Trino, Iceberg and the Battle for the Lakehouse | Justin Borgman, CEO, Starburst

26:31It's like adoption or conversion. And you need both. You need to grow the pie and then you need to make sure that you're getting a good piece of it. And so I think it's more art than science. But certainly, you know, things around performance and security are very logical places to kind of draw some lines where you know that enterprise customers value that. And, you know, that's something that can be monetized. Do you have any kind of, you know, in your license, anything that forces companies above a certain size to pay or not? We don't. And that's something that at least philosophically my co-founders have been pretty, I guess, consistent on is a desire to continue to use the Apache license, which gives widespread flexibility and freedom to users of the technology.

27:23And so we haven't created any, you know, more, I guess, restrictive licenses like some open source companies have. And so you were saying the on-prem version of Starbucks enterprise is particularly popular with large companies and financial services in particular. Yes. It's like another super interesting topic of, you know, cloud versus not cloud and who will resist ever going to the cloud. So your experience is that those companies are not going to the cloud. Yeah. I mean, they're pretty emphatic about it. I had dinner with the CEO of one of the largest banks in the country. I won't name his name just to protect his innocence, I guess.

28:07But, you know, he told me point blank, like, we're never going to move everything to the cloud. We're going to have four clouds, in his words, and the fourth one being his own, you know, on-prem, essentially. And so I do think that's the future. I think also there's, you know, an interesting case to be made that there may be some repatriation with the AI emergence. And, you know, our partner Dell is very much betting on that as well. They're selling a ton of servers right now, AI servers, which are basically, you know, have a lot of GPUs in them, a lot of NVIDIA products in there. and are seeing customers that are trying to get economies of scale by deploying that infrastructure on-prem and running it themselves.

28:49So, yeah, I think this dichotomy is going to exist for as far as I can see. Yeah, it's been fascinating, actually, to see the Dell stock price. Everybody's talked about NVIDIA, you know, for all the right reasons, but it's amazing how Dell, as the quintessential on-prem player, has seen their fortunes accelerate as well last year. Okay, great. So that's Starburst Enterprise and Starburst Galaxy, the managed version. How does that work? Yeah, so that's a classic, you know, SaaS product that's hosted and managed by us. It is connecting to your storage. So it's your own S3 buckets, your own, you know, RDS, your own MySQL database.

29:32But the compute and the control plane is managed by us. And so we're able to offer a very seamless, easy to use, turnkey, push button type of approach while still giving you all the performance and functionality that you need and that you're looking for. And that product has actually evolved very, very quickly for us. We've been able to get a lot of new interesting features and functionality there. And one of the use cases where we've seen a lot of adoption and interest is actually customers who are building their own data applications and using Galaxy as the embedded engine. where, you know, there's a data analytics portion of the SaaS app that they provide to their customers.

30:14And the analytics are essentially powered by our engine. And, you know, we were digging into, like, you know, why are they choosing us for this? And I think it goes down to, you know, if you're going to be part of someone else's margin, essentially, their cogs, right, you need good TCO. I think what we're seeing is data application developers are choosing Iceberg for storage because that gives them a lot of flexibility. They want something that's based on open source so that they always have that optionality as well. And then, you know, we can deliver them pound for pound the best cost performance, we believe, in the industry.

30:50And so those things all really matter when you're, you know, ultimately fitting into their margin. Yeah, very interesting. And that's in contrast to like this whole series of companies that specialize in embedded analytics and BI. So the. Yes. Size sense and good data. Yes. Well, I'll clarify. We're not doing the visualization piece. Yeah. So some of those companies. But you help them do the analytics and then embedded math. Exactly. Yeah. We're doing the query execution behind the scenes. We're the engine for that. Exactly. Okay. All right. So that's Galaxy. And then we alluded to the Dell thing.

31:26That's more recent, right? Yes, that was almost 12 months ago that we released what's called the Dell Lakehouse. So it's their product powered by Starburst. And that's essentially, you know, Starburst inside with Dell's object storage, Dell's hardware. And it's become a real centerpiece of their AI strategy in selling this into large customers. Some of them themselves are CSPs that are building out their own infrastructure for analytics and AI. And I think at the end of the day, AI is only as good as the data that you're training the models on, only as good as the data that you're accessing through RAG workflows.

