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People know, if you're going to
excel, you look at a spreadsheet,

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you have rose and columns. Well, rose typically contain lots of different types

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of data first name, last name, phone number, email address, address,

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all this kind of fun stuff notes
that's all in a record. That's

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one record, and then the columns
tend to be the same or they should

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be the same of first names,
last names, phone numbers, et cetera.

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One reason why that's important is because
compression is easier on a column,

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and so Verdica was one of the
first major column oriented databases. Wasn't the

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first, what was one of the
first cybased IQ was one before that years

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ago. It was one of the
first to say, you know what people

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want to do analytics. People want
to do analysis compression it is a big

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part of that. And being able
to rapidly slice and dice information is a

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big part of analytics. That's what
we need to be able to do.

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So Michael Stonebreaker who invented the modern
database. By the way, if you

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look this guy up, he's amazing
way back in MIT, like I get

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fifty odd years ago. He came
up with the postgress and the ingress,

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which were database architectures that have now
been used by tons of different companies.

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Well, I'm telling you this backstory
just help you understand how we got here.

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And what's fascinating is this data fabric
stuff came really after the had do

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movement. People started thinking, all
right, we have these incredibly topocryphal,

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the topographically challenging environments. How are
we going to be able to provision across

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multinational corporations with many, many different
users, sometimes tens of thousands of people.

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You'll learn in the database world,
concurrency is a real issue, and

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being able to serve many concurrent users
is a real issue because it's like having

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ten thousand people come into your boutique
shops someday. Well, you can't even

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fit them all in there. This
is what happens with concurrency. You have

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to purposely design your architecture to handle
that kind of stuff. As my buddy

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Mark Madson, who's an analyst again
these days, once told me. He

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said, it takes ten years to
find the edge case for a database that

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will crash it. Ten years like
after your in production before you finally figure

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out what it's going to throw it
off. All right, this is how

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complex databases are. So data fabric
is even more complicated than that. I

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mean, quite frankly, it really
is a whole array of data pipelines and

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triggers and monitors and sensors. Now
we have data observeability these days, which

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is all about watching for upstream changes
in data. Did a field change?

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Is there a new column in this
database? Did we not get the data?

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Well, these kinds of early indicators
are very useful for data engineering teams

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and for analysts because hither too you
show up and start doing your quer and

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you didn't get the data that you
want. Well, what the heck happened?

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Let's look into it like raise a
ticket with it and wait a couple

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of weeks. Well those days have
to be gone, folks, and data

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fabric is designed to be able to
solve for that and then data matches.

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I suggested, Well, it's a
fascinating concept. Exactly what it is,

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I don't know. We're going to
find out, but I think I've been

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talking for a mom now, so
I'm going to go ahead and pipe down

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and throw it over to Steve Starsfield
from open Text Vertigo. Steve, you've

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been around maybe almost as long as
I have, or about as long as

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I have so you've seen this whole
evolution. It's kind of amazing, it's

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kind of bewildering. But I do
feel like we're getting to a place now

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where it leads, to a certain
extent, the nuts and bolts of the

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data administration don't have to concern the
analysts and the end users who just want

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to have fun with the data.
What do you think, Yeah, absolutely,

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you know, it's funny. I
think over the years, what has

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happened is the data center of gravity
has changed quite a bit. Right,

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So it used to be that the
data warehouse with the center of gravity,

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but we can't do that anymore,
right, We can't just continually move all

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the data in the data warehouse.
So we have things like data lakes,

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and we have object stores that we
can store our data in. But the

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center of gravity has changed because as
the volume of data is getting so much

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greater and we can't possibly load that
in, the number of people who want

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access to analytics has changed, and
so we have different challenges now, we

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have big challenges that we can have. And so really what data fabric and

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data mash really represent is that change
in gravity where the data sits, whether

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it's you know, in locally object
store on the cloud various places. I

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think it kind of represents accessing and
managing and using that data, not in

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one central place, right. Yeah, And that's that's a really good point.

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And you know, you bring up
on data lakes, and of course

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now we've gone from data Lake to
data lake house to lake house architecture and

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you know all this stuff. And
I think the key is to remember the

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product that focused from a business perspective
of the data product. But you know,

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I'm kind of reminded too of the
no sequel movement. So out of

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Haddob you had this sort of no
sequel which meant not SQL and sometimes ONLYQL,

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not only SQL. And what happened
is very shortly after that, these

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no SQL databases started strapping SEQL engines
on top of them, because guess what,

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SQL is a standard, that's the
structured query language for those who don't

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know as QL, it's a standard, and so you need that stuff.

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But I remember when the data Lake
craze came about, I started wondering myself,

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are we making some of the stain
mistakes again that we made last time,

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where you just throw a bunch of
stuff out there and then Okay,

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you hope to be able to find
it, and people talk about the data

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swamp and things of this nature,
and that's why you have people saying,

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now lake house architecture, they're coming
up with more clever ways to be able

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to get access to it. But
that's really the bottom line is access path

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and efficiency, right whenever your information
architecture is the key is you want to

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optimize the access path for the important
users and you want to be efficient and

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durable and secure and all that other
stuff, right, Steve, Yeah,

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I think we don't really have an
ETL problem or ELP problem anymore. Right,

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We're not trying to move the data
around. What we're trying to do

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is access it in place. You're
absolutely right about that, Eric, And

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so you know we need to do
some things in order to access in place.

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It could be data measure data fabric, and you know, with a

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data mash, we want to make
sure that we have access to the metadata

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so we can access that data on
top of the data and then perform our

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analysis. With a data fabric,
that metadata may not exist, right,

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so we may have to create a
semantic layer, our own semantic layer using

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a graph or some other technology.
And so again it's about accessing the data

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without having to move it into a
single consolidated data warehouse. Yeah, that's

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a really good point. And the
metadata, so I talked about that in

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the semantics and data catalogs and some
of these other things. They are all

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there to help us make sense of
the data. And the other interesting development

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here from a business analyst perspective is
that in the data warehousing world, we've

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stripped out all the context to be
able to get it through thin pipes,

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to slow processors and be able to
to slice and dice the data. Well,

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you don't have to do a lot
of that stuff anymore. I mean,

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I remember the schema on read concept
coming out of the Data lath and

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even that's a bit challenging, right, because it's going to slow the process

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down a little bit. You have
to make sure you get that correct.

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But the general thrust I'm throwing out
here is that you don't necessarily need to

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strip out so much context read metadata
anymore to be able to facilitate use down

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the road, right, Steve,
Yeah, that's absolutely true. You know,

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I think that metadata, that context
is super important for the organization.

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It kind of describes how you use
the data and what you want to do

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with the data, and so stripping
that out can be you know, detrimental

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to whatever you want to accomplish in
terms of business. Right, we do

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these things because we want to understand
our business better. We want to drive

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through areas. Area one is we
want to make sure that we can drive

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additional revenue within the organization. We
want to make sure that we're super efficient

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and using only the servers that we
need to have and only the processes that

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we need to do. And we
want to make sure that we're compliant.

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So if someone asked us for reports, we should be able to go out

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to those data sources very easily get
access to that. And so you know,

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having that meta data, having that
sort of whatever it is fabric or

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mesh or whatever to allow us to
access that those data sources and perform those

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three things I think is super crucial. Yeah, we have got a couple

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of good questions from our studio audience, by the way, so I'm just

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going to throw this one out there. One of our attendees is writing,

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so does that mean that users would
access to data at the source. That

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is usually not a good idea because
it could put too much pressure on source

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databases even with data measure fabric.
Don't we need to move the data to

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some central location for analytics access?
And this is this is a good question,

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but I will say you are starting
to see a greater focus on federated

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data access and leaving the data where
it sits, just to be able to

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touch it as needed. But there
is a point about overburdening source systems.

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That's why we came up with these
things in the first place. Right.

