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Learn more at Inside analysis dot Comsideanalysis dot com. And now here's your host,
Eric Kavanaughs. Oh, ladies and gentlemen, Hello, and welcome back
once again to the only coast to coast radio show that's all about the information
economy. It's time for Inside Analysis, your host Eric Cavanaugh here in folks.
I'm very pleased to have an industry visionary on the call today. We're
to be talking to Justin Borgman. He is the co founder and CEO of
a company called Starburst, and they are shaking up the industry of analytics.
They've come up with a way to allow you to query data pretty much wherever
it is. Maybe it's in your relational database, maybe it's in in open
formats like Iceberg. We're to be talking a lot about Iceberg. I'm going
to be attending their virtual event shortly too. I think it's in about a
week or two. And they've shaken up the market in significant ways. And
justin welcome to the show. Thank you for having me. Eric super excited
to be here. Yeah. Absolutely, And as we were just discussing before
the show, I've been in the data warehousing space for a long time,
almost twenty five years. It's hard to believe. And boy have things changed.
I mean, if you go back in you know two thousand and one
time frame, storage was expensive, processors were relatively slow, the pipes were
fairly thin, so it was a different architecture. And these days, I
mean, goodness, gracious, we've had so many innovations in distributed systems for
example, and then being able to leverage data where it lives. And I
think that's one of the keys to success in the future is being able to
analyze data wherever it's sitting. And that was one of your early concepts right
when you rolled this out. Please talk about that. Yeah, exactly.
So you've got me beat in the number of years, department, But I've
been in the space for fifteen years, and even fifteen years ago. You
know, it was very interesting. That was really the early days of Hindu
kind of the first data lake, and a lot of the concepts that are
so important today were actually kind of pioneered back then, you know, the
idea of open formats like parquet files and avro and rc file and storing that
in cheap storage and a distributed file system and so forth. And then my
first company was acquired by Terra Data. I spent a few years there,
and to me, one of the things that I realized was that despite terror
Data being the industry leader certainly at the time, not one of their customers
actually had all of their data sitting in a classical data warehouse. That single
source of truth in one proprietary EDW just wasn't actually achievable, and that you
would always have data that lived outside of that, and even where you could
centralize, it actually probably made more sense for you to centralize in more of
a lake model. And around the same time as I was having these realizations,
I discovered an open source project coming out of Facebook that was originally called
Presto it later became Trino as it's known today, and decided to partner with
the creators of this project while they were at Facebook. We started collaborating together
between Terra Data and Facebook to improve this project, make it really enterprise grade,
and in twenty seventeen left ter Data to form Starburst as the company behind
this project, and the creators of Preston Trino joined me from Facebook and sort
of, you know, from there we went, yeah, that's fantastic.
You know, I'm the biggest fan of open source. And it was in
fact, back in two thousand and five when I was working for the Data
Warehousing Institute that I did a bunch of research into it. And way back
then, the Apache web server had just eclipsed the Microsoft the web server as
the number one web server, and I was like, oh, this is
very interesting stuff. And I remember doing some research and doing some writing around
the nexus of open source, and back then, of course it was service
oriented architecture, and I was thinking to myself, hmmmm, this is very
interesting because ideally, if you have an SOA and you are using open source
technologies, you should be able to rip and replace different component parts build a
composable architecture. And that doesn't sound like good news for the big Oracles and
ibms of the world. Of course, IBM though, went all in an
open source and really supported so they saw that vision. But wow, you
look back in time now and that was a very pivotal moment in this industry,
right when open source really took over. Now a patchy is huge with
all these different projects. That's pretty cool stuff, right absolutely. I mean
that was one of my key lessons from that period as well. I think
like when when you have an open option and a proprietary option, that the
open option is is probably going to win, and it's it's better for customers.
I mean, at the end of the day, I think customers,
you know, get smart about these things, and they want to build,
you know, architecture that can stand the test of time, you know,
something that's truly future proof. And and that's why it's so important to use
open components because they are you know, generally modular and swappable and you can
adapts as things change. And at least from where we said, that means,
you know, how you store your data can change. You know,
maybe it's Oracle and tereartate on prem today, and maybe it's you know,
storing data and Iceberg and S three and you know, Mango deb for for
your operational database in the future. Right, And being able to have analytics
across all of that, regardless of where it lives, is really core to
our mission and providing customers that optionality. Yeah, and that's so cool because
it's always in the context that you understand a particular business problem. And as
I as I'm sure you know, in the old days if you will,
of data warehousing, the whole idea was to pull data from source systems,
put it into a relational model that then you can analyze it. Because we
learned that these ERP systems and other production systems were not designed for analytics.
