Something breaks and then you have to jump out of the bed in the middle
of the night to get those things fixed.
Hello and welcome back to Data Driven, the podcast where we explore the emerging
industry that is AI, data science, and of course, none of
it's all possible without data engineering. Now, unfortunately,
my favorite, most favorite data engineer in the world can't make it here
today. And I am actually enjoying the sunny but not too
ridiculously hot sunny day here in the suburbs of Baltimore,
Maryland. But I am excited here because I have Pranesh Patel,
who is the co-founder and CEO of a company that
does— makes data engineering a lot more palatable, it sounds like, Ultima
AI. Welcome to the show. Hey, um,
Frank, thanks for having me here. Super excited to chat with you
today. Yeah, so, so tell me about your company, Ultimate AI. Is, uh,
tell me about what it is and what led you to make it.
So Ultimate AI, we are a startup based out of the San Francisco Bay Area.
What we do is we use AI to
automate a bunch of data engineering tasks for the teams out there
who do data work, right? The tasks like building
ELT pipeline, extract and loading of the data, transforming
data, and not just building those pipelines but maintaining
those as well. It's always nightmares. Something breaks and then you have to
jump out of the bed in the middle of the night to get those things
fixed. So building and maintaining data pipelines or
managing your data infrastructure Like in the modern data
stack today, we have Snowflake, Databricks, BigQuery, those things, right?
We need to spend a bunch of effort to manage that infrastructure as well as
optimize that infrastructure, like making sure you have turned on the right knobs on the
infrastructure side as well as you have optimized those queries and pipelines,
etc. So we work on that as well. The whole idea is how we
can use the agents to automate a bunch of that work. so
we can scale our teams even further. I'm glad
you mentioned— I'm sorry, go ahead. No, you asked
me that, hey, how did it all start? So me and my co-founder,
we have worked in B2B enterprise space for a long time. You must have seen
all those data teams every company has. There's so much work involved. There is a
long backlog. We went through the same experiences, and we started
thinking there must be a better solution so that we can handle that
backlog. At the same time, a few years ago, this whole AI wave started
coming in. We jumped in headfirst and started building
agents first to sort of automate simple tasks like writing
documentation or writing some data quality tests. And through that,
the tool and the product evolved from there. And now
we automate bunch of things that are day-to-day things for
data engineering folks. That's a good point. You brought up a good point
because you mentioned Snowflake, you mentioned Databricks, you mentioned all of these
technology tools that the stack used to be a lot simpler in data,
in the data space, right? You, you know, you were either an Oracle shop
or a SQL Server shop and you had, you know, SQL
Server tooling, which those I'm way more familiar with, but also the
Oracle kind of stack too, right? So it went from being kind of a
The tools may have been limited, yes, but the tools also—
there only wasn't really that much of them, right? You picked one side
and stuck with it, right, for most organizations. And then we
had things like Hadoop and Pig and Hive and all of those things kind of
come out. And now this many decade and a half or so
or more, you can tell by my gray hair, that now we
have just an unlimited assortment, it seems, of these
tools, right? And each one of them has their own quirks, their own settings, as
you said, the knobs and dials. Is that something that your
solution offers a solution to? Yeah,
absolutely. Because as you mentioned, the use
cases exploded, and with that, the tool stack exploded as
well. You can't be exploiting 35 different tools to make sure
everything works perfectly and keep your tabs on everything.
So we help tremendously with that, like all those configurations in your
infrastructure as well as building out things. You can't be expert in so many tools.
And on the other side with AI, what has happened is
even these tools as a software are evolving so fast. In a
year, there are like 100 features come out. How can you keep tab on what's
the latest and greatest and what actually fits in my environment and to my use
cases? And, you know, AI for Rescue there, it can
do a bunch of those things for you. No, absolutely. And
I hadn't really thought about that angle of you have to be an expert.
It's like what happened in the software development world
where you had the notion of a full-stack developer where suddenly,
you know, it went from you would be a Visual Basic developer, right?
