 Good morning everyone, welcome back to theCUBE's coverage of Snowflake Summit 22 live from Caesars Forum in Las Vegas. Lisa Martin here with Dave Vellante. This is day three of our coverage. We've had an amazing, amazing time, great conversations talking with Snowflake executives, partners, customers. We're going to be digging into data mesh with data.world. Please welcome John Loyans, the Chief Product Officer. Great to have you on the program, John. Thank you so much for having me here. I mean, the summit, like you said, has been incredible. So many great people, such a good time. Really, really nice to be back in person with folks. It is fabulous to be back in person. The fact that we're on day four for them, and this is the solution so-case, is as packed as it is at 11 in the morning, is saying something. People were chomping up the bit to hear what they're doing and innovate. Absolutely, usually those last days of conferences, everybody starts getting a little tired, but we're not seeing that at all here. Especially in Vegas, this is impressive. Talk to the audience a little bit about data.world, what you guys do, and talk about the Snowflake relationship. Absolutely, data.world is the only true cloud-native enterprise data catalog. We've been an incredible Snowflake partner, and Snowflake's been an incredible partner to us. Really, since 2018, when we became the first data catalog in the Snowflake Partner Connect experience. Snowflake and the data cloud make it so possible, and it's changed so much in terms of being able to very easily transition data into the cloud to break down those silos, and to have a platform that enables folks to be incredibly agile with data from an engineering and infrastructure standpoint. Data.world is able to provide a layer of discovery and governance that matches that agility and the ability for a lot of different stakeholders to really participate in the process of data management and data governance. So, Data Mesh, basically, Jermak Dagoni lays out the, first of all, the fault domains of existing data and big data initiatives, and she boils it down to the fact that it's just this monolithic architecture with hyper-specialized teams that you have to go through, and it slows everything down, and it doesn't scale, they don't have domain context, so she came up with four principles, if I may. Domain ownership, so push it out to the businesses, they have the context, they should own the data. The second is data as product, we're certainly hearing a lot about that this week. The third is that, so that makes sounds good, push out the data, great, but it creates two problems. Self-serve infrastructure, okay, but her premise is infrastructure should be an operational detail, and then the fourth is computational governance. So, you talked about data, where do you fit in those four principles? You know, honestly, we are able to help teams realize the Data Mesh architecture, and we know that Data Mesh is really, it's both a process and a culture change, but then, when you want to enact a process and a culture change like this, you also need to select the appropriate tools to match the culture that you're trying to build, the process and the architecture that you're trying to build, and the data.world data catalog can really help along all four of those axes. When you start thinking first about, let's say, let's take the first one, data as a product, right? We, even like a very meta of us from metadata management platform at the end of the day, the very meta of us, when you talk about data as a product, we track adoption and usage of all your data assets within your organization and provide program teams and offices of the CDO with incredible event analytics, very detailed, that gives them the right audit trail that enables them to direct very scarce data engineering, data architecture resources to make sure that their data assets are getting adopted and used properly. On the domain driven side, we are entirely knowledge graph and open standards based enabling those different domains. We have incredible joint Snowflake customers like ProLogists and we chatted a lot about this in our session here yesterday, where because of our knowledge graph underpinnings, because of the flexibility of our metadata model, it enables those domains to actually model their assets uniquely from group to group without having to relaunch or run different environments. Like you can do that all within one data catalog platform without having to have separate environments for each of those domains. Federated governance, again, the amount of data exhaust that we create that really enables ambient governance and participatory governance as well. We call it agile data governance, really the adoption of agile and open principles applied to governance to make it more inclusive and transparent and we provide that in a way that can federate across those domains and make it consistent. Okay, so you facilitate across that whole spectrum of principles and so in the early examples of data mesh that I've studied and actually collaborated with, like with JPMC who I don't think is, who's not using your data catalog, hello, fresh, you may or may not be, but I mean there are numbers and I want to get to that. But what they've done is they've enabled the domains to spin up their own, whatever, data lakes, data warehouses, data hubs, at least in concept, most of them are data lakes on AWS, but still in concept they want to be inclusive and they've created a master data catalog and then each domain has a sub-catalog which feeds into the master and that's how they get consistency and governance and everything else. Is that the right way to think about it? Do you have a different spin on that? Yeah, I have a slightly different spin on it. I think organizationally it's the right way to think about it and in absence of a catalog that can truly have multiple federated metadata models, multiple graphs in one platform, that is really kind of the only way to do it. With Data.World you don't have to do that. You can have one platform, one environment, one instance of Data.World that spans all of your domains, enable them to operate independently and then federate across them. So you just answered my question as to why I should use Data.World versus Amazon Glue. Oh, absolutely. I mean, that's awesome that you've done now. How have you done that? What's your secret sauce? The secret sauce there is really, in all credit to our CTO, one of my closest friends who was a true student of knowledge graph practices and principles and really felt that the right way to manage metadata and knowledge about the data analytics ecosystem that companies were building was through federated linked data, right? So we use standards and we've built an open and extensible metadata model that we call COS that really takes the best parts of existing open standards in the semantic space, things like schema.org, dcat, Dublin Core brings them together and models out the most typical enterprise data assets, providing you with an ontology that's ready to go. But because of the graph nature of what we do is instantly accessible without having to rebuild environments, without having to do a lot of management against it. It's really quite something and it's something all of our customers are very impressed with and are getting a lot of leverage out of it. And we have a lot of time today so we're not going to short change this topic. One last question then I'll shut up and let you jump in. This