(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)

>>data by its very nature is distributed and siloed. But most data architectures today are highly centralized. Organizations are increasingly challenged to organize and manage data and turn that data into insights this idea of a single monolithic platform for data, it's giving way to new thinking. We're a decentralized approach with open cloud native principles and Federated governance will become an underpinning underpinning of digital transformations. Hi everybody, this is Day Volonte. Welcome back to HP discover 2021 the virtual version. You're watching the cubes continuous coverage of the event and we're here with Matt Mako is the field C T O for Israel software at H P E. And we're gonna talk about HP software strategy and esmeralda and specifically how to take a I analytics to scale and ensure the productivity of data teams. Matt, welcome to the cube. Good to see you. >>Good to see you again. Dave thanks for having me today. >>You're welcome. So talk a little bit about your role as CTO. Where do you spend your time? >>Yeah. So I spend about half of my time talking to customers and partners about where they are on their digital transformation journeys and where they struggle with this sort of last phase where we start talking about bringing those cloud principles and practices into the data world. How do I take those data warehouses, those data lakes, those distributed data systems into the enterprise and deploy them in a cloud like manner. And then the other half of my time is working with our product teams to feed that information back so that we can continually innovate to the next generation of our software platform. >>So when I remember I've been following HP and HP for a long, long time, the cube is documented. We go back to sort of when the company was breaking in two parts and at the time a lot of people were saying, oh HP is getting rid of the software business to get out of software. I said no, no, no hold on, they're really focusing and and the whole focus around hybrid cloud and and now as a service and so you're really retooling that business and sharpen your focus. So so tell us more about asthma, it's cool name. But what exactly is as moral software, >>I get this question all the time. So what is Israel? Israel is a software platform for modern data and analytics workloads using open source software components. And we came from some inorganic growth. We acquired a company called citing that brought us a zero trust approach to doing security with containers. We bought blue data who came to us with an orchestrator before kubernetes even existed in mainstream. They were orchestrating workloads using containers for some of these more difficult workloads, clustered applications, distributed applications like Hadoop. And then finally we acquired Map are which gave us this scale out, distributed file system and additional analytical capabilities. And so what we've done is we've taken those components and we've also gone out into the marketplace to see what open source projects exist, to allow us to bring those club principles and practices to these types of workloads so that we can take things like Hadoop and spark and Presto and deploy and orchestrate them using open source kubernetes, leveraging Gpu s while providing that zero trust approaches security. That's what Israel is all about. Is taking those cloud practices and principles but without locking you in again using those open source components where they exist and then committing and contributing back to the open source community where those projects don't exist. >>You know, it's interesting. Thank you for that history. And when I go back, I always been there since the early days of big data and Hadoop and so forth. The map are always had the best product. But but they can't get back then. It was like Kumbaya open source and they had this kind of proprietary system, but it worked and that's why it was the best product. And so at the same time they participated in open source projects because everybody that that's where the innovation is going. So you're making that really hard to use stuff easier to use with kubernetes orchestration. And then obviously I'm presuming with the open source chops, sort of leaning into the big trends that you're seeing in the marketplace. So my question is, what are those big trends that you're seeing when you speak to technology executives, which is a big part of what you do? >>Yeah. So the trends I think are a couple of fold and it's funny about Duke, I think the final nails in the coffin have been hammered in with the Hadoop space now. And so that that leading trend of of where organizations are going. We're seeing organizations wanting to go cloud first, but they really struggle with these data intensive workloads. Do I have to store my data in every cloud? Am I going to pay egress in every cloud? Well, what if my data scientists are most comfortable in AWS? But my data analysts are more comfortable in Azure. How do I provide that multi cloud experience for these data workloads? That's the number one question I get asked. And that's the probably the biggest struggle for these Chief Data Officers. Chief Digital Officer XYZ. How do I allow that innovation but maintaining control over my data compliance especially, we talk international standards like G. D. P. R. To restrict access to data, the ability to be forgotten in these multinational organizations. How do I sort of square all of those components and then how do I do that in a way that just doesn't lock me into another appliance or software vendors stack? I want to be able to work within the confines of the ecosystem. Use the tools that are out there but allow my organization to innovate in a very structured, compliant way. >>I mean I love this conversation. And just to me you hit on the key word which is organization. I want to I want to talk about what some of the barriers are. And again, you heard my wrap up front. I I really do think that we've created not only from a technology standpoint and yes, the tooling is important, but so is the organization. And as you said, you know, an analyst might want to work in one environment, a data scientist might want to work in another environment. The data may be very distributed. They maybe you might have situations where they're supporting the line of business. The line of business is trying to build new products. And if I have to go through this, hi this monolithic centralized organization, that's a barrier uh for me. And so we're seeing that change that kind of alluded to it upfront. But what do you see as the big, you know, barriers that are blocking this vision from becoming a reality? >>It very much is organization dave it's the technology is actually no longer the inhibitor here. We have enough technology, enough choices out there. That technology is no longer the issue. It's the organization's willingness to embrace some of those technologies and put just the right level of control around accessing that data because if you don't allow your data scientists and data analysts to innovate, they're going to do one of two things, they're either going to leave and then you have a huge problem keeping up with your competitors or they're gonna do it anyway, and they're gonna do it in a way that probably doesn't comply with the organizational standards. So the more progressive enterprises that I speak with have realized that they need to allow these various analytical users to choose the tools, they want to self provision those as they need to and get access to data in a secure and compliant way. And that means we need to bring the cloud to generally where the data is because it's a heck of a lot easier than trying to bring the data where the cloud is while conforming to those data principles. And that's, that's Hve strategy, you've heard it from our CEO for years now, everything needs to be delivered as a service. It's essential software that enables that capability, such as self service and secure data provisioning, etcetera. >>Again, I love this conversation because if you go back to the early days of the Duke, that was what was profound about. Do bring bring five megabytes of code, do a petabyte of data and it didn't happen. We shoved it all into a data lake and it became a data swamp. And so it's okay, you know, and that's okay. It's a one dato maybe maybe in data is is like data warehouses, data hubs data lake. So maybe this is now a four dot Oh, but we're getting there. Uh, so an open but open source one thing's for sure. It continues to gain momentum. It's where the innovation is. I wonder if you could comment on your thoughts on the role that open source software plays for large enterprises. Maybe some of the hurdles that are there, whether they're legal or licensing or or or just fears. How important is open source software today? >>I think the cloud native development, you know, following the 12 factor applications microservices based, pave the way over the last decade to make using open source technology tools and libraries mainstream, we have to tip our hats to red hat right for allowing organizations to embrace something. So core is an operating system within the enterprise. But what everyone realizes that its support, that's what has to come with that. So we can allow our data scientists to use open source libraries, packages and notebooks. But are we going to allow those to run in production? And so if the answer is no, then that if we can't get support, we're not going to allow that. So where HP es Merrill is taking the lead here is again embracing those open source capabilities, but if we deploy it, we're going to support it or we're going to work with the organization that has the committees to support it. You call HPD the same phone number you've been calling for years for tier 1 24 by seven support and we will support your kubernetes, your spark your presto your Hadoop ecosystem of components were that throat to choke and we'll provide all the way up to break fix support for some of these components and packages giving these large enterprises the confidence to move forward with open source but knowing that they have a trusted partner in which to do so >>and that's why we've seen such success with, say, for instance, managed services in the cloud or versus throwing out all the animals in the zoo and say, okay, figure it out yourself. But of course what we saw, which was kind of ironic was we, we saw people finally said, hey, we can do this in the cloud more easily. So that's where you're seeing a lot of data. A land. However, the definition of cloud or the notion of cloud is changing no longer. Is it just this remote set of services somewhere out there? In the cloud? Some data center somewhere. No, it's, it's moving on. Prem on prem is creating hybrid connections you're seeing, you know, co location facility is very proximate to the cloud. We're talking now about the edge, the near edge and the far edge deeply embedded, you know? And so that whole notion of cloud is, is changing. But I want to ask you, there's still a big push to cloud, everybody is a cloud first mantra. How do you see HP competing in this new landscape? >>I I think collaborating is probably a better word, although you could certainly argue if we're just leasing or renting hardware than it would be competition. But I think again, the workload is going to flow to where the data exists. So if the data is being generated at the edge and being pumped into the cloud, then cloud is prod, that's the production system. If the data is generated, the on system on premises systems, then that's where it's going to be executed, that's production. And so HBs approach is very much coexist, coexist model of if you need to do deaf tests in the cloud and bring it back on premises, fine or vice versa. The key here is not locking our customers and our prospective clients into any sort of proprietary stack, as we were talking about earlier, giving people the flexibility to move those workloads to where the data exists. That is going to allow us to continue to get share of wallet. Mindshare, continue