>>Okay. Okay. We're back at Caesar's Forum. The Snowflake summit 2022. The cubes. Continuous coverage this day to wall to wall coverage. We're so excited to have the analyst panel here, some of my colleagues that we've done a number. You've probably seen some power panels that we've done. David McGregor is here. He's the senior vice president and research director at Ventana Research. To his left is Tony Blair, principal at DB Inside and my in the co host seat. Sanjeev Mohan Sanremo. Guys, thanks so much for coming on. I'm glad we can. Thank you. You're very welcome. I wasn't able to attend the analyst action because I've been doing this all all day, every day. But let me start with you, Dave. What have you seen? That's kind of interested you. Pluses, minuses. Concerns. >>Well, how about if I focus on what I think valuable to the customers of snowflakes and our research shows that the majority of organisations, the majority of people, do not have access to analytics. And so a couple of things they've announced I think address those are helped to address those issues very directly. So Snow Park and support for Python and other languages is a way for organisations to embed analytics into different business processes. And so I think that will be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most most people in the organisation or not, analysts, they're doing some line of business function. Their HR managers, their marketing people, their salespeople, their finance people right there, not sitting there mucking around in the data. They're doing a job and they need analytics in that job. So, >>Tony, I thank you. I've heard a lot of data mesh talk this week. It's kind of funny. Can't >>seem to get away from it. You >>can't see. It seems to be gathering momentum, but But what have you seen? That's been interesting. >>What I have noticed. Unfortunately, you know, because the rooms are too small, you just can't get into the data mesh sessions, so there's a lot of interest in it. Um, it's still very I don't think there's very much understanding of it, but I think the idea that you can put all the data in one place which, you know, to me, stuff like it seems to be kind of sort of in a way, it sounds like almost like the Enterprise Data warehouse, you know, Clouded Cloud Native Edition, you know, bring it all in one place again. Um, I think it's providing, sort of, You know, it's I think, for these folks that think this might be kind of like a a linchpin for that. I think there are several other things that actually that really have made a bigger impression on me. Actually, at this event, one is is basically is, um we watch their move with Eunice store. Um, and it's kind of interesting coming, you know, coming from mongo db last week. And I see it's like these two companies seem to be going converging towards the same place at different speeds. I think it's not like it's going to get there faster than Mongo for a number of different reasons, but I see like a number of common threads here. I mean, one is that Mongo was was was a company. It's always been towards developers. They need you know, start cultivating data, people, >>these guys going the other way. >>Exactly. Bingo. And the thing is that but they I think where they're converging is the idea of operational analytics and trying to serve all constituencies. The other thing, which which also in terms of serving, you know, multiple constituencies is how snowflake is laid out Snow Park and what I'm finding like. There's an interesting I economy. On one hand, you have this very ingrained integration of Anaconda, which I think is pretty ingenious. On the other hand, you speak, let's say, like, let's say the data robot folks and say, You know something our folks wanna work data signs us. We want to work in our environment and use snowflake in the background. So I see those kind of some interesting sort of cross cutting trends. >>So, Sandy, I mean, Frank Sullivan, we'll talk about there's definitely benefits into going into the walled garden. Yeah, I don't think we dispute that, but we see them making moves and adding more and more open source capabilities like Apache iceberg. Is that a Is that a move to sort of counteract the narrative that the data breaks is put out there. Is that customer driven? What's your take on that? >>Uh, primarily I think it is to contract this whole notion that once you move data into snowflake, it's a proprietary format. So I think that's how it started. But it's hugely beneficial to the customers to the users, because now, if you have large amounts of data in parquet files, you can leave it on s three. But then you using the the Apache iceberg table format. In a snowflake, you get all the benefits of snowflakes. Optimizer. So, for example, you get the, you know, the micro partitioning. You get the meta data. So, uh, in a single query, you can join. You can do select from a snowflake table union and select from iceberg table, and you can do store procedures, user defined functions. So I think they what they've done is extremely interesting. Uh, iceberg by itself still does not have multi table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache iceberg in a raw format, they don't have it. But snowflake does, >>right? There's hence the delta. And maybe that maybe that closes over time. I want to ask you as you look around this I mean the ecosystems pretty vibrant. I mean, it reminds me of, like reinvent in 2013, you know? But then I'm struck by the complexity of the last big data era and a dupe and all the different tools. And is this different, or is it the sort of same wine new new bottle? You guys have any thoughts