For trend from this year's nearly three percent average to two point one percent in
twenty twenty four. I'm Chris Karagio, NBC News Radio. You're on board
caseaa's Inland Extress KCAA. Come Linda ten fifty am, the station that needs
notice. Year behind the information economy as a ride. The world is teeming
with innovation as new business models reinvent every industry industry. Inside Analysis is your
source of information and insight about how to make the most of this exciting new
era. Learn more at inside analysis dot Comsideanalysis dot com. And now here's
your host, Eric Kavanaugh. Yes, all right, both, hello and
welcome back to the only coast to ghost radio show that's all about the information
economy. And now, of course we're part of a TV show called future
Proof. It's Inside That Analysis, or as Truly. Eric Cavanaugh is here
with an all star panel. We've got Steve Sarrosfield from Open Text Vertica on
the line and my good buddy Eugene Burt from Digital Strategies Group. We're to
talk about data mesh versus data fabric cut from the same cloth. Short answer
is yes. So for our broad listening audience out there on the radio data
fabric, data mesh. These are relatively new concepts, and really they're just
they're sort of extrapolations of the database. We all know. A database runs
basically every application that there is out there. Databases has been around for decades
now, I mean really for probably sixty years if you go all the way
back, maybe close to seventy years, we've had database technology, and there
have been tremendous advances in the database space, especially over the ten twelve years
leading up till today. So I remember when the hell Haddoup thing came around.
So some of you may know, there's a concept, a construct called
Haddoup and base. That was how Yahoo would index the web. And then
there was open sourced and a number of companies jumped on it, like Cloudera
and Horton Works. A company called Matt Bar jumped on it. Matt Bar
got absorbed by the borg he his HP a number of years ago. Cloud
are still around, but they absorbed Horton Works, which was odd because they
were staunched, I mean, nasty competitors, and then they sort of merged.
They're doing some interesting things still, but that was that wasn't the beginning
of the database explosion, because before that you had some other things happening.
Right, You've got my sequel as a database. You've got Mango DP,
you've got couch Base, You've got all these different open source databases. You
have cloud databases. Now Amazon has their own Dynamo dB. There are all
these different databases. Well, on our show today we have Steve from a
company called Vertica. Well, the product called Vertica is now part of OpenText.
I remember interviewing doctor Michael Stonebreaker back in two thousand and five. I
believe it was when he was just rolling out Vertica. And the reason they
have that name Vertica is because it's vertical for column orientation. So a lot
of 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,
etc. One reason why that's important is because compression is easier on a column
and so Vertica was one of the first major column oriented databases. Wasn't the
first, what was one of the first sybased 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's 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 at MIT like 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 to 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 topocryphally 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. And 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 is
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 hitherto you'd show up and start doing your quer and you didn't get the
data that you want. Well, what the heck happened? I don't know.
Let's look into it like raise a ticket with it and wait a couple
of weeks. Well those days half be gone, folks, and data fabric
is designed to be able to solve for that and then data matches. I
suggested, Well it is. It's a fascinating concept. Exactly what it is,
I don't know. We're gonna find out, but I think I've been
talking for a while now, so I'm gonna 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, and 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 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 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, to focus from a business perspective of the data products. But you
know, I'm kind of reminded two of the no sequel movement. So out
of Haddub you had this sort of no sequel which meant not SQL and then
sometimes only SQL, right, 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 SQL, it's a standard, and so you
need that stuff. But I remember when the data lake craze came about.
I started wondering to myself, are we making some of the stame 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, whatever your information architecture is,
the key is you want to opt 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 can 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 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 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 lay and even that's a bit challenging, right, because it's
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 the organization. That 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 three 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 metadata, 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 the to measure fabric. Don't
we need to move the data to some central location for analytics access? And
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 s a P e 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 object stores these
days, so they're taking a lot of their data and just dumping it into
an object store. That provides you with some pretty interesting capabilities. So a
lot of the databases, including Bertica, 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 store base, and it's a kind of a new
way of doing in 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 like with a
multidimensional 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 the next evolution of MDM. And
you know, because we have such large volumes and everyone wants to 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 coarse grained services, right, And even though no one
really talks about SA anymore, I think what happened is the principles of service
oriented arch architecture 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?