to deploy those workloads and yes, there's going to be competition that comes along. Do you run this on a G C P or do you run it on a green lake on premises? Sure. We'll have those conversations. But again, if we're using open source software as the foundation for that, then actually where you run it is less relevant. >>So a lot of, there's a lot of choices out there when it comes to containers generally and kubernetes specifically, uh, you may have answered this, you get zero trust component, you've got the orchestrator, you've got the, the scale out, you know, peace. But I'm interested in hearing in your words why an enterprise would or should consider s morale instead of alternatives to kubernetes solutions? >>It's a fair question. And it comes up in almost every conversation. We already do kubernetes, so we have a kubernetes standard and that's largely true. And most of the enterprises I speak to their using one of the many on premises distributions of the cloud distributions and they're all fine. They're all fine for what they were built for. Israel was generally built for something a little different. Yes, everybody can run microservices based applications, devoPS based workloads, but where is Meryl is different is for those data intensive and clustered applications. Those sort of applications require a certain degree of network awareness, persistent storage etcetera, which requires either a significant amount of intelligence. Either you have to write in go lang or you have to write your own operators or Israel can be that easy button. We deploy those state full applications because we bring a persistent storage later that came from that bar we're really good at deploying those stable clustered applications and in fact we've open sourced that as a project cube director that came from Blue data and we're really good at securing these using spiffy inspire to ensure that there is that zero trust approach that came from side tail and we've wrapped all of that in kubernetes so now you can take the most difficult, gnarly, complex data intensive applications in your enterprise and deploy them using open source and if that means we have to coexist with an existing kubernetes distribution, that's fine. That's actually the most common scenario that I walk into is I start asking about what about these other applications you haven't done yet? The answer is usually we haven't gotten to him yet or we're thinking about it and that's when we talk about the capabilities of s role and I usually get the response, oh, a we didn't know you existed and be, well, let's talk about how exactly you do that. So again, it's more of a coexist model rather than a compete with model. Dave >>Well, that makes sense. I mean, I think again, a lot of people think, oh yeah, Kubernetes, no big deal, it's everywhere. But you're talking about a solution, I'm kind of taking a platform approach with capabilities, you've got to protect the data. A lot of times these microservices aren't some micro uh and things are happening really fast, You've got to be secure, you've got to be protected. And like you said, you've got a single phone number, you know, people say one throat to choke, Somebody said the other day said no, no single hand to shake, it's more of a partnership and I think that's a proposed for HPV met with your >>hair better. >>So you know, thinking about this whole, you know, we've gone through the pre big data days and the big data was all, you know, the hot buzz where people don't maybe necessarily use that term anymore, although the data is bigger and getting bigger, which is kind of ironic. Um where do you see this whole space going? We've talked about that sort of trends are breaking down the silos, decentralization. Maybe these hyper specialized roles that we've created maybe getting more embedded are lined with the line of business. How do you see it feels like the last, the next 10 years are going to be different than the last 10 years. How do you see it matt? >>I completely agree. I think we are entering this next era and I don't know if it's well defined, I don't know if I would go out on an edge to say exactly what the trend is going to be. But as you said earlier, data lakes really turned into data swamps. We ended up with lots of them in the enterprise and enterprises had to allow that to happen. They had to let each business unit or each group of users collect the data that they needed and I. T. Sort of had to deal with that down the road. And so I think the more progressive organizations are leading the way they are again taking those lessons from cloud and application developments, microservices and they're allowing a freedom of choice there, allowing data to move to where those applications are. And I think this decentralized approach is really going to be king. And you're gonna see traditional software packages, you're gonna see open source, you're going to see a mix of those. But what I think we'll probably be common throughout all of that is there's going to be this sense of automation, this sense that we can't just build an algorithm once released and then wish it luck that we've got to treat these these analytics and these these data systems as living things that there's life cycles that we have to support, which means we need to have devops for our data science. We need a ci cd for our data analytics. We need to provide engineering at scale like we do for software engineering. That's going to require automation and an organizational thinking process to allow that to actually occur. And so I think all of those things that sort of people process product, but it's all three of those things are going to have to come into play. But stealing those best ideas from cloud and application development, I think we're going to end up with probably something new over the next decade or so >>again, I'm loving this conversation so I'm gonna stick with it for a second. I it's hard to predict, but I'll some takeaways that I have matt from our conversation. I wonder if you could, you could comment. I think, you know, the future is more open source. You mentioned automation deV's are going to be key. I think governance as code, security designed in at the point of code creation is going to be critical. It's not no longer to be a bolt on and I don't think we're gonna throw away the data warehouse or the data hubs or the data lakes. I think they become a node. I like this idea and you know, jim octagon. But she has this idea of a global data mesh where these tools lakes, whatever their their node on the mesh, they're discoverable. They're shareable. They're they're governed uh in a way and that really I think the mistake a lot of people made early on in the big data movement, Oh we have data, we have to monetize our data as opposed to thinking about what products that I can I build that are based on data that then I can, you know, can lead to monetization. And I think and I think the other thing I would say is the business has gotten way too technical. All right. It's an alienated a lot of the business lines and I think we're seeing that change. Um and I think, you know, things like Edinburgh that simplify that are critical. So I'll give you the final thoughts based on my rent. >>I know you're ready to spot on. Dave. I think we we were in agreement about a lot of things. Governance is absolutely key. If you don't know where your data is, what it's used for and can apply policies to it, it doesn't matter what technology throw at it, you're going to end up in the same state that you're essentially in today with lots of swamps. Uh I did like that concept of of a note or a data mesh. It kind of goes back to the similar thing with a service smashed or a set of a P I is that you can use. I think we're going to have something similar with data that the trick is always how heavy is it? How easy is it to move about? And so I think there's always gonna be that latency issue. Maybe not within the data center, but across the land, latency is still going to be key, which means we need to have really good processes to be able to move data around. As you said, government determine who has access to what, when and under what conditions and then allow it to be free, allow people to bring their choice of tools, provision them how they need to while providing that audit compliance and control. And then again, as as you need to provision data across those notes for those use cases do so in a well measured and govern way. I think that's sort of where things are going. But we keep using that term governance. I think that's so key. And there's nothing better than using open source software because that provides traceability, the audit ability and this frankly openness that allows you to say, I don't like where this project is going. I want to go in a different direction and it gives those enterprises that control over these platforms that they've never had before. >>Matt. Thanks so much for the discussion. I really enjoyed it. Awesome perspectives. >>Well, thank you for having me. Dave are excellent conversation as always. Uh, thanks for having me again. >>All right. You're very welcome. And thank you for watching everybody. This is the cubes continuous coverage of HP discover 2021 of course, the virtual version next year. We're gonna be back live. My name is Dave a lot. Keep it right there. >>Yeah.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Okay, >>we're now going to explore the vision of the future of cloud computing From the perspective of one of the leaders in the field, J G >>Share >>a pure off is the vice president of As Your Data ai and Edge at Microsoft G. Welcome to the Cuban cloud. Thanks so much for participating. >>Well, thank you, Dave, and it's a real pleasure to be here with you. And I just wanna welcome the audience as well. >>Well, jg judging from your title, we have a lot of ground to cover, and our audience is definitely interested in all the topics that are implied there. So let's get right into it. You know, we've said many times in the Cube that the new innovation cocktail comprises machine intelligence or a I applied to troves of data. With the scale of the cloud. It's it's no longer, you know, we're driven by Moore's law. It's really those three factors, and those ingredients are gonna power the next wave of value creation and the economy. So, first, do you buy into that premise? >>Yes, absolutely. we do buy into it. And I think, you know, one of the reasons why we put Data Analytics and Ai together is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from a I as being on that continues off, having started off with really things like analytics and proceeding toe. You know, machine learning and the use of data. Interesting breaks. Yes. >>I'd like to get some more thoughts around a data and how you see the future data and the role of cloud and maybe how >>Microsoft, you >>know, strategy fits in there. I mean, you, your portfolio, you got you got sequel server, Azure, Azure sequel. You got arc, which is kinda azure everywhere for people that aren't familiar with that. You've got synapse. Which course that's all the integration a data warehouse, and get things ready for B I and consumption by the business and and the whole data pipeline and a lot of other services as your data bricks you got You got cosmos in their, uh, Blockchain. You've got open source services like Post Dress and my sequel. So lots of choices there. And I'm wondering, you know, how do you think about the future of Of of Cloud data platforms? It looks like your strategies, right tool for the right job? Is that fair? >>It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today is that customer was the Sikh really a comprehensive proposition? And when I say a comprehensive proposition, it is sometimes not just about saying that. Hey, listen way No, you're a sequel server company. We absolutely trust that you have the best Azure sequel database in the cloud, but tell us more. We've got data that's sitting in her group systems. We've got data that's sitting in Post Press in things like mongo DB, right? So that open source proposition today