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We realized that sap ERP was not
easy to query and so we pulled data

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out of that put it into the
warehouse. But what do you think about

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that? There is some concern,
but I think the processes are getting more

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and more elegant and efficient at being
able to do that. What do you

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think, steem, Yeah, it's
a case by case basis. I know

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for Vertico what we do is we
do have data virtualization. So if you're,

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for example, you wanted to run
important do a joint against a table

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that's sitting in Oracle, that's pretty
easy for us to do and a lot

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of other databases have that too,
So being able to go out and access

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that third party is key. It
will tax the third party system though,

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And what I see a lot of
companies doing if they're concerned about that,

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is they're leveraging a stores these days, so they're taking a lot of their

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data and just dumping it into an
object store. The the that provides you

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with some pretty interesting capabilities. So
a lot of the databases, including Pertica,

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has this whole concept of separation of
compute and storage. So I have

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my data, it's sitting in a
separate storage object store. I want to

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do some data loading. I spin
up three nodes. I do data loading.

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I want marketing to run reports.
I spin up three nodes. It

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does the reports. You know,
I want various my dashboards throwing really fast.

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It's spent up five nodes for the
CEO, and I run the reports.

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They're all operating on that same data, but we're able to access that

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through one location, through one engine. And we see that happening a lot

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in the market. It's happening within
you know, our within our customer base,

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and it's a kind of a new
way of doing an architecture. Is

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that whole separation of compute and storage. Yeah, and these are all deep

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architectural determinations that get made. And
kind of where I was going with that

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comment about large language models is we
don't really I don't really understand how they

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work. But what I do know
is they can get around all kinds of

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these issues because you're dealing with a
multi dimensional member mole app right, multidimensional

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online analytical processing, the micro strategy
folks, you want multiple different dimensions.

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But these large language models have like
three point one billion vertices or something,

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and there are lots of different ways
you can slice and dice stuff. But

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again, we don't know about the
veracity, we don't know about the clarity.

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We're not sure. And of course, with something like a data warehouse,

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you want to be really gosh darned
sure about what you're doing, right.

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We want to know, you know, what the most current information is,

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what the most trusted information is,
and we want to be able to

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use that. And so you know, to some extent, MDM used to

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help us with that. We used
to take all the data and create an

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MDM system around that. I think
this is the next evolution of ETL.

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It's an evolution of MDM. And
you know, because we have such large

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volumes and everyone wants access to the
data, data mesh, data fabric are

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is the next thing? Is the
next evolution of that? Yeah, and

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so you have some of those principles
baked in, right. I mean I

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think that's the key is that we've
learned over the years. I remember asking

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myself a rhetorical question, is MDM
the next SLA? If you think about

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service oriented architecture? We had fine
grain services and course grain services, right,

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And even though no one really talks
about SA anymore, I think what

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happened is the principles of service oriented
ar chitecture kind of got baked into how

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we do cloud and now that's just
the norm. But real quick, what

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do you think about that state?
Yeah? I think you're right. I

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think it is, you know,
the basic way that we do cloud.

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It's it's but you know, I
can't tell you how many customers I talk

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to that have, you know,
like a pub subsystem to where they're publishing

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data and subscribing to data cosca where
some of the open source tools that are

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available for that, so you know, yeah, there is you know,

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this concept of a data bus that
I think a lot of companies deal with,

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including Tafka, that kind of help
that. Yeah, it's funny,

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you know, going down memory lane
again. I worked for Daman Consulting back

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in two thousand and one. That's
when I got into this whole space and

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Michael Haston, super smart guy at
consultant, as the same birthday as me,

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and we were excited about that.
He would talk about what he called

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an enterprise backplane, and what we
was talking about is what you just mentioned

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this bus. Basically, it's a
staging area of data. And I meant

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to mention this in my opening remarks
as well, that intelligent caching is older

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than the hills. I mean,
it's something we came up with a long

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time ago, and it's a very
useful construct for being able to facilitate access

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to often used data. Right now, how you manage that, how you

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construct that matters a lot and will
have a big impact on whether or not

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it works. But it's not a
new concept, right I mean, caches

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are a huge part of data virtualization
for example, now, a big part

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of data fabric That's what I was
talking about, the pre processing stuff.

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And you know, the beautiful thing
is machines. You know, unless you

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turn them off, they don't sleep. And machine learning unless you turn it

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off, is just learning and learning
and learning. They're just kind of crawling

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around looking for patterns, and we
humans are pretty darn predictable if you get

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right down to it. So if
you do have a machine learning layer and

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it's monitoring usage of data, it's
going to know when the peaks and valleys

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are. And to your point earlier, you can spin up three ohs or

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spin up four ohs, or spin
up two ohs, or whatever the case

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may be. Not that you're never
going to have a hiccup again, there

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will always be hiccups, there will
always be downtimes and things of this nature.

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But the point is we're getting really
close. And I think that's the

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key with data fabric at least,
is it's trying to be as prepared as

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it can be for whatever data usage
you're going to need in the next hour,

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the next day, whatever the case
may be. Well, don't touch

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out to folks, will be right
back on a fantastic episode of Inside Analysis.

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Welcome back to Inside Analysis. Here's
your host, Eric Tabanac. All

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right, folks, back here on
Inside Analysis, part of the dm Radio

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Broadcasting Network. We're talking to Steve
Sarsfield of Open Text Vertica and Eugene Burke

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from Digital Strategies Group. Eugene,
you heard us ranting and raving about data

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fabric versus data mesh in the opening, and I had a great question around

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logical data models and where is this
is all going? I mean, you

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know, when I think about these
large language models, again, they have

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absorbed far more than just text.
They have absorbed concepts. They have absorbed

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formulae, whole spreadsheets. You know, one of my buddies was saying,

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Goes, I just go to these
things to get the numbers of things,

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because that was absorbed as well.
Now again you do have this whole issue

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of moorings and anchors of truth as
some people call these things, and you

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have to worry about all that.
But what's your take on the data mesh

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versus data fabric religious war or is
it that big a deal at all?

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So yeah, so, Steve,
I guess I would have a two part

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question to get us started on this
segment. Are data fabric and data mesh

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twin sons of different mothers? Are
they destined to fight or do they have

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different objectives and different mountains to conquer? And how do they relate to lms?

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And this kind of adoption of a
completely different paradigm or enterprise ask and

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answer computing. I can answer that
if you want. You know, I

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kind of look at data mesh and
data fabric as I look at the origin

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story. You know, every superhero
has an origin story, right, and

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I think data fabric and data mesh
have different origin stories. So in the

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case of data fabric, the origin
story has a lot to do with graph

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databases. You know, graph databases
to some extent, they are a solution

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looking for a problem. To some
extent, I won't want to I don't

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want to completely paint them like that, but you know the problem that they

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solved. The one problem that they
really solve is that if you have disparate

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data, and that data is sparse, and that data is it doesn't have

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any metadata attached to it, it's
sort of like an unknown graph. Databases

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do a really good job of building
linkages between the data. Data that's sitting

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in different files, data that's sitting
in different columns and rows, and so

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graph databases do a really good job
at that. Thus, data fabric the

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uh. You know, Eric mentioned
the Hadoop model, and I think the

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origin story of data mesh is hadoop. You know, we have data that's

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kind of sitting in there in the
data lake. We want to have access

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to it. We want to make
sure we know probably that the metadata is

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accurate, and we know that maybe
our company grew through acquisition. So I've

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got data warehouses that have multiple data
warehouses in our organization. I've got a

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data lake in our organization, but
the metadata associate with that is probably pretty

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good and we can sort of kind
of trust it. That's data mesh and

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putting all that data together is kind
of where that comes from. So when

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I look at data fabric and I
look at data mesh, I look at

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those origin stories and it doesn't really
answer though which one I should use.

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And so you know, if you
kind of turn that around and you are

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a pharmaceutical company and you have a
lot of dis data and the data is

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a little bit messy and it's a
little bit sparse, maybe the thing to

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do is to set up a semantic
layer access that and use a data fabric.

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If you're a company that has grown
to acquisition, you've got multiple data

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warehouses, a CRM system in the
ERP system. But guess what, the

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data is pretty good, pretty fit
for use. Maybe data mesh is the

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solution, and I think that's the
big difference in my head of what the

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differences are between those solutions. Do
Eugene you want to comment on that,

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sure, I guess the follow on
is, do you agree with some people's

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assessment that the data fabric is more
IT driven and a data mesh is more

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business pull or business driven and organized
around the business typology or top top topography.