on that? >>I think it's different and I'll tell you why. I think it's different because it's based around sequel. So if back to Tony's point, these vendors are coming at this from different angles, right? You've got data warehouse vendors and you've got data lake vendors and they're all going to meet in the middle. So in your case, you're taught operational analytical. But the same thing is true with Data Lake and Data Warehouse and Snowflake no longer wants to be known as the Data Warehouse. There a data cloud and our research again. I like to base everything off of that. >>I love what our >>research shows that organisation Two thirds of organisations have sequel skills and one third have big data skills, so >>you >>know they're going to meet in the middle. But it sure is a lot easier to bring along those people who know sequel already to that midpoint than it is to bring big data people to remember. >>Mrr Odula, one of the founders of Cloudera, said to me one time, John Kerry and the Cube, that, uh, sequel is the killer app for a Yeah, >>the difference at this, you know, with with snowflake, is that you don't have to worry about taming the zoo. Animals really have thought out the ease of use, you know? I mean, they thought about I mean, from the get go, they thought of too thin to polls. One is ease of use, and the other is scale. And they've had. And that's basically, you know, I think very much differentiates it. I mean, who do have the scale, but it didn't have the ease of use. But don't I >>still need? Like, if I have, you know, governance from this vendor or, you know, data prep from, you know, don't I still have to have expertise? That's sort of distributed in those those worlds, right? I mean, go ahead. Yeah. >>So the way I see it is snowflake is adding more and more capabilities right into the database. So, for example, they've they've gone ahead and added security and privacy so you can now create policies and do even set level masking, dynamic masking. But most organisations have more than snowflake. So what we are starting to see all around here is that there's a whole series of data catalogue companies, a bunch of companies that are doing dynamic data masking security and governance data observe ability, which is not a space snowflake has gone into. So there's a whole ecosystem of companies that that is mushrooming, although, you know so they're using the native capabilities of snowflake, but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other, like relational databases, you can run these cross platform capabilities in that layer. So so that way, you know, snowflakes done a great job of enabling that ecosystem about >>the stream lit acquisition. Did you see anything here that indicated there making strong progress there? Are you excited about that? You're sceptical. Go ahead. >>And I think it's like the last mile. Essentially. In other words, it's like, Okay, you have folks that are basically that are very, very comfortable with tableau. But you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency, um, to San James Point. I think part of it, this kind of plays into it is what makes this different from the ado Pere is the fact that this all these capabilities, you know, a lot of vendors are taking it very seriously to make put this native obviously snowflake acquired stream. Let's so we can expect that's extremely capabilities are going to be native. >>And the other thing, too, about the Hadoop ecosystem is Claudia had to help fund all those different projects and got really, really spread thin. I want to ask you guys about this super cloud we use. Super Cloud is this sort of metaphor for the next wave of cloud. You've got infrastructure aws, azure, Google. It's not multi cloud, but you've got that infrastructure you're building a layer on top of it that hides the underlying complexities of the primitives and the a p I s. And you're adding new value in this case, the data cloud or super data cloud. And now we're seeing now is that snowflake putting forth the notion that they're adding a super path layer. You can now build applications that you can monetise, which to me is kind of exciting. It makes makes this platform even less discretionary. We had a lot of talk on Wall Street about discretionary spending, and that's not discretionary. If you're monetising it, um, what do you guys think about that? Is this something that's that's real? Is it just a figment of my imagination, or do you see a different way of coming any thoughts on that? >>So, in effect, they're trying to become a data operating system, right? And I think that's wonderful. It's ambitious. I think they'll experience some success with that. As I said, applications are important. That's a great way to deliver information. You can monetise them, so you know there's there's a good economic model around it. I think they will still struggle, however, with bringing everything together onto one platform. That's always the challenge. Can you become the platform that's hard, hard to predict? You know, I think this is This is pretty exciting, right? A lot of energy, a lot of large ecosystem. There is a network effect already. Can they succeed in being the only place where data exists? You know, I think that's going to be a challenge. >>I mean, the fact is, I mean, this is a classic best of breed versus the umbrella play. The thing is, this is nothing new. I mean, this is like the you know, the old days with enterprise applications were basically oracle and ASAP vacuumed up all these. You know, all these applications in their in their ecosystem, whereas with snowflake is. And if you look at the cloud, folks, the