and data and data management and database management has become front and center, so are really sort of push. There is when it comes to migration management, modernization of data to present the broadest possible choice to our customers so we can meet them where they are. However, when it comes to analytics. One of the things they asked for is give us a lot more convergence use. You know it, really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like synapse Fitzer, where in just land any kind of data in the leg and then use any compute engine on top of it to drive insights from it. So, fundamentally, you know, it is that flexibility that we really sort of focus on to meet our customers where they are and really not pushing our dogma and our beliefs on it. But to meet our customers according to the way they have deployed stuff like this. >>So that's great. I want to stick on this for a minute because, you know, I know when when I have guests on like yourself, do you never want to talk about you know, the competition? But that's all we ever talk about. That's all your customers ever talk about, because because the counter to that right tool for the right job and that I would say, is really kind of Amazon's approach is is that you got the single unified data platform, the mega database that does it all. And that's kind of Oracle's approach. It sounds like you wanna have your cake and eat it, too, so you you got the right tool for the right job approach. But you've got an integration layer that allows you to have that converge database. I wonder if you could add color to that and you confirm or deny what I just said. >>No, that's a That's a very fair observation, but I I say there's a nuance in what I sort of describe when it comes to data management. When it comes to APS, we have them customers with the broadest choice. Even in that, even in that perspective, we also offer convergence. So, case in point, when you think about Cosmos TV under that one sort of service, you get multiple engines, but with the same properties, right global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native AB toe, adopt cosmos Davey and adopted in a way that it's and choose an engine that is most flexible. Tow them, however you know when it comes to say, you know, writing a sequel server, for example from organizing it you know you want. Sometimes you just want to lift and shift it into things. Like I asked In other cases, you want to completely rewrite it, so you need to have the flexibility of choice there that is presented by a legacy off What's its on premises? When it moved into things like analytics, we absolutely believe in convergence, right? So we don't believe that look, you need to have a relation of data warehouse that is separate from a loop system that is separate from, say, a B I system. That is just, you know, it's a bolt on for us. We love the proposition off, really building things that are so integrated that once you land data, once you prep it inside the lake, you can use it for analytics. You can use it for being. You can use it for machine learning. So I think you know, are sort of differentiated. Approach speaks for itself there. Well, >>that's that's interesting, because essentially, again, you're not saying it's an either or, and you're seeing a lot of that in the marketplace. You got some companies say no, it's the Data Lake and others saying No, no put in the data warehouse and that causes confusion and complexity around the data pipeline and a lot of calls. And I'd love to get your thoughts on this. Ah, lot of customers struggled to get value out of data and and specifically data product builders of frustrated that it takes too long to go from. You know, this idea of Hey, I have an idea for a data service and it could drive monetization, but to get there, you gotta go through this complex data lifecycle on pipeline and beg people to add new data sources. And do you do you feel like we have to rethink the way that we approach data architectures? >>Look, I think we do in the cloud, and I think what's happening today and I think the place where I see the most amount of rethink the most amount of push from our customers to really rethink is the area of analytics in a I. It's almost as if what worked in the past will not work going forward. Right? So when you think about analytics on in the Enterprise today, you have relational systems, you have produced systems. You've got data marts. You've got data warehouses. You've got enterprise data warehouses. You know, those large honking databases that you use, uh, to close your books with right? But when you start to modernize it, what deep you are saying is that we don't want to simply take all of that complexity that we've built over say, you know, 34 decades and simply migrated on mass exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see shape or form inside the lake. Once you landed inside the lake, you can essentially use a synapse studio toe. Prep it in the way that you like, use any compute engine of your choice and and operate on this data in any way that you see fit. So, case in point, if you want to hydrate relation all data warehouse, you can do so if you want to do ad hoc analytics using something like spark. You can do so if you want to invoke power. Bi I on that data or b i on that data you can do so if you want to bring in a machine learning model on this breath data you can do so, so inherently. So when customers buy into this proposition, what it solves for them and what it gives them is complete simplicity, right? One way to land the data, multiple ways to use it. And it's all eso. >>Should we think of synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way toe? Think about it. >>Yeah, you can think of it that way. It abstracts away, Dave a couple of things. It takes away the type of data, you know, sort of the complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data and fundamentally puts it in a place where it can be now consumed by any sort of entity inside the actual proposition. And by that token, even data breaks. You know, you can, in fact, use data breaks in in sort off an integrated way with a synapse, Right, >>Well, so that leads me to this notion of and then wonder if you buy into it s Oh, my inference is that a data warehouse or a data lake >>could >>just be a node in inside of a global data >>mesh on. >>Then it's synapses sort of managing, uh, that technology on top. Do you buy into that that global data mesh concept >>we do. And we actually do see our customers using synapse and the value proposition that it brings together in that way. Now it's not where they start. Often times when a customer comes and says, Look, I've got an enterprise data warehouse, I want to migrate it or I have a group system. I want to migrate it. But from there, the evolution is absolutely interesting to see. I give you an example. You know, one of the customers that we're very proud off his FedEx And what FedEx is doing is it's completely reimagining its's logistics system that basically the system that delivers What is it? The three million packages a day on in doing so in this covert times, with the view of basically delivering our covert vaccines. One of the ways they're doing it is basically using synapse. Synapse is essentially that analytic hub where they can get complete view into their logistic processes. Way things are moving, understand things like delays and really put all that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, you know, is one of my favorite, uh, we see once customers buy into it, they essentially can do other things with it. So an example of this is, uh is really my favorite story is Peace Parks Initiative. It is the premier Air White Rhino Conservancy in the world. They essentially are using data that has landed in azure images in particular. So, basically, you know, use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue so that this part with about 200 rangers can scramble surgically versus having to read range across the last area that they cover. So What do you see here is you know, the importance is really getting your data in order. Landed consistently. Whatever the kind of data ideas build the right pipelines and then the possibilities of transformation are just endless. >>Yeah, that's very nice how you worked in some of the customer examples. I appreciate that. I wanna ask you, though, that that some people might say that putting in that layer while it clearly adds simplification and e think a great thing that they're begins over time to be be a gap, if you will, between the ability of that layer to integrate all the primitives and all the peace parts on that, that you lose some of that fine grain control and it slows you down. What would you say to that? >>Look, I think that's what we excel at, and that's what we completely sort of buy into on. It's our job to basically provide that level off integration that granularity in the way that so it's an art, absolutely admit it's an art. There are areas where people create simplicity and not a lot of you know, sort of knobs and dials and things like that. But there are areas where customers want flexibility, right? So I think just to give you an example of both of them in landing the data inconsistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. Just 100 to do it. When it comes to computing and reducing this data analyzing this data, they want flexibility. This is one of the reasons why we say, Hey, listen, you want to use data breaks? If you're you're buying into that proposition and you're absolutely happy with them, you can plug plug it into it. You want to use B I and no, essentially do a small data mart. You can use B I If you say that. Look, I've landed in the lake. I really only want to use em melt, bringing your animal models and party on. So that's where the flexibility comes in. So that's sort of really sort of think about it. Well, >>I like the strategy because, you know, my one of our guest, Jim Octagon, e E. I think one of the foremost thinkers on this notion of off the data mesh and her premises that that that data builders, data product and service builders air frustrated because the the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system, you could get products to market much, much, much faster. So but that seems to be your philosophy. But I'm gonna jump ahead to do my ecosystem question. You've mentioned data breaks a couple of times. There's another partner that you have, which is snowflake. They're kind of trying to build out their own, uh, data cloud, if you will, on global mesh in and the one hand, their partner. On the other hand, there are competitors. How do you sort of balance and square that circle? >>Look, when I see snowflake, I actually see a partner. You know that when we essentially you know, we are. When you think about as you know, this is where I sort of step back and look at Azure as a whole and in azure as a whole. Companies like snowflakes are vital in our ecosystem, right? I mean, there are places we compete, but you know, effectively by helping them build the best snowflake service on Asia. We essentially are able toe, you know, differentiate and offer a differentiated value proposition compared to, say, a Google or on AWS. In fact, that's being our approach with data breaks as well, where you know they are effectively on multiple club, and our opportunity with data breaks is toe essentially integrate them in a way where we offer the best experience. The best integrations on Azure Barna That's always been a focus. >>That's hard to argue with. Strategy. Our data with our data partner eat er, shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I wanna come back thio ai a little bit. It's obviously one of the fastest growing areas in our in our survey data. As I said, clearly, Microsoft is a leader in this space. What's your what's your vision of the future of machine intelligence and how Microsoft will will participate in that opportunity? >>Yeah, so fundamentally, you know, we've built on decades of research around, you know, around, you know, essentially, you know, vision, speech and