June 13, 2025 • 58 mins
Joe Reis joins us to reflect on life after Fundamentals of Data Engineering, what makes data content worth consuming, and why good taste matters as much as technical skill. We talk about burnout in big tech, the myth of AI replacing everyone, and how Discord communities, DJ sets, and a sense of humor are helping shape the future of data. This one’s part industry pulse check, part real talk.
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What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:15):
Well, we have the famous Joe Rice here on this
episode of what's New in Data.
I'm putting you on the spot.
How are you doing?
Speaker 2 (00:23):
Joe, I'm doing good.
I'm just slightly jet lagged.
I've just been getting overthat, but otherwise I think I'm
good yeah yeah, I'm just in.
German.
I think net is nice, so yep.
How are you?
Speaker 1 (00:41):
I'm doing good.
Every time I catch up with you,I know I always like to ask you
where you've been, and it'salways some exotic answer.
I'll just ask you.
I mean, we're recording this inmid-March, you know, joe, where
were you last?
Speaker 2 (00:56):
week spent some time in Munich, then Zellemsee, I
think that's how you pronounceit in Austria, which is in the
Alps, so a winter dataconference put on by, actually,
(01:18):
chris Tabb.
Yes, great data crew.
Yeah, my kid drew a Chris Tabbmug for me, so, yeah, it was a
great one.
I think you were at Skid lastyear over in.
Gerbier.
Yeah, that was a lot of fun.
This is the new version of itwith probably a more official
sounding event name Not skiersand data, but a winter data
(01:41):
conference instead.
So it was a lot of fun.
Great turnout, you know.
The Alps are lovely, as always,so, yeah, but now I'm back in
salt lake city or it actuallysnows more here than it does in
the alps, apparently.
Speaker 1 (01:55):
So yeah, yeah, I remember last year when we had
berbier.
You were mentioning that.
You know you're from salt lakecity and there's like 50
berbiers in salt lake city, soyeah it was trying to kind of
Utah.
Speaker 2 (02:05):
It seemed to be cocky about that, huh, but it is true
yeah it is true, so it's notcocky.
Speaker 1 (02:11):
If it's true.
Everyone knows that you put outthis book, the Fundamentals of
Data Engineering, and it'sreceived such great acclaim.
It's one of my favorite books.
I recommend it to every dataengineer because it's both
theoretical but also verypractical in how data
engineering is applied.
And now you know all your worldtravel and events you speak at
(02:33):
and companies you work with.
You know it's all kind ofrooted in.
You know the success and howpeople have really been
captivated by your book.
What it, what's it like just uh, continuing to to work with
people who've read your book orknow you through your book?
Speaker 2 (02:51):
it's kind of surreal really.
I mean, yeah, I mean it'scoming on three years old now,
which is kind of crazy.
It was published, so it's.
So it's interesting in that waywhere you know there's
definitely you know, I think weknew it was going to be
successful just because the youknow, on amazon it was a number
one new release, a bunch ofcategories before it came out
(03:11):
and then it obviously still doesreally well.
But it's definitely a bit ofit's still a bit surreal, um,
you got to see posts about itevery day on social media, like
literally every day there's anew post about it still.
So that's that's pretty cool.
You know, I'm definitelygrateful for the success it's
brought um, and I feel like, um,you know, just just thankful
(03:32):
that at this point, you know, abook will be successful on its
own merits and through word ofmouth, not because of marketing.
So apparently people liked it.
So I think that that's prettycool.
It's definitely opened up a lotof opportunities.
I think changed, um, you know,definitely my trajectory and
stuff.
At the same time I'm always onto the next thing, you know.
(03:53):
So I don't want to be likeuncle rico from napoleon
dynamite who, uh, I rememberthat character.
So he's just so for theaudience.
Uncle rico, if you haven'twatched the play in dynamite,
he's the uh kind of washed uphigh school football player who
still reminisces about his glorydays when he almost won state
championship.
If coach just put him in he'd bein the NFL, he'd have mansions
(04:16):
and women and all this stuff,cars, yeah, and I mean that's,
that's a danger though, right,because if you, I think, if you
rest on your laurels, um, youknow, and aren't working on the
next thing, and then, uh, youknow that I think you do risk
becoming kind of uncle rico inthat way.
So, once the book was done, I Ikind of, you know, definitely
(04:40):
gave a lot of talks and you knowI've been very grateful for the
success, but I was always juston to the next thing working on
that.
Uh, we're next things, I guess,but you know, it's definitely
cool, but it's, it's weird.
You know, um, at this stage Ithink I'll first look through
the podcasts and being morevisible, um, you know, going to
conferences and, um, definitelygetting noticed or some cases,
(05:04):
you know, I hate to say it, butprobably mobbed, and then, you
know, even in public, justwalking around, getting noticed
in public.
So that's always interesting.
Speaker 1 (05:14):
I've witnessed it.
Yeah, I mean, people are like,hey, dude, get out of the way.
Joe Rice is here.
I want to talk to him.
Speaker 2 (05:22):
Yeah, it's funny.
Speaker 1 (05:23):
Yeah.
Speaker 2 (05:24):
It is what it is.
I mean I'm thankful for it.
I mean it's it's.
It's definitely takes a bit ofgetting used to, but by this
point you know that's what it is.
Speaker 1 (05:33):
So, yeah, and you know people, ultimately I mean
it's, it's, it's one of thosebooks that you know it, it is,
it's in the title, it's, it'svery fundamental and you know,
now data engineering is ischanging in a lot of ways, uh,
but also going to stay the samein in others, like a lot of the
core principles of datamanagement and pipelines and
(05:56):
data modeling.
I don't expect that to changetoo much, but the way people
write code for data um is isdefinitely going to change, just
because code generation is alsogetting disrupted in its own
way.
Speaker 2 (06:11):
Oh yeah, it's ubiquitous.
Yeah, that's how it goes.
But I always wondered our bookcame out right before ChatGPT
hit the scene.
No, not right before.
It was like five months, fourmonths actually.
But what's interesting withthat is, you know, I kind of
called it.
You know the data engineeringlifecycle is not going to change
.
The undercurrents won't change.
(06:32):
You know you're still going toget data from source systems.
You're still going to need tomake it secure, your system
secure, and I would say, morethan ever.
Actually, data management anddata operations and governance
and modeling is actually moreimportant than ever, essentially
because of AI.
So I think it's been driving alot of interest in data
engineering.
At the same time, as you pointout, data engineering itself, at
(06:56):
least the way we do it, itcertainly is changing.
You're not handcrafting codeanymore.
Code gen is becoming more andmore of a thing, which I think
is a blessing and a curse, butit's reality.
You're not getting away from it.
So you know.
Yeah.
Speaker 1 (07:11):
It's interesting, yeah, and you know, but I also
want to ask you.
So you know, we talked aboutfundamentals of data engineering
.
Being, you know, just overthree years old now you know
what are you working on now.
Speaker 2 (07:28):
Well, I have a new company, kind of hinted at it,
you know, it's publishing, it'seducation, it's media.
It's a lot of things that I'vebeen doing.
So you know you want to talkabout like, definitely, I think,
jumping into what the bookprovided.
It provided me a lot ofopportunities to, I think,
become more involved in mediaand education and so forth.
You know, I did a course withAndering and the deep learning
(07:52):
fine folks there at deeplearning last year.
That's on Coursera and that'sdone really well.
But you know, I think doingthat and a few other things you
know reminded me of like Ireally do like, and I want to
focus more on that.
I think one of the biggest gapswe have in the industry is, you
know, we have great tools.
I think our ability to usethese tools to its fullest
(08:13):
potential is really hampered byour skills and knowledge, and so
I think helping upskill a newgeneration of data an existing
one of engineers will help pushus forward.
That plus AI, great skills, andAI will help you do great
things.
But AI on its own, withouthaving great skills, I think is
(08:34):
could be a recipe for, you know,pretty interesting outcomes.
So that's that's one thing I'mfocusing on right now.
So, yeah, my new book is goingto be out on this new company.
There's a few of the books.
That's one thing I'm focusingon right now.
So, yeah, my new book is goingto be out on, uh, this new
company.
Um, there's a few of the booksbeing written as well, but yeah,
but it's, it's an interestingthing, um, and I'll ask you.
I'll answer a question youdidn't ask, um, which is, uh,
(08:56):
why do publishing in an agewhere you could seemingly just
make a book in an hour you know,probably a half hour and get it
all published on?
Speaker 1 (09:06):
Amazon, that's what I was thinking about seeing you
so thank you, yeah, just perfectfor that.
Speaker 2 (09:13):
Thanks, john, great question.
So you're welcome.
But I think with AI, people areyou're already seeing it people
are getting tired ofAI-generated content, especially
AI-generated slop, as it'scalled, which is just
low-quality writing, low-qualityimages and videos and so forth.
(09:35):
I think that, more than ever,there's going to be a really
strong need for people forreally strong human-generated,
human-created ideas that aregoing to help push our industry
forward.
This is something that I don'tthink AI can do, at least not
yet.
But at least for now, I don'twant to write technical books
(09:59):
per se, like how to do AIengineering with a python or
something.
I don't think that needs to bea book.
These days, that's somethingyou could ask claude or chat gpt
and get a pretty good answer,uh.
So I think the tactical workand the tactical types of books
I don't really want to do, but Ithink the big idea books, the
(10:20):
ones that are really going topush our industry forward, those
are the ones I'm interested in.
So so for the audience, yeah, Ihave an idea like that.
You know, hit me up, uh, butyeah and that's what's so
important these days.
Speaker 1 (10:31):
You know there's, you know, as you, as you mentioned,
ai slop is definitely, uh, astrong label that's been
attached to a lot of the youknow, not to sound redundant,
but slop that's been put outthere.
And you log into linkedin andyou know there's like so many
posts that are like clearly aigenerated how many?
they all use the same uh, likethey all use the same kind of
(10:52):
writing style.
Uh, you know they'll, they'll,uh, because it's trained on.
You know very, very specifictypes of media and you know you
can just tell, like, within youknow five seconds of looking at
a post, whether it's generatedby chat gpt or not, and and uh.
But I think what people aregonna be craving now more than
(11:13):
ever, is hearing from experts,right, and hearing from people
who, like you said I'm, bigideas that will actually have a
big impact within anorganization, because people are
kind of more scrutinous aboutyou know, just things being
built, because we know ai canbuild things too.
So you know what's to say thatyou know someone just didn't
(11:35):
tell chat gpt to build somethingfor them, but, yeah, they're
doing it in a way that'sgrounded in fundamentals and,
you know, based on experienceand really solving business
problems well, absolutely, and Ithink the litmus test is going
to be, people gravitate towardslike, really good books, really
good writing, really goodpodcasts, right, these are, you
know, human exchanges, um, andin person.
Speaker 2 (11:59):
Right, I think this is increasingly going to become
more and more viable and what Ithink is actually just doing a
podcast.
Today with Jordan Morrow, we'retalking about public speaking
(12:27):
and it's interesting because Ithink, increasingly, giving
talks is going to be one of thedifferentiators between people
who write real stuff and cantalk about it.
Um, you know, in a publicsetting, especially during the q
a, like, I think that's goingto distinguish the experts, the
people who wrote books and whoknow what they're talking about
when they write the book, versusjust having chat gpt generate a
bunch of garbage.
Um, they probably couldn'tspeak to you know, or understand
um, so I know, with my book Imean, I counted probably over a
dozen, you know, throughout,throughout the you know a few
(12:48):
years, probably a dozendifferent books titled
fundamentals of data engineering, uh, you know, which are all
kind of knockoffs of that nicebook and um, I download the
samples of them, they check themout and kindle and it's exactly
what you think it's.
It's, there's no thought putinto it?
Um, there's not.
You know, if you're to askthese people to give a lecture
(13:09):
on data engineering, I'm prettysure none of them would, because
they're probably not people.
They're, they're uh, they'rebots under pen names.
There's one guy, on linkedin, Ithink, who wrote a book under
my name, but you know, I'd lovefor him to give a talk on the
topic and let's see what he cando.
Speaker 1 (13:28):
Well, yeah, I mean, yeah, there's a lot of people
who generate a lot of AI content.
They just want to publish it.
But if you ask them about anyof those topics, ask them one
hard question about it.
Unless they have Claude infront of them, them, uh, won't
be able to tell you much.
That's just it.
That's just it.
(13:49):
I mean like, yeah, yeah, and I,I deal, I deal with that a lot
too, because if, being in thisindustry, you're constantly
researching, you're constantlylearning, you're constantly like
trying all the new um patternsand frameworks that are coming
out.
Like you know, model contextprotocol, for example, was a uh,
you was kind of an opinionatedway of writing your AI apps to
(14:10):
fetch external context.
And they say it's like having aUSB-C input for AI and there's
so much slop generated around itwhere, like the first two or
three times I I read about it, Iwas like this is garbage, mcp
is stupid.
