --> 00:15:13,520
k.com/podcast For more
information or to schedule a 30

241
00:15:13,520 --> 00:15:15,360
minute call to get
started with them today.

242
00:15:16,080 --> 00:15:20,440
Remember Intelligent focuses on the
Microsoft cloud so you can focus on your

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00:15:20,680 --> 00:15:25,280
business. Oh co-pilot everywhere,

244
00:15:25,280 --> 00:15:29,080
you know where else co-pilot's
coming everywhere to Edge <laugh>.

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00:15:29,580 --> 00:15:29,800
Did.

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00:15:29,800 --> 00:15:33,800
You see co-pilot as coming to Edge too?
Yes. So this one could be interesting.

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00:15:33,830 --> 00:15:38,240
This one of some of them intrigues me a
little bit where it essentially uses the

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website as context. So some of these news
articles or if you pull up a website,

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this one would be interesting to play
with co-pilot to get like a summary of a

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00:15:47,470 --> 00:15:51,240
webpage or to find out
more about the webpage.

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This one is one of the co-pilots
that intrigues me more.

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00:15:54,480 --> 00:15:57,280
I haven't played with like Power Apps
or Power Automate or some of those

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00:15:57,470 --> 00:15:58,303
co-pilots.

254
00:15:58,660 --> 00:16:01,400
I'm really looking forward to getting
my hands on all the co-pilots and seeing

255
00:16:01,400 --> 00:16:05,600
which ones I actually like and I find
useful and which ones are just kind of eh.

256
00:16:05,980 --> 00:16:10,480
So for Power Automate I haven't
used it much but I'm not

257
00:16:11,000 --> 00:16:13,000
a large power automate person.

258
00:16:13,070 --> 00:16:17,920
Like I'm not using it in my
daily life but I do use Power

259
00:16:18,060 --> 00:16:21,840
BI almost daily and okay, I've got
Power BI desktop open all the time.

260
00:16:22,260 --> 00:16:24,520
So there's a quick little video preview.

261
00:16:24,830 --> 00:16:28,440
It's actually on the news site
from like Microsoft Global Comms.

262
00:16:28,750 --> 00:16:33,440
I'll put a link in the show notes
to that which is copilot in Power BI

263
00:16:34,140 --> 00:16:35,880
and you can kind of see how it works.

264
00:16:36,060 --> 00:16:40,520
So one of the interesting things to me
about copilot and Power BI is you know

265
00:16:40,520 --> 00:16:45,360
how Power BI has kind of the q
and a widgets and things available

266
00:16:45,370 --> 00:16:48,600
today? Yeah. Where you can go in and
it understands your data set and so,

267
00:16:48,740 --> 00:16:53,560
you know, show me sales by quarter
for the past two years ranked by

268
00:16:54,320 --> 00:16:57,880
industry of company, blah blah
blah or industry of customer. Uh,

269
00:16:57,880 --> 00:16:58,920
and and it can do all that.

270
00:16:59,340 --> 00:17:04,240
So that already exists today but all
that's doing is building out like singular

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00:17:04,900 --> 00:17:06,200
visualizations for you.

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00:17:06,820 --> 00:17:11,600
The co-pilot in Power BI lets you
go in and it says hey here's a

273
00:17:11,600 --> 00:17:15,280
data set, like here's a database
and here's some tables in it.

274
00:17:15,510 --> 00:17:19,320
Like maybe go figure that out for me
and then tell me what I can do with it.

275
00:17:19,620 --> 00:17:23,440
Oh and by the way go ahead and
actually build out the P B I.

276
00:17:23,500 --> 00:17:25,080
For me it's kind of like I,

277
00:17:25,160 --> 00:17:29,840
I get it's all demoware and when it
works it works <laugh> but it's super

278
00:17:30,190 --> 00:17:32,800
cool just to kind of like
see it just, you know,

279
00:17:33,140 --> 00:17:36,280
get released and go against things
like that. Cause if you think about it,

280
00:17:36,280 --> 00:17:39,800
like lots of companies are the
same at the end of the day.