language that's being the three core building blocks and for the for a really focused period of time we focused on essentially ensuring human parody. So if you ever wondered what the keys to the kingdom are it, czar, it's the most we built in ensuring that the research posture that we've taken there, what we then done is essentially a couple of things we focused on, essentially looking at the spectrum. That is a I both from saying that, Hollis and you know it's gotta work for data. Analysts were looking toe basically use machine learning techniques, toe developers who are essentially, you know, coding and building a machine learning models from scratch. So for that select proposition manifesto us, as you know, really a. I focused on all skill levels. The other court thing we've done is that we've also said, Look, it will only work as long as people trust their data and they can trust their AI models. So there's a tremendous body of work and research we do in things like responsibility. So if you ask me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum off a I, and you can sort of come together for any skill level, and we keep that responsibly. I proposition. Absolutely strong now against that canvas, Dave. I'll also tell you that you know, as edge devices get way more capable, right where they can input on the edge, see a camera or a mike or something like that, you will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality. But the core really is all skill levels and that responsible denia. >>Yeah, So that that brings me to this notion of wanna bring an edge and and hybrid cloud Understand how you're thinking about hybrid cloud multi cloud. Obviously one of your competitors, Amazon won't even say the word multi cloud you guys have, Ah, you know, different approach there. But what's the strategy with regard? Toe, toe hybrid. You know, Do you see the cloud you bringing azure to the edge? Maybe you could talk about that and talk about how you're different from the competition. >>Yeah, I think in the edge from Annette, you know, I live in I'll be the first one to say that the word nge itself is conflated. Okay, It's, uh but I will tell you, just focusing on hybrid. This is one of the places where you know I would say the 2020 if I would have looked back from a corporate perspective. In particular, it has Bean the most informative because we absolutely saw customers digitizing moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became really from a cloud computing perspective and an example of this is we understood it's not all or nothing. So sometimes customers want azure consistency in their data centers. This is where things like Azure stack comes in. Sometimes they basically come to us and say, We want the flexibility of adopting flexible pattern, you know, platforms like, say, containers orchestra, Cuban Pettis, so that we can essentially deployed wherever you want. And so when we design things like art, it was built for that flexibility in mind. So here is the beauty of what's something like our can do for you. If you have a kubernetes endpoint anywhere we can deploy and as your service onto it, that is the promise, which means if for some reason, the customer says that. Hey, I've got this kubernetes endpoint in AWS and I love as your sequel. You will be able to run as your sequel inside AWS. There's nothing that stops you from doing it so inherently you remember. Our first principle is always to meet our customers where they are. So from that perspective, multi cloud is here to stay. You know, we're never going to be the people that says, I'm sorry, we will never see a But it is a reality for our customers. >>So I wonder if we could close. Thank you for that by looking, looking back and then and then ahead. And I wanna e wanna put forth. Maybe it's, Ah criticism, but maybe not. Maybe it's an art of Microsoft, but But first you know, you get Microsoft an incredible job of transitioning. It's business as your nominee president Azzawi said. Our data shows that so two part question First, Microsoft got there by investing in the cloud, really changing its mind set, I think, in leveraging its huge software state and customer base to put Azure at the center of its strategy, and many have said me included that you got there by creating products that air Good enough. You know, we do a 1.0, it's not that great. And the two Dato, and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expanding market. >>How >>do you respond to that? Is that is that a fair comment? Ume or than good enough? I wonder if you could share your >>thoughts, gave you? You hurt my feelings with that question. I don't hate me, g getting >>it out there. >>So there was. First of all, thank you for asking me that. You know, I am absolutely the biggest cheerleader. You'll find a Microsoft. I absolutely believe you know that I represent the work off almost 9000 engineers and we wake up every day worrying about our customer and worrying about the customer condition and toe. Absolutely. Make sure we deliver the best in the first time that we do. So when you take the platter off products we've delivered in nausea, be it as your sequel, be it as your cosmos TV synapse as your data breaks, which we did in partnership with data breaks, a za machine learning and recently when we prevail, we sort off, you know, sort of offered the world's first comprehensive data government solution in azure purview. I would humbly submit to you that we're leading the way and we're essentially showing how the future off data ai and the actual work in the cloud. >>I'd be disappointed if you if you had If you didn't, if you capitulated in any way J g So so thank you for that. And the kind of last question is, is looking forward and how you're thinking about the future of cloud last decade. A lot about your cloud migration simplifying infrastructure management, deployment SAS if eyeing my enterprise, lot of simplification and cost savings. And, of course, the redeployment of resource is toward digital transformation. Other other other valuable activities. How >>do >>you think this coming decade will will be defined? Will it be sort of more of the same? Or is there Is there something else out there? >>I think I think that the coming decade will be one where customers start one law outside value out of this. You know what happened in the last decade when people leave the foundation and people essentially looked at the world and said, Look, we've got to make the move, you know, the largely hybrid, but we're going to start making steps to basically digitize and modernize our platforms. I would tell you that with the amount of data that people are moving to the cloud just as an example, you're going to see use of analytics ai for business outcomes explode. You're also going to see a huge sort of focus on things like governance. You know, people need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all other privacy and compliance regulations out there. Essentially, they're complying this posture. So I think the unlocking of outcomes versus simply Hey, I've saved money Second, really putting this comprehensive sort off, you know, governance, regime in place. And then, finally, security and trust. It's going to be more paramount than ever before. Yeah, >>nobody's gonna use the data if they don't trust it. I'm glad you brought up your security. It's It's a topic that hits number one on the CEO list. J G. Great conversation. Obviously the strategy is working, and thanks so much for participating in Cuba on cloud. >>Thank you. Thank you, David. I appreciate it and thank you to. Everybody was tuning in today. >>All right? And keep it right there. I'll be back with our next guest right after this short break.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. >>We continue >>with Cuban Cloud. We're here with Milan Thompson Bukovec, who's the vice president? Block and object storage at A W s, which comprise comprises elastic block storage, AWS s three and Amazon Glacier. Milan. Great to see you again. Thanks so much for coming on the program. >>Nice to be here. Thanks for having me, David. >>You're very welcome it So here we are. We're unpacking the future of cloud. And we'd love to get your perspectives on how customers should think about the future of infrastructure, things like applying machine intelligence to their data. But just to set the stage when we look back at the history of storage in the Cloud is obviously started with us three. And then a couple years later was introduced CBS for block storage. And those are the most well known services in the portfolio. But there's there's Mawr, this cold storage and new capabilities that you announced recently. It reinvent around, you know, super duper block storage and in tearing is another example. But it looks like AWS is really starting to accelerate and pick up the pace of customer >>options in >>storage. So my first question is, how should we think about this expanding portfolio? >>Well, I think you have to go all the way back to what customers air trying to do with their data. Dave, The path to innovation is paved by data. If you don't have data, you don't have machine learning. You don't have the next generation of analytics applications. That helps you chart a path forward into a world that seems to be changing every week. And so in orderto have that insight in orderto have that predictive forecasting that every company needs, regardless of what industry that you're in today. It all starts from data, and I think the key shift that I've seen is how customers are thinking about that data about being instantly usable, whereas in the past it might have been a backup. Now it's part of a data lake, and if you could bring that data into a data lake, you can have not just analytics or machine learning or auditing applications. It's really what does your application do for your business, and how can it take advantage of that vast amount of shared data set in your business. Awesome. >>So thank you. So I wanna I wanna make sure we're hitting on the big trends that you're seeing in the market. That kind of informing your strategy around the portfolio and what you're seeing with customers Instant usability. You you bring in machine learning into the equation. I think, um, people have really started to understand the benefits of of of cloud storage as a service on the pay paid by the drink and that whole whole model, obviously co vid has accelerated that cloud migration has accelerated. Anything else we're missing there. What are the other big trends that you see if any? >>Well, Dave, you did a good job of capturing a lot of the drivers. The one thing I would say that just sits underneath All of it is the massive growth of digital data year over year I. D. C. Says digital data is growing at a rate of 40% year over year, and that has been true for a while. And it's not going to stop. It's gonna keep on growing because the sources of that data acquisition keeps on expanding and whether it's coyote devices whether it is content created by users. That data is going to grow, and everything you're talking about depends on the ability to not just capture it and store it. But as you say, use it well, >>you know, and we talk about data growth a lot, and sometimes it becomes bromide. But I think the interesting thing that I've observed over the last a couple of decades really is that the growth is nonlinear on. It's really the curve is starting. Thio used to shape exponentially. You guys always talk about that flywheel. Effect it. It's really hard to believe, You know, people say trees don't grow to the moon. It seems like data does. >>It does. And what's interesting about working in the world of AWS storage Dave is that it's counterintuitive. But our goal without data growth is to make it cost effective. And so year over year, how could we make it cheaper and cheaper? Just have customers store more and more data so they can use it. But it's also to think about the definition of usage. And what kind of data is that? Eyes being tapped by businesses for their insights and make that easier than it's ever been before. Let me ask >>you