WEBVTT

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Comedy The Fall Guy finished second with
thirteen point seven million. Tennis drama Challengers

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finished third again this week with four
point six million. Low budget horror film

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Taro finished fourth with three point four
million, and Godzilla Xkong and New Empire

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refuses to leave the top five with
two point five million. I'm Chris Karagio,

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NBC News Radio, NBC News on
CACAA Lomolinda sponsored by Teamsters Local nineteen

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thirty two, Protecting the Future of
Working Families Teamsters nineteen thirty two. Dot

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org. The information economy has a
rod. The world is teeming with innovation

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as new business models reinvent every industry
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information and insight about how to make
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Learn more at Inside analysis dot Comsideanalysis
dot com. And now here's your host,

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Eric Kavanaughs. Oh, ladies and
gentlemen, Hello, and welcome back

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once again to the only coast to
coast radio show that's all about the information

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economy. It's time for Inside Analysis, your host Eric Cavanaugh here in folks.

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I'm very pleased to have an industry
visionary on the call today. We're

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to be talking to Justin Borgman.
He is the co founder and CEO of

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a company called Starburst, and they
are shaking up the industry of analytics.

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They've come up with a way to
allow you to query data pretty much wherever

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it is. Maybe it's in your
relational database, maybe it's in in open

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formats like Iceberg. We're to be
talking a lot about Iceberg. I'm going

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to be attending their virtual event shortly
too. I think it's in about a

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week or two. And they've shaken
up the market in significant ways. And

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justin welcome to the show. Thank
you for having me. Eric super excited

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to be here. Yeah. Absolutely, And as we were just discussing before

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the show, I've been in the
data warehousing space for a long time,

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almost twenty five years. It's hard
to believe. And boy have things changed.

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I mean, if you go back
in you know two thousand and one

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time frame, storage was expensive,
processors were relatively slow, the pipes were

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fairly thin, so it was a
different architecture. And these days, I

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mean, goodness, gracious, we've
had so many innovations in distributed systems for

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example, and then being able to
leverage data where it lives. And I

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think that's one of the keys to
success in the future is being able to

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analyze data wherever it's sitting. And
that was one of your early concepts right

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when you rolled this out. Please
talk about that. Yeah, exactly.

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So you've got me beat in the
number of years, department, But I've

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been in the space for fifteen years, and even fifteen years ago. You

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know, it was very interesting.
That was really the early days of Hindu

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kind of the first data lake,
and a lot of the concepts that are

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so important today were actually kind of
pioneered back then, you know, the

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idea of open formats like parquet files
and avro and rc file and storing that

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in cheap storage and a distributed file
system and so forth. And then my

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first company was acquired by Terra Data. I spent a few years there,

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and to me, one of the
things that I realized was that despite terror

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Data being the industry leader certainly at
the time, not one of their customers

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actually had all of their data sitting
in a classical data warehouse. That single

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source of truth in one proprietary EDW
just wasn't actually achievable, and that you

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would always have data that lived outside
of that, and even where you could

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centralize, it actually probably made more
sense for you to centralize in more of

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a lake model. And around the
same time as I was having these realizations,

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I discovered an open source project coming
out of Facebook that was originally called

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Presto it later became Trino as it's
known today, and decided to partner with

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the creators of this project while they
were at Facebook. We started collaborating together

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between Terra Data and Facebook to improve
this project, make it really enterprise grade,

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and in twenty seventeen left ter Data
to form Starburst as the company behind

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this project, and the creators of
Preston Trino joined me from Facebook and sort

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of, you know, from there
we went, yeah, that's fantastic.

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You know, I'm the biggest fan
of open source. And it was in

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fact, back in two thousand and
five when I was working for the Data

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Warehousing Institute that I did a bunch
of research into it. And way back

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then, the Apache web server had
just eclipsed the Microsoft the web server as

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the number one web server, and
I was like, oh, this is

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very interesting stuff. And I remember
doing some research and doing some writing around

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the nexus of open source, and
back then, of course it was service

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oriented architecture, and I was thinking
to myself, hmmmm, this is very

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interesting because ideally, if you have
an SOA and you are using open source

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technologies, you should be able to
rip and replace different component parts build a

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composable architecture. And that doesn't sound
like good news for the big Oracles and

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ibms of the world. Of course, IBM though, went all in an

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open source and really supported so they
saw that vision. But wow, you

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look back in time now and that
was a very pivotal moment in this industry,

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right when open source really took over. Now a patchy is huge with

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all these different projects. That's pretty
cool stuff, right absolutely. I mean

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that was one of my key lessons
from that period as well. I think

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like when when you have an open
option and a proprietary option, that the

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open option is is probably going to
win, and it's it's better for customers.

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I mean, at the end of
the day, I think customers,

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you know, get smart about these
things, and they want to build,

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you know, architecture that can stand
the test of time, you know,

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something that's truly future proof. And
and that's why it's so important to use

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open components because they are you know, generally modular and swappable and you can

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adapts as things change. And at
least from where we said, that means,

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you know, how you store your
data can change. You know,

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maybe it's Oracle and tereartate on prem
today, and maybe it's you know,

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storing data and Iceberg and S three
and you know, Mango deb for for

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your operational database in the future.
Right, And being able to have analytics

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across all of that, regardless of
where it lives, is really core to

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our mission and providing customers that optionality. Yeah, and that's so cool because

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it's always in the context that you
understand a particular business problem. And as

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I as I'm sure you know,
in the old days if you will,

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of data warehousing, the whole idea
was to pull data from source systems,

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put it into a relational model that
then you can analyze it. Because we

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learned that these ERP systems and other
production systems were not designed for analytics.

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They were designed for doing stuff for
transactions. So it's a different architecture,

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it's a different workflow, and the
idea was, well, gosh, how

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can we query these things? Well, you do that by bringing it into

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a data warehouse. But by enabling
analysis of data and many different systems and

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formats, you've obviated the big,
huge need of all of this ETL extract

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transform load, extract transform load.
It's never going to go away. But

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I remember seventeen years ago when we
launched DM radio thinking to myself, this

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has got to stop. I mean, there's so much time, effort,

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and money spent moving data around,
and we now have evidence to suggest that,

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you know what, eighty percent of
that data isn't even really needed for

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your queries. So you're moving data
that you don't even need to move.

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Well, that's silly in and of
itself, but then when you can analyze

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it where it lives, now you've
enabled the kind of rich context that's going

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to help answer business questions. Right, that's right, Yeah, that's exactly

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right. Yeah. So, I
mean I think it's fascinating that you're able

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to do that. Can you kind
of get into the weeds a bit about

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how so obviously of connectors, ODBC, JDBC traditional ways of connecting to data,

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but then you can sort of infer
the format of the data and then

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transform that I guess in real time
to be able to do analysis. Is

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that right, walk us through how
that whole process works. Yeah. So

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you know, the way I like
to explain it to folks is that Trino

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and Starburst are like the top half
of the database. And what I mean

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by that is it's a database without
storage, so we access the storage wherever

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that may be. It could be
a traditional relational database as you said,

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maybe it's Oracle or Terra Data or
SQL server or Postcress or mycequl or what

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have you. It could be no
SQL database like Mango. It could even

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be cough Ga topics. It could
be these open formats, which I think

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are a big deal, and I
know we're going to spend some time talking

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about those in a bit like Iceberg. But regardless of where you store it,

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what we're doing is we're essentially connecting
to it, connecting to the catalog

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that has the metadata about how that
data is laid out, and and executing

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that query in memory in a in
a distributed MPP architecture massively parallel processing architecture,

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so it's scalable and super fast.
And that's really I think what what

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gave it its momentum as a as
a project when it was first created at

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Facebook years ago was the fact that
this thing could really work at petabyte scale

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with you know, tons of concurrency
and complexity around the types of analytics that

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you're trying to do and do that
in a performance and scalable way. And

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so today it's it's leveraged by some
of the largest organizations of the world LinkedIn

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Netflix, AIRBNBU, you know,
large enterprises like Comcast, a ton of

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the big banks, and and really
using this at at at scale to do

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exactly as you described. Yeah,
and I mean this whole issue of being

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able to analyze data where it lives, no matter what format it's in,

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Like you said, a no SQL
database Cassandra Mango dB our relational database.