And then, finally, like I foundpeople credible talking about
(14:33):
it and the way they talked aboutit, I was like, oh, okay, now
it makes sense to me, right,right.
So you're almost it's it's,it's almost hard to not throw
the baby out with the bath waterwhen, when there's like all
these people generating a I slopabout things that are actually
valuable.
Yeah it's.
It's hard to get around that,so it's good to like like with
(14:54):
the company you're buildingyou're, you're gonna sort of
have those lists of like curatedminds that's just it, they just
have.
Speaker 2 (15:01):
You know you get the best minds of the planet.
You know producing the, and Ithink magical things would
happen and treat them well.
That's the thing.
You've got to treat people withrespect, especially authors and
creators.
These days, I think that, morethan ever, coming up with an
idea is one thing, but thenyou've got to help market and
distribute, which is somethingmost traditional publishers
(15:23):
don't do.
The sad thing is, if you writea book, you're also responsible
for marketing it, and that'ssomething that's.
It's a difficult skill set forsomebody to have, right, and so
that's.
That's another thing.
Where get the ideas out there?
Get good ideas from greatpeople out there, and I think
the rest will take care ofitself.
But you know, if you, if youmarket a book though right, I
(15:45):
mean, marketing is only going toprobably push a book or you
know, a piece of content formaybe a week or two, right, yeah
, it has to be good to center onits own.
Speaker 1 (15:57):
And I guess what's the definition of good?
Speaker 2 (16:00):
People will tell their friends about it.
People will tell their friendslike this is worth your time,
you know yeah.
Speaker 1 (16:08):
It's really interesting because it's similar
to the music industry where,like music industry has you know
what are called tastemakers andthey're the ones who influence.
You know the big playlists, youknow who the big music
publications will talk aboutwhat plays on the radio, even
though radio is kind of uh, lostin the space yeah you know,
(16:29):
okay, the big spotify playlists,right, uh, the big apple music
playlists, etc.
So, like you see, these djs who,who basically decide, kind of
like you know they, they havethe curated mindset to say you
know what's what's good and bad,and, uh, and and joe, you're a
big uh music aficionado yourself.
(16:50):
You're, you're, you actuallyare a dj, right, so you do have
incredible taste.
Just because you know, and I'velistened to your sets, they're
good.
I think just being able to, tocurate a great like dj set
already shows that you know, youunderstand what, what people
want to hear.
You know one way or another, Ithink.
I think it's the same thing asuh, understanding you know what,
(17:13):
what, what data patterns makesense, or adoptable, adoptable
and and practical, um, and Idon't know when was the last
time you DJ'd actually in publicgosh.
Speaker 2 (17:29):
That must have been maybe a year ago or something we
both did.
Chad's event that was Novemberit was November 23 actually, so
that was a while ago, right ohyeah but yeah, I always carry my
usb stick around with me if Itravel.
You never know when you're gonnafind turntables and it's kind
(17:52):
of fun.
But, um, yeah, I've just beenpretty booked.
I haven't had time.
I mean, I've been, um, there'sa, there's a very good chance I
might be doing some some localgigs here just to go dj at um
some clubs and probably do somemore live sets too.
Like I have a whole uh live um,you know kind of hardware setup
that I like to play on, uh, butbut curation, right, I mean, I
(18:16):
think djing taught me a lotabout kind of how I approach you
know what I'm doing now, right,uh, like, I think you just sort
of develop a sixth sense forwhere things are going or, more
importantly, need to go in ourindustry and and so you, you
think at a certain point you canhelp shape the discussion, you
(18:37):
can help shape where things go,and that's pretty cool to do.
I'm doing that with datamodeling right now.
Um, that wasn't really a cooltopic for a bit until I
mentioned, you know, startwriting a book on it.
I'm not saying you can write itfor all of it, but I think it
definitely helped push that backinto the foray, right?
So I think at a certain pointyou can just start.
You can kind of determine wheretrends go, or you can at least
(19:00):
see where they go Right.
But, um, or you can at leastsee where they go right, but you
have to be in it for a longtime to to know the patterns
that are hidden behind thepatterns and uh, you know it's
um, but everything you know, Ithink, a lot of things in our
industry.
They definitely move in cycles,they kind of go on a pendulum
(19:20):
and uh, you just you know.
I think once you understand therhythm of it, I can't say you
can predict the future, but youcan certainly tell where things
are likely going to be going.
So even with AI right now right, I mean, that's, it's new, you
know might shake things up a bit, but when you get down to it,
there isn't much different fromwhen you saw other.
(19:42):
You know, significanttechnology patterns emerge in
the world.
Of course there will bedifferences, but it's not like
this is brand new out of thescene.
It never happened before.
Speaker 1 (19:51):
We never had a new technology it happens a bunch,
yeah, and you know, like when I,when I look at the way you
approach it, I mean you haveboth.
You know a STEM, technical,academic background and actual
industry background building.
You know analytics and datascience and data infrastructure.
You know for, for, for largecompanies, intuition about kind
(20:18):
of.
You know what's good and what'sbad and that's always
subjective, right, people debateit a lot.
You know who's to say oneperson's right, one person's
wrong but I think what's sort ofindisputable is that you know
when you put pen to paper andyou put out that book with Matt.
You know Fundamentals of DataEngineering and you know it was
just widespread acclaim.
(20:38):
People agree with it.
And when you generally go talk,people are captivated and
interested.
And then, even coming back toDJing great DJ sets we were just
talking about you were puttingme on to FIAC and speaking of
misconstruing names.
I call them FJAC and it kind ofreminds me of this quote I
actually saw it on X from SamLambert, the CEO ofo of planet
(21:02):
scale.
It's a database company.
Yeah, he says do not work foranyone who doesn't love music.
They will never build anythingthat that humans want, and I
think you were one of the firstpeople I thought about there
because I was like becausethat's a, that's the ceo of a
database company saying that so,like, what does a ceo of a
database company care about?
Like, like people havingmusical taste?
But then kind of, when youconnect the dots and you meet
(21:23):
people you know, uh, across theindustry, like there there is
actually some relation there.
Speaker 2 (21:31):
Oh tons, I mean, you're a musician, I would say
you're.
You're a very classicallytrained musician, right?
Speaker 1 (21:39):
Yeah, yeah, I uh, you know, yeah, definitely, uh, my
days at san franciscoconservatory music, and then I
was, I, you know, I had my ownartist project.
I go under a pseudonym, thoughit's very, it's very secret and
you know, uh, I try to keep itseparate, right, so people don't
kind of, because I, I always, Iwas always kind of insecure
about that too like, okay,people know about, like my music
(22:01):
.
You know that they, they mightnot take my uh, uh work in data
and engineering as credibly,because I'll be like, well, how
do you, how do you do both?
Like, because I feel like, yeah, confuse people I think it's.
Speaker 2 (22:12):
I think those walls are diminishing though, right,
like social media sort of brokendown a lot of the barriers
between, you know, public andprivate persona.
Um, I mean, for god's sake, youknow, the ceo of golden sacks
is a dj.
Um, you know, I think a prettypopular one at that, diesel
david solomon, right, yeah, like, yeah, I don't think he has any
you know qualms about.
Oh, I'm the ceo of golden sacks.
(22:33):
Nobody can know that I dj, youknow, I think he uses that to
almost to his advantage in a bit.
I mean, we're talking about it.
Um, yeah, you know, and it says, I think this is the walls
between public and private,whatever, I mean it's, you know,
depending on what you do,obviously, but I don't see any.
I think data needs more of thatreally, where it's kind of a
(22:53):
boring industry, we selldatabase systems.
Woo, I mean, that's's cool, ithelps make the world go around,
but so does music, right, and Ithink, you know, and I meet a
lot of interesting people, asyou do too, in the industry and
I think, um, you know thatbecause you see, especially at
conferences, conferences arehilarious because you meet
people and they have to put onthe conference face and then
(23:16):
where you know, where theconference you know, attire
usually a suit of some sort, butthen you hang out with these
people after the after hours ofthe conference.
These people are nuts.
So I think that's I kind ofhope that a lot of these walls
(23:36):
get broken down.
I just think that I'm much moreimpressed when I think there's
there's a, there's a level ofcredibility to somebody and a
level of authenticity.
I, I think we're.
If you know, if the, thebarriers are pretty
indistinguishable, then I thinkI can trust you a lot more for
one right, because I know you'renot like hiding, I know you're
not, you know, full of crap.
(23:57):
So, um, you know, yeah, yeah'sme, but that's how it is.
That's the trajectory of theworld.
The world is going though right, like it or not, it just is
what it is.
It's John Coutet.
You can either change your nameto your pseudonym or unleash
the music into the world, intoyour real name, but I think it
(24:18):
would benefit you.
Speaker 1 (24:22):
I tease a bit on your on your discord, which is
something else that you know I Iwant to talk to you about.
So you, you launched a, adiscord channel called practical
data.
Tell me about that practicaldata is a.
Speaker 2 (24:35):
It's really an extension of the subsec that I
had.
So I'm releasing, uh, earlysections of my book, usually in
draft form, on practical datamodeling.
That's sub stack, uhcom.
If I had half a brain I wouldhave just called it practical
data, because then I can havemore books that I'll be writing
on there as well.
But here we are and so we hadthis chat group going for a bit
(24:56):
on sub stack.
But sub stack chat, it's prettymid, as the kids say.
I was like okay, so let's makea chat community.
I was like okay, do I want touse Slack or do I want to use
Discord?
I joked, you know we'd betteruse Teams, but I'll just keep it
(25:20):
really corporate.
Can you bring Skype back back?
Skype like yeah, bring skypeback.
That makes it pretty funny.
Uh, we these webex, um, that'samazing that'd be hilarious
actually.
Maybe I should do that for aprilfools.
Um, I'll write that one downactually, but anyway, so I think
it's.
Yeah.
So I started a discord groupand, uh, you know, soft launch
(25:42):
into about 100 people and thatwas really fun, just to see how
it would go right, is this evenworth pursuing or not?
Um, and then, you know, open itup to a few more people and now
we're almost like 1100 people.
It's not not huge, but I thinkthe quality of the conversations
I've heard from quite a fewpeople that it's definitely
their favorite data community tobe involved in because it's
just very candid.
(26:02):
Um, conversations you know about, about our practice, right, and
I have other channels as wellso you can talk about.
If you want to talk aboutpolitics, you can.
There's a politics channel.
I don't care, right, as long asyou keep it civil.
There's an unhinged channel.
You want to talk about just thecraziest stuff you can think of
?
Go put it in there, I don'tcare.
Um, you know, we opened up anew one about sports the other
day, so we can just talk aboutwhatever sports you're into.
(26:23):
So I think it's because whatyou realize is there's when you
start a data community.
Most people want to talk abouteverything except data, and
again this just comes back toyou know um djing and working in
clubs for a while too, or Ithink you just get to know human
behavior.
At the end of the day, peoplehave a lot of facets to
(26:46):
themselves.
They want to talk about a lotof things, and a lot of times
the last thing they want to talkabout is work, especially in a
chat setting.
Speaker 1 (26:54):
That's one reason I didn't want to do Slack or
anything, because it's like thatformat is too familiar to you,
because you associate that withyour job yeah, I, I think just
having yeah, like and just like,you understand people better
and you know, because all thisyou know, talk about like data
infrastructure, like everyonehas a as an opinion on it, and
(27:16):
unless someone has like a,really you know uh is really
grounded in something.
That's just absolutely proven.
It's all up for debate.
When you get people together tochat about stuff, it's the same
way Like, oh, I think thisfootball team is going to win
the Super Bowl this year, basedon my opinion, people just chat
about it the same way.
It's just open-ended and casual.
(27:38):
I really like the, the kind ofthe discord, because people both
talk about data the same way.
They just talk about otherstuff right right yeah what you
realize too, is it culturally.
Speaker 2 (27:52):
Data is one of these topics where they're all
cultural nuances to how we workwith data right, how we
architect systems and how webuild them.
It's not like.
So what I realized, you know,especially in the travels as I'm
sure you have around the worldand talking with people is like
they're you have to take intoaccount the geographical
situation that you're in.
You have to take into accountthe business culture that you're
(28:14):
in.
Nothing is monolithic.
When you're trying to sell, forexample, you, you know, say,
stream into a company, it's likethere are other factors you
need to consider besides justtypical.
Am I talking to the qualifiedbuyer?
Am I, you know, am I hitting apain point?
It's like that's basic stuff,but it's also, you know, how do
they get to the situationthey're in?
(28:34):
You know what's the team likethat's going to support this and
a what's the team like that'sgoing to support this?
A lot of this comes down to thenuances of the culture that
they happen to live in.
In our Discord group we havepeople from all over the world,
even people in Europe.
Eastern Europe is differentthan Western Europe.
It's different from the UK.
It's different from Nordics.
It should be obvious, but I gotcalled out on this.