a follow up question on that my life could I get asked this a lot? Or guy here comments a lot that yes, A W s continuously and rigorously reduces pricing. But it's just >>kind of >>following the natural curve of Moore's law or, you know, whatever. How >>do you >>respond to that? And there are other factors involved. Obviously, labor is another cost reducing factor. But what's the trend line say, >>Well, cost efficiencies in our DNA, Dave. We come to work every day and aws across all of our services, and we ask ourselves, How can we lower our costs and be able to pass that along to customers? As you say, there are many different aspects to cost. There's the cost of the storage itself is the cost of the data center. And that's really what we've seen impact a lot of customers that were slower or just getting started with removed. The cloud is they entered 2020 and then they found out exactly how expensive that data center was to maintain because they had to put in safety equipment and they had to do all the things that you have to do in a pandemic in a data center. And so sometimes that cost is a little bit hidden or won't show up until you really don't need to have it land. But the cost of managing that explosive growth of data is very riel. And when we're thinking about cost, we're thinking about cost in terms of how can I lower it on a per gigabyte per month basis? But we're also building into the product itself adaptive discounts like we have a storage class in S three that's called intelligent hearing. And in intelligence hearing, we have built in monitoring where, if particular objects aren't frequently accessed in a given month, ah, customer will automatically get a discounted price for that storage or a customer Can you know, as of late last year, say that they wanna automatically move storage in the storage class that has been stored, for example, longer than 100 and 80 days and saves 95% by moving it into archive storage, deep archives storage? And so it's not just, you know, relentlessly going after and lowering the cost of storage. It's also building into the products these new ways where we can adaptive Lee discount storage based on what a customer's storage is actually doing >>well. And I would, I would add to our audience, is the other thing that does has done is it's really forced transparency almost the same way that Amazon has done on retail. And now my mom, When we talked last I mentioned that s three was an object store. And of course, that's technically technically correct. But your comment to me was Dave. It's more than that. And you started to talk about sage Maker and AI and bringing in machine learning. And I wonder if you could talk a little bit about the future of how storage is gonna be leveraged in the cloud that's may be different than what we've been, you know, used to in the early days of s three and how your customers should be thinking about infrastructure not as bespoke services but as a suite of capabilities and maybe some of those adjacent adjacent services that you see as most leverage a ble for customers And why? >>Well, to tell this story, dude, we're gonna have to go a little bit back in time all the way back to the 19 nineties. Or before then, when all you had waas, a set of hardware appliance vendors that sold you appliances that you put in your data center and inherently created a data silo because those hardware appliances were hardwired to your application. And so an individual application that was dealing with auditing as an example wouldn't really be able to access the storage for another application. Because you know, the architecture er of that legacy world is tied to a data silo and s tree came out launched in 2000 and six and introduced very low cost storage. That is an object. And I'll tell you, Dave, you know, over the last 10 plus years, we have seen all kinds of data come into us three, whereas before it might have been backups or it might have been images and videos. Now a pretty substantial data set is our parquet files and orc files. Thes files are there for business analytics for more real time type of processing. And that has really been the trend of the future. Is taking these different files putting them in a shared file layer, So any application today or in the future can tap into that data. And so this idea of the shared file layer is a major trend that has been taking off for the last. I would say five or six years, and I expect that to not only keep on going, but to really open up the type of services that you can then do on that shared file layer and whether that sage maker or some of the machine learning introduced by our connect service, it's bringing together the data as a starting point. And then the applications can evolve very rapidly. On top of that, I want to >>ask your opinion about big data architectures. One of our guests, Jim Octagon E. She's amazing, uh, data architect, and she's put forth this notion of a distributed global mesh, and I picked him picking up on some of the comments. Andy Jassy made it at reinvent How essentially Hey, we're bringing a W s to the edge. We see the data center is just another edge. Notes. You're seeing this massive distributed system evolving. You guys have talked about that for a while, and data by its very nature is distributed. But we've had this tendency to put into it monolithic Data Lake or a data warehouse on bits sort of antithetical to that distributed nature. So how >>do >>you see that playing out? What do you see customers in the future doing in terms of their big data architectures? And what does that mean for storage? >>It comes down to the nature of the data and again, the usage and Dave. That's where I see the biggest difference in these modern data architectures from the legacy of 20 years ago is the idea that the data need drives the data storage. So let's taken example of the type of data that you always wanna have on the edge. We have customers today that need tohave storage in the field and whether the field of scientific research or oftentimes, it's content creation in the in the film industry or if it's for military operations. There's a lot of data that needs to be captured and analyzed in the field and for us, what that means is that you know we have a suite of products called Snowball and whether it's snowball or snow cone, take your pick. That whole portfolio of AWS services is targeted at customers that need to do work with storage at the edge. And so it you know, if you think about the need for multiple applications acting on the same data set, that's when you keep it in an AWS region. And what we've done in AWS storage is we've recognized that depending on the need of usage, where you put your data and how you interactive, it may vary. But we've built a whole set of services like data transfer to help make sure that we can connect data from, for example, that new snow cone into a region automatically. And so our goal Dave, is to make sure that when customers air operating at the edge or they're operating in the region, they have the same quality of storage service, and they have easy ways to go between them. You shouldn't have to pick. You should be able to do it all. >>So in the spirit of do it all, this is sort of age old dynamic in the tech business, where you've got the friction between the the best of breed and the integrated suite, and my question is around what you're optimizing for for customers. And can you have your cake and eat it too? In other words, why A W S storage does what makes a compelling? Is it because it's kind of a best of breed storage service? Or is it because it's integrated with a W S? Would you ever sub optimize one in in order to get an advantage to the other? Or can you actually, >>you >>know, have your cake and eat it, too? >>The way that we build storage is to focus on being both the breath of capabilities on the depth of capabilities. And so where we identify ah, particular need where we think that it takes a whole new service to deliver, we'll go build that service and example for that is FTP, our AWS sftp service, which you know there's a lot of sftp usage out there and there will be for a while because of the you know, the Legacy B two b type of architectures that still live in the business world today. And so we looked at that problem. We said, How are we gonna build that in the best depth way and the best focus? And we launched a separate service for them. And so our goal is to take the individual building blocks of CBS and Glacier and s three and make the best of class and the most comprehensive in the capabilities of what we can dio and where we identify very specific need. We'll go build a service for. But, Dave, you know, as an example for that idea of both depths and breath s three storage lands is a great example of that s three storage lands is a new capability that we launched last year. And what it does is it lets you look across all your regions and all your accounts and get a summary view of all your s three storage and whether that's buckets or, you know, the most active prefixes that you have and be able to drill down from that and that is built in to the S three service and available for any customer that wants to turn it on in the AWS Management Council. >>Right? And we we saw just recently made I called it super duper block storage. But you made some, you know, improvements and really addressing the highest performance. Um, I want to ask you So we've all learned about an experience the benefits of cloud over the last several years, and especially in the last 10 months during the pandemic. But one >>of >>the challenges, and it's particularly acute with bio is, of course, Leighton see and moving data around and accessing data remotely. It's It's a challenge for customers, you know, due to speed of light, etcetera. So my question is, how was a W s thinking about all that data that still resides on premises? I think we heard that reinvent. That's still 90% of the opportunities or or the workloads. They're still on Prem that live inside a customer's data center. So how do you tap into those and help customers innovate with on Prem data, particularly from a storage >>angle? Well, we always want to provide the best of class solution for those little Leighton see workloads, and that's why we launched Block Express just late last year. It reinvent and Black expresses a new capability and preview on top of our Iot to provisioned eye ops volume type, and what's really interesting about Block Express Dave, is that the way that we're able to deliver the performance of Block Express, which is sound performance with cloud elasticity, is that we went all the way down to the network layer and we customize the hardware software. And at the network Lehrer, we built a Block Express on something called SRD, which stands for a scalable, reliable diagrams. And basically, what is letting us to do is offload all of our EBS operations for Block Express on the Nitro card on hardware. And so that type of innovation where we're able Thio, you know, take advantage of modern cop commodity, multi tenant data center networks where we're sending in this new network protocol across a large number of network paths, and that that type of innovation all the way down to that protocol level helps us innovate in a way that's hard. In fact, I would say impossible for for other sound providers to kind of really catch up and keep up. And so we feel that the amount of innovation that we have for delivering those low latency workloads in our AWS cloud storage is is unlimited, really, Because of that ability to customize software, hardware and network protocols as we go along without requiring upgrades from a customer it just gets better and the customer benefits. Now if you want to stay in your data center, that's why we built outposts. And for outpost, we have EBS and we have s three for outposts. And our goal there is that some customers will have workloads where they want to keep them resident in the data center And for those customers, we want to give them that AWS storage opportunities as well. So >>thank you for coming back to block Express. So you call it