32:06And so having a lake house at the core of that architecture is important. Okay, great. So those are the three offerings. And still on the topic of product, maybe walk us through the overall architecture. And you have a chart somewhere on the website that maybe we'll put in the video where, you know, you have the classic sources on the left and then, you know, Starburst in the middle and magic on the right side. So walk us through that. So in terms of sources, how do the data go into or how does Starburst access the data? What kind of data? Sure. Yeah. So at the heart of Starburst is this notion of connectors.

32:50And we think of everything as a connector. So even if you're just accessing S3 and you're going to be querying iceberg tables, that's technically our S3 connector or data lake connector to access that. And so every connector is basically just connecting to the underlying catalog of the system that you're connecting to. And then as soon as you've connected, which is like a one-time, you know, setup thing, now you can run queries. And you can do that at the command line, like just start to write SQL queries, joining tables across different systems. Or you can use a BI tool like Tableau or ThoughtSpot or others.

33:27Or, and then this goes to the sort of data application side, we're seeing customers, you know, build more programmatic ways to interact with the data that we have access to. One of the features that we've built that's really nice, especially for internal purposes, is something called data products, which is basically allowing you to stitch together a view of your data across these different data sources. And that's where you really start to get some interesting optionality because you can decide to materialize that view or not materialize that view. And there are trade-offs to both. You know, if you're querying the data source directly, you're going to get very fresh access to the data directly where it lives, but you might trade some performance.

34:07And if you're really optimizing for performance to power some kind of dashboard, well, that might be a case where you want to materialize that view in the lake. And so we have a lot of flexibility on allowing you to do either one. The data can be batch or real-time? Yes. Yep. So meaning that you just have a Kafka endpoint in gesture? Yes. Is that a word? Ingestion. Yes. Of some sort. Yeah. Yeah. And actually, that's something we've really optimized recently is we call it streaming ingest. And that's connecting to a Kafka stream and landing it in iceberg tables automatically for you. And we're able to do that at incredible throughput.

34:48And that's an architecture I would say that we're seeing a lot of out there is Kafka as opposed to more traditional batch-oriented ETL. And so you can land your data, have it there, you know, it might be 15 seconds old, you know, from when it was created. And that enables, again, more real-time analytics as a result. The data itself can be structured versus unstructured. Does it matter? Yeah, typically it's structured or semi-structured for our SQL analytics offerings. But we're also, you know, doing some interesting things around vector search. We can connect to different vector databases. There's some things that we're working on there.

35:30But yes, today I would say primarily SQL analytics on structured or semi-structured data. Semi-structured would be something like JSON files or XML files. Yes. All right. So that's sort of the, for the most part, the left side of the magic chart. So the middle, so there's a SQL query engine, which is what we talked about, augmented by warp speed. That's what it's called. And then there's a governance component as well. Exactly. Yes. And that's very important for our large enterprise customers. You know, the access control piece in particular, having good auditing controls of being able to see who accessed what, who saw what, and being able to do both what's called RBAC, role-based access control, but also ABAC, attribute-based access control.

36:18And that's important because that allows you to tag certain data sets as being, you know, PII data. Maybe this one has certain data sovereignty constraints. You know, only people in Switzerland can see this data, for example. So it gives you a lot of flexibility. Is there other bits in that middle that I forget? You know, I'm quizzing you on one chart on your website. Yeah. Because as a CEO, of course, you know all the charts on your website. I've probably seen them all at some point. Yeah, I mean, I think the other is just obviously if you're using Galaxy, then we have a lot of automation of the cluster management itself.

36:54You know, you can scale it up, scale it down. You can have an auto scale on its own. and all those things are just going to save you money if you don't have a cluster running all the time. So I'm totally cheating because I'm bringing up the chart in front of me, but I'm not showing it to you. So you have, joke aside, you have, so ingestion we talked about, you have table maintenance. So governance we talked about, accelerated SQL analytics we talked about. The two things we didn't talk about is table maintenance and automatic capacity management. Do you want to go? Okay, yeah. Yeah, so the table maintenance piece, That's, you know, there's a lot of activities that you want to do to optimize for performance reasons how the tables are structured and laid out.

