sds podcast episode 229: data-driven approach of … · 2019-01-24 · be data driven, data...
TRANSCRIPT
Kirill Eremenko: This is episode number 229 with Co-Founder at
Cursor, Adam Weinstein.
Kirill Eremenko: Welcome to the SuperDataScience Podcast. My name
is Kirill Eremenko, Data Science Coach, and Lifestyle
Entrepreneur. Each week we bring you inspiring
people and ideas to help you build your successful
career in Data Science. Thanks for being here today,
and now let's make the complex simple.
Kirill Eremenko: Welcome back to the SuperDataScience Podcast ladies
and gentlemen. Super excited to have you back here
on the show. Today, we've got a very special guest
joining us for this episode, Adam Weinstein, who is a
Co-Founder at Cursor. Now, what you need to know
about Cursor is it's a company tool that helps organize
Data Science assets. So, if in your company you're
working on many different Data Science projects, you
have lots of different types of code, different
dashboards, different meta data, different teams
working on these projects. All that can be organized
with Cursor.
Kirill Eremenko: In this podcast, you will find out quite a lot of
interesting things. First of all, we'll talk about Adam's
own journey, his background. How he went from
working at Deloitte, all the way to working at LinkedIn,
and then founding his own company. So, if you're
interested in actually being an entrepreneur in the
space of Data Science, this podcast is definitely for
you. Plus, we'll talk about the concepts of Data
Literacy and Citizen Data Scientist, and you will find
out how Cursor can help you out in this journey. Of
course, in general what it means for an organization to
be data driven, data literate, and what Citizen Data
Scientists are.
Kirill Eremenko: So, if you are a founder of an organization or, if you
are an executive, this podcast is also for you. And, in
general, if you are interested in becoming more data
literate, and interested in the concept of Citizen Data
Scientist, whatever your level is in the organization,
once again this podcast is for you.
Kirill Eremenko: One more thing I wanted to mention before we get
started is that this podcast is available in a video
version. So, if you'd like to watch the video of us
chatting with Adam, head on over to
www.superdatascience.com/229. Then, you can enjoy
the video experience there. However, if you're listening
to an audio while you're running or in the car or
something else, then feel free to continue with the
audio because you will still get all the valuable insights
from here.
Kirill Eremenko: And now, without further ado, I bring to you Co-
Founder of Cursor, Adam Weinstein.
Kirill Eremenko: Welcome to the SuperDataScience Podcast ladies and
gentlemen. Today got a very exciting guest on the
show, Adam Weinstein. Adam, how are you doing
today?
Adam Weinstein: Doing great. Doing great. How about yourself?
Kirill Eremenko: Very good as well, thanks. We were just chatting
before how cool it is, the time difference. I'm in
Brisbane, Australia. It's almost 10:00 AM. What's the
time for you in San Francisco?
Adam Weinstein: Yeah, it's like a little before 4:00 PM the day before.
Kirill Eremenko: We were talking about it-
Adam Weinstein: [crosstalk 00:03:21] the day almost.
Kirill Eremenko: Yeah, I can tell you all about like in the morning you'll
have some rain. Then it'll get sunny. Whole bunch of
[inaudible 00:03:28] your day.
Adam Weinstein: We could use some rain here. There's about six years
of drought that we're trying to dig our way out of.
Kirill Eremenko: That is crazy. That is crazy. I heard about the fires
that were happening in California. Is that still going
on?
Adam Weinstein: Yeah. No, so they're luckily all ... they've all burned
out, I guess, at this point. Unfortunately, they finally
contained the fire about 12 hours before the rain
came. So, it was poor timing but, yeah. The impact has
been pretty massive. It's fascinating, Vice just did an
interesting special over the weekend on sort of what
does this mean in the long run, because we've had
now, two or even three years in a row, we've had tons
and tons of acreage burn, and houses burn. People
displaced, people killed, et cetera. Just because of this
wildfires that have started. Interesting to think if that's
not necessarily an anomaly anymore, right? Is it
becoming the normal?
Kirill Eremenko: Yeah.
Adam Weinstein: Maybe a topic for another time.
Kirill Eremenko: Yeah, I know what you mean. I originally saw a
visualization of they had ... what was it? I think they
had the 50 states of the US, and they had how often
abnormal weather events happened over the past like
60 years. And, all the same pictures, and it's
animated. You can see like, okay, if the start was here,
something popped up here, here, here, here, here.
Then, as you get into the 2000 it's like everything is
red every year like, something is popping up. It's crazy.
We might actually include that in the video version of
this podcast so people can see.
Adam Weinstein: Yeah, no it's interesting. I mean, the size of the area
that burned, I think, was roughly equivalent to almost
six San Francisco's or, you know ...
Kirill Eremenko: Wow.
Adam Weinstein: About 12 New York Cities, at least. So, if you imagine
like ... no grant that, these aren't densely populated
areas, but still, that ... if you've been to one of these
towns and you say, "Okay, that entire town burned.
Multiply that by 12 or six [inaudible 00:05:27]." It's a
huge amount of land that just totally been destroyed.
Kirill Eremenko: Wow, that's crazy. All right, well let's move on to Data
Science. Hopefully that situation will get better with
the fires. Data Science, so Adam, very excited to have
you on the show. You have an amazing journey
through Data Science with lots of highlights, LinkedIn,
Bright, and now your own company. I don't even know
where to start. Let's ...
Adam Weinstein: Yeah.
Kirill Eremenko: Let's maybe talk about if somebody were to ask you off
the street for the first time, and you were introducing
your ... how do you introduce yourself to people? Who
is Adam Weinstein, and what do you do?
Adam Weinstein: Yeah. I guess I'm a data geek, right? No. You know,
background is interesting, like you said. I started life
out of school like every undecided undergraduate. I
was a consultant for a couple of years. And, I always-
Kirill Eremenko: Sign me up for that as well. Yeah, same story.
Adam Weinstein: So, I'd always been interested in the hardware side of
technology. So, I actually got into infrastructure
consulting. We were helping really large companies
figure out how to deploy data centers around the
world. At the time, there was this big wave of
virtualization that was occurring, right? So, back in
the day you'd have one application on one server, and
even if it only ran a job for 10 minutes a day, it would
be an individual server that would be wasted for the
other 23 hours and 50 minutes.
