(electronic music) >> Welcome back to our studios in Palo Alto, California. My name is Dave Vellante, I'm here with John Furrier, who is taking a quick break. You know, in one of the early examples that we used of so called super cloud was Snowflake. We called it a super data cloud. We had, really, a lot of fun with that. And we've started to evolve our thinking. Years ago, we said that data was going to form in the cloud around industries and ecosystems. And Benoit Dogeville is a many time guest of theCube. He's the co-founder and president of products at Snowflake. Benoit, thanks for spending some time with us, at Supercloud 22, good to see you. >> Thank you, thank you, Dave. >> So, you know, like I said, we've had some fun with this meme. But it really is, we heard on the previous panel, everybody's using Snowflake as an example. Somebody how builds on top of hyper scale infrastructure. You're not building your own data centers. And, so, are you building a super data cloud? >> We don't call it exactly that way. We don't like the super word, it's a bit dismissive. >> That's our term. >> About our friends, cloud provider friends. But we call it a data cloud. And the vision, really, for the data cloud is, indeed, it's a cloud which overlays the hyper scaler cloud. But there is a big difference, right? There are several ways to do this super cloud, as you name them. The way we picked is to create one single system, and that's very important, right? There are several ways, right. You can instantiate your solution in every region of the cloud and, you know, potentially that region could be AWS, that region could be GCP. So, you are, indeed, a multi-cloud solution. But Snowflake, we did it differently. We are really creating cloud regions, which are superimposed on top of the cloud provider region, infrastructure region. So, we are building our regions. But where it's very different is that each region of Snowflake is not one instantiation of our service. Our service is global, by nature. We can move data from one region to the other. When you land in Snowflake, you land into one region. But you can grow from there and you can, you know, exist in multiple cloud at the same time. And that's very important, right? It's not different instantiation of a system, it's one single instantiation which covers many cloud regions and many cloud provider. >> So, we used Snowflake as an example. And we're trying to understand what the salient aspects are of your data cloud, what we call super cloud. In fact, you've used the word instantiate. Kit Colbert, just earlier today, laid out, he said, there's sort of three levels. You can run it on one cloud and communicate with the other cloud, you can instantiate on the clouds, or you can have the same service running 24/7 across clouds, that's the hardest example. >> Yeah. >> The most mature. You just described, essentially, doing that. How do you enable that? What are the technical enablers? >> Yeah, so, as I said, first we start by building, you know, Snowflake regions, we have today 30 regions that span the world, so it's a world wide system, with many regions. But all these regions are connected together. They are meshed together with our technology, we name it Snow Grid, and that makes it hard because, you know, Azure region can talk to a WS region, or GCP regions, and as a user for our cloud, you don't see, really, these regional differences, that regions are in different potentially cloud. When you use Snowflake, you can exist, your presence as an organization can be in several regions, several clouds, if you want, geographic, both geographic and cloud provider. >> So, I can share data irrespective of the cloud. And I'm in the Snowflake data cloud, is that correct? I can do that today? >> Exactly, and that's very critical, right? What we wanted is to remove data silos. And when you insociate a system in one single region, and that system is locked in that region, you cannot communicate with other parts of the world, you are locking data in one region. Right, and we didn't want to do that. We wanted data to be distributed the way customer wants it to be distributed across the world. And potentially sharing data at world scales. >> Does that mean if I'm in one region and I want to run a query, if I'm in AWS in one region, and I want to run a query on data that happens to be in an Azure cloud, I can actually execute that? >> So, yes and no. The way we do it is very expensive to do that. Because, generally, if you want to join data which are in different region and different cloud, it's going to be very expensive because you need to move data every time you join it. So, the way we do it is that you replicate the subset of data that you want to access from one region from other region. So, you can create this data mesh, but data is replicated to make it very cheap and very performing too. >> And is the Snow Grid, does that have the metadata intelligence to actually? >> Yes, yes. >> Can you describe that a little? >> Yeah, Snow Grid is both a way to exchange metadata. So, each region of Snowflake knows about all the other regions of Snowflake. Every time we create a new region, the metadata is distributed over our data cloud, not only region knows all the region, but knows every organization that exists in our cloud, where this organization is, where data can be replicated by this organization. And then, of course, it's also used as a way to exchange data, right? So, you can exchange data by scale of data size. And I was just receiving an email from one of our customers who moved more than four petabytes of data, cross region, cross cloud providers in, you know, few days. And it's a lot of data, so it takes some time to move. But they were able to do that online, completely online, and switch over to the other region, which is very important also. >> So, one of the hardest parts about super cloud that I'm still trying to struggling through is the security model. Because you've got the cloud as your sort of first line of defense. And now we've got multiple clouds, with multiple first lines of defense, I've got a shared responsibility model across those clouds, I've got different tools in each of those clouds. Do you take care of that? Where do you pick up from the cloud providers? Do you abstract that security layer? Do you bring in partners? It's a very complicated. >> No, this is a great question. Security has always been the most important aspect of Snowflake sense day one, right? This is the question that every customer of ours has. You know, how can you guarantee the security of my data? And, so, we secure data really tightly in region. We have several layers of security. It starts by creating every data at rest. And that's very important. A lot of customers are not doing that, right? You hear of these attacks, for example, on cloud, where someone left their buckets. And then, you know, you can access the data because it's a non-encrypted. So, we are encrypting everything at rest. We are encrypting everything in transit. So, a region is very secure. Now, you know, from one region, you never access data from another region in Snowflake. That's why, also, we replicate data. Now the replication of that data across region, or the metadata, for that matter, is really our least secure, so Snow Grid ensures that everything is encrypted, everything is, we have multiple encryption keys, and it's stored in hardware secure modules, so, we bit Snow Grid such that it's secure and it allows very secure movement of data. >> Okay, so, I know we kind of, getting into the technology here a lot today, but because super cloud is the future, we actually have to have an architectural foundation on which to build. So, you mentioned a bucket, like an S3 bucket. Okay, that's storage, but you also, for instance, taking advantage of new semi-conductor technology. Like Graviton, as an example, that drives efficiency. You guys talk about how you pass that on to your customers. Even if it means less revenue for you, so, awesome, we love that, you'll make it up in volume. And, so. >> Exactly. >> How do you deal with the lowest common denominator problem? I was talking to somebody the other day and this individual brought up what I thought was a really good point. What if we, let's say, AWS, have the best, silicon. And we can run the fastest and the least expensive, and the lowest power. But another cloud provider hasn't caught up yet. How do you deal with that delta? Do you just take the best of and try to respect that? >> No, it's a great question. I mean, of course, our software is extracting all the cloud providers infrastructure so that when you run in one region, let's say AWS, or Azure, it doesn't make any difference, as far as the applications are concerned. And this abstraction, of course, is a lot of work. I mean, really, a lot of work. Because it needs to be secure, it needs to be performance, and every cloud, and it has to expose APIs which are uniform. And, you know, cloud providers, even though they have potentially the same concept, let's say block storage, APIs are completely different. The way these systems are secure, it's completely different. There errors that you can get. And the retry mechanism is very different from one cloud to the other. The performance is also different. We discovered that when we starting to port our software. And we had to completely rethink how to leverage block storage in that cloud versus that cloud, because just off performance too. And, so, we had, for example, to stripe data. So, all this work is work that you don't need as an application because our vision, really, is that application, which are running in our data cloud, can be abstracted for this difference. And we provide all the services, all the workload that this application need. Whether it's transactional access to data, analytical access to data, managing logs, managing metrics, all of this is abstracted too, so that they are not tied to one particular service of one cloud. And distributing this application across many region, many cloud, is very seamless. >> So, Snowflake has built, your team has built a true abstraction layer across those clouds that's available today? It's actually shipping? >> Yes, and we are still developing it. You know, transactional, Unistore, as we call it, was announced last summit. So, they are still, you know, work in progress. >> You're not done yet. >> But that's the vision, right? And that's important, because we talk about the infrastructure, right. You mention a lot about storage and compute. But it's not only that, right. When you think about application, they need to use the transactional database. They need to use an analytical system. They need to use machine learning. So, you need to provide, also, all these services which are consistent across all the cloud providers. >> So, let's talk developers. Because, you know, you think Snowpark, you guys announced a big application development push at the Snowflake summit recently. And we have said that a criterion of super cloud is a super paz layer, people wince when I say that, but okay, we're just going to go with it. But the point is, it's a purpose built application development layer, specific to your particular agenda, that supports your vision. >> Yes. >> Have you essentially built a purpose built paz layer? Or do you just take them off the shelf, standard paz, and cobble it together? >> No, we build it a custom build. Because, as you said, what exist in one cloud might not exist in another cloud provider, right. So, we have to build in this, all these components that a multi-application need. And that goes to machine learning, as I said, transactional analytical system, and the entire thing. So that it can run in isolation physically. >> And the objective is the developer experience will be identical across those clouds? >> Yes, the developers doesn't need to worry about cloud provider. And, actually, our system will have, we didn't talk about it, but a marketplace that we have, which allows, actually, to deliver. >> We're getting there. >> Yeah, okay. (both laughing) I won't divert. >> No, no, let's go there, because the other aspect of super cloud that we've talked about is the ecosystem. You have to enable an ecosystem to add incremental value, it's not the power of many versus the capabilities of one. So, talk about the challenges of doing that. Not just the business challenges but, again, I'm interested in the technical and architectural challenges. >> Yeah, yeah, so, it's really about, I mean, the way we enable our ecosystem and our partners to create value on top of our data cloud, is via the marketplace. Where you can put shared data on the marketplace. Provide listing on this marketplace, which are data sets. But it goes way beyond data. It's all the way to application. So, you can think of it as the iPhone. A little bit more, all right. Your iPhone is great. Not so much because the hardware is great, or because of the iOS, but because of all the applications that you have. And all these applications are not necessarily developed by Apple, basically. So, we are, it's the same model with our marketplace. We foresee an environment where providers and partners are going to build these applications. We call it native application. And we are going to help them distribute these applications across cloud, everywhere in the world, potentially. And they don't need to worry about that. They don't need to worry about how these applications are going to be instantiated. We are going to help them to monetize these applications. So, that unlocks, you know, really, all the partner ecosystem that you have seen, you know, with something like the iPhone, right? It has created so many new companies that have developed these applications. >> Your detractors have criticized you for being a walled garden. I've actually used that term. I used terms like defacto standard, which are maybe less sensitive to you, but, nonetheless, we've seen defacto standards actually deliver value. I've talked to Frank Slootman about this, and he said, Dave, we deliver value, that's what we're all about. At the same time, he even said to me, and I want your thoughts on this, is, look, we have to embrace open source where it makes sense. You guys announced Apache Iceberg. So, what are your thoughts on that? Is that to enable a developer ecosystem? Why did you do Iceberg? >> Yeah, Iceberg is very important. So, just to give some context, Iceberg is an open table format. >> Right. >> Which was first developed by Netflix. And Netflix put it open source in the Apache community. So, we embraced that open source standard because it's widely used by many companies. And, also, many companies have really invested a lot of effort in building big data, Hadoop Solutions, or DataX Solution, and they want to use Snowflake. And they couldn't really use Snowflake, because all their data were in open format. So, we are embracing Iceberg to help these companies move through the cloud. But why we have been reluctant with direct access to data, direct access to data is a little bit of a problem for us. And the reason is when you direct access to data, now you have direct access to storage. Now you have to understand, for example, the specificity of one cloud versus the other. So, as soon as you start to have direct access to data, you lose your cloud data sync layer. You don't access data with API. When you have direct access to data, it's very hard to sync your data. Because you need to grant access, direct access to tools which are not protected. And you see a lot of hacking of data because of that. So, direct access to data is not serving well our customers, and that's why we have been reluctant to do that. Because it is not cloud diagnostic. You have to code that, you need a lot of intelligence, why APIs access, so we want open APIs. That's, I guess, the way we embrace openness, is by open API versus you access, directly, data. >> iPhone. >> Yeah, yeah, iPhone, APIs, you know. We define a set of APIs because APIs, you know, the implementation of the APIs can change, can improve. You can improve compression of data, for example. If you open direct access to data now, you cannot evolve. >> My point is, you made a promise, from governed, security, data sharing ecosystem. It works the same way, so that's the path that you've chosen. Benoit Dogeville, thank you so much for coming on theCube and participating in Supercloud 22, really appreciate that. >> Thank you, Dave. It was a great pleasure. >> All right, keep it right there, we'll be right back with our next segment, right after this short break. (electronic music)

(calm music) >> Welcome to theCUBE's live coverage of Snowflake Summit '22 from Las Vegas, Caesars Forum. Lisa Martin here with Dave Vellante, we're going to unpack some really cool stuff next, in the next 10 minutes with you, Carl Perry joins us, the Director of Product Management at Snowflake, he's here to talk about Snowflake's new Unistore workloads, how it's driving the next phase of innovation, welcome to the program. >> Oh, thank you so much for having me, this is awesome. >> There's a ton of momentum here, I saw the the numbers from fiscal 23Q1, product revenue 394 million, 85% growth, a lot of customers here, the customer growth is incredible as well, talk to us about Unistore, what is it? Unpack it and how have the customers been influential in it's development? >> Yeah, so Unistore is a way for customers to take their transactional workloads, for their enterprise applications and now have them run on or be built on top of Snowflake and now, you have your transactional data, along with all of your historical data, so now you have a single unified platform for doing anything you need to do with your data, whether it's transactional, single row look-ups, we can do that, whether it's the analytical data across again, transactional and historical data in a single query, our customers are super excited about this. >> So, what are Hybrid Tables? Is that just an extension of external tables? >> Yeah, that's a great question. So, Hybrid Tables are a new table-type that we've added to Snowflake and Hybrid Tables are really kind of just like another table with a couple of key differences, so number one is that Hybrid Tables provide fast, fine-grain read and write operations, so when you do something like a select star from customers where customer ID=832, that's going to return extremely fast, but on top of that same data, your transactional data, you can actually perform amazing analytical queries that return extremely fast and that's what Hybrid Tables at their core are. >> So, what does this mean for, so you're bringing that world of transaction and analytics together, what does it mean for customers? Walk us through Carl, an example of- >> Yeah, so it's great, so Adobe is a customer that is looking at using and leveraging Hybrid Tables today, and then more broadly Unistore, and frankly, Adobe has been an amazing customer since they started their journey, just really quickly, they're in phase three, the first phase was customers had data in Snowflake that they wanted to take advantage of with the Adobe Campaign Platform and so what they did is they built a connector basically into and being able to access customer data, and then they started to look at, "Well, this thing's working really well, let's try to leverage Snowflake for all our analytical needs." And so that was kind of phase two, and now phase three is like, look let's go and reimagine what we can do with the Adobe Campaign Platform by having both the transactional and analytical data in the same platform, so that they can really enable their customers to do personalization, ad campaign management, understanding the ethicacy of those things at a scale that they haven't been able to do before. >> Prior to this capability, they would what? Have to go outside of the Snowflake Data Cloud? And do something else? And then come back in? >> Exactly, right? So, they'd have a transactional system where all of the transactional state for what the customer was doing inside Adobe Campaign, setting up all their campaigns and everything, and that would be stored inside a database, right? And then they would need to ensure that, that data was moved over to Snowflake for further analytical purposes, right? You know you imagine the complexity that our customers have to manage every single day, a separate transactional system, an ETL pipeline to keep that data flowing and then Snowflake, right? And with Unistore, we really believe that customers will be able to remove that complexity from their lives and have that single platform that really makes their lives easier. >> I mean, they'll still have a transactional system, will they not? Or do you see a day where they sort of sunset that? >> I mean, there's a set of workloads that are not going to be the best choice today for Unistore and Hybrid Tables, right? And so we know that customers will continue to have their own transactional systems, right? And there's lots of transactional systems that customers rely and have entire applications, and systems built around, right? Right now with Hybrid Tables and Unistore, customers can take those enterprise applications, not consumer-facing applications and move them over to leverage Snowflake, and then really think about re-imagining how they can use their data that's both realtime transactional, as well as all the historical data without the need to move things between systems or use a ton of different services. >> The Adobe example that you just gave seems like, I loved how you described the phases they're in, they're discovering, it's like peeling the onion and just discovering more, and more, but what it sounds like is that Snowflake has enabled Adobe to transform part of it's business, how is Unistore positioned to be so transformational for your customers? >> Well, I mean I think there's a couple of things, so one, they have this like level of complexity today for a set of applications that they can completely stop worrying about, right? No need to maintain that separate transactional system for that again, enterprise application, no need to maintain that ETL pipeline, that's kind of like one step, the next step is, I mean all your data's in Snowflake, so you can start leveraging that data for insight and action immediately, there's no delay in being able to take advantage of that data, right? And then number three, which I think is the most compelling part is because it's part of Snowflake, you getting the benefit of Snowflake's entire ecosystem, whether it's first party capabilities like easy to manage and enforce really powerful governance, and security policies, right? Being able to take data from the market place and actually join it with my realtime transactional data, this is game-changing and then most importantly is the third-party ecosystem of partners who are building all these incredible solutions on top of Snowflake, I can't even begin to imagine what they're going to do with Hybrid Tables in Unistore. >> So, Carl I have to ask you, so I talked to a lot of customers and I talked to a lot of technology companies, explain, so Snowflake obviously was the first to separate compute from storage and you know the cloud, cloud database and then tons of investment came into that space, kind of follow you on, so that's cool, you reached escape velocity, awesome, but a lot of the companies that I talked to are saying, "We're converging transaction and analytics," I think (speaking softly) calls it HTAP or something, they came up with a name, explain the difference between what you're doing and what everybody else is doing, and why, what customer benefits you're delivering? >> Yeah, so I mean I think that's a really great question and to use the term you used HTAP, right? It's a industry understood term, really when people think about HTAP, what that is about is taking your transactional data that you have and enabling you to do fast analytical capabilities on that, and that's great, but there are a couple of problems that historical HTAP solutions have suffered from, so number one, that acceleration, that colander format of data is all in memory, so you're bound by the total amount of memory that you can use to accelerate the queries that you want to, so that's kind of problem one, this is not the approach that Snowflake is taking, most importantly, it's not just about accelerating queries on transactional data, whether it's a single-row lookup or a complex aggregate, it's about being able to leverage that data within the data cloud, right? I don't want to have a separate dataset on a transactional system or an HTAP system that can give me great analytics on transactional data and then I can't use it with all the other data that I have, it's truly about enabling the transformation with the data cloud and completely taking away silos, so that your data, whether it's realtime, whether it's historical, can be treated as a single dataset, this is the key thing that is different about Unistore, you can take the power of the data cloud, all of it, all of the partners, all the solutions and all the capabilities we continue to add, and leverage your data in ways that nobody's thought of possible before. >> Governance is a huge, huge component of that, right? So, in the press release, you have this statement, "As part of the Unistore Snowflake is introducing Hybrid Tables," you explained that, "Which offer fast, single-row operations and allow customers to build transactional business applications directly on Snowflake"- >> Yep. >> That's a little interesting tidbit, so you expect customers are going to build transactional applications inside the data cloud? And somewhat minimize the work that is going to be required by their existing transactional databases, correct? >> Exactly and I think, so let me say a couple things on this, right? So, first of all, there's a class of applications that will be able to just build on top of Hybrid Tables and run on Snowflake directly, for their transactional needs, I think what's super interesting here though is when you again start to talk about all your data, one example that we're going to walk through tomorrow in our talk is being able to do a transaction that updates data in a Hybrid Table and then updates data in a Standard Snowflake Table, and then either being able to atomically commit, or rollback that transaction, this is a transaction that's spanning multiple different table types inside Snowflake and you'll have consistency of either the rollback or the commit, this type of functionality doesn't exist elsewhere and being able to take, and build transactional applications with these capabilities, we think is transformative- >> And that's all going to happen inside the Snowflake Data Cloud, with all the capabilities and it's not like you know what you're doing with Dell and Pure, it's nice, but it's read-only, you can't you know add and delete, and do all that stuff, this is Native? First class citizen inside the database? >> Yep, just like other table types, you'll be able to take on and leverage the power of the data cloud as a normal table that you'd be able to use elsewhere. >> Got to ask you, your energy in the way that you're talking about this is fantastic, the transformation that it's going to be, how central it is to the product innovations that Snowflake is coming out with, what's been the feedback from customers? As there's so many thousands of folks here today, the keynote was standing in your room only, there was an overflow, what are you hearing on the floor here? >> Well, I mean, I think it was funny in the talk when I announced that primary keys are going to be required and enforced, and we got a standing ovation, I was like, "Wow, I didn't expect people to be so excited about primary key enforcement." I mean, what's been amazing both about the private preview and the feedback we're getting there, and then some of the early feedback we're getting from customers is that they want to understand and they're really thinking about like, "Wait, I can use Snowflake for all of this now?" And honestly I think that people are kind of like, "But wait, what would I do if I could have those applications running on Snowflake and not have to worry about multiple systems? Wait, I can combine it with all my historical data and anything that's in the data cloud, like what can I do?" Is the question they're asking and I think that this is the most fascinating thing, customers are going to build things they haven't been able to build before and I'm super excited to see what they do. >> But more specifically, my takeaway is that customers, actually application builders are going to be able to build applications that have data inherent to those apps, I mean John Furrier years ago said, "You know data is the new development kit." And it never happened the data, the data stack if you will separate from the application development stack, you're bringing those two worlds together, so what do you think the implications are of that? >> Well, I mean I think that we're going to dramatically simplify our customers lives, right? A thing that we focus on at Snowflake is relentless customer innovation, so we can make their lives better, so I mean frankly we talk to customers like, "Wait, I can do all this? Wait, are you sure that I'll be able to do this?" And we walk through what we can do, and what we can't do, and they really are like, "Wow, this could just dramatically simplify our lives and wait, what could we do with our data here?" And so, I think with the announcement of Unistore, and also all the Native app stuff that we're announcing today, I think we're really trying to enable customers and app developers there to think about, and being able to leverage Snowflake as their transactional system, the system of source, so I mean, I'm super excited about this, I came to Snowflake to work on this and I'm like, "Can't believe we get to talk about it." >> How do you, how, how? How does this work? What's the secret sauce behind it? Is it architecture or is it? >> Yeah, so I mean I think a big part of it is the architecture that we chose, so you know number one, a key product philosophy that we have at Snowflake is we have one product, we don't have many, we don't put the onus of complexity onto our customers and so building that into Snowflake is actually really hard, so underlying Hybrid Tables, which is the feature that powers Unistore is a row storage engine, a row-based storage engine, right? And then data is asynchronously copied over into a colander format and what this provides, because it's just another table that's deeply integrated with Snowflake is the compiler's completely aware of this, so you can write a query that spans multiple tables and take advantage of it, and we'll take over all the complexity, whether it needs to be a fast response to a single-row lookup, or it needs to aggregate and scan a ton of data, we'll make sure that we choose the right thing and provide you with the best performance that we have- >> You built that intelligence inside of that? >> Completely built in and amazing, but provided in a very simple fashion. >> You said you came to Snowflake to do this? How long ago was that? >> I came here a little over a year and a half. >> Okay, and had they started working on this obviously beforehand, or at least envisioning it, right? >> Yeah, this I mean, this is absolutely incredible, I have been working on this now for a year and a half, some of the team members have been working on it for more and it's incredible to finally be able to talk to customers and everybody about it, and for them to tell us what they're trying to do. I've already talked to a bunch of customers like, "Well wait, I could do this, or this, what about this scenario?" And it's awesome to hear their requirements, right? The thing that's been most amazing and you'll hear it in the talk tomorrow with Adobe who's been a great customer is like, "Customers give us insanely hard requirements." And what I love about this company is not, "Well, you know it's easier to do it this way." It's like, "No, how can we actually make their life easier?" And so, we really focus on doing that with Snowflake. >> And that's one of the things Frank talked about this morning with that mission alignment being critical there. So, it's in private preview now, when can folks expect to get their hands on it? >> Well, we don't have a date right now we're talking about, but you can go signup to be notified of the public preview when we get there, I think it's like snowflake.com/try-unistore, but we'll publish that later and you know if you're interested in the private preview, talk to your account team and we'll see if we can get you in. >> Carl, thank you so much for joining Dave and me in an action-packed 15 minutes, talking about the power of Unistore, what it's going to enable organizations to do and it sounds like you're tapping the surface, there's just so much more innovation that's to come, you're going to have to come back. >> Yes, that sounds awesome, thank you so much. >> Our pleasure. For Carl and Dave Vellante, I'm Lisa Martin, you're watching theCUBE's live coverage of Snowflake Summit '22 from the show floor in Las Vegas, we're going to be right back with our next guest. (calm music)