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Of course my SQL postcress SQL.
You know as well as I do.

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There are millions and millions of instances
of these databases all around the globe.

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But the bigger the company gets,
the more you're likely to have multiple different

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systems, maybe you know, a
call center system, a CRM, customer

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databases, all these different kinds of
systems, which hitherto in the old days

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you would have had to run extraction
routines to pull the data out load it

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into a particular schema. Well instead, what you're doing is like you say,

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you got that top half of the
database, and you don't care where

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it's stored. So now your business
analyst can go, you know, I

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want to be able to triangulate how
much we're selling with our with our current

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supplies, with our customer needs,
with what we're seeing on our website,

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all these different things. You can
pull that together in a query and hit

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boom, get and get answers.
Right, that's exactly right. And I

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think to your point, you know, if you're a large enterprise fortune five

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hundred and forty one thousand enterprise,
that that sort of Cambrian explosion of data

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sources is inherent. You know,
it's unavoidable. And in fact, there's

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a venture capitalist named Matt Turk at
First Mark Capital who's only I mean you've

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probably you know where I'm going with
this here. He maintains this data landscape

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of all the different types of database
systems and data sources basically that exist in

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our space. And he's updated it
every year since back when I was doing

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my first startup fifteen years ago.
And so you can watch this progression.

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It never simplifies, it never seems
to get smaller. It's just more and

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more and more data sources, to
the point where if you're a large organization,

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you have probably most of what's on
that on that market map. Yeah,

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and to your point, the world
is getting more diverse. There are

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more tools, there are more data
types, and there's value in those data

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types. There's value in them hills, as they say, there's gold in

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them hills. Right. So to
be able to have this abstraction layer above

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the panoply of data sources underneath,
that's very powerful because again, otherwise you

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would have to manually go in and
connect to this source pull all that stuff

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out. Those pipelines break all the
time. They're expensive, they're cumbersome.

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Typically you lose some data when you're
doing that. You can have all sorts

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of data quality issues occur. So
instead just leaving it where it is and

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being able to query it, and
you still want observeability, you still want

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to know when it's not coming through
the way it's supposed to come through.

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Things of this nature, that whole
space is exploded as well. The key

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is to be able to enable your
professionals, your analysts, to ask whatever

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questions they want from data that's in
any number of different sources, right,

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yep, exactly, that's exactly right. That's pretty cool stuff. And give

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us some examples maybe of some of
your clients how they're able to pull together

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data from multiple different sources to see
something and to understand something that's key to

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their business. So one example that
we've been working with Comcasts for a number

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of years. Actually, they've been
a customer CHISE, I don't know,

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probably maybe six years now, since
the earliest days of the company's existence,

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and I always love chatting about some
of their use cases because they're very relatable

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to a wide audience. We all
watch TV in some former fashion. And

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when they first started working with us, they had all of the classical billing

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data in a traditional enterprise data warehouse
on prem and they had all the viewing

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behavior basically every time you change that
channel, every time you interact with that

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cable box that was stored in a
Hadoo data lake. And what they really

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wanted to do was be able to
correlate the shows that you watch with how

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much you spend and do cross sell
and upsell based on your viewing behavior,

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which makes a ton of sense,
but is a perfect example of one data

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set living in one data source which
is huge volume, right, like all

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that event data or every time you
change the remote that needs to be in

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a data lake. I mean,
you really wouldn't want to put that in

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a data warehouse. Its cost you
a fortune. But then they had all

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that classical billing data from you know, decades of customer history in the in

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their data warehouse, and so they
wanted to be able to combine that join

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tables across these two systems uh.
And and we were able to deliver that

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value very quickly for them simply by
being this abstraction layer and being able to

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join across these two data sources over
time. What that let them do also

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is start a migration effort, a
digital transformation effort, if you will,

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or cloud transformation effort maybe more precisely, uh, and start to move some

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of that data into the cloud into
you know, object storage like S three

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or across multiple clouds uh, and
leverage more of a data lake or lake

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house architecture for large portions of that
data, where we become essentially the engine.

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And and again I know we'll get
to the but basically, when you

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combine you know, Starburst or Treno
and a open format like Parquet or Iceberg

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or Hoodie or what have you.
You essentially have a warehouse. It's just

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an open source warehouse with an open
engine combined with an open format, which

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can now today in today's world,
give you the same performance and functionality that

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you might traditionally have gotten from a
proprietary data warehouse. Yeah, you know,

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that's very interesting for lots of reasons. One, you don't want to

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hamstring your analysts. You want your
analysts to be able to ask whatever questions

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are going to come to their mind, and you want them to be able

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to leverage new data sources quickly.
Yeah. Right, Because there's an opportunity

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cost of doing the old model in
that people just don't think outside the box.

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They think very much inside the box, and that greatly limits the kinds

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of questions you can ask, the
kinds of analysis you can do. So

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what you've really done is open the
doors to all kinds of exploration. Right,

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that's exactly right. You literally have
the freedom to be curious, uh

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and be able to get a response
and iterate on that curiosity. So for

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exploratory analytics, it's it's truly a
game changer to have everything at at your

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fingertips. And you know, in
some of the large organizations that we work

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at, big banks and so forth. I'll tell you another anecdote. Uh,

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this one, I can't say the
name, but I'll just say one

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of the largest banks, Uh,
you know, the the CEO used to

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call their data analytics team and say, hey, can you tell me you

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know X, y Z, you
know how many how many times a day

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are our customers checking the banking app
today? Or you know what's our what's

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our mortgage default risk or what have
you? And uh, it used to

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take that team, you know,
a week or two weeks and they'd say,

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hey, yeah, we'll get we'll
get that answer for you. But

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now by having connectivity to all those
data sources, that the analytics team can

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say, hey, just wait,
wait one second and get the answer while

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he's on the phone, you know. And that's a that's a powerful,

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powerful thing. Well, it is
amazing because again, once you acclimate to

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being able to discover I love that
line you said, you have the freedom

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to be curious. It's very interesting
because the mind is a very powerful thing,

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and I know that these analytical engines
are very powerful, but human creativity

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is the key ingredient in all this
stuff. Right, you can have all

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this data, all this analysis,
reports, dashboards, everything you want.

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It's a human being that's going to
have to really look at that and map

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it all through the business and the
plans and where you want to go.

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And these days you have to be
able to change plans pretty quickly. I

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mean, I've been in business myself
for on and off, self employed for

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twenty five years and two long instances, and I've never seen a more dynamic

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marketplace than what we have today.
I mean, especially for mid sized businesses,

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but even for big businesses. You
have to be able to know what's

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happening. Where's the money coming from, where are the expenses coming from,

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where's the margin coming from, what
are the people doing. All these things

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are crucial to understand and be able
to pivot, because otherwise you have to

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lay people off, and a lot
of times we've seen a lot of layoffs

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these days. It's very difficult to
recover from that stuff. Just knowing what

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people we're working on is half the
battle. Then being able to reallocate that

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worked as someone else is another whole
half of the battle. And then by

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the time you've done that we'll guess
what the market's changed again. So the

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long winded point I'm making here is
that it's really important to be able to

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analyze your data to understand what it's
telling you, no matter which system it's

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coming from. And folks, don't
touch up that. I'll be right back.

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You're listening to Inside Analysis. Welcome
back to Inside Analysis. Here's your

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host, Eric Tavanaugh. All right, folks, back here on Inside Analysis

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talking to Justin Borgman, co founder
and CEO of Starburst, a very cool

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company that is doing amazing things with
data analytics. And we were just talking

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on the break just at about COVID, how COVID came along and really forced

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everyone to pay attention to their processes, to their workflows, to their data.

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You better understand what's happening. And
of course all these behaviors changed.