(29:00):
The other day I was in Munichand I asked one of my friends
what is her thought on AIinnovation or something like
that in Europe?
And she's like, well, butyou've got to understand, we're
in Germany, that's differentthan it's not Europe.
I was like thanks for thereminder.
You're absolutely right, butyou realize the data world is a
(29:20):
pretty big place and everyone'sgot their own opinions on how to
do stuff and you can't just.
I think all too often,especially in America, we try
and paint it from the broadstrokes of well, this is how
it's done in the Bay Area, forexample, so therefore everybody
is the same right, or this ishow we do it in New York or
wherever, but that's certainlynot the case.
Speaker 1 (29:43):
So yeah, yeah, and I think, coming back to taste, you
know a lot of the status stuffdoes and it's not new
necessarily.
Like when you look at, like,the role of a data architect in
the enterprise, like usually,like the you know some domain
specific team, a software teamor a business team will say, hey
, we want to do these things.
(30:04):
Uh, it'll technically work forus.
Data architect, what do youthink?
And the architect will shoot itdown just because they think
it's a bad design.
And you know, there might besome, some technical principles
that they're applying there.
But a lot of times when I see it, it is sort of like a form of
taste making, like I just like,oh, I don't know that pattern,
like I've never heard of it andyou know it's, even though it
(30:26):
technically might work that youknow, I don't know if it'll be
resilient or scale.
They might shoot something downbecause of that.
Right, and it can be, it can bea little arbitrary, so it's
it's.
It's super interesting to seehow that also correlates with
what you're doing with theDiscord, where I see people
talking about announcements ofnew data tools and open source
(30:48):
frameworks and what sub-vendorsare pitching and they haven't
tried it yet Some of them havetried some of the open source
stuff.
But even with stuff theyhaven't tried, they're just
inserting their tastes and theirview on just what they think
about it broadly yeah, yeah, Ialways get a kick out of that.
Speaker 2 (31:09):
Um, yeah, sort of having an opinion before trying
it.
Um, yeah, that's something Ipersonally always, always want
to try and avoid.
I uh, I, uh, well, I veryrarely will talk about vendors
specifically too, just because I, I mean, for better or for
worse, if I say something, itdoes have an impact on things
sometimes, so I usually keepquiet.
(31:30):
But other people, you know, Imean, they definitely post
things and it's cool to see theenthusiasm and sometimes lack of
enthusiasm, maybe warranted orunwarranted, but uh, um, but
that's how it is.
I think, you know, people bringa lot of biases to how they
evaluate tools and technologiesand, um, but that's the fun part
about our field, I suppose, is,uh, you know, if a vendor is
(31:52):
liked or disliked, that could,that could change.
Um, uh, I'll give you know,I'll give you an example, I will
call it a vendor.
Um, uh, microsoft.
They I'll give you an example,I will call it a vendor
Microsoft.
They were sort of, I think fordecades considered sort of the
great Satan in the tech world bydevelopers.
Now they've made a huge change.
I remember 10 years ago one ofmy friends, she went there to go
work at Microsoft as a Pythonadvocate.
(32:15):
I was like what are you doing?
This is nuts.
They're making some of thebiggest strides with Python and
Guido's there, and so you knowthey have amazing tools right.
I mean VS Code is widely used,so I mean things could change
right, and so I never writeanybody off.
And you know, and at the sametime I don't really get too hot
(32:36):
on things because it's like it'sgood for today, who knows how
it is tomorrow.
I take a very, I guess,zen-like approach to all this
stuff because, as you know,you're around long enough as
things kind of flow like waterand that's how it is.
So you know.
But I think that's a goodexample, you know, of what
Microsoft did.
I mean they've developer to thenon-Microsoft people using it.
(32:58):
I think that's awesome to see.
I mean I used WSL on my Windowslaptop and I think it's
fantastic.
So kudos to them.
They did a great job.
Speaker 1 (33:06):
Yeah, and Power BI is ubiquitous and Azure is
everywhere.
Speaker 2 (33:12):
It's everywhere.
Speaker 1 (33:13):
And, speaking of BI, one thing that you mentioned
with AI BI is that old-school BIdashboards are kind of
following the Lindy effect.
Do you see them being disruptedby this concept of AI, bi or AI
in general?
Speaker 2 (33:34):
a bit ago and that spurred a lot of discussion.
I think it was a podcast I didabout old school versus new
school BI, and you are seeingthis.
I think that there is chatterabout how we can just put a
chatbot in front of everything.
You don't need dashboardsanymore in the old school sense
(33:58):
you can chat with your data.
So I guess the questions I hadwas you know, did the data
change along with this newinterface, or is it the same
data?
Because if it's the same data,most of the questions that you
need to run your business areprobably already answered, I'm
guessing, in a dashboard that'sbeen there for probably quite a
(34:19):
while.
Right, like what are the trendsof my sales?
What's my?
In a dashboard that's beenthere for probably quite a while
, what are the trends of mysales, what's my operating
margin and so forth?
Whatever user statistics youhave in your app, whatever
you're into, those probably havebeen, I'm guessing.
I hope, answered in a dashboardof some sort or a report.
But who uses these?
(34:40):
I think by most accounts, bitool adoption has never hit
above 25% in most companies.
So my question was if usage hasbeen pretty low across the
board or at that level, whatmakes you think that, given the
ability to chat with your data,that you're going to ask
(35:02):
different questions, and theability to ask questions is the
only thing that stopped you fromnot looking at your dashboard
before?
And my answer to this is it'sgoing to probably be both.
I think old school BI, it stillaccomplishes 80-90% of what you
need.
Those other 10-20%, sure, ifyou want, that'll certainly um
make it faster than trying totalk to an analyst who might
(35:23):
take weeks to build a report foryou, um.
But so I think the question isboth, because I kept seeing
these very um binary reactionswhere it's, yes, you know you
need new school, ai ei, and theold school stuff is done for.
Or you know the other camps,like no, that's all garbage and
it's has to be the old schoolstuff is done for.
Or you know the other camp'slike no, that's all garbage and
it has to be the old schoolstuff.
I'm like maybe it's both, Idon't know.
(35:45):
I mean, like I said, I alwaystry to be flexible in these
ideas because I don't believe inabsolutes.
When it comes to technology,things move too fast and things
improve, like, I think, thepeople who say that it's always
going to be old school bi andlike large language models are
useless.
It's like, yeah, they're justlooking at today's
hallucinations, right, it's notnot accounting for the fact you
know these will improve.
(36:06):
They have to yeah um, it's likeyou know, so um yeah, we're like
.
Speaker 1 (36:13):
I mean, we were chatting about this or a text
and like, for a long time peoplewere ragging on including me,
ragging on llms for being bad atmath.
Right, like, okay, if you giveit like a, uh, like a division
problem, it'll get it wrong.
Uh, I think, I think I sent youa passage from a chip wenz book
.
Uh, yeah, you can just give aia calculator, you know, or it'll
write some python code you know, and suddenly it's amazing at
(36:36):
math, right, yeah, so ai justgets better and better, with,
with, with tooling orimprovements.
Uh, you know, reinforcementlearning, you know better models
, better inference, time tools,rag, getting better.
So, like you said, I mean, yeah, it's like, yeah, you can't
just look at how it's workingtoday and see it hallucinate
once and be like, oh, you know,it's never gonna work oh yeah, I
(36:59):
mean.
Speaker 2 (36:59):
The classic counter example is like how many times
you've asked, you know, maybeit's never going to work?
Oh, yeah, I mean.
The classic counter example islike how many times have you
asked, you know, maybe yourteammate or an employee about
something and maybe they get itwrong?
You know, yeah, what are youjust going to discount humans
Because one of them got aquestion wrong?
You know?
Are you going to fire thatperson?
Probably not, I mean, that'd bepretty heartless.
Fire all humans fire.
(37:22):
I mean.
Yeah, I mean, some aiaccelerationist thinks it's what
you should do.
Um, but it's a, it's a classic,you know, upton sinclair quote
what is it?
It's difficult to get a man tounderstand something when his
salary depends on his notunderstanding it.
Um, so I think that's one ofthe truisms in our industry, and
in every industry really, it's.
You know, if you'reincentivized to, um, you know,
to ridicule or or promotesomething, that that's what
(37:44):
you'll do, often at the expenseof having a?
Um, uh, you know being able tohold two opposing viewpoints in
your mind at once, which, uh,apparently, is a sign of
intelligence if you're able todo that.
But that's the paradox we're inright now is more and more.
Vai tools are getting prettygood, and now this is supposed
(38:08):
to be the year of agents, and sowe'll see how that goes, but
it's only March, so we have sometime.
Speaker 1 (38:18):
Yeah, I expect a lot to happen between now and the
end of the year for sure,especially with with agents and
just the.
You know there's so much goingon with ai engineering as well
and we were, you know we, like Isaid, we were chatting about
that over over text and uh, uh,especially as it applies to data
.
You know the.
(38:40):
If people can just kind of chatwith data, they're going to ask
different questions than.
Okay, I get this pre-cannedreport with some filters and
dimensions that I can apply toit.
But if I can just say, hey,really colloquially or casually,
tell me how my business isdoing or show me which customers
you know, show me whichcustomers are happy, show me
(39:02):
which customers are unhappy, youknow there has to be this
person's sort of interpretationof what the user means, right,
and then has to go kind of preprocess that against the
existing you know data models,which comes back to data quality
.
But first, looking ahead, right,you have to come up with the
right SQL query.
(39:23):
Like this is a classic TexasSQL.
So like understanding whattypes of questions people ask
and how it relates to your dataand then understanding is that
something, is that answer whatthey're actually looking for or
not right, and this is just youknow.
Naturally, like an agenticproblem, right, it requires
reasoning, it requires a chainof thought.
(39:43):
So do you see agents becoming abig part of BI as well?
Speaker 2 (39:50):
I suppose.
So I mean, you got me thinking,though, about something where
it's, if you think aboutdashboards, sort of as we used
to think about the three big TVnetworks back in the day when
people would watch TV.
I mean, you brought up radio,too, earlier in the conversation
, right, so it was that famousLA station KROQ, I think it was.
People used to have limitedsources of information, right,
(40:15):
and in some ways I think thatwas good, where there was a
common set of beliefs and commonset of the quorum and standards
of ideas.
Internet happens we're able tosearch for whatever we want.
Social media happens Now we getinto filter bubbles, and so if
(40:39):
you draw these same conclusionsto being able to chat freely
with data but you don't have, II guess, a deterministic way of
knowing which queries people arelooking at, the sql queries,
that is right, um, I I thinkthis, this could be a very
interesting outcome inbusinesses, because, john,
(41:02):
you'll, you'll be looking atyour reports and say, well, I
asked this prompt here and I gotthis answer.
That's nice, john.
I asked this question here andwithout us knowing SQL, do you
think that that's going toimprove the situation where BI
is already messy?
People have a lot of questionsabout data.
So this is an interestingscenario you just brought up to
(41:24):
me that I was thinking about.
Like that, because it remindsme of what happened with um uh
news and information in greatersociety, where now everybody,
there is no sense of truthanymore, there is no sense of
ground truth anywhere.
You can't agree on what.
What's a fact right now in inin the news, it's whatever, it's
whatever your social media feedtells you.
And I think it's interestingbecause I do think that this
(41:46):
actually could happen with thesebots, unless there's some sort
of governance on the queries,making sure these are consistent
.
So what does this do for dataquality?
I mean, you better hope thatyour data is good quality
because bad quality data, witheven SQL queries that are
(42:07):
slightly different, butdifferent in very important ways
, uh, that'll be just veryinteresting outcomes, um, ones I
would shudder to think about ifI was running a business yeah,
yeah, absolutely, you know it.
Speaker 1 (42:23):
It could just become like an evolution of like the
existing bi products.
I mean, oh, I didn't even get achance to look at what open ai
put out.
I mean we're oh yeah, it'smid-march and they just put out
like their first uh, dataanalysts, uh, analysis agent, uh
, let's see, yeah, and it's,it's gonna be.
(42:43):
I still don't really think it'spossible for ai to really,
especially with just how messydata is and, like you know, the
relationship between, like whatthe data says and what's reality
within a business.
Like you know, even like if youlook at like data in salesforce
, where you have you know 500columns that have like similar
(43:04):
semantic meaning, but you knowyou have to know the sales ops
uses actually this column, notthis one.
When they say you know leadsource, right, they have like
five.
You know they have like dozensof columns for lead source, but
the one that you Lead source one, two, three, four, 12, yeah.
So, like an agent, being able tounderstand that right is almost
(43:26):
impossible because everythingkind of relies on yeah, that
person in sales ops to explainit to me, Right?
Speaker 2 (43:37):
Right.
So yeah, I mean you've seenthis before, I mean because you
live in reality.
So it's like this is the kindof stuff you get to deal with
all day.
I'm a data integration vendor.
I'm sure it's just how you seedatabases.
You're like what exactly isthis that we're looking at right
now?