281
00:17:40,110 --> 00:17:43,040
Like if you're a publicly traded company,

282
00:17:43,110 --> 00:17:47,000
like the way you're keeping the books
is gonna be very similar to all other

283
00:17:47,000 --> 00:17:50,280
publicly traded companies usually in
your country cuz that's some type of

284
00:17:50,280 --> 00:17:51,200
regulated industry.

285
00:17:51,580 --> 00:17:54,440
So there's gonna be certain requirements
for you for reporting your data,

286
00:17:54,820 --> 00:17:55,640
for storing your data.

287
00:17:55,640 --> 00:17:57,880
Like it's always gonna have to
come out in a common format anyway.

288
00:17:58,180 --> 00:18:02,040
So it's one of those things that you
can absolutely train a model on and then

289
00:18:02,070 --> 00:18:06,520
have that model come back and kind
of immediately have context to your

290
00:18:06,790 --> 00:18:11,640
data but it already knows things like
common schema, common terminology, all,

291
00:18:11,660 --> 00:18:12,493
all that kind of stuff.

292
00:18:13,040 --> 00:18:15,240
Interesting. This is, I'm Scott,

293
00:18:15,500 --> 00:18:17,640
I'm committing to not adding
everything to my list.

294
00:18:17,830 --> 00:18:19,440
This one I don't use Power bi enough.

295
00:18:19,440 --> 00:18:24,240
This is not gonna get added to my list
but it would be interesting and maybe if

296
00:18:24,240 --> 00:18:27,560
I started pulling more data into
it, yeah, I'll have to play with it.

297
00:18:27,690 --> 00:18:31,560
Maybe this is a maybe how about
it? Maybe on my list, maybe not.

298
00:18:31,660 --> 00:18:33,000
Uh, maybe on your list. Okay.

299
00:18:33,140 --> 00:18:36,480
So maybe one last one for today
before we run out of time,

300
00:18:36,580 --> 00:18:39,200
cuz I think we both
have meetings coming up.

301
00:18:39,700 --> 00:18:44,480
And this was in the same article with
introducing co-pilot and Power BI

302
00:18:44,900 --> 00:18:47,200
was introducing Microsoft Fabric.

303
00:18:48,320 --> 00:18:52,440
I have seen a boatload of
headlines about Microsoft Fabric.

304
00:18:53,080 --> 00:18:57,960
I am not a data guy and this looks

305
00:18:57,960 --> 00:19:01,080
like it's tied in a lot
more to Synapse data,

306
00:19:01,080 --> 00:19:04,880
factory data warehousing data science,
that type of stuff from the headlines.

307
00:19:05,180 --> 00:19:09,320
And what I've gathered is
that it's like this cross

308
00:19:10,130 --> 00:19:15,000
cloud data ingestion thing
where I can like go grab all my

309
00:19:15,000 --> 00:19:19,400
data from all the things everywhere
and pull it into Microsoft Fabric and

310
00:19:19,400 --> 00:19:24,400
integrate it into some of these data
analytics tools into Synapse Data

311
00:19:24,400 --> 00:19:27,040
Factory. Is that right? Or like what,

312
00:19:27,830 --> 00:19:29,840
what is fabric at a high level?

313
00:19:29,840 --> 00:19:32,440
Because I also don't know that we
have time to dive into it in detail.

314
00:19:32,840 --> 00:19:35,280
I don't know <laugh>, I don't
know enough about Fabric. All.

315
00:19:35,280 --> 00:19:39,240
Right. Fabric. Well first of all
it's a horribly named product.

316
00:19:39,760 --> 00:19:42,400
I think some marketers
fell asleep at the Wheel.

317
00:19:42,840 --> 00:19:43,390
<Laugh> again.

318
00:19:43,390 --> 00:19:45,680
When they came up with
the name for it. Y yeah.