(upbeat music) >> Good afternoon, everyone. Welcome back to theCUBE's live coverage of Snowflake Summit 22, the fourth annual Snowflake Summit. Lisa Martin here with Dave Vellante, We're live in Vegas, as I mentioned. We've got a couple of guests here with us. We're going to be unpacking some more great information that has come out of the show news today. Please welcome Chris Child back to theCUBE, Senior Director of Product Management at Snowflake, and Lisa Cramer is here, Head of Embedded Products at LiveRamp, guys welcome. >> Thank you. >> Hi. >> Tell us a little bit about LiveRamp, what you guys do, what your differentiators are and a little bit about the Snowflake partnership? >> Sure, well, LiveRamp makes it safe and easy to connect data. And we're powered by core identity resolution capabilities, which enable our clients to resolve their data, and connect it with other data sets. And so we've brought these identity infrastructure capabilities to Snowflake, and built into the Native Application Framework. We focused on two initial products around device resolution, which enables our clients to connect customer data from the digital ecosystem. This powers things like, measurement use cases, and understanding campaign effectiveness and ROI. And the second capability we built into the Native Application Framework is called transcoding. And this enables a translation layer between identifiers, so that parties can safely and effectively share data at a person-based view. >> Chris, talk to us about, Snowflake just announced a lot of news this morning, just announced, the new Snowflake Native Application Framework. You alluded to this, Lisa, talk to us about that. What does it mean for customers, what does it do? Give us all the backstory. >> Yeah, so we had seen a bunch of cases for our customers where they wanted to be able to take application logic, and have other people use it. So LiveRamp, as an example of that, they've built a bunch of complicated logic to help you figure out who is the same person in different systems. But the problem was always that, that application had to run outside of the Data Cloud. And that required you to take your data outside of Snowflake, entrust your data to a third party. And so every time that companies have to go, become a vendor, they have to go through a security review, and go through a long onerous process, to be able to be allowed to process the really sensitive data that these customers have. So with the Native Applications Framework, you can take your application code, all of the logic, and the data that's needed to build it together, and actually push that through secure data sharing into a customer's account, where it runs, and is able to access their data, join it with data from the provider, all without actually having to give that provider access to your core data assets themselves. >> Is it proper to think of the Native Application Framework as a PaaS layer within the Data Cloud? >> That's a great way to think about it. And so, this is where we've integrated with the marketplace as well. So providers like LiveRamp will be able to publish these applications. They'll run entirely on effectively a PaaS layer that's powered by Snowflake, and be able to deliver those to any region, any cloud, any place that Snowflake runs. >> So, we get a lot of grief for this term, but we've coined a term called "supercloud". Okay, and the supercloud is an abstraction layer that hovers above the hyperscale infrastructure. Companies like yours, build on top of that. So you don't have to worry about the underlying complexities. And we've said that, in order to make that a reality, you have to have a super PaaS. So is that essentially what you're doing? You're building your product on top of that? You're not worrying about, okay, now I'm going to go to Azure, I'm going to go to AWS, or I'm going to go to, wherever, is that a right way to think about it? >> That's exactly right. And I think, Snowflake has really helped us, kind of shift the paradigm in how we work with our customers, and enabled us to bring our capabilities to where their data lives, right? And enabled them to, kind of run the analytics, and run the identity resolution where their data sits. And so that's really exciting. And I think, specifically with the Native Application Framework, Snowflake delivered on the promise of minimizing data movement, right? The application is installed. You don't have to move your data at all. And so for us, that was a really compelling reason to build into it. And we love when our customers can maintain control of their data. >> So the difference between what you are doing as partners, and a SaaS, is that, you're not worrying about all the capabilities, there in the data, all the governance, and the security components. You're relying on the Data Cloud for that, is that right? Or is it a SaaS? >> Yeah, I think there's components, like certainly parts of our business still run in the SaaS model. But I think the ability to rely on some of the infrastructure that Snowflake provides, and honestly kind of the connectivity, and the verticalized solutions that Snowflake brings to bear with data providers, and technology providers, that matter most to that vertical, really enable us to kind of rely on some of that to ensure that we can serve our customers as they want us to. >> So you're extending your SaaS platform and bringing new capabilities, as opposed to building, or are you building new apps in the Data Cloud? This is, I'm sorry to be so pedantic, but I'm trying to understand from your perspective. >> Oh yeah, so we built new capabilities within the Data Cloud. It's based on our core identity infrastructure capabilities, but we wanted to build into the Native Application Framework, so that data doesn't have to move and we can serve our customers, and they can maintain control over their data in their environment. So we built new capabilities, but it's all based on our core identity infrastructure. >> So safe sharing reminds me of like when procurement says, do we have an MSA? Yes, okay, go. You know, it's just frictionless. Versus no, okay, send some paper, go back and forth and it just takes forever. >> That's one of the big goals that we see. And to your point on, is it a PaaS, is it a SaaS? We honestly think of it as something a little bit different, in a similar way to where, at Snowflake we saw a whole generation of SaaS business models, and as a utility, and a consumption-based model, we think of ourselves as different from a SaaS business model. We're now trying to enable application providers, like LiveRamp, to take the core technology in IP that they've built over many, many years, but deliver it in a completely new different way that wasn't possible. And so part of this is extending what they're doing, and making it a little easier to deploy, and not having to go through the MSA process in the same way. But also we do think that this will allow entirely new capabilities to be brought that wouldn't be possible, unless they could be deployed and run inside the Data Cloud. >> Is LiveRamp a consumption pricing model, or is it a subscription, or a combo? >> We are actually a subscription, but with some usage capabilities. >> It's an hybrid. >> Chris, talk a little bit about the framework that you guys have both discussed. How is it part of the overall Snowflake vision of delivering secure and governed, powerful analytics, and data sharing to customers, and ecosystem partners? >> So this, for us we view this as kind of the next evolution of Snowflake. So Snowflake was all built on helping people consolidate their data, bring all your data into one place and then run all of your different workloads on it. And what we've seen over the years is, there are still a lot of different use cases, where you need to take your data out of the Data Cloud, in order to do certain different things. So we made a bunch of announcements today around machine learning, so that you don't have to take your data out to train models. And native applications is built on the idea of don't bring your data to the applications you need. Whether they're machine learning models, whether they're identity resolution, whether they're really even just analytics. Instead, take the application logic and bring that into the Data Cloud, and run it right on your data where it is. And so the big benefit of that is, I don't need copies of my data that are getting out of sync, and getting out of date. I don't need to give a copy of my data to anyone else. I get to keep it, I get to govern it. I get to secure it. I know exactly what's going on. But now, we can open this up to workloads, not just ones that Snowflake's building, but workloads that partners like LiveRamp, or anyone else is building. All those workloads can then run in a single copy of your data, in a single secure environment. >> And when you say in one place, Chris, people can get confused by that, 'cause it's really not in one place. it's the global thing that Benoit stressed this morning >> And that right, and so these, once you write a native app once, so the native app that they've written is one piece of code, one application, that now can be deployed by customers in any region, or on any cloud that they're running on without any changes at all. So to your point on the PaaS, that's where it gets very PaaS-like, because they write once to the Snowflake APIs, and now it can run literally anywhere the Snowflake runs. >> But the premise that we've put forth in supercloud is that, this is a new era. It's not multicloud. And it's consistent with a digital business, right? You're building, you've got a digital business, and this is a new value layer of a digital business. If I've got capabilities, I want to bring them to the cloud. I want to bring them to, every company's a software company, software's eating the world, data's eating software. I mean, I could go on and on and on, but it's not like 10 years ago. This is a whole new life cycle that we're just starting. Is that valid? I mean do you feel that way about LiveRamp? >> Definitely, I mean, I think it's really exciting to see all of the data connectivity that is happening. At the same time, I think the challenges still remain, right? So there are still challenges around being able to resolve your data, and being able to connect your data to a person-based view in a privacy safe way, to be able to partner with others in a data collaboration model, right? And to be able to do all of that without sharing anything from a sensitive identifier standpoint, or not having a resolved data set. And so I think you're absolutely right. There's a lot of really cool, awesome innovation happening, but the customer challenges, kind of still exist. And so that's why it's exciting to build these applications that can now solve those problems, where that data is. >> It's the cloud benefit, the heavy lifting thing, for data? 'Cause you don't have to worry about all that. You can focus on campaign ROI, or whatever new innovation that you want to bring out. >> And think about it from the end customer's perspective. They now, can come into their single environment where they have all their data, they can say, I need to match the identity, and they can pull in LiveRamp with a few clicks, and then they can say, I'm ready to take some actions on this. And they can pull in action tools with just a few more clicks. And they haven't made current marketing stack that you see. There's 20 different tools and you're schlepping data back and forth between each of them, and LiveRamp's just one stop on your journey to get this data out to where I'm actually sending emails or targeting ads. Our vision is that, all that happens on one copy of the data, each of these different tools are grabbing the parts they need, again in a secure well-governed, well-controlled way, enriching in ways that they need, taking actions that they need, pulling in other data sets that they need. But the end consumer maintains control over the data, and over the process, the entire way through. >> So one copy data. So you sometimes might make a copy, right? But you'd make as many copies as you need to, but no more, kind of thing, to paraphrase Einstein, or is that right? >> There's literally one copy of the data. So one of the nice things with Snowflake, with data sharing, and with native applications, the data is stored once in one file on disc and S3, which eventually is a disc somewhere. >> Yeah, yeah, right. >> But what can happen is, I'm really just granting permission to these different applications, to read and write from that single copy of the data. So as soon as a new customer touches my website, that immediately shows up in my data. LiveRamp gets access to that instantly. They enrich it. Before I've even noticed that that new customer signed up, the data's already been enriched, the identity's been matched, and they're already put into a bucket about what campaign I should run against them. >> So the data stays where it is. You bring the ISO compute, but the application. And then you take the results, right? And then I can read them back? >> You bring the next application, right to that same copy of the data. So what'll happen is you'll have a view that LiveRamp is accessing and reading and making changes on, LiveRamp is exposing its own view, I have another application reading from the LiveRamp view, exposing its own view. And ultimately someone's taking an action based on that. But there's one copy of the data all the way through. That's the really powerful thing. >> Okay, so yeah, so you're not moving the data. So you're not dealing with latency problems, but I can, if I'm in Australia and I'm running on US West, it's not a problem? >> Yes, so there, if you do want to run across different clouds, we will copy the data in that case, we've found it's much faster. >> Okay, great, I thought I was losing my mind. >> No, but as long as you're staying within a single region, there will be no copies of the data. >> Yeah, okay, totally makes sense, great. >> One of the efficiency there in speed to be able to get the insights. That's what it's all about, being able to turn the volume up on the data from a value perspective. Thanks so much guys for joining us on the program today talking about what LiveRamp and Snowflake are doing together and breaking down the Snowflake Native Application Framework. We appreciate your insights and your time, And thanks for joining us. >> Thank you both. >> Thank you guys. >> Thank you. >> For our guests, and Dave Vellante, I'm Lisa Martin. You're watching theCUBE Live from Snowflake Summit 22 from Las Vegas. We'll be right back with our next guest. (upbeat music)

>>mhm. >>Good morning. Live from Las Vegas. That snowflake Summit 22. Lisa Martin With Day Volonte David's Great. We have three wall to wall days of coverage at Snowflake Summit 22 this year. >>Yeah, it's all about data and bringing data to applications. And we've got some big announcements coming this week. Super exciting >>collaboration around data. We are excited to welcome our first two guests before the keynote. We have seen Nielsen in S V. P of data and Analytics Service offering head at emphasis. And Chris Dignan alumni is back with us to chief revenue officer at stuff like guys. Great to have you on the programme. Thanks for having us. Thank you very much. So he'll tell us what's going on with emphasis and snowflake and the partnership. Give us all that good stuff. >>Yeah, No, I think with the convergence of, uh, data digital and computing economy, um, you know that convergence is creating so much possibilities for for customers, uh, snowflake and emphases working together to help our customers realise the vision and these possibilities that are getting driven. We share a very strategic partnership where we are thinking ahead for our customers in terms of what, uh, we can do together in order to build solutions in order to bring out the expertise that is needed for such transformations and also influencing the thinking, Um, and the and the point of view in the market together so that, you know there is there is cohesive approach to doing this transformation and getting to those business outcomes. So it's a It's a partnership that's very successful and its strategic for for our customers, and we continue to invest for the market. >>Got some great customer. Some of my favourite CVS, Nike, William Sanoma. Gotta love that one. Chris talked to us about the snowflake data cloud. What makes it so unique and compelling in the market? >>Well, I think our customers, really they are going through digital transformation today, and they're moving from on premise to the cloud and historically speaking, there just hasn't been the right tool set to help them do that. I think snowflake brings to the table an opportunity for them to take all of their data and take it and and allow it to go from one cloud to the other so they can sit on a W s it can sit on Azure can sit on G, C, P and I can move around from cloud to cloud, and they can do analytics on top of that. >>So data has been traditionally really hard. And we saw that in the big data movement. But we learned a lot. Uh, and AI has been, you know, challenging. So what are you seeing with with customers? What are they struggling with? And how are you guys helping them? >>Yeah. So if you look at the customer journey, they have invested in a number of technologies in the past and are now at a juncture where they need to transform that landscape. They have the challenges of legacy debt that they need to, you know, get rid of or transform. They have the challenges of really bringing, you know, a cohesive understanding within the enterprise as to what these possibilities are for their business. Given the strategy that they are pursuing, um, business and I t cycles are not necessarily aligned. Um, you have the challenge of very fragmented data landscape that they have created over a period of time. How do you, you know, put all these together and work with a specific outcome in mind so that you're not doing transformation for the purpose of transformation. But to be able to actually drive new business models, new data driven products and services ability for you to collaborate with your partners and create unique competitive advantage in the market. And how do you bring those purposes together with the transformation that that's really happening? And and that's where you know our our customers, um, you know, grapple with the challenges of bringing it together. So, >>Chris, how do you see? Because it was talking about, uh, legacy that I think technical debt. Um, you kind of started out making the data warehouse easier. Then this data cloud thing comes out. You're like, Oh, that's an interesting vision and all of a sudden it's way more than vision. You get this huge ecosystem you're extending, we're gonna hear the announcements this morning. We won't. We won't spill the beans, but but really expanding the data cloud. So it's hard to keep up with with where you're at. So I think modernisation, right? So how do you think about modernisation? How are your customers thinking about it? And what's the scope of Snowflake. >>Well, you know, I think historically, you asked about AI and Ml and, you know, in the A I world historically, they've lacked data, and I think because we're the data cloud, we're bringing data, you know, and making it available and democratising it for everybody. And then, you know, partners like emphasis are actually helping us bring, you know, applications and new business models to to the table to our customers and their innovating on top of the data that we already have in the Snowflake Data Club. >>Chris, can you talk about some of the verticals where you guys are successful with emphasis that the three that I mentioned are retailers, But I know that finance, healthcare and life sciences are are huge for smooth, like talk to me, give us a perspective of the verticals that are coming to you. Guys saying help us out with transport. >>You know, I'll give you just an example. So So in the in the retail space, for example, Kraft Heinz is a is a joint customer of ours. And, you know, they've been all in on on snowflakes, Data Cloud and one of our big customers as well it is is Albertsons, and Albertans realises, Oh my gosh, I have all this information around the consumer in in the grocery stores and Kraft Heinz. They want access to that, and they actually can make supply chain decisions a lot faster if they have access to it. So with snowflakes data sharing, we can actually allow them to share data. Albertans share data directly with Kraft, Heinz and Kraft. Heinz can actually make supply chain decisions in real time so that these are some of the stuff that emphasis and stuff like help our customers self. >>So traditionally, the data pipeline goes through some very highly specialised individuals, whether the data engineer, the data scientists and data analyst. So that example that you just gave our organisation you mentioned before democratisation. So democratisation needs to be as a businessperson, I actually can get access to the data. So in that example that you gave between Kraft, Heinz and and and Albertson, is it the the highly hyper specialised teams sharing that data? Or is it actually extending into the line of business focus? >>That's so that's the interesting part for us is I think, snowflake, we just recently reorganise my sales team this year into verticals, and the reason we did that is customers no longer want to talk to us about speeds and feeds of how fast my database goes. They want to actually talk about business outcomes. How do I solve for demand forecasting? How do I supply fix my supply chain issues? Those are things. Those are the. That's how we're aligning with emphasis. So well is they've been doing this for a long time, Can only we haven't. And so we need their help on getting us to the next level of of the sales motion and talking to our customers on solving these business challenges in >>terms of that next level. So no question for you. Where are the customer conversations happening? At what level? I mean, we've seen such dramatic changes in the market in the last couple of years. Now we're dealing with inflation rising interest rates. Ukraine. Are you seeing the conversations in terms of building data platforms rising up the C suite? As every company recognises, we're going to be a data company. We're not gonna be a business. >>Absolutely. And I think all the macroeconomic forces that you talked about that's working on the enterprises globally is actually leading them to think about how to future proof their business models. Right? And there are tonnes of learning that they've hired in the last two or three years and digitising in embracing more digital models. The conversation with the customers have really pivoted towards business outcome. It is a C suite conversation. It is no longer just an incremental change for the for the companies they recognise. That data has been touted as a strategic asset for a long time, but I think it's taking a purpose and a meaning as to what it does for for the customers, the conversations are around industry verticals. You know, what are the specific challenges and opportunities that the the enterprises have, uh, and how you realise those and these cuts across multiple different layers. You know, we're talking about how your democratised data, which in our point of view, is absolute, must in terms of putting a foundation that doesn't take super specialised people to be able to run every operation and every bit of data that you process we have invested in building autonomous data and a state that can process data as it comes in without any manual intervention and take it all the way to consumption but also investing in those industry solutions. Along with snowflake, we launched the healthcare and life Sciences solution. We launched the only channel for retail and CPG. And these are great examples of how Snowflake Foundation enables democratisation on one side but also help solve business problems. In fact, with Snowflake, we have a very, uh, special partnership because our point of view on data economy is about how you connect with the network partners externally, and snowflake brings native capabilities. On this, we leverage that to Dr Exchanges for our customers and one of the services company in the recycling business. Uh, we're actually building and in exchange, which will allow the data points from multiple different sources and partners to come together. So they have a better understanding of their customers, their operations, the field operations and things >>like building a data ecosystem. Yes. Alright, They they Is it a two sided market place where you guys are observers and providing the the technology and the process, you know, guidance. What's your role in that? >>Yeah. So, um, we were seeing their revolution coming? Uh, two stages. Maybe even more. Um, customers are comfortable building an ecosystem. That's kind of private for them. Which means that they know who they are sharing data with. They know what the data is getting used for. And how do you really put governance on this? So that on one side you can trust it on the other side. There is a good use of that data, Uh, and not, uh, you know, compromise on their quality or privacy and some of the other regulations. But we do see this opening up to the two sided market places as well. Uh, some of the industry's lend themselves extremely well for that kind of play. We have seen that happening in trading area. We've seen that happen. And, uh, you know, the credit checks and things like that which are usually open for, you know, those kind of ecosystem. But the conversations and the and the programmes are really leading towards towards that in the market. >>You know, Lisa, one of things I wrote about this weekend is I was decided to come to stuff like summit and and see one of the, you know, thesis I have is that we're going to move not just beyond analytics, including analytics, but also building data products that can be monetised and and I'm hoping we're going to see some of that here. Are you seeing that Christian in the customer? It's It's >>a great question, David. So So we have You know, I just thought of it as as he was talking about. We have a customer who's a very large customer of ours who's in the financial services space, and they handle roughly 40% of the credit card transactions that happen in the US and they're coming to us and saying they want to go from zero in data business today to a $2 billion business over the next five years, and they're leaning on us to help them do that. And one of the things that's exciting for me is they're coming to us not saying Hey, how do you do it? You know, they're saying, Hey, we want to build a consumption model on top of snowflake and we want to use you as the delivery mechanism and the billing mechanism to help us actually monetise that data. So yes, the answer is. You know, I I used to sell to, you know, chief Data Officers and and see IOS. Now I'm talking to VPs of sales and I'm talking to chief operating officers and I'm talking to CEOs about how do we actually create a new revenue stream? And that's just I mean, it's exhilarating to have those conversations. That's >>data products. They don't have to worry about the infrastructure that comes from the cloud. They don't have to worry about the governance, as Senior was saying, Just put >>it in stuff like Just >>put stuff like that. So I call it The super cloud is kind of a, you know, a funny little tongue in cheek. But it's happening. It's this layer. It's not just multiple clouds. You see a lot of your critical competitors adjacent competitors saying, Hey, we're now running in in Google or we're running in Azure. We've been running on AWS. This is different. This is different, isn't it? It's a cloud that floats above the The infrastructure of the hyper scale is, and that's that's a new era. I think >>it's a new error. I think they're you know, I think the hyper scholars want to, you know, keep us as a as a data warehouse and and we're not. The customers are not letting them so So I think that's you know where emphasis kind of saw the light early on. And they were our innovation partner of the year, uh, this past year and they're helping us in our customers innovate, >>but you're uniquely qualified to do that where? I don't think it's the hyper scholars agenda. At least I never say never with the hyper scale is, but yeah, they have focused on providing infrastructure. And, yeah, they have databases and other tools. But that that cross cloud that continuum to your point, talking to VPs of sales and how do you generate revenue? That maybe, is a conversation that they have, but not explicitly as to how to actually do it in a data >>cloud. That's right. I mean, those and those are the Those are the fun conversations because you're you're saying, Hey, we can actually create a new revenue stream. And how can we actually help you solve our joint customers problems? So, yes, it is. Well, >>that's competitive differentiation for businesses. I mean, this is, as I mentioned Every company has to be a data company. If they're not, they're probably not going to be around much longer. They've got to be able to to leverage a data platform like snowflake, to find insights, be able to act on them and create value new services, new products to stay competitive, to stay ahead of the competition. That's no longer nice to have >>100%. I mean, I think they're they're all scared. I mean, you know, like if you look in the financial services space, they look at some of the fintech, as you know, the giant £800 gorillas look at the small fintech has huge threats to the business, and they're coming to us and say, How can we innovate our business now? And they're looking at us as the the innovator, and they're looking at emphasis to help them do that. So I think these are These are incredible times. >>So the narrative on Wall Street, of course, this past earnings season was consumption and who has best visibility and and they they were able to snowflake had a couple of large customers dial down consumption, some consumer facing. Here's the thing. If you're selling a data product for more than it costs you to make. If you dial down consumption in the future, you're gonna dial down revenue. So that's it's going to become less and less discretionary over time. And that, to me, is the next error. That's really exciting. >>The key, The key there is understanding the unit of measure. I think that's the number. One question that we get from customers is what is the unit of measure that we care about, that we want to monetise because to your point, it costs you more to make the product. You're not going to sell it right? And so I think that those are the things that the energy that we're spending with customers today is advising them, jointly advising them on how to actually monetise the specific, you know, unit of measure that they care >>about because when they get the Amazon bill or the snowflake bill, the CFO starts knocking the door. The answer has to be well, look at all the revenue that we generated and all the operating profit and the free cash flow that we drove, and then it's like, Oh, I get it. Keep doing it well, if I'm >>if I'm going on sales calls with the VP of sales and his their sales team, fantastic, right generated helping them generate revenue, right? That's a great conversation >>dynamic. And I think the adoption is really driven through the value, uh, that they can drive in their ecosystem. Their products are similar to products and services that these companies sell. And if you're embedding data inside Syria into your products services, that makes you that much more competitive in the market and drive value for your stakeholders. And that's essentially the future business model that we're talking about. On one side, the other one is the agility. Things aren't remaining constant, they are constantly changing, and we talked about some of those forces earlier. All of this is changing. The landscape is changing the the needs in the economy and things like that, and how you adapt to those kind of models in the future and pivoted on data capabilities that lets you identify new opportunities and and create new value. >>Speaking of creating new value last question guys, before we wrap, what's the go to market approach here between the two companies working customers go to get engaged. I imagine both sides. >>Yeah. I mean, the way that partnership looks good to me is is sell with co selling. So So I think, you know, we look at developing joint solutions with emphasis. They've done a wonderful job of leading into our partnership. So, you know, Sue Neill and I have a regular cadence where we talked every quarter, and our sales teams and our partner teams are are all leaning in and co selling. I don't know if you >>have Absolutely, um, you know, we we proactively identify, you know, the opportunities for our customers. And we work together at all levels within, you know, between the two companies to be able to bring a cohesive solution and a proposition for the customers. Really help them understand how to, you know, what is it that they can, um, get to and how you get that journey actually executed. And it's a partnership that works very seamlessly through that entire process, not just upstream when we're selling, but also downstream and we're executing. And we've had tremendous success together and look forward to more. >>Congratulations on that success, guys. Thank you so much for coming on talking about new possibilities with data and AI and sharing some of the impact that the technologies are making. We appreciate your insights. >>Thank you. Thank >>you. Thank you So much >>for our guests and a Volonte. I'm Lisa Martin. You're watching the Cube live in Las Vegas from Snowflake Summit 22 back after the keynote with more breaking news. Mhm, mhm.