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And that's one fascinating aspect of the
industry of business in general that I found

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so curious because everyone tells me this
is the case. And what happened is

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you have all these algorithms that were
designed to optimize sales and marketing and operations

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and different aspects of a business,
but they're predicated on old behavior of how

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people acted before COVID. Then COVID
hits and all that stuff went out the

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window. So you had to come
up with new ideas, new ways of

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doing things. And that's where analytics
plays a crucial role. Right, That's

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exactly right. I mean, we
live in vollats ole dynamic times and being

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able to react and understand how changes
in the world around you are impacting your

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business and drive strategy in accordance with
that is so important, and that time

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to inst is critical in driving that. And I think Covid is a fantastic

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example. Right, all of a
sudden, overnight we started ordering our own

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groceries and doing our banking online,
and those mobile apps you know, became

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so important, and delivery services became
so important, and you know, calculating

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that incorrectly, you know, could
have huge, huge impact. I'll tell

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you a quick story here in the
Boston area. You know, one of

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our great tech success stories is a
company called Toast, which has basically,

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you know, reinvented the point of
sale system and restaurants. And there was

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this moment in the very beginning of
COVID where they thought, oh my god,

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our business is screwed, basically,
and yeah, toasts. Yeah,

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no, pun intended. And so
you know, tragically they let go of

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half their company. You know,
it's like a thousand people were suddenly laid

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off. It ended up being the
case that their business actually ended up growing

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dramatically during that period, and you
know, but they couldn't see it yet

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that that COVID was actually putting a
lot of restaurants out of business but creating

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the opportunity to create new ones.
And as new ones were created, the

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first thing that somebody decides when they
open up a restaurant is which point of

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sales system do I want to buy? And so you know that that prospect

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of selling to legacy restaurants, existing
restaurants and getting them to change is much

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much harder for them than selling to
a brand new restaurant. And so it

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ended up being this huge boon to
their business. But but you know,

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they just couldn't see that at the
time, and you know, weren't able

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to react early in that and unfortunately, you know a lot of people lost

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their job early. And now to
you know, TOA is a huge success

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story public company, you know,
great great business. Well, yeah,

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and being able to pull data from
systems like that for example, is very

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useful. So, especially if you've
got ten twenty thirty stores around the country,

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being able to analyze the data coming
in through a system like that like

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toast point of sale is great for
apply chain, it's great for planning,

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it's great for you know, understanding
what mixture of products to offer in your

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stores. I mean a lot of
people don't realize how much work goes into

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rolling out a new product at a
company like that, like at a Chipotle

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with their brisket or something like that. Yeah, a lot of work goes

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into the understanding what is the sourcing, what is the pricing, How are

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you going to be able to pull
this off? That's what analytics is here

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for. And because you have this
abstraction layer that allows you to dig into

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any number of different data sources,
you've greased the tracks to that kind of

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analysis. Right, that's right,
that's exactly right. Yeah, Because speed

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matters these days, you have to
know and you have to be able to

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fail. Fast forward, I think
is the real key. Try something,

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if it doesn't work, know that
it doesn't work, move on. No

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sacred cows in business, Right,
that's right, that's exactly right. Yeah,

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Yeah, that's interesting. Well,
let's dive into this open table format

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stuff. We talked about open source
earlier in the show, and how amazed

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I am still at the number of
open source projects we mentioned had dupe briefly,

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and I watched that whole evolution a
little bit skeptical, to be honest,

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I'm like, hmmm, I'm not
sure if this is if this is

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one NiFe that's going to solve all
these problems and that produce you know,

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it's great for indexing. The Web
had a very good use case, that's

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why it was designed right, but
it was there were security issues, there's

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overhead, there were network issues.
All kinds of different things came into play.

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But the point is we learned a
lot. We learned a lot about

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distributed systems. We learned a lot
about file storage, for example, and

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now we have these interesting developments in
file formats, in table formats. There's

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parque right, which has been adopted
as a standard, I think, and

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one of the reason real quick is
because it has that little analytical box at

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top right in the beginning of the
file. It has a little area that's

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designed to give metrics on what's in
that file, which is great for analytics.

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Right, do you know much about
Park? Yeah, yeah, no,

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you're You're absolutely right, And I
think that's one of the one of

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them most important, you know,
creations of that Hadoop era that you were

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describing, you know, maybe fifteen
years ago, you know, when I

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first got involved. Back then Park
didn't exist, and you know, the

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query performance was also exceptionally slow.
Map produce itself was exceptionally slow, and

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the file formats that you were reading
from were also very slow to read from.

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And so Park was a columnar format
first and foremost in terms of how

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the data was laid out, which
allowed for much better performance and mimics,

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as you say, like a lot
of the capabilities of a of a traditional

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database. And I think to your
earlier point, you know, Hadoo was

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a test ground of a lot of
new concepts that I think have been perfected

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now ten to fifteen years later.
And this is really like you know the

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Lake two point zero, if you
will, where every little component has been

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upgraded. You know, in the
early days it was Hive or maybe I

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Paula, if you were a cloud
era customer to it might be you know,

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Treno and Starburst on the query engine
layer. And similarly, while it

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was, you know, part of
k for a long time. You can

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now leverage Iceberg or other formats like
Iceberg, there's Hoodie, there's Delta.

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When can talk about some of the
pros and cons of each if you'd like.

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But you know, these really allow
you to do updates and deletes,

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which is sort of like finishing the
last mile on the file format piece.

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So you can now modify the data. You know, if you have a

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GDPR use case where you need to
take somebody out of a mailing database or

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mailing list if you will, because
they've opted out, you can now do

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that. And historically you couldn't do
that with data lakes. They were append

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only, and so it was a
real pain in the butt to try to

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actually modify your late. But with
Iceberg you can do that, you can

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00:25:44.920 --> 00:25:49.720
do time travel, you get all
this new functionality that is much more historically

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associated with a proprietary data warehouse like
tear Data or snowflake. That's great,

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it's another abstraction layer, right,
what was the on one of these shows

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years ago someone joked that in it
we always think that one more abstraction layer

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will solve all our problems, and
it kind of does at least it solves

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many problems. Right, then you
have to do all the mappings and make

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sure that you've doubted your eyes and
cross your t's. But nonetheless, that

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is incredibly important, and it's a
great example you just gave too, being

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able to delete something, say,
okay, get it out of this person

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that's unsubscribed. We don't want to
hit them anymore. Yep. Most people

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think, oh, can't you do
that? Not in that old format,

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no, but now in this format
you can. So we're always making sacrifices

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to achieve new ends in this business. The key is to kind of understand

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what do we need to keep,
what can we let go of? How

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can we solve these problems? And
these abstraction layers are very powerful, being

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able to solve many many problems in
one layer, right exactly, Yeah,

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yeah, I think you're exactly right. And you know the history of computing

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is a lot of abstraction on top
of abstraction, right, Like, thank

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goodness, we're not deciding exactly where
to write every bitten byte to a hard

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drive anymore, right, So,
yeah, well, there are lots of

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things happening. So how how would
a starburst work with Iceberg for example in

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a client situation. How does that
all come together? Yeah, So this

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is this is what's got me really
excited right now currently is that we've seen

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first and foremost widespread adoption of the
Iceberg format and tremendous momentum there. We

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see so many customers are looking at
it because it is open, it is

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independent. You know, number of
vendors have embraced it. Even Snowflake is

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embracing it, which is interesting on
its own right. But the industry has

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really rallied behind this this format,
and I think it becomes the de facto

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standard. It becomes the new new
parquet and you know, in the sense

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of its ubiquity is our view.
And one of the reasons that's exciting to

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us is because when Iceberg was first
created at Netflix, it was created to

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go pair with Treno or Pressed as
it was first known. Now today Netflix,

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a huge Traino user, uses that
with Iceberg, so that pairing of

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Trino and Iceberg goes back to its
earliest beginnings. And one of the reasons

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was that Netflix was a big data
warehouse customer of my former employer, and

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they were trying to figure out,
how can we really do the full functionality

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of data warehousing in an open warehouse
essentially open and warehousing model, and that

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was one of the motivations for the
creation of this format and it's been it's

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been very successful. So the combination
of trino and Iceberg is something that we

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see a large portion of the Internet
companies already embracing again LinkedIn, Netflix,

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many others, Apple and so forth. And we call this the ice house,

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you know, the Iceberg wear and
I think that's very exciting for those

401
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that are going to centralize data we
would say centralized that are in an ice

402
00:29:00.680 --> 00:29:03.559
house, and then you can also
leverage us of course to query the other

403
00:29:03.640 --> 00:29:07.720
data sources you have as well.
That's very interesting. You know. What

404
00:29:07.799 --> 00:29:11.079
also excites me, and I've seen
this now several times, is people in

405
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these very large organizations, in the
data teams dealing with massive amounts of data.

