Company, we have Michael Gordon, who is the Chief Financial Officer, COO, and, and Serge Tanjga, who is the Senior Vice President of Finance. So thanks so much for being here.
Thanks for having us.
Thank you for having us.
You know, Michael, maybe just for the sake of the audience, just to help level set, you can provide just a little bit of background on MongoDB and kind of your core, you know, target market and kinda how you differentiate yourself, and then we'll kinda jump into a fireside format.
Great. Again, thanks for having us. I know there are number of generalists in the crowd who maybe don't follow all of this, so we'll take a little bit of a step back. So we at MongoDB make database software. So if you think about, today, you hear these phrases about software eating the world or every company trying to become a technology company.
Really, that's all shorthand for the fact that today, companies are driving their competitive advantage from internally built software, right? That's how they're getting competitive advantage. And at the heart of every one of those applications, whether it's a customer-facing application, a system of record, an internal application, those applications are built on a database, and the database is at the core. And so that's what we provide.
If you think about the need to have scalable, nimble, agile database, if you think about how much applications are changing, that's what really created the opportunity for us a number of years ago. And so we have the leading modern general purpose database that we provide. It's an incredibly large market, you know, $80+ billion spent on databases in 2023, and growing at 11%, 12% a year.
So the market's forecast to be I think it's $138 billion over the next couple of years. And one of the reasons typically, you know, think a large market like databases, maybe that should be more mature, maybe that should more grow in line with GDP.
But at the core, the reason why it's not is because of what I said at the beginning. It's so strategic and it's so foundational to how companies are driving competitive advantage. That's what's really powering the growth when you think about the growth of new applications and companies being able to deliver better user experiences and differentiate themselves from the competition. So at a high level, that's where we compete.
Okay. No, that's great. And of course, you just reported earnings in the last week.
We did.
You know, a number of moving pieces there. You know, perhaps just a, maybe a quick overview. We can jump into some of those in more detail.
Yeah. So, and I apologize, I seemed to have started losing my voice last night. So we'll get through as much until I expire, but we'll, i f you think about the quarter, we reported quarterly results last week as Will mentioned. Top line growth was 22%. Atlas, which is our database as a service offering, which is now 70% of the revenue, is growing at 32% year-over-year.
We beat the top end of our guidance, and on the bottom line, beat as well. Bottom line margin, about 7%, operating margin in Q1. And generally, you know, I think operationally, you know, a fine quarter. It was not our best quarter, and those who follow will know, but we'll probably get into that a little bit in detail. If I think about sort of some of the things that didn't go as well in the quarter, we saw slower growth from existing applications, and we'll talk about, there are a bunch of different subparts and reasons for that.
And then for us, we had some self-inflicted operational missteps in the first quarter. January is our fiscal year-end, so this quarter is our first fiscal quarter. And we got a little slower start operationally from a new business standpoint. We mostly caught up, but not quite by the end of the quarter, so that's sort of a one-time thing. You know, when we talk about macro and macro impacts in the business, I think it's really important to keep a few things in mind.
One, when we think about the business, we think about the business in two pieces. We think about the new business that we're winning, the new workloads that we're bringing on board, whether they're new applications altogether, whether they're migrations of existing technology.
For the last couple of years, despite the more challenging macroeconomic environment, we've been successfully able to win new workloads, and we're very happy with our progress on new workloads. The biggest driver of outcomes and results in the short period is what is the growth of the installed base, right? The existing workloads that we've already won.
And that's where we've seen we have a very tight linkage between the underlying usage of the application and what our customers pay, and what we've seen is slower underlying growth in the read/write activity at that, and that's when we kind of bake that all in in Q1, and look out and give our updated revised fiscal 2025 guide, that's where it's really factoring in, and, you know, despite the beat in Q1, had us, you know, lower outlook for the balance of the year.
So yeah, let's try to dig into that a little bit. I mean, you've called out some of the self-inflicted issues, go-to-market being a piece of that. Is there a way to kinda help frame, you know, how much that impacted the outlook versus, you know, what you're seeing on the macro front?
Yeah, go ahead.
Yeah. So, maybe I'll try to do it and sort of do it in the pieces and where we quantify, try to help people sort of understand the change in the guide. So I think the most important thing for the investors who kind of closely follow the stock has been the fact that we lowered the guide for the full year.