Speaker 1 (43:53):
Right, that's reality though, yeah.
I see all these LinkedIn postsI'm sure you've seen them too
where it's like oh, bad dataequals bad AI.
It's really popular to say Isee all these LinkedIn posts and
I'm sure you've seen them toowhere it's like oh, you know,
bad data equals bad AI, and youknow it's really popular to say
you know, and it's very true,but I don't know if that's like
(44:16):
a and every vendor claimsthey're the solution to bad data
.
Of course, right, all you needis one more vendor, a data
quality vendor, and suddenly allyour, all your problems will be
fixed.
Uh, but, uh, but I don't.
I don't know if that's asurmountable problem, right,
because just when you see, okay,like the last 30, 40, 50 years
(44:38):
of data infrastructure and eventhese new sophisticated startups
, tech startups, startups comingup, I mean the thing that's
always the least you know,sanitized and at least
structured is like the analyticsprocess, right?
So, yeah, you get these curatedreports that solve, like very
specific burning businessproblems or questions that you
(45:00):
know the data team knows the CEOis going to ask, but just
having this you know broad datalake that you can chat with and
answer questions, no one'scracked that yet, but it
requires a lot of other problemsto be solved which just take a
lot of brainpower and, honestly,people who have like intuition
for how the business actuallyworks.
Speaker 2 (45:21):
Yeah, it turns out, and a lot of these problems that
you see in see data sources,for example, or whatever, right,
if you look at again theclassic continuum of people
processing technology.
Speaker 1 (45:35):
Yeah, that, exactly, that's exactly it.
Speaker 2 (45:40):
The culprits of a lot of bad data models, for example
.
It's not because you don't havetechnology to do it Right.
It's like often it's at leastthese days that the common thing
I hear from people that want tomodel their data and want to
put some cycles through it is Idon't have enough time to do it,
I don't, I'm not given the timeto do this, I don't have, I'm
not given the budget, I'm notgiven the support to do data
(46:05):
modeling, and so I just need tocram some stuff into a database,
and we'll call it that.
It was interesting.
I was talking to someonerecently who runs engineering at
a fintech company.
This company started out as astartup and now they're publicly
traded.
He described his job asbasically buying more bubble
(46:28):
wrap and duct tape to put aroundthe data systems, and that's
pretty much what happens on aweekly basis.
Decisions were made early at astartup, which at a startup.
That's how it goes.
Things move fast, but therewasn't the attention to say well
, is this thing going to scalewhen we're successful?
(46:48):
And now that we're successful,should we?
Maybe not?
Maybe we should figure out analternative to putting bubble
wrap on this thing.
Nah, just keep putting bubblewrap on it, right?
So it's always about incentivesand outcomes, like if you don't
have everyone incentivized to dosomething, the outcome is well,
I guess you get to be a masterof bubble wrap and duct tape.
Speaker 1 (47:07):
Yeah, I mean speaking of that, I mean you even
brought this up in yourcommunity, your Discord and your
pod which is that there's a lotof people feeling more burnout
in the past in the data industry, more burnout in the past in
the data industry, and people inbig tech who just feel burned
(47:28):
out because there's too muchgoing on, too much.
You know bubble wrap, duct tapeand you know not enough
incentives on a budget.
Speaker 2 (47:33):
You feel like it's worse now than it was in in
previous years I think it is, Ithink it is, and it's not just
in tech, really.
It's like I think it's justthis general sense of malaise
I'm seeing with people, nomatter where I go in the world.
I think there's sort of a Maybea bit of reflection, I suppose,
(47:54):
on what exactly are we doing.
When I talk to people at techcompanies, for sure, it's
definitely a bit of a meatgrinder right now you get paid a
lot of money, but that's noguarantee that that will be
around tomorrow, uh.
So you know, I think, when yousee, you know, it's such a
contrast to where we were in 20and 2020 and 2021, when, uh,
(48:18):
well, around this time actuallywas about the uh, uh, 50th
anniversary of COVID lockdowns.
But back on that time, right,like everything is kind of
falling out from underneath.
Everybody Gave it a few weeksand then all of a sudden, it's
like okay, let's hire, like mad,and so that's what happened,
(48:38):
and it was a euphoric time.
It was like the sky's the limit.
I would hear people say that,you know, nothing's going to
stop, this is going to keepgoing, you know.
And then it did.
But what's interesting is a lotof companies are posting, you
know, pretty decent profits,sometimes record profits, and
these same companies are lettinggo of workers, and I think that
(49:01):
just creates a tremendousamount of insecurity and burnout
for sure.
I mean, I've done severalpodcast episodes on burnout and
it's a real thing.
I mean people who are, you know, making great money by any
standard, or just you knowthey're table flipping, so to
speak, and so it is interesting.
And then, you know, with therise of AI, I suppose there's
(49:22):
the speculation like well, youknow, do we need to hire more
engineers?
Are we just good doing AI now,because it's really amazing?
Yeah, so it's a fascinatingtime, but you know this is
actually happening in, you know,non-technical fields as well.
So I just think there's a lotof trepidation about what is?
What does AI hold for for theworkforce, you know, in the next
(49:45):
10 years, for example?
Nobody knows.
Speaker 1 (49:48):
So yeah, and that's why I think your discord is so
important, because it just kindof gives people a place to feel
community and and chat and youknow, just chat with people and
and that does make people feelbetter about burnout it.
And that does make people feelbetter about burnout.
It's like do you feel likeyou're overworked?
Yeah, I feel overworked.
Why?
Because you know there's allthis AI stuff around us that you
know I have to research everyday and answer questions about.
(50:08):
You know, while you know thestuff that we built 10 years ago
, I'm still maintaining it Right.
So it's like, how do you go?
Speaker 2 (50:16):
It's hard.
It's hard for people who are atstages of burnout in their
career.
I mean, I've been there before.
It sucks.
I mean I decided that Iprobably wasn't fit to have a
proper job, so just neverhaven't had one for years.
But yeah, but that's not foreverybody, right?
And I think some people findsatisfaction in the community
that a job brings them and thesense of purpose.
(50:39):
So, it's definitely interestingright now, for sure.
But I think one of the reasonsI wanted to start the discord,
uh community was just, I think,to provide people a place where
they can talk and, um, you know,again, kind of reading the room
, right.
I think that's what peoplewanted.
They wanted a place where theycould have conversations, I
think in a very candid way.
I don't jump in and censoranybody.
(51:00):
Really, I'm like, if you wantto, you know, whatever you say
is on you, as long as it's notlike grossly, like terrible, um,
that I might have a problemwith it.
But I, you know, I definitelybelieve in setting up a free
speech zone for people.
They can just say what theywant I don't care.
Yeah, yeah, but uh, yeah,community is important.
I think more and more and moreit's going to be probably the
(51:21):
most important thing there is.
Speaker 1 (51:23):
Yeah, that human connection in the time of AI is
going to be much more unique anddesired by people.
It'll definitely be interestingto see what part I mean.
The lazy trope these days isthat AI is going to replace
(51:44):
everybody.
Anyone who's actually practicalknows that AI is not even close
to doing that, but you do haveto figure out how is AI going to
come into your workflow?
Because I think what willhappen is people that refuse to
use AI the right way and wehaven't decided what the right
way is yet but people who refuseto use AI will right way, and
we haven't decided what theright way is yet but people who
refuse to use AI will probablyget replaced to some extent.
Speaker 2 (52:09):
I mean it's a cool person.
In the 80s and 90s I rememberthere were people who just
insisted on using paper foreverything.
I had an old boss who wouldhave all the emails printed out,
you know, and he was boss too,so whatever.
But I mean the whole point islike you know some uh, his boss
too, so whatever.
But I mean the whole point islike you didn't.
You know, some people catch upwith the time, some people don't
, I don't know.
Speaker 1 (52:27):
But yeah, and he's all the time.
You know it's gonna just createlike a new category of
productive people, right?
So you know, like uh, anthropicceo dario amode, you know,
famously said oh yeah, I'm likeyou know, within 12 to 18 months
, ai is going to replaceengineers.
Why do you have all these jobpostings to hire all these
(52:50):
engineers that you're supposedlygoing to replace?
Speaker 2 (52:53):
If you're applying for that job, you'd be like,
okay, I have a job, for how long?
Speaker 1 (52:57):
Yeah, writing the code to replace myself forever.
The reality is that AIengineers will, will, will be,
will, be the future, rightpeople?
Engineers who know how to useai and you know, uh, fin ops.
People who know how to use aiand data people that know how to
use ai and those those areefficiencies.
Or people who just come up witha really good reason why they
(53:19):
should never use ai.
Um, and they're credible enoughwhere their word is essentially
bond with their organizationsand they trust them enough to
believe that we can't have thisprocess run through an LLM,
because it's too critical.
But I think the best way tofollow along with it is just
(53:44):
being active there and thinkingabout how you can solve real
problems.
Speaker 2 (53:47):
Absolutely.
Yeah, I mean, lean into it,there's.
It's awesome, there's this.
The cool thing was, what'shappening now is just the rate
of innovation, the rate ofreleases.
Is it's mind bending Just howmuch stuff is announced every,
every week.
Basically, it's awesome.
I mean it's jarring.
I can't keep up.
I don't know if you can.
I mean, I'm just like I'll justfind what's interesting to my
(54:10):
use case and go with that.
I don't have time or the energyto expound on every single
possibility of this stuff, butmaybe that's the whole point.
You just pick what works foryou, for your situation and go
with it.
But there's no shortage ofgreat tools and offerings out
there.
The models get better everyweek.
So cool, yeah, yeah, you knowit's like in the internet, in
(54:33):
the internet age.
It would be like if you had, ifyou went from like copper, fiber
optic that whole transition inlike an hour across societies,
like that's about how fastthings are happening right now.
I just made up.
Yeah, maybe do the math on it,but it's like it's about that
quick.
I mean, I grew up on coppermodems, I think what 1200 odd or
(54:55):
something like that, maybe,maybe less, but it was like
that's what I started on oncopper line now it's yeah, now I
have like eight gig googlefiber at my house up and down.
Yeah, it's like that's that.
If you were told me as athere's a uh scrappy teenager
hitting bulletin board servicesin the early 90s that I'd be
using eight gig of fiber, I'd belike that's that's.
(55:18):
It's impossible.
Look how it's like sciencefiction, right.
Speaker 1 (55:22):
Yeah, it's moving incredibly fast.
You just got to find the bestway to make it work for you.
I actually tried this stuff outwith MCP.
Like I was saying, that was thebig craze last week.
It didn't really hit me whyit's useful until I went through
Matt Palmer at Replit.
(55:42):
I ran through his example codeand I was like, okay, I could
see how this could be useful now.
And you know, you ultimatelyhave to just, you know, wade
through, you know kind of the AI, slop out there, talk to people
in the community, you know, goto that curated list of experts
who really can kind of discernthis stuff.
(56:03):
And you, you know, I thinkthat's the best thing people can
do right now and it ultimatelywill save them time oh yeah,
embrace the AI yeah, why not?
yeah, well, joe, always goodcatching up with you, man.
I'm sure by the next time I seeyou would have circled the
(56:25):
globe four more times.
Who knows?
Speaker 2 (56:28):
If I see you next week, then probably not, but who
knows?
There's a few trips in thedocket coming up, yeah, but I'll
probably actually see you.
I'm going to be in SanFrancisco soon, so release the
area.
Speaker 1 (56:39):
Yeah, let me know when you're here.
Speaker 2 (56:40):
Yeah, we'll do.
Speaker 1 (56:41):
Yeah, a lot going on in San Francisco of course.
Speaker 2 (56:47):
There's so much happening there.
It's so cool.
Speaker 1 (56:50):
Yeah, it's definitely .
San Francisco is definitely.
I mean, I left when it was deadand now I'm kind of back and I
can.
It was really dead when we leftin 2020, like in the middle of
COVID, it was like a zombieghost town, basically.
I mean things like in themiddle of covid, it was like a
(57:10):
zombie, it was like a zombieghost town.
Basically, I mean things wereboring.
It felt like that I was there,it was nothing going on.
Now, now you can definitelyfeel the like, just like the,
the ai and like the builderenergy for sure.
So I I do mention that topeople that it's, it's palpable
and you know, I see it everyweek yeah, you know it's.
Speaker 2 (57:21):
It's so many events going on, just so much action
happening, and um, it's, it'sawesome.
I'm inspired every time I getout there.
I also don't really want tolive there, so I'm glad I live
in salt lake city where I canjust take an hour 20 minute
flight there.
Speaker 1 (57:33):
Um, but it's uh yeah, exactly yeah, same same story
for me, you know, living in laon weekends and san francisco
during the week, and yeah,that's kind of the balance that
we're that we're looking for.
It's a little bit right, butyeah.
Well, good, catching up.
Speaker 2 (57:48):
Joe, likewise See you around.
Speaker 1 (57:50):
Yeah, see you around.