hyper scale is still building out their own portfolios as well. Some are, You know, some hyper skills are more partner friendly than others. What? What Snowflake is saying is that we're going to give all of you folks who basically are competing against the hyper skills in various areas like data catalogue and pipelines and all that sort of wonderful stuff will make you basically, you know, all equal citizens. You know the burden is on you to basically we will leave. We will lay out the A P. I s Well, we'll allow you to basically, you know, integrate natively to us so you can provide as good experience. But the but the onus is on your back. >>Should the ecosystem be concerned, as they were back to reinvent 2014 that Amazon was going to nibble away at them or or is it different? >>I find what they're doing is different. Uh, for example, data sharing. They were the first ones out the door were data sharing at a large scale. And then everybody has jumped in and said, Oh, we also do data sharing. All the hyper scholars came in. But now what snowflake has done is they've taken it to the next level. Now they're saying it's not just data sharing. It's up sharing and not only up sharing. You can stream the thing you can build, test deploy, and then monetise it. Make it discoverable through, you know, through your marketplace >>you can monetise it. >>Yes. Yeah, so So I I think what they're doing is they are taking it a step further than what hyper scale as they are doing. And because it's like what they said is becoming like the data operating system You log in and you have all of these different functionalities you can do in machine learning. Now you can do data quality. You can do data preparation and you can do Monetisation. Who do you >>think is snowflakes? Biggest competitor? What do you guys think? It's a hard question, isn't it? Because you're like because we all get the we separate computer from storage. We have a cloud data and you go, Okay, that's nice, >>but there's, like, a crack. I think >>there's uniqueness. I >>mean, put it this way. In the old days, it would have been you know, how you know the prime household names. I think today is the hyper scholars and the idea what I mean again, this comes down to the best of breed versus by, you know, get it all from one source. So where is your comfort level? Um, so I think they're kind. They're their co op a Titian the hyper scale. >>Okay, so it's not data bricks, because why they're smaller. >>Well, there is some okay now within the best of breed area. Yes, there is competition. The obvious is data bricks coming in from the data engineering angle. You know, basically the snowflake coming from, you know, from the from the data analyst angle. I think what? Another potential competitor. And I think Snowflake, basically, you know, admitted as such potentially is mongo >>DB. Yeah, >>Exactly. So I mean, yes, there are two different levels of sort >>of a on a longer term collision course. >>Exactly. Exactly. >>Sort of service now and in salesforce >>thing that was that we actually get when I say that a lot of people just laughed. I was like, No, you're kidding. There's no way. I said Excuse me, >>But then you see Mongo last week. We're adding some analytics capabilities and always been developers, as you say, and >>they trashed sequel. But yet they finally have started to write their first real sequel. >>We have M c M Q. Well, now we have a sequel. So what >>were those numbers, >>Dave? Two thirds. One third. >>So the hyper scale is but the hyper scale urz are you going to trust your hyper scale is to do your cross cloud. I mean, maybe Google may be I mean, Microsoft, perhaps aws not there yet. Right? I mean, how important is cross cloud, multi cloud Super cloud Whatever you want to call it What is your data? >>Shows? Cloud is important if I remember correctly. Our research shows that three quarters of organisations are operating in the cloud and 52% are operating across more than one cloud. So, uh, two thirds of the organisations are in the cloud are doing multi cloud, so that's pretty significant. And now they may be operating across clouds for different reasons. Maybe one application runs in one cloud provider. Another application runs another cloud provider. But I do think organisations want that leverage over the hyper scholars right they want they want to be able to tell the hyper scale. I'm gonna move my workloads over here if you don't give us a better rate. Uh, >>I mean, I I think you know, from a database standpoint, I think you're right. I mean, they are competing against some really well funded and you look at big Query barely, you know, solid platform Red shift, for all its faults, has really done an amazing job of moving forward. But to David's point, you know those to me in any way. Those hyper skills aren't going to solve that cross cloud cloud problem, right? >>Right. No, I'm certainly >>not as quickly. No. >>Or with as much zeal, >>right? Yeah, right across cloud. But we're gonna operate better on our >>Exactly. Yes. >>Yes. Even when we talk about multi cloud, the many, many definitions, like, you know, you can mean anything. So the way snowflake does multi cloud and the way mongo db two are very different. So a snowflake says we run on all the hyper scalar, but you have to replicate your data. What Mongo DB is claiming is that one cluster can have notes in multiple different clouds. That is right, you know, quite something. >>Yeah, right. I mean, again, you hit that. We got to go. But, uh, last question, um, snowflake undervalued, overvalued or just about right >>in the stock market or in customers. Yeah. Yeah, well, but, you know, I'm