They were designed for doing stuff for transactions. So it's a different architecture,
it's a different workflow, and the idea was, well, gosh, how
can we query these things? Well, you do that by bringing it into
a data warehouse. But by enabling analysis of data and many different systems and
formats, you've obviated the big, huge need of all of this ETL extract
transform load, extract transform load. It's never going to go away. But
I remember seventeen years ago when we launched DM radio thinking to myself, this
has got to stop. I mean, there's so much time, effort,
and money spent moving data around, and we now have evidence to suggest that,
you know what, eighty percent of that data isn't even really needed for
your queries. So you're moving data that you don't even need to move.
Well, that's silly in and of itself, but then when you can analyze
it where it lives, now you've enabled the kind of rich context that's going
to help answer business questions. Right, that's right, Yeah, that's exactly
right. Yeah. So, I mean I think it's fascinating that you're able
to do that. Can you kind of get into the weeds a bit about
how so obviously of connectors, ODBC, JDBC traditional ways of connecting to data,
but then you can sort of infer the format of the data and then
transform that I guess in real time to be able to do analysis. Is
that right, walk us through how that whole process works. Yeah. So
you know, the way I like to explain it to folks is that Trino
and Starburst are like the top half of the database. And what I mean
by that is it's a database without storage, so we access the storage wherever
that may be. It could be a traditional relational database as you said,
maybe it's Oracle or Terra Data or SQL server or Postcress or mycequl or what
have you. It could be no SQL database like Mango. It could even
be cough Ga topics. It could be these open formats, which I think
are a big deal, and I know we're going to spend some time talking
about those in a bit like Iceberg. But regardless of where you store it,
what we're doing is we're essentially connecting to it, connecting to the catalog
that has the metadata about how that data is laid out, and and executing
that query in memory in a in a distributed MPP architecture massively parallel processing architecture,
so it's scalable and super fast. And that's really I think what what
gave it its momentum as a as a project when it was first created at
Facebook years ago was the fact that this thing could really work at petabyte scale
with you know, tons of concurrency and complexity around the types of analytics that
you're trying to do and do that in a performance and scalable way. And
so today it's it's leveraged by some of the largest organizations of the world LinkedIn
Netflix, AIRBNBU, you know, large enterprises like Comcast, a ton of
the big banks, and and really using this at at at scale to do
exactly as you described. Yeah, and I mean this whole issue of being
able to analyze data where it lives, no matter what format it's in,
Like you said, a no SQL database Cassandra Mango dB our relational database.
Of course my SQL postcress SQL. You know as well as I do.
There are millions and millions of instances of these databases all around the globe.
But the bigger the company gets, the more you're likely to have multiple different
systems, maybe you know, a call center system, a CRM, customer
databases, all these different kinds of systems, which hitherto in the old days
you would have had to run extraction routines to pull the data out load it
into a particular schema. Well instead, what you're doing is like you say,
you got that top half of the database, and you don't care where
it's stored. So now your business analyst can go, you know, I
want to be able to triangulate how much we're selling with our with our current
supplies, with our customer needs, with what we're seeing on our website,
all these different things. You can pull that together in a query and hit
boom, get and get answers. Right, that's exactly right. And I
think to your point, you know, if you're a large enterprise fortune five
hundred and forty one thousand enterprise, that that sort of Cambrian explosion of data
sources is inherent. You know, it's unavoidable. And in fact, there's
a venture capitalist named Matt Turk at First Mark Capital who's only I mean you've
probably you know where I'm going with this here. He maintains this data landscape
of all the different types of database systems and data sources basically that exist in
our space. And he's updated it every year since back when I was doing
my first startup fifteen years ago. And so you can watch this progression.
It never simplifies, it never seems to get smaller. It's just more and
more and more data sources, to the point where if you're a large organization,
you have probably most of what's on that on that market map. Yeah,
and to your point, the world is getting more diverse. There are
more tools, there are more data types, and there's value in those data
types. There's value in them hills, as they say, there's gold in
them hills. Right. So to be able to have this abstraction layer above
the panoply of data sources underneath, that's very powerful because again, otherwise you
would have to manually go in and connect to this source pull all that stuff
out. Those pipelines break all the time. They're expensive, they're cumbersome.
Typically you lose some data when you're doing that. You can have all sorts
of data quality issues occur. So instead just leaving it where it is and
being able to query it, and you still want observeability, you still want
to know when it's not coming through the way it's supposed to come through.