And thanks everyone for tuninginto this episode of what's New
in Data.
We'll have links out to Joe'sDiscord and all the other places
you can follow him down down inthe show notes.
All right, joe, see you around.
All right, take care.
Well, we have the famous Joe Rice here on this
episode of what's New in Data.
I'm putting you on the spot.
How are you doing?
Speaker 2 (00:23):
Joe, I'm doing good.
I'm just slightly jet lagged.
I've just been getting overthat, but otherwise I think I'm
good yeah yeah, I'm just in.
German.
I think net is nice, so yep.
How are you?
Speaker 1 (00:41):
I'm doing good.
Every time I catch up with you,I know I always like to ask you
where you've been, and it'salways some exotic answer.
I'll just ask you.
I mean, we're recording this inmid-March, you know, joe, where
were you last?
Speaker 2 (00:56):
week spent some time in Munich, then Zellemsee, I
think that's how you pronounceit in Austria, which is in the
Alps, so a winter dataconference put on by, actually,
(01:18):
chris Tabb.
Yes, great data crew.
Yeah, my kid drew a Chris Tabbmug for me, so, yeah, it was a
great one.
I think you were at Skid lastyear over in.
Gerbier.
Yeah, that was a lot of fun.
This is the new version of itwith probably a more official
sounding event name Not skiersand data, but a winter data
(01:41):
conference instead.
So it was a lot of fun.
Great turnout, you know.
The Alps are lovely, as always,so, yeah, but now I'm back in
salt lake city or it actuallysnows more here than it does in
the alps, apparently.
Speaker 1 (01:55):
So yeah, yeah, I remember last year when we had
berbier.
You were mentioning that.
You know you're from salt lakecity and there's like 50
berbiers in salt lake city, soyeah it was trying to kind of
Utah.
Speaker 2 (02:05):
It seemed to be cocky about that, huh, but it is true
yeah it is true, so it's notcocky.
Speaker 1 (02:11):
If it's true.
Everyone knows that you put outthis book, the Fundamentals of
Data Engineering, and it'sreceived such great acclaim.
It's one of my favorite books.
I recommend it to every dataengineer because it's both
theoretical but also verypractical in how data
engineering is applied.
And now you know all your worldtravel and events you speak at
(02:33):
and companies you work with.
You know it's all kind ofrooted in.
You know the success and howpeople have really been
captivated by your book.
What it, what's it like just uh, continuing to to work with
people who've read your book orknow you through your book?
Speaker 2 (02:51):
it's kind of surreal really.
I mean, yeah, I mean it'scoming on three years old now,
which is kind of crazy.
It was published, so it's.
So it's interesting in that waywhere you know there's
definitely you know, I think weknew it was going to be
successful just because the youknow, on amazon it was a number
one new release, a bunch ofcategories before it came out
(03:11):
and then it obviously still doesreally well.
But it's definitely a bit ofit's still a bit surreal, um,
you got to see posts about itevery day on social media, like
literally every day there's anew post about it still.
So that's that's pretty cool.
You know, I'm definitelygrateful for the success it's
brought um, and I feel like, um,you know, just just thankful
(03:32):
that at this point, you know, abook will be successful on its
own merits and through word ofmouth, not because of marketing.
So apparently people liked it.
So I think that that's prettycool.
It's definitely opened up a lotof opportunities.
I think changed, um, you know,definitely my trajectory and
stuff.
At the same time I'm always onto the next thing, you know.
(03:53):
So I don't want to be likeuncle rico from napoleon
dynamite who, uh, I rememberthat character.
So he's just so for theaudience.
Uncle rico, if you haven'twatched the play in dynamite,
he's the uh kind of washed uphigh school football player who
still reminisces about his glorydays when he almost won state
championship.
If coach just put him in he'd bein the NFL, he'd have mansions
(04:16):
and women and all this stuff,cars, yeah, and I mean that's,
that's a danger though, right,because if you, I think, if you
rest on your laurels, um, youknow, and aren't working on the
next thing, and then, uh, youknow that I think you do risk
becoming kind of uncle rico inthat way.
So, once the book was done, I Ikind of, you know, definitely
(04:40):
gave a lot of talks and you knowI've been very grateful for the
success, but I was always juston to the next thing working on
that.
Uh, we're next things, I guess,but you know, it's definitely
cool, but it's, it's weird.
You know, um, at this stage Ithink I'll first look through
the podcasts and being morevisible, um, you know, going to
conferences and, um, definitelygetting noticed or some cases,
(05:04):
you know, I hate to say it, butprobably mobbed, and then, you
know, even in public, justwalking around, getting noticed
in public.
So that's always interesting.
Speaker 1 (05:14):
I've witnessed it.
Yeah, I mean, people are like,hey, dude, get out of the way.
Joe Rice is here.
I want to talk to him.
Speaker 2 (05:22):
Yeah, it's funny.
Speaker 1 (05:23):
Yeah.
Speaker 2 (05:24):
It is what it is.
I mean I'm thankful for it.
I mean it's it's.
It's definitely takes a bit ofgetting used to, but by this
point you know that's what it is.
Speaker 1 (05:33):
So, yeah, and you know people, ultimately I mean
it's, it's, it's one of thosebooks that you know it, it is,
it's in the title, it's, it'svery fundamental and you know,
now data engineering is ischanging in a lot of ways, uh,
but also going to stay the samein in others, like a lot of the
core principles of datamanagement and pipelines and
(05:56):
data modeling.
I don't expect that to changetoo much, but the way people
write code for data um is isdefinitely going to change, just
because code generation is alsogetting disrupted in its own
way.
Speaker 2 (06:11):
Oh yeah, it's ubiquitous.
Yeah, that's how it goes.
But I always wondered our bookcame out right before ChatGPT
hit the scene.
No, not right before.
It was like five months, fourmonths actually.
But what's interesting withthat is, you know, I kind of
called it.
You know the data engineeringlifecycle is not going to change
.
The undercurrents won't change.
(06:32):
You know you're still going toget data from source systems.
You're still going to need tomake it secure, your system
secure, and I would say, morethan ever.
Actually, data management anddata operations and governance
and modeling is actually moreimportant than ever, essentially
because of AI.
So I think it's been driving alot of interest in data
engineering.
At the same time, as you pointout, data engineering itself, at
(06:56):
least the way we do it, itcertainly is changing.
You're not handcrafting codeanymore.
Code gen is becoming more andmore of a thing, which I think
is a blessing and a curse, butit's reality.
You're not getting away from it.
So you know.
Yeah.
Speaker 1 (07:11):
It's interesting, yeah, and you know, but I also
want to ask you.
So you know, we talked aboutfundamentals of data engineering
.
Being, you know, just overthree years old now you know
what are you working on now.
Speaker 2 (07:28):
Well, I have a new company, kind of hinted at it,
you know, it's publishing, it'seducation, it's media.
It's a lot of things that I'vebeen doing.
So you know you want to talkabout like, definitely, I think,
jumping into what the bookprovided.
It provided me a lot ofopportunities to, I think,
become more involved in mediaand education and so forth.
You know, I did a course withAndering and the deep learning
(07:52):
fine folks there at deeplearning last year.
That's on Coursera and that'sdone really well.
But you know, I think doingthat and a few other things you
know reminded me of like Ireally do like, and I want to
focus more on that.
I think one of the biggest gapswe have in the industry is, you
know, we have great tools.
I think our ability to usethese tools to its fullest
(08:13):
potential is really hampered byour skills and knowledge, and so
I think helping upskill a newgeneration of data an existing
one of engineers will help pushus forward.
That plus AI, great skills, andAI will help you do great
things.
But AI on its own, withouthaving great skills, I think is
(08:34):
could be a recipe for, you know,pretty interesting outcomes.
So that's that's one thing I'mfocusing on right now.
So, yeah, my new book is goingto be out on this new company.
There's a few of the books.
That's one thing I'm focusingon right now.
So, yeah, my new book is goingto be out on, uh, this new
company.
Um, there's a few of the booksbeing written as well, but yeah,
but it's, it's an interestingthing, um, and I'll ask you.
I'll answer a question youdidn't ask, um, which is, uh,
(08:56):
why do publishing in an agewhere you could seemingly just
make a book in an hour you know,probably a half hour and get it
all published on?
Speaker 1 (09:06):
Amazon, that's what I was thinking about seeing you
so thank you, yeah, just perfectfor that.
Speaker 2 (09:13):
Thanks, john, great question.
So you're welcome.
But I think with AI, people areyou're already seeing it people
are getting tired ofAI-generated content, especially
AI-generated slop, as it'scalled, which is just
low-quality writing, low-qualityimages and videos and so forth.
(09:35):
I think that, more than ever,there's going to be a really
strong need for people forreally strong human-generated,
human-created ideas that aregoing to help push our industry
forward.
This is something that I don'tthink AI can do, at least not
yet.
But at least for now, I don'twant to write technical books
(09:59):
per se, like how to do AIengineering with a python or
something.
I don't think that needs to bea book.
These days, that's somethingyou could ask claude or chat gpt
and get a pretty good answer,uh.
So I think the tactical workand the tactical types of books
I don't really want to do, but Ithink the big idea books, the
(10:20):
ones that are really going topush our industry forward, those
are the ones I'm interested in.
So so for the audience, yeah, Ihave an idea like that.
You know, hit me up, uh, butyeah and that's what's so
important these days.
Speaker 1 (10:31):
You know there's, you know, as you, as you mentioned,
ai slop is definitely, uh, astrong label that's been
attached to a lot of the youknow, not to sound redundant,
but slop that's been put outthere.
And you log into linkedin andyou know there's like so many
posts that are like clearly aigenerated how many?
they all use the same uh, likethey all use the same kind of
(10:52):
writing style.
Uh, you know they'll, they'll,uh, because it's trained on.
You know very, very specifictypes of media and you know you
can just tell, like, within youknow five seconds of looking at
a post, whether it's generatedby chat gpt or not, and and uh.
But I think what people aregonna be craving now more than
(11:13):
ever, is hearing from experts,right, and hearing from people
who, like you said I'm, bigideas that will actually have a
big impact within anorganization, because people are
kind of more scrutinous aboutyou know, just things being
built, because we know ai canbuild things too.
So you know what's to say thatyou know someone just didn't
(11:35):
tell chat gpt to build somethingfor them, but, yeah, they're
doing it in a way that'sgrounded in fundamentals and,
you know, based on experienceand really solving business
problems well, absolutely, and Ithink the litmus test is going
to be, people gravitate towardslike, really good books, really
good writing, really goodpodcasts, right, these are, you
know, human exchanges, um, andin person.
Speaker 2 (11:59):
Right, I think this is increasingly going to become
more and more viable and what Ithink is actually just doing a
podcast.
Today with Jordan Morrow, we'retalking about public speaking
(12:27):
and it's interesting because Ithink, increasingly, giving
talks is going to be one of thedifferentiators between people
who write real stuff and cantalk about it.
Um, you know, in a publicsetting, especially during the q
a, like, I think that's goingto distinguish the experts, the
people who wrote books and whoknow what they're talking about
when they write the book, versusjust having chat gpt generate a
bunch of garbage.
Um, they probably couldn'tspeak to you know, or understand
um, so I know, with my book Imean, I counted probably over a
dozen, you know, throughout,throughout the you know a few
(12:48):
years, probably a dozendifferent books titled
fundamentals of data engineering, uh, you know, which are all
kind of knockoffs of that nicebook and um, I download the
samples of them, they check themout and kindle and it's exactly
what you think it's.
It's, there's no thought putinto it?
Um, there's not.
You know, if you're to askthese people to give a lecture
(13:09):
on data engineering, I'm prettysure none of them would, because
they're probably not people.
They're, they're uh, they'rebots under pen names.
There's one guy, on linkedin, Ithink, who wrote a book under
my name, but you know, I'd lovefor him to give a talk on the
topic and let's see what he cando.
Speaker 1 (13:28):
Well, yeah, I mean, yeah, there's a lot of people
who generate a lot of AI content.
They just want to publish it.
But if you ask them about anyof those topics, ask them one
hard question about it.
Unless they have Claude infront of them, them, uh, won't
be able to tell you much.
That's just it.
That's just it.
(13:49):
I mean like, yeah, yeah, and I,I deal, I deal with that a lot
too, because if, being in thisindustry, you're constantly
researching, you're constantlylearning, you're constantly like
trying all the new um patternsand frameworks that are coming
out.
Like you know, model contextprotocol, for example, was a uh,
you was kind of an opinionatedway of writing your AI apps to
(14:10):
fetch external context.
And they say it's like having aUSB-C input for AI and there's
so much slop generated around itwhere, like the first two or
three times I I read about it, Iwas like this is garbage, mcp
is stupid.
And then, finally, like I foundpeople credible talking about
(14:33):
it and the way they talked aboutit, I was like, oh, okay, now
it makes sense to me, right,right.
