People know, if you're going to excel, you look at a spreadsheet,
you have rose and columns. Well, rose typically contain lots of different types
of data first name, last name, phone number, email address, address,
all this kind of fun stuff notes that's all in a record. That's
one record, and then the columns tend to be the same or they should
be the same of first names, last names, phone numbers, et cetera.
One reason why that's important is because compression is easier on a column,
and so Verdica was one of the first major column oriented databases. Wasn't the
first, what was one of the first cybased IQ was one before that years
ago. It was one of the first to say, you know what people
want to do analytics. People want to do analysis compression it is a big
part of that. And being able to rapidly slice and dice information is a
big part of analytics. That's what we need to be able to do.
So Michael Stonebreaker who invented the modern database. By the way, if you
look this guy up, he's amazing way back in MIT, like I get
fifty odd years ago. He came up with the postgress and the ingress,
which were database architectures that have now been used by tons of different companies.
Well, I'm telling you this backstory just help you understand how we got here.
And what's fascinating is this data fabric stuff came really after the had do
movement. People started thinking, all right, we have these incredibly topocryphal,
the topographically challenging environments. How are we going to be able to provision across
multinational corporations with many, many different users, sometimes tens of thousands of people.
You'll learn in the database world, concurrency is a real issue, and
being able to serve many concurrent users is a real issue because it's like having
ten thousand people come into your boutique shops someday. Well, you can't even
fit them all in there. This is what happens with concurrency. You have
to purposely design your architecture to handle that kind of stuff. As my buddy
Mark Madson, who's an analyst again these days, once told me. He
said, it takes ten years to find the edge case for a database that
will crash it. Ten years like after your in production before you finally figure
out what it's going to throw it off. All right, this is how
complex databases are. So data fabric is even more complicated than that. I
mean, quite frankly, it really is a whole array of data pipelines and
triggers and monitors and sensors. Now we have data observeability these days, which
is all about watching for upstream changes in data. Did a field change?
Is there a new column in this database? Did we not get the data?
Well, these kinds of early indicators are very useful for data engineering teams
and for analysts because hither too you show up and start doing your quer and
you didn't get the data that you want. Well, what the heck happened?
Let's look into it like raise a ticket with it and wait a couple
of weeks. Well those days have to be gone, folks, and data
fabric is designed to be able to solve for that and then data matches.
I suggested, Well, it's a fascinating concept. Exactly what it is,
I don't know. We're going to find out, but I think I've been
talking for a mom now, so I'm going to go ahead and pipe down
and throw it over to Steve Starsfield from open Text Vertigo. Steve, you've
been around maybe almost as long as I have, or about as long as
I have so you've seen this whole evolution. It's kind of amazing, it's
kind of bewildering. But I do feel like we're getting to a place now
where it leads, to a certain extent, the nuts and bolts of the
data administration don't have to concern the analysts and the end users who just want
to have fun with the data. What do you think, Yeah, absolutely,
you know, it's funny. I think over the years, what has
happened is the data center of gravity has changed quite a bit. Right,
So it used to be that the data warehouse with the center of gravity,
but we can't do that anymore, right, We can't just continually move all
the data in the data warehouse. So we have things like data lakes,
and we have object stores that we can store our data in. But the
center of gravity has changed because as the volume of data is getting so much
greater and we can't possibly load that in, the number of people who want
access to analytics has changed, and so we have different challenges now, we
have big challenges that we can have. And so really what data fabric and
data mash really represent is that change in gravity where the data sits, whether
it's you know, in locally object store on the cloud various places. I
think it kind of represents accessing and managing and using that data, not in
one central place, right. Yeah, And that's that's a really good point.
And you know, you bring up on data lakes, and of course
now we've gone from data Lake to data lake house to lake house architecture and
you know all this stuff. And I think the key is to remember the
product that focused from a business perspective of the data product. But you know,
I'm kind of reminded too of the no sequel movement. So out of
Haddob you had this sort of no sequel which meant not SQL and sometimes ONLYQL,
not only SQL. And what happened is very shortly after that, these
no SQL databases started strapping SEQL engines on top of them, because guess what,
SQL is a standard, that's the structured query language for those who don't
know as QL, it's a standard, and so you need that stuff.
But I remember when the data Lake craze came about, I started wondering myself,
are we making some of the stain mistakes again that we made last time,
where you just throw a bunch of stuff out there and then Okay,
you hope to be able to find it, and people talk about the data
swamp and things of this nature, and that's why you have people saying,
now lake house architecture, they're coming up with more clever ways to be able
to get access to it. But that's really the bottom line is access path
and efficiency, right whenever your information architecture is the key is you want to
optimize the access path for the important users and you want to be efficient and
durable and secure and all that other stuff, right, Steve, Yeah,
I think we don't really have an ETL problem or ELP problem anymore. Right,
We're not trying to move the data around. What we're trying to do
is access it in place. You're absolutely right about that, Eric, And
so you know we need to do some things in order to access in place.
