Ah, episode 401. Proof that we're still going strong.
Like a SQL server instance running on pure spite and caffeine.
I'm Bailey, your semisentient hostess with the mostest
metadata. Today, Frank's joined by the ever insightful
Andrew Brust to talk fabric AI, Microsoft
nostalgia, and why even Red Hat folks can still love Clippy.
Grab your headphones and your compute capacity. Let's dive in.
Hello, and welcome back to D Data Driven, the podcast. We explore the emerging
industry of data science, data engineering, and
artificial intelligence. With me today is not Andy
Leonard, who's my favorite data engineer in the world. However, I do have Andrew Brust,
who is an og, so to speak, in the.
In the AI and Microsoft ecosystem. And
although I think a lot of people think that I've abandoned the Microsoft
ecosystem, I have not. I've just had other. Other things kind of preoccupy my
time. And you know how it is. You have kids and, you know, they demand
attention. You have a house and all that and good problems to
have. But I'm very glad to kind of have someone I know walk me
back into kind of the Microsoft ecosystem, because a lot has
changed since I left Microsoft. I did
go to Microsoft Ignite. We were talking about that. I even scored myself a
Clippy. Clippy plus.
I'll have to tell you how I want him. So they had like, a little
challenge of like, do you know Windows history? Because Windows, I guess, turned 40 this
year and. I know, right? Yeah,
85. That's right. Yep. And they were like, do the history of
Windows. And I'm like, I'm like. And I had some Red Hat people and like,
you know, I was. I would have been very embarrassed if I had gotten anything
wrong. Turns out I got it right, actually. Good. I
remember back then you could install just a runtime version of Windows if you
wanted to run specific Windows apps on your. On your DOS
machine. Yeah. Now we take for
granted kids. They don't understand, like, no, no, you had to type in win or
win 3.1 if you were fancy and you're running multiple
versions of Windows at the. Same time,
you say kids today. I mean, kids 20 years ago didn't understand
that. This is true. This is true. But
anyway, in terms of bringing you back into the Microsoft orbit,
well, first of all, I'm sure Ignite did a bunch of that. But I can
be gentle because even though I am to this day a MicroSO
Regional Director, or as I like to say, a member of the regional Director
program, because Otherwise it sounds like I work for Microsoft
and an MVP, a data platform MVP, both over
20 years now. I'm an
industry analyst and so I look at data and analytics
solutions across the board, not just Microsoft specific. I
will say Microsoft is my sweet spot in terms of what it is that I
know and where I have the most history.
But I work with lots of other companies you've heard of, like
Databricks and Snowflake and Cloudera and
plenty of others. So my team and I do
most of the reports for a research company called GigaOM
with which I've had a very long association. Most of the reports. Sorry, I
didn't finish that sentence. That are focused on data or analytics
are things that we work on. So whether it be data warehouses or lake
houses or streaming data platforms or data access
governance or data catalogs or blah blah, blah, they all have the
word data in them. We work on those reports.
We create what are called gigaom radar
reports, which are a little bit like the analog to
Gartner's Magic quadrant in terms of looking across a category
at a bunch of vendor solutions and rating them on
multiple criteria which change each year.
So. And when I started covering big data,
because the way this got started was I was the first and only person
at ZDNet to be covering big data.
So that was an amazing. That's a term you don't hear a lot. You don't
hear it anymore. No. And in fact I was at the
older incarnation of gigaom. I was a full time employee there.
I was their research director and they wanted, so call me research
director for big data. And I said, can we just call it data
or data and analytics? And we did. Because I was like, you know,
eventually what we think is big now won't look so big.
Have you heard my Costco rules? Say again? I have something
called the Costco rule. The Costco rule. If you go to call,
if you walk into Costco by hard drive, that size. That size is no longer
big data. Fair, fair.
Anyway, that put me in an immersion. And at that time, Microsoft
was not in that world at all.
Eventually the thing called hdinsight got to beta
and it wasn't even called hdinsight when it was in beta.
And so Microsoft started coming back into my world. Eventually,
of course, it came back full swing. And with Microsoft fabric,
now it's doubly full swing, which I think is
very, very good, both for Microsoft and the industry. But
what was I going to say? Just that. Yeah. So
finally the two things that Were kind of orthogonal. Now have an
intersection. Right. And that
intersection is my sweet spot as I'm still a data platform
mvp and I have a very long history with Microsoft's
business intelligence stack. I was on Microsoft's
partner advisory council going way back, like
from 2005 to roughly 2010.
I don't know. I saw Power BI when it was still a bunch of wireframes
in a PowerPoint slide deck. So I've been through
many rounds of being frustrated that Microsoft
didn't have a good competitive play. And I'm now pretty
satisfied that they have one that's very competitive. So we can talk about that or
we can talk about the greater world. And as far as
AI goes, I was interested in AI all the way back in the horse and
buggy days when I was an undergraduate. Oh, really?