319
00:19:45,730 --> 00:19:50,640
Cause there there are already things
called fabric in the Azure ecosystem

320
00:19:51,060 --> 00:19:54,600
and and in the broader Microsoft
ecosystem as well, there's.

321
00:19:54,600 --> 00:19:57,280
Lots of fabric. Like my
first thought was App Fabric.

322
00:19:57,520 --> 00:20:00,440
I can't remember what App Fabric was
from. Was that a SharePoint thing?

323
00:20:00,630 --> 00:20:04,800
Well it was Azure. There's App
fabrics. Service fabrics, yeah, yeah,

324
00:20:04,800 --> 00:20:06,400
there there's fabrics for service fabrics.

325
00:20:06,400 --> 00:20:07,220
All the things.

326
00:20:07,220 --> 00:20:11,200
So Microsoft Fabric is a

327
00:20:12,140 --> 00:20:12,973
SAS service.

328
00:20:13,500 --> 00:20:18,200
So it's kind of interesting
in that it is something

329
00:20:18,310 --> 00:20:23,040
that wraps a bunch of
Azure services while not

330
00:20:23,510 --> 00:20:27,840
necessarily being Azure itself.

331
00:20:28,870 --> 00:20:32,120
What I mean by that is if you think
about some of these things, like we were,

332
00:20:32,120 --> 00:20:34,480
we were talking about Power
BI like a couple minutes ago.

333
00:20:34,890 --> 00:20:39,720
Power BI is a SaaS service.
It's licensed by the user.

334
00:20:39,870 --> 00:20:44,280
Like you don't go out and buy
a Power BI tenant and then

335
00:20:44,700 --> 00:20:48,800
buy a Power BI database and then kind of
put all these pieces together yourself.

336
00:20:48,800 --> 00:20:49,633
You say like, oh no,

337
00:20:49,940 --> 00:20:53,200
I'm just gonna buy a per user license
to this thing called Power bi and it's

338
00:20:53,200 --> 00:20:57,200
gonna let me do visualizations based on
a bunch of data sources that exist out

339
00:20:57,200 --> 00:20:58,320
there. So it's,

340
00:20:58,320 --> 00:21:00,440
it's a little bit of a weird model
for me to wrap my head around,

341
00:21:00,440 --> 00:21:04,440
I guess is just like data
as a service that's not

342
00:21:05,030 --> 00:21:08,280
necessarily Azure focused.

343
00:21:08,550 --> 00:21:12,120
It's like it's Microsoft
focused. So it's not even M 365,

344
00:21:12,120 --> 00:21:16,040
like it's a whole new weird
thing. But at a high level it's,

345
00:21:16,040 --> 00:21:21,000
it's a SaaS offering that brings
together a bunch of building blocks for

346
00:21:21,000 --> 00:21:24,560
you inside of a single service offering.

347
00:21:25,020 --> 00:21:29,400
So while you have all these component
pieces and parts like Power BI and

348
00:21:29,590 --> 00:21:34,480
Synapse and Data Factory and Azure
Data Explorer and all these things

349
00:21:34,480 --> 00:21:36,720
that sit out there, you
know, they are disjointed.

350
00:21:37,060 --> 00:21:40,800
So you could go down the path of picking
up a Power BI license for your users

351
00:21:40,860 --> 00:21:41,520
and then saying, okay,

352
00:21:41,520 --> 00:21:44,360
I'm gonna use my existing Azure
data explorer thing over here.

353
00:21:44,780 --> 00:21:49,560
But then what do you do when you want
to go grab data from like a raw data set

354
00:21:49,560 --> 00:21:51,680
from a data lake and
transform it with Spark? Okay,

355
00:21:51,680 --> 00:21:53,000
well now I gotta go create a,

356
00:21:53,320 --> 00:21:57,640
a storage account and activate
hierarchical namespace on it and then,

357
00:21:57,700 --> 00:22:02,560
oh by the way, I might need like a, a
synapse spark pool to spin that up and,

358
00:22:02,560 --> 00:22:06,720
and you just end up with all these pieces
that you need to manage and it might

359
00:22:06,720 --> 00:22:08,520
be a little bit easier to
have it in a managed offering,

360
00:22:08,520 --> 00:22:10,280
which is kind of what Fabric does.

