WEBVTT

1
00:00:00.040 --> 00:00:03.919
For trend from this year's nearly three
percent average to two point one percent in

2
00:00:04.000 --> 00:00:08.679
twenty twenty four. I'm Chris Karagio, NBC News Radio. You're on board

3
00:00:08.759 --> 00:00:15.080
caseaa's Inland Extress KCAA. Come Linda
ten fifty am, the station that needs

4
00:00:15.119 --> 00:00:26.079
notice. Year behind the information economy
as a ride. The world is teeming

5
00:00:26.120 --> 00:00:32.039
with innovation as new business models reinvent
every industry industry. Inside Analysis is your

6
00:00:32.200 --> 00:00:37.039
source of information and insight about how
to make the most of this exciting new

7
00:00:37.119 --> 00:00:42.640
era. Learn more at inside analysis
dot Comsideanalysis dot com. And now here's

8
00:00:42.679 --> 00:00:51.240
your host, Eric Kavanaugh. Yes, all right, both, hello and

9
00:00:51.600 --> 00:00:55.960
welcome back to the only coast to
ghost radio show that's all about the information

10
00:00:56.000 --> 00:00:58.640
economy. And now, of course
we're part of a TV show called future

11
00:00:58.719 --> 00:01:02.320
Proof. It's Inside That Analysis,
or as Truly. Eric Cavanaugh is here

12
00:01:02.359 --> 00:01:06.719
with an all star panel. We've
got Steve Sarrosfield from Open Text Vertica on

13
00:01:06.799 --> 00:01:11.200
the line and my good buddy Eugene
Burt from Digital Strategies Group. We're to

14
00:01:11.200 --> 00:01:15.760
talk about data mesh versus data fabric
cut from the same cloth. Short answer

15
00:01:15.799 --> 00:01:22.560
is yes. So for our broad
listening audience out there on the radio data

16
00:01:22.560 --> 00:01:26.560
fabric, data mesh. These are
relatively new concepts, and really they're just

17
00:01:26.079 --> 00:01:30.560
they're sort of extrapolations of the database. We all know. A database runs

18
00:01:30.599 --> 00:01:36.560
basically every application that there is out
there. Databases has been around for decades

19
00:01:36.640 --> 00:01:40.000
now, I mean really for probably
sixty years if you go all the way

20
00:01:40.000 --> 00:01:44.000
back, maybe close to seventy years, we've had database technology, and there

21
00:01:44.000 --> 00:01:48.719
have been tremendous advances in the database
space, especially over the ten twelve years

22
00:01:48.799 --> 00:01:53.640
leading up till today. So I
remember when the hell Haddoup thing came around.

23
00:01:55.000 --> 00:01:57.519
So some of you may know,
there's a concept, a construct called

24
00:01:57.599 --> 00:02:02.480
Haddoup and base. That was how
Yahoo would index the web. And then

25
00:02:02.519 --> 00:02:07.359
there was open sourced and a number
of companies jumped on it, like Cloudera

26
00:02:07.400 --> 00:02:10.000
and Horton Works. A company called
Matt Bar jumped on it. Matt Bar

27
00:02:10.120 --> 00:02:15.159
got absorbed by the borg he his
HP a number of years ago. Cloud

28
00:02:15.199 --> 00:02:19.759
are still around, but they absorbed
Horton Works, which was odd because they

29
00:02:19.759 --> 00:02:23.039
were staunched, I mean, nasty
competitors, and then they sort of merged.

30
00:02:23.400 --> 00:02:29.439
They're doing some interesting things still,
but that was that wasn't the beginning

31
00:02:29.479 --> 00:02:32.000
of the database explosion, because before
that you had some other things happening.

32
00:02:32.080 --> 00:02:36.360
Right, You've got my sequel as
a database. You've got Mango DP,

33
00:02:36.560 --> 00:02:39.280
you've got couch Base, You've got
all these different open source databases. You

34
00:02:39.319 --> 00:02:45.280
have cloud databases. Now Amazon has
their own Dynamo dB. There are all

35
00:02:45.280 --> 00:02:49.919
these different databases. Well, on
our show today we have Steve from a

36
00:02:49.960 --> 00:02:53.639
company called Vertica. Well, the
product called Vertica is now part of OpenText.

37
00:02:53.360 --> 00:02:59.439
I remember interviewing doctor Michael Stonebreaker back
in two thousand and five. I

38
00:02:59.479 --> 00:03:04.159
believe it was when he was just
rolling out Vertica. And the reason they

39
00:03:04.199 --> 00:03:07.680
have that name Vertica is because it's
vertical for column orientation. So a lot

40
00:03:07.719 --> 00:03:10.520
of people know if you're going to
excel, you look at a spreadsheet,

41
00:03:10.719 --> 00:03:15.039
you have rose and columns. Well, rose typically contain lots of different types

42
00:03:15.039 --> 00:03:19.039
of data. First name, last
name, phone number, email address,

43
00:03:19.479 --> 00:03:22.879
address, all this kind of fun
stuff notes that's all in a record.

44
00:03:23.000 --> 00:03:25.039
That's one record, and then the
columns tend to be the same, or

45
00:03:25.039 --> 00:03:30.000
they should be the same of first
names, last names, phone numbers,

46
00:03:30.039 --> 00:03:36.039
etc. One reason why that's important
is because compression is easier on a column

47
00:03:36.400 --> 00:03:39.840
and so Vertica was one of the
first major column oriented databases. Wasn't the

48
00:03:39.840 --> 00:03:44.759
first, what was one of the
first sybased IQ was one before that years

49
00:03:44.759 --> 00:03:47.360
ago. It was one of the
first to say, you know what people

50
00:03:47.400 --> 00:03:52.360
want to do analytics. People want
to do analysis compression it's a big part

51
00:03:52.360 --> 00:03:55.599
of that. And being able to
rapidly slice and dice information is a big

52
00:03:55.639 --> 00:03:59.759
part of analytics. That's what we
need to be able to do. So

53
00:04:00.280 --> 00:04:02.759
Michael Stonebreaker, who invented the modern
database. By the way, if you

54
00:04:02.800 --> 00:04:06.879
look this guy up, he's amazing. Way back at MIT like fifty odd

55
00:04:06.960 --> 00:04:10.759
years ago, he came up with
the postgress and the ingress, which were

56
00:04:10.840 --> 00:04:15.319
database architectures that have now been used
by tons of different companies. Well,

57
00:04:15.319 --> 00:04:17.720
I'm telling you this backstory just to
help you understand how we got here.

58
00:04:18.040 --> 00:04:24.360
And what's fascinating is this data fabric
stuff came really after the had do movement.

59
00:04:24.399 --> 00:04:30.759
People started thinking, all right,
we have these incredibly topocryphally topographically challenging

60
00:04:30.879 --> 00:04:35.360
environments. How are we going to
be able to provision across multinational corporations with

61
00:04:35.519 --> 00:04:41.319
many, many different users, sometimes
tens of thousands of people. You'll learn

62
00:04:41.319 --> 00:04:45.759
in the database world concurrency is a
real issue, and being able to serve

63
00:04:45.879 --> 00:04:49.560
many concurrent users is a real issue
because it's like having ten thousand people come

64
00:04:49.600 --> 00:04:54.439
into your boutique shops someday. Well, you can't even fit them all in

65
00:04:54.480 --> 00:04:59.199
there. This is what happens with
concurrency. You have to purposely design your

66
00:04:59.319 --> 00:05:02.160
architecture to handle that kind of stuff. And as my buddy Mark Madson,

67
00:05:02.199 --> 00:05:05.279
who's an analyst again these days,
once told me. He said, it

68
00:05:05.319 --> 00:05:11.560
takes ten years to find the edge
case for a database that will crash it.

69
00:05:12.120 --> 00:05:15.160
Ten years like after your in production
before you finally figure out what is

70
00:05:15.199 --> 00:05:18.720
going to throw it off. All
right, this is how complex databases are.

71
00:05:18.920 --> 00:05:23.160
So data fabric is even more complicated
than that. I mean, quite

72
00:05:23.160 --> 00:05:29.199
frankly, it really is a whole
array of data pipelines and triggers and monitors

73
00:05:29.240 --> 00:05:32.240
and sensors. Now we have data
observeability these days, which is all about

74
00:05:32.639 --> 00:05:38.720
watching for upstream changes in data.
Did a field change? Is there a

75
00:05:38.759 --> 00:05:42.519
new column in this database? Did
we not get the data? Well,

76
00:05:42.560 --> 00:05:46.800
these kinds of early indicators are very
useful for data engineering teams and for analysts

77
00:05:46.839 --> 00:05:51.680
because hitherto you'd show up and start
doing your quer and you didn't get the

78
00:05:51.720 --> 00:05:54.720
data that you want. Well,
what the heck happened? I don't know.

79
00:05:55.120 --> 00:05:58.040
Let's look into it like raise a
ticket with it and wait a couple

80
00:05:58.079 --> 00:06:00.639
of weeks. Well those days half
be gone, folks, and data fabric

81
00:06:00.720 --> 00:06:03.879
is designed to be able to solve
for that and then data matches. I

82
00:06:03.920 --> 00:06:09.800
suggested, Well it is. It's
a fascinating concept. Exactly what it is,

83
00:06:10.319 --> 00:06:13.319
I don't know. We're gonna find
out, but I think I've been

84
00:06:13.319 --> 00:06:15.759
talking for a while now, so
I'm gonna go ahead and pipe down and

85
00:06:15.800 --> 00:06:19.839
throw it over to Steve Starsfield from
Open Text Vertigo. Steve, you've been

86
00:06:19.879 --> 00:06:23.879
around maybe almost as long as I
have, or about as long as I

87
00:06:23.959 --> 00:06:27.600
have, so you've seen this whole
evolution, and it's kind of amazing.

88
00:06:27.639 --> 00:06:30.480
It's kind of bewildering. But I
do feel like we're getting to a place

89
00:06:30.560 --> 00:06:35.399
now where it leads, to a
certain extent, the nuts and bolts of

90
00:06:35.439 --> 00:06:41.439
the data administration don't have to concern
the analysts and the end users who just

91
00:06:41.519 --> 00:06:44.279
want to have fun with the data. What do you think? Yeah?

92
00:06:44.319 --> 00:06:46.639
Absolutely, you know it's funny.
I think over the years, what has

93
00:06:46.639 --> 00:06:50.759
happened is the data center of gravity
has changed quite a bit, right,

94
00:06:50.839 --> 00:06:55.160
So it used to be that the
data warehouse with the center of gravity,

95
00:06:55.920 --> 00:06:59.439
but we can't do that anymore,
right, We can't just continually move all

96
00:06:59.480 --> 00:07:02.120
the data the data warehouse. So
we have things like data lakes and we

97
00:07:02.199 --> 00:07:06.399
have object stores that we can store
our data in. But the center of

98
00:07:06.399 --> 00:07:12.800
gravity has changed because the volume of
data is getting so much greater and we

99
00:07:12.839 --> 00:07:15.759
can't possibly load that in. The
number of people who want access to analytics

100
00:07:15.839 --> 00:07:19.199
has changed, and so we have
different challenges now. We have big challenges

101
00:07:19.199 --> 00:07:25.600
that we can have. And so
really what data fabric and data mash really

102
00:07:25.639 --> 00:07:30.160
represent is that change in gravity where
the data sits, whether it's you know,

103
00:07:30.480 --> 00:07:33.319
in locally, object store, on
the cloud, various places. I

104
00:07:33.360 --> 00:07:40.720
think it kind of represents accessing and
managing and using that data, not in

105
00:07:40.759 --> 00:07:44.759
one central place, right, Yeah, And that's that's a really good point.