Or a developer of, you know, web developer. But
then it became, no, you had to be a full-stack developer. You had to know
the data side. You had to know the CSS. You had to know the HTML
and the JavaScript, right? Yeah. Data, I think, has followed a very similar trajectory
in that regard of no one person can do it all. At least
no one person can do it all without the assistance of some kind of
AI. And is that what your product enables? Like
somebody to like basically be a— in the DC area,
they love the term force multiplier. Is that kind of like what
your tool does? Exactly. Exactly. Because
as I think things exploded, use cases exploded, and even
the use of AI has exploded overall, your data
actually fuels all of it. And what's happening is the data projects
are growing exponentially, but our teams are not growing in that size. And
that's why there is a huge backlog that's happening. So the idea is then,
in your words, how we can have some sort of force multiplier that
can do a bunch of things for me automatically. And I focus on some of
the more complex things or something where AI needs more help
and guidance. So think of this as these bunch of agents
are my minions. They're getting the work done. I have like 30 of those and
I'm giving them directions, course correcting, telling them what to do. And
that's how basically the teams are scaling themselves today. But there are so many challenges
in doing that as well, in that whole process. And our
goal is how we can streamline and make that whole process smooth. So you have
teams of these minions which are getting a lot of work done for you.
Yeah, and I think you brought up a very real pain
point, right? They're not hiring tens of people in the data engineering
teams anymore, right? Data engineers are expected to do, to all
be 10x engineers, right? And so
I like the idea of you calling these AI agents minions, one, 'cause
I think the movies are really cute and I have small kids. But
how do you— what's the governance look like on that? Is that something that you—
how did you address that problem? 'Cause I'm sure that comes up quite a bit.
No, I think you touched on very important area, right? Because as a
human, we usually have general sense of understanding and
what needs to be done and what shouldn't be done. But for agents,
it's a piece of code, it's a machine, right? A lot of times it
doesn't have that, compass of common sense. So for example,
that's why you might have seen situations where agent ran a query and that cost
you thousands of dollars. Oh, and there's so many stories of agent
deleted my sensitive data or replicated it. So whether it's sensitive
data, access controls, cost guardrails,
all of those are very important factors around which
we need to develop a solution. Now we just talked about Lot
of tools in the data stack. Every tool has their access layer, RBAC
layer. Now, how are we going to do this, right? So what we have been
doing is, and that's the reason we launch the, our product as open
source project, we call it Ultimate Core. There is a governance
layer that's built in, which allows people to define these guardrails, rules,
and permissions. Lot of guardrails come inbuilt.
And the beauty of that is this is a layer that sits on top of
these bunch of different tools that you are using in your data stack. and gives
us the common ground around governance. So for example,
cost, it makes sure the agent doesn't spend more money than this
for your specific task. Or for agent
itself, you can create a separate view, separate tables so that they don't
inherit like service user permissions or user permissions directly. Because as
a user, I might have so many permissions, but I don't want my agent to
have the same permissions and use my same credentials, et cetera.
So that part also we have solved pretty well.
Yeah, I think, I think calling them minions works out pretty well too, because, you
know, the minions always— there's a whole sequence in the first movie about the dart
gun. Parents, if you know, you know. But
the minions misheard it and built something else. But
no, I think you're right. Making sure these things have guardrails on them,
I think, is— people are going to learn very quickly what happens if you don't
have guardrails, right? millions of dollars a query, or etc., etc.
Actually, recently in the news, and they haven't disclosed what was the core— what
was the core problem, but apparently AWS had—
was giving out bills that were like orders of magnitude higher.
And I can only— again, I have no inside information,
but I can only imagine that there was probably some kind of rogue AI doing
a math error or doing something crazy like that. What do you think the
future of these— this tool space is going to be? Do you think You know,
there'll be more MCP servers, right? What do you think,
um, what do you think that's going to look like, the ecosystem,
the data ecosystem? So how I
see it is based on what we want to do, the tools
get built, right? And as we sort of expand our
horizons to do more and more things, the tools get expanded also, right?