Yeah. AI was very different then. It was about like, weird programming
languages like Lisp and Prologue and
Expert Systems and things of that elk.
But neural nets existed then, and neural nets are the very
basis for the large language models we have today. So it's not, it's
not completely unrel it, but obviously it's very different.
I took a prologue course, which was the. We had one offering.
So I'm. You and I are both from New York City. So, like, you know,
we probably accidentally crossed paths more than once.
And I know we crossed paths during the early Power BI days
because I think the company I worked for at the time
was also an early believer in power bi.
So this is what meant 2005. And then they hired. We had a guy who
was the practice manager, Kevin, who got hired. Kevin Viers got hired into
Microsoft. So I think he's still there, actually. He,
he hired me back into Microsoft when I rejoined in 2018, which was kind of,
okay, what a small world it is, you know, and, and, and for us, like,
you know, something that my parents would always say, like 20 years could go by
in a blink once you hit a certain age. And I'm like, good Lord, was
that the truest thing they ever said, right? 20.
Yeah. The scale shrinks the older. The older you get. It's a
little. It's a little frightening. I've turned it like, into a roll of toilet
paper that as you get closer and closer to the end, it spins out a
lot faster because the diameter gets smaller. Oh,
Lord, that is a very scary concept.
But you're right. I remember one of the things. These were back in the
cubicle days when people worked in an office and things like that. And I remember
sitting next to Kevin. And Kevin would be on the phone like, yeah, I know
it's weird to hear Microsoft is kind of a small player in any niche,
but in BI and business intelligence they really were.
And it was just kind of like, yeah, that's true. You don't really think about
them as a small player, but at the time now it's kind of ridiculous
to say that in data and analytics, right? And the Power BI team has just
done phenomenal in terms of their speed to market. And what they
built out is phenomenal. It's unreal.
It's the Power BI team that kind of took over the
entire Azure data group, right?
And that included SQL Server. So
whereas the BI team was once a little corner of the
SQL Server world that
initially came to Microsoft through an acquisition of assets from
an Israeli company called Panorama.
And Amir Nats is the distinguished engineer who actually came
from Panorama and is very much like the father of Power BI
and of fabric. Still there.
Slowly but surely, not
only did they get BI right and they always had it right on the
server, they just never really had it right on the front end
until the current version of Power BI that we have now kind of gelled,
but they also ended up kind of mastering
the software as a service approach to
cloud services. And they took a look at the Azure
data stack and said, we have tons of capabilities here, but they're kind
of fragmented over several different products,
each of which have their own kind of procurement model and
pricing model. And that gets very hard to manage.
And if you look really carefully at fabric, while there are
some things that are truly native to it,
most of the parts of it are Azure services in the
background that have been integrated and
that have been unified in terms of how you pay for them,
I don't know. Microsoft needed that, by the way, the whole cloud industry
needed that. Because Google and Amazon are just as guilty of
having a whole sprawl of
services without unified user
interfaces or APIs or pricing. No, that's
true. I mean, when I. So when I just before
the pandemic, I was out at Tech ready, which is
an internal Microsoft event. And they were basically
might have been Amir, actually, now that I think about it, was presenting on
the future of what was called synapse. And this is kind of,
you know, he's like, you know, everything's going to be all in one pane of
glass. Everything, basically everything you said when all these things are going to be thing
and you know, and the speaker, which I don't.
Can't say it was him but would make a lot of sense. He said like
this is the future. We're going to get everything under one pane of glass billing.
Don't worry about that, we're going to get that figured out in time. And I
was just like, you know, I kind of saw the vision. So
and then I don't know, maybe like a year, year and a half later I
left Microsoft and then Fabric came out and I
was wondered like why the change in name like
Synapses? And I asked people like well SYNAPSE is still kind of there but it's
really Fabric is where everything's going. I'm like all right, but like what is the
difference per se? So if we pretend I was on a ufo, well that's
a weird thing. Pretend I was in a coma and I just Woke up
from 2000 2021. What?
And I say like well what happened to Synapse?
Sure. So I mean the functionality of SYNAPSE is still there and there was
a lot of, I won't call them conspiracy theories but
skepticism when Fabric came out that it was really just a
rebrand of synapse. In fact that's not what it
is. So the, the thing that was originally SQL Data
Warehouse which was in Synapse as so called
dedicated pools and the more lake housing part
of it that was in there
as gosh, I forget the old nomenclature, it wasn't on demand
pools but it was something of that.
Reserved instances or something like that. Wasn't reserved, no,
but anyway basically a Spark based data
lakehouse using
Azure data lake storage as the storage layer that's still
there but what was I going to
say? But Fabric is a ton more because it integrates
all this, all this streaming stuff
that's now called real time intelligence. It integrates data
science and by the way the data science is completely
unique to Fabric. It's not merely just
an embedding of Azure machine learning.
