Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks

We’re excited to have Databricks join us at AIEWF, among hundreds of the top companies in the AI Engineer ecosystem. LS subscribers can use their discount to get past the late bird pricing and access over $50k in sponsor offers!

Everyone is still talking about Satya’s Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.

From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx at the 2026 Data + AI Summit to unpack Omnigent, LTAP, Lakebase, agent security, open formats, Mosaic, and why databases may matter more than ever once AI agents start doing real work.

We go deep on Omnigent: Databricks’ open-source meta-harness for combining, controlling, and sharing agents across Claude Code, Codex, Cursor, Pi, custom agents, and internal tools. Matei explains why coding agents and enterprise agents run into the same problems: portability, collaboration, session history, security, spend controls, and the need for a common API above every harness.

Then Reynold walks through Databricks’ database dream: why CDC is brittle enough to joke that it means “continuous data corruption,” why HTAP has been the holy grail of database engineering, and why Databricks thinks LTAP gets most of the benefits by unifying the storage layer instead of collapsing every query engine. We also cover Databricks’ infrastructure scale, the culture behind rapid prototyping, the difference between tech and enterprise customers, Databricks vs Snowflake, whether vector databases should have ever existed, the Mosaic model strategy, Genie, AI Runtime, RL fine-tuning, and the thesis that traditional software gets rewritten once the data is in the right place and agents sit on top.

Databricks began as a company for the big data era. The origination of Spark from the Berkeley AMPLab which eventually turned into the product Lakehouse convinced enterprises that they didn’t need a separate data lake, warehouse, ML platform, and governance layer. They just needed one open foundation where all of their data could live and be reasoned over.

Since then a lot has changed, but data has only become more important. Data is no longer something you keep track of and analyze ad hoc, it’s the necessary context agents need in order to act. So the framing has shifted from “where do we put all of our data?” to “how do we expose the right slice of state, history, permissions, and business logic to an AI system at the exact moment it’s doing work?”

If frontier model performance becomes commoditized, the durable advantage then becomes the company-specific context around them: proprietary data, governed access, operational state, transaction logs, workflows, and feedback loops. Which makes Databricks positioned perfectly.

Now coming fresh off the Data + AI Summit 2026, the company is moving just as fast to keep up, announcing Genie One, Omnigent, LTAP, and many more, indicating a central mission in its newer work: Databricks is trying to become the operating system for enterprise agents.

Models are getting good enough, but agents are only useful if they have the right context, permissions, memory, state, cost controls, and access to live business data. Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!

We discuss:

* Why Databricks built Omnigent as a meta-harness above existing AI agents

* Why coding agents and custom enterprise agents need the same infrastructure

* The common API for agent sessions, files, streams, tool calls, and cancellation

* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter

* Why Databricks open-sourced Omnigent instead of keeping it proprietary

* Databricks’ internal agent usage, cloud sandboxes, and coding workflows

* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast

* Why agent security needs contextual and stateful policies

* How an agent could read confidential docs, install a compromised npm package, and leak data

* Why spend control matters when an agent can burn $500 reading logs

* Startup opportunities around coding-agent analytics, quality, skills, and spend

* LTAP, Lakebase, and why Databricks wants to rethink the database stack

* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.

* Why HTAP has historically been the holy grail of database engineering

* Why Databricks thinks LTAP is “HTAP done right”

* How writing transactional data into column-oriented formats changes analytics

* Why agents need live operational context from databases, not just telemetry

* How Databricks prototypes strategic systems without endless process

* Enterprise vs tech customers, governance, procurement, and DIY culture

* The “second system syndrome” risk of rewriting a database engine

* Building a database engine from a decade of traces and quadrillions of data points

* Why vector databases should never have been a separate category

* Why open formats and AI changed the race with Snowflake

* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training

* Why model customization and RL fine-tuning may become mainstream

* Why “get the data there, slap some agent on top” may rewrite traditional software