in sand in the cloud eso Is that essentially you've you've comprises a custom built, essentially storage storage network. Is that is that right? What kind of what you just described? SRD? I think you call it. >>Yeah, it's SRT is used by other AWS services as well, but it is a custom network protocol that we designed to deliver the lowest latency experience on We're taking advantage of it with Block Express >>sticking with traditional data centers for a moment, I'm interested in your thoughts on the importance of the cloud you know, pricing approach I e. The consumption model to paid by the drink. Obviously, it's one of the most attractive features But But And I ask that because we're seeing what Andy Jassy first, who is the old Guard Institute? Flexible pricing models. Two of the biggest storage companies HP with Green Lake and Dell has this thing called Apex. They've announced such models for on Prem and and presumably, Cross Cloud. How >>do you think >>this is going to impact your customers Leverage of AWS cloud storage? Is it something that you have ah, opinion on? >>Yeah, I think it all comes down to again that usage of the storage And this is where I think there is an inherent advantage for our cloud storage. So there might be an attempt by the old guard toe lower prices or add flexibility. But the end of the day it comes down to what the customer actually needs to to. And if you think about gp three, which is the new E. B s volume, the idea with GP three is we're gonna pass along savings to the customer by making the storage 20% cheaper than GP two. And we're gonna make the product better by giving a great, reliable baseline performance. But we're also going to let customers who want to run work clothes like Cassandra on TBS tune their throughput separately, for example, from their capacity. So if you're running Cassandra, sometimes you don't need to change your capacity. Your storage capacity works just fine, but what happens with for example, Cassandra were quote is that you may need more throughput. And if you're buying hardware appliance, you just have to buy for your peak. You have to buy for the max of what you think, your throughput in the max of what your storage is and this inherent flexibility that we have for AWS storage and being able to tune throughput separate from IOP, separate from capacity like you do for GP three. That is really where the future is for customers having control over costs and control over customer experience without compromising or trading off either one. >>Awesome. Thank you for that. So another time we have remaining my line. I want to talk about the topic of diversity. Uh, social impact on Daz. Ah, woman leader, women executive on. I really wanna get your perspectives on this, and I've shared with the audience previously. One of my breaking analysis segments your your boxing video, which is awesome and eso so you've got a lot of unique, non traditional aspects to your to your life, and and I love it. But I >>want to >>ask you this. So it's obviously, you know, certainly politically and socially correct to talk about diversity, the importance of diversity. There's data that suggests that that that diversity is good both economically, not just socially. And of course, it's the right thing to do. But there are those. Peter Thiel is probably the most prominent, but there are others who say, You know what, >>But >>get that. Just hire people just like you will be able to go faster, ramp up more quickly, hit escape velocity. It's natural. And that's what you should dio. Why is that not the right approach? Why is diversity both course socially responsible, but also good for business? >>For Amazon, we think about diversity as something that is essential toe how we think about innovation. And so, Dave, you know, as you know, from listening to some of the announcements I reinvent, we launched a lot of new ideas, new concepts and new services in AWS and just bringing that lends down to storage U. S. Tree has been reinventing itself every year since we launched in 2000 and six. PBS introduced the first Son on the Cloud late last year and continues to reinvent how customers think about block storage. We would not be able Thio. Look at a product in a different way and think to ourselves Not just what is the legacy system dio in a data center today. But how do we want to build this new distributed system in a way that helps customers achieve not just what they're doing today, but what they want to do in five and 10 years? You can't get that innovative mindset without bringing different perspectives to the table. And so we strongly believe in hiring people who are from underrepresented groups and whether that's gender or it's related racial equality or if its geographic, uh, diversity and bringing them in tow have the conversation. Because those divers viewpoints inform how we can innovate at all levels in a W s >>right. And so I really appreciate the perspectives on that, and we've had a zoo. You probably know the Cube has been, you know, a very big advocate of diversity, you know, generally, but women in tech Specifically, we participated a lot. And you know, I often ask this question is, you know, as a smaller company, uh, I and some of my other colleagues in in small business Sometimes we struggle. Um and so my question is, how >>how do >>you go beyond What's your advice for going beyond, you know, the good old boys network? I think its large companies like AWS and the big players you've got a responsibility to that. You can put somebody in charge and make it you know, their full time job. How should smaller companies, um, that are largely white, male dominated? How should they become more diverse? What should they do? Thio increase that diversity? >>Well, I think the place to start his voice. A lot of what we try to dio is make sure that the underrepresented voice is heard. And so, Dave, any small business owner of any industry can encourage voice for your under represented or your unheard populations. And honestly, it is a simple as being in a meeting and looking around that table, we're on your screen as it were and asking yourself Who hasn't talked? Who hasn't weighed in particularly if the debate is contentious or even animated. And you will see, particularly if you note this. Over time you will see that there may be somebody and whether it's an underrepresented, a group or its ah woman whose early career or it's it's not. It's just a member of your team who happens to be a white male to who's not being hurt. And you can ask that person for their perspective. And that is a step that every one of us can and should do, which is asked toe, have everyone's voice at the table, toe listen and to weigh in on it. So I think that is something everyone should dio. I think if you are a member of an underrepresented groups, as for example, I'm Vietnamese American and I'm the female in Tech. I think it z something to think about how you can make sure that you're always taking that bold step forward. And it's one of the topics that we covered it at reinvent. We had a great discussion with a group of women CEOs, and a lot of it we talked about is being bolt, taking the challenge of being bold in tough situations, and that is an important thing, I think, for anybody to keep in mind, but especially for members of underrepresented groups, because sometimes Dave, that bold step that you kind of think of is like, Oh, I don't know if I should ask for that promotion or I don't know if I should volunteer for that project It's not. It's not a big ask, but it's big in your head. And so if you can internalize as a member of some, you know, a group that maybe hasn't heard or seen as much how you can take those bold challenges and step forward and learn, maybe fell also because that's how you learn. Then that is a way toe. Also have people learn and develop and become leaders in whatever industry it ISS. It's >>great advice, and I reminds me of, I mean, I think most of us can relate to that my land, because when we started in the industry, we may be timid. You didn't want to necessarily speak up, and I think it's incumbent upon those in a position of power. And by the way, power might just be running a meeting agenda to maybe calling those folks that are. Maybe it's not diversity of gender or, you know, our or race. And maybe it's just the underrepresented. Maybe that's a good way to start building muscle memory. So that's unique advice that I hadn't heard before. So thank you very much for that. Appreciate it. And, uh hey, listen, thanks so much for coming on the Cuban cloud. Uh, we're out of time and and really, always appreciate your perspectives. And you're doing a great job, and thank you. >>Great. Thank you, Dave. Thanks for having me and have a great day. >>All right? And keep it right, everybody. You're watching the cube on cloud right back.

>>from around the globe. It's the Cube presenting Cuban cloud brought >>to you by silicon angle. Okay, we're back. Here. Live Cuban Cloud. And this is Dave. Want with my co host, John Ferrier Were all remote. We're getting into the analyst power half hour. Really pleased to have Maribel Lopez here. She's the principal and founder of Lopez Research and Zias Caraballo, who is the principal and founder of ZK research. Guys, great to see you. Let's get into it. How you doing? >>Great. How you been? Good, >>thanks. Really good. John's hanging in there quarantining and, uh, all healthy, So I hope you guys are too. Hey, Mary, But let's start with you. You know, here we are on 2021 you know, just exited one of the strangest years, if not the strangest year of our lives. But looking back in the past decade of cloud and we're looking forward. How do you see that? Where do we come from? Where we at and where we going >>When we obviously started with the whole let's build a public cloud and everything was about public cloud. Uh, then we went thio the notion of private cloud than we had hybrid cloud and multi cloud. So we've done a lot of different clouds right now. And I think where we are today is that there's a healthy recognition on the cloud computing providers that you need to give it to the customers the way they want it, not the way you've decided to build it. So how do you meet them where they are so that they can have a cloud like experience wherever they want their data to be? >>Yes and yes, you've, you know, observed, This is well, in the early days of cloud, you heard a lot of rhetoric. It was private cloud And and then now we're, you know, hearing a lot of multi cloud and so forth. But initially, a lot of the traditional vendors kind of pooh poohed it. They called us analysts. We said we were all cloud crazy, but they seem to have got their religion. >>Well, everything. Everyone's got a definition of cloud, but I actually think we are right in the midst of another transformation of clouds Miracle talked about. We went from, you know, private clouds, which is really hosting the public cloud to multi cloud hybrid cloud. And if you look at the last post that put on Silicon Angle, which was talking about five acquisition of Volterra, I actually think we're in the midst of the transition to what's called distributed Club, where if you look at modernized cloud apps today, they're actually made up of services from different clouds on also distributed edge locations. And that's gonna have a pretty profound impact on the way we build out, because those distributed edges be a telco edge, cellular vagina. Th whatever the services that lived there are much more ephemeral in nature, right? So the way we secure the way we connect changes quite a bit. But I think that the great thing about Cloud is we've seen several several evolutionary changes. So what the definition is and we're going through that now, which is which is pretty cool to think about, right? It's not a static thing. Um, it's, uh, you know, it's a it's an ongoing transition. But I think, uh, you know, we're moving into this distributed Cloudera, which to me is a lot more complex than what we're dealing with in the Palace. >>I'm actually pretty excited about that because I think that this move toe edge