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So what mesh is trying to solve
is to put the business back in

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the data driver's seat. Yeah,
I think that's true. And you know,

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one of the reasons for that,
again is the introduction of a graph

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database and a semantic player. Right. That's a pretty tough thing to do.

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It requires a specialized set of knowledge
that not a lot of people have,

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only a handful of people. And
I'm trying to understand what sparkle and

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cipher is and trying to understand basically
what a triple is versus you know,

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a standard column in row. That
requires specialized knowledge that we just don't have

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normally in the normal database work.
So it is IT driven. It usually

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has a component of services and technology
that are bound together. That kind of

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work together to create that semantic player
with data mesh. You know, we

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could almost pull that off if we
have a good understanding of metadata and you

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know, how to manage all of
those and data virtualization and some of the

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other tools that you might use for
a data mesh. Yeah, that's you

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know, it's more of a business
initiative. Yeah, thank you. Guys

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could comment like that. That's how
I see it though. Yeah, you

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know, I also have had difficulty
wrapping my head around two bowls, right,

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And I remember when the semantic web
was going to solve all the world's

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problems and it never really kind of
got there. Now there are semantic layers

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that you can use for a database, and that's a very useful thing.

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It's very similar to data catalogs,
right, I mean, the data catalog

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is there to capture the meaning of
things and to enable business people to connect

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dots in their systems basically, right, I mean, that's what a data

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catalog is supposed to to. But
again, these things are all sort of

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it's just interesting how they're all sort
of moving forward at their own pace.

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Although I'd never heard before until now, so thank you that the origin story

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of data mesh was had dupe.
I did not know that. I listen,

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it may be wrong about that.
All of these technologies, all of

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these technologies though, you know,
they rely on multiple technologies, right,

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it's the coming together of multiple technologies. So we have databases, and databases

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that used to run data warehouses are
having capabilities around data mesh. Right.

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They're able to go and access data
that's outside of them and form analysis on

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them like they couldn't ever before.
And we have query engines, you know,

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Presto and Trino and some of those
technologies. They get access data that's

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sitting in a data lake that doesn't
have any metadata that it's not sitting in

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a database, visualization tools, graph
databases, data virtualization, data catalog.

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So a lot of those things are
coming together as technologies to form data mesh.

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Data data fabric. Yeah, that's
a really good way to put it.

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A data fabric, I think is
the amalgam of all these different things.

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Yeah. It is designed to be
an efficient and thoroughly capable data foundation

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to handle whatever data uses the business
may have. Which is interesting too because

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it's almost like we're we're moving in
the direction of so called h TAP right,

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hybrid transactional analytical processing, which always
made me kind of wonder there was

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this approach where a query would come
in and you could have sort of a

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sniffer there awaiting. It's like,
okay, is this an analytical quer and

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operational queer? And if it's operational, okay, I'll go this way.

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If then I'll go that way.
I wonder about that. Anytime you have

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these sort of if then statements at
the at the foundation of a database technology,

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well, that's going to affect performance, right. I mean, all

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this when you get right down to
it has to do with performance. Can

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this thing perform the tasks I wanted
to perform quickly enough, efficiently enough,

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and accurately enough. And you know
that's where I see tremendous pressure from these

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lms on basically everything that's in the
data stack, everything that's in the information

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stack, because you know these The
key is to have your embedding strategy and

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to know and that gets back to
your point Steve from earlier in the show

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that you, as an organization,
you need to start getting your data prepared

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for these large language models and make
sure that it's trusted, make sure that

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it's governed, understand your processes and
one of the things that you can use

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to do that is a large language
model, because they're actually pretty good at

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being able to ascertain and then articulate
specific processes that you need to go to.

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You know, it's really interesting.
I was talking to Steve Lucas,

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the CEO of Boomy, former president
of sp America's he's over at MARKETO for

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a while. He was saying,
when they got in there, he started

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looking at what they were seeing inside
their own systems, and he asked it

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show me all the different versions of
order to cash that we have. It

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was like, okay, that just
started, Like holy love it. Wow.

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So we're just now scratching the surface. Now. It doesn't mean all

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this other stuff is going to go
away, not right away, but it

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does mean you have to get your
data ready, and that means quality checks,

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that means lineage checks, things of
that nature. What else would you

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suggest, Steve and then maybe Eugene
from your experience, how can organizations get

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ready for that? How can you
prepare your data for large language models?

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First deed, I mean a big
one for me is access and security,

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right, that is a huge one. A lot of companies have a PII

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seeing in a data storage somewhere.
It may be encrypted, it may not

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be. How do you identify that
PII and make sure that you're not exposed

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as a company to all the potential
finds that you could get around that that

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is a regulatory mess that could you
know, you want to make sure that

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that problem is solved. We have
some technologies that open texts that allow you

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to actually go and take a look
at even free form text, even video

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and audio allow you to access that
can take a look and make sure that

359
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there's no PII there, so that
you know you're not vulnerable to law,

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lawsuits and stuff. So security access
encryption, that's a real key one for

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you know, making sure that your
data is in order for for the next

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generation. Yeah, Eugene, that's
one I can probably go on, but

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Eugene. Any so, there are
few things like an l l M for

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exposing flaws in your data, and
exposing one of the flaws would be security

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holes. So to your point,
if you have PHI or p I I

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and the l ll M has a
way to find it, it will find

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it right because that's what it's built
to do. And so now is the

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time to understand your I A M
architecture and to make sure that if you're

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going to use a large language model
for customer service, patient service, provider

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service, that you really understand the
pathways for accessing your highly sensitive data.

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And because you don't want to have
an audit come up to say, Okay,

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here's violation, violation, violation or
God forbid of breach, right,

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because you didn't adequately think through your
architecture. Yeah, so that's a technical

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component, you know, there's there's
also sort of like the operational components of

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it, right. You know,
when data governance we used to talk about

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people, processes and technology, and
so the people in the processes are also

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something you need to look at.
How is data handled, is data copied?

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You know regarding processes, and then
people who has access to it,

379
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how are they handling it and so
on. So again, you know,

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there is a component of data governance
to data mession, data forever. So

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the mixture of a wild West data
culture in lll MS is quite potentially dangerous.

382
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Yeah, that is true. The
other cool thing here and some of

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the cooler technologies I've go across have
this capacity to scan your environment. Some

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of the data catalogs have this capacity. Very very useful stuff. There's a

385
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company it's really more in the security
and governance space Extra Hop. I haven't

386
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taken a brief infront of in a
long time, but what I loved about

387
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them is that they will scan.
They basically just siphon off your network traffic

388
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and then create a digital twint of
your entire information landscape to show you every

389
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database, every application, anything that's
touching it. You see an object for

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that, and that's the kind of
tech that you can use. And of

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course LMS, I mean, like
I said, we've said before, if

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you point one at your information architecture
is going to start sucking that stuff up.

393
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And if you weren't careful about what
went in there, it's going to

394
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be very difficult to get it out. It's like unlearning things. It's hard

395
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to unlearn something you know. And
I often use the analogy of raising children

396
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to explain how to train your large
language model. If you've got a two

397
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year old, you don't want to
let her hang out with a bunch of

398
00:29:04.960 --> 00:29:08.279
gangsters in the hood for a while, like, because they're gonna absorb all

399
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this information these behavioral patterns. So
you have to be careful about how you

400
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how you train what access you give
to these models, just as you do

401
00:29:18.759 --> 00:29:22.000
racing your child, because all of
a sudden, your kid'll be reflecting things

402
00:29:22.039 --> 00:29:22.799
back to you that you don't like, and you're like, well, where

403
00:29:22.799 --> 00:29:26.559
did that come from? Well,
I don't know where did you allow this

404
00:29:26.640 --> 00:29:30.200
child to go? What did you
share with this child? Well, just

405
00:29:30.200 --> 00:29:33.680
a funny story. One of our
babysitters watch with our I guess three year

406
00:29:33.720 --> 00:29:37.160
old the Chucky movie, like The
Chucky, the Little Killer, and we're

407
00:29:37.200 --> 00:29:41.039
just like, all right, why
did you do that? Now? Our

408
00:29:41.119 --> 00:29:44.079
kid loves these really dark, disturbing
movies. We're like, all right,

409
00:29:44.160 --> 00:29:47.640
I don't know that was the best
move on our part. But you know,

410
00:29:47.720 --> 00:29:51.680
once it's in there, you're not
gonna get it back out unless you

411
00:29:51.839 --> 00:29:55.920
like, unplug the whole thing and
start from scratch. That's not gonna be

412
00:29:56.000 --> 00:29:59.240
fun. That's not gonna be fun. And I think that this is a

413
00:29:59.359 --> 00:30:03.480
major trend line that we are going
to see here over the next number of

414
00:30:03.519 --> 00:30:06.880
years. It's going to be very
interesting. But this is what I seen.