>> From theCUBE Studios in Palo Alto, in Boston bringing you Data Driven Insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> In the pre-cloud era hardware companies would run benchmarks, showing how database and or application performance ran better on their systems relative to competitors or previous generation boxes. And they would make a big deal out of it. And the independent software vendors, you know they'd do a little golf clap if you will, in the form of a joint press release it became a game of leaprog amongst hardware competitors. That was pretty commonplace over the years. The Dell Snowflake Deal underscores that the value proposition between hardware companies and ISVs is changing and has much more to do with distribution channels, volumes and the amount of data that lives On-Prem in various storage platforms. For cloud native ISVs like Snowflake they're realizing that despite their Cloud only dogma they have to grit their teeth and deal with On-premises data or risk getting shut out of evolving architectures. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we unpack what little is known about the Snowflake announcement from Dell Technologies World and discuss the implications of a changing Cloud landscape. We'll also share some new data for Cloud and Database platforms from ETR that shows Snowflake has actually entered the Earth's orbit when it comes to spending momentum on its platform. Now, before we get into the news I want you to listen to Frank's Slootman's answer to my question as to whether or not Snowflake would ever architect the platform to run On-Prem because it's doable technically, here's what he said, play the clip >> Forget it, this will only work in the Public Cloud. Because it's, this is how the utility model works, right. I think everybody is coming through this realization, right? I mean, excuses are running out at this point. You know, we think that it'll, people will come to the Public Cloud a lot sooner than we will ever come to the Private Cloud. It's not that we can't run a private Cloud. It's just diminishes the potential and the value that we bring. >> So you may be asking yourselves how do you square that circle? Because basically the Dell Snowflake announcement is about bringing Snowflake to the private cloud, right? Or is it let's get into the news and we'll find out. Here's what we know at Dell Technologies World. One of the more buzzy announcements was the, by the way this was a very well attended vet event. I should say about I would say 8,000 people by my estimates. But anyway, one of the more buzzy announcements was Snowflake can now run analytics on Non-native Snowflake data that lives On-prem in a Dell object store Dell's ECS to start with. And eventually it's software defined object store. Here's Snowflake's clark, Snowflake's Clark Patterson describing how it works this past week on theCUBE. Play the clip. The way it works is I can now access Non-native Snowflake data using what materialized views, external tables How does that work? >> Some combination of the, all the above. So we've had in Snowflake, a capability called External Tables, which you refer to, it goes hand in hand with this notion of external stages. Basically there's a through the combination of those two capabilities, it's a metadata layer on data, wherever it resides. So customers have actually used this in Snowflake for data lake data outside of Snowflake in the Cloud, up until this point. So it's effectively an extension of that functionality into the Dell On-Premises world, so that we can tap into those things. So we use the external stages to expose all the metadata about what's in the Dell environment. And then we build external tables in Snowflake. So that data looks like it is in Snowflake. And then the experience for the analyst or whomever it is, is exactly as though that data lives in the Snowflake world. >> So as Clark explained, this capability of External tables has been around in the Cloud for a while, mainly to suck data out of Cloud data lakes. Snowflake External Tables use file level metadata, for instance, the name of the file and the versioning so that it can be queried in a stage. A stage is just an external location outside of Snowflake. It could be an S3 bucket or an Azure Blob and it's soon will be a Dell object store. And in using this feature, the Dell looks like it lives inside of Snowflake and Clark essentially, he's correct to say to an analyst that looks exactly like the data is in Snowflake, but uh, not exactly the data's read only which means you can't do what are called DML operations. DML stands for Data Manipulation Language and allows for things like inserting data into tables or deleting and modifying existing data. But the data can be queried. However, the performance of those queries to External Tables will almost certainly be slower. Now users can build things like materialized views which are going to speed things up a bit, but at the end of the day, it's going to run faster than the Cloud. And you can be almost certain that's where Snowflake wants it to run, but some organizations can't or won't move data into the Cloud for a variety of reasons, data sovereignty, compliance security policies, culture, you know, whatever. So data can remain in place On-prem, or it can be moved into the Public Cloud with this new announcement. Now, the compute today presumably is going to be done in the Public Cloud. I don't know where else it's going to be done. They really didn't talk about the compute side of things. Remember, one of Snowflake's early innovations was to separate compute from storage. And what that gave them is you could more efficiently scale with unlimited resources when you needed them. And you could shut off the compute when you don't need us. You didn't have to buy, and if you need more storage you didn't have to buy more compute and vice versa. So everybody in the industry has copied that including AWS with Redshift, although as we've reported not as elegantly as Snowflake did. RedShift's more of a storage tiering solution which minimizes the compute required but you can't really shut it off. And there are companies like Vertica with Eon Mode that have enabled this capability to be done On-prem, you know, but of course in that instance you don't have unlimited elastic compute scale on-Prem but with solutions like Dell Apex and HPE GreenLake, you can certainly, you can start to simulate that Cloud elasticity On-prem. I mean, it's not unlimited but it's sort of gets you there. According to a Dell Snowflake joint statement, the companies the quote, the companies will pursue product integrations and joint go to market efforts in the second half of 2022. So that's a little vague and kind of benign. It's not really clear when this is going to be available based on that statement from the two first, but, you know, we're left wondering will Dell develop an On-Prem compute capability and enable queries to run locally maybe as part of an extended apex offering? I mean, we don't know really not sure there's even a market for that but it's probably a good bet that again, Snowflake wants that data to land in the Snowflake data Cloud kind of makes you wonder how this deal came about. You heard Sloop on earlier Snowflake has always been pretty dogmatic about getting data into its native snowflake format to enable the best performance as we talked about but also data sharing and governance. But you could imagine that data architects they're building out their data mesh we've reported on this quite extensively and their data fabric and those visions around that. And they're probably telling Snowflake, Hey if you want to be a strategic partner of ours you're going to have to be more inclusive of our data. That for whatever reason we're not putting in your Cloud. So Snowflake had to kind of hold its nose and capitulate. Now the good news is it further opens up Snowflakes Tam the total available market. It's obviously good marketing posture. And ultimately it provides an on ramp to the Cloud. And we're going to come back to that shortly but let's look a little deeper into what's happening with data platforms and to do that we'll bring in some ETR data. Now, let me just say as companies like Dell, IBM, Cisco, HPE, Lenovo, Pure and others build out their hybrid Clouds. The cold hard fact is not only do they have to replicate the Cloud Operating Model. You will hear them talk about that a lot, but they got to do that. So it, and that's critical from a user experience but in order to gain that flywheel momentum they need to build a robust ecosystem that goes beyond their proprietary portfolios. And, you know, honestly they're really not even in the first inning most companies and for the likes of Snowflake to sort of flip this, they've had to recognize that not everything is moving into the Cloud. Now, let's bring up the next slide. One of the big areas of discussion at Dell Tech World was Apex. That's essentially Dell's nascent as a service offering. Apex is infrastructure as a Service Cloud On-prem and obviously has the vision of connecting to the Cloud and across Clouds and out to the Edge. And it's no secret that database is one of the most important ingredients of infrastructure as a service generally in Cloud Infrastructure specifically. So this chart here shows the ETR data for data platforms inside of Dell accounts. So the beauty of ETR platform is you can cut data a million different ways. So we cut it. We said, okay, give us the Cloud platforms inside Dell accounts, how are they performing? Now, this is a two dimensional graphic. You got net score or spending momentum on the vertical axis and what ETR now calls Overlap formally called Market Share which is a measure of pervasiveness in the survey. That's on the horizontal axis that red dotted line at 40% represents highly elevated spending on the Y. The table insert shows the raw data for how the dots are positioned. Now, the first call out here is Snowflake. According to ETR quote, after 13 straight surveys of astounding net scores, Snowflake has finally broken the trend with its net score dropping below the 70% mark among all respondents. Now, as you know, net score is measured by asking customers are you adding the platform new? That's the lime green in the bar that's pointing from Snowflake in the graph and or are you increasing spend by 6% or more? That's the forest green is spending flat that's the gray is you're spend decreasing by 6% or worse. That's the pinkish or are you decommissioning the platform bright red which is essentially zero for Snowflake subtract the reds from the greens and you get a net score. Now, what's somewhat interesting is that snowflakes net score overall in the survey is 68 which is still huge, just under 70%, but it's net score inside the Dell account base drops to the low sixties. Nonetheless, this chart tells you why Snowflake it's highly elevated spending momentum combined with an increasing presence in the market over the past two years makes it a perfect initial data platform partner for Dell. Now and in the Ford versus Ferrari dynamic. That's going on between the likes of Dell's apex and HPE GreenLake database deals are going to become increasingly important beyond what we're seeing with this recent Snowflake deal. Now noticed by the way HPE is positioned on this graph with its acquisition of map R which is now part of HPE Ezmeral. But if these companies want to be taken seriously as Cloud players, they need to further expand their database affinity to compete ideally spinning up databases as part of their super Clouds. We'll come back to that that span multiple Clouds and include Edge data platforms. We're a long ways off from that. But look, there's Mongo, there's Couchbase, MariaDB, Cloudera or Redis. All of those should be on the short list in my view and why not Microsoft? And what about Oracle? Look, that's to be continued on maybe as a future topic in a, in a Breaking Analysis but I'll leave you with this. There are a lot of people like John Furrier who believe that Dell is playing with fire in the Snowflake deal because he sees it as a one way ticket to the Cloud. He calls it a one way door sometimes listen to what he said this past week. >> I would say that that's a dangerous game because we've seen that movie before, VMware and AWS. >> Yeah, but that we've talked about this don't you think that was the right move for VMware? >> At the time, but if you don't nurture the relationship AWS will take all those customers ultimately from VMware. >> Okay, so what does the data say about what John just said? How is VMware actually doing in Cloud after its early missteps and then its subsequent embracing of AWS and other Clouds. Here's that same XY graphic spending momentum on the Y and pervasiveness on the X and the same table insert that plots the dots and the, in the breakdown of Dell's net score granularity. You see that at the bottom of the chart in those colors. So as usual, you see Azure and AWS up and to the right with Google well behind in a distant third, but still in the mix. So very impressive for Microsoft and AWS to have both that market presence in such elevated spending momentum. But the story here in context is that the VMware Cloud on AWS and VMware's On-Prem Cloud like VMware Cloud Foundation VCF they're doing pretty well in the market. Look, at HPE, gaining some traction in Cloud. And remember, you may not think HPE and Dell and VCF are true Cloud but these are customers answering the survey. So their perspective matters more than the purest view. And the bad news is the Dell Cloud is not setting the world on fire from a momentum standpoint on the vertical axis but it's above the line of zero and compared to Dell's overall net score of 20 you could see it's got some work to do. Okay, so overall Dell's got a pretty solid net score to you know, positive 20, as I say their Cloud perception needs to improve. Look, Apex has to be the Dell Cloud brand not Dell reselling VMware. And that requires more maturity of Apex it's feature sets, its selling partners, its compensation models and it's ecosystem. And I think Dell clearly understands that. I think they're pretty open about that. Now this includes partners that go beyond being just sellers has to include more tech offerings in the marketplace. And actually they got to build out a marketplace like Cloud Platform. So they got a lot of work to do there. And look, you've got Oracle coming up. I mean they're actually kind of just below the magic 40% in the line which is pro it's pretty impressive. And we've been telling you for years, you can hate Oracle all you want. You can hate its price, it's closed system all of that it's red stack shore. You can say it's legacy. You can say it's old and outdated, blah, blah, blah. You can say Oracle is irrelevant in trouble. You are dead wrong. When it comes to mission critical workloads. Oracle is the king of the hill. They're a founder led company that knows exactly what it's doing and they're showing Cloud momentum. Okay, the last point is that while Microsoft AWS and Google have major presence as shown on the X axis. VMware and Oracle now have more than a hundred citations in the survey. You can see that on the insert in the right hand, right most column. And IBM had better keep the momentum from last quarter going, or it won't be long before they get passed by Dell and HP in Cloud. So look, John might be right. And I would think Snowflake quietly agrees that this Dell deal is all about access to Dell's customers and their data. So they can Hoover it into the Snowflake Data Cloud but the data right now, anyway doesn't suggest that's happening with VMware. Oh, by the way, we're keeping an eye close eye on NetApp who last September ink, a similar deal to VMware Cloud on AWS to see how that fares. Okay, let's wrap with some closing thoughts on what this deal means. We learned a lot from the Cloud generally in AWS, specifically in two pizza teams, working backwards, customer obsession. We talk about flywheel all the time and we've been talking today about marketplaces. These have all become common parlance and often fundamental narratives within strategic plans investor decks and customer presentations. Cloud ecosystems are different. They take both competition and partnerships to new heights. You know, when I look at Azure service offerings like Apex, GreenLake and similar services and I see the vendor noise or hear the vendor noise that's being made around them. I kind of shake my head and ask, you know which movie were these companies watching last decade? I really wish we would've seen these initiatives start to roll out in 2015, three years before AWS announced Outposts not three years after but Hey, the good news is that not only was Outposts a wake up call for the On-Prem crowd but it's showing how difficult it is to build a platform like Outposts and bring it to On-Premises. I mean, Outpost isn't currently even a rounding era in the marketplace. It really doesn't do much in terms of database support and support of other services. And, you know, it's unclear where that that is going. And I don't think it has much momentum. And so the Hybrid Cloud Vendors they've had time to figure it out. But now it's game on, companies like Dell they're promising a consistent experience between On-Prem into the Cloud, across Clouds and out to the Edge. They call it MultCloud which by the way my view has really been multi-vendor Chuck, Chuck Whitten. Who's the new co-COO of Dell called it Multi-Cloud by default. (laughing) That's really, I think an accurate description of that. I call this new world Super Cloud. To me, it's different than MultiCloud. It's a layer that runs on top of hyperscale infrastructure kind of hides the underlying complexity of the Cloud. It's APIs, it's primitives. And it stretches not only across Clouds but out to the Edge. That's a big vision and that's going to require some seriously intense engineering to build out. It's also going to require partnerships that go beyond the portfolios of companies like Dell like their own proprietary stacks if you will. It's going to have to replicate the Cloud Operating Model and to do that, you're going to need more and more deals like Snowflake and even deeper than Snowflake, not just in database. Sure, you'll need to have a catalog of databases that run in your On-Prem and Hybrid and Super Cloud but also other services that customers can tap. I mean, can you imagine a day when Dell offers and embraces a directly competitive service inside of apex. I have trouble envisioning that, you know not with their historical posture, you think about companies like, you know, Nutanix, you know, or Cisco where they really, you know those relationships cooled quite quickly but you know, look, think about it. That's what AWS does. It offers for instance, Redshift and Snowflake side by side happily and the Redshift guys they probably hate Snowflake. I wouldn't blame them, but the EC Two Folks, they love them. And Adam SloopesKy understands that ISVs like Snowflake are a key part of the Cloud ecosystem. Again, I have a hard time envisioning that occurring with Dell or even HPE, you know maybe less so with HPE, but what does this imply that the Edge will allow companies like Dell to a reach around on the Cloud and somehow create a new type of model that begrudgingly accommodates the Public Cloud but drafts of the new momentum of the Edge, which right now to these companies is kind of mostly telco and retail. It's hard to see that happening. I think it's got to evolve in a more comprehensive and inclusive fashion. What's much more likely is companies like Dell are going to substantially replicate that Cloud Operating Model for the pieces that they own pieces that they control which admittedly are big pieces of the market. But unless they're able to really tap that ecosystem magic they're not going to be able to grow much beyond their existing install bases. You take that lime green we showed you earlier that new adoption metric from ETR as an example, by my estimates, AWS and Azure are capturing new accounts at a rate between three to five times faster than Dell and HPE. And in the more mature US and mere markets it's probably more like 10 X and a major reason is because of the Cloud's robust ecosystem and the optionality and simplicity of transaction that that is bringing to customers. Now, Dell for its part is a hundred billion dollar revenue company. And it has the capability to drive that kind of dynamic. If it can pivot its partner ecosystem mindset from kind of resellers to Cloud services and technology optionality. Okay, that's it for now? Thanks to my colleagues, Stephanie Chan who helped research topics for Breaking Analysis. Alex Myerson is on the production team. Kristen Martin and Cheryl Knight and Rob Hof, on editorial they helped get the word out and thanks to Jordan Anderson for the new Breaking Analysis branding and graphics package. Remember these episodes are all available as podcasts wherever you listen. All you do is search Breaking Analysis podcasts. You could check out ETR website @etr.ai. We publish a full report every week on wikibon.com and siliconangle.com. You want to get in touch. @dave.vellente @siliconangle.com. You can DM me @dvellante. You can make a comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (upbeat music)

>> theCube presents Dell Technologies World, brought to you by Dell. >> Hi everyone, welcome back to Dell Technologies World 2022. You're watching theCube's coverage of this, three-day coverage wall to wall. My name is David Vellante John Furrier's here, Lisa Martin, David Nicholson. Talk of the town here is data. And one of the big announcements at the show is Snowflake and Dell partnering up, building ecosystems. Snowflake reaching into on-prem, allowing customers to actually access the Snowflake Data Cloud without moving the data or if they want to move the data they can. This is really one of the hotter announcements of the show. Martin Glynn is here, he's the Senior Director of Storage Product Management at Dell Technologies. And Clark Patterson, he's the Head of Product Marketing for Snowflake. Guys, welcome. >> Thanks for having us. >> So a lot of buzz around this and, you know, Clark, you and I have talked about the need to really extend your data vision. And this really is the first step ever you've taken on-prem. Explain the motivation for this from your customer's perspective. >> Yeah. I mean, if you step back and think about Snowflake's vision and our mission of mobilizing the world's data, it's all around trying to break down silos for however customers define what a silo is, right? So we've had a lot of success breaking down silos from a workload perspective where we've expanded the platform to be data warehousing, and data engineering, and machine learning, and data science, and all the kind of compute intensive ways that people work with us. We've also had a lot of success in our sharing capabilities and how we're breaking down silos of organizations, right? So I can share data more seamlessly within my team, I can do it across totally disparate organizations, and break down silos that way. So this partnership is really like the next leg of the stool, so to speak, where we're breaking down the silos of the the data and where the data lives ultimately, right? So up until this point, Cloud, all focus there, and now we have this opportunity with Dell to expand that and into on-premises world and people can bring all those data sets together. >> And the data target for this Martin, is Dell ECS, right? Your object store, and it's got S3 compatibility. Explain that. >> Yeah, we've actually got sort of two flavors. We'll start with ECS, which is our turnkey object storage solution. Object storage offers sort of the ultimate in flexibility, you know, potential performance, ease of use, right? Which is why it fits so well with Snowflake's mission for sort of unlocking, you know, the data within the data center. So we'll offer it to begin with ECS, and then we also recently announced our software defined object scale solution. So add even more flexibility there. >> Okay. And the clock, the way it works is I can now access non-native Snowflake data using what? Materialized views, external tables, how does that work? >> Some combination of all the above. So we've had in Snowflake a capability called external tables which we refer to, it goes hand in hand with this notion of external stages. Basically through the combination of those two capabilities, it's a metadata layer on data wherever it resides. So customers have actually used this in Snowflake for data lake data outside of Snowflake in the Cloud up until this point. So it's effectively an extension of that functionality into the Dell on-premises world, so that we can tap into those things. So we use the external stages to expose all the metadata about what's in the Dell environment. And then we build external tables in Snowflake so that data looks like it is in Snowflake. And then the experience for the analyst or whomever it is, is exactly as though that data lives in the Snowflake world. >> Okay. So for a while you've allowed non-native Snowflake data but it had to be in the Cloud. >> Correct. >> It was the first time it's on-prem, >> that's correct >> that's the innovation here. Okay. And if I want to bring it into the Cloud, can I? >> Yeah, the connection here will help in a migration sense as well, right? So that's the good thing is, it's really giving the user the choice. So we are integrating together as partners to make connection as seamless as possible. And then the end user will say like, look I've got data that needs to live on-premises, for whatever reasons, data sovereignty whatever they decide. And they can keep it there and still do the analytics in another place. But if there's a need and a desire to use this as an opportunity to migrate some of that data to Cloud, that connection between our two platforms will make that easier. >> Well, Michael always says, "Hey, it's customer choice, we're flexible." So you're cool with that? That's been the mission since we kind of came together, right? Is if our customers needed to stay in their data center, if that makes more sense from a cost perspective or, you know, a data gravity perspective, then they can do that. But we also want to help them unlock the value of that data. So if they need to copy it up to the public Cloud and take advantage of it, we're going to integrate directly with Snowflake to make that really easy to do. >> So there are engineering integrations here, obviously that's required. Can you describe what that looks like? Give us the details on when it's available. >> Sure. So it's going to be sort of second half this year that you'll see, we're demoing it this week, but the availability we second half this year. And fundamentally, it's the way Clark described it, that Snowflake will reach into our S3 interface using the standard S3 interface. We're qualifying between the way they expect that S3 interface to present the data and the way our platform works, just to ensure that there's smooth interaction between the two. So that's sort of the first simplest use case. And then the second example we gave where the customer can copy some of that data up to the public Cloud. We're basically copying between two S3 buckets and making sure that Snowflake's Snowpipe is aware that data's being made available and can easily ingest it. >> And then that just goes into a virtual warehouse- >> Exactly. >> and customer does to know or care. >> Yep Exactly. >> Yeah. >> The compute happens in Snowflake the way it does in any other manner. >> And I know you got to crawl, walk, run second half of this year, but I would imagine, okay, you're going to start with AWS, correct? And then eventually you go to other Clouds. I mean, that's going to take other technical integrations, I mean, obviously. So should we assume there's a roadmap here or is this a one and done? >> I would assume that, I mean, based on our multi-Cloud approach, that's kind of our approach at least, yeah. >> Kind of makes sense, right? I mean, that would seem to be a natural progression. My other thought was, okay, I've got operational systems. They might be transaction systems running on a on a PowerMax. >> Yeah. >> Is there a way to get the data into an object store and make that available, now that opens up even more workloads. I know you're not committing to doing that, but it just, conceptually, it seems like something a customer might want to do. >> Yeah. I, a hundred percent, agree. I mean, I think when we brought our team together we started with a blank slate. It was what's the best solution we can build. We landed on this sort of first step, but we got lots of feedback from a lot of our big joint customers about you know, this system over there, this potential integration over here, and whether it's, you know, PowerMax type systems or other file workloads with native Snowflake data types. You know, I think this is just the beginning, right? We have lots of potential here. >> And I don't think you've announced pricing, right? It's premature for that. But have you thought about, and how are you thinking about the pricing model? I mean, you're a consumption based pricing, is that kind of how this is going to work? Or is it a sort of a new pricing model or haven't you figured that out yet? >> I don't know if you've got any details on that, but from a Snowflake perspective, I would assume it's consistent with how our customers engage with us today. >> Yeah. >> And we'll offer both possibilities, right? So you can either continue with the standard, you know, sort of CapEx motion, maybe that's the most optimal for you from a cost perspective, or you can take advantage through our OpEx option, right? So you can do consumption on-prem also. >> Okay. So it could be a dual model, right? Depending on what the customer wants. If they're a Snowflake customer, obviously it's going to be consumption based, however, you guys price. What's happening, Clark, in in the market? Explain why Snowflake has so much momentum and, you know, traction in the marketplace. >> So like I spent a lot of time doing analysis on why we win and lose, core part of my role. And, you know, there's a couple of, there's really three things that come up consistently as to why people people are really excited about Snowflake platform. One is the most simplest thing of all. It feels like is just ease of use and it just works, right? And I think the way that this platform was built for the Cloud from the ground up all the way back 10 years ago, really a lot allows us to deliver that seamless experience of just like instant compute when you want it, it goes away, you know, only pay for what you use. Very few knobs to turn and things like that. And so people absolutely love that factor. The other is multi-Cloud. So, you know, there's definitely a lot of organizations out there that have a multi-Cloud strategy, and, you know, what that means to them can be highly variable, but regardless, they want to be able to interact across Clouds in some capacity. And of course we are a single platform, like literally one single interface, consistent across all the three Cloud providers that we work upon. And it gives them that flexibility to mix and match Cloud infrastructure under any Snowflake however they see fit. The last piece of it is sharing. And, you know, I think it's that ability as I kind of alluded to around like breaking down organizational silos, and allow people to be able to actually connect with each other in ways that you couldn't do before. Like, if you think about how you and I would've shared data before, I'd be like, "Hey, Dave, I'm going to unload this table into a spreadsheet and I'm going to send it over in email." And there's the whole host of issues that get introduced in that and world, now it's like instantly available. I have a lot of control over it, it's governed it's all these other things. And I can create kind of walled gardens, so to speak, of how far out I want that to go. It could be in a controlled environment of organizations that I want to collaborate with, or I can put it on our marketplace and expose it to the whole world, because I think there's a value in that. And if I choose I can monetize it, right? So those, you know, the ease of use aspect of it, absolutely, it's just a fantastic platform. The multi-Cloud aspect of it and our unique differentiation around sharing in our marketplace and monetization. >> Yeah, on the sharing front. I mean, it's now discoverable. Like if you send me an email, like what'd you call that? When did you send that email? And then the same time I can forward that to somebody else's not governed. >> Yeah. >> All right. So that just be creates a nightmare for the compliance. >> Right. Yeah. You think about how you revoke access in that situation. You just don't, right? Now I can just turn it off and you go in to run your query. >> Don't get access on that data anymore. Yeah. Okay. And then the other thing I wanted to ask you, Clark is Snowflake started really as analytics platform, simplifying data warehousing, you're moving into that world of data science, you know, the whole data lake movement, bringing those two worlds together. You know, I was talking to Ben Ward about this, maybe there's a semantic layer that helps us kind of talk between those two worlds, but you don't care, right? If it's in an object store, it can play in both of those worlds, right? >> That's right. >> Yeah, it's up to you to figure it out and the customer- >> Yeah. >> from a storage standpoint. Here it is, serve it up. >> And that's the thrust of this announcement, right? Is bringing together two great companies, the Dell platform, the Snowflake platform, and allowing organizations to bring that together. And they decide like it, as we all know, customers decide how they're going to build their architecture. And so this is just another way that we're helping them leverage the capabilities of our two great platforms. >> Does this push or pull or little bit of both? I mean, where'd this come from? Or customers saying, "Hey, it would be kind of cool if we could have this." Or is it more, "Hey, what do you guys think?" You know, where are you at with that? >> It was definitely both, right? I mean, so we certainly started with, you know, a high level idea that, you know, the technologies are complimentary, right? I mean, as Clark just described, and at the same time we had customers coming to us saying, "Hey, wait a minute, I'm doing this over here, and this over here, how can I make this easier?" So that was like I said, we started with a blank sheet and lots of long customer conversations and this is what resulted. So >> So what are the sequence of events to kind of roll this out? You said it's second half, you know, when do you start getting customers involved? Do you have your already, you know, to poke at this and what's that look like? >> Yeah, sure. I can weigh in there. So, absolutely. We've had a few of our big customers that have been involved sort of in the design already who understand how they want to use it. So I think our expectation is that now that the sort of demonstrations have been in place, we have some pre functionality, we're going to see some initial testing and usage, some beta type situations with our customers. And then second half, we'll ramp from there. >> It's got to be a huge overlap between Dell customers and Snowflake customers. I mean, it's hundred billion. You can't not bump into Dell somewhere. >> Exactly. Yeah, you know. >> So where do you guys want to see this relationship go, kind of how should we measure success? Maybe you could each give your perspectives of that. >> I mean, for us, I think it's really showing the value of the Snowflake platform in this new world where there's a whole new ecosystem of data that is accessible to us, right? So seeing those organizations that are saying like, "Look, I'm doing new things with on-premises data that I didn't think that I could do before", or, "I'm driving efficiency in how I do analytics, and data engineering, and data science, in ways that I couldn't do before," 'cause they were locked out of using a Snowflake-like technology, right? So I think for me, that's going to be that real excitement. I'm really curious to see how the collaboration and the sharing component comes into this, you know, where you can think of having an on-premises data strategy and a need, right? But you can really connect to Cloud native customers and partners and suppliers that live in the Snowflake ecosystem, and that wasn't possible before. And so that is very conceivable and very possible through this relationship. So seeing how those edges get created in in our world and how people start to collaborate across data, both in the Cloud and on-prem is going to be really exciting. >> I remember I asked Frank, it was kind of early in the pandemic. I asked him, come on, tell me about how you're managing things. And he was awesome. And I asked him to at the time, you know, "You're ever going to do, you know, bring this platform on-prem?" He's like unequivocal, "No way, that's never going to happen. We're not going to do it halfway house ware Cloud only." And I kept thinking, but there's got to be a way to expand that team. There's so much data out there, and so boom, now we see the answer . Martin, from your standpoint, what does success look like? >> I think it starts with our partnership, right? So I've been doing this a long time. Probably the first time I've worked so closely with a partner like Snowflake. Joint customer conversations, joint solutioning, making sure what we're building is going to be really, truly as useful as possible to them. And I think we're going to let them guide us as we go forward here, right? You mentioned, you know, systems or record or other potential platforms. We're going to let them tell us where exactly the most value will come from the integration between the two companies. >> Yeah. Follow data. I mean, remember in the old days a hardware company like Dell would go to an ISP like Snowflake and say, "Hey, we ran some benchmarks. Your software runs really fast on our hardware, can we work together?" And you go, "Yeah, of course. Yeah, no problem." But wow! What a different dynamic it is today. >> Yeah. Yeah, absolutely. >> All right guys. Hey, thanks so much for coming to theCube. It's great to see you. We'll see you at the Snowflake Summit in June. >> Snowflake Summit in a month and a half. >> Looking forward to that. All right. Thank you again. >> Thank you Dave. >> All right. Keep it right there everybody. This is Dave Vellante, wall to wall coverage of Dell Tech World 2022. We'll be right back. (gentle music)