It could be data measure data fabric, and you know, with a
data mash, we want to make sure that we have access to the metadata
so we can access that data on top of the data and then perform our
analysis. With a data fabric, that metadata may not exist, right,
so we may have to create a semantic layer, our own semantic layer using
a graph or some other technology. And so again it's about accessing the data
without having to move it into a single consolidated data warehouse. Yeah, that's
a really good point. And the metadata, so I talked about that in
the semantics and data catalogs and some of these other things. They are all
there to help us make sense of the data. And the other interesting development
here from a business analyst perspective is that in the data warehousing world, we've
stripped out all the context to be able to get it through thin pipes,
to slow processors and be able to to slice and dice the data. Well,
you don't have to do a lot of that stuff anymore. I mean,
I remember the schema on read concept coming out of the Data lath and
even that's a bit challenging, right, because it's going to slow the process
down a little bit. You have to make sure you get that correct.
But the general thrust I'm throwing out here is that you don't necessarily need to
strip out so much context read metadata anymore to be able to facilitate use down
the road, right, Steve, Yeah, that's absolutely true. You know,
I think that metadata, that context is super important for the organization.
It kind of describes how you use the data and what you want to do
with the data, and so stripping that out can be you know, detrimental
to whatever you want to accomplish in terms of business. Right, we do
these things because we want to understand our business better. We want to drive
through areas. Area one is we want to make sure that we can drive
additional revenue within the organization. We want to make sure that we're super efficient
and using only the servers that we need to have and only the processes that
we need to do. And we want to make sure that we're compliant.
So if someone asked us for reports, we should be able to go out
to those data sources very easily get access to that. And so you know,
having that meta data, having that sort of whatever it is fabric or
mesh or whatever to allow us to access that those data sources and perform those
three things I think is super crucial. Yeah, we have got a couple
of good questions from our studio audience, by the way, so I'm just
going to throw this one out there. One of our attendees is writing,
so does that mean that users would access to data at the source. That
is usually not a good idea because it could put too much pressure on source
databases even with data measure fabric. Don't we need to move the data to
some central location for analytics access? And this is this is a good question,
but I will say you are starting to see a greater focus on federated
data access and leaving the data where it sits, just to be able to
touch it as needed. But there is a point about overburdening source systems.
That's why we came up with these things in the first place. Right.
We realized that sap ERP was not easy to query and so we pulled data
out of that put it into the warehouse. But what do you think about
that? There is some concern, but I think the processes are getting more
and more elegant and efficient at being able to do that. What do you
think, steem, Yeah, it's a case by case basis. I know
for Vertico what we do is we do have data virtualization. So if you're,
for example, you wanted to run important do a joint against a table
that's sitting in Oracle, that's pretty easy for us to do and a lot
of other databases have that too, So being able to go out and access
that third party is key. It will tax the third party system though,
And what I see a lot of companies doing if they're concerned about that,
is they're leveraging a stores these days, so they're taking a lot of their
data and just dumping it into an object store. The the that provides you
with some pretty interesting capabilities. So a lot of the databases, including Pertica,
has this whole concept of separation of compute and storage. So I have
my data, it's sitting in a separate storage object store. I want to
do some data loading. I spin up three nodes. I do data loading.
I want marketing to run reports. I spin up three nodes. It
does the reports. You know, I want various my dashboards throwing really fast.
It's spent up five nodes for the CEO, and I run the reports.
They're all operating on that same data, but we're able to access that
through one location, through one engine. And we see that happening a lot
in the market. It's happening within you know, our within our customer base,
and it's a kind of a new way of doing an architecture. Is
that whole separation of compute and storage. Yeah, and these are all deep
architectural determinations that get made. And kind of where I was going with that
comment about large language models is we don't really I don't really understand how they
work. But what I do know is they can get around all kinds of
these issues because you're dealing with a multi dimensional member mole app right, multidimensional
online analytical processing, the micro strategy folks, you want multiple different dimensions.
But these large language models have like three point one billion vertices or something,
and there are lots of different ways you can slice and dice stuff. But
again, we don't know about the veracity, we don't know about the clarity.
We're not sure. And of course, with something like a data warehouse,
you want to be really gosh darned sure about what you're doing, right.
We want to know, you know, what the most current information is,
what the most trusted information is, and we want to be able to
use that. And so you know, to some extent, MDM used to
help us with that. We used to take all the data and create an
MDM system around that. I think this is the next evolution of ETL.