And we beat a little bit in Q1, so then if you think about the balance of the year, Q2, Q3, and Q4, we lowered the guide by roughly $40 million. So, Michael mentioned a couple of the puts and takes, but I'll sort of double-click on those and maybe add a couple more. But the first thing we said was, if you look at our non-Atlas business, we lowered the guide for the year there 'cause we're seeing less benefit from multi-year deals.
So just as a brief aside, when we sign a multi-year deal for our Enterprise Advanced products or for some of our licensing deals, we take under 606, we take the entire term license revenue component up front. And so when you sign a multi-year deal, that can be a significant benefit or a significant, you know, difficult compare a year later.
Because of that, that dynamic, we're expecting to see a modest decline and EA business, instead now we're seeing more like mid-single digits decline. So that's a part of the $40 million, that slight difference in terms of the rate of decline, and then the rest is Atlas.
And so just on the Atlas piece, I think it's important to call it, that is macro hesitancy to sign multi-year deals.
Right.
Right.
So, and somewhat consistent with what other software providers have been recently saying.
Correct.
Then, if you talk about Atlas, we, there's sort of three components. We didn't quantify them or stack rank them, but let's try to sort of maybe explain, at least conceptually, how they work. First is the macro component. So, we've seen slower usage than a year ago, and therefore slower growth of our consumption, and that's pretty uniform, sort of across the base.
And so we've had a macro slowdown two years ago, and some investors wanted to sort of draw comparisons between that time period and this time period. What we see right now isn't quite as stark as what we saw two years ago when, you know, inflation was spiking, when interest rates were going up, when startup funding dried up, and people were bracing for, you know, an economic impact.
We are seeing a slowdown in usage, but it's not, you know, sort of to the same extent that we saw a year ago. But again, it applies to the entire base, so it matters. So that's bucket number one. Bucket number two is we called out specifically workloads that we acquired last year, so a subset of the base.
They are important because they are still meaningful contributors to growth this year, 'cause they are still in the healthy growth phase. We've seen those grow more slowly than we've expected, so that's another element of contribution. And then the third is the third on the Atlas side, that Michael was talking about, was the fact that we didn't quite deliver on our new business targets for Q1.
We're not changing our new business outlook for the rest of the year, but, you know, that missing piece will stay with us, sort of for the rest of the year. But then if you kind of pull it together, $40 million, a chunk of that goes to EA, the rest is Atlas. Maybe the best way to quantify it is, like, Atlas did $1.1 billion in revenue last year. So it gives you a sense of sort of the magnitude of total change between these three factors that we're talking about.
So maybe if I try to unpack that a little bit. So net new workloads, at least through Q1, wider than expected. It sounds like you're starting to see some improvement, I guess, as you go into Q2. What's driving that, or what kinda changed then i n your go-to-market function.
Yeah. I would say it slightly differently and just, you know, following up on what Michael has said, operationally, we were a little slower to start the year. So, you know, we just in terms of just blocking and tackling that you gotta do at the beginning of the year in terms of finalizing territories, finalizing org structure, issuing final quota numbers.
We were just a little bit slower b ut, you know, in, in a quarter of 12 weeks, that matters. And so because of that, like, we didn't quite get to hit the ground running with our sales team the way that we would do in a normal year. And we actually mostly caught up by the end of the quarter. So in the end, we were behind, but, but, but we, we made it most of the way back.
And so that gives us confidence that it's not a pipeline issue, that it's not a competitive issue, our win rates remain the same. It was an operational issue, but one that we know, we'll fix for next year, but in the near term, it just doesn't recur because now we're executing for the rest of the year.
Okay, and I think one of the other things you cited was go to market. I guess there's the question I've gotten is, you know, did you just emphasize quantity over quality? And where is that now in terms of getting that incentive structure right, and.
Yeah. So, maybe a few different things to think about, and I think this is specifically talking about the more recent workloads that we acquired, right? And, c orrect. Over the last year, right, because newer workloads tend to grow more quickly, that's an important driver of our outlook for fiscal 2025 as Serge sort of walked through.
And so I would put this in the category of we continue to, you know, learn and iterate, and we've been on sort of a multi-year journey, to consumption and to workload focus and reducing the importance of upfront commitments. So for those who've been around this story for a while, that'll sound familiar and be very clear.