>> theCube presents Dell Technologies World, brought to you by Dell. >> Hi everyone, welcome back to Dell Technologies World 2022. You're watching theCube's coverage of this, three-day coverage wall to wall. My name is David Vellante John Furrier's here, Lisa Martin, David Nicholson. Talk of the town here is data. And one of the big announcements at the show is Snowflake and Dell partnering up, building ecosystems. Snowflake reaching into on-prem, allowing customers to actually access the Snowflake Data Cloud without moving the data or if they want to move the data they can. This is really one of the hotter announcements of the show. Martin Glynn is here, he's the Senior Director of Storage Product Management at Dell Technologies. And Clark Patterson, he's the Head of Product Marketing for Snowflake. Guys, welcome. >> Thanks for having us. >> So a lot of buzz around this and, you know, Clark, you and I have talked about the need to really extend your data vision. And this really is the first step ever you've taken on-prem. Explain the motivation for this from your customer's perspective. >> Yeah. I mean, if you step back and think about Snowflake's vision and our mission of mobilizing the world's data, it's all around trying to break down silos for however customers define what a silo is, right? So we've had a lot of success breaking down silos from a workload perspective where we've expanded the platform to be data warehousing, and data engineering, and machine learning, and data science, and all the kind of compute intensive ways that people work with us. We've also had a lot of success in our sharing capabilities and how we're breaking down silos of organizations, right? So I can share data more seamlessly within my team, I can do it across totally disparate organizations, and break down silos that way. So this partnership is really like the next leg of the stool, so to speak, where we're breaking down the silos of the the data and where the data lives ultimately, right? So up until this point, Cloud, all focus there, and now we have this opportunity with Dell to expand that and into on-premises world and people can bring all those data sets together. >> And the data target for this Martin, is Dell ECS, right? Your object store, and it's got S3 compatibility. Explain that. >> Yeah, we've actually got sort of two flavors. We'll start with ECS, which is our turnkey object storage solution. Object storage offers sort of the ultimate in flexibility, you know, potential performance, ease of use, right? Which is why it fits so well with Snowflake's mission for sort of unlocking, you know, the data within the data center. So we'll offer it to begin with ECS, and then we also recently announced our software defined object scale solution. So add even more flexibility there. >> Okay. And the clock, the way it works is I can now access non-native Snowflake data using what? Materialized views, external tables, how does that work? >> Some combination of all the above. So we've had in Snowflake a capability called external tables which we refer to, it goes hand in hand with this notion of external stages. Basically through the combination of those two capabilities, it's a metadata layer on data wherever it resides. So customers have actually used this in Snowflake for data lake data outside of Snowflake in the Cloud up until this point. So it's effectively an extension of that functionality into the Dell on-premises world, so that we can tap into those things. So we use the external stages to expose all the metadata about what's in the Dell environment. And then we build external tables in Snowflake so that data looks like it is in Snowflake. And then the experience for the analyst or whomever it is, is exactly as though that data lives in the Snowflake world. >> Okay. So for a while you've allowed non-native Snowflake data but it had to be in the Cloud. >> Correct. >> It was the first time it's on-prem, >> that's correct >> that's the innovation here. Okay. And if I want to bring it into the Cloud, can I? >> Yeah, the connection here will help in a migration sense as well, right? So that's the good thing is, it's really giving the user the choice. So we are integrating together as partners to make connection as seamless as possible. And then the end user will say like, look I've got data that needs to live on-premises, for whatever reasons, data sovereignty whatever they decide. And they can keep it there and still do the analytics in another place. But if there's a need and a desire to use this as an opportunity to migrate some of that data to Cloud, that connection between our two platforms will make that easier. >> Well, Michael always says, "Hey, it's customer choice, we're flexible." So you're cool with that? That's been the mission since we kind of came together, right? Is if our customers needed to stay in their data center, if that makes more sense from a cost perspective or, you know, a data gravity perspective, then they can do that. But we also want to help them unlock the value of that data. So if they need to copy it up to the public Cloud and take advantage of it, we're going to integrate directly with Snowflake to make that really easy to do. >> So there are engineering integrations here, obviously that's required. Can you describe what that looks like? Give us the details on when it's available. >> Sure. So it's going to be sort of second half this year that you'll see, we're demoing it this week, but the availability we second half this year. And fundamentally, it's the way Clark described it, that Snowflake will reach into our S3 interface using the standard S3 interface. We're qualifying between the way they expect that S3 interface to present the data and the way our platform works, just to ensure that there's smooth interaction between the two. So that's sort of the first simplest use case. And then the second example we gave where the customer can copy some of that data up to the public Cloud. We're basically copying between two S3 buckets and making sure that Snowflake's Snowpipe is aware that data's being made available and can easily ingest it. >> And then that just goes into a virtual warehouse- >> Exactly. >> and customer does to know or care. >> Yep Exactly. >> Yeah. >> The compute happens in Snowflake the way it does in any other manner. >> And I know you got to crawl, walk, run second half of this year, but I would imagine, okay, you're going to start with AWS, correct? And then eventually you go to other Clouds. I mean, that's going to take other technical integrations, I mean, obviously. So should we assume there's a roadmap here or is this a one and done? >> I would assume that, I mean, based on our multi-Cloud approach, that's kind of our approach at least, yeah. >> Kind of makes sense, right? I mean, that would seem to be a natural progression. My other thought was, okay, I've got operational systems. They might be transaction systems running on a on a PowerMax. >> Yeah. >> Is there a way to get the data into an object store and make that available, now that opens up even more workloads. I know you're not committing to doing that, but it just, conceptually, it seems like something a customer might want to do. >> Yeah. I, a hundred percent, agree. I mean, I think when we brought our team together we started with a blank slate. It was what's the best solution we can build. We landed on this sort of first step, but we got lots of feedback from a lot of our big joint customers about you know, this system over there, this potential integration over here, and whether it's, you know, PowerMax type systems or other file workloads with native Snowflake data types. You know, I think this is just the beginning, right? We have lots of potential here. >> And I don't think you've announced pricing, right? It's premature for that. But have you thought about, and how are you thinking about the pricing model? I mean, you're a consumption based pricing, is that kind of how this is going to work? Or is it a sort of a new pricing model or haven't you figured that out yet? >> I don't know if you've got any details on that, but from a Snowflake perspective, I would assume it's consistent with how our customers engage with us today. >> Yeah. >> And we'll offer both possibilities, right? So you can either continue with the standard, you know, sort of CapEx motion, maybe that's the most optimal for you from a cost perspective, or you can take advantage through our OpEx option, right? So you can do consumption on-prem also. >> Okay. So it could be a dual model, right? Depending on what the customer wants. If they're a Snowflake customer, obviously it's going to be consumption based, however, you guys price. What's happening, Clark, in in the market? Explain why Snowflake has so much momentum and, you know, traction in the marketplace. >> So like I spent a lot of time doing analysis on why we win and lose, core part of my role. And, you know, there's a couple of, there's really three things that come up consistently as to why people people are really excited about Snowflake platform. One is the most simplest thing of all. It feels like is just ease of use and it just works, right? And I think the way that this platform was built for the Cloud from the ground up all the way back 10 years ago, really a lot allows us to deliver that seamless experience of just like instant compute when you want it, it goes away, you know, only pay for what you use. Very few knobs to turn and things like that. And so people absolutely love that factor. The other is multi-Cloud. So, you know, there's definitely a lot of organizations out there that have a multi-Cloud strategy, and, you know, what that means to them can be highly variable, but regardless, they want to be able to interact across Clouds in some capacity. And of course we are a single platform, like literally one single interface, consistent across all the three Cloud providers that we work upon. And it gives them that flexibility to mix and match Cloud infrastructure under any Snowflake however they see fit. The last piece of it is sharing. And, you know, I think it's that ability as I kind of alluded to around like breaking down organizational silos, and allow people to be able to actually connect with each other in ways that you couldn't do before. Like, if you think about how you and I would've shared data before, I'd be like, "Hey, Dave, I'm going to unload this table into a spreadsheet and I'm going to send it over in email." And there's the whole host of issues that get introduced in that and world, now it's like instantly available. I have a lot of control over it, it's governed it's all these other things. And I can create kind of walled gardens, so to speak, of how far out I want that to go. It could be in a controlled environment of organizations that I want to collaborate with, or I can put it on our marketplace and expose it to the whole world, because I think there's a value in that. And if I choose I can monetize it, right? So those, you know, the ease of use aspect of it, absolutely, it's just a fantastic platform. The multi-Cloud aspect of it and our unique differentiation around sharing in our marketplace and monetization. >> Yeah, on the sharing front. I mean, it's now discoverable. Like if you send me an email, like what'd you call that? When did you send that email? And then the same time I can forward that to somebody else's not governed. >> Yeah. >> All right. So that just be creates a nightmare for the compliance. >> Right. Yeah. You think about how you revoke access in that situation. You just don't, right? Now I can just turn it off and you go in to run your query. >> Don't get access on that data anymore. Yeah. Okay. And then the other thing I wanted to ask you, Clark is Snowflake started really as analytics platform, simplifying data warehousing, you're moving into that world of data science, you know, the whole data lake movement, bringing those two worlds together. You know, I was talking to Ben Ward about this, maybe there's a semantic layer that helps us kind of talk between those two worlds, but you don't care, right? If it's in an object store, it can play in both of those worlds, right? >> That's right. >> Yeah, it's up to you to figure it out and the customer- >> Yeah. >> from a storage standpoint. Here it is, serve it up. >> And that's the thrust of this announcement, right? Is bringing together two great companies, the Dell platform, the Snowflake platform, and allowing organizations to bring that together. And they decide like it, as we all know, customers decide how they're going to build their architecture. And so this is just another way that we're helping them leverage the capabilities of our two great platforms. >> Does this push or pull or little bit of both? I mean, where'd this come from? Or customers saying, "Hey, it would be kind of cool if we could have this." Or is it more, "Hey, what do you guys think?" You know, where are you at with that? >> It was definitely both, right? I mean, so we certainly started with, you know, a high level idea that, you know, the technologies are complimentary, right? I mean, as Clark just described, and at the same time we had customers coming to us saying, "Hey, wait a minute, I'm doing this over here, and this over here, how can I make this easier?" So that was like I said, we started with a blank sheet and lots of long customer conversations and this is what resulted. So >> So what are the sequence of events to kind of roll this out? You said it's second half, you know, when do you start getting customers involved? Do you have your already, you know, to poke at this and what's that look like? >> Yeah, sure. I can weigh in there. So, absolutely. We've had a few of our big customers that have been involved sort of in the design already who understand how they want to use it. So I think our expectation is that now that the sort of demonstrations have been in place, we have some pre functionality, we're going to see some initial testing and usage, some beta type situations with our customers. And then second half, we'll ramp from there. >> It's got to be a huge overlap between Dell customers and Snowflake customers. I mean, it's hundred billion. You can't not bump into Dell somewhere. >> Exactly. Yeah, you know. >> So where do you guys want to see this relationship go, kind of how should we measure success? Maybe you could each give your perspectives of that. >> I mean, for us, I think it's really showing the value of the Snowflake platform in this new world where there's a whole new ecosystem of data that is accessible to us, right? So seeing those organizations that are saying like, "Look, I'm doing new things with on-premises data that I didn't think that I could do before", or, "I'm driving efficiency in how I do analytics, and data engineering, and data science, in ways that I couldn't do before," 'cause they were locked out of using a Snowflake-like technology, right? So I think for me, that's going to be that real excitement. I'm really curious to see how the collaboration and the sharing component comes into this, you know, where you can think of having an on-premises data strategy and a need, right? But you can really connect to Cloud native customers and partners and suppliers that live in the Snowflake ecosystem, and that wasn't possible before. And so that is very conceivable and very possible through this relationship. So seeing how those edges get created in in our world and how people start to collaborate across data, both in the Cloud and on-prem is going to be really exciting. >> I remember I asked Frank, it was kind of early in the pandemic. I asked him, come on, tell me about how you're managing things. And he was awesome. And I asked him to at the time, you know, "You're ever going to do, you know, bring this platform on-prem?" He's like unequivocal, "No way, that's never going to happen. We're not going to do it halfway house ware Cloud only." And I kept thinking, but there's got to be a way to expand that team. There's so much data out there, and so boom, now we see the answer . Martin, from your standpoint, what does success look like? >> I think it starts with our partnership, right? So I've been doing this a long time. Probably the first time I've worked so closely with a partner like Snowflake. Joint customer conversations, joint solutioning, making sure what we're building is going to be really, truly as useful as possible to them. And I think we're going to let them guide us as we go forward here, right? You mentioned, you know, systems or record or other potential platforms. We're going to let them tell us where exactly the most value will come from the integration between the two companies. >> Yeah. Follow data. I mean, remember in the old days a hardware company like Dell would go to an ISP like Snowflake and say, "Hey, we ran some benchmarks. Your software runs really fast on our hardware, can we work together?" And you go, "Yeah, of course. Yeah, no problem." But wow! What a different dynamic it is today. >> Yeah. Yeah, absolutely. >> All right guys. Hey, thanks so much for coming to theCube. It's great to see you. We'll see you at the Snowflake Summit in June. >> Snowflake Summit in a month and a half. >> Looking forward to that. All right. Thank you again. >> Thank you Dave. >> All right. Keep it right there everybody. This is Dave Vellante, wall to wall coverage of Dell Tech World 2022. We'll be right back. (gentle music)

