 Hey gang, welcome back to theCUBE's coverage of Snowflake Summit 22 live on the show floor at Caesars Forum in Las Vegas. Lisa Martin here with Dave Vellante. Couple of guests joining us to unpack more of what we've been talking about today. George Fraser joins us, the CEO of FiveTran and Veronica Durgan, the head of data at Saks Fifth Avenue. Guys, welcome to the program. Thank you for having us. Hello. George, talk to us about FiveTran for the audience that may not be super familiar. Talk to us about the company, your vision, your mission, your differentiation and then maybe the partnership with Snowflake? Well, a lot of people in the audience here at Snowflake Summit probably are familiar with FiveTran. We have almost 2,000 shared customers with them. So a considerable amount of the data that we're all talking about here flows through FiveTran. But in brief, what FiveTran is, is we're data pipeline and that means that we go get all the data of your company in all the places that it lives. So all your tools and systems that you use to run your company, we go get that data and we bring it all together in one place like Snowflake. And that is the first step in doing anything with data is getting it all in one place. So you've been considerable amount of shared customers. I think I saw this morning on the slide over 5,900 but you're saying you're already at around 2,000 shared customers, lots of innovation, I'm sure with between both companies. But talk to us about some of the latest developments at FiveTran in terms of product, in terms of company growth, what's going on? Well, one of the biggest things that happened recently with FiveTran is we acquired another data integration company called HVR. And HVR's specialty has always been replicating the biggest, baddest enterprise databases like Oracle and SQL server databases that are enormous, that are run within an inch of their capabilities by their DBAs and HVR was always known as the best in the business at that scenario. And by bringing that together with FiveTran, we now really have the full spectrum of capabilities we can replicate all types of data for all sizes of company. And so that's a really exciting development for us and for the industry. So Veronica, ahead of data at Saks, what does that entail? How do you spend your time? What's your purview? So the cool thing about Saks is a very old company. Saks is the premier luxury e-commerce platform and we help our Saks with having your customers just express themselves through fashion. So we're trying to modernize the very old company and we do have the biggest, baddest databases of any flavor you can imagine. So my job is to modernize, to bring us to near real-time data to make sure data is available to all of our users so they can actually take advantage of it. So let's talk about some of those biggest, baddest hairballs that you've got and how you deal with that. So a lot of, over time, you've built up a lot of data. You've got different data stores. So what are you doing with that and how, what role does FiveTran and Snowflake play in helping you modernize? Yeah, FiveTran helps us ingest data from all of those data sources into Snowflake near real-time. It's very important to us and one of the examples that I give is within a matter of maybe a few weeks we were able to get data from over a dozen of different data sources into Snowflake in near real-time and some of those data sources were not available to our users in the past and everybody was so excited and the reason they weren't available is because they require a lot of engineering effort to actually build those data pipelines to manage them and maintain them. Oh, sorry. It was just a follow-up. So FiveTran is kind of the consolidator of all that data and Snowflake plays that role as well. We bring it all together and the place that it has consolidated is Snowflake and from there you can really do anything with it and there's really three things you were kind of touching on it that make data integration hard. One is volume and that's the one that people tend to talk about just size of data and that is important but it's not the only thing. It's also latency. How fresh is the data in the locus of consolidation? Before FiveTran the state of the art was nightly snapshots. Once a day was considered pretty good and we consider now once a minute pretty good and we're trying to make it even better and then the last challenge which people then tend not to talk about it sort of the dark secret of our industry is just incidental complexity. All of these data sources have a lot of strange behaviors and rules and corner cases. Every data source is a little bit different and so a lot of what we bring to the table is that we've done the work over 10 years and in the case of HBR since the 90s to map out all of these little complexities of all these data sources that as a user you don't have to see it. You just connect source, connect destination and that's it. So you don't have to do the M word, migrate off of all those databases. You can maybe allow them to dial them down over time and create new value with using FiveTran and Snowflake. Is that the right way to think about it? Well, FiveTran like it's, I just want to like it's incredibly simple. You just connect it to whatever source and then the matter of minutes you have a pipeline and I mean it's like for us it's in the matter of minutes for FiveTran there's hundreds of engineers. We're kind of like extending our data engineering team to now FiveTran and we can pick and choose which tables we want to replicate which fields and once data lands on Snowflake now we have data across different sources in one place, in central place and now we can do all kinds of different things. We can integrate data together. We can do validations. We can do reconciliations. We now have ability to do point and time historical kind of journey in the past in transactional system. You don't see that. You only see data that's right now but now that we replicate everything to Snowflake and Snowflake being so powerful as an analytical platform we can do what did it look like two months ago? What did it look like two years ago? You've got all that time series data. And to address that word you mentioned a moment ago migrate, this is something people often get confused about. What we're talking about here is not a