(upbeat music) >> Welcome back everyone, theCUBE's three days of wall to wall coverage of Snowflake Summit '22 is coming to an end, but Dave Vellante and I, Lisa Martin are so pleased to have our final guest as none other than the co-founder and president of products at Snowflake, Benoit Dageville. Benoit, thank you so much for joining us on the program. Welcome. >> Thank you. Thank you, thank you. >> So this is day four, 'cause you guys started on Monday. This is Thursday. The amount of people that are still here speaks volumes. We've had close to 10,000 people here. >> Yeah. >> Could you ever have imagined back in the day, 10 years ago that it would come to something like this in such a short period of time? >> Absolutely not. And I always say if I had imagined that I might not have started Snowflake, right. This is somehow scary. I mean and yeah, it's huge. And you can feel the excitement of everyone. It is like mind boggling and the fact that so many people are still there after four days is great. >> Your keynote on Tuesday was fantastic. Your energy was off the charts. It was standing room only. There were overflow rooms. Like we just mentioned, a lot of people are still here. Talk about the evolution of Snowflake, this week's announcements and what it means for the future of the data cloud. >> Yeah, so evolution, I mean, I will start with the evolution. It's true that that's what we have announced. This week is not where we started necessarily. So we started really very quickly with big data combined with data warehouse as one thing. We saw that the world was moving into fragmented siloing data and we thought with Thierry, we are going to combine big data and data warehouse in one system for the cloud with this elasticity and this service simplicity. So simplicity, amazing elasticity, which is this multi workload architecture that I was explaining during the keynotes and really extreme simplicity with the service. Then we realized that there is one other attribute in the cloud, which is unique, which doesn't exist on-premise, which is collaboration. How you can connect different tenets of the platform together. And Google showed that with Google Docs. I always say to me, it was amazing that you could share document and have direct access to document that you didn't produce and you can collaborate on this document. So we wanted to do the same thing for data and this is where we created the data cloud and the marketplace where you can have all these data sets available and really the next evolution I would say is really about applications that are (indistinct) by that data, but are way simpler to use for all the tenets of the data cloud. And this is the way you can share expertise also, including, ML model, everyone talks about ML and the democratization of ML. How are you going to democratize ML? It's not by making necessary training super easy. Such that everyone can train their ML for themselves. It's by having very specialized application where data and ML is at the core, which are shared, through the marketplace and we shall leverage by many tenets of this marketplace that have no necessary knowledge about building this ML models. So that's where, yeah. >> When you and Thierry started the company, I go back to the improbable rise of Kubernetes and there were other more sophisticated container management systems back then, but they chose to focus on simplicity. And you've told me before, that was our main tenet. We are not going to worry about all the complex database stuff. You knew how to do that, but you chose not to. So my question is, did you envision solving those complex problems over time yourselves or through an ecosystem? Was this by design or did you... As you started to get into it, say let's not even try to go there let's partner to go there. >> Yeah, I mean, it's both. It's a combination of both. Snowflake, the simplicity of the platform is really important because if our partners are struggling to put their solution and build solution on top of Snowflake they will not build it. So it's very important that number one, our platform is really easy to use from day one. And that really has to be built inside the platform. You cannot build simplicity on top. You cannot have a complex solution and all of a sudden realize that, oh, this is complex. I need to build another layer on top of it to make it simpler, that will not work. So it had to be built from day one, but you're right. What is going to be Snowflake? I always say in 10 years from now, we just turn 10 years old or we are going to turn 10 years old in few months. Actually a few months, yes. >> Right. >> So for the next 10 years I really believe that most of Snowflake will not be built by Snowflake. And that's the power of the partners and these applications. When you are going to say I'm using Snowflake, actually, probably you are not going to use directly code developed by Snowflake. That code will leverage our platform, but you will use a solution that has been built on top of Snowflake. And this is the way we are going to decouple, the effort of Snowflake and multiply it. >> It's an interesting balance, isn't it? When I think of what you did with Apache Iceberg, if I use Iceberg and I'm not going to get as much functionality, but I may want that openness, but I'm going to get more functionality inside of the data cloud. And I don't know, but if you know the answer to what's going to happen. >> No, that's a super good question. So to explain what we did with Apache Iceberg, and the fact that now it's a native format for us. So everything that you can do with our internal formats, you can do it with Apache Iceberg, including security, defining masking, data masking all the governors that we have, fine grain security aspects, the replications you can define you can use (indistinct) on top of... >> But there's a but, right? But if I do that with native Snowflake tools, I'm going to get an even greater advantage, am I not? >> Yes. So that's what I'm saying. So that's why we embraced Iceberg, because I think we can bring all the benefit of Snowflake to people who have decided to use Iceberg, I mean open formats. Iceberg is a table format. So and why it was important because people had massive investments in open source in Hadoop. And we had a lot of companies saying, we love Snowflake. We want to be a Snowflake customer, but we cannot really migrate all our data. I mean, it will be really costly. And we have a lot of tools that need access, direct access. So this is why we created Iceberg because we can really... I mean, we really think that we can bring the benefit of Snowflake to this data. >> Gives customers optionality. Okay. I use this term super cloud. You don't use the term, but that's okay. And I get a lot of heat for it. But to me, what you're doing is quite a bit different than multicloud because you're creating that abstraction layer. You're bringing value above it. My question to you is, the most of the heat I get is, oh, that's just SaaS. Are you just SaaS? >> No. I mean, no, absolutely not. I mean, you're right we are a super cloud. I mean it's a much better word than saying we are multicloud. Multicloud is often viewed as oh, I have my system and now I can run this system in the different cloud providers. Snowflake is different. We have one single platform for the world, which happens to have some regions are AWS region, some regions are Azure, some regions are GCP, Google and we merge them together. We have this Snowgrid technology that connects all our regions together so that we have really one platform for the world. And that's very important because when you talk about connections of data and expertise applications you want to have global reach, right. It doesn't exist. We are not siloed by region of the world, right? You have a lot of companies which are multinational that have presence everywhere. And you want to have this global reach. The world is not a independent set of regions and countries, right. And that's the realization. So we had to create this global platform for our customers. >> And now you have people building clouds on top of your data cloud, well that to me is the next signal. In your keynote, you talked about seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace, governance, which ones are the most important? >> All of them. It's like when you have kids, you don't want to pick and say, this one is my preferred one, so they are really important. All of them, as I said without data, there is no Snowflake, right? So all data is so important that we can reach every data, wherever it is. And Iceberg is a part of that, but all workload is really important because you don't want to put your data in one platform, if you cannot run all your workloads and workloads are much broader than just data warehousing, there is data engineering, data science, ML engineering, (indistinct) all these workloads applications. So that's critical. Programmable is where we are moving, right. We want to be the place where data applications are built. And we think we have a lot of advantages because data application needs to use many workloads at once, right? It's not that that application will do only data warehousing, they need to store their states, they need to use this new workload that we define, which is Unistore. They need to do data engineering because they need to get data, right. They have to save this data. So they need to combine many workload and if they have to stitch this workload, because the platform was not designed as one single product where everything is consistent and works together, that you have to stitch, it's complicated for this application to make it work. So Snowflake is we believe an ideal platform to run these data applications. So all workloads, programmable, obviously, so that you can program. And programmable has two aspects, which is big part of our announcement. Is both data programmability, which is running Python against petabyte, terabytes of data at scale and doing it scale out. So that's what we call data programmability. So both Java, Python and (indistinct), but also running applications like UI. And we had this acquisition of Streamlit. Streamlit now has been fully integrated in Snowflake. We announced that such that not only you can have this data programmability, but you can expose your data through this nice UIs, interactive UI to business users potentially. So it goes all the way there. Global is super important. As we say, we want to be one platform for the world. And of course, as I said, the last pillar, which is somehow critical for us, because we are cloud, we need to have governance. We need to have security of our data. And why it took us so long to do Python is not because it's out to run Python, right? Everyone can run Python it's because we had to secure it. And I talk about it creating this amazing sandboxing technology, such that when you include third party libraries and third party codes, you are guaranteed that this third party code will not reach to infiltrate your data, right. We control the environment that Snowflake provides. >> Can you share us some of the feedback from the customer? You probably had many customer conversations over the last four days. >> Look at that smile. (interviewer laughing) (Lisa laughing) >> Actually not because I was so busy everywhere. Unfortunately, I didn't speak to many customers. Saying that, I had everyone stopping me and talking about what they heard and yeah, there is a huge excitement about all of this. >> What's been the feedback around the theme of the event? The world of data collaboration. Data collaboration is so critical as every company these days must be a data company to compete, to win. What's been from just some of the feedback that you've had customers really embracing data collaboration, what Snowflake is enabling. >> Yeah. I mean, almost every company which is using Snowflake, is collaborating with data. You have heard, the number of stable edges that we have, and there is a real need for that because your data alone... You cannot make sense of your data if it is just alone. It needs to be connected with other data. You haven't not generated. So all data, when you say the first pillar of Snowflake is all data is not only about your data, but is about all the data that's created around you. That puts perspective on your own data. And that's critical and it's so painful to get. I mean, even your data is difficult to have access to your data, but imagine data that you didn't produce. And so yes, so the data collaboration is critical, and then now we expanded it to application and expertise, sharing models, for example, That's going to have a huge impact. >> All data includes now transaction data, right? >> Yes. >> That's a big part of the announcements that you guys made. >> Yeah. So and that's the motivation for that was really, if we want to run application, full application, we announced native applications, which are fully executed and run inside the (indistinct) data cloud, right. They need all the services that application need and in particular managing their states. And so we created Unistore, which is a new workload, which allows you to combine transactional data, which are generated by this application. And at the same time being able to do analytics directly on this data. So we call it Hybrid Table because it has this hybrid aspect. You can do both transactional access to this data and at the same time analytic here without having data pipeline and moving data and transforming it from the transactional system to the analytical system, right. Snowflake is one system. Again, in the spirit of simplifying everything, this is the Snowflake (indistinct). >> I can ask the same question I ask at first, (indistinct) when was the aha moment that you and Thierry had that said, this is not just a better data warehouse, it's actually more than that. You probably didn't call it a data cloud until later on, but did you know that from the beginning or was that something you kind of stumbled into? >> No. So as I said, we founded Snowflake in 2012 and Thierry and I, we locked in my apartment and we were doing the blueprint of Snowflake and trying to find what is the revolution with the cloud for this data warehouse system and analytical system, both big data and data warehouse. And the aha moment was but of course cloud, okay. What is cloud? It's elasticity, it's service and later collaboration. So in the elasticity aspect, when you ask database people, what is elasticity, they will tell you, oh, you have a cluster of nodes. Like if it is Oracle, it would be a (indistinct) cluster. And the elasticities that you can add one node, two node to this cluster without having too much impact on the existing workload, because you need to shuffle data, right. It's hard and doing it online, right, that's elasticity. If you can do that, you are elastic. We thought that that was not very interesting to do that. What is interesting with elasticity is to plug new workloads. You can plug a workload like that and that workload is running without having any impact on other workloads, which are running on the platform. So elasticity for us was having dedicated computer resources to workloads. And these computer resources could start and be part as soon as the workload starts and will shut down when the workload finishes and they will be sized exactly for the demand of that workload. And we thought the aha moment was, okay if we can do that, now we can run a workload with, let's say 10X more computer resources than what you would have used or 100X more. Okay, let's say 100X more because we paralyzed things. Now this workload can run 100X faster, right? That's assuming we do a good job in the scale, which is our IP. And if we can do that, now the computer resources that you have used, you have used them for 100 times less. So you have used 100 times more resources because you have more nodes, but because you go fast, you use them for less time, right? So if you multiply the two it's constant. So you can run and accelerate workload dramatically 10X, 100X for the same price. Even if we are not better in efficiency than competition, just having that was the magic, right? >> You know how Google founders originally had trouble raising money because who needs another search engine? Did you get from original, like when you started going to raise money, Amazon's got a database, so who needs another cloud database? Did you get that early on or was it just obvious Speiser and companies as well. >> Speiser is a little bit on the crazy side and ambitious and so Speiser is Speiser. And of course he had no doubt, but even him was saying Benoit, Thierry, Hadoop, right. Everyone is saying Hadoop is going to be the revolution. And you guys are betting actually against Hadoop because we told Speiser, Hadoop is a bad system, it's going to fail, but at the time everyone was so bullish about Hadoop, everyone was implementing Hadoop that it didn't look like it was going to fail and we were probably wrong. So there was a lot of skepticism about not leveraging Hadoop and not being an Hadoop. Okay, something being on top of Hadoop. That was number one. There was no cloud warehouse at the time we started. Redshift was not started. It was the pioneer somewhere when Snowflake was founded. So creating a data warehouse in the cloud sounded crazy to people. How am I going to move my data over there? And security and what about security, the cloud is not secure. So that was another... >> So you guys predated that Parexel move by... >> Yes. >> Okay, so that's interesting. And I thought when Redshift... I mean, Amazon announced Redshift, I was sure that Mike Speiser will come and say, guys it's too sad, but they beat you guys and they build something and actually it was the reverse. Mike Speiser was super excited and so it was interesting to me. >> Wow, that's amazing. 'Cause John Furrier and I, we were early with theCUBE. when theCUBE started it was like the beginning of Hadoop. And so we brought theCUBE to, I think it was the second Hadoop World and we was rubbing nickels together at the time. And I was so excited bring compute to storage and it made so much sense. But I remember and I won't say who it was, but an early Hadoop committer told me this is going to fail. And I'm like, what? And he started going age basis crap and all this stuff. And I was sad because I was so excited, but it turned out that you had the same (indistinct). >> Because of complexity. Okay, Hadoop failed for two reasons. One is because they decided that, oh, a lot of this database thing, you don't need transaction, you don't need SQL, you don't necessarily, you don't need to go fast. It'll be batch, normal real time interaction with data, no one needs that. >> Cheap storage. >> So a lot of compromise on the very important technology. And at the same time, extreme complexity and complexity for me was, where I was I knew that it was going to fail big time and we bet Snowflake on the failure of Hadoop indeed. >> And there was no cloud early on in Hadoop. >> And there was no cloud too. >> And that was what killed it. That was like... >> You're right. And the model that Hadoop had for data didn't work on block storage. Block storage is not as efficient as HGFS. So that was also another figure. >> Do you ever sit back and think about... So you think about how much money has poured in to separating compute from storage and cloud databases and you started it all. (interviewer laughing) >> Yeah. No, this is... >> Pretty amazing. >> Yeah. >> Right, so that's good. That means that you're onto a good idea, but a lot of people get confused that again, they think that you're a cloud data warehouse and you're not, I mean, you're much more than that. >> Yeah, I hate that. I have to say, because from day one we were not a cloud data warehouse. As I said, it was all about combining the big data, massive amount of unstructured data, petabytes stored as files. Okay, that's very important, store as files where it's very easy to drop data in the system without... Very low cost to combine with data warehouse, full multi statement transaction when people will tell you today, oh, now we are a data warehouse. They don't have multi statement transaction, right. So we had from day one multi statement transaction really efficient SQL. You could run your dashboard. So combining these two worlds was I think the crazy thing, that's the crazy innovation that Snowflake did initially. >> Yeah. >> And I know it's really easy to build data warehouse somewhere, because if you don't think about big data, petabytes, extremely structured data, you remove a lot of complexity. >> This is why Lisa, when you get excited about technology, but you always have to have a, somebody who really deeply understands technology to stink test it, all right so awesome. Thank you for sharing that story. >> Yeah. >> Fantastic. So over 5,900 customers now. I saw over 500 in the Forbes G2K, over almost 10,000 people here this year. If we think back to 2019, there was about what? Less than 2000 people. >> Yeah. >> What do you think is going to happen next year? >> I don't know. I don't like to think about next year. I mean, I always say, Snowflake is so exciting to me because it is like a TV show, right. Where you wait the next season and we have one season every year. So I'm really excited to know what is going to happen next year. And I don't want to project what I think will happen, but all these movements to the Snowflake being the platform for data application. I want to see what people are going to build on our platform. I mean, that's the excitement. >> Season 11 coming up. >> Yes. Season 11. Yes. >> No binge watching here. Benoit, it's been a pleasure to have you on the program. >> Thank you. >> Congratulations on incredible success, the momentum, the energy is contagious. We love it. (Benoit laughing) >> Thank you so much. >> Thank you. >> Bye bye. >> For Benoit Dageville and Dave Vellante, I'm Lisa Martin. You're watching theCUBE's coverage of Snowflake Summit '22. Dave and I will be right back with a wrap. (upbeat music)

(upbeat music) >> Hi, everyone, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're wrapping up four days of coverage, two sets. Two remote sets, one in Boston, one in Palo Alto. And really, it's a pleasure to introduce Benoit Dageville. He's the Press Co-founder of Snowflake and President of Products. Benoit, thanks for taking some time out and coming to theCUBE. >> Yeah, thank you for having me, Dave. >> You know, it's really a pleasure. We've been watching Snowflake since, maybe not 2012, but mid last decade you hit our radar. We said, "Wow, this company is going to go places." And yeah, we made that call correctly. But it's been a pleasure to sort of follow you. We've talked a little bit remotely. I kind of want to go back to some of the fundamentals. First of all, I wanted mention your earnings last night. If you guys didn't see it, again, triple digit growth, $1.8 billion RPO, cashflow actually looking pretty good. So, pretty amazing. Oh, and 173% NRR, you know, wow. And Mike Scarpelli is kind of bummed that you did so well. And I know why, right? Because it's going to be at some point, and he dials it down for the expectations and Wall Street says, "Oh, he's sandbagging." And then at some point you're actually going to meet expectations and people are going to go, "Oh, they met expectations." But anyway, he's a smart guy, he know what he's doing. (Benoit laughing) I loved it, it was so funny listening to him last night. But anyway, I want to go back to, when I talked to practitioners about data warehousing pre-cloud, they would say sound bites like, it's like a snake swallowing a basketball, they would tell me. And the other thing they said, "We just chased the chips. Every time a new Intel chip comes out, we have to bring in new servers, and we're struggling." The cloud changed all that. Your vision and Terry's vision changed all that. Maybe go back to the fundamentals of what you saw. >> Yeah, we really wanted to address what we call the data challenges. And if you remember at that time, data challenge was first of the volume of data, machine-generated data. So it was way more than just structured data, right? Machine-generated data is weblogs, and it's at petabyte scale. And there was no good solution for that type of data. Big data was not a great solution, Hadoop was really bad. And there was no good solution for that. So we thought we should do something for big data. The other aspect was concurrency, right? Everyone wants to use these data analytic platform in an enterprise, right? And you have more and more workload running against the same data, and the systems that were built were not scaling for these workloads. So you had to silo data, right? That's the only way big enterprise could deal with that, is to create many different silos, Oracle, Teradata, data mass, you would hear data mass. All of it was to afloat, right, this data? And then there was the, what do we call, data sharing. How to get access to data which is not born inside the enterprise, right? So with Terry, we wanted to solve all these challenges and we thought the only way to solve it was the cloud. And the cloud has really two free aspects. One is the elasticity, for all of a sudden, you can run every workload that you want concurrently, in parallel, on different computer resources, and you can run them against the same data. So this is kind of the data lake model, if you want. At the same time, you can, in the cloud, create a service. So you can remove complexity from users and make it really easy for new workloads to be added to the system, because you can manage, you can create a managed service, where all the sudden our customers, they don't need to manage infrastructure, they don't need to patch, they don't need to tune. Everything is done by Snowflake, the service, and they can just load in and run their query. And the third aspect is really collaboration. Is how to connect data sets together. And that's almost a new product for Snowflake, this data sharing. So we really at Snowflake was all about combining big data and data warehouse in one system in the cloud, and have only one single system where you can put all your data and all your workload. >> So you weren't necessarily trying to solve the data warehouse problem, you were trying to solve a data problem. And then it just so happened data warehouse was a logical entry point for you. >> It's really not that. Yes, we wanted to solve the data problem. And for us big data was a really important problem to solve. So from day one, Snowflake was all about machine generated data, petabyte scale, but we wanted to do it right. And for us, right was not compromising on data warehouse principle, which is a CDT of transaction, which is really fast response time, and which is also simplicity. So as I said, we wanted to solve kind of all the problems at the time of volume of data, concurrency, and these sharing aspects. >> This was 2012. You knew at that time that Hadoop wasn't going to be the answer. >> No, I mean, we were really, I mean, everyone knew that. Everyone knew Hadoop was really bad. You know, complex to manage, really slow. It had good aspects, right? This was the only system that could manage petabyte scale data sets. That's the only thing- >> Cheaply. >> Yeah, and cheaply which was good. And we wanted really to do that, plus have all the good attributes of data warehouse system. And at the same time, we wanted to build a system where if you are data warehouse customer, if you are coming from Teradata, you can migrate to Snowflake and you will get to a system which is faster than what you had on-premise, right. That's why it's pretty cool. So we wanted to do big data without compromising on data warehouse. >> So several years ago we looked at the hyperscalers and said, "Wow, last year they spent $100 billion in CapEx." And so, we started to think about this abstraction layer. And then we saw what you guys announced with the data cloud. We call it super clouds. And we see that as exactly what you're building. So that's clearly not just a data warehouse or database, it's technology that really hides the underlying complexity of all those clouds, and it allows you to have federated governance and data sharing, all those things. Can you talk about sort of how you think about that architecture? >> So for me, what I say is that really Snowflake is the worldwide web of data. And we are indeed a super cloud, or we are super-posed to the infrastructure cloud, which is our friends at Amazon, and of course, Azure, I mean, Microsoft and Google. And as any cloud, we have regions, Snowflake regions all over the world, and located on different cloud providers. At the same time, our platform is global in the sense that every region interconnects with all the other regions, this is our snow grid and data mesh, if you want. So that as an organization you can have your presence on several Snowflake region. It doesn't matter which cloud provider, so you can mix AWS with Azure. You can use our cloud like that. And indeed you can, this is a cloud where you can store your data, that's the thing that really matters, and data is structured, but it's machine structure, as I say, machine generated, petabyte scale, but there's also unstructured, right? We have added support for images, text, videos, where you can process this data in our system, and that's the workload spout. And workload, what is very important is that you can run this workload, any number of workloads. So the number of workloads is effectively unlimited with Snowflake because each workload can have its dedicated set of compute resources all operating on the same data set. And the type of workloads is also very important. It's not only about dashboards and data warehouse, it's data engineering, it's data science, it's building application. We have many of our customers who are building full-scale cloud applications on top of Snowflake. >> Yeah so the other thing, if you're not familiar with Snowflake, I don't know, maybe your head has been in the sand for a while, but separating compute and storage, I don't know if you were the first, but you were certainly the first to popularize it. And that allowed you to solve that chasing the chips problem and the swallowing the basketball, right? Because you have virtually infinite resources now at your disposal. >> Yeah, this is really the concurrency challenge that I was mentioning. Everyone wants to access the data. And of course, if everyone runs on the same set of compute resources, you have a bottleneck. So Snowflake was really about this multi-workload. We call it Multi-Cluster Shared Data Architecture. But it's not difficult to run multiple cluster if you don't have consistency of data. So how to do that while maintaining transactional property of data as CDT, right? You cannot modify data from different clusters. And when you commit, every other cluster will immediately see the change, right, as if everyone was running on the same cluster. So that was the challenge that we solve when we started Snowflake. >> Used the term data mesh. What is data mesh to Snowflake? Is it a concept, is it fabric? >> No, it's a very interesting point. As much as we like to centralize data, this becomes a bottleneck, right? When you are a large organization with different independent units, everyone wants to manage their own data and they have domain-specific expertise about that data. So having it centralized in IT is not practical. At the same time, you really want to be able to connect these different data sets together and join different data together, right? So that's the data mesh architecture. Each data set is managed independently by business owners, and then there is a contract which is exposed to others, and you can combine. And Snowflake architectures with data sharing, right. Data sharing that can happen within an organization, or across organization, allows you to connect any data with any other data on our platform. >> Yeah, so when I first heard that term, you guys using the term data mesh, I got very excited because it was kind of the data mesh is, my view, anyway, is going to be the fundamental architecture of this decade and beyond. And the principles, if I understand it correctly, you're applying the principles of Jim Octagon's data mesh within Snowflake. So decentralized data doesn't have to be physically in one place. Logically it's in the data cloud. >> It's logically decentralized, right? It's independently managed, and the reason, right, is the data that you need to use is not produced by your, even if in your company you want to centralize the data and having only one organization, let's say IT managing that, let's say, pretend. Yet you need to connect with other datasets, which is managed by other organizations. So by nature, the data that you use cannot be centralized, right? So now that you have this principle, if you have a platform where you can store all the data, wherever it is, and you can connect these data very seamlessly, then we can use that platform for your enterprise, right? To have different business units independently manage their data sets, connects these together so that as a company you have a 360 view of your customers, for example. But you can expand that outside of your enterprise and connect with data sets, which are from your vertical, for example, financial data set that you don't have in your company, or any public data set. >> And the other key principles, I think, that you've touched on really is the line of business now. Increasingly they're building data products that are creating value, and then also there's a self-service component. Assuming there's the fourth principle, governance. You got to have federated governance. And it seems like you've kind of ticked the boxes, more than tick the boxes, but engineered a solution to solve for those. >> No, it's very true. So Snowflake was really built to be really simple to use. And you're right. Our vision was, it would be more than IT, right? Who is going to use Snowflake is going now to be business unit, because you do not have to manage infrastructure. You do not have to patch. You do not have to do these things that business cannot do. You just have to load your data and run your queries, and run your applications. So now business can directly use Snowflake and create value from that. And yes, you're right, then connect that data with other data sets and to get maximum insights. >> Can you please talk about some of the things you do with AWS here at the event. I'm interested in what you're doing with your machine learning initiatives that you've recently announced, the AI piece. >> Yes, so one key aspects is data is not only about SQL, right? We started with SQL, but we expanded our platform to what we call data programmability, which is really about running program at scale across a large volume of data. And this was made popular with a programming model which was introduced by Pendal, DataFrames. Later taken by Spark, and now we have DataFrames in Snowflake, Where we are different than other systems, is that these DataFrame programs, which are in Python, or Java, or Scala, you program with data. These DataFrames are compiled to our single execution platforms. So we have one single execution platform, which is a data flow execution platform, which can run both SQL very efficiently, as I said, data warehouse speed, and also these very complex programs running Python and Java against this data. And this is a single platform. You don't need to use two different systems. >> Now so, you kind of really attack the traditional analytics base. People said, "Wow, Snowflake's really easy." Now you're injecting AI and machine intelligence. I see Databricks coming at it from the other angle. They started with machine learning, now they're sort of going after the analytics. Does there need to be a semantic layer to connect, 'cause it's the same raw data. Does there need to be a semantic layer to connect those two worlds? >> Yes, and that's what we are doing in our platform. And that's very novel to Snowflake. As I said, you interact with data in different program. You