(gentle music) >> Welcome to the new abnormal. Yes, you know, the pandemic, it did accelerate the shift to digital, but it's also created disorder in our world. I mean, every day it seems that companies are resetting their office reopening playbooks. They're rethinking policies on large gatherings and vaccination mandates. There's an acute labor shortage in many industries, and we're seeing an inventory glutton in certain goods, like bleach and hand sanitizer. Airline schedules and pricing algorithms, they're all unsettled. Is inflation transitory? Is that a real threat to the economy? GDP forecasts are seesawing. In short, the world is out of whack and the need for fast access to quality, trusted and governed data has never been greater. Can coherent data strategies help solve these problems, or will we have to wait for the world to reach some type of natural equilibrium? And how are companies, like Google, helping customers solve these problems in critical industries, like financial services, retail, manufacturing, and other sectors? And with me to share his perspectives on data is a long-time CUBE alum, Bruno Aziza. He's the head of data analytics at Google. Bruno, my friend, great to see you again, welcome. >> Great to see you, thanks for having me, Dave. >> So you heard my little narrative upfront, how do you see this crazy world of data today? >> I think you're right. I think there's a lot going on in the world of data analytics today. I mean, certainly over the last 30 years, we've all tried to just make the life of people better and give them access more readily to the information that they need. But certainly over the last year and half, two years, we've seen an amazing acceleration in digital transformation. And what I think we're seeing is that even after three decades of investment in the data analytics world, you know, the opportunity is still really out wide and is still available for organizations to get value out of their data. I was looking at some of the latest research in the market, and, you know, only 32% of companies are actually able to say that they get tangible, valuable insights out of their data. So after all these years, we still have a lot of opportunity ahead of us, of course, with the democratization of access to data, but also the advent in machine learning and AI, so that people can make better decisions faster than their competitors. >> So do you think that the pandemic has heightened that sort of awareness as they were sort of forced to pivot to digital, that they're maybe not getting enough out of their data strategies? That maybe their whatever, their organization, their technology, their way they were thinking about data was not adequate and didn't allow them to be agile enough? Why do you think that only 32% are getting that type of value? >> I think it's true. I think, one, digital transformation has been accelerated over the last two years. I think, you know, if you look at research the last two years, I've seen almost a decade of digital acceleration, you know, happening. But I also think that we're hitting a particular time where employees are expecting more from their employers in terms of the type of insights that can get. Consumers are now evolving, right? So they want more information. And I think now technology has evolved to a point where it's a lot easier to provision a data cloud environment so you can get more data out to your constituents. So I think the connection of these three things, expectation of employees, expectation of customers to better customer experiences, and, of course, the global environment, has accelerated quite a bit, you know, where the space can go. And for people like me, you know, 20 years ago, nobody really cared about databases and so forth. And now I feel like, you know, everybody's, you know, understands the value that we can get out of it. And we're kind of getting, you know, in the sexy territory, finally, data now is sexy for everyone and there's a lot of interest in the space. >> You and I met, of course, in the early days of Hadoop. And there were many things about Hadoop that were profound and, of course, many things that, you know, just were overly complex, et cetera. And one of the things we saw was this sort of decentralization. We thought that Hadoop was going to send five megabytes of code to petabytes of data. And what happened is everything, you know, came into this centralized repository and that centralized thinking, the data pipeline organization was very centralized. Are you seeing companies rethink that? I mean, has the cloud changed their thinking? You know, especially as the cloud expands to the edge, on-prem, everywhere. How are you seeing organizations rethink their regimes for data? >> Yeah, I think, you know, we've seen over the last three decades kind of the pendulum, right, from really centralizing everything and making the IT organization kind of the center of excellence for data analytics, all the way to now, you know, providing data as a self-service, you know, application for end-users. And I think what we're seeing now is there's a few forces happening. The first one is, of course, multicloud, right? So the world today is clearly multicloud and it's going to be multicloud for many, many years. So I think not only are now people considering their on-prem information, but they're also looking at data across multiple clouds. And so I think that is a huge force for chief data officers to consider is that, you know, you're not going to have data centralized in one place, nicely organized, because sometimes it's going to be a factor of where you want to be as an organization. Maybe you're going to be partnering with other organizations that have data in other clouds. And so you want to have an architecture that is modern and that accommodates this idea of an open cloud. The second problem that we see is this idea around data governance, intelligent data governance, right? So the world of managing data is becoming more complex because, of course, you're now dealing with many different speeds, you're dealing with many different types of data. And so you want to be able to empower people to get access to the information, without necessarily having to move this data, so they can make quick decisions on the data. So this idea of a data fabric is becoming really important. And then the third trend that we see, of course, is this idea around data sharing, right? People are now looking to use their own data to create a data economy around their business. And so the ability to augment their existing data with external data and create data products around it is becoming more and more important to the chief data officers. So it's really interesting we're seeing a switch from, you know, this chief data officer really only worried about governance, to this we're now worried about innovation, while making sure that security and governance is taken care of. You know, we call this freedom within the framework, which is a great challenge, but a great opportunity for many of these data leaders. >> You mentioned several things there. Self-service, multicloud, the governance key, especially if we can federate that governance in a decentralized world. Data fabric is interesting. I was talking to Zhamak Dehghani this weekend on email. She coined the term data mesh. And there seems to be some confusion, data mesh, data fabric. I think Gartner's using the term fabric. I know like NetApp, I think coined that term, which to me is like an infrastructure layer, you know. But what do you mean by data fabric? >> Well, the first thing that I would say is that it's not up to the vendors to define what it is. It really is up to the customer. The problem that we're seeing these customers trying to fix is you have a diversity of data, right? So you have data stored in the data mart, in a data lake, in a data warehouse, and they all have their specific, you know, reasons for being there. And so this idea of a data fabric is that without moving the data, can you, one, govern it intelligently? And, two, can you provide landing zones for people to actually do their work without having to go through the pain of setting up new infrastructure, or moving information left and right, and creating new applications? So it's this idea of basically taking advantage of your existing environment, but also governing it centrally, and also now providing self-service capabilities so people can do their job easily. So, you know, you might call it a data mesh, you might call it a data fabric. You know, the terminology to me, you know, doesn't seem to be the barrier. The issue today is how do we enable, you know, this freedom for customers? Because, you know, I think what we've seen with vendors out there is they're trying to just take the customer down to their paradigms. So if they believe in all the answers need to be in a data warehouse, they're going to guide the customer there. If they believe that, you know, everything needs to be in a data lake, they're going to guide the customer there. What we believe in is this idea of choice. You should be able to do every single use case. And we should be able to enable you to manage it intelligently, both from an access standpoint, as well as a governance standpoint. >> So when you think about those different, and I like that, you're making it somewhat technology agnostic, so whether it's a data warehouse, or a data lake, or a data hub, a data mart, those are nodes within the mesh or the fabric, right? That are discoverable, accessible, I guess, governed. I think that there's got to be some kind of centralized governance edict, but in a federated governance model so you don't have to move the data around. Is that how you're thinking about it? >> Absolutely, you know, in our recent event, in the Data Cloud Summit, we had Equifax. So the gentleman there was the VP of data governance and data fabric. So you can start seeing now these roles, you know, created around this problem. And really when you listen to what they're trying to do, they're trying to provide as much value as they can without changing the habits of their users. I think that's what's key here, is that the minute you start changing habits, force people into paradigms that maybe, you know, are useful for you as a vendor, but not so useful to the customer, you get into the danger zone. So the idea here is how can you provide a broad enough platform, a platform that is deep enough, so the data can be intelligently managed and also distributed and activated at the point of interaction for the end-user, so they can do their job a lot easier? And that's really what we're about, is how do you make data simpler? How do you make, you know, the process of getting to insight a lot more fluid without changing habits necessarily, both on the IT side and the business side? >> I want to get to specifics on what Google is doing, but the last sort of uber-trends I want to ask you about 'cause, again, we've known each other for a long time. We've seen this data world grow up. And you're right, 20, 30 years ago, nobody cared about database. Well, maybe 30 years ago. But 20 years ago, it was a boring market, right now it's like the hottest thing going. But we saw, you know, bromide like data is the new oil. Well, we found out, well, actually data is more valuable than oil 'cause you can use, you know, data in a lot of different places, oil you can use once. And then the term like data as an asset, and you said data sharing. And it brings up the notion that, you know, you don't want to share your assets, but you do want to share your data as long as it can be governed. So we're starting to change the language that we use to describe data and our thinking is changing. And so it says to me that the next 10 years, aren't going to be like the last 10 years. What are your thoughts on that? >> I think you're absolutely right. I think if you look at how companies are maturing their use of data, obviously the first barrier is, "How do I, as a company, make sure that I take advantage of my data as an asset? How do I turn, you know, all this information into a sustainable, competitive advantage, really top of mind for organizations?" The second piece around it is, "How do I create now this innovation flywheel so that I can create value for my customers, and my employees, and my partners?" And then, finally, "How do I use data as the center of a product that I can then further monetize and create further value into my ecosystem?" I think the piece that's been happening that people have not talked a lot about I think, with the cloud, what's come is it's given us the opportunity to think about data as an ecosystem. Now you and I are partnering on insights. You and I are creating assets that might be the combination of your data and my data. Maybe it's an intelligent application on top of that data that now has become an intelligent, rich experience, if you will, that we can either both monetize or that we can drive value from. And so I think, you know, it's just scratching the surface on that. But I think that's where the next 10 years, to your point, are going to be, is that the companies that win with data are going to create products, intelligent products, out of that data. And they're just going to take us to places that, you know, we are not even thinking about right now. >> Yeah, and I think you're right on. That is going to be one of the big differences in the coming years is data as product. And that brings up sort of the line of business, right? I mean the lines of business heads historically have been kind of removed from the data group, that's why I was asking you about the organization before. But let's get into Google. How do you describe Google's strategy, its approach, and why it's unique? >> You know, I think one of the reasons, so I just, you know, started about a year ago, and one of the reasons for why I found, you know, the Google mission interesting, is that it's really rooted at who we are and what we do. If you think about it, we make data simple. That's really what we're about. And we live that value. If you go to google.com today, what's happening? Right, as an end-user, you don't need any training. You're going to type in whatever it is that you're looking for, and then we're going to return to you highly personalized, highly actionable insights to you as a consumer of insights, if you will. And I think that's where the market is going to. Now, you know, making data simple doesn't mean that you have to have simple infrastructure. In fact, you need to be able to handle sophistication at scale. And so simply our differentiation here is how do we go from highly sophisticated world of the internet, disconnected data, changing all the time, vast volume, and a lot of different types of data, to a simple answer that's actionable to the end-user? It's intelligence. And so our differentiation is around that. Our mission is to make data simple and we use intelligence to take the sophistication and provide to you an answer that's highly actionable, highly relevant, highly personalized for you, so you can go on and do your job, 'cause ultimately the majority of people are not in the data business. And so they need to get the information just like you said, as a business user, that's relevant, actionable, timely, so they can go off and, you know, create value for their organization. >> So I don't think anybody would argue that Google, obviously, are data experts, arguably the best in the world. But it's interesting, some of the uniqueness here that I'm hearing in your language. You used the word multicloud, Amazon doesn't, you know, use that term. So that's a differentiation. And you sell a cloud, right? You sell cloud services, but you're talking about multicloud. You sell databases, but, of course, you host other databases, like Snowflake. So where do you fit in all this? Do you see your role, as the head of data analytics, is to sort of be the chef that helps combine all these different capabilities? Or are you sort of trying to help people adopt Google products and services? How should we think about that? >> Yeah, the best way to think about, you know, I spend 60 to 70% of my time with customers. And the best way I can think about our role is to be your innovation partner as an organization. And, you know, whichever is the scenario that you're going to be using, I think you talked about open cloud, I think another uniqueness of Google is that we have a very partner friendly, you know, approach to the business. Because we realized that when you walk into an enterprise or a digital native, and so forth, they already have a lot of assets that they have accumulated over the years. And it might be technology assets, but also might be knowledge, and know-how, right? So we want to be able to be the innovation vendor that enables you to take these assets, put them together, and create simplicity towards the data. You know, ultimately, you can have all types of complexity in the backend. But what we can do the best for you is make that really simple, really integrated, really unified, so you, as a business user, you don't have to worry about, "Where is my data? Do I need to think about moving data from here to there? Are there things that I can do only if the data is formatted that way and this way?" We want to remove all that complexity, just like we do it on google.com, so you can do your job. And so that's our job, and that's the reason for why people come to us, is because they see that we can be their best innovation partner, regardless where the data is and regardless, you know, what part of the stack they're using. >> Well, I want to take an example, because my example, I mean, I don't know Google's portfolio like you do, obviously, but one of the things I hear from customers is, "We're trying to inject as much machine intelligence into our data as possible. We see opportunities to automate." So I look at something like BigQuery, which has a strong affinity in embedded machine learning and machine intelligence, as an example, maybe of that simplification. But maybe you could pick up on that and give us some other concrete examples. >> Yeah, specifically on products, I mean, there are a lot products we can talk about, and certainly BigQuery has tremendous market momentum. You know, and it's really anchored on this idea that, you know, the idea behind BigQuery is that just add data and we'll do the rest, right? So that's kind of the idea where you can start small and you can scale at incredible, you know, volumes without really having to think about tuning it, about creating indexes, and so forth. Also, we think about BigQuery as the place that people start in order to build their ecosystem. That's why we've invested a lot in machine learning. Just a few years ago, we introduced this functionality called BigQuery Machine Learning, or BigQuery ML, if you're familiar with it. And you notice out of the top 100 customers we have, 80% of these customers are using machine learning right out of, you know, BigQuery. So now why is that? Why is it that it's so easy to use machine learning using BigQuery is because it's built in. It was built from the ground up. Instead of thinking about machine learning as an afterthought, or maybe something that only data scientists have access to that you're going to license just for narrow scenarios, we think about you have your data in a warehouse that can scale, that is equally awesome at small volume as very large volume, and we build on top of that. You know, similarly, we just announced our analytics exchange, which is basically the place where you can now build these data analytics assets that we discussed, so you can now build an ecosystem that creates value for end-users. And so BigQuery is really at the center of a lot of that strategy, but it's not unlike any of the other products that we have. We want to make it simple for people to onboard, simple to scale, to really accomplish, you know, whatever success is ahead of them. >> Well, I think ecosystems is another one of those big differences in the coming decade, because you're able to build ecosystems around data, especially if you can share that data, you know, and do so in a governed and secure way. But it leads to my question on industries, and I'm wondering if you see any patterns emerging in industries? And each industry seems to have its own unique disruption scenario. You know, retail obviously has been, you know, disrupted with online commerce. And healthcare with, of course, the pandemic. Financial services, you wonder, "Okay, are traditional banks going to lose control of payment systems?" Manufacturing you see our reliance on China's supply chain in, of course, North America. Are you seeing any patterns in industry as it pertains to data? And what can you share with us in terms of insights there? >> Yeah, we are. And, I mean, you know, there's obviously the industries that are, you know, very data savvy or data hungry. You think about, you know, the telecommunication industry, you think about manufacturing, you think about financial services and retail. I mean, financial services and retailers are particularly interesting, because they're kind of both in the retail business and having to deal with this level of complexity of they have physical locations and they also have a relationship with people online, so they really want to be able to bring these two worlds together. You know, I think, you know, about those scenarios of Carrefour, for instance. It's a large retailer in Europe that has been able to not only to, you know, onboard on our platform and they're using, you know, everything from BigQuery, all the way to Looker, but also now create the data assets that enable them to differentiate within their own industry. And so we see a lot of that happening across pretty much all industries. It's difficult to think about an industry that is not really taking a hard look at their data strategy recently, especially over the last two years, and really thought about how they're creating innovation. We have actually created what we call design patterns, which are basically blueprints for organization to take on. It's free, it's free guidance, it's free datasets and code that can accelerate their building of these innovative solutions. So think about the, you know, ability to determine propensity to purchase. Or build, you know, a big trend is recommendation systems. Another one is anomaly detection, and this was great because anomaly detection is a scenario that works in telco, but also in financial services. So we certainly are seeing now companies moving up in their level of maturity, because we're making it easier and simpler for them to assemble these technologies and create, you know, what we call data-rich experiences. >> The last question is how you see the emerging edge, IoT, analytics in that space? You know, a lot of the machine learning or AI today is modeling in the cloud, as you well know. But when you think about a lot of the consumer applications, whether it's voice recognition or, you know, or fingerprinting, et cetera, you're seeing some really interesting use cases that could bleed into the enterprise. And we think about AI inferencing at the edge as really driving a lot of value. How do you see that playing out and what's Google's role there? >> So there's a lot going on in that space. I'll give you just a simple example. Maybe something that's easy for the community to understand is there's still ways that we define certain metrics that are not taking into account what actually is happening in reality. I was just talking to a company whose job is to deliver meals to people. And what they have realized is that in order for them to predict exactly the time it's going to take them from the kitchen to your desk, they have to take into account the fact that distance sometimes it's not just horizontal, it's also vertical. So if you're distributing and you're delivering meals, you know, in Singapore, for instance, high density, you have to understand maybe the data coming from the elevators. So you can determine, "Oh, if you're on the 20th floor, now my distance to you, and my ability to forecast exactly when you're going to get that meal, is going to be different than if you are on the fifth floor. And, particularly, if you're ordering at 11:32, versus if you're ordering at 11:58." And so what's happening here is that as people are developing these intelligent systems, they're now starting to input a lot of information that historically we might not have thought about, but that actually is very relevant to the end-user. And so, you know, how do you do that? Again, and you have to have a platform that enables you to have a large diversity of use cases, and that thinks ahead, if you will, of the problems you might run into. Lots and lots of innovation in this space. I mean, we work with, you know, companies like Ford to, you know, reinvent the connected, you know, cars. We work with companies like Vodafone, 700 use cases, to think about how they're going to deal with what they call their data ocean. You know, I thought you would like this term, because we've gone from data lakes to data oceans. And so there is certainly a ton of innovation and certainly, you know, the chief data officers that I have the opportunity to work with are really not short of ideas. I think what's been happening up until now, they haven't had this kind of single, unified, simple experience that they can use in order to onboard quickly and then enable their people to build great, rich-data applications. >> Yeah, we certainly had fun with that over the years, data lake or data ocean. And thank you for remembering that, Bruno. Always a pleasure seeing you. Thanks so much for your time and sharing your perspectives, and informing us about what Google's up to. Can't wait to have you back. >> Thanks for having me, Dave. >> All right, and thank you for watching, everybody. This is Dave Vellante. Appreciate you watching this CUBE Conversation, and we'll see you next time. (gentle music)

>>from around the globe. It's the >>cube presenting >>active de que intelligent automation for data quality brought to you by Iota Ho >>Welcome to the sixth episode of the I. O. Tahoe data automation series. On the cube. We're gonna start off with a segment on how to accelerate the adoption of snowflake with Glenn Grossman, who is the enterprise account executive from Snowflake and yusef khan, the head of data services from Iota. Gentlemen welcome. >>Good afternoon. Good morning, Good evening. Dave. >>Good to see you. Dave. Good to see you. >>Okay glenn uh let's start with you. I mean the Cube hosted the snowflake data cloud summit in November and we heard from customers and going from love the tagline zero to snowflake, you know, 90 minutes very quickly. And of course you want to make it simple and attractive for enterprises to move data and analytics into the snowflake platform but help us understand once the data is there, how is snowflake helping to achieve savings compared to the data lake? >>Absolutely. dave. It's a great question, you know, it starts off first with the notion and uh kind of, we coined it in the industry or t shirt size pricing. You know, you don't necessarily always need the performance of a high end sports car when you're just trying to go get some groceries and drive down the street 20 mph. The t shirt pricing really aligns to, depending on what your operational workload is to support the business and the value that you need from that business? Not every day. Do you need data? Every second of the moment? Might be once a day, once a week through that t shirt size price and we can align for the performance according to the environmental needs of the business. What those drivers are the key performance indicators to drive that insight to make better decisions, It allows us to control that cost. So to my point, not always do you need the performance of a Ferrari? Maybe you need the performance and gas mileage of the Honda Civic if you would just get and deliver the value of the business but knowing that you have that entire performance landscape at a moments notice and that's really what what allows us to hold and get away from. How much is it going to cost me in a data lake type of environment? >>Got it. Thank you for that yussef. Where does Io Tahoe fit into this equation? I mean what's, what's, what's unique about the approach that you're taking towards this notion of mobilizing data on snowflake? >>Well, Dave in the first instance we profile the data itself at the data level, so not just at the level of metadata and we do that wherever that data lives. So it could be structured data could be semi structured data could be unstructured data and that data could be on premise. It could be in the cloud or it could be on some kind of SAAS platform. And so we profile this data at the source system that is feeding snowflake within snowflake itself within the end applications and the reports that the snowflake environment is serving. So what we've done here is take our machine learning discovery technology and make snowflake itself the repository for knowledge and insights on data. And this is pretty unique. Uh automation in the form of our P. A. Is being applied to the data both before after and within snowflake. And so the ultimate outcome is that business users can have a much greater degree of confidence that the data they're using can be trusted. Um The other thing we do uh which is unique is employee data R. P. A. To proactively detect and recommend fixes the data quality so that removes the manual time and effort and cost it takes to fix those data quality issues. Uh If they're left unchecked and untouched >>so that's key to things their trust, nobody's gonna use the data. It's not trusted. But also context. If you think about it, we've contextualized are operational systems but not our analytic system. So there's a big step forward glen. I wonder if you can tell us how customers are managing data quality when they migrate to snowflake because there's a lot of baggage in in traditional data warehouses and data lakes and and data hubs. Maybe you can talk about why this is a challenge for customers. And like for instance can you proactively address some of those challenges that customers face >>that we certainly can. They have. You know, data quality. Legacy data sources are always inherent with D. Q. Issues whether it's been master data management and data stewardship programs over the last really almost two decades right now, you do have systemic data issues. You have siloed data, you have information operational, data stores data marks. It became a hodgepodge when organizations are starting their journey to migrate to the cloud. One of the things that were first doing is that inspection of data um you know first and foremost even looking to retire legacy data sources that aren't even used across the enterprise but because they were part of the systemic long running operational on premise technology, it stayed there when we start to look at data pipelines as we onboard a customer. You know we want to do that era. We want to do QA and quality assurance so that we can, And our ultimate goal eliminate the garbage in garbage out scenarios that we've been plagued with really over the last 40, 50 years of just data in general. So we have to take an inspection where traditionally it was E. T. L. Now in the world of snowflake, it's really lt we're extracting were loading or inspecting them. We're transforming out to the business so that these routines could be done once and again give great business value back to making decisions around the data instead of spending all this long time. Always re architect ng the data pipeline to serve the business. >>Got it. Thank you. Glenda yourself of course. Snowflakes renowned for customers. Tell me all the time. It's so easy. It's so easy to spin up a data warehouse. It helps with my security. Again it simplifies everything but so you know, getting started is one thing but then adoption is also a key. So I'm interested in the role that that I owe. Tahoe plays in accelerating adoption for new customers. >>Absolutely. David. I mean as Ben said, you know every every migration to Snowflake is going to have a business case. Um uh and that is going to be uh partly about reducing spending legacy I. T. Servers, storage licenses, support all those good things um that see I want to be able to turn off entirely ultimately. And what Ayatollah does is help discover all the legacy undocumented silos that have been built up, as Glenn says on the data estate across a period of time, build intelligence around those silos and help reduce those legacy costs sooner by accelerating that that whole process. Because obviously the quicker that I. T. Um and Cdos can turn off legacy data sources the more funding and resources going to be available to them to manage the new uh Snowflake based data estate on the cloud. And so turning off the old building, the new go hand in hand to make sure those those numbers stack up the program is delivered uh and the benefits are delivered. And so what we're doing here with a Tahoe is improving the customers are y by accelerating their ability to adopt Snowflake. >>Great. And I mean we're talking a lot about data quality here but in a lot of ways that's table stakes like I said, if you don't trust the data, nobody's going to use it. And glenn, I mean I look at Snowflake and I see obviously the ease of use the simplicity you guys are nailing that the data sharing capabilities I think are really exciting because you know everybody talks about sharing data but then we talked about data as an asset, Everyone so high I to hold it. And so sharing is is something that I see as a paradigm shift and you guys are enabling that. So one of the things beyond data quality that are notable that customers are excited about that, maybe you're excited about >>David, I think you just cleared it out. It's it's this massive data sharing play part of the data cloud platform. Uh you know, just as of last year we had a little over about 100 people, 100 vendors in our data marketplace. That number today is well over 450 it is all about democratizing and sharing data in a world that is no longer held back by FTp s and C. S. V. S and then the organization having to take that data and ingested into their systems. You're a snowflake customer. want to subscribe to an S and P data sources an example, go subscribe it to it. It's in your account there was no data engineering, there was no physical lift of data and that becomes the most important thing when we talk about getting broader insights, data quality. Well, the data has already been inspected from your vendor is just available in your account. It's obviously a very simplistic thing to describe behind the scenes is what our founders have created to make it very, very easy for us to democratize not only internal with private sharing of data, but this notion of marketplace ensuring across your customers um marketplace is certainly on the type of all of my customers minds and probably some other areas that might have heard out of a recent cloud summit is the introduction of snow park and being able to do where all this data is going towards us. Am I in an ale, you know, along with our partners at Io Tahoe and R. P. A. Automation is what do we do with all this data? How do we put the algorithms and targets now? We'll be able to run in the future R and python scripts and java libraries directly inside Snowflake, which allows you to even accelerate even faster, Which people found traditionally when we started off eight years ago just as a data warehousing platform. >>Yeah, I think we're on the cusp of just a new way of thinking about data. I mean obviously simplicity is a starting point but but data by its very nature is decentralized. You talk about democratizing data. I like this idea of the global mesh. I mean it's very powerful concept and again it's early days but you know, keep part of this is is automation and trust, yussef you've worked with Snowflake and you're bringing active D. Q. To the market what our customers telling you so far? >>Well David the feedback so far has been great. Which is brilliant. So I mean firstly there's a point about speed and acceleration. Um So that's the speed to incite really. So where you have inherent data quality issues uh whether that's with data that was on premise and being brought into snowflake or on snowflake itself, we're able to show the customer results and help them understand their data quality better Within Day one which is which is a fantastic acceleration. I'm related to that. There's the cost and effort to get that insight is it's a massive productivity gain versus where you're seeing customers who've been struggling sometimes too remediate legacy data and legacy decisions that they've made over the past couple of decades, so that that cost and effort is much lower than it would otherwise have been. Um 3rdly, there's confidence and trust, so you can see Cdos and see IOS got demonstrable results that they've been able to improve data quality across a whole bunch of use cases for business users in marketing and customer services, for commercial teams, for financial teams. So there's that very quick kind of growth in confidence and credibility as the projects get moving. And then finally, I mean really all the use cases for the snowflake depend on data quality, really whether it's data science, uh and and the kind of snow park applications that Glenn has talked about, all those use cases work better when we're able to accelerate the ri for our joint customers by very quickly pushing out these data quality um insights. Um And I think one of the one of the things that the snowflake have recognized is that in order for C. I. O. Is to really adopt enterprise wide, um It's also as well as the great technology with Snowflake offers, it's about cleaning up that legacy data state, freeing up the budget for CIA to spend it on the new modern day to a state that lets them mobilise their data with snowflake. >>So you're seeing the Senate progression. We're simplifying the the the analytics from a tech perspective. You bring in Federated governance which which brings more trust. Then then you bring in the automation of the data quality piece which is fundamental. And now you can really start to, as you guys are saying, democratized and scale uh and share data. Very powerful guys. Thanks so much for coming on the program. Really appreciate your time. >>Thank you. I appreciate as well. Yeah.

