>>Welcome back to Vegas guys, Lisa Martin and Dave Lanta here wrapping up our coverage of day two of snowflake summit. We have given you a lot of content in the last couple of days. We've had a lot of great conversations with snowflake folks with their customers and with partners. And we have an alumni back with us. Please. Welcome back to the queue. Mike Palmer, CEO of Sigma computing. Mike. It's great to see you. >>Thanks for having me. And I guess again >>Exactly. >>It's fantastic me. >>So talk to the audience about Sigma before we get into the snowflake partnership and what you guys are doing from a technical perspective, give us that overview of the vision and some of the differentiators. >>Sure. You know, you've over the last 12 years, companies have benefited from enormous investments and improvements in technology in particular, starting with cloud technologies, obviously going through companies like snowflake, but in terms of the normal user, the one that makes the business decision in the marketing department and the finance team, you know, in the works in the back room of the supply chain, doing inventory very little has changed for those people. And the time had come where the data availability, the ability to organize it, the ability to secure it was all there, but the ability to access it for those people was not. And so what Sigma's all about is taking great technology, finding the skillset they have, which happens to be spreadsheets. There are billion license spreadsheet users in the world and connecting that skillset with all of the power of the cloud. >>And how do you work with snowflake? What are some of the, the what's the joint value proposition? >>How are they as an investor? That's what I wanna know. Ah, >>Quiet, which is the way we like them. No, I'm just kidding. Snowflake is, well, first of all, investment is great, but partnership is even better. Right. You know, and I think snowflake themselves are going through some evolution, but let's start with the basics of technology where this all starts because you know, all of the rest doesn't matter if the product is not great, we work directly on snowflake. And what that means is as an end user, when I, when I sit on that marketing team and I want to understand and, and connect, how did I get a, a customer where I had a pay to add? And they showed up on my website and from my website, they went to a trial. And from there, they touched a piece of syndicated contents. All of that data sits in snowflake and I, as a marketer, understand what it means to me. >>So for the first time, I want to be able to see that data in one place. And I want to understand conversion rates. I want to understand how I can impact those conversion rates. I can make predictions. What that user is doing is going to, to Sigma accessing live data in snowflake, they're able to ask ad hoc questions, questions that were never asked questions, that they don't exist in a filter that were never prepped by a data engineer. So they could truly do something creative and novel in a very independent sort of way. And the connection with Snowflake's live data, the performance, the security and governance that we inherit. These are all facilitators to really expand that access across the enterprise. So at, at a product level, we were built by a team of people, frankly, that also were the original investors in snowflake by two amazing engineers and founders, Rob will and Jason France, they understood how snowflake worked and that shows up in the product for our end customers. >>So, but if I may just to follow up on that, I mean, you could do that without snowflake, but what, it would be harder, more expensive. Describe what you'd have to go through to accomplish that outcome. >>And I think snowflake does a good job of enabling the ecosystem at large. Right. But you know, you always appreciate seeing early access to understand what the architecture's going to look like. You know, some of the things that I will, you know, leaning forward that we've heard here that we're very excited about is snowflake going to attack the TP market, right? The transactional market, one of the transactional database market. I, yeah. Right. You know, one of the things that we see coming, and, and one of the bigger things that we'll be talking about in Sigma is not just that you can do analytics out of snowflake. I think that's something that we do exceptionally well on an ad hoc basis, but we're gonna be the first that allow you to write into snowflake and to do that with good performance. And to do that reliably, we go away from OAP, which is the terminology for data warehousing. >>And we go toward transactional databases. And in that world, understanding snowflake and working collaboratively with them creates again, a much better experience for the end customer. So they, they allow us into those programs, even coming to these conferences, we talk to folks that run the industry teams, trying to up level that message and not just talk database and, and analytics, but talk about inventory management. How do we cut down the gap that exists between POS systems and inventory ordering, right? So that we get fewer stockouts, but also that we don't overorder. So that's another benefit, >>Strong business use cases. >>That's correct. >>And