361
00:22:10,700 --> 00:22:14,640
So it brings together Data
factory synapse, data engineering,

362
00:22:14,750 --> 00:22:18,800
synapse data warehouse, synapse data
science, synapse real-time analytics,

363
00:22:18,890 --> 00:22:21,200
Azure Data Explorer, power bi,

364
00:22:22,140 --> 00:22:26,640
and it brings together
cloud storage as well

365
00:22:27,140 --> 00:22:31,760
and even multi-cloud storage into a single

366
00:22:32,190 --> 00:22:35,840
service offering. So now
rather than saying, Hey user,

367
00:22:36,100 --> 00:22:40,320
go to Power BI and then go talk to the
developer over here to get a connection

368
00:22:40,340 --> 00:22:43,720
to that data lake and then go talk
to the developer over there to get a

369
00:22:43,720 --> 00:22:46,800
connection to their, their data
set. And then, oh by the way,

370
00:22:46,940 --> 00:22:51,080
go talk to this other developer in this
other team who's been doing this Delta

371
00:22:51,080 --> 00:22:55,960
Lake project and blah blah blah. You
can just have it all sit in one place,

372
00:22:56,170 --> 00:23:00,480
which is your one lake, which is kind
of like your foundational data set.

373
00:23:00,820 --> 00:23:05,640
And then all these services automatically
have connectivity and they know how

374
00:23:05,780 --> 00:23:09,920
to talk to that one lake
and consume data out of it.

375
00:23:10,180 --> 00:23:13,800
So it's a little weird. Like you
get all these Azure services,

376
00:23:13,860 --> 00:23:18,640
you get some M 365 or like
data SaaS services like

377
00:23:18,640 --> 00:23:23,080
Power bi, then you can also pull
in data from outside sources.

378
00:23:23,390 --> 00:23:27,640
Like we'll see how it goes. I,
I would recommend there's a,

379
00:23:27,820 --> 00:23:29,200
in the getting started docs,

380
00:23:29,430 --> 00:23:34,000
there's a fabric terminology
bit of documentation and it

381
00:23:34,240 --> 00:23:37,520
takes you through like general
terms of the service. Like hey,

382
00:23:37,580 --> 00:23:41,720
what's a workspace? You know, how do
they measure capacity? Things like that.

383
00:23:42,100 --> 00:23:46,400
But then it breaks down
into the individual pieces
and components like inside

384
00:23:46,710 --> 00:23:49,640
Data Factory inside of Microsoft Fabric.

385
00:23:50,150 --> 00:23:52,080
What do you get inside of uh,

386
00:23:52,080 --> 00:23:56,200
synapse data science inside of when
it's wrapped in Microsoft Fabric,

387
00:23:56,870 --> 00:23:57,703
what do you get?

388
00:23:57,900 --> 00:24:02,560
So I can see it like making things
a lot easier for people. You know,

389
00:24:02,560 --> 00:24:03,920
if you think about like, you know,

390
00:24:03,920 --> 00:24:08,600
just that headache of like getting access
to a data set and getting like my VM

391
00:24:08,660 --> 00:24:12,280
and my managed identity to be able to
talk to this storage account or this thing

392
00:24:12,280 --> 00:24:15,280
over here, this database over here.
Like that's as painful, right?

393
00:24:15,280 --> 00:24:19,440
Like why not just have it all managed by
the service and wrapped around a common

394
00:24:19,470 --> 00:24:21,160
data layer? Like that makes sense.

395
00:24:21,500 --> 00:24:25,440
So I think that's kind of like how
the service manifests and comes out.