>>with me or three amazing guest Panelists. One of the things that we can do today with data that we say weren't able to do maybe five years ago. >>Yes, certainly. Um, I think there's lots of things that we can integrate specific actions. But if you were to zoom out and look at the big picture, our ability to reason through data to inform our choices to data with data is bigger than ever before. There are still many companies have to decide to sample data or to throw away older data, or they don't have the right data from from external companies to put their decisions and actions in context. Now we have the technology and the platforms toe, bring all that data together, tear down silos and look 3 60 of a customer or entire action. So I think it's reasoning through data that has increased the capability of organizations dramatically in the last few years. >>So, Milan, when I was a young pup at I D. C. I started the storage program there many, many moons ago, and and so I always pay attention to what's going on storage back in my mind. And as three people forget. Sometimes that was actually the very first cloud product announced by a W s, which really ushered in the cloud era. And that was 2006 and fundamentally changed the way we think about storing data. I wonder if you could explain how s three specifically and an object storage generally, you know, with get put really transform storage from a blocker to an enabler of some of these new workloads that we're seeing. >>Absolutely. I think it has been transformational for many companies in every industry. And the reason for that is because in s three you can consolidate all the different data sets that today are scattered around so many companies, different data centers. And so if you think about it, s three gives the ability to put on structure data, which are video recordings and images. It puts semi structured data, which is your CSP file, which every company has lots of. And it has also support for structure data types like parquet files which drive a lot of the business decisions that every company has to make today. And so if you think about S three, which launched on Pi Day in March of 2000 and six s three started off as an object store, but it has evolved into so much more than that where companies all over the world, in every industry are taking those different data sets. They're putting it in s three. They're growing their data and then they're growing the value that they capture on top of that data. And that is the separation we see that snowflake talks about. And many of the pioneers across different industries talk about which is a separation of the growth of storage and the growth of your computer applications. And what's happening is that when you have a place to put your data like s three, which is secure by default and has the availability in the durability of the operational profile, you know, and can trust, then the innovation of the application developers really take over. And you know, one example of that is where we have a customer and the financial sector, and they started to use us three to put their customer care recordings, and they were just using it for storage because that obviously data set grows very quickly, and then somebody in their fraud department got the idea of doing machine learning on top of those customer care recordings. And when they did that, they found really interesting data that they could then feed into their fraud detection models. And so you get this kind of alchemy of innovation that that happens when you take the data sets of today and yesterday and tomorrow you put them all in one place, which is dust free and the innovation of your application. Developers just takes over and builds not just what you need today, but what you need in the future as well. >>Thank you for that Mark. I want to bring you into this panel. It's it's great to have you here, so so thank you. I mean, Tableau has been a game changer for organizations. I remember my first by tableau conference, passionate, uh, customers and and really bringing cloud like agility and simplicity. Thio visualization just totally change the way people thought about data and met with massive data volumes and simplified access. And now we're seeing new workloads that are developing on top of data and snowflake data in the cloud. Can you talk about how your customers are really telling stories and bringing toe life those stories with data on top of things like, that's three, which my mom was just talking about. >>Yeah, for sure. Building on what Christian male I have already said you are. Our mission tableau has always been to help people see and understand data. And you look at the amazing advances they're happening in storage and data processing and now you, when you that the data that you can see and play with this so amazing, right? Like at this point in time, yeah, it's really nothing short of a new microscope or a new telescope that really lets you understand patterns. They were always there in the world, but you literally couldn't see them because of the limitations of the amount of data that you could bring into the picture because of the amount of processing power in the amount of sharing of data that you could bring into the picture. And now, like you said, these three things are coming together. This amazing ability to see and tell stories with your data, combined with the fact that you've got so much more data at your fingertips, the fact that you can now process that data. Look at that data. Share that data in ways that was never possible. Again, I'll go back to that analogy. It feels like the invention of a new microscope, a new telescope, a new way to look at the world and tell stories and get thio. Insights that were just were never possible before. >>So thank you for that. And Christian, I want to come back to this notion of the data cloud, and, you know, it's a very powerful concept, and of course it's good marketing. But But I wonder if you could add some additional color for the audience. I mean, what more can you tell us about the data cloud, how you're seeing it, it evolving and maybe building on some of the things that Mark was just talking about just in terms of bringing this vision into reality? >>Certainly. Yeah, Data Cloud, for sure, is bigger and more concrete than than just the marketing value of it. The big insight behind our vision for the data cloud is that just a technology capability, just a cloud data platform is not what gets organizations to be able to be, uh, data driven to be ableto make great use of data or be um, highly capable in terms of data ability. Uh, the other element beyond technology is the access and availability off Data toe put their own data in context or enrich, based on the no literal data from other third parties. So the data cloud the way to think about it is is a combination of both technology, which for snowflake is our cloud data platform and all. The work loves the ability to do data warehousing, enquiries and speeds and feeds fit in there and data engineering, etcetera. But it's also how do we make it easier for our customers to have access to the data they need? Or they could benefit to improve the decisions for for their own organizations? Think of the analogy off a set top box. I can give you a great, technically set top box, but if there's no content on the other side, it makes it difficult for you to get value out of it. That's how we should all be thinking about the data cloud. It's technology, but it's also seamless access to data >>in my life. Can >>you give us >>a sense of the scope And what kind of scale are you seeing with snowflake on on AWS? >>Well, Snowflake has always driven as Christian. That was a very high transaction rate, the S three. And in fact, when Chris and I were talking, uh, just yesterday we were talking about some of the things that have really been, um, been remarkable about the long partnership that we've had over the years. And so I'll give you an example of of how that evolution has really worked. So, as you know, as three has eyes, you know, the first a W s services launched, and we have customers who have petabytes hundreds of petabytes and exabytes of storage in history. And so, from the ground up, s three has been built for scale. And so when we have customers like Snowflake that have very high transaction rates for requests for ESRI storage, we put our customer hat on and we asked, we asked customers like like, Snowflake, how do you think about performance? Not just what performance do you need, but how do you think about performance? And you know, when Christians team were walking through the demands of making requests? Two, there s three data. They were talking about some pretty high spikes over time and just a lot of volume. And so when we built improvements into our performance over time, we put that hat on for work. You know, Snowflake was telling us what they needed, and then we built our performance model not around a bucket or an account. We built it around a request rate per prefix, because that's what Snowflake and other customers told us they need it. And so when you think about how we scale our performance, we Skillet based on a prefix and not a popular account, which other cloud providers dio, we do it in this unique way because 90% of our customer roadmap across AWS comes from customer request. And that's what Snowflake and other customers were saying is that Hey, I think about my performance based on a prefix of an object and not some, you know, arbitrary semantic of how I happened to organize my buckets. I think the other thing I would also throw out there for scale is, as you might imagine, s Tree is a very large distributed system. And again, if I go back to how we architected for our performance improvements. We architected in such a way that a customer like snowflake could come in and they could take advantage of horizontally scaling. They can do parallel data retrievals and puts in gets for your data. And when they do that, they can get tens of thousands of requests for second because they're taking advantage of the scale of s tree. And so you know when when when we think about scale, it's not just scale, which is the growth of your storage, which every customer needs. I D. C says that digital data is growing at 40% year over year, and so every customer needs a place to put all of those storage sets that are growing. But the way we also to have worked together for many years is this. How can we think about how snowflake and other customers are driving these patterns of access on top of the data, not just elasticity of the storage, but the access. And then how can we architect, often very uniquely, as I talked about with our request rate in such a way that they can achieve what they need to do? Not just today but in the future, >>I don't know you. Three companies here there don't often take their customer hats off. Mark, I wonder if you could come to you. You know, during the Data Cloud Summit, we've been exploring this notion that innovation in technology is really evolved from point products. You know, the next generation of server or software tool toe platforms that made infrastructure simpler, uh, are called functions. And now it's evolving into leveraging ecosystems. You know, the power of many versus the resource is have one. So my question is, you know, how are you all collaborating and creating innovations that your customers could leverage? >>Yeah, for sure. So certainly, you know, tableau and snowflake, you know, kind of were dropped that natural partners from the beginning, right? Like putting that visualization engine on top of snowflake thio. You know, combine that that processing power on data and the ability to visualize it was obvious as you talk about the larger ecosystem. Now, of course, tableau is part of salesforce. Um and so there's a much more interesting story now to be told across the three companies. 1, 2.5, maybe a zoo. We talk about tableau and salesforce combined together of really having this full circle of salesforce. You know, with this amazing set of business APS that so much value for customers and getting the data that comes out of their salesforce applications, putting it into snowflakes so that you can combine that share, that you process it, combine it with data not just for across salesforce, but from your other APS in the way that you want and then put tableau on top of it. Now you're talking about this amazing platform ecosystem of data, you know, coming from your most valuable business applications in the world with the most, you know, sales opportunity, objects, marketing service, all of that information flowing into this flexible data platform, and then this amazing visualization platform on top of it. And there's really no end of the things that our customers can do with that combination. >>Christian, we're out of time. But I wonder if you could bring us home and I want to end with, you know, let's say, you know, people. Some people here, maybe they don't Maybe they're still struggling with cumbersome nature of let's say they're on Prem data warehouses. You know the kids just unplug them because they rely on them for certain things, like reporting. But But let's say they want to raise the bar on their data and analytics. What would you advise for the next step? For them? >>I think the first part or first step to take is around. Embrace the cloud and they promise and the abilities of cloud technology. There's many studies where relative to peers, companies that embracing data are coming out ahead and outperforming their peers and with traditional technology on print technology. You ended up with a proliferation of silos and copies of data, and a lot of energy went into managing those on PREM systems and making copies and data governance and security and cloud technology. And the type of platform the best snowflake has brought to market enables organizations to focus on the data, the data model, data insights and not necessarily on managing the infrastructure. So I think that with the first recommended recommendation from from our end embraced cloud, get into a modern cloud data platform, make sure you're spending your time on data not managing infrastructure and seeing what the infrastructure lets you dio. >>Okay, this is Dave, Volunteer for the Cube. Thank you for watching. Keep it right there with mortgage rate content coming your way.

>>Hello, buddy. And welcome back to the cubes. Coverage of Snowflake Data Cloud Summer 2020. We're tracking the rise of the data cloud and fresh off the keynotes. Hear Frank's Luqman, the chairman and CEO of Snowflake, and Anita Lynch, the vice president of data governance at Disney Streaming Services. Folks. Welcome E Need a Disney plus. Awesome. You know, we signed up early. Watched all the Marvel movies. Hamilton, the new Pixar movie Soul. I haven't gotten to the man DeLorean yet. Your favorite, but I really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud because I never liked the term Enterprise Data Warehouse. What you're doing is is so different from the sort of that legacy world that I've known all these years. But start with why the data cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing? >>Yeah, I know. You know, we have We've come a long way in terms of workload execution, right? In terms of scale and performance and, you know, concurrent execution. We really taking the lid off sort of the physical constraints that that have existed on these types of operations. But there's one problem, uh, that were not yet, uh solving. And that is the silo ing and bunkering of data. Essentially, you know, data is locked in applications. It's locked in data centers that's locked in cloud cloud regions incredibly hard for for data science teams to really, you know, unlocked the true value of data. When you when you can address patterns that that exists across data set. So we're perpetuate, Ah, status we've had for for ever since the beginning off computing. If we don't start Thio, crack that problem now we have that opportunity. But the notion of a data cloud is like basically saying, Look, folks, you know, we we have to start inside, lowing and unlocking the data on bring it into a place where we can access it. Uh, you know, across all these parameters and boundaries that have historically existed, it's It's very much a step level function. Customers have always looked at things won't workload at that time. That mentality really has to go. You really have to have a data cloud mentality as well as a workload orientation towards towards managing data. Yeah, >>Anita is great here in your role at Disney, and you're in your keynote and the work. You're doing the governance work, and you're you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. You know, maybe you can expand on some of these initiatives here and share what you you're seeing as some of the biggest challenges to success. And, of course, the opportunities that you're unlocking. >>Sure. I mean, in my role leading data to governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them, they can also understand really easily and quickly whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance. And a lot of the work that we would normally have to do manually is actually done for us through the data. Clean rooms. >>Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you could elucidate on that. >>Sure, I mean data complexities air going to evolve over time in any traditional data architecture. Er, simply because you often have different teams at different periods in time trying thio, analyze and gather data across Ah, whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders, their time constraints. And quite often, um, it's not always clear how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down the silos is allow individuals to see up front how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away, and by ensuring that essentially, as they're continuing to kind of scale the use cases that they're focused on. They're no longer required. Thio make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world, >>for sure. I mean, copy creep, because it be the silent killer. Frank, I followed you for a number of years. You know, your big thinker. You and I have had a lot of conversations about the near term midterm and long term. I wonder if you could talk about you know, when you're Kino. You talk about eliminating silos and connecting across data sources, which really powerful concept. But really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe What are some of the blockers there? >>Well, there's there's certainly, ah natural friction there. I still remember when we first started to talk to to Salesforce, you know, they had discovered that we were top three destination off sales first data, and they were wondering, you know why that was. And and the reason is, of course, that people take salesforce data, push it to snowflake because they wanna overlay it with what data outside of Salesforce. You know, whether it's adobe or any other marketing data set. And then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS is always like, no, we're an island were planning down to ourselves. Everybody needs to come with us as opposed to we We go, you know, to a different platform to run these type of processes. It's no different for the for the public club. Venter Day didn't mean they have, you know, massive moats around there. Uh, you know, their stories to, you know, really prevent data from from leaving their their orbit. Eso there is natural friction in in terms off for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on on the power and potential of data unless we allow it to come together. Uh, snowflake is the platform that allows that to happen. You know, we were pleased with our relationship with Salesforce because they did appreciate you know why this was important and why this was necessary. And we think you know, other parts of the industry will gradually come around to it as well. So the the idea of a data cloud has really come, right? People are recognizing, you know, why does this matters now? It's not gonna happen overnight, And there's a step global function of very big change in mentality and orientation. You know, >>it's almost as though the SAS ification of our industries sort of repeated some of the application silos, and you build a hardened top around it. All the processes are hardened around it, and Okay, here we go. And you're really trying to break that, aren't you? Yeah, Exactly. Anita. Again, I wanna come back to this notion of governance. It's so it's so important. It's the first role in your title, and it really underscores the importance of this. Um, you know, Frank was just talking about some of the hurdles, and and this is this is a big one. I mean, we saw this in the early days of big data. Where governance was this after thought it was like, bolted on kind of wild, Wild West. I'm interested in your governance journey, and maybe you can share a little bit about what role Snowflake has played there in terms of supporting that agenda. Bond. Kind of What's next on that journey? >>Sure. Well, you know, I've I've led data teams in a numerous, uh, in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance. And what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >>Well, I mean a big part of what you were talking about, at least my inference in your your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it. But they're not the wonder about and and about the privacy, the concerns, etcetera. You've taken care of all that. It's sort of transparent to them. Is that >>yeah, right. That's right. Absolutely. So we focus on ensuring compliance across all the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring, you know, that we're able Thio do this. We don't We don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these air really important components of our strategy. >>I got. So I have a question. Maybe each of you can answer. I I sort of see this our industry moving from, you know, products. So then the platforms and platforms even involving into ecosystems. And then there's this ecosystem of of data. You guys both talked a lot about data sharing. But maybe Frank, you could start in Anita. You can add on to Frank's answer. You're obviously both both passionate about the use of of data and trying to do so in a responsible way. That's critical, but it's also gonna have business impact. Frank, where's this passion come from? On your side. And how are you putting in tow action in your own organization? >>Well, you know, I'm really gonna date myself here, but, you know, many, many years ago, you know, I saw the first glimpse off, uh, multidimensional databases that were used for reporting. Really, On IBM mainframes on debt was extraordinarily difficult. We didn't even have the words back then. In terms of data, warehouses and business. All these terms didn't exist. People just knew that they wanted to have, um, or flexible way of reporting and being able Thio pivot data dimensionally and all these kinds of things. And I just whatever this predates, you know, Windows 3.1, which, really, you know, set off the whole sort of graphical in a way of dealing with systems which there's not a whole generations of people that don't know any different. Right? So I I've lived the pain off this problem on sort of been had a front row seat to watching this This transpire over a very long period of time. And that's that's one of the reasons um, you know why I'm here? Because I finally seen, you know, a glimpse off, you know, also as an industry fully fully just unleashing and unlocking the potential were not in a place where the technology is ahead of people's ability to harness it right, which we've We've never been there before, right? It was always like we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just heads are spinning with what's now possible, which is why you see markets evolved very rapidly right now. Way we were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on the world. The world's changing right in front of your eyes right now. >>Sonita. Maybe you could add on to what Frank just said and share some of the business impacts and and outcomes that air notable since you're really applied your your love of data and maybe maybe touch on culture, your data culture. You know any words of wisdom for folks in the audience who might be thinking about embarking on a data cloud journey similar to what you've been on? >>Yeah, sure, I think for me. I fell in love with technology first, and then I fell in love with data, and I fell in love with data because of the impact the data can have on both the business and the technology strategy. And so it's sort of that nexus, you know, between all three and in terms of my career journey and and some of the impacts that I've seen I mean, I think with the advent of the cloud, you know before, Well, how do I say that before the cloud actually became, you know, so prevalent in such a common part of the strategy that's required? It was so difficult, you know, so painful. It took so many hours to actually be able to calculate, you know, the volumes of data that we had. Now we have that accessibility, and then on top of it with the snowflake data cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have toe have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact has has only been possible with the volumes of data that we have available to us today. And it's just it's phenomenal to see the speed at which we can operate and really, truly understand our customers, interests and their preferences, and then tailor the experiences that they really want and deserve for them. Um, it's It's been a great feeling. Thio, get to this point in time. >>That's fantastic. So, Frank, I gotta ask you if you're still in your spare time, you decided to write a book? I'm loving it. Um, I don't have a signed copy, so I'm gonna have to send it back and have you sign it. But you're I love the inside baseball. It's just awesome. Eso really appreciate that. So But why did you decide to write a book? >>Well, there were a couple of reasons. Obviously, we thought it was an interesting tale to tell for anybody you know who is interested in, You know what's going on. How did this come about, You know, where the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, because this is such a step function, it's so non incremental. We felt like, you know, we really needed quite a bit of real estate to really lay out what the full narrative and context is on. Do you know we thought books titled The Rise of the Data Cloud. That's exactly what it ISS and We're trying to make the case for that mindset, that mentality, that strategy. Because all of us, you know, I think is an industry or were risk off persisting, perpetuating, You know, where we've been since the beginning off computing. So we're really trying to make a pretty forceful case for Look, you know, there is an enormous opportunity out there, The different choices you have to make along the way. >>Guys, we got to leave it there. Frank. I know you and I are gonna talk again. Anita. I hope we have a chance to meet face to face and and talking the Cube live someday. You're phenomenal, guest. And what a great story. Thank you both for coming on. And thank you for watching. Keep it right there. You're watching the Snowflake Data Cloud Summit on the Cube.

>>Hi, I'm Frank's Luqman CEO of Snowflake. And welcome to the Snowflake Data Cloud Summit. I'd like to take the next few minutes to introduce you to >>the data cloud on why it matters to the modern enterprise. As an industry, we have struggled to mobilize our data, meaning that has been hard to put data into service of our enterprises. We're not living in a data economy and for most data central how we run our lives, our businesses and our institutions, every single interaction we have now, whether it's in social media, e commerce or any other service, engagement generates critical data. You multiply this out with the number of actors and transactions. The volume is overwhelming, growing in leaps and bounds every day. There was a time when data operations focused mostly on running reports and populating dashboards to inform people in the enterprise of what had happened on what was going on. And we still do a ton of that. But the emphasis is shifting to data driving operations from just data informing people. There is such a thing as the time value off data meaning that the faster data becomes available, the more impactful and valuable it ISS. As data ages, it loses much of its actionable value. Digital transformation is an overused term in our industry, but the snowflake it means the end to end automation of business processes, from selling to transacting to supporting to servicing customers. Digital processes are entirely disinter mediated in terms of people. Involvement in are driven into end by data. Of course, many businesses have both physical and digital processes, and they are >>intertwined. Think of retail, logistics, delivery services and so on. So a data centric operating discipline is no longer optional data operations Air now the beating heart >>of the modern enterprise that requires a massively scalable data platform talented data engineering and data science teams to fully exploit the technology that now is becoming available. Enter snowflake. Chances are that, you know, snowflake as a >>world class execution platform for a diverse set of workloads. Among them data warehousing, data engineering, data, lakes, data, science, data applications and data sharing. Snowflake was architected from scratch for cloud scale computing. No legacy technology was carried forward in the process. Snowflake reimagined many aspects of data management data operations. The result was a cloud data platform with massive scale, blistering performance, superior economics and world class data governance. Snowflake innovated on a number of vectors that wants to deliver this breakthrough. First scale and performance. Snowflake is completely designed for cloud scale computing, both in terms of data volume, computational performance and concurrent workload. Execution snowflake features numerous distinct innovations in this category, but none stands up more than the multi cluster shared stories. Architectural Removing the control plane from the individual cluster led to a dramatically different approach that has yielded tremendous benefits. But our customers love about Snowflake is to spin up new workloads without limitation and provisioned these workloads with his little or as much compute as they see fit. No longer do they fear hidden capacity limits or encroaching on other workloads. Customers can have also scale storage and compute independent of each other, something that was not possible before second utility and elasticity. Not only can snowflake customer spin up much capacity for as long as they deem necessary. Three. Utility model in church, they only get charged for what they consumed by the machine. Second, highly granular measurement of utilization. Ah, lot of the economic impact of snowflake comes from the fact that customers no longer manage capacity. What they do now is focused on consumption. In snowflake is managing the capacity. Performance and economics now go hand in hand because faster is now also cheaper. Snowflake contracts with the public cloud vendors for capacity at considerable scale, which then translates to a good economic value at the retail level is, well, third ease of use and simplicity. Snowflake is a platform that scales from the smallest workloads to the largest data estates in the world. It is unusual in this offer industry to have a platform that controversy the entire spectrum of scale, a database technology snowflake is dramatically simple fire. To compare to previous generations, our founders were bent on making snowflake, a self managing platform that didn't require expert knowledge to run. The role of the Deba has evolved into snowflake world, more focused on data model insights and business value, not tuning and keeping the infrastructure up and running. This has expanded the marketplace to nearly any scale. No job too small or too large. Fourth, multi cloud and Cross Cloud or snowflake was first available on AWS. It now also runs very successfully on mark yourself. Azure and Google Cloud Snowflake is a cloud agnostic platform, meaning that it doesn't know what it's running on. Snowflake completely abstracts the underlying cloud platform. The user doesn't need to see or touch it directly and also does not receive a separate bill from the cloud vendor for capacity consumed by snowflake. Being multi cloud capable customers have a choice and also the flexibility to change over time snowflakes. Relationships with Amazon and Microsoft also allow customers to transact through their marketplaces and burned down their cloud commit with their snowflakes. Spend Snowflake is also capable of replicating across cloud regions and cloud platforms. It's not unusual to see >>the same snowflake data on more than one public cloud at the time. Also, for disaster recovery purposes, it is desirable to have access to snowflake on a completely different public cloud >>platform. Fifth, data Security and privacy, security and privacy are commonly grouped under the moniker of data governance. As a highly managed cloud data platform, snowflake designed and deploys a comprehensive and coherent security model. While privacy requirements are newer and still emerging in many areas, snowflake as a platform is evolving to help customers steer clear from costly violations. Our data sharing model has already enabled many customers to exchange data without surrendering custody of data. Key privacy concerns There's no doubt that the strong governance and compliance framework is critical to extracting you analytical value of data directly following the session. Police Stay tuned to hear from Anita Lynch at Disney Streaming services about how >>to date a cloud enables data governance at Disney. The world beat a >>path to our door snowflake unleashed to move from UN promised data centers to the public cloud platforms, notably AWS, Azure and Google Cloud. Snowflake now has thousands of enterprise customers averaging over 500 million queries >>today across all customer accounts, and it's one of the fastest growing enterprise software companies in a generation. Our recent listing on the New York Stock Exchange was built is the largest software AIPO in history. But the data cloth conversation is bigger. There is another frontier workload. Execution is a huge part of it, but it's not the entire story. There is another elephant in the room, and that is that The world's data is incredibly fragmented in siloed, across clouds of old sorts and data centers all over the place. Basically, data lives in a million places, and it's incredibly hard to analyze data across the silos. Most intelligence analytics and learning models deploy on single data sets because it has been next to impossible to analyze data across sources. Until now, Snowflake Data Cloud is a data platform shared by all snowflake users. If you are on snowflake, you are already plugged into it. It's like being part of a Global Data Federation data orbit, if you will, where all other data can now be part of your scope. Historically, technology limitations led us to build systems and services that siloed the data behind systems, software and network perimeters. To analyze data across silos, we resorted to building special purpose data warehouses force fed by multiple data sources empowered by expensive proprietary hardware. The scale limitations lead to even more silos. The onslaught of the public cloud opened the gateway to unleashing the world's data for access for sharing a monetization. But it didn't happen. Pretty soon they were new silos, different public clouds, regions within the and a huge collection of SAS applications hoarding their data all in their own formats on the East NC ations whole industries exist just to move data from A to B customer behavior precipitated the silo ing of data with what we call a war clothes at a time mentality. Customers focused on the applications in isolation of one another and then deploy data platforms for their workload characteristics and not much else, thereby throwing up new rules between data. Pretty soon, we don't just have our old Silas, but new wants to content with as well. Meanwhile, the promise of data science remains elusive. With all this silo ing and bunkering of data workload performance is necessary but not sufficient to enable the promise of data science. We must think about unfettered data access with ease, zero agency and zero friction. There's no doubt that the needs of data science and data engineering should be leading, not an afterthought. And those needs air centered on accessing and analyzing data across sources. It is now more the norm than the exception that data patterns transcend data sources. Data silos have no meaning to data science. They are just remnants of legacy computing. Architectures doesn't make sense to evaluate strictly on the basis of existing workloads. The world changes, and it changes quickly. So how does the data cloud enabled unfettered data access? It's not just a function of being in the public cloud. Public Cloud is an enabler, no doubt about it. But it introduces new silos recommendation by cloud, platform by cloud region by Data Lake and by data format, it once again triggered technical grandstands and a lot of programming to bring a single analytical perspective to a diversity of data. Data was not analytics ready, not optimized for performance or efficiency and clearly lacking on data governance. Snowflake, address these limitations, thereby combining great execution with great data >>access. But, snowflake, we can have the best of both. So how does it all work when you join Snowflake and have your snowflake account? You don't just >>avail yourself of unlimited stories. And compute resource is along with a world class execution platform. You also plug into the snowflake data cloud, meaning that old snowflake accounts across clouds, regions and geography are part of a single snowflake data universe. That is the data clouds. It is based on our global data sharing architectures. Any snowflake data can be exposed and access by any other snowflake user. It's seamless and frictionless data is generally not copied. Her moves but access in place, subject to the same snowflake governance model. Accessing the data cloth can be a tactical one on one sharing relationship. For example, imagine how retailer would share data with a consumer back. It's good company, but then it easily proliferate from 1 to 1. Too many too many. The data cloud has become a beehive of data supply and demand. It has attracted hundreds of professional data listings to the Snowflake Data Marketplace, which fuels the data cloud with a rich supply of options. For example, our partner Star Schema, listed a very detailed covert 19 incident and fatality data set on the Snowflake Data Marketplace. It became an instant hit with snowflake customers. Scar schema is not raw data. It is also platform optimize, meaning that it was analytics ready for all snowflake accounts. Snowflake users were accessing, joining and overlaying this new data within a short time of it becoming available. That is the power of platform in financial services. It's common to see snowflake users access data from snowflake marketplace listings like fax set and Standard and Poor's on, then messed it up against for example. Salesforce data There are now over 100 suppliers of data listings on the snowflake marketplace That is, in addition to thousands of enterprise and institutional snowflake users with their own data sets. Best part of the snowflake data cloud is this. You don't need to do or buy anything different. If your own snowflake you're already plugged into the data clouds. A whole world data access options awaits you on data silos. Become a thing of the past, enjoy today's presentations. By the end of it, you should have a better sense in a bigger context for your choices of data platforms. Thank you for joining us.