So you're almost it's it's,it's almost hard to not throw
the baby out with the bath waterwhen, when there's like all
these people generating a I slopabout things that are actually
valuable.
Yeah it's.
It's hard to get around that,so it's good to like like with
(14:54):
the company you're buildingyou're, you're gonna sort of
have those lists of like curatedminds that's just it, they just
have.
Speaker 2 (15:01):
You know you get the best minds of the planet.
You know producing the, and Ithink magical things would
happen and treat them well.
That's the thing.
You've got to treat people withrespect, especially authors and
creators.
These days, I think that, morethan ever, coming up with an
idea is one thing, but thenyou've got to help market and
distribute, which is somethingmost traditional publishers
(15:23):
don't do.
The sad thing is, if you writea book, you're also responsible
for marketing it, and that'ssomething that's.
It's a difficult skill set forsomebody to have, right, and so
that's.
That's another thing.
Where get the ideas out there?
Get good ideas from greatpeople out there, and I think
the rest will take care ofitself.
But you know, if you, if youmarket a book though right, I
(15:45):
mean, marketing is only going toprobably push a book or you
know, a piece of content formaybe a week or two, right, yeah
, it has to be good to center onits own.
Speaker 1 (15:57):
And I guess what's the definition of good?
Speaker 2 (16:00):
People will tell their friends about it.
People will tell their friendslike this is worth your time,
you know yeah.
Speaker 1 (16:08):
It's really interesting because it's similar
to the music industry where,like music industry has you know
what are called tastemakers andthey're the ones who influence.
You know the big playlists, youknow who the big music
publications will talk aboutwhat plays on the radio, even
though radio is kind of uh, lostin the space yeah you know,
(16:29):
okay, the big spotify playlists,right, uh, the big apple music
playlists, etc.
So, like you see, these djs who,who basically decide, kind of
like you know they, they havethe curated mindset to say you
know what's what's good and bad,and, uh, and and joe, you're a
big uh music aficionado yourself.
(16:50):
You're, you're, you actuallyare a dj, right, so you do have
incredible taste.
Just because you know, and I'velistened to your sets, they're
good.
I think just being able to, tocurate a great like dj set
already shows that you know, youunderstand what, what people
want to hear.
You know one way or another, Ithink.
I think it's the same thing asuh, understanding you know what,
(17:13):
what, what data patterns makesense, or adoptable, adoptable
and and practical, um, and Idon't know when was the last
time you DJ'd actually in publicgosh.
Speaker 2 (17:29):
That must have been maybe a year ago or something we
both did.
Chad's event that was Novemberit was November 23 actually, so
that was a while ago, right ohyeah but yeah, I always carry my
usb stick around with me if Itravel.
You never know when you're gonnafind turntables and it's kind
(17:52):
of fun.
But, um, yeah, I've just beenpretty booked.
I haven't had time.
I mean, I've been, um, there'sa, there's a very good chance I
might be doing some some localgigs here just to go dj at um
some clubs and probably do somemore live sets too.
Like I have a whole uh live um,you know kind of hardware setup
that I like to play on, uh, butbut curation, right, I mean, I
(18:16):
think djing taught me a lotabout kind of how I approach you
know what I'm doing now, right,uh, like, I think you just sort
of develop a sixth sense forwhere things are going or, more
importantly, need to go in ourindustry and and so you, you
think at a certain point you canhelp shape the discussion, you
(18:37):
can help shape where things go,and that's pretty cool to do.
I'm doing that with datamodeling right now.
Um, that wasn't really a cooltopic for a bit until I
mentioned, you know, startwriting a book on it.
I'm not saying you can write itfor all of it, but I think it
definitely helped push that backinto the foray, right?
So I think at a certain pointyou can just start.
You can kind of determine wheretrends go, or you can at least
(19:00):
see where they go Right.
But, um, or you can at leastsee where they go right, but you
have to be in it for a longtime to to know the patterns
that are hidden behind thepatterns and uh, you know it's
um, but everything you know, Ithink, a lot of things in our
industry.
They definitely move in cycles,they kind of go on a pendulum
(19:20):
and uh, you just you know.
I think once you understand therhythm of it, I can't say you
can predict the future, but youcan certainly tell where things
are likely going to be going.
So even with AI right now right, I mean, that's, it's new, you
know might shake things up a bit, but when you get down to it,
there isn't much different fromwhen you saw other.
(19:42):
You know, significanttechnology patterns emerge in
the world.
Of course there will bedifferences, but it's not like
this is brand new out of thescene.
It never happened before.
Speaker 1 (19:51):
We never had a new technology it happens a bunch,
yeah, and you know, like when I,when I look at the way you
approach it, I mean you haveboth.
You know a STEM, technical,academic background and actual
industry background building.
You know analytics and datascience and data infrastructure.
You know for, for, for largecompanies, intuition about kind
(20:18):
of.
You know what's good and what'sbad and that's always
subjective, right, people debateit a lot.
You know who's to say oneperson's right, one person's
wrong but I think what's sort ofindisputable is that you know
when you put pen to paper andyou put out that book with Matt.
You know Fundamentals of DataEngineering and you know it was
just widespread acclaim.
(20:38):
People agree with it.
And when you generally go talk,people are captivated and
interested.
And then, even coming back toDJing great DJ sets we were just
talking about you were puttingme on to FIAC and speaking of
misconstruing names.
I call them FJAC and it kind ofreminds me of this quote I
actually saw it on X from SamLambert, the CEO ofo of planet
(21:02):
scale.
It's a database company.
Yeah, he says do not work foranyone who doesn't love music.
They will never build anythingthat that humans want, and I
think you were one of the firstpeople I thought about there
because I was like becausethat's a, that's the ceo of a
database company saying that so,like, what does a ceo of a
database company care about?
Like, like people havingmusical taste?
But then kind of, when youconnect the dots and you meet
(21:23):
people you know, uh, across theindustry, like there there is
actually some relation there.
Speaker 2 (21:31):
Oh tons, I mean, you're a musician, I would say
you're.
You're a very classicallytrained musician, right?
Speaker 1 (21:39):
Yeah, yeah, I uh, you know, yeah, definitely, uh, my
days at san franciscoconservatory music, and then I
was, I, you know, I had my ownartist project.
I go under a pseudonym, thoughit's very, it's very secret and
you know, uh, I try to keep itseparate, right, so people don't
kind of, because I, I always, Iwas always kind of insecure
about that too like, okay,people know about, like my music
(22:01):
.
You know that they, they mightnot take my uh, uh work in data
and engineering as credibly,because I'll be like, well, how
do you, how do you do both?
Like, because I feel like, yeah, confuse people I think it's.
Speaker 2 (22:12):
I think those walls are diminishing though, right,
like social media sort of brokendown a lot of the barriers
between, you know, public andprivate persona.
Um, I mean, for god's sake, youknow, the ceo of golden sacks
is a dj.
Um, you know, I think a prettypopular one at that, diesel
david solomon, right, yeah, like, yeah, I don't think he has any
you know qualms about.
Oh, I'm the ceo of golden sacks.
(22:33):
Nobody can know that I dj, youknow, I think he uses that to
almost to his advantage in a bit.
I mean, we're talking about it.
Um, yeah, you know, and it says, I think this is the walls
between public and private,whatever, I mean it's, you know,
depending on what you do,obviously, but I don't see any.
I think data needs more of thatreally, where it's kind of a
(22:53):
boring industry, we selldatabase systems.
Woo, I mean, that's's cool, ithelps make the world go around,
but so does music, right, and Ithink, you know, and I meet a
lot of interesting people, asyou do too, in the industry and
I think, um, you know thatbecause you see, especially at
conferences, conferences arehilarious because you meet
people and they have to put onthe conference face and then
(23:16):
where you know, where theconference you know, attire
usually a suit of some sort, butthen you hang out with these
people after the after hours ofthe conference.
These people are nuts.
So I think that's I kind ofhope that a lot of these walls
(23:36):
get broken down.
I just think that I'm much moreimpressed when I think there's
there's a, there's a level ofcredibility to somebody and a
level of authenticity.
I, I think we're.
If you know, if the, thebarriers are pretty
indistinguishable, then I thinkI can trust you a lot more for
one right, because I know you'renot like hiding, I know you're
not, you know, full of crap.
(23:57):
So, um, you know, yeah, yeah'sme, but that's how it is.
That's the trajectory of theworld.
The world is going though right, like it or not, it just is
what it is.
It's John Coutet.
You can either change your nameto your pseudonym or unleash
the music into the world, intoyour real name, but I think it
(24:18):
would benefit you.
Speaker 1 (24:22):
I tease a bit on your on your discord, which is
something else that you know I Iwant to talk to you about.
So you, you launched a, adiscord channel called practical
data.
Tell me about that practicaldata is a.
Speaker 2 (24:35):
It's really an extension of the subsec that I
had.
So I'm releasing, uh, earlysections of my book, usually in
draft form, on practical datamodeling.
That's sub stack, uhcom.
If I had half a brain I wouldhave just called it practical
data, because then I can havemore books that I'll be writing
on there as well.
But here we are and so we hadthis chat group going for a bit
(24:56):
on sub stack.
But sub stack chat, it's prettymid, as the kids say.
I was like okay, so let's makea chat community.
I was like okay, do I want touse Slack or do I want to use
Discord?
I joked, you know we'd betteruse Teams, but I'll just keep it
(25:20):
really corporate.
Can you bring Skype back back?
Skype like yeah, bring skypeback.
That makes it pretty funny.
Uh, we these webex, um, that'samazing that'd be hilarious
actually.
Maybe I should do that for aprilfools.
Um, I'll write that one downactually, but anyway, so I think
it's.
Yeah.
So I started a discord groupand, uh, you know, soft launch
(25:42):
into about 100 people and thatwas really fun, just to see how
it would go right, is this evenworth pursuing or not?
Um, and then, you know, open itup to a few more people and now
we're almost like 1100 people.
It's not not huge, but I thinkthe quality of the conversations
I've heard from quite a fewpeople that it's definitely
their favorite data community tobe involved in because it's
just very candid.
(26:02):
Um, conversations you know about, about our practice, right, and
I have other channels as wellso you can talk about.
If you want to talk aboutpolitics, you can.
There's a politics channel.
I don't care, right, as long asyou keep it civil.
There's an unhinged channel.
You want to talk about just thecraziest stuff you can think of
?
Go put it in there, I don'tcare.
Um, you know, we opened up anew one about sports the other
day, so we can just talk aboutwhatever sports you're into.
(26:23):
So I think it's because whatyou realize is there's when you
start a data community.
Most people want to talk abouteverything except data, and
again this just comes back toyou know um djing and working in
clubs for a while too, or Ithink you just get to know human
behavior.
At the end of the day, peoplehave a lot of facets to
(26:46):
themselves.
They want to talk about a lotof things, and a lot of times
the last thing they want to talkabout is work, especially in a
chat setting.
Speaker 1 (26:54):
That's one reason I didn't want to do Slack or
anything, because it's like thatformat is too familiar to you,
because you associate that withyour job yeah, I, I think just
having yeah, like and just like,you understand people better
and you know, because all thisyou know, talk about like data
infrastructure, like everyonehas a as an opinion on it, and
(27:16):
unless someone has like a,really you know uh is really
grounded in something.
That's just absolutely proven.
It's all up for debate.
When you get people together tochat about stuff, it's the same
way Like, oh, I think thisfootball team is going to win
the Super Bowl this year, basedon my opinion, people just chat
about it the same way.
It's just open-ended and casual.
(27:38):
I really like the, the kind ofthe discord, because people both
talk about data the same way.
They just talk about otherstuff right right yeah what you
realize too, is it culturally.
Speaker 2 (27:52):
Data is one of these topics where they're all
cultural nuances to how we workwith data right, how we
architect systems and how webuild them.
It's not like.
So what I realized, you know,especially in the travels as I'm
sure you have around the worldand talking with people is like
they're you have to take intoaccount the geographical
situation that you're in.
You have to take into accountthe business culture that you're
(28:14):
in.
Nothing is monolithic.
When you're trying to sell, forexample, you, you know, say,
stream into a company, it's likethere are other factors you
need to consider besides justtypical.
Am I talking to the qualifiedbuyer?
Am I, you know, am I hitting apain point?
It's like that's basic stuff,but it's also, you know, how do
they get to the situationthey're in?
(28:34):
You know what's the team likethat's going to support this and
a what's the team like that'sgoing to support this?
A lot of this comes down to thenuances of the culture that
they happen to live in.
In our Discord group we havepeople from all over the world,
even people in Europe.
Eastern Europe is differentthan Western Europe.
It's different from the UK.
It's different from Nordics.
It should be obvious, but I gotcalled out on this.
(29:00):
The other day I was in Munichand I asked one of my friends
what is her thought on AIinnovation or something like
that in Europe?
And she's like, well, butyou've got to understand, we're
in Germany, that's differentthan it's not Europe.
I was like thanks for thereminder.