migration. These source systems are not going away. You know these databases are the systems powering sacs.com and they're staying right there. They're the systems you interact with when you place an order on the site. The purpose of our tool and the whole stack that Veronica has put together is to serve other workloads in Snowflake that need to have access to all of the data together. But if you didn't have Snowflake you would have to push those other data stores and try to have them do things that they have sometimes a tough time doing. Okay. You can't run analytical workloads. You cannot do reporting on the transactional database. It's not meant for that. It's supporting capability of an application and it's configured to be optimized for that. So we always had to offload that those specific analytical reporting functionality or machine learning somewhere else and Snowflake is excellent for that. It's meant for that. What was, I was going to ask you what you were doing before what you just answered that. What was the aha moment for realizing you needed to work with the power of FiveTran and Snowflake? If we look at, you talked about, you know, Saks being a legacy history company that's obviously been very successful at transforming to the digital age, but what was that? One thing is the head of the data that this is it. Great question. I've worked with FiveTran in the past. This is my third company saying with Snowflake I actually brought FiveTran into two companies at this point. My first experience with both FiveTran and Snowflake was this, like, this is where I want to be. Like, this is the stack and the tooling and just the engineering behind it. So as I moved on to the next company that that was, you know, I'm bringing tools, you know, with me, so that was kind of like part. And the other thing I want to mention, like, when we look, when we evaluate tools for a new platform we kind of look at things in like three dimensions, right? One, we're cloud first. We want to have native, you know, cloud native tools and they have to be modular, but we also don't want to have too many tools. So FiveTran certainly checks that off their, you know, cloud first, cloud native, and they also have a very long list of connectors. The other thing is for us, it's very important that, you know, data engineering effort is spent on actually analyzing data, not building pipelines and supporting infrastructure. And FiveTran, you know, reliable, it's secure, it has various connectors, so it checks off that box as well. And another thing is that we're looking for companies we can partner with. So companies that help us grow and grow with us, we're looking at company culture, their maturity, how they treat their customers and how they innovate. And again, FiveTran checks off that box as well. And I imagine Snowflake does as well, Frank Slutman on stage this morning talked about mission alignment. And it seemed to me like, ah, one of the missions of Snowflake is to align with its customers' missions. It sounds like from the conversations that Dave and I have had today, that it's the same with partners, but it sounds like you have that cultural alignment with FiveTran and Snowflake. Oh, absolutely. And FiveTran has that with, obviously with 2,000 shared customers. Yeah, I think that, well, not quite there yet, but we're close. I think that, you know, the most important way that we've always been aligned with our customers is that we've been very clear on what we do and don't do. And that our job is to get the data from here to there, that the data be accurately replicated, which means in practice, I often joke that it is exactly as messed up as it was in the source, no better and no worse. But we really will accomplish that task. You do not need to worry about that. You can well and fully delegate it to us. But then what you do with the data, you know, we don't claim that we're gonna solve that problem for you. That's up to you. And then anyone who claims that they're gonna solve that problem for you, you should be very skeptical. So how do you solve that problem? You have? Well, that's where modeling comes in, right? Like you get data from point A to point B and it's like, you know, bed in, bed out. Like that's it. And that's where, you know, we do those reconcilations and that's where we model our data. We actually try to understand what our business is, how our, you know, users, how they talk about data, how they talk about business. And you know, that's where data warehouse is important and in our cases, data vault. Talk to me a little bit before we wrap here about the benefits to the end user, the consumer. Say I'm on sacs.com, I'm looking for a particular item. What is it about this foundation that sacs has built with Fivetran and with Snowflake that's empowering me as a consumer to be able to get, find what I want, get it, get the transaction done like that? So getting access to, like, our end goal is to, you know, help our customers, right? Make their experience, you know, beautiful, luxurious. We want to make sure that what we put in front of you is what you're looking for so you can actually make that purchase and you're happy with it. So having that data, having that data coming from various different sources into one place enables us to do that near real-time analytics so we can help you as a customer to find what you're looking for. Magic on the back end, delighting customers. The world is kind of still messed up, right? I mean, airlines are out of whack. There's supply imbalances. You've got the situation in Ukraine with oil prices, the Fed kind of missed the mark. So can data solve these problems? I mean, you know, if you think about the context of the macro environment and you bring it down to what you're seeing at Saks, with your relationship with Fivetran and with Snowflake, do you see the light at the end of that confusion tunnel? That's such a great question, very philosophical. I don't think data can solve it. Is the people looking at data and working together that can solve it? I think data can help. Data can't stop a war. Data can't help you forecast supply chain misses and mitigate those problems so data can help. Can be a facilitator. Can be a facilitator. Yeah, it can be a facilitator of whatever you end up doing with it. Data can be used for good or evil. It's ultimately up to the user. Do you bring a