In this episode of Data Driven,
frank and Andy get back to the
data engineering side of the
equation by speaking with Sakit
Saurab, cofounder of Nexler.
Nexler specializes in tools for
automating data engineering
processes. Now onto the show.
Hello and welcome to Data
Driven, the podcast where we
explore the emergency fields of
data science, machine learning,
and of course, the ever present
data engineering. This is season
seven that we're now in and we
are welcoming. Andy is shaking
his head. If you're not watching
the video, it is hard to believe
that we hit season seven
seasons. But by the time this is
launched you probably have heard
our one or two shows where we
did kind of delve in deep. So
you're probably tired of hearing
us bang on about that. I really
like kind of kicking off this
first guest interview for season
seven with a Saket Sarab who
runs, who is co founder and CEO
of a company called Nexla, whose
tagline is automation for data
engineering. And if there's
anything we've heard about in
the last, say, six to ten
months, it's all about
automation this, automation
that. Whether it's Chat, GPT or
any other kind of low code, no
code, automation is all the
rage. And he also very much like
a previous guest, has a cool
vendor tag from Gartner. So
we're going to talk to that.
Welcome to the show.
Soquette thank you Frank, and
thank you Andy. Good to be here.
So very fascinated about kind of
your story. In the virtual green
room we were talking about. You
used to write Linux drivers for
video card manufacturers and we
spent a few minutes on waxing
poetic about how easy Linux has
become. So what exactly does
automation for data Engineering
mean to you? I think let's start
there.
Yeah, I think when we look at
enterprises and companies out
there with a lot more data, with
a lot more people who need to
use data, there are two ways you
can achieve scale. One is
through automation and the other
is through collaboration and
automation. Or achieving scale
through automation means that
the tasks that we do today, can
they be automated, can they
become more intelligent? So for
example, if I had to create a
data pipeline and I have to
connect to a data system and
read that data, process it,
maybe transform that, push the
data somewhere, let's say it
takes me four weeks or six weeks
to write that code, test it QA,
take it to production.
Automation would basically mean
that can a lot of these things
be done automatically and
faster? So can I, for example,
not have to write a connector?
It can get auto generated there,
right? Can I not have to write,
test or error conditions and
check for them because the
system can look at the data,
understand its properties and
say, oh, this would be a good
validation for this type of
data. For example, or if I had
to process or run the same
pipeline. But now the data
volume has grown ten x, I don't
have to go and do a whole bunch
of engineering to manage that
scale. The system can
understand, oh, the scale is
increasing. My bottom neck is in
this part of the processing. Let
me allocate more containers to
that and just let it run
smoothly. So automation is a lot
about doing the same tasks that
we do, but doing that faster.
Why? Because something can
figure out certain tasks, do it
for us, create more reliability,
create more repeatability,
create better performance
without us having to do that
manual work. So when we go back
into automation for data
engineering and you understand
that there is so much data
engineering work to do, I think
it's almost impossible for the
data engineers out there to just
support all that demand that
they have. Automation for them
is like something that helps
them and supports them. And it's
like a lot of easy use cases can
be done automatically and
quickly. And a lot of difficult
use cases can have big chunks
taken care of in various
aspects. So that's kind of where
that direction is and automation
is one of the key parts to that
scale.
Interesting.
Yeah. I really like your
description of this. I'm
wondering if it's okay if you
share a little more detail. I've
worked some with automating data
engineering in the past and I
find that it's very applicable
when you're doing pretty much
straight one to one type stuff
and that's not throwing off on
your product by any stretch. I
don't know if you agree or
disagree, but I think about
staging data so I can pull data
from extracts, text files, flat
files and load that into some
data store, usually a database.
And once I get it there, I find
a couple of things are true. And
I may not pull it from an
extract, I may pull it from the
system of record. I want to get
in, get the data and get out
with doing as little harm as
possible, stealing as little
cycles. But once I get my copy
of it, then I can start applying
rules, looking for opportunities
to apply strong data types and
the like. And automation really
works well there. Your product,
I'm I'm assuming, does that and
does that part well?
Yeah, absolutely. But there are
also parts where you're getting
that extract and there is a
slight change in schema now and
it's more or less the same. But
can automation cache that for
you and take care of a few of
those things? Or do you have to
go back and write that piece of
code there? So there are many
places where you can benefit
from that. So there is what we
call when we talk about
automation, what is the driver
of automation, what is the
source of that? And we put that
on an aspect of applying
intelligence to the metadata. So
when we look at the data and we
understand that the metadata is
actually things like the Schema
or I have a price attribute in
my extract, but this is the
behavior of that attribute, this
is how it looks like, these are
the characteristics. And based
on that metadata, can I apply a
validation rule to it, for
example, automatically without
having to define that. And
something does it for me that is
bringing automation. So actually
the roots of that come from
there is so much information.