not sure that's the right question. >>That's the question I'm asking. You know, >>I'll say the question is undervalued or overvalued for customers, right? That's really what matters. Um, there's a different audience. Who cares about the investor side? Some of those are watching, but But I believe I believe that the from the customer's perspective, it's probably valued about right, because >>the reason I I ask it, is because it has so hyped. You had $100 billion value. It's the past service now is value, which is crazy for this student Now. It's obviously come back quite a bit below its IPO price. So But you guys are at the financial analyst meeting. Scarpelli laid out 2029 projections signed up for $10 billion.25 percent free time for 20% operating profit. I mean, they better be worth more than they are today. If they do >>that. If I If I see the momentum here this week, I think they are undervalued. But before this week, I probably would have thought there at the right evaluation, >>I would say they're probably more at the right valuation employed because the IPO valuation is just such a false valuation. So hyped >>guys, I could go on for another 45 minutes. Thanks so much. David. Tony Sanjeev. Always great to have you on. We'll have you back for sure. Having us. All right. Thank you. Keep it right there. Were wrapping up Day two and the Cube. Snowflake. Summit 2022. Right back. Mm. Mhm.

>>Welcome everyone to our continuous coverage on the cube of AWS reinvent 2021. I'm your host, Dave Nicholson. And I am absolutely delighted to be joined by two folks from Unisys. I have a company that has been in the business of helping people with everything related to it for a very, very long time. We heard a talk about data monetization at modernization with ANU Priya, rom Raj vice president of cloud solution management at Unisys, along with ANU palms, the high VP and CTO of cloud solution engineering at UNISIS. And, uh, just so that we keep everything clear, I'm just going to call you on new and ANU Palm, and we'll all know who we're talking to. Sure. The funny thing is I'm David Nicholson or Dave Nicholson. Dave Vellante is one of the founders of Silicon angle, the cube. So usually it's two Dave's battling in >>So I get to be David and he's Dave typically. So we're completely, we're completely used to this, right? So, so tell me about what Eunice is doing UNISIS is doing in the arena of app modernization and data modernization and migration into cloud. You Unisys has a long and storied history of managing it in people's environments, you know, in the sort of on-premise world, as well as, as well as cloud now. But, uh, I knew tell us, tell us a little about what you'd assist is doing in this space. And then we'll, we'll double click and dive in. >>Um, so you, you're probably very, very familiar with the six RS of modernization, right? All the way from migration modernization, all the way from replatform rehost to, to the other side of the spectrum, refactor and rearchitect, right? So what is DASA is that it takes clients on that journey, right? So we see clients in different stages of that journey. There are clients that come to us, uh, recently brought on board a pipeline they're very early in their journey. They just talking about their first set of migrations. There are clients that have taken the leap and done 75% of their workload is on cloud, even for Unisys 95% of more than 95% of our workload actually runs on cloud public cloud. So different stages of the journey, but no matter where they are in the journey, really moving the needle on modernization. Right. And what did he mean by modernization? It's it's taking advantage of the innovation in cloud, whether it's seven containers are AI and bringing that to the client so that they can drive those business outcomes. That's what we are passionate about doing. And we can talk to you about a couple of clients where we've done this on a, but I like to unopened to add on. >>Sure. Yeah. And, and just, and before you dive in on a Palm, I want to hear specifically about the inhibitors that you're seeing, the things that causing friction, right. Movement to cloud. >>Yeah. So cloud of the transformative technology is as disruptive and it brings about lots of benefits that are very well understood, but not realized, um, lower total cost of ownership, higher security, innovation, and agility. But the challenges that you see for customers really benefit from moving and migrating to the cloud are related to security and compliance. That comes up to be the top pain point, followed by cost of ownership that are optimizations that you need to do before you can benefit from really leveraging the benefits from the cloud and then innovation and agility, how to drive that. And there are certain things around app and data about innovation, data analytics, AIML that really helps realize those values, but it needs a concerted effort and a drive and a push to transform your infrastructure from where you are today to really get to derive the true benefits from the cloud. >>And we do a cloud barometer study of about thousands of organizations from a Unisys perspective, Dave, and as a Oklahoma saying, um, more than 60% of our clients say security is the biggest inhibitor they want help with security. You >>No, you're saying the inhibitor to going to cloud is security >>To accelerating the cloud journey. They always are perceptive. >>Is that, is that hesitancy, uh, just perception or is it reality? >>That's a great question, >>Dave, and you don't have to be gentle with me. Like you might with a client, you know, you can, you can reach over and smack me and say get over