37:35If you've been around for a while, you know, this is kind of like defragging a disk drive from like a long time ago, right? Like you want to take a lot of small files and bring them together into larger files. And that's called compaction. And that's a particular thing that you want to do with iceberg tables to get better performance. And you could do that yourself. It can be tedious, time-consuming, error-prone. There's a lot of work involved. Or you can use Galaxy and we do it all for you, basically. So that's sort of the table maintenance side of things. And then on the capacity management, that's really those autoscaling capabilities where you can spin clusters up and down and have them automatically scale based on incoming compute.

38:13It's a more serverless type of experience behind the scenes. The data apps you just described, is that – so that's a materialized view that you were describing? Well, that's the data products piece. That's the data products. All right, great. So there's data products and there's data apps. So what are the data apps? So data apps is really what I would say is an emerging use case where customers are building their own applications, leveraging our platform, and doing that because, again, we become part of their COGS, part of their margin. So they want a platform that's going to be very cost-effective to them.

38:47And so we're powering the analytics behind some of the large SaaS vendors out there. As I'm reviewing my notes, I joked down things like AML, Anti-Money Laundering, Customer 360. Yep. So those are examples. So those are apps that are delivered by SaaS companies just to play it back, but powered by Starburst? Well, they can either be apps built internally. So like an AML use case, anti-money laundering use case might be something that a bank has built themselves to meet their own regulatory requirements. But, you know, for example, one of our customers is called Vectra, Vectra.ai. And they're a – Because that's Amr's company, right?

39:32Yes. Yes. And they're a cybersecurity-oriented company, and we're powering the analytics behind Vectra. So that's an example. Yep. Okay. But so for the internal use case, like I'm a bank and I want an AML and I'm an online drink. So what walk me between the difference between a data app, like a Starburst data app versus just a use case? So you formalize, you have like a building block, like a Lego block that helps them build the AML app? So not necessarily for AML specifically. I would say like, you know, this is a horizontal platform, but because of, you know, our REST API, they can start building their application directly against that.

40:20And so now access to all of the data that they need to, you know, either show or power dashboards or analytics within the app itself or deliver reports or whatever analysis is sort of taking place, we can be the engine behind that. And we've tried to make it as easy as possible for those developers to build against that. Okay. Very cool. So that's a lot of things you guys have built because you also have, you know, we were talking about connectors. Like I wrote down, you have like 50 plus data sources. That's a lot of building. What turned out to be the hardest to build in the history of Starburst?

41:02Ah, the hardest to build. You know, I would say building Galaxy, the SaaS platform, you know, there's a story there where we actually built it twice. And, you know, customers never really saw the first version because at the last minute we said, you know what, we can't deliver the seamless, easy to use, consistent experience that we're going for. Oh, wow. So you had to go back to the drawing board? and went with a totally different approach. And actually what's interesting is that first architecture was actually fairly similar to Databricks' architecture where we did not control the compute plane.

41:41We were just this sort of control plane and we were running everything in the customer's environment. And we saw potential benefits to that, but the biggest drawback is you don't have as much control over the experience. And, you know, we wanted to take a more, I'll call it like a more of an Apple approach, you know, to really delivering, you know, customer joy. And so we re-architected that. And, you know, that basically took another year, you know. So it was a big, expensive, you know, re-platforming. But ultimately, we're really happy with the way Galaxy turned out. That must have been a – was it an obvious decision or was it like a super nerve-wracking decision?

42:15It was a nerve-wracking decision. It was obvious that we had prototyped this different approach that became Galaxy as it is today. and it was obvious that that was going to be better. But even still, it was a nerve-wracking approach because you're sort of throwing away like two years of development and starting over again. And, you know, of course, we're venture-backed and we spent a lot of money to build that. And that was actually one of the reasons we raised venture in the first place. You know, we have a little unusual history that we were bootstrapped the first two years and we were running a nice little profitable business.

42:47It was great. But we thought, you know what, we can't build a SaaS solution. We can't build a cloud platform off our little bootstrapped, you know, small business. It's just, you know, too expensive. And so we raised venture, built this cloud platform, and then, you know, threw it out and built it again. So that was definitely stressful. Good times. I'm sure that must have been made for good board meetings. Yes, for sure. We're lucky we have very patient investors. Yes. And the flip side to having a lot of different products in addition to the effort and time to build them is that It's a lot of surface to manage, a lot of like product management that's involved.