pick your program. You are a SQL programmer, use SQL. You are a Python programmer, use DataFrames with Python. It doesn't really matter. And then the semantic layer is our compiler and our processing engine, is going to translate both your program and my program in Python, your program in SQL, to the same execution platform and to the same programming language that Snowflake internally, we don't expose our programming language, but it's a data flow programming language that our execution platform executes. So at the end, we might execute exactly the same program, potentially. And that's very important because we spent all our IP and all our time, engineering time to optimize this platform, to make it the fastest platform. And we want to use that platform for any type of workloads, whether it's data programs or SQL. >> Now, you and Terry were at Oracle, so you know a lot about bench marketing. As Larry would stand up and say, "We killed the competition." You guys are probably behind it, right. So you know all about that. >> We are very behind it. >> So you know a lot about that. I've had some experience, I'm not a technologist, but I'm an observer and analyst. You have to take benchmarking with a very big grain of salt. So you guys have generally stayed away from that. Databricks came out and they came up with all these benchmarks. So you had to respond, because otherwise it's out there. Now you reran the benchmarks, you took out the materialized views and all the expensive stuff that they included in your cost, your price performance, but then you wrote, I thought, a very cogent blog. Maybe you could talk about sort of why you did that and your general philosophy around bench marketing. >> Yeah, from day one, with Terry we say never again we will participate in this really stupid benchmark war, because it's really not in the interest of customers. And we have been really at the frontline of that war with Terry, both of us, really doing special tricks, right? And optimizing this query to death, this query that no one runs apart from the synthetic benchmark. We optimize them to death to have the best number when we were at Oracle. And we decided that this is really not helping customers in the end. So we said, with Snowflake, we'll not do that. And actually, we are not the only one not to do that. If you look at who has published TPC-DS, you will see no one, none of the big vendors. It's not because they cannot run TPC-DS, Oracle can run it, I know that. And all the other big data warehouse vendor can, but it's something of a little bit of past. And TPC was really important at some point, and is not really relevant now. So we are not going to compete. And that's what we said is basically now our blog. We are not interesting in participating in this war. We want to invest our engineering effort and our IP in solving real world issues and performance issues that we have. And we want to improve our engine for these real world customers. And the nice thing with Snowflake, because it's a service, we see exactly all the queries that our customers are executing. So we know where we are struggling as a system, and that's where we want to invest and we want to improve. And if you look at many announcements that we made, it's all about under-the-cover improving Snowflake and getting the benefit of this improvement to our customer. So that was the message of that blog. And yes, the message was okay. Mr. Databricks, it's nice, and it's perfect that, I mean, everyone makes a decision, right? We made the decision not to participate. Databricks made another decision, which is very fine, and that's fine that they publish their number on their system. Where it is not fine is that they published number using Snowflake and misrepresenting our performance. And that's what we wanted also to correct. >> Yeah, well, thank you for going into that. I know it's, look, leaders don't necessarily have to get involved in that mudslide. (crosstalk) Enough said about that, so that's cool. I want to ask you, I interviewed Frank last spring, right after the lockdown, he was kind enough to come on virtually, and I asked him about on-prem. And he was, you know Frank, he doesn't mix words, He said, "We're not getting into a halfway house. That's not going to happen." And of course, you really can't do what you do on-prem. You can't separate compute, some have tried, but it's not the same. But at the same time that you see like Andreessen comes out with this blog that says a huge portion of your cost of goods sold is going to be the cloud, so you're going to have to repatriate. Help me square that circle. Is it cloud forever? Is it will you never say never? What can you share of that? >> I will never say never, it's not my style. I always say you can always change your mind, and maybe different factors can change your mind. What was true at some point might not be true at a later point. But as of now, I don't see any reason for us to go on-premise. As you mentioned at the beginning, right, Snowflake is growing like crazy. The world is moving to the cloud. I think maybe it goes both ways, but I would say 90% or 99% of the world is moving to the cloud. Maybe 1% is coming back for some very specific reasons. I don't think that the world is going to move back on-premise. So in the end we might miss a small percentage of the workload that will stay on-premise and that's okay. >> And as well, if you dig into some of the financial statements you'll see, read the notes where you've renegotiated, right? We're talking big numbers. Hundreds and hundreds of millions of dollars of cost reduction, actually more, over a 10 year period. Billions of your cloud bills. So the cloud suppliers, they don't want to lose you as a customer, right? You're one of their biggest customer. So it's awesome. Last question is kind of, your work now is to really drive the data cloud, get adoption up, build that supercloud, we call it. Maybe you could talk a little bit about how you see the future. >> The future is really broadened, the scope of Snowflake, and really, I would say the marketplace, and data sharing, and services, which are directly built natively on Snowflake and are shared through our platform, and can operate, it can mix data on provider-side with data on consumer-side, and creating this collaboration within the Snowflake data cloud, I think is really the future. And we are really only scratching the surface of that. And you can see the enthusiasm of Snowflake data cloud and vertical industry We have nuanced the final show data cloud. Industry, complete vertical industry, latching on that concept and collaborating via Snowflake, which was not possible before. And I think you talked about machine learning, for example. Machine learning, collaboration through machine learning, the ones who are building this advanced model might not be the same as the one who are consuming this model, right? It might be this collaboration between expertise and consumer of that expertise. So we are really at the beginning of this interconnected world. And to me the world wide web of data that we are creating is really going to be amazing. And it's all about connecting. >> And I'm glad you mentioned the ecosystem. I didn't give enough attention to that. Because as a cloud provider, which essentially you are, you've got to have a strong ecosystem. That's a hallmark of cloud. And then the other, vertical, that we didn't touch on, is media and entertainment. A lot of direct-to-consumer. I think healthcare is going to be a huge vertical for you guys. All right we got to go, Terry. Thanks so much for coming on "theCUBE." I really appreciate you. >> Thanks, Dave. >> And thank you for watching. This a wrap from AWS re:Invent 2021. "theCUBE," the leader in global tech coverage. We'll see you next time. (upbeat music)

>>Okay, We're now going into the technical deep dive. We're gonna geek out here a little bit. Ben Wa Dodgeville is here. He's co founder of Snowflake and president of products. And also joining us is Christian Kleinerman. Who's the senior vice president of products. Gentlemen, welcome. Good to see you. >>Yeah, you that >>get this year, they Thanks for having us. >>Very welcome. So it been well, we've heard a lot this morning about the data cloud, and it's becoming my view anyway, the linchpin of your strategy. I'm interested in what technical decisions you made early on. That that led you to this point and even enabled the data cloud. >>Yes. So? So I would say that that a crowd was built in tow in three phases. Really? The initial phase, as you call it, was it was really about 20 minutes. One regions Teoh, Data Cloud and and that region. What was important is to make that region infinity, infinity scalable, right. And that's our architectural, which we call the beauty cross to share the architectural er so that you can plug in as many were clues in that region as a Z without any limits. The limit is really the underlying prop Provide the, you know, resource is which you know, Cal provide the region as a really no limits. So So that z you know, region architecture, I think, was really the building block of the snowflake. That a cloud. But it really didn't stop there. The second aspect Waas Well, it was really data sharing. How you know munity internets within the region, how to share data between 10 and off that region between different customers on that was also enabled by architectures Because we discover, you know, compute and storage so compute You know clusters can access any storage within the region. Eso that's based off the data cloud and then really faced three Which is critical is the expansion the global expansion how we made you know, our cloud domestic layers so that we could talk You know the snowflake vision on different clouds on DNA Now we are running in three cloud on top of three cloud providers. We started with the ws and US West. We moved to assure and then uh, Google g c p On how this this crowd region way started with one crowd region as I said in the W S U S West, and then we create we created, you know, many you know, different regions. We have 22 regions today, all over the world and all over the different in the cloud providers. And what's more important is that these regions are not isolated. You know, Snowflake is one single, you know, system for the world where we created this global data mesh which connects every region such that not only there's no flex system as a whole can can be aware of for these regions, But customers can replicate data across regions on and, you know, share. There are, you know, across the planet if need be. So So this is one single, you know, really? I call it the World Wide Web. Off data that, that's, you know, is this vision of the data cloud. And it really started with this building block, which is a cloud region. >>Thank you for that. Ben White Christian. You and I have talked about this. I mean, that notion of a stripping away the complexity and that's kind of what the data cloud does. But if you think about data architectures, historically they really had no domain knowledge. They've really been focused on the technology toe ingest and analyze and prepare And then, you know, push data out to the business and you're really flipping that model, allowing the sort of domain leaders to be first class citizens if you will, uh, because they're the ones that creating data value, and they're worrying less about infrastructure. But I wonder, do you feel like customers air ready for that change? >>I I love the observation. They've that, uh, so much energy goes in in in enterprises, in organizations today, just dealing with infrastructure and dealing with pipes and plumbing and things like that and something that was insightful from from Ben Juan and and our founders from from Day one WAAS. This is a managed service. We want our customers to focus on the data, getting the insights, getting the decisions in time, not just managing pipes and plumbing and patches and upgrades, and and the the other piece that it's it's it's an interesting reality is that there is this belief that the cloud is simplifying this, and all of a sudden there's no problem but actually understanding each of the public cloud providers is a large undertaking, right? Each of them have 100 plus services, uh, sending upgrades and updates on a constant basis. And that just distracts from the time that it takes to go and say, Here's my data. Here's my data model. Here's how it make better decisions. So at the heart of everything we do is we wanna abstract the infrastructure. We don't wanna abstract the nuance of each of the cloud providers. And as you said, have companies focus on This is the domain expertise or the knowledge for my industry. Are all companies ready for it? I think it's a It's a mixed bag. We we talk to customers on a regular basis every way, every week, every day, and some of them are full on. They've sort of burned the bridges and, like I'm going to the cloud, I'm going to embrace a new model. Some others. You can see the complete like, uh, shock and all expressions like What do you mean? I don't have all these knobs. 2 to 3 can turn. Uh, but I think the future is very clear on how do we get companies to be more competitive through data? >>Well, Ben Ben. Well, it's interesting that Christian mentioned to manage service and that used to be in a hosting. Guys run around the lab lab coats and plugging things in. And of course, you're looking at this differently. It's high degrees of automation. But, you know, one of those areas is workload management. And I wonder how you think about workload management and how that changes with the data cloud. >>Yeah, this is a great question. Actually, Workload management used to be a nightmare. You know, traditional systems on it was a nightmare for the B s and they had to spend most a lot of their time, you know, just managing workloads. And why is that is because all these workloads are running on the single, you know, system and a single cluster The compete for resources. So managing workload that always explain it as explain Tetris, right? You had the first to know when to run. This work will make sure that too big workers are not overlapping. You know, maybe it really is pushed at night, you know, And And you have this 90 window which is not, you know, efficient. Of course, for you a TL because you have delays because of that. But but you have no choice, right? You have a speaks and more for resource is and you have to get the best out of this speaks resource is. And and for sure you don't want to eat here with her to impact your dash boarding workload or your reports, you know, impact and with data science and and And this became a true nine man because because everyone wants to be that a driven meaning that all the entire company wants to run new workers on on this system. And these systems are completely overwhelmed. So so, well below management was, and I may have before Snowflake and Snowflake made it really >>easy. The >>reason is it's no flag. We leverage the crowds who dedicates, you know, compute resources to each work. It's in the snowflake terminology. It's called a warehouse virtual warehouse, and each workload can run in its own virtual warehouse, and each virtual warehouse has its own dedicated competition resources. It's on, you know, I opened with and you can really control how much resources which workload gas by sizing this warehouses. You know, I just think the compute resources that they can use When the workload, you know, starts to execute automatically. The warehouse, the compute resources are turned off, but turned on by snowflake is for resuming a warehouse and you can dynamically resized this warehouse. It can be done by the system automatically. You know if if the conference see of the workload increases or it can be done manually by the administrator or, you know, just suggesting, you know, uh, compute power. You know, for each workload and and the best off that model is not only it gives you a very fine grain. Control on resource is that this work can get Not only workloads are not competing and not impacting it in any other workload. But because of that model, you can hand as many workload as you want. And that's really critical because, as I said, you know, everyone in the organization wants to use data to make decisions, So you have more and more work roads running. And then the Patriots game, you know, would have been impossible in in a in a centralized one single computer, cross the system On the flip side. Oh, is that you have to have a zone administrator off the system. You have to to justify that. The workload is worth running for your organization, right? It's so easy in literally in seconds, you can stand up a new warehouse and and start to run your your crazy on that new compute cluster. And of course, you have to justify if the cost of that because there is a cost, right, snowflake charges by seconds off compute So that cost, you know, is it's justified and you have toe. You know, it's so easy now to hire new workflow than you do new things with snowflake that that that you have to to see, you know, and and look at the trade off the cost off course and managing costs. >>So, Christian been while I use the term nightmare, I'm thinking about previous days of workload management. I mean, I talked to a lot of customers that are trying to reduce the elapsed time of going from data insights, and their nightmare is they've got this complicated data lifecycle. Andi, I'm wondering how you guys think about that. That notion of compressing elapsed time toe data value from raw data to insights. >>Yeah, so? So we we obsess or we we think a lot about this time to insight from the moment that an event happens toe the point that it shows up in a dashboard or a report or some decision or action happens based on it. There are three parts that we think on. How do we reduce that life cycle? The first one which ties to our previous conversation is related toe. Where is their muscle memory on processes or ways of doing things that don't actually make us much sense? My favorite example is you say you ask any any organization. Do you run pipelines and ingestion and transformation at two and three in the morning? And the answer is, Oh yeah, we do that. And if you go in and say, Why do you do that? The answer is typically, well, that's when the resource is are available Back to Ben Wallace. Tetris, right? That's that's when it was possible. But then you ask, Would you really want to run it two and three in the morning? If if you could do it sooner, we could do it. Mawr in time, riel time with when the event happened. So first part of it is back to removing the constraints of the infrastructures. How about running transformations and their ingestion when the business best needs it? When it's the lowest time to inside the lowest latency, not one of technology lets you do it. So that's the the the easy one out the door. The second one is instead of just fully optimizing a process, where can you remove steps of the process? This is where all of our data sharing and the snowflake data marketplace come into place. How about if you need to go in and just data from a SAS application vendor or maybe from a commercial data provider and imagine the dream off? You wouldn't have to be running constant iterations and FTP s and cracking C S V files and things like that. What if it's always available in your environment, always up to date, And that, in our mind, is a lot more revolutionary, which is not? Let's take away a process of ingesting and copying data and optimize it. How about not copying in the first place? So that's back to number two on, then back to number three is is what we do day in and day out on making sure our platform delivers the best performance. Make it faster. The combination of those three things has led many of our customers, and and And you'll see it through many of the customer testimonials today that they get insights and decisions and actions way faster, in part by removing steps, in part by doing away with all habits and in part because we deliver exceptional performance. >>Thank you, Christian. Now, Ben Wa is you know, we're big proponents of this idea of the main driven design and data architecture. Er, you know, for example, customers building entire applications and what I like all data products or data services on their data platform. I wonder if you could talk about the types of applications and services that you're seeing >>built >>on top of snowflake. >>Yeah, and And I have to say that this is a critical aspect of snowflake is to create this platform and and really help application to be built on top of this platform. And the more application we have, the better the platform will be. It is like, you know, the the analogies with your iPhone. If your iPhone that no applications, you know it would be useless. It's it's an empty platforms. So So we are really encouraging. You know, applications to be belong to the top of snowflake and from there one actually many applications and many off our customers are building applications on snowflake. We estimated that's about 30% are running already applications on top off our platform. And the reason is is off course because it's it's so easy to get compute resources. There is no limit in scale in our viability, their ability. So all these characteristics are critical for for an application on DWI deliver that you know from day One Now we have improved, you know, our increased the scope off the platform by adding, you know, Java in competition and Snow Park, which which was announced today. That's also you know, it is an enabler. Eso in terms off type of application. It's really, you know, all over and and what I like actually needs to be surprised, right? I don't know what well being on top of snowflake and how it will be the world, but with that are sharing. Also, we are opening the door to a new type of applications which are deliver of the other marketplace. Uh, where, You know, one can get this application died inside the platform, right? The platform is distributing this application, and today there was a presentation on a Christian T notes about, >>you >>know, 20 finds, which, you know, is this machine learning, you know, which is providing toe. You know, any users off snowflake off the application and and machine learning, you know, to find, you know, and apply model on on your data and enrich your data. So data enrichment, I think, will be a huge aspect of snowflake and data enrichment with machine learning would be a big, you know, use case for these applications. Also, how to get there are, you know, inside the platform. You know, a lot of applications led him to do that. Eso machine learning. Uh, that engineering enrichments away. These are application that we run on the platform. >>Great. Hey, we just got a minute or so left in. Earlier today, we ran a video. We saw that you guys announced the startup competition, >>which >>is awesome. Ben, while you're a judge in this competition, what can you tell us about this >>Yeah, >>e you know, for me, we are still a startup. I didn't you know yet, you know, realize that we're not anymore. Startup. I really, you know, you really feel about you know, l things, you know, a new startups, you know, on that. That's very important for Snowflake. We have. We were started yesterday, and we want to have new startups. So So the ends, the idea of this program, the other aspect off that program is also toe help, you know, started to build on top of snowflake and to enrich. You know, this this pain, you know, rich ecosystem that snowflake is or the data cloud off that a cloud is And we want to, you know, add and boost. You know that that excitement for the platform, so So the ants, you know, it's a win win. It's a win, you know, for for new startups. And it's a win, ofcourse for us. Because it will make the platform even better. >>Yeah, And startups, or where innovation happens. So registrations open. I've heard, uh, several, uh, startups have have signed up. You goto snowflake dot com slash startup challenge, and you can learn mawr. That's exciting program. An initiative. So thank you for doing that on behalf of of startups out there and thanks. Ben Wa and Christian. Yeah, I really appreciate you guys coming on Great conversation. >>Thanks for David. >>You're welcome. And when we talk, Thio go to market >>pros. They >>always tell us that one of the key tenets is to stay close to the customer. Well, we want to find out how data helps us. To do that in our next segment. Brings in to chief revenue officers to give us their perspective on how data is helping their customers transform. Business is digitally. Let's watch.

>> Hello everyone, welcome back to theCUBE'S wall to wall coverage of the Snowflake Data Cloud Summit. My name is Dave Vellante and with me are two world-class technologists, visionaries, and entrepreneurs. Benoit Dageville is the, he co-founded Snowflake. And he's now the president of the Product division and Florian Douetteau is the co-founder and CEO of Dataiku. Gentlemen, welcome to theCUBE, two first timers, love it. >> Great time to be here. >> Now Florian, you and Benoit, you have a number of customers in common. And I've said many times on theCUBE that, the first era of cloud was really about infrastructure, making it more agile taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake and Dataiku make a good match for customers? >> I think that because it's our values that align. When it gets all about actually today, and knowing complexity per customer, so you close the gap or we need to commoditize the access to data, the access to technology, it's not only about data, data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible within an organization? And another value is about just the openness of the platform, building a future together. I think a platform that is not just about the platform but also for the ecosystem of partners around it, bringing the little bit of accessibility and flexibility, you need for the 10 years of that. >> Yes, so that's key, but it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we all know that, you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so really the challenge before Snowflake, I would say, was really to put all the data, in one place and run all the computes, all the workloads that you wanted to run, against that data. And of course, existing legacy platforms were not able to support that level of concurrency, many workload. We talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place, didn't make sense at all. And therefore, what customers did, is to create silos, silos of data everywhere, with different systems having a subset of the data. And of course now you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data in the cloud. So it's a really cloud native. We really thought about how to solve that problem, how to create leverage cloud and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake at least dedicate compute resources to run. And that makes it very agile, right. Florian talked about data scientist having to run analysis. So they need a lot of compute resources, but only for few hours and with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system. It will automatically shut down. Therefore they would not pay for the resources that they don't choose. So it's a very agile system, where you can do these analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. To me, I mean, because everybody's trying to copy it now. It's like, I remember the notion of bringing compute to the data in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say, the first data scientist I ever interviewed on theCUBE was the amazing Hilary Mason, right after she started at Bitly. And she made data science sounds so compelling, but data science is hard. So same question for you. What do you see is the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective is that once you solve the issue of the data silo with Snowflake, you don't want to bring another silo, which would be a silo of skills. And essentially, thanks to that talent gap between the talent and labor of the markets, or how it is to actually find, recruit and train data scientists and what needs to be done. And so you need actually to simplify the access to technology such as every organization can make it, whatever the talents by bridging that gap. And to get there, there is a need of actually breaking up the silos. I think a collaborative approach, where technologies and business work together and actually all put some of their ends into those data projects together. >> Yeah, it makes sense. So Florian, Let's stay with you for a minute, if I can. Your observation spaces, is pretty, pretty global. And so, you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends, maybe differences between regions or maybe within different industries? What are you seeing? >> Yep. Yeah, definitely, I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to store data as well as few years and know each level of maturity where they can invest more and get to the next steps. And it's really reliant to reach out to certain details, certain organization, actually to have built this longterm data strategy a few years ago, and no stocks ripping off the benefits. >> You know, a decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientists. And then everybody, all the statisticians became data scientists and they got a raise. But data science requires more than just statistics acumen. What skills do you see is critical for the next generation of data science? >> Yeah, it's a good question because I think the first generation of data scientists became better scientists because they could learn some Python quickly and be flexible. And I think that skills of the next generation of data scientists will definitely be different. It will be first about being able to speak the language of the business, meaning all you translate data insight, predictive modeling, all of this into actionable insights or business impact. And it will be about who you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python or do quantity models of some sorts. It's about how you actually build this bridge with the business. And obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools in technologies, and they will still need to get this level of flexibility and get to understand quickly what are the next tools, they need to use or new languages or whatever to get there. >> Thank you for that. Benoit, let's come back to you. This year has been tumultuous to say the least for everyone, but it's a good time to be in tech, ironically. And if you're in cloud, it's even better. But you look at Snowflake and Dataiku, you guys had done well, despite the economic uncertainty and the challenges of the pandemic. As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right. We, this crisis has told us that the world really can change from one day to the next. And this has dramatic and profound aspects. For example, companies all of a sudden, saw their revenue line dropping and they had to do less with data. And some of the companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely change from one day to the other. So this agility of adjusting the resources that you have to do the task, a need that can change, using solution like Snowflake, really helps that. And we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID and their business benefited, but also, as you know, had to drop and what is nice with cloud, it allows to adjust compute resources to your business needs and really address it in-house. The other aspect is understanding what is happening, right? You need to analyze. So we saw all our customers basically wanted to understand, what is it going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data, which are not necessarily data about their business, but also data from the outside. For example, COVID data. Where is the state, what is the impact, geographic impact on COVID all the time. And access to this data is critical. So this is the promise of the data cloud, right? Having one single place where you can put all the data of the world. So, our customers all of a sudden, started to consume the COVID data from our data marketplace. And we have the unit already thousands of customers looking at this data, analyzing this data to make good decisions. So this agility and this adapting from one hour to the next is really critical and that goes with data, with cloud, more interesting resources and that's doesn't exist on premise. So, indeed I think the lesson learned is, we are living in a world which is changing all the time, and we have to understand it. We have to adjust and that's why cloud, some way is great. >> Excellent, thank you. You know, in theCUBE, we like to talk about disruption, of course, who doesn't. And also, I mean, you look at AI and the impact that it's beginning to have and kind of pre-COVID, you look at some of the industries that were getting disrupted by, everybody talks about digital transformation and you had on the one end of the spectrum, industries like publishing, which are highly disrupted or taxis, and you can say, "Okay well, that's Bits versus Adam, the old Negroponte thing." But then the flip side of this, it says, "Look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is right for disruption, defense." So the more the number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian, I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science and what I call machine intelligence or AI in the coming years and decades? >> Honestly, I think it's all of them, or at least most of them. Because for some industries, the impact is very visible because we are talking about brand new products, drones, flying cars, or whatever is that are very visible for us. But for others, we are talking about spectrum changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted when you look at it from the consumer side or the outside. In fact internally, it's probably impacted just because of the way you use data to develop for flexibility you need, is there kind of a cost gain you can get by leveraging the latest technologies, is just enormous. And so it will, actually comes from the industry, that also. And overall, I think that 2020 is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience. Maturity, meaning that when you've got a crisis, you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions and look forward and not backward. And I think that's a very important learning from 2020 that will tell things about 2021. And resilience, it's like, yeah, data analytics today is a function consuming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient. So probably not on trend and not fully on trend, at some point and the kind of residence where you need to be able to plan for literally anything. like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we are just getting into a next step for all data analytics. >> I wonder Benoit, if you have anything to add to that, I mean, I often wonder, you know, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? You know, what's going to happen to big retail stores? I mean, may be bring us home with maybe some of your final thoughts. >> Yeah, I would say, I don't see that as a negative, right? The human being will always be involved very closely, but then the machine and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So, I think it's going to be a compliment, not a replacement and everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do. That I would not be worried about the effect of being more efficient and better at our work. And indeed, I fundamentally think that, data, processing of images and doing AI on these images and discovering patterns and potentially flagging disease, way earlier than it was possible, it is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, Guys, I wish we had more time. We got to leave it there but so thanks so much for coming on theCUBE. It was really a pleasure having you. >> [Benoit & Florian] Thank you. >> You're welcome but keep it right there, everybody. We'll back with our next guest, right after this short break. You're watching theCUBE.