You're absolutely right, butyou realize the data world is a
(29:20):
pretty big place and everyone'sgot their own opinions on how to
do stuff and you can't just.
I think all too often,especially in America, we try
and paint it from the broadstrokes of well, this is how
it's done in the Bay Area, forexample, so therefore everybody
is the same right, or this ishow we do it in New York or
wherever, but that's certainlynot the case.
Speaker 1 (29:43):
So yeah, yeah, and I think, coming back to taste, you
know a lot of the status stuffdoes and it's not new
necessarily.
Like when you look at, like,the role of a data architect in
the enterprise, like usually,like the you know some domain
specific team, a software teamor a business team will say, hey
, we want to do these things.
(30:04):
Uh, it'll technically work forus.
Data architect, what do youthink?
And the architect will shoot itdown just because they think
it's a bad design.
And you know, there might besome, some technical principles
that they're applying there.
But a lot of times when I see it, it is sort of like a form of
taste making, like I just like,oh, I don't know that pattern,
like I've never heard of it andyou know it's, even though it
(30:26):
technically might work that youknow, I don't know if it'll be
resilient or scale.
They might shoot something downbecause of that.
Right, and it can be, it can bea little arbitrary, so it's
it's.
It's super interesting to seehow that also correlates with
what you're doing with theDiscord, where I see people
talking about announcements ofnew data tools and open source
(30:48):
frameworks and what sub-vendorsare pitching and they haven't
tried it yet Some of them havetried some of the open source
stuff.
But even with stuff theyhaven't tried, they're just
inserting their tastes and theirview on just what they think
about it broadly yeah, yeah, Ialways get a kick out of that.
Speaker 2 (31:09):
Um, yeah, sort of having an opinion before trying
it.
Um, yeah, that's something Ipersonally always, always want
to try and avoid.
I uh, I, uh, well, I veryrarely will talk about vendors
specifically too, just because I, I mean, for better or for
worse, if I say something, itdoes have an impact on things
sometimes, so I usually keepquiet.
(31:30):
But other people, you know, Imean, they definitely post
things and it's cool to see theenthusiasm and sometimes lack of
enthusiasm, maybe warranted orunwarranted, but uh, um, but
that's how it is.
I think, you know, people bringa lot of biases to how they
evaluate tools and technologiesand, um, but that's the fun part
about our field, I suppose, is,uh, you know, if a vendor is
(31:52):
liked or disliked, that could,that could change.
Um, uh, I'll give you know,I'll give you an example, I will
call it a vendor.
Um, uh, microsoft.
They I'll give you an example,I will call it a vendor
Microsoft.
They were sort of, I think fordecades considered sort of the
great Satan in the tech world bydevelopers.
Now they've made a huge change.
I remember 10 years ago one ofmy friends, she went there to go
work at Microsoft as a Pythonadvocate.
(32:15):
I was like what are you doing?
This is nuts.
They're making some of thebiggest strides with Python and
Guido's there, and so you knowthey have amazing tools right.
I mean VS Code is widely used,so I mean things could change
right, and so I never writeanybody off.
And you know, and at the sametime I don't really get too hot
(32:36):
on things because it's like it'sgood for today, who knows how
it is tomorrow.
I take a very, I guess,zen-like approach to all this
stuff because, as you know,you're around long enough as
things kind of flow like waterand that's how it is.
So you know.
But I think that's a goodexample, you know, of what
Microsoft did.
I mean they've developer to thenon-Microsoft people using it.
(32:58):
I think that's awesome to see.
I mean I used WSL on my Windowslaptop and I think it's
fantastic.
So kudos to them.
They did a great job.
Speaker 1 (33:06):
Yeah, and Power BI is ubiquitous and Azure is
everywhere.
Speaker 2 (33:12):
It's everywhere.
Speaker 1 (33:13):
And, speaking of BI, one thing that you mentioned
with AI BI is that old-school BIdashboards are kind of
following the Lindy effect.
Do you see them being disruptedby this concept of AI, bi or AI
in general?
Speaker 2 (33:34):
a bit ago and that spurred a lot of discussion.
I think it was a podcast I didabout old school versus new
school BI, and you are seeingthis.
I think that there is chatterabout how we can just put a
chatbot in front of everything.
You don't need dashboardsanymore in the old school sense
(33:58):
you can chat with your data.
So I guess the questions I hadwas you know, did the data
change along with this newinterface, or is it the same
data?
Because if it's the same data,most of the questions that you
need to run your business areprobably already answered, I'm
guessing, in a dashboard that'sbeen there for probably quite a
(34:19):
while.
Right, like what are the trendsof my sales?
What's my?
In a dashboard that's beenthere for probably quite a while
, what are the trends of mysales, what's my operating
margin and so forth?
Whatever user statistics youhave in your app, whatever
you're into, those probably havebeen, I'm guessing.
I hope, answered in a dashboardof some sort or a report.
But who uses these?
(34:40):
I think by most accounts, bitool adoption has never hit
above 25% in most companies.
So my question was if usage hasbeen pretty low across the
board or at that level, whatmakes you think that, given the
ability to chat with your data,that you're going to ask
(35:02):
different questions, and theability to ask questions is the
only thing that stopped you fromnot looking at your dashboard
before?
And my answer to this is it'sgoing to probably be both.
I think old school BI, it stillaccomplishes 80-90% of what you
need.
Those other 10-20%, sure, ifyou want, that'll certainly um
make it faster than trying totalk to an analyst who might
(35:23):
take weeks to build a report foryou, um.
But so I think the question isboth, because I kept seeing
these very um binary reactionswhere it's, yes, you know you
need new school, ai ei, and theold school stuff is done for.
Or you know the other camps,like no, that's all garbage and
it's has to be the old schoolstuff is done for.
Or you know the other camp'slike no, that's all garbage and
it has to be the old schoolstuff.
I'm like maybe it's both, Idon't know.
(35:45):
I mean, like I said, I alwaystry to be flexible in these
ideas because I don't believe inabsolutes.
When it comes to technology,things move too fast and things
improve, like, I think, thepeople who say that it's always
going to be old school bi andlike large language models are
useless.
It's like, yeah, they're justlooking at today's
hallucinations, right, it's notnot accounting for the fact you
know these will improve.
(36:06):
They have to yeah um, it's likeyou know, so um yeah, we're like
.
Speaker 1 (36:13):
I mean, we were chatting about this or a text
and like, for a long time peoplewere ragging on including me,
ragging on llms for being bad atmath.
Right, like, okay, if you giveit like a, uh, like a division
problem, it'll get it wrong.
Uh, I think, I think I sent youa passage from a chip wenz book
.
Uh, yeah, you can just give aia calculator, you know, or it'll
write some python code you know, and suddenly it's amazing at
(36:36):
math, right, yeah, so ai justgets better and better, with,
with, with tooling orimprovements.
Uh, you know, reinforcementlearning, you know better models
, better inference, time tools,rag, getting better.
So, like you said, I mean, yeah, it's like, yeah, you can't
just look at how it's workingtoday and see it hallucinate
once and be like, oh, you know,it's never gonna work oh yeah, I
(36:59):
mean.
Speaker 2 (36:59):
The classic counter example is like how many times
you've asked, you know, maybeit's never going to work?
Oh, yeah, I mean.
The classic counter example islike how many times have you
asked, you know, maybe yourteammate or an employee about
something and maybe they get itwrong?
You know, yeah, what are youjust going to discount humans
Because one of them got aquestion wrong?
You know?
Are you going to fire thatperson?
Probably not, I mean, that'd bepretty heartless.
Fire all humans fire.
(37:22):
I mean.
Yeah, I mean, some aiaccelerationist thinks it's what
you should do.
Um, but it's a, it's a classic,you know, upton sinclair quote
what is it?
It's difficult to get a man tounderstand something when his
salary depends on his notunderstanding it.
Um, so I think that's one ofthe truisms in our industry, and
in every industry really, it's.
You know, if you'reincentivized to, um, you know,
to ridicule or or promotesomething, that that's what
(37:44):
you'll do, often at the expenseof having a?
Um, uh, you know being able tohold two opposing viewpoints in
your mind at once, which, uh,apparently, is a sign of
intelligence if you're able todo that.
But that's the paradox we're inright now is more and more.
Vai tools are getting prettygood, and now this is supposed
(38:08):
to be the year of agents, and sowe'll see how that goes, but
it's only March, so we have sometime.
Speaker 1 (38:18):
Yeah, I expect a lot to happen between now and the
end of the year for sure,especially with with agents and
just the.
You know there's so much goingon with ai engineering as well
and we were, you know we, like Isaid, we were chatting about
that over over text and uh, uh,especially as it applies to data
.
You know the.
(38:40):
If people can just kind of chatwith data, they're going to ask
different questions than.
Okay, I get this pre-cannedreport with some filters and
dimensions that I can apply toit.
But if I can just say, hey,really colloquially or casually,
tell me how my business isdoing or show me which customers
you know, show me whichcustomers are happy, show me
(39:02):
which customers are unhappy, youknow there has to be this
person's sort of interpretationof what the user means, right,
and then has to go kind of preprocess that against the
existing you know data models,which comes back to data quality
.
But first, looking ahead, right,you have to come up with the
right SQL query.
(39:23):
Like this is a classic TexasSQL.
So like understanding whattypes of questions people ask
and how it relates to your dataand then understanding is that
something, is that answer whatthey're actually looking for or
not right, and this is just youknow.
Naturally, like an agenticproblem, right, it requires
reasoning, it requires a chainof thought.
(39:43):
So do you see agents becoming abig part of BI as well?
Speaker 2 (39:50):
I suppose.
So I mean, you got me thinking,though, about something where
it's, if you think aboutdashboards, sort of as we used
to think about the three big TVnetworks back in the day when
people would watch TV.
I mean, you brought up radio,too, earlier in the conversation
, right, so it was that famousLA station KROQ, I think it was.
People used to have limitedsources of information, right,
(40:15):
and in some ways I think thatwas good, where there was a
common set of beliefs and commonset of the quorum and standards
of ideas.
Internet happens we're able tosearch for whatever we want.
Social media happens Now we getinto filter bubbles, and so if
(40:39):
you draw these same conclusionsto being able to chat freely
with data but you don't have, II guess, a deterministic way of
knowing which queries people arelooking at, the sql queries,
that is right, um, I I thinkthis, this could be a very
interesting outcome inbusinesses, because, john,
(41:02):
you'll, you'll be looking atyour reports and say, well, I
asked this prompt here and I gotthis answer.
That's nice, john.
I asked this question here andwithout us knowing SQL, do you
think that that's going toimprove the situation where BI
is already messy?
People have a lot of questionsabout data.
So this is an interestingscenario you just brought up to
(41:24):
me that I was thinking about.
Like that, because it remindsme of what happened with um uh
news and information in greatersociety, where now everybody,
there is no sense of truthanymore, there is no sense of
ground truth anywhere.
You can't agree on what.
What's a fact right now in inin the news, it's whatever, it's
whatever your social media feedtells you.
And I think it's interestingbecause I do think that this
(41:46):
actually could happen with thesebots, unless there's some sort
of governance on the queries,making sure these are consistent
.
So what does this do for dataquality?
I mean, you better hope thatyour data is good quality
because bad quality data, witheven SQL queries that are
(42:07):
slightly different, butdifferent in very important ways
, uh, that'll be just veryinteresting outcomes, um, ones I
would shudder to think about ifI was running a business yeah,
yeah, absolutely, you know it.
Speaker 1 (42:23):
It could just become like an evolution of like the
existing bi products.
I mean, oh, I didn't even get achance to look at what open ai
put out.
I mean we're oh yeah, it'smid-march and they just put out
like their first uh, dataanalysts, uh, analysis agent, uh
, let's see, yeah, and it's,it's gonna be.
(42:43):
I still don't really think it'spossible for ai to really,
especially with just how messydata is and, like you know, the
relationship between, like whatthe data says and what's reality
within a business.
Like you know, even like if youlook at like data in salesforce
, where you have you know 500columns that have like similar
(43:04):
semantic meaning, but you knowyou have to know the sales ops
uses actually this column, notthis one.
When they say you know leadsource, right, they have like
five.
You know they have like dozensof columns for lead source, but
the one that you Lead source one, two, three, four, 12, yeah.
So, like an agent, being able tounderstand that right is almost
(43:26):
impossible because everythingkind of relies on yeah, that
person in sales ops to explainit to me, Right?
Speaker 2 (43:37):
Right.
So yeah, I mean you've seenthis before, I mean because you
live in reality.
So it's like this is the kindof stuff you get to deal with
all day.
I'm a data integration vendor.
I'm sure it's just how you seedatabases.
You're like what exactly isthis that we're looking at right
now?
Speaker 1 (43:53):
Right, that's reality though, yeah.
I see all these LinkedIn postsI'm sure you've seen them too
where it's like oh, bad dataequals bad AI.