43:26How does that work practically having, you know, I guess now three different products that need to be sort of synced in terms of functionality? Yeah, definitely there's complexity to it. I think the way that we try to simplify it is find as many common components between that. You know, if you think of like a car manufacturer, you know, they usually, the big ones, the VWs, you know, of the world, you know, they'll build on one platform and that platform will then get used by, you know, five different car models or maybe 10, you know, where they've built the chassis, you know, once. And they'll use the engine, you know, the same engine in, you know, five different cars.

44:03So we've basically taken that kind of approach where there are common elements that are used across all three of those. But even still, there are differences among those different products. And that requires a lot of, you know, intentionality, puts some added pressure on our product managers to really make sure that they have their ICP really nailed down, their ideal customer profile that that particular product caters to, you know. And we touched on some of those things. You know, if you're a big multinational bank with an on-prem footprint, enterprise is probably your best bet. If you're more digital native, then you probably want to use Galaxy, you know.

44:39And so each one has its own unique components. And how does that work from a team perspective? Do you have different teams working on the different versions? We do, yeah. Yep, different engineering teams, different PMs. And again, there's common elements that are shared, but yep. And then so they all come to you and you have to decide on the priority and who gets more money to go faster? Yes, we're actually doing that right now for, you know, our next fiscal year. Very cool. And, you know, it's really, you know, one of the very interesting parts of this conversation is precisely that kind of on-prem versus cloud because, you know, the dominant narrative has been in open source has been that, you know, people who did that kind of like hybrid approach kind of ended up not being, not really liking it.

45:37and then recommending to anyone that would listen that people should do cloud only. But you've embraced the complexity and you do both. Yes. For better or worse, that's right. That's exactly right. I mean, you know, we started the company with this idea of optionality. It was probably one of the words that I used, actually, the data-driven event that you hosted, geez, I don't know, five or six years ago, right? Yeah, 2019. Yes. That's the second one we did. The one we did for Starburst, yes. Yes, yes. You remember words you used in 2019? I do because it was a word that I used a lot, and I still do.

46:14You know, optionality is certainly a word that you may hear in finance context, but not necessarily used that much in technology architectures. And, you know, we sort of founded the company on this idea, which is to really give customers, you know, the power of optionality, the flexibility to build a future-proof architecture that you can change out components, you can work on-prem, you can connect to different data sources. And I think part of that was also our bootstrapped orientation where we were very customer obsessed because we depended on that revenue to like pay the next month's payroll.

46:48And so optionality became sort of a core ethos. Now, the consequence of that is complexity for us as a vendor. So you're right to touch on that. But it's one of the reasons customers choose us. Okay. So we talked about the different offerings. We talked about sort of the architecture under the hood, maybe to close on architecture. So we did talk about Iceberg. You support the other two as well? We do. Optionality. Optionality. There you go. So Hoody and Delta. And Delta, yep. But you see most of your customer use Iceberg? Yes. I feel like the summer of 2024, the world said, okay, it is Iceberg.

47:33And that was like VHS, Betamax, you know, decision made. What triggered it? Was that the acquisition of Tabular by Databricks? I think it was two things. I think that was a big, big piece of it. I think the other thing that happened just maybe two weeks before that was that Snowflake also decided to support Iceberg. Yeah. And of course, you know, maybe some of your viewers are aware there was a bidding war for Tabular, which is what led to such an incredible outcome for them. Yes. And to put this so. Actually, I don't know what the numbers were, but I heard that Snowflake was bidding like 300 and then 600.

48:11And then what was the final price for? It was like 2 billion, right? Databricks. So Databricks won over Snowflake with a$2 billion deal. Yep. And now... Which is fascinating, which is probably partly why Ali wants to... One of the reasons why Ali wants to keep the company private for a bit longer is because he can do that kind of thing, which is a very bold move and exciting move. Yes. But they can do that without the scrutiny of public markets. A hundred percent. Yes, I totally agree. And what do you make of the acquisition from a strategic standpoint while we're on the topic? Because so Tabular, for anybody that may or may not follow the space very quickly, was a young Series B company that was the commercial company on top of Iceberg.

48:59and, but ultimately Tabular is an open source project. Yep. Well, Iceberg, yes, is. What did I say? I'm sorry. Iceberg, yes. Iceberg is an open source project. And it's sort of unclear what you buy. If your ultimate strategic goal is to take control over an open source project, sort of unclear what you actually buy by acquiring the company that's a commercial company on top of the open source project. I mean, on there, you know, this, you know, certainly better than me. But aren't there like a bunch of like iceberg contributors like AWS? Yeah. Starburst as well. Starburst and Microsoft, right.