For example, your data extract
happens every day at 04:00 p.m.
And you expect the finished data
to be ready by five and on
someday. It doesn't happen. It's
06:00 and it's not there. So is
there automation going and
saying alert it didn't come
through. Oh, by the way, the
reason it didn't come through
was that your stuff was all
great except these 20 records in
between completely threw it off
because it was wrongly formatted
or whatever. So stuff like that
is where I think that automation
really becomes an assist in
this. So you know the business
problem, you know what you're
trying to do, this is how you
get there faster.
So I've heard that unexpected
changes in formats, which I see
it all the time because it's
what I do. But I hear that
addressed under the topic of
Schema drift. And it can happen
a couple of different ways. You
can miss a tab or comma whatever
delimiter you're using. You can
either miss one or an extra one
be inserted in the extract
process. You can have missing
files, completely missing files
for a variety of reasons, some
of which are legit, like you're
doing an incremental extract and
nothing changed and stuff like
that. And I guess your product
addresses that has rules for
saying if this is missing, just
keep going. It's a slowly
changing dimension and if we
miss ten in a row, it's no big
deal.
So what we do at a very high
level, right, when we think
about data engineering, one of
the key problems that it solves
is integrating data, getting
data from point A to point B and
making sure it's valid, it is
trusted, it can be used by the
downstream application. There is
often an implicit contract that
dashboard is relying on this
sort of information. So what we
do basically at a high level is
one we are like hey, we can
figure out how to connect to new
systems. This is a part where we
bring automation to the
connector creation. So instead
of writing code for connectors,
we are able to generate most of
the connectors out there. So
that's one part. But when we
scan so the data we understand,
we do understand what the Schema
is and all of that, and we
present that and automatically
sort of package that into what
we call as a logical data
product that becomes much more
easily understandable by an
average data user person who
understand it. So in that
process, in between that, yes,
the Schema Drift is an important
part, but it's not as
straightforward because what
happens is you're getting data
with first name and last name
and email address and suddenly
you get data maybe two records
which don't match that. Is that
an error? Is that a change? Is
that an evolution? You got first
name, last name, email address
and now you're also getting
phone number. Well, it's a
sparse schema potentially and
it's a drifting schema. Well
does it break the downstream
contract because something got
renamed or does it just simply
add to that? So there are a few
of those aspects. We do cover
all of those by sort of saying
that when I connect to a data
system, I'm going to present
that data in a certain sort of a
data product view is we call it
a logical data product. So
here's a logical data product,
this is all there is, these are
the characteristics and stuff
and you decide what you want to
do with it and how you want to
use it. But once you have a
consumer for a data product,
then it sort of implicitly
creates a contract and we keep
track of that. And there's some
interesting concepts that we do
there, which is you can take a
data product and create some
derivatives out of that. So you
can say I have an orders or
transactions and it has credit
card number, I'll mask it and I
have an order ID and I'll look
up the items from a different
entity and now I have a new data
product which is enriched and
which is maybe more Pisa. So
some interesting you know what
I.
Like about that is we think
about tools that visualize
lineage, Atlas and Purview and
tools like that, but those are
very reactive, even though both
Atlas and Microsoft's
implementation of that. Purview
introduce automatic scans,
automated scans and they manage
Schema Drift to a certain
extent. You know this if you've
ever tried to code a tool to
react to Schema drift, then you
know how complex that is. And I
can't wait to see large language
models integrated into that
process because I suspect it'll
do a much better job of managing
that than trying to guess what
this data type should be and
such. But what I hear you
describing in your contract
sounds like a proactive piece to
that where you meet with your
client and you say, yeah, here's
whatever you want to call it,
our data dictionary, what have
you of all of our source data
and our fields and columns,
fields, columns, metadata for
all of that. And then you're
saying these particular fields
are important because they're
used downstream in these dozen
reports at the very end of the
process. But what you're saying
is if another related field was
to show up in that list, then
you're going to be able to make
an educated guess at whether
that's schema drift or whether
it's additional attributes. Is
that what I'm understanding we
make.
An educated guess about? Is this
a one off thing and we should
treat it as an error? Hey, this
record is an error, it didn't
actually meet certain criteria
or this is a change that is
showing up. And the way you do
that is you observe that data
over a certain period of time to
make the determination and we do
a certain level of drift
analysis. And if the drift is
very significant, then what we
do is we today actually do not
go and make assumptions on
behalf of the user. We actually
create a notification and saying
we saw a significant change and
we think this might be a new
data product that we have
detected. So we are connected to
a store. So looking at the
schemas and stuff and we're
saying here's a data product,
that data product is
transactions and this is what it
looks like. Oh, some came here
and whatnot. But then at some
point we may say, hey, this
looks significantly different,
would you like to consider this
as a new entity? And then it
sort of notifies the user and
lets them do that. I think when
building automation, at least my
understanding is that's very
important to understand, when to
make a reasonable assumption and
when to actually let the user
decide. But even creating that
workflow is a big value add
because this is stuff that
actually would have gotten
missed maybe for a few days, but
you are getting notified about
that upfront. So we try to be
maybe a little bit more
conservative, if anything about
making assumptions on behalf of
the customer or the user because
you go wrong and it's not fun at
all.