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I mean, goodness, greacious,
Netflix, think about how much data

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00:29:19.319 --> 00:29:25.039
that they're dealing with. It's a
tremendous amount and relatively unwieldy data too.

408
00:29:25.079 --> 00:29:27.160
A lot of machine data, but
a lot of user data, so not

409
00:29:27.200 --> 00:29:32.160
your traditional types of just transactions buying
this and that. I mean, they

410
00:29:32.240 --> 00:29:34.640
have that too, but they have
all these other things they have to manage.

411
00:29:34.920 --> 00:29:40.440
Well, what's interesting is how you
see these diasporas every so often where

412
00:29:40.480 --> 00:29:42.640
people will come up with an idea
in this organization that spring out and start

413
00:29:42.640 --> 00:29:47.000
their own thing. It started with
Yahoo I think was the first big one

414
00:29:47.400 --> 00:29:51.599
back in like the early aughts.
Basically a whole bunch of people sprang out

415
00:29:51.640 --> 00:29:55.799
of there and they found it,
I mean forty different companies, and that

416
00:29:55.880 --> 00:29:59.759
became the had dup ecosystem, right
because first there was adup hdfs. Then

417
00:29:59.759 --> 00:30:03.640
you had this whole ecosystem around it
to do all the stuff like governance and

418
00:30:03.680 --> 00:30:06.920
analytics and different things like that.
And now we're seeing it come out.

419
00:30:07.000 --> 00:30:08.759
People come out of Google, people
come out of LinkedIn, that's where Kafka

420
00:30:08.799 --> 00:30:12.880
came from. People come out of
Facebook and Meta. It's really interesting,

421
00:30:12.960 --> 00:30:17.720
right because they were at the coal
face, as they would say, hacking

422
00:30:17.799 --> 00:30:21.680
through these incredibly difficult challenges, and
then they went and they rolled their own

423
00:30:21.759 --> 00:30:23.680
like Cassandra, I mean that came
out of Facebook too, right, yep.

424
00:30:25.480 --> 00:30:27.000
I'm so with you on that,
Eric, And these are sort of

425
00:30:27.079 --> 00:30:33.279
some of my own personal truisms that
I've you know, learned over the years

426
00:30:33.599 --> 00:30:38.559
is a open is always is generally
better than proprietary, and then I think

427
00:30:38.599 --> 00:30:42.319
the markets move in that direction.
And b if you want to see what

428
00:30:42.359 --> 00:30:47.240
the future looks like, look at
the Internet companies because generally they're on the

429
00:30:47.240 --> 00:30:52.319
frontier, and I think architectures start
to evolve to mimic you know how those

430
00:30:52.359 --> 00:30:55.720
folks deal with things, because they're
always ahead of the game in terms of

431
00:30:55.759 --> 00:31:00.720
scale and performance and really pushing the
limits of those systems. Yeah, and

432
00:31:00.759 --> 00:31:03.119
then to open source stuff, I
mean, how cool is that, right

433
00:31:03.240 --> 00:31:10.039
for for the folks at LinkedIn to
roll out Copka, open source it,

434
00:31:10.160 --> 00:31:14.799
makeing it Apache project so that do
it yourselfers can go grab the code and

435
00:31:14.839 --> 00:31:17.799
stand things up. Now you're going
to have to maintain it, and that

436
00:31:17.880 --> 00:31:19.359
takes a lot of time and effort. Right, So, Like, there

437
00:31:19.359 --> 00:31:23.759
are pluses and minuses to using open
source stacks, and there are security issues

438
00:31:23.759 --> 00:31:27.839
we've learned about recently. There was
that one case of a developer who caught

439
00:31:27.839 --> 00:31:33.599
some little potential hack and it was
saved us all from a lot of trouble,

440
00:31:33.599 --> 00:31:37.920
I think. But nonetheless, I
think you're right that openness foster's innovation.

441
00:31:38.119 --> 00:31:44.079
It also builds trust right, totally, absolutely, Yeah, you know,

442
00:31:44.200 --> 00:31:49.519
and that's really important because, especially
in industries that have regulations, you

443
00:31:49.599 --> 00:31:52.720
want to have an audit trail.
You want to be able to explain what

444
00:31:52.880 --> 00:31:56.799
this thing is doing. Black boxes
are not so good. Chat GPT was

445
00:31:56.920 --> 00:31:59.799
open, open AI, now it's
not open. Now it's a black box.

446
00:32:00.160 --> 00:32:01.880
So these things are issues we have
to worry about. And I think

447
00:32:01.920 --> 00:32:06.200
that by and large, the black
boxes are are kind of over for a

448
00:32:06.240 --> 00:32:09.000
big business. But what do you
think about that? Yeah? I think

449
00:32:09.079 --> 00:32:12.759
again, you know, over time, you know, where there's an open

450
00:32:12.920 --> 00:32:15.200
alternative, that's where people are going
to want to graduate for all all the

451
00:32:15.240 --> 00:32:21.079
reasons that you described. You know, even at let's say JPMC, Jamie

452
00:32:21.160 --> 00:32:23.680
Diamond talks about you know, he's
never going to use one cloud, he's

453
00:32:23.680 --> 00:32:28.359
going to have four clouds, the
fourth I guess being his own on prem

454
00:32:29.279 --> 00:32:31.200
You know, he doesn't want to
choose one vendor. He doesn't want to

455
00:32:31.200 --> 00:32:36.000
be locked into anything. I mean, and this is like a banking CEO

456
00:32:35.759 --> 00:32:40.359
who has the the you know,
I guess foresight to think about his his

457
00:32:40.440 --> 00:32:45.759
technology strategy in a similar risk management
way. And I think, you know,

458
00:32:45.839 --> 00:32:50.039
Jamie has just been a fantastic risk
manager in all aspects of his business.

459
00:32:51.000 --> 00:32:53.920
But you know, just another great
example of you know, someone even

460
00:32:53.960 --> 00:32:58.400
at his stature, thinking about I
think a lot of these principles you're speaking

461
00:32:58.480 --> 00:33:00.960
of, Yeah, well, I
mean they are at the front end of

462
00:33:00.960 --> 00:33:04.400
innovation as well. I mean they
kind of have to be, because you

463
00:33:04.519 --> 00:33:07.319
have to protect that money first of
all, so it's all going to be

464
00:33:07.640 --> 00:33:13.400
focused on governance and security and compliance
and transactional integrity. You can't lose your

465
00:33:13.400 --> 00:33:16.640
money. But they're also very forward
thinking in terms of how to use analytics

466
00:33:16.680 --> 00:33:22.519
to understand customer behavior. And you
talked about risk too. I mean,

467
00:33:22.559 --> 00:33:25.759
what a complex feel that is.
I had a whole past life moderating webinars

468
00:33:25.759 --> 00:33:30.839
for a group called GARP. That's
the Global Association of Risk Professionals, or

469
00:33:30.880 --> 00:33:36.279
as I call them, the defacto
Illuminati, like chief risk officers for every

470
00:33:36.319 --> 00:33:38.920
central bank in the world, right, And these folks are like heavy hard

471
00:33:39.000 --> 00:33:44.519
eye on the market on what's happening. When COVID went down, they were

472
00:33:44.599 --> 00:33:47.079
the first to figure out, we
got to do something about this. And