For example, I think MCP server is a great example. At one point
we realized, hey, we need to sort of feed in all this
information to MCP server, uh, to AI agents, right? And then
MCP servers were built as a solution to it. Now
we are hitting the limits of MCP servers themselves where,
you know, okay, governance— anybody can install any MCP server and I have
no control over who uses which MCP server in the organization. Right. That's
happening. Second is The MCB servers are putting out so
much output that my tokens are getting consumed like candy,
right? So then how do I control it where MCB tool outputs are
limited? And we have built some functionality around that, around context comparison
and tool output curtails, etc., especially for data engineering
tasks. Our third big thing is MCB server is like
API, is going to just pull the data from the other system and bring it
to you. But it is not going to do any intelligent filtering
or connecting those dots together. And then now people are looking at
semantic layers and those things, how I can do it also. So now we are
running into the limitations of NCP servers themselves, and then people are trying
to figure out, hey, what's the next thing we need to do to solve this
problem really well? And then people are talking about context graphs
or context store where all of your information is already there,
curated, filtered, smartly arranged, And then you use MCP
server to pull only right information instead of directly interfacing with the
tool, like for example Salesforce or HubSpot, and just dumping everything into your
AI agent as well. So how I see it is, I think
agents are going to become more and more autonomous, more and more ambient
as well, and the information we are going to feed them
is going to become much more curated as well. And there are so
many facets to this. There is a right information feeding angle around
context. There is a governance angle to it, and now. And now I think you
just touched on it. The one big angle that's coming into the play is cost
as well. There, there have been so many stories coming out where people spend
their entire year's worth of budget in like 3 months. I know some stories
which are not public where people's usage like 30x'd in
6 months and now they're like, oh my God, my AI bill is actually same
as my cloud bill now. I never planned for this.
And what is exactly the ROI that people are trying to measure also?
Yeah. So all these questions are coming up, and as the questions and use cases
come up, I believe we'll have more tooling and better solutions.
Is anything— is there anything in particular that your product addresses
to any of these problems? Yeah, so we talked about the governance
piece of it. On the cost side as well, what we started doing
is one of the features we have in the product is context compaction.
So I was talking about MCP tools. Dumping a lot of data, like
especially data-related MCP tools. So what we do is
we curtail the output correctly. We know all these MCP
servers, etc., so that you are not spending too much of a token cost. And
not just the cost, right? What they call is a context rot. If you
dump in too much information, LLM has too many directions to go in. So we
curtail and do that context compaction. Second part we
have introduced is Specifically for data tasks, we create
memories automatically. So not every time agent is starting from scratch,
and it's very curated for data-related tasks. So in that way,
next time when the agent does the same task, it can tap into those memories
that are shared across the organization and get the task done in
very less number of steps. That's an interesting
point because I noticed that, that was one of the When I started
poking around my OpenClaw instance, right, inside of there, there's the
soul, but there's also kind of this memory type of notion and managing
that memory so the context doesn't have to start from
zero every time. And does that really save on tokens? Like, what's
the rough order of magnitude in terms of what the token save is?
No, the memory will save you tremendously because, for example,
let's take an example of data pipelines. Right? So if your data
pipelines are failing, usually there is a pattern, same issues you're going
to see again and again. Hey, data hasn't landed, that's why this pipeline particularly
fails. Now if that thing gets stored in the memory, your agent
is going to check the first thing is has data already
landed? That will save you other 10 other things
that agent would normally try out before coming to that, right? Boom, your
multiple workflows are saved. Your token cost saved, your time is saved
also. We fixed it very quickly, right? So it helps us
tremendously in that way. And the beauty of that is even the memory
layer cannot be very generic, right? You need to understand
as tasks are happening, what kind of memories are important. It needs
to be curated. There needs to be, say for example, somebody's writing
agents and harness for finance, there needs to be a finance-specific memory. that
will remember finance-related things. Similarly, for data work, what we
have created is data agent-specific memories, which works amazingly
well. And since we are talking about memory, right, memory
is not just limited to one session. So for example, what I'm trying to say
is, now if you have data pipelines, probably you have a team of
engineers maintaining that data pipeline. So for example,
I fixed this data pipeline today, for that data not landing
issue, and it gets saved in my memory. Of course, next time
in the regular systems, I try to fix it. Next time it will read from
the memory stored in my, say, code editor or Cloud Code or something like that,
and I can draw to it. But what about somebody else on my team? They
are also going to work on that pipeline, and they might be on on-call. That's
when the pipeline failed. So the memory layer that we have
built, it's a layer that gets shared across the teams and
organization as well. So then it's the— I call this a
hive-like mind in which you are storing the memory, and anybody can
come in and use that hive-like mind and sort of use
that knowledge to do things better. And the big benefit of this is
even for newer people joining your team, right, they don't have to start from scratch.