For those who are newer, what it basically means is that we've moved away over the last several years from a, you know, upfront, large commitment, you know, even if it was just a one-year commitment, of how much are you going to spend, in return, maybe what discount am I going to get? And the reason why we've moved away from that is because that negotiation or that discussion, rarely impacts the underlying usage, right?
And when you think about the database as a service offering, the way that we, you know, generate revenue from that is from the underlying reads and writes and the activity and the consumption on our platform. And so because we have this huge market and we have relatively thin footprint coverage relative to the opportunity, there was a huge amount of wasted time if the rep has an incentive to try and get you to commit to $20,000 more, $50,000 more, none of which was gonna change the actual underlying consumption of the subsequent year.
And so what we did is the final piece of that multi-year journey, which we started before, you know, COVID hit and everything else, was removing the incentives or for one-year commitments that sales reps had. And so instead, really focusing them on winning workloads. And if you think about a reps comp plan, simplistically, there are kind of two components, right? They're the net dollars of ARR that I drive, right? What's the new business that I'm driving?
And then as we were, you know, continuing to iterate on this evolution, a quantity of workloads, right, that you had to acquire. And what we saw is that in reducing the commitment emphasis, the sales reps wound up getting the velocity benefits that we wanted, right? We had a great year in fiscal 2024, in terms of winning new workloads, right? We talked about that throughout. And so we got the kind of payoff or the benefit that we did.
There's one thing that we learned, and like I said, I put this sort of in the category of unintended consequences, that as a result of us now not having this long, protracted, time-consuming, inefficient conversation, I also missed some specific data about the application, right? And so I kind of want a quantity of workloads, and our plan or our thought process had been, you'll win a portfolio of workloads and, you know, they'll look like a normal portfolio of workloads.
Well, what ends up happening is if you don't get some of that information, and the workload part of your comp plan is only focused on a uniform dollar threshold of like, you know, you need to be above this spend level in order to kind of qualify, there wasn't enough of a mix of the workloads.
And so the tweak that we made to the current comp plan is same setup, same structure, but instead of just saying, "Okay, X number of workloads, and then you declare this plan," is like, we're gonna specify the portfolio. You're gonna get, you know, two, two that have a minimum spend of this, and three that have a minimum spend of that, and two that spend of that, to sort of force the portfolio that was happening naturally and organically previously.
But there's a chance that in the focus and the evolution to workloads, you may be over-rotated too much to volume, in part 'cause you just didn't know, 'cause you weren't having that conversation and you couldn't say, "Hey, what's the, what, what are the highest growing workloads gonna be? What are the biggest, largest workloads gonna be?
Where are you in that evolution then, of that sales change?
So yeah, we put that in place, we put that in place in Q1. And it was relatively straightforward. I've spent more time talking about it now than we've probably had to explain it to an actual rep. Like they get it, they understand it. This is not going back on commits or rewinding the clock or anything. It's just saying, "Hey, you know, have a little more intentionality around what the mix of, of the new workloads that you're winning look like.
But probably too early to tell whether you're starting to see something r eturns from that.
Yeah, too early to tell, for sure. Yeah.
Okay. I guess probably the single biggest question then is, you know, the change in Atlas consumption trends. It sounds like you think just based on the broad-based nature, it ties to macro, but it sounds like it started in April, and it's continued into May, and you're just kind of assuming?
Yeah, I'd say slightly differently.
Okay.
So, if you rewind back to our prior call, which was in early March, at the time we obviously had the February actuals, so we knew what we were working with there. But the pattern of consumption we were expecting, and that would be in line with prior years, were that March and April are stronger than February.
And they were a little bit stronger, but not sort of to the extent expected. And once we sort of concluded that this is not just a week or two aberration, but you know, a more consistent sort of trend or lack of the seasonal recovery, we sort of dug into it, and we really found it to be very broad-based across segments, across geographies, across customer tenures. That plus sort of the year-over-year usage decline indicates to us that, you know, the phenomenon at play is macro.
Just not a year-over-year usage decline, but slower growth.
Sorry, slower growth. Thank you.
Yes. Just very important.
And then in May, we saw trends consistent with Q1 trends. And May is usually consistent with Q1, so that leads us to believe that, you know, we are in a slightly slower macro environment, but one that's been consistent now for several months, and that's what we're using to base our guide on.
So a re you assuming then normal seasonality from here?