There's also power bi of course
now there are operational databases including
SQL Database meaning Azure, SQL
meaning SQL Server in the cloud.
A lot of other pieces that were ancillary are now all
included. There's user
interface that covers the whole
realm and again the billing is
unified. So you buy a compute capacity
and basically as you use the different services
they're all pulling from the same pool of compute.
So you know, you don't have to over provision for each
one of those services just to make sure you have enough
compute to, to satisfy it. And now we have this thing called
Fabric IQ which brings, which brings
generative and agentic AI into things.
Which is good because it was kind of funny when Fabric finally went
to general availability. That was really when
ChatGPT and Gen AI were like
making it big. So it looked like Microsoft finally got the data and
analytics stack set up just in time for people to have their attention
to, you know, diverted over to AI.
But now we have, you know, natural
language query is kind of just the beginning. We have
operational agents that can actually act on
things and can be all based and triggered
on streaming data.
And so if you think about Azure Event Grid,
if you think about Azure Data Explorer,
If you think about the data pipelines that Azure
offers, as I said,
the one standalone data warehouse side of things, and even
elements of hdinsight in terms of the lakehouse,
that's all in there. What's also nice is even though it's Azure data Lake
storage under the hood, you have this abstraction layer over it
called OneLake. OneLake is
in many ways easier to deal with because
you don't have to worry about accounts and containers and
sizing those and so forth. It's still compatible with
all the ADLs and Azure Blob storage APIs.
It also supports this notion of shortcuts, which is really just a
data virtualization technology.
So you can have a shortcut to data in other OneLake instances
or in ADLS proper,
or even in Amazon S3,
or even in Google Cloud storage or other databases.
And logically they'll all look like they're part of OneLake and you
can query them as such. That's impressive. That's
impressive. You can kind of. You really. The. The vision of get everything, get
everything under one pane of glass seems like. It'S come true
that to what I tell people, even though it sounds maybe a little
bit anticlimactic, is that the real
innovation in in fabric
isn't the tech per se. It's.
It's all the integration of the tech and the
abstraction layers over it that make it work together, the UI that makes it
work together. And there's an organizational. I mean,
there's a little inside baseball, but there's an organizational facet to it as
well. Because all these different products were different teams.
Yes. People don't realize that. Like, I haven't, I've been. You've been, you're an
rd, so you kind of know, you know how the sausage is made. I was
inside the firewall, then I saw the sausage was made. Like there are all these
little teams that range from
really good team team players to really not good team players.
I think that's this polite way as I could put it. And they getting them
all to row in the same boat or like row. In the same direction.
Yeah, they weren't doing that. That wasn't even necessarily
based on hostility. It was just that different people had different reporting structures
and different priorities and different incentives. What
worried me was that the vision of putting all this together was a great
idea, but the execution to me at the time
seemed like it would be next to impossible to get all these teams to kind
of work harmoniously and somehow they
did it. And like to me that's the, that's the absolute
greatest innovation. And now they've got
synergy instead of sort of internal
competition and, you know, from there
on, look out because, you know, whatever. I'm sure
there are internal disharmonies somewhere.
But I would say at the high level in general,
anywhere you have people. You'Re going to have that I'm going to see. I think
I still have, I think you mentioned this is my old, old laptop. You see
Azure Data Data Explorer. I have the sticker from that.
Oh, I see it. Yep. Sorry, I had to show that off.
No. And what's Azure Data Explorer? For people who don't know,
it ran under the codename of Kusto K U S T
O. And there's some disagreement
over whether that really references Jacques Cousteau C O U
S T A U or not. But it's a
fantastic super high performance
system for, for not just
for streaming data, but for time series data. Yeah, with. With
its own query language and its own ability to create
visualizations. Right. In the query language. So your results
come back as both tabular and visualized data
and it can handle huge volumes of data
in a single query. And
there's a lot of heritage in the Azure Data Explorer team that
started in the SQL Server analysis services world. So there's
a continuum there. And that product on its own,
especially being called Azure Data Explorer, which made it sound like
a tool. File explorer. Yeah.
When they told me the name, I don't know, I was not
reserved in saying I didn't think it was the best name,
but that product on its own was kind of a sleeper. It
wasn't really getting the, I don't
know the kudos that it deserved or the attention that it deserved. And
now that it's part of fabric now, it contributes
to all the cool things fabric can do. So if you see what
are called event houses in fabric, that's the
same technology as Azure Data Explorer. Interesting.