>> Narrator: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards brought to you by Amazon Web Services. Hello everyone, welcome back to theCUBE's Coverage of "AWS Public Sector Partner Awards Program". I'm John Furrier your host of theCUBE here in Palo Alto, California doing the remote interviews, during this pandemic we have our remote crews and getting all the stories and celebrating the award winners and here to feature the most Innovative Connect Deployment. We have Accenture of Canada and the Department of Employment and Social Development of Canada known as ESDC. Guys, congratulations Joel Marchildon, Accenture Canada, managing director and Benoit Long, ESDC of Canada chief transformation officer. Gentlemen, thanks for coming on, and congratulations on the award. >> Thank you. >> Thank you and nice to be here >> So obviously, during this pandemic, a lot of disruption and a lot of business still needs to go on including government services. But the citizens and people need to still do their thing you got a business got to run, and you got to get things going. But the disruptions caused a little bit of how the user experiences are. So this Connect has been interesting. Its been a featured part of what we've been hearing at the Public Sector Summit with Teresa Carlson. You guys, this is a key product. Tell us about the award. What is the solution that is starving and deserving of the award? >> Maybe I'll go first and then pass it over to Benoit. But I think the solution is Amazon Connect based Virtual Contact Center that was stood up fairly quickly, over the course of about four days and really in support of benefit that the Government of Canada was was releasing as part of its economic response to the pandemic. And in the end, its a fully functioning featured contact center solution. Includes an IVR. And, we stood it up for about 1500 to 2000 agents. So that's the the crux of the solution. And maybe Benoit can give a bit of insight as to how it came about so quickly. >> Yeah, we're happy to actually, we were obviously like every other government facing enormous pressures at that time to deliver benefits directly to people who were in true need. The jobs are being lost, our current systems were in trouble because of their age and their archaic nature. And so the challenge was quickly how do we actually support a lot of people really fast. And so it came through immediately that after our initial payments were made under what was called the Canada emergency response benefit that we had to support clients directly and so people turn to the transformation team of all teams. If you wish during a firestorm, to say, well, what could you do? And how could you help. And so we had an established relationship with a number of our system integrators, including Accenture. And we were able to run a competition very rapidly, and Accenture won. And then we deployed in, as Joel said, in a matter of four days, what for us was an exceptional and high quality solution to a significant client problem. And I say that because I think you can imagine how people feel in a pandemic of all things, but with the uncertainty that comes with loss of income, loss of jobs, the question of being able to deal with somebody a real; a human being, as well as to be able to efficiently answer a very simple but straightforward questions rapidly and with high quality, was pretty fundamental for us. So the the people in the groups that we're talking through here we're speaking to millions of people, who were literally being asked to accept the payment rapidly and to be able to connect with us quickly. And without this solution, which was exceptionally well done and of high quality personally as a technology solution, it would not have been possible to even answer any of these queries quickly. >> And well, that's a great point. One of the things that you see with the pandemic, its a disaster in the quote disaster kind of readiness thing. Unforeseen, right. So like other things, you can kind of plan for things, hypothetical, you got scenarios. But this is truly a case where every day counts, every minute counts, because humans are involved. There's no ROI calculation. Its not like, well, what's the payback of our system? The old kind of way to think. This is real results, fast. This is what cloud is all about. This is the promise of cloud, can I stand up something quick, and you did it with a partner, okay. This is like not, like normal. Its like, its like unheard of, right. Four days, with critical infrastructure, critical services that were unforeseen. Take us through what was going on in the war room. As you guys knew this was here. Take us through through what happened. >> So I think I can start. As you can imagine the set of executives that were overseeing the payment process was an exceptional, it was like a bunker, frankly, for about two weeks. We had to suspend the normal operations of the vast majority of our programming. We had to launch brand new payments and benefits systems and programs that nobody has seen before the level of simplicity was maximized in order to deliver the funds quickly. So you can imagine its a Warpath if you wish, because the campaign is really around timing. Timing is fundamental. People are literally losing their jobs, there is no support, there is no funding money for them to be able to buy groceries. So, and the trust that people have in the government is pretty much at risk right there. And there is straightforward but extraordinarily powerful magic moment, if you wish. If you can deliver a solution, then you make a difference for a long time. And so the speed is unheard of on all fronts. When it came to the call center capability and the ability for us to support in a service context, the clients that were desperate to reach us, and we're talking hundreds of thousands of calls a day. We're not talking a few thousand here, ultimately, at some point we were literally getting in overtaken by volumes, call centers, because we had our regular ones still operating. Over a million calls were coming in the day. With the capacity to answer 10s of thousands and so the reality is that the Call Centers that we put up here, very quickly became capable of answering more calls than our regular call centers. And that speaks to the the speed of delivery, the quality of the solution, of course, but the scalability of it. And I have to say maybe unheard of, it may be difficult to replicate the conditions to lead to this are rare. But I have to say that my bosses and most of the government is probably now wondering why we can't do this more often. Why can't we operate with that kind of speed and agility. So I think what you've got is a client in our case, under extreme circumstances, now realizing the new normal will never be the same. That these types of solutions and technology and their scalability, their agility, their speed of deployment, is frankly something we want we want all the time. Now we'd like to be able to do them during normal timeline conditions, but even those will be a fraction of what it used to take. It would have taken us a while I can actually tell you because I was the lead technologist to deploy at scale for the government, Canada, all the call center capabilities under a single software as a service platform. It took us two years to design it two years to procure it, and five years to install it. That's the last experience we have of call center, enterprise scale capabilities. And in this case we went from years, to literally days. >> Well, it takes a crisis sometimes to kind of wire up the simplicity solution that you say, why didn't we do this before? The waterfall meetings getting everyone arguing kind of gets in the way and the old software model, I want to come back to the transformation Benoit a minute, Because I think that's going to be a great success story and some learnings and I want to get your thoughts on that. But I want to go to Joel, because Joel, we've talked to many Accenture executives over the years and most recently, this past 24 months. And the message we've been hearing is, "We're going to be faster. We're not going to be seen as that, a consulting firm, taking our times trying to get a pound of flesh from the client." This is an example of my opinion of a partner working with a problem statement that kind of matches the cloud speed. So you guys have been doing this is not new to Accenture. So take us through how you guys reacted, because one, you got to sync up and get the cadence of what Benoit was trying to do sync up and execute take us through what happened on your side. >> Yeah, I mean, so its an unprecedented way of operating for us as well, frankly. And, we've had to look at, to get this specific solution out the door and respond to an RFP and the commercial requirements that go with that we had to get pretty agile ourselves internally on, how we go through approvals, etc, to make sure that we were there to support Benoit and his team and I think that we saw this as a broader opportunity to really respond to it. To help Canada in a time of need. So I think we had to streamline a lot of our internal processes and make quick decisions that normally even for our organization would have taken, could have taken weeks, right, and we were down to hours and a lot of instances. So it forces us to react and act differently as well. But I mean to Benoit's point I think this is really going to hopefully change the way... It illustrates the art of the possible and hopefully will change how quickly we can look at problems and we reduce deployment timeframes from years to months and months to weeks, etc. For solutions like this. And I think the AWS platform specifically in this case, Benoit touched on a lot of things beat the market scalability, but just as the benefit itself has to be simplified to do this quickly. I think one of the one of the benefits of the solution itself is, its simple to use technologically. I mean, we trained, as I said, I think 1600 agents on how to use the platform over the course of a weekend. And they're not normal agents. These were people who were furloughed from other jobs potentially within the government. So they're not necessarily contact center agents, by training, but they became contact center agents over the course of 48 hours. And I think, from that perspective, that was important as well to have something that people could use to answer those calls that we know that we knew were going to come. >> Benoit this is the transformation dream scenario in the sense of capabilities. I know its under circumstances of the pandemic and you guys did solve a big problem really fast and saved lives and then help people get on with their day. But transformation is about having people closest to the problem, execute. And also the people equation people process technology, as they say, is kind of playing out in real time. This is kind of the playbook. Amazon came in and said, "Hey, you want to stand something up?" You wired it together the solution quickly, you have close to it. Looking back now its almost like, hey, why aren't we doing this before, as you said, and then you had to bring people in, who weren't trained and stood them up and they were delivering the service. This is the playbook to share your thoughts on this because this is what you're you're thinking about all the time, and it actually is playing out in real time. >> Well, I would definitely endorse the idea that its a playbook. Its I would say its an ideal and dream playbook to bid like showing up on a basketball court with all the best players in the entire league playing together magically. It is exactly that. So a lot of things had to happen quickly but also correctly, because you can't pull all these things properly together without that. So I would say the partnership with the private sector here was fundamental. And I have to applaud the work that Accenture did particularly I think, as Canadians we were very proud of the fact that we needed to respond quickly. Everyone was in this our neighbors, we knew people who were without support and Accenture's team, I mean, all the way up and down across the organization was fundamental in and delivering this but also literally putting themselves into these roles and to make sure that we would be able to respond and quickly do so. I think the playbook around the readiness for change, I was shocked into existence. I mean, I won't talk about quantum physics, but clearly some higher level of energy was thrown in quickly, mobilize everybody all at once. Nobody was said he is sitting around saying, I wonder if we have changed management covered off, this was changed readiness at its best. And so I think for me from a learning perspective, apart from just the technology side, which is pretty fundamental, if you don't have ready enough technology to deploy quickly, then the best pay your plans in the world won't work. The reality is that to mobilize an organization going forward into that level of spontaneous driving change, exception, acceptance, and adoption, is really what I would aim for. And so our challenge now will be continuing that kind of progression going forward. And we now found the way and we certainly use the way to work with the private sector in an innovative capacity and innovative ways with brand new solutions that are truly agile and scalable, to be able to pull all of the organization all at once very rapidly and I have to admit that it is going to shift permanently our planning, we had 10 year plans for our big transformations, because some of our programs are the most important in the country in many ways. We support people about 8 million Canadians a month, depending on the benefits payments that we deliver. And they're the most marginal needing and requires our support from seniors, to the unemployed, to job seekers and whatnot. So if you think about that group itself, and to be able to support them clearly with the systems that we have its just unsustainable. But the new technologies are clearly going to show us a way that we had never forecast, and I have to say I had to throw up my 10 year plan. And now I'm working my way down from 10 to nine to eight year plans going forward. And so its exciting and nerve wracking sometimes, but then, obviously as a change leader, our goal is to get there as quickly as possible. So the benefits of all these solutions can make a difference in people's lives. >> What's interesting is that you can shorten that timetable, but also frees you up to be focused on what's contemporary and what's needed at the time to leverage the people and the resources you have. And take advantage of that versus having something that you're sitting on that's needs to be refreshed, you can always be on that bleeding edge. And this just brings up the DevOps kind of mindset, agility, the lean startup, the lean company, this is a team effort between Amazon Accenture and ESDC. Its, pass, shoot, score really fast. So this is the new reality. Any commentary from you guys on this, new pass, shoot, score combination because you got speed, you got agility, you're leaner, which makes you more flexible for being contemporary in solving problems? What's your thoughts? >> Yeah. So my perspective on that is most definitely right. I think what we were able to show in what's coming out of a lot of different responses to the pandemic by government is, perfection isn't the most important thing out of the gate, getting something out there that's going to reassure citizens, that's going to allow them to answer their questions or access benefits quickly, is what's becoming more important, obviously, security and privacy, those things are of the utmost importance as well. But its ability to get stuff out there, quickly, test it, change it, test it again, and just always be iterating on the solution. Like I can say what we put out on April 6, within four days, is the backbone of what's out there still today. But we've added an integrated workforce management solution from NICE, and we added some other ISVs to do outbound dialing from Acquia and things like that. So the solution has grown from that MVP. And I think that's one other thing that's going to be a big takeaway. If you're not going to do anything till you got the final end product out there, then its going to be late. So let's go quickly and let's adapt from there. >> Benoit, talk about that dynamic because that's about building blocks, on foundational things and then services. Its the cloud model. >> Yeah, I mean, before the pandemic, I had lunch with Mark Schwartz, which I believe you are quite familiar with. And, I spent an hour and a half with him. We were talking and he was so exciting and energized by what the technologies could do. And I was listening to him and I used to be the chief technology officer for the Government of Canada, right. And so I've seen a lot of stuff and I said, Well, that's really exciting. And I'm sure its possible in some other places, and maybe in some other countries where they didn't have infrastructure and legacy. I guess if I see him again soon. I'll have to apologize for not believing him enough. I think the building blocks of Agile the building blocks sprints and MVPs. I mean, they're enough fundamental to the way we're going to solve our biggest Harriers and scariest problems technologically. And then from a business perspective, service candidate itself has 18,000 employees involved in multiple channels, where the work has always been very lethargic, very difficult. Arduous you make change over years, not months, not days, for sure. And so I think that new method is not only a different way of working, its a completely revamped way of assembling solutions. And I think that the concept of engineering is probably going to be closer to what we're going to do. And I have to borrow the Lego metaphor, but the building blocks are going to be assembled. We know in working, I'm saying this in front of Joel, he doesn't know that yet. (all laughing) (indistinct) partners. We're going to be assembling MVP maps of an entire long program and its going to be iterative, it is going to be designed built, it will be agile as much as we can implement it. But more importantly, as much as we can govern it because the government is... We may have changed a lot, but the government is not necessarily caught on to most of these approaches. But the reality is that, that's where we're heading. And I will say, I'll close perhaps on this answer. The biggest reason for doing that apart from we've proved it is the fact that the appetite inside the organization for that level of mobilization, speed and solutioning, and being engaged rapidly, you just can't take that away from an organization once they've tasted that. If you let them down, well, they'll remember and frankly, they do remember now because they want more of this. And its going to be hard. But its a better hard, better challenge, than the one of having to do things over a decade, then to go fast and to kind of iterate quickly through the challenges and the issues and then move on very much to the next one as rapidly as possible. I think the the other comment I would add is most of this was driven by a client need. And that's not inconsequential because it mobilized everybody to a common focus. If it had been just about, well, we need to get people on side and solutions in place just to make our lives better as providers. Yeah, would it work perhaps, but it would have been different than the mobilization that comes when the client is put in the middle. The client is the focus, and then we drive everyone to that solution. >> Shared success and success is contagious. And when you ride the new wave, you're oh, we need a new board, right? So once you get it, it then spreads like wildfire. This is what we've been seeing. And it also translates down to the citizens because again, being contemporary, none of this just look could feel its success and performance. So as people in business start to adopt cloud. It becomes a nice synergy. This is a key! Joe, take us home here on the Accenture. The award winner, you guys did a great job. Final thoughts. >> Yeah, I mean, I think final thoughts would be happy to have had the opportunity to help. And it was a it was a complete team effort and continues to be. Its not a bunch of eccentric technologists in the background doing this. The commitment from everyone to get this in place and to continue to improve it from Benoit team and from other folks across the government has been paramount to the success. So its been a fantastic if world win like experience and look forward to continuing to build on it. And it has been well said, I think one thing that's done is its created demand for speed on some of these larger transformations. So I looking forward to continuing to innovate with with Benoit team. >> Well, congratulations for the most innovative Connect Deployment. And because you guys from Canada, I have to use the Hockey-Reference. You get multiple people working together in a cohesive manner. Its pass, shoot, score every time and its contagious. (Benoit laughs) Gentlemen, thank you very much for your time and congratulations for winning the election. Take care! >> Thanks. >> Take care. >> Okay, this is theCUBE's Coverage "AWS Public Sector Partner Awards" show. I'm John Furrier, host of theCUBE. Thanks for watching. (upbeat music)

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Welcome back to the Cube's coverage of AWS Public Sector Partner Awards program. I'm John Furrow, your host of the Cube here in Palo Alto, California In the remote interviews during this pandemic, we have our remote crews and getting all the stories and celebrating the award winners. And here to feature the most innovative connect deployment. We have a center of Canada and the Department of Employment and Social Development of Canada, known as E S D. C guys. Congratulations, Joel. More Children Censure Canada Managing director and Ben while long sdc of Canada Chief Transformation officer. Gentlemen, thanks for coming on. And congratulations on the award. >>Thank you. >>Thank you. >>So, Ashley, during this pandemic, a lot of disruption and a lot of business still needs to go on, including government services. But the citizens and people need to still do their thing. Business got to run, and you got to get things going. But the disruptions caused a little bit of how the user experiences are. So this connect has been interesting. It's been a featured part of where you've been hearing at the Public Sector summit with Theresa Carlson. You guys, this is a key product. Tell us about the award. What is the solution? That disturbing of deserving reward? >>Maybe I'll get I'll go first and then pass it over to Benoit. But I think the solution is Amazon Connect based Virtual Contact Center that we stood up fairly quickly over the course of about four days and really in support of of benefit that the government of Canada was was releasing as part of its economic response to the pandemic. And in the end that, you know, it's a fully functioning featured contact center solution includes an I V r. And, uh, you know, we stood it up for about 1500 to 2000 agents so that that's the crux of the solution. And maybe Benoit can give a bit of insight as to to how it came about so quickly. >>Yeah, happy to actually wear obviously, like every other government, facing enormous pressures at that time to deliver benefits directly to people who were in true need, the jobs are being lost. Our current systems were in trouble because of their age and barricade cake nature. And so the challenge is was quickly how to actually support a lot of people really fast. And so it came through immediately that after our initial payments were made under what was called Canada Emergency Response Benefit, then we have to support our clients directly. And so people turn to the transformation team of all teams. If you wish during a fire firestorm to say, Well, what could you do and how could you help? And so we had an established relationship with a number of other system integrators, including Accenture, and we were able to run a competition very rapidly. Accenture one. And then we deployed. And as you all said, in a matter of four days, what for us was a new, exceptional on high quality solution to a significant client problem. And I say that because I think you can imagine how people feel in the endemic of all of all things. But with the uncertainty that comes with the loss of income, loss of jobs, the question of being able to deal with somebody really a human being, as well as to be able to be efficiently answer a very simple but straightforward questions rapidly and with high quality, with pretty fundamental for us. So the people in the groups that were talking through here are talking, speaking to millions of people who were literally being asked to to accept the pavement rapidly and to be able to connect with us quickly. And without this solution, which was exceptionally well done and deployed and of high quality personally, just a technology, uh, solution. I would not have been possible to even answer any of these queries quickly. >>And while that's a great 0.1 of the things that you see with the pandemic it's a disaster in the quote disaster kind of readiness thing. Unforeseen, right? So, like other things, you can kind of plan for things that hypothetical. You've got scenarios, but this >>is >>truly a case where every day counts. Every minute counts because humans are involved is no our ROI calculation. It's not like it's not like, Well, what's the payback of our system? The old kind of way to think this is really results fast. This is what cloud is all about. This is the promise of cloud. Can I stand up something quick and you did it with a partner. Okay, this is, like, not, like, normal again. It's like it's, you know, it's like, unheard of, right? Four days with critical infrastructure, critical services that were unforeseen. Take us through what was going on in the war room, as you guys knew this was here. Take us through the through what happened. Yeah, >>So I think I can start a Z. You can imagine the set of executives that we're seeing a payment process. Uh, was an exceptional. It was like a bunker. Frankly, for about two weeks, we had to suspend the normal operations off the vast majority of our programming. We had to launch brand new payments and benefits systems and programs that nobody had seen before. The level of simplicity was maximized to delivered the funds quickly. So you could imagine it's a warpath if you wish, because the campaign is really around. A timing. Timing is fundamental. People are are literally losing their jobs. There is no support. There's no funding money for them to be able to buy groceries. So on the trust that people have in the government, Ai's pretty much at risk right there and then in a very straightforward but extraordinarily powerful magic moment. If you wish. If you can deliver a solution, then you make a difference for a long time. And so the speed unheard off on old friends when he came to the call center capability and the ability for us to support and service context the clients that were desperate to reach us on. We're talking hundreds of thousands of calls, right? We're not talking a few 1000 year. Ultimately, at some point we were literally getting in our over over, taken by volumes, call centers. But we had a regular one still operating over a 1,000,000 calls for coming in today with the capacity to answer, um, you know, tens of thousands. And so the reality is that the counselor that we put up here very quickly became capable of answering more calls than our regular costumes. And that speaks to the speed of delivery, the quality of the solution, of course, but the scalability of it and I have to say, maybe unheard of, it may be difficult to replicate. The conditions to lead to this are rare, but I have to say that my bosses and most of the government is probably now wondering why we can't do this more often, like we can't operate with that kind of speed and agility. So I think what you've got is a client in our case, under extreme circumstances. Now, realizing the new normal will never be the same, that these types of solutions and technology. And then there's scalability. There's agility there, the speed of deployment. It's frankly, something we want. We want all the time. Now we'd like to be able to do it under your whole timeline conditions. But even those will be a fraction of what it used to take. It would have taken us well, actually, I can actually tell you because I was the lead, Ah, technologists to deploy at scale for the government. Canada all the call center capabilities under a single software as a service platform. It took us two years to design it two years to procure it and five years to install it. That's the last experience. We have a call center enterprise scale capabilities, and in this case, we went from years to literally days. >>Well, you know, it takes a crisis sometimes to kind of wire up the simplicity solution that you say. Why didn't we do this before? You know, the waterfall meetings, Getting everyone arguing gets kind of gets in the way of the old the old software model. I want to come back to the transformation been wanna minute, cause I think that's gonna be a great success story and some learnings, and I want to get your thoughts on that. But I want to go to Joel because Joel, we've talked to many Accenture executives over the years and most recently this past 24 months. And the message we've been hearing is we're going to be faster. We're not going to be seen as that. You know, a consulting firm taking our times. Try and get a pound of flesh from the client. This is an example. In my opinion of a partner working with a problem statement that kind of matches the cloud speed. So you guys have been doing this. This is not new to a censure. So take us through how you guys reacted because one you got to sync up and get the cadence of what, Ben? What I was trying to do sync up and execute. Take us through what happened on your side. >>Yeah, I mean, so it's It's Ah, it's an unprecedented way of operating for us as well, frankly, and, um and, uh and, you know, we've had to look at to get this specific solution at the door and respond to an RFP and the commercial requirements that go with that way. Had Teoh get pretty agile ourselves internally on on how we go through approvals, etcetera, to make sure that that we were there to support Ben Wan is team. And I think you know that we saw this is a broader opportunity to really respond to it, to help Canada in a time of need. So So I think we, you know, we had to streamline a lot of our internal processes that make quick decisions that normally even for our organization, would have taken, um, could it could have taken weeks, right? And we were down to hours in a lot of instances. So it helps. It forces us to react and act differently as well. But I mean, to Benoit's point, I think this is really going to to hopefully change the way it illustrates the art of the possible and hopefully will change How, How quick We can look at problems and and we reduced deployment timeframes from from years to months and months to weeks, etcetera for solutions like this. Um, and I think that the AWS platform specifically in this case but what touched on a lot of things to beat the market scale ability But just as the benefit itself was, you know has to be simplified to do this quickly. I think one of the one of the benefits of the solution itself is it's simple to use technologically. I mean, we know least retrained. As I said, I think 1600 agents on how to use the platform over the course of a weekend on and and were able, and they're not normal agents. These were people who are firm from other jobs, potentially within the government. So they're not necessarily contact center agents by training. But they became contact center agents over the course of 48 hours, and I think from that perspective, you know, that was important as well have something that people could could use. The answer those calls that we know that when you were gonna come so >>Ben what this is. This is the transformation dream scenario in the sense of capabilities. I know it's under circumstances of the pandemic, and you guys didn't solve a big, big problem really fast and saved lives and help people get on with their day. But transformations about having people closest to the problem execute and the the also the people equation people process technology, as they say, is kind of playing out in real time. This >>is >>the this is kind of the playbook, you know? Amazon came in said, Hey, you want to stand something up? You wired it together. The solution quickly. You're close to it. Looking back now, it's almost like, Hey, why aren't we doing this before? As you said and then you had to bring people in who weren't trained and stood them up and they were delivering the service. This >>is >>the playbook to share your thoughts on this, because this is what you're you're thinking about all the time and it actually playing out in real time. >>Well, I would definitely endorsed the idea that it's a playbook. It's I would say it's an ideal and dream playbook timidly showing up on the basketball court with all the best players in the entire league playing together magically, it is exactly that. So a lot of things have to happen quickly, but also, um, correctly because you know, you can't pull these things properly together without that. So I would say the partnership with the private sector here was fundamental, and I have to applaud the work that Accenture did particularly, I think, as Canadians, we're very proud of the fact that we needed to respond quickly. Everyone was in this, our neighbors, we knew people who were without support and Accenture's team, I mean, all the way up and down across the organization was fundamental and delivering this, but also literally putting themselves into, uh, these roles and to make sure that we would be able to respond quickly to do so. I think the playbook around the readiness for change I was shocked into existence every night. I won't talk about quantum physics, but clearly some some high level of energy was thrown in very quickly, mobilized everybody all at once. Nobody was said. He's sitting around saying, I wonder if we have change management covered off, you know this was changed readiness at its best. And so I think for me from a learning perspective, apart from just the technology side, which is pretty fundamental if you don't have ready enough technology to deploy quickly than the best paid plans in the world won't work. The reality is that to mobilize an organization going for it into that level of of spontaneous driving, change, exception, acceptance and adoption is really what I would aim for. And so our challenge now we'll be continuing that kind of progression going forward, and we now found the way. We certainly use the way to work with private sector in an innovative capacity in the new, innovative ways with brand new solutions that are truly agile and and and scalable to be able to pull all of the organization. All that one's very rapidly, and I have to admit that it is going to shift permanently our planning. We had 10 year plans for our big transformation, so some of our programs are the most important in the country. In many ways. We support people about eight million Canadians a month and on the benefits payments that we deliver, and they're the most marginal needed meeting and and requires our support from senior study, unemployed jobseekers and whatnot. So if you think about that group itself and to be able to support them clearly with the systems that we have is just unsustainable. But the new technologies are clearly going to show us the way that we had never for forecast. And I have to say I had to throw up, like in your plan. And now I'm working my way down from 10 denying date your plants going forward. And so it's exciting and nerve wracking sometimes, but then obviously has a change leader. Our goal is to get there as quickly as possible, so the benefit of all of these solutions could make a difference in people's lives. >>What's interesting is that you can shorten that timetable but also frees you up to be focused on what's contemporary and what's needed at the time. So leverage the people on the resource is You have and take advantage of that versus having something that you're sitting on that need to be refreshed. You can always be on that bleeding edge, and this brings up the Dev ops kind of mindset agility. The lean startup glean company. You know this is a team effort between Amazon and center and SDC. It's pass, shoot, score really fast. So this isn't the new, the new reality. Any commentary from you guys on this, you know, new pass shoot score combination. Because you got speed, you got agility. You're leaner, which makes you more flexible for being contemporary and solving problems. What's your thoughts? >>So my perspective on that is most definitely right. I think what we what we were able to show and what's. You know, what's coming out of a lot of different responses to the pandemic by government is, um, you know, perfection isn't the most important thing out of the gate. Getting something out there that's going to reassure citizens that's gonna allow them to answer their questions or access benefits quickly is what's becoming more important. Obviously, security and privacy. Those things are of the utmost importance as well. But it's ability to get stuff out there, quickly, test it, change it, tested again and and just always be iterating on the solutions. Like I can say what we put out on April 6th within four days is the backbone of what's out there still today. But we've added, you know, we added an integrated workforce management solution from Nice, and we added some other eyes views to do outbound dialing from acquisition, things like that. So the solution has grown from that M v p. And I think that's one other thing that that's going to be a big takeaways if you're not gonna do anything. So you got the final and product out there, then it's going to be here, right? So let's go quickly and let's adapt from there. >>Then we'll talk about that dynamic cause that's about building blocks, fund foundational things and then services. It's the cloud model. >>Yeah, I mean, before the pandemic, I had lunch with Mark Schwarz, which I believe you're quite familiar with, and, you know, I spent an hour and 1/2 with it. We were talking, and he was so exciting and and energized by what the technologies could do. And I was listening to him, and I used to be the chief technology officer for the government can right? And so I've seen a lot of stuff and I said, Well, that's really exciting, and I'm sure it's possible in some other places. And maybe it's some other countries where you know they didn't have infrastructure and legacy. I guess if I see him again soon, I'll have to. I apologize for not believing him enough, I think the building blocks of edge of the building, blocks of sprints and MVP's I mean they're not fundamental to the way we're gonna. So our biggest, various and scariest problems, technologically and then from a business perspective, Service candidate itself has 18,000 employees involved in multiple channels where the work has always been very lethargic, very difficult, arduous. You make change over years, not months, not days for sure. And so I think that that new method is not only a different way of working, it's a completely re HVAC way of assembly solutions, and I think the concept of engineering is probably going to be closer to what we're going to do on. And I have to borrow the Lego metaphor, but the building blocks are gonna be assembled. We now and working. I'm saying this in front of goal. He doesn't know that you should practice partners. We're gonna be assembling MPP maps of an entire long program, and it's gonna be iterative. It is gonna be designed, built. It will be agile as much as we can implement it. But more importantly, and punches weaken govern. It is, you know, the government is we may have changed. A lot of the government is not necessarily can count on to Most of these things approaches, But the reality is that that's where we're heading. And I will say, Oh, close. Perhaps on this on this answer. The biggest reason for doing that apart from we've proved it is the fact that the appetite inside the organization for that level of globalization, speed solution ing and being engaged rapidly you just can't take that away from an organization. Must be a piece of that. Uh, if you let them down, well, they'll remember. And frankly, they do remember now, cause they want more and it's gonna be hard. But it's a better heart. Ah, a better challenge that the one of having to do things over a decade, then to go fast and to kind of iterating quickly through the challenges and the issues and then move on very much to the next one as rapidly as possible. I think the other company, I would add is most of this was driven by a client need, and that's not inconsequential because it mobilized everybody to comment focused. If you have been just about, well, you know, we need to get people on side and solutions in place just to make our lives better, it providers. Yeah, it would have worked, perhaps, but it would have been different than the mobilisation It comes when the client is put in the middle, the client is the focus, and then we drive. Everyone's with that solution, >>you know, shared success and success is contagious. And when you ride the new way to oh, we need a new board, right? So once you get it, it then spreads like wildfire. This is what we've been seeing. And it also translates down to the citizens because again, being contemporary, none of us just looked could feel it's success in performance. So, as you know, people in business start to adopt cloud. It becomes a nice, nice, nice synergy. This is key. I'll take a year on a center. Um, the award winner. You guys did a great job. Final thoughts. >>Yeah. I mean, I think final thoughts would be happy to have the opportunity that help. And it was a It was a complete team effort and continues to be, um, it's not. It's not a bunch of Accenture technologists in the background in this, you know the commitment from everyone to get this in place. And can you continue to improvement from Benoit's team and from other folks across the government has been, uh, has been paramount to the success. So, um um, it's been a fantastic if world win like experience and, uh, look forward to continuing to build on it. And it has been said, I think one thing this is done is it's created demand for speed on some of these larger transformations. So I'm looking forward to continuing to innovate with with Ben wanting. >>Well, congratulations. The most innovative connect deployment. And because you guys from Canada, I have to use the hockey reference. You get multiple people working together in a cohesive manner. It's pass, shoot, score every time. And you know it's contagious. Thank you very much for your time. And congratulations for winning the >>West. Thanks. Thank you. Okay, this is the >>Cube's coverage of AWS Public Sector Partner Award show. I'm John Furrier, host of the Cube. Thanks for watching. Yeah, Yeah, yeah, yeah.

