# Databricks Unifies AI Agents, Storage, and Security

**Podcast:** Latent Space: The AI Engineer Podcast
**Published:** 2026-06-24

## Transcript

Mattheon Reynolds from Databricks, welcome to Alien Space.
MATHEI RENOLDS, Databricks.
Thanks for having us.
MATTHEI RENOLDS, Databricks.
Yeah, thanks so much.
Thanks for taking time out.
You have your Databricks Data AI Summit going on.
You were just telling me how the first summit that you guys ran was just 50 people in Berkeley.
MATTHEI RENOLDS, Databricks.
Yeah, it was a little meetup at Berkeley, I think.
We put together.
We do these tutorials and just teach people Spark.
MATTHEI RENOLDS, Databricks.
Yeah.
Obviously, now it's like.
I think the headline number is like 100,000 people around the world, 30,000 in person.
It's a crazy community.
Well, I mean, I just saw the keynote.
Ali is just, did you know, was it obvious back when that Ali was being such a great CEO?
What do you think?
I mean, I think among our group of founders, it was clear that I think he'd be the best at this.
And yeah, it turned out great.
I mean, he's ramped up on so many topics going to a company.
He would just go in and study it and talk to all the experts.
Even if he can't hire the person, learn enough about finance and sales and whatever it was, and go from there.
Yeah.
I mean, he's obviously very high IQ and very high EQ, but it wasn't like Ali today is quite different from Ali from like 10 years ago.
I think there's a lot of work that he put in.
to get to this point.
Yeah.
I mean, to me, the most appealing thing about him is that he's funny.
And like, you know, it's hard to make jokes about, you know, data warehouses and security and what have you.
Oh, yeah.
That's for sure.
Yeah.
So you guys launched a whole bunch of things.
I'll just kind of name check briefly the stuff because we're not going to cover everything.
Omnigens, your baby.
LTAP, your baby.
Your dream engine.
We're also going to cover Genie, cover Customer League, you acquired Panther, OpenSharing, and there's Unity AI Gateway.
A lot of these, I think, are things that you would expect a Databricks to do.
It's like part of the roadmap.
Everyone in your category has similar things.
But I think probably the two of you are leading the two most unique and differentiated initiatives in the landscape.
Maybe we'll start with Omnigint and we'll go into it.
I do think that a lot of people are exploring this sort of meta harness concept what led you to it Yeah, there were actually a couple of like converging lines, which I think is a good sign that you need something new.
So on the one hand, there's all the coding agent infra internally.
We have a really great dev infra team.
They built something called Isaac that's basically like a wrapper on cloud code and codex and lets you use them either on the web and like sandboxes or just on your dev machine or on your laptop or whatever.
And then, you know, they were adding all.
kinds of stuff there.
And we saw all the...
sort of more advanced engineers, like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top, or even on top of that.
And then the other one was, like, us building agents.
We shipped this, like, data science agent called Genie on the research team, which I co-lead, basically.
We also built a lot of internal ones for various things, and then we have all the customer ones.
And all of them running into this thing of, like, oh, I need to switch.
model and harness and so on, you know, every few months.
Plus, the agent is like completely useless if you can't share sessions with someone and have history and have search and all this like layer on top of it for collaboration.
I thought a bit about it from both contexts.
And at first, people thought it was weird.
They're like, why are you doing coding agents?
custom agents in the same thing.
But I said, it's basically the same problems.
And you just want to build the stuff that lets you deliver the agent, maybe control it if you care about security, and make it portable across things.
And then we prototype some things as experiments.
We say, yeah, actually, we can make it work.
And then we, you know, we sort of built that for real.
I'm wondering if this kind of...
let's call it architecture, maps to anything in your careers in the past.
You know, like I always think about how a lot of things actually just tie back to operating systems.
A lot of operating systems tie back to databases or the other way around.
So the thing, I do think it ties a lot to like network.
protocols, you know, internet protocol.
Communication between entities.
Yeah, we did stuff with like data sharing also, which is probably, you know, most viewers probably won't know unless they're...
Yeah, open protocol is the term.
Yeah.
Open sharing.
Open sharing.
Yeah, so it's like you have a company, you maintain some kind of table, like let's say like Walmart or something, they have like, you know, inventory and what's been sold in each store.
And then you also have suppliers and they would love to produce more things and ship them like exactly the moment you need them.
So they...
would love real-time access to your table.
So instead of sending emails around or Excel sheets or phone calls, why can't you share a view of that table in real-time with them?
Then they query, they join it with their data, and they decide what to send.
So it's one of these things where you like.
You might ask, like, today, since we can Vibe code anything so fast, why do we even need to design, like, protocols or APIs or software?
Can't you just Vibe code things on demand?
But actually, for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do want to design it and build it.
So it reminds me of that, like agents talking to each other and users talking to agents and tools.
Reynold, any other comments or alternative viewpoints?
I think, brother, we had a debate on exactly which set of benefits would matter a lot.
And I think around the time we decided to do this thing, I was telling Matei, hey, it just happened to be there's a particular week that I was coding nonstop.
From the moment I woke up to the moment I went to bed, I was like, looking at my clock sessions, my codex sessions.
And one of the things that was particularly annoying was having to keep my laptop open.
I was actually driving to a doctor's appointment.
And I remember because I wanted to make sure the whole thing continues working.
By the way, it's so comforting to hear you say that because I'm like, I don't know if I'm a clown and I'm doing this.
Honestly, I was driving and I was tethering my laptop to my phone.
Keeping it on the side, whenever I hit a red light, I started looking at what's going on on my laptop.
And I just felt that was ridiculous.
It felt like we went back to the dark ages of programming.
I mean, the productivity you gain from all this coding age is amazing.
Have you heard of cloud?
It was crazy to me.
MARK MANDELAVYIERIENIQI WONG- The thing you were working on was the sandboxes or was this before that?
FRANCESC CAMPOYOVICIERIENIQI WONG- It was a sandbox.
MARK MANDELAVYIERIENIQI WONG- So I was approaching from a very different angle.
I wanted to say, hey, we're going to have cloud sandboxes that actually doesn't shut down.
You can get one very quickly, but not just for running agentic sessions.
It's actually also for running development.
So I was actually personally building that that week.
And through building that, I ran into all these issues.
And then I wrote actually a document for my case.
Like, here's my wish list of what the actual environment should do.
And I think he actually ended up almost implementing every single one of them.
Yeah, I remember Reynolds saying, because my first prototype of this had just chats with your agent.
And he said, I have to be able to open a shell, like my own shell, and list files and tail them and stuff.
So actually, it has that.
Yeah, actually, it has that.
Tallying my log.
Yeah.
And also, another thing I think I asked was, I still use cursor for the sole purpose of rendering markdown files.
Uh-huh.
Yes.