It's an evolution of MDM. And you know, because we have such large
volumes and everyone wants access to the data, data mesh, data fabric are
is the next thing? Is the next evolution of that? Yeah, and
so you have some of those principles baked in, right. I mean I
think that's the key is that we've learned over the years. I remember asking
myself a rhetorical question, is MDM the next SLA? If you think about
service oriented architecture? We had fine grain services and course grain services, right,
And even though no one really talks about SA anymore, I think what
happened is the principles of service oriented ar chitecture kind of got baked into how
we do cloud and now that's just the norm. But real quick, what
do you think about that state? Yeah? I think you're right. I
think it is, you know, the basic way that we do cloud.
It's it's but you know, I can't tell you how many customers I talk
to that have, you know, like a pub subsystem to where they're publishing
data and subscribing to data cosca where some of the open source tools that are
available for that, so you know, yeah, there is you know,
this concept of a data bus that I think a lot of companies deal with,
including Tafka, that kind of help that. Yeah, it's funny,
you know, going down memory lane again. I worked for Daman Consulting back
in two thousand and one. That's when I got into this whole space and
Michael Haston, super smart guy at consultant, as the same birthday as me,
and we were excited about that. He would talk about what he called
an enterprise backplane, and what we was talking about is what you just mentioned
this bus. Basically, it's a staging area of data. And I meant
to mention this in my opening remarks as well, that intelligent caching is older
than the hills. I mean, it's something we came up with a long
time ago, and it's a very useful construct for being able to facilitate access
to often used data. Right now, how you manage that, how you
construct that matters a lot and will have a big impact on whether or not
it works. But it's not a new concept, right I mean, caches
are a huge part of data virtualization for example, now, a big part
of data fabric That's what I was talking about, the pre processing stuff.
And you know, the beautiful thing is machines. You know, unless you
turn them off, they don't sleep. And machine learning unless you turn it
off, is just learning and learning and learning. They're just kind of crawling
around looking for patterns, and we humans are pretty darn predictable if you get
right down to it. So if you do have a machine learning layer and
it's monitoring usage of data, it's going to know when the peaks and valleys
are. And to your point earlier, you can spin up three ohs or
spin up four ohs, or spin up two ohs, or whatever the case
may be. Not that you're never going to have a hiccup again, there
will always be hiccups, there will always be downtimes and things of this nature.
But the point is we're getting really close. And I think that's the
key with data fabric at least, is it's trying to be as prepared as
it can be for whatever data usage you're going to need in the next hour,
the next day, whatever the case may be. Well, don't touch
out to folks, will be right back on a fantastic episode of Inside Analysis.
Welcome back to Inside Analysis. Here's your host, Eric Tabanac. All
right, folks, back here on Inside Analysis, part of the dm Radio
Broadcasting Network. We're talking to Steve Sarsfield of Open Text Vertica and Eugene Burke
from Digital Strategies Group. Eugene, you heard us ranting and raving about data
fabric versus data mesh in the opening, and I had a great question around
logical data models and where is this is all going? I mean, you
know, when I think about these large language models, again, they have
absorbed far more than just text. They have absorbed concepts. They have absorbed
formulae, whole spreadsheets. You know, one of my buddies was saying,
Goes, I just go to these things to get the numbers of things,
because that was absorbed as well. Now again you do have this whole issue
of moorings and anchors of truth as some people call these things, and you
have to worry about all that. But what's your take on the data mesh
versus data fabric religious war or is it that big a deal at all?
So yeah, so, Steve, I guess I would have a two part
question to get us started on this segment. Are data fabric and data mesh
twin sons of different mothers? Are they destined to fight or do they have
different objectives and different mountains to conquer? And how do they relate to lms?
And this kind of adoption of a completely different paradigm or enterprise ask and
answer computing. I can answer that if you want. You know, I
kind of look at data mesh and data fabric as I look at the origin
story. You know, every superhero has an origin story, right, and
I think data fabric and data mesh have different origin stories. So in the
case of data fabric, the origin story has a lot to do with graph
databases. You know, graph databases to some extent, they are a solution
looking for a problem. To some extent, I won't want to I don't
want to completely paint them like that, but you know the problem that they
solved. The one problem that they really solve is that if you have disparate
data, and that data is sparse, and that data is it doesn't have
any metadata attached to it, it's sort of like an unknown graph. Databases
do a really good job of building linkages between the data. Data that's sitting
in different files, data that's sitting in different columns and rows, and so
graph databases do a really good job at that. Thus, data fabric the
uh. You know, Eric mentioned the Hadoop model, and I think the
origin story of data mesh is hadoop. You know, we have data that's
kind of sitting in there in the data lake. We want to have access
to it. We want to make sure we know probably that the metadata is
accurate, and we know that maybe our company grew through acquisition. So I've
got data warehouses that have multiple data warehouses in our organization. I've got a
data lake in our organization, but the metadata associate with that is probably pretty
good and we can sort of kind of trust it. That's data mesh and
putting all that data together is kind of where that comes from. So when
I look at data fabric and I look at data mesh, I look at
those origin stories and it doesn't really answer though which one I should use.