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Welcome back to the Cube's coverage of AWS Public Sector Partner Awards program. I'm John Furrow, your host of the Cube here in Palo Alto, California In the remote interviews during this pandemic, we have our remote crews and getting all the stories and celebrating the award winners. And here to feature the most innovative connect deployment. We have a center of Canada and the Department of Employment and Social Development of Canada, known as E S D. C guys. Congratulations, Joel. More Children Censure Canada Managing director and Ben while long sdc of Canada Chief Transformation officer. Gentlemen, thanks for coming on. And congratulations on the award. >>Thank you. >>Thank you. >>So, Ashley, during this pandemic, a lot of disruption and a lot of business still needs to go on, including government services. But the citizens and people need to still do their thing. Business got to run, and you got to get things going. But the disruptions caused a little bit of how the user experiences are. So this connect has been interesting. It's been a featured part of what we've been hearing at the public sector summit with Theresa Carlson. You guys, this is a key product. Tell us about the award. What is the solution? That disturbing of deserving reward? >>Maybe I'll get I'll go first and then pass it over to Benoit. But I think the solution is Amazon. Connect a spiritual contact center that we stood up fairly quickly over the course of about four days and really in support of of benefit that the government of Canada was was releasing as part of its economic response to the pandemic. And in the end that, you know, it's a fully functioning featured contact center solution includes an ai VR and, uh, you know, we stood it up for 1500 to 2000 agents so that that's the crux of the solution. And maybe Benoit can give a bit of insight as to to how it came about so quickly. >>Yeah, I'd be happy to actually wear obviously, like every other government, facing enormous pressures at that time to deliver benefits directly to people who were in true need, the jobs are being lost. Our current systems were in trouble because of their age in the arcade cake Nature. And so the challenge is was quickly how to actually support a lot of people really fast. And so it came through immediately that after our initial payments were made under what was called Canada Emergency Response Benefit, then we have to support our clients directly. And so people turn to the transformation team of all teams. If you wish during a fire firestorm to say, Well, what could you do and how could you help? And so we had an established relationship with a number of other system integrators, including Accenture, and we were able to run a competition very rapidly. Accenture one. And then we deployed in, as you all said, in a matter of four days, what for us was a new, exceptional on high quality solution to a significant client problem. And I say that because I think you can imagine how people feel in that endemic of all of all things. But with the uncertainty that comes with the loss of income, loss of jobs, the question of being able to deal with somebody really a human being, as well as to be able to be efficiently answer a very simple but straightforward questions rapidly and with high quality, with pretty fundamental for us. So the people in the groups that were talking through here are talking, speaking to millions of people who were literally being asked to to accept the pavement rapidly and to be able to connect with us quickly. And without this solution, which was exceptionally well done and deployed and of high quality personally, just a technology, uh, solution. I would not have been possible to even answer any of these queries quickly. >>And while that's a great 0.1 of the things that you see with the pandemic it's a disaster in the quote disaster kind of readiness thing. Unforeseen, right? So, like other things, you can kind of plan for things that hypothetical. You've got scenarios, but this >>is >>truly a case where every day counts. Every minute counts because humans are involved is no our ROI calculation. It's not like it's not like, Well, what's the payback of our system? The old kind of way to think this is really results fast. This is what cloud is all about. This is the promise of cloud. Can I stand up something quick and you did it with a partner. Okay, this is, like, not, like, normal again. It's like it's, you know, it's like, unheard of right? Four days with critical infrastructure, critical services that were unforeseen. Take us through what was going on in the war room, as you guys knew this was here. Take us through the through what happened. Yeah, >>So I think I can start a Z. You can imagine the set of executives that we're seeing a payment process. Uh, was an exceptional. It was like a bunker. Frankly, for about two weeks, we had to suspend the normal operations off the vast majority of our programming. We had to launch brand new payments and benefits systems and programs that nobody had seen before. The level of simplicity was maximized to delivered the funds quickly. So you could imagine it's a warpath if you wish, because the campaign is really around. A timing. Timing is fundamental. People are are literally losing their jobs. There is no support. There's no funding money for them to be able to buy groceries. So on the trust that people have in the government, Ai's pretty much at risk right there and then, in a very straightforward but extraordinarily powerful magic moment. If you wish. If you can deliver a solution, then you make a difference for a long time. And so the speed unheard off on old friends when he came to the call center capability and the ability for us to support and service context the clients that were desperate to reach us on. We're talking hundreds of thousands of calls, right? We're not talking a few 1000 year. Ultimately, at some point we were literally getting in our over over, taken by volumes, call centers, but we had a regular one still operating over a 1,000,000 calls for coming in today. Uh, with the capacity to answer, um, you know, tens of thousands. And so the reality is that the counselor that we put up here very quickly became capable of answering more calls than our regular costumes. And that speaks to the speed of delivery, the quality of the solution, of course, but the scalability of it and I have to say, maybe unheard of, it may be difficult to replicate. The conditions to lead to this are rare, but I have to say that my bosses and most of the government is probably now wondering why we can't do this more often like we can't operate with that kind of speed and agility. So I think what you've got is a client in our case, under extreme circumstances. Now, realizing the new normal will never be the same, that these types of solutions and technology. And then there's scalability. There's agility there, the speed of deployment. It's frankly, something we want. We want all the time. Now we'd like to be able to do it under your whole timeline conditions. But even those will be a fraction of what it used to take. It would have taken us well, actually, I can actually tell you because I was the lead. Ah, technologist, to deploy at scale for the government, Canada, all the call center capabilities under a single software as a service platform. It took us two years to design it. Two years to procure it and five years to install it. That's the last experience. We have a call center enterprise scale capabilities, and in this case, we went from years to literally days. >>Well, you know, it takes a crisis sometimes to kind of wire up the simplicity solution that you say. Why didn't we do this before? You know the waterfall meetings, Getting everyone arguing gets kind of gets in the way of the old, the old software model. I want to come back to the transformation been wanna minute, cause I think that's going to be a great success story and some learnings, and I want to get your thoughts on that. But I want to go to Joel because Joel we've talked to many Accenture executives over the years and most recently this past 24 months, And the message we've been hearing is we're going to be faster. We're not going to be seen as that. You know, a consulting firm taking our times. Try and get a pound of flesh from the client. This is an example, in my opinion of a partner working with the problem statement that kind of matches the cloud speed. So you guys have been doing this. This is not new to a censure. So take us through how you guys reacted because one you got to sync up and get the cadence of what? Ben? What I was trying to do, sync up and execute. Take us through what happened on your side. >>Yeah. I mean, so it's It's Ah, It's an unprecedented way of operating for us as well, frankly, and, um and, uh and, you know, we've had to look at to get this specific solution at the door and respond to an RFP and the commercial requirements that go with that way. Had Teoh get pretty agile ourselves internally on on how we go through approvals, etcetera, to make sure that that we were there to support Ben Wan is team. And I think you know that we saw this is a broader opportunity to really respond to it, to help Canada in a time of need. So So I think we, you know, we had to streamline a lot of our internal processes and make quick decisions that normally, even for our organization, would have taken, um, could it could have taken weeks, right? And we were down to hours in a lot of instances. So it helps. It forces us to react and act differently as well. But I mean, to Benoit's point, I think this is really going to to hopefully change the way it illustrates the art of the possible and hopefully will change how, How quickly we can look at problems and and we reduce deployment timeframes from from years to months and months to weeks, etcetera for solutions like this. Um, and I think that the AWS platform specifically in this case but what touched on a lot of things to beat the market scale ability But just as the benefit itself was, you know has to be simplified to do this quickly. I think one of the one of the benefits of the solution itself is it's simple to use technologically. I mean, we know least retrained. As I said, I think 1600 agents on how to use the platform over the course of a weekend on and and were able, and they're not normal agents. These were people who are firm from other jobs, potentially within the government. So they're not necessarily contact center agents by training. But they became contact center agents over the course of 48 hours that I think from that perspective, you know, that was important as well have something that people could could use. The answer those calls that you know that when you're gonna come So, >>Ben, what this is This is the transformation dream scenario in the sense of capabilities. I know it's under circumstances of the pandemic, and you guys didn't solve a big, big problem really fast and saved lives and help people get on with their day. But transformations about having people closest to the problem execute and the the also the people equation. People process technology, as they say, is kind of playing out in real time. This >>is >>the this is kind of the playbook, you know, Amazon came in said, Hey, you want to stand something up? You wired it together. The solution quickly. You're close to it. Looking back now, it's almost like, Hey, why aren't we doing this before? As you said and then you had to bring people in who weren't trained and stood them up and they were delivering the service. This >>is >>the playbook to share your thoughts on this, because this is what you're you're thinking about all the time and it actually playing out in real time. >>Well, I would definitely endorsed the idea that it's a playbook. It's I would say it's an ideal and dream playbook to build like showing up on the basketball court with all the best players in the entire league playing together magically, it is exactly that. So a lot of things have to happen quickly, but also correctly because you know you can't pull these things properly together without that. So I would say the partnership with the private sector here was fundamental. And I have to applaud the work that Accenture did particularly, I think, as Canadians, we're very proud of the fact that we needed to respond quickly. Everyone was in this, our neighbors, we knew people who were without support and Accenture's team, I mean all the way up and down across the organization was fundamental in and delivering this, but also literally putting themselves into, uh, these roles and to make sure that we would be able to respond quickly, do so. I think the playbook around the readiness for change. I was shocked into existence every night. I won't talk about quantum physics, but clearly some some high level of energy was thrown in very quickly, mobilized everybody all at once. Nobody was said. He's sitting around saying, I wonder if we have change management covered off, you know this was changed readiness at its best. And so I think for me from a learning perspective, apart from just the technology side, which is pretty fundamental if you don't have ready enough technology to deploy quickly than the best plans in the world won't work. The reality is that to mobilize an organization going forward into that level of of spontaneous driving, change, exception, acceptance and adoption is really what I would ain't for. And so our challenge Now we'll be continuing that kind of progression going forward, and we now found a way. And we certainly use the way to work with private sector in an innovative capacity and in innovative ways with brand new solutions that are truly agile and and scalable to be able to pull all of the organization. All that one's very rapidly, and I have to admit that it is going to shift permanently our planning. We had 10 year plans for our big transformation, so some of our programs are the most important in the country. In many ways. We support people about eight million Canadians a month and on the benefits payments that we deliver, and they're the most marginal needed meeting and and requires our support from senior studio, unemployed jobseekers and whatnot. So if you think about that group itself and to be able to support them clearly with their systems that we have is just unsustainable. But the new technologies are clearly going to show us the way that we had never for forecast. And I have to say I had to throw up, like in your plan. And now I'm working my way down from 10 denying date your plants going forward. And so it's exciting and nerve wracking sometimes. But then, obviously, as a change leader, our goal is to get there as quickly as possible, so the benefit of all of these solutions could make a difference in people's lives. >>What's interesting is that you can shorten that timetable but also frees you up to be focused on what's contemporary and what's needed at the time. So leverage the people on the resource is You have and take advantage of that versus having something that you're sitting on that need to be refreshed. You can always be on that bleeding edge, and this brings up the Dev ops kind of mindset agility. The lean startup glean company. You know this is a team effort between Amazon and center and SDC. It's pass, shoot, score really fast. So this isn't the new, the new reality. Any commentary from you guys on this, you know, new pass shoot score combination. Because you got speed, you got agility. You're leaner, which makes you more flexible for being contemporary and solving problems. What's your thoughts? >>Yeah, So my perspective on that is most definitely right. I think what we what we were able to show and what's. You know, what's coming out of a lot of different responses to the pandemic by government is, um, you know, perfection isn't the most important thing out of the gate. Getting something out there that's going to reassure citizens that's going to allow them to answer their questions or access benefits quickly is what's becoming more important. Obviously, security and privacy. Those things are of the utmost importance as well. But it's ability to get stuff in there, quickly, test it, change it tested again and just always be iterating on the solutions. Like I can say what we put out on April 6th within four days is the backbone of what's out there still today. But we've added, you know, we added an integrated workforce management solution from Nice, and we added some other eyes views to do outbound dialing from acquisition, things like that. So the solution has grown from that M v p. And I think that's one other thing that that's going to be a big takeaways if you're not gonna do anything. So you got the final and product out there, then it's going to be here, right? So let's go quickly and let's adapt from there. >>Then we'll talk about that dynamic cause that's about building blocks, fund foundational things and then services. It's the cloud model. >>Yeah, I mean, before the pandemic, I had lunch with Mark Schwarz, which I believe you're quite familiar with, and, you know, I spent an hour and 1/2 with it. We were talking, and he was so exciting and and energized by what the technologies could do. And I was listening to him, and I used to be the chief technology officer for the government. Can't right. And so I've seen a lot of stuff and I said, Well, that's really exciting, and I'm sure it's possible in some other places. And maybe it's some other countries where you know they didn't have infrastructure and legacy. I guess if I see him again soon, I'll have to. I apologize for not believing him enough, I think the building blocks of agile, the building blocks of sprints and MVP's I mean, they're not fundamental to the way we're going to solve our biggest various and scariest problems technologically and then from a business perspective. Service candidate itself has 18,000 employees involved in multiple channels, where the work has always been very lethargic, very difficult, arduous. You make change over years, not months, not days for sure. And so I think that that new method is not only a different way of working, it's a completely revamped way of assembly solutions, and I think the concept of engineering is probably going to be closer to what we're going to do. Um, and I have to borrow the Lego metaphor, but the building blocks are gonna be assembled. We now and working. I'm saying this in front of goal. He doesn't know that you should practice partners. We're gonna be assembling MPP maps of an entire long program, and it's gonna be iterative. It is gonna be designed, built. It will be agile as much as we can implement it. But more importantly, and punches weaken govern. It is, you know, the government is we may have changed. A lot of the government is not necessarily can count on to Most of these things approaches. But the reality is that that's where we're headed. And I will say, Oh, close. Perhaps on this on this answer. The biggest reason for doing that apart from we've proved it is the fact that the appetite inside the organization for that level of globalization, speed solution ing and being engaged rapidly you just can't take that away from an organization. Must be a piece of that. Uh, if you let them down, well, they don't remember. And frankly, they do remember now, cause they want more and it's gonna be hard. But it's a better heart. Ah, a better challenge that the one of having to do things over a decade, then to go fast and to kind of iterating quickly through the challenges and the issues and then move on very much to the next one as rapidly as possible. I think The other company, I would add, is most of this was driven by a client need, and that's not inconsequential because it mobilized everybody to comment focused. It could have been just about well, you know, we need to get people on side and solutions in place just to make our lives better. It is his providers. Yeah, it would have worked, perhaps, but it would have been different than the mobilisation It comes when the client is put in the middle. The client is the focus. And then we drive. Everyone's with that, >>you know, shared success and and successes contagious. And when you ride the new way to oh, we need a new board, right? So once you get it, it then spreads like wildfire. This is what we've been seeing. And it also translates down to the citizens because again, being contemporary numbers just look and feel. It's success in performance. So, as you know, people in business start to adopt cloud. It becomes a nice, nice, nice synergy. This is key. I'll take a year on a center. Um, the award winner. You guys did a great job. Final thoughts. >>Yeah. I mean, I think final thoughts would be happy to have the opportunity that help. And it was a It was a complete team effort and continues to be, um, it's not. It's not a bunch of Accenture technologists in the background in this, you know the commitment from everyone to get this in place. And can you continue to improvement from Benoit's team and from other folks across the government has been has been paramount to the success. So, um um, it's been a fantastic world win like experience and, uh, look forward to continuing to build on it. And it has been said, I think one thing this is done is it's created demand for speed on some of these larger transformations. So I'm looking forward to continuing to innovate with with Ben wanting. >>Well, congratulations. The most innovative connect deployment. And because you guys from Canada, I have to use the hockey reference. You get multiple people working together in a cohesive manner. It's pass, shoot, score every time. And you know it's contagious. Thank you very much for your time. And congratulations for winning the West. Thanks. Okay, this is the Cube's coverage of AWS Public Sector Partner Award show. I'm John Furrier, host of the Cube. Thanks for watching. Yeah, Yeah, >>yeah, yeah, yeah

(upbeat music) >> Welcome back to Supercloud 2. This is Dave Vellante. We're here exploring the intersection of data and analytics in the future of cloud and data. In this segment, we're going to look deeper into the life sciences business with Jesse Cugliotta, who leads the Healthcare and Life Sciences industry practice at Snowflake. And Nicholas Nick Taylor, who's the executive director of Informatics at Ionis Pharmaceuticals. Gentlemen, thanks for coming in theCUBE and participating in the program. Really appreciate it. >> Thank you for having us- >> Thanks for having me. >> You're very welcome, okay, we're go really try to look at data sharing as a use case and try to understand what's happening in the healthcare industry generally and specifically, how Nick thinks about sharing data in a governed fashion whether tapping the capabilities of multiple clouds is advantageous long term or presents more challenges than the effort is worth. And to start, Jesse, you lead this industry practice for Snowflake and it's a challenging and vibrant area. It's one that's hyper-focused on data privacy. So the first question is, you know there was a time when healthcare and other regulated industries wouldn't go near the cloud. What are you seeing today in the industry around cloud adoption and specifically multi-cloud adoption? >> Yeah, for years I've heard that healthcare and life sciences has been cloud diverse, but in spite of all of that if you look at a lot of aspects of this industry today, they've been running in the cloud for over 10 years now. Particularly when you look at CRM technologies or HR or HCM, even clinical technologies like EDC or ETMF. And it's interesting that you mentioned multi-cloud as well because this has always been an underlying reality especially within life sciences. This industry grows through acquisition where companies are looking to boost their future development pipeline either by buying up smaller biotechs, they may have like a late or a mid-stage promising candidate. And what typically happens is the larger pharma could then use their commercial muscle and their regulatory experience to move it to approvals and into the market. And I think the last few decades of cheap capital certainly accelerated that trend over the last couple of years. But this typically means that these new combined institutions may have technologies that are running on multiple clouds or multiple cloud strategies in various different regions to your point. And what we've often found is that they're not planning to standardize everything onto a single cloud provider. They're often looking for technologies that embrace this multi-cloud approach and work seamlessly across them. And I think this is a big reason why we, here at Snowflake, we've seen such strong momentum and growth across this industry because healthcare and life science has actually been one of our fastest growing sectors over the last couple of years. And a big part of that is in fact that we run on not only all three major cloud providers, but individual accounts within each and any one of them, they had the ability to communicate and interoperate with one another, like a globally interconnected database. >> Great, thank you for that setup. And so Nick, tell us more about your role and Ionis Pharma please. >> Sure. So I've been at Ionis for around five years now. You know, when when I joined it was, the IT department was pretty small. There wasn't a lot of warehousing, there wasn't a lot of kind of big data there. We saw an opportunity with Snowflake pretty early on as a provider that would be a lot of benefit for us, you know, 'cause we're small, wanted something that was fairly hands off. You know, I remember the days where you had to get a lot of DBAs in to fine tune your databases, make sure everything was running really, really well. The notion that there's, you know, no indexes to tune, right? There's very few knobs and dials, you can turn on Snowflake. That was appealing that, you know, it just kind of worked. So we found a use case to bring the platform in. We basically used it as a logging replacement as a Splunk kind of replacement with a platform called Elysium Analytics as a way to just get it in the door and give us the opportunity to solve a real world use case, but also to help us start to experiment using Snowflake as a platform. It took us a while to A, get the funding to bring it in, but B, build the momentum behind it. But, you know, as we experimented we added more data in there, we ran a few more experiments, we piloted in few more applications, we really saw the power of the platform and now, we are becoming a commercial organization. And with that comes a lot of major datasets. And so, you know, we really see Snowflake as being a very important part of our ecology going forward to help us build out our infrastructure. >> Okay, and you are running, your group runs on Azure, it's kind of mono cloud, single cloud, but others within Ionis are using other clouds, but you're not currently, you know, collaborating in terms of data sharing. And I wonder if you could talk about how your data needs have evolved over the past decade. I know you came from another highly regulated industry in financial services. So what's changed? You sort of touched on this before, you had these, you know, very specialized individuals who were, you know, DBAs, and, you know, could tune databases and the like, so that's evolved, but how has generally your needs evolved? Just kind of make an observation over the last, you know, five or seven years. What have you seen? >> Well, we, I wasn't in a group that did a lot of warehousing. It was more like online trade capture, but, you know, it was very much on-prem. You know, being in the cloud is very much a dirty word back then. I know that's changed since I've left. But in, you know, we had major, major teams of everyone who could do everything, right. As I mentioned in the pharma organization, there's a lot fewer of us. So the data needs there are very different, right? It's, we have a lot of SaaS applications. One of the difficulties with bringing a lot of SaaS applications on board is obviously data integration. So making sure the data is the same between them. But one of the big problems is joining the data across those SaaS applications. So one of the benefits, one of the things that we use Snowflake for is to basically take data out of these SaaS applications and load them into a warehouse so we can do those joins. So we use technologies like Boomi, we use technologies like Fivetran, like DBT to bring this data all into one place and start to kind of join that basically, allow us to do, run experiments, do analysis, basically take better, find better use for our data that was siloed in the past. You mentioned- >> Yeah. And just to add on to Nick's point there. >> Go ahead. >> That's actually something very common that we're seeing across the industry is because a lot of these SaaS applications that you mentioned, Nick, they're with from vendors that are trying to build their own ecosystem in walled garden. And by definition, many of them do not want to integrate with one another. So from a, you know, from a data platform vendor's perspective, we see this as a huge opportunity to help organizations like Ionis and others kind of deal with the challenges that Nick is speaking about because if the individual platform vendors are never going to make that part of their strategy, we see it as a great way to add additional value to these customers. >> Well, this data sharing thing is interesting. There's a lot of walled gardens out there. Oracle is a walled garden, AWS in many ways is a walled garden. You know, Microsoft has its walled garden. You could argue Snowflake is a walled garden. But the, what we're seeing and the whole reason behind the notion of super-cloud is we're creating an abstraction layer where you actually, in this case for this use case, can share data in a governed manner. Let's forget about the cross-cloud for a moment. I'll come back to that, but I wonder, Nick, if you could talk about how you are sharing data, again, Snowflake sort of, it's, I look at Snowflake like the app store, Apple, we're going to control everything, we're going to guarantee with data clean rooms and governance and the standards that we've created within that platform, we're going to make sure that it's safe for you to share data in this highly regulated industry. Are you doing that today? And take us through, you know, the considerations that you have in that regard. >> So it's kind of early days for us in Snowflake in general, but certainly in data sharing, we have a couple of examples. So data marketplace, you know, that's a great invention. It's, I've been a small IT shop again, right? The fact that we are able to just bring down terabyte size datasets straight into our Snowflake and run analytics directly on that is huge, right? The fact that we don't have to FTP these massive files around run jobs that may break, being able to just have that on tap is huge for us. We've recently been talking to one of our CRO feeds- CRO organizations about getting their data feeds in. Historically, this clinical trial data that comes in on an FTP file, we have to process it, take it through the platforms, put it into the warehouse. But one of the CROs that we talked to recently when we were reinvestigate in what data opportunities they have, they were a Snowflake customer and we are, I think, the first production customer they have, have taken that feed. So they're basically exposing their tables of data that historically came in these FTP files directly into our Snowflake instance now. We haven't taken advantage of that. It only actually flipped the switch about three or four weeks ago. But that's pretty big for us again, right? We don't have to worry about maintaining those jobs that take those files in. We don't have to worry about the jobs that take those and shove them on the warehouse. We now have a feed that's directly there that we can use a tool like DBT to push through directly into our model. And then the third avenue that's came up, actually fairly recently as well was genetics data. So genetics data that's highly, highly regulated. We had to be very careful with that. And we had a conversation with Snowflake about the data white rooms practice, and we see that as a pretty interesting opportunity. We are having one organization run genetic analysis being able to send us those genetic datasets, but then there's another organization that's actually has the in quotes "metadata" around that, so age, ethnicity, location, et cetera. And being able to join those two datasets through some kind of