So I said, just give me a way to see my markdown files and render.
them properly i don't need a separate two anymore yeah i think you also build that yeah we did that yeah Yeah, we had a lot of engineers building their own Vibe coding setup.
But then the other thing they all said is, okay, I built something that's amazing for me, but no one else on the team can use it because I don't have a server to collaborate.
And this is why we tried to set up Omnigens so you can have a server and have the security set up in there, like log in with Google or whatever and actually securely share stuff.
And that's where we've seen a lot of other agents hit things.
People think they prototyped an awesome agent, but it's not allowed to connect to some really important data or whatever because of the security team.
Yeah.
At this point, for those watching along on YouTube, we're going to bring up an image of the structure here, and we can talk a little bit of the architecture.
I just want to have people understand, because it's like...
When we're talking about software, it can be very abstract.
And here is actually what we're talking about.
You've worked out in open source this entire platform, basically.
And there's a runner component, a server component, with a sort of uniform API that you've figured out.
Any other sort of element?
And obviously, you can plug in all these persistence layers and compute layers.
This is a whole cloud.
It's an agent cloud.
Yeah, I mean, it's got these components to work with it.
You know, a lot of the action happens like on the machine where you deploy your agent too.
So whatever you've got on there, you can go on.
But yeah, I think it's sort of the minimal thing.
You want to have hosted like collaborative agents and to have that server.
And one of the reasons we open sourced it is anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too.
Like someone would make a nice...
you know, agent app, and then other teams would ask, oh, can I just use yours for my agent?
Yeah, I think we had like five or six different agentic frameworks built by every different team.
They all do more or less the same thing.
Yeah, you need to, basically people want to take something that works in 4Kid, and you might as well have something open source.
Yeah, which also was another question, which is interesting for a database, like what do you choose to open source, what do you choose to make it?
proprietary.
I mean, this goes back to Spark, right?
Yeah.
So, I mean, one of the reasons to open source something is if you think it's a layer that will actually, there'll be some network effect, it'll benefit from many people collaborating on it.
So, for example, with Spark, I don't know if you know, when Spark came out, we also focused a lot on letting you have libraries on top.
So, like, there used to be different distributed computing engines for, like, machine learning and graph computation.
We said they should all be libraries that you can compose.
And we made it super easy to add connectors to data sources too.
And then we benefit because, you know, we don't have the time to write like connectors to like, you know, a thousand like different databases and file formats, but we can just use the ones people make.
And of course they benefit from joining, you know, kind of this thing.
So that's like one of these.
Another way to think about it is like imagine.
you know, our thing wasn't open.
We had some kind of agent hosting thing, but it's not open.
And then there is an open one.
Which one is going to win in the long run?
So like here, because there is this benefit from like people hiding integrations, it'll be that.
And then there are other things that like you just can't even deliver as open source that are things the company does.
Like, for example, how do you make sure you're like streaming, you know, jobs or your lake-based database doesn't like...
you know lose all your data at night well that requires an operational team that's going to sit there there's no way it has to be a service so like we want to make sure as a company we're really good at those in front services and then we're as open as as we can in terms of like what you build on top i mean speaking from the benefits i think we're already seeing poor request all kinds of ecosystem integration even though it was only released on saturday yeah saturday yeah so someone let's see what's going on yeah yeah you can look at the merge lines um I actually asked Omnigen this morning about the...
400 merge already?
Yeah.
I think quite, I would guess around half are not from our team.
But for example, someone added support for running it on Kubernetes.
People added many cloud sandboxes.
So this can launch a cloud sandbox and run your agent in there, which is great for sharing too, because it's not like on your laptop and someone's like running scary code on there.
So yeah, many startups have put those in.
And we expect to see more of them.
We also have more agent harnesses already.
Courser, CLI, and anti-gravity also.
Yeah, that's all beautiful.
I feel like the last time this happened, there was the rise of the modern data stack.
I don't know if it's that useful.
I'm actually kind of curious.
Your post-mortem, I think most people will agree that it finally did.
But maybe this arises to a new modern AI stack that does the same thing.
I don't know.
I mean, I think the modern data stack was a pretty useful thing, probably even up until this day.
I think what, maybe for the audience who don't actually understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data.
And all of this runs on some sort of data warehouse.
or later on as we're doing data warehouse also lake house i think that concepts are all very powerful and very useful it's able to enable a lot of workloads what people eventually run into it's kind of a question of unification and consolidation is hey do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get like a very simple visualization done right so i think Like over time, everybody started realizing that customers are pushing us.
We started to realize that.
So we started building more and more capabilities and trying to consolidate.
And at the end of the day now, customers don't have to worry about having me hook up five different systems in order to produce a chart.
But I think honestly, something like this is probably happening in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.
Just to be clear, I would say the core of this is this common API on top of all the harnesses.
So the API is basically like you've got an agent session and you can send in a message or like a file, basically.
That's what you can send in.
And then you get out, you know, these streams as it's streaming text or as it's doing tool calls.
And the other thing you can send in is you can like tell it to cancel or turn.
So that's the API.
Now, the thing we did is we...
could get you that on top of like cloud code running in a terminal codex you know pi OpenAI SDK, all that stuff, we map them all to that same interface.
So that is something that you'd have to maintain yourself if you built your own agent orchestrator.
And then whenever Cloud changes its API, you've got to tweak your thing or it's going to lose some messages.
So that's the thing that's valuable to maintain.
Then on top of that, we built a few apps.
I think we built a pretty cool UI and stuff.
And we built the security and control piece, which I'm excited about.
But it's that common interface.
That doesn't try to be a stack.
And in fact, you could plug in your own UI on top of this server.
That's one of the use cases we care a lot about because we want to use this in our own products.
Yeah, it should be everywhere.
Yeah.
I think one of those things that is really interesting to me is, first of all, I'll endeavor to do everything and not call it the modern AI stack because I think we need a different name.
But yes, so one of the first people that told me about...
compute sandboxing was Nikita from Neon.
Because a lot of people think about Neon as like, well, it's serverless Postgres with like the separation of compute and storage and, you know, instant branching and all those things.
But actually, every database company is also a compute company.
Yeah.
And so he was actually showing to me his sandboxing solution.
I don't think he ever launched it.
So our sandbox solution, the reason we could have built it so quickly was because we realized if you just take the actual lake-based architecture and remove the database from it, if I know it's coming from you, you have the sandbox.
Now there are some differences.
For example, the one to support this particular workload is important to have local persistence because you want your state to persist.
You don't have to install your library every time.
Whereas the Neon architecture, because of the separation of storage from compute, you don't need persistent local disks.
So there's some differences.
But at the end of the day, yeah, it's...
Yeah, so this is when you run a coding sandbox.
If I use it, we have the dev info internally at Databricks.
There's many, many tens of gigabytes of data just for all the source code and artifacts and stuff that I built, and I want that to come back next time.
But, yeah.
Before the show, we were talking about some statistics that might be surprising at the adoption.