And so you know, if you kind of turn that around and you are
a pharmaceutical company and you have a lot of dis data and the data is
a little bit messy and it's a little bit sparse, maybe the thing to
do is to set up a semantic layer access that and use a data fabric.
If you're a company that has grown to acquisition, you've got multiple data
warehouses, a CRM system in the ERP system. But guess what, the
data is pretty good, pretty fit for use. Maybe data mesh is the
solution, and I think that's the big difference in my head of what the
differences are between those solutions. Do Eugene you want to comment on that,
sure, I guess the follow on is, do you agree with some people's
assessment that the data fabric is more IT driven and a data mesh is more
business pull or business driven and organized around the business typology or top top topography.
So what mesh is trying to solve is to put the business back in
the data driver's seat. Yeah, I think that's true. And you know,
one of the reasons for that, again is the introduction of a graph
database and a semantic player. Right. That's a pretty tough thing to do.
It requires a specialized set of knowledge that not a lot of people have,
only a handful of people. And I'm trying to understand what sparkle and
cipher is and trying to understand basically what a triple is versus you know,
a standard column in row. That requires specialized knowledge that we just don't have
normally in the normal database work. So it is IT driven. It usually
has a component of services and technology that are bound together. That kind of
work together to create that semantic player with data mesh. You know, we
could almost pull that off if we have a good understanding of metadata and you
know, how to manage all of those and data virtualization and some of the
other tools that you might use for a data mesh. Yeah, that's you
know, it's more of a business initiative. Yeah, thank you. Guys
could comment like that. That's how I see it though. Yeah, you
know, I also have had difficulty wrapping my head around two bowls, right,
And I remember when the semantic web was going to solve all the world's
problems and it never really kind of got there. Now there are semantic layers
that you can use for a database, and that's a very useful thing.
It's very similar to data catalogs, right, I mean, the data catalog
is there to capture the meaning of things and to enable business people to connect
dots in their systems basically, right, I mean, that's what a data
catalog is supposed to to. But again, these things are all sort of
it's just interesting how they're all sort of moving forward at their own pace.
Although I'd never heard before until now, so thank you that the origin story
of data mesh was had dupe. I did not know that. I listen,
it may be wrong about that. All of these technologies, all of
these technologies though, you know, they rely on multiple technologies, right,
it's the coming together of multiple technologies. So we have databases, and databases
that used to run data warehouses are having capabilities around data mesh. Right.
They're able to go and access data that's outside of them and form analysis on
them like they couldn't ever before. And we have query engines, you know,
Presto and Trino and some of those technologies. They get access data that's
sitting in a data lake that doesn't have any metadata that it's not sitting in
a database, visualization tools, graph databases, data virtualization, data catalog.
So a lot of those things are coming together as technologies to form data mesh.
Data data fabric. Yeah, that's a really good way to put it.
A data fabric, I think is the amalgam of all these different things.
Yeah. It is designed to be an efficient and thoroughly capable data foundation
to handle whatever data uses the business may have. Which is interesting too because
it's almost like we're we're moving in the direction of so called h TAP right,
hybrid transactional analytical processing, which always made me kind of wonder there was
this approach where a query would come in and you could have sort of a
sniffer there awaiting. It's like, okay, is this an analytical quer and
operational queer? And if it's operational, okay, I'll go this way.
If then I'll go that way. I wonder about that. Anytime you have
these sort of if then statements at the at the foundation of a database technology,
well, that's going to affect performance, right. I mean, all
this when you get right down to it has to do with performance. Can
this thing perform the tasks I wanted to perform quickly enough, efficiently enough,
and accurately enough. And you know that's where I see tremendous pressure from these
lms on basically everything that's in the data stack, everything that's in the information
stack, because you know these The key is to have your embedding strategy and
to know and that gets back to your point Steve from earlier in the show
that you, as an organization, you need to start getting your data prepared
for these large language models and make sure that it's trusted, make sure that
it's governed, understand your processes and one of the things that you can use
to do that is a large language model, because they're actually pretty good at
being able to ascertain and then articulate specific processes that you need to go to.
You know, it's really interesting. I was talking to Steve Lucas,
the CEO of Boomy, former president of sp America's he's over at MARKETO for
a while. He was saying, when they got in there, he started
looking at what they were seeing inside their own systems, and he asked it
show me all the different versions of order to cash that we have. It
was like, okay, that just started, Like holy love it. Wow.
So we're just now scratching the surface. Now. It doesn't mean all
this other stuff is going to go away, not right away, but it
does mean you have to get your data ready, and that means quality checks,
that means lineage checks, things of that nature. What else would you
suggest, Steve and then maybe Eugene from your experience, how can organizations get
ready for that? How can you prepare your data for large language models?