it. You're going to be fine, Dave, >>Actually, I'm a new from leaned into it already. In many cases, when you, when you actually get to your cloud configuration, right. You probably be more secure in the cloud, but it's getting clients confident with that setup. That's where the rubber meets the road. Right. And that's where we come in to say, um, do you understand the shared responsibility model with cloud? What is the cloud provider do? What does being here at AWS reinvent? What has AWS bring to the table for security? This is what the client is responsible for. For example, application security is completely their client's responsibility, right? In most cases. So, um, just working with the clients so that they understand the shared responsibility model and then making sure we protect all the different layers of the stack, but security, right? Even, even as apps are developed, you need to have DevSecOps pipeline, right? So I didn't say dev ops, I said, dev sec ops, because we want to make security a part of developing your applications and deploying them in cloud as well. So that's what we bring to the table and making sure clients feel confident in, in accelerating their cloud journey. So >>You can deal with customers like me, who, who truly believed that my money is safer in a coffee, can buried in my backyard than it is in a bank, right. With all those banking people wandering around. Um, so when you start looking at an environment and you, and you look at the totality of an it infrastructure landscape, how do you go about determining what is the low hanging fruit? What makes sense to move first from is that, is that always an ROI discussion that comes into play and are your customers, I like to give like five questions at the same time to confuse you and are your customers expecting to immediately save money? And how big is the ROI conversation in this? >>Uh, great question. So a couple of things need to be considered first, just to understand where does the customer in the digital transformation journey are there green fee where they only have on premise data center and they're trying to get to the cloud, or they already have dipped their toes and move to the cloud. And in the cloud, how far in advance are they in their transformation journey, have them not have the done apps and data modernization? Do they have, uh, uh, management operations capability for day one and day two cloud ops and fin ops and security ops, and other leveraging the power of the cloud, the copious amounts of data that cloud brings to the table. Uh, the, the important thing to understand is that 80% of the tools that work in the on-prem do not work in the cloud. So you have to understand the very nature of the cloud and to deal with it differently. >>The same old tools and creeks will not work in the cloud. And I call it the three V's in the cloud, velocity volume, and variety of data is different in the cloud. So when you're talking about security, you need to look at the cloud infrastructure, posture management. You need to look at the cloud workload, pasture management. You need to look at the data that's available and analyze and harness the data using AIML and data analytics. So you need a new set of clicks as it were to really harness the power of the cloud to derive the benefits from increased security, lower cost of ownership and innovation and agility. >>And it makes sense. Yeah. >>I mean, I think you touched on touched on it, but fin ops, right. And you asked the question David on, is that the biggest driver in terms of savings to get to the, to the cloud. And I think it's definitely one of the bigger factors, um, because, and be believe to, to realize that we offer a fin op service. And if you know, Upserve is not just for the cloud, but choosing models at different, right. It's not like your data center planning. We talked about the tools being different. It's more than the tools, right? So you could do reserved instances or you could do spot instances, completely different ballgame with AWS, right. Or you could do AWS savings plans. Are you maximizing all of that? And even beyond that, are you thinking beyond that into like AWS container suppose, um, EKS, are you talking about seven less and that could completely change your bill and your total kind of cost of ownership. You talk Dave about past databases, right? So platform as a service, and that could completely change your total cost of ownership there as well. So are you really maximizing that? And do you have a service around that? Do you have a trusted partner who can help you with fin ops is I think an important consideration there? >>Well, I don't know. Pretty, I know you're dying to talk about a customer example, make it real for us. Give us an example of, uh, of this process inaction where UNISIS has helped a customer on the journey. >>Absolutely. Dave. So, um, uh, one example that comes to mind is a large public university and they've got about a half a million students and they've got 20 plus campuses around the U S in California, Sarah, I might've given myself away there. And, and, uh, in, into what they've done is, um, initially they are big into AWS and they are into their cloud, uh, higher into the IBM cloud journey, uh, big time. And they are a hybrid deployment at this point. And initially, uh, they, uh, when they subscribed to our fin ops service, uh, we, we brought in all the different, uh, thinking around working with different organizations, they need to like business planning, right? You need to know which is your most significant apps and what do you want to invest in them in terms of modernization and in tuning your AWS spend. And