106
00:07:44.800 --> 00:07:46.439
And you know, you bring up
on data lakes, and of course

107
00:07:46.480 --> 00:07:50.360
now we've gone from data lake to
data lake house to lake house architecture and

108
00:07:50.600 --> 00:07:54.879
you know all this stuff. And
I think the key is to remember the

109
00:07:54.920 --> 00:07:58.800
product, to focus from a business
perspective of the data products. But you

110
00:07:58.839 --> 00:08:03.199
know, I'm kind of reminded two
of the no sequel movement. So out

111
00:08:03.240 --> 00:08:07.920
of Haddub you had this sort of
no sequel which meant not SQL and then

112
00:08:07.959 --> 00:08:13.839
sometimes only SQL, right, not
only SQL. And what happened is very

113
00:08:13.879 --> 00:08:18.600
shortly after that, these no SQL
databases started strapping SEQL engines on top of

114
00:08:18.639 --> 00:08:24.279
them because guess what, SQL is
a standard, that's the structured query language.

115
00:08:24.279 --> 00:08:28.079
For those who don't know SQL,
it's a standard, and so you

116
00:08:28.199 --> 00:08:33.000
need that stuff. But I remember
when the data lake craze came about.

117
00:08:33.080 --> 00:08:35.000
I started wondering to myself, are
we making some of the stame mistakes again

118
00:08:35.080 --> 00:08:39.039
that we made last time? Where
you just throw a bunch of stuff out

119
00:08:39.080 --> 00:08:41.919
there and then okay, you hope
to be able to find it. And

120
00:08:41.960 --> 00:08:45.440
people talk about the data swamp and
things of this nature, and that's why

121
00:08:45.519 --> 00:08:48.240
you have people saying now lake house
architecture, they're coming up with more clever

122
00:08:48.279 --> 00:08:52.759
ways to be able to get access
to it. But that's really the bottom

123
00:08:52.799 --> 00:08:58.559
line is access path and efficiency.
Right, whatever your information architecture is,

124
00:08:58.600 --> 00:09:01.519
the key is you want to opt
the access path for the important users,

125
00:09:01.919 --> 00:09:05.840
and you want to be efficient and
durable and secure and all that other stuff,

126
00:09:05.879 --> 00:09:07.960
right, Steve, Yeah, I
think we don't really have an ETL

127
00:09:09.039 --> 00:09:13.000
problem or ELP problem anymore. Right, We're not trying to move the data

128
00:09:13.039 --> 00:09:16.600
around. What we're trying to do
is access it in place. You're absolutely

129
00:09:16.679 --> 00:09:20.240
right about that, Eric, And
so you know, we need to do

130
00:09:20.360 --> 00:09:22.919
some things in order to access in
place. It can be data measure data

131
00:09:22.960 --> 00:09:26.039
fabric, and you know, with
a data mash, we want to make

132
00:09:26.080 --> 00:09:31.840
sure that we have access to the
metadata so we can access that data on

133
00:09:31.919 --> 00:09:37.039
top of the data and then perform
our analysis. With a data fabric,

134
00:09:37.600 --> 00:09:41.279
that metadata may not exist, right, so we may have to create a

135
00:09:41.279 --> 00:09:45.759
semantic layer, our own semantic layer
using a graph or some other technology.

136
00:09:46.080 --> 00:09:50.360
And so again it's about accessing the
data without having to move it into a

137
00:09:50.440 --> 00:09:56.120
single consolidated data warehouse. Yeah,
that's a really good point. And the

138
00:09:56.200 --> 00:10:00.120
metadata, so I talked about that
in the semantics and data catalogs and of

139
00:10:00.200 --> 00:10:03.159
these other things. They are all
there to help us make sense of the

140
00:10:03.240 --> 00:10:09.080
data. And the other interesting development
here from a business analyst perspective is that

141
00:10:09.600 --> 00:10:13.799
in the data warehousing world, we've
stripped out all the context to be able

142
00:10:13.840 --> 00:10:18.279
to get it through thin pipes to
slow processors and be able to slice and

143
00:10:18.320 --> 00:10:20.840
dice the data. Well, you
don't have to do a lot of that

144
00:10:20.879 --> 00:10:24.440
stuff anymore. I mean, I
remember the schema on read concept coming out

145
00:10:24.720 --> 00:10:28.200
of the data lay and even that's
a bit challenging, right, because it's

146
00:10:28.639 --> 00:10:31.080
it's going to slow the process down
a little bit. You have to make

147
00:10:31.120 --> 00:10:35.720
sure you get that correct. But
the general thrust I'm throwing out here is

148
00:10:35.799 --> 00:10:41.480
that you don't necessarily need to strip
out so much context read metadata anymore to

149
00:10:41.480 --> 00:10:45.879
be able to facilitate use down the
road, right, Steve, Yeah,

150
00:10:45.919 --> 00:10:48.960
that's absolutely true. You know,
I think that metadata, that context is

151
00:10:50.000 --> 00:10:54.279
super important the organization. That kind
of describes how you use the data and

152
00:10:54.399 --> 00:10:58.120
what you want to do with the
data, and so stripping that out can

153
00:10:58.200 --> 00:11:03.600
be you know, detrimental to whatever
you want to accomplish in terms of business.

154
00:11:03.720 --> 00:11:07.440
Right, we do these things because
we want to understand our business better.

155
00:11:07.720 --> 00:11:11.360
We want to drive three areas.
Area one is we want to make

156
00:11:11.360 --> 00:11:16.200
sure that we can drive additional revenue
within the organization. We want to make

157
00:11:16.240 --> 00:11:20.519
sure that we're super efficient and using
only the servers that we need to have

158
00:11:20.759 --> 00:11:24.240
and only the processes that we need
to do, and we want to make

159
00:11:24.279 --> 00:11:26.440
sure that we're compliant. So if
someone asked us for reports, we should

160
00:11:26.440 --> 00:11:30.639
be able to go out to those
data sources very easily get access to that.

161
00:11:31.159 --> 00:11:35.080
And so you know, having that
metadata, having that sort of whatever

162
00:11:35.120 --> 00:11:39.159
it is fabric or mesh or whatever
to allow us to access that those data

163
00:11:39.159 --> 00:11:45.039
sources and perform those three things,
I think is super crucial. Yeah,

164
00:11:45.039 --> 00:11:46.840
we have got a couple of good
questions from our studio audience, by the

165
00:11:46.840 --> 00:11:50.559
way, so I'm just going to
throw this one out there. One of

166
00:11:50.600 --> 00:11:54.360
our attendees is writing, so does
that mean that users would access to data

167
00:11:54.399 --> 00:11:56.720
at the source? That is usually
not a good idea because it could put

168
00:11:56.759 --> 00:12:01.360
too much pressure on source databases.
Even with the to measure fabric. Don't

169
00:12:01.360 --> 00:12:05.519
we need to move the data to
some central location for analytics access? And

170
00:12:05.600 --> 00:12:09.600
this is a good question. But
I will say you are starting to see

171
00:12:11.360 --> 00:12:16.440
a greater focus on federated data access
and leaving the data where it sits just

172
00:12:16.480 --> 00:12:20.000
to be able to touch it as
needed. But there is a point about

173
00:12:20.320 --> 00:12:22.720
overburdening source systems. That's why we
came up with these things in the first

174
00:12:22.759 --> 00:12:26.840
place. Right we realized that s
a P e ERP was not easy to

175
00:12:26.919 --> 00:12:30.399
query, and so we pulled data
out of that put it into the warehouse.

176
00:12:30.879 --> 00:12:33.559
But what do you think about that? There is some concern, but

177
00:12:33.600 --> 00:12:37.600
I think the processes are getting more
and more elegant and efficient at being able

178
00:12:37.600 --> 00:12:39.480
to do that. What do you
think, steem, Yeah, it's a

179
00:12:39.519 --> 00:12:41.720
case by case basis. I know
for Vertico what we do is we do

180
00:12:41.799 --> 00:12:46.080
have data virtualization. So if you're, for example, you wanted to run

181
00:12:46.080 --> 00:12:50.679
important do a joint against a table
that's sitting in oracle, that's pretty easy

182
00:12:50.679 --> 00:12:52.480
for us to do. And a
lot of other databases have that too,

183
00:12:52.600 --> 00:13:00.360
So being able to go out and
access that third party is key. It

184
00:13:00.440 --> 00:13:03.799
will tax the third party system though, And what I see a lot of

185
00:13:05.200 --> 00:13:09.679
companies doing, if they're concerned about
that, is they're leveraging object stores these

186
00:13:09.759 --> 00:13:11.799
days, so they're taking a lot
of their data and just dumping it into

187
00:13:11.879 --> 00:13:20.360
an object store. That provides you
with some pretty interesting capabilities. So a

188
00:13:20.360 --> 00:13:24.440
lot of the databases, including Bertica, has this whole concept of separation of

189
00:13:24.519 --> 00:13:28.159
compute and storage. So I have
my data, it's sitting in a separate

190
00:13:28.240 --> 00:13:31.720
storage object store. I want to
do some data loading. I spin up

191
00:13:31.720 --> 00:13:35.080
three nodes. I do data loading. I want marketing to run reports,

192
00:13:35.120 --> 00:13:39.279
I spin up three nodes. It
does the reports. You know, I

193
00:13:39.320 --> 00:13:45.720
want various my dashboards throwing really fast. It's spent up five nodes for the

194
00:13:45.759 --> 00:13:48.320
CEO, and I run the reports. They're all operating on that same data,

195
00:13:50.200 --> 00:13:54.679
but we're able to access that through
one location, through one engine.

196
00:13:54.039 --> 00:13:58.840
And we see that happening a lot
in the market. It's happening within you

197
00:13:58.879 --> 00:14:01.159
know, our within our store base, and it's a kind of a new

198
00:14:01.200 --> 00:14:07.200
way of doing in architecture. Is
that whole separation of compute and storage.