and the distribution that you've talked about, it's like we now have processing everywhere. We've got it on devices, we've got it in, cars were moving, the data centers closer and closer to where the action's happening. And I think that's gonna be a huge trend for 2021. Is that distributed that you were talking about a lot of edge discussion? You >>know what? The >>reason we're doing This, too, is we want. It's not just we're moving the data closer to the user, right? And some. If you think you brought up the autonomous vehicle right in the car being an edge, you think of the data that generates right? There's some things such as the decision to stop or not right that should be done in car. I don't wanna transport that data all the way back to Google him back to decide whether I want to stop. You could also use the same data determine whether drivers driving safely for insurance purposes, right? So the same data give me located at the edge or in a centralized cloud for different purposes, and I think that's what you know, kind of cool about this is we're being able to use our data and much different ways. Now. >>You know, it's interesting is it's so complex. It's mind blowing because this is distributed computing. Everyone kind of agrees this is where it is. But if you think about the complexity and I want to get your guys reaction to this because you know some of the like side fringe trend discussions are data sovereignty, misinformation as a vulnerability. Okay, you get the chips now you got gravitas on with Amazon in front. Apple's got their own chips. Intel is gonna do a whole new direction. So you've got tons of computer. And then you mentioned the ephemeral nature. How do you manage those? What's the observe ability look like? They're what's the trust equation? So all these things kind of play into it. It sounds almost mind blowing, just even thinking about it. But how do you guys, this analyst tryto understand where someone's either blowing bullshit or kind of like has the real deal? Because all those things come into play? I mean, you could have a misinformation campaign targeting the car. Let's say Hey, you know that that data is needs to be. This is this is misinformation who's a >>in a lot of ways, this creates almost unprecedented opportunity now for for starts and for companies to transform right. The fundamental tenet of my research has always been share shifts happen when markets transition and we're in the middle of the big one. If the computer resource is we're using, John and the application resource will be using or ephemeral nature than all the things that surrounded the way we secured the way we connect. Those also have to be equal, equally agile, right, So you can't have, you know, you think of a micro services based application being secured with traditional firewalls, right? Just the amount of, or even virtual the way that the length of time it takes to spend those things up is way too long. So in many ways, this distributed cloud change changes everything in I T. And that that includes all of the services in the the infrastructure that we used to secure and connect. And that's a that is a profound change, and you mentioned the observe ability. You're right. That's another thing that the traditional observe ability tools are based on static maps and things and, you know, traditional up, down and we don't. Things go up and down so quickly now that that that those don't make any sense. So I think we are going to see quite a rise in different types of management tools and the way they look at things to be much more. I suppose you know Angela also So we can measure things that currently aren't measurable. >>So you're talking about the entire stack. Really? Changing is really what you're inferring anyway from your commentary. And that would include the programming model as well, wouldn't it? >>Absolutely. Yeah. You know, the thing that is really interesting about where we have been versus where we're going is we spent a lot of time talking about virtual izing hardware and moving that around. And what does that look like? And that, and creating that is more of a software paradigm. And the thing we're talking about now is what is cloud is an operating model look like? What is the manageability of that? What is the security of that? What? You know, we've talked a lot about containers and moving into a different you know, Dev suck ups and all those different trends that we've been talking about, like now we're doing them. So we've only got into the first crank of that. And I think every technology vendor we talked to now has to address how are they going to do a highly distributed management and security landscape? Like, what are they gonna layer on top of that? Because it's not just about Oh, I've taken Iraq of something server storage, compute and virtualized it. I now have to create a new operating model around it. In a way, we're almost redoing what the OS I stack looks like and what the software and solutions are for that. >>So >>it was really Hold on, hold on, hold on their lengthened. Because that side stack that came up earlier today, Mayor. But we're talking about Yeah, we were riffing on the OSC model, but back in the day and we were comparing the S n a definite the, you know, the proprietary protocol stacks that they were out there and someone >>said Amazon's S N a. Is that recall? E think that's what you said? >>No, no. Someone in the chest. That's a comment like Amazon's proprietary meaning, their scale. And I said, Oh, that means there s n a But if you think about it, that's kind of almost that can hang. Hang together. If the kubernetes is like a new connective tissue, is that the TCP pipe moment? Because I think Os I kind of was standardizing at the lower end of the stack Ethernet token ring. You know, the data link layer physical layer and that when you got to the TCP layer and really magic happened right to me, that's when Cisco's happened and everything started happening then and then. It kind of stopped because the application is kinda maintain their peace there. A little history there, but like that's kind of happening now. If you think about it and then you put me a factor in the edge, it just kind of really explodes it. So who's gonna write that software? E >>think you know, Dave, your your dad doesn't change what you build ups. It's already changed in the consumer world, you look atyou, no uber and Waze and things like that. Those absolute already highly decomposed applications that make a P I calls and DNS calls from dozens of different resource is already right. We just haven't really brought that into the enterprise space. There's a number, you know, what kind of you know knew were born in the cloud companies that have that have done that. But they're they're very few and far between today. And John, your point about the connectivity. We do need to think about connectivity at the network layer. Still, obviously, But now we're creating that standardization that standardized connectivity all the way a player seven. So you look at a lot of the, you know, one of the big things that was a PDP. I calls right, you know, from different cloud services. And so we do need to standardize in every layer and then stitch that together. So that does make It does make things a lot more complicated. Now I'm not saying Don't do it because you can do a whole lot more with absolute than you could ever do before. It's just that we kind of cranked up the level of complexity here, and flowered isn't just a single thing anymore, right? That's that. That's what we're talking about here It's a collection of edges and private clouds and public clouds. They all have to be stitched together at every layer in orderto work. >>So I was I was talking a few CEOs earlier in the day. We had we had them on, I was asking them. Okay, So how do you How do you approach this complexity? Do you build that abstraction layer? Do you rely on someone like Microsoft to build that abstraction layer? Doesn't appear that Amazon's gonna do it, you know? Where does that come from? Or is it or is it dozens of abstraction layers? And one of the CEO said, Look, it's on us. We have to figure out, you know, we get this a p I economy, but But you guys were talking about a mawr complicated environment, uh, moving so so fast. Eso if if my enterprise looks like my my iPhone APs. Yes, maybe it's simpler on an individual at basis, but its app creep and my application portfolio grows. Maybe they talk to each other a little bit better. But that level of complexity is something that that that users are gonna have to deal >>with what you thought. So I think quite what Zs was trying to get it and correct me if I'm wrong. Zia's right. We've got to the part where we've broken down what was a traditional application, right? And now we've gotten into a P. I calls, and we have to think about different things. Like we have to think about how we secure those a p I s right. That becomes a new criteria that we're looking at. How do we manage them? How do they have a life cycle? So what was the life cycle of, say, an application is now the life cycle of components and so that's a That's a pretty complex thing. So it's not so much that you're getting app creep, but you're definitely rethinking how you want to design your applications and services and some of those you're gonna do yourself and a lot of them are going to say it's too complicated. I'm just going to go to some kind of SAS cloud offering for that and let it go. But I think that many of the larger companies I speak to are looking for a larger company to help them build some kind of framework to migrate from what they've used with them to what they need tohave going forward. >>Yeah, I think. Where the complexities. John, You asked who who creates the normalization layer? You know, obviously, if you look to the cloud providers A W s does a great job of stitching together all things AWS and Microsoft does a great job of stitching together all things Microsoft right in saying with Google. >>But >>then they don't. But if if I want to do some Microsoft to Amazon or Google Toe Microsoft, you know, connectivity, they don't help so much of that. And that's where the third party vendors that you know aviatrix on the network side will tear of the security side of companies like that. Even Cisco's been doing a lot of work with those companies, and so what we what we don't really have And we probably won't for a while if somebody is gonna stitch everything together at every >>you >>know, at every layer. So Andi and I do think we do get after it. Maribel, I think if you look at the world of consumer APS, we moved to a lot more kind of purpose built almost throwaway apps. They serve a purpose or to use them for a while. Then you stop using them. And in the enterprise space, we really haven't kind of converted to them modeling on the mobile side. But I think that's coming. Well, >>I think with micro APS, right, that that was kind of the issue with micro APS. It's like, Oh, I'm not gonna build a full scale out that's gonna take too long. I'm just gonna create this little workflow, and we're gonna have, like, 200 work flows on someone's phone. And I think we did that. And not everybody did it, though, to your point. So I do think that some people that are a little late to the game might end up in in that app creep. But, hey, listen, this is a fabulous opportunity that just, you know, throw a lot of stuff out and do it differently. What What? I think what I hear people struggling with ah lot is be to get it to work. It typically is something that is more vertically integrated. So are you buying all into a Microsoft all you're buying all into an Amazon and people are starting to get a little fear about doing the full scale buy into any specific platform yet. In absence of that, they can't get anything to work. >>Yeah, So I think again what? What I'm hearing from from practitioners, I'm gonna put a micro serve. And I think I think, uh, Mirabelle, this is what you're implying. I'm gonna put a micro services layer. Oh, my, my. If I can't get rid of them, If I can't get rid of my oracle, you know, workloads. I'm