you're enabling those business users to have access to that data. I presume in near real time or near real time, so that they can make decisions that drive marketing forward or finance forward or legal >>Forward. Exactly. We had a customer panel yesterday. An example of that go puff is hopefully most of the viewers are familiar with, as a delivery company. This is a complicated business to run. It's run on the fringes. When we think about how to make money at it, which means that the decisions need to be accurate. They need to be real time. You can't have a batch upload for delivery when they're people are on the street, and then there's an issue. They need to understand the exact order at that time, not in 10 minutes, not from five minutes ago, right. Then they need to understand, do I have inventory in the warehouse when the order comes in? If they don't, what's a replacement product. We had a Mike came in from go puff and walked us through all of the complexity of that and how they're using Sigma to really just shorten those decision cycles and make them more accurate. You know, that's where the business actually benefits and, >>And actually create a viable business model. Cuz you think back to the early, think back to the.com days and you had pets.com, right? They couldn't make any money. Yeah. Without chewy. Okay. They appears to be a viable business model. Right? Part of that is just the efficiencies. And it's sort of a, I dunno if those are customers that they may or may not be, but they should be if they're not >>Chewy is, but okay. You know, and that's another example, but I'll even pivot to the various REI and other retailers. What do they care about cohorts? I'm trying to understand who's buying my product. What can I sell to them next? That, that idea of again, I'm sitting in a department, that's not data engineering, that's not BI now working collaboratively where they can get addend engineer, putting data sets together. They have a BI person that can help in the analytics process. But now it's in a spreadsheet where I understand it as a marketer. So I can think about new hierarchies. I wanna know it by customer, by region, by product type. I wanna see it by all of those things. I want to be able to do that on the fly because then it creates new questions that sort of flow. If you' ever worked in development, we use the word flow constantly, right? And as people that flow is when we have a question, we get an answer that generates a question. We have, we just keep doing that iteratively. That that is where Sigma really shines for them. >>What does a company have to do to really take advantage of, of this? I, if they're kind of starting from a company that's somewhat immature, what are the sort of expectations, maybe even outta scope expectations so they can move faster, accelerate analytics, a lot of the themes that we've heard today, >>What does an immature company is actually even a question in, in and of itself? You know, I think a lot of companies consider themselves to be immature simply because for various constraint reasons, they haven't leveraged the data in the way that they thought possible. Good, >>Good, good definition. Okay. So not, not, >>Not, I use this definition for digital transformation. It very simple. It is. Do you make better decisions, faster McKenzie calls this corporate metabolism, right? Can you speed up the metabolism of, of an enterprise and for me and for the Sigma customer base, there's really not much you have to do once. You've adopted snowflake because for the first time the barriers and the silos that existed in terms of accessing data are gone. So I think the biggest barrier that customers have is curiosity. Because once you have curiosity and you have access, you can start building artifacts and assets and asking questions. Our customers are up and running in the product in hours. And I mean that literally in hours, we are a user in snowflake, that's a direct live connection. They are able to explore tables, raw. They can do joins themselves if they want to. They can obviously work with their data engineering team to, to create data sets. If that's the preferred method. And once they're there and they've ever built a pivot table, they can be working in Sigma. So our customers are getting insights in the first one to two days, you referenced some, those of us are old enough to remember pest.com. Also old enough to remember shelfware that we would buy. We are very good at showing customers that within hours they're getting value from their investment in Sigma. And that, that just creates momentum, right? Oh, >>Tremendous momentum and >>Trust and trust and expansion opportunities for Sigma. Because when you're in one of those departments, someone else says, well, you know, why do you get access to that data? But I don't, how are you doing this? Yeah. So we're, you know, I think that there's a big movement here. People, I often compare data to communication. If you go back a hundred years, our communication was not limited. As it turns out by our desire to communicate, it was limited by the infrastructure. We had the typewriter, a letter and the us postal service and a telephone that was wired. And now we have walk around here. We, everything is, is enabled for us. And we send, you know, hundreds