415
00:30:06.920 --> 00:30:10.759
We'll pick this up after the break
and see what does Steve and Eugene

416
00:30:10.759 --> 00:30:14.559
think about this. But I think
most large organizations, even men. They

417
00:30:14.559 --> 00:30:18.079
need to Small companies are going to
pick their poison. They're going to choose.

418
00:30:18.160 --> 00:30:19.880
Okay, I'm going to use BART
or I'm going to use open AI,

419
00:30:21.039 --> 00:30:23.680
or I'm going to use anthropic.
I'm sure somebody's will come out and

420
00:30:23.720 --> 00:30:29.559
they're going to begin this process of
training that large language model, that AI

421
00:30:29.640 --> 00:30:33.440
model on their corporate data. Well, that process, I promise you,

422
00:30:33.519 --> 00:30:37.480
is a really really important process.
And I often i'm throwing out this concept.

423
00:30:37.759 --> 00:30:41.559
I'm saying it's a second chance for
data. And what I mean by

424
00:30:41.599 --> 00:30:45.920
that is we've spent the last forty
odd years doing all sorts of things to

425
00:30:47.000 --> 00:30:49.519
move data, cleansed data and enrich
data, load data, access, analyze,

426
00:30:49.599 --> 00:30:55.200
parse, all this stuff to get
some value from it. And now

427
00:30:55.319 --> 00:30:59.000
this is a big reset. We're
going to hit the reset button non data.

428
00:30:59.440 --> 00:31:02.160
And it's called the large Language model. But folks, don't touch that.

429
00:31:02.279 --> 00:31:11.279
I'll be right back. You're listening
to Inside Analysis. Welcome back to

430
00:31:11.440 --> 00:31:19.680
Inside Analysis. Here's your host,
Eric Tabanat all right, folks, welcome

431
00:31:19.680 --> 00:31:23.960
back here to Inside Analysis, part
of the DM Radio Broadcasting Network. Your

432
00:31:25.000 --> 00:31:29.759
host here, Eric Kavanaugh, was
Steve Sarsfield of OpenText Vertica and Eugene Burke

433
00:31:29.839 --> 00:31:33.359
of Digital Strategies Group, and I
wanted to throw this question at both of

434
00:31:33.400 --> 00:31:37.000
you. It's one of our attendees
is writing talking about logical data models.

435
00:31:37.279 --> 00:31:41.680
Right, we had logical data warehouses, which was a sort of virtual data

436
00:31:41.680 --> 00:31:44.440
warehouse. There are lots of different
ways you could do these things, and

437
00:31:44.519 --> 00:31:48.640
he writes that ldms are the semantic
layer that is missing. I presently create

438
00:31:48.759 --> 00:31:53.559
ldms to document primary key, alternate
keys, business definitions, relationships, PII

439
00:31:53.640 --> 00:31:59.000
classifications, and many other things that
are not in the physical implementation layer.

440
00:32:00.079 --> 00:32:01.880
That's a very clever way to go
about things, and I think that you

441
00:32:01.920 --> 00:32:08.000
could even load some of those as
embeddings into your large language model. But

442
00:32:08.039 --> 00:32:13.160
these are really interesting observations, so
I'll throw it over to Steve first.

443
00:32:13.279 --> 00:32:16.119
You know, I've got this concept
that these large language models represent a second

444
00:32:16.240 --> 00:32:20.240
chance for data. What do you
think about all that? Well, yeah,

445
00:32:20.319 --> 00:32:23.559
I think that's true. You know, it's interesting how over the years

446
00:32:23.599 --> 00:32:30.079
we have these different ways of accessing
and managing data. I think you're right

447
00:32:30.119 --> 00:32:36.559
that large language models will be the
way that create semantic models and access data

448
00:32:36.640 --> 00:32:39.279
of the future. So yeah,
I'm not sure I have any more to

449
00:32:39.279 --> 00:32:44.400
add to that, that's a great
way to access data. Yeah, I'll

450
00:32:44.400 --> 00:32:47.680
throw it over to Eugene, because
it's not like from my perspective, it's

451
00:32:47.839 --> 00:32:52.400
yeah, yes, First of all, it's these are text generators. That's

452
00:32:52.400 --> 00:32:55.039
really what they're designed to do.
Of course they're also art generators and different

453
00:32:55.079 --> 00:32:59.799
things like that. But when I'm
thinking about this is what I'm really thinking

454
00:32:59.839 --> 00:33:05.000
of in the darkest corners of my
mind. Here are that you know,

455
00:33:06.279 --> 00:33:10.160
a multidimensional structure that it has like
four or five dimensions that can be pretty

456
00:33:10.200 --> 00:33:15.279
complex. These things have billions of
vertices, which means the complexity is through

457
00:33:15.480 --> 00:33:19.640
the roof. It's just all over
the dark place. And that allows you

458
00:33:19.880 --> 00:33:22.000
and I'll throw one last little story
Eugene, and then you can comment on

459
00:33:22.039 --> 00:33:25.519
it. I remember watching Carl Sagan
when I was like ten or eleven on

460
00:33:25.640 --> 00:33:30.799
his show to Cosmotion was like billions
and billions of stars, and he gave

461
00:33:30.839 --> 00:33:35.160
this presentation where he had a table
and he had a bunch of little pieces

462
00:33:35.160 --> 00:33:38.200
of paper and like squares and circles
on the table and he goes, this

463
00:33:38.240 --> 00:33:42.680
is a two dimensional world, and
all these little creatures are in their two

464
00:33:42.720 --> 00:33:45.079
dimensional world. Well, imagine if
someone could come along and pick up one

465
00:33:45.079 --> 00:33:49.240
of these two dimensional creatures and lift
it into the air, and now all

466
00:33:49.279 --> 00:33:52.720
of a sudden it can see all
the two dimensional creatures floating around. What

467
00:33:53.000 --> 00:33:58.599
kind of impact that would have on
your thought processes and on your ability to

468
00:33:58.720 --> 00:34:00.960
extrapolate it to come up with new
ideas. Oh, I mean I can

469
00:34:01.000 --> 00:34:05.720
see that like it was yesterday,
and that was like forty two years ago

470
00:34:06.200 --> 00:34:10.039
that I saw this. But it
was just such an excellent way of articulating

471
00:34:10.079 --> 00:34:15.880
the power of perception and of perspective. But what do you think of at

472
00:34:15.880 --> 00:34:17.960
all that ugene? Is it?
Is it a second chance for data?

473
00:34:19.760 --> 00:34:23.639
It is? And so back to
the analogy, or that's the story of

474
00:34:25.079 --> 00:34:31.599
feeding something that a three year old
ought not to have. If you're trying

475
00:34:31.639 --> 00:34:40.199
to use LLMS in a customer interaction
scenario, you only get one chance to

476
00:34:40.199 --> 00:34:49.679
make that first impression, and if
the consumers or customers lose trust in the

477
00:34:49.719 --> 00:34:52.840
implementation of a large language model,
it's going to be very, very difficult

478
00:34:52.960 --> 00:34:58.000
to get it back if you don't
have the guardrails to say, oh,

479
00:34:58.039 --> 00:35:01.320
it's still learning. Well, most
people want understand that. But if it's

480
00:35:01.960 --> 00:35:08.559
an all large language model doing bank
customer service, provider customer service. It

481
00:35:08.639 --> 00:35:14.400
needs to be fed the good Gerber
food, right, and so here's the

482
00:35:14.519 --> 00:35:17.639
second chance for data, So feed
it only the good stuff, right.