All this tribal knowledge is stored in that hive mind, which
we call as a memory layer. I like that because then
your first few rounds of learning it, right, you're
literally onboarding this virtual employee, it sounds like, right? It sounds somewhere between a minion
and a virtual employee that can kind of capture that tribal knowledge,
capture that institutional kind of wisdom. Yeah. And so
it's not so much you're spending tokens, you're kind of investing tokens, right,
for the future and training this virtual employee. And
presumably you'll get that, you'll see dividends later on.
Exactly. And this system works with agentic
frameworks that are out there already. People use, say, Claude Code, GitHub
Copilot, Cursor. The system works with those
already. We don't build LLMs, we don't build—
give agentic frameworks, but we build this harness which makes
these tools extremely suitable or powerful when it
comes to data engineering work.
Interesting. Can— does it learn on its own or
can you edit those memories? Right. So like, what if— what,
let's just say we have a, somebody makes a mistake, right? You obviously
wanna mark that and kind of remove that from the memory. Is that, is that
like, obviously you probably edit, it probably adds its own. And,
and the reason why I mentioned this is because when I dove into my
my OpenCLAWS memory file. I thought it was funny what it read about me.
So if people are watching this, you'll see I'm kind of like winking my eyes.
Apparently allergies are really bad today, which I did not factor that in when
sitting outside. And one of the things it learned about me was I
always ask about the pollen levels for the day, which I think is kind of
funny. It said that, you know, I ask about the weather, I ask about stocks,
and I ask about, you know, AI innovations and pollen report,
right? Does this— but obviously I can go in, I can open up a terminal
and edit it myself. But how does your solution— is it built into
the UI or is it like just a config file? So
as you start using the solution, you install, it will start creating
memories automatically. You can, of course, in your prompt give a
specific instruction. As you give the instructions, those get saved also. But you
can say specifically create a memory also. And through our
MCP, it will get that memory created as well. Now the harder
part usually is what if there are bad memories and you want to change memories
or you want to erase those out? The good news is it comes with that
flash stick that I think I remember it from the movie Men in
Black, right? It comes with that. So you can go in, delete your
memories because as I was telling you, we store these memories in a SaaS. So
in that way, they're shared with your team members, they're shared with the rest
of the organization, and there is a granular control you can do who they get
shared with. But at the same time, if you want to update
it, you can go to the UI and get those updated or
deleted as well. Oh, interesting. So you
have that neuralyzer built in. I think that's what the thing is called in Men
in Black. But so you can go back
and you can remove like, hey, everything I did today was terrible, so don't remember
that. What about
security, right? Obviously, There's a lot of trade, you know,
there's a lot of sensitive information that are going to be floated around in the
data engineering space. How does your solution, how does Ultimate AI
kind of address that? So first and foremost,
we don't look at the data directly. Okay, so it's a
harness that comes in, and this harness people can install locally
as well. So if you're some sensitive industry, maybe healthcare
or something like that, You can use it completely
locally, and the LLM solution that you use in the
background, people can use their LLM solution. As I was talking about, Claude
Core subscription or Codex subscription, they can use
that. They can hook up their own models also. We support, say for example,
OpenRouter, all these different models people can use. Even if they want to use
on-premise LLM, we support that as well. On the other side,
as a company and as a platform, We are SOC 2 certified,
pen tested, a bunch of big Fortune 500 companies
use our product already. So even on that side, I'm
sure we can make people's security teams happy because we have done a bunch of
work around it already. Oh, that's interesting. That's good.
Because I mean, the security conversation
comes up in AI, but I don't think it comes up often enough or early
enough. And Obviously, I think that's going to change as more and
more systems get deployed. And obviously, the Fortune
500 companies obviously also take that into account as
well. What would be your advice to people who are data
engineers who are curious about how do I make— how do I get my own
minions, right? Like, how do I start thinking about— let's roll
that up. How do I start thinking about
my job as a data engineer in terms of
I'm managing a dozen potential virtual employees
as opposed to doing it myself, quote unquote, the old-fashioned way.