and thousands of messages a day and probably could do more. You will find that is true. And we're seeing it in our product is true of data. If you give people access, they have 10 times as many questions as they thought they had. And that's the change that we're gonna see in business over the next few years, >>Frank Salman's first book, what he was was CEO of snowflake was rise of the data cloud. And he talked about network effects. Basically what he described was Metcalf's law. Again, go back to the.com days, right? And he, Bob Metcalf used the phone system. You know, if there's two people in the phone system, it's not that valuable, right. >>You know, exactly, >>You know, grow it. And that's where the value is. And that's what we're seeing now applied to data. >>And even more than that, I think that's a great analogy. In fact, the direct comparison to what Sigma is doing actually goes one step beyond everything that I've been talking about, which is great at the individual level, but now the finance team and the marketing team can collaborate in the platform. They can see data lineage. In fact, one of our, our big emphasis points here is to eliminate the sweet products. You know, the ones where, you know, you think you're buying something, but you really have a spreadsheet product here and a document product there and a slide product over there. And they, you know, you can do all of that in Sigma. You can write a narrative. You can real time live, edit on numbers. You, you know, if you want to, you could put a picture in it. But you know, at Sigma we present everything out of our product. Every meeting is live data. Every question is answered on the spot. And that's when, you know, you know, to your point about met cap's law. Now everybody's involved in the decision making. They're doing it real time. Your meetings are more productive. You have fewer of them because they're no action items, right. We're answering our questions there and we're, and we're moving forward. >>You know, view were meeting sounds good. Productivity is, is weird now with the, the pandemic. But you know, if you go back to the nineties here am I'm, I'm dating myself again, but that's okay. You know, you, you didn't see much productivity going on when the PC boom started in the eighties, but the nineties, it kicked in and pre pandemic, you know, productivity in the us and Europe anyway has been going down. But I feel like Mike, listen to what you just described. I, how many meetings have we been in where people are arguing about them numbers, what are the assumptions on the numbers wasting so much time? And then nothing gets done and they, then they, they bolt cut that away and you drive in productivity. So I feel like we're on a Renaissance of productivity and a lot of that's gonna be driven by, by data. Yeah. And obviously communications the whole 5g thing. We'll see how that builds out. But data is really the main spring of, I think, a new, new Renaissance in productivity. >>Well, first of all, if you could find an enterprise where you ask the question, would you rather use your data better? And they say, no, like, you know, show me, tell me that I'll short their stock immediately. But I do agree. And I, unfortunately I have a career history in that meeting that you just described where someone doesn't like, what you're showing them. And their first reaction is to say, where'd you get that data? You know, I don't trust it. You know? So they just undermined your entire argument with an invalid way of doing so. Right. When you walk into a meeting with Sigma where'd, where'd you get that data? I was like, that's the live data right now? What question do you want answer >>Lineage, right. Yeah. And you know, it's a Sen's book about, you know, gotta move faster. I mean, this is an example of just cutting through making decisions faster because you're right. Mike and the P the P and L manager in a meeting can, can kill the entire conversation, you know, throw FUD at it. Yeah. You know, protect his or her agenda. >>True. But now to be fair to the person, who's tended to do that. Part of the reason they've done that is that they haven't had access to that data before the meeting and they're getting blindsided. Right. So going back to the collaboration point. Yes. Right. The fact we're coming to this discussion more informed in and of itself takes care of some of that problem. Yeah. >>For sure. And if, and if everybody then agrees, we can move on and now talk about the really important stuff. Yeah. That's good. It >>Seems to me that Sigma is an enabler of that curiosity that you mentioned that that's been lacking. People need to be able to hire for that, but you've got a platform that's going here. You go ask >>Away. That's right in the we're very good. You know, we love being a SaaS platform. There's a lot of telemetry. We can watch what we call our mouse to Dows, you know, which is our monthly average users to our daily average users. We can see what level of user they are, what type of artifacts they build. Are they, you know, someone that creates things from scratch, are they people that tend to increment them, which by the way, is helpful to our customers because we can then advise them, Hey, here's, what's really going on. You might wanna work with this team over