Correct. Yeah, so normal seasonality from here, which would mean that the back half of Q2 is a little bit slower, the back half of Q3 is better and then we have the holiday slowdown in December.
Okay. And what provides the, I guess, conviction that it's macro as opposed to, I don't know, any kind of competitive share shift? I mean, it sounds like the new workloads suggest to you that, you know, in terms of win rates. You're still, you know, stacking up well.
But one of the questions I've gotten is, you know, the hyperscalers, because they're taking in so much more of this generative AI workload activity, are they starting to pick up some incremental share? What's kind of your reaction to kind of what you're seeing more broadly, competitively, that could help allay any concerns that might be out there on that front?
Yeah, so I think it goes back to what Michael started, which is the best way to the best mental model for our business is to separate new and existing. So if we were seeing a competitive issue, it would be on the new. If we were seeing saturation issue, it would be on the new.
If we were seeing a pricing error, pick any sort of, you know, fundamental factor, it would affect the new side of the business, and we're not seeing it there. We're happy where our win rates are where they are, where they have been very high.
We were disappointed with our Q1 new business, but it's a function of a slow start as opposed to any fundamental change in the environment. And then on the existing side, the really underlying driver is the growth of the underlying applications, and that's where we've seen slightly slower usage, which we believe is a macro phenomenon.
Okay. Yeah, I've got a number of other questions, but if there are any from the audience, there you do have instructions where you can submit questions, and I can get those to the team here as well. Maybe kind of sticking with that kind of generative AI theme, I guess I'm kind of curious what you are seeing or what your thought process is around this idea that you look at the reacceleration from the hyperscalers.
It feels like a lot of that is generated by, you know, generative AI applications. You know, is that kind of taking the oxygen out of the room in terms of spending in other areas? I mean, is that one of the areas that could be impacting you, or is maybe that less of an impact? A ny particular kind of viewpoint on, you know, where dollars are being allocated broadly across software?
Yeah. I'll start, and Michael will jump in as needed.
Thank you.
So clearly, we're seeing evidence of gen AI spending in the hyperscaler numbers. What we hear from them and what we hear in the market is that it's happening sort of at the infrastructure layer.
So a lot of the money goes into GPUs, model training, so the sort of activities that predate, effectively, the creation of apps, and that would be sort of the next layer up, the layer where we play and the layer where we would expect to see benefit, sort of in the medium term. So we can get back to, like, what that means and what it looks like.
But sort of sticking around this sort of near-term narrative, what we heard from investors is, "Okay, well, that money, and it's significant, must be coming from somewhere. Where do you guys think it's coming from, and is it coming from software? Is it coming from, you know, in-house app development? Where exactly is it coming from?"
Obviously, a difficult question for us to answer from the perspective of, look, we have small market share, so, you know, our visibility would be less than if we were, you know, the cloud providers themselves. Tautologically, the money is coming from somewhere.
And we anecdotally see that customers are playing with AI, that developer teams are building proof-of-concept applications, that the C-suite is engaged to figure out what exactly AI means for them, what is the strategy, and how they, how most quickly do they execute it in a cost-efficient way.
So there's definitely an element of AI distraction that is sort of impacting the market and maybe siphoning all the dollars. Now, how that impacts or doesn't impact us? Again, in the new versus existing paradigm, it would impact the new. It would impact our ability to go win workloads that are currently being built because there's fewer of them being built while people are paused their regular, you know, software development in order to focus on AI.
We don't think that this magnitude of switch is such that it should impact our ability to generate new business, or as our CEO, Dev, would say, like, he would not accept that as an acceptable excuse, only because our market share is so low. To Michael's point, the market is enormous.
You would need to have some sort of, you know, AI winter, you know, fully freeze regular sort of ongoing business and software development for it to really impact us, given our starting point. Yes, anecdotally and conceptually, it makes sense that it would be happening. We don't think it's happening at a scale that is impacting our new business, and just to close the loop, it shouldn't be or doesn't have anything to do with our existing business.
And just, just to your hyperscaler point, Will, I mean, I think, A, yeah, huge amount of that is on the training side. There's very little that's taking place on the inference side, certainly from everything we've seen and heard, not just from our own business, but from talking to everyone else, talking to customers, things like that.