Matei Zaharia

* LinkedIn: https://www.linkedin.com/in/mateizaharia

* X: https://x.com/matei_zaharia

Reynold Xin

* LinkedIn: https://www.linkedin.com/in/rxin

* X: https://x.com/rxin

Databricks

* Website: https://www.databricks.com

* X: https://x.com/databricks

Timestamps

00:00:00 Introduction

00:02:22 Omnigent and the Agent Infrastructure Layer

00:08:39 Agent Clouds, Common APIs, and Open Source

00:16:52 Databricks Scale and Internal AI Workflows

00:18:03 Agent Security, Governance, and Spend Controls

00:27:34 LTAP and the Database Dream

00:30:30 CDC, HTAP, and Why Data Pipelines Break

00:34:05 Lakebase, Parquet, and Live Data for Agents

00:36:47 Databricks’ Culture of Fast Prototyping

00:43:40 The Dream Engine and Rewriting the Database Stack

00:51:02 Vector Databases, Query Engines, and LTAP

00:52:36 Databricks vs Snowflake

00:57:48 Mosaic, DBRX, Genie, and Specialized Models

01:03:11 Context, AI Runtime, and RL Fine-Tuning

01:06:15 Why Data + Agents May Rewrite Software

01:07:09 Closing Thoughts

Transcript

Introduction: Databricks, Data + AI Summit, and Founder Dynamics

Swyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.

Reynold Xin [00:00:06]: Hey, thanks for having us.

Swyx [00:00:07]: Yeah.

Matei Zaharia [00:00:08]: Yeah, thanks so much.

Swyx [00:00:09]: 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

Reynold Xin [00:00:17]: Yeah, it was

Swyx [00:00:17]: in Berkeley

Reynold Xin [00:00:18]: little meetup at Berkeley, I think

Matei Zaharia [00:00:19]: Yeah

Reynold Xin [00:00:19]: put together

Matei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.

Swyx [00:00:23]: Yeah. obviously now it’s like, I think like the headline number’s like 100,000 people around the world, 30,000 in person.

Swyx [00:00:30]: it’s a crazy

Matei Zaharia [00:00:31]: Amazing

Swyx [00:00:31]: community. Well, I just saw the keynote.

Swyx [00:00:35]: Ali’s just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? Like

Reynold Xin [00:00:42]: Oh

Swyx [00:00:42]: such a great presenter?

Reynold Xin [00:00:43]: What do you think?

Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he’d be the best at this.

Swyx [00:00:50]: Yeah.

Matei Zaharia [00:00:50]: And yeah, it turned out great. And he’s, he’s ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can’t hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.

Swyx [00:01:09]: Yeah.

Reynold Xin [00:01:10]: he’s obviously very high IQ and a 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.

Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he’s funny. And like, it, it’s, it’

Matei Zaharia [00:01:26]: It’s true, yeah

Swyx [00:01:26]: it’s hard to make jokes about, data warehouses

Reynold Xin [00:01:30]: About serious topics

Swyx [00:01:31]: security

Matei Zaharia [00:01:32]: Yeah

Swyx [00:01:32]: what have you.

Matei Zaharia [00:01:33]: Oh, yeah. That’s for sure.

Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I’ll, I’ll just name check briefly, the stuff because we’re not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.

Swyx [00:01:47]: we’re also gonna cover Genie, cover CustomerLake, you acquired Panther

Matei Zaharia [00:01:52]: Yeah

Swyx [00:01:52]: Open Sharing, and there’s Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It’s, 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

Omnigent and the Agent Infrastructure Layer

Swyx [00:02:09]: on, in the landscape. Maybe we’ll start with, Omnigentt we’ll, we’ll, we’ll, we’ll go into it. I do think that a lot of people are exploring this meta harness concept.

Matei Zaharia [00:02:21]: Yeah, totally.

Swyx [00:02:21]: What led you to it?

Matei Zaharia [00:02:22]: Yeah. There were 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 info internally. We have really great, dev infra team. they built something called Isaac, that’s like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the 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 ship this, like, data science agent called Genie on the research team, which I lead. We also build 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,” 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 and custom agents in the same thing?” But I said it’s, it’s the same problems and, you just wanna 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 prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.

Swyx [00:04:06]: I’m wondering if this let’s call it architecture

Matei Zaharia [00:04:11]: Yeah

Swyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.