Yeah, I really like it.
Now that's interesting. And I
see your company has been around
about seven years.
Yeah.
What has changed in those seven
years? Because I think seven
years ago AI was not on the top
of everyone's lips, obviously. I
think certainly since Chat GPT
came out. Right. It's a big part
of the conversation. Have you
also seen kind of the data
engineering world be kind of
touched by AI and if so, how?
Not as much, but I think it is
getting there. So when we
started very early, as I said,
seven years ago, we had actually
come in with the question when
starting the company was do we
want to build an AI or a machine
learning company? Because
actually in 2016 it was hot, it
was hype. There was a lot of
like, oh, this is going to
change the world. Always the
hype is ahead of the reality.
And it took a while before
things like LLM came around and
generative AI is starting to
succeed. But even otherwise
there are a lot of other AI
initiatives that are still
figuring their way out. But we
were very clear that we want to
build the technology for the
users of data. We want to focus
on users of data and allow the
user of data to get the data
wherever they need and do
whatever they want to do with it
in whatever tool they want to do
with. So we came with that
approach because we said that
the user of data is not going to
be necessarily very expert in
the data system. So there's
expertise in data, which is I
understand what the data is, and
there's expertise in data
systems, which is with the more
engineering side, we said the
data user will not be an expert
in data. So if you have a lot
more variety of data that's
coming, how do they use it?
We'll create an abstraction.
We'll create an abstraction that
will give them a clean,
consistent view of data no
matter where it comes from. So
now you're like, I don't care if
the data was a stream or API or
JSON or document or whatever, I
have something consistent to
work with. So we went in and
looked at metadata and started
to apply metadata intelligence
to create that. I would say that
when we were doing it, a lot of
people early on were like, why
are you doing that? Why not just
create like this straightforward
thing that everybody does? What
has changed for us is in the
last two years or so, our
approach of creating this
logical entity around data and
using that started to catch on
with the concept of data
products. So where we were
initially struggling to say what
does this mean? And why is it
valuable? Has suddenly become
like, oh, it makes so much sense
that you guys have done it this
way. So that has certainly
changed. I think that
application of intelligence to
the metadata itself to make data
tasks themselves more automated
is a very valid use case. The
generative models are doing
quite well in things like can
you generate a description for
this data if this data looks
like this? There's also been
some really interesting stuff in
terms of generating code as far
as data engineering is
concerned. Hey, can you generate
code that reads it up from here
and pushes it out there? So I
think there is that happening as
well. So there's a lot of, I
think, places transforming data.
For example, if the data looks
like this and it has to become
like that, can we figure out
what is the logic in between? So
I think I would say that it's
good that we're moving in that
direction because I believe that
the number of things to do is so
massive that some degree of
automation is essential.
I totally agree. And I was just
scrolling, so apologies, Frank,
scrolling around on Nexla.com
and looking at your data
operations. And first I want to
commend you for next sets.
That's a cool play. And some of
the fields that you cover here
in my career, I've been doing
this for a long time. I'm old
socket but Continuous Metadata
intelligence caught my eye. That
is a very cool concept. And I've
done what you described here
sounds like some stuff that I've
done but that's a much cooler
name. Continuous Metadata CMI
that just rolls off the tongue.
The idea, the automated Error
management and quarantine. I'm
just kind of going from the
bottom up here on the page for
data operations. Those are just
key pieces of functionality that
time and time again data
Warehouse ETL includes something
like that. But everybody's
rolling it from scratch. This is
just very cool. I love this idea
of abstracting that out and I'm
just going to throw this out
there. I'm going to be signing
up for a demo. I want to see
more.
Oh absolutely. You're very
welcome for that. And I think
the other problem that it helped
us solve, as I said, there are
two ways to get scale in
enterprise. One is through
automation and the other is
through collaboration. By
creating this abstracted entity
we were able to say hey, this is
a lot more easy to understand as
access control. I can be really
good at connecting to the data
from a transaction system and
cleaning it up and doing some
applying some compliance to it.
But then the output of my work,
which is the cool thing about
these next sets of the data
products, is you take one and
you apply some operations to the
output is another next set,
which is identical in behavior
and consistency, but it is a
slightly different view of the
data and you can give somebody
else access to that and you can
keep repeating that process. You
can imagine in a large company
people are finding these,
they're creating their own
variants and they're using it.