473
00:33:47.119 --> 00:33:50.880
you know, just real quick,
I had a guy from my healthcare company

474
00:33:50.920 --> 00:33:54.359
on the show. Yes, I
guess about maybe nine months after COVID struck,

475
00:33:54.400 --> 00:33:59.880
and he said that in the first
six months after COVID hit they not

476
00:34:00.119 --> 00:34:05.720
out five years of digital transformation because
they had to and they knew they had

477
00:34:05.720 --> 00:34:07.920
to. And so when the pressure
mounts and you know you have to act,

478
00:34:08.039 --> 00:34:10.599
amazing things can happen. And it
was all the stuff we talked about

479
00:34:10.599 --> 00:34:15.960
a COVID of the spirit workforce.
You've got to understand your workflows, all

480
00:34:15.000 --> 00:34:17.039
that fun stuff. But folks,
don't touch up that. I'll be right

481
00:34:17.079 --> 00:34:29.480
back. You're listening to Inside Analysis. Welcome back to Inside Analysis. Here's

482
00:34:29.519 --> 00:34:35.440
your host, Eric Tabanaugh. All
right, folks, back here on Inside

483
00:34:35.480 --> 00:34:39.000
Analysis talking all things data and analytics
with Justin Bordman, co founder and CEO

484
00:34:39.079 --> 00:34:42.840
of Starburst. I love your company
name. By the way, you got

485
00:34:42.880 --> 00:34:45.199
a Starburst. That's good stuff.
We have a lot of fun with that.

486
00:34:45.400 --> 00:34:49.480
Yeah, those of you watching the
video, at least you can see

487
00:34:49.480 --> 00:34:53.119
this the background. That's one of
my favorite words in Spanish is Istria,

488
00:34:53.320 --> 00:34:59.360
the star Istria fugas the shooting star. Right, cool stuff. Yeah,

489
00:34:59.400 --> 00:35:02.559
but let's talk about the stuff you're
doing with Iceberg. And I understand you

490
00:35:02.599 --> 00:35:07.079
have a great team member who has
just joined you. But what you're doing

491
00:35:07.199 --> 00:35:10.599
in an in jest right, because
in jest has always fascinated me. You

492
00:35:10.679 --> 00:35:15.320
point your your connectors to data sources
to pull all that stuff in. How

493
00:35:15.360 --> 00:35:21.920
can you do that efficiently fast?
It's just really important to be able to

494
00:35:22.000 --> 00:35:23.519
do that because the longer you wait, the longer you're waiting to do someone

495
00:35:23.519 --> 00:35:28.039
else is tell us about that,
yeah, exactly. So building on that

496
00:35:28.239 --> 00:35:32.119
idea of a ice house architecture,
really what we're going for is to create

497
00:35:32.119 --> 00:35:38.360
a full end and experience that is, you know, just like a traditional

498
00:35:38.440 --> 00:35:44.719
data warehouse, but leveraging a open
format at its core. And again that's

499
00:35:44.719 --> 00:35:49.320
what an ice house is. It's
a open lakehouse leveraging Iceberg at its core.

500
00:35:50.000 --> 00:35:53.360
And that means, you know,
addressing a few areas, one being

501
00:35:53.480 --> 00:36:00.039
data ingestion, both streaming and batch
turning that data into Iceberg tables. A

502
00:36:00.079 --> 00:36:01.320
lot of people just you know,
don't know how to do that. They've

503
00:36:01.320 --> 00:36:05.559
never had to do that before.
So how do you create those tables.

504
00:36:06.400 --> 00:36:13.440
There's the governance aspects of access control, lineage, auditing. There's also just

505
00:36:13.480 --> 00:36:17.159
the management of those tables. There's
something called compaction where you want to compact

506
00:36:17.199 --> 00:36:22.960
a lot of small files into larger
files to get better performance. And so

507
00:36:23.039 --> 00:36:29.280
that's just kind of a utility that
needs to take place for performance optimization.

508
00:36:30.239 --> 00:36:36.239
There's you know retention and snapshot expiration. There's you know, capacity management,

509
00:36:36.320 --> 00:36:43.360
increasing or decreasing your size of your
cluster for performance to match the demand and

510
00:36:43.400 --> 00:36:47.360
being able to do that elastically as
well. So these are all the things

511
00:36:47.400 --> 00:36:52.119
that we've been working on from the
Starbars side. You're absolutely right. We

512
00:36:52.280 --> 00:36:58.079
just hired Carl Steinbach. He was
a very early member in the Iceberg community,

513
00:36:58.079 --> 00:37:00.239
has been working on Iceberg since twenty
eighteen, and he's a PMC member

514
00:37:00.280 --> 00:37:06.000
and actually was one of the co
founders of Tabula, which is an Iceberg

515
00:37:07.039 --> 00:37:12.719
company, and we're super excited to
have him here working with us on continuing

516
00:37:12.719 --> 00:37:15.760
to drive this this roadmap. And
one of the reasons he joined is I

517
00:37:15.800 --> 00:37:20.800
think exactly what we believe, which
is that you know, open formats belong

518
00:37:20.840 --> 00:37:25.079
with open engines, and this is
really the stack for giving you a end

519
00:37:25.079 --> 00:37:30.239
to end, you know, open
warehouse experience. I got to tell you,

520
00:37:30.280 --> 00:37:32.599
I love this ice house concept,
right, I mean, I've been

521
00:37:32.639 --> 00:37:36.880
around a long time. As we
were talking about data warehousing the data lakes.

522
00:37:37.119 --> 00:37:39.000
I will tell you I was a
little bit concerned in the early days

523
00:37:39.000 --> 00:37:42.679
of the data lake. I'm like, hmmm, are we making the same

524
00:37:42.800 --> 00:37:46.599
mistake again of thinking we can store
all this data in one discrete location.

525
00:37:47.159 --> 00:37:51.480
Because data is going to live wherever
it lives, and moving it is always

526
00:37:51.519 --> 00:37:54.760
going to take time and effort.
This is concept of data gravity. Right.

527
00:37:54.840 --> 00:37:59.079
Of course data doesn't have actual gravity, but the point is in a

528
00:37:59.119 --> 00:38:01.800
conceptual way it does because you have
to pull it out of somewhere, push

529
00:38:01.840 --> 00:38:05.360
it to some other location, land
it there, and make sure it all

530
00:38:05.480 --> 00:38:08.679
lands properly. And again this whole
federated worldview. I remember the first time

531
00:38:08.840 --> 00:38:13.920
I learned about Apache Arrow, I
was like, oh wow, that sounds

532
00:38:14.000 --> 00:38:16.960
very interesting. So enabling the federated
queries, I think in memory was their

533
00:38:16.960 --> 00:38:22.800
whole vision with Apache Arrow and again
another Apache project. Right. So here

534
00:38:22.840 --> 00:38:28.760
you have the best minds who come
together and in an open way develop technology

535
00:38:28.800 --> 00:38:32.880
that is open source, right,
because open leads to future connectivity, it

536
00:38:32.960 --> 00:38:38.480
leads to future collaboration. It's not
a closed door. Proprietary as a closed

537
00:38:38.559 --> 00:38:43.119
door. And you know, this
is what I saw way back when in

538
00:38:43.159 --> 00:38:45.719
like two thousand and five time frame
or something when I'm like, wait a

539
00:38:45.800 --> 00:38:51.079
minute, this open store stuff is
pretty cool. What's gonna stop it?

540
00:38:51.599 --> 00:38:53.719
And you know things have happened.
I won't name names, but there have

541
00:38:53.800 --> 00:38:58.920
been some movements to truncate open source. But I think the cats out of

542
00:38:58.920 --> 00:39:01.880
the bag. What do you think? I totally agree. I totally agree.