Things of this nature, that whole space is exploded as well. The key
is to be able to enable your professionals, your analysts, to ask whatever
questions they want from data that's in any number of different sources, right,
yep, exactly, that's exactly right. That's pretty cool stuff. And give
us some examples maybe of some of your clients how they're able to pull together
data from multiple different sources to see something and to understand something that's key to
their business. So one example that we've been working with Comcasts for a number
of years. Actually, they've been a customer CHISE, I don't know,
probably maybe six years now, since the earliest days of the company's existence,
and I always love chatting about some of their use cases because they're very relatable
to a wide audience. We all watch TV in some former fashion. And
when they first started working with us, they had all of the classical billing
data in a traditional enterprise data warehouse on prem and they had all the viewing
behavior basically every time you change that channel, every time you interact with that
cable box that was stored in a Hadoo data lake. And what they really
wanted to do was be able to correlate the shows that you watch with how
much you spend and do cross sell and upsell based on your viewing behavior,
which makes a ton of sense, but is a perfect example of one data
set living in one data source which is huge volume, right, like all
that event data or every time you change the remote that needs to be in
a data lake. I mean, you really wouldn't want to put that in
a data warehouse. Its cost you a fortune. But then they had all
that classical billing data from you know, decades of customer history in the in
their data warehouse, and so they wanted to be able to combine that join
tables across these two systems uh. And and we were able to deliver that
value very quickly for them simply by being this abstraction layer and being able to
join across these two data sources over time. What that let them do also
is start a migration effort, a digital transformation effort, if you will,
or cloud transformation effort maybe more precisely, uh, and start to move some
of that data into the cloud into you know, object storage like S three
or across multiple clouds uh, and leverage more of a data lake or lake
house architecture for large portions of that data, where we become essentially the engine.
And and again I know we'll get to the but basically, when you
combine you know, Starburst or Treno and a open format like Parquet or Iceberg
or Hoodie or what have you. You essentially have a warehouse. It's just
an open source warehouse with an open engine combined with an open format, which
can now today in today's world, give you the same performance and functionality that
you might traditionally have gotten from a proprietary data warehouse. Yeah, you know,
that's very interesting for lots of reasons. One, you don't want to
hamstring your analysts. You want your analysts to be able to ask whatever questions
are going to come to their mind, and you want them to be able
to leverage new data sources quickly. Yeah. Right, Because there's an opportunity
cost of doing the old model in that people just don't think outside the box.
They think very much inside the box, and that greatly limits the kinds
of questions you can ask, the kinds of analysis you can do. So
what you've really done is open the doors to all kinds of exploration. Right,
that's exactly right. You literally have the freedom to be curious, uh
and be able to get a response and iterate on that curiosity. So for
exploratory analytics, it's it's truly a game changer to have everything at at your
fingertips. And you know, in some of the large organizations that we work
at, big banks and so forth. I'll tell you another anecdote. Uh,
this one, I can't say the name, but I'll just say one
of the largest banks, Uh, you know, the the CEO used to
call their data analytics team and say, hey, can you tell me you
know X, y Z, you know how many how many times a day
are our customers checking the banking app today? Or you know what's our what's
our mortgage default risk or what have you? And uh, it used to
take that team, you know, a week or two weeks and they'd say,
hey, yeah, we'll get we'll get that answer for you. But
now by having connectivity to all those data sources, that the analytics team can
say, hey, just wait, wait one second and get the answer while
he's on the phone, you know. And that's a that's a powerful,
powerful thing. Well, it is amazing because again, once you acclimate to
being able to discover I love that line you said, you have the freedom
to be curious. It's very interesting because the mind is a very powerful thing,
and I know that these analytical engines are very powerful, but human creativity
is the key ingredient in all this stuff. Right, you can have all
this data, all this analysis, reports, dashboards, everything you want.
It's a human being that's going to have to really look at that and map
it all through the business and the plans and where you want to go.
And these days you have to be able to change plans pretty quickly. I
mean, I've been in business myself for on and off, self employed for
twenty five years and two long instances, and I've never seen a more dynamic
marketplace than what we have today. I mean, especially for mid sized businesses,
but even for big businesses. You have to be able to know what's
happening. Where's the money coming from, where are the expenses coming from,
where's the margin coming from, what are the people doing. All these things
are crucial to understand and be able to pivot, because otherwise you have to
lay people off, and a lot of times we've seen a lot of layoffs
these days. It's very difficult to recover from that stuff. Just knowing what
people we're working on is half the battle. Then being able to reallocate that
worked as someone else is another whole half of the battle. And then by
the time you've done that we'll guess what the market's changed again. So the
long winded point I'm making here is that it's really important to be able to
analyze your data to understand what it's telling you, no matter which system it's
coming from. And folks, don't touch up that. I'll be right back.
You're listening to Inside Analysis. Welcome back to Inside Analysis. Here's your
host, Eric Tavanaugh. All right, folks, back here on Inside Analysis
talking to Justin Borgman, co founder and CEO of Starburst, a very cool
company that is doing amazing things with data analytics. And we were just talking
on the break just at about COVID, how COVID came along and really forced
everyone to pay attention to their processes, to their workflows, to their data.
You better understand what's happening. And of course all these behaviors changed.