hammer to a gunfight? No, but it's a tool in the right hands, you know, for the right purpose, it can definitely help. So you have this great foundation. You're able to delight customers as, especially from a luxury brand perspective, I imagine that luxury customers have high expectations. What's next for Saks from a data perspective? Well, we want to first and foremost modernize our data platform. We want to make sure we actually bring that near real time data to our customers. We want to make sure data is reliable, that well understood that we do the, you know, data engineering and the modeling behind the scenes so that people that are using our data can rely on it. Because it's like, you know, there is bad data is bad data, but we want to make sure it's very clear. And what's next? I mean, the sky's the limit. Is your, can you describe your data team teams? Is it highly centralized? What's your philosophy in terms of the architecture of the organization? So right now we're starting with a centralized team. It just works for us as we're trying to rebuild our platform and modernize it. But as we become more mature, we establish, you know, our practices, our data governance, our definitions, then I see a future where we kind of like decentralize a little bit. And actually each team has their own, you know, analytical function or potentially data engineering function as well. That'll be an interesting discussion when you get there. That's a hot topic. It's one of the hardest problems in building a data team is whether decentralized or decentralized. We're still centralized at FiveTrain, but the company's now over a thousand people and we're starting to feel the strain of that. And inevitably you eventually have to find a way to find seams and create specialization. You just have to be fluid, right? And go with the company as the company grows and things change. Yeah, I mean, I've worked with some companies. I mean, JPMC is here. They've got a little kind of, I'll call it a skunkworks. They're probably understates what they're doing, but they're testing that out. Company like Hello Fresh is doing some things because their Hadoop cluster just couldn't scale. So they have to begin to decentralize. It is a hot topic these days. You know, I'm not sure there's a right or a wrong. It's really a situational, but I think in a lot of situations, it's maybe the trend. Yeah, I think centralized versus decentralized technology is a different question than centralized versus decentralized teams. They're both valid, but they're very different. And sometimes people conflate them and that's very dangerous because you might want one to be centralized and the other to be decentralized. Well, it's true. And I think a lot of folks look at a centralized team and say, hey, it's more efficient to have these specialized roles, but at the same time, what's the outcome? If the outcome can be optimized and it's maybe a little bit more people-expensive or I don't know, and they're in the lines of business where there's data context, that might be a better solution for a company. I mean, to truly understand the value of data, you have to specialize in that specific area. So I see people kind of like deep diving into specific vertical or whatever that is and truly understanding what data they have and how to take advantage of it. Well, all this talk about monetization and building data products. I mean, you're there, right? You're on the cusp of that. And so who's going to build those data products? It's going to be somebody in the business. Today, they don't quote unquote own the life cycle of the data. They don't feel responsible for it, but they complain when it's not what they want. And so I feel as though what Snowflake is doing is actually attacking some of those problems. I mean, not 100% there, obviously, but a lot of work to do. You know, great analysts are great navigators of organizations, amongst other things. And one of the best things that's happened as part of this evolution from technology like Hadoop to technology like Snowflake is the new stack is a lot simpler. There's a lot less technical knowledge that you need. You still need technical knowledge, but not nearly what you used to. And that has made it accessible to more people, people who bring different skills to the table. And in many cases, those are the skills you really need to deliver value from data. Is not, you know, do you know the inner workings of HDFS, but do you know how to extract from your constituents in the organization a precise version of the question that they're trying to ask. You really want them spending their time. The technical infrastructure is an operational detail. Right. You know, so you can put your teams on those types of questions, not how do we make it work? And that's kind of what Hadoop was. Hey, we got it to work. And that's something we're obsessed with. We're always trying to hide the technical complexities of the problem of data centralization behind the scenes. Even if it's harder for us, even if it's more expensive for us, we will pay any costs so that you don't have to see it because that allows our customers to focus on more high impact costs. Well, this is a case where a technology vendor's R&D is making your life, you know, easier. Easier, right. I would presume you'd rather spend money to save time than spend your time to save, engineering time to save money. That's true. And at the end of the day, hiring, you know, three data engineers to do custom work that a tool does, it's actually not saving money. It costs more in the end. But to your point, pulling business people into those data teams gives them ownership and they feel like they're part of the solution and it's such a great feeling so that they're excited to contribute, they're excited to help us. So I love where the industry's going like in that direction. And of course, that's the theme of the show, the world around data collaborations. Absolutely critical. Guys, thank you so much for joining David and me, talking about five trans notes, like together what you're doing to empower stocks, to be a data company. I'm going to absolutely have a different perspective next time I shop there. Thanks for joining us. Thank you. Thanks you guys. For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit 22 from Vegas. Stick around, our next guest joins us momentarily.