Adam Weinstein: So, virtualization was like, okay, can you compact
multiple processes over the same box. Now, it's
containerization, or [Kubernetes 00:07:12] or
whatever. Fast forward 15 years. So, I happened to get
a little bit of a focus in data infrastructure. So, after I
had done my sort of tour in the consulting world I
joined a company, a start up actually, called Exact
Target. We had an office in Sydney ironically but,
never [inaudible 00:07:32].
Adam Weinstein: But, Exact Target was-
Kirill Eremenko: You worked in Sydney?
Adam Weinstein: Say what?
Kirill Eremenko: You worked in Sydney?
Adam Weinstein: No, we had an office in Sydney. I [inaudible 00:07:41]
in Sydney. Wish I worked there, but no. I worked in
Indianapolis, which is ... I lived in Chicago as a
consultant and moved back to Indianapolis where I
grew up after being a consultant for a while. Exact
Target was in the email marketing space. So, it's now
... it's currently Sales Force Marketing Cloud. Sales
Force bought the company about five years ago. It's
now the ... I think it's the largest email center in the
world still. Like, most large brands that send out any
quantity of email, whether it's Nike, or large banks, or
anything in between, right? If you're sending ... if you
need to send a few hundred emails, a few hundred
million emails in a few minutes, you tend to use
something like an Exact Target, or today's Sales Force
Marketing Cloud.
Adam Weinstein: But, they didn't really have a data team at the time.
So, that was a role I kind of jumped into shortly after
getting there. It was fascinating, right? We were
running the world's largest Microsoft sequel server,
which I'm not sure that's something you want to brag
about, but it was a fascinating time, right? The
company had gone from sending a few million emails
on behalf a few small businesses, to you know,
hundreds of millions of emails on behalf of Groupon,
and Nike, Bank of America.
Kirill Eremenko: How crazy is that, that like a company that's running
the world's biggest SEL server, and working with so
many users, and companies, they didn't have a data
team. Like, right now in this day and age, 10 years
later, it's unfathomable for a company [crosstalk
00:09:04] data team.
Adam Weinstein: Yeah, there was like an infrastructure team that kept
the lights on, right? They literally were hiring the
architects from Microsoft just to keep the thing
running. Like, [inaudible 00:09:14] Microsoft sequel
server because it was such a kind of a fire ... I'll refrain
from using other language, but yeah. It was a mess.
Adam Weinstein: But it's interesting, right? Even data companies
struggle sometimes I feel like, to step away from it and
say, "Okay, how can we look at the data that we have
and be more intelligent about how we use it?" So,
yeah. We were really a data driven company. We
helped companies identify, okay, you've got this list of
emails that you've accumulated from your website, or
from orders. There was a lot of retail at that time. How
do you market to them in a more cost effective
manner, as opposed to TV ads, or print mailers and
things like that. The company was growing through
the recession in 2008.
Adam Weinstein: So, I got there in 2007. Ironically, we probably went
public at the end of the year. Although, we ended up
pulling it because it was such a terrible time, but the
business did phenomenal through the recession,
mostly because it was a lost cost alternative. You
know, the cost of sending an email is a fraction of a
penny. The cost to send something in a mail or put a
TV ad is infinitely higher. So, yeah-
Kirill Eremenko: One of the first players there. One of the first
companies-
Adam Weinstein: Yeah, first data player at least there. So, we did
everything from how do you identify when a customer
is like, you know, high risk and looking to turn, to hey,
this customer just signed up yesterday and they've
already blown through their utilization so, why don't
we talk to them and find a better product. That kind of
thing. It was very early days of data, and I'm not even
sure it was called Data Science. It was probably more
business intelligence.
Kirill Eremenko: Yeah.
Adam Weinstein: But, it was a fun time. So, was there for a few years.
We got to about 1200 employees and I decided I
wanted to go do something small again. I actually took
kind of a brief hiatus from data. So, I was always a
sarcastic greeting card sender. I used to send cards to
family and friends. I had this crazy idea that the
challenge with cards is you can go down the street and
you can buy some cards, but if they're not the style
that you like or in the language that you need you're
kind of ... you have no options, right? It's not like you
can just go find another store. Card stores were kind
of dying too.
Adam Weinstein: So, I came up with this idea like, okay, if you just print
everything on demand you can have a selection of an
infinite number of cards. Someone could come online
in Australia, order a card, have it printed and mailed
in New York today and get to the person tomorrow.
You know, if you just have this distributive print
infrastructure. So, that was a company called Engreet.
We were a small team. Grew it, never raised any
money ironically, but debated to moving it out west to
do so. Coincidentally it was bought by the printer, so
the printer that was doing all this was like, hey we
want to get in that business. We talked over drinks
and they decided, okay, well what if we just brought
you on board. So, ended up selling them Engreet, and
now it's called The Greeting Card Shop. So, it still
exist. It's funny, I still get their emails every holiday
season.
Adam Weinstein: Then, I moved out west, and this is probably were my
real start in data kind of occurs, at least modern day
data. To work for a company called Bright. Bright was
a company in the machine learning space that helped
match jobs to people. So, if you think about the job
search process going back a few years, you'd go to a
career builder on Monster and Indeed. You'd type in a
job title and say, hey, I want to be a software engineer
or a product manager, or marketing coordinator or
whatever it may be. Then, they'd show you all the lists
of jobs that had those titles. But, companies were
getting a little creative with titles. I think the joke we
used to use was, what if the job was called ninja.
Nowadays, I don't think, hopefully, anybody uses that
title, but maybe they do.
Adam Weinstein: So, we built an algorithm that would basically parse a
resume and a job description. Calculate the
normalized skills that were basically being used, right?
So, Oracle, and Facebook, and SAP, and Microsoft and
Google. They all recruit people that can write Java, but
how they describe it is very different. So, we would
come up with a way to normalize the skills being
represented on both the job description and the
resume. Then, score the fit between the two of them.
Adam Weinstein: So, instead of searching for a job title you would
actually just [inaudible 00:13:21] show you all the jobs
you were qualified for. So, that was a fun couple of
years.
Kirill Eremenko: Sorry, just kind of like a recommender engine, right?
Adam Weinstein: Yeah.
Kirill Eremenko: Like you upload your resume and then instantly you
get jobs that you're ... is that still used online when I
go on Indeed or Glassdoor, and stuff like that?
Adam Weinstein: Yeah. So, LinkedIn bought Bright in 2014. So, when
you do ... when you perform a job search on LinkedIn
that scoring algorithm is actually being used behind
the scenes to [inaudible 00:13:53] a job. So, you still
do search for a title but, there's sort of a marriage of
the title, and then that score that help recommend
what jobs to see.
Kirill Eremenko: Oh okay.