is an open standard, it's not open source. No, it's an open standard. Built on open standards. We also fundamentally believe in extensibility and openness. We do not want to vertically lock you into our platform. So everything we have is API driven API available. Your metadata belongs to you if you need to export your graph instantly available in open machine readable formats. That's really, we come from the open data community. That was a lot of the founding of data.world. We've worked a lot with the open data community and we fundamentally believe in that. And that's enabled a lot of our customers as well to truly take data.world and not have it be a data catalog application but really an entire metadata management platform and extend it even further into their enterprise to really catalog all of their assets but also to build incredible integrations to things like corporate search. Having data assets show up in corporate wiki search along with all the descriptive metadata that people need has been incredibly powerful and an incredible extension of our platform that I'm so happy to see our customers in need to be doing. So it's not exclusive to Snowflake. It's not exclusive to AWS. You can bring it anywhere of Azure, GCP, anything. Yeah, you know, where we are, where we love Snowflake, look, we're at the Snowflake summit and we've always had a great relationship with Snowflake though I'm really leaned in there because we really believe Snowflake's principles particularly around cloud and being cloud native and the operating advantages that it affords companies that's really aligned with what we do. And so Snowflake was really the first of the cloud data catalogs that we ultimately or say the first cloud data warehouses that we integrated with and to see them transition to building really out the data cloud has been awesome. Talk about how data.world and stuff like enable companies like Prologis to be data companies these days, every company has to be a data company but they have to be able to do so quickly to be competitive and to really win. How do you help them if we like up level the conversation to really impacting the overall business? That's a great question, especially right now, everybody knows and Prologis is a great example. They're a logistics and supply chain company at the end of the day and we know how important logistics and supply chain is nowadays. And for them and for a lot of our customers, I think one of the advantages of having a data catalog is the ability to build trust, transparency and inclusivity into their data analytics practice. By adopting agile principles, by adopting a data mesh, you're able to extend your data analytics practice to a much broader set of stakeholders and to involve them in the process while the work is getting done. One of the greatest things about agile software development when it became a thing in the early 2000s was how inclusive it was and that inclusivity led to a much faster ROI on software projects. And we see the same thing happening in data analytics. People, you know, we have amazing data scientists and data analysts coming up with these insights that could be business changing, that could make their company significantly more resilient, especially in the face of economic uncertainty. But if you have to sit there and argue with your business stakeholders about the validity of the data, about the techniques that were used to do the analysis and it takes you three months to get people to trust what you've done, that opportunity's passed. So how do we shorten those cycles? How do we bring them closer? And that's really a huge benefit that like prologists has realized just tightening that cycle time, building trust, building inclusion and making sure ultimately humans learn by doing. And if you can be inclusive, it even increases things like that we all want to help because Lord knows that the world needs it, things like data literacy, right? So data.world can inform me as to where on the spectrum of data quality my data set lives so I can say, okay, this is usable, shareable, gold standard versus fix this. Okay. And you could do that with one data catalog, not a bunch of, yeah. And trust is really a multifaceted and multi-angle idea, right? It's not just necessarily data quality or data observability. We have incredible partnerships in that space like our partnership with Monte Carlo where we can ingest all their amazing observability information and display that in a really consumable way in our data catalog. But it also includes things like the lineage, who touched it, who was involved in the process of it? Can I get a question answered quickly about this data? What's it been used for previously and do I understand that? It's so multifaceted that you have to be able to really model and present that in a way that's unique to any given organization, even unique within domains, within a single organization. Yeah, so you're not, I mean to suggest you're a data quality supplier, but you'll partner with them and then that you become the master catalog. Right, exactly. Exactly. And you just raised a series C, 15 million. We did, yeah. We're really lucky to have incredible investors like Goldman Sachs who led our series C. It really, I think communicates the trust that they have in our vision and what we're doing and the impact that we can have on organizations' ability to be agile and resilient around data analytics. Enabling customers to have that single source of truth is so critical, you talked about trust. That is no joke. Absolutely. That is critical and there's a tremendous amount of business impact, positive business impact that can come from that. What are some of the things that are next for data.world that we're going to see? Oh, you know, I love this. We have such an incredibly innovative team that's so dedicated to this space and the mission of what we're doing. We're out there trying to fundamentally change how people get data analytics work done together. One of the big reasons I founded the company is I really truly believe that data analytics needs to be a team sport. It needs to go from single player mode to team mode and everything that we've worked on in the last six years has leaned into that. Our architecture, being cloud native, we've done over a thousand releases a year that nobody has to manage. You don't have to worry about upgrading your environment. It's a lot of the same story that's made Snowflake so great. We are really excited to have announced and March in our own summit and we're rolling this suite of features out over the course of the year. A new package of features that we call data.world Eureka, which is a suite of automations and knowledge driven functionality that really helps you leverage a knowledge graph to make decisions faster and to operationalize your data in the data ops way. With significantly less effort. Big, big impact there. John, thank you so much for joining Dave and me unpacking what data.world is doing, the data mesh, the opportunities that you're giving to customers in every industry. We appreciate your time and congratulations on the news and the funding. Yeah, thank you. It's been a true pleasure. Thank you for having me on. And I hope you guys enjoy the rest of the day and your other guests that you have. Thank you, we will. All right, for our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE's third day of coverage of Snowflake Summit 22 live from Vegas. Dave and I will be right back with our next guest. So stick around.