Yeah, yeah. No, I think that's how we should start
thinking about it if anybody hasn't started going on that
path. There are a bunch of tools out there, and I
believe it's easy to get lost also because there are just so many tools and
things that are happening. on the AI side of the things. And what I have
seen is people try to retrofit the tools for software engineers to data engineering,
and usually that leaves bad taste in their mouth. And sometimes
I heard, hey, AI doesn't work. It's because you're using the wrong tools for data
engineering most of the times. So you need to use the
harnesses or tools that are specifically done for data engineering.
On our side, we open source Ultimate Code Project, so definitely try it out.
It's MIT licensed, completely open source. But in addition to that, what
we have done is on our website, we have put this
course called AI Data Engineer. So there is a section on our
ultimate.ai website, AI Data Engineer, where we have put
curated articles and we keep spending time to make sure all the updated
information is there. So somebody might be like at day 1, somebody might
already be at day 50. You go through that material and it will coach
you. What are the latest tools and what are the best things you can possibly
use to get these things done? Oh, that's really cool. So you
have built-in training, and you did mention that your product is open source. We'll make
sure that the link to the GitHub repo and as well as the, um,
this onboarding, um, this kind of this ment— what would you call it,
a mentoring tool, an onboarding tool? Like, what would you call
that? I just call it training boot camp. So
join it. Even if you're brand new, there is a bunch of useful
information. Even if you have been using it for a while, you know, world
of AI and technology changes so fast. Definitely it will help you
keep tabs on what are the things changing as well.
Yeah, no, I— it's a, it's a very fast-moving space
and it, it's not gotten slower. It's— I think the pace of innovation is
accelerating for good or for bad. What
What would be your advice to someone? I started asking this in the question, like,
there's a lot of computer science students that are very still
in school and they're very worried about AI taking their jobs. I think you and
I have gotten to the point where AI doesn't really take away your job. I
think it changes the nature of your job.
Yeah. But what would be first? My advice would be stick it
through, kids. But like aside from that, what would be
your advice to particularly, so I think a lot of people
are getting started in data engineering and if they hear that there's AI tools
assisting with that or doing that, they may be a little bit afraid or concerned
about the future here. I think the future looks bright, but that's just my opinion
as a, I wouldn't say I'm a perpetual
optimist, but I've kind of been through that cycle of,
oh, you know, I, AI is taking away my job, but I really kind of
see the nature of it. It just changes the nature of my job.
Yeah, yeah. If I can summarize this, I have heard those
things, hey, is AI going to take my job? And I always tell
if anybody says this to me, in my opinion,
it's not AI that's going to take your job, but it's going to be somebody
who can use AI is going to take your job. So if you don't upscale
yourself, because I completely resonate with your point. Our
jobs and roles are changing rapidly in this new world, so
upskilling yourself for AI is very, very important. And being
on that cutting edge of the AI and data engineering and all that data
work, that's the most important part. How I'm feeling
is going to unfold is like how 100, almost 100 years
ago, Industrial Revolution unfolded, right? There were, for example,
there were people who are making clothes by hand, right?
Then the machines came and we got operators who would operate those machines
to produce clothes at a humongous scale and see how
many clothes we have today, right? Same thing is going to happen
with AI in general. It's going to give us those machines
which will increase the overall throughput of all the people
out there for different professions. It's going to bring in a lot of prosperity.
But at that time, people who were making clothes by hand, they went out of
job. Maybe some of them learned how to operate the machines, but that's
the important part. Learn to operate the machines. Like, don't just say that, hey,
AI is going to take my jobs. Learn to use AI, embrace it. Your job
is not going anywhere there. That's a really good way to put it.
One of the examples I like is, um, there's self-driving cars,
right? But there's also a lot of cars have adaptive cruise
control. So for those not familiar with it, you know, it's basically cruise
control, which will keep your speed, but it also has proximity sensors. So
it'll apply the brake, right? And kind of keep you within a certain distance of
the car ahead of you, et cetera, et cetera, et cetera. And also can keep
you inside your lane. When I got that in, in
my car for the first time, I felt like— It's a good analogy for AI
because I'm still controlling the car. Right. I'm not fancy enough
to have a full-on, you know, self-driving car, but, um, but I
did notice that I became more like a captain of a ship, so to speak,
where I would— I felt like I was guiding the car which direction to
go. Now, in that case, I'm not a big fan of driving around
all the time, but so it would be nice to have a full driving car.