483
00:35:19.000 --> 00:35:24.639
So thinking about strategy for preparing these
models, maybe don't expose it to kind

484
00:35:24.679 --> 00:35:29.960
of the swamp, right, because
it's going to ingest some of the swampiness.

485
00:35:30.840 --> 00:35:37.599
So I was reading yesterday really incisive
kind of analysis. Tech debt is

486
00:35:37.679 --> 00:35:44.679
one thing. Data tech debt is
actually more insidious than that, because once

487
00:35:44.800 --> 00:35:52.039
your business customers, your internal customers
or your external customers, lose trust in

488
00:35:52.079 --> 00:35:57.199
the data that you're showing them,
the information that you're saying this is the

489
00:35:57.679 --> 00:36:04.519
represents the business, or this represents
your customer status and it's wrong, then

490
00:36:04.559 --> 00:36:09.639
you have almost no end of pulling
your hair out because so I guess the

491
00:36:09.679 --> 00:36:15.440
moral to that story is prepare your
models very well. Hey, just an

492
00:36:15.440 --> 00:36:17.920
add on to that, it's pretty
interesting. I read an article in Time

493
00:36:19.000 --> 00:36:24.519
magazine that talked about how chat GPT
used canyon workers to actually get rid of

494
00:36:24.559 --> 00:36:29.639
the toxicity. Right, So they
used workers to actually go through the data

495
00:36:29.679 --> 00:36:32.599
and said, is this toxic content
that we're putting into chat cheap or not?

496
00:36:34.159 --> 00:36:36.400
Right, and that was one of
the ways that they got it,

497
00:36:36.400 --> 00:36:43.119
so they got out they didn't have
too much toxic information in chat chiaptake.

498
00:36:44.239 --> 00:36:47.719
But you know, I think we
should think about that is, you know,

499
00:36:47.760 --> 00:36:53.159
how can human intelligence and AI intelligence
augment each other as we're building these

500
00:36:53.199 --> 00:36:58.639
models, so we can look to
that too kind of as a test.

501
00:36:59.719 --> 00:37:02.920
Yeah, that's that's an excellent point
to make, Steve. And I think

502
00:37:04.000 --> 00:37:07.760
the upshot of what I'd like to
share at the audience here today is that

503
00:37:08.199 --> 00:37:12.920
your data warehouse, your data marge, the things that you've worked very hard

504
00:37:13.000 --> 00:37:19.760
on need to be a crucial and
foundational component of your AI model, and

505
00:37:19.840 --> 00:37:22.840
they should be front and center as
a trusted source. That is the primary

506
00:37:22.880 --> 00:37:27.639
source that you want to use,
is something like a data warehouse, because

507
00:37:27.679 --> 00:37:31.079
you have governance, because you have
all this attention that's been paid to the

508
00:37:31.159 --> 00:37:37.119
model. And remember, the data
of your organization reflects the organization itself,

509
00:37:37.719 --> 00:37:40.559
you know. And I think we
could see some really interesting things happening of

510
00:37:40.679 --> 00:37:45.119
dynamically generated data models that look at
the data and the flow of data and

511
00:37:45.159 --> 00:37:51.119
go, you know, maybe you
should maybe you should reconfigure your data model

512
00:37:51.159 --> 00:37:54.400
to better reflect this new way that
things are working in your organization. I

513
00:37:54.480 --> 00:37:58.400
think that's coming to There are a
lot of fun things that can kind of

514
00:37:58.440 --> 00:38:01.119
spin out of this. But you
know, the other fun point i'd throw

515
00:38:01.159 --> 00:38:05.039
out here, maybe it's going to
comment from each of you, is I've

516
00:38:05.039 --> 00:38:08.719
been seeing when you connect these engines
to your data sources, there are lots

517
00:38:08.719 --> 00:38:13.119
of cool things that you can do
and think about. If you've ever done

518
00:38:13.159 --> 00:38:17.760
a Google search for instructions for a
particular app, how can I use xyz

519
00:38:17.960 --> 00:38:22.280
app? And you get something that
says, okay, step one, go

520
00:38:22.320 --> 00:38:24.320
to the file and click on the
red button. And you go there and

521
00:38:24.360 --> 00:38:27.599
there's no red button. You're like, all right, what is this talking

522
00:38:27.599 --> 00:38:30.840
about? Because it's an old version. It's an old version of the previous

523
00:38:30.880 --> 00:38:35.119
app or the previous version of the
app. It is a very difficult problem

524
00:38:35.159 --> 00:38:37.920
to solve, or at least has
been historically, because you don't control Google

525
00:38:37.920 --> 00:38:40.840
search engines, right, you don't
control that stuff, and so they're just

526
00:38:40.840 --> 00:38:45.079
going to find old stuff that is
difficult to manage. Well, if you

527
00:38:45.119 --> 00:38:50.000
do this correctly, the large language
model can sense like change data capture when

528
00:38:50.039 --> 00:38:52.599
something has changed, and when the
source file has changed, it goes Aha,

529
00:38:52.840 --> 00:38:57.800
update and let's go grab that stuff
dynamically. So you start thinking about

530
00:38:57.880 --> 00:39:01.880
that's really powerful stuff because when you
make the change to the system of record,

531
00:39:02.159 --> 00:39:07.360
it's almost instantaneously reflected in the large
language model that you're using to interact

532
00:39:07.440 --> 00:39:10.000
with the environment. But the closing
thoughts from U Steve, what do you

533
00:39:10.000 --> 00:39:14.280
think? Yeah, I just want
to comment on that. So databases have

534
00:39:14.400 --> 00:39:19.360
things like indexes and materialized views,
and we have something called projections, right,

535
00:39:19.960 --> 00:39:23.199
projections. What they allow you to
do is based on your queries,

536
00:39:24.480 --> 00:39:29.639
how can I optimize the data?
So that is all aged I'd driven now

537
00:39:29.679 --> 00:39:31.079
for a lot of companies out there, for Vertica and for some of the

538
00:39:31.119 --> 00:39:37.480
other database companies that exist. So
I'm watching as an AI, I'm watching

539
00:39:37.480 --> 00:39:39.480
the queries that take place, and
I'm saying, you know, if I

540
00:39:39.719 --> 00:39:45.920
had this materialized view, or if
I had index configured this way, I

541
00:39:45.960 --> 00:39:49.800
could switch it around and I could
run that theory that query ten times faster.

542
00:39:50.440 --> 00:39:53.639
Some of that functionally exists today.
That's something that we're always looking to

543
00:39:53.639 --> 00:39:58.639
build out, you know, for
optimizing speed. So yeah, I mean

544
00:39:58.719 --> 00:40:02.960
there's that component too. Yeah,
And I think that you can also again

545
00:40:04.360 --> 00:40:09.000
dynamically provision these things. So that's
the other fun thing my buddy Loo Simon

546
00:40:09.119 --> 00:40:13.679
was mentioning, is he's like,
think about this. Historically, you had

547
00:40:13.760 --> 00:40:16.920
to learn to speak computer in order
to talk to your computer. You had

548
00:40:16.960 --> 00:40:21.599
to learn the structured query language,
you had to learn Python, you had

549
00:40:21.599 --> 00:40:25.599
to learn some language to be able
to communicate with this machine. You don't

550
00:40:25.639 --> 00:40:30.480
have to do that anymore. Now
you can use natural language to communicate with

551
00:40:30.480 --> 00:40:32.519
these things. And I mean,
I'm telling you this prompt engineering stuff,

552
00:40:34.039 --> 00:40:37.760
it's really it's taking off as well
it should, and it's going to be

553
00:40:37.800 --> 00:40:40.400
amazing when we hit what's called interactive
AI, which I think is really the

554
00:40:40.440 --> 00:40:45.039
next big phase that's coming, and
it's going to be wild because if you

555
00:40:45.119 --> 00:40:49.800
let these intelligent bots loose on your
environment, we'll start I absolutely agree with

556
00:40:49.840 --> 00:40:52.280
that. I think the next big
thing, the next big snowflake, the

557
00:40:52.320 --> 00:40:55.760
that next big database if you will, or analytics engine that comes to market

558
00:40:55.840 --> 00:41:00.719
that solves that problem, that says
we're going to be really simple. You're

559
00:41:00.760 --> 00:41:05.360
going to type natural language queries and
we're not going to care at all about