543
00:39:01.960 --> 00:39:06.480
And and you know there's another point
that you reference there on sort of

544
00:39:06.519 --> 00:39:10.800
centralization versus decentralization. Our answer is
both, like you're going to have you

545
00:39:10.800 --> 00:39:15.920
know, some centralization and in those
cases leverage open formats like Iceberg and the

546
00:39:15.920 --> 00:39:19.800
ice House model. And you're going
to have other cases where you're going to

547
00:39:19.840 --> 00:39:22.280
want to keep the data where it
is and query it there directly. And

548
00:39:22.320 --> 00:39:27.360
that's where you know federated queries adds
value. And so you know, we

549
00:39:27.400 --> 00:39:30.519
think it's a it's a both,
not a not an either or an or.

550
00:39:30.400 --> 00:39:36.440
And absolutely you know you want to
be leveraging open components wherever you possibly

551
00:39:36.440 --> 00:39:39.480
can in that sack to give you
that flexibility, right, well, and

552
00:39:39.599 --> 00:39:43.679
who likes lock in? Right?
Nobody wants lock in? Ye? I

553
00:39:43.719 --> 00:39:46.880
saw one of the big cloud providers, was it Google? Maybe that lowered

554
00:39:46.920 --> 00:39:52.760
their egress fees. Did you see
this recently? It was like, yeah,

555
00:39:52.880 --> 00:39:58.559
what's happening there? It's these you
know again, no one likes lock

556
00:39:58.639 --> 00:40:00.559
in, right, there's no CTO
that wants lock in. There's no CFO

557
00:40:00.639 --> 00:40:05.679
that wants to lock in. Nobody
wants that. And open formats allow you

558
00:40:06.039 --> 00:40:09.280
to stay open and to mix and
match to bring some new technology, because

559
00:40:09.280 --> 00:40:12.639
that's I mean you mentioned the term
future proof. Of course, that's the

560
00:40:12.679 --> 00:40:15.440
name of our show or our TV
show future Proof. It's really important these

561
00:40:15.519 --> 00:40:21.039
days to leave doors open because amazing
new things come down the pike. I

562
00:40:21.039 --> 00:40:23.480
mean Iceberg. I don't think too
many people saw Iceberg coming five six years

563
00:40:23.480 --> 00:40:28.000
ago. That's right, and here
it is, and I know for sure

564
00:40:28.079 --> 00:40:30.800
that all the other big guns look, oh we have to talk to Iceberg.

565
00:40:30.880 --> 00:40:35.000
Yeah, you probably should, because
it's just it's taken off. Right.

566
00:40:35.000 --> 00:40:38.360
There was Hoodi, Delta Lake,
and Iceberg are the three different versions

567
00:40:38.360 --> 00:40:43.679
you could use. And of course
Delta Lake is proprietary for data bricks.

568
00:40:44.039 --> 00:40:47.280
Hoody was for someone else, can
remember who launched Hoodi came out of Uber

569
00:40:47.480 --> 00:40:52.360
and there's a company behind that one
called One House. Whody's interesting. It

570
00:40:52.480 --> 00:40:58.800
just hasn't gained as much momentum,
you know, and sometimes it's just the

571
00:40:58.840 --> 00:41:01.199
most popular wins, right, and
I think Iceberg is that, But who

572
00:41:01.199 --> 00:41:05.280
do you whods a great format too, and we do support all three,

573
00:41:05.440 --> 00:41:07.800
you know, back to that point
like optionality is what creates that that that

574
00:41:07.840 --> 00:41:14.400
future proof ability, right and and
and then allows you to kind of double

575
00:41:14.440 --> 00:41:17.599
down where where the market moves.
And I think in this case you know

576
00:41:17.639 --> 00:41:22.039
that that's Iceberg. Yeah, no, that's a good point. And uh,

577
00:41:22.280 --> 00:41:27.599
and getting that momentum, getting enough
developers focused on something that's one of

578
00:41:27.639 --> 00:41:30.360
the key too. Maybe we should
dive into that for the developer world,

579
00:41:30.480 --> 00:41:35.760
right, because it's I heard a
great stat that every year, like the

580
00:41:35.840 --> 00:41:39.960
number of developers is growing by some
astronomical number, which means there are new

581
00:41:39.960 --> 00:41:45.280
developers all over the place every year. And so how do you attract those

582
00:41:45.320 --> 00:41:50.039
folks to work on your stuff?
To understand your architecture, your your languages,

583
00:41:50.159 --> 00:41:52.360
your vision. That's all important,
right, And I've seen this develop

584
00:41:52.440 --> 00:41:58.000
over the last really less i'd say
seven to ten years. Software companies not

585
00:41:58.199 --> 00:42:00.719
just trying to get clients, but
working for or to get developers to pay

586
00:42:00.760 --> 00:42:04.559
attention to them to work in their
communities, right, talk about that for

587
00:42:04.559 --> 00:42:09.280
a minute. Yeah, that's an
excellent point, And I think developers like

588
00:42:09.360 --> 00:42:15.599
to work on open source frankly,
it actually allows them to not only you

589
00:42:15.599 --> 00:42:20.239
know, build their resume on technologies
that they know are going to be useful

590
00:42:20.280 --> 00:42:23.760
wherever they go in their career,
but also if they choose to participate in

591
00:42:23.800 --> 00:42:29.559
those communities and contribute, they're building
a different kind of resume on GitHub,

592
00:42:29.639 --> 00:42:32.480
you know, and that's really valuable
to them. And so I think you're

593
00:42:32.519 --> 00:42:36.679
absolutely right. I think this is
one of the secrets, by the way

594
00:42:36.880 --> 00:42:44.039
of Facebook's success from an engineering organization
perspective, They've always attracted tremendous talent,

595
00:42:44.440 --> 00:42:46.440
and I think one of the reasons
is that they have a very open source

596
00:42:47.519 --> 00:42:54.280
centric culture that allows their developers to
participate in a variety of projects and really

597
00:42:54.280 --> 00:42:59.320
become almost like famous, if you
will, within those projects. And that's

598
00:42:59.320 --> 00:43:04.480
certainly the history of my co founders
creating Presto and Trino when they were there,

599
00:43:04.800 --> 00:43:07.920
and so many other great you know, open technologies that have come out

600
00:43:07.960 --> 00:43:12.599
of companies like that, you know, like Cofka, at LinkedIn and others.

601
00:43:13.599 --> 00:43:16.760
Yeah, I think it's really amazing, and developers are the front lines

602
00:43:16.800 --> 00:43:21.400
these days. They are building the
products that you use. When your products

603
00:43:21.400 --> 00:43:25.239
are web based software as a service, or you're selling all sorts of things

604
00:43:25.360 --> 00:43:29.159
using the web to sell all that
stuff. Understanding all that, you need

605
00:43:29.199 --> 00:43:31.920
developers and you want the developers to
be motivated. You want them to be

606
00:43:32.000 --> 00:43:36.599
interested in things. And you made
an excellent point about how GitHub is like

607
00:43:36.679 --> 00:43:39.199
your resume. It's a meritocracy.
So you're not just saying you can do

608
00:43:39.239 --> 00:43:43.079
stuff. You're saying, look at
the stuff I've done, this is how

609
00:43:43.079 --> 00:43:45.239
it works. And this is just
such a white hot space. It's a

610
00:43:45.280 --> 00:43:52.920
great place to be for a developer
working with data, working with the unique

611
00:43:52.000 --> 00:43:55.239
nature of any business. I think
that's probably the exciting thing, right,

612
00:43:55.320 --> 00:44:00.880
is that every company is different.
Every business has its own, its own

613
00:44:00.920 --> 00:44:04.360
way of doing things, its own
people. Of course, businesses a bunch

614
00:44:04.400 --> 00:44:07.239
of people. It's people, data, systems, technology, vision, where

615
00:44:07.280 --> 00:44:10.760
things are going, and what Starburst
is doing and the other people in this

616
00:44:10.880 --> 00:44:17.159
industry. To be fair, you
are providing the mechanisms of action for inventing

617
00:44:17.199 --> 00:44:22.519
new things, for trying new things, for deploying new things. And it's

618
00:44:22.559 --> 00:44:25.159
going to be all about efficiency in
this economy. I mean people say it's

619
00:44:25.159 --> 00:44:30.360
the information economy. I think it's
more like the execution economy these days.