199
00:14:07.000 --> 00:14:13.840
Yeah, and these are all deep
architectural determinations that get made. And kind

200
00:14:13.840 --> 00:14:18.080
of where I was going with that
comment about large language models is we don't

201
00:14:18.120 --> 00:14:20.960
really I don't really understand how they
work. But what I do know is

202
00:14:20.000 --> 00:14:24.399
they can get around all kinds of
these issues because you're dealing like with a

203
00:14:24.440 --> 00:14:30.120
multidimensional member mole app right, multidimensional
online analytical processing, the micro strategy folks,

204
00:14:30.679 --> 00:14:33.840
you want multiple different dimensions. But
these large language models have like three

205
00:14:35.120 --> 00:14:37.919
point one billion vertices or something,
and there are lots of different ways you

206
00:14:37.960 --> 00:14:41.559
can slice and dice stuff. But
again, we don't know about the veracity,

207
00:14:41.600 --> 00:14:45.919
we don't know about the clarity.
We're not sure. And of course,

208
00:14:45.919 --> 00:14:48.879
with something like a data warehouse,
you want to be really gosh darned

209
00:14:48.200 --> 00:14:52.240
sure about what you're doing, right. We want to know, you know,

210
00:14:52.279 --> 00:14:56.559
what the most current information is,
what the most trusted information is,

211
00:14:56.919 --> 00:14:58.679
and we want to be able to
use that. And so you know,

212
00:14:58.759 --> 00:15:03.159
to some extent, MDM used to
help us with that. We used to

213
00:15:03.200 --> 00:15:05.799
take all the data and create an
MDM system around that. I think this

214
00:15:05.879 --> 00:15:09.960
is the next evolution of ETL.
It's the next evolution of MDM. And

215
00:15:11.120 --> 00:15:13.960
you know, because we have such
large volumes and everyone wants to access to

216
00:15:13.960 --> 00:15:18.960
the data, data mesh data fabric
are is the next thing. Is the

217
00:15:18.000 --> 00:15:22.200
next evolution of that. Yeah,
and so you have some of those principles

218
00:15:22.200 --> 00:15:24.519
baked in, right. I mean, I think that's the key is that

219
00:15:24.559 --> 00:15:31.000
we've learned over the years. I
remember asking myself a rhetorical question, is

220
00:15:31.159 --> 00:15:35.720
MDM the next SLA? If you
think about service oriented architecture? We had

221
00:15:37.120 --> 00:15:39.759
fine grain services and coarse grained services, right, And even though no one

222
00:15:39.759 --> 00:15:46.000
really talks about SA anymore, I
think what happened is the principles of service

223
00:15:46.080 --> 00:15:50.600
oriented arch architecture kind of got baked
into how we do cloud and now that's

224
00:15:50.759 --> 00:15:52.159
just the norm. But real quick, what do you think about that?

225
00:15:52.200 --> 00:15:56.240
Steam? Yeah, I think you're
right. I think it is, you

226
00:15:56.279 --> 00:16:00.600
know, the basic way that we
do cloud. It's it's but you know,

227
00:16:00.080 --> 00:16:03.720
I can't tell you how many customers
I talked to that have, you

228
00:16:03.720 --> 00:16:07.120
know, like a pub subsystem too, where they're publishing data and subscribing to

229
00:16:07.200 --> 00:16:11.440
data via Kafka, where some of
the open source tools that are available for

230
00:16:11.559 --> 00:16:15.519
that. So you know, yeah, there is you know, this concept

231
00:16:15.519 --> 00:16:19.960
of a data bus that I think
a lot of companies deal with, including

232
00:16:21.039 --> 00:16:25.159
Kafka. That kind of helped that. Yeah, it's funny, you know,

233
00:16:25.600 --> 00:16:29.559
going down memory lane again. I
worked for Damon Consulting back in two

234
00:16:29.600 --> 00:16:33.240
thousand and one. That's when I
got into this whole space and Michael Hayston,

235
00:16:33.559 --> 00:16:36.360
super smart guy at Consultant, as
the same birthday as me, and

236
00:16:36.360 --> 00:16:40.080
we were excited about that. He
would talk about what he called an enterprise

237
00:16:40.240 --> 00:16:44.080
backplane, and what we was talking
about is what you just mentioned this bus.

238
00:16:44.159 --> 00:16:48.600
Basically it's a staging area of data
and I meant to mention this in

239
00:16:48.639 --> 00:16:53.879
my opening remarks as well, that
intelligent caching is older than the hills.

240
00:16:53.960 --> 00:16:56.720
I mean, it's something we came
up with a long time ago, and

241
00:16:56.759 --> 00:17:03.559
it's a very useful construct for being
able to facilitate access to often used data.

242
00:17:03.680 --> 00:17:07.519
Right now, how you manage that, how you construct that matters a

243
00:17:07.559 --> 00:17:11.440
lot and will have a big impact
on whether or not it works. But

244
00:17:11.519 --> 00:17:15.400
it's not a new concept, right
I mean, cache is are a huge

245
00:17:15.400 --> 00:17:19.480
part of data virtualization for example,
now, a big part of data fabric

246
00:17:19.759 --> 00:17:22.720
That's what I was talking about,
the pre processing stuff. And you know,

247
00:17:22.720 --> 00:17:26.279
the beautiful thing is machines. You
know, unless you turn them off,

248
00:17:26.319 --> 00:17:29.920
they don't sleep. And machine learning, unless you turn it off,

249
00:17:30.000 --> 00:17:33.079
is just learning and learning and learning. They're just kind of crawling around looking

250
00:17:33.079 --> 00:17:36.960
for patterns. And we humans are
pretty darn predictable if you get right down

251
00:17:37.000 --> 00:17:41.880
to it. So if you do
have a machine learning layer and it's monitoring

252
00:17:41.039 --> 00:17:45.319
usage of data, it's going to
know when the peaks and valleys are.

253
00:17:45.359 --> 00:17:48.079
And to your point earlier, you
can spin up three ohs or spin up

254
00:17:48.079 --> 00:17:49.720
four ohs, or spin up two
ohs, or whatever the case may be,

255
00:17:51.400 --> 00:17:53.240
not that you're never going to have
a hiccup again, there will always

256
00:17:53.279 --> 00:17:57.200
be hiccups, there will always be
down times and things of this nature.

257
00:17:57.240 --> 00:18:00.359
But the point is we're getting really
close. And I think that's the key

258
00:18:00.480 --> 00:18:06.319
with data fabric at least, is
it's trying to be as prepared as it

259
00:18:06.400 --> 00:18:10.079
can be for whatever data usage you're
going to need in the next hour,

260
00:18:10.200 --> 00:18:11.640
the next day, whatever the case
may be. Well, don't touch out

261
00:18:11.680 --> 00:18:19.400
to folks, will be right back
on a fantastic episode of Inside Analysis.

262
00:18:22.960 --> 00:18:30.319
Welcome back to Inside Analysis. Here's
your host, Eric Tabanaugh. All right,

263
00:18:30.359 --> 00:18:34.000
folks, back here on Inside Analysis, part of the dm Radio Broadcasting

264
00:18:34.039 --> 00:18:38.759
Network. We're talking to Steve Sarsfield
of Open Text Vertica and Eugene Burke from

265
00:18:38.839 --> 00:18:44.519
Digital Strategies Group. And Eugene,
you heard us ranting and raving about data

266
00:18:44.559 --> 00:18:48.880
fabric versus data mesh in the opening, and I had a great question around

267
00:18:48.880 --> 00:18:52.759
logical data models and where is this
is all going. I mean, you

268
00:18:52.799 --> 00:18:56.519
know, when I think about these
large language models again, they have absorbed

269
00:18:56.559 --> 00:19:02.880
far more than just text. They
have absorbed concepts, They have absorbed formulae,

270
00:19:03.079 --> 00:19:06.160
whole spreadsheets. You know, one
of my Buddies was saying, he

271
00:19:06.200 --> 00:19:08.279
goes, I just go to these
things to get the numbers of things,

272
00:19:08.640 --> 00:19:14.000
because that was absorbed as well.
Now again you do have this whole issue

273
00:19:14.039 --> 00:19:17.519
of moorings and anchors of truth as
some people call these things, and you

274
00:19:17.559 --> 00:19:19.880
have to worry about all that.
But what's your take on the data mesh

275
00:19:19.960 --> 00:19:25.359
versus data fabric religious war or is
it that big of deal at all?

276
00:19:26.200 --> 00:19:29.480
So yeah, so, Steve,
I guess I would have a two part

277
00:19:29.599 --> 00:19:37.079
question to get us started on this
segment. Are data fabric and data mesh

278
00:19:37.160 --> 00:19:42.440
twin sons of different mothers? Are
they destined to fight or do they have

279
00:19:42.519 --> 00:19:51.599
different objectives and different mountains to conquer? And how do they relate to lms

280
00:19:52.000 --> 00:20:00.319
and this kind of adoption of a
completely different paradigm or enterprise ask an answer

281
00:20:00.359 --> 00:20:04.119
computing. I can answer that if
you want. You know, I kind

282
00:20:04.119 --> 00:20:08.759
of look at data mesh and data
fabric as I look at the origin story.

283
00:20:08.839 --> 00:20:15.599
You know, every superhero has an
origin story, and I think data

284
00:20:15.640 --> 00:20:19.279
fabric and data mesh have different origin
stories. So in the case of data

285
00:20:19.359 --> 00:20:25.599
fabric, the origin story has a
lot to do with graph databases. You

286
00:20:25.640 --> 00:20:30.279
know, graph databases to some extent, they are a solution looking for a

287
00:20:30.319 --> 00:20:36.799
problem. To some extent, I
won't want to I don't want to completely

288
00:20:36.799 --> 00:20:40.039
paint them like that, but you
know, the problem that they solved,

289
00:20:40.039 --> 00:20:42.640
The one problem that they really solve
is that if you have disparate data,

290
00:20:44.079 --> 00:20:48.559
and that data is sparse, and
that data is it doesn't have any metadata

291
00:20:48.559 --> 00:20:55.960
attached to it, it's sort of
like an unknown graph. Databases do a

292
00:20:56.000 --> 00:21:00.599
really good job of building linkages between
the data. Data that's sitting in different

293
00:21:00.599 --> 00:21:06.759
files, data that's sitting in different
columns and rows, and so graft databases

294
00:21:06.799 --> 00:21:12.559
do a really good job at that. Thus, data fabric the you know,

295
00:21:14.079 --> 00:21:18.279
Eric mentioned the Hadoop model, and
I think the origin story of data

296
00:21:18.400 --> 00:21:22.240
mesh is Hadoop. You know,
we have data that's kind of sitting in

297
00:21:22.279 --> 00:21:25.039
there in the data lake. We
want to have access to it. We

298
00:21:25.039 --> 00:21:29.920
want to make sure we know probably
that the metadata is accurate, and we

299
00:21:30.039 --> 00:21:34.400
know that maybe our company grew through
acquisition, so I've got data warehouses that

300
00:21:34.720 --> 00:21:38.200
have multiple data warehouses in our organization. I've got a data lake in our

301
00:21:38.400 --> 00:21:42.680
organization, but the metadata associate with
that is probably pretty good, and we

302
00:21:42.720 --> 00:21:48.160
can sort of kind of trust it. That's data mesh and putting all that

303
00:21:48.240 --> 00:21:52.200
data together is kind of where that
comes from. So when I look at

304
00:21:52.240 --> 00:21:55.279
data fabric and I look at data
mesh, I look at those origin stories

305
00:21:55.480 --> 00:22:02.799
and it doesn't really answer though which
one I should use. And so you

306
00:22:02.839 --> 00:22:07.880
know, if you kind of turn
that around and you are a pharmaceutical company

307
00:22:07.920 --> 00:22:11.200
and you have a lot of disparate
data and the data is a little bit

308
00:22:11.279 --> 00:22:14.680
messy and it's a little bit sparse, maybe the thing to do is to