Adam Weinstein: And then, [inaudible 00:14:03] LinkedIn emails on job
recommendations. I'm sure there are still some
mistakes in the algorithm but yeah, those
recommendations are being informed by the same
score.
Kirill Eremenko: Interesting.
Adam Weinstein: [crosstalk 00:14:14] owned by LinkedIn, but yeah.
Kirill Eremenko: So, it uses the information on your profile when you
search for stuff? That's so cool. That's so cool. It's
important to get your profile right, not just for other
people to see it, but also for your searches also be
most relevant to you.
Adam Weinstein: Right. And, increasingly, so LinkedIn's core business is
this recruiter product that if you're a recruiter inside of
a large corporation you pay a substantial fee to be able
to search across the entire LinkedIn network. You
can't ... you know, you can then send an email to
anybody. Increasingly, that search process is
converting to a recommendation process. So, instead
of the recruiter saying, "Hey, I'm looking for software
engineers that have three years of job experience and
have worked at these five companies." LinkedIn is
trying to push candidates to those recruiters. That's
being informed by the same recommendation
algorithms. I think, you know, the downside of the
hey, you have to put all your sort of life's work into
this profile, but the more you put there the better off
people that might be looking to hire you will be.
Although, as a Data Science you're probably not
hurting for inbound interest in being hired.
Kirill Eremenko: Yeah. [crosstalk 00:15:22]. And, just maybe a year or
two ago LinkedIn started ... when somebody endorses
somebody for their skills, now it's not as easy as
before. Just like click, click, click. Now you have to
explain. How do you know the person, what level of
endorsement and so on. I think that's probably has to
do with that whole system as well.
Adam Weinstein: Yeah, it has to do with how knowledgeable, or what's
the quality of that recommendation. So, before
anybody could go and endorse anybody for anything. I
could endorse somebody for material science
engineering and I don't know the first thing about
material science, and vise versa. Somebody could
endorse me for something I might good or might not be
good at but, even if they don't know anything.
Adam Weinstein: There was actually a time, maybe embarrassing length
in history, where you could create your own skills. We
famously endorsed people for very inappropriate things
like, you know, I don't know what a good example
would be. Like dropping things on the floor, or tripping
in public places, or things like this.
Kirill Eremenko: Yeah.
Adam Weinstein: You know, not skills that anybody would want in their
profile but, you could just endorse anybody for
anything. So, why not?
Kirill Eremenko: You'd endorse them ... you'd create a skill for them,
right? You endorse them for something they don't even
have on the profile.
Adam Weinstein: Exactly. Exactly.
Kirill Eremenko: Good times, yeah.
Adam Weinstein: So, no. Yeah, they're trying to get the quality aspect
figured out because it's ... that really is what tells you
whether somebody is good at something. If somebody
that's in a domain can endorse somebody else in that
same domain for something, that should be a very
valuable endorsement, and that's what they're trying
to get to with skills.
Kirill Eremenko: Yeah, totally. Totally gotcha. What happened after
LinkedIn? You were there ... you were at Brights until
they got acquired, then, you were for what? Two years.
Then, at LinkedIn you worked for another three years?
Adam Weinstein: Yeah, exactly. So, here I was, this sort of start up data
guy at Bright, right? We had a small team but it wasn't
LinkedIn size. When I got to LinkedIn, they actually ...
the joke was they didn't necessarily know what to do
with me. They're okay, here's a guy that knows how to
build data teams in small organizations. We've already
got 200 of those people. What do you want to do?
Adam Weinstein: But, it turned out that LinkedIn had just built a ...
sorry. An office in China, so in Beijing. And, the way
doing business in China works it was technically a
subsidiary. So, we had a couple of folks there but we
were building it out as if it were an independent
company. It had to be autonomous, right? LinkedIn in
California didn't know the first thing about succeeding
in China. We hired a team that had been really
successful previously, a couple from Google, from
Apple, and elsewhere. We just wanted to give them the
autonomy to do so.
Adam Weinstein: So, I became the data ops guy that was sent there to
help build out all the tooling that they needed to be
successful against most of the LinkedIn data that
already existed. But, the recommendation was to don't
necessarily use what we already have. Think of things
as if you were doing it from the ground up. So, I was
there like two weeks when they asked me to do this.
Adam Weinstein: I started getting on a plane, going back and forth every
six weeks. My first question was okay, how are things
done around here? What are the metrics we care
about? Where is the data? Show me models that are
relevant. How do I get an understanding of this
business that was about 5000 employees at the time.
The challenge I found was that there was no one place
to go. So, I had about 200 or so, it was like 180
coffees. Ironically I don't drink coffee, but 180
meetings over the course of about 18 months where I
just met leaders in different domains. And said, "Okay,
you're the marketer for this product. How do you
measure new customers? What is the definition of a
customer? What's the definition of a customer at risk?
What's the definition of a successful customer?" Like,
all these things that were not captured in one place.
They were just in individuals peoples' heads or on
their local machines.
Kirill Eremenko: Sorry, this isn't the main LinkedIn? You're getting that
information on the main LinkedIn [crosstalk 00:19:17]-
Adam Weinstein: Then, I would take that, go to China and say, "Okay,
here's how we do it in the US. You could use this if
you want." Or, I should say the rest of the world. We
were supporting a global business, kind of sort of
being as one carve out. Yeah, that was sort of my 18
month window of life. Where literally, come back, I'd
pick a new domain, a new product, new area, go learn
as much as I could about it. Then, fly sort of like ...
show the team what I'd figure out. Then, go do the
same thing all over again.
Adam Weinstein: What I ended up developing was this great corpus of
knowledge of, hey, here's data inside LinkedIn.
Knowing every time I interviewed someone like
chances are three weeks later it would change. But,
you know, it was relatively up to date. A bunch of
people around the business started using it as well,
right? So, it was a collection of code, of terminology,
nomenclature, like data definitions. A little bit of like,
okay here's were we keep ... we had a bunch of
different reporting systems because we were a software
company. So, we built them every time we needed
them.
Adam Weinstein: So, here's the reporting system for this metric. Here's
the reporting system for that metric. Sometimes it was
Tableau, sometimes it was homegrown. Sometimes it
was something that had been there for 13 years that
we didn't know why it was there. Yeah, it was a
fascinating journey, but I think it taught me that even
in a really innovative company it can be hard to keep
your arms around what's going on where, and how to
find answers to sort of even the most basic data
questions, which I think as a Data Scientist is ...
before you can have fun with data as a Data Scientist,
you have to know where things are. You have to know
what it means. Can you trust the data? Is it of high
quality? Is it being refreshed? Is this the source of
truth, if you will, right?