396
00:24:26,000 --> 00:24:26,833
I haven't seen,

397
00:24:26,980 --> 00:24:31,840
and I I didn't do too deep a dive
on it about things like pricing

398
00:24:32,660 --> 00:24:37,600
and what that's gonna look like,
like impacts to existing users.

399
00:24:38,030 --> 00:24:40,920
It's kind of like if you're
a Power BI <laugh>, uh, the,

400
00:24:40,920 --> 00:24:43,520
the way it is in the docs, if you go
to the docs today and you say, Hey,

401
00:24:43,600 --> 00:24:45,600
I wanna start a fabric preview trial,

402
00:24:45,980 --> 00:24:50,480
it immediately opens up with what
if you're an existing Power BI user.

403
00:24:50,860 --> 00:24:54,880
And I think that kind of gives you a
big hint as the target audience and,

404
00:24:54,940 --> 00:24:56,400
and where they're going with it. Yeah.

405
00:24:56,470 --> 00:24:57,320
They do have,

406
00:24:57,320 --> 00:25:02,320
like I was looking through some of the
documentation where they have by fabric

407
00:25:02,820 --> 00:25:06,680
now or by Pro and I don't
see though, to your point,

408
00:25:06,800 --> 00:25:11,240
I don't see purchase fabric capacity.
There's some pricing but man,

409
00:25:11,270 --> 00:25:12,960
some of the naming in here too, Scott,

410
00:25:13,150 --> 00:25:15,200
like I'm gonna go back
to you with marketing.

411
00:25:15,320 --> 00:25:19,520
I was flipping through this
article and now you have

412
00:25:20,270 --> 00:25:25,080
Data Lake but you also
have one Lake data and then

413
00:25:25,100 --> 00:25:26,920
you have uh,

414
00:25:26,920 --> 00:25:29,760
there was something else in there that
I saw that was named really similar to

415
00:25:29,760 --> 00:25:30,280
that.

416
00:25:30,280 --> 00:25:35,280
I think that fabric terminology site is
gonna be a good site to keep handy as

417
00:25:35,280 --> 00:25:38,040
you go sort through what
all of this stuff is.

418
00:25:38,240 --> 00:25:40,040
I think the big thing to keep
in mind today is, like I said,

419
00:25:40,040 --> 00:25:42,160
like it is a wrapped SaaS offering.

420
00:25:42,860 --> 00:25:46,840
So you don't go and buy Data
Factory by itself anymore.

421
00:25:46,910 --> 00:25:50,560
Like you or you still can like I'm,
I'm not saying you can't do that,

422
00:25:50,560 --> 00:25:53,400
like absolutely like the component
pieces and parts are there,

423
00:25:53,780 --> 00:25:57,680
but if you walk into it from a service
offering side of things, it's just,

424
00:25:57,980 --> 00:26:02,040
I'm gonna go buy Fabric And Fabric has a
data factory component within it. Yeah.

425
00:26:02,040 --> 00:26:05,200
Well and it looks like
because of that, as you need,

426
00:26:05,200 --> 00:26:06,880
depending on the size of your data,

427
00:26:06,880 --> 00:26:10,200
it looks like there is
different capacities and
different levels and there's,

428
00:26:10,510 --> 00:26:13,640
this isn't just gonna be go buy fabric,

429
00:26:14,180 --> 00:26:18,920
but you're gonna have to buy
different levels or different capacity

430
00:26:19,060 --> 00:26:23,760
of fabric based on the amount of
data, how much is gonna be in there,

431
00:26:24,140 --> 00:26:25,280
et cetera, et cetera.

432
00:26:25,800 --> 00:26:28,760
Capacity is an interesting
one. So it's a SaaS product,

433
00:26:28,980 --> 00:26:31,880
so it it's a multi-tenant
kind of product. So you,

434
00:26:31,880 --> 00:26:36,440
you're basically buying a fabric tenant
is is really what you're coming into

435
00:26:36,790 --> 00:26:38,400
when you go ahead and purchase that.