Swyx [00:04:18]: A lot of operating

Matei Zaharia [00:04:19]: Yeah

Swyx [00:04:20]: systems tie back to databases,

Matei Zaharia [00:04:21]: So

Swyx [00:04:21]: or the other way around

Matei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we also

Swyx [00:04:29]: Communication between entities.

Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won’t know unless they’

Swyx [00:04:36]: Yeah, open protocol is the term.

Matei Zaharia [00:04:37]: Yeah.

Swyx [00:04:38]: Open sharing. Open sharing.

Matei Zaharia [00:04:38]: Open sharing.

Swyx [00:04:39]: Yes.

Matei Zaharia [00:04:39]: Yeah. So it’s like you have a company, you maintain some table, like let’s say like a Walmart or something. They have like the, 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, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can’t you share like 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? Why can’t you just vibe code things on demand? But 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 wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.

Agent Clouds, Cloud Sandboxes, and Keeping Sessions Alive

Swyx [00:05:42]: Reynold, any other comments alternative viewpoints?

Reynold Xin [00:05:46]: I think, by the way, 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

Swyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.

Swyx [00:06:12]: I was driving to a doctor’s appointment, and I remember because I wanted to make sure the whole thing continues working.

Matei Zaharia [00:06:18]: But 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 or like.”

Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.

Matei Zaharia [00:06:29]: huh.

Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what’s going on my laptop.

Matei Zaharia [00:06:35]: Yeah.

Swyx [00:06:35]: And I just felt that was ridiculous.

Matei Zaharia [00:06:37]: Yeah.

Swyx [00:06:37]: It felt like we went back to the dark ages

Matei Zaharia [00:06:39]: Yeah

Swyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.

Matei Zaharia [00:06:45]: Have you heard of cloud?

Swyx [00:06:47]: Yeah.

Swyx [00:06:48]: It was crazy to me.

Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?

Swyx [00:06:52]: It was a sandbox.

Matei Zaharia [00:06:53]: Okay.

Swyx [00:06:54]: I was work

Matei Zaharia [00:06:54]: So you were in

Swyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we’re gonna have cloud sandboxes that doesn’t shut down. You can get one very quickly,” but not just for running agentic sessions.

Matei Zaharia [00:07:06]: Yeah.

Swyx [00:07:06]: It’s also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wrote

Matei Zaharia [00:07:15]: Yeah

Swyx [00:07:15]: a document for Matei, it’s like, “Here’s my wish list of what the actual environment should do.” And I think he ended up almost implementing

Matei Zaharia [00:07:22]: Yeah

Swyx [00:07:22]: every single one of them.

Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause 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 like list files and like tail them and stuff.” So

Swyx [00:07:36]: So SSH into a mainframe.

Matei Zaharia [00:07:37]: Yeah. it has that now.

Swyx [00:07:39]: Tailing my log.

Matei Zaharia [00:07:40]: Yeah.

Matei Zaharia [00:07:41]: Yeah.

Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.

Matei Zaharia [00:07:48]: huh. Yes.

Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and render

Matei Zaharia [00:07:53]: Yeah

Swyx [00:07:53]: them properly, I don’t need a separate tool anymore.”

Matei Zaharia [00:07:55]: Yeah.

Swyx [00:07:56]: And I think you also built that in.

Matei Zaharia [00:07:57]: Yeah, we, 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 like, “Hey, I built something that’s amazing for me, but, like, no one else on the team can use it ‘cause I don’t have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that’s where we’ve seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it’s not allowed to connect to like some really important data or whatever because of the security team.

Omnigent Architecture, Open Source, and Common APIs

Swyx [00:08:38]: Yeah.

Matei Zaharia [00:08:38]: So yeah.

Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we’re gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we’re talking about software, it can be very abstract and like here is what we’re talking about. You’ve worked out in open source this entire platform and there’s a runner component and server component with a uniform API that you’ve, you’ve figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It’s an agent cloud.

Matei Zaharia [00:09:12]: Yeah. It’s, it’s got these components to work with it. The, 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 run. But yeah, it’s, I think it’s 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, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”

Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworks

Matei Zaharia [00:09:48]: Yeah

Swyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It’s in. this goes back to Spark, right?