Adam Weinstein: So, that drove me to start Cursor, which I can talk
about but, that was sort of my journey at LinkedIn. I
left last March officially to ... you know, decided that a
paycheck was no longer worth it.
Kirill Eremenko: Did you already know when you were leaving that you
have this idea for Cursor, or did you leave and then,
come up with the idea for Cursor later on?
Adam Weinstein: Yeah, so I had a good understanding of what I wanted
to do. It wasn't ... I don't think it was perfectly nailed
down. What I wanted to do was spend a couple of
months talking to other companies, particularly
outside of the area of technology. Silicon Valley can be
a little bit of a bubble in terms of how we look at
problems, and how we solve them. So, I wanted to talk
to banks and industrial companies, and retailers and
understand what is data inside their organization and
how do people interact with it.
Adam Weinstein: I had a good sense though of what I wanted to build in
terms of somewhat of a data catalog but something
that was more interactive for the average business
user. So, that was the premise. I think we honed it for
a few months before we actually started the company,
and raised money and that kind of thing. I had a
strong sense of what it was we were going build at
maybe [inaudible 00:22:14].
Kirill Eremenko: Okay. Awesome. So, that leads us to Cursor.
Adam Weinstein: Yeah.
Kirill Eremenko: First of all, why the name? Why Cursor?
Adam Weinstein: Yeah. So, I've liked the name Cursor since long before I
came up with the idea. You know, I guess you could
say the concept to me is like, in a knowledge
management kind of problem, which I guess you could
generically call Cursor a knowledge management
solution, although it's not generic knowledge
management. It's specific to data.
Adam Weinstein: I think that the notion of a cursor helping you seek, or
find something is really I think powerful. Then, I think,
you know, there's also a database Cursor concept,
although we talked to a DVA about a database Cursor
although they'll run you out of the room because
they're not very conferment. But, I think the sort of
marriage of those two, right, that it's steeped in data.
At least the concept of a cursor, or even code right? I
mean, cursors have existed in [inaudible 00:23:11] for
a long time.
Adam Weinstein: Then, the notion of like, okay, people can relate to a
cursor helping to find something. Whether it's on the
screen, or potentially buried in a data link somewhere,
right? Like, I've always liked the name. So, it just so
happened I found the dot com was available.
Kirill Eremenko: Oh wow.
Adam Weinstein: Between the two of them, I was like okay, well this is
the right name. So, we had the name before we had
the company and the idea.
Kirill Eremenko: Yeah. Gotcha. As in Cursor like the cursor you have
on the screen, that type?
Adam Weinstein: Yeah, exactly.
Kirill Eremenko: That's so cool. Rarely those domain names are
available, short ones like that.
Adam Weinstein: Yeah. It'd be interesting to see if domain names last.
You know, they're sort of a real estate gold rush for
domain names now that you've got so many other
TLDs, right? Dot IO, dot APT. You know? Other things,
right, is the dot com is valuable? I guess we'll find-
Kirill Eremenko: Yeah, we'll see. As soon as Google updates their search
algorithms. Right now I think [crosstalk 00:24:10].
Adam Weinstein: Yeah, exactly. Exactly.
Kirill Eremenko: Easiest to find. Okay, well Cursor. Tell us about the
company. What does it do? We've heard your story.
Obviously, you've built up a lot of experience,
knowledge and data, and then some pressing issues
that you actually saw first hand. How does Cursor go
about solving? And just in general. Give us an
overview.
Adam Weinstein: Yeah. So Cursor, the challenge if I had to sort of boil it
down that we had at LinkedIn is that we had a bunch
of users across the organization that were creating
content, right? Could be Ad Hawk Sequel code, could
be dashboards in Tableau. Could be an Excel
spreadsheet. Could be a Python model, right? There
was no one place to go find all of that. Mostly because
everybody was using their own set of tools. So, you
had people that had locally installed sequel editors.
Tableau, I guess if you were looking for a Tableau
dashboard, you could search if it had been published
to the server. But, there was a lot of work that was
being done on the local machine. Even Jupiter
notebooks, right, for the most part were installed in
local environments.
Adam Weinstein: So, Cursor is a tool that a user can start with, or a
team, that if they work inside the product it has a built
in sequel editor, it has a built in Python environment.
It connects to all these places where data lives, and to
BI solutions like the Tableau and any database that
you might use. It basically curates in sort of an
intelligent way all the data that it's seen. Or, I should
say meta data that it's seeing. It's actually not looking
at the raw data itself.
Adam Weinstein: So, if you connected to three databases, you've written
some sequel, you've written some Python, you've
connected to Tableau, it helps build a single corpus of
knowledge that any user in that business can come
search. And, helps ... and the goal of defining things
that have already been done, or answers that may
already exist. So, an example might be I'm an analyst
and I'm trying to figure out how many products have
we sold today? Generically speaking. If somebody else
has done that work, how do I find them? If they
haven't, how do I find what table has the product data
in it? If I do find that table, how do I know that table is
the right table? So, we help built a place where people
can come find what they're looking ... you know, find
an answer to a data question. Make use of data if they
don't necessarily know what the answer may be, and
then understand what they're seeing.
Adam Weinstein: So, it's you know, simply speaking you can think of it
like an Evernote or Dropbox for an analyst, or for a
data user. It could be also for a Data Scientist, but it's
designed to scale as wide as need be. So, data we
know is siloed, right? As are the teams that use it. So,
the solution's kind of designed to fit that. You can
start with one team. You can let another team come on
later. This was a challenge that we saw on LinkedIn.
We looked for a solution in the market to try and solve
it for the business, and the problem was we couldn't
get everybody to agree. The perfect prevented was the
enemy of the good, right? You can't have a solution for
everybody, nobody had anything.
Adam Weinstein: So, this is designed to solve that challenge with hey,
one team can start using Cursor. They can at least
start sharing with themselves, and then, typically what
you'll see is okay, another user gets jealous of this
corpus of knowledge. They'll come on, and that brings
their team with them, and it kind of grows from there.
Kirill Eremenko: Wow. That is such a cool idea. It's like, and I'm already
hooked because I think of myself as a very organized
person, and what you described it sounds like a tool to
organize Data Science assets. You know, whether it's
code, whether it's data, whether it's like anything to do
with the Data Science projects. Very cool.