mechanism would be really beneficial to the organization. Being able to build a data white room so we can put that genetic data in a secure place, anonymize it, and then share the amalgamated data back out in a way that's able to be joined to the anonymized metadata, that could be pretty huge for us as well. >> Okay, so this is interesting. So you talk about FTP, which was the common way to share data. And so you basically, it's so, I got it now you take it and do whatever you want with it. Now we're talking, Jesse, about sharing the same copy of live data. How common is that use case in your industry? >> It's become very common over the last couple of years. And I think a big part of it is having the right technology to do it effectively. You know, as Nick mentioned, historically, this was done by people sending files around. And the challenge with that approach, of course, while there are multiple challenges, one, every time you send a file around your, by definition creating a copy of the data because you have to pull it out of your system of record, put it into a file, put it on some server where somebody else picks it up. And by definition at that point you've lost governance. So this creates challenges in general hesitation to doing so. It's not that it hasn't happened, but the other challenge with it is that the data's no longer real time. You know, you're working with a copy of data that was as fresh as at the time at that when that was actually extracted. And that creates limitations in terms of how effective this can be. What we're starting to see now with some of our customers is live sharing of information. And there's two aspects of that that are important. One is that you're not actually physically creating the copy and sending it to someone else, you're actually exposing it from where it exists and allowing another consumer to interact with it from their own account that could be in another region, some are running in another cloud. So this concept of super-cloud or cross-cloud could becoming realized here. But the other important aspect of it is that when that other- when that other entity is querying your data, they're seeing it in a real time state. And this is particularly important when you think about use cases like supply chain planning, where you're leveraging data across various different enterprises. If I'm a manufacturer or if I'm a contract manufacturer and I can see the actual inventory positions of my clients, of my distributors, of the levels of consumption at the pharmacy or the hospital that gives me a lot of indication as to how my demand profile is changing over time versus working with a static picture that may have been from three weeks ago. And this has become incredibly important as supply chains are becoming more constrained and the ability to plan accurately has never been more important. >> Yeah. So the race is on to solve these problems. So it start, we started with, hey, okay, cloud, Dave, we're going to simplify database, we're going to put it in the cloud, give virtually infinite resources, separate compute from storage. Okay, check, we got that. Now we've moved into sort of data clean rooms and governance and you've got an ecosystem that's forming around this to make it safer to share data. And then, you know, nirvana, at least near term nirvana is we're going to build data applications and we're going to be able to share live data and then you start to get into monetization. Do you see, Nick, in the near future where I know you've got relationships with, for instance, big pharma like AstraZeneca, do you see a situation where you start sharing data with them? Is that in the near term? Is that more long term? What are the considerations in that regard? >> I mean, it's something we've been thinking about. We haven't actually addressed that yet. Yeah, I could see situations where, you know, some of these big relationships where we do need to share a lot of data, it would be very nice to be able to just flick a switch and share our data assets across to those organizations. But, you know, that's a ways off for us now. We're mainly looking at bringing data in at the moment. >> One of the things that we've seen in financial services in particular, and Jesse, I'd love to get your thoughts on this, is companies like Goldman or Capital One or Nasdaq taking their stack, their software, their tooling actually putting it on the cloud and facing it to their customers and selling that as a new monetization vector as part of their digital or business transformation. Are you seeing that Jesse at all in healthcare or is it happening today or do you see a day when that happens or is healthier or just too scary to do that? >> No, we're seeing the early stages of this as well. And I think it's for some of the reasons we talked about earlier. You know, it's a much more secure way to work with a colleague if you don't have to copy your data and potentially expose it. And some of the reasons that people have historically copied that data is that they needed to leverage some sort of algorithm or application that a third party was providing. So maybe someone was predicting the ideal location and run a clinical trial for this particular rare disease category where there are only so many patients around the world that may actually be candidates for this disease. So you have to pick the ideal location. Well, sending the dataset to do so, you know, would involve a fairly complicated process similar to what Nick was mentioning earlier. If the company who was providing the logic or the algorithm to determine that location could bring that algorithm to you and you run it against your own data, that's a much more ideal and a much safer and more secure way for this industry to actually start to work with some of these partners and vendors. And that's one of the things that we're looking to enable going into this year is that, you know, the whole concept should be bring the logic to your data versus your data to the logic and the underlying sharing mechanisms that we've spoken about are actually what are powering that today. >> And so thank you for that, Jesse. >> Yes, Dave. >> And so Nick- Go ahead please. >> Yeah, if I could add, yeah, if I could add to that, that's something certainly we've been thinking about. In fact, we'd started talking to Snowflake about that a couple of years ago. We saw the power there again of the platform to be able to say, well, could we, we were thinking in more of a data share, but could we share our data out to say an AI/ML vendor, have them do the analytics and then share the data, the results back to us. Now, you know, there's more powerful mechanisms to do that within the Snowflake ecosystem now, but you know, we probably wouldn't need to have onsite AI/ML people, right? Some of that stuff's very sophisticated, expensive resources, hard to find, you know, it's much better for us to find a company that would be able to build those analytics, maintain those analytics for us. And you know, we saw an opportunity to do that a couple years ago and we're kind of excited about the opportunity there that we can just basically do it with a no op, right? We share the data route, we have the analytics done, we get the result back and it's just fairly seamless. >> I mean, I could have a whole another Cube session on this, guys, but I mean, I just did a a session with Andy Thurai, a Constellation research about how difficult it's been for organization to get ROI because they don't have the expertise in house so they want to either outsource it or rely on vendor R&D companies to inject that AI and machine intelligence directly into applications. My follow-up question to you Nick is, when you think about, 'cause Jesse was talking about, you know, let the data basically stay where it is and you know bring the compute to that data. If that data lives on different clouds, and maybe it's not your group, but maybe it's other parts of Ionis or maybe it's your partners like AstraZeneca, or you know, the AI/ML partners and they're potentially on other clouds or that data is on other clouds. Do you see that, again, coming back to super-cloud, do you see it as an advantage to be able to have a consistent experience across those clouds? Or is that just kind of get in the way and make things more complex? What's your take on that, Nick? >> Well, from the vendors, so from the client side, it's kind of seamless with Snowflake for us. So we know for a fact that one of the datasets we have at the moment, Compile, which is a, the large multi terabyte dataset I was talking about. They're on AWS on the East Coast and we are on Azure on the West Coast. And they had to do a few tweaks in the background to make sure the data was pushed over from, but from my point of view, the data just exists, right? So for me, I think it's hugely beneficial that Snowflake supports this kind of infrastructure, right? We don't have to jump through hoops to like, okay, well, we'll download it here and then re-upload it here. They already have the mechanism in the background to do these multi-cloud shares. So it's not important for us internally at the moment. I could see potentially at some point where we start linking across different groups in the organization that do have maybe Amazon or Google Cloud, but certainly within our providers. We know for a fact that they're on different services at the moment and it just works. >> Yeah, and we learned from Benoit Dageville, who came into the studio on August 9th with first Supercloud in 2022 that Snowflake uses a single global instance across regions and across clouds, yeah, whether or not you can query across you know, big regions, it just depends, right? It depends on latency. You might have to make a copy or maybe do some tweaks in the background. But guys, we got to jump, I really appreciate your time. Really thoughtful discussion on the future of data and cloud, specifically within healthcare and pharma. Thank you for your time. >> Thanks- >> Thanks for having us. >> All right, this is Dave Vellante for theCUBE team and my co-host, John Furrier. Keep it right there for more action at Supercloud 2. (upbeat music)

>> From every ISV to solve the problems. You want there to be tools in place that you can use, either open source tools or whatever it is that help you build it. And slowly over time, that building will become easier and easier. So my question to you was, where do you see you playing? Do you see yourself playing to ISVs as a set of tools, which will make their life a lot easier and provide that work? >> Absolutely. >> If they don't have, so they don't have to do it. Or you're providing this for the end users? Or both? >> So it's a progression. If you go to the ISVs first, you're doomed to starved before you have time for that other option. >> Yeah. >> Right? So it's a question of phase, the phasing of it. And also if you go directly to end users, you can demonstrate the power of it and get the attention of the ISVs. I believe that the ISVs, especially those with the biggest footprints and the most, you know, coveted estates, they have already made massive investments at trying to solve decentralization of their software stack. And I believe that they have used it as a hook to try to move to a software as a service model and rope people into leasing their infrastructure. So if you look at the clouds that have been propped up by Autodesk or by Adobe, or you name the company, they are building proprietary makeshift solutions for decentralizing or hybrid clouding. Or maybe they're not even doing that at all and all they're is saying hey, if you want to get location agnosticness, then what you should just, is just move into our cloud. >> Right. >> And then they try to solve on the background how to decentralize it between different regions so they can have decent offerings in each region. But those who are more advanced have already made larger investments and will be more averse to, you know, throwing that stuff away, all of their makeshift machinery away, and using a platform that gives them high performance parallel, low level file system access, while at the same time having metadata-driven, you know, policy-based, intent-based orchestration to manage the diffusion of data across a decentralized infrastructure. They are not going to be as open because they've made such an investment and they're going to look at how do they monetize it. So what we have found with like the movie studios who are using us already, many of the app they're using, many of those software offerings, the ISVs have their own cloud that offers that software for the cloud. But what we got when I asked about this, 'cause I was dealt specifically into this question because I'm very interested to know how we're going to make that leap from end user upstream into the ISVs where I believe we need to, and they said, look, we cannot use these software ISV-specific SAS clouds for two reasons. Number one is we lose control of the data. We're giving it to them. That's security and other issues. And here you're talking about we're doing work for Disney, we're doing work for Netflix, and they're not going to let us put our data on those software clouds, on those SAS clouds. Secondly, in any reasonable pipeline, the data is shared by many different applications. We need to be agnostic as to the application. 'Cause the inputs to one application, you know, the output for one application provides the input to the next, and it's not necessarily from the same vendor. So they need to have a data platform that lets them, you know, go from one software stack, and you know, to run it on another. Because they might do the rendering with this and yet, they do the editing with that, and you know, et cetera, et cetera. So I think the further you go up the stack in the structured data and dedicated applications for specific functions in specific verticals, the further up the stack you go, the harder it is to justify a SAS offering where you're basically telling the end users you need to park all your data with us and then you can run your application in our cloud and get this. That ultimately is a dead end path versus having the data be open and available to many applications across this supercloud layer. >> Okay, so-- >> Is that making any sense? >> Yes, so if I could just ask a clarifying question. So, if I had to take Snowflake as an example, I think they're doing exactly what you're saying is a dead end, put everything into our proprietary system and then we'll figure out how to distribute it. >> Yeah. >> And and I think if you're familiar with Zhamak Dehghaniis' data mesh concept. Are you? >> A little bit, yeah. >> But in her model, Snowflake, a Snowflake warehouse is just a node on the mesh and that mesh is-- >> That's right. >> Ultimately the supercloud and you're an enabler of that is what I'm hearing. >> That's right. What they're doing up at the structured level and what they're talking about at the structured level we're doing at the underlying, unstructured level, which by the way has implications for how you implement those distributed database things. In other words, implementing a Snowflake on top of Hammerspace would have made building stuff like in the first place easier. It would allow you to easily shift and run the database engine anywhere. You still have to solve how to shard and distribute at the transaction layer above, so I'm not saying we're a substitute for what you need to do at the app layer. By the way, there is another example of that and that's Microsoft Office, right? It's one thing to share that, to have a file share where you can share all the docs. It's something else to have Word and PowerPoint, Excel know how to allow people to be simultaneously editing the same doc. That's always going to happen in the app layer. But not all applications need that level of, you know, in-app decentralization. You know, many of them, many workflows are pipelined, especially the ones that are very data intensive where you're doing drug discovery or you're doing rendering, or you're doing machine learning training. These things are human in the loop with large stages of processing across tens of thousands of cores. And I think that kind of data processing pipeline is what we're focusing on first. Not so much the Microsoft Office or the Snowflake, you know, parking a relational database because that takes a lot of application layer stuff and that's what they're good at. >> Right. >> But I think... >> Go ahead, sorry. >> Later entrance in these markets will find Hammerspace as a way to accelerate their work so they can focus more narrowly on just the stuff that's app-specific, higher level sharing in the app. >> Yes, Snowflake founders-- >> I think it might be worth mentioning also, just keep this confidential guys, but one of our customers is Blue Origin. And one of the things that we have found is kind of the point of what you're talking about with our customers. They're needing to build this and since it's not commercially available or they don't know where to look for it to be commercially available, they're all building themselves. So this layer is needed. And Blue is just one of the examples of quite a few we're now talking to. And like manufacturing, HPC, research where they're out trying to solve this problem with their own scripting tools and things like that. And I just, I don't know if there's anything you want to add, David, but you know, but there's definitely a demand here and customers are trying to figure out how to solve it beyond what Hammerspace is doing. Like the need is so great that they're just putting developers on trying to do it themselves. >> Well, and you know, Snowflake founders, they didn't have a Hammerspace to lean on. But, one of the things that's interesting about supercloud is we feel as though industry clouds will emerge, that as part of company's digital transformations, they will, you know, every company's a software company, they'll begin to build their own clouds and they will be able to use a Hammerspace to do that. >> A super pass layer. >> Yes. It's really, I don't know if David's speaking, I don't want to speak over him, but we can't hear you. May be going through a bad... >> Well, a regional, regional talks that make that possible. And so they're doing these render farms and editing farms, and it's a cloud-specific to the types of workflows in the median entertainment world. Or clouds specifically to workflows in the chip design world or in the drug and bio and life sciences exploration world. There are large organizations that are kind of a blend of end users, like the Broad, which has their own kind of cloud where they're asking collaborators to come in and work with them. So it starts to even blur who's an end user versus an ISV. >> Yes. >> Right? When you start talking about the massive data is the main gravity is to having lots of people participate. >> Yep, and that's where the value is. And that's where the value is. And this is a megatrend that we see. And so it's really important for us to get to the point of what is and what is not a supercloud and, you know, that's where we're trying to evolve. >> Let's talk about this for a second 'cause I want to, I want to challenge you on something and it's something that I got challenged on and it has led me to thinking differently than I did at first, which Molly can attest to. Okay? So, we have been looking for a way to talk about the concept of cloud of utility computing, run anything anywhere that isn't addressed in today's realization of cloud. 'Cause today's cloud is not run anything anywhere, it's quite the opposite. You park your data in AWS and that's where you run stuff. And you pretty much have to. Same with with Azure. They're using data gravity to keep you captive there, just like the old infrastructure guys did. But now it's even worse because it's coupled back with the software to some degree, as well. And you have to use their storage, networking, and compute. It's not, I mean it fell back to the mainframe era. Anyhow, so I love the concept of supercloud. By the way, I was going to suggest that a better term might be hyper cloud since hyper speaks to the multidimensionality of it and the ability to be in a, you know, be in a different dimension, a different plane of existence kind of thing like hyperspace. But super and hyper are somewhat synonyms. I mean, you have hyper cars and you have super cars and blah, blah, blah. I happen to like hyper maybe also because it ties into the whole Hammerspace notion of a hyper-dimensional, you know, reality, having your data centers connected by a wormhole that is Hammerspace. But regardless, what I got challenged on is calling it something different at all versus simply saying, this is what cloud has always meant to be. This is the true cloud, this is real cloud, this is cloud. And I think back to what happened, you'll remember, at Fusion IO we talked about IO memory and we did that because people had a conceptualization of what an SSD was. And an SSD back then was low capacity, low endurance, made to go military, aerospace where things needed to be rugged but was completely useless in the data center. And we needed people to imagine this thing as being able to displace entire SAND, with the kind of capacity density, performance density, endurance. And so we talked IO memory, we could have said enterprise SSD, and that's what the industry now refers to for that concept. What will people be saying five and 10 years from now? Will they simply say, well this is cloud as it was always meant to be where you are truly able to run anything anywhere and have not only the same APIs, but you're same data available with high performance access, all forms of access, block file and object everywhere. So yeah. And I wonder, and this is just me throwing it out there, I wonder if, well, there's trade offs, right? Giving it a new moniker, supercloud, versus simply talking about how cloud is always intended to be and what it was meant to be, you know, the real cloud or true cloud, there are trade-offs. By putting a name on it and branding it, that lets people talk about it and understand they're talking about something different. But it also is that an affront to people who thought that that's what they already had. >> What's different, what's new? Yes, and so we've given a lot of thought to this. >> Right, it's like you. >> And it's because we've been asked that why does the industry need a new term, and we've tried to address some of that. But some of the inside baseball that we haven't shared is, you remember the Web 2.0, back then? >> Yep. >> Web 2.0 was the same thing. And I remember Tim Burners Lee saying, "Why do we need Web 2.0? "This is what the Web was always supposed to be." But the truth is-- >> I know, that was another perfect-- >> But the truth is it wasn't, number one. Number two, everybody hated the Web 2.0 term. John Furrier was actually in the middle of it all. And then it created this groundswell. So one of the things we wrote about is that supercloud is an evocative term that catalyzes debate and conversation, which is what we like, of course. And maybe that's self-serving. But yeah, HyperCloud, Metacloud, super, meaning, it's funny because super came from Latin supra, above, it was never the superlative. But the superlative was a convenient byproduct that caused a lot of friction and flack, which again, in the media business is like a perfect storm brewing. >> The bad thing to have to, and I think you do need to shake people out of their, the complacency of the limitations that they're used to. And I'll tell you what, the fact that you even have the terms hybrid cloud, multi-cloud, private cloud, edge computing, those are all just referring to the different boundaries that isolate the silo that is the current limited cloud. >> Right. >> So if I heard correctly, what just, in terms of us defining what is and what isn't in supercloud, you would say traditional applications which have to run in a certain place, in a certain cloud can't run anywhere else, would be the stuff that you would not put in as being addressed by supercloud. And over time, you would want to be able to run the data where you want to and in any of those concepts. >> Or even modern apps, right? Or even modern apps that are siloed in SAS within an individual cloud, right? >> So yeah, I guess it's twofold. Number one, if you're going at the high application layers, there's lots of ways that you can give the appearance of anything running anywhere. The ISV, the SAS vendor can engineer stuff to have the ability to serve with low enough latency to different geographies, right? So if you go too high up the stack, it kind of loses its meaning because there's lots of different ways to make due and give the appearance of omni-presence of the service. Okay? As you come down more towards the platform layer, it gets harder and harder to mask the fact that supercloud is something entirely different than just a good regionally-distributed SAS service. So I don't think you, I don't think you can distinguish supercloud if you go too high up the stack because it's just SAS, it's just a good SAS service where the SAS vendor has done the hard work to give you low latency access from different geographic regions. >> Yeah, so this is one of the hardest things, David. >> Common among them. >> Yeah, this is really an important point. This is one of the things I've had the most trouble with is why is this not just SAS? >> So you dilute your message when you go up to the SAS layer. If you were to focus most of this around the super pass layer, the how can you host applications and run them anywhere and not host this, not run a service, not have a service available everywhere. So how can you take any application, even applications that are written, you know, in a traditional legacy data center fashion and be able to run them anywhere and have them have their binaries and their datasets and the runtime environment and the infrastructure to start them and stop them? You know, the jobs, the, what the Kubernetes, the job scheduler? What we're really talking about here, what I think we're really talking about here is building the operating system for a decentralized cloud. What is the operating system, the operating environment for a decentralized cloud? Where you can, and that the main two functions of an operating system or an operating environment are the process scheduler, the thing that's scheduling what is running where and when and so forth, and the file system, right? The thing that's supplying a common view and access to data. So when we talk about this, I think that the strongest argument for supercloud is made when you go down to the platform layer and talk of it, talk about it as an operating environment on which you can run all forms of applications. >> Would you exclude--? >> Not a specific application that's been engineered as a SAS. (audio distortion) >> He'll come back. >> Are you there? >> Yeah, yeah, you just cut out for a minute. >> I lost your last statement when you broke up. >> We heard you, you said that not the specific application. So would you exclude Snowflake from supercloud? >> Frankly, I would. I would. Because, well, and this is kind of hard to do because Snowflake doesn't like to, Frank doesn't like to talk about Snowflake as a SAS service. It has a negative connotation. >> But it is. >> I know, we all know it is. We all know it is and because it is, yes, I would exclude them. >> I think I actually have him on camera. >> There's nothing in common. >> I think I have him on camera or maybe Benoit as saying, "Well, we are a SAS." I think it's Slootman. I think I said to Slootman, "I know you don't like to say you're a SAS." And I think he said, "Well, we are a SAS." >> Because again, if you go to the top of the application stack, there's any number of ways you can give it location agnostic function or you know, regional, local stuff. It's like let's solve the location problem by having me be your one location. How can it be decentralized if you're centralizing on (audio distortion)? >> Well, it's more decentralized than if it's all in one cloud. So let me actually, so the spectrum. So again, in the spirit of what is and what isn't, I think it's safe to say Hammerspace is supercloud. I think there's no debate there, right? Certainly among this crowd. And I think we can all agree that Dell, Dell Storage is not supercloud. Where it gets fuzzy is this Snowflake example or even, how about a, how about a Cohesity that instantiates its stack in different cloud regions in different clouds, and synchronizes, however magic sauce it does that. Is that a supercloud? I mean, so I'm cautious about having too strict of a definition 'cause then only-- >> Fair enough, fair enough. >> But I could use your help and thoughts on that. >> So I think we're talking about two different spectrums here. One is the spectrum of platform to application-specific. As you go up the application stack and it becomes this specific thing. Or you go up to the more and more structured where it's serving a specific application function where it's more of a SAS thing. I think it's harder to call a SAS service a supercloud. And I would argue that the reason there, and what you're lacking in the definition is to talk about it as general purpose. Okay? Now, that said, a data warehouse is general purpose at the structured data level. So you could make the argument for why Snowflake is a supercloud by saying that it is a general purpose platform for doing lots of different things. It's just one at a higher level up at the structured data level. So one spectrum is the high level going from platform to, you know, unstructured data to structured data to very application-specific, right? Like a specific, you know, CAD/CAM mechanical design cloud, like an Autodesk would want to give you their cloud for running, you know, and sharing CAD/CAM designs, doing your CAD/CAM anywhere stuff. Well, the other spectrum is how well does the purported supercloud technology actually live up to allowing you to run anything anywhere with not just the same APIs but with the local presence of data with the exact same runtime environment everywhere, and to be able to correctly manage how to get that runtime environment anywhere. So a Cohesity has some means of running things in different places and some means of coordinating what's where and of serving diff, you know, things in different places. I would argue that it is a very poor approximation of what Hammerspace does in providing the exact same file system with local high performance access everywhere with metadata ability to control where the data is actually instantiated so that you don't have to wait for it to get orchestrated. But even then when you do have to wait for it, it happens automatically and so it's still only a matter of, well, how quick is it? And on the other end of the spectrum is you could look at NetApp with Flexcache and say, "Is that supercloud?" And I would argue, well kind of because it allows you to run things in different places because it's a cache. But you know, it really isn't because it presumes some central silo from which you're cacheing stuff. So, you know, is it or isn't it? Well, it's on a spectrum of exactly how fully is it decoupling a runtime environment from specific locality? And I think a cache doesn't, it stretches a specific silo and makes it have some semblance of similar access in other places. But there's still a very big difference to the central silo, right? You can't turn off that central silo, for example. >> So it comes down to how specific you make the definition. And this is where it gets kind of really interesting. It's like cloud. Does IBM have a cloud? >> Exactly. >> I would say yes. Does it have the kind of quality that you would expect from a hyper-scale cloud? No. Or see if you could say the same thing about-- >> But that's a problem with choosing a name. That's the problem with choosing a name supercloud versus talking about the concept of cloud and how true up you are to that concept. >> For sure. >> Right? Because without getting a name, you don't have to draw, yeah. >> I'd like to explore one particular or bring them together. You made a very interesting observation that from a enterprise point of view, they want to safeguard their store, their data, and they want to make sure that they can have that data running in their own workflows, as well as, as other service providers providing services to them for that data. So, and in in particular, if you go back to, you go back to Snowflake. If Snowflake could provide the ability for you