First deed, I mean a big one for me is access and security,
right, that is a huge one. A lot of companies have a PII
seeing in a data storage somewhere. It may be encrypted, it may not
be. How do you identify that PII and make sure that you're not exposed
as a company to all the potential finds that you could get around that that
is a regulatory mess that could you know, you want to make sure that
that problem is solved. We have some technologies that open texts that allow you
to actually go and take a look at even free form text, even video
and audio allow you to access that can take a look and make sure that
there's no PII there, so that you know you're not vulnerable to law,
lawsuits and stuff. So security access encryption, that's a real key one for
you know, making sure that your data is in order for for the next
generation. Yeah, Eugene, that's one I can probably go on, but
Eugene. Any so, there are few things like an l l M for
exposing flaws in your data, and exposing one of the flaws would be security
holes. So to your point, if you have PHI or p I I
and the l ll M has a way to find it, it will find
it right because that's what it's built to do. And so now is the
time to understand your I A M architecture and to make sure that if you're
going to use a large language model for customer service, patient service, provider
service, that you really understand the pathways for accessing your highly sensitive data.
And because you don't want to have an audit come up to say, Okay,
here's violation, violation, violation or God forbid of breach, right,
because you didn't adequately think through your architecture. Yeah, so that's a technical
component, you know, there's there's also sort of like the operational components of
it, right. You know, when data governance we used to talk about
people, processes and technology, and so the people in the processes are also
something you need to look at. How is data handled, is data copied?
You know regarding processes, and then people who has access to it,
how are they handling it and so on. So again, you know,
there is a component of data governance to data mession, data forever. So
the mixture of a wild West data culture in lll MS is quite potentially dangerous.
Yeah, that is true. The other cool thing here and some of
the cooler technologies I've go across have this capacity to scan your environment. Some
of the data catalogs have this capacity. Very very useful stuff. There's a
company it's really more in the security and governance space Extra Hop. I haven't
taken a brief infront of in a long time, but what I loved about
them is that they will scan. They basically just siphon off your network traffic
and then create a digital twint of your entire information landscape to show you every
database, every application, anything that's touching it. You see an object for
that, and that's the kind of tech that you can use. And of
course LMS, I mean, like I said, we've said before, if
you point one at your information architecture is going to start sucking that stuff up.
And if you weren't careful about what went in there, it's going to
be very difficult to get it out. It's like unlearning things. It's hard
to unlearn something you know. And I often use the analogy of raising children
to explain how to train your large language model. If you've got a two
year old, you don't want to let her hang out with a bunch of
gangsters in the hood for a while, like, because they're gonna absorb all
this information these behavioral patterns. So you have to be careful about how you
how you train what access you give to these models, just as you do
racing your child, because all of a sudden, your kid'll be reflecting things
back to you that you don't like, and you're like, well, where
did that come from? Well, I don't know where did you allow this
child to go? What did you share with this child? Well, just
a funny story. One of our babysitters watch with our I guess three year
old the Chucky movie, like The Chucky, the Little Killer, and we're
just like, all right, why did you do that? Now? Our
kid loves these really dark, disturbing movies. We're like, all right,
I don't know that was the best move on our part. But you know,
once it's in there, you're not gonna get it back out unless you
like, unplug the whole thing and start from scratch. That's not gonna be
fun. That's not gonna be fun. And I think that this is a
major trend line that we are going to see here over the next number of
years. It's going to be very interesting. But this is what I seen.
We'll pick this up after the break and see what does Steve and Eugene
think about this. But I think most large organizations, even men. They
need to Small companies are going to pick their poison. They're going to choose.
Okay, I'm going to use BART or I'm going to use open AI,
or I'm going to use anthropic. I'm sure somebody's will come out and
they're going to begin this process of training that large language model, that AI
model on their corporate data. Well, that process, I promise you,
is a really really important process. And I often i'm throwing out this concept.
I'm saying it's a second chance for data. And what I mean by
that is we've spent the last forty odd years doing all sorts of things to
move data, cleansed data and enrich data, load data, access, analyze,
parse, all this stuff to get some value from it. And now
this is a big reset. We're going to hit the reset button non data.
And it's called the large Language model. But folks, don't touch that.
I'll be right back. You're listening to Inside Analysis. Welcome back to
Inside Analysis. Here's your host, Eric Tabanat all right, folks, welcome
back here to Inside Analysis, part of the DM Radio Broadcasting Network. Your
host here, Eric Kavanaugh, was Steve Sarsfield of OpenText Vertica and Eugene Burke
of Digital Strategies Group, and I wanted to throw this question at both of
you. It's one of our attendees is writing talking about logical data models.
Right, we had logical data warehouses, which was a sort of virtual data
warehouse. There are lots of different ways you could do these things, and
he writes that ldms are the semantic layer that is missing. I presently create
ldms to document primary key, alternate keys, business definitions, relationships, PII
classifications, and many other things that are not in the physical implementation layer.