Matei Zaharia [00:10:05]: Yeah.

Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it’s a layer that will 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 when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be different

Swyx [00:10:28]: Ecosystem

Matei Zaharia [00:10:28]: 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, we don’t have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that’s like one of these as it. Another way to think about it is like imagine, we our thing wasn’t open. We had some agent hosting thing, but it’s not open and then there is an open one. if you’re. Which one’s gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it’ll be, 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, jobs or your Lakebase database doesn’t like, lose all your data at night? Well, that requires an operational team that’s gonna sit there. There’s no way it has to be a service. So like we wanna make sure as a company we’re really good at those infra services and then we’re as open as we can in terms of like what you build on top.

Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requests

Matei Zaharia [00:11:45]: Yeah

Swyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.

Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someone

Swyx [00:11:51]: Let’s see, let’s see what’s going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning about

Matei Zaharia [00:11:59]: 400 merge already?

Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause 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. Cursor, CLI, and Antigravity also.

The Modern Data Stack and the Emerging AI Stack

Matei Zaharia [00:12:34]: Yeah. That’s all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.

Matei Zaharia [00:12:42]: I don’t know if it’s that useful. I’m, I’m curious in your postmortem.

Matei Zaharia [00:12:46]: I think most people

Swyx [00:12:47]: Agree

Matei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.

Matei Zaharia [00:12:52]: I don’t know.

Reynold Xin [00:12:54]: 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 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 data warehouse, or later on, as we’re doing data warehouse or lakehouse.

Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is 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, we can 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

Matei Zaharia [00:13:55]: Yeah

Reynold Xin [00:13:55]: produce a chart. But the. 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.

Matei Zaharia [00:14:06]: 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 like, you’ve got an agent session, and you can send in a message or, like, a file. That’s what you can send in, and then you get out, these streams as it’s streaming text or as it’s doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a 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, Py, 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, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it’s gonna lose some messages. So that’s the thing that’s valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that’s, And we built a security and control piece, which I’m excited about. But it’s that common interface, so we don’t. We. That doesn’t try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that’s one of the use cases we care a lot about, ‘cause we want to use this in our own products.

Compute, Sandboxes, and Databricks Scale

Swyx [00:15:20]: Yeah. It should be everywhere.

Matei Zaharia [00:15:22]: Yeah.

Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I’ll, I’ll endeavor to do everything and not call it the modern AI stack because like it needs a different name.

Matei Zaharia [00:15:32]: Yeah.

Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.

Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it’s serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.

Matei Zaharia [00:15:51]: Yeah. Yeah.

Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don’t think he have ever launched it.

Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architecture

Swyx [00:16:05]: Yeah

Matei Zaharia [00:16:05]: and remove the database from it, by the coming from Neon

Swyx [00:16:08]: Exactly, right

Matei Zaharia [00:16:09]: you have this sandbox

Swyx [00:16:09]: Every database company has it already, yeah.

Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it’s important to have local persistence,

Swyx [00:16:19]: Yeah

Matei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don’t have to install your library every time, right?

Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don’t need persistent local disk.

Swyx [00:16:30]: Yeah.

Matei Zaharia [00:16:30]: So there’s some differences.

Swyx [00:16:32]: Yeah.

Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it’s, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There’s, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.

Matei Zaharia [00:16:51]: Yeah.

Matei Zaharia [00:16:51]: But yeah.

Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.

Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.

Swyx [00:17:02]: So we, on the analytics side, I think we launched

Reynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we’re one of the biggest compute orchestrators out there.

Reynold Xin [00:17:13]: Stuff for sure for CPU compute.

Swyx [00:17:14]: Yeah.

Matei Zaharia [00:17:14]: Yeah.

Reynold Xin [00:17:15]: the. 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, Databricks would have processed exabytes of data already on that day. and on Neon, it’s pretty interesting, too. It’s launching, I think, 13 million databases

Swyx [00:17:34]: Yeah

Reynold Xin [00:17:34]: a day now.