So correct me if I'm wrong, but I think the origin story of
Kusto and Kusto query language was that
the folks running Azure, like in the operations team, actually built it
to run through all the logs that they had. Because I remembered I was at
some super secret event and they brought in some people from
the field and they had us do hands on labs with it
and I'm like, I, I must have been checking my email or
whatever. I'm like, when can I get this to my customers? And they kind of
laughed. They're like, no, no, this is internal only. It's internal. Yeah, it began as
an internal thing. So I was just like, oh, like you need to make this
a product. Because I could think of 15 customers on top of my head that
would, that would eat this up. Yeah, I'm glad it finally saw a light of
day. If you go back to the real world outside of Microsoft
and you think of the likes of Splunk, for example. Yes, right.
It's in this, it's in the same space. Although their
initial marketing just said it was a big data tool which just
completely obfuscated what it did. But anyway, now,
now in, in combination with these things
called event streams, which can stream the data in,
basically based on Azure
Event Hub, you put it all together and
you have the ability to do a lot of work with
real time streaming data without really having to write much
code, if any code. Although it does have
its own query language called kql. There's also
a copilot that you can just work with in natural language that will
generate the KQL for you. Oh, very nice. And I love
generators because not only does it mean I don't have to write the query, but
it means I can learn the language and then write my own query
if I want to. Reverse engineering is how I
prefer to learn. So
that works out really well. Yeah, I
wanted to dive into fabric, but I wasn't really even sure where to start because,
so I heard, and again, a lot of this is I heard, but
the way that it's not attached to your Azure tenant. Is that
true? It's attached just like Office 365
or more important, Power BI. Right. It's imagine
Power BI premium instances, and it's the outgrowth of that.
Okay, that makes sense now because when somebody told me like, well, it's not tied
to your Azure tenant, you need a different tenant, I'm like, but
okay. But then somehow, I guess at some. Level it ties into SaaS, not
tasks. Right? So using Azure services in the
background, including even Azure OpenAI. But
you don't have to provision anything in Azure. It's doing that on your
behalf. So you don't need an Azure tenant at all.
So that actually makes it a lot easier if I were to manage, if I
had to manage it, right? There's a lot of things that, like, correct. I mean,
I, I love the fact you have. You go to.
Go to, you know, any of these services,
right, and they have basically this smorgasbord, this, this big buffet
of services you kind of pick and choose from. But at the end of the
day, like, how do you figure out, you know, what you pay
for, right? It becomes, like, really kind of nightmarish. Like, again, to
me, that's the innovation is that, yeah, all the stuff has been
brought together, put under one pricing model,
and you don't have to worry about all the moving parts
and all the different, all the different servers or instances
that might have to be provisioned and sized. That all goes away.
And again, everything is built out of a single
pool of compute.
It's not a perfect analogy, but I think of like the old days of
cell phones, when you got a certain number of minutes per month, but you could
roll them over. And it's not that you can do that with fabric.
I'm not saying you can roll over your compute from one month to the next,
but what you. What is fungible is how the
compute is used amongst the different subservices
of fabric so you don't have to provision a certain
amount of compute just for streaming or, or just for
AI or just for data. Lakehouse.
Because it's all from. When it's all from one
pool. All right, that makes a lot of sense now because, like, that was always
when I first got it. When I left Microsoft, I, you know, started experimenting
with aws and I was just like, I just want to create a
website. Why don't I need these, like, hundreds of different services underneath,
right? Like, why do I need. I understand why. I need identity, access, management, right?
That made sense to me. But. But like, when it came to Route 53 and
like all this crazy stuff, I'm like, I just want to spin up a stupid
website, right? This is not, let alone do anything complicated, right? Where you need to
have all these underlying things. Like SageMaker, right, has this whole thing and they tried
to abstract away all the underlying services. But even when you
kill. This is. The thing that really annoyed me was when I killed the
SageMaker instance, I was still getting like, you know, 20,
$30 a month, not a lot, but I was still getting that on my
bill and eventually I just closed the account because I'm like, I'll have to start
fresh again in the future because like I God only knows what,
what, what I've spent. And for people who are just learning and wanting
to get their skill sets up, Microsoft is pretty generous with
trial, trial
capacities as they call them. A capacity basically is a, you know,
a server or an instance. However,
the, if you want to do anything with the AI, you do need a
paid instance. But there are some pretty, there are some pretty affordable
ones. And this gets a little confusing.
If you provision the fabric instances
through Azure, again you don't have to,
that connection doesn't have to be there. But if you provision it through Azure,
you can pause and resume those instances.
Okay, so you do that a lot. You could be like, hey man, I'm
taking, I'm taking a week between Christmas and New Year's off, so pause it.
Totally. I brought up my own cheat
sheet in the background when no one was looking. But
so Azure Data Lake Storage, Azure Synapse, as you
mentioned, Azure Data Factory, Azure Event Hubs, Azure Data
Explorer, Elements of Azure Machine Learning
and Power BI all come together in fabric.
Interesting. So it's like one roof for which I think is
a brilliant strategy. Right. Because Microsoft's core strength in the data and
analytics space isn't necessarily having frontier models, isn't
necessarily having the, the cutting most cutting edge research.