here. They could probably be a little better of us using the data, but look at this team over here, you know, they've originated five workbooks in the last, you know, six days they're really on it. There's, there's, you know, that ability to even train for the curiosity that you're referring to is now there, >>Where are your customer conversations? Are they at the lines of business? Are they with the chief data officer? What does that look like these days? >>Great question. So stepping back a bit, what, what is Sigma here to do? And, and our first phase is really to replace spreadsheets, right? And so one of the interesting things about the company is that there isn't a department where a spreadsheet isn't used. So Sigma has an enormous Tam, but also isn't necessarily associated with any particular department or any particular vertical. So when we tend to have conversations, it really depends on, you know, either what kind of investment are you making? A lot of mid-market companies are making best technology investments. They're on a public cloud, they're buying snowflake and they wanna understand what's, what's built to really make this work best over the next number of years. And those are very short sales for us because we, we prove that, you know, in, in minutes to hours, if you're working at a large enterprise and you have three or four other tools, you're asking a different question. >>And often you're asking a question of what I call exploration. We have a product that has dashboards and they've been working for us and we don't wanna replace the dashboard. But when we have a question about the data in the dashboard, we're stuck, how do we get to the raw data? How do we get to the example that we can actually manage? You can't manage a dashboard. You can't manage a trend line, but if you get into the data behind the trend line, you can make decisions to change business process, to change quality, accuracy, to change speed of execution. That is what we're trying to enable. Those conversations happen between the it team who runs technology and the business teams who are responsible for the decisions. So we are, you know, we have a cross departmental sale, but across every department, >>One of the things we're not talking about at this event, which is kind of interesting, cause it's all we've been talking about is the macro supply chain challenges, Ukraine, blah, blah, blah, and the stock market. But, but how are you thinking about that? Macro? The impacts you're seeing, you know, a lot of private companies being, you know, recapped, et cetera, you guys obviously very well funded. Yeah. But how do you think about, I mean, I asked Frank a similar question. He's like, look, it's a marathon. We don't worry about it. We, you know, they made the public market, they get 5 billion in cash. Yeah. Yeah. How are you thinking about it? >>You know, first of all, what's the expression, right? You never, never waste a good, you know, in this case recession, no, we don't have one yet, but the impetus is there, right. People are worried. And when they're worried, they're thinking about their bottom lines, they're thinking about where they're going to get efficiency and their costs. They're already dealing with the supply chain issues of inventory. We all have it in our personal lives. If you've ordered anything in the last six months, you're used to getting it in, you know, days to weeks. And now you're getting in months, you know, we had customers like us foods as a good example, like they're constantly trying to align inventory. They have with transportation that gets that inventory to their end customers, right? And they do that with better data accuracy at the end point, working with us on what we are launching. >>And I mentioned earlier, having more people be able to update that data creates more data, accuracy creates better decisions. We align that then with them and better collaboration with the folks that then coordinate the trucks with Prologis and the panel yesterday, they're the only commercial public company that reports their, their valuations on a quarterly basis. They work with Sigma to trim the amount of time it takes their finance team to produce that data that creates investor confidence that holds up your stock price. So I mean the, the importance of data relative to all the stakeholders in enterprise cannot be overstated. Supply chain is a great example. And yes, it's a marathon because a lot of the technology that drives supply chain is old, but you don't have to rip out those systems to put your data into snowflake, to get better access through Sigma, to enable the people in your environment to make better decisions. And that's the good news. So for me, while I agree, there's a marathon. I think that most of the, I dunno if I could continue this metaphor, but I think we could run quite far down that marathon without an awful lot of energy by just making those couple of changes. >>Awesome. Mike, this has been fantastic. Last question. I, I can tell, I know a lot of growth for Sigma. I can feel it in your energy alone. What are some of the key priorities that you're gonna be focusing on for the rest of the year? >>Our number one priority, our number two priority and number three priority are always build the best product on the