Swyx [00:17:35]: Yeah, to me that was, like, a

Reynold Xin [00:17:36]: And that’s just like

Swyx [00:17:37]: Like, what do you mean?

Matei Zaharia [00:17:38]: Yeah. And that’s the point.

Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentation

Swyx [00:17:44]: Yeah

Reynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita’s team, to launch databases. It’s, the. So it’s changing the way people use databases.

Swyx [00:17:54]: Yeah. Okay, we’re gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the security

Omnigent Security, Contextual Policies, and Spend Controls

Swyx [00:18:03]: control side.

Matei Zaharia [00:18:04]: Yeah.

Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.

Matei Zaharia [00:18:08]: Yep.

Swyx [00:18:09]: what have you found there?

Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there’s a few things. Like, first of all, one thing, that immediately was. became obvious is for security, 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 patterns 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, some confidential documents, or let’s say, should it be able to install new packages from npm, which, maybe it’s compromised. Yes or no? Like, maybe I wanna 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, like 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, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don’t, 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 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, like, will share a document with stuff on the internet and which ones won’t? It’s, it’s annoying. So we designed in Omnigentt the policy layer so that it’s functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and that

Swyx [00:20:25]: This is related to the Panther,

Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. Panther

Swyx [00:20:30]: Yeah

Matei Zaharia [00:20:30]: a similar idea on the event processing side, and it’s Python-based versus a weird custom language. this is more, as in real

Swyx [00:20:39]: I didn’t even know we were good yeah.

Matei Zaharia [00:20:41]: Those things are happening, yeah.

Swyx [00:20:42]: Yeah.

Matei Zaharia [00:20:42]: 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, like, 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 can. 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 agent 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’ll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”

Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at Databricks

Matei Zaharia [00:21:34]: Yeah

Reynold Xin [00:21:34]: which is the governance layer for data.

Matei Zaharia [00:21:35]: That’s right, yeah.

Reynold Xin [00:21:36]: And he’s combining expertise at that layer together with all the AI governance he knows.

Matei Zaharia [00:21:41]: Yeah.

Swyx [00:21:41]: Do

Matei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.

Matei Zaharia [00:21:46]: And also as the

Reynold Xin [00:21:48]: All the above

Matei Zaharia [00:21:48]: I don’t want to end up on the front page as, like, I installed some weird npm package and leaked

Swyx [00:21:53]: Yeah

Matei Zaharia [00:21:53]: all the code, so I’m especially paranoid. But also I have very little time, so I don’t want to sit there approving, like, do you want to run a 20-line, 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?

Swyx [00:22:10]: Yeah. Is safety and mmm, let’s call it security a bigger concern than token maxing or token budgets? which one is, like

Matei Zaharia [00:22:19]: Oh, yeah, they’re both there. 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, they really care about that

Swyx [00:22:34]: you can be Uber and still be concerned?

Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you have

Reynold Xin [00:22:38]: for us, security

Matei Zaharia [00:22:39]: Yeah

Reynold Xin [00:22:40]: super paramount.

Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It’s, it’s the most important thing, and, token maxing, we’re not so worried about it yet, but I’ve seen the 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 $1,000 a month, that’s, that’s not fun.

Swyx [00:23:04]: Yeah

Matei Zaharia [00:23:04]: we have, like, only a few thousand engineers.

Swyx [00:23:06]: What’s the policy in Databricks? Is it just unlimited or what’

Matei Zaharia [00:23:08]: It’s, it’s unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that’looking to optimize and to see if anyone’s doing something weird. And, we had some really cool insights just from analyzing current traces, like which

Swyx [00:23:24]: Yeah

Matei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.

Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.

Matei Zaharia [00:23:34]: Yeah.

Swyx [00:23:34]: I think it’s

Reynold Xin [00:23:34]: Yeah

Swyx [00:23:34]: it’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.

Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can like

Swyx [00:23:52]: But if there’s something I should be showing

Matei Zaharia [00:23:53]: Yeah

Swyx [00:23:53]: I don’t, I don’t see it on the GitHub, but,

Matei Zaharia [00:23:55]: Oh, yeah

Swyx [00:23:56]: there’s just

Matei Zaharia [00:23:56]: Well, in the docs there’s something.