And that's one fascinating aspect of the industry of business in general that I found
so curious because everyone tells me this is the case. And what happened is
you have all these algorithms that were designed to optimize sales and marketing and operations
and different aspects of a business, but they're predicated on old behavior of how
people acted before COVID. Then COVID hits and all that stuff went out the
window. So you had to come up with new ideas, new ways of
doing things. And that's where analytics plays a crucial role. Right, That's
exactly right. I mean, we live in vollats ole dynamic times and being
able to react and understand how changes in the world around you are impacting your
business and drive strategy in accordance with that is so important, and that time
to inst is critical in driving that. And I think Covid is a fantastic
example. Right, all of a sudden, overnight we started ordering our own
groceries and doing our banking online, and those mobile apps you know, became
so important, and delivery services became so important, and you know, calculating
that incorrectly, you know, could have huge, huge impact. I'll tell
you a quick story here in the Boston area. You know, one of
our great tech success stories is a company called Toast, which has basically,
you know, reinvented the point of sale system and restaurants. And there was
this moment in the very beginning of COVID where they thought, oh my god,
our business is screwed, basically, and yeah, toasts. Yeah,
no, pun intended. And so you know, tragically they let go of
half their company. You know, it's like a thousand people were suddenly laid
off. It ended up being the case that their business actually ended up growing
dramatically during that period, and you know, but they couldn't see it yet
that that COVID was actually putting a lot of restaurants out of business but creating
the opportunity to create new ones. And as new ones were created, the
first thing that somebody decides when they open up a restaurant is which point of
sales system do I want to buy? And so you know that that prospect
of selling to legacy restaurants, existing restaurants and getting them to change is much
much harder for them than selling to a brand new restaurant. And so it
ended up being this huge boon to their business. But but you know,
they just couldn't see that at the time, and you know, weren't able
to react early in that and unfortunately, you know a lot of people lost
their job early. And now to you know, TOA is a huge success
story public company, you know, great great business. Well, yeah,
and being able to pull data from systems like that for example, is very
useful. So, especially if you've got ten twenty thirty stores around the country,
being able to analyze the data coming in through a system like that like
toast point of sale is great for apply chain, it's great for planning,
it's great for you know, understanding what mixture of products to offer in your
stores. I mean a lot of people don't realize how much work goes into
rolling out a new product at a company like that, like at a Chipotle
with their brisket or something like that. Yeah, a lot of work goes
into the understanding what is the sourcing, what is the pricing, How are
you going to be able to pull this off? That's what analytics is here
for. And because you have this abstraction layer that allows you to dig into
any number of different data sources, you've greased the tracks to that kind of
analysis. Right, that's right, that's exactly right. Yeah, Because speed
matters these days, you have to know and you have to be able to
fail. Fast forward, I think is the real key. Try something,
if it doesn't work, know that it doesn't work, move on. No
sacred cows in business, Right, that's right, that's exactly right. Yeah,
Yeah, that's interesting. Well, let's dive into this open table format
stuff. We talked about open source earlier in the show, and how amazed
I am still at the number of open source projects we mentioned had dupe briefly,
and I watched that whole evolution a little bit skeptical, to be honest,
I'm like, hmmm, I'm not sure if this is if this is
one NiFe that's going to solve all these problems and that produce you know,
it's great for indexing. The Web had a very good use case, that's
why it was designed right, but it was there were security issues, there's
overhead, there were network issues. All kinds of different things came into play.
But the point is we learned a lot. We learned a lot about
distributed systems. We learned a lot about file storage, for example, and
now we have these interesting developments in file formats, in table formats. There's
parque right, which has been adopted as a standard, I think, and
one of the reason real quick is because it has that little analytical box at
top right in the beginning of the file. It has a little area that's
designed to give metrics on what's in that file, which is great for analytics.
Right, do you know much about Park? Yeah, yeah, no,
you're You're absolutely right, And I think that's one of the one of
them most important, you know, creations of that Hadoop era that you were
describing, you know, maybe fifteen years ago, you know, when I
first got involved. Back then Park didn't exist, and you know, the
query performance was also exceptionally slow. Map produce itself was exceptionally slow, and
the file formats that you were reading from were also very slow to read from.
And so Park was a columnar format first and foremost in terms of how
the data was laid out, which allowed for much better performance and mimics,
as you say, like a lot of the capabilities of a of a traditional
database. And I think to your earlier point, you know, Hadoo was
a test ground of a lot of new concepts that I think have been perfected
now ten to fifteen years later. And this is really like you know the
Lake two point zero, if you will, where every little component has been
upgraded. You know, in the early days it was Hive or maybe I
Paula, if you were a cloud era customer to it might be you know,
Treno and Starburst on the query engine layer. And similarly, while it
was, you know, part of k for a long time. You can
now leverage Iceberg or other formats like Iceberg, there's Hoodie, there's Delta.
When can talk about some of the pros and cons of each if you'd like.
But you know, these really allow you to do updates and deletes,
which is sort of like finishing the last mile on the file format piece.