It could be internal, it could be external, whatever comes to mind.
Just to impress people to scale this is happening.
So we, on the analytics side, I think we launched maybe 50 or 60 million virtual machines a day across all three clouds.
So we're one of the biggest compute orchestrators out there.
For sure, for CPU compute.
And all of this process, I think, exabytes of data.
I joked about...
depending on which time zone you are, typically before you have breakfast, with the process exabytes of data already on that day.
And on Neon, it's actually pretty interesting, too.
It's launching, I think, 13 million databases a day now.
Yeah, to me, that was like a big...
And that's just like...
What do you mean?
And a lot of those were thanks to agents and branching experimentation because we made it so easy and so quickly.
and thanks a lot to Nikita's team, to launch databases.
So it's changing the way people use databases.
Yeah.
Okay, we're going to go into more database talk in a bit, but I want to make sure we close up anything on Omnigens.
You mentioned you were excited about the security and control side.
A lot of companies are figuring that out right now, as well as the spend side.
What have you found there?
Yeah, so I spent quite a bit of time talking to internal users, developers, security team, you know, managers, and also lots of customers.
And there's a few things.
Like, first of all, one thing, you know, that immediately became obvious is for security, you know, there's this tension between, like, usability and security.
And the way people do, like, a lot of coding agents today have very basic things, like, you can tell me which tool path.
I'll allow or disallow or whatever.
It's like yes or no.
But that puts you in a very tough spot.
So just as an example, like, should my agent be able to read, you know, some confidential documents or let's say, should it be able to install new packages from NPM, which, you know, maybe it's compromised, yes or no.
Like maybe I want to allow it.
Should my agent be able to publish stuff to the company website?
Well, if I'm using it to code on the website, yes.
But should it be able to do both so it can grab a confidential document and be prompt injected and leak it?
Probably not.
So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session.
It's not like, is it allowed to push to the...
marketing site or not, but like, hey, if it did a risky thing, like it installed, you know, a one day old package from NPM or it read like a thousand confidential docs, then no, then don't do it.
Otherwise, maybe it's okay.
That's one example of like moving that trade off.
So it's both more secure and more useful by having a more powerful engine, essentially.
This requires tracking sessions.
The other piece that was interesting there is like there are these very low-level events it's doing, and you want some libraries on top that parse them.
Like, for example, we have a MCP server on Google Drive internally.
It's got 60 API calls.
Like, how do I know which of those will share a document with stuff on the internet and which ones won't?
It's annoying.
We designed in Omnigen the policy layer so that it functions and you can have libraries.
Like someone can make something that maps the low-level events to high-level ones, and then you write a policy about the high-level things that came out.
This is related to the panther.
Yeah, Panther will help with that.
Panther is kind of a similar idea on the event processing side, and it's Python-based versus a weird custom language.
This is sort of more as in real time as things are happening.
Yeah.
So, yeah, but these are the cool things.
I think the contextual or stateful part and then the way it can be libraries, and that was another reason to make it open source because others will write libraries, and we and our customers can use them.
And the final thing, because it's stateful, one of the states we track is how much you spent in that session.
So I've had, like, I ask an agent to debug something and it spent $500 because it decided to read a lot of log files and burn a lot of tokens.
But I can literally say, okay, launch a subagent to do this and cap it to spending $5.
Like, ask me for permission if it needs more.
And because we're counting that within that session, it will pop up and tell me, okay, you spent $5.
$5 do you want to go on?
So I'm more than context here.
Matei spent the last five years, a lot of his time was architecting UND catalog.
at the Inbricks, which is the governance layer for data.
That's right, yeah.
And it's sort of combining expertise at that layer together with all the AI governance.
Yeah, but I also spend a lot of time being annoyed by coding agents and getting prompts.
And also, as the CTO, I don't want to end up on the front page as like, I installed some weird NPM package and leaked all the code.
So I'm especially paranoid.
But also, I have very little time, so I don't want to sit there proving, like, do you want to run a...
20 line, you know, bash script, yes or no.
So that's why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?
Yeah.
Is safety and let's call it security a bigger concern than token maxing or token budgets?
You know, which one is like, oh, yeah, they're both there.
I mean, I don't know.
I guess it depends on the type of company you are.
So I think some companies, like the budget is limited and, you know, they really care about that.
I mean, you can be Uber and still be concerned, you know?
Yeah, oh yeah, totally.
I mean, for our security.
Yeah, for us, security is absolutely critical as a cloud provider.
It's the most important thing.
And token maxing, we're not so worried about it yet, but I've seen that.
Like, for example, I talked to some consulting companies.
They have like 100,000.
employees who are all coding for customers, if those each spend like an extra thousand dollars a month, that's not fun.
You know, we have like only a few thousand engineers.
What's the policy in Databricks?
Is it just unlimited?
It's unlimited, but we do, you know, we use our own product to like analyze the traces and stuff.
And we have a team that's...
looking to optimize and to see if anyone's doing something weird.
And we actually had some really cool insights just from analyzing current traces, like which models are better at, say, Rust versus TypeScript or whatever.
So yeah, at least in our code base.
Yeah, amazing.
Obviously, I have to ask the token mixing question, obviously.
I think that's a key thing.
But yes, security and control.
above that and figuring out a sane layer there you can have some autonomy, but not too much.
Yeah, and we want to make it super easy.
As an engineer, you should set the thing.
So in Omnigence, you can ask your agent, set a policy on yourself to do this.
If there's something I should be showing, I don't see it on the GitHub.
Oh, yeah.
In the docs.
You can look at it later.
Just look in the docs on contextual policies if you want to see.
I just like to point people...
Look at the built-in policies.
Yeah, if you want to follow up on this, this is exactly where to look, right?
Yeah, yeah.
Yeah, and the story of these is like I just wrote...
I wrote a doc with 10 ideas for things before, as you were working on them.
Well, that was my wish list of things people asked.
And I told the team, hey, can you do at least five of these for the launch?
And then they just got back with all of them.
Oh, wow.
So you can come up with more.
But some of them are just meant to be examples.
Really, you can intercept any event the agent is making, and you can then either block or force a task to ask the user or allow.
update state to track stuff.
Yeah, because ultimately, I think of you as a systems designer.
You let people plug in, right?
That's the whole modus operandi of what you do.
Yeah, yeah.
And we care a lot about also compose.
Can someone else write a library that others use, which this is meant to.
There's also a batteries included philosophy here, probably very similar to how you did Spark, which is you could just start using.
Yeah, that's right.
It has to be good out of the box at certain things, and then you can build your own things on top that we don't want to do.
But in Spark, if you just want to, I don't know, read a table or do an aggregation, it should be awesome out of the box.
Yeah.
People want to catch up on Omnigene.
They should watch your keynote.
They should go through the GitHub and the docs.