620
00:44:30.400 --> 00:44:32.920
Because the information is everywhere, the
data is all over the place. The

621
00:44:34.039 --> 00:44:37.480
question is what do you do with
it? How quickly can you make use

622
00:44:37.599 --> 00:44:40.000
of it? And that's really what
we're talking about with these federated queries and

623
00:44:40.039 --> 00:44:45.280
these distributed systems, make use of
it quickly, such as you can ideate,

624
00:44:45.880 --> 00:44:50.800
create, test market and then redo, reinvent, try something different.

625
00:44:50.800 --> 00:44:53.000
It's all going to be very fluid
in the next few years, and the

626
00:44:53.000 --> 00:44:58.199
companies that manage their data best are
going to have the best chances of victory.

627
00:44:58.239 --> 00:45:00.199
But podcast bonus segments coming up next, folks, don't touch that Valu're

628
00:45:00.239 --> 00:45:07.559
listening to Inside Analysis, all right, folks, back here on Inside Analysis.

629
00:45:07.559 --> 00:45:10.760
Time for the podcast bonus segment with
Justin Borgman of Starburst CEO and co

630
00:45:10.880 --> 00:45:16.239
founder. And Starburst Galaxy is your
cloud based system. Like many vendors,

631
00:45:16.239 --> 00:45:22.119
you started off with a non prem
version and you realize that SaaS is probably

632
00:45:22.119 --> 00:45:24.039
the future. So now you've got
star Wars Galaxy, which I think you

633
00:45:24.039 --> 00:45:29.400
said is the easy button for the
ice house. That's right, that's pretty

634
00:45:29.480 --> 00:45:34.559
cool. So up in the cloud. This is metadata management largely right up

635
00:45:34.599 --> 00:45:37.440
in the cloud. You would then
point to your different data sources and it

636
00:45:37.480 --> 00:45:40.639
pulls the metadata up in there,
and then you can kind of rationalize and

637
00:45:40.719 --> 00:45:45.599
reconcile it and work your magic.
Is that about right? Yeah. You

638
00:45:45.920 --> 00:45:50.000
absolutely can connect to all of your
different data sources. You can query them

639
00:45:50.039 --> 00:45:53.559
directly. You have the ability to
hook up your favorite bi tool or query

640
00:45:53.920 --> 00:46:00.079
editor or Python notebooks or whatever the
case may be, and and interact with

641
00:46:00.119 --> 00:46:02.880
that data directly. And as you
said, it's the easy button. We

642
00:46:02.960 --> 00:46:08.679
try to make this, you know, super sophisticated distributed MPP engine as easy

643
00:46:08.719 --> 00:46:15.079
to use, uh, with as
few knobs to turn as possible to make

644
00:46:15.119 --> 00:46:20.599
it, you know, really low
low maintenance for you from a operational perspective.

645
00:46:21.320 --> 00:46:23.000
We also have built in a lot
of these features we were talking about

646
00:46:23.039 --> 00:46:28.480
earlier in the on the ice House
topic of you know, streaming in jest

647
00:46:28.760 --> 00:46:35.119
and creating Iceberg tables, and you
know, doing compaction and data profiling and

648
00:46:35.199 --> 00:46:39.559
data lineage and creating curated data products
off the data that you have. You

649
00:46:39.559 --> 00:46:46.280
can do all of that within this
Galaxy experience. And you know you touched

650
00:46:46.320 --> 00:46:51.280
on in the earlier segment the important
of efficiency. One one of one element

651
00:46:51.320 --> 00:46:58.960
of efficiency is cost performance efficiency,
and compared to cloud data warehouses, we

652
00:46:59.199 --> 00:47:04.760
are roughly one third the costs of
a traditional cloud data warehouse. So you

653
00:47:05.360 --> 00:47:09.880
can save a bunch of money leveraging
this open lakehouse model or ice house as

654
00:47:09.920 --> 00:47:15.800
we call it, and Galaxy makes
it easy to do that. Now,

655
00:47:15.119 --> 00:47:19.719
I kind of hear some hints of
governance in there. I mean, if

656
00:47:19.760 --> 00:47:22.920
you can access the systems and manage
metadata and things of that nature, is

657
00:47:23.000 --> 00:47:28.559
governance in the roadmap? Can you
do data governance to some extent? Yeah.

658
00:47:28.599 --> 00:47:32.159
So access control has always been actually
part of our strategy, at least

659
00:47:32.159 --> 00:47:37.719
as a company. So we built
in fine brained role based access controls,

660
00:47:37.800 --> 00:47:42.159
you know, many years ago,
maybe five years ago. More recently in

661
00:47:42.239 --> 00:47:47.039
last couple we've actually upgraded that to
attribute based access control as well, and

662
00:47:47.079 --> 00:47:52.480
so you can tag your data and
leverage that for your access controls. But

663
00:47:52.559 --> 00:48:00.079
also we've even added the ability to
sort of automate tagging of data. And

664
00:48:00.360 --> 00:48:04.480
this is where we actually incorporate some
AI in the product itself, where we

665
00:48:04.519 --> 00:48:08.280
can automatically tag p I I data
to help you out in your in your

666
00:48:08.960 --> 00:48:14.320
in your governance strategy. By by
no means do you have to leverage that

667
00:48:14.360 --> 00:48:16.880
you should also be checking all of
all of your data, but it helps

668
00:48:16.920 --> 00:48:22.559
with with that process. And again
very fine grain, so row level,

669
00:48:22.679 --> 00:48:25.400
column level, data masking, query
auditing, all of that's built into the

670
00:48:25.440 --> 00:48:30.559
platform and it's enforced across all the
data that you connect to, So not

671
00:48:30.800 --> 00:48:35.760
just your Iceberg tables, although of
course that's that's a common common use case,

672
00:48:36.079 --> 00:48:39.559
but also across you know, my
SQL postgrass oracle, you know what,

673
00:48:39.559 --> 00:48:45.199
whatever data sources you may be connecting
to. That's very interesting. I

674
00:48:45.199 --> 00:48:50.159
mean, I think it sounds like
a wonderful way to have this management layer

675
00:48:50.239 --> 00:48:52.119
because then it doesn't matter where you
are. You could be on the road,

676
00:48:52.159 --> 00:48:54.679
you could be at your home office, you could be anywhere. You

677
00:48:54.760 --> 00:49:00.039
just log into your cloud based system. You can see this whole environment and

678
00:49:00.400 --> 00:49:01.719
you can run your queries from that
environment. Right, I mean, you

679
00:49:01.800 --> 00:49:06.079
have to do your connectors to your
various systems on prem or whatever they are,

680
00:49:07.000 --> 00:49:08.639
and see that. This is what
I think is so exciting is being

681
00:49:08.679 --> 00:49:13.360
able to connect to SaaS sources on
prem sources whatever the case may be,

682
00:49:13.920 --> 00:49:17.480
from one marsting area then to be
able to build queries, ask questions across

683
00:49:17.519 --> 00:49:22.039
this whole environment and under the covers
star Wars Galaxy does that right? That's

684
00:49:22.079 --> 00:49:25.760
exactly right. Yep, that's exactly
right. And for the power user,

685
00:49:25.800 --> 00:49:30.000
there's even a built in query editor, so you can literally just open it

686
00:49:30.079 --> 00:49:34.199
up and start using it. Of
course, if customers prefer their favorite tool

687
00:49:34.320 --> 00:49:37.960
maybe Tableau, powerbi Looker or what
have you, you can use that as

688
00:49:37.960 --> 00:49:42.599
well. But that's exactly right.
All that powers at your fingertips. This

689
00:49:42.719 --> 00:49:45.840
is wild, wildly stuff, folks. While we've been talking to Justin Borgman,

690
00:49:45.000 --> 00:49:50.880
CEO and co founder of Starburst,
hop online to Starburst dot io to

691
00:49:50.960 --> 00:49:54.000
learn more about those folks, and
it's moving fast, folks. I like

692
00:49:54.039 --> 00:49:58.679
the ice house, the open lake
house. I think the ice house sounds

693
00:49:58.679 --> 00:50:00.760
good. That's tough. Talt you
next time. Send me need if you

694
00:50:00.760 --> 00:50:02.800
want to be in the show.
Info at inside analysis dot com. We'll

695
00:50:02.840 --> 00:52:00.760
talk you next time. You've been
listening to Inside Analysis, jerfully everything to

696
00:52:00.880 --> 00:52:06.119
everyone? Or can we the station
that leaves no listener behind? KCAA.