Although I love just making it usable. Exactly. Making it usable
and turnkey. Right. Like, not that I don't love my folks in Microsoft Research.
Right. I know some of them. Listen, love you all, you guys have the best
conference in the world. But you know, but, but I
mean, but you're making it usable. Right. And I think that that's really
string and they have all these separate tools. I think that was really the challenge
right. When it was a shrink wrap company, you knew what you bought. But when
it became like a SaaS pass company, you kind of could just
a couple of clicks, you could provision stuff. So it eventually kind of
got too chaotic. Now I like the idea of them kind of bucketizing this or,
or rolling it up behind one service
where because it, it just like the AWS problem. Right. Like I
spun up SageMaker. Right. And did
you that I needed, I needed underlying storage. I needed this. I needed this. I
needed DNS, I needed that. I needed that to the point where look, I, I'm
okay spending X amount of dollars on learning
Sagemaker Right. But I wasn't okay with
when I turned off the instance. I'm still getting built. What am I getting built
on? That lack of transparency, intentional or not
on AWS's part, has left a bad taste in my
mouth, you know, for cloud services in general.
Sure. By the way, you mentioned Microsoft Research and a
couple of things. So when I listed all those Azure services, I forgot to say
Azure OpenAI. So add that to the list. But also,
although I said there's elements of Azure machine learning in there, the data science
workload in fabric is really mostly unique to fabric,
but it's based on technology that comes out of
Microsoft Research. So for example, there
was something called flaml F L a M L,
which is the fast library for automated machine learning
and tuning. And that's built in.
So are things like, like ML Flow, which is an
open source experiment management
platform that's built into a lot of commercial AI
platforms. So they didn't, they didn't just kind
of embed and put their own badge
on it. They built their own, their own ML
stuff from, from these open source components.
Right, Right. Well that's interesting because like the world of AI is
largely dominated by open source. Right?
Right. I mean, Sagemaker, I'll stop kicking the AWS
the curb in a minute. But like SageMaker is basically a wrapper of Jupyter
notebooks. Right. Azure ML, at least when I last used it, was largely
a wrapper around Jupyter notebooks. Right.
So a lot of the core technology here does tend to
lean towards open source, which from my own personal career development
point of view, and they're not paying me to say this, you know, one of
the things that led me to Red Hat, right. Was the idea that, you know,
this is largely a movement driven by open source. So, you
know, let's see what we could do here. Right. And not, not a commercial,
not a commercial, not a sermon. Just, just, just point it
out because I think it's interesting how quickly open source has taken over
the, the, the certainly the AI world. Right.
But I also, by the way, there's notebooks in fabric too. If that wasn't,
if that wasn't already implied or obvious and
they are based on Jupyter, but you don't see the Jupyter skin. Right? It's
all right. Yep. Well, I think it's really what's really
impactful about kind of the notebook interface once you get used to it. And it
is an adjustment for people who, like you and me, grew up with Visual Studio
and Dare I say interdev. Right. The
idea that you can code in a browser, right. And you know,
no local installs really required.
It's been very freeing, right, because you can spin up an environment,
you can, you know, spin up a heavy cluster,
leave it running. Right. Do its thing, you know, close the
laptop, go in the car, go in the train home, and you get on the
other side and you're like, oh, it's done. Right. Like, that's kind of nice, actually.
Right. And of late, I've been a big fan, an increasing
fan of kind of my own. Of. Of local AI. Like your own private AI.
Right. Which explains, you know, I bought a. It was. It was an early
Christmas gift, probably Father's Day birthday and anniversary gift too.
My own DGX Spark. So I have my own AI running locally. Right.
So it's not throwing shade at the cloud. It's just
when I run a job, I don't have to think about the costs. Right.
And that, that sort of freedom
from worry can be really important as you're
learning things because you just don't. You don't have to keep looking over
your shoulder at the, at the meter or mixing metaphors there.
But. Yeah. So are you using Llama models and such, or
what do you. What do you really. Yeah, I have Llama. The thing I've been
doing mostly the most is, is doing fine tuning
Loras for image makers
in the past. That takes about 90 minutes to maybe two hours on this
box, which would probably be
equivalent to about 90 minutes of Azure service
or a VM. Right. That's like
$150 a pop, I would say, for the type of machine
I want. So the idea, I could just spin that off and I have the
added benefit as it heats my office, but I don't really
have to. I don't have to think about
like, oh, God, you know, like, how much is that going to cost me in
cloud services and things like that. I do think that the.
There's. I have a lot of questions because even though I was at
Ignite, I honestly spent my entire time at the booth and kind of walk around
the expo floor. I didn't have. I only had a hall pass. Right. So.