But I think that's kind of a good analogy for how,
how jobs are going to look like, right? And if you're a software engineer, you're
not going to be writing every line of code by hand anymore. I think,
to your example, I think, you know, when humans were sewing, doing the
sewing, right, individual stitch by stitch, clothes were made. And accordingly,
clothes were expensive. And I think we're going to see that kind of happen with
AI, right? Whether it's code, whether it's data engineering tasks.
Because certainly I think anyone out there who is in a professional
position where they're doing data engineering, There's plenty of
things that are just kind of on the back burner that they would do to
make their jobs more efficient, right? And I just think that,
I think by having AI enabling,
you have a lot more cognitive free room. And all those things
are on the back burner, on the whiteboard somewhere, or kind of on a Post-it
note somewhere about, hey, you know, I bet I can improve this process by
X number of percent, but they're too busy keeping the machines
working, right, keeping everything working. So with AI, I think you can have a
bit of cognitive surplus where you can tackle those. Sorry, I cut you off. You
were about to say something. No, nothing to add there. I think
that's so spot on. This is where the world is going.
That's cool. That's cool. So what's next for your company? Like, what,
what's, like, what other big challenges do you think you
all are going to take on? So one thing is, of course,
right now we have been automating few of the data engineering use cases,
but we are planning to expand to more and more use cases and at
the same time improve the support of the data stacks
we support as well. For example, you're talking about SQL Server,
maybe older Hadoop, Hive stacks. There's so much information
out there, and being early-stage company, we support certain
stacks, certain stacks we don't support. So the plan is to expand
into more ecosystems and at the same time expand for more
use cases as well. Yeah, that's cool. Yeah, and it's funny
because I know there's a lot of Hadoop legacy solutions out
there. Somebody told me, and I don't want to pick on Hadoop, right? But
Hadoop was one of the first, you know, big data solutions out there. And
I would imagine there's a lot of— somebody told me that it was moved to
the— what's Apache call it? The attic? the attic or the basement where it's basically
considered a legacy product now. And, you know, obviously
there's a lot of engineering— data engineering projects historically
have not turned over that quickly, right? Like, you know, there were— there are probably
batch jobs I wrote in the early 2000s that are still running, right?
And that's not that unusual. So I think that
because data modernization projects even if they're
needed, they may not happen because people are too busy keeping
the machines running, right? So if you can kind of— I think people are missing
the point about AI taking their jobs. I know we're back on this again. You
can always tell when the coffee hits me, right? Like, my mind goes in like
3 different places at once. But the release
of cognitive service, cognitive surplus,
I think is going to do a lot of good things for The IT
industry, and ultimately, I think, for the broader economy as a whole, right?
I think you, you mentioned prosperity before, and I know it's a, a lot of
people are very anxious about what AI can do. And you live, you know,
you're in the Bay Area, you're probably at the epicenter of a lot of this
back and forth, right? But I think that
by releasing some of this, the human minds
from the drudgery of some of the, the machinery that we have today,
I think has enormous potential for improving IT, right?
You know, take any kind of like regular person out there who, who has to
interact with their IT department. Would they describe it as a wonderful experience, right?
Would they describe it as a, you know, or is it more like going to
the DMV, right? Probably more like the DMV. Although
I will say credit where credit is due. In Maryland, I had to spend— I
recently got a new car and it wasn't as bad as I feared
it to be. You know, maybe they're using AI, I don't
know. Yeah, yeah. And as you mentioned, right, a lot
of this work we sort of parked it for later. Like you were talking
about batch jobs or Hadoop jobs, etc. There's still a bunch of those things running
because migrations had been nightmares, right?