And then I think also people try to do these read-across, where they look at the hyperscalers and try and say: What can we learn from that? And I think one of the things that's important to understand for the hyperscalers is, you know, a year ago they were under a fair amount of cost pressure, optimization headwinds, and all of those kinds of things that aren't our dynamic. Even though that we, you know, recognize revenue on the basis of consumption, I think sometimes people mistake that revenue recognition, and make it equivalent to a business model.
The business models are a little different, and so they went through a series of optimization, optimizations that we didn't go through, that they're now lapping now, and so their headwind from last year becomes a tailwind for them this year, but that's just not our dynamic, and so I think that also helps, like, put things in context for folks.
Yeah.
Sorry.
Yeah, no, it's helpful. I guess maybe kinda sticking with that vein a little bit, I mean, you're coming off of local New York here just you know, few weeks ago. Seemed like a lot of enthusiasm there. Any kind of green shoots you'd point to with respect to, you know, AI application development?
I mean, that's kinda what we're kinda waiting for, right? Is those applications that you can help, you know, underpin effectively, you know, moving forward. You know, maybe, and maybe along those lines with vector search, you know, the integration you have there, with the broader platform, you know, what are you kinda seeing on that front?
Yeah, I'll come, come at it a couple different ways, but the one thing I would say is we are not waiting for it in the sense that there's plenty of opportunity to go win new business until sort of the AI wave actually washes out.
So just, you know, we think of that as an incremental vector of growth out of already plenty of other incremental vectors of growth. But where we're seeing interest is sort of, we've seen interest primarily and where we've seen it for the past year, is a tremendous amount of innovation by the startup community.
So we have pages and pages of reference use cases of startups doing very cool things on MongoDB, using all sorts of varieties of data, from video to voice, to PDFs, to combinations of all of them, to build interesting use cases, but they tend to be new. You know, they need to build a product, then they need to find the product market fit and all of that.
But it gives us a lot of confidence in our positioning in the market to see just the amount of innovation that's happening on the kinda startup and in the market for us. And when it comes to enterprises, they are very interested. Back to prior conversations, the conversation really spans from, you know, the C-suite all the way down to the developer. And they're tinkering.
They're building proof of concepts. They're excited about when those come together. The question is, A, they're hard to build, B, they are sometimes expensive to build 'cause some of the pieces of the AI stack are quite expensive still.
And then finally, they need to have confidence that they can deliver the user experience that's necessary to generate the ROI on that investment, while solving things like security, like IP, and on and on and on. So we see a lot of experimentation. We hear of exceptionally cool outcomes, which gives us confidence that they will be scaling some of those applications into production as time goes on, but it'll take a little bit of time.
Okay. I've got one question, just to interject. I think it's kind of a clarification, asking if new business trends have improved recently, and I think that was coming back to the net new workload comment.
Yeah, yeah, that's right. So, like, we started off slow in Q1, and it has been better since then.
Yeah, proof of them.
Yeah, I would just clarify, that's not a comment on the market. That's a comment on we got a delayed start to the year, and then we pushed well through the quarter and almost caught up, not somehow we are seeing some change in the market dynamics or anything like that. That's more of an internal execution, operational issue.
Yeah.
Right. Okay, maybe just to, you know, circle back to the MongoDB.local event here. Anything else that really stood out for customers or customers were particularly excited about, or you came out thinking, "This is something we're gonna start to get traction on?" I mean, what, what's kinda the next product, I guess, that you're most, maybe most excited about, or product or two that you think can start to generate more usage here over the next, you know, 12, 18 months?
It actually wasn't the product per se that we got most or maybe most relative to expectations, customer feedback, but it's this program that we announced called MongoDB AI Applications Program, where we've announced effectively a series of partnerships that makes it easier for customers to get started with AI.
So we've curated a set of partners in basically every sub-sector that are needed in the AI stack. So whether that is the hyperscalers themselves are participating, whether that is model providers, model hosting companies, embeddings management, orchestration frameworks, including services providers as well, and sort of give a list to customers of, like, "Here are partners that we believe, we, MongoDB, believe are innovators in the space.
And if you're looking how you're gonna piece together your AI application, you should start with this list. And the feedback from customers was positive because they need help to cut through the noise. The noise is significant and growing, and every week, if not every day, there's a new press release and a new leader in every one of those quadrants.