Kirill Eremenko: So, basically I can not only search ... as I understand
it you are combining, first of all the tools. Sort of like if
something was done Python or in R, or in Tableau, I
don't know, right? I might only know Tableau or might
only know Python. I don't know what other people
have done in other tools. Or, even I've worked in many
tools. I can actually put those entries into Cursor, and
that way I will know what I've done across different
tools like keep track of it across different platforms.
Adam Weinstein: [crosstalk 00:28:25] right? We don't want to have to
have you pull in everything manually. So, in many
cases we've built connectors. Like, if you've got
Tableau already, you can just plug in your credentials
once. We'll automatically suck everything in. We'll pull
all the queries behind every dashboard, make it
searchable, and same thing goes for other
environments too. The goal, again, it's like we don't
want to replace every tool. We just want to bring them
all together into one sort of searchable interface.
Kirill Eremenko: Gotcha. So, that's tools. Then, on the other hand you
also organize across people and departments, right?
So, in a bigger organization, or even if it's like ... even
if it's a small organization, but decentralized. Like, our
business is across different countries. So, if somebody
has worked on a project and I don't know if they
worked. So, again, you want to reduce double work,
right?
Adam Weinstein: Yep. That's exactly right. So, what we separate is that
to know that somebody has worked on something
versus, being able to see the results. So, we have
teams where, let's just say sales and finance. There
may be certain things that finance produces that
they're comfortable knowing, like okay they worked on
a quarterly sales pipeline. But, they may not be
comfortable sharing the results of it. So, what that
separates is like, okay the sequel query or the Python
code that's been written, you can see that but then,
the only way you can actually see the results is if you
have the credentials to actually execute it. So, we allow
you to sort of separate those two, because the model or
the code is often times less sensitive than the actual
results.
Adam Weinstein: That's a challenge we see time and time again, that
you know, why have somebody start from scratch
when they can reuse 80% of something that someone
else produced just because you know, you're on a
different team.
Kirill Eremenko: Gotcha. Yeah, that's a really cool idea. I'm surprised
nobody has done it before. Were you shocked that it
didn't exist?
Adam Weinstein: Yeah. I think it's ... I think it would have been difficult
to do it too many years ago. The reason being, like if
you look at how fragmented ... I mean, I would say it's
becoming more fragmented, but if you look at how
fragmented the tool space was even just a few years
ago and how few of those were web accessible. So, it's
really easy to build an integration to Tableau because
they have rich APIs that you can connect to, and it
allows you to extract a lot of the relevant information
you want to add into some sort of search interface.
But, if you go back to the world of SAP and Oracle,
where that was commonly what you would see in big
enterprise, there weren't rich APIs. There weren't great
ways to stitch things together.
Adam Weinstein: So, it would have been harder to build a solution that
was trying to do what we're doing. To be fair, we get
asked to plug into things and depending on the
product, we can do good sometimes and less good
others. It is a ... it is something in this web era where
things are built with, I don't know if it's collaboration,
but certainly accessibility in mind, and the ability to
come from third party platforms. You know, it's getting
easier to do.
Adam Weinstein: But yeah, I was surprised that there was nothing
focused on this search problem. There were data
catalogs, and data catalogs was sort of like a V1 of this
problem set, which is like how do you at least provide
just a dictionary. Think of like a telephone directory of
data inside of a business. But, the problem I saw with
those ... we looked to deploy one at LinkedIn too. The
problem I saw with those is that everybody has to go
upload the dictionary manually, and by the time you're
done uploading it and surveying the entire business,
it's already out of date. Like, ingrained in the person's
workflow. So, if they're not using it on a daily basis,
and they have to take time to separately go document
something, just like documentation in general, right?
It's not going to get done.
Adam Weinstein: So, we tried to build something thoughtfully that was
part of a user's daily workflow. That's why I hope we
can succeed.
Kirill Eremenko: Gotcha. What kind of integrations do you have at the
moment? You mentioned Python, Tableau, R. Can you
give us a quick overview?
Adam Weinstein: Yeah, yeah. So, we've sort of focused on three areas.
Any data store that you'd want to plug into from ... so,
big data, like a Hive or a Spark, to you know,
traditional data stores like an Oracle, or Terra Data or
Microsoft Sequel, right? Any database we want to be
able to plug into on the BI front. So, we think of that
as sort of layered to you know, that there's Tableau,
there's Click, there's Looker, there's Power BI. We
started with those just because they're sort of the
larger, more popular ones on the market.
Adam Weinstein: Then, you're right, on the language front we have
Python. R is in process. Not there just yet, but it's on
the horizon. And, sequel, lots of sequel and various
flavors of sequel. If you're writing P sequel, Microsoft
world, they support that of course. Then, support a
number of different operating systems. So, we have a
Mac client, a Windows client, and a Linux client too if
you want that.
Adam Weinstein: The product is ... you know, it's cloud based in the
sense that when you share something, like if you write
some code and we're on the same team, and you want
to share it with me, that's shared via the cloud. But,
there is a client aspect to deal with the certain
networks if you layer in between. It's like often times
inside big companies, you know all the places where
data live are not accessible to the clouds. We couldn't
directly connect to it from our cloud layer. We'd have
some sort place, or some place internal to be able to
get into that.
Adam Weinstein: So, you can use the client as a means of doing that, or
you can actually deploy it on a server internally if you
want. It's up to you. It's much like an R server, or a
Jupiter notebook environment, right? You need some
place internally for it to live in order to connect to
data.
Kirill Eremenko: Okay, gotcha. Let's talk about actually people using
this. How has it been received? Have you had people
and companies try it out, because I can imagine it
actually solves a lot of pain points, and for some of our
listeners listening in, they're probably already seeing
this at the company they work in, or maybe have
experienced this in the past, or maybe it's their own
business so they're seeing it. So, tell us about how
others have perceived this, and what kind of benefits
has this been able to deliver?
Adam Weinstein: Yeah. So, I think it depends on the audience, right?
So, there's probably three or four audiences that have
crept up. I don't know if they were intentional or not.
In no particular order, right? So, there's an
engineering audience that like more traditional
software engineering. They may support a data
organization, or an analytics team, but they'll often
times have queries that they want people just to be
able to see. They could be health checks, they could be
just actually like business insight type information.
Like hey, here's a metric that we look at that we
monitor. They've used the tool as a way to democratize
that, make it easy for other people to come find it. You
know, if they want to go on vacation not have to worry
about they're going to get a phone call just to get a
snippet of code. You know, like get ...