If they wanted to contribute or they want to build on this ecosystem, where would you call out as the most?
high leverage places to get involved yeah do get involved in the discord and in github our team is is there is monitoring and uh some of the things people ask for we just built ourselves some of them you know we're collaborating with with them to build it uh and also tell us like how you would like to use that because i think especially for developers like everyone wants it to work their own way and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize.
So we love to hear like, if you think, hey, I don't want it to work this way, tell us.
We really just want to get that compatibility layer across agents and then let you do stuff on top.
Yeah.
Is there any, you know, in terms of like the startup side, I'm a founder.
I want to, I see an opportunity.
I want to get in front of you.
What's your request for like a startup that like?
I wish someone was working on this.
Oh, for a startup.
Yeah.
You got your own startup.
It's doing well.
But if you weren't working on your own startup, what is obvious that you should...
You advise many startups too, obviously.
I mean, I do think...
just as a company with a lot of engineers, anything that helps me make sense of how people are using coding agents and spend, but also quality, or you should add this skill or you should write this thing, or your agents are really horrible at tasks involving this service, so go spend time.
That would be nice.
Yeah, the closest I found is this team, Git AI.
Oh, cool, yeah.
They started with, we will just do...
uh code and human attribution but they're basically building the analytics layer on top of them uh i do think like there are a bunch of like artificial analysis is obviously uh doing super well with their stuff um so there's there will be people i think i think this is like the domain of consultants first but then people will actually build software that has the management plane for coding engines.
Yeah, I think there'll be a lot of insights there.
You have it in other areas.
Okay, well, and then the other big thing is your dream engine.
If you want to tell the story of LTAP.
And background, I'm going to make people listen to our Ankur Goyal episode where we talk about single store, HTAP, and all that history.
Yeah, yeah.
The LTAB idea is actually pretty simple.
So if people have heard of Ankur's talk about HTAP, it's effectively the world of databases.
Sorry, there's maybe a lot of context needs to be injected here.
I am happy to be the database podcast that I'm forcing people to learn your databases, guys.
You cannot Vibe code with just markdown files.
It's one of the most important fundamental systems technologies out there.
But the world of databases is effectively split into it.
roughly two halves.
There's what we call OLTP databases, which are transactional.
And think of your Postgres, your MySQL, your Oracle databases.
And the other side is what we call analytics.
And sometimes might have a term OLAP.
And the difference is on OLTP, you typically have maybe run some transactions on some event that looks up at one specific role.
We update that role, right?
It's a very role-oriented.
data structure.
And on analytics, you're trying to reason on the data.
You're trying to compute, hey, what's my revenue per store?
How's my website doing every day?
And then you eventually want to probably end up running machine learning on it to predict, hey, how will my maybe sales be going in the future?
They are so very different architecture and everybody starts with OLTV databases, every app.
When you become serious enough, they need more than markdown files.
You need to have a database.
You want to lose your data.
You want to have some transactional consistency.
But once you want to reason on the data, if you only have like 100 rows, it's probably okay to run it on your Postgres or your MySQL database.
But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database.
So you start doing...
getting data out of the database.
Replicate them into the analytic systems.
Yeah, which for people, Elasticsearch is like a big...
Yeah, so some of them actually get into Elasticsearch for like block analysis.
A lot of our customers obviously get into Databricks to run more sophisticated things.
And there's this term called CDC, which changes data capture.
And what it does, it reads the bin log of the database.
And if you don't understand what bin logs, fine.
But it's a little delta.
of the data and it reconstructs based on the delta, the state of the database on the analytics side.
But CEC is like a very painful thing.
It's how basically standard in the industry, everybody uses it.
But it ends up being, I think many data engineers ends up being waken up at like 3 a.m.
because there's some pipeline thing.
My explanation is like Airbyte became a $5 billion company just doing CDC.
Yeah, exactly.
CEC is like a very, it's one of the...
most boring, but one of the most fundamental operations powering modern society.
But it's so brittle that a weak joke that it should be called continuous data corruption, because you might change your schema on your LTP database, and then the CEC pipeline fails to handle the schema change.
And then everything goes out.
MARK MANDELAVANIERIENIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII At my keynote, I asked the audience to put up their hand if they love their CDC pipeline.
Only maybe two people put it out.
So a single store, about maybe a decade ago, I think the industry had this idea, hey, what if I build a single database that can handle both workloads?
MARK MANDELAWIERIENIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII I think one of the first issues is that, hey, each, they say Postgres has a massive ecosystem.
You want to be using the tools that's built for Postgres.
And Spark, for example, has a massive ecosystem.
There's a lot of libraries you want to use.
If you were to create now a new thing, you don't have an ecosystem.
You tend to create a new, smaller proprietary API, and you're lacking both.
And it's also very difficult to make it performance-wise to be sort of comparable on either side.
So it ends up being actually sucking on both.
And our whole idea of LTAP is kind of obviously a word play on the term HTAP.
It's that we think this is HTAP done, right?
HTAP wants to build a single engine for both.
We think you can get 99% of what you need by unifying the storage and just have a single storage layer.
And once you have the single storage layer, if your Postgres databases are writing data in a column oriented format, everything analytics can just go read that data directly without any delay.
right there's no pipeline in between so all the data will immediately be available for reasoning analytics i think i was telling some customers earlier hey when we talked about this is going to be super useful for agents i actually at first didn't really believe in myself even though we wrote that positioning but then last night i was having dinner with the australian customer and they actually told me oh hey one of the big issue we have is we have all these logs from our services and we see sli dips and want to investigate But then there's no way for those agents to even understand what's going on in the actual databases themselves.
All we see is just like product telemetry of the database and the services.
It would actually make those agents 10 times more powerful if they understand, for example, who's actually placing those orders?
What is happening?
What exactly are they doing?
So now I'm actually sold on our own message.
I think it's really kind of, it gets you basically almost all of the benefits of the HTAP holy grail, which is, hey, make the data available in media for reasoning analytics.
Yeah, I think in the way that humans are generally intelligent and want to have the ability and access to query anything.
While they do the work, they also need history and context.
And where else is it going to get context?
It's an analytical workload.
Exactly.
I remember when we had incidents with our databases and engineers said, well, I can't just run a giant query on it to see what's going on because that's going to bring down the database and hoard it even more.
That's the kind of stuff that this gets rid of because you spin up a whole separate fleet of machines.
doing the analytics, you're not overloading the main database that's still trying to serve stuff.
Yeah.
So this has been a dream for a while.
What had to get done in order to get to today?
Yeah.
I feel like you have announced variants of this several times, but it wasn't as clear as LTAP.
Yeah.
I think LTAP is like, OK, we've got it, guys.
I was talking to somebody at Meta, and he was asking me, hey, what's the catch?
Why is it possible now?
And I think the reality is we took a lot of time to actually work on the lake-based architecture.
I mean, obviously, a lot of it came from the Neon team, which is a separation of storage from compute.