Like people spend millions for years. But now I think, now
if I look at it with the angle of AI, AI can automate
90-95% of those migrations as well. Now, who knows, like, even
that whole modernization and digitization
in the data world, that will start accelerating much faster because of
this. And a lot faster, different companies can be on the
most modern technology. And they can, they can take advantage of
the newer features, the optimization technology. Yeah, I mean,
because no, no company in their right mind is going to suddenly, you know, say
we're going to hire, you know, we have this system, we can either
A, keep throwing a little bit of money at
it per year, right? And keep it going indefinitely. Or
B, we're going to hire 100 new data engineers and all these project
managers and things like that. We're going to spend billions on upgrading it. They're not
going to do that, right? But they do need to upgrade, right? So then if
it becomes a, well, you know, if you just spend just a little bit
more than what you do have to do to keep the lights on, and you
kind of have AI take a lot of the brunt work of that, right? Instead
of hiring 400 people, you can probably hire, you know, say
40, right, to do the migration. It starts to become more
palatable, right? It starts to become more justifiable in a, in
a balance sheet. And, and, you know, I think a lot of the breaches
we've seen have been from improperly
configured systems, right? Or legacy software
that hasn't been patched or legacy software that has no business still running
in this day and age. And I think you're right. I think AI is really—
I think if you put the right type of glasses on, AI
is a way to solve the problems that we've just
been pushing back for years and years. And technical
debt, that's the word I'm looking for. I think AI has the potential to
be a great way to pay down technical debt. Yeah.
But So while we're talking about
that, like, what is the thing that your customers who are successful on your
platform, what is the thing that they're most happy with at the end
of the day? What are they like, they call you up and they say, wow,
Pranesh, I'm so glad we did this because
what is it, increased productivity? Is it something else?
A few things, right? So for example, one example I gave was
we can manage people's infrastructure using AI. Right? And
we can optimize it. Now AI does it at scale
at which humans can't even do it. For example, it can change the configurations
300 times in a day. We can't do it, right? Like, how can we
change the configuration of single machine 300 times a day? You need 150
people doing just that. Yeah. And
especially in the space of data, your workloads are always changing, right?
As your data changes, your workload changes. So one of the things we have done
is that infrastructure optimization, where we have built agents which can
automatically analyze your infrastructure, change the configurations. We have built agents
which can analyze the data pipelines and automatically optimize them. And
of course, that saves tons of time for engineers, but at the same
time, your infrastructure runs at 90 to 100%
utilization, and those pipelines are always top-notch when it comes to
optimization because people run millions of SQL queries and hundreds of thousands of
pipelines. Now AI does that. The result is not just the
engineering hours saving, but real dollar savings on
the infrastructure cost also. So some of our biggest customers, we
have saved them millions of dollars in their Snowflake and
Databricks environment by optimizing this.
And yeah, that's super happy about it. They're like, hey, tool pays for
itself multiple times over. We are getting all these engineering productivity also. We're saving
tons of engineering hours. But hey, right there and then you're saving me
infrastructure costs also by automating that process by a huge margin.
No, it's a great way to— that's a great way to look at it. And
I really think there's so much opportunity. I know there's a lot of people, there
are a lot of naysayers now about what AI looks like and,
and, you know, will we, will we realize the gains that were been promised?
I think we will. I think it's going to be in places where we may
not think of, right? No one You know, and data engineers will be the first
people to tell you, like, they're not usually first of mind for a lot of
people in the C-suite, right? Data is like air. You don't think
about it. Good data engineering is like air. You don't think about it
until you don't have any, right? Like, it, you know, if you, you know, it's
kind of lost in, in, in the haze, so to speak. And
I think that, I think if people,
organizations that get their data estates kind of sorted out,
are at a competitive advantage to anyone that doesn't, right? And
it's very often— that's why I always make a big deal in the intro is
that people don't think about data engineering. I've been in hundreds of meetings where
the data engineering aspect is often neglected, right? One
story in particular was this guy had this great idea for this,
that for his organization, and he was going to hire
He was going to do all this sort of stuff, integrating all these different data
sources, you know, from public, private, enterprise, you name it,
right? You name it, he mentioned it, right? It was that type of guy. And
he goes, I'm going to need 20 data scientists. And I'm looking at it, I'm
like, you're going to need 18
data engineers and 2 data scientists, right? Like,
you know, like, you know, because like the data engineers, Quote unquote,
the old school kind of proper data engineers, I
mean, data scientists, they don't want to deal with SQL, right? They just want to
get kind of their data. But like, in terms of the sheer
amount of systems this guy wanted to integrate and do all the
translation, I mean, he's going to need— and he's probably going to need,
you know, 50 data engineers realistically, right? Or 20 of them doing
the work of 50. But yeah, it just goes to show you
that no one appreciates data engineering, right?