to have your data where you wanted, you were in charge of that, would that make Snowflake a supercloud? >> I'll tell you, in my mind, they would be closer to my conceptualization of supercloud if you can instantiate Snowflake as software on your own infrastructure, and pump your own data to Snowflake that's instantiated on your own infrastructure. The fact that it has to be on their infrastructure or that it's on their, that it's on their account in the cloud, that you're giving them the data and they're, that fundamentally goes against it to me. If they, you know, they would be a pure, a pure plate if they were a software defined thing where you could instantiate Snowflake machinery on the infrastructure of your choice and then put your data into that machinery and get all the benefits of Snowflake. >> So did you see--? >> In other words, if they were not a SAS service, but offered all of the similar benefits of being, you know, if it were a service that you could run on your own infrastructure. >> So did you see what they announced, that--? >> I hope that's making sense. >> It does, did you see what they announced at Dell? They basically announced the ability to take non-native Snowflake data, read it in from an object store on-prem, like a Dell object store. They do the same thing with Pure, read it in, running it in the cloud, and then push it back out. And I was saying to Dell, look, that's fine. Okay, that's interesting. You're taking a materialized view or an extended table, whatever you're doing, wouldn't it be more interesting if you could actually run the query locally with your compute? That would be an extension that would actually get my attention and extend that. >> That is what I'm talking about. That's what I'm talking about. And that's why I'm saying I think Hammerspace is more progressive on that front because with our technology, anybody who can instantiate a service, can make a service. And so I, so MSPs can use Hammerspace as a way to build a super pass layer and host their clients on their infrastructure in a cloud-like fashion. And their clients can have their own private data centers and the MSP or the public clouds, and Hammerspace can be instantiated, get this, by different parties in these different pieces of infrastructure and yet linked together to make a common file system across all of it. >> But this is data mesh. If I were HPE and Dell it's exactly what I'd be doing. I'd be working with Hammerspace to create my own data. I'd work with Databricks, Snowflake, and any other-- >> Data mesh is a good way to put it. Data mesh is a good way to put it. And this is at the lowest level of, you know, the underlying file system that's mountable by the operating system, consumed as a real file system. You can't get lower level than that. That's why this is the foundation for all of the other apps and structured data systems because you need to have a data mesh that can at least mesh the binary blob. >> Okay. >> That hold the binaries and that hold the datasets that those applications are running. >> So David, in the third week of January, we're doing supercloud 2 and I'm trying to convince John Furrier to make it a data slash data mesh edition. I'm slowly getting him to the knothole. I would very much, I mean you're in the Bay Area, I'd very much like you to be one of the headlines. As Zhamak Dehghaniis going to speak, she's the creator of Data Mesh, >> Sure. >> I'd love to have you come into our studio as well, for the live session. If you can't make it, we can pre-record. But you're right there, so I'll get you the dates. >> We'd love to, yeah. No, you can count on it. No, definitely. And you know, we don't typically talk about what we do as Data Mesh. We've been, you know, using global data environment. But, you know, under the covers, that's what the thing is. And so yeah, I think we can frame the discussion like that to line up with other, you know, with the other discussions. >> Yeah, and Data Mesh, of course, is one of those evocative names, but she has come up with some very well defined principles around decentralized data, data as products, self-serve infrastructure, automated governance, and and so forth, which I think your vision plugs right into. And she's brilliant. You'll love meeting her. >> Well, you know, and I think.. Oh, go ahead. Go ahead, Peter. >> Just like to work one other interface which I think is important. How do you see yourself and the open source? You talked about having an operating system. Obviously, Linux is the operating system at one level. How are you imagining that you would interface with cost community as part of this development? >> Well, it's funny you ask 'cause my CTO is the kernel maintainer of the storage networking stack. So how the Linux operating system perceives and consumes networked data at the file system level, the network file system stack is his purview. He owns that, he wrote most of it over the last decade that he's been the maintainer, but he's the gatekeeper of what goes in. And we have leveraged his abilities to enhance Linux to be able to use this decentralized data, in particular with decoupling the control plane driven by metadata from the data access path and the many storage systems on which the data gets accessed. So this factoring, this splitting of control plane from data path, metadata from data, was absolutely necessary to create a data mesh like we're talking about. And to be able to build this supercloud concept. And the highways on which the data runs and the client which knows how to talk to it is all open source. And we have, we've driven the NFS 4.2 spec. The newest NFS spec came from my team. And it was specifically the enhancements needed to be able to build a spanning file system, a data mesh at a file system level. Now that said, our file system itself and our server, our file server, our data orchestration, our data management stuff, that's all closed source, proprietary Hammerspace tech. But the highways on which the mesh connects are actually all open source and the client that knows how to consume it. So we would, honestly, I would welcome competitors using those same highways. They would be at a major disadvantage because we kind of built them, but it would still be very validating and I think only increase the potential adoption rate by more than whatever they might take of the market. So it'd actually be good to split the market with somebody else to come in and share those now super highways for how to mesh data at the file system level, you know, in here. So yeah, hopefully that answered your question. Does that answer the question about how we embrace the open source? >> Right, and there was one other, just that my last one is how do you enable something to run in every environment? And if we take the edge, for example, as being, as an environment which is much very, very compute heavy, but having a lot less capability, how do you do a hold? >> Perfect question. Perfect question. What we do today is a software appliance. We are using a Linux RHEL 8, RHEL 8 equivalent or a CentOS 8, or it's, you know, they're all roughly equivalent. But we have bundled and a software appliance which can be instantiated on bare metal hardware on any type of VM system from VMware to all of the different hypervisors in the Linux world, to even Nutanix and such. So it can run in any virtualized environment and it can run on any cloud instance, server instance in the cloud. And we have it packaged and deployable from the marketplaces within the different clouds. So you can literally spin it up at the click of an API in the cloud on instances in the cloud. So with all of these together, you can basically instantiate a Hammerspace set of machinery that can offer up this file system mesh. like we've been using the terminology we've been using now, anywhere. So it's like being able to take and spin up Snowflake and then just be able to install and run some VMs anywhere you want and boom, now you have a Snowflake service. And by the way, it is so complete that some of our customers, I would argue many aren't even using public clouds at all, they're using this just to run their own data centers in a cloud-like fashion, you know, where they have a data service that can span it all. >> Yeah and to Molly's first point, we would consider that, you know, cloud. Let me put you on the spot. If you had to describe conceptually without a chalkboard what an architectural diagram would look like for supercloud, what would you say? >> I would say it's to have the same runtime environment within every data center and defining that runtime environment as what it takes to schedule the execution of applications, so job scheduling, runtime stuff, and here we're talking Kubernetes, Slurm, other things that do job scheduling. We're talking about having a common way to, you know, instantiate compute resources. So a global compute environment, having a common compute environment where you can instantiate things that need computing. Okay? So that's the first part. And then the second is the data platform where you can have file block and object volumes, and have them available with the same APIs in each of these distributed data centers and have the exact same data omnipresent with the ability to control where the data is from one moment to the next, local, where all the data is instantiate. So my definition would be a common runtime environment that's bifurcate-- >> Oh. (attendees chuckling) We just lost them at the money slide. >> That's part of the magic makes people listen. We keep someone on pin and needles waiting. (attendees chuckling) >> That's good. >> Are you back, David? >> I'm on the edge of my seat. Common runtime environment. It was like... >> And just wait, there's more. >> But see, I'm maybe hyper-focused on the lower level of what it takes to host and run applications. And that's the stuff to schedule what resources they need to run and to get them going and to get them connected through to their persistence, you know, and their data. And to have that data available in all forms and have it be the same data everywhere. On top of that, you could then instantiate applications of different types, including relational databases, and data warehouses and such. And then you could say, now I've got, you know, now I've got these more application-level or structured data-level things. I tend to focus less on that structured data level and the application level and am more focused on what it takes to host any of them generically on that super pass layer. And I'll admit, I'm maybe hyper-focused on the pass layer and I think it's valid to include, you know, higher levels up the stack like the structured data level. But as soon as you go all the way up to like, you know, a very specific SAS service, I don't know that you would call that supercloud. >> Well, and that's the question, is there value? And Marianna Tessel from Intuit said, you know, we looked at it, we did it, and it just, it was actually negative value for us because connecting to all these separate clouds was a real pain in the neck. Didn't bring us any additional-- >> Well that's 'cause they don't have this pass layer underneath it so they can't even shop around, which actually makes it hard to stand up your own SAS service. And ultimately they end up having to build their own infrastructure. Like, you know, I think there's been examples like Netflix moving away from the cloud to their own infrastructure. Basically, if you're going to rent it for more than a few months, it makes sense to build it yourself, if it's at any kind of scale. >> Yeah, for certain components of that cloud. But if the Goldman Sachs came to you, David, and said, "Hey, we want to collaborate and we want to build "out a cloud and essentially build our SAS system "and we want to do that with Hammerspace, "and we want to tap the physical infrastructure "of not only our data centers but all the clouds," then that essentially would be a SAS, would it not? And wouldn't that be a Super SAS or a supercloud? >> Well, you know, what they may be using to build their service is a supercloud, but their service at the end of the day is just a SAS service with global reach. Right? >> Yeah. >> You know, look at, oh shoot. What's the name of the company that does? It has a cloud for doing bookkeeping and accounting. I forget their name, net something. NetSuite. >> NetSuite. NetSuite, yeah, Oracle. >> Yeah. >> Yep. >> Oracle acquired them, right? Is NetSuite a supercloud or is it just a SAS service? You know? I think under the covers you might ask are they using supercloud under the covers so that they can run their SAS service anywhere and be able to shop the venue, get elasticity, get all the benefits of cloud in the, to the benefit of their service that they're offering? But you know, folks who consume the service, they don't care because to them they're just connecting to some endpoint somewhere and they don't have to care. So the further up the stack you go, the more location-agnostic it is inherently anyway. >> And I think it's, paths is really the critical layer. We thought about IAS Plus and we thought about SAS Minus, you know, Heroku and hence, that's why we kind of got caught up and included it. But SAS, I admit, is the hardest one to crack. And so maybe we exclude that as a deployment model. >> That's right, and maybe coming down a level to saying but you can have a structured data supercloud, so you could still include, say, Snowflake. Because what Snowflake is doing is more general purpose. So it's about how general purpose it is. Is it hosting lots of other applications or is it the end application? Right? >> Yeah. >> So I would argue general purpose nature forces you to go further towards platform down-stack. And you really need that general purpose or else there is no real distinguishing. So if you want defensible turf to say supercloud is something different, I think it's important to not try to wrap your arms around SAS in the general sense. >> Yeah, and we've kind of not really gone, leaned hard into SAS, we've just included it as a deployment model, which, given the constraints that you just described for structured data would apply if it's general purpose. So David, super helpful. >> Had it sign. Define the SAS as including the hybrid model hold SAS. >> Yep. >> Okay, so with your permission, I'm going to add you to the list of contributors to the definition. I'm going to add-- >> Absolutely. >> I'm going to add this in. I'll share with Molly. >> Absolutely. >> We'll get on the calendar for the date. >> If Molly can share some specific language that we've been putting in that kind of goes to stuff we've been talking about, so. >> Oh, great. >> I think we can, we can share some written kind of concrete recommendations around this stuff, around the general purpose, nature, the common data thing and yeah. >> Okay. >> Really look forward to it and would be glad to be part of this thing. You said it's in February? >> It's in January, I'll let Molly know. >> Oh, January. >> What the date is. >> Excellent. >> Yeah, third week of January. Third week of January on a Tuesday, whatever that is. So yeah, we would welcome you in. But like I said, if it doesn't work for your schedule, we can prerecord something. But it would be awesome to have you in studio. >> I'm sure with this much notice we'll be able to get something. Let's make sure we have the dates communicated to Molly and she'll get my admin to set it up outside so that we have it. >> I'll get those today to you, Molly. Thank you. >> By the way, I am so, so pleased with being able to work with you guys on this. I think the industry needs it very bad. They need something to break them out of the box of their own mental constraints of what the cloud is versus what it's supposed to be. And obviously, the more we get people to question their reality and what is real, what are we really capable of today that then the more business that we're going to get. So we're excited to lend the hand behind this notion of supercloud and a super pass layer in whatever way we can. >> Awesome. >> Can I ask you whether your platforms include ARM as well as X86? >> So we have not done an ARM port yet. It has been entertained and won't be much of a stretch. >> Yeah, it's just a matter of time. >> Actually, entertained doing it on behalf of NVIDIA, but it will absolutely happen because ARM in the data center I think is a foregone conclusion. Well, it's already there in some cases, but not quite at volume. So definitely will be the case. And I'll tell you where this gets really interesting, discussion for another time, is back to my old friend, the SSD, and having SSDs that have enough brains on them to be part of that fabric. Directly. >> Interesting. Interesting. >> Very interesting. >> Directly attached to ethernet and able to create a data mesh global file system, that's going to be really fascinating. Got to run now. >> All right, hey, thanks you guys. Thanks David, thanks Molly. Great to catch up. Bye-bye. >> Bye >> Talk to you soon.

(bright music) >> "Supermetafragilisticexpialadotious." What's in a name? In an homage to the inimitable Charles Fitzgerald, we've chosen this title for today's session because of all the buzz surrounding "supercloud," a term that we introduced last year to signify a major architectural trend and shift that's occurring in the technology industry. Since that time, we've published numerous videos and articles on the topic, and on August 9th, kicked off "Supercloud22," an open industry event designed to advance the supercloud conversation, gathering input from more than 30 experienced technologists and business leaders in "The Cube" and broader technology community. We're talking about individuals like Benoit Dageville, Kit Colbert, Ali Ghodsi, Mohit Aron, David McJannet, and dozens of other experts. And today, we're pleased to welcome David Linthicum, who's a Chief Strategy Officer of Cloud Services at Deloitte Consulting. David is a technology visionary, a technical CTO. He's an author and a frequently sought after keynote speaker at high profile conferences like "VMware Explore" next week. David Linthicum, welcome back to "The Cube." Good to see you again. >> Oh, it's great to be here. Thanks for the invitation. Thanks for having me. >> Yeah, you're very welcome. Okay, so this topic of supercloud, what you call metacloud, has created a lot of interest. VMware calls it cross-cloud services, Snowflake calls it their data cloud, there's a lot of different names, but recently, you published a piece in "InfoWorld" where you said the following. "I really don't care what we call it, "and I really don't care if I put "my own buzzword into the mix. "However, this does not change the fact "that metacloud is perhaps the most important "architectural evolution occurring right now, "and we need to get this right out of the gate. "If we do that, who cares what it's named?" So very cool. And you also mentioned in a recent article that you don't like to put out new terms out in the wild without defining them. So what is a metacloud, or what we call supercloud? What's your definition? >> Yeah, and again, I don't care what people call it. The reality is it's the ability to have a layer of cross-cloud services. It sits above existing public cloud providers. So the idea here is that instead of building different security systems, different governance systems, different operational systems in each specific cloud provider, using whatever native features they provide, we're trying to do that in a cross-cloud way. So in other words, we're pushing out data integration, security, all these other things that we have to take care of as part of deploying a particular cloud provider. And in a multicloud scenario, we're building those in and between the clouds. And so we've been tracking this for about five years. We understood that multicloud is not necessarily about the particular public cloud providers, it's about things that you build in and between the clouds. >> Got it, okay. So I want to come back to that, to the definition, but I want to tie us to the so-called multicloud. You guys did a survey recently. We've said that multicloud was mostly a symptom of multi-vendor, Shadow Cloud, M&A, and only recently has become a strategic imperative. Now, Deloitte published a survey recently entitled "Closing the Cloud Strategy, Technology, Innovation Gap," and I'd like to explore that a little bit. And so in that survey, you showed data. What I liked about it is you went beyond what we all know, right? The old, "Our research shows that on average, "X number of clouds are used at an individual company." I mean, you had that too, but you really went deeper. You identified why companies are using multiple clouds, and you developed different categories of practitioners across 500 survey respondents. But the reasons were very clear for "why multicloud," as this becomes more strategic. Service choice scale, negotiating leverage, improved business resiliency, minimizing lock-in, interoperability of data, et cetera. So my question to you, David, is what's the problem supercloud or metacloud solves, and what's different from multicloud? >> That's a great question. The reality is that if we're... Well, supercloud or metacloud, whatever, is really something that exists above a multicloud, but I kind of view them as the same thing. It's an architectural pattern. We can name it anything. But the reality is that if we're moving to these multicloud environments, we're doing so to leverage best of breed things. In other words, best of breed technology to provide the innovators within the company to take the business to the next level, and we determine that in the survey. And so if we're looking at what a multicloud provides, it's the ability to provide different choices of different services or piece parts that allows us to build anything that we need to do. And so what we found in the survey and what we found in just practice in dealing with our clients is that ultimately, the value of cloud computing is going to be the innovation aspects. In other words, the ability to take the company to the next level from being more innovative and more disruptive in the marketplace that they're in. And the only way to do that, instead of basically leveraging the services of a particular walled garden of a single public cloud provider, is to cast a wider net and get out and leverage all kinds of services to make these happen. So if you think about that, that's basically how multicloud has evolved. In other words, it wasn't planned. They didn't say, "We're going to go do a multicloud." It was different developers and innovators in the company that went off and leveraged these cloud services, sometimes with the consent of IT leadership, sometimes not. And now we have these multitudes of different services that we're leveraging. And so many of these enterprises are going from 1000 to, say, 3000 services under management. That creates a complexity problem. We have a problem of heterogeneity, different platforms, different tools, different services, different AI technology, database technology, things like that. So the metacloud, or the supercloud, or whatever you want to call it, is the ability to deal with that complexity on the complexity's terms. And so instead of building all these various things that we have to do individually in each of the cloud providers, we're trying to do so within a cross-cloud service layer. We're trying to create this layer of technology, which removes us from dealing with the complexity of the underlying multicloud services and makes it manageable. Because right now, I think we're getting to a point of complexity we just can't operate it at the budgetary limits that we are right now. We can't keep the number of skills around, the number of operators around, to keep these things going. We're going to have to get creative in terms of how we manage these things, how we manage a multicloud. And that's where the supercloud, metacloud, whatever they want to call it, comes that. >> Yeah, and as John Furrier likes to say, in IT, we tend to solve complexity with more complexity, and that's not what we're talking about here. We're talking about simplifying, and you talked about the abstraction layer, and then it sounds like I'm inferring more. There's value that's added on top of that. And then you also said the hyperscalers are in a walled garden. So I've been asked, why aren't the hyperscalers superclouds? And I've said, essentially, they want to put your data into their cloud and keep it there. Now, that doesn't mean they won't eventually get into that. We've seen examples a little bit, Outposts, Anthos, Azure Arc, but the hyperscalers really aren't building superclouds or metaclouds, at least today, are they? >> No, they're not. And I always have the predictions for every major cloud conference that this is the conference that the hyperscaler is going to figure out some sort of a multicloud across-cloud strategy. In other words, building services that are able to operate across clouds. That really has never happened. It has happened in dribs and drabs, and you just mentioned a few examples of that, but the ability to own the space, to understand that we're not going to be the center of the universe in how people are going to leverage it, is going to be multiple things, including legacy systems and other cloud providers, and even industry clouds that are emerging these days, and SaaS providers, and all these things. So we're going to assist you in dealing with complexity, and we're going to provide the core services of being there. That hasn't happened yet. And they may be worried about conflicting their market, and the messaging is a bit different, even actively pushing back on the concept of multicloud, but the reality is the market's going to take them there. So in other words, if enough of their customers are asking for this and asking that they take the lead in building these cross-cloud technologies, even if they're participating in the stack and not being the stack, it's too compelling of a market that it's not going to drag a lot of the existing public cloud providers there. >> Well, it's going to be interesting to see how that plays out, David, because I never say never when it comes to a company like AWS, and we've seen how fast they move. And at the same time, they don't want to be commoditized. There's the layer underneath all this infrastructure, and they got this ecosystem that's adding all this tremendous value. But I want to ask you, what are the essential elements of supercloud, coming back to the definition, if you will, and what's different about metacloud, as you call it, from plain old SaaS or PaaS? What are the key elements there? >> Well, the key elements would be holistic management of all of the IT infrastructure. So even though it's sitting above a multicloud, I view metacloud, supercloud as the ability to also manage your existing legacy systems, your existing security stack, your existing network operations, basically everything that exists under the purview of IT. If you think about it, we're moving our infrastructure into the clouds, and we're probably going to hit a saturation point of about 70%. And really, if the supercloud, metacloud, which is going to be expensive to build for most of the enterprises, it needs to support these things holistically. So it needs to have all the services, that is going to be shareable across the different providers, and also existing legacy systems, and also edge computing, and IoT, and all these very diverse systems that we're building there right now. So if complexity is a core challenge to operate these things at scale and the ability to secure these things at scale, we have to have commonality in terms of security architecture and technology, commonality in terms of our directory services, commonality in terms of network operations, commonality in term of cloud operations, commonality in terms of FinOps. All these things should exist in some holistic cross-cloud layer that sits above all this complexity. And you pointed out something very profound. In other words, that is going to mean that we're hiding a lot of the existing cloud providers in terms of their interfaces and dashboards and things like that that we're dealing with today, their APIs. But the reality is that if we're able to manage these things at scale, the public cloud providers are going to benefit greatly from that. They're going to sell more services because people are going to find they're able to leverage them easier. And so in other words, if we're removing the complexity wall, which many in the industry are calling it right now, then suddenly we're moving from, say, the 25 to 30% migrated in the cloud, which most enterprises are today, to 50, 60, 70%. And we're able to do this at scale, and we're doing it at scale because we're providing some architectural optimization through the supercloud, metacloud layer. >> Okay, thanks for that. David, I just want to tap your CTO brain for a minute. At "Supercloud22," we came up with these three deployment models. Kit Colbert put forth the idea that one model would be your control planes running in one cloud, let's say AWS, but it interacts with and can manage and deploy on other clouds, the Kubernetes Cluster Management System. The second one, Mohit Aron from Cohesity laid out, where you instantiate the stack on different clouds and different cloud regions, and then you create a layer, a common interface across those. And then Snowflake was the third deployment model where it's a single global instance, it's one instantiation, and basically building out their own cloud across these regions. Help us parse through that. Do those seem like reasonable deployment models to you? Do you have any thoughts on that? >> Yeah, I mean, that's a distributed computing trick we've been doing, which is, in essence, an agent of the supercloud that's carrying out some of the cloud native functions on that particular cloud, but is, in essence, a slave to the metacloud, or the supercloud, whatever, that's able to run across the various cloud providers. In other words, when it wants to access a service, it may not go directly to that service. It goes directly to the control plane, and that control plane is responsible... Very much like Kubernetes and Docker works, that control plane is responsible for reaching out and leveraging those native services. I think that that's thinking that's a step in the right direction. I think these things unto themselves, at least initially, are going to be a very complex array of technology. Even though we're trying to remove complexity, the supercloud unto itself, in terms of the ability to build this thing that's able to operate at scale across-cloud, is going to be a collection of many different technologies that are interfacing with the public cloud providers in different ways. And so we can start putting these meta architectures together, and I certainly have written and spoke about this for years, but initially, this is going to be something that may escape the detail or the holistic nature of these meta architectures that people are floating around right now. >> Yeah, so I want to stay on this, because anytime I get a CTO brain, I like to... I'm not an engineer, but I've been around a long time, so I know a lot of buzzwords and have absorbed a lot over the years, but so you take those, the second two models, the Mohit instantiate on each cloud and each cloud region versus the Snowflake approach. I asked Benoit Dageville, "Does that mean if I'm in "an AWS east region and I want to do a query on Azure West, "I can do that without moving data?" And he said, "Yes and no." And the answer was really, "No, we actually take a subset of that data," so there's the latency problem. From those deployment model standpoints, what are the trade-offs that you see in terms of instantiating the stack on each individual cloud versus that single instance? Is there a benefit of the single instance for governance and security and simplicity, but a trade-off on latency, or am I overthinking this? >> Yeah, you hit it on the nose. The reality is that the trade-off is going to be latency and performance. If we get wiggy with the distributed nature, like the distributed data example you just provided, we have to basically separate the queries and communicate with the databases on each instance, and then reassemble the result set that goes back to the people who are recording it. And so we can do caching systems and things like that. But the reality is, if it's distributed system, we're going to have latency and