And it turned out it was just a tiny little step away, going from that to this LTAP idea, which is, hey, we just, in the Neon architecture and in lake-based architecture, we're writing data in role-oriented format to the open data lake.
But in there, we're writing in Postgres pages.
Actually, Ali and I were spending a lot of time debating, hey, can we actually just change that to write in column-oriented format?
And we're just debating.
And one day, one of our engineers who was super smart came in.
He's like, hey, I just prototype it.
It works.
Wait, so prototype what?
Prototype, instead of storing the data in the data lake in the role-oriented format, like Postgres pages, write them in parquet.
And he just makes the observation that, hey, our storage fleet has a lot of extra idle CPUs.
And we can use those CPUs to do the transcoding from row to column, where row is good for LTP, but column is good for analytics.
So let's do that transcoding at that time.
And as a matter of fact, once you transcode the data, the data compresses better.
So from those services writing to, for example, S3 or other data lake, like object stores, you can actually write them faster because now they are now smaller.
So there's no overhead.
It's no compromise in performance.
Some CPU overhead.
Yeah, but we had extra CPUs anyway.
We had that fleet anyway.
So the debate ended.
I mean, it's one of the classics of a tech issue of a lot of debate, but then somebody actually went ahead and just tried to prototype it, and it worked.
But something this strategic and important to the company, I expect there to be a kickoff thing, a design dog, nothing like that.
Nothing like that.
We were debating at many, many meetings, and then we're just debating whether it's...
possible or not from first principle and then somebody just did it Yeah, I mean, if you set yourself up so people do that, that'll be great.
That happened a bit with Omnigine, too.
I think if I just had a doc on, like, we can make these together, everyone would, you know, would think, oh, what about this?
What about this?
But then if you try it out, it helps.
And then if you have real users and they bash it and, like, it's still working, or in this case, if you have the workload, you know what the workload looks like.
You can just test the same pattern.
Tech aside, which is very cool, this is, like, the most important thing.
the culture of innovation and you don't have to ask my permission you don't have to like do a whole form formal process just do it especially these days i think with yeah yeah it's actually easier to do it i think you are very rare i mean i made a lot of c-suite of like large companies and like i think that at scale things slow down and i'm sure you felt it already but somehow you have this core of people that like are exempt how i think we hire And we work with really, really good people.
And that's a very important part of it.
And empowering them, but also spending a lot of time, maybe us in the trenches, matter a lot also.
Yeah, I think people can adapt to being in the larger company.
So that helps.
And we want to make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before or launch a thing in beta or whatever.
And then the other thing, I do think as a company, despite the size, we don't launch that many products.
We try to keep it pretty coherent.
That was actually the whole theory of the company was instead of having 20 Amazon services, you need to set up analytics and machine learning stack, you just have one.
And it's the same API, the same semantics across all of them, the same copy of the data.
So that requires unification.
basically added one more thing at a time.
Like we added storage with Delta Lake.
We didn't used to do any storage.
Then we added SQL.
You know, we added machine learning platform stuff.
So, but yeah, don't do too many, but do those things well.
And that also helps, you know, helps keep it manageable.
Yeah.
The other thing we kind of encourage a lot is instead of building to boil the ocean for everything.
Let's figure out how do we do it incrementally.
How do we do it very quickly?
Many of our products, they're built in the span of weeks.
And then we go to, hey, usually my first question to whoever team is building is, who is the target customer?
Who are you working with?
Are you on a first name basis with them?
Are you texting with them?
I think having that very tight loop.
Can you bring up another launch that?
comes to mind in this kind of thing.
I just want to give examples.
Omnigrant itself happened that way.
Who's the customer?
Yeah, Omnigrant was more of an internal thing, actually, because we would use it for our developer.
Basically, the whole AI team got access to it and was using it.
And we made sure it works from the beginning with our internal code base, which is a monorepo that's enormous.
We gave them some infrastructure.
We gave them lots of token capacity.
So it's all the developers.
We had others.
This is, I think, a public story.
I was going to ask.
Marketplace, open sharing, all of them had.
I just don't remember exactly which ones publicly referenced.
Yeah, they had earlier.
Well, very early in the company, there was like Delta Lake, which is the transactional storage layer we did.
We had our largest customer at the time said like, okay, I want something in the cloud because if the rest of our network is compromised, this thing needs to be separate to store and create.
the events and then talked to us.
He said, okay, this is the rate of events per second.
This is like the freshness I want.
Can you do it?
So that was like way larger than any workload we had.
And we had our engineer working on that, Michael Armbrust, and he worked just to make this work.
And once it worked for them, you know, it worked for everyone else.
Yeah, this was early in the company, probably like four years in or something.
2018?
Yeah, 2017, 2018.
Do you have other examples?
Maybe you have others.
Yeah, Cleanroom, which is basically how you share data in a way without sharing underlying data, but you allow specific operations.
Those were done effectively initially just for two customers.
I think the industry has a sense of, hey, maybe if you overfit to like one or two customers, it's going to be really bad for you.
But I think...
the downside overfitting is much smaller than the upside itself.
And if you sort of try to be too ambitious and boil the ocean, it's a much bigger problem.
Yeah.
Because you might end up actually having no customer.
Yeah.
That's the more likely outcome.
Yeah.
And then you can sort of pivot from there.
I do think there is such a thing as a bad customer that sometimes you shoot fire.
They could exist sometimes if you drive.
But one of the challenges I think we probably see, and maybe many newer generation companies are seeing is, So tech companies are very, very different from non-tech companies or traditional enterprises.
And if you optimize everything just for tech companies, you might have various challenges scaling them outside of tech companies.
OK, what are the top three differences that you always think about?
Governance is a big one.
I think, yeah, a big one is like, yeah, security, you know, data piracy, governance, all that stuff.
So usually if you're building some kind of like B2B or developer tool, like your biggest market is going to be enterprises.
But it's just very different.
A company that's existed for like, you know, it's had some form of IT for like 30 years.
They have so many legacy systems or they operate in a regulated space.
Whereas a startup or even like, you know, like sort of.
more recent tech company, everything is new and sort of pristine.
So yeah, it's just different.
And if you've never worked with enterprises or been in one, you just won't know about it.
And the procurement process is probably quite different.
There's actually far more stakeholders.
Yeah, that is one.
Another piece that's interesting is I think some tech companies...
you know, people will say, oh, I can build that myself, right?
I'll just build that myself.
So then you go, but- I don't think people say that about Databricks.
Yeah, they do, they do.
Yeah, and it depends on the teams and things.
But on the other hand, like many of the enterprises, actually, I don't, I never want to be in the business of building that.
Like I don't want my, you know, whatever, I'm a retailer or something.
I never want to be down because like some weird like nerd, like couldn't get streaming pipelines.
That is not what I'm doing.
This makes them great customers, to be honest.
Yeah, but you have to understand it.