But what they are doing becomes
an input to somebody else and
they can go take it out of
there. And what we did a month
back was introduced the concept
of you can take all of these
logical data products and Nexus
that we are creating and make
them into, put them into a
marketplace that is internal to
the company. You can allow
people to go find it request to
get that you're not really
buying. You're saying hey, can I
get access to that? And there is
a mechanism to approve and give
people access to that. Now the
interesting thing is that these
are all very importantly, these
Nexus or Data products are
logical entities. They're not
making copies of data. So
they're bringing the same sort
of benefits that
containerization for example,
has done on the compute side is
like you have an abstracted
entity, it's consistent, you
don't have to worry about what
was under the hood. Where did it
come from, was it XML data, was
it CSV? Now you have something
consistent to work with and it
opens up a whole bunch of
interfaces. I can take that data
product, that next set and say,
I would like this data in a
warehouse or I would like this
data as an API. And you realize
that the same entity can have
benefits for different users and
they can approach it in
different ways. So we think that
is what is bringing
collaboration. So when you bring
together automation on one hand,
collaboration on other, and then
you really get the benefits of
scale from both technology and
process.
I absolutely love you. Now I get
why you keep saying data product
and it makes perfect sense now.
You're creating a very
interesting, almost an
integration layer in between the
idea of Containerized for code
and you're containerizing data.
And that's what I believe your
data product represents. And now
I'm really interested in that
demo.
Well, especially if my recent
forays into OpenShift and kind
of what Containerization has
done for developers, I think
it's only a matter of time
before containerization kind of
hits the data world. And there's
something I want to point out is
that it was very smart, I think,
of you to focus on the data
engineering side, right. Because
AI is the hype machine, right. I
fully admit that I say this as a
data scientist, right? But one
of the things that we've kind of
discovered, both Andy and I, and
in both our professional careers
is that data scientists will
tend to brush aside the simple
basics of it's. Five words,
right? First you get the data,
or first we got the data. Right.
Behind that is months of work.
It's orders of magnitude of
work. In fact, one of the spiels
I have now is kind of like the
idea of rock stars and roadies,
right? And for every rock star
there's, there's an army of
Rhodeies that set up the lights
to move the chairs around and
take set up the equipment and
manage the sound. So I think
that the data engineering, I
think, is one of those things
that I think has not it's like
the Rodney Dangerfield of kind
of the data world where it
didn't get any respect up until
lately because Chat GPT will get
all the headlines, right? But
think about the data that went
into it. I've heard numbers,
billions and trillions of
parameters for four. I think
it's smart that you picked that
and I think that actually worked
out pretty well for you.
Actually what worked,
fortunately for us, was that I
actually really looked at
machine learning as a way to I'd
been an entrepreneur before. I'd
built a company I really enjoyed
sort of building the data aspect
of it. I had built a company in
the advertising at Tech Space,
built one of the earliest mobile
ad servers. We became part of
the largest ad exchange at the
time outside of Google. So we
were processing over 300 billion
records a day, and my co founder
here was running that
infrastructure. And we're like,
at some level, be candid, I
didn't really enjoy being in
advertising, but I did like the
sort of data challenges that
were there. So we were looking
to figure out what is the next
because we had taken that
company public and all that
stuff, and we moved on. I was
like, okay, what is the next
thing you want to work on? And I
really seriously looked at
building something because
Machine Learning and AI was hot
topic even in 20, 15, 16. But
when I looked at it, I
understood that at some level,
it is so specific to that
business and that company and
that problem. It almost becomes
consultative. And I was like,
how do you platform it? It's
hard to platform because even
you can take two retail
companies and they're solving
maybe the same problem of
recommending products to people
and their models will be very
different. And what you do there
is not easily translatable. So I
hesitated for that reason. And
when I look back, in hindsight,
almost some of the major
companies that came out on
machine Learning, ultimately,
when you look deeper into it,
there are large professional
services organizations under the
hood. And that's what gave us a
hesitation. I'm more of an
engineer. I like to build a
platform and make it once and
let people use it. And the data
engineering part of it is what
looked like that thing. But I
don't know if you have read this
paper called The Hidden Debt in
Machine Learning Systems. It's a
Google paper and it actually
talks about there's a very cool
diagram in there. I don't know
if you can screen share here,
but actually it's a diagram
which shows that in all these
different boxes, there's a tiny
box called Machine Learning, and
there's huge boxes around it
which are all essentially data.
I think it's a 2016 paper.
If I'm not, I'm looking it up.
Yeah. Hidden technical debt in
machine learning systems is the
paper. And there's a diagram on
the third page which shows that.
But it is interesting that even
at that time, people who were
working on these things were
seeing the same pattern.
Yes, I have seen this diagram.