So, they need that. They need assurance from a credible third party of, like, where should they focus their efforts, and also assurance that over time, as the market develops, we, we will be there to help them. And we'll do more on map. We'll do things like reference architectures. We'll do things like, you know, packaging, or sort of co-selling arrangements.
But the feedback was positive in sort of validating two ideas: one, that we play a critical part of the stack, and B, that customers really need help to figure out how to put it all together. And then the second thing I would say is, which we've been talking about for a while, and talked actually at the investor session about is, and it's also, it's also AI-related, which is that, we see great opportunity to use AI to make it easier to migrate off of relational databases and onto MongoDB. We call that, App Modernization Factory.
We talked about a few pilots that we've done to start the years, and, and several more coming in the back half. The first results are very encouraging in that AI, meaning large language models, make it much easier to rewrite the code of a legacy application, and significantly easier and less risky to build testing to make sure that the new MongoDB application performs the same functions effectively as the relational application.
Those two things together, as well as the feedback from the first pilots, gives us a sense that the opportunity is increasingly feeling real. It's gonna take time to execute because, again, we do it one customer at a time, but those are the things that, you know, in and around our MongoDB.local and, and sort of fortified by customer feedback, gives us a sort of incremental confidence that those are vectors of growth going forward.
Yeah, I would just underscore the relational migration piece, app modernization, whatever you wanna call it, very early, but over the long tail should help and is a great example of sort of using AI, not necessarily in an AI application, but how that's increasingly, you know, important and certainly helping us or has the potential to help us over time.
The other perspective that I would just add is, one of the beautiful things about MongoDB from my perspective is, we have this very small share in this incredibly large market, so we don't need some new product to be some sort of like, you know, big unlock or access some adjacent TAM or power future growth opportunities.
As boring as it is, a lot of it's just about executing against the opportunity that we have against us, or in front of us. That said, we are investing against a broader vision and a broader roadmap, and excited about, you know, what that holds in the future.
Yeah, I think one of the other broader questions I get is trying to understand, you know, why you all are well-positioned to help customers with generative AI and build generative AI applications versus, you know, some of the other platforms out there, data warehouse approach, data lakehouse approach, you know, Snowflake or Databricks, or even the hyperscalers.
What, what's kind of your, you know, answer to that? I mean, obviously, you've got great developer presence, you know, great set of capabilities. How do you kind of set yourself apart to be that kind of go-to partner?
Right. So if you think about building an application, so our persona is the developer. So not an analyst or an MLOps engineer or a data scientist, but just your, your developer. And so if they're building an application, they are gonna require a number of components, but ultimately what an app does is write data.
And for that, you need a persistence layer, an OLTP persistence layer, and that's the core space that we perform in. That's not a function that a data warehouse or a lakehouse or any other sort of repository or of already created data serves to perform. And, you know, every application, including an AI application, needs a database, and that's not gonna change going forward. So that sort of defines our competitive set for that portion of the AI stack.
Now, within that, what's becoming increasingly obvious to us, not just because it makes intellectual sense to us, but also because we're hearing it from both customers and partners, is the importance for that OLTP layer, the database layer, to be flexible, to be performant, to be fully distributed, and to be able to work with heterogeneous data.
And, our sort of foundational architectural advantage, i.e., the document model, is perfectly suited for exactly that type of use case. And that's different than relational, the rows and columns. It's different, frankly, for effectively all the other NoSQL solutions that don't have that flexibility, versatility, and heterogeneity of data.
And that is gonna be needed as you think about the exciting AI applications that we think are gonna power our lives, whatever, 5, 10 years from now. And again, conceptually, it makes sense. It's made sense to us, you know, since the beginning of the AI conversation over a year ago, but that's what we're hearing brings customers into conversation with us, and that's what brings partners to the table who want to partner with us.
And then just more broadly, I know we're up on time, but just more broadly, I think our view is that applications will get smarter over time, whether they're actually AI-specific applications or other applications that just become sort of more intelligent, AI-powered, whatever you want to call those. And the couple of key trends that are important there are, one, that's the domain of the developer, the developer's creating those applications.
So we think more and more responsibility is gonna shift to the developer, where we're, you know, incredibly well-positioned. And then secondly, it's leveraging that operational data that's being created in the application. It's not some, you know, stored after the fact in some data warehouse or some data lake. This is the operational data being created in the application itself.
Yeah, no, that makes a lot of sense. All right. Well, thank you all. Those are great insights. Really-