49:39So. Snowflake? Yeah. I mean. So what do you get? Then you spend$2 billion, you buy a company. Yeah. But like to which extent, in your opinion, does that help you take control over that project? Yeah. I think this is a really interesting question. I don't think it does allow them to take over the project, to be perfectly honest. And I think that the market is actually resolutely determined to ensure that it continues to be independent, which is important actually for Iceberg. And that's what made Iceberg popular in the first place over Delta, you know, which was Databricks' own format. The market wants an independent standard.

50:17That's what they want, independent of any vendor. And so fortunately, there's enough groundswell of people like ourselves, like Snowflake, like some of the others you mentioned, where it is – and it's also, by the way, an Apache Software Foundation governed project. So you have the Apache Software Foundation also ensuring independent governance, which is important and makes it truly, I think, independent. I mean, Ryan works for Databricks now, and he will for some period of time. And Ryan being Ryan Blue, the CEO of Tabular. Exactly. But Iceberg is bigger than Databricks, honestly. And it will continue to be, I think, the ubiquitous format.

50:58You know, so if they had hopes of like killing it, I don't think that worked. If they had hopes of controlling it, I don't really think that will work either. I think probably the biggest thing that they get out of the acquisition is the ability to tell the market that they can do iceberg two and not look like they had made a mistake with Delta. You know, that they can they get sort of a marketing win out of it. But as a practical enduring matter, I don't think that they get extra influence beyond that. I also don't think like, I think Ryan cares too much about the future of Iceberg, like even to like, even if Ali was like, do this thing.

51:36I don't know that Ryan would do it, you know? Yeah. And why do you think Snowflake embraced Iceberg before that? Because that's fundamentally against their interest, right? Snowflake is all about put all your data in our proprietary database and leave it here forever. Yeah. So was it just pressure? Customer pressure. I think that's exactly right. I think that the market is basically saying we want to store our data in open formats. That's better for us. And by the way, this was sort of one of my theses, if you will, for starting Starburst is I believe gradually over time, enterprise software technology, at least infrastructure technology, wherever there are two things that are mostly the same and one is open and one is not, the open is going to win over time just because economics will eventually drive long-term decision-making.

52:26And, you know, that's what I saw with Cloudera. Again, Teradata was the better system at the time, but Cloudera was the open, open-source system. And that got a lot of adoption. Oh, that's right, right. Because so Teradata as in HADAPT at Teradata because, yeah, because HADAPT was basically a competitor in Pala, right? That's exactly right. Yeah. So we're going back memory lane, but... That's right. Yes. That's right. But Impala was open source. It was. And Hedapp slash shared data was proprietary. That's a lesson learned there. Exactly. I am the product of many lessons learned. And that's making me feel old.

53:04But that's, no, that's exactly right. Yeah. Which is a good thing in this space of enterprise software. Like having learned the lessons and try different things is very much a strength. Okay. Another thing that seems to be a part of the overall Starburst story and positioning is the data mesh. I would say, I would ask, what's up with the data mesh? That seems to be, you know, a very powerful idea. And we had Jarmak on, you know, either a data-driven or on earlier version of this podcast. But that was, you know, like all the rage sort of like a year or two ago. And I guess what's the current state of it?

53:50Is that as vibrant and exciting as ever? What's your take? No, I think the phrase or the concept has lost a bit of momentum. I think that there are companies who are deploying data meshes for sure, but some of the attention has certainly waned. I think the lasting legacy of that, though, is this concept of data products, creating these sort of curated data sets from data that can live in multiple places and thinking about them from a product perspective with a product mindset, which is to say that there's a clear owner of the data. And so I think like any, you know, major sort of wave or, you know, phenomenon, there's a lasting impact of that.

54:36Yeah. I mean, Jamak has her own company now. She's been very focused on, she probably hasn't been quite as publicly vocal on the data mesh concept. And I think what we've seen with customers is there's a people and process element that's actually harder than the technology part in implementing a data mesh. So we're very capable of helping customers implement data meshes from an architecture perspective. And we do do that. But I think, you know, it does require some retraining of sort of how you do things and how you organize your people internally. Yeah, yeah. And I think we're saying the same thing, but I think the term itself is perhaps a little like past the initial excitement, but the fundamental concept remains as something powerful.