This is something that comes up
in my day job quite a bit, where
we talk about how the machine
learning is only one part of it,
right? There's a whole lot that
has to go into that. So yeah,
and that's a good point. And I
think that everybody wants to be
the rock star, right? Everybody
wants to have their name up on
lights, but the amount of people
that goes to make that rock star
look good, there's a lot of
opportunity in there. And he's
been a guest on the show. He's
famous within a certain Internet
circle. John Lee Dumas. Right?
He has a phrase where I like
boring boring is good because no
one is competing to do the
boring stuff. Not that data
engineering is boring. I want to
head off the hate mail right
there. But no, I mean it's one
of those things where there's
enormous opportunity. If you
look that box, it's one part of
the the whole operation and.
There'S a tiny little ML code
box.
In the it takes out all the
oxygen in the room. But
realistically, in order to have
that little box, you need to
stand on a lot of other
operations. Right. Andy and I
kind of had this back and forth
and obviously data science is
important, blah, blah, blah. AI
is important, machine learning
is important, but it stands on
the shoulders of giants. Another
analogy I've seen where it shows
like a rocket, right, in a
little capsule that holds
people. But it's sitting on top
of a massive rocket, which of
course has the launch pad and
all the other accessories to it.
That's another way to look at
it.
Right.
It is crucial. And I think it's
a shame that we kind of not we,
but it doesn't get the attention
that it deserves.
Yeah, I think data engineering
is complex. It is also painful.
It is also something that has to
be done at a massive, massive
scale. And it's challenging. But
remember, it's a means to the
end and people get fascinated
about that end. That happens
eventually, but the means to the
end takes a lot of work. And
sometimes, to be candid, it
becomes tankless work. Because
why are we such a big proponent
of bringing automation into data
engineering work? You cannot
automate all of data
engineering, just to be clear.
But when you bring in
automation, you're saying that
if I'm running literally, I have
customers running thousands of
pipelines in our system, for
example. And you don't want to
be waken up on that Saturday
night because one of those is
not working. You want automation
in there. Right? You want that
system to work for you.
Otherwise all you get in data
engineering is a lot of lot of
work to do and complaints when
it doesn't work. But it is a
very key niece then. So I do
celebrate the work that they do.
And if you look at OpenAI, for
example, because it's such a hot
company right now, that billion
dollar plus in funding, very few
companies could have done what
they have done because they had
that kind of money okay.
Right.
To begin with. But I bet if you
look at how that money got
spent, I'm sure a big chunk is
in pipelines because of data and
processing that and moving that
around and we don't talk about
it. But the reason that Chat GPD
works so well is because it can
look into all of that data and
talk intelligently about it,
right?
No, absolutely. I don't know
when this switch happened, but
in terms of staffing, a.
Previous job, at the end of
2021, they wanted to do
something. Can't say what it
was, but they wanted to do
something. And they said, oh,
it'll be challenging to find the
data scientist for this job. And
my manager and I can look each
other. Actually, at this point
in time, it's going to be a lot
harder to find a data engineers
that you're going to need for
that.
Right.
Because they only really needed
two data scientists. But just
based on what the aggressive
thing that they were trying to
build, they would need, I would
just spitball and I would say a
dozen data engineers.
But from a technology provider
and a tool provider perspective,
I would say the interesting
thing about data engineering is
it is very complex, but the
challenges are very consistent.
I can look at our customers in
retail, like a BedBath, or
Forever 21, or in delivery like
DoorDash, or in Pharma like
Yansen JNJ, or in financial
services, or in cybersecurity,
all of them. The challenges at
fundamental level are very
similar, which is large amounts
of diverse heterogeneous data.
Being able to take that process
that do that, reliably detect
the issues, all of that stuff.
Have data quality monitoring,
make that data usable by people
scaling. All of those are very
similar. Which means that it
fits very nicely into the
traditional sort of problems
that can be solved by software,
problems that can be solved by
automation sort of model. Right.
So I think that is definitely a
part where the challenges are
not very unique to a certain
problem. And of course, there
are unique flavors to it. Some
have real time data, some have
data from devices, some have
data from legacy systems and so
on. But yeah, there is
structure.
Excellent.
Cool.
Very cool. All right, I'm
tweeting about Nexla.
Cool.
Thank you.
Right now.
And for all the stalkers, we are
recording this on April 12, just
FYI. So if you look at Andy's
feed and you're like, where is
the tweet? You have to go back.
So at this point in the show, we
want to switch to kind of the
pre found questions. And given
your background, the first one,
I really have to know the
answer, right? We always ask,
how did you find your way into
data? Did the data life find you
or did you find data?