Adam Weinstein: Our tools like that do a great job of documenting code,
and sort of version control, but they may not have the
business context. So, they'll use our product as a
means of sharing that. That's sort of software
engineering.
Adam Weinstein: On the Data Science front, I think it's probably more
in collaboration with a BI team or an analytics team,
where too much of Data Science has become data
prep. How do you get dirty data or the right data in a
format that you can then actually start performing
machine learning on, or for that matter, even just
modeling. So, where Data Science teams, and BI teams
have come out of the platform like, if the BI team
comes first, which has been a common trend, they'll
get all their code in there. Then, a Data Scientist, they
might want to go look at A, is there something
predictive in this data set that we could use, or we
could monetize? They'll at least know, okay, I'll pick
the code the BI guy uploaded. I'll get the result set,
and then I can just go and I don't have to waste time
finding the data, prepping it, getting it ready for
whatever I'm trying to do to it. So, that's probably
audience number two is this sort of joint BI/Data
Science audience.
Adam Weinstein: Then, audience number three, coincidentally is like a
business user. So, somebody who spends all day
looking for a report or an answer to a question. They
don't know whether it's in sales force, or in Tableau, or
they just need to ask the analyst sitting next to them.
They're looking for a quicker way to not bug people
over email or slack or whatever it may be. So, they're
using the product sort of asking the team like, hey,
can you start using something like this so that I can
not bug you as much. That's sort of one of our selling
points. It's like, hey, if you're a business leader and
you're constantly bugging someone for answers to
questions, for your sake and theirs, put it all in one
place so that you can come find it.
Kirill Eremenko: Yeah, yeah. So, it's kind of like you're benefiting from
this network effect. Yeah, it's classic Silicon Valley
start ups.
Adam Weinstein: Yeah, exactly. We didn't invent that, right? Same thing
as [inaudible 00:37:31], same thing ... you know, even
self service BI, the Tableau's [inaudible 00:37:36].
Same thing, like hey, if you've got a dashboard come
find it. Right? But, not everything is in a dashboard,
and for that matter, not every dashboard is accurate.
Kirill Eremenko: Not every dashboard is Tableau.
Adam Weinstein: But yeah, those are probably the three audiences.
Engineers, analyst and business leaders that use the
project, or that are driving to push the adoption of the
product.
Kirill Eremenko: You mentioned four audiences, no?
Adam Weinstein: Do what?
Kirill Eremenko: You said four.
Adam Weinstein: Data Scientist and business analyst.
Kirill Eremenko: Oh okay. Gotcha. Very cool. Very cool. I actually want
to talk a bit more about the business audience, right?
So, the way I see it is it's not just like business data is
for sure, executives and directors, but also I think this
could be useful for really anybody in the business.
Like, as an organization, and the world is moving on to
[inaudible 00:38:26] more kind of data driven type of
environment approach of doing business. Every
business is starting to try to become data driven. You
actually, you talk about this concept. The whole
notion. Maybe it's a good time to talk about this. The
Citizen Data Scientist, right? So, let's talk about that
for a bit.
Adam Weinstein: Yeah. Data Science is fascinating right? I think it
almost feels to me like the early days of BI, or I should
I say self service BI. Self service BI, I think the sales
pitch was like oh, you build this cube, which was what
it used to be, right? Excuse me. Then, anybody can
come to this system and ask a question and it'll give
you the answer. How many [inaudible 00:39:09] did we
sell yesterday? How many employees do we have in
this country? How many of them graduated from this
college? You can always come up with a question that
a self service BI system may or may not be able to
answer, right?
Adam Weinstein: Data Science sort of feels like a similar problem set in
the sense that there are really hard Data Science
problems that require someone with extensive
statistical understanding, and math capabilities, and
the ability to code, and all that. But, there's also a set
of Data Science problems that should be approachable
to what I call like a technical business analyst or a
Citizen Data Scientist. So, you know, I think helping
those folks feel comfortable exploring data, and playing
with it, and using tools, whether it's Cursor or there's
sort of even a growing auto ML set of solutions, right?
How do you automatically model ... throw a number of
different models against the data set and figure out
what's predicted, right? Someone should be able to feel
comfortable using that if they're comfortable writing
sequel.
Kirill Eremenko: Something like Data Robot you mean.
Adam Weinstein: Yeah, Data Robot, or there's a number of different ... I
mean, Acer has one that fits in the [inaudible
00:40:20]. Amazon is in the process of making one. I
think that ... there's an audience for that type of use
case where like maybe 60% of the ML problems might
be solvable by that audience in the next five years. Not
today, but at some point soon. Maybe it's more than
60, I don't know.
Adam Weinstein: I think that the challenge is sort of like how do you
help breed these folks that they may be stuck in their
current day job, and how do you help sort of
encourage that type of exploration, and
understanding? So, I think that's a little bit of what
Cursor can hopefully help with, but it's not just
Cursor, right? It's how do you encourage people to
take that leap. So, we saw that a lot at LinkedIn where
somebody that was a technical analyst would just
start playing with Python. They'd take a course, and
sometimes on Udemy right? They'd figure out, hey,
there's something more than just pulling data that I
can do that might be more valuable to the business,
and just understanding that that opportunity is out
there is ... it's the only thing stopping them.
Adam Weinstein: I don't know if that answer is where you're getting at,
but yeah, there's a growing audience there and I see it.
It's probably going to be the sequel user of today that's
the Citizen Data Scientist of tomorrow.
Kirill Eremenko: Yeah, yeah. And, to your point, I recently read a ... I
think it was like a study somewhere. It was not
recently obviously, I'd remember it better. But, it was a
while ago, and it ... what they did ... a bit of a different
situation but, to illustrate the same concept, that they
were developing certain, I think it was certain drugs, to
fight some kind of diseases. With drugs, you need to
put the chemical formula together in order to you
know ... and, they had the modeled environment
prepared, so basically there's this environment where
all the tests can be run. But now, it's just about
iterating and trying out these millions variations of the
chemical compounds and formula.
Kirill Eremenko: So, instead of doing it internally or running brute force
through it, and running simulations, what they did
was they opened up a online place where people,
anybody, could go, and just try it out for themselves.
So, people, random people from all around the world,
would log in. Not even log in, just go there and drag
and drop these chemical compounds and, click run
and see what comes up. In the end, they came up with
the most non standard, and they solved all the
problem. They found all the right composite they
needed. So, that just shows that even people who don't
understand chemicals and drugs-
Adam Weinstein: Sure.