market, right? We, we want customers to increase usage. We want them to be delighted. You know, we want them to be RA. Like we have customers at our booth that walk up and it's like, you're building a great company. We love your product. I, if you want to show up happy at work, have customers come up proactively and tell you how your products changed their life. And that is, that is the absolute, most important thing because the real marathon here is that enablement over the long term, right? It is being a great provider to a bunch of great companies under that. We are growing, you know, we've been tripling the company for the fast few years, every year, that takes a lot of hiring. So I would've alongside product is building a great culture with bringing the best people to the company that I guess have my energy level. >>You know, if you could get paid in energy, we would've more than tripled it, you know, but that's always gonna be number two, where we're focused on the segment side, you know, is really the large enterprise customer. At this point, we are doing a great job in the mid-market. We have customer, we have hundreds of customers in our free trial on a constant basis. I think that without wanting to seem over confident or arrogant, I think our technology speaks for itself and the product experience for those users, making a great ROI case to a large enterprise takes effort. It's a different motion. We're, we're very committed to building that motion. We're very committed to building out the partner ecosystem that has been doing that for years. And that is now coming around to the, the snowflake and all of the ecosystem changes around snowflake because they've learned these customers for decades and now have a new opportunity to bring to them. How do we enable them? That is where you're gonna see Sigma going over the next couple of years. >>Wow, fantastic. Good stuff. And a lot of momentum, Mike, thank you so much for joining Dave and me talking about Sigma, the momentum, the flywheel of what you're doing with snowflake and what you're enabling customers to achieve the massive business outcomes. Really cool stuff. >>Thank you. And thank you for continuing to give us a platform to do this and glad to be back in conferences, doing it face to face. It's fantastic. >>It it's the best. Awesome. Mike, thank you for Mike Palmer and Dave ante. I'm Lisa Martin. You've been watching the cube hopefully all day. We've been here since eight o'clock this morning, Pacific time giving you wall the wall coverage of snowflake summit 22 signing off for today. Dave and I will see you right bright and early tomorrow morning. I will take care guys.

>> Narrator: Live from Las Vegas, it's The Cube, covering AWS re:Invent 2017. Presented by AWS, Intel, and our ecosystem of partners. >> Bob: It's actually a little quieter here. >> Hey, welcome back to AWS re:Invent 2017. I am Lisa Martin. We're all very chatty. You can hear a lot of chatty folks behind us. This is day two of our continuing coverage. 42,000 people here, amazing. I'm Lisa Martin with my co-host Keith Townsend, and we're very excited to be joined by a Cube alumni Bob Muglia, CEO and President of Snowflake. >> Thank you. >> Lisa: Welcome back. >> Thank you, good to be back. >> And Kelly Mungary, the Director of Enterprise Data and Analytics from Lionsgate. A great use case from Snowflake. Thanks so much guys for joining us. So one of the hot things going on today at the event is your announcement Bob with AWS and Snowpipe. What is Snowpipe? How do customers get started with it? >> Great, well thanks. We're excited about Snowpipe. Snowpipe is a way of ingesting data into Snowflake in a streaming, continuous way. You simply can drop new data that's coming in into S3 and we'll ingest it for you automatically. Makes that super, super simple. Brings the data in continuously into your data warehouse, ensuring that you're always up to date and your analysts are getting the latest insights and the latest data. >> So, when you guys were founded, about five years ago, as the marketing says on your website, a complete data warehouse built for the Cloud. What was the opportunity back then? What did you see that was missing, and how has Snowflake evolved to really be a leader in this space? >> So you know, if you go back five years this was a time frame where no SQL was the big rage, and everybody was talking about how SQL was passe and it's something that you're not see in the future. Our founders had a different view, they had been working on true relational databases for almost 20 years, and they recognized the power of SQL and relational technology but they also saw that customers were experiencing significant limits with existing technology, and those limits really restricted what people could do. They saw in the Cloud and what Amazon had done the ability to build a all new database that takes advantage of the full elasticity and power of the Cloud to deliver whatever set of analytics capabilities that the business requires. However much data you want, however many queries simultaneously. Snowflake takes what you love about a relational database and removes all the limits, and allows you to operate in a very different way. And our founders had that vision five years ago, and really successfully