309
00:22:14.680 --> 00:22:18.160
set up a semantic layer access that
and use a data fabric. If you're

310
00:22:18.200 --> 00:22:22.799
a company that has grown to acquisition, you've got multiple data warehouses, a

311
00:22:22.839 --> 00:22:26.440
CRM system in the ERP system.
But guess what, the data is pretty

312
00:22:26.440 --> 00:22:30.680
good, pretty fit for use.
Maybe data mesh is the solution. And

313
00:22:30.759 --> 00:22:34.000
I think that's the big difference in
my head of what the differences are between

314
00:22:34.039 --> 00:22:40.480
those solutions. Eugene, you want
to comment on that, sure, I

315
00:22:40.519 --> 00:22:47.039
guess the follow on is do you
agree with some people's assessment that the data

316
00:22:47.079 --> 00:22:52.599
fabric is more it driven and a
data mesh is more business pull or business

317
00:22:52.680 --> 00:23:00.119
driven and organized around the business typology
or top top topography. So and what

318
00:23:00.400 --> 00:23:07.160
mesh is trying to solve is to
put the business back in the data driver's

319
00:23:07.200 --> 00:23:11.720
seat. Yeah, I think that's
true. And you know, one of

320
00:23:11.759 --> 00:23:15.119
the reasons for that, again is
the introduction of a graph database and a

321
00:23:15.160 --> 00:23:18.680
semantic player. Right. That's a
pretty tough thing to do. It requires

322
00:23:18.720 --> 00:23:22.480
a specialized set of knowledge that not
a lot of people have, only a

323
00:23:22.480 --> 00:23:26.119
handful of people. And I'm trying
to understand what sparkle and cipher is and

324
00:23:26.519 --> 00:23:32.119
trying to understand basically what a triple
is versus you know, a standard column

325
00:23:32.119 --> 00:23:40.480
in row. That requires specialized knowledge
that we just don't have normally in the

326
00:23:40.480 --> 00:23:45.319
normal database world. So it is
it driven. It usually has a component

327
00:23:45.400 --> 00:23:52.039
of services and technology that are bound
together that kind of work together to create

328
00:23:52.079 --> 00:23:56.640
that semantic layer. With data mesh, you know, we could almost pull

329
00:23:56.720 --> 00:24:00.079
that off if we have a good
understanding of metadata and you know, how

330
00:24:00.079 --> 00:24:03.599
to manage all of those and data
virtualization and some of the other tools that

331
00:24:03.599 --> 00:24:08.000
you might use for a data mesh. Yeah, that's you know, that's

332
00:24:08.039 --> 00:24:11.920
more of a business initiative. Yeah, thank you. You guys could comment

333
00:24:12.079 --> 00:24:15.559
like that. That's how I see
it though. Yeah, you know,

334
00:24:17.000 --> 00:24:21.480
I also have had difficulty wrapping my
head around two bowls. Right, And

335
00:24:21.480 --> 00:24:23.839
I remember when the semantic web was
going to solve all the world's problems and

336
00:24:23.880 --> 00:24:29.720
it never really kind of got there. Now there are semantic layers that you

337
00:24:29.880 --> 00:24:33.160
can use for a database, and
that's a very useful thing. It's very

338
00:24:33.160 --> 00:24:37.960
similar to data catalogs, right,
I mean, the data catalog is there

339
00:24:37.039 --> 00:24:44.960
to capture the meaning of things and
to enable business people to connect dots in

340
00:24:45.039 --> 00:24:48.599
their systems basically, right, I
mean that's what a data catalog is supposed

341
00:24:48.599 --> 00:24:52.039
to do. But again, these
things are all sort of it's just interesting

342
00:24:52.079 --> 00:24:56.000
how they're all sort of moving forward
at their own pace. Although I'd never

343
00:24:56.039 --> 00:24:57.920
heard before until now, So thank
you that the origin story of data mesh

344
00:24:59.000 --> 00:25:03.599
was had do but I did not
know that. I listen, it may

345
00:25:03.599 --> 00:25:07.000
be wrong about that. All of
these technologies, all of these technologies,

346
00:25:07.000 --> 00:25:11.160
though, you know, they kind
of rely on multiple technologies, right,

347
00:25:11.200 --> 00:25:15.240
it's the coming together of multiple technologies. So we have databases and databases that

348
00:25:15.359 --> 00:25:21.759
used to run data warehouses are having
capabilities around data mesh. Right. They're

349
00:25:21.799 --> 00:25:26.559
able to go and access data that's
outside of them and form analysis on them

350
00:25:26.839 --> 00:25:32.039
like like they couldn't ever before.
And we have query engines, you know,

351
00:25:32.119 --> 00:25:36.000
Presto and Trino and some of those
technologies. They get access data that's

352
00:25:36.000 --> 00:25:38.519
sitting in a data leake that doesn't
have any metadata that does it's not sitting

353
00:25:38.559 --> 00:25:45.400
in a database, visualization tools,
graph databases, data virtualization, data catalog.

354
00:25:45.440 --> 00:25:48.200
So a lot of those things are
coming together as technologies to form data

355
00:25:48.240 --> 00:25:53.000
mesh, data data fabric. H
Yeah, that's a really good way to

356
00:25:53.000 --> 00:25:56.920
put it. A data fabric I
think is the amalgam of all these different

357
00:25:56.920 --> 00:26:03.720
things. Yeah. It is designed
to be an efficient and thoroughly capable data

358
00:26:03.799 --> 00:26:10.119
foundation to handle whatever data uses the
business may have. Which is interesting too

359
00:26:10.200 --> 00:26:15.319
because it's almost like we're moving in
the direction of so called h TAP right,

360
00:26:15.480 --> 00:26:19.960
hybrid transactional analytical processing, which always
made me kind of wonder. There

361
00:26:21.000 --> 00:26:25.599
was this approach where a query would
come in and you could have sort of

362
00:26:25.640 --> 00:26:27.519
a sniffer there awaiting. It's like, okay, is this an analytical queer

363
00:26:27.559 --> 00:26:30.599
and operational quer and if it's operational, okay, I'll go this way,

364
00:26:30.640 --> 00:26:33.000
if then go that way, you
know, I wonder about that anytime you

365
00:26:33.079 --> 00:26:37.559
have these sort of if then statements
at the at the foundation of a database

366
00:26:37.640 --> 00:26:41.240
technology. Well, that's going to
affect performance, right. I mean,

367
00:26:41.839 --> 00:26:45.279
all this when you get right down
to it, has to do with performance.

368
00:26:45.319 --> 00:26:51.559
Can this thing perform the tasks I
wanted to perform quickly enough, efficiently

369
00:26:51.680 --> 00:26:55.759
enough, and accurately enough. And
you know that's where I see tremendous pressure

370
00:26:55.799 --> 00:27:00.759
from these llms on basically everything that's
in the data stack, everything that's in

371
00:27:00.400 --> 00:27:07.039
the information stack. Because you know
these the key is to have your embedding

372
00:27:07.079 --> 00:27:11.519
strategy and to know and that gets
back to your point Steve from earlier in

373
00:27:11.559 --> 00:27:15.480
the show that you as an organization, you need to start getting your data

374
00:27:15.519 --> 00:27:21.880
prepared for these large language models and
make sure that it's trusted, make sure

375
00:27:21.880 --> 00:27:25.759
that it's governed, understand your processes. And one of the things that you

376
00:27:25.839 --> 00:27:30.000
can use to do that is a
large language model because they're actually pretty good

377
00:27:30.440 --> 00:27:36.279
at being able to ascertain and then
articulate specific processes that you need to go

378
00:27:36.400 --> 00:27:38.720
to. You know, it's really
interesting. I was talking to Steve Lucas,

379
00:27:38.759 --> 00:27:44.640
the CEO of Boomy, former president
of sp America's he's over at MARKETO

380
00:27:44.680 --> 00:27:47.920
for a while. He was saying
when they got in there, he started

381
00:27:47.920 --> 00:27:51.480
looking at what they were seeing inside
their own systems, and he asked it

382
00:27:51.960 --> 00:27:55.200
showing me all the different versions of
order to cash that we have. It

383
00:27:55.240 --> 00:28:02.400
was like, okay, that just
started, Like holy dem yeah wow,

384
00:28:02.640 --> 00:28:06.759
So we're just now scratching the surface. Now. It doesn't mean all this

385
00:28:06.799 --> 00:28:10.920
other stuff is going to go away, at least not right away. But

386
00:28:11.000 --> 00:28:15.160
it does mean that you have to
get your data ready and that means quality

387
00:28:15.359 --> 00:28:19.039
checks, That means lineage checks,
things of that nature. What else would

388
00:28:19.079 --> 00:28:23.519
you suggest, Steve and then maybe
Eugene from your experience, how can organizations

389
00:28:23.559 --> 00:28:27.920
get ready for that? How can
you prepare your data for large language models?

390
00:28:27.960 --> 00:28:32.559
First deed? I mean A big
one for me is access and security,

391
00:28:32.680 --> 00:28:36.240
right, that is a huge one. A lot of companies have a

392
00:28:36.359 --> 00:28:41.400
PII seeing in a data storage somewhere. It may be encrypted, it may

393
00:28:41.440 --> 00:28:45.640
not be. How do you identify
that PII and make sure that you're not

394
00:28:45.759 --> 00:28:51.519
exposed as a company to all of
the potential finds that you could get around

395
00:28:51.559 --> 00:28:56.400
that. That is a regulatory mess
that could you know, you want to

396
00:28:56.400 --> 00:29:00.720
make sure that that problem is solved. We have some technologies at open Texts

397
00:29:00.759 --> 00:29:04.680
that allow you to actually go and
take a look at even free form text,

398
00:29:04.880 --> 00:29:08.680
even in video and audio, allow
you to access that and take a

399
00:29:08.720 --> 00:29:14.559
look and make sure that there's no
PII there, so that you know you're

400
00:29:14.559 --> 00:29:19.720
not vulnerable to law, lawsuits and
stuff. So security access encryption, that's

401
00:29:19.720 --> 00:29:22.799
a real key one for you know, making sure that your data is in

402
00:29:23.039 --> 00:29:30.599
order for the next generation. Yeah, Gene, that's one I can probably

403
00:29:30.599 --> 00:29:34.680
go on, but Eugene anything,
So there are few things like an lll

404
00:29:34.839 --> 00:29:41.160
M for exposing flaws in your data, and exposing one of the flaws would

405
00:29:41.200 --> 00:29:47.200
be security holes. So to your
point, if you have PHI or PII

406
00:29:48.359 --> 00:29:51.759
and the lll M has a way
to find it, it will find it,

407
00:29:52.119 --> 00:29:56.119
right, because that's what it's built
to do. And so now is

408
00:29:56.160 --> 00:30:00.680
the time to understand your I A
M R texture and to make sure that

409
00:30:00.720 --> 00:30:06.640
if you're going to use a large
language model for customer service, patient service,

410
00:30:07.200 --> 00:30:12.400
provider service, that you really understand
the pathways for accessing your highly sensitive

411
00:30:12.480 --> 00:30:18.200
data. And because you don't want
to have an audit come up to say,

412
00:30:18.440 --> 00:30:23.119
okay, here's violation, violation,
violation or God forbid of breach,

413
00:30:23.319 --> 00:30:29.160
right because you didn't adequately think through
your architecture. Yeah, so that's a

414
00:30:29.160 --> 00:30:33.079
technical component. You know, there's
there's also sort of like the operational components

415
00:30:33.079 --> 00:30:36.400
of it, right. You know
when data governance we used to talk about

416
00:30:37.440 --> 00:30:41.119
people, processes and technology, and
so the people in the processes are also

417
00:30:41.480 --> 00:30:45.119
something you need to look at.
How is data handled, is data copied?