(upbeat music) >> Hi everyone, thank you for joining us at the Data Cloud Summit. The last couple of months have been an exciting time at Snowflake. And yet, what's even more compelling to all of us at Snowflake is what's ahead. Today I have the opportunity to share new product developments that will extend the reach and impact of our Data Cloud and improve the experience of Snowflake users. Our product strategy is focused on four major areas. First, Data Cloud content. In the Data Cloud silos are eliminated and our vision is to bring the world's data within reach of every organization. You'll hear about new data sets and data services available in our data marketplace and see how previous barriers to sourcing and unifying data are eliminated. Second, extensible data pipelines. As you gain frictionless access to a broader set of data through the Data Cloud, Snowflakes platform brings additional capabilities and extensibility to your data pipelines, simplifying data ingestion, and transformation. Third, data governance. The Data Cloud eliminates silos and breaks down barriers and in a world where data collaboration is the norm, the importance of data governance is ratified and elevated. We'll share new advancements to support how the world's most demanding organizations mobilize your data while maintaining high standards of compliance and governance. Finally, our fourth area focuses on platform performance and capabilities. We remain laser focused on continuing to lead with the most performant and capable data platform. We have some exciting news to share about the core engine of Snowflake. As always, we love showing you Snowflake in action, and we prepared some demos for you. Also, we'll keep coming back to the fact that one of the characteristics of Snowflake that we're proud as staff is that we offer a single platform from which you can operate all of your data workloads, across clouds and across regions, which workloads you may ask, specifically, data warehousing, data lake, data science, data engineering, data applications, and data sharing. Snowflake makes it possible to mobilize all your data in service of your business without the cost, complexity and overhead of managing multiple systems, tools and vendors. Let's dive in. As you heard from Frank, the Data Cloud offers a unique capability to connect organizations and create collaboration and innovation across industries fueled by data. The Snowflake data marketplace is the gateway to the Data Cloud, providing visibility for organizations to browse and discover data that can help them make better decisions. For data providers on the marketplace, there is a new opportunity to reach new customers, create new revenue streams, and radically decrease the effort and time to data delivery. Our marketplace dramatically reduces the friction of sharing and collaborating with data opening up new possibilities to all participants in the Data Cloud. We introduced the Snowflake data marketplace in 2019. And it is now home to over 100 data providers, with half of them having joined the marketplace in the last four months. Since our most recent product announcements in June, we have continued broadening the availability of the data marketplace, across regions and across clouds. Our data marketplace provides the opportunity for data providers to reach consumers across cloud and regional boundaries. A critical aspect of the Data Cloud is that we envisioned organizations collaborating not just in terms of data, but also data powered applications and services. Think of instances where a provider doesn't want to open access to the entirety of a data set, but wants to provide access to business logic that has access and leverages such data set. That is what we call data services. And we want Snowflake to be the platform of choice for developing discovering and consuming such rich building blocks. To see How the data marketplace comes to live, and in particular one of these data services, let's jump into a demo. For all of our demos today, we're going to put ourselves in the shoes of a fictional global insurance company. We've called it Insureco. Insurance is a data intensive and highly regulated industry. Having the right access control and insight from data is core to every insurance company's success. I'm going to turn it over to Prasanna to show how the Snowflake data marketplace can solve a data discoverability and access problem. >> Let's look at how Insureco can leverage data and data services from the Snowflake data marketplace and use it in conjunction with its own data in the Data Cloud to do three things, better detect fraudulent claims, arm its agents with the right information, and benchmark business health against competition. Let's start with detecting fraudulent claims. I'm an analyst in the Claims Department. I have auto claims data in my account. I can see there are 2000 auto claims, many of these submitted by auto body shops. I need to determine if they are valid and legitimate. In particular, could some of these be insurance fraud? By going to the Snowflake data marketplace where numerous data providers and data service providers can list their offerings, I find the quantifying data service. It uses a combination of external data sources and predictive risk typology models to inform the risk level of an organization. Quantifying external sources include sanctions and blacklists, negative news, social media, and real time search engine results. That's a wealth of data and models built on that data which we don't have internally. So I'd like to use Quantifind to determine a fraud risk score for each auto body shop that has submitted a claim. First, the Snowflake data marketplace made it really easy for me to discover a data service like this. Without the data marketplace, finding such a service would be a lengthy ad hoc process of doing web searches and asking around. Second, once I find Quantifind, I can use Quantifind service against my own data in three simple steps using data sharing. I create a table with the names and addresses of auto body shops that have submitted claims. I then share the table with Quantifind to start the risk assessment. Quantifind does the risk scoring and shares the data back with me. Quantifind uses external functions which we introduced in June to get results from their risk prediction models. Without Snowflake data sharing, we would have had to contact Quantifind to understand what format they wanted the data in, then extract this data into a file, FTP the file to Quantifind, wait for the results, then ingest the results back into our systems for them to be usable. Or I would have had to write code to call Quantifinds API. All of that would have taken days. In contrast, with data sharing, I can set this up in minutes. What's more, now that I have set this up, as new claims are added in the future, they will automatically leverage Quantifind's data service. I view the scores returned by Quantifind and see the two entities in my claims data have a high score for insurance fraud risk. I open up the link returned by Quantifind to read more, and find that this organization has been involved in an insurance crime ring. Looks like that is a claim that we won't be approving. Using the Quantifind data service through the Snowflake data marketplace gives me access to a risk scoring capability that we don't have in house without having to call custom APIs. For a provider like Quantifind this drives new leads and monetization opportunities. Now that I have identified potentially fraudulent claims, let's move on to the second part. I would like to share this fraud risk information with the agents who sold the corresponding policies. To do this, I need two things. First, I need to find the agents who sold these policies. Then I need to share with these agents the fraud risk information that we got from Quantifind. But I want to share it such that each agent only sees the fraud risk information corresponding to claims for policies that they wrote. To find agents who sold these policies, I need to look up our Salesforce data. I can find this easily within Insureco's internal data exchange. I see there's a listing with Salesforce data. Our sales Ops team has published this listing so I know it's our officially blessed data set, and I can immediately access it from my Snowflake account without copying any data or having to set up ETL. I can now join Salesforce data with my claims to identify the agents for the policies that were flagged to have fraudulent claims. I also have the Snowflake account information for each agent. Next, I create a secure view that joins on an entitlements table, such that each agent can only see the rows corresponding to policies that they have sold. I then share this directly with the agents. This share contains the secure view that I created with the names of the auto body shops, and the fraud risk identified by Quantifind. Finally, let's move on to the third and last part. Now that I have detected potentially fraudulent claims, I'm going to move on to building a dashboard that our executives have been asking for. They want to see how Insureco compares against other auto insurance companies on key metrics, like total claims paid out for the auto insurance line of business nationwide. I go to the Snowflake data marketplace and find SNL U.S. Insurance Statutory Data from SNP. This data is included with Insureco's existing subscription with SMP so when I request access to it, SMP can immediately share this data with me through Snowflake data sharing. I create a virtual database from the share, and I'm ready to query this data, no ETL needed. And since this is a virtual database, pointing to the original data in SNP Snowflake account, I have access to the latest data as it arrives in SNPs account. I see that the SNL U.S. Insurance Statutory Data from SNP has data on assets, premiums earned and claims paid out by each us insurance company in 2019. This data is broken up by line of business and geography and in many cases goes beyond the data that would be available from public financial filings. This is exactly the data I need. I identify a subset of comparable insurance companies whose net total assets are within 20% of Insureco's, and whose lines of business are similar to ours. I can now create a Snow site dashboard that compares Insureco against similar insurance companies on key metrics, like net earned premiums, and net claims paid out in 2019 for auto insurance. I can see that while we are below median our net earned premiums, we are doing better than our competition on total claims paid out in 2019, which could be a reflection of our improved claims handling and fraud detection. That's a good insight that I can share with our executives. In summary, the Data Cloud enabled me to do three key things. First, seamlessly fine data and data services that I need to do my job, be it an external data service like Quantifind and external data set from SNP or internal data from Insureco's data exchange. Second, get immediate live access to this data. And third, control and manage collaboration around this data. With Snowflake, I can mobilize data and data services across my business ecosystem in just minutes. >> Thank you Prasanna. Now I want to turn our focus to extensible data pipelines. We believe there are two different and important ways of making Snowflakes platform highly extensible. First, by enabling teams to leverage services or business logic that live outside of Snowflake interacting with data within Snowflake. We do this through a feature called external functions, a mechanism to conveniently bring data to where the computation is. We announced this feature for calling regional endpoints via AWS gateway in June, and it's currently available in public preview. We are also now in public preview supporting Azure API management and will soon support Google API gateway and AWS private endpoints. The second extensibility mechanism does the converse. It brings the computation to Snowflake to run closer to the data. We will do this by enabling the creation of functions and procedures in SQL, Java, Scala or Python ultimately providing choice based on the programming language preference for you or your organization. You will see Java, Scala and Python available through private and public previews in the future. The possibilities enabled by these extensibility features are broad and powerful. However, our commitment to being a great platform for data engineers, data scientists and developers goes far beyond programming language. Today, I am delighted to announce Snowpark a family of libraries that will bring a new experience to programming data in Snowflake. Snowpark enables you to write code directly against Snowflake in a way that is deeply integrated into the languages I mentioned earlier, using familiar concepts like DataFrames. But the most important aspect of Snowpark is that it has been designed and optimized to leverage the Snowflake engine with its main characteristics and benefits, performance, reliability, and scalability with near zero maintenance. Think of the power of a declarative SQL statements available through a well known API in Scala, Java or Python, all these against data governed in your core data platform. We believe Snowpark will be transformative for data programmability. I'd like to introduce Sri to showcase how our fictitious insurance company Insureco will be able to take advantage of the Snowpark API for data science workloads. >> Thanks Christian, hi, everyone? I'm Sri Chintala, a product manager at Snowflake focused on extensible data pipelines. And today, I'm very excited to show you a preview of Snowpark. In our first demo, we saw how Insureco could identify potentially fraudulent claims. Now, for all the valid claims InsureCo wants to ensure they're providing excellent customer service. To do that, they put in place a system to transcribe all of their customer calls, so they can look for patterns. A simple thing they'd like to do is detect the sentiment of each call so they can tell which calls were good and which were problematic. They can then better train their claim agents for challenging calls. Let's take a quick look at the work they've done so far. InsureCo's data science team use Snowflakes external functions to quickly and easily train a machine learning model in H2O AI. Snowflake has direct integrations with H2O and many other data science providers giving Insureco the flexibility to use a wide variety of data science libraries frameworks or tools to train their model. Now that the team has a custom trained sentiment model tailored to their specific claims data, let's see how a data engineer at Insureco can use Snowpark to build a data pipeline that scores customer call logs using the model hosted right inside of Snowflake. As you can see, we have the transcribed call logs stored in the customer call logs table inside Snowflake. Now, as a data engineer trained in Scala, and used to working with systems like Spark and Pandas, I want to use familiar programming concepts to build my pipeline. Snowpark solves for this by letting me use popular programming languages like Java or Scala. It also provides familiar concepts in APIs, such as the DataFrame abstraction, optimized to leverage and run natively on the Snowflake engine. So here I am in my ID, where I've written a simple scalar program using the Snowpark libraries. The first step in using the Snowpark API is establishing a session with Snowflake. I use the session builder object and specify the required details to connect. Now, I can create a DataFrame for the data in the transcripts column of the customer call logs table. As you can see, the Snowpark API provides native language constructs for data manipulation. Here, I use the Select method provided by the API to specify the column names to return rather than writing select transcripts as a string. By using the native language constructs provided by the API, I benefit from features like IntelliSense and type checking. Here you can see some of the other common methods that the DataFrame class offers like filters like join and others. Next, I define a get sentiment user defined function that will return a sentiment score for an input string by using our pre trained H2O model. From the UDF, we call the score method that initializes and runs the sentiment model. I've built this helper into a Java file, which along with the model object and license are added as dependencies that Snowpark will send to Snowflake for execution. As a developer, this is all programming that I'm familiar with. We can now call our get sentiment function on the transcripts column of the DataFrame and right back the results of the score transcripts to a new target table. Let's run this code and switch over to Snowflake to see the score data and also all the work that Snowpark has done for us on the back end. If I do a select star from scored logs, we can see the sentiment score of each call right alongside the transcript. With Snowpark all the logic in my program is pushed down into Snowflake. I can see in the query history that Snowpark has created a temporary Java function to host the pre trained H20 model, and that the model is running right in my Snowflake warehouse. Snowpark has allowed us to do something completely new in Snowflake. Let's recap what we saw. With Snowpark, Insureco was able to use their preferred programming language, Scala and use the familiar DataFrame constructs to score data using a machine learning model. With support for Java UDFs, they were able to run a train model natively within Snowflake. And finally, we saw how Snowpark executed computationally intensive data science workloads right within Snowflake. This simplifies Insureco's data pipeline architecture, as it reduces the number of additional systems they have to manage. We hope that extensibility with Scala, Java and Snowpark will enable our users to work with Snowflake in their preferred way while keeping the architecture simple. We are very excited to see how you use Snowpark to extend your data pipelines. Thank you for watching and with that back to you, Christian. >> Thank you Sri. You saw how Sri could utilize Snowpark to efficiently perform advanced sentiment analysis. But of course, if this use case was important to your business, you don't want to fully automate this pipeline and analysis. Imagine being able to do all of the following in Snowflake, your pipeline could start far upstream of what you saw in the demo. By storing your actual customer care call recordings in Snowflake, you may notice that this is new for Snowflake. We'll come back to the idea of storing unstructured data in Snowflake at the end of my talk today. Once you have the data in Snowflake, you can use our streams and past capabilities to call an external function to transcribe these files. To simplify this flow even further, we plan to introduce a serverless execution model for tasks where Snowflake can automatically size and manage resources for you. After this step, you can use the same serverless task to execute sentiment scoring of your transcript as shown in the demo with incremental processing as each transcript is created. Finally, you can surface the sentiment score either via snow side, or through any tool you use to share insights throughout your organization. In this example, you see data being transformed from a raw asset into a higher level of information that can drive business action, all fully automated all in Snowflake. Turning back to Insureco, you know how important data governance is for any major enterprise but particularly for one in this industry. Insurance companies manage highly sensitive data about their customers, and have some of the strictest requirements for storing and tracking such data, as well as managing and governing it. At Snowflake, we think about governance as the ability to know your data, manage your data and collaborate with confidence. As you saw in our first demo, the Data Cloud enables seamless collaboration, control and access to data via the Snowflake data marketplace. And companies may set up their own data exchanges to create similar collaboration and control across their ecosystems. In future releases, we expect to deliver enhancements that create more visibility into who has access to what data and provide usage information of that data. Today, we are announcing a new capability to help Snowflake users better know and organize your data. This is our new tagging framework. Tagging in Snowflake will allow user defined metadata to be attached to a variety of objects. We built a broad and robust framework with powerful implications. Think of the ability to annotate warehouses with cost center information for tracking or think of annotating tables and columns with sensitivity classifications. Our tagging capability will enable the creation of companies specific business annotations for objects in Snowflakes platform. Another key aspect of data governance in Snowflake is our policy based framework where you specify what you want to be true about your data, and Snowflake enforces those policies. We announced one such policy earlier this year, our dynamic data masking capability, which is now available in public preview. Today, we are announcing a great complimentary a policy to achieve row level security to see how role level security can enhance InsureCo's ability to govern and secure data. I'll hand it over to Artin for a demo. >> Hello, I'm Martin Avanes, Director of Product Management for Snowflake. As Christian has already mentioned, the rise of the Data Cloud greatly accelerates the ability to access and share diverse data leading to greater data collaboration across teams and organizations. Controlling data access with ease and ensuring compliance at the same time is top of mind for users. Today, I'm thrilled to announce our new row access policies that will allow users to define various rules for accessing data in the Data Cloud. Let's check back in with Insureco to see some of these in action and highlight how those work with other existing policies one can define in Snowflake. Because Insureco is a multinational company, it has to take extra measures to ensure data across geographic boundaries is protected to meet a wide range of compliance requirements. The Insureco team has been asked to segment what data sales team members have access to based on where they are regionally. In order to make this possible, they will use Snowflakes row access policies to implement row level security. We are going to apply policies for three Insureco's sales team members with different roles. Alice, an executive must be able to view sales data from both North America and Europe. Alex in North America sales manager will be limited to access sales data from North America only. And Jordan, a Europe sales manager will be limited to access sales data from Europe only. As a first step, the security administrator needs to create a lookup table that will be used to determine which data is accessible based on each role. As you can see, the lookup table has the row and their associated region, both of which will be used to apply policies that we will now create. Row access policies are implemented using standard SQL syntax to make it easy for administrators to create policies like the one our administrators looking to implement. And similar to masking policies, row access policies are leveraging our flexible and expressive policy language. In this demo, our admin users to create a row access policy that uses the row and region of a user to determine what row level data they have access to when queries are executed. When users queries are executed against the table protected by such a row access policy, Snowflakes query engine will dynamically generate and apply the corresponding predicate to filter out rows the user is not supposed to see. With the policy now created, let's log in as our Sales Users and see if it worked. Recall that as a sales executive, Alice should have the ability to see all rows from North America and Europe. Sure enough, when she runs her query, she can see all rows so we know the policy is working for her. You may also have noticed that some columns are showing masked data. That's because our administrator's also using our previously announced data masking capabilities to protect these data attributes for everyone in sales. When we look at our other users, we should notice that the same columns are also masked for them. As you see, you can easily combine masking and row access policies on the same data sets. Now let's look at Alex, our North American sales manager. Alex runs to st Korea's Alice, row access policies leverage the lookup table to dynamically generate the corresponding predicates for this query. The result is we see that only the data for North America is visible. Notice too that the same columns are still masked. Finally, let's try Jordan, our European sales manager. Jordan runs the query and the result is only the data for Europe with the same columns also masked. And you reintroduced masking policies, today you saw row access policies in action. And similar to our masking policies, row access policies in Snowflake will be accepted Hands of capability integrated seamlessly across all of Snowflake everywhere you expect it to work it does. If you're accessing data stored in external tables, semi structured JSON data, or building data pipelines via streams or plan to leverage Snowflakes data sharing functionality, you will be able to implement complex row access policies for all these diverse use cases and workloads within Snowflake. And with Snowflakes unique replication feature, you can instantly apply these new policies consistently to all of your Snowflake accounts, ensuring governance across regions and even across different clouds. In the future, we plan to demonstrate how to combine our new tagging capabilities with Snowflakes policies, allowing advanced audit and enforcing those policies with ease. And with that, let's pass it back over to Christian. >> Thank you Artin. We look forward to making this new tagging and row level security capabilities available in private preview in the coming months. One last note on the broad area of data governance. A big aspect of the Data Cloud is the mobilization of data to be used across organizations. At the same time, privacy is an important consideration to ensure the protection of sensitive, personal or potentially identifying information. We're working on a set of product capabilities to simplify compliance with privacy related regulatory requirements, and simplify the process of collaborating with data while preserving privacy. Earlier this year, Snowflake acquired a company called Crypto Numerix to accelerate our efforts on this front, including the identification and anonymization of sensitive data. We look forward to sharing more details in the future. We've just shown you three demos of new and exciting ways to use Snowflake. However, I want to also remind you that our commitment to the core platform has never been greater. As you move workloads on to Snowflake, we know you expect exceptional price performance and continued delivery of new capabilities that benefit every workload. On price performance, we continue to drive performance improvements throughout the platform. Let me give you an example comparing an identical set of customers submitted queries that ran both in August of 2019, and August of 2020. If I look at the set of queries that took more than one second to compile 72% of those improved by at least 50%. When we make these improvements, execution time goes down. And by implication, the required compute time is also reduced. Based on our pricing model to charge for what you use, performance improvements not only deliver faster insights, but also translate into cost savings for you. In addition, we have two new major announcements on performance to share today. First, we announced our search optimization service during our June event. This service currently in public preview can be enabled on a table by table basis, and is able to dramatically accelerate lookup queries on any column, particularly those not used as clustering columns. We initially support equality comparisons only, and today we're announcing expanded support for searches in values, such as pattern matching within strings. This will unlock a number of additional use cases such as analytics on logs data for performance or security purposes. This expanded support is currently being validated by a few customers in private preview, and will be broadly available in the future. Second, I'd like to introduce a new service that will be in private preview in a future release. The query acceleration service. This new feature will automatically identify and scale out parts of a query that could benefit from additional resources and parallelization. This means that you will be able to realize dramatic improvements in performance. This is especially impactful for data science and other scan intensive workloads. Using this feature is pretty simple. You define a maximum amount of additional resources that can be recruited by a warehouse for acceleration, and the service decides when it would be beneficial to use them. Given enough resources, a query over a massive data set can see orders of magnitude performance improvement compared to the same query without acceleration enabled. In our own usage of Snowflake, we saw a common query go 15 times faster without changing the warehouse size. All of these performance enhancements are extremely exciting, and you will see continued improvements in the future. We love to innovate and continuously raise the bar on what's possible. More important, we love seeing our customers adopt and benefit from our new capabilities. In June, we announced a number of previews, and we continue to roll those features out and see tremendous adoption, even before reaching general availability. Two have those announcements were the introduction of our geospatial support and policies for dynamic data masking. Both of these features are currently in use by hundreds of customers. The number of tables using our new geography data type recently crossed the hundred thousand mark, and the number of columns with masking policies also recently crossed the same hundred thousand mark. This momentum and level of adoption since our announcements in June is phenomenal. I have one last announcement to highlight today. In 2014, Snowflake transformed the world of data management and analytics by providing a single platform with first class support for both structured and semi structured data. Today, we are announcing that Snowflake will be adding support for unstructured data on that same platform. Think of the abilities of Snowflake used to store access and share files. As an example, would you like to leverage the power of SQL to reason through a set of image files. We have a few customers as early adopters and we'll provide additional details in the future. With this, you will be able to leverage Snowflake to mobilize all your data in the Data Cloud. Our customers rely on Snowflake as the data platform for every part of their business. However, the vision and potential of Snowflake is actually much bigger than the four walls of any organization. Snowflake has created a Data Cloud a data connected network with a vision where any Snowflake customer can leverage and mobilize the world's data. Whether it's data sets, or data services from traditional data providers for SaaS vendors, our marketplace creates opportunities for you and raises the bar in terms of what is possible. As examples, you can unify data across your supply chain to accelerate your time and quality to market. You can build entirely new revenue streams, or collaborate with a consortium on data for good. The possibilities are endless. Every company has the opportunity to gain richer insights, build greater products and deliver better services by reaching beyond the data that he owns. Our vision is to enable every company to leverage the world's data through seamless and governing access. Snowflake is your window into this data network into this broader opportunity. Welcome to the Data Cloud. (upbeat music)