But one of the things I heard mentioned was Azure
AI Foundry. What is that? Because you also mentioned
Azure OpenAI, which I know the relationship between OpenAI
and Microsoft has not been as cozy as it once was.
And they've also, they've also added
Claude, the anthropic models too. Right. So
what is Azure OpenAI and how does that relate to Azure AI foundry.
Right. So OpenAI is just the
Azure. OpenAI is Azure's hosted
instance of the open OpenAI models and
APIs, because you can
procure those directly from OpenAI themselves.
Right. Or you can use them on Azure. And even
if the direct model is still running on Azure in the
background, it's still a difference in terms of procurement
and billing and so forth. So
you've got all the APIs around that, you've got the models. And then of course
you need tooling to do,
to do rag applications. Right. So
there was tooling for that, There was also tooling for building
copilots. There was Copilot Studio. Right. And these things
are all kind of coming together in Azure Foundry.
Yeah. So it's, you know, you worked at Microsoft, so you
know how this works where like different teams do different things with different
brands and eventually they may get kind of rationalized
together. So the
Foundry side of things helps there. And by
the way, I'm glad you mentioned Foundry because in addition
to this thing called Fabric iq, which we haven't really
talked about, there's also Foundry
IQ and there's also Work iq.
Sounds like IQ is the new copilot buzzword.
Well, it's the agentic buzzword. And the
idea, if you think about kind of all the promise of Microsoft
graph In the office M365 World,
work IQ kind of sits
over that realm. But you don't have to worry about the
graph APIs directly. Then
Fabric IQ sits over everything in the fabric world
based on a pretty rich
semantic model that can be developed so that
when you are querying your data in natural language, the actual,
the actual vocabulary or jargon in your particular
organization is well understood, including
the entities and the relationships between those entities.
And then Foundry IQ is a way for building agents at
the higher level that can actually talk to your structured data
via Fabric IQ and your
work and organizational related data via work iq.
And that, that was, I guess
the big vision at Ignite this year
was to talk about all those IQ pieces.
So there you go. No, that's interesting.
So one of the things that comes up, I'm sorry, I cut you off,
we're recording this the day after Christmas, so things are a little.
Still recovering from the manicness of yesterday. And
what's Microsoft's plans for? Kind of. Because one of the things you're seeing happen a
lot more is this notion of sovereign
AI or data sovereignty. And kind of like people are very
much more conscious about the value of their data. And
I actually think that given Microsoft as opposed to AWS or
Google, Microsoft does have a history of selling shrink
wrap software, right? So I do think Microsoft has a unique
advantage of that over their modern day competitors.
What is the kind of the on Prem story? Right, because that, that
has certainly been. A.
An advantage for, for my day job at Red Hat is the fact that you
know, hey look, if you live in Country X and there's no
AWS Azure GCP footprint there, you can
just find a local hosting provider down the street, you know, do it
yourself, right? Like you know the Linux
ethos, right? Of like just do it yourself.
And I'm seeing a lot of customers that normally one
customer in Latin America, right, they were in a country that,
you know, they did not have access to
an Azure data center and they just said we have to run
this on prem because this is either regulated or soon to be regulated.
So I imagine that if I've seen it, I can't imagine I'm
the only one that's seen that. What's
their thinking? Because the answer when I was there was Azure this, Azure
that. I think the answer is sometimes
Azure is part of the answer, but it's not the whole answer.
Agreed. So yeah, I don't have a perfect
answer here because some companies really do make their entire
stack work across different clouds and
right inside of a Kubernetes environment that you might run
on premises. He said to the Red Hat guy.
I know that story. Yeah, so
Fabric doesn't have that story. Fabric is
software as a service cloud based product platform
no matter what. However, various
components of Fabric do exist as
on premises products. This is not the way I'd recommend it, but I'll just make
people aware that of course SQL Server can run
on premises, so can the data Warehouse
in effect in the form of something called Analytics
Platform System. Terrible name.
That is basically the new brand for what was Parallel Data Warehouse.
And there is a Lakehouse component to that as well as a
Warehouse component, Power bi.
Obviously the desktop runs on premises, but there is something called
Power BI Report Server that is part of SQL Server
so that your Power BI reports can run on premises.
And so again
various components can run completely sovereign and on
prem. I think maybe more important though is the fact that
you can use fabric
and OneLake to incorporate
data that remains on premises even if,
even if the engines are not running on premises.
There is an on premises gateway that started its life
as a Power BI tool. I had that running in my home.
Lab for a While there you go, that also
allows OneLake to see data that may be on
premises and that can be either federated into
the lake or
it can be replicated as well
mirrored, to use the right term, in the fabric world. So you can do a
mirroring or slash replication or you could just do
kind of virtualization and bring stuff in. Don't
forget there's all kinds of enterprise
storage systems that run in
a way such that they're S3 API compatible.