418
00:30:45.599 --> 00:30:51.160
You know, regarding processes, and
then people who has access to it,

419
00:30:52.039 --> 00:30:55.559
how are they handling it and so
on. So again, you know,

420
00:30:55.599 --> 00:31:00.799
there is a component of data governance
to data messed data feb So the

421
00:31:00.880 --> 00:31:08.119
mixture of a wild West data culture
in llm's is quite potentially dangerous. Yeah,

422
00:31:08.160 --> 00:31:11.960
that is true. The other cool
thing here and some of the cooler

423
00:31:11.000 --> 00:31:17.839
technologies I'll go across, have this
capacity to scan your environments. Some of

424
00:31:17.839 --> 00:31:22.000
the data catalogs have this capacity,
very very useful stuff. There's a company

425
00:31:22.039 --> 00:31:26.200
it's really more in the security and
governance space Extra Hop. I haven't taken

426
00:31:26.319 --> 00:31:30.440
a brief infront of in a long
time, but what I loved about them

427
00:31:30.759 --> 00:31:33.680
is that they will scan. They
basically just siphon off your network traffic and

428
00:31:33.720 --> 00:31:38.640
then create a digital twin of your
entire information landscape to show you every database,

429
00:31:38.680 --> 00:31:44.359
every application, anything that's touching it. You see an object for that,

430
00:31:45.039 --> 00:31:47.160
and that's the kind of tech that
you can use. And of course

431
00:31:47.680 --> 00:31:49.400
LMS, I mean, like I
said, we've said before, if you

432
00:31:49.480 --> 00:31:55.920
point one at your information architecture is
going to start sucking that stuff up,

433
00:31:55.960 --> 00:31:59.119
and if you weren't careful about what
went in there, it's going to be

434
00:31:59.119 --> 00:32:01.759
a very difficulty get it out.
It's like unlearning things. It's hard to

435
00:32:01.880 --> 00:32:07.519
unlearn something you know. And I
often use the analogy of raising children to

436
00:32:07.839 --> 00:32:12.359
explain how to train your large language
model. If you've got a two year

437
00:32:12.400 --> 00:32:15.599
old, you don't want to let
her hang out with a bunch of gangsters

438
00:32:15.640 --> 00:32:17.200
in the hood for a while,
like, because they're going to absorb all

439
00:32:17.200 --> 00:32:22.240
this information, these behavioral patterns.
So you have to be careful about how

440
00:32:22.640 --> 00:32:27.279
you how you train, what access
you give to these models, just as

441
00:32:27.319 --> 00:32:30.079
you do raising your child, because
all of a sudden, your kid'll be

442
00:32:30.119 --> 00:32:31.480
reflecting things back to you that you
don't like and you're like, well,

443
00:32:31.519 --> 00:32:34.960
where did that come from? Well, I don't know, where did you

444
00:32:35.039 --> 00:32:37.640
allow this child to go? What
did you share with this child? Well,

445
00:32:38.039 --> 00:32:43.319
just funny story. One of our
babysitters watch whether our I guess three

446
00:32:43.440 --> 00:32:46.039
year old the Chucky movie, like
the Chucky the Little Killer, and we're

447
00:32:46.119 --> 00:32:49.960
just like, all right, why
did you do that? Now? Our

448
00:32:50.039 --> 00:32:52.960
kid loves these really dark, disturbing
movies. We're like, all right,

449
00:32:52.079 --> 00:32:55.440
I don't know if that was the
best move on our part. But you

450
00:32:55.440 --> 00:32:59.559
know, once it's in there,
you're not going to get it back out

451
00:33:00.200 --> 00:33:04.720
unless you like unplug the all thing
and start from scratch. That's not gonna

452
00:33:04.759 --> 00:33:07.039
be fun. That's not gonna be
fun. And I think that this is

453
00:33:07.079 --> 00:33:12.279
a major trend line that we are
going to see here over the next number

454
00:33:12.319 --> 00:33:15.799
of years. It's gonna be very
interesting. But this is what I seen.

455
00:33:15.839 --> 00:33:19.680
We'll pick this up after the break
and see what does Steve and Eugene

456
00:33:19.680 --> 00:33:22.599
think about this. But I think
most large organizations, even men they need

457
00:33:22.640 --> 00:33:27.319
to small companies are gonna pick their
poison. They're gonna choose. Okay,

458
00:33:27.400 --> 00:33:30.640
I'm gonna use BARD or I'm gonna
use open AI, or I'm gonna use

459
00:33:30.720 --> 00:33:35.319
Anthropic. I'm sure somebody's will come
out and they're going to begin this process

460
00:33:35.359 --> 00:33:39.519
of training that large language model,
that AI model on their corporate data.

461
00:33:40.000 --> 00:33:45.559
Well, that process, I promise
you is a really really important process and

462
00:33:45.559 --> 00:33:49.759
I often I'm throwing out this concept. I'm saying it's a second chance for

463
00:33:49.920 --> 00:33:52.519
data. And what I mean by
that is we've spent the last forty odd

464
00:33:52.599 --> 00:33:57.759
years doing all sorts of things to
move data, cleansed data, enrich data,

465
00:33:57.799 --> 00:34:01.640
load data, access, analyze,
parcel the stuff to get some value

466
00:34:01.680 --> 00:34:06.480
from it. And now this is
a big reset. We're going to hit

467
00:34:06.519 --> 00:34:09.320
the reset button on data. And
it's called the large language model. But

468
00:34:09.360 --> 00:34:17.800
folks, don't touch that. Delbi
right back. You're listening to Inside Analysis.

469
00:34:20.400 --> 00:34:28.360
Welcome back to Inside Analysis. Here's
your host, Eric Tabanac. All

470
00:34:28.480 --> 00:34:31.000
Right, folks, welcome back here
to Inside Analysis, part of the DM

471
00:34:31.119 --> 00:34:36.400
Radio Broadcasting Network. Your host here, Eric Kavanaugh, was Steve Sarsfield of

472
00:34:36.480 --> 00:34:40.480
openext Vertica and Eugene Burke of Digital
Strategies Group, and I wanted to throw

473
00:34:40.519 --> 00:34:45.519
this question at both of you.
It's one of our attendees is writing talking

474
00:34:45.559 --> 00:34:49.400
about logical data models. Right,
we had logical data warehouses, which was

475
00:34:49.440 --> 00:34:52.440
a sort of virtual data warehouse.
There are lots of different ways you can

476
00:34:52.480 --> 00:34:55.320
do these things. And he writes
that ldms are the semantic layer that is

477
00:34:55.360 --> 00:35:00.519
missing. I presently create ldms to
document primary key, alternate keys, business

478
00:35:00.559 --> 00:35:05.920
definitions, relationships, PII classifications,
and many other things that are not in

479
00:35:06.000 --> 00:35:10.000
the physical implementation layer. That's a
very clever way to go about things,

480
00:35:10.039 --> 00:35:15.719
and I think that you could even
load some of those as embeddings into your

481
00:35:15.039 --> 00:35:20.480
large language model. But these are
really interesting observations, So I'll throw it

482
00:35:20.519 --> 00:35:23.440
over to Steve first. You know, I've got this concept that these large

483
00:35:23.519 --> 00:35:27.599
language models represent a second chance for
data. What do you think about all

484
00:35:27.599 --> 00:35:30.159
that? Wow? Yeah, I
think that's true. You know, it's

485
00:35:30.519 --> 00:35:37.599
interesting how over the years we have
these different ways of accessing and managing data.

486
00:35:37.280 --> 00:35:43.280
I think you're right that large language
models will be the way that creates

487
00:35:43.559 --> 00:35:47.480
semantic models and access data will the
future. So yeah, I'm not sure

488
00:35:47.519 --> 00:35:51.679
I have any more to add to
that. That's a great way to access

489
00:35:51.760 --> 00:35:55.719
data. Yeah, I'll throw it
over to Eugene because it's not like from

490
00:35:55.719 --> 00:35:59.920
my perspective, it's yeah, yes, First of all, it's these are

491
00:36:00.159 --> 00:36:02.400
text generators. That's really what they're
designed to do. Of course, they're

492
00:36:02.400 --> 00:36:07.440
also art generators and different things like
that. But when I'm thinking about this,

493
00:36:07.639 --> 00:36:12.800
what I'm really thinking of in the
darkest corners of my mind here are

494
00:36:12.880 --> 00:36:19.400
that you know, a multidimensional structure
that it has like four or five dimensions

495
00:36:19.400 --> 00:36:23.039
that can be pretty complex. These
things have billions of vertices, which means

496
00:36:23.039 --> 00:36:27.360
the complexity is through the roof.
It's just all over the dark place.

497
00:36:27.800 --> 00:36:30.119
And that allows you and I'll throw
one last little story, Eugene, and

498
00:36:30.119 --> 00:36:34.239
then you can comment on it.
I remember watching Carl Sagan when I was

499
00:36:34.280 --> 00:36:37.760
like ten or eleven, on his
show The Cosmos. It was like billions

500
00:36:37.760 --> 00:36:40.719
and billions of stars, and he
gave this presentation where he had a table

501
00:36:42.039 --> 00:36:45.360
and he had a bunch of little
pieces of paper and like squares and circles

502
00:36:45.440 --> 00:36:49.880
on the table, and he goes, this is a two dimensional world,

503
00:36:50.000 --> 00:36:52.320
and all these little creatures are in
their two dimensional world. Well, imagine

504
00:36:52.320 --> 00:36:55.320
if someone could come along and pick
up one of these two dimensional creatures and

505
00:36:55.400 --> 00:36:59.280
lift it into the air, and
now all of a sudden it can see

506
00:36:59.280 --> 00:37:04.480
all the two dimends creatures floating around. What kind of impact that would have

507
00:37:04.920 --> 00:37:08.079
on your thought processes and on your
ability to extrapolate and to come up with

508
00:37:08.119 --> 00:37:12.440
new ideas. No, I mean
I can see that like it was yesterday,

509
00:37:12.559 --> 00:37:15.920
and that was like forty two years
ago. That I saw this,

510
00:37:15.480 --> 00:37:22.119
but it was just such an excellent
way of articulating the power of perception and

511
00:37:22.159 --> 00:37:25.280
of perspective. But what do you
think about all that utene? Is it?