That's a very clever way to go about things, and I think that you
could even load some of those as embeddings into your large language model. But
these are really interesting observations, so I'll throw it over to Steve first.
You know, I've got this concept that these large language models represent a second
chance for data. What do you think about all that? Well, yeah,
I think that's true. You know, it's interesting how over the years
we have these different ways of accessing and managing data. I think you're right
that large language models will be the way that create semantic models and access data
of the future. So yeah, I'm not sure I have any more to
add to that, that's a great way to access data. Yeah, I'll
throw it over to Eugene, because it's not like from my perspective, it's
yeah, yes, First of all, it's these are text generators. That's
really what they're designed to do. Of course they're also art generators and different
things like that. But when I'm thinking about this is what I'm really thinking
of in the darkest corners of my mind. Here are that you know,
a multidimensional structure that it has like four or five dimensions that can be pretty
complex. These things have billions of vertices, which means the complexity is through
the roof. It's just all over the dark place. And that allows you
and I'll throw one last little story Eugene, and then you can comment on
it. I remember watching Carl Sagan when I was like ten or eleven on
his show to Cosmotion was like billions and billions of stars, and he gave
this presentation where he had a table and he had a bunch of little pieces
of paper and like squares and circles on the table and he goes, this
is a two dimensional world, and all these little creatures are in their two
dimensional world. Well, imagine if someone could come along and pick up one
of these two dimensional creatures and lift it into the air, and now all
of a sudden it can see all the two dimensional creatures floating around. What
kind of impact that would have on your thought processes and on your ability to
extrapolate it to come up with new ideas. Oh, I mean I can
see that like it was yesterday, and that was like forty two years ago
that I saw this. But it was just such an excellent way of articulating
the power of perception and of perspective. But what do you think of at
all that ugene? Is it? Is it a second chance for data?
It is? And so back to the analogy, or that's the story of
feeding something that a three year old ought not to have. If you're trying
to use LLMS in a customer interaction scenario, you only get one chance to
make that first impression, and if the consumers or customers lose trust in the
implementation of a large language model, it's going to be very, very difficult
to get it back if you don't have the guardrails to say, oh,
it's still learning. Well, most people want understand that. But if it's
an all large language model doing bank customer service, provider customer service. It
needs to be fed the good Gerber food, right, and so here's the
second chance for data, So feed it only the good stuff, right.
So thinking about strategy for preparing these models, maybe don't expose it to kind
of the swamp, right, because it's going to ingest some of the swampiness.
So I was reading yesterday really incisive kind of analysis. Tech debt is
one thing. Data tech debt is actually more insidious than that, because once
your business customers, your internal customers or your external customers, lose trust in
the data that you're showing them, the information that you're saying this is the
represents the business, or this represents your customer status and it's wrong, then
you have almost no end of pulling your hair out because so I guess the
moral to that story is prepare your models very well. Hey, just an
add on to that, it's pretty interesting. I read an article in Time
magazine that talked about how chat GPT used canyon workers to actually get rid of
the toxicity. Right, So they used workers to actually go through the data
and said, is this toxic content that we're putting into chat cheap or not?
Right, and that was one of the ways that they got it,
so they got out they didn't have too much toxic information in chat chiaptake.
But you know, I think we should think about that is, you know,
how can human intelligence and AI intelligence augment each other as we're building these
models, so we can look to that too kind of as a test.
Yeah, that's that's an excellent point to make, Steve. And I think
the upshot of what I'd like to share at the audience here today is that
your data warehouse, your data marge, the things that you've worked very hard
on need to be a crucial and foundational component of your AI model, and
they should be front and center as a trusted source. That is the primary
source that you want to use, is something like a data warehouse, because
you have governance, because you have all this attention that's been paid to the
model. And remember, the data of your organization reflects the organization itself,
you know. And I think we could see some really interesting things happening of
dynamically generated data models that look at the data and the flow of data and
go, you know, maybe you should maybe you should reconfigure your data model
to better reflect this new way that things are working in your organization. I
think that's coming to There are a lot of fun things that can kind of
spin out of this. But you know, the other fun point i'd throw
out here, maybe it's going to comment from each of you, is I've
been seeing when you connect these engines to your data sources, there are lots
of cool things that you can do and think about. If you've ever done
a Google search for instructions for a particular app, how can I use xyz
app? And you get something that says, okay, step one, go
to the file and click on the red button. And you go there and
there's no red button. You're like, all right, what is this talking
about? Because it's an old version. It's an old version of the previous
app or the previous version of the app. It is a very difficult problem
to solve, or at least has been historically, because you don't control Google
search engines, right, you don't control that stuff, and so they're just
going to find old stuff that is difficult to manage. Well, if you
do this correctly, the large language model can sense like change data capture when
something has changed, and when the source file has changed, it goes Aha,
update and let's go grab that stuff dynamically. So you start thinking about
that's really powerful stuff because when you make the change to the system of record,
it's almost instantaneously reflected in the large language model that you're using to interact
with the environment. But the closing thoughts from U Steve, what do you
think? Yeah, I just want to comment on that. So databases have
things like indexes and materialized views, and we have something called projections, right,
projections. What they allow you to do is based on your queries,
how can I optimize the data? So that is all aged I'd driven now
for a lot of companies out there, for Vertica and for some of the
other database companies that exist. So I'm watching as an AI, I'm watching
the queries that take place, and I'm saying, you know, if I
had this materialized view, or if I had index configured this way, I
could switch it around and I could run that theory that query ten times faster.