560
00:41:06.679 --> 00:41:10.719
SQL or Sparkle or any kind of
language cipher. We're going to answer that

561
00:41:10.800 --> 00:41:15.000
query based on that language that you
enter in. I think the company that

562
00:41:15.119 --> 00:41:20.039
does that is going to succeed and
be the next big thing. Yeah,

563
00:41:20.119 --> 00:41:23.039
I think you're right. Well,
folks, this has been an absolute blast

564
00:41:23.519 --> 00:41:28.679
talking to two experts, Steve stars
Field of open text Vertica look them up

565
00:41:28.679 --> 00:41:32.719
on LinkedIn, and Eugene Burk of
Digital Strategies Group. Things are changing very,

566
00:41:32.840 --> 00:41:37.519
very rapidly. And you know,
one of the fun quotes I heard

567
00:41:37.800 --> 00:41:42.599
the other day with respect to understanding
machine learning and artificial intelligence and where it's

568
00:41:42.599 --> 00:41:47.800
all going was learning meaning human learning, meaning we as humans need to learn

569
00:41:47.880 --> 00:41:51.639
how these things work. And the
way you do that is by playing with

570
00:41:51.679 --> 00:41:53.400
them. Send me an email info
at inside analysis dot com. You've been

571
00:41:53.400 --> 00:42:00.199
listening to Inside Analysis and now it's
time for today's podcast Bonus, in which

572
00:42:00.320 --> 00:42:07.559
host Eric Cavana talks about the transformative
impact of large language models such as chin

573
00:42:07.599 --> 00:42:14.119
jept and other projective analytics tools on
the future of data analysis and decision making

574
00:42:14.199 --> 00:42:17.840
in the industry. All right,
ladies and gentlemen, Hello and welcome to

575
00:42:17.880 --> 00:42:23.079
this virtual summit on Inside Analysis or
truly Eric Kavanaugh here, I can never

576
00:42:23.119 --> 00:42:28.920
miss an opportunity to promote future proof
of the world's first made for TV webinar

577
00:42:29.199 --> 00:42:32.119
series. Very excited about that.
Check your local listenings. We're now in

578
00:42:32.239 --> 00:42:37.159
Washington, DC and Silicon Valley and
Los Alamos and lots of other fun places,

579
00:42:37.519 --> 00:42:40.960
So check your local listings or hop
onto YouTube to see past shows.

580
00:42:42.280 --> 00:42:45.519
Let's dive right in data fabric versus
data mesh. Cut from the same cloth,

581
00:42:46.440 --> 00:42:50.840
Yes, indeed, so let's talk
about what this really comes from,

582
00:42:50.880 --> 00:42:53.320
and that's the modern data stack.
I'm sure many of you've heard this concept,

583
00:42:53.360 --> 00:42:57.719
the modern data stack. One of
my favorite lines about this is that

584
00:42:57.760 --> 00:43:00.320
we kind of sacrifice state at the
altar of sk And what I mean by

585
00:43:00.360 --> 00:43:07.280
that is we broke apart all the
different component parts of a database into separate

586
00:43:07.360 --> 00:43:12.280
layers for storage, for integration,
for processing, analytics orchestrations, some antics,

587
00:43:12.280 --> 00:43:16.079
governance, artificial intelligence, machine learning, of course, security, and

588
00:43:16.400 --> 00:43:20.480
well guess what. All of that
can be done inside of a database.

589
00:43:20.800 --> 00:43:25.039
But the modern data stack really attempted
to solve for scale issues, to be

590
00:43:25.079 --> 00:43:29.760
able to scale out any one of
these component parts, to scale out and

591
00:43:29.760 --> 00:43:32.119
then to scale back down. That's
what you want, that's the optimal scenario,

592
00:43:32.360 --> 00:43:36.400
because I'm sure many of you recalled
back in the day before we had

593
00:43:36.440 --> 00:43:39.400
something like the modern data stack.
You then had the provision for the highest

594
00:43:39.440 --> 00:43:44.039
workload irrespective of whatever workload you had
or what your budget was going to be.

595
00:43:44.559 --> 00:43:46.480
And at times when you had peak
usage, that was okay. But

596
00:43:46.519 --> 00:43:50.239
when you don't have peak usage,
you're spending a lot of money that you

597
00:43:50.280 --> 00:43:54.639
don't really have to spend. So
that's kind of where the that's kind of

598
00:43:54.639 --> 00:44:00.679
where this thing came from. I
panelists is having trouble getting in, so

599
00:44:00.800 --> 00:44:05.079
let me try to multitask while I'm
talking to you here. But basically what's

600
00:44:05.079 --> 00:44:12.039
happening here is you have this situation
where we're trying to be able to again

601
00:44:12.679 --> 00:44:15.119
leverage the power of compute wherever it
is needed. So if I need to

602
00:44:15.280 --> 00:44:17.719
ingeest a ton of data, that's
what I do. If I need to

603
00:44:17.760 --> 00:44:22.159
process a bunch of data, that's
what I do. If I need to

604
00:44:22.199 --> 00:44:24.400
scale out the governance components of the
equation here, then maybe that's what I

605
00:44:24.440 --> 00:44:29.119
need to do. So that's where
the modern data stack came from. But

606
00:44:29.280 --> 00:44:34.920
there are a lot of component parts
that well, they complicate things because anytime

607
00:44:34.960 --> 00:44:37.960
you have multiple parts, while there
are connections between all these parts and that

608
00:44:38.000 --> 00:44:42.599
can slowly but surely cause you some
trouble, so you want to watch out

609
00:44:42.639 --> 00:44:45.920
for that, and that's one of
the downsides. But let's kind of dive

610
00:44:45.000 --> 00:44:49.960
deeper in. So what is the
data fabric. It is a substitute for

611
00:44:50.000 --> 00:44:52.840
a database. It's supposed to be
more flexible, more versatile, more durable,

612
00:44:53.199 --> 00:44:57.239
faster, better, easier to govern. All that fun stuff. Sounds

613
00:44:57.239 --> 00:45:00.199
great. Is it easier to manage? No, it's not easier to manage.

614
00:45:00.239 --> 00:45:05.159
It's going to be significantly harder to
manage than a single database. Right,

615
00:45:05.199 --> 00:45:08.000
And I've been joking to myself our
DBA is going away. We have

616
00:45:08.119 --> 00:45:13.519
DFA's data fabric administrators. I don't
think that's going to happen. I think

617
00:45:13.559 --> 00:45:17.519
you're still going to have data based
administrators. Really, data engineers is the

618
00:45:17.599 --> 00:45:22.719
key. So data engineers are sort
of the new DBAs, if you will.

619
00:45:22.800 --> 00:45:24.880
So debas will still be around,
they're just doing slightly different things.

620
00:45:25.360 --> 00:45:30.599
So what else is a big part
of the whole world of data fabric is

621
00:45:30.639 --> 00:45:34.000
this whole concept of automation. Right, That's what we really want to be

622
00:45:34.000 --> 00:45:38.639
able to do is automate things.
And so we're automating various components of integration.