436
00:26:38,580 --> 00:26:42,600
So each fabric tenant has

437
00:26:43,640 --> 00:26:47,880
capacity that's measured in
things like compute, like hey,

438
00:26:47,900 --> 00:26:49,480
I'm spinning up synapse in the background.

439
00:26:49,990 --> 00:26:52,040
What are the different SKUs
that are available to me?

440
00:26:52,300 --> 00:26:54,640
How many cores is that gonna come with?

441
00:26:55,020 --> 00:26:58,880
So immediately like if you start going
down the fabric licensing hole and you

442
00:26:58,880 --> 00:27:00,400
get into like capacity and SKUs,

443
00:27:00,400 --> 00:27:04,920
it opens up with compute where you've
gotta figure out like what's the skew and

444
00:27:04,920 --> 00:27:09,400
fabric to the equivalent skew and
Power BI and what does that translate

445
00:27:09,790 --> 00:27:14,160
into V cores and how much you use

446
00:27:14,690 --> 00:27:17,240
along the way Gets a little
bit weird on that side.

447
00:27:17,700 --> 00:27:22,600
The whole OneLink concept is
interesting as well because it's

448
00:27:22,660 --> 00:27:26,960
not a data lake that you manage. So
I guess coming from Azure storage,

449
00:27:27,180 --> 00:27:29,760
you know my take on, it's like
just bring your data lake, right?

450
00:27:29,760 --> 00:27:33,360
Like you've already got it out there,
it's just a, a gen two storage account,

451
00:27:34,020 --> 00:27:36,480
GV two with hierarchical namespace
enabled, blah blah blah. Yep.

452
00:27:36,540 --> 00:27:41,480
But now with one lake, because
it's a multi-tenant model,

453
00:27:41,870 --> 00:27:45,680
they've effectively like provisioned
some storage on the background for you.

454
00:27:45,980 --> 00:27:50,600
So I think that's gonna be interesting
to see like how they just price capacity

455
00:27:50,860 --> 00:27:52,320
and and kind of figure all that out.

456
00:27:52,700 --> 00:27:57,400
And then they built connectors from
things like one lake over to all

457
00:27:57,640 --> 00:28:02,240
the other clouds as well to to
Microsoft's cloud and then over to S3 and

458
00:28:02,820 --> 00:28:05,680
GCP as well. So if you have a one lake,

459
00:28:05,710 --> 00:28:08,720
there's a connector in there
where you can go and say like,

460
00:28:08,740 --> 00:28:13,200
Hey I have an S3 bucket that sits over
here that maybe has a data set that you

461
00:28:13,200 --> 00:28:17,400
want in it and you can actually connect
your one lake over to that S3 dataset

462
00:28:17,420 --> 00:28:22,080
and then the proxy layer of one lake
just makes it super easy for Power BI to

463
00:28:22,080 --> 00:28:25,120
talk to it. Like Power BI doesn't
need to know that it's talking to s3,

464
00:28:25,140 --> 00:28:28,000
it just needs to know that it's talking
to one lake and then you're done.

465
00:28:28,400 --> 00:28:32,320
Interesting. Lots of
stuff to go figure out.

466
00:28:32,590 --> 00:28:34,040
This is not what on my list either.

467
00:28:34,280 --> 00:28:36,560
This is not a high priority
for me given what I do,

468
00:28:36,700 --> 00:28:40,000
but with all the news I've
seen around it about it,

469
00:28:40,160 --> 00:28:42,440
I figured I should go
dig into it a little bit.