Steam? 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 talked to that have, you
know, like a pub subsystem too, where they're publishing data and subscribing to
data via Kafka, 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
Kafka. That kind of helped that. Yeah, it's funny, you know,
going down memory lane again. I worked for Damon Consulting back in two
thousand and one. That's when I got into this whole space and Michael Hayston,
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, cache is 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 down times 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 Tabanaugh. 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. And 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, he
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 of 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 an 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, 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 graft databases
do a really good job at that. Thus, data fabric the 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 disparate 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. 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 and 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 world. 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 layer. 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, that's
more of a business initiative. Yeah, thank you. 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 do. 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 do but 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 kind of 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 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 leake that doesn't have any metadata that does 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. H 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 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 queer
and operational quer and if it's operational, okay, I'll go this way,
if then go that way, you know, 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 llms 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
showing me all the different versions of order to cash that we have. It
was like, okay, that just started, Like holy dem yeah wow,
So we're just now scratching the surface. Now. It doesn't mean all this
other stuff is going to go away, at least not right away. But
it does mean that 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 of 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 at open Texts
that allow you to actually go and take a look at even free form text,
even in video and audio, allow you to access that and 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 the next generation. Yeah, Gene, that's one I can probably
go on, but Eugene anything, So there are few things like an lll
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 PII
and the lll 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 R texture 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 messed data feb So the
mixture of a wild West data culture in llm's is quite potentially dangerous. Yeah,
that is true. The other cool thing here and some of the cooler
technologies I'll go across, have this capacity to scan your environments. 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 twin 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
a very difficulty 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 going to 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 raising 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 funny story. One of our babysitters watch whether 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 if that was the best move on our part. But you
know, once it's in there, you're not going to get it back out
unless you like unplug the all 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 gonna 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 gonna pick their poison. They're gonna choose. Okay,
I'm gonna use BARD or I'm gonna use open AI, or I'm gonna 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, enrich data,
load data, access, analyze, parcel the stuff to get some value
from it. And now this is a big reset. We're going to hit
the reset button on data. And it's called the large language model. But
folks, don't touch that. Delbi right back. You're listening to Inside Analysis.
Welcome back to Inside Analysis. Here's your host, Eric Tabanac. 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
openext 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 can
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? Wow? 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 creates
semantic models and access data will 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,
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 The Cosmos. It 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 dimends creatures floating around. What kind of impact that would have
on your thought processes and on your ability to extrapolate and to come up with
new ideas. No, 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 about all that utene? 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 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 the strategy for preparing
these models, maybe not 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, loan 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 Kenyon 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 it the chat cheap or not? Right? And that
was one of the ways that they got it. So they got out of
they didn't have too much toxic information in chat cheap take. But you know,
I think we should think about that is, you know, how can
human intelligence and and AI intelligence augment each other as we're building these models,
which 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 margs, 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, like 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 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 dots 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 age 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 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 Lou 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 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 SEQL 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 expert 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 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 segment, in which
host Eric Cavana talks about the transformative impact of large language models such as ched
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 listings. 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 have 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 scale, 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 scale back down. That's what you want, that's the optimal scenario,
because I'm sure many of you recall 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 stent to spend. So that's kind of where the that's
kind of where this thing came from. One of my 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 ingest 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, well 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, 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 DBAs 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 pre processing 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 close 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 process 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 fabric.
So companies, according to Gardner 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.
Informatica is not open source, Matillion is not open source, Abnisio 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
Analysis one O six point five FM and KCAA ten fifty am. Open for
takeout and delivery. Hell Tapiac, Mexican food restaurant in the Tri City Center
of Redlands is back. Their entire family is on hand to serve up their
delicious burritos, but chaka charizo, wavos, manche zos, steak and eggs
just part of their mouth watering great food. Since fifteen thirty one, people
have marveled at the miracle of El Tapiac, and now you can marvel at
the great food the Lugo family has been serving up for over two decades,
nestled quietly in the corner of the Tri City Center shopping mall next to Burlington
Code Factory. Support them. They can't wait to serve you some of their
delectable, authentic South of the Border Mexican fair at great prices, served up
with love. Support the area's best loved Mexican food restaurant in these tough times.
Order up a tasty meal on the phone for delivery or takeout for breakfast,
lunch, or dinner from ten am to six pm called nine to nine
three seven zero zero one seven that's nine O nine three oh seven zero zero
seventeen or google Eltapiac Redlands and treat yourself to Hebot Club's original pure poundy rco
Super tea comes from the only tree in the world that fungus does not grow
on. As a result, it naturally has anti fungal, anti infection,
anti viral, antibacterial, anti inflammation, and anti parasite properties. So the
tea is great for healthy people because it helps build the immune system, and
it can truly be miraculous for someone fighting a potentially life threatening disease due to
an infection, diabetes, or cancer. The t is also organic and naturally
caffeine free. A one pound package of T is forty nine ninety five which
includes shipping. To order, please visit to Hebot club dot com. T
hebo is spelled T like tom, a h ee b like boy oh.
Then continue with the word T and then the word club. The complete website
is to Hebot club dot com or call us at eight one eight sixty one
zero eight zero eight eight Monday through Saturday nine am to five pm California time.
That's eight one eight sixty one zero eight zero eight eight t ebot club
dot com with sixty years of fascinating facts. This is the man from yesterday
and back in time to this time in nineteen eighty six. Looks like Platoon
is a huge hit. Platoon is written and directed by Vietnam morevet Oliver Stone
and stars Charlie Sheen, Tom Behringer, and William the Fall. In nineteen
sixty seven, Oliver Stone was a combat infantryman in Vietnam. He was wounded
twice and received a Medal for gallantry and action. Ten years later he was
a Hollywood screenwriter and from about this time. In nineteen sixty eight, December,
NBC broadcasts Dean Martin Christmas, but this one's a little different. At
the end of the show, celebrities tell kids to keep a sharp eye out
for Santa at oster homes, orphanages, and children's hospitals around the country.