55:24Yes, yes, exactly. Of course, the topic that we need to talk about is AI. Ah, yes. I'm hearing good things about AI. Apparently it's going to be big. It's going to be big. Yes. To which extent is that part of your positioning? We've talked a lot about SQL search and structured or semi-structured data. Yep. Equally, you were saying that for Dell, you were part of the AI story. So, like, where do you fit in the sort of AI stack world? Yeah. So, two places, I would say. First and foremost, on the training of models for those who are actually building their own models. Your models are only as good as the data that you train them on, and, you know, access to more data or better data is going to influence that.

56:10And so we become this sort of access layer to all the data in our organization. There is no piece of data that we cannot get to, essentially, through our federated architecture and being able to work across on-prem and cloud. And so that's one element is, you know, you're going to access data, you're going to do some transformation of data, prepare data, train a model with that data. The other is the more practical aspects of putting AI into production, which, you know, we see RAG workflows as an essential part of that. You know, people are building these agents that need to access contextual information, pass that along to the LLM to get the appropriate response as part of the agent or part of the application that they're building.

56:53And we think we can play a central role in that, both in terms of, you know, the access to structured data, but also as performing vector search. And so we expect to be doing more, and you'll hear a lot more about us, I think, this year around RAG workflows and how Starburst plays a unique role in that. A few words on the go-to markets, you know, lessons learned selling enterprise software. What has worked? What has not worked? How do you currently sell? Are you mostly outbound, sales driven, presumably given the type of customers? Yeah. Anything you can talk to? Sure. Yeah. Yeah, we're mostly direct.

57:36And then, of course, we have an important partnership with Dell that we touched on as a channel for us. You know, things we've learned. I think the biggest thing that we learned was that we are, and this is going to sound silly now in retrospect, but it was an important lesson. We're hardcore enterprise software. You know, we're not a PLG motion. And I say that that feels silly in retrospect because, like, it's so clear to us now today. But there was a period where we thought we wanted to be PLG, you know. Well, I think just about any company in 2021 thought they were PLG, right? Yeah, for sure.

58:11For sure. And so, you know, we hired sales leaders who had PLG experience and we thought that would make us PLG. But the reality is like PLG starts with the P, which is the product itself. And we're pretty hardcore, complex, you know, data infrastructure stuff. And our solution architects are some of the superheroes of our go-to-market. Like they're the guys and gals who know how to, you know, deploy this and work within the specific unique data ecosystem that a particular customer has. And each one is different and unique. And that's part of what makes it hard to really create a true PLG motion around this is that, you know, every customer is a little bit different in terms of what they're trying to do and how they want to deploy it.

58:55And do you charge services? Is like service is a component? We do. Yes, yes. And we do that both ourselves. We have our own professional services organization, and then we've trained up some amazing partners. We work with one that's pretty prevalent here in New York City called Kubrick, which does amazing stuff. And we work with a number of SIs outside of that, too. But, yeah, services are definitely helpful, especially for large, complex enterprises that are deploying this. So, actually, a very interesting topic, services for, you know, any founder listening to this. How did you go about building that network, so Kubrick or others?

59:35When did you feel it was the right time to start working with SIs? That's a good question. I would say, you know, you should probably learn how to do it yourself first. So I would say not at the very, very beginning. But then my advice would be try to start training up one or two and probably boutique firms like don't don't go after Accenture on day one, but start with a smaller boutique firm. Because what's most important, especially in the early parts of developing the services piece, is the quality of the delivery. And you want to control the quality of that as much as possible. Obviously, you control it when you do it yourself, but that's difficult to scale.

1:00:17And it's not the margins that your investors probably care about. They don't want you to build a services company. So that's probably not going to be the dominant way you want to deliver ultimately. But by choosing just one or two and really focusing on them, there's a mutual benefit there. A, you're helping to bring them business and you're creating that positive reinforcement cycle that learning how to deploy your software is going to lead to more revenue for them. But also you're creating a reliable partner that you can count on, that you know when you recommend that firm to deliver services at customer X, you know they're not gonna make you look bad, you know, and that's really important.