Kirill Eremenko: Bacteria and all these diseases, and stuff, they still
have creativity, right? People can still ... you just
provide a self serve drag and drop type of environment.
They can solve probably like half, or like you say, 60%
of your business problems can be solved by people just
in their spare time. Like, oh you know, let me try this
machine learning algorithm and things like that.
Kirill Eremenko: I think what you're doing in Cursor is like a massive
step towards that. I think that with time, businesses
not only need to leverage their data more, but also the
creativity of the people that work there in general.
Adam Weinstein: Yeah, no that's a really good point. I think there's ... if
you open the newspaper every morning, and you look
at headline of okay, this company had this much of a
data breech, and the sort of repercussions, right?
There's sort of this desire to just crawl into a shell. We
used to joke. The last role I was in in LinkedIn I
actually helped work on the security side of the house.
It was interesting. We'd walk into meeting and
sometimes you've have some pessimist or, there'd be a
negative tone to it. I say, "Well, okay. You can just turn
off all the servers and go home. Then, there's no
security risks." No business either, but you know.
Adam Weinstein: So, I think there needs to be a comfortable way to
allow people, like you said, experiment, explore, learn
because your employees are your biggest advocates. I
mean, generally speaking. There's always going to be
bad actors, but you know, rarely are they internal. So,
this balance of like, okay, how do you trust but then,
excuse me, also have some security around how you
do it is an important one to strike.
Adam Weinstein: So, yeah. I couldn't agree more. How do you open
things up as much as you can without putting yourself
at risk? That's a question I think people are grappling
with, and even Cursor, we often live in a hybrid
environment. So, companies have some data in the
cloud, and some on prim. I don't see that mix
changing. I don't think it's going to go 100% cloud any
time soon. Yet, if it did, it would open up so many
different opportunities from an infrastructure
perspective, or a tool perspective and what they could
use, and how it would actually benefit the company.
But, because of this security fear they have data is
probably one of the last things to go to the cloud
unfortunately.
Kirill Eremenko: Mm-hmm (affirmative). Yeah, gotcha. Also, big
companies, like a lot of these large corporations have
so much momentum that it's going to take years
before things change there. Okay. Obviously Cursor is
solving a very interesting problem, and looking very
forward, [inaudible 00:45:55] tool. What would you say
to those listening who are ... they see the value of
Cursor, but they're not ready to go ahead yet. They
want to build a data driven culture with Citizen Data
Scientists but, not yet there that to invest in a tool like
Cursor. What would you ... any advise for business
leaders, or even people in organizations that are of that
mindset?
Adam Weinstein: Yeah. I mean, I think the key is just to always
experiment. So, you know, whether it's to try open
source tools, and I know there's some apprehension in
large corporations around open source. Not because of
cost, but because of support, and security and that
kind of thing. But, I think if you're a company that's
not always experimenting and looking for ways to use
data to drive efficiency or productivity or even, you
know, if you want to use the phrase like, monetary
gain, right? Not doing that, then your competitors are.
Adam Weinstein: So, you know, we were joking the other day. It's like
okay, what companies have been displaced in the last
10 years by the Amazon's, the Uber/Lyft's, the ... what
industries had been turned upside down, and
[inaudible 00:47:12] turned upside down. And, it's all
just data, right? Uber and Lyft are still using the same
cars, but you know, they may reduce the number of
needed cars on the road in the next 10 years because
of, whether it's self driving or just data and being able
to put cars at the right place at the right time. Same
thing with Amazon, right? They're not selling any
different products than all the retailers down the
street. They're just delivering it in a better fashion.
Adam Weinstein: It's fascinating, I think to me, that companies would be
afraid to experiment. I think, you know, often times I
see that coming from ... this is actually something I've
seen with Cursor and before Cursor as a consultant.
You know, not listening to people that are actually in
the trenches on a daily basis is usually where that sort
of mindset with set in, and people that are actually
interacting with and, you know, there are plenty of
people in the world that are still looking at an Excel
spreadsheet every day, spending hours a day manually
cleaning data. Not helping them find a solution to get
out of that is like, you're wasting a very valuable and
productive person's time doing something that can be
automated in an instant.
Adam Weinstein: So, you're not helping anybody. The company,
yourself, that person. What's more likely to happen is
that person will quit and then, go find a better job and
your company will have to suffer the pain and
consequences, right? But yeah. I think just always
experiment, and find time to do it. Carve out 20% of
the quarter, or the year to just ... maybe it's less.
Maybe it's 5%, who knows? Whatever it is, but some
amount of time to look at ways to do things better.
Kirill Eremenko: Gotcha. So, experiment. Very valuable advise. What
about spreading Data Literacy? Any thoughts on that?
How does an executive inspire people in the company
to become more ... to want to become more Data
Literate?
Adam Weinstein: Yeah. I think there's always going to be a crowd that is
literate, right? It may be a small analytics team. It
maybe a CIO's organization or whatever. But, I think
making ... I don't know if it's a requirement, but
inspiring them to teach the rest of the organization.
So, we had these brown bags constantly at LinkedIn
where we would invite almost anybody to come listen
to a talk on a data topic. It was a big deal for the
author to put together the content, and to be able to
actually articulate it, and document it in a way that
was easy to understand. But, it was also really exciting
to go listen to it if it wasn't a domain that you were a
part of.
Adam Weinstein: So, having that kind of a conversation and, giving it a
forum, I think is one way to start increasing Data
Literacy. That's not even doing it in a systematic way,
right? That's just hey, how do you have sort of a
conversation about it. You know two, I think is, teams
that work with data finding a way for them to share
with those that might care what [inaudible 00:50:07].
You know, we made it a basic goal every quarter for all
the teams to send out an email update of all the work
they were doing. It's like, what are the priorities? What
got done this quarter? What's going on next quarter?
We actually emailed it to basically the entire company,
even in sales you would get updates from data
infrastructure that would say, "Hey, we're adding
10,000 Hadoop notes and, here's what that's going to
do for us."
Adam Weinstein: You know, they may not care, but the ones that do
care you'll quickly identify because they'll raise their
hand and say, "Hey, I want to know more." And they
want to help. So, that's a great way to, I think, get
started around literacy, and certainly collaboration
products. Products that could help. It doesn't have to
be ours, right? There's tons of tools in the market,
whether it's Jira, or Slack, or something like that,
right? Just allowing people to have a conversation
helps create empathy, and ultimately helps, I think,
solve problems.
Kirill Eremenko: Yeah, gotcha. Why did you call them brown bags? I
didn't quite understand that reference.