I think it was happening
together, right? I guess so. My
decision to start a company, and
I mentioned to you guys, I'd
been at Nvidia, on the compute
side of the world, really, and
at some point I decided to start
my own company. And when I was
looking to do that in 2009, I
was like, where do I go? Build a
platform, if you will. And I
felt that at the time, in 2009,
apps were new and I'm going to
build a monetization platform
for app developers in the mobile
space. That whole approach about
building something around ad
servers and one of the early ad
servers in the mobile space, you
realize that it's a very data
driven world in advertising. And
there is a reason why a lot of
the data innovation, I would
say, if you trace its roots,
come from advertising. Whether
it was Yahoo, whether it was
Google, whether it was Facebook,
what were these guys doing with
data in the first place? They
were dealing with a huge number
of people visiting those pages
and clicking on those ads, and
they had to really figure out
how to show the performance and
say, which ad should you spend
more money in, where should you
not? And a lot of machine
learning systems that we built
early on back in 2011, twelve
actually were for that purpose.
We ran one of the largest ad
auction systems at the time, and
if you're running an automated
ad auction with 15 billion
auctions happening and 300
billion bids on that, you have a
lot of data. But you can also
figure out that, hey, based on
certain patterns, I can decide
who to invite for an auction, I
can decide what the floor price
should be. And those were all
machine learning systems. So I
ended up actually building this
advertising technology and
system in those days to solve
that developer problem of like,
I'm building apps, how do I
monetize it? But realizing that
a whole chunk of it was data,
and this was a lot of the data
stuff that we did was pre kafka,
even when big data hadoop was
relatively early at the time, so
the technologies were limited.
Did a lot of homegrown stuff at
the time, but realized that this
is a massive problem. And I
think data and I sort of met in
that time, but I'm coming from
this compute land. I'm building
software for embedded systems
where you are trained to squeeze
every single kilobyte and every
single ounce of performance in
some of these systems. Like I
mentioned, I was building
software for the PlayStation
Three when I was at Nvidia. And
you come from that mindset of
high performance squeezing the
most of the system, and you see
the data challenges. And I
thought it sort of intersected
nicely for me in sort of a
developer or a product approach.
And then we said, well, more
people need to be using data.
That's where the world is
headed. Everybody is going to
become a data user. I could see
my second grade kid at the time,
and they do simple survey in the
class, and they created a
histogram like, okay,
everybody's going to be data
user. How do you really get
there? Not everybody's going to
be technical and engineer. So
that is kind of where my sort of
direct experience in the data
world started to happen, is
like, we want to solve that
problem. We want to make it
possible for anybody to use
data. And what is standing in
their way is that data is
complex. It's everywhere. It is
hard to work with. Only
developers are able to do that.
So we're going to automate that.
We're going to bring that and
present that data to this user
and they'll be able to use it
wherever they want. And that was
the driver for me.
Wow, that's fascinating. Our
next question is what's your
favorite part of your current
job?
I think the CEO job and the co
founder job is a new challenge,
I would say every day and all
sorts of unexpected things. I'm
still a very product person at
heart, so it's like those things
are always fun to look at. But I
would say no day is similar to
the last one is the best part of
it. I think about a month back,
all of a sudden we were like,
oh, the bank that we are banking
with is going under and what do
you do? Did I go in or did any
of those CEOs go in on that
Thursday or Friday morning to
say, oh, this is what I'm going
to do. I'm going to spend the
whole weekend figuring out if we
have any money as a company or
we're out. All of a sudden the
rug pulled underneath us. So I
think that's the challenge. But
that's also the fun of this
particular role. I do enjoy the
thought that we are doing some
very cool stuff in data. And the
number one source of
satisfaction for me is when our
customers come to us and say,
this user of ours in this pharma
industry, they're like, you know
what, this data that we're using
with you guys, it was processing
in multiple hours and now it
happens in nine minutes. I was
like, wow, my goodness. And when
I hear those kind of stories,
I'm like, okay, we are actually
delivering value. I don't know
how to make a medicine or a
medical device, but these guys
who know how to do that, we are
somehow enabling them to do
their job better. And that is, I
think, ultimately the
satisfaction of the work.
Right?
Very cool.
Interesting. So we have three
complete the sentences. And the
first one is when I'm not
working, I enjoy blank.
So many things. I love road
biking. I do that a lot right
now, but I love snowboarding and
a bunch of activities. I don't
do flying anymore actively, but
that's another one I would do.
Nice. Very cool. Our next
complete the sentence. I think
the coolest thing in technology
today is blank.
I would have to say that
Generative AI is certainly one
of the coolest things out there.
I think I'm still trying to
understand from a technical
engineering perspective, like
the ins and outs of it, but it
is fascinating. It is also
scary, to be honest. I wouldn't
deny that either.