So you can now modify the data. You know, if you have a
GDPR use case where you need to take somebody out of a mailing database or
mailing list if you will, because they've opted out, you can now do
that. And historically you couldn't do that with data lakes. They were append
only, and so it was a real pain in the butt to try to
actually modify your late. But with Iceberg you can do that, you can
do time travel, you get all this new functionality that is much more historically
associated with a proprietary data warehouse like tear Data or snowflake. That's great,
it's another abstraction layer, right, what was the on one of these shows
years ago someone joked that in it we always think that one more abstraction layer
will solve all our problems, and it kind of does at least it solves
many problems. Right, then you have to do all the mappings and make
sure that you've doubted your eyes and cross your t's. But nonetheless, that
is incredibly important, and it's a great example you just gave too, being
able to delete something, say, okay, get it out of this person
that's unsubscribed. We don't want to hit them anymore. Yep. Most people
think, oh, can't you do that? Not in that old format,
no, but now in this format you can. So we're always making sacrifices
to achieve new ends in this business. The key is to kind of understand
what do we need to keep, what can we let go of? How
can we solve these problems? And these abstraction layers are very powerful, being
able to solve many many problems in one layer, right exactly, Yeah,
yeah, I think you're exactly right. And you know the history of computing
is a lot of abstraction on top of abstraction, right, Like, thank
goodness, we're not deciding exactly where to write every bitten byte to a hard
drive anymore, right, So, yeah, well, there are lots of
things happening. So how how would a starburst work with Iceberg for example in
a client situation. How does that all come together? Yeah, So this
is this is what's got me really excited right now currently is that we've seen
first and foremost widespread adoption of the Iceberg format and tremendous momentum there. We
see so many customers are looking at it because it is open, it is
independent. You know, number of vendors have embraced it. Even Snowflake is
embracing it, which is interesting on its own right. But the industry has
really rallied behind this this format, and I think it becomes the de facto
standard. It becomes the new new parquet and you know, in the sense
of its ubiquity is our view. And one of the reasons that's exciting to
us is because when Iceberg was first created at Netflix, it was created to
go pair with Treno or Pressed as it was first known. Now today Netflix,
a huge Traino user, uses that with Iceberg, so that pairing of
Trino and Iceberg goes back to its earliest beginnings. And one of the reasons
was that Netflix was a big data warehouse customer of my former employer, and
they were trying to figure out, how can we really do the full functionality
of data warehousing in an open warehouse essentially open and warehousing model, and that
was one of the motivations for the creation of this format and it's been it's
been very successful. So the combination of trino and Iceberg is something that we
see a large portion of the Internet companies already embracing again LinkedIn, Netflix,
many others, Apple and so forth. And we call this the ice house,
you know, the Iceberg wear and I think that's very exciting for those
that are going to centralize data we would say centralized that are in an ice
house, and then you can also leverage us of course to query the other
data sources you have as well. That's very interesting. You know. What
also excites me, and I've seen this now several times, is people in
these very large organizations, in the data teams dealing with massive amounts of data.
I mean, goodness, greacious, Netflix, think about how much data
that they're dealing with. It's a tremendous amount and relatively unwieldy data too.
A lot of machine data, but a lot of user data, so not
your traditional types of just transactions buying this and that. I mean, they
have that too, but they have all these other things they have to manage.
Well, what's interesting is how you see these diasporas every so often where
people will come up with an idea in this organization that spring out and start
their own thing. It started with Yahoo I think was the first big one
back in like the early aughts. Basically a whole bunch of people sprang out
of there and they found it, I mean forty different companies, and that
became the had dup ecosystem, right because first there was adup hdfs. Then
you had this whole ecosystem around it to do all the stuff like governance and
analytics and different things like that. And now we're seeing it come out.
People come out of Google, people come out of LinkedIn, that's where Kafka
came from. People come out of Facebook and Meta. It's really interesting,
right because they were at the coal face, as they would say, hacking
through these incredibly difficult challenges, and then they went and they rolled their own
like Cassandra, I mean that came out of Facebook too, right, yep.
I'm so with you on that, Eric, And these are sort of
some of my own personal truisms that I've you know, learned over the years
is a open is always is generally better than proprietary, and then I think
the markets move in that direction. And b if you want to see what
the future looks like, look at the Internet companies because generally they're on the
frontier, and I think architectures start to evolve to mimic you know how those
folks deal with things, because they're always ahead of the game in terms of
scale and performance and really pushing the limits of those systems. Yeah, and
then to open source stuff, I mean, how cool is that, right
for for the folks at LinkedIn to roll out Copka, open source it,
makeing it Apache project so that do it yourselfers can go grab the code and
stand things up. Now you're going to have to maintain it, and that
takes a lot of time and effort. Right, So, Like, there
are pluses and minuses to using open source stacks, and there are security issues
we've learned about recently. There was that one case of a developer who caught
some little potential hack and it was saved us all from a lot of trouble,
I think. But nonetheless, I think you're right that openness foster's innovation.