Adam Weinstein: Oh, bring a lunch. Brown bag, like a brown paper bag.
Kirill Eremenko: Oh okay, gotcha.
Adam Weinstein: Yeah, people literally didn't [inaudible 00:51:10]. We
had a cafeteria. We were spoiled. But, in the older
days, right, you'd bring a brown paper bag lunch. So,
that was ... yeah, you'd have your sandwich and your
soda, and your chips, and that's what you'd-
Kirill Eremenko: So, you're enjoying lunchtime. I remember we had
those at Deloitte as well. That was really ...
Adam Weinstein: Yeah. It was back to a different time.
Kirill Eremenko: Yeah. I gotcha. So, experiment. Don't be afraid to
experiment, and empower people by good
conversations about Data Literacy because, you're
right, that's where the world is going. Organizations
are going to be doing more and more of that, and
people want that. That's what I find. People are so
fascinated with data these days that surprisingly a
very large segment of employees who actually want to
be more involved in this business because they see the
value and they see this as something that ... inevitably
it's part of our lives more and more. Like, with social
media and stuff. So, they're like, oh cool, I can do this
in business. Something exciting and interesting.
Adam Weinstein: There's not a person in the organization, whether
you're hanging up the phone and realizing that okay,
we need a prompt for people that have this question,
or you know, making lunch and realizing oh, I got to
refresh this paper food more often because ... there's
always ... everyone has a thought on data. So, giving
them a forum to do that and ... or an executive, right?
That's wondering why can't I get a quicker answer to
this question to take six weeks. There's always
someone that needs help, and yeah. I think it makes
sense that making it easier to get to would be positive
for everybody.
Kirill Eremenko: Yeah. Adam, I wanted to ask you another thing. I know
you guys have, for Cursor, you have like a free version.
How does that work? Because I understand, you would
need ... like an executive would need to approve it and
install into the business. That's a long process. How
does the free version work?
Adam Weinstein: Yeah. So, the free version is actually pretty good. It
does quite a few things, I think, out of the box. So, the
free version uses our cloud. You would download a
client on a local machine, much like you would a
Python editor, or a sequel editor. And, just like you use
other sort of cloud based tools, like Dropbox, or
Evernote, or that kind of thing, the work that you do
gets shared to the cloud, right? You can determine
how you want that shared. You can determine if you
want it visible to your team, or just to you. But, the
idea of being that like the data never leaves your
network. So, if you're running code the data lives in
your local machine, but the code and the meta data,
like hey, you worked with this table, or this was some
query that you wrote, and this is what actually-
Kirill Eremenko: The columns and the rows of the table, that kind of
stuff.
Adam Weinstein: Yeah, the names or the columns, that kind of thing.
That gets shared to the cloud. So, if you wrote
something that says, okay, how many laptops did we
sell in Brisbane this year? There's a guy that's in New
York that wants to know that same question. They can
discover that code. They still need the credentials to be
able to actually run it, it doesn't share that. But, it
does share anything that's being done. So, they would
be able to see the database you connected to. Be able
to see the table names that you used. But again, if
they don't have the credentials to that database they
can't actually do anything with it.
Adam Weinstein: So, it's sort of a light weight way to get started, and the
idea is ... you know, what we've seen is that even
though often times IT or legal or security may need to
get involved. Most companies will have a way or a user
that'll try it on their side time at home to be able to
play with something, and if they see that hey, this is
great, this is useful. It makes the process of getting it
in the enterprise version a little bit easier. So, it's a
pretty fully featured ... we call it the Cursor Core
Product, which is just sort of like the lighter weight
version of it, but it doesn't have every integration. It
doesn't have every language, but it has most.
Adam Weinstein: So, you should be able to get a decent amount of value
out of it.
Kirill Eremenko: That's cool. That's very nice of you to share that as
well, because you know especially the start ups that
don't really have ... like data is not being shared. So,
they don't really care about their intellectual property
at this stage. They could use that, especially [inaudible
00:55:31] maybe I'll sign up and use them for our
company now.
Adam Weinstein: You should try it.
Kirill Eremenko: Because we're decentralized and we have that problem
a lot. Everybody is all over the world, and it's different
time zones. It's so hard to get to the bottom of things
sometimes. So yeah, the free version would work there
as well.
Adam Weinstein: Yeah.
Kirill Eremenko: Yeah, very cool. Well, Adam, thanks so much. It's been
a pleasure. We're coming close to the hour mark.
Before I do let you go, I wanted to ask you what are
some of the best ways that our listeners can get in
touch, follow you, your career, or maybe get in contact
to learn more about Cursor?
Adam Weinstein: Yeah. So, certainly feel free to reach out to me directly.
I mean, my email is just [email protected]. Our
website is Cursor.com. Check it out. Feel free to
download the product. Follow us on twitter, Cursor
Data. But yeah. We'd love to chat and hear what
people think, right? Good, bad or indifferent.
Kirill Eremenko: Awesome. Awesome. Okay for people to connect with
you on LinkedIn as well?
Adam Weinstein: Sure. Always.
Kirill Eremenko: Fantastic. Okay, Adam, thanks so much for coming on
the show. It's been a massive pleasure for having you.
Adam Weinstein: Thanks for having me. It's really been awesome talking
to you as well.
Kirill Eremenko: So there you have it. That is Adam Weinstein, Co-
Founder of Cursor. Hope you enjoyed this podcast. My
personal favorite part was the whole notion of
organizing Data Science assets. I'm very surprised that
no company in the world has been doing this as
actively as Cursor, and I think it's a very APT problem
that needs to be solved because more and more
companies will want to become Data Literate, data
driven, and will want to introduce Citizen Data
Scientist kind of tool like that can really help out with
that.
Kirill Eremenko: So, on that note, if you'd like to get the show notes for
this episode, head on over to
SuperDataScience.com/229. You'll find all the
materials that we mentioned in this podcast, plus the
URL to connect with Adam, and of course, the URL to
Cursor, which is Cursor.com. If you are interested in
building a Data Literate organization and, helping
organize your data size assets then check out
Cursor.com. Check out their product and see if it can
help you. So, they have, as you know, they have the
Core of Cursor, which is a paid product. It might be
interesting to larger organizations that are ready to
make the jump.
Kirill Eremenko: If you are not there yet, then they have a free version,
which you can try out in the cloud and see how that
works for you. On that note, thank you so much for
being here, and spending this hour with us. Can't wait
to see you back here next time. Until then, happy
analyzing.