It's hard without having worked there and stuff like you may not appreciate.
Look, I think they all agree.
Don't get me wrong.
They have different challenges.
But many of the tech companies, for sure, there's a lot far more DIY.
On the flip side, you have people who are very much experts in their domain.
Like they're building airplanes, they're designing medicines, whatever.
And they just want to bridge the technology.
They don't want to learn.
databases or whatever, as cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit.
They just never want to know.
They just say, I have a giant matrix or whatever with my clinical data.
How do I cluster it or whatever?
Yeah, that's true.
Okay, so and then I wanted to actually build out the sort of Dream Engine vision.
Where does this all lead?
So one of the things we realized maybe a couple years back is that actually every single database engine out there, especially on the analytics side, are kind of a decade old.
Pretty much everything that have reasonable traction are about a decade old.
And they all started targeting some very specific, narrow use cases.
And then over time, it's become more and more successful.
They've grown in their ambition.
And then they try to support more and more use cases.
But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created that were not for those use cases.
But you can kind of support them more or less okay.
And before you know it, after 10 years of organic evolution that way, it becomes a gigantic pile of shit.
But that includes Databricks.
And very, very few companies or very few systems, I think, have the gut to say, let's go start from scratch.
Let's go back to the drawing board, the design, knowing everything we know today after a decade of workloads and probably billions in revenue.
Let's attempt to rewrite it from scratch and actually make sure it will work.
And then we can support all of these use cases.
So we started doing that, but it's a very ambitious project.
By the way, you can search on Wikipedia.
There's a thing called second system syndrome.
Yeah, I know that.
Our second system effect.
Every developer must know what a second sentence is.
It's basically, you build your first thing, and it works out great.
And the second one's bound to fail because you become too ambitious.
And then you add so many requirements.
You think you know everything, and then you're like, I'm going to design the perfect system this time.
Yeah.
And it turned out it's not perfect.
And then it starts failing, and you're too ambitious, never launch.
And you get killed.
And the engineering team that actually started this, they were brilliant.
I think we hired some of the best database engineers on the planet into Databricks.
And they were brilliant.
Thank God it's not their second system.
Many of them have built more than two in the past.
Ah, nice.
But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is going to take like five years.
to mature.
This would be a very long-term project.
It could fail.
I think one of the engineers kind of jokingly said, hey, maybe we'll just call it Reynolds Dream Engine.
If we name after a co-founder, maybe we'll then get canceled or killed.
But I think they built something pretty remarkable.
They went back to, they kind of changed the way the database engines were built from a paradigm point of view.
Usually when you build a database engine, you read a lot of academic papers.
You try to understand what the latest algorithms and data structures, and you put them together and see if they work or not.
And there's a high risk of failure there also because whatever that looks really good on paper might actually look really good in 70% of the workloads, but then it backfires on the other 30%.
They actually, when built more of a factory for building the database, so they spent more time building this factory.
And the factory takes the decade of traces we have.
I think they count as a quadrillion data points in the trace table.
You don't drop anything?
Oh, you see sample?
We for sure sample, but there's like massive amount of things.
And they use that to build a model, like a machine learning model.
Machine learning model basically can very, very quickly tell us how any algorithm and how any implementation will perform for any specific type of queries with very, very high fidelity.
And based on that, they can pick the most likely algorithm and data structure that will actually help.
with the different kinds of workloads, both at runtime as well as at implementation time.
Because there's an unlimited number of papers.
MARK MANDEL WONG- It sounds like you want to route to different data structures.
FRANCESC CAMPOYOVICHI- Yeah.
I mean, if you think about it, a single database has many things implemented together.
but you want to make sure they all work well with each other.
And then for any given operation, there might be more than one implementation.
So it would make it actually run really, really.
The reality is things, algorithm that works super well, for example, for very, very low latency, might not work very well for, say, scanning through petabytes of data.
Actually, most often, there's a trade-off there between throughput and latency.
What are the key dimensions, like scale, throughput, latency, anything else?
And the distribution of data.
How sparse the data is, that matters very a lot.
How frequently do you hit the same data?
Yeah, how many distinct values and stuff like that.
Those things matter a lot.
Number of distinct value basically impacts the memory consumption of your aggregation.
At some point, there's a hash table.
In my write-up, I'm going to try and list all these up because I really want a taxonomy.
To me, taxonomies are so helpful because it covers everything they should think about.
I think if you actually try to list it out, it could be like a million different features.
I always want, like, okay, give me, like, 12.
You know, like, someone did, I think, an Oracle paper in, like, 40 years ago.
These are the eight fallacies of distributed systems.
That kind of thing is super useful.
It's like, okay, think through these eight.
But let me give you a very weird example, but it actually has a profound implication of performance, which is, like, is your string just ASCII, or does it have Unicode in it?
How should you encode it?
I mean, strings are the most complex data type.
Yeah.
So the end that, like for example, if string is super dense, you could actually convert every string into a, like imagine you have to do a irrigation.
Instead of having a hash table, you could actually have an array.
Because if your string is dense enough, if you only have 256 options, you don't need a hash table.
You can just do array lookup.
Yeah.
And that'll be far fast.
Like if the string is like a country code or something.
Yeah.
Yeah.
So it's actually like.
probably millions of features in that model.
But using that, they can, one, basically prioritize the different algorithms that might actually impact in practice.
And many of them are very counterintuitive.
It isn't actually things that you think might work super well actually don't work that well in practice.
But also, more importantly, at runtime, you can dispatch the right algorithm and structure.
I'm listening to the dream.
I feel like Databricks is doing a really good job of the incremental evolution.
Do you have to hard cut to a new system at any point?
We designed it in a way that it can be incremental.
So first, we're releasing a new endpoint.
But this goes to the border ocean versus what we wanted to do is wanted to by design, this new engine should be able to do everything we were able to do before and better.
In particular, the better part refers to very low latency workloads that can finish in 10 milliseconds.
want to roll it out incrementally with incremental capabilities so it doesn't take like five years to actually see the light at the end of the tunnel.
I think that's a heroic task.
I don't know what other way to say it.
I am really interested in any sort of new workload and new databases.
I mean, obviously, I think I've maybe established that I'm a little bit of a database nerd.
The transactional databases, sorry, the accounting databases, like the Tiger Beatles, I don't know if you've seen those.
What do they do?
Dual entry accounting database.
Like, it's meant to really model, like...
financial accounts and credit systems.
It's like a very specific brand.
Very high throughput.
Yeah.
So when you were talking about how everyone starts with a thing and then they scale up and then they tack on other things, it's exactly that.
And then I recently interviewed Simon from Turbo Puffer.
Same thing.
And Chroma as well.
All the vector database companies of 2023 are suddenly now just general storage.
Vector database should have never been a separate category.
I think it used to be a hot take.
Now it's like the conventional wisdom nowadays.
What should be a separate category?
If everything becomes LTAP, what's?