It also builds trust right, totally, absolutely, Yeah, you know,
and that's really important because, especially in industries that have regulations, you
want to have an audit trail. You want to be able to explain what
this thing is doing. Black boxes are not so good. Chat GPT was
open, open AI, now it's not open. Now it's a black box.
So these things are issues we have to worry about. And I think
that by and large, the black boxes are are kind of over for a
big business. But what do you think about that? Yeah? I think
again, you know, over time, you know, where there's an open
alternative, that's where people are going to want to graduate for all all the
reasons that you described. You know, even at let's say JPMC, Jamie
Diamond talks about you know, he's never going to use one cloud, he's
going to have four clouds, the fourth I guess being his own on prem
You know, he doesn't want to choose one vendor. He doesn't want to
be locked into anything. I mean, and this is like a banking CEO
who has the the you know, I guess foresight to think about his his
technology strategy in a similar risk management way. And I think, you know,
Jamie has just been a fantastic risk manager in all aspects of his business.
But you know, just another great example of you know, someone even
at his stature, thinking about I think a lot of these principles you're speaking
of, Yeah, well, I mean they are at the front end of
innovation as well. I mean they kind of have to be, because you
have to protect that money first of all, so it's all going to be
focused on governance and security and compliance and transactional integrity. You can't lose your
money. But they're also very forward thinking in terms of how to use analytics
to understand customer behavior. And you talked about risk too. I mean,
what a complex feel that is. I had a whole past life moderating webinars
for a group called GARP. That's the Global Association of Risk Professionals, or
as I call them, the defacto Illuminati, like chief risk officers for every
central bank in the world, right, And these folks are like heavy hard
eye on the market on what's happening. When COVID went down, they were
the first to figure out, we got to do something about this. And
you know, just real quick, I had a guy from my healthcare company
on the show. Yes, I guess about maybe nine months after COVID struck,
and he said that in the first six months after COVID hit they not
out five years of digital transformation because they had to and they knew they had
to. And so when the pressure mounts and you know you have to act,
amazing things can happen. And it was all the stuff we talked about
a COVID of the spirit workforce. You've got to understand your workflows, all
that fun stuff. But folks, don't touch up that. I'll be right
back. You're listening to Inside Analysis. Welcome back to Inside Analysis. Here's
your host, Eric Tabanaugh. All right, folks, back here on Inside
Analysis talking all things data and analytics with Justin Bordman, co founder and CEO
of Starburst. I love your company name. By the way, you got
a Starburst. That's good stuff. We have a lot of fun with that.
Yeah, those of you watching the video, at least you can see
this the background. That's one of my favorite words in Spanish is Istria,
the star Istria fugas the shooting star. Right, cool stuff. Yeah,
but let's talk about the stuff you're doing with Iceberg. And I understand you
have a great team member who has just joined you. But what you're doing
in an in jest right, because in jest has always fascinated me. You
point your your connectors to data sources to pull all that stuff in. How
can you do that efficiently fast? It's just really important to be able to
do that because the longer you wait, the longer you're waiting to do someone
else is tell us about that, yeah, exactly. So building on that
idea of a ice house architecture, really what we're going for is to create
a full end and experience that is, you know, just like a traditional
data warehouse, but leveraging a open format at its core. And again that's
what an ice house is. It's a open lakehouse leveraging Iceberg at its core.
And that means, you know, addressing a few areas, one being
data ingestion, both streaming and batch turning that data into Iceberg tables. A
lot of people just you know, don't know how to do that. They've
never had to do that before. So how do you create those tables.
There's the governance aspects of access control, lineage, auditing. There's also just
the management of those tables. There's something called compaction where you want to compact
a lot of small files into larger files to get better performance. And so
that's just kind of a utility that needs to take place for performance optimization.
There's you know retention and snapshot expiration. There's you know, capacity management,
increasing or decreasing your size of your cluster for performance to match the demand and
being able to do that elastically as well. So these are all the things
that we've been working on from the Starbars side. You're absolutely right. We
just hired Carl Steinbach. He was a very early member in the Iceberg community,
has been working on Iceberg since twenty eighteen, and he's a PMC member
and actually was one of the co founders of Tabula, which is an Iceberg
company, and we're super excited to have him here working with us on continuing
to drive this this roadmap. And one of the reasons he joined is I
think exactly what we believe, which is that you know, open formats belong
with open engines, and this is really the stack for giving you a end
to end, you know, open warehouse experience. I got to tell you,
I love this ice house concept, right, I mean, I've been
around a long time. As we were talking about data warehousing the data lakes.
I will tell you I was a little bit concerned in the early days
of the data lake. I'm like, hmmm, are we making the same
mistake again of thinking we can store all this data in one discrete location.