623
00:45:38.880 --> 00:45:45.519
So think preprocessing for example, think
monitoring for usage patterns and then provisioning

624
00:45:45.800 --> 00:45:50.719
additional access at a certain time,
like maybe at the end of the week,

625
00:45:51.000 --> 00:45:52.039
maybe at the end of the month, for example, when you can

626
00:45:52.119 --> 00:45:57.239
have a clothes to do. Situations
like that, you want to be able

627
00:45:57.280 --> 00:46:00.440
to get additional resources. Well,
with automation of a data fabric, you

628
00:46:00.480 --> 00:46:05.119
can pre provision, you can pre
process data, and that's a big part

629
00:46:05.159 --> 00:46:08.599
of what facilitates the data fabric's ultimate
goal, which is to make life a

630
00:46:08.639 --> 00:46:14.039
lot easier for the consumption of data, whether for people by people or for

631
00:46:14.199 --> 00:46:16.280
machines, whether from machine learning,
et cetera. You want to be able

632
00:46:16.280 --> 00:46:21.559
to detect these patterns, and that's
actually a really strong use case for machine

633
00:46:21.639 --> 00:46:25.159
learning, because even though we might
think that we're not very predictable as human

634
00:46:25.199 --> 00:46:30.679
beings, the truth is we are
very predictable, and our behavior patterns can

635
00:46:30.760 --> 00:46:35.880
easily be ascertained, understood, and
then codified by machines. And so that's

636
00:46:35.880 --> 00:46:38.800
a big part of data fabric is
to watch for patterns of usage and then

637
00:46:38.840 --> 00:46:44.519
be able to pre provision pre processed
data, for example, to do some

638
00:46:44.639 --> 00:46:49.360
work before someone shows up such that
it's already ready to go. So I

639
00:46:49.400 --> 00:46:52.760
do want to mention one fun thing
about data fabrics. So in companies,

640
00:46:52.840 --> 00:46:57.519
according to Gartner, in the data
fabric space was Talent. Many of you

641
00:46:57.559 --> 00:47:00.400
may know Talent. Lots of companies
use Talent. It was an open source

642
00:47:00.559 --> 00:47:06.280
company Talent. Open Studio has been
really an open source starward for the last

643
00:47:06.639 --> 00:47:09.760
gosh twenty years almost. I think
it was around two thousand and three or

644
00:47:09.840 --> 00:47:14.079
so that they started to take off. I remember talking to them in two

645
00:47:14.079 --> 00:47:16.320
thousand and five when I worked for
the Data Warehousing Institute. Well, Click

646
00:47:16.400 --> 00:47:22.440
bought Talent. The company Qlik sorry, Click bought talent. Click of course

647
00:47:22.519 --> 00:47:28.920
is a business intelligence platform, and
they finally finished their acquisition of Talent a

648
00:47:28.960 --> 00:47:32.000
number of months ago, and guess
what happened. They announced that the open

649
00:47:32.079 --> 00:47:37.119
source version of open studio is going
bye bye, that's going away. Well,

650
00:47:37.159 --> 00:47:39.519
as I said, they were a
leader in the data fabric space.

651
00:47:39.559 --> 00:47:44.199
So what does this mean about the
future of data fabric sharp answer is I

652
00:47:44.239 --> 00:47:47.679
don't really know. And you can
look up online you'll find lots of commentary

653
00:47:47.719 --> 00:47:52.480
about this. It is a hot
topic for sure, but the bottom line

654
00:47:52.519 --> 00:47:55.559
is it is going to be closed
source from now on, so now branded.

655
00:47:55.559 --> 00:48:00.239
There are lots of integration vendors that
are not open source. Informatic is

656
00:48:00.280 --> 00:48:02.360
not open source, Matillion is not
open source. Ab an Issue is not

657
00:48:02.400 --> 00:48:06.440
open source. Lots of companies in
the integration space are not open source.

658
00:48:06.760 --> 00:48:08.880
But Talent was, and it was
very well known for that. It was

659
00:48:08.960 --> 00:48:13.920
kind of like the Lancelot of open
source. Right, the strongest night in

660
00:48:14.000 --> 00:48:16.519
King Arthur's court, if you will, was Talent and that is now not

661
00:48:16.679 --> 00:48:20.679
open source. It's all going to
be closed source. What does that mean

662
00:48:20.719 --> 00:48:23.360
for the future of data fabric I'm
not entirely sure, but it's probably not

663
00:48:23.760 --> 00:48:28.800
the best news. Thanks so much
of your time you've been listening to Inside

664
00:48:28.840 --> 00:48:32.719
Analysis. This segment brought to you
by Christmas and The Fat Greek in Ukaipa.

665
00:48:32.880 --> 00:48:36.880
Still have some Christmas shopping or gifts
to buy for that special person,

666
00:48:37.239 --> 00:48:40.800
business, associate, or friend.
Nothing says love like a Fat Greek food

667
00:48:40.840 --> 00:48:45.199
certificate. Nick Chris and their families
at the Fat Greek want you to know

668
00:48:45.480 --> 00:48:49.679
about their holiday gift certificates. How
you can also get whole holiday meals for

669
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the family of friends too quick,
easy and affordable, and you get money

670
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back too. You could see more
about the Fat Greek on the big Ukypa

671
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led monster signs on the ten Freeway. The gateway to u Kaipa. Fat

672
00:49:00.719 --> 00:49:06.239
Greek is your holiday relief station.
Kick your feet up and enjoy the holidays

673
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with all kinds of Greek comfort food
to take that load off your feet,

674
00:49:08.679 --> 00:49:13.000
and you might want to partake in
an adult beverage at their full cocktail bar.

675
00:49:13.239 --> 00:49:15.239
You can also catch a game on
their big eighty five inch TV too.

676
00:49:15.360 --> 00:49:19.920
The Fat Greek in Ukaipa on Ukuipa
Boulevard across from the golf course near

677
00:49:19.960 --> 00:49:22.719
oaklenn Boulevard, open every day except
Tuesdays. You can pre order a holiday

678
00:49:22.760 --> 00:49:28.519
meal at gofat Greek dot com.
That's gofat Greek dot com and Happy Holidays

679
00:49:28.519 --> 00:49:30.159
from the Fat Creek. And while
you're at it, don't forget to say

680
00:49:30.239 --> 00:49:43.840
ohpah this holiday season. Was your
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help with dense Allmagic Paint and Body
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They're highly trained staff and certified technicians
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the business and treat each car as
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Paint and Body Collision Centers are family
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and tools to ensure optimum results.
They use the latest technology in computerized color

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matching and specialize in frame repairs with
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certified and they have four locations to
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Collision Centers offer rental car assistance with
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and their work has a lifetime guarantee. All Magic Paint and Body Collision Centers

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are in Narco, East Vale,
Marina Valley and in Fontana. Call them

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at one eight hundred and sixty one
Magic. That's one eight hundred sixty one

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Magic. All Magic Paint and Body
Collision Centers one eight hundred sixty one Magic

693
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All Magic Paint and Auto Bodies says, drive carefully. This segment is sponsored

694
00:50:47.159 --> 00:50:52.039
by Dickie's Barbecue, now in you
Kaipa at three point thirty five sixty two

695
00:50:52.119 --> 00:50:55.760
you Kuipa Boulevard in the Bomb Shopping
Center Dicky's Barbecue where you can get sauced

696
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with five delicious barbecue sauces. Well
the holidays, there's the Dickies holiday feast

697
00:51:01.480 --> 00:51:07.320
options everything you need for a festive
gathering with delicious, hassle free meals if

698
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you just eat the serve. Whatever
your needs are, they have the perfect

699
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option including the complete feest, the
dinner fees or the single holiday meats and

700
00:51:15.480 --> 00:51:20.480
sides available for pickup and delivery from
Dickies. And there's no charge for kids

701
00:51:20.519 --> 00:51:23.280
on Sundays. In fact, the
kids get free ice cream. Dickie's Barbecue

702
00:51:23.440 --> 00:51:28.880
now open in Yukaipa at three point
thirty five sixty two you Kaipa Boulevard and

703
00:51:28.920 --> 00:51:37.679
the Bond Shopping Center Dicky's Barbecue.
Whatever your needs are. This segment brought

704
00:51:37.719 --> 00:51:42.960
to you by Christmas and the Fat
Greek in U Kaipeon. Still have some

705
00:51:43.039 --> 00:51:46.000
Christmas shopping or gifts to buy for
that special person, business, associate or

706
00:51:46.119 --> 00:51:51.679
friend. Nothing says love like a
Fat Greek food certificate. Nick Chris and

707
00:51:51.719 --> 00:51:54.599
their families at the Fact Creek want
you to know about their holiday gift certificates.

708
00:51:54.719 --> 00:51:59.360
How you can also get whole holiday
meals for the family or friends too

709
00:51:59.679 --> 00:52:02.760
quick, easy and affordable, and
you get money back too. You could

710
00:52:02.760 --> 00:52:07.599
see more about the Fat Greek on
the Big Ukaypa led Monster signs on the

711
00:52:07.639 --> 00:52:12.480
ten Freeway at the gateway to Ukaipa. Fat Greek is your holiday relief station.