I think the thesis of LTAP is we're not collapsing the databases at the actual query layer.
We're just collapsing the storage layer.
And that's, I think, a very important part.
And we actually don't think it makes sense to collapse the query layer into a single HTAP style database.
And part of it, the other thing I think a lot of people had is, hey, it would be nice if there's only one query language I have to worry about.
Instead of worrying about PostgreSQL and maybe Spark SQL, why not just one?
But I don't think that's an issue for agents.
Agents are very eloquent in PostgreSQL or Spark SQL.
It's never going to get confused.
As long as the data is there and it's accessible, agents will do fine.
That might have been, so five years ago might have been a problem for humans.
That could arise over time also, and this leads to how to do things incrementally, right?
Like we realize you don't need it right now.
We don't need to solve that problem to have a lot of value from the current teletap.
Yeah, okay.
I'm going to end the pod with a little bit more sort of spicier things.
Everyone has had the receive within a separation and storage compute and tried to build the clouds.
I had the same...
pitches from Snowflake.
How have you succeeded where they failed?
That's rough.
Respecting that they are a competitor, objectively, you have outpaced them.
What is the core insight from your point of view that you guys just went different directions?
FRANCESC CAMPOYOVICIERIENCIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGANIENGIAO CHANGAN Databricks never had the proprietary format.
We started with the open ecosystem, started with Parquet, and then evolved into Delta and Iceberg and all that.
That's one big thing.
I think it matters a lot.
The other one is AI.
I mean, before October 2022, when Chagibiti came out, we had always pitched Databricks as a machine learning plus data.
And a lot of the platform were built with machine learning use cases in mind.
And obviously AI is a little bit different.
And Mateusz spent far more time there than I do.
But the whole platform was, we never felt, hey, we're just a data infrastructure platform.
Like analytics only.
I think they started with, they thought, okay, we'll just manage the most valuable data and try to make it really fast.
For that, we'll have...
our own storage, you know, which is optimized with the engine.
And then we'll just target like the small amount of data that like the managers and whatever, you know, finance people and so on look at and make that super fast to serve.
And, you know, it was a different space.
Whereas we started with like, we'll do the bulk processing and ingest.
Like you got a bunch of, you know.
JSON log files, you've got whatever.
We do that very large scale stuff because that's what Spark was for, the large scale mapages like stuff.
And then we'll keep the data in an open format.
It might be slower, but like it's already out there.
You can consume it downstream.
And it turned out that, you know...
It's easier to go from that bod thing that's really good at the scale and ingesting and super low cost and create versions in it that have the speed and features of the...
you know, super easy to use, like smaller data for business users thing.
So start open, then optimize.
Yeah, start open and start large.
Like in some sense, we started upstream of them.
And there was a time actually when we both like sort of listed each other as partners because he said, if you use both solutions together, use Databricks for like your ingest and compute and then serve the tables out of Snowflake, you get all the...
visualization, all the very fast stuff, like that's great.
And then, you know, we both realized, like, customers were telling us, like, why do I need this other thing?
Why can't I just square your tables?
And we said, no, we're horrible at that.
Like, please use our partner for the SQL warehouse stuff.
And then they realized, like, wait a minute, so much of the computer is moving upstream into this other thing.
You have to go into each other's territory, yeah.
But I think we did start with the bigger scope and with the open thing, and that's important architecture.
Again, it goes to enterprises.
If your company's existed for 30 years, you've experienced being locked into Oracle and all kinds of crazy things.
the CTO there and you're setting up the architecture for the future for your company, you're going to want to pick a foundation that's open.
And you only want like one way to manage data in your company.
Ideally, you don't want like seven different systems.
But the open data format had won.
I think now every enterprise wants to put data in open data format.
But it was actually very controversial back then, I think.
five six when exactly as one of the snowflake full founders actually wrote a blog called choosing open wisely which basically argue against yeah i think they might have taken it down you have to find your archive now oh i mean it's it's never going away now uh no no it's still there i love the the sort of perspective that only you guys will have because obviously you you run the company uh and i Thank you for indulging.
That's an incredible perspective.
Maybe one last one.
As you were talking, I think I have to give Ali a lot of credit.
He's an incredible CEO.
I think he's the perfect combination of IQ, EQ, technology obsession, execution, business acumen.
And he's also a founder, which makes it a lot easier for him to mobilize and execute.
I think that's...
Oh, that was it?
So you have Ali and they don't?
Like, okay.
Well, I think Ali played a pretty big role.
I thought there was going to be some technical choice that he contributed to.
He did for a lot of these.
There were sort of forks in the road where he pushed for one way and then it became clear that that was the right way.
Yeah.
I mean, there's a whole book that needs to be written about how the eight of you...
work together and all that.
I think there's been profiles that people have done.
Second one, not a cleared question again.
Mosaic.
A lot of people in our community are curious on what's the model story of Databricks, right?
When you guys bought Mosaic, the thing was like, okay, well, we're going to do fine tuning.
We're going to do in-house model because they had the Mosaic models.
And it seems like you're not doing that.
And it seems like you're going towards more of the LTAP and the Harness stuff.
What's the story there?
Yeah, I guess when Mosaic started, I think it was well known or became most well known for releasing open source LLMs early on.
And they were general models.
Actually, before that, they were doing other things.
They were about optimizing training systems, basically.
So they had the fastest image model training stack in the world and stuff like that.
And then they decided to do LLMs.
which was smart.
They moved into it before ChatGPD, so they had some of the first open-source LLMs.
Yeah, we interviewed John Franco and Avi for MPC7B.
Yeah, exactly.
Oh, yeah, very cool.
Yeah, so we decided, even though we did launch an open-source model DBRX, and we went up to sort of above the LAMAR-3 scale, we decided that we really want to focus on there'll be so many people releasing models, and instead of doing the general...
model where a big part of the recipe is just throw in a lot of compute and just scale.
We want to focus on the next step also of, let's say you have the very smart model, how do you make it useful?
For us, it was a lot about automating, making it very good at querying data.
That's the first.
party agents we have called Genie.
So it's like a virtual data scientist.
Imagine, you know, there's someone who already knows all the stuff in your company inside out and knows all the machine learning libraries, all the data libraries, you know, all the stuff on the web.
And you can ask them.
questions.
That's what we wanted to do first.
So that meant let's not focus as much on let's just train some kind of frontier model, but let's build this system using either external models or fine-tuned customized components.
We're still doing quite a bit of model training, though, and in fact, we're always procuring lots of GPUs and stuff all the time to do it.
And there's a few places where we're doing it.
One is There are many high volume use cases where if you have a specialized model, it's just so much better than any of the general models you get.
A nice example of that is understanding documents, like PDF, Word documents, stuff like that, parsing them.
If you've ever tried to do that, it's...
frustrating because you send it to like, you know, like Claude Fable or whatever, it like almost gets it, but it gets some things wrong and it's super expensive.