512
00:37:25.599 --> 00:37:30.280
Is it a second chance for data? It is? And so back

513
00:37:30.320 --> 00:37:37.239
to the analogy, or that's the
story of feeding something that a three year

514
00:37:37.239 --> 00:37:44.000
old ought not to have. If
you're trying to use llms in a customer

515
00:37:44.079 --> 00:37:52.039
interaction scenario, you only get one
chance to make that first impression. And

516
00:37:52.480 --> 00:38:00.840
if the consumers or customers lose trust
in the implementation of a large language model,

517
00:38:00.960 --> 00:38:05.000
it's going to be very, very
difficult to get it back if you

518
00:38:05.079 --> 00:38:07.119
don't have the guardrails to say,
oh, it's still learning. Well,

519
00:38:07.320 --> 00:38:15.199
most people understand that. But if
it's an all large language model doing bank

520
00:38:15.320 --> 00:38:20.840
customer service, provider customer service,
it needs to be fed the good Gerber

521
00:38:20.880 --> 00:38:24.320
food, right, And so here's
the second chance for data. So feed

522
00:38:24.360 --> 00:38:30.239
it only the good stuff, right. So thinking about the strategy for preparing

523
00:38:30.280 --> 00:38:36.000
these models, maybe not don't expose
it to kind of the swamp, right,

524
00:38:36.280 --> 00:38:40.840
because it's going to ingest some of
the swampiness. So I was reading

525
00:38:40.960 --> 00:38:49.599
yesterday really incisive kind of analysis tech
debt is one thing. Data tech debt

526
00:38:49.719 --> 00:38:57.239
is actually more insidious than that,
because once your business customers, your internal

527
00:38:57.239 --> 00:39:02.480
customers or your external customers, loan
trust in the data that you're showing them,

528
00:39:02.519 --> 00:39:07.239
the information that you're saying, this
is the represents the business or this

529
00:39:07.320 --> 00:39:15.679
represents your customer status and it's wrong, then you have almost no end of

530
00:39:15.360 --> 00:39:21.199
pulling your hair out because so I
guess the moral to that story is prepare

531
00:39:21.239 --> 00:39:24.280
your models very well. Hey,
just an add on to that. It's

532
00:39:24.280 --> 00:39:30.599
pretty interesting. I read an article
in Time magazine that talked about how chat

533
00:39:30.639 --> 00:39:35.559
GPT used Kenyon workers to actually get
rid of the toxicity. Right. So

534
00:39:35.599 --> 00:39:38.840
they used workers to actually go through
the data and said, is this toxic

535
00:39:38.920 --> 00:39:43.800
content that we're putting it the chat
cheap or not? Right? And that

536
00:39:43.880 --> 00:39:46.639
was one of the ways that they
got it. So they got out of

537
00:39:47.440 --> 00:39:52.719
they didn't have too much toxic information
in chat cheap take. But you know,

538
00:39:53.320 --> 00:39:58.280
I think we should think about that
is, you know, how can

539
00:39:58.679 --> 00:40:02.480
human intelligence and and AI intelligence augment
each other as we're building these models,

540
00:40:04.239 --> 00:40:07.880
which so we can look to that
too kind of as a test. Yeah,

541
00:40:07.960 --> 00:40:12.960
that's that's an excellent point to make. Steve, and I think the

542
00:40:13.239 --> 00:40:17.360
upshot of what I'd like to share
at the audience here today is that your

543
00:40:17.440 --> 00:40:22.119
data warehouse, your data margs,
the things that you've worked very hard on,

544
00:40:22.599 --> 00:40:28.719
need to be a crucial and foundational
component of your AI model, and

545
00:40:28.760 --> 00:40:32.079
they should be front and center as
a trusted source that is the primary source

546
00:40:32.119 --> 00:40:36.920
that you want to use, is
something like a data warehouse because you have

547
00:40:37.039 --> 00:40:40.440
governance, because you have all this
attention that's been paid to the model.

548
00:40:40.800 --> 00:40:45.760
And remember, like the data of
your organization reflects the organization itself, you

549
00:40:45.800 --> 00:40:51.400
know. And I think we could
see some really interesting things happening of dynamically

550
00:40:51.840 --> 00:40:54.239
generated data models that look at the
data and the flow of data and go,

551
00:40:54.480 --> 00:41:00.000
you know, maybe you should,
maybe you should reconfigure your data model

552
00:41:00.079 --> 00:41:04.360
to better reflect this new way that
things are working in your organization. I

553
00:41:04.440 --> 00:41:07.360
think that's coming to There are a
lot of fun things that can kind of

554
00:41:07.360 --> 00:41:10.440
spin out of this. But the
other fun point i'd throw out here,

555
00:41:10.480 --> 00:41:14.880
maybe it's going to comment from each
of you, is I've been seeing when

556
00:41:14.920 --> 00:41:19.360
you connect these engines to your data
sources, there are lots of cool things

557
00:41:19.400 --> 00:41:22.000
that you can do and think about. If you've ever done a Google search

558
00:41:22.440 --> 00:41:28.639
for instructions for a particular app.
How can I use xyz app? And

559
00:41:28.679 --> 00:41:31.039
you get something that says, okay, step one go to the file and

560
00:41:31.360 --> 00:41:35.079
click on the red button. And
you go there and there's no red button.

561
00:41:35.119 --> 00:41:37.159
You're like, all right, what
is this talking about? Because it's

562
00:41:37.159 --> 00:41:40.000
an old version. It's an old
version of the previous app or the previous

563
00:41:40.079 --> 00:41:44.559
version of the app. It is
a very difficult problem to solve, or

564
00:41:44.599 --> 00:41:47.760
at least has been historically, because
you don't control Google search engines, right,

565
00:41:47.800 --> 00:41:51.559
you don't control that stuff, and
so they're just going to find old

566
00:41:51.559 --> 00:41:53.920
stuff that is difficult to manage.
Well, if you do this correctly,

567
00:41:54.800 --> 00:41:59.960
the large language model can sense like
change data capture when something has changed,

568
00:42:00.400 --> 00:42:02.679
and when the source file has changed, it goes aha, update and let's

569
00:42:02.719 --> 00:42:07.880
go grab that stuff dynamically. So
you start thinking about that's really powerful stuff

570
00:42:08.280 --> 00:42:13.800
because when you make the change to
the system of record, it's almost instantaneously

571
00:42:13.840 --> 00:42:17.159
reflected in the large language model that
you're using to interact with the environment.

572
00:42:17.199 --> 00:42:20.719
But the closing dots from U Steve, what do you think? Yeah,

573
00:42:20.800 --> 00:42:23.519
I just want to comment on that. So databases have things like indexes and

574
00:42:23.599 --> 00:42:29.639
materialized views, and we have something
called projections, right, projections, what

575
00:42:29.679 --> 00:42:35.599
they allow you to do is based
on your queries, how can I optimize

576
00:42:35.599 --> 00:42:38.039
the data. So that is all
age i'd driven now for a lot of

577
00:42:38.039 --> 00:42:42.559
companies out there, for Vertica and
for some of the other database companies that

578
00:42:42.639 --> 00:42:46.199
exist. So I'm watching as an
AI, I'm watching the queries that take

579
00:42:46.239 --> 00:42:51.119
place, and I'm saying, you
know, if I had this materialized view,

580
00:42:51.199 --> 00:42:54.800
or if I had index configured this
way, I could switch it around

581
00:42:54.880 --> 00:42:59.760
and I could run that theory that
query ten times faster. Some of that

582
00:43:00.119 --> 00:43:05.440
functionally exists today. That's something that
we're always looking to build out for optimizing

583
00:43:05.480 --> 00:43:09.599
speed. So yeah, I mean
there's that component too. Yeah, and

584
00:43:09.639 --> 00:43:15.119
I think that you can also again
dynamically provision these things. So that's the

585
00:43:15.159 --> 00:43:20.480
other fun thing my buddy Lou Simon
was mentioning is He's like, think about

586
00:43:20.480 --> 00:43:24.440
this. Historically, you had to
learn to speak computer in order to talk

587
00:43:24.519 --> 00:43:29.320
to your computer. You had to
learn the structured query language, you had

588
00:43:29.320 --> 00:43:32.960
to learn Python, you had to
learn some language to be able to communicate

589
00:43:34.000 --> 00:43:37.480
with this machine. You don't have
to do that anymore. Now you can

590
00:43:37.599 --> 00:43:40.000
use natural language to communicate with these
things. And I mean, I'm telling

591
00:43:40.039 --> 00:43:45.400
you this prompt engineering stuff, it's
really it's taking off as well it should,

592
00:43:45.880 --> 00:43:49.239
and it's going to be amazing when
we hit what's called interactive AI,

593
00:43:49.360 --> 00:43:52.880
which I think is really the next
big phase that's coming, and it's going

594
00:43:52.920 --> 00:43:55.679
to be wild because if you let
these intelligent bots loose on your environment,

595
00:43:57.320 --> 00:44:00.000
we'll start I absolutely agree with that. I think the next big thing,

596
00:44:00.119 --> 00:44:04.440
the next big snowflake, the next
big database if you will, or analytics

597
00:44:04.480 --> 00:44:07.480
engine that comes to market that solves
that problem, that says we're going to

598
00:44:07.480 --> 00:44:13.400
be really simple. You're going to
type natural language queries and we're not going

599
00:44:13.480 --> 00:44:19.320
to care at all about SEQL or
Sparkle or any kind of language cipher.

600
00:44:19.679 --> 00:44:22.519
We're going to answer that query based
on that language that you enter in.

601
00:44:22.840 --> 00:44:27.440
I think the company that does that
is going to succeed and be the next

602
00:44:27.440 --> 00:44:30.599
big thing. Yeah, I think
you're right. Well, folks, this

603
00:44:30.639 --> 00:44:36.639
has been an absolute blast talking to
two expert Steve stars Field of open text

604
00:44:36.760 --> 00:44:40.599
Vertica look them up on LinkedIn and
Eugene Burk of Digital Strategies Group. Things

605
00:44:40.639 --> 00:44:45.760
are changing very very rapidly, and
one of the fun quotes I heard the

606
00:44:45.840 --> 00:44:51.719
other day with respect to understanding machine
learning and artificial intelligence and where it's all

607
00:44:51.760 --> 00:44:58.039
going was learning meaning human learning,
meaning we as humans need to learn how

608
00:44:58.039 --> 00:45:00.840
these things work, and the way
you do that is by playing with them.

609
00:45:00.079 --> 00:45:02.599
Send me an email info at inside
analysis dot com. You've been listening

610
00:45:02.639 --> 00:45:09.159
to Inside Analysis and now it's time
for today's podcast bonus segment, in which

611
00:45:09.280 --> 00:45:15.440
host Eric Cavana talks about the transformative
impact of large language models such as ched

612
00:45:15.559 --> 00:45:22.079
JEPT and other projective analytics tools on
the future of data analysis and decision making

613
00:45:22.159 --> 00:45:27.800
in the industry. All right,
ladies and gentlemen, Hello and welcome to

614
00:45:27.840 --> 00:45:32.039
this virtual summit on Inside Analysis or
truly Eric Kavanaugh here, I can never

615
00:45:32.079 --> 00:45:37.880
miss an opportunity to promote future Proof
of the world's first made for TV webinar

616
00:45:37.159 --> 00:45:42.079
series. Very excited about that.
Check your local listings. We're now in

617
00:45:42.159 --> 00:45:46.119
Washington, DC and Silicon Valley and
Los Alamos and lots of other fun places,

618
00:45:46.480 --> 00:45:50.920
So check your local listings or hop
onto YouTube to see past shows.