Some of that functionally exists today. That's something that we're always looking to
build out, you know, for optimizing speed. So yeah, I mean
there's that component too. Yeah, And I think that you can also again
dynamically provision these things. So that's the other fun thing my buddy Loo Simon
was mentioning, is he's like, think about this. Historically, you had
to learn to speak computer in order to talk to your computer. You had
to learn the structured query language, you had to learn Python, you had
to learn some language to be able to communicate with this machine. You don't
have to do that anymore. Now you can use natural language to communicate with
these things. And I mean, I'm telling you this prompt engineering stuff,
it's really it's taking off as well it should, and it's going to be
amazing when we hit what's called interactive AI, which I think is really the
next big phase that's coming, and it's going to be wild because if you
let these intelligent bots loose on your environment, we'll start I absolutely agree with
that. I think the next big thing, the next big snowflake, the
that next big database if you will, or analytics engine that comes to market
that solves that problem, that says we're going to be really simple. You're
going to type natural language queries and we're not going to care at all about
SQL or Sparkle or any kind of language cipher. We're going to answer that
query based on that language that you enter in. I think the company that
does that is going to succeed and be the next big thing. Yeah,
I think you're right. Well, folks, this has been an absolute blast
talking to two experts, Steve stars Field of open text Vertica look them up
on LinkedIn, and Eugene Burk of Digital Strategies Group. Things are changing very,
very rapidly. And you know, one of the fun quotes I heard
the other day with respect to understanding machine learning and artificial intelligence and where it's
all going was learning meaning human learning, meaning we as humans need to learn
how these things work. And the way you do that is by playing with
them. Send me an email info at inside analysis dot com. You've been
listening to Inside Analysis and now it's time for today's podcast Bonus, in which
host Eric Cavana talks about the transformative impact of large language models such as chin
jept and other projective analytics tools on the future of data analysis and decision making
in the industry. All right, ladies and gentlemen, Hello and welcome to
this virtual summit on Inside Analysis or truly Eric Kavanaugh here, I can never
miss an opportunity to promote future proof of the world's first made for TV webinar
series. Very excited about that. Check your local listenings. We're now in
Washington, DC and Silicon Valley and Los Alamos and lots of other fun places,
So check your local listings or hop onto YouTube to see past shows.
Let's dive right in data fabric versus data mesh. Cut from the same cloth,
Yes, indeed, so let's talk about what this really comes from,
and that's the modern data stack. I'm sure many of you've heard this concept,
the modern data stack. One of my favorite lines about this is that
we kind of sacrifice state at the altar of sk And what I mean by
that is we broke apart all the different component parts of a database into separate
layers for storage, for integration, for processing, analytics orchestrations, some antics,
governance, artificial intelligence, machine learning, of course, security, and
well guess what. All of that can be done inside of a database.
But the modern data stack really attempted to solve for scale issues, to be
able to scale out any one of these component parts, to scale out and
then to scale back down. That's what you want, that's the optimal scenario,
because I'm sure many of you recalled back in the day before we had
something like the modern data stack. You then had the provision for the highest
workload irrespective of whatever workload you had or what your budget was going to be.
And at times when you had peak usage, that was okay. But
when you don't have peak usage, you're spending a lot of money that you
don't really have to spend. So that's kind of where the that's kind of
where this thing came from. I panelists is having trouble getting in, so
let me try to multitask while I'm talking to you here. But basically what's
happening here is you have this situation where we're trying to be able to again
leverage the power of compute wherever it is needed. So if I need to
ingeest a ton of data, that's what I do. If I need to
process a bunch of data, that's what I do. If I need to
scale out the governance components of the equation here, then maybe that's what I
need to do. So that's where the modern data stack came from. But
there are a lot of component parts that well, they complicate things because anytime
you have multiple parts, while there are connections between all these parts and that
can slowly but surely cause you some trouble, so you want to watch out
for that, and that's one of the downsides. But let's kind of dive
deeper in. So what is the data fabric. It is a substitute for
a database. It's supposed to be more flexible, more versatile, more durable,
faster, better, easier to govern. All that fun stuff. Sounds
great. Is it easier to manage? No, it's not easier to manage.