697
00:52:08.480 --> 00:52:12.920
Hi, this is Chris Klin,
investment manager for Capstone Wealth Management. I've

698
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been through just about every market imaginable
since the early nineties, and you know

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what they have in common. We
helped people just like you navigate them and

700
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that's given our investors peace of mind. Now, my boys say being in

701
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the game this long just makes me
old, but I say it makes me

702
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battle tested. I've been blessed to
work for a lot of people who have

703
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entrusted tens of millions of dollars of
their hard earned capital, and me and

704
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my team, if you'd like to
see how we can successfully manage your money,

705
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let's start a conversation. The best
way to do that is to shoot

706
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eight Monday through Saturday, nine am
to five pm California time. That's eight

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one eight six one zero eight zero
eight eight to hebot Club dot com with

733
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sixty years of fascinating facts. This
is the man from yesterday and back in

734
00:54:47.880 --> 00:54:52.119
time. We go to this time
in nineteen eighty two. Well, it's

735
00:54:52.199 --> 00:54:55.679
official. Don Henley and Glenn Fry
have broken up. The Eagles each has

736
00:54:55.760 --> 00:55:00.880
solo albums in the works. Here
in nineteen eighty two, the new King

737
00:55:00.119 --> 00:55:14.039
in Town, Everybody Loves You,
and from this time in two thousand and

738
00:55:14.079 --> 00:55:17.480
five, Ken Jennings, who won
seventy four games on Jeopardy, loses a

739
00:55:17.519 --> 00:55:22.880
three day Tournament of Champs and a
two million dollar prize to a Pennsylvania contestant.

740
00:55:23.039 --> 00:55:28.760
This term for a long handled gardening
tool can also mean an immoral pleasure

741
00:55:28.840 --> 00:55:35.400
seeker. Ken, What's a hoe
no, whoa whoa, and back in

742
00:55:35.400 --> 00:55:38.599
time to this time. In nineteen
sixty nine, while conducting your Montreal Bedding

743
00:55:38.639 --> 00:55:43.880
for Peace at the Queen Elizabeth Hotel, John and Yoko Lenin put out a

744
00:55:43.880 --> 00:55:47.679
call for recording equipment. Someone wrote
oversized lyrics to their new song Give Piece

745
00:55:47.679 --> 00:55:52.000
a Chance and placed them up on
a wall in their sweet so everyone could

746
00:55:52.039 --> 00:56:14.159
see with more at man from Yesterday
dot com This Mother's Day, help fight

747
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breast cancer, schedule your mammogram.
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Body Shop. Route supporters in the
Battle against breast cancer. Tune into the

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Faran Doozier Show USIC marks Place in
Time the soundtrack to Life Sunday nights at

755
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eight pm on KCAA Radio, playing
the hottest hits in the Coolest Conversations Sunday

756
00:56:57.079 --> 00:57:00.239
Nights at a PM on The Faran
Dozier Show with an array of music,

757
00:57:00.639 --> 00:57:06.800
talk, sports, in the outreach
and veteran resources with the hits for your

758
00:57:06.920 --> 00:57:12.800
sixties, seventies, eighties, nineties, and today's hits. The Farando zih

759
00:57:12.880 --> 00:57:20.440
Show on KCAKA Radio on all available
streaming platforms and almost six point five FM

760
00:57:20.480 --> 00:57:39.119
and ten fifty AM The Farando zi
Show on KCNA Radio, NBC News Radio.

761
00:57:39.239 --> 00:57:44.079
I'm Chris Garagio. The United Nations
says tens of thousands of Palestinians are

762
00:57:44.079 --> 00:57:49.480
fleeing Southern Gaza daily as the fighting
there intensifies. UNI spokesperson Tess Ingram calls

763
00:57:49.519 --> 00:57:52.440
it a desperate situation. Do we
know that in the last week, three

764
00:57:52.519 --> 00:57:57.679
hundred thousand people have been forced to
leave Rafa. They're piling onto donkey carts

765
00:57:57.679 --> 00:58:01.760
and trucks and buses possessions. You
and officials released the figure today as the

766
00:58:01.800 --> 00:58:07.320
Israeli military called for more evacuations from
Southern Gaza's largest city, The Idea of

767
00:58:07.360 --> 00:58:10.320
reports dozens of Hamas fighters have been
killed in heavy fighting near the area where

768
00:58:10.360 --> 00:58:15.880
more than one million Palestinians have been
seeking shelter. Former Trump lawyer Michael Cohen

769
00:58:15.960 --> 00:58:19.639
is set to testify tomorrow at the
hush money trial. Donald Trump is accused

770
00:58:19.639 --> 00:58:22.960
of falsifying business records to hide a
hush money payment that Cohen made adult film

771
00:58:23.000 --> 00:58:28.320
actress Stormy Daniels ahead of the twenty
sixteen presidential election. The New York judge

772
00:58:28.320 --> 00:58:31.039
presiding over the trial has been telling
Cohen to stop talking about the case.

773
00:58:31.199 --> 00:58:36.440
Judge Van Marshan issued the warning on
Friday after Donald Trump's legal team brought up

774
00:58:36.480 --> 00:58:38.960
recent statements by Cohen, who went
on TikTok last week wearing a t shirt

775
00:58:38.960 --> 00:58:44.039
that appeared to show Trump behind bars. The controlled demolition of debris lying on

776
00:58:44.039 --> 00:58:47.239
the cargo ship that struck the Key
Bridge as being delayed until tomorrow. According

777
00:58:47.239 --> 00:58:52.800
to the Baltimore Sun. Keybridge Unified
Command says the operation is being pushed back

778
00:58:52.840 --> 00:58:57.280
another day due to weather concerns.
Cruz initially had planned to use explosive charges

779
00:58:57.320 --> 00:59:00.480
over the weekend to break apart a
large bridge trust and haul it away.

780
00:59:00.679 --> 00:59:05.599
Removing the debris will allow the ship
to be refloated and returned to the Port

781
00:59:05.639 --> 00:59:08.159
of Baltimore. Kingdom of The Planet
of the Apes is finishing first at the

782
00:59:08.159 --> 00:59:12.960
box office. On its opening weekend, The action adventure film earned fifty six

783
00:59:13.000 --> 00:59:16.719
point five million dollars, topping expectations. Action comedy The fall Guy finished second

784
00:59:16.719 --> 00:59:22.039
with thirteen point seven million. Tennis
drama Challengers finished third again this week with

785
00:59:22.079 --> 00:59:27.320
four point six million. Low budget
horror film Tarot finished fourth with three point

786
00:59:27.320 --> 00:59:30.039
four million, and Godzilla ex cong
and New Empire refuses to leave the top

787
00:59:30.079 --> 00:59:34.880
five with two point five million.
I'm Chris Karragio, NBC News Radio,

788
00:59:37.199 --> 00:59:43.119
NBC News on KCAA Lomelanda sponsored by
Teamsters Local nineteen thirty two, Protecting the

789
00:59:43.159 --> 00:59:52.480
Future of Working Families Teamsters nineteen thirty
two dot org. You're listening to an

790
00:59:52.639 --> 01:00:00.360
encore presentation of this program KCAA The
Inland Talk Express. Thank you for tuning

791
01:00:00.400 --> 01:00:06.079
in for this edition of Justice Watch
with Attorney Zulu Ali. I am Attorney

792
01:00:06.159 --> 01:00:08.320
Zulu Ali, with a Justice Watch
crew throws a new