bandwidth issues that are going to be limiting us. And also security issues, because if we're removing lots of information over the open internet, or even private circuits, that those are going to be attack vectors that hackers can leverage. You have to keep that in mind. We're trying to reduce those attack vectors. So it would be, in many instances, and I think we have to think about this, that we're going to keep the data in the same physical region for just that. So in other words, it's going to provide the best performance and also the most simplistic access to dealing with security. And so we're not, in essence, thinking about where the data's going, how it's moving across things, things like that. So the challenge is going to be is when you're dealing with a supercloud or metacloud is, when do you make those decisions? And I think, in many instances, even though we're leveraging multiple databases across multiple regions and multiple public cloud providers, and that's the idea of it, we're still going to localize the data for performance reasons. I mean, I just wrote a blog in "InfoWorld" a couple of months ago and talked about, people who are trying to distribute data across different public cloud providers for different reasons, distribute an application development system, things like that, you can do it. With enough time and money, you can do anything. I think the challenge is going to be operating that thing, and also providing a viable business return based on the application. And so why it may look like a good science experiment, and it's cool unto itself as an architect, the reality is the more pragmatic approach is going to be a leavitt in a single region on a single cloud. >> Very interesting. The other reason I like to talk to companies like Deloitte and experienced people like you is 'cause I can get... You're agnostic, right? I mean, you're technology agnostic, vendor agnostic. So I want to come back with another question, which is, how do you deal with what I call the lowest common denominator problem? What I mean by that is if one cloud has, let's say, a superior service... Let's take an example of Nitro and Graviton. AWS seems to be ahead on that, but let's say some other cloud isn't quite quite there yet, and you're building a supercloud or a metacloud. How do you rationalize that? Does it have to be like a caravan in the army where you slow down so all the slowest trucks can keep up, or are the ways to adjudicate that that are advantageous to hide that deficiency? >> Yeah, and that's a great thing about leveraging a supercloud or a metacloud is we're putting that management in a single layer. So as far as a user or even a developer on those systems, they shouldn't worry about the performance that may come back, because we're dealing with the... You hit the nail on the head with that one. The slowest component is the one that dictates performance. And so we have to have some sort of a performance management layer. We're also making dynamic decisions to move data, to move processing, from one server to the other to try to minimize the amount of latency that's coming from a single component. So the great thing about that is we're putting that volatility into a single domain, and it's making architectural decisions in terms of where something will run and where it's getting its data from, things are stored, things like that, based on the performance feedback that's coming back from the various cloud services that are under management. And so if you're running across clouds, it becomes even more interesting, because ultimately, you're going to make some architectural choices on the fly in terms of where that stuff runs based on the active dynamic performance that that public cloud provider is providing. So in other words, we may find that it automatically shut down a database service, say MySQL, on one cloud instance, and moved it to a MySQL instance on another public cloud provider because there was some sort of a performance issue that it couldn't work around. And by the way, it does so dynamically. Away from you making that decision, it's making that decision on your behalf. Again, this is a matter of abstraction, removing complexity, and dealing with complexity through abstraction and automation, and this is... That would be an example of fixing something with automation, self-healing. >> When you meet with some of the public cloud providers and they talk about on-prem private cloud, the general narrative from the hyperscalers is, "Well, that's not a cloud." Should on-prem be inclusive of supercloud, metacloud? >> Absolutely, I mean, and they're selling private cloud instances with the edge cloud that they're selling. The reality is that we're going to have to keep a certain amount of our infrastructure, including private clouds, on premise. It's something that's shrinking as a market share, and it's going to be tougher and tougher to justify as the public cloud providers become better and better at what they do, but we certainly have edge clouds now, and hyperscalers have examples of that where they run a instance of their public cloud infrastructure on premise on physical hardware and software. And the reality is, too, we have data centers and we have systems that just won't go away for another 20 or 30 years. They're just too sticky. They're uneconomically viable to move into the cloud. That's the core thing. It's not that we can't do it. The fact of the matter is we shouldn't do it, because there's not going to be an economic... There's not going to be an economic incentive of making that happen. So if we're going to create this meta layer or this infrastructure which is going to run across clouds, and everybody agrees on, that's what the supercloud is, we have to include the on-premise systems, including private clouds, including legacy systems. And by the way, include the rising number of IoT systems that are out there, and edge-based systems out there. So we're managing it using the same infrastructure into cloud services. So they have metadata systems and they have specialized services, and service finance and retail and things like doing risk analytics. So it gets them further down that path, but not necessarily giving them a SaaS application where they're forced into all of the business processes. We're giving you piece parts. So we'll give you 1000 different parts that are related to the finance industry. You can assemble anything you need, but the thing is, it's not going to be like building it from scratch. We're going to give you risk analytics, we're giving you the financial analytics, all these things that you can leverage within your applications how you want to leverage them. We'll maintain them. So in other words, you don't have to maintain 'em just like a cloud service. And suddenly, we can build applications in a couple of weeks that used to take a couple of months, in some cases, a couple of years. So that seems to be a large take of it moving forward. So get it up in the supercloud. Those become just other services that are under managed... That are under management on the supercloud, the metacloud. So we're able to take those services, abstract them, assemble them, use them in different applications. And the ability to manage where those services are originated versus where they're consumed is going to be managed by the supercloud layer, which, you're dealing with the governance, the service governance, the security systems, the directory systems, identity access management, things like that. They're going to get you further along down the pike, and that comes back as real value. If I'm able to build something in two weeks that used to take me two months, and I'm able to give my creators in the organization the ability to move faster, that's a real advantage. And suddenly, we are going to be valued by our digital footprint, our ability to do things in a creative and innovative way. And so organizations are able to move that fast, leveraging cloud computing for what it should be leveraged, as a true force multiplier for the business. They're going to win the game. They're going to get the most value. They're going to be around in 20 years, the others won't. >> David Linthicum, always love talking. You have a dangerous combination of business and technology expertise. Let's tease. "VMware Explore" next week, you're giving a keynote, if they're going to be there. Which day are you? >> Tuesday. Tuesday, 11 o'clock. >> All right, that's a big day. Tuesday, 11 o'clock. And David, please do stop by "The Cube." We're in Moscone West. Love to get you on and continue this conversation. I got 100 more questions for you. Really appreciate your time. >> I always love talking to people at "The Cube." Thank you very much. >> All right, and thanks for watching our ongoing coverage of "Supercloud22" on "The Cube," your leader in enterprise tech and emerging tech coverage. (bright music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR, This is "Breaking Analysis with Dave Vellante". >> Black Hat 22 was held in Las Vegas last week, the same time as theCUBE Supercloud event. Unlike AWS re:Inforce where words are carefully chosen to put a positive spin on security, Black Hat exposes all the warts of cyber and openly discusses its hard truths. It's a conference that's attended by technical experts who proudly share some of the vulnerabilities they've discovered, and, of course, by numerous vendors marketing their products and services. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis", we summarize what we learned from discussions with several people who attended Black Hat and our analysis from reviewing dozens of keynotes, articles, sessions, and data from a recent Black Hat Attendees Survey conducted by Black Hat and Informa, and we'll end with the discussion of what it all means for the challenges around securing the supercloud. Now, I personally did not attend, but as I said at the top, we reviewed a lot of content from the event which is renowned for its hundreds of sessions, breakouts, and strong technical content that is, as they say, unvarnished. Chris Krebs, the former director of Us cybersecurity and infrastructure security agency, CISA, he gave the keynote, and he spoke about the increasing complexity of tech stacks and the ripple effects that that has on organizational risk. Risk was a big theme at the event. Where re:Inforce tends to emphasize, again, the positive state of cybersecurity, it could be said that Black Hat, as the name implies, focuses on the other end of the spectrum. Risk, as a major theme of the event at the show, got a lot of attention. Now, there was a lot of talk, as always, about the expanded threat service, you hear that at any event that's focused on cybersecurity, and tons of emphasis on supply chain risk as a relatively new threat that's come to the CISO's minds. Now, there was also plenty of discussion about hybrid work and how remote work has dramatically increased business risk. According to data from in Intel 471's Mark Arena, the previously mentioned Black Hat Attendee Survey showed that compromise credentials posed the number one source of risk followed by infrastructure vulnerabilities and supply chain risks, so a couple of surveys here that we're citing, and we'll come back to that in a moment. At an MIT cybersecurity conference earlier last decade, theCUBE had a hypothetical conversation with former Boston Globe war correspondent, Charles Sennott, about the future of war and the role of cyber. We had similar discussions with Dr. Robert Gates on theCUBE at a ServiceNow event in 2016. At Black Hat, these discussions went well beyond the theoretical with actual data from the war in Ukraine. It's clear that modern wars are and will be supported by cyber, but the takeaways are that they will be highly situational, targeted, and unpredictable because in combat scenarios, anything can happen. People aren't necessarily at their keyboards. Now, the role of AI was certainly discussed as it is at every conference, and particularly cyber conferences. You know, it was somewhat dissed as over hyped, not surprisingly, but while AI is not a panacea to cyber exposure, automation and machine intelligence can definitely augment, what appear to be and have been stressed out, security teams can do this by recommending actions and taking other helpful types of data and presenting it in a curated form that can streamline the job of the SecOps team. Now, most cyber defenses are still going to be based on tried and true monitoring and telemetry data and log analysis and curating known signatures and analyzing consolidated data, but increasingly, AI will help with the unknowns, i.e. zero-day threats and threat actor behaviors after infiltration. Now, finally, while much lip service was given to collaboration and public-private partnerships, especially after Stuxsnet was revealed early last decade, the real truth is that threat intelligence in the private sector is still evolving. In particular, the industry, mid decade, really tried to commercially exploit proprietary intelligence and, you know, do private things like private reporting and monetize that, but attitudes toward collaboration are trending in a positive direction was one of the sort of outcomes that we heard at Black Hat. Public-private partnerships are being both mandated by government, and there seems to be a willingness to work together to fight an increasingly capable adversary. These things are definitely on the rise. Now, without this type of collaboration, securing the supercloud is going to become much more challenging and confined to narrow solutions. and we're going to talk about that little later in the segment. Okay, let's look at some of the attendees survey data from Black Hat. Just under 200 really serious security pros took the survey, so not enough to slice and dice by hair color, eye color, height, weight, and favorite movie genre, but enough to extract high level takeaways. You know, these strongly agree or disagree survey responses can sometimes give vanilla outputs, but let's look for the ones where very few respondents strongly agree or disagree with a statement or those that overwhelmingly strongly agree or somewhat agree. So it's clear from this that the respondents believe the following, one, your credentials are out there and available to criminals. Very few people thought that that was, you know, unavoidable. Second, remote work is here to stay, and third, nobody was willing to really jinx their firms and say that they strongly disagree that they'll have to respond to a major cybersecurity incident within the next 12 months. Now, as we've reported extensively, COVID has permanently changed the cybersecurity landscape and the CISO's priorities and playbook. Check out this data that queries respondents on the pandemic's impact on cybersecurity, new requirements to secure remote workers, more cloud, more threats from remote systems and remote users, and a shift away from perimeter defenses that are no longer as effective, e.g. firewall appliances. Note, however, the fifth response that's down there highlighted in green. It shows a meaningful drop in the percentage of remote workers that are disregarding corporate security policy, still too many, but 10 percentage points down from 2021 survey. Now, as we've said many times, bad user behavior will trump good security technology virtually every time. Consistent with the commentary from Mark Arena's Intel 471 threat report, fishing for credentials is the number one concern cited in the Black Hat Attendees Survey. This is a people and process problem more than a technology issue. Yes, using multifactor authentication, changing passwords, you know, using unique passwords, using password managers, et cetera, they're all great things, but if it's too hard for users to implement these things, they won't do it, they'll remain exposed, and their organizations will remain exposed. Number two in the graphic, sophisticated attacks that could expose vulnerabilities in the security infrastructure, again, consistent with the Intel 471 data, and three, supply chain risks, again, consistent with Mark Arena's commentary. Ask most CISOs their number one problem, and they'll tell you, "It's a lack of talent." That'll be on the top of their list. So it's no surprise that 63% of survey respondents believe they don't have the security staff necessary to defend against cyber threats. This speaks to the rise of managed security service providers that we've talked about previously on "Breaking Analysis". We've seen estimates that less than 50% of organizations in the US have a SOC, and we see those firms as ripe for MSSP support as well as larger firms augmenting staff with managed service providers. Now, after re:Invent, we put forth this conceptual model that discussed how the cloud was becoming the first line of defense for CISOs, and DevOps was being asked to do more, things like securing the runtime, the containers, the platform, et cetera, and audit was kind of that last line of defense. So a couple things we picked up from Black Hat which are consistent with this shift and some that are somewhat new, first, is getting visibility across the expanded threat surface was a big theme at Black Hat. This makes it even harder to identify risk, of course, this being the expanded threat surface. It's one thing to know that there's a vulnerability somewhere. It's another thing to determine the severity of the risk, but understanding how easy or difficult it is to exploit that vulnerability and how to prioritize action around that. Vulnerability is increasingly complex for CISOs as the security landscape gets complexified. So what's happening is the SOC, if there even is one at the organization, is becoming federated. No longer can there be one ivory tower that's the magic god room of data and threat detection and analysis. Rather, the SOC is becoming distributed following the data, and as we just mentioned, the SOC is being augmented by the cloud provider and the managed service providers, the MSSPs. So there's a lot of critical security data that is decentralized and this will necessitate a new cyber data model where data can be synchronized and shared across a federation of SOCs, if you will, or mini SOCs or SOC capabilities that live in and/or embedded in an organization's ecosystem. Now, to this point about cloud being the first line of defense, let's turn to a story from ETR that came out of our colleague Eric Bradley's insight in a one-on-one he did with a senior IR person at a manufacturing firm. In a piece that ETR published called "Saved by Zscaler", check out this comment. Quote, "As the last layer, we are filtering all the outgoing internet traffic through Zscaler. And when an attacker is already on your network, and they're trying to communicate with the outside to exchange encryption keys, Zscaler is already blocking the traffic. It happened to us. It happened and we were saved by Zscaler." So that's pretty cool. So not only is the cloud the first line of defense, as we sort of depicted in that previous graphic, here's an example where it's also the last line of defense. Now, let's end on what this all means to securing the supercloud. At our Supercloud 22 event last week in our Palo Alto CUBE Studios, we had a session on this topic on supercloud, securing the supercloud. Security, in our view, is going to be one of the most important and difficult challenges for the idea of supercloud to become real. We reviewed in last week's "Breaking Analysis" a detailed discussion with Snowflake co-founder and president of products, Benoit Dageville, how his company approaches security in their data cloud, what we call a superdata cloud. Snowflake doesn't use the term supercloud. They use the term datacloud, but what if you don't have the focus, the engineering depth, and the bank roll that Snowflake has? Does that mean superclouds will only be developed by those companies with deep pockets and enormous resources? Well, that's certainly possible, but on the securing the supercloud panel, we had three technical experts, Gee Rittenhouse of Skyhigh Security, Piyush Sharrma who's the founder of Accurics who sold to Tenable, and Tony Kueh, who's the former Head of Product at VMware. Now, John Furrier asked each of them, "What is missing? What's it going to take to secure the supercloud? What has to happen?" Here's what they said. Play the clip. >> This is the final question. We have one minute left. I wish we had more time. This is a great panel. We'll bring you guys back for sure after the event. What one thing needs to happen to unify or get through the other side of this fragmentation and then the challenges for supercloud? Because remember, the enterprise equation is solve complexity with more complexity. Well, that's not what the market wants. They want simplicity. They want SaaS. They want ease of use. They want infrastructure risk code. What has to happen? What do you think, each of you? >> So I can start, and extending to the previous conversation, I think we need a consortium. We need a framework that defines that if you really want to operate on supercloud, these are the 10 things that you must follow. It doesn't matter whether you take AWS, Slash, or TCP or you have all, and you will have the on-prem also, which means that it has to follow a pattern, and that pattern is what is required for supercloud, in my opinion. Otherwise, security is going everywhere. They're like they have to fix everything, find everything, and so on and so forth. It's not going to be possible. So they need a framework. They need a consortium, and this consortium needs to be, I think, needs to led by the cloud providers because they're the ones who have these foundational infrastructure elements, and the security vendor should contribute on providing more severe detections or severe findings. So that's, in my opinion, should be the model. >> Great, well, thank you, Gee. >> Yeah, I would think it's more along the lines of a business model. We've seen in cloud that the scale matters, and once you're big, you get bigger. We haven't seen that coalesce around either a vendor, a business model, or whatnot to bring all of this and connect it all together yet. So that value proposition in the industry, I think, is missing, but there's elements of it already available. >> I think there needs to be a mindset. If you look, again, history repeating itself. The internet sort of came together around set of IETF, RSC standards. Everybody embraced and extended it, right? But still, there was, at least, a baseline, and I think at that time, the largest and most innovative vendors understood that they couldn't do it by themselves, right? And so I think what we need is a mindset where these big guys, like Google, let's take an example. They're not going to win at all, but they can have a substantial share. So how do they collaborate with the ecosystem around a set of standards so that they can bring their differentiation and then embrace everybody together. >> Okay, so Gee's point about a business model is, you know, business model being missing, it's broadly true, but perhaps Snowflake serves as a business model where they've just gone out and and done it, setting or trying to set a de facto standard by which data can be shared and monetized. They're certainly setting that standard and mandating that standard within the Snowflake ecosystem with its proprietary framework. You know, perhaps that is one answer, but Tony lays out a scenario where there's a collaboration mindset around a set of standards with an ecosystem. You know, intriguing is this idea of a consortium or a framework that Piyush was talking about, and that speaks to the collaboration or lack thereof that we spoke of earlier, and his and Tony's proposal that the cloud providers should lead with the security vendor ecosystem playing a supporting role is pretty compelling, but can you see AWS and Azure and Google in a kumbaya moment getting together to make that happen? It seems unlikely, but maybe a better partnership between the US government and big tech could be a starting point. Okay, that's it for today. I want to thank the many people who attended Black Hat, reported on it, wrote about it, gave talks, did videos, and some that spoke to me that had attended the event, Becky Bracken, who is the EIC at Dark Reading. They do a phenomenal job and the entire team at Dark Reading, the news desk there, Mark Arena, whom I mentioned, Garrett O'Hara, Nash Borges, Kelly Jackson, sorry, Kelly Jackson Higgins, Roya Gordon, Robert Lipovsky, Chris Krebs, and many others, thanks for the great, great commentary and the content that you put out there, and thanks to Alex Myerson, who's on production, and Alex manages the podcasts for us. Ken Schiffman is also in our Marlborough studio as well, outside of Boston. Kristen Martin and Cheryl Knight, they help get the word out on social media and in our newsletters, and Rob Hoff is our Editor-in-Chief at SiliconANGLE and does some great editing and helps with the titles of "Breaking Analysis" quite often. Remember these episodes, they're all available as podcasts, wherever you listen, just search for "Breaking Analysis Podcasts". I publish each on wikibon.com and siliconangle.com, and you could email me, get in touch with me at david.vellante@siliconangle.com or you can DM me @dvellante or comment on my LinkedIn posts, and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis". (upbeat music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante at our inaugural super cloud 22 event we further refined the concept of a super cloud iterating on the definition the salient attributes and some examples of what is and what is not a super cloud welcome to this week's wikibon cube insights powered by etr you know snowflake has always been what we feel is one of the strongest examples of a super cloud and in this breaking analysis from our studios in palo alto we unpack our interview with benoit de javille co-founder and president of products at snowflake and we test our super cloud definition on the company's data cloud platform and we're really looking forward to your feedback first let's examine how we defl find super cloudant very importantly one of the goals of super cloud 22 was to get the community's input on the definition and iterate on previous work super cloud is an emerging computing architecture that comprises a set of services which are abstracted from the underlying primitives of hyperscale clouds we're talking about services such as compute storage networking security and other native tooling like machine learning and developer tools to create a global system that spans more than one cloud super cloud as shown on this slide has five essential properties x number of deployment models and y number of service models we're looking for community input on x and y and on the first point as well so please weigh in and contribute now we've identified these five essential elements of a super cloud let's talk about these first the super cloud has to run its services on more than one cloud leveraging the cloud native tools offered by each of the cloud providers the builder of the super cloud platform is responsible for optimizing the underlying primitives of each cloud and optimizing for the specific needs be it cost or performance or latency or governance data sharing security etc but those primitives must be abstracted such that a common experience is delivered across the clouds for both users and developers the super cloud has a metadata intelligence layer that can maximize efficiency for the specific purpose of the super cloud i.e the purpose that the super cloud is intended for and it does so in a federated model and it includes what we call a super pass this is a prerequisite that is a purpose-built component and enables ecosystem partners to customize and monetize incremental services while at the same time ensuring that the common experiences exist across clouds now in terms of deployment models we'd really like to get more feedback on this piece but here's where we are so far based on the feedback we got at super cloud 22. we see three deployment models the first is one where a control plane may run on one cloud but supports data plane interactions with more than one other cloud the second model instantiates the super cloud services on each individual cloud and within regions and can support interactions across more than one cloud with a unified interface connecting those instantiations those instances to create a common experience and the third model superimposes its services as a layer or in the case of snowflake they call it a mesh on top of the cloud on top of the cloud providers region or regions with a single global instantiation a single global instantiation of those services which spans multiple cloud providers this is our understanding from a comfort the conversation with benoit dejaville as to how snowflake approaches its solutions and for now we're going to park the service models we need to more time to flesh that out and we'll propose something shortly for you to comment on now we peppered benoit dejaville at super cloud 22 to test how the snowflake data cloud aligns to our concepts and our definition let me also say that snowflake doesn't use the term data cloud they really want to respect and they want to denigrate the importance of their hyperscale partners nor do we but we do think the hyperscalers today anyway are building or not building what we call super clouds but they are but but people who bar are building super clouds are building on top of hyperscale clouds that is a prerequisite so here are the questions that we tested with snowflake first question how does snowflake architect its data cloud and what is its deployment model listen to deja ville talk about how snowflake has architected a single system play the clip there are several ways to do this you know uh super cloud as as you name them the way we we we picked is is to create you know one single system and that's very important right the the the um [Music] there are several ways right you can instantiate you know your solution uh in every region of a cloud and and you know potentially that region could be a ws 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 superposed on top of the cloud provider you know region infrastructure region so we are building our regions but but where where it's very different is that each region of snowflake is not one in 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 but you can grow from there and you can you know exist in multiple clouds at the same time and that's very important right it's not one single i mean different instantiation of a system is one single instantiation which covers many cloud regions and many cloud providers snowflake chose the most advanced level of our three deployment models dodgeville talked about too presumably so it could maintain maximum control and ensure that common experience like the iphone model next we probed about the technical enablers of the data cloud listen to deja ville talk about snow grid he uses the term mesh and then this can get confusing with the jamaicani's data mesh concept but listen to benoit's explanation well as i said you know first we start by building you know snowflake regions we have today furry region that spawn you know the world so it's a worldwide worldwide system with many regions but all these regions are connected together they are you know meshed together with our technology we name it snow grid and that makes it hard because you know regions you know azure region can talk to a ws region or gcp regions and and as a as a user of our cloud you you don't see really these regional differences that you know regions are in different you know potentially clown when you use snowflake you can exist your your presence as an organization can be in several regions several clouds if you want geographic and and and both geographic and cloud provider so i can share data irrespective of the the cloud and i'm in the snowflake data cloud is that correct i can do that today exactly and and that's very critical right what we wanted is to remove data silos and and when you instantiate 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 the data in one region right and we didn't want to do that we wanted you know data to be distributed the way customer wants it to be distributed across the world and potentially sharing data at world scale now maybe there are many ways to skin the other cat meaning perhaps if a platform does instantiate in multiple places there are ways to share data but this is how snowflake