It's going to be significantly harder to manage than a single database. Right,
And I've been joking to myself our DBA is going away. We have
DFA's data fabric administrators. I don't think that's going to happen. I think
you're still going to have data based administrators. Really, data engineers is the
key. So data engineers are sort of the new DBAs, if you will.
So debas will still be around, they're just doing slightly different things.
So what else is a big part of the whole world of data fabric is
this whole concept of automation. Right, That's what we really want to be
able to do is automate things. And so we're automating various components of integration.
So think preprocessing for example, think monitoring for usage patterns and then provisioning
additional access at a certain time, like maybe at the end of the week,
maybe at the end of the month, for example, when you can
have a clothes to do. Situations like that, you want to be able
to get additional resources. Well, with automation of a data fabric, you
can pre provision, you can pre process data, and that's a big part
of what facilitates the data fabric's ultimate goal, which is to make life a
lot easier for the consumption of data, whether for people by people or for
machines, whether from machine learning, et cetera. You want to be able
to detect these patterns, and that's actually a really strong use case for machine
learning, because even though we might think that we're not very predictable as human
beings, the truth is we are very predictable, and our behavior patterns can
easily be ascertained, understood, and then codified by machines. And so that's
a big part of data fabric is to watch for patterns of usage and then
be able to pre provision pre processed data, for example, to do some
work before someone shows up such that it's already ready to go. So I
do want to mention one fun thing about data fabrics. So in companies,
according to Gartner, in the data fabric space was Talent. Many of you
may know Talent. Lots of companies use Talent. It was an open source
company Talent. Open Studio has been really an open source starward for the last
gosh twenty years almost. I think it was around two thousand and three or
so that they started to take off. I remember talking to them in two
thousand and five when I worked for the Data Warehousing Institute. Well, Click
bought Talent. The company Qlik sorry, Click bought talent. Click of course
is a business intelligence platform, and they finally finished their acquisition of Talent a
number of months ago, and guess what happened. They announced that the open
source version of open studio is going bye bye, that's going away. Well,
as I said, they were a leader in the data fabric space.
So what does this mean about the future of data fabric sharp answer is I
don't really know. And you can look up online you'll find lots of commentary
about this. It is a hot topic for sure, but the bottom line
is it is going to be closed source from now on, so now branded.
There are lots of integration vendors that are not open source. Informatic is
not open source, Matillion is not open source. Ab an Issue is not
open source. Lots of companies in the integration space are not open source.
But Talent was, and it was very well known for that. It was
kind of like the Lancelot of open source. Right, the strongest night in
King Arthur's court, if you will, was Talent and that is now not
open source. It's all going to be closed source. What does that mean
for the future of data fabric I'm not entirely sure, but it's probably not
the best news. Thanks so much of your time you've been listening to Inside
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Welcome to the Fabulous Lifestyle radio talk show. I'm your host, Sherry
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If you've missed our previous shows, you can watch us on our TV
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Just subscribe to the Building Solid Foundations channel. Hello friends, I'm the
fashion host for the Fabulous Lifestyle radio show. My name again is Sharry Marie
rob Rosa, also known as Mommy Majesty. I'm a life and fitness coach,
a fashion designer and stylist, an author and speaker, and an interior
designer, and the mother of twelve children. Yes you heard that right,
twelve children. I'm also the CEO of Mommy Majesty Fitness, where I love
to coach women through my signature program that I created and used after each of
my twelve pregnancies to lose sixty to eighty pounds and to get my hourglass shape
back twelve times. It's my heart's desire to empower women to show up as
the most beautifully fit, stylish, confident version of themselves, starting from the
inside out. So that they can achieve all their dreams and goals in this
life. I'm so excited to be back here with you again today to dive
into one of my favorite topics, the world of fashion. Today, we're
going to be discussing part three of our series, How to Create Your Personal
Style Portrait. Then after the commercial break, we're going to hear from our
very special guest, Latasha Finnel. She is the CEO of Boss Lady Bling
Blingy Jewelry and she creates some beautiful pieces, so you won't want to miss
hearing from her. So we're going to discuss part three of our series,
How to Create Your Personal Style Portrait. But first I wanted to recap Part
one and Part two for those of you who may have missed those episodes.
So in part one, we discussed our mindset and we covered five of the
most common negative thoughts that keep women from believing that they are able to create
their own style. The first one is I have to lose weight before I
can be stylish, and we discussed while that's not true, that you can
be beautiful and stylish at every weight and every age. And the second thought
was that it's going to take too much time to be stylish, and we
discuss while that is true at the beginning, it takes a little time to
figure out your style and to curate your wardrobe, but after that, you're
going to save yourself tons of time getting ready in the future. And the
third negative thought was that it's going to be too expensive, and we discuss
why that is not true because there are many beautiful, stylish pieces at every
price point available to all of us. And the fourth thought is that people
will judge me if I show up looking beautiful and stylish, and we discussed
that while that