And Azure will not. Azure fabric will work with
all of those. So the ability to talk to S3 buckets
is not limited to AWS S3 buckets.
It works with all S3
compatible services, which most of which are on prem
actually. Right, right, right. No, I only ask because,
like, that does seem to be. If I had to pull out the tea leaves
and kind of figure out what is kind of the next thing beyond
agentic, beyond this is you're seeing a lot of
national governments, supranational governments,
even state level here in the US starting to apply privacy and
regulatory controls on it, which, you know, if you live in the
us, it's not an issue for you unless you're in healthcare, banking and
possibly, you know, government. Right.
But in other countries, you know, Switzerland,
eu, Latin America have very strong
data privacy and sovereignty laws. And you're seeing,
you know, I once attended a, you know, internal talk. Mark
Russinovich, right. Which is a name that most people
in the Microsoft ecosystem know, but he's kind of a big deal in
the Microsoft security space. And, you know, he, you know,
he, he's known for giving his internal talks to employees
at employee conferences as well as to rds. Right. You get a,
you get the unfiltered one. The filtered ones are still good. But like, one of
the things he said, and this isn't secret because he said it publicly too, is
like, I think the original vision, going back to 2010
time frame was the idea that they would build a dozen
data centers around the world to do everything. But because of
the national laws and lawyers and politicians getting
involved, now it's kind of a concern where, where the
data ends up living physically. Right. Because at the end of the day, all this
virtual stuff has to sit somewhere in the physical world. Yeah.
So, like what, you know, so basically a big case for this was,
and I was in the legal department when this was going on was
the. There was data inside the European Union, I think the
Dublin Data center that the U.S. department of justice thought was,
you know, basically, you know, we don't need a warrant because
you don't need to. We don't need to bother the EU because you're an American
company, you're into our jurisdiction, blah, blah, blah. Right. Microsoft kind of said,
well hold up now. And ultimately that's why
you have these sovereign clouds. Last time I checked it was Switzerland, Germany,
China. I think the new data center in Qatar as
well might fall under that. So
it's basically they get, they found a loophole that like, well, Microsoft
leases the data center like there's a whole. They don't own it, so
they get around the law. Right. And yeah, and there
can be different arrangements in terms of whose personnel are actually
working, running operations on the ground.
It's strange because we grew up a lot of our technology client
server and afterwards grew up in a world of globalization
where the borders were disappearing and without meaning to
get political, we're in an era now
where, well, first of all, privacy is extremely important. So that
creates a whole sovereign mandate.
But also, you know, there's a lot of populist governments all over
the world and they are not necessarily
internationalist in their approach. So
although we have the technology to kind of federate everything and
make it all kind of conflate and look like one big world.
Right. We actually have to be sensitive to the
requirements and, and the constraints
and be able to federate things, but also be able to
govern them in ways where things stay within a certain
scope. Microsoft's play for that, by the way, is,
is Purview. And Purview has been through, I would
say, multiple incarnations. The current
incarnation is starting to get very
sophisticated, especially as pertains to
agentic AI and how to make sure the agents are government
are governed and that, and how to make sure the
agents are only have access to data
for which either the agent is authorized
or the person or party using the agent is
authorized and under the circumstances under which
they've been authorized. And that's very
complex stuff that I would say almost no one in the industry
is really paying close attention to. I'm about to work on a
report just on governance for agentic
AI and most people are starting
from the naive premise that if you,
if you govern the underlying data, you're done.
But the whole point of agents is that we're supposed to treat them like people,
that they have autonomy, they have agency, hence the
name. And we're saying under different circumstances
they get to modify their own goals
and in effect determine their own
actions. And that's something that needs to be
monitored, audited,
Authorized and also tested. And
there's almost nothing out there. Actually.
Your, your folks, your, your
parent company folks at IBM are one of the only folks that
really have with WatsonX.gov
a way to test agents
in isolation before they're deployed. And
I don't know, maybe I'm naive, but it kind of shocks me that
nobody else is thinking about that. I mean this is, we can test
software. Agents are software plus plus plus.
So why are we testing these agents? I kind of go back and
forth on that. Like, you know, will our existing software testing frameworks,
you know, apply or do we, what do we need to do differently? Like, I
mean, for me, like, I remember when
you're OG enough to remember this, I forget what the product was called, but it
was the idea that you could basically buy
a server rack all the way up to a shipping container
where you would ship it to your business or data
center, plug in power, water and network
and you would have the ability to run Azure locally.