Because data is going to live wherever it lives, and moving it is always
going to take time and effort. This is concept of data gravity. Right.
Of course data doesn't have actual gravity, but the point is in a
conceptual way it does because you have to pull it out of somewhere, push
it to some other location, land it there, and make sure it all
lands properly. And again this whole federated worldview. I remember the first time
I learned about Apache Arrow, I was like, oh wow, that sounds
very interesting. So enabling the federated queries, I think in memory was their
whole vision with Apache Arrow and again another Apache project. Right. So here
you have the best minds who come together and in an open way develop technology
that is open source, right, because open leads to future connectivity, it
leads to future collaboration. It's not a closed door. Proprietary as a closed
door. And you know, this is what I saw way back when in
like two thousand and five time frame or something when I'm like, wait a
minute, this open store stuff is pretty cool. What's gonna stop it?
And you know things have happened. I won't name names, but there have
been some movements to truncate open source. But I think the cats out of
the bag. What do you think? I totally agree. I totally agree.
And and you know there's another point that you reference there on sort of
centralization versus decentralization. Our answer is both, like you're going to have you
know, some centralization and in those cases leverage open formats like Iceberg and the
ice House model. And you're going to have other cases where you're going to
want to keep the data where it is and query it there directly. And
that's where you know federated queries adds value. And so you know, we
think it's a it's a both, not a not an either or an or.
And absolutely you know you want to be leveraging open components wherever you possibly
can in that sack to give you that flexibility, right, well, and
who likes lock in? Right? Nobody wants lock in? Ye? I
saw one of the big cloud providers, was it Google? Maybe that lowered
their egress fees. Did you see this recently? It was like, yeah,
what's happening there? It's these you know again, no one likes lock
in, right, there's no CTO that wants lock in. There's no CFO
that wants to lock in. Nobody wants that. And open formats allow you
to stay open and to mix and match to bring some new technology, because
that's I mean you mentioned the term future proof. Of course, that's the
name of our show or our TV show future Proof. It's really important these
days to leave doors open because amazing new things come down the pike. I
mean Iceberg. I don't think too many people saw Iceberg coming five six years
ago. That's right, and here it is, and I know for sure
that all the other big guns look, oh we have to talk to Iceberg.
Yeah, you probably should, because it's just it's taken off. Right.
There was Hoodi, Delta Lake, and Iceberg are the three different versions
you could use. And of course Delta Lake is proprietary for data bricks.
Hoody was for someone else, can remember who launched Hoodi came out of Uber
and there's a company behind that one called One House. Whody's interesting. It
just hasn't gained as much momentum, you know, and sometimes it's just the
most popular wins, right, and I think Iceberg is that, But who
do you whods a great format too, and we do support all three,
you know, back to that point like optionality is what creates that that that
future proof ability, right and and and then allows you to kind of double
down where where the market moves. And I think in this case you know
that that's Iceberg. Yeah, no, that's a good point. And uh,
and getting that momentum, getting enough developers focused on something that's one of
the key too. Maybe we should dive into that for the developer world,
right, because it's I heard a great stat that every year, like the
number of developers is growing by some astronomical number, which means there are new
developers all over the place every year. And so how do you attract those
folks to work on your stuff? To understand your architecture, your your languages,
your vision. That's all important, right, And I've seen this develop
over the last really less i'd say seven to ten years. Software companies not
just trying to get clients, but working for or to get developers to pay
attention to them to work in their communities, right, talk about that for
a minute. Yeah, that's an excellent point, And I think developers like
to work on open source frankly, it actually allows them to not only you
know, build their resume on technologies that they know are going to be useful
wherever they go in their career, but also if they choose to participate in
those communities and contribute, they're building a different kind of resume on GitHub,
you know, and that's really valuable to them. And so I think you're
absolutely right. I think this is one of the secrets, by the way
of Facebook's success from an engineering organization perspective, They've always attracted tremendous talent,
and I think one of the reasons is that they have a very open source
centric culture that allows their developers to participate in a variety of projects and really
become almost like famous, if you will, within those projects. And that's
certainly the history of my co founders creating Presto and Trino when they were there,
and so many other great you know, open technologies that have come out
of companies like that, you know, like Cofka, at LinkedIn and others.
Yeah, I think it's really amazing, and developers are the front lines
these days. They are building the products that you use. When your products
are web based software as a service, or you're selling all sorts of things
using the web to sell all that stuff. Understanding all that, you need
developers and you want the developers to be motivated. You want them to be
interested in things. And you made an excellent point about how GitHub is like
your resume. It's a meritocracy. So you're not just saying you can do
stuff. You're saying, look at the stuff I've done, this is how
it works. And this is just such a white hot space. It's a
great place to be for a developer working with data, working with the unique
nature of any business. I think that's probably the exciting thing, right,
is that every company is different. Every business has its own, its own
way of doing things, its own people. Of course, businesses a bunch
of people. It's people, data, systems, technology, vision, where
things are going, and what Starburst is doing and the other people in this
industry. To be fair, you are providing the mechanisms of action for inventing
new things, for trying new things, for deploying new things. And it's
going to be all about efficiency in this economy. I mean people say it's
the information economy. I think it's more like the execution economy these days.