executed on it. The product has worked beyond our dreams, and our customers, our response from our customers is what we get so excited about. >> So, the saying is "Data is the new oil". However, just as oil is really hard to drill for and find, finding the data to service up, to even put in a data lake to analyze has been a challenge. How did you guys go about identifying what data should even be streamed to Snowpipe? >> Well, yeah, that's a great question. I mean, in entertainment today, we're experiencing probably like in pretty much every type of business. A data explosion. We have, you know, streaming is big now. We have subscription data coming in, billing data, social media data, and on and on. And the thing is, it's not coming in a normal, regular format. It's coming in what we call a semi-structured, structured, json, xml. So, up until Snowflake came onto the scene with a truly Cloud based SAAS solution for data warehousing pretty much everyone was struggling to wrangle in all these data sets. Snowpipe is a great example of one of the avenues of bringing in these multiple data sets, merging them real time, and getting the analytics out to your business in an agile way that has never been seen before. >> So, can you talk a little bit about that experience? Kinda that day one up, you were taking these separate data sources, whether it's ERP solution, data from original content, merging that together and then being able to analyze that. What was that day one experience like? >> Well, you know, I gotta tell you, it evolves around a word, that word is "Yes", okay? And data architects and executives and leaders within pretty much every company are used to saying, "We'll get to that" and "We'll put it on the road map", "We could do that six months out", "Three months out". So what happened when I implemented Snowflake was I was just walking into meetings and going, "Yes". "You got it". "No worries, let's do it". >> Lisa: It liberated. >> Well, it's changes, it's not only liberating, it changes the individual's opportunities, the team's opportunities, the company's opportunities, and ultimately, revenue. So, I think it's just an amazing new way of approaching data warehousing. >> So Bob, can you talk a little bit about the partnership with AWS, and the power to bring that type of capability to customers? Data lakes are really hard to do that type of thing run a query against to get instant answers. Talk about the partnership with AWS to bring that type of capability. >> Well Amazon's been a fantastic partner of ours, and we really enjoy working with Amazon. We wind up working together with them to solve customer problems. Which is what I think is so fantastic. And with Snowflake, on top of Amazon, you can do what Kelly's saying. You can say yes, because all of a sudden you can now bring all of your data together in one place. Technology has limited, it's technology that has caused data to be in disparate silos. People don't want their data all scattered all over the place. It's all in these different places because limits to technology force people to do that. With the Cloud, and with what Amazon has done and with a product like Snowflake, you can bring all of that data together, and the thing that's interesting, where Kelly is going, is it can change the culture of a company, and the way people work. All of a sudden, data is not power. Data is available to everyone, and it's democratizing. Every person can work with data and help to bring the business forward. And it can really change the dynamics about the way people work. >> And Kelly, you just spoke at the multi-city Cloud Analytics Tour that Snowflake just did. You spoke in Santa Monica, one of my favorite places. You talked about a data driven culture. And we hear data driven in so many different conversations, but how did you actually go about facilitating a data driven culture. Who are some of the early adopters, and what business problems have you been able to solve by saying yes? >> Well, I can speak entertainment in general. I think that it's all about technology it's about talent, and it's about teaching. And with technology being the core of that. If we go back five years, six years, seven years, it was really hard to walk into a room, have an idea, a concept, around social media, around streaming data, around billing, around accounting. And to have an agile approach that you could bring together within a week or so forth. So what's happening is, now that we've implemented Snowflake on AWS and some of the other what I call dream tools on top of that. The dream stack, which includes Snowflake. It's more about integrating with the business. Now we can speak the same language with them. Now we can walk into a room and they're glad to see me now. And at the end of the day, it's new, it's all new. So, this is something that I say sometimes, in kidding, but it's actually true. It's as if Snowflake had a time traveler on staff that went forward in the future ten years to determine how things should be done in the big data space, and then came back and developed it. And that's how futuristic they are, but proven at the same time. And that allows us to cultivate that data driven culture within entertainment, because we have tools and we have the agile approach that the business is looking