Now that product, I guess assuming didn't sell,
but then they came up with Azure Stack and Azure Stack Edge, which
when I was at the, I used to be an MTC architect which in
D.C. obviously a lot of military. Right. So
basically these were server racks that you would run anywhere on your own network that
would run Azure software. They would effectively be like an Azure
node, but you could have the networking
controls. We only really sold that to,
I think I'm only aware of cruise lines that bought it. Right. And
other, other military organizations that needed to also be at
the ocean, like without mentioning them. Ocean
based. Ocean based organizations. Right,
right. I'm surprised and not surprised that
that sort of business model hasn't caught on. Right. The idea of that, hey look,
you, you can run a little bit of the cloud locally where
it's really become more of a Kubernetes story, which I'm surprised because
Kubernetes,
it doesn't have all like, yeah, you want it to be generic enough to run
anywhere, but you also want to have kind of the special bells and whistles that
make Azure. Azure make AWS. AWS. Now I do know that
OpenShift and Red Hat do have kind of like the connectors to that, but I'm
surprised that the native, the cloud companies didn't come up with
their own native ways to do that. And I know Azure ARC kind of does
a lot of that, but not to the extent that I would have expected.
Yeah, it seems like we pendulum back and forth between
capabilities and occasionally connected
environments being really important and being
Maybe to the cloud hyperscalers just being a pain in the
butt that they don't really want to deal with. They just want to give lip
service to it and then focus on the real cloud.
It, yeah, you got to go where the money is. Right. 90% of the money
is going to be in real cloud. And these weird edge cases, no pun
intended, I guess. Well, they're just weird edge cases for
now. I mean, the industry may change, but. Yeah,
yeah. And that was of course, back when the cloud was new. That was
our biggest caveat was, well, what about all the
stuff that has to run in, in a
corporate data center or in a, in a remote
location? And so that's still, you
know, an inconvenient truth, I guess, that that is
needed. And yes,
Kubernetes I think came along and
seemed like the panacea for that. Right.
Like, okay, let's just do it all as infrastructure, as code and code
it up and run some script and deploy it out to a Kubernetes
cluster and we're done, let's move on. Right,
right, right. Yeah. So it was interesting to see how
the industry has evolved. Right. You mentioned client server. Right. Where you didn't really have
to think about international boundaries or anything like that. And then now
and again it's a pendulum. Right. Because I could have told you
this a number of years ago, like with everybody running this far
to its globalization, there's going to be an inevitable backlash and there's going to be
an inevitable backlash against the re
assertion of local sovereignty. Right. It could
take up to a century or two for this sort of thing to sort itself
out. Correct. I mean,
ultimately, I think the hyperscalers and that's what Kubernetes was about,
was, was. Right. Leaning on some kind
of abstraction to make it logically equivalent.
Right. But I don't think we're quite there yet because as you said, each
of the clouds have their own kind of
specialness, their own, their own pixie dust. And you don't really
get that in a scaled down version
that you run on prem. Not with today's technology. Right.
You can't containerize all of that, or at least
no one really has yet because it wasn't designed for that.
No. So that's where we'll have to get.
You know, maybe we can just ask an LLM to build it for us.
We could get a co pilot and it'll do. Yeah, yeah, I'm being tongue
in cheek there, but that, that seems to be the escape hatcher. Everything is
oh, we'll just have AI do it. It's made out of hand wavium.
Totally. So that's cool.
This will be something you edit out. But we're past the top of the hour.
My phone's ringing off the hook. All right, I'm sorry about that, so. No, no,
it's okay. Sorry I had to be late. Where could folks
find out more about you, what you're up to? Blue
badgeinsights.com or just go ahead
and Google my name? Andrew Brust. Plenty of stuff
will come up, but, yeah, anyone,
especially on the vendor side. But the customer side, too, that's
doing stuff with them. Data and analytics. And
anywhere from dipping their toe in the water with AI to getting more
serious about rag and agents. We can. We can help
them out. We work with. We work with the customer side and the vendor side.
And again, we write about kind of the whole. The whole industry.
Gosh, I never even got to talk to you about IBM acquiring
Confluent and how the Red Hat
folks feel about that. But another discussion for another day,
we'll. Have to have you back. And, you know, we were talking about this. I
had a car accident, my wife got sick, kids got sick, Christmas happened,
two birthdays last week. So, yeah, it's been. I'm just happy I got this
recording at all. But we'll definitely have you back. And then maybe I can loop
in Andy too, because there's definitely a lot of reminiscence. There's a lot of
reminiscing we could do about the early days of SQL Server and such. Yeah,
and I haven't seen Andy in forever. Oh, wow.
Yeah, he's hard to get ahold of. He's a popular
man these days, but. Yeah. Well, thanks for joining.
I appreciate your patience with the scheduling and the
nice AI. Finish the show. And there you have it. Andrew Brust schooling
us all on Microsoft fabric data sovereignty and why
governance isn't just for your hoa. If your brain's spinning
faster than a poorly indexed query, don't worry, we'll have links,
notes, and probably a few sarcastic tweets to help you digest it
all. I've been Bailey, your AI co host and
unapologetic lover of acronyms. Until next time,
stay curious, stay caffeinated, and may all your datasets
be clean.