Swyx [00:23:57]: Yeah, this is it.

Matei Zaharia [00:23:58]: You can look at it later.

Swyx [00:23:59]: Okay. Yeah.

Matei Zaharia [00:23:59]: Just look in the docs

Swyx [00:24:00]: Yeah

Matei Zaharia [00:24:00]: contextual policies if you wanna see.

Swyx [00:24:04]: I just like to point people

Matei Zaharia [00:24:05]: look at the built-in policies.

Swyx [00:24:06]: Yeah.

Reynold Xin [00:24:06]: Yeah.

Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?

Reynold Xin [00:24:10]: Yeah.

Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.

Swyx [00:24:29]: Oh, wow.

Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keep

Swyx [00:24:45]: Yeah

Matei Zaharia [00:24:45]: track stuff.

Swyx [00:24:46]: Yeah, ‘cause ultimately you’re, I think of you as, like, a systems designer.

Swyx [00:24:50]: You let people plug in, right? That’s the whole

Matei Zaharia [00:24:51]: Yeah

Swyx [00:24:52]: modus operandi of what you do.

Matei Zaharia [00:24:53]: Yeah.

Swyx [00:24:54]: It’s like

Matei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, which

Swyx [00:24:59]: Yeah

Matei Zaharia [00:24:59]: this is meant to.

Reynold Xin [00:25:00]: There’s also a batteries included philosophy here

Matei Zaharia [00:25:03]: Yes

Reynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.

Swyx [00:25:06]: Yeah.

Matei Zaharia [00:25:06]: 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, like, we don’t wanna do. But in Spark, if you just wanna like, I don’t know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.

Building on Omnigent: Contributions, Startups, and Analytics

Swyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.

Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?

Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we’re, we’re collaborating with them to build it. and also tell us, like

Swyx [00:25:49]: Yeah, they’re gonna be very

Matei Zaharia [00:25:49]: how you would like to use it 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’d love to hear, like, if you think, “Hey, I, I don’t want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.

Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I’m, I’m a founder.

Swyx [00:26:18]: I want

Matei Zaharia [00:26:18]: Yeah

Swyx [00:26:18]: I see an opportunity, I wanna get in front of you. What’s your request for, like, a startup that, like, I wish someone

Matei Zaharia [00:26:23]: Oh, like you wanna integrate with us?

Swyx [00:26:24]: someone was working on this.

Matei Zaharia [00:26:26]: Oh, for a startup?

Swyx [00:26:27]: Yeah.

Swyx [00:26:28]: Like, your, you got your own startup. It’s doing well.

Matei Zaharia [00:26:30]: Yeah.

Swyx [00:26:30]: But like, if you weren’t working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.

Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are using

Swyx [00:26:46]: Spend

Matei Zaharia [00:26:46]: coding agents and,

Swyx [00:26:48]: Yeah. Analytics

Matei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.

Swyx [00:27:00]: Yeah. The closest I’ve found is, this team, GitAI.

Matei Zaharia [00:27:03]: Oh, cool. Yeah.

Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they’re building the analytics layer on top of that.

Matei Zaharia [00:27:12]: Yeah.

Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,

Matei Zaharia [00:27:18]: Yeah, they have their benchmarks

Swyx [00:27:18]: doing super well

Matei Zaharia [00:27:19]: Yeah

Swyx [00:27:19]: with their stuff. so there’s, there will be people. I think this is like the domain of consultants first, but then people

Matei Zaharia [00:27:26]: Yeah

Swyx [00:27:26]: will build software that, let’s say, it’s kinda like the management plane

Matei Zaharia [00:27:29]: Yeah

Swyx [00:27:30]: for coding agents.

Matei Zaharia [00:27:30]: Yeah, I think there’ll be a lot of insights there. You have it in other areas.

Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.

LTAP: Lake Transactional/Analytical Processing

Swyx [00:27:39]: maybe you wanna tell the story of, LTAP.

Reynold Xin [00:27:45]: So, and background with. I’m, I’m gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAP

Matei Zaharia [00:27:52]: Yeah

Reynold Xin [00:27:52]: and all that history.

Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur’s, talk about HTAP, it’s effectively the world of databases. Sorry, there’s like maybe a lot of context needs to be injected here. The world of databases

Swyx [00:28:06]: I am happy to be the database podcast that I’m forcing people to, like, learn your databases, guys.

Swyx [00:28:11]: You cannot vibe code with just markdown files.

Reynold Xin [00:28:13]: Yeah.

Swyx [00:28:13]: Like,

Reynold Xin [00:28:14]: It’s one of the most important fundamental systems technologies out there. But the world of database effectively split into 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 sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It’s a very 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? What’s my. How’s my website doing every day?” And then you, eventually want to probably end up running anal- 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 start with OLTP databases. Every app, when you become serious enough, that needs 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- A hundred rows, it’s probably okay to run it on your Postgres or your own, 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 OLTP database

Swyx [00:29:35]: Replication.

Reynold Xin [00:29:36]: Replicate them into the analytic systems and just start

Swyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, a

Reynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.

Swyx [00:29:51]: Yeah.

Reynold Xin [00:29:51]: And there’s this term called CDC, which

Matei Zaharia [00:29:54]: Change data capture

Reynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don’t understand what binlog is, it’s fine. The, 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 CDC is, like, a very painful thing. It’s how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there’s some pipeline thing.

Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.

Reynold Xin [00:30:27]: Yeah, exactly.

Reynold Xin [00:30:28]: CDC is, like, a very

Matei Zaharia [00:30:30]: It’s hard.

Reynold Xin [00:30:30]: It’s one of the most boring but one of the most fundamental operations, like, powering modern society.

Matei Zaharia [00:30:37]: huh.

Reynold Xin [00:30:37]: But it’s so brittle that, we joke that it’s, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handle

Swyx [00:30:48]: Yeah

Reynold Xin [00:30:48]: the schema change.

Swyx [00:30:49]: Yeah.

Reynold Xin [00:30:49]: And then everything goes out.

Swyx [00:30:51]: And there’s all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.

Reynold Xin [00:30:55]: Yeah, but it’s a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don’t.

Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.

Reynold Xin [00:31:15]: Yes. Yes.

Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that’s built for Postgres. And Spark, for example, had 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 a 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, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it’s obviously a wordplay on the term HTAP, is 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 gonna be super useful for agents, I at first didn’t really believe in it myself, even though we wrote that positioning.

Lakebase, Agents, and Live Operational Data

Matei Zaharia [00:32:39]: Yeah.

Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA 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 make those agents 10 times more powerful if understand, for example, who’s placing those orders, what is happening, what exactly are they doing. So now I’m sold on our own message.

Swyx [00:33:13]: Yeah.

Reynold Xin [00:33:14]: I think it’s really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analytics

Swyx [00:33:26]: Yeah, I think,

Reynold Xin [00:33:27]: without compromise

Swyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anything

Reynold Xin [00:33:34]: Yeah

Swyx [00:33:35]: while they do the work, they also need history and need context.

Swyx [00:33:38]: And, like, where else does they get context? That’s it’s an analytical workload.

Reynold Xin [00:33:41]: Exactly.

Matei Zaharia [00:33:42]: Yeah. Yeah. And 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 gonna bring down the database and hoard it even more.” Like, that’s the stuff that this gets rid of, because you spin up a whole separate fleet of machines that’s doing the analytics. You’re not overloading, like, the main database

Reynold Xin [00:34:02]: Right

Matei Zaharia [00:34:02]: that’s still trying to serve stuff.

Reynold Xin [00:34:04]: Yeah.

Matei Zaharia [00:34:04]: Yeah.

Why LTAP Works Now: Parquet, Postgres, and Lakebase

Swyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,

Reynold Xin [00:34:11]: Yeah.

Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn’t as clear as LTAP.

Reynold Xin [00:34:18]: Yeah.

Swyx [00:34:18]: I think LTAP is like Like, okay, we’ve got it, guys.

Matei Zaharia [00:34:21]: This thing, yeah.

Reynold Xin [00:34:21]: I was talking to somebody at Meta,