You know, it's almost invisible. It's certainly invisible when it works
well, and it's certainly visible when it
doesn't work. But it's also when it's working kind of mediocre,
it's also kind of invisible too, right? Yeah, yeah.
A lot of times it gets underestimated how much time
the work is required to put, I think, right amount of data
on the table, right? And that's why a lot of times people don't
understand why there is a huge backlog. Hey, I'm asking for simple data, why you
are telling me it's too much? So there are 50 people ahead of you and
every project is going to take 4 days, right? Takes time to find that data
curated and put it somewhere where it can be consumed by, say,
analytics use cases or data science use cases.
You know, one of the things that comes up a lot is the joke is
that DBAs used to stand for don't bother
asking, right? But it— but I think that
we had an earlier guest a long time ago now basically talk about that
one of the important shifts, I think AI really puts the acceleration
on the shift of mentality, is that data
DBAs, Data engineering managers have to think of
themselves less as gatekeepers and more like shopkeepers,
right? They're not the bouncers at the door anymore, right? They're the people
behind the counter that say, how can I help you? Bonus points if they can
set up a system that is— again, I'll go back to the DMV, right? If
you go to the Maryland DMV, I'm sure it's even more so where you
live, given it's the Valley, right? There's plenty of self-service kiosks
where if you just need a new XYZ, You can just scan
your driver's license, enter some information, of course,
enter some payment information as well, and you, you know, they'll,
they'll print up or it's almost fully self-service. And I
think that the data engineering organization in the enterprise in the future
is going to look a lot more like that than in the way it's looked
like historically. Yeah, a lot of it is going to
be self-service also, right? People just come in if it's simple tasks.
Hey, as an organization, data organization, will
serve it for you. You don't need to bother us, or we don't need to
bother you. Like, you can just get what you need. If it's complex stuff,
then we will put our minions to work. Otherwise, our minions will work
completely independently with you. Yeah. So do you
have— do you track metrics in your product that says,
hey, you know, this saves X number of hours, or is that something
that Will come later? No,
absolutely. We track it today. So it tracks what kind of tasks
have been done already by AI for you, and we
try to make sort of a guesstimate also. If this task was
done by you manually without using AI, this is how much
timewise it would have costed you. So that's one part of it. Second
is we do a bunch of benchmarking. against some other
tools out there. For example, if you use— if you try to do this
task using just vanilla Claude Code or GitHub Copilot, or
if you use some other AI solutions that Snowflake and Databricks has
put out also, like Codex Code, or dbt has put out
Visit, how much better we do against that also. So
we do the benchmarking, we publish those benchmarks. That's great. There are open-source
benchmarks around this. One is called Agentic Data Engineering,
ADE, and the other one is Data Agent Benchmark, DAB.
So we do that testing, we put out those benchmarks, and those keep continuously changing
as the space itself is so fast evolving. Wow.
It's not a real technology until there's benchmarks, right?
Yes. In the data world, in infrastructure, we love our
benchmarks, right? So why not do it? It's the easiest way to compare different
tools and different options. Well, awesome. Uh,
and the website is Ultima— Ultima—
I'm sorry, an Ultima just drove by. I'm sorry. Let me
help you there. It's called Ultimate, so
ultimate.ai, and our open source
project is called Ultimate Code, so ultimate
and code. Just search on it and you'll find the repository.
You should find our website also right there. Excellent. Thank you very
much. And I appreciate— I know we had some issues rescheduling, so
I appreciate your patience with that. And I'm really looking forward to
checking this out. You have my curiosity because there's all these like little ideas that
I have in terms of, you know, I have datasets and things like that, personal
and otherwise, that yes, I would love to go through and
organize those. However, I don't have time. Right?
Just as a personal project. I'm telling you, when you start running your own home
lab, you really start to feel the pain of what IT organizations
feel, right? I have an LLM server, and when it's down, my
kids tell me, and it just becomes like this, like, nightmare of
situations. So, like, the best way to learn is to do. And
I encourage everyone out there to build out a home lab with whatever you got
and just start messing around with the AI tools to make things easier.
And, uh, with that, I'll let the outro music play.