619
00:45:51.239 --> 00:45:54.480
Let's dive right in data fabric versus
data mesh cut from the same cloth.

620
00:45:55.360 --> 00:45:59.840
Yes, indeed, so let's talk
about what this really comes from, and

621
00:46:00.039 --> 00:46:02.239
that's the modern data stack. I'm
sure many of you have heard this concept,

622
00:46:02.320 --> 00:46:06.679
the modern data stack. One of
my favorite lines about this is that

623
00:46:06.719 --> 00:46:09.079
we kind of sacrifice state at the
altar of scale, And what I mean

624
00:46:09.119 --> 00:46:15.880
by that is we broke apart all
the different component parts of a database into

625
00:46:15.920 --> 00:46:20.639
separate layers for storage, for integration, for processing, analytics orchestrations, some

626
00:46:20.760 --> 00:46:24.320
antics, governance, artificial intelligence,
machine learning, of course, security,

627
00:46:24.880 --> 00:46:29.440
and well guess what all of that
can be done inside of a database.

628
00:46:29.760 --> 00:46:35.000
But the modern data stack really attempted
to solve for scale issues, to be

629
00:46:35.039 --> 00:46:37.719
able to scale out any one of
these component parts, to scale out and

630
00:46:37.760 --> 00:46:42.079
then scale back down. That's what
you want, that's the optimal scenario,

631
00:46:42.320 --> 00:46:45.679
because I'm sure many of you recall
back in the day before we had something

632
00:46:45.760 --> 00:46:49.880
like the modern data stack, you
then had the provision for the highest workload,

633
00:46:49.880 --> 00:46:52.039
irrespective of whatever workload you had or
what your budget was going to be.

634
00:46:52.519 --> 00:46:55.440
And at times when you had peak
usage, that was okay. But

635
00:46:55.480 --> 00:46:59.199
when you don't have peak usage,
you're spending a lot of money that you

636
00:46:59.239 --> 00:47:04.320
don't really have to stent to spend. So that's kind of where the that's

637
00:47:04.360 --> 00:47:07.119
kind of where this thing came from. One of my panelists is having trouble

638
00:47:07.159 --> 00:47:12.400
getting in, so let me try
to multitask while I'm talking to you here.

639
00:47:12.760 --> 00:47:17.800
But basically what's happening here is you
have this situation where we're trying to

640
00:47:17.880 --> 00:47:23.559
be able to again leverage the power
of compute wherever it is needed. So

641
00:47:23.599 --> 00:47:27.119
if I need to ingest a ton
of data, that's what I do.

642
00:47:27.159 --> 00:47:29.280
If I need to process a bunch
of data, that's what I do.

643
00:47:29.360 --> 00:47:32.800
If I need to scale out the
governance components of the equation here, then

644
00:47:32.800 --> 00:47:36.760
maybe that's what I need to do. So that's where the modern data stack

645
00:47:36.800 --> 00:47:40.840
came from. But there are a
lot of component parts that well, they

646
00:47:40.840 --> 00:47:46.159
complicate things because anytime you have multiple
parts, well there are connections between all

647
00:47:46.159 --> 00:47:50.719
these parts, and that can slowly
but surely cause you some trouble. So

648
00:47:50.760 --> 00:47:52.639
you want to watch out for that, and that's one of the downsides.

649
00:47:53.000 --> 00:47:57.320
But let's kind of dive deeper in. So what is the data fabric.

650
00:47:57.440 --> 00:48:00.440
It is a substitute for a database, supposed to be more flexible, more

651
00:48:00.519 --> 00:48:05.519
versatile, more durable, faster,
better, easier to govern all that fun

652
00:48:05.519 --> 00:48:08.360
stuff. Sounds great. Is it
easier to manage? No, it's not

653
00:48:08.440 --> 00:48:12.880
easier to manage. It's going to
be significantly harder to manage than a single

654
00:48:13.000 --> 00:48:16.519
database, right. And I've been
joking to myself our DBAs going away,

655
00:48:16.760 --> 00:48:22.000
we have DFA's data fabric administrators.
I don't think that's going to happen.

656
00:48:22.239 --> 00:48:25.079
I think you're still going to have
data based administrators. Really, data engineers

657
00:48:27.280 --> 00:48:30.280
is the key. So data engineers
are sort of the new DBAs, if

658
00:48:30.320 --> 00:48:34.280
you will. So debas will still
be around, they're just doing slightly different

659
00:48:34.320 --> 00:48:37.840
things. So what else is a
big part of the whole world of data

660
00:48:37.880 --> 00:48:42.800
fabric is this whole concept of automation, Right, That's what we really want

661
00:48:42.840 --> 00:48:46.599
to be able to do is automate
things, and so we're automating various components

662
00:48:46.639 --> 00:48:53.400
of integration. So think pre processing
for example, think monitoring for usage patterns

663
00:48:53.440 --> 00:48:59.199
and then provisioning additional access at a
certain time, like maybe at the end

664
00:48:59.239 --> 00:49:00.599
of the week, maybe at the
end of the month, for example,

665
00:49:00.599 --> 00:49:05.840
when you can have a close to
do situations like that, you want to

666
00:49:05.880 --> 00:49:09.039
be able to get additional resources.
Well, with automation of a data fabric,

667
00:49:09.320 --> 00:49:13.559
you can pre provision, you can
pre process data. And that's a

668
00:49:13.599 --> 00:49:17.199
big part of what facilitates the data
fabric's ultimate goal, which is to make

669
00:49:17.239 --> 00:49:22.760
life a lot easier for the consumption
of data, whether for people by people

670
00:49:22.880 --> 00:49:25.000
or for machines, whether from machine
learning, et cetera. You want to

671
00:49:25.000 --> 00:49:29.840
be able to detect these patterns.
And that's actually a really strong use case

672
00:49:29.920 --> 00:49:34.599
for machine learning, because even though
we might think that we're not very predictable

673
00:49:34.639 --> 00:49:37.960
as human beings, the truth is
we are very predictable, and our behavior

674
00:49:38.000 --> 00:49:44.400
patterns can easily be ascertained, understood, and then codified by machines. And

675
00:49:44.440 --> 00:49:47.519
so that's a big part of data
fabric is to watch for patterns of usage

676
00:49:47.559 --> 00:49:52.400
and then be able to pre provision, pre process data, for example,

677
00:49:52.800 --> 00:49:55.599
to do some work before someone shows
up, such that it's already ready to

678
00:49:55.679 --> 00:50:00.960
go. So I do want to
mention one fun thing about data fabric.

679
00:50:00.079 --> 00:50:06.280
So companies, according to Gardner in
the data fabric space was Talent. Many

680
00:50:06.320 --> 00:50:08.679
of you may know Talent. Lots
of companies use Talent. It was an

681
00:50:08.719 --> 00:50:14.280
open source company, Talent. Open
Studio has been really an open source starward

682
00:50:14.599 --> 00:50:19.159
for the last gosh twenty years almost. I think it was around two thousand

683
00:50:19.199 --> 00:50:22.679
and three or so that they started
to take off. I remember talking to

684
00:50:22.719 --> 00:50:24.639
them in two thousand and five when
I worked for the Data Warehousing Institute.

685
00:50:24.800 --> 00:50:30.840
Well, Click bought Talent. The
company Qlik sorry, Click bought talent.

686
00:50:30.840 --> 00:50:37.440
Click of course is a business intelligence
platform. And they finally finished their acquisition

687
00:50:37.480 --> 00:50:40.440
of Talent a number of months ago, and guess what happened. They announced

688
00:50:40.480 --> 00:50:45.079
that the open source version of open
studio is going bye bye. That's going

689
00:50:45.119 --> 00:50:47.800
away. Well, as I said, they were a leader in the data

690
00:50:47.800 --> 00:50:52.480
fabric space. So what does this
mean about the future of data fabric sharp

691
00:50:52.559 --> 00:50:55.760
answer is I don't really know.
And you can look up online you'll find

692
00:50:55.800 --> 00:51:00.719
lots of commentary about this. It
is a hot topic for sure, but

693
00:51:00.760 --> 00:51:02.679
the bottom line is it is going
to be closed source from now on.

694
00:51:02.880 --> 00:51:07.400
So now branded. There are lots
of integration vendors that are not open source.

695
00:51:07.440 --> 00:51:12.239
Informatica is not open source, Matillion
is not open source, Abnisio is

696
00:51:12.239 --> 00:51:15.400
not open source. Lots of companies
in the integration space are not open source.

697
00:51:15.719 --> 00:51:17.880
But Talent was and it was very
well known for that. It was

698
00:51:17.960 --> 00:51:22.880
kind of like the Lancelot of open
source. Right. The strongest night in

699
00:51:22.000 --> 00:51:25.480
King Arthur's Court, if you will, was talent and that is now not

700
00:51:25.679 --> 00:51:29.679
open source. It's all going to
be closed source. What does that mean

701
00:51:29.679 --> 00:51:32.320
for the future of data fabric I'm
not entirely sure, but it's probably not

702
00:51:32.719 --> 00:51:37.840
the best news. Thanks so much
of your time you've been listening to Inside

703
00:51:37.840 --> 00:51:47.000
Analysis one O six point five FM
and KCAA ten fifty am. Open for

704
00:51:47.079 --> 00:51:52.840
takeout and delivery. Hell Tapiac,
Mexican food restaurant in the Tri City Center

705
00:51:52.880 --> 00:51:55.760
of Redlands is back. Their entire
family is on hand to serve up their

706
00:51:55.760 --> 00:52:00.440
delicious burritos, but chaka charizo,
wavos, manche zos, steak and eggs

707
00:52:00.519 --> 00:52:05.599
just part of their mouth watering great
food. Since fifteen thirty one, people

708
00:52:05.639 --> 00:52:07.920
have marveled at the miracle of El
Tapiac, and now you can marvel at

709
00:52:07.920 --> 00:52:12.400
the great food the Lugo family has
been serving up for over two decades,

710
00:52:12.519 --> 00:52:15.760
nestled quietly in the corner of the
Tri City Center shopping mall next to Burlington

711
00:52:15.800 --> 00:52:19.920
Code Factory. Support them. They
can't wait to serve you some of their

712
00:52:19.920 --> 00:52:23.400
delectable, authentic South of the Border
Mexican fair at great prices, served up

713
00:52:23.400 --> 00:52:28.800
with love. Support the area's best
loved Mexican food restaurant in these tough times.