It's really popular to say Isee all these LinkedIn posts and
I'm sure you've seen them toowhere it's like oh, you know,
bad data equals bad AI, and youknow it's really popular to say
you know, and it's very true,but I don't know if that's like
(44:16):
a and every vendor claimsthey're the solution to bad data
.
Of course, right, all you needis one more vendor, a data
quality vendor, and suddenly allyour, all your problems will be
fixed.
Uh, but, uh, but I don't.
I don't know if that's asurmountable problem, right,
because just when you see, okay,like the last 30, 40, 50 years
(44:38):
of data infrastructure and eventhese new sophisticated startups
, tech startups, startups comingup, I mean the thing that's
always the least you know,sanitized and at least
structured is like the analyticsprocess, right?
So, yeah, you get these curatedreports that solve, like very
specific burning businessproblems or questions that you
(45:00):
know the data team knows the CEOis going to ask, but just
having this you know broad datalake that you can chat with and
answer questions, no one'scracked that yet, but it
requires a lot of other problemsto be solved which just take a
lot of brainpower and, honestly,people who have like intuition
for how the business actuallyworks.
Speaker 2 (45:21):
Yeah, it turns out, and a lot of these problems that
you see in see data sources,for example, or whatever, right,
if you look at again theclassic continuum of people
processing technology.
Speaker 1 (45:35):
Yeah, that, exactly, that's exactly it.
Speaker 2 (45:40):
The culprits of a lot of bad data models, for example
.
It's not because you don't havetechnology to do it Right.
It's like often it's at leastthese days that the common thing
I hear from people that want tomodel their data and want to
put some cycles through it is Idon't have enough time to do it,
I don't, I'm not given the timeto do this, I don't have, I'm
not given the budget, I'm notgiven the support to do data
(46:05):
modeling, and so I just need tocram some stuff into a database,
and we'll call it that.
It was interesting.
I was talking to someonerecently who runs engineering at
a fintech company.
This company started out as astartup and now they're publicly
traded.
He described his job asbasically buying more bubble
(46:28):
wrap and duct tape to put aroundthe data systems, and that's
pretty much what happens on aweekly basis.
Decisions were made early at astartup, which at a startup.
That's how it goes.
Things move fast, but therewasn't the attention to say well
, is this thing going to scalewhen we're successful?
(46:48):
And now that we're successful,should we?
Maybe not?
Maybe we should figure out analternative to putting bubble
wrap on this thing.
Nah, just keep putting bubblewrap on it, right?
So it's always about incentivesand outcomes, like if you don't
have everyone incentivized to dosomething, the outcome is well,
I guess you get to be a masterof bubble wrap and duct tape.
Speaker 1 (47:07):
Yeah, I mean speaking of that, I mean you even
brought this up in yourcommunity, your Discord and your
pod which is that there's a lotof people feeling more burnout
in the past in the data industry, more burnout in the past in
the data industry, and people inbig tech who just feel burned
(47:28):
out because there's too muchgoing on, too much.
You know bubble wrap, duct tapeand you know not enough
incentives on a budget.
Speaker 2 (47:33):
You feel like it's worse now than it was in in
previous years I think it is, Ithink it is, and it's not just
in tech, really.
It's like I think it's justthis general sense of malaise
I'm seeing with people, nomatter where I go in the world.
I think there's sort of a Maybea bit of reflection, I suppose,
(47:54):
on what exactly are we doing.
When I talk to people at techcompanies, for sure, it's
definitely a bit of a meatgrinder right now you get paid a
lot of money, but that's noguarantee that that will be
around tomorrow, uh.
So you know, I think, when yousee, you know, it's such a
contrast to where we were in 20and 2020 and 2021, when, uh,
(48:18):
well, around this time actuallywas about the uh, uh, 50th
anniversary of COVID lockdowns.
But back on that time, right,like everything is kind of
falling out from underneath.
Everybody Gave it a few weeksand then all of a sudden, it's
like okay, let's hire, like mad,and so that's what happened,
(48:38):
and it was a euphoric time.
It was like the sky's the limit.
I would hear people say that,you know, nothing's going to
stop, this is going to keepgoing, you know.
And then it did.
But what's interesting is a lotof companies are posting, you
know, pretty decent profits,sometimes record profits, and
these same companies are lettinggo of workers, and I think that
(49:01):
just creates a tremendousamount of insecurity and burnout
for sure.
I mean, I've done severalpodcast episodes on burnout and
it's a real thing.
I mean people who are, you know, making great money by any
standard, or just you knowthey're table flipping, so to
speak, and so it is interesting.
And then, you know, with therise of AI, I suppose there's
(49:22):
the speculation like well, youknow, do we need to hire more
engineers?
Are we just good doing AI now,because it's really amazing?
Yeah, so it's a fascinatingtime, but you know this is
actually happening in, you know,non-technical fields as well.
So I just think there's a lotof trepidation about what is?
What does AI hold for for theworkforce, you know, in the next
(49:45):
10 years, for example?
Nobody knows.
Speaker 1 (49:48):
So yeah, and that's why I think your discord is so
important, because it just kindof gives people a place to feel
community and and chat and youknow, just chat with people and
and that does make people feelbetter about burnout it.
And that does make people feelbetter about burnout.
It's like do you feel likeyou're overworked?
Yeah, I feel overworked.
Why?
Because you know there's allthis AI stuff around us that you
know I have to research everyday and answer questions about.
(50:08):
You know, while you know thestuff that we built 10 years ago
, I'm still maintaining it Right.
So it's like, how do you go?
Speaker 2 (50:16):
It's hard.
It's hard for people who are atstages of burnout in their
career.
I mean, I've been there before.
It sucks.
I mean I decided that Iprobably wasn't fit to have a
proper job, so just neverhaven't had one for years.
But yeah, but that's not foreverybody, right?
And I think some people findsatisfaction in the community
that a job brings them and thesense of purpose.
(50:39):
So, it's definitely interestingright now, for sure.
But I think one of the reasonsI wanted to start the discord,
uh community was just, I think,to provide people a place where
they can talk and, um, you know,again, kind of reading the room
, right.
I think that's what peoplewanted.
They wanted a place where theycould have conversations, I
think in a very candid way.
I don't jump in and censoranybody.
(51:00):
Really, I'm like, if you wantto, you know, whatever you say
is on you, as long as it's notlike grossly, like terrible, um,
that I might have a problemwith it.
But I, you know, I definitelybelieve in setting up a free
speech zone for people.
They can just say what theywant I don't care.
Yeah, yeah, but uh, yeah,community is important.
I think more and more and moreit's going to be probably the
(51:21):
most important thing there is.
Speaker 1 (51:23):
Yeah, that human connection in the time of AI is
going to be much more unique anddesired by people.
It'll definitely be interestingto see what part I mean.
The lazy trope these days isthat AI is going to replace
(51:44):
everybody.
Anyone who's actually practicalknows that AI is not even close
to doing that, but you do haveto figure out how is AI going to
come into your workflow?
Because I think what willhappen is people that refuse to
use AI the right way and wehaven't decided what the right
way is yet but people who refuseto use AI will right way, and
we haven't decided what theright way is yet but people who
refuse to use AI will probablyget replaced to some extent.
Speaker 2 (52:09):
I mean it's a cool person.
In the 80s and 90s I rememberthere were people who just
insisted on using paper foreverything.
I had an old boss who wouldhave all the emails printed out,
you know, and he was boss too,so whatever.
But I mean the whole point islike you know some uh, his boss
too, so whatever.
But I mean the whole point islike you didn't.
You know, some people catch upwith the time, some people don't
, I don't know.
Speaker 1 (52:27):
But yeah, and he's all the time.
You know it's gonna just createlike a new category of
productive people, right?
So you know, like uh, anthropicceo dario amode, you know,
famously said oh yeah, I'm likeyou know, within 12 to 18 months
, ai is going to replaceengineers.
Why do you have all these jobpostings to hire all these
(52:50):
engineers that you're supposedlygoing to replace?
Speaker 2 (52:53):
If you're applying for that job, you'd be like,
okay, I have a job, for how long?
Speaker 1 (52:57):
Yeah, writing the code to replace myself forever.
The reality is that AIengineers will, will, will be,
will, be the future, rightpeople?
Engineers who know how to useai and you know, uh, fin ops.
People who know how to use aiand data people that know how to
use ai and those those areefficiencies.
Or people who just come up witha really good reason why they
(53:19):
should never use ai.
Um, and they're credible enoughwhere their word is essentially
bond with their organizationsand they trust them enough to
believe that we can't have thisprocess run through an LLM,
because it's too critical.
But I think the best way tofollow along with it is just
(53:44):
being active there and thinkingabout how you can solve real
problems.
Speaker 2 (53:47):
Absolutely.
Yeah, I mean, lean into it,there's.
It's awesome, there's this.
The cool thing was, what'shappening now is just the rate
of innovation, the rate ofreleases.
Is it's mind bending Just howmuch stuff is announced every,
every week.
Basically, it's awesome.
I mean it's jarring.
I can't keep up.
I don't know if you can.
I mean, I'm just like I'll justfind what's interesting to my
(54:10):
use case and go with that.
I don't have time or the energyto expound on every single
possibility of this stuff, butmaybe that's the whole point.
You just pick what works foryou, for your situation and go
with it.
But there's no shortage ofgreat tools and offerings out
there.
The models get better everyweek.
So cool, yeah, yeah, you knowit's like in the internet, in
(54:33):
the internet age.
It would be like if you had, ifyou went from like copper, fiber
optic that whole transition inlike an hour across societies,
like that's about how fastthings are happening right now.
I just made up.
Yeah, maybe do the math on it,but it's like it's about that
quick.
I mean, I grew up on coppermodems, I think what 1200 odd or
(54:55):
something like that, maybe,maybe less, but it was like
that's what I started on oncopper line now it's yeah, now I
have like eight gig googlefiber at my house up and down.
Yeah, it's like that's that.
If you were told me as athere's a uh scrappy teenager
hitting bulletin board servicesin the early 90s that I'd be
using eight gig of fiber, I'd belike that's that's.
(55:18):
It's impossible.
Look how it's like sciencefiction, right.
Speaker 1 (55:22):
Yeah, it's moving incredibly fast.
You just got to find the bestway to make it work for you.
I actually tried this stuff outwith MCP.
Like I was saying, that was thebig craze last week.
It didn't really hit me whyit's useful until I went through
Matt Palmer at Replit.
(55:42):
I ran through his example codeand I was like, okay, I could
see how this could be useful now.
And you know, you ultimatelyhave to just, you know, wade
through, you know kind of the AI, slop out there, talk to people
in the community, you know, goto that curated list of experts
who really can kind of discernthis stuff.
(56:03):
And you, you know, I thinkthat's the best thing people can
do right now and it ultimatelywill save them time oh yeah,
embrace the AI yeah, why not?
yeah, well, joe, always goodcatching up with you, man.
I'm sure by the next time I seeyou would have circled the
(56:25):
globe four more times.
Who knows?
Speaker 2 (56:28):
If I see you next week, then probably not, but who
knows?
There's a few trips in thedocket coming up, yeah, but I'll
probably actually see you.
I'm going to be in SanFrancisco soon, so release the
area.
Speaker 1 (56:39):
Yeah, let me know when you're here.
Speaker 2 (56:40):
Yeah, we'll do.
Speaker 1 (56:41):
Yeah, a lot going on in San Francisco of course.
Speaker 2 (56:47):
There's so much happening there.
It's so cool.
Speaker 1 (56:50):
Yeah, it's definitely .
San Francisco is definitely.
I mean, I left when it was deadand now I'm kind of back and I
can.
It was really dead when we leftin 2020, like in the middle of
COVID, it was like a zombieghost town, basically.
I mean things like in themiddle of covid, it was like a
(57:10):
zombie, it was like a zombieghost town.
Basically, I mean things wereboring.
It felt like that I was there,it was nothing going on.
Now, now you can definitelyfeel the like, just like the,
the ai and like the builderenergy for sure.
So I I do mention that topeople that it's, it's palpable
and you know, I see it everyweek yeah, you know it's.
Speaker 2 (57:21):
It's so many events going on, just so much action
happening, and um, it's, it'sawesome.
I'm inspired every time I getout there.
I also don't really want tolive there, so I'm glad I live
in salt lake city where I canjust take an hour 20 minute
flight there.
Speaker 1 (57:33):
Um, but it's uh yeah, exactly yeah, same same story
for me, you know, living in laon weekends and san francisco
during the week, and yeah,that's kind of the balance that
we're that we're looking for.
It's a little bit right, butyeah.
Well, good, catching up.
Speaker 2 (57:48):
Joe, likewise See you around.
Speaker 1 (57:50):
Yeah, see you around.
And thanks everyone for tuninginto this episode of what's New
in Data.
We'll have links out to Joe'sDiscord and all the other places
you can follow him down down inthe show notes.
All right, joe, see you around.
All right, take care.
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