Because the information is everywhere, the data is all over the place. The
question is what do you do with it? How quickly can you make use
of it? And that's really what we're talking about with these federated queries and
these distributed systems, make use of it quickly, such as you can ideate,
create, test market and then redo, reinvent, try something different.
It's all going to be very fluid in the next few years, and the
companies that manage their data best are going to have the best chances of victory.
But podcast bonus segments coming up next, folks, don't touch that Valu're
listening to Inside Analysis, all right, folks, back here on Inside Analysis.
Time for the podcast bonus segment with Justin Borgman of Starburst CEO and co
founder. And Starburst Galaxy is your cloud based system. Like many vendors,
you started off with a non prem version and you realize that SaaS is probably
the future. So now you've got star Wars Galaxy, which I think you
said is the easy button for the ice house. That's right, that's pretty
cool. So up in the cloud. This is metadata management largely right up
in the cloud. You would then point to your different data sources and it
pulls the metadata up in there, and then you can kind of rationalize and
reconcile it and work your magic. Is that about right? Yeah. You
absolutely can connect to all of your different data sources. You can query them
directly. You have the ability to hook up your favorite bi tool or query
editor or Python notebooks or whatever the case may be, and and interact with
that data directly. And as you said, it's the easy button. We
try to make this, you know, super sophisticated distributed MPP engine as easy
to use, uh, with as few knobs to turn as possible to make
it, you know, really low low maintenance for you from a operational perspective.
We also have built in a lot of these features we were talking about
earlier in the on the ice House topic of you know, streaming in jest
and creating Iceberg tables, and you know, doing compaction and data profiling and
data lineage and creating curated data products off the data that you have. You
can do all of that within this Galaxy experience. And you know you touched
on in the earlier segment the important of efficiency. One one of one element
of efficiency is cost performance efficiency, and compared to cloud data warehouses, we
are roughly one third the costs of a traditional cloud data warehouse. So you
can save a bunch of money leveraging this open lakehouse model or ice house as
we call it, and Galaxy makes it easy to do that. Now,
I kind of hear some hints of governance in there. I mean, if
you can access the systems and manage metadata and things of that nature, is
governance in the roadmap? Can you do data governance to some extent? Yeah.
So access control has always been actually part of our strategy, at least
as a company. So we built in fine brained role based access controls,
you know, many years ago, maybe five years ago. More recently in
last couple we've actually upgraded that to attribute based access control as well, and
so you can tag your data and leverage that for your access controls. But
also we've even added the ability to sort of automate tagging of data. And
this is where we actually incorporate some AI in the product itself, where we
can automatically tag p I I data to help you out in your in your
in your governance strategy. By by no means do you have to leverage that
you should also be checking all of all of your data, but it helps
with with that process. And again very fine grain, so row level,
column level, data masking, query auditing, all of that's built into the
platform and it's enforced across all the data that you connect to, So not
just your Iceberg tables, although of course that's that's a common common use case,
but also across you know, my SQL postgrass oracle, you know what,
whatever data sources you may be connecting to. That's very interesting. I
mean, I think it sounds like a wonderful way to have this management layer
because then it doesn't matter where you are. You could be on the road,
you could be at your home office, you could be anywhere. You
just log into your cloud based system. You can see this whole environment and
you can run your queries from that environment. Right, I mean, you
have to do your connectors to your various systems on prem or whatever they are,
and see that. This is what I think is so exciting is being
able to connect to SaaS sources on prem sources whatever the case may be,
from one marsting area then to be able to build queries, ask questions across
this whole environment and under the covers star Wars Galaxy does that right? That's
exactly right. Yep, that's exactly right. And for the power user,
there's even a built in query editor, so you can literally just open it
up and start using it. Of course, if customers prefer their favorite tool
maybe Tableau, powerbi Looker or what have you, you can use that as
well. But that's exactly right. All that powers at your fingertips. This
is wild, wildly stuff, folks. While we've been talking to Justin Borgman,
CEO and co founder of Starburst, hop online to Starburst dot io to
learn more about those folks, and it's moving fast, folks. I like
the ice house, the open lake house. I think the ice house sounds
good. That's tough. Talt you next time. Send me need if you
want to be in the show. Info at inside analysis dot com. We'll
talk you next time. You've been listening to Inside Analysis, jerfully everything to
everyone? Or can we the station that leaves no listener behind? KCAA.
Hi, this is Chris Klin, investment manager for Capstone Wealth Management. I've
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I'm Chris Garagio. The United Nations says tens of thousands of Palestinians are
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