712
00:52:12.760 --> 00:52:15.280
Kick your feet up and enjoy the
holidays with all kinds of Greek comfort

713
00:52:15.280 --> 00:52:19.239
food to take that load off your
feet, and you might want to partake

714
00:52:19.320 --> 00:52:22.599
in an adult beverage at their full
cocktail bar. You can also catch a

715
00:52:22.639 --> 00:52:25.039
game on their big eighty five inch
TV too. The Fat Greek in ukaipe

716
00:52:25.159 --> 00:52:30.119
on Ukuipa Boulevard across from the golf
course near Oaklen Boulevard, open every day

717
00:52:30.119 --> 00:52:34.079
except Tuesdays. You can pre order
a holiday meal at gofat Greek dot com.

718
00:52:34.119 --> 00:52:37.079
That's gofat Greek dot com and Happy
Holidays from the Fat Creek. And

719
00:52:37.159 --> 00:52:40.800
while you're at it, don't forget
to say opah. This holiday season.

720
00:52:44.400 --> 00:52:49.559
Hill's Country Kitchen is now open for
business. Hills Country Kitchen the newest restaurant

721
00:52:49.559 --> 00:52:53.079
in Ukaipa at the corner of fifteenth
in Ukaipa Boulevard across from Craft and Hills

722
00:52:53.119 --> 00:52:57.679
College, located in the collection at
Craft and Hills Shopping Center, along with

723
00:52:57.840 --> 00:53:01.800
Laser Legacy, the original Rosie Hills
Country Kitchen, where you're always welcomed.

724
00:53:01.960 --> 00:53:08.000
Hills has the recipe for delicious breakfast, reasonably priced lunches an amazingly scrumptious sninner.

725
00:53:08.079 --> 00:53:12.960
Hills Country Kitchen in Yukaipa is now
open for a breakfast and lunch an

726
00:53:13.039 --> 00:53:17.119
amazingly scrumptious sninner where you're always welcomed. Hills has the recipe at the corner

727
00:53:17.159 --> 00:53:30.079
of fifteenth and Ukaipa Boulevard, across
from Craft and Hills Colin. Redlands.

728
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Auto Electric reminds everyone that the blood
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729
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Somedate that someone might be a friend, a loved one, or even

730
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you, so please give blood and
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731
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courtesy of Redlands Auto Electric at one
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732
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Known for quality, integrity and knowledgeable
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733
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ninety two four seven seven six Redlands
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734
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This segment sponsored by the All RAMS
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735
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the RAMS Ultimate One Stop Shop at
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736
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crafton Hills Avenue, just across from
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737
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738
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739
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When you buy gas inside the one
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743
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Rams is now open at twenty ninety
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746
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RAMS for their community spirits and sponsoring
this radio station. RAMS in mentone is

747
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waiting for you. As a small
business owner decision maker looking to start a

748
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new business. Are you frustrated with
having to handle all the administrative business for

749
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your company, a constant turnover,
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750
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running and out of trouble while you
are trying to grow your company. You

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can't do all this paperwork and expand
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752
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your company with chief financial officer services
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financial statement preparation and analysis services,
loan package preparation, payroll marketing services,

754
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notary services, new business formations,
business liability insurance services, and an

755
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Call Executive Services at eight hundred seven
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handle all of your companies administrative issues
and problems. Call eight hundred and seven

760
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oh seven fourteen twenty seven or visit
the Executive Services website www dot xscbs dot

761
00:56:05.119 --> 00:56:15.400
com for more information. KCAA Loma
Linda, The Legacy KCAA ten fifty am

762
00:56:15.599 --> 00:56:20.760
and Express one oh six point five
FU. You're listening to The Inland Talk

763
00:56:20.800 --> 00:56:24.599
Express ten fifty am and one o
six point five FM. Ky Loma Linda,

764
00:56:24.920 --> 00:56:38.079
you're listening to an encore presentation of
this program KCAA The Inland Talk Express.

765
00:56:39.320 --> 00:56:45.119
Welcome to the Fabulous Lifestyle radio talk
show. I'm your host, Sherry

766
00:56:45.199 --> 00:56:52.920
Marie robi Rosa here on kcaa broadcasting
network. We're affiliated with CNBC and NBC

767
00:56:53.119 --> 00:57:00.000
News and Sports, where we cover
over five million households in Greater Los Angeles.

768
00:57:00.719 --> 00:57:04.599
If you've missed our previous shows,
you can watch us on our TV

769
00:57:04.760 --> 00:57:10.679
streaming channels, distributions on Roku TV, Amazon fireTV, and the Android app.

770
00:57:12.199 --> 00:57:16.719
Just subscribe to the Building Solid Foundations
channel. Hello friends, I'm the

771
00:57:16.760 --> 00:57:22.159
fashion host for the Fabulous Lifestyle radio
show. My name again is Sharry Marie

772
00:57:22.400 --> 00:57:29.440
rob Rosa, also known as Mommy
Majesty. I'm a life and fitness coach,

773
00:57:29.920 --> 00:57:34.960
a fashion designer and stylist, an
author and speaker, and an interior

774
00:57:35.039 --> 00:57:39.360
designer, and the mother of twelve
children. Yes you heard that right,

775
00:57:39.920 --> 00:57:45.960
twelve children. I'm also the CEO
of Mommy Majesty Fitness, where I love

776
00:57:46.079 --> 00:57:52.960
to coach women through my signature program
that I created and used after each of

777
00:57:52.039 --> 00:57:58.679
my twelve pregnancies to lose sixty to
eighty pounds and to get my hourglass shape

778
00:57:58.760 --> 00:58:04.679
back twelve times. It's my heart's
desire to empower women to show up as

779
00:58:04.760 --> 00:58:10.079
the most beautifully fit, stylish,
confident version of themselves, starting from the

780
00:58:10.199 --> 00:58:15.199
inside out. So that they can
achieve all their dreams and goals in this

781
00:58:15.360 --> 00:58:20.760
life. I'm so excited to be
back here with you again today to dive

782
00:58:20.840 --> 00:58:24.519
into one of my favorite topics,
the world of fashion. Today, we're

783
00:58:24.559 --> 00:58:30.480
going to be discussing part three of
our series, How to Create Your Personal

784
00:58:30.559 --> 00:58:35.079
Style Portrait. Then after the commercial
break, we're going to hear from our

785
00:58:35.199 --> 00:58:40.639
very special guest, Latasha Finnel.
She is the CEO of Boss Lady Bling

786
00:58:40.840 --> 00:58:46.239
Blingy Jewelry and she creates some beautiful
pieces, so you won't want to miss

787
00:58:46.280 --> 00:58:52.079
hearing from her. So we're going
to discuss part three of our series,

788
00:58:52.199 --> 00:58:57.639
How to Create Your Personal Style Portrait. But first I wanted to recap Part

789
00:58:57.679 --> 00:59:00.400
one and Part two for those of
you who may have missed those episodes.

790
00:59:01.119 --> 00:59:07.599
So in part one, we discussed
our mindset and we covered five of the

791
00:59:07.639 --> 00:59:13.679
most common negative thoughts that keep women
from believing that they are able to create

792
00:59:13.679 --> 00:59:17.159
their own style. The first one
is I have to lose weight before I

793
00:59:17.199 --> 00:59:22.599
can be stylish, and we discussed
while that's not true, that you can

794
00:59:22.599 --> 00:59:28.719
be beautiful and stylish at every weight
and every age. And the second thought

795
00:59:28.960 --> 00:59:32.199
was that it's going to take too
much time to be stylish, and we

796
00:59:32.280 --> 00:59:37.480
discuss while that is true at the
beginning, it takes a little time to

797
00:59:37.559 --> 00:59:40.880
figure out your style and to curate
your wardrobe, but after that, you're

798
00:59:40.920 --> 00:59:46.039
going to save yourself tons of time
getting ready in the future. And the

799
00:59:46.119 --> 00:59:52.400
third negative thought was that it's going
to be too expensive, and we discuss

800
00:59:52.480 --> 00:59:58.360
why that is not true because there
are many beautiful, stylish pieces at every

801
00:59:58.440 --> 01:00:02.840
price point available to all of us. And the fourth thought is that people

802
01:00:02.880 --> 01:00:08.000
will judge me if I show up
looking beautiful and stylish, and we discussed

803
01:00:08.000 --> 01:00:08.440
that while that