chose to approach the problem next question how do you deal with latency in this big global system this is really important to us because while snowflake has some really smart people working as engineers and and the like we don't think they've solved for the speed of light problem the best people working on it as we often joke listen to benoit deja ville's comments on this topic so yes and no the the way we do it it's very expensive to do that because generally if you want to join you know data which is in which are in different regions and different cloud it's going to be very expensive because you need to move you know 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 regions so you can create this data mesh but data is replicated to make it very cheap and very performant too and is the snow grid does that have the metadata intelligence yes to actually can you describe that a little bit yeah snow grid is both uh a way to to exchange you know metadata about so each region of snowflake knows about all the other regions of snowflake every time we create a new region diary you know the metadata is distributed over our data cloud not only you know region knows all the regions but knows you know every organization that exists in our clouds where this organization is where data can be replicated by this organization and then of course it's it's also used as a way to uh uh exchange data right so you can exchange you know beta by scale of data size and we just had i was just receiving an email from one of our customers who moved more than four petabytes of data cross-region cross you know cloud providers in you know few days and you know it's a lot of data so it takes you know some time to move but they were able to do that online completely online and and switch over you know to the diff to the other region which is failover is very important also so yes and no probably means typically no he says yes and no probably means no so it sounds like snowflake is selectively pulling small amounts of data and replicating it where necessary but you also heard him talk about the metadata layer which is one of the essential aspects of super cloud okay next we dug into security it's one of the most important issues and we think one of the hardest parts related to deploying super cloud so we've talked about how the cloud has become the first line of defense for the cso but now with multi-cloud you have multiple first lines of defense and that means multiple shared responsibility models and multiple tool sets from different cloud providers and an expanded threat surface so listen to benoit's explanation here please play the clip this is a great question uh security has always been the most important aspect of snowflake since day one right this is the question that every customer of ours has you know how you 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 by encrypting it every data at rest and that's very important a lot of customers are not doing that right you hear these attacks for example on on cloud you know where someone left you know their buckets uh uh open and then you know you can access the data because it's a non-encrypted uh so we are encrypting everything at rest we are encrypting everything in transit so a region is very secure now you know you never 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 is really highly secure so snow grits ensure that everything is encrypted everything is you know we have multiple you know encryption keys and it's you know stored in hardware you know secure modules so we we we built you know snow grids such that it's secure and it allows very secure movement of data so when we heard this explanation we immediately went to the lowest common denominator question meaning when you think about how aws for instance deals with data in motion or data and rest it might be different from how another cloud provider deals with it so how does aws uh uh uh differences for example in the aws maturity model for various you know cloud capabilities you know let's say they've got a faster nitro or graviton does it do do you have to how does snowflake deal with that do they have to slow everything else down like imagine a caravan cruising you know across the desert so you know every truck can keep up let's listen it's a great question i mean of course our software is abstracting you know all the cloud providers you know 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 and this abstraction of course is a lot of work i mean really really a lot of work because it needs to be secure it needs to be performance and you know every cloud and it has you know to expose apis which are uniform and and you know cloud providers even though they have potentially the same concept let's say blob storage apis are completely different the way you know these systems are secure it's completely different the errors that you can get and and the retry you know mechanism is very different from one cloud to the other performance is also different we discovered that when we were starting to port our software and and and you know we had to completely rethink how to leverage blob storage in that cloud versus that cloud because just of performance too so we had you know for example to you know stripe data so all this work is work that's you know you don't need as an application because our vision really is that applications which are running in our data cloud can you know be abstracted of all this difference and and we provide all the services all the workload that this application need whether it's transactional access to data analytical access to data you know managing you know logs managing you know metrics all of these is abstracted too such that they are not you know tied to one you know particular service of one cloud and and distributing this application across you know many regions many cloud is very seamless so from that answer we know that snowflake takes care of everything but we really don't understand the performance implications in you know in that specific case but we feel pretty certain that the promises that snowflake makes around governance and security within their data sharing construct construct will be kept now another criterion that we've proposed for super cloud is a super pass layer to create a common developer experience and an enabler for ecosystem partners to monetize please play the clip let's listen we build it you know a custom build because because as you said you know what exists in one cloud might not exist in another cloud provider right so so we have to build you know on this all these this components that modern application mode and that application need and and and and that you know goes to machine learning as i say transactional uh analytical system and the entire thing so such that they can run in isolation basically and the objective is the developer experience will be identical across those clouds yes right the developers doesn't need to worry about cloud provider and actually our system we have we didn't talk about it but the marketplace that we have which allows actually to deliver we're getting there yeah okay now we're not going to go deep into ecosystem today we've talked about snowflakes strengths in this regard but snowflake they pretty much ticked all the boxes on our super cloud attributes and definition we asked benoit dejaville to confirm that this is all shipping and available today and he also gave us a glimpse of the future play the clip and we are still developing it you know the transactional you know unistore as we call it was announced in last summit so so they are still you know working properly but but but that's the vision right and and and that's important because we talk about the infrastructure right you mentioned 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 you know machine learning so you need to provide also all these services which are consistent across all the cloud providers so you can hear deja ville talking about expanding beyond taking advantage of the core infrastructure storage and networking et cetera and bringing intelligence to the data through machine learning and ai so of course there's more to come and there better be at this company's valuation despite the recent sharp pullback in a tightening fed environment okay so i know it's cliche but everyone's comparing snowflakes and data bricks databricks has been pretty vocal about its open source posture compared to snowflakes and it just so happens that we had aligotsy on at super cloud 22 as well he wasn't in studio he had to do remote because i guess he's presenting at an investor conference this week so we had to bring him in remotely now i didn't get to do this interview john furrier did but i listened to it and captured this clip about how data bricks sees super cloud and the importance of open source take a listen to goatzee yeah i mean let me start by saying we just we're big fans of open source we think that open source is a force in software that's going to continue for you know decades hundreds of years and it's going to slowly replace all proprietary code in its way we saw that you know it could do that with the most advanced technology windows you know proprietary operating system very complicated got replaced with linux so open source can pretty much do anything and what we're seeing with the data lake house is that slowly the open source community is building a replacement for the proprietary data warehouse you know data lake machine learning real-time stack in open source and we're excited to be part of it for us delta lake is a very important project that really helps you standardize how you lay out your data in the cloud and with it comes a really important protocol called delta sharing that enables you in an open way actually for the first time ever share large data sets between organizations but it uses an open protocol so the great thing about that is you don't need to be a database customer you don't even like databricks you just need to use this open source project and you can now securely share data sets between organizations across clouds and it actually does so really efficiently just one copy of the data so you don't have to copy it if you're within the same cloud so the implication of ellie gotzi's comments is that databricks with delta sharing as john implied is playing a long game now i don't know if enough about the databricks architecture to comment in detail i got to do more research there so i reached out to my two analyst friends tony bear and sanji mohan to see what they thought because they cover these companies pretty closely here's what tony bear said quote i've viewed the divergent lake house strategies of data bricks and snowflake in the context of their roots prior to delta lake databrick's prime focus was the compute not the storage layer and more specifically they were a compute engine not a database snowflake approached from the opposite end of the pool as they originally fit the mold of the classic database company rather than a specific compute engine per se the lake house pushes both companies outside of their original comfort zones data bricks to storage snowflake to compute engine so it makes perfect sense for databricks to embrace the open source narrative at the storage layer and for snowflake to continue its walled garden approach but in the long run their strategies are already overlapping databricks is not a 100 open source company its practitioner experience has always been proprietary and now so is its sql query engine likewise snowflake has had to open up with the support of iceberg for open data lake format the question really becomes how serious snowflake will be in making iceberg a first-class citizen in its environment that is not necessarily officially branding a lake house but effectively is and likewise can databricks deliver the service levels associated with walled gardens through a more brute force approach that relies heavily on the query engine at the end of the day those are the key requirements that will matter to data bricks and snowflake customers end quote that was some deep thought by by tony thank you for that sanjay mohan added the following quote open source is a slippery slope people buy mobile phones based on open source android but it's not fully open similarly databricks delta lake was not originally fully open source and even today its photon execution engine is not we are always going to live in a hybrid world snowflake and databricks will support whatever model works best for them and their customers the big question is do customers care as deeply about which vendor has a higher degree of openness as we technology people do i believe customers evaluation criteria is far more nuanced than just to decipher each vendor's open source claims end quote okay so i had to ask dodgeville about their so-called wall garden approach and what their strategy is with apache iceberg here's what he said iceberg is is very important so just to to give some context iceberg is an open you know table format right which was you know first you know developed by netflix and netflix you know put it open source in the apache community so we embrace that's that open source standard because because it's widely used by by many um many you know companies and also many companies have you know really invested a lot of effort in building you know big data hadoop solution or data like solution and they want to use snowflake and they couldn't really use snowflake because all their data were in open you know formats so we are embracing icebergs to help these companies move through the cloud but why we have been relentless with direct access to data direct access to data is a little bit of a problem for us and 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 you know your cloud diagnostic layer you don't access data with api when you have direct access to data it's very hard to secure data because you need to grant access direct access to tools which are not you know protected and you see a lot of you know hacking of of data you know because of that so so that was not you know direct access to data is not serving well our customers and that's why we have been relented to do that because it's it's cr it's it's not cloud diagnostic it's it's you you have to code that you have to you you you need a lot of intelligence while apis access so we want open apis that's that's i guess the way we embrace you know openness is is by open api versus you know you access directly data here's my take snowflake is hedging its bets because enough people care about open source that they have to have some open data format options and it's good optics and you heard benoit deja ville talk about the risks of directly accessing the data and the complexities it brings now is that maybe a little fud against databricks maybe but same can be said for ollie's comments maybe flooding the proprietaryness of snowflake but as both analysts pointed out open is a spectrum hey i remember unix used to equal open systems okay let's end with some etr spending data and why not compare snowflake and data bricks spending profiles this is an xy graph with net score or spending momentum on the y-axis and pervasiveness or overlap in the data set on the x-axis this is data from the january survey when snowflake was holding above 80 percent net score off the charts databricks was also very strong in the upper 60s now let's fast forward to this next chart and show you the july etr survey data and you can see snowflake has come back down to earth now remember anything above 40 net score is highly elevated so both companies are doing well but snowflake is well off its highs and data bricks has come down somewhat as well databricks is inching to the right snowflake rocketed to the right post its ipo and as we know databricks wasn't able to get to ipo during the covet bubble ali gotzi is at the morgan stanley ceo conference this week they got plenty of cash to withstand a long-term recession i'm told and they've started the message that they're a billion dollars in annualized revenue i'm not sure exactly what that means i've seen some numbers on their gross margins i'm not sure what that means i've seen some numbers on their net retention revenue or net revenue retention again i'll reserve judgment until we see an s1 but it's clear both of these companies have momentum and they're out competing in the market well as always be the ultimate arbiter different philosophies perhaps is it like democrats and republicans well it could be but they're both going after a solving data problem both companies are trying to help customers get more value out of their data and both companies are highly valued so they have to perform for their investors to paraphrase ralph nader the similarities may be greater than the differences okay that's it for today thanks to the team from palo alto for this awesome super cloud studio build alex myerson and ken shiffman are on production in the palo alto studios today kristin martin and sheryl knight get the word out to our community rob hoff is our editor-in-chief over at siliconangle thanks to all please check out etr.ai for all the survey data remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcasts i publish each week on wikibon.com and siliconangle.com and you can email me at david.vellante at siliconangle.com or dm me at devellante or comment on my linkedin posts and please as i say etr has got some of the best survey data in the business we track it every quarter and really excited to be partners with them this is dave vellante for the cube insights powered by etr thanks for watching and we'll see you next time on breaking analysis [Music] you

(gentle upbeat music) >> Welcome back everyone, to "theCUBE"'s live stage performance here in Palo Alto, California at "theCUBE" Studios. I'm John Furrier with Dave Vellante, kicking off our first inaugural Supercloud event. It's an editorial event, we wanted to bring together the best in the business, the smartest, the biggest, the up-and-coming startups, venture capitalists, everybody, to weigh in on this new Supercloud trend, this structural change in the cloud computing business. We're about to run the Ecosystem Speaks, which is a bunch of pre-recorded companies that wanted to get their voices on the record, so stay tuned for the rest of the day. We'll be replaying all that content and they're going to be having some really good commentary and hear what they have to say. I had a chance to interview and so did Dave. Dave, this is our closing segment where we kind of unpack everything or kind of digest and report. So much to kind of digest from the conversations today, a wide range of commentary from Supercloud operating system to developers who are in charge to maybe it's an ops problem or maybe Oracle's a Supercloud. I mean, that was debated. So so much discussion, lot to unpack. What was your favorite moments? >> Well, before I get to that, I think, I go back to something that happened at re:Invent last year. Nick Sturiale came up, Steve Mullaney from Aviatrix; we're going to hear from him shortly in the Ecosystem Speaks. Nick Sturiale's VC said "it's happening"! And what he was talking about is this ecosystem is exploding. They're building infrastructure or capabilities on top of the CapEx infrastructure. So, I think it is happening. I think we confirmed today that Supercloud is a thing. It's a very immature thing. And I think the other thing, John is that, it seems to me that the further you go up the stack, the weaker the business case gets for doing Supercloud. We heard from Marianna Tessel, it's like, "Eh, you know, we can- it was easier to just do it all on one cloud." This is a point that, Adrian Cockcroft just made on the panel and so I think that when you break out the pieces of the stack, I think very clearly the infrastructure layer, what we heard from Confluent and HashiCorp, and certainly VMware, there's a real problem there. There's a real need at the infrastructure layer and then even at the data layer, I think Benoit Dageville did a great job of- You know, I was peppering him with all my questions, which I basically was going through, the Supercloud definition and they ticked the box on pretty much every one of 'em as did, by the way Ali Ghodsi you know, the big difference there is the philosophy of Republicans and Democrats- got open versus closed, not to apply that to either one side, but you know what I mean! >> And the similarities are probably greater than differences. >> Berkely, I would probably put them on the- >> Yeah, we'll put them on the Democrat side we'll make Snowflake the Republicans. But so- but as we say there's a lot of similarities as well in terms of what their objectives are. So, I mean, I thought it was a great program and a really good start to, you know, an industry- You brought up the point about the industry consortium, asked Kit Colbert- >> Yep. >> If he thought that was something that was viable and what'd they say? That hyperscale should lead it? >> Yeah, they said hyperscale should lead it and there also should be an industry consortium to get the voices out there. And I think VMware is very humble in how they're putting out their white paper because I think they know that they can't do it all and that they do not have a great track record relative to cloud. And I think, but they have a great track record of loyal installed base ops people using VMware vSphere all the time. >> Yeah. >> So I think they need a catapult moment where they can catapult to the cloud native which they've been working on for years under Raghu and the team. So the question on VMware is in the light of Broadcom, okay, acquisition of VMware, this is an opportunity or it might not be an opportunity or it might be a spin-out or something, I just think VMware's got way too much engineering culture to be ignored, Dave. And I think- well, I'm going to watch this very closely because they can pull off some sort of rallying moment. I think they could. And then you hear the upstarts like Platform9, Rafay Systems and others they're all like, "Yes, we need to unify behind something. There needs to be some sort of standard". You know, we heard the argument of you know, more standards bodies type thing. So, it's interesting, maybe "theCUBE" could be that but we're going to certainly keep the conversation going. >> I thought one of the most memorable statements was Vittorio who said we- for VMware, we want our cake, we want to eat it too and we want to lose weight. So they have a lot of that aspirations there! (John laughs) >> And then I thought, Adrian Cockcroft said you know, the devs, they want to get married. They were marrying everybody, and then the ops team, they have to deal with the divorce. >> Yeah. >> And I thought that was poignant. It's like, they want consistency, they want standards, they got to be able to scale And Lori MacVittie, I'm not sure you agree with this, I'd have to think about it, but she was basically saying, all we've talked about is devs devs devs for the last 10 years, going forward we're going to be talking about ops. >> Yeah, and I think one of the things I learned from this day and looking back, and some kind of- I've been sauteing through all the interviews. If you zoom out, for me it was the epiphany of developers are still in charge. And I've said, you know, the developers are doing great, it's an ops security thing. Not sure I see that the way I was seeing before. I think what I learned was the refactoring pattern that's emerging, In Sik Rhee brought this up from Vertex Ventures with Marianna Tessel, it's a nuanced point but I think he's right on which is the pattern that's emerging is developers want ease-of-use tooling, they're driving the change and I think the developers in the devs ops ethos- it's never going to be separate. It's going to be DevOps. That means developers are driving operations and then security. So what I learned was it's not ops teams leveling up, it's devs redefining what ops is. >> Mm. And I think that to me is where Supercloud's going to be interesting- >> Forcing that. >> Yeah. >> Forcing the change because the structural change is open sources thriving, devs are still in charge and they still want more developers, Vittorio "we need more developers", right? So the developers are in charge and that's clear. Now, if that happens- if you believe that to be true the domino effect of that is going to be amazing because then everyone who gets on the wrong side of history, on the ops and security side, is going to be fighting a trend that may not be fight-able, you know, it might be inevitable. And so the winners are the ones that are refactoring their business like Snowflake. Snowflake is a data warehouse that had nothing to do with Amazon at first. It was the developers who said "I'm going to refactor data warehouse on AWS". That is a developer-driven refactorization and a business model. So I think that's the pattern I'm seeing is that this concept refactoring, patterns and the developer trajectory is critical. >> I thought there was another great comment. Maribel Lopez, her Lord of the Rings comment: "there will be no one ring to rule them all". Now at the same time, Kit Colbert, you know what we asked him straight out, "are you the- do you want to be the, the Supercloud OS?" and he basically said, "yeah, we do". Now, of course they're confined to their world, which is a pretty substantial world. I think, John, the reason why Maribel is so correct is security. I think security's a really hard problem to solve. You've got cloud as the first layer of defense and now you've got multiple clouds, multiple layers of defense, multiple shared responsibility models. You've got different tools for XDR, for identity, for governance, for privacy all within those different clouds. I mean, that really is a confusing picture. And I think the hardest- one of the hardest parts of Supercloud to solve. >> Yeah, and I thought the security founder Gee Rittenhouse, Piyush Sharrma from Accurics, which sold to Tenable, and Tony Kueh, former head of product at VMware. >> Right. >> Who's now an investor kind of looking for his next gig or what he is going to do next. He's obviously been extremely successful. They brought up the, the OS factor. Another point that they made I thought was interesting is that a lot of the things to do to solve the complexity is not doable. >> Yeah. >> It's too much work. So managed services might field the bit. So, and Chris Hoff mentioned on the Clouderati segment that the higher level services being a managed service and differentiating around the service could be the key competitive advantage for whoever does it. >> I think the other thing is Chris Hoff said "yeah, well, Web 3, metaverse, you know, DAO, Superclouds" you know, "Stupercloud" he called it and this bring up- It resonates because one of the criticisms that Charles Fitzgerald laid on us was, well, it doesn't help to throw out another term. I actually think it does help. And I think the reason it does help is because it's getting people to think. When you ask people about Supercloud, they automatically- it resonates with them. They play back what they think is the future of cloud. So Supercloud really talks to the future of cloud. There's a lot of aspects to it that need to be further defined, further thought out and we're getting to the point now where we- we can start- begin to say, okay that is Supercloud or that isn't Supercloud. >> I think that's really right on. I think Supercloud at the end of the day, for me from the simplest way to describe it is making sure that the developer experience is so good that the operations just happen. And Marianna Tessel said, she's investing in making their developer experience high velocity, very easy. So if you do that, you have to run on premise and on the cloud. So hybrid really is where Supercloud is going right now. It's not multi-cloud. Multi-cloud was- that was debunked on this session today. I thought that was clear. >> Yeah. Yeah, I mean I think- >> It's not about multi-cloud. It's about operationally seamless operations across environments, public cloud to on-premise, basically. >> I think we got consensus across the board that multi-cloud, you know, is a symptom Chuck Whitten's thing of multi-cloud by default versus multi- multi-cloud has not been a strategy, Kit Colbert said, up until the last couple of years. Yeah, because people said, "oh we got all these multiple clouds, what do we do with it?" and we got this mess that we have to solve. Whereas, I think Supercloud is something that is a strategy and then the other nuance that I keep bringing up is it's industries that are- as part of their digital transformation, are building clouds. Now, whether or not they become superclouds, I'm not convinced. I mean, what Goldman Sachs is doing, you know, with AWS, what Walmart's doing with Azure connecting their on-prem tools to those public clouds, you know, is that a supercloud? I mean, we're going to have to go back and really look at that definition. Or is it just kind of a SAS that spans on-prem and cloud. So, as I said, the further you go up the stack, the business case seems to wane a little bit but there's no question in my mind that from an infrastructure standpoint, to your point about operations, there's a real requirement for super- what we call Supercloud. >> Well, we're going to keep the conversation going, Dave. I want to put a shout out to our founding supporters of this initiative. Again, we put this together really fast kind of like a pilot series, an inaugural event. We want to have a face-to-face event as an industry event. Want to thank the founding supporters. These are the people who donated their time, their resource to contribute content, ideas and some cash, not everyone has committed some financial contribution but we want to recognize the names here. VMware, Intuit, Red Hat, Snowflake, Aisera, Alteryx, Confluent, Couchbase, Nutanix, Rafay Systems, Skyhigh Security, Aviatrix, Zscaler, Platform9, HashiCorp, F5 and all the media partners. Without their support, this wouldn't have happened. And there are more people that wanted to weigh in. There was more demand than we could pull off. We'll certainly continue the Supercloud conversation series here on "theCUBE" and we'll add more people in. And now, after this session, the Ecosystem Speaks session, we're going to run all the videos of the big name companies. We have the Nutanix CEOs weighing in, Aviatrix to name a few. >> Yeah. Let me, let me chime in, I mean you got Couchbase talking about Edge, Platform 9's going to be on, you know, everybody, you know Insig was poopoo-ing Oracle, but you know, Oracle and Azure, what they did, two technical guys, developers are coming on, we dig into what they did. Howie Xu from Zscaler, Paula Hansen is going to talk about going to market in the multi-cloud world. You mentioned Rajiv, the CEO of Nutanix, Ramesh is going to talk about multi-cloud infrastructure. So that's going to run now for, you know, quite some time here and some of the pre-record so super excited about that and I just want to thank the crew. I hope guys, I hope you have a list of credits there's too many of you to mention, but you know, awesome jobs really appreciate the work that you did in a very short amount of time. >> Well, I'm excited. I learned a lot and my takeaway was that Supercloud's a thing, there's a kind of sense that people want to talk about it and have real conversations, not BS or FUD. They want to have real substantive conversations and we're going to enable that on "theCUBE". Dave, final thoughts for you. >> Well, I mean, as I say, we put this together very quickly. It was really a phenomenal, you know, enlightening experience. I think it confirmed a lot of the concepts and the premises that we've put forth, that David Floyer helped evolve, that a lot of these analysts have helped evolve, that even Charles Fitzgerald with his antagonism helped to really sharpen our knives. So, you know, thank you Charles. And- >> I like his blog, by the I'm a reader- >> Yeah, absolutely. And it was great to be back in Palo Alto. It was my first time back since pre-COVID, so, you know, great job. >> All right. I want to thank all the crew and everyone. Thanks for watching this first, inaugural Supercloud event. We are definitely going to be doing more of these. So stay tuned, maybe face-to-face in person. I'm John Furrier with Dave Vellante now for the Ecosystem chiming in, and they're going to speak and share their thoughts here with "theCUBE" our first live stage performance event in our studio. Thanks for watching. (gentle upbeat music)