You just burnt a huge amount of tokens plopping in an image into there.
So our team built this document sort of vision model that takes a page and gives you back a nice JSON with all the components.
And it's very competitive.
It's like.
probably like 100x cheaper than those frontier models and still better.
And that's actually done by one of the researchers who came from DeepMind, was a co-founder of Adapt, like very early LLM scaling person, but focused on this.
Likewise, we're doing specialized sub-agents for part of what the coding agent does.
And if you've seen the stuff on advisor models from Harvey, also from Ananthropic.
Commission also, yeah.
And UC Berkeley, actually, one of my grad students there wrote a paper called Advisor Models, I think, before those came out.
I mean, I'm sure others had the idea at the same time, but that's something that helps a ton.
So, yeah, we actually showed some stuff just today at the keynote on...
Is it Parf?
Oh, you know Parf?
Parf, yeah, yeah.
Oh, he's speaking of my thing.
He's doing Continual Learning Bench.
Yes, yes, yeah.
I'm one of his advisors at Berkeley.
We interviewed his brother, Chai, because he's also at a bridge.
Yeah, yeah, yeah.
That family's very smart.
Yeah, yeah, yeah, they're awesome.
Yeah.
So, yeah, so we're doing some of that.
And as we get experience with these in the first party agents, we're also doing them with customers.
So my feeling is like customizing models is actually going to get way easier over time.
That's what we're finding because the base models are smarter.
So they generate better traces and.
RL already, and then RL is about learning from your own past races.
Then synthetic data generation is way better, way easier now.
We have pipelines just using open source models.
The same model generates training environments and trains itself and beats like Opus and GPT 5.5 and stuff at a task.
So I do think it's going to pick up.
The thing is, the ease of training, the algorithms is only going to go up over time.
There's a question of when it crosses into mainstream.
Instead of this specialized document parsing thing we did where you need a hardcore LLM researcher, when does it get easy enough that anyone can sort of...
plop in some stuff and describe a task.
Yeah.
Well, you know what makes it easy?
Interfaces.
Yeah.
And unified APIs.
Because obviously, if it's not interoperable, then you cannot switch.
That's what we're seeing with Omnigent and the composable agents.
You can have subagents with specialized models, and then you can train the whole thing.
I think that'll help a lot, too.
Yeah.
The last thing I was going to leave, actually, I'm sequencing this, so I'm actually kind of proud of myself.
Satya is talking about this.
I interviewed him at Microsoft Build a couple weeks ago.
And then he wrote this essay, which I'm sure you've seen, which is talking about building a frontier ecosystem.
He sounded, when I was talking to him, more like a Databricks CEO than I've ever heard.
Is there a comment?
This thing presumably went viral in my circles.
I don't know if it's in your circles.
What's the sort of theory of like, you know, I guess tokens as IP, building up the context, you know, he basically said everything, but data is the new oil or context is the new oil.
There's some version of that, you know, that you guys have heard before.
Yeah, I agree.
I think the data you have, as you get better technology around that, like you can just do more in your domain with it.
It's not even just about AI.
Even when people, you know, started collecting stuff in real time, like I remember all the power companies put like the smart meters and stuff and all the car manufacturers started putting like sensors and cameras and stuff.
Any technology like makes data more valuable and can give you some advantage.
Anything that helps you do something with it and make some decisions.
And AI is.
the same way.
Like you had all this stuff that's just sitting there.
Now you can have an agent automatically tell you.
Like, for example, you know, instead of I discovered as what feature in my product is broken because a customer complained, the agent tells me I noticed no one is like uploading files anymore because they get errors or whatever.
And as you saw with like Raiden, like as a database company, because we have all these, the history of all the queries and all the table layouts and like how they work, we can build a new engine.
quickly that actually is good, and we're confident that it's going to be good.
So I think this is right.
I think the question is exactly how it will land.
But I do think model customization, which Sajja talked about, is going to get easier over time.
Yeah, which is why, by the way, I brought up the model thing, because they have their MEI things, and you guys don't.
That was to be the mental question.
Yeah, we do have, we're doing like RL fine tuning as a service and with a bunch of customers.
We don't have like, basically, you know, we have like preview customers and we have a general, something called AI runtime.
That's like, we get you GPU clusters on demand with a software stack in there that makes it easy to do training.
So we didn't like sort of, that's existed for a while.
We've had like GPU compute for a while and that's where a lot of the mosaic.
uh stack went to to help scale that but yeah we found that the engagements like some of the There's two types of customers.
There's some who just want GPUs and libraries to get data in and out and monitor.
So that's what AI runtime is.
And then there's some that say, hey, can you actually work with me, build evals, build synthetic data?
The more forward deploy solutions architects.
And then that's what we're doing.
And more things will transition from being custom to not.
But that's sort of how it is today.
Going back to your original question, I think one of the theses we have is actually, the ones you can get the data in the right place.
The AI models are becoming pretty good.
The generic agents are fairly, I mean, Ali talked about EGIS already here.
They have pretty good reasoning capabilities.
Actually, I think many of the traditional software will be sort of rewritten with this new paradigm, which is just get the data to be there.
And then slap some agent on top.
Magic will come out.
But without the right data, you can't really do that.
And it's actually our approach to going to security.
and our approach to go into the customer data platform space.
Yeah.
It's like we launched two products at Data and AI Summit, one targeting security teams and the other one targeting marketing teams.
And those all have a lot of existing technologies out there.
And I think our approach is, hey, once you get the data in, everything is a lot easier with agents on that.
Yeah.
Yeah.
Well, you guys have.
been fantastic guests.
I just love this discussion.
I just love the ability to dive in on the tech side, but also culture and strategy.
I hope this isn't the last time we chat.
I mean, congrats on all the success so far.
Thank you.
Congrats on your success also.
Yeah.
Yeah, I mean, Databricks is actually supporting my event, which is, so I run a conference and it is actually...
I've been attendee of Data AI Summit for a long time.
And I noticed that it was like kind of, this is back in 2022.
It was like 90% data and then 10% AI.
And I was just like, well, okay, we need the community thing that is like just 90% AI, which like now everybody is.
Yeah, yeah.
So Data Brinks will be at the conference.
And I know it's just amazing to see you guys build out the most like...
interesting cloud that I haven't seen outside of the big three.
And it's amazing how far you've grown.
One of the most insightful, I'm not a VC, but I play one on TV, Ben Horowitz.
When he was talking to you guys, advising you on just where is this company going, he was like, don't sell until $100 billion or some version of that story, right?
It was like, the company should be worth a trillion dollars.
You're underselling it for $10 billion.
And he doesn't do that for everyone.
for some reason, I think he saw the vision, but also the infinite runway that you have.
We're lucky to have Ben.
He's a big supporter.
Yeah, amazing.
Okay, well, thank you so much.
All right, thank you so much, Swix.