1:00:57So I would say, you know, rather than going far and wide and signing up hundreds and thousands of SIs, which we did that too along the way, I would say go deep with one or two, probably on the smaller end, just because you'll be able to get more attention span and try to make them as good as delivering as you are. And then you can build on that relationship. And that can be very symbiotic. And then the Dell partnership, which is more of a, you know, ISV kind of, which is the other sort of side of the partnership world. To the extent you can talk about it, how did that come about? Because, you know, a lot of people want to do that.

1:01:31A lot of startups want to do that. And that actually happens very rarely. So what was the history and how long did he take and any lessons learned there? They actually came to us. And I think that's a really important element. Hard to recreate, of course, for any entrepreneurs who are listening to this. But the reason that's important is whenever a partner is coming to you, you know that there's some motivation behind it. Maybe that you don't even know initially on why that's so important. And that's going to just drive momentum and focus and interest because the biggest challenge working with somebody so much larger than you is how do they get focus on it, right?

1:02:05I mean, they have tens of thousands of employees. How do we know this is going to be important to them? Well, because, you know, a VP within Dell was like, this is important. We need to close this gap. Michael himself thinks we need to close this gap. And a VP of product. A VP of product. That's right. Not a VP of partnerships. That's actually a good point. Yeah, I think of the partnerships organization, at least in this context, as the facilitators of the relationship. But the real impetus, the real motivation has to, I think, come from product. It could come from sales. That's also not bad. You know, in some cases, it's a revenue leader who's saying, hey, we need partnership with this company because we know it helps us get into these customers or close these deals.

1:02:45I think that's great motivation as well. But I think you do need that stakeholder who's willing to stand up and sort of sponsor the initiative to get the right resources around it. So in our case, it was on the product side. They had come to us. They looked at all the players in the market. They did some bake-offs and decided, hey, we're the best technology for what they were trying to do. And that led to this OEM agreement. We started as a reseller. So we did about a year as a reseller agreement, meaning that they could resell our software. And then that emerged or evolved into a true OEM where we're now embedded in their product offering.

1:03:21It is a Dell product. You know, for all their sellers, it's a first-party product. They're getting comped in full. You know, it is treated as a Dell product in every way. So how long was the whole cycle from first call to where you are now, like a couple of years? Yeah, I would say probably, yeah, we're probably now starting year three. And I would say it was two years to get it really out the door as a skew that they could sell. So it is also long. Yeah. Yeah. So people should, yeah, should view those as very long term projects. And who drove that on your end? Like, how did you make it successful on the Starburst end?

1:04:01So I think that idea of focus needs to be present on both sides to make it work. And so in my case, I have a SVP of alliances. His name is Tony, who sits on my executive team. He works directly for me, reports to me, who shepherded that deal on our end. And that meant that it was always visible to me. You know, it was something we talked about in every one of our one-on-ones. How's it going? Where are we? What are the negotiation points? What are we working through? And so I think it really needs to be that kind of priority on both sides to make it work. Okay. Maybe to close, what's the next year going to look like for you?

1:04:40Or I guess we're at the beginning of 2025. So what's the next year going to be for you and then for the industry? So that's the prediction, 2025 prediction part of the question. Yeah, question. The thing I'm most excited about is really seeing AI start to get implemented in real production use cases. I think, you know, the past year or year and a half has been a lot of experimentation, a lot of prototyping. And I think we're now just starting to see AI actually matriculate into real production use cases. And that's going to get us over the hype cycle. Because I think even in my own organization, when we started to get into AI, you know, some people were like, is this just a fad?

1:05:22Is this, you know, like – Is this like crypto? Yeah, yeah, exactly. No, like we're going to quickly get into, I think, a real like, you know, plateau of productivity or whatever they call it in the hype curve here where we're going to actually see material results, which is only going to just reinforce, I think, the demand for it. And I think, you know, RAG in particular will be a really important element of making AI production ready. Okay. So that's both what you have on the docket that starburst and your prediction for the industry. Yes, exactly. Very cool. All right. Well, Justin, thank you so much.

1:05:59That's been wonderful. Really interesting chat. Appreciate it. Thank you for having me. Always a pleasure. Hi, it's Matt Turk again. Thanks for listening to this episode of the MAD podcast. If you enjoyed it, we'd be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from. This really helps us build a podcast and get great guests. Thanks and see you at the next episode.