gonna connect them to my modernize them with a layer, and I'm gonna impart build that. I'm gonna, you know, partner to get that done. But that seems to be a a critical path forward. If I don't take that step, gonna be stuck in the path in the past and not be able to move forward. >>Yeah, absolutely. I mean, you do have to bridge to the past. You you aren't gonna throw everything out right away. That's just you can't. You can't drive the bus and take the wheels off that the same time. Maybe one wheel, but not all four of them at the same time. So I think that this this concept of what are the technologies and services that you use to make sure you can keep operational, but that you're not just putting on Lee new workloads into the cloud or new workloads as decomposed APS that you're really starting to think about. What do I want to keep in whatever I want to get rid of many of the companies you speak Thio. They have thousands of applications. So are they going to do this for thousands of applications? Are they gonna take this as an opportunity to streamline? Yeah, >>well, a lot of legacy never goes away, right? And I was how companies make this transition is gonna be interesting because there's no there's no really the fact away I was I was talking to this one company. This is New York Bank, and they've broken their I t division down into modern I t and legacy I t. And so modern. Everything is cloud first. And so imagine me, the CEO of Legacy i e 02 miracles. But what they're doing, if they're driving the old bus >>and >>then they're building a new bus and parallel and eventually, you know, slowly they take seats out of the old bus and they take, you know, the seat and and they eventually start stripping away things. That old bus, >>But >>that old bus is going to keep running for a long time. And so stitching the those different worlds together is where a lot of especially big organizations that really can't commit to everything in the cloud are gonna struggle. But it is a It is a whole new world. And like I said, I think it creates so much opportunity for people. You know, e >>whole bus thing reminds me that movie speed when they drive around 55 miles an hour, just put it out to the airport and just blew up E >>got But you know, we all we all say that things were going to go away. But to Zia's point, you know, nothing goes away. We're still in 2021 talking about mainframes just as an aside, right? So I think we're going to continue tohave some legacy in the network. But the But the issue is ah, lot will change around that, and they're gonna be some people. They're gonna make a lot of money selling little startups that Just do one specific piece of that. You know, we just automation of X. Oh, >>yeah, that's a great vertical thing. This is the This is the distributed network argument, right? If you have a note in the network and you could put a containerized environment around it with some micro services um, connective tissue glue layer, if you will software abstract away some integration points, it's a note on the network. So if in mainframe or whatever, it's just I mean makes the argument right, it's not core. You're not building a platform around the mainframe, but if it's punching out, I bank jobs from IBM kicks or something, you know, whatever, Right? So >>And if those were those workloads probably aren't gonna move anywhere, right, they're not. Is there a point in putting those in the cloud? You could say Just leave them where they are. Put a connection to the past Bridge. >>Remember that bank when you talk about bank guy we interviewed in the off the record after the Cube interviews like, Yeah, I'm still running the mainframe, so I never get rid of. I love it. Run our kicks job. I would never think about moving that thing. >>There was a large, large non US bank who said I buy. I buy the next IBM mainframe sight unseen. Andi, he's got no choice. They just write the check. >>But milliseconds is like millions of dollars of millisecond for him on his back, >>so those aren't going anywhere. But then, but then, but they're not growing right. It's just static. >>No, no, that markets not growing its's, in fact. But you could make a lot of money and monetizing the legacy, right? So there are vendors that will do that. But I do think if you look at the well, we've already seen a pretty big transition here. If you look at the growth in a company like twilio, right, that it obviates the need for a company to rack and stack your own phone system to be able to do, um, you know, calling from mobile lapse or even messaging. Now you just do a P. I calls. Um, you know, it allows in a lot of ways that this new world we live in democratizes development, and so any you know, two people in the garage can start up a company and have a service up and running another time at all, and that creates competitiveness. You know much more competitiveness than we've ever had before, which is good for the entire industry. And, you know, because that keeps the bigger companies on their toes and they're always looking over their shoulder. You know what, the banks you're looking at? The venues and companies like that Brian figure out a way to monetize. So I think what we're, you know well, that old stuff never going away. The new stuff is where the competitive screen competitiveness screen. >>It's interesting. Um IDs Avery. Earlier today, I was talking about no code in loco development, how it's different from the old four g l days where we didn't actually expand the base of developers. Now we are to your point is really is democratizing and, >>well, everybody's a developer. It could be a developer, right? A lot of these tools were written in a way that line of business people create their own APs to point and click interface is, and so the barrier. It reminds me of when, when I started my career, I was a I. I used to code and HTML build websites and then went to five years. People using drag and drop interface is right, so that that kind of job went away because it became so easy to dio. >>Yeah, >>sorry. A >>data e was going to say, I think we're getting to the part. We're just starting to talk about data, right? So, you know, when you think of twilio, that's like a service. It's connecting you to specific data. When you think of Snowflake, you know, there's been all these kinds of companies that have crept up into the landscape to feel like a very specific void. And so now the Now the question is, if it's really all about the data, they're going to be new companies that get built that are just focusing on different aspects of how that data secured, how that data is transferred, how that data. You know what happens to that data, because and and does that shift the balance of power about it being out of like, Oh, I've created these data centers with large recommend stack ums that are virtualized thio. A whole other set of you know this is a big software play. It's all about software. >>Well, we just heard from Jim Octagon e You guys talking earlier about just distributed system. She basically laid down that look. Our data architectures air flawed there monolithic. And data by its very nature is distributed so that she's putting forth the whole new paradigm around distributed decentralized data models, >>which Howie shoe is just talking about. Who's gonna build the visual studio for data, right? So programmatic. Kind of thinking around data >>I didn't >>gathering. We didn't touch on because >>I do think there's >>an opportunity for that for, you know, data governance and data ownership and data transport. But it's also the analytics of it. Most companies don't have the in house, um, you know, data scientists to build on a I algorithms. Right. So you're gonna start seeing, you know, cos pop up to do very specific types of data. I don't know if you saw this morning, um, you know, uniforms bought this company that does, you know, video emotion detection so they could tell on the video whether somebody's paying attention, Not right. And so that's something that it would be eso hard for a company to build that in house. But I think what you're going to see is a rise in these, you know, these types of companies that help with specific types of analytics. And then you drop you pull those in his resource is into your application. And so it's not only the storage and the governance of the data, but also the analytics and the analytics. Frankly, there were a lot of the, uh, differentiation for companies is gonna come from. I know Maribel has written a lot on a I, as have I, and I think that's one of the more exciting areas to look at this year. >>I actually want to rip off your point because I think it's really important because where we left off in 2020 was yes, there was hybrid cloud, but we just started to see the era of the vertical eyes cloud the cloud for something you know, the cloud for finance, the cloud for health care, the telco and edge cloud, right? So when you start doing that, it becomes much more about what is the specialized stream that we're looking at. So what's a specialized analytic stream? What's a specialized security stack stream? Right? So until now, like everything was just trying to get to what I would call horizontal parody where you took the things you had before you replicated them in a new world with, like, some different software, but it was still kind of the same. And now we're saying, OK, let's try Thio. Let's try to move out of everything, just being a generic sort of cloud set of services and being more total cloud services. >>That is the evolution of everything technology, the first movement. Everything doing technology is we try and make the old thing the new thing look like the old thing, right? First PCs was a mainframe emulator. We took our virtual servers and we made them look like physical service, then eventually figure out, Oh, there's a whole bunch of other stuff that I could do then I couldn't do before. And that's the part we're trying to hop into now. Right? Is like, Oh, now that I've gone cloud native, what can I do that I couldn't do before? Right? So we're just we're sort of hitting that inflection point. That's when you're really going to see the growth takeoff. But for whatever reason, and i t. All we ever do is we're trying to replicate the old until we figure out the old didn't really work, and we should do something new. >>Well, let me throw something old and controversial. Controversial old but old old trope out there. Consumerism ation of I t. I mean, if you think about what year was first year you heard that term, was it 15 years ago? 20 years ago. When did that first >>podcast? Yeah, so that was a long time ago >>way. So if you think about it like, it kind of is happening. And what does it mean, right? Come. What does What does that actually mean in today's world Doesn't exist. >>Well, you heard you heard. Like Fred Luddy, whose founder of service now saying that was his dream to bring consumer like experiences to the enterprise will. Well, it didn't really happen. I mean, service not pretty. Pretty complicated compared toa what? We know what we do here, but so it's It's evolving. >>Yeah, I think there's also the enterprise ation of consumer technology that John the companies, you know, you look a zoom. They came to market with a highly consumer facing product, realized it didn't have the security tools, you know, to really be corporate great. And then they had to go invest a bunch of money in that. So, you know, I think that waken swing the pendulum all the way over to the consumer side, but that that kind of failed us, right? So now we're trying to bring it back to center a little bit where we blend the two together. >>Cloud kind of brings that I never looked at that way. That's interesting and surprising of consumer. Yeah, that's >>alright, guys. Hey, we gotta wrap Zs, Maribel. Always a pleasure having you guys on great great insights from the half hour flies by. Thanks so much. We appreciate it. >>Thank >>you guys. >>Alright, keep it right there. Mortgage rate content coming from the Cuban Cloud Day Volonte with John Ferrier and a whole lineup still to come Keep right there.