Here's the first one done by Dean Martin, and I'd like to start by
telling all you kids at the Saint John's Hospital in Steubenville, Ohio, where
I was born. To keep a sharp eye out for Santa, because he's
on his way with lots of gifts for you. And from this time December
of nineteen seventy five. WMAQ TV Channel five news anchor Jane Pawley recently subbed
on nbctv's Today Show. Some say Jane Pauley is headed for a network post.
Let's see Jane Pauley, who comes to us from Indiana and Chicago.
And as I said earlier, any family would be happy to welcome someone so
bright and energetic and enterprising and just incidentally pretty as well. With more at
Man from Yesterday dot Com. Every Wednesday at three pm, It's The Uncommon
Sense Democrat with host Eric Bauman. I love when his people talk about them
old Joe Biden, but he's just a couple of years behind him. You'll
get the best political commentary and stuff like this. Good night, Well,
I join us for the Uncommon Sense Democrat every Wednesday at three pm on the
stations that leave no listener behind casey AA ten fifty am Man one oh six
point five. That's them. It's that time of year again, No,
not the holidays. Medicare open enrollment and if you have questions about Medicare,
you should talk to the local expert, Paul Barrett and Associates. All of
his agents are certified with plans that are accepted by most of the medical groups
in our area. Call nine oh nine seven ninety three oh three eighty five.
Their service is free and after forty two years of the business, their
agents are trained to help you pick the plan that's right for you. Hey,
y'all, merle here good news for once. My neighbors is jealous of
me. You want to know why, because my grass is growing and looking
green, and I can sell on my sofa out in the front yard and
I don't even have to overwater it anymore. You know how I did it.
I listened to damn water Boys under Waters on every Thursday night on KCIA.
Well, I got me a smart controller and now a water's at night
mir looks darn tooting. No more sneaking around and hooking up my horse to
my neighbor's pigott in the middle of the night, and his dog won't bite
me anymore. And you can do it too. Listening is easier than ever.
KCIA is now screaming online. Eh, streaming. Why it's streaming,
you dummy. Well, I don't know much about streaming, but they doing
it apparently at KCA radio dot com. So AnyWho listen to to WA zone
and fix your yacht up rant right here at KCIA, the station that leaves
no listener behind e digits, lock them in for more information, recreation and
guaranteed fun. Kcaa NBC News Radio. I'm Chris Karagio. Defense Secretary of
Lloyd Austin is talking with counterparts in the Middle East as the US pushes for
an end to Israel's airstrikes and ground combat operations in the Gaza Strip. The
visit comes amid protests over the deaths of three hostages mistakenly killed by Israeli forces.
Prime Minister Benjamin Netnyah, who has hinted that he's now open to new
negotiations with a moss A crowd of about one hundred thousand people turned out for
a rally last night until Aviv to demand that the government negotiate the release of
the remaining hostages. A Texas congressman, as the Senate and the White House
will have to sweeten the current border security deal to get an aid package for
Ukraine and Israel through the House. Speaking on CBS's Face the Nation, GOP
Congressman Tony Gonzalez said labeling cartels as terrorist organizations would be a major step in
getting an aid package through. Homeland Security Secretary Alejandro Majorcis has been spending the
weekend meeting with a bipartisan group of three senators to try to work out a
tentative deal on border security. GOP presidential hopeful Nikki Haley calls herself and New
Hampshire Governor Chris Snunu a team, but one is more outspoken about the front
runner than the other. We love the idea that he was going to drain
the swamp. That was an amazing opportunity. Didn't even try. I mean
literally didn't even try. Speaking on ABC's this Week, both Haley and Sinunu
said it would be a disaster of former President Trump got back into the White
House. Snunu said Trump only talks about retribution so he can avoid talking about
his failures as president. When asked if Trump should be granted immunity for any
crimes he may have committed while he was in office, Haley said that's up
to the courts to decide. It's a sweet weekend for Wonka, which tops
the box office by a wide margin. The candy theme fantasy musical took in
an estimated thirty nine million bucks in its opening weekend, which is more than
the combined total of the other ten movies that made at least a million dollars
over the weekend. The Hunger Games prequel and the Japanese film The Boy and
the Heron rounded out the top three at the box office. I'm Chris Karagio,
NBC News Radio listene the KCAA Loma Linda at one O six point five
FM, K two ninety three c f Brito Valley. Did you know here
at KCAA ten fifty am that we developed an app for all your Android devices.
We're talking about your smartphone, your tablets, you name it. You
have an Android format. You can take kca with you everywhere you go.
We're talking about our audio stream, our video stream, and even our podcast.
Go to KCAA Express dot com. That's KSECAA Express dot com, KCAA
Express dot com. Ram's tire service in Oma. Linda reminds everyone that the
blood you donate gives someone another chance at life some day that someone might be a