(lively music) >> Thank you, Frank, for kicking us off, setting the stage, and providing the vision for the Snowflake Data Cloud. Hi, everyone, I hope you're all doing well and staying safe. Thank you for joining me at the Snowflake Summit today to dive into the role of the Data Cloud in mobilizing data at Disney Streaming. Together, we're going to discuss data governance and how to leverage some of the unique benefits of Snowflake's data platform to unlock business value for better customer experiences. I am Anita Lynch, Vice President of Data Governance at Disney Streaming, home of Disney+. I fell in love with technology at an early age. My family is originally from Chicago and we came to the Bay Area when my dad's sales career led him to Silicon Valley. Because of the exciting advancements he saw in the devices he sold and the engineers he worked with, I am so fortunate that my father created the early opportunities for me to learn about technology, like starting to code when I was 10. Decades later, over the course of my career spanning tech startups, business school, strategy consulting, and leading data at global enterprises, I have learned it is not enough to create a technology solution. It takes a real understanding of what problems your customers are trying to solve, and what resources or capabilities they can mobilize to do it. Today, this is the focus of my career in data. At Disney Streaming, we pride ourselves on delighting our customers. We commit each day to bringing beloved characters, timeless stories, and epic sporting events to a global audience. I am one member of a global data team at Disney Streaming, continuing to work through these challenging times for our world. We are deeply appreciative to be able to continue doing our part to deliver the entertainment people love on Disney+, including my new, personal favorite series, "The Mandalorian." It is important to all of us that we maintain our viewers' highest level of trust. As our data volume grows continuously on a daily basis, we need to ensure data is compliant, secure, and well-governed. Therefore, how we execute is critical. Our work ensures our business is guiding decisions with high-quality data. Doing this empowers us to challenge convention and innovate, which brings us to the role of the organization I lead at Disney Streaming. I lead data governance, which includes instrumentation, compliance, integrations, and data architecture. Collectively, we are responsible for the value, protection, and mobilization of data for Disney+. With data volumes in the thousands of petabytes after just one year and global teams depending on us to be able to perform their analysis, data science modeling, and machine learning, it is critical to maintain compliance protocols and governance standards. However, our approach to locking down the data and limiting access without becoming a blocker to critical information needs is key. Poorly informed business decisions could ultimately lead to suboptimal customer experiences. Recognizing this, I've established eight operating principles to maintain a balance between technology, people, and process. Data lifecycle, stewardship, and data quality together define the mechanisms by which we maintain, measure, and improve the value of data as an asset. Regulatory compliance and data access establish key partnerships with our legal and information security to help us ensure data complies with internal and external legal guidelines in each region. Auditability, traceability, and risk management ensure we monitor, educate, influence, and enforce best practices. And lastly, data sharing, which serves to socialize valuable datasets and shared definitions in a secure, easy way that allows us to keep pace with the fast-moving and rapidly changing nature of our world today. Principles serve only as guardrails. In real practice, we measure the value data governance delivers based on these six, quantifiable goals for the teams we serve. Underpinning all of them is the Snowflake Data Cloud. It is our platform to store, secure, integrate, and mobilize data across the organization. It enables us to make compliant data accessible for teams to collaborate without copying, moving, or reprocessing. Going beyond the notion of a single source of truth, Snowflake's Data Cloud allows us to truly have a single copy of the data, plus the ability to scale to support a near-unlimited number of concurrent users without contention for resources, and the flexibility to prioritize or deprioritize compute workloads where concurrency matters less than our ability to manage cost. What does this mean to me? Put simply, it means the ability to support business intelligence, analytics, data science, and machine learning use cases on-demand, exceeding expectations for speed and performance where they matter without sacrificing anything on governance. And that is how we deliver value through data governance for Disney+. Data sharing is at the heart of how we make this work. We'll look at three important use cases, data clean rooms that enable restricted data sharing, data discovery that ensures data is easily found and understood, and partner data management for collaboration outside of our team. Data sharing creates the opportunity to access the power of the integrated dataset in an environment that ensures both quality and compliance. Let's start with data clean rooms and the example of restricted data sharing. Better understanding the interests and preferences of our audience through analysis is how we improve experiences for our customers, such as in-app personalization or making a recommendation on what to watch. The challenge is to mobilize the right data as it is needed while blocking distribution of any data that is not required, preventing the disclosure of sensitive information and prohibiting the merging of data that should not be combined. Simultaneously, while we seek to deliver compliance, we also want to avoid the typical process delays and enormous manual repetitive work that often comes with it. Data clean rooms enable the secure sharing of data, again, without creating copies, the combining of datasets without PII or sensitive information, and the restricting of queries by use of parameterized inputs and filtered query outputs, so only permissible data can be extracted. Outlining in advance how data will be used properly ensures consistency and execution of our compliance workflows and improves transparency on constraints, so teams don't waste their valuable time. This accelerates our ability to act on data insights. Decisions can be made for the benefit of our customers. For example, for me on Disney+, I would see right away the season two trailer for "The Mandalorian," including exciting scenes with Baby Yoda, more formerly known to some of you as the Child. Sometimes unintended data silos arise due to architectural complexities. In a traditional model for data infrastructure, complexity can evolve over time as various teams need to access, integrate, and transform data from different data sources in ways that uniquely serve their specific stakeholders. This proliferation in the analytical supply chain could result in multiple instances of copying, loading, and transforming the same data and introduce significant risks to data quality throughout the system, such as a lack of traceability. For example, changing one data pipeline may create unforeseen consequences in the calculations that occur in downstream tables and reports with no clear resolution. In the spirit of challenging convention to innovate, we knew we had to do better. With the Snowflake Data Cloud, our teams are able to discover the data sources they need through a centrally organized platform for data management and data sharing. Each user knows the data visible to them is available to them. They know they can trust it, and they know how it can properly be used to drive broader customer insights. And if a team wants to share their insights for further collaboration, they can easily publish those datasets to the Data Cloud, where they benefit from the protection of our managed platform, making sure all governance protocols are in place, including who can access for what purpose and at what level of granularity. This facilitates data sharing without the administration worry that comes with sharing files. And since there is one single copy, future updates happen at once for all consumers of the data, keeping it fresh for everyone without sacrificing business continuity. Finally, data sharing improves the performance of our partner relationships with the same degree of simplicity. In this model, our partner teams can also participate in the Data Cloud by invitation to access data specifically shared to them. Or conversely, a partner can request to share their data, and upon authorization for quality and compliance, we can safely publish that data, making it simultaneously available to all the right teams who need it. As a thought exercise, one way for us to envision making it easier to work with partners is in the way we collect and analyze data from media serving and content distribution networks. Today, customer stream Disney+ on more than 13 different types of devices. Their streaming is made possible through a collection of services that vary by geography and consumer choice. Better understanding the experience for an individual client may require integration of data collected across the unique combination of services available to that customer. To better serve our content and delight our customers, data-driven analysis to detect anomalies and service impacts might benefit from a data management platform for partner data that requires a high level of data governance similar to what we do today through our Snowflake Data Cloud. Now in closing, data is at the core of our mission at Disney Streaming to delight our customers. And when it comes to data governance, we strive to always hold ourselves to the highest standard. With the Data Cloud, we power our business with a single source of truth. As we grow, it enables data sharing with data governance at massive scale and performance. I will also leave you with this often quoted African proverb I like. "If you want to go fast, go alone. But if you want to go far, go together." We share an important cultural value. Commitment to innovation accelerated our ability to address unique use cases and the successful growth of Disney+. It was both the technology and the commitment to meet our data governance needs that has resulted in more than just another cloud data platform. We have a solution that works for us. Thank you for joining me on this journey, and thank you to Snowflake for the ongoing partnership. With the product keynote coming up next, I'm excited to see how future innovation will continue to enable us to challenge convention going forward.

>>Hey, Sue, my great to see you. Welcome to the Data Cloud Summit. Super excited to have you welcome. >>Hey, Chris. Very nice to be there. Thank you for having me >>tell us a little bit about alien spending lakhs. Tell us a little bit about yourself and your role. Italy and Benelux >>aliens, Benelux zits. Basically the aliens business in the region. Belgium, Netherlands and Luxembourg. We serve the needs of the customer here by securing the future. We actually do both PNC asses. We call it properly and casualities in life investment management and health. We do retail, uh, small and medium enterprises. I am a regional chief Data and Biggs, officer for aliens. Benelux. I report directly to the regional CEO my job here in alliance to basically drive the data and analytics agenda for aliens. Vanilla, >>cinnamon. I understand you're getting your PhD in data science. It would be great for the audience to learn a little bit more about what's driving you to do that. And kind of what? What's most interesting to you about data science? A I m l >>the reason why I started to do this because there's so much relevance. Push that which is basically driving the agenda. We need to really look at the theoretical part off it as well. To kind of concrete eyes, Andi toe bring in a certain develop dependency, consistency, timelessness, etcetera. And obviously that which we're doing is very innovative. Here, Italians, monologues driven again by relevance and which is very good for the business. But the timelessness needs to also be the sustainability the scalability needs also has to be given to this particular relevance driven topic so that we don't just create superficial impact. But we create a long lasting and everlasting impact in our competitive intelligence intelligence that building against monologues. >>That's awesome. I mean, thanks for sharing that. So So I think. Cinnamon. When when you and I met back in March 1 of the big things that you were you were considering is, you know, uh, signing up with snowflake and becoming a customer. But part of that journey was convincing Ali on spent lakhs to move to the cloud in your journey. So kind of it would be great for you to explain to the audience. You know what that journey has been like. Was it hard to convince your organization moved to the cloud, What hurdles might you have seen in your journey to the cloud? >>It was not very different to any kind of a change on the kind of effort that you need to put in a change for a normal status go set up that which exists today. So, of course, in any kind of a change, your status could change or challenge that which you bring in. There is a considerable, uh, effort that you need to put in. And it's also your responsibility to basically do that because if you don't have that energy or if you don't have that commitment and you are not able to sustain the energy of the commitment that you show in the new agenda that you bring in, then probably you're not gonna be there to see the change through. Of course, it waas difficult, obviously, because, uh, there is already existing status. Go. And there we have a lot of benefits by moving to cloud, and obviously the benefits seems very interesting. But there is skepticism, and we s alliance is from a group perspective, and Benelux perspective is full of very, very clear on a point that we cannot take advantage off the data that which we have. We want to ensure that privacy is by design. Security is by design. And we give utmost care to our customer data. Um, mhm. And all of this basically brings in tow the concept off. Okay, what is it about moving to the cloud and where are we getting exposed? Where should we basically put together? A security by design privacy with some kind of concepts before we do it and etc. Are you ready? Can be ensured that we still keep the customers data A to a place where we basically can't bust. Well, those are the things that which had to be explained. A certain level of sensitization had to be created. A certain level of awareness. Uh, then the consideration part. Yeah, all of this basically takes its own cycle. >>Awesome. Thanks for sharing that. So we're super excited to call Ali on spending lakhs of customer. Now, what are you excited about with snowflake? And I know that you're you're looking at snowflake. Is this kind of data cloud and data cloud transformation project. Tell us a little bit more about, you know, What? What excites you about Snowflake? How you think you might use stuff like, um, in this kind of transformation of Ali on spending lakhs? >>I know that snowflake is brought to us as a product by you guys, but we look at snowflake is a kind off message. We are breaking down the silos. Literally. Onda. We look at snowflake as a kind often agent to do this. Uh, this is something that which is very important to understand that whatever you do with the organizational level, you still end up with a situation where you kind of reinforce the silos. But, snowflake, we have an opportunity here to even challenge that on break the data silos. Once the data silos is broke, you basically improve the find ability of data. You basically improve the understand ability of the data accessibility of the data interpret ability on everyone sees pretty much the same truth. And that's how the silos disappear. We're very, very excited about the journey that which, which we have in front of us because we're pretty new in it. In the sense that we are going toe haven't very exciting journey as we progress, we are also looking forward to see how Snowflakes road map is going to take us to the point off arrival, as I would call it in our own data revenge in >>today we live in this kind of multi cloud, multi cloud application world. What are some of the concerns you have as you transition from, you know, having stuff in a data center to using multiple clouds to using multiple tools? You know, what's what's some of the challenges you for? See having? What are the things that you're looking for from Snowflake to help you? Um, in that journey, >>there is always a reason why we basically make a change. And the reason is always mostly towards more efficiency, effectiveness and so on and so forth, right? I mean, basically, we have Catholics challenges on this. Catholic challenges can also be addressed with this move to the cloud, except but what We should be careful and should avoid us that the cost that which we have in terms of Camp X is just does not get re attributed into another cost called articulation, cost or arbitration cost. So having a multi cloud is definitely a challenge until you have a kind off orchestrator because we are doing a business here and we don't want to care about pretty much the orchestration. The are part off it on. This needs to be taken taken into account because there is this application cloud and there is this infrastructure cloud. You can have as many clothes as you want, whatever function that which is is supporting you. But that has to be encapsulate, er abstracted away from us so that we're able to focus on the business that we're here to do. And these are certain constraints that I really had as I was thinking about multi cloud or hybrid cloud and I was even focusing on how am I going toe orchestrate all of these different things Eso that you know, you kind of feel abstracted from those things. So well, those are the constraints that I think we still have toe conquer as we progress. I think we are evolving very fastly in that area. And you are the experts in that area, and you know exactly what you're doing there. But for me, what is very important is that uh, yeah, it gets abstracted away from us, and we just get the scalability that we need the elasticity that which we need the security by design the privacy by design on. Then I think this is perfect for us. >>Awesome. So? So I think a lot of customers that are listening to this are about to jump on the same journey that you're you're embarking on. What, is there a specific use case that you decided to kind of go? You know, you know, all in on Snowflake. What was the what was the kind of the initial driver for you to say? Hey, then the business driver on you saying, Hey, I'm gonna use this use case to drive transformation within within Ali and spend lakhs, >>I think virtualization, uh, it's the keep point that comes up the top of my head the moment you speak about what even did drive me to think about snowflake as an option, right? Why virtualization? Because obviously I don't want to move huge amount of data from left, right and center, because you know that when you start optimizing such a kind of an architectural, you end up creating pockets silos, which is totally against what we want to do. We want to break silos. But in the end, just because off the infrastructure needs in the computational needs, etcetera on the response rates and stuff like that, you start to create silos, bring with virtualization and especially with the performance that with Snowflake and provide us in that area. Now it seems like a possibility that we will be able to do that. I mean, it was not something that we just thought about, let's say, a few years back, but now it's definitely possible virtualization. It's one of the key points, but when you talk in the terms of use cases, we Italians monologues do not look at use cases. Actually, we look at business initiatives, so the reason why we don't look at it as use cases is because use cases used, kind off a start and stop. But we were not in the game. Off use cases were in the game off delivering future, that which our customer really wants to be secured. That's what the business we are in and that there are no use cases. There are initiatives there that which matches to the agenda for our customer. So when you start thinking about like that one of the most important things that snowflake offices is an opportunity is to obviously create on environment, so to say, on elastic scalable, uh, situation with the computer that which we need that which basically matches one on one with the agenda for our customer. So what I mean is the data warehousing on the cloud through data warehousing on the cloud is what waas on off our driving thought processes for We did not want to go and say that we will just do, uh, do Data Lake. We will just do data hub way don't belong toe religion. So to say, we basically are very opportunistic in this approach where we say we will have a data lake. We will have a data warehouse. We will have a data hub on. We will integrate it, you know, very a semantic way that which will match to the agenda of the customer and treat the customer as a sort of centric point. >>That's great. I appreciate that. So So, um, Suderman, thank you so much for for, you know, joining us today. Um, And again, thank you for your partnership. We snowflake is super excited. I'm I'm super excited Thio participate in this journey with you. Is there anything that you kind of like to let the audience know before we wrap up? >>Very happy about the way we started Toe talk. Converse. I think the proof of value as we did was a very good engagement with you guys. I mean, you guys were really there. I really appreciate the way that you took the proof of what I've worked with many other windows in terms of proof of value. But I think you had a marked difference in the way you you brought Snowflake. Tow us. Thank you so much and keep doing the good work. >>Thanks so much cinnamon for the partnership and were super pumped on, you know, making you very successful in your project. So thank you so much. >>Thank you.

>> We're here at the Data Cloud Summit 2020, tracking the rise of the data cloud. And we're talking about the ecosystem powering the next generation of innovation in cloud, you know, for decades, the technology industry has been powered by great products. Well, the cloud introduced a new type of platform that transcended point products and the next generation of cloud platforms is unlocking data-centric ecosystems where access to data is at the core of innovation, tapping the resources of many versus the capabilities of one. Casey McGee is here. He's the vice president of global ISV sales at Microsoft, and he's joined by Colleen Kapase, who is the VP of partnerships and global alliances at Snowflake. Folks, welcome to theCUBE. It's great to see you. >> Thanks Dave, good to see you. Thank you. >> Thanks for having us here. >> You're very welcome. So, Casey, let me start with you please. You know, Microsoft's got a long heritage, of course, working with partners, you're renowned in that regard, built a unbelievable ecosystem, the envy of many in the industry. So if you think about as enterprises, they're speeding up their cloud adoption, what are you seeing as the role and the importance of ecosystem, the ISV ecosystem specifically, in helping make customers' outcomes successful? >> Yeah, let me start by saying we have a 45 year history of partnership, so from our very beginning as a company, we invested to build these partnerships. And so let me start by saying from day one, we looked at a diverse ecosystem as one of the most important strategies for us, both to bring innovation to customers and also to drive growth. And so we're looking to build that environment even today. So 45 years later, focused on how do we zero in on the business outcomes that matter most to customers, usually identified by the industry that they're serving. So really building an ecosystem that helps us serve both the customers and the business outcomes they're looking to drive. And so we're building that ecosystem of ISVs on the Microsoft cloud and focused on bringing that innovation as a platform provider through those companies. >> So Casey, let's stay on that for a moment, if we can. I mean, you work with a lot of ISVs and you got a big portfolio of your own solutions. Now, sometimes they overlap with the ISV offerings of your partners. How do you balance the focus on first party solutions and third-party ISV partner solutions? >> Yeah, first and foremost, we're a platform company. So our whole intent is to bring value to that partner ecosystem. Well, sometimes that means we may have offers in market that may compliment one another. Our focus is really on serving the customer. So anytime we see that, we're looking at what is the most desired outcome for our customer, driving innovation into that specific business requirement. So for us, it's always focusing on the customer, and really zeroing in on making sure that we're solving their business problems. Sometimes we do that together with partners like Snowflake. Sometimes that means we do that on our own, but the key for us is really deeply understanding what's important to the customer and then bringing the best of the Microsoft and Snowflake scenarios to bear. >> You know, Casey, I appreciate that. A lot times people say "Dave, don't ask me that question. It's kind of uncomfortable." So Colleen, I want to bring you into the discussion. How does Snowflake view this dynamic, where you're simultaneously partnering and competing sometimes with some of the big cloud companies on the planet? >> Yeah, Dave, I think it's a great question, and really in this era of innovation, so many large companies like Microsoft are so diverse in their product set, it's almost impossible for them to not have some overlap with most of their ecosystem. But I think Casey said it really well, as long as we stay laser focused on the customer, and there are a lot of very happy Snowflake customers and happy Azure customers, we really win together. And I think we're finding ways in which we're working better and better together, from a technology standpoint, and from a field standpoint. And customers want to see us come together and bring best of breed solutions. So I think we're doing a lot better, and I'm looking forward to our future, too. >> So Casey, Snowflake, you know, they're really growing, they've got a pretty large footprint on Azure. You're talking hundreds of customers here that are active on that platform. I wonder if you could talk about the product integration points that you kind of completed initially, and then kind of what's on the horizon that you see as particularly important for your joint customers? >> You have to say, so one of the things that I love about this partnership is that, well, we start with what the customer wants. We bring that back into the engineering-level relationship that we have between the two companies. And so that's produced some pretty incredibly rich functionality together. So let me start by saying, you know, we've got eight Azure regions today with nine coming on soon. And so we have a geographic diversity that is important for many of our customers. We've also got a series of engineering-level integrations that we've already built. So that's functionality for Azure Private Link, as well as integration between Power BI, Azure Data Factory, and Azure Data Lake, all of this back again to serve the business outcomes that are required for our customers. So it's this level of integration that I think really speaks to the power of the partnership. So we are intently focused on the democratization of data. So we know that Snowflake is the premier partner to help us do that. So getting that right is key to enabling high concurrency use cases with large numbers of businesses, users coming together, and getting the performance they expect. >> Yeah, I appreciate that Casey, because a lot of times I'll, you know, I'll look at the press release. Sometimes we laugh, we call them Barney deals. You know, "I love you. You love me." But I listen for the word engineering and integration. Those are sort of important triggers. Colleen, or Casey too, but I want to start with Colleen. I mean, anything you would add to that, are there things that you guys have worked on together that you're particularly proud of, or maybe that have pushed the envelope and enabled new capabilities for customers where they've given you great feedback? Any examples you can share? >> Great question. And we're definitely focusing on making sure stability is a core value for both of us, so that what we offer, that our customers can trust, is going to work well and be dependable, so that's a key focus for us. We're also looking at how can we advance into the future, what can we do around machine learning, it's an area that's really exciting for a lot of the CXO-level leadership at our customers, so we're certainly focused on that. And also looking at Power BI and the visualization of how do we bring these solutions together as well. I'd also say at the same time, we're trying to make the buying experience frictionless for our customers, so we're also leveraging and innovating with Azure's Marketplace, so that our customers can easily acquire Snowflake together with Azure. And even that is being helpful for our customers. Casey, what are your thoughts, too? >> Yeah, let me add to that. I think the work that we've done with Power BI is pretty, pretty powerful. I mean, ultimately, we've got customers out there that are looking to better visualize the data, better inform decisions that they're making. So as much as AI and ML and the inherent power of the data that's being stored within Snowflake is important in and of itself, Power BI really unlocks that and helps drive better decisions, better visualization, and help drive to decision outcomes that are important to the customer. So I love the work that we're doing on Power BI and Snowflake. >> Yeah, and you guys both mentioned, you know, machine learning. I mean, they really are an ecosystem of tools. And the thing to me about Azure, it's all about optionality. You mentioned earlier, Casey, you guys are a platform. So, you know, customer A may want to use Power BI. Another customer might want to use another visualization tool, fine, from a platform perspective, you really don't care, do you? So I wonder Colleen, if we could, and again, maybe Casey can chime in afterwards. You guys, obviously everybody these days, but you in particular, you're focused on customer outcomes. That's the sort of starting point, and Snowflake for sure has built pretty significant experience working with large enterprises and working alongside of Microsoft to get other partners. In your experience, what are customers really looking for out of the two joint companies when they engage with Snowflake and Microsoft, so that one plus one is, you know, much bigger than two. Maybe Colleen, you could start. >> Yeah, I definitely think that what our customers are looking for is both trust and seamlessness. They just want the technology to work. The beauty of Snowflake is our ease of use. So many customers have questions about their business, more so now in this pandemic world than ever before. So the seamlessness, the ease of use, the frictionless, all of these things really matter to our joint customers, and seeing our teams come together, too, in the field, to show here's how Snowflake and Azure are better together, in your local area, and having examples of customers where we've had win-wins, which I'd say Casey, we're getting more and more of those every day, frankly, so it's pretty exciting times. And having our sales teams work as a partnership, even though we compete, we know where we play well together, and I see us doing that over and over again, more and more, around the world, too, which is really important as Snowflake pushes forward, beyond the North America geographies into stronger and stronger in the global regions, where frankly, Microsoft's had a long, storied history at. That's very exciting, especially in Europe and Asia. >> Casey, anything you'd add to that? >> Yeah. Colleen, it's well said. I think ultimately, what customers are looking for is that when our two companies come together, we bring new innovation, new ideas, new ways to solve old problems. And so I think what I love about this partnership is ultimately when we come together, whether it's engineering teams coming together to build new product, whether it's our sales and marketing teams out in front of the customers, across that spectrum, I think customers are looking for us to help bring new ideas. And I love the fact that we've engineered this partnership to do just that. And ultimately we're focused on how do we come together and build something new and different. And I think we can solve some of the most challenging problems with the power of the data and the innovation that we're bringing to the table. >> I mean, you know, Casey, I mean, everybody's really quite in awe and amazed at Microsoft's transformation, and really openness and willingness to really, change and lean into some of the big waves. I wonder if you could talk about your multi-platform strategy and what problems that you're solving in conjunction with Snowflake. >> Yeah, let me start by saying, you know, I think as much as we appreciate that feedback on the progress that we've been striving for, I mean, we're still learning every day, looking for new opportunities to learn from customers, from partners, and so a lot of what you see on the outside is the result of a really focused culture, really focusing on what's important to our customers, focusing on how do we build diversity and inclusion to everything we do, whether that's within Microsoft, with our partners, our customers, and ultimately, how do we show up as one Microsoft, I call one Microsoft kind of the partner's gift. It's ultimately how do our companies show up together? So I think if you look multi-platform, we have the same concept, right? We have the Microsoft cloud that we're offering out in the marketplace. The Microsoft cloud consists of what we're serving up as far as the platform, consists of what we're serving up for data and AI, modern workplace and business applications. And so this multi-cloud strategy for us is really focused on how do we bring innovation across each of the solution areas that matter most to customers. And so I see really the power of the Snowflake partnership playing in there. >> Awesome. Colleen, are there any examples you can share where, maybe this partnership has unlocked the customer opportunity or unique value? >> Yeah, I can't speak about the customer-specific, but what I can do and say is, Casey and I play very corporate roles in terms of we're thinking about the long-term partnership, we're driving the strategy. But hey, look, we'll get called in, we're working a deal right now, it's almost close of the quarter for us, we're literally working on an opportunity right now, how can we win together, how can we be competitive, the customers, the CIO has asked us to come together, to work on that solution. Very large, well-known brand. And we're able to get up to the very senior levels of our companies very quickly to make decisions on what do we need to do to be better and stronger together. And that's really what a partnership is about, you can do the long-term plans and the strategics and you can have great products, but when your executives can pick up the phone and call each other to work on a particular deal, for a particular customer's need, I think that's where the power of the partnership really comes together, and that's where we're at. And that's been a growth opportunity for us this year, is, wasn't necessarily where we were at, and I really have to thank Casey for that. He's done a ton, getting us the right glue between our executives, making sure the relationships are there, and making sure the trust is there, so when our customers need us to come together, that dialogue and that shared diction of putting customers first is there between both companies. So thank you, Casey. >> Oh, thanks, Colleen, the feeling's mutual. >> Well, I think this is key because as I said up front, we've gone from sort of very product-focused to platform-focused. And now we're tapping the power of the ecosystem. That's not always easy to get all the parts moving together, but we live in this API economy. You could say "Hey, I'm a company, everything's going to be homogeneous. Everything is going to be my stack." And maybe that's one way to solve the problem, but really that's not how customers want to solve the problem. Casey, I'll give you the last word. >> Yeah, let me just end by saying, you know, first off the cultures between our two companies couldn't be more well aligned. So I think ultimately when you ask yourself the question, "What do we do to best show up in front of our customers?" It is, focus on their business outcomes, focus on the things that matter most to them. And this partnership will show up well. And I think ultimately our greatest opportunity is to tap into that need, to that interest. And I couldn't be happier about the partnership and the fact that we are so well aligned. So thank you for that. >> Well guys, thanks very much for coming on theCUBE and unpacking some of the really critical aspects of the ecosystem. It was really a pleasure having you. >> Thank you so much for having us. >> Okay, and thank you for watching. Keep it right there. We've got more great content coming your way at the Data Cloud Summit.