Well, it's funny you mentioned
that because I was thinking on
that earlier today. Even. And
the whole idea, the moving
parts, when you start thinking
about the image generation, just
take a subset and you think
about what goes into that. You
describe something, so it has to
understand what you describe,
and that has that LLM component
to it. Right. And then it
interprets that in such a way
and then probably tokenizes it,
and then it generates this
image. And I was reading a
blurb, a quote from someone at
Nvidia today, and that's what
kind of got me off doing
Billable work, mind you, and
running down the rabbit hole.
And I think it was a guy from
Nvidia. And if it wasn't, then I
apologize. But it was a person
at Nvidia who made the statement
that we're approaching that
point where we're no longer
rendering the pixels we're
generating at the pixel level.
So now they're rendering
splotches of it. Yes, generated,
but probably pulled in from
someplace based on the
description. A tokenized image
from a tokenized description.
But they're talking about
generating the pixels suck in.
Wow.
I would like to understand how
lighting because lighting has
always been the biggest part in
doing this, right? And how that
applies to it. But I would say
the reason I used to have
unconditional love for
technology innovation was ten
years ago. Everything that is
better is always or faster is
better. But I would say that
post social media and YouTube
and all of the stuff have become
a little bit concerned that we
really have to understand what
is this technology going to do?
And nothing scares me more than
that about genetic AI. Is that
okay? It is a cool piece of
innovation, but unlike a faster
chip, which was almost a no
brainer, I think now the
question is like, oh, okay, what
is it going to do that we can't
even think about today?
Yeah, I'm with you. And I get
the hesitancy and the thinking
part of our population calling
for a moratorium, kind of a six
month pause. Knowing what I know
about geeks and engineers, even
knowing what I know about me,
that's not going to happen. I
found a quote. It's from
digitalnative Substac.com, and
here's a quote from it. It's in
a talk with Sequoia last week,
nvidia CEO Jensen Huang said,
every single pixel will be
generated soon. Not rendered,
generated. And that was from, I
think, at the time of this
recording. It's either the
latest from Digital native or
next to the latest.
And he's a very smart guy, and
he knows the stuff better than
anybody out there. So if he's
saying it, I believe it. Okay?
Yeah.
But I don't know if everybody
gets how big of a leap that is
to go from what we were doing
before to generating pixels. I
don't know. Maybe I'm making too
much out of it, but it boggles
my mind.
At least the game logic and
stuff is going there. I was
reading something about how they
put these AI agents in the game.
In this paper that came out, I
think, two or three days back,
and they figured out to do a
Valentine's Day party in there
and invite people and all that
sort of social mechanics were
happening in that sort of
generative way. So they're not
pre programming these goodness.
That was also sort of crazy
fascinating, because when it
means to game development and
the gaming experience, you can
have a truly sort of a multiplat
storyline of any sort and you
don't know what everybody's
gaming experience is going to do
different. I mean, there was a
thing at a time when you would
code those things and make
people have a different
experience.
Right. One more thing and then
we'll shut up. Do you remember
that Black Mirror episode where
the lady's social score was just
crashing as she went to some
gathering and by the time she
got there, she couldn't get in
because she didn't have a high
enough social score? It's like
we're getting to more and more
to that point in some place.
Of the world that already
exists.
Your next fill in the blank. I'm
sorry.
Sure. No, it's all good stuff.
That's what makes this podcast I
mean, dare I say, it makes this
podcast look cool. But that's
what makes this field
interesting. Right. It's not
just about the bits anymore.
Right. There's social
connotations. Now, I used to
also be unquestioned fan of
technology. It makes everything
better, it's going to solve the
problems. And here we are some
ten years later. It's like we
actually created a whole bunch
of new problems. And I don't
know, is that maturity? I'm ten
years older, or is that kind of
the state of the technology that
we have created? No, I'll let
the philosophers debate that
one.
I think it's evolution because
we went from building the basic
infrastructure, which is like
chips and compute and stuff, and
those things are but we didn't
see that end application to the
level that we are seeing now. So
it's better building technology.
Bricks, cement, all that is
good, but suddenly cities are
built that look crazy and
whatever, and you're like, oh,
is that what we're trying to do
here? So I think it's just
applying technology to every
kind of problem.
Yeah, no, absolutely. All right.
And our third and final complete
the sentence I look forward to
today when I can use technology
to blank.
I think that I would say, like,
the self driving car. I think it
would save me a good bunch of
time if it really becomes
reliable. I don't know if I'll
trust it, but.
Why is it that all the engineers
are suspicious of self driving
cars?
I'll be candid with you. At some
level I feel like when I'm
driving the car, it's not that
much work. And I'm sitting
there, I might as well just have
my hand on, because most of the
stuff is got an automated like
those adaptive cruises and lane
management and stuff. So, yeah,
that much work.
But yeah, adaptive cruise
control is not self driving in
the purest sense, but I will
tell you, I can't live without
it. Now, my car, this is first
world problems, right? My car
will break all the way to 0.