470
00:28:42,680 --> 00:28:47,320
I think you're gonna see it more
than you think <laugh> in your world

471
00:28:47,870 --> 00:28:52,600
because your customers are
Microsoft 365 SaaS customers who

472
00:28:52,900 --> 00:28:57,880
use things like Power bi. So if
Power BI is an entrance point today,

473
00:28:58,190 --> 00:29:01,160
like looking at the way
that it's positioned,

474
00:29:01,300 --> 00:29:04,920
it was announced and the
way the documentation's
oriented and things like that,

475
00:29:05,790 --> 00:29:09,960
it's kind of like Microsoft is
positioning it as every Power BI customer

476
00:29:10,420 --> 00:29:14,240
should just become a fabric customer
and then your life will be a lot easier,

477
00:29:14,240 --> 00:29:14,560
right?

478
00:29:14,560 --> 00:29:18,200
Because you get all the compute and it's
managed and identities are managed and

479
00:29:18,200 --> 00:29:20,200
you don't need to worry about
security and things like that.

480
00:29:20,580 --> 00:29:23,720
So if you in your ecosystem have
customers who are doing Power bi,

481
00:29:24,460 --> 00:29:28,160
it might be something you wanna spend
some more time with cuz I bet that's where

482
00:29:28,270 --> 00:29:32,880
like a bunch of the bleed outcomes from
is existing Power BI customers going

483
00:29:32,880 --> 00:29:36,880
like, huh, should I just be doing Power
BI anymore or should I be doing uh,

484
00:29:36,880 --> 00:29:37,600
fabric?

485
00:29:37,600 --> 00:29:40,520
I feel like it'll depend a lot on
the pricing just looking through,

486
00:29:40,640 --> 00:29:42,280
I posted a link to it in Discord too,

487
00:29:42,280 --> 00:29:45,840
looking through the whole
Microsoft Fabric licensing.

488
00:29:46,140 --> 00:29:50,440
It feels way more confusing than Power
BI <laugh>. But I guess to your point,

489
00:29:50,440 --> 00:29:54,960
if you're starting to bundle in
some of those locations where your

490
00:29:55,190 --> 00:29:56,720
data might be housed,

491
00:29:56,810 --> 00:30:00,400
maybe it's that the bundle
does end up being cheaper,

492
00:30:00,700 --> 00:30:04,480
the licensing does not look like it's
gonna be straightforward based on that

493
00:30:04,580 --> 00:30:08,640
fabric licenses. So I'll be curious
and I'm gonna keep an eye on it.

494
00:30:08,670 --> 00:30:12,640
It's gonna take time to get
there because Fabric is kind of,

495
00:30:13,100 --> 00:30:14,640
cuz you're buying Rebundled capacity.

496
00:30:14,940 --> 00:30:19,600
So now you're back to like this
weird model of saying like, oh, okay,

497
00:30:19,750 --> 00:30:24,720
like what's the skew I get and what's
the capacity that comes with it

498
00:30:25,090 --> 00:30:26,320
along the way,

499
00:30:26,930 --> 00:30:30,160
which can get confusing pretty quickly.

500
00:30:30,550 --> 00:30:34,720
Well, with that, I should probably go
jump to my next meeting. I can let you go.

501
00:30:34,930 --> 00:30:37,120
We've been chatting for
a while now. All right,

502
00:30:37,380 --> 00:30:42,200
we can continue on Friday cuz
our recording schedule is a bit

503
00:30:42,220 --> 00:30:43,880
on with some vacations coming up,

504
00:30:44,060 --> 00:30:47,400
but we'll talk to you again in
like two days for more topics. All.

505
00:30:47,400 --> 00:30:48,920
Right, sounds good. Thanks Ben. Thanks.

506
00:30:48,920 --> 00:30:53,800
A lot Scott. We'll talk to you
later. If you enjoyed the podcast,

507
00:30:54,340 --> 00:30:56,640
go leave us a five star rating in iTunes.

508
00:30:56,780 --> 00:31:01,320
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509
00:31:01,320 --> 00:31:02,153
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510
00:31:02,420 --> 00:31:06,160
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511
00:31:06,180 --> 00:31:10,600
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512
00:31:10,860 --> 00:31:13,160
Thanks again for listening
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