714
00:52:29.000 --> 00:52:31.760
Order up a tasty meal on the
phone for delivery or takeout for breakfast,

715
00:52:31.800 --> 00:52:36.159
lunch, or dinner from ten am
to six pm called nine to nine

716
00:52:36.239 --> 00:52:40.000
three seven zero zero one seven that's
nine O nine three oh seven zero zero

717
00:52:40.239 --> 00:52:47.760
seventeen or google Eltapiac Redlands and treat
yourself to Hebot Club's original pure poundy rco

718
00:52:47.840 --> 00:52:52.159
Super tea comes from the only tree
in the world that fungus does not grow

719
00:52:52.199 --> 00:52:54.880
on. As a result, it
naturally has anti fungal, anti infection,

720
00:52:55.000 --> 00:53:00.039
anti viral, antibacterial, anti inflammation, and anti parasite properties. So the

721
00:53:00.079 --> 00:53:04.760
tea is great for healthy people because
it helps build the immune system, and

722
00:53:04.800 --> 00:53:08.199
it can truly be miraculous for someone
fighting a potentially life threatening disease due to

723
00:53:08.280 --> 00:53:14.000
an infection, diabetes, or cancer. The t is also organic and naturally

724
00:53:14.039 --> 00:53:16.880
caffeine free. A one pound package
of T is forty nine ninety five which

725
00:53:16.880 --> 00:53:21.840
includes shipping. To order, please
visit to Hebot club dot com. T

726
00:53:22.000 --> 00:53:27.119
hebo is spelled T like tom,
a h ee b like boy oh.

727
00:53:27.239 --> 00:53:30.360
Then continue with the word T and
then the word club. The complete website

728
00:53:30.400 --> 00:53:35.400
is to Hebot club dot com or
call us at eight one eight sixty one

729
00:53:35.519 --> 00:53:39.639
zero eight zero eight eight Monday through
Saturday nine am to five pm California time.

730
00:53:39.840 --> 00:53:45.039
That's eight one eight sixty one zero
eight zero eight eight t ebot club

731
00:53:45.079 --> 00:53:52.039
dot com with sixty years of fascinating
facts. This is the man from yesterday

732
00:53:52.480 --> 00:53:57.239
and back in time to this time
in nineteen eighty six. Looks like Platoon

733
00:53:57.360 --> 00:54:00.320
is a huge hit. Platoon is
written and directed by Vietnam morevet Oliver Stone

734
00:54:00.360 --> 00:54:06.480
and stars Charlie Sheen, Tom Behringer, and William the Fall. In nineteen

735
00:54:06.559 --> 00:54:10.440
sixty seven, Oliver Stone was a
combat infantryman in Vietnam. He was wounded

736
00:54:10.440 --> 00:54:15.800
twice and received a Medal for gallantry
and action. Ten years later he was

737
00:54:15.840 --> 00:54:19.960
a Hollywood screenwriter and from about this
time. In nineteen sixty eight, December,

738
00:54:20.119 --> 00:54:23.199
NBC broadcasts Dean Martin Christmas, but
this one's a little different. At

739
00:54:23.239 --> 00:54:27.639
the end of the show, celebrities
tell kids to keep a sharp eye out

740
00:54:27.639 --> 00:54:30.920
for Santa at oster homes, orphanages, and children's hospitals around the country.

741
00:54:31.000 --> 00:54:36.320
Here's the first one done by Dean
Martin, and I'd like to start by

742
00:54:36.360 --> 00:54:40.039
telling all you kids at the Saint
John's Hospital in Steubenville, Ohio, where

743
00:54:40.039 --> 00:54:45.159
I was born. To keep a
sharp eye out for Santa, because he's

744
00:54:45.159 --> 00:54:47.440
on his way with lots of gifts
for you. And from this time December

745
00:54:47.480 --> 00:54:53.519
of nineteen seventy five. WMAQ TV
Channel five news anchor Jane Pawley recently subbed

746
00:54:53.559 --> 00:54:59.199
on nbctv's Today Show. Some say
Jane Pauley is headed for a network post.

747
00:54:59.480 --> 00:55:02.519
Let's see Jane Pauley, who comes
to us from Indiana and Chicago.

748
00:55:04.039 --> 00:55:07.679
And as I said earlier, any
family would be happy to welcome someone so

749
00:55:07.840 --> 00:55:13.800
bright and energetic and enterprising and just
incidentally pretty as well. With more at

750
00:55:13.800 --> 00:55:21.960
Man from Yesterday dot Com. Every
Wednesday at three pm, It's The Uncommon

751
00:55:22.000 --> 00:55:25.079
Sense Democrat with host Eric Bauman.
I love when his people talk about them

752
00:55:25.119 --> 00:55:30.159
old Joe Biden, but he's just
a couple of years behind him. You'll

753
00:55:30.199 --> 00:55:38.280
get the best political commentary and stuff
like this. Good night, Well,

754
00:55:38.440 --> 00:55:44.880
I join us for the Uncommon Sense
Democrat every Wednesday at three pm on the

755
00:55:44.880 --> 00:55:49.559
stations that leave no listener behind casey
AA ten fifty am Man one oh six

756
00:55:49.599 --> 00:55:52.039
point five. That's them. It's
that time of year again, No,

757
00:55:52.320 --> 00:55:57.960
not the holidays. Medicare open enrollment
and if you have questions about Medicare,

758
00:55:58.159 --> 00:56:01.039
you should talk to the local expert, Paul Barrett and Associates. All of

759
00:56:01.119 --> 00:56:06.159
his agents are certified with plans that
are accepted by most of the medical groups

760
00:56:06.199 --> 00:56:09.719
in our area. Call nine oh
nine seven ninety three oh three eighty five.

761
00:56:09.880 --> 00:56:14.480
Their service is free and after forty
two years of the business, their

762
00:56:14.519 --> 00:56:17.480
agents are trained to help you pick
the plan that's right for you. Hey,

763
00:56:17.559 --> 00:56:22.719
y'all, merle here good news for
once. My neighbors is jealous of

764
00:56:22.760 --> 00:56:25.480
me. You want to know why, because my grass is growing and looking

765
00:56:25.559 --> 00:56:29.719
green, and I can sell on
my sofa out in the front yard and

766
00:56:29.800 --> 00:56:32.239
I don't even have to overwater it
anymore. You know how I did it.

767
00:56:32.599 --> 00:56:37.440
I listened to damn water Boys under
Waters on every Thursday night on KCIA.

768
00:56:37.960 --> 00:56:42.599
Well, I got me a smart
controller and now a water's at night

769
00:56:42.920 --> 00:56:46.239
mir looks darn tooting. No more
sneaking around and hooking up my horse to

770
00:56:46.280 --> 00:56:50.360
my neighbor's pigott in the middle of
the night, and his dog won't bite

771
00:56:50.360 --> 00:56:53.280
me anymore. And you can do
it too. Listening is easier than ever.

772
00:56:53.639 --> 00:57:00.480
KCIA is now screaming online. Eh, streaming. Why it's streaming,

773
00:57:00.599 --> 00:57:05.719
you dummy. Well, I don't
know much about streaming, but they doing

774
00:57:05.719 --> 00:57:09.920
it apparently at KCA radio dot com. So AnyWho listen to to WA zone

775
00:57:10.000 --> 00:57:15.280
and fix your yacht up rant right
here at KCIA, the station that leaves

776
00:57:15.280 --> 00:57:21.119
no listener behind e digits, lock
them in for more information, recreation and

777
00:57:21.199 --> 00:57:31.199
guaranteed fun. Kcaa NBC News Radio. I'm Chris Karagio. Defense Secretary of

778
00:57:31.239 --> 00:57:35.760
Lloyd Austin is talking with counterparts in
the Middle East as the US pushes for

779
00:57:35.800 --> 00:57:39.159
an end to Israel's airstrikes and ground
combat operations in the Gaza Strip. The

780
00:57:39.239 --> 00:57:45.480
visit comes amid protests over the deaths
of three hostages mistakenly killed by Israeli forces.

781
00:57:45.679 --> 00:57:49.239
Prime Minister Benjamin Netnyah, who has
hinted that he's now open to new

782
00:57:49.239 --> 00:57:52.920
negotiations with a moss A crowd of
about one hundred thousand people turned out for

783
00:57:52.960 --> 00:57:55.920
a rally last night until Aviv to
demand that the government negotiate the release of

784
00:57:55.960 --> 00:58:00.519
the remaining hostages. A Texas congressman, as the Senate and the White House

785
00:58:00.559 --> 00:58:05.480
will have to sweeten the current border
security deal to get an aid package for

786
00:58:05.599 --> 00:58:08.559
Ukraine and Israel through the House.
Speaking on CBS's Face the Nation, GOP

787
00:58:08.639 --> 00:58:15.360
Congressman Tony Gonzalez said labeling cartels as
terrorist organizations would be a major step in

788
00:58:15.559 --> 00:58:20.800
getting an aid package through. Homeland
Security Secretary Alejandro Majorcis has been spending the

789
00:58:20.800 --> 00:58:23.679
weekend meeting with a bipartisan group of
three senators to try to work out a

790
00:58:23.719 --> 00:58:30.000
tentative deal on border security. GOP
presidential hopeful Nikki Haley calls herself and New

791
00:58:30.000 --> 00:58:34.679
Hampshire Governor Chris Snunu a team,
but one is more outspoken about the front

792
00:58:34.719 --> 00:58:37.599
runner than the other. We love
the idea that he was going to drain

793
00:58:37.639 --> 00:58:40.280
the swamp. That was an amazing
opportunity. Didn't even try. I mean

794
00:58:40.360 --> 00:58:45.400
literally didn't even try. Speaking on
ABC's this Week, both Haley and Sinunu

795
00:58:45.679 --> 00:58:49.079
said it would be a disaster of
former President Trump got back into the White

796
00:58:49.079 --> 00:58:53.079
House. Snunu said Trump only talks
about retribution so he can avoid talking about

797
00:58:53.079 --> 00:58:58.039
his failures as president. When asked
if Trump should be granted immunity for any

798
00:58:58.039 --> 00:59:00.239
crimes he may have committed while he
was in office, Haley said that's up

799
00:59:00.280 --> 00:59:04.679
to the courts to decide. It's
a sweet weekend for Wonka, which tops

800
00:59:04.719 --> 00:59:07.800
the box office by a wide margin. The candy theme fantasy musical took in

801
00:59:07.840 --> 00:59:12.639
an estimated thirty nine million bucks in
its opening weekend, which is more than

802
00:59:12.679 --> 00:59:15.360
the combined total of the other ten
movies that made at least a million dollars

803
00:59:15.400 --> 00:59:19.960
over the weekend. The Hunger Games
prequel and the Japanese film The Boy and

804
00:59:20.000 --> 00:59:22.800
the Heron rounded out the top three
at the box office. I'm Chris Karagio,

805
00:59:22.920 --> 00:59:28.400
NBC News Radio listene the KCAA Loma
Linda at one O six point five

806
00:59:28.599 --> 00:59:31.599
FM, K two ninety three c
f Brito Valley. Did you know here

807
00:59:31.679 --> 00:59:37.079
at KCAA ten fifty am that we
developed an app for all your Android devices.

808
00:59:37.280 --> 00:59:40.760
We're talking about your smartphone, your
tablets, you name it. You

809
00:59:40.800 --> 00:59:45.679
have an Android format. You can
take kca with you everywhere you go.

810
00:59:45.960 --> 00:59:51.519
We're talking about our audio stream,
our video stream, and even our podcast.

811
00:59:52.000 --> 00:59:58.679
Go to KCAA Express dot com.
That's KSECAA Express dot com, KCAA

812
00:59:58.840 --> 01:00:04.960
Express dot com. Ram's tire service
in Oma. Linda reminds everyone that the

813
01:00:05.039 --> 01:00:08.400
blood you donate gives someone another chance
at life some day that someone might be a