for. >> So, Kelly, I'm really interested, and I love the concept of making data available to everyone. That's been a theme of this conference from the keynote this morning, which is putting tools in builder's hands, and allowing builders to do what they do. >> Kelly: That's right. >> And we're always surprised at what users come back with. What's one of the biggest surprises from the use cases, now that you've enabled your users. >> Well, I'm gonna give you one that's based on AWS and Snowflake. A catch phrase you hear a lot of is "Data center of excellence", and a lot of us are trying to build out these data centers of excellence, but it's a little bit of an oxymoron to the fact that a data center of excellence is really about enabling your business and finding champions within marketing, within sales, within accounting, and giving them the ability to have self-service business intelligence, self-service data warehousing. The kinds of things that, again, we go back five, six years ago, you couldn't even have that conversation. I'll tell you today, I can walk into a room, and say, "Okay, who here is interested in learning "about data warehousing?". And there'll be somebody, "Okay, great". Within an hour, I'll have you being dangerous in terms of setting up, standing up, configuring and loading a data warehouse. That's unheard of, and it's all due to Snowflake and their new technology. >> I'd love to understand Bob, from your perspective. First of all, it sounds like you have a crystal ball according to Kelly, which is awesome. But second of all, collaboration, we talked about that earlier. Andy Jassy is very well known and very vocal about visiting customers every week. And I love their bottom, their backwards approach to, before building a product, to try to say, "What problem can we solve?". They're actually working with customers first. What are their requirements? Tell me a little bit Bob about the collaboration that Snowflake has with Lionsgate, or other customers. How are they helping to influence your crystal ball? >> You know what, this is where I think what Amazon has done, and Andy has done a fantastic job. There's so much to learn from them, and the customer centricity that Amazon has always had is something that we have really focused to bring into Snowflake, and really build deeply into our culture. I've sort of said many, many times, Snowflake is a value space company. Our values are important to us, they're prominent in our website. Our first value is we put our customer's first. What I'm most proud of is, every customer who has focused on deploying Snowflake, has successfully deployed Snowflake, and we learn from them. We engage with them. We partner with them. All of our customers are our partners. Kelly and Lionsgate are examples of customers that we learn from every day, and it's such a rewarding thing to hear what they want to do. You look at Snowpipe and what Snowpipe is, that came from customers, we learned that from customers. You look at so many features, so many details. It's iterative learning with customers. And what's interesting about that, it's listening to customers, but it's also understanding what they do. One of the things that's interesting about Snowflake is is that as a company we run Snowflake on Snowflake. All of our data is in Snowflake. All of our sales data, our financial data, our marketing data, our product support data, our engineering data. Every time a user runs a query, that query is logged in Snowflake and intrinsics about it are logged. So what's interesting is because it's all in one place, and it's all accessible, we can answer essentially any question, about what's been done. And then, driving the culture to do that is an important thing. One of the things I do find interesting is, even at Snowflake, even at this data centered company, even where everything is all centralized, I still find sometimes people don't reference it. And I'm constantly reinforcing that your intuition, you know, you're really smart, you're really intuitive, but you could be wrong. And if you can answer the question based on what's happened, what your customers are doing, because it's in the data, and you can get that answer quickly, it's a totally different world. And that's what you can do when you have a tool with the power of what Snowflake can deliver, is you could answer effectively any business question in just a matter of minutes, and that's transformative, it's transformative to the way people work, and that, to me, that's about what it means to build a data driven culture. Is to reinforce that the answer is inside what customers are doing. And so often, that is encapsulated in the data. >> Wow, your energy is incredible. We thank you so much Bob and Kelly for coming on and sharing your story. And I think a lot of our viewers are gonna learn some great lessons from both of you on collaboration on transformations. So thanks so much for stopping by. >> Yeah. >> Thank you so much, we really enjoyed it. Thanks a lot. >> Likewise, great to meet you. >> Thanks Kelly. >> Thank you. >> For my co-host Keith Townsend, and for Kelly and Bob, I am Lisa Martin. You've been watching The Cube, live on day two, continuing coverage at AWS re:Invent 2017. Stick around, we have great more guests coming up. (upbeat music)