Wife's car will stop at around
22 when I'm stuck in a traffic
jam in my car. I wouldn't say
it's no big deal, but I can just
kind of sit back and let the car
handle the braking in my wife's
car when it goes below a certain
speed. Now I'm on the hook. It
actually is annoying, which is
pretty funny. But next question.
Andy?
I'm in. Yeah, I was going to
interrupt you there, Frank.
Before you yell at you to put
down the shovel and climb out,
share something different about
yourself. Socket. But we remind
our interviewees that it's a
family podcast, so we want to
keep our clean ratings. So
something different about you.
And you already mentioned a few.
Yeah, from a family perspective,
I'm a dad of three kids, two
boys who are 13 and ten and a
daughter who is six. And there
is definitely a joy to seeing
all of that happen. So, yeah,
I'm a pretty regular individual
in that way, but definitely
bitten by the desire to build.
And I'm like, I see a problem, I
have to solve it. And that's
kind of what landed me in this
entrepreneur boat. Okay.
Yeah, that's awesome.
Awesome. And the next question,
although technically speaking,
it's out of order, so I need to
fix that. Audible sponsors data
driven. Do you do audiobooks?
And if so, can you recommend a
good one? And if you don't do
audiobooks, any book
recommendation will do.
I do actually love them. That's
where the driving part comes in,
right? I mean, that's the way to
to make the most of your driving
time is to be listening into a
book. I do a lot of technical
stuff, actually, I think the
most recent book that I was
listening into was I'm just
looking back at my bookshelf
because always get a physical
copy as well.
I do the same thing.
Yeah, I think my most recent
book that I really enjoyed was I
think the book called Sapiens. I
don't know if you have read yes.
Oh, I've heard of that.
Yeah, it is an older book, but
yeah, I got to it more recently.
I've got it.
Haven't read it yet. That author
wrote something else that I
read. Was it the guns? Germs and
steel or something like that?
That's an amazing book. It's a
difficult read, I would say.
That'S why it is.
We can sit back and listen to
it.
Yeah, crank that dude up to 1.25
and let it go.
Yeah, exactly. I actually just
finished The Wolf of Wall
Street, the Abridged version,
and I'd seen the movie, and I
follow a lot of the other things
that Jordan Belford had kind of
done. But after listening to the
story, there's a lot that didn't
make it into the movie. And the
impression I'm left with was
truth really is stranger than
fiction. I think they didn't put
some of that stuff in because no
one would have believed it. And
if you've seen the movie, it's a
pretty wild story. Anyway. So
there's like even crazier stuff
in there.
Oh, my goodness.
Yeah. So it's hard to imagine
there's a.
Second edition of The Black Swan
out.
Oh, really?
Yeah, it's got a few extra stuff
in it. There's definitely an
appendix, I think a brand new
appendix at the end where he
talks about implications and
applications. But there's new
stuff all the way. Even in the
introduction. He added some
nassim. Nicholas Tilleb and I
believe the first book of his
encerta. So very interesting
listening to that.
I have to pick up the New Blacks
one. That'll be interesting.
Yeah. As a product person, I
actually love also listening to
a lot of these books on the
business side of things. So I
was listening to this book
called The Ultimate Sales
Machine maybe a couple of months
back. And what I really enjoyed
was that while you're listening
to these books in the car and
you have this free time, which
is so hard to get, by the way,
these days, is that gives me the
space to think about things and
reconstruct my ideas and
thoughts. Most of the ideas that
I get about maybe writing a blog
post and stuff like that happen
typically in the car, listening
to something that triggers some
idea in my head and do that
because otherwise the whole day
is like meetings and chasing
stuff and whatever.
Sure.
Awesome. That's a good point.
It's interesting how it always
good to stretch your brain,
because our brain is pretty much
I'm not physically fit, but I
like to think my brain is, at
least. It's just good to kind of
I get a lot out of just walking
around or driving and kind of
thinking about things and
listening to books that are kind
of outside my norm. Right. Which
is probably why I picked up The
Wolf of Wall Street because I'd
been doing so many technical
books and kind of sales books,
and I was like, Let me check
that out.
Sometimes reading something or
listening to something in one
context, which is completely
different, suddenly starts
connecting to all the stuff that
we are doing, like day to day.
Absolutely. Yeah.
I totally get that.
Well, cool. So with that, is
there anything where people can
find you? I know@nextla.com.
Yeah. next.com. And on LinkedIn,
actually, I do engage with a lot
of folks in conversations over
there as well.
Excellent.
Awesome. Well, thank you for
being on the show. And we'll let
Bailey wrap us up now.
That was some show. Is it me, or
are the shows getting better? It
could be my bias that leads me
to say that. But I figured I
would ask to get more input.
After all, what's an AI without
good input and a feedback loop?
Speaking of feedback, have you
checked out Data Driven Magazine
yet?