so we did that. And so we got them about a 33% cost saving and what they did was then they took, looked at all of their AWS accounts across the campuses and said, we want fin ops across all of them. Let's consolidate all of them. So that's, that's the power of a synopsis is about 33% saving right there. Well, that is >>Particularly exciting for me because I assume that they're going to be lowering my kid's tuition next year. So I'll be, I'll be looking forward to that. And now I know Palm, we know why she was kicked out of the, uh, you know, the, the intelligence agency can't keep a secret. Let's, let's, let's talk about an amusement park, uh, famous for its rodent, but I'm not going to say the name. So, so out upon talk about, uh, the technology space that we're in the midst of here at AWS reinvent, right? Um, each time we have a keynote, we're hit with an, almost a mind boggling number of announcements, right? Customers can't keep, keep this stuff straight. They're 575 different kinds of instances. It used to be, we have S3 and we have VC too. Right. Would you like, would you like one, or would you like both, right? How do you help customers make sense of this? >>Yeah, no, that's, that's a great question because, um, the cloud is, uh, I, as I said, cloud has three V's velocity, variety and volume of data and, and the new kinds of services that are available. Day-by-day, it's growing the keys to really figure out, again, map the business objectives that you as a customer or a company are trying to achieve, understand where you are in your digital transformation journey. And then based on the two, uh, and assess where you at and, and companies like Unisys can work with the customer to assess their, what I call the digital transformation posture, which will then give, uh, give us clear indications or recommendations on what are the next stages in the transformation of journey. So whether it's whether you want to improve your security posture, whether you want to improve your cost of ownership, posture, whether you want to go to go to the cloud and leverage DevSecOps to benefit from the innovation and agility, we can help you. >>Unisys has DevSecOps as a service, uh, containers as a service where we can help our customers and partners migrate to the cloud, modernize the apps. And again, based on research, that's out there, you can speed up app deployment and development by 60% by leveraging the power of the cloud. So the benefits out there for customers to get access to, it's a question of finding the right combination of people, process and technology to get you there. And Unisys being a very trusted advisor is certainly able to help you accelerate that journey and get you to meet your business outcomes. So me, >>Um, let me ask the two of you, what might be an uncomfortable question, and that is obviously Unisys is in the business of managing things that aren't in cloud. Also, you have very, very large existing customers that are spending money with you, right? And if they'll just stay still and not do anything and not change, you'll keep making money into the future. Aren't some of these things that you're doing as a trusted advisor, almost counterintuitive from a, from a finance perspective at Unisys, at least in the short term, how do you, how do you balance that? >>It's a, it's a great question, Dave, and for us, we are customer obsessed. So that's, I know one of the AWS principles and we, we live by that as well. Right? So customer comes first and doing the right thing by them, whether it is the total cost of ownership when it's getting the security posture, right. That comes first for us. And if, if moving them to a public cloud will help them achieve that. We will do that. Right. So even if it means that our bill is going to be lower, right. So we'll give you a great example there. Um, Eunice's, as you know, Dave has been in the mainstream business and we've got customers that are still on clear path, right? So even with those customers, we help them with both transitions. We can run clear path to the, on public cloud and we also help with modernization, right? So we always do the right thing by the customer. It's really the customer's tries in terms of what does the business warrant, how much business disruption are they willing to take as we do this modernization journey. And that's what determines us. And that's what makes us trusted advisers. Um, you're not looking out for the bottom line there in terms of how much our bill would be. Yup. >>Well, that's a, that's actually a great place to wrap up. Uh, I think it's hilarious that you mentioned mainframe since you were five years old, you gave me, you gave me a blank stare. When I mentioned stuff, Unisys was doing 20 years ago on a free auto Palm from Unisys. Thank you so much. It's a great point to close on. You're a trusted advisor when you're doing things that are truly in the customer's best interest and not just in your own company's best interests. I'm Dave Nicholson for the cube. We'd like to thank you for joining our continuous coverage at AWS reinvent 2021 stay tuned because we are your leader in hybrid tech event coverage.

>>I am. So take a week off representing my work functional encryption for attribute. With that sums, this is tracked, what with Michelle dolla and twin Cinco you go. So the context of this work is to do a private data based analysis. So consider we have a database off an attribute value past comprising expire public values and see a private value. So thank for concreteness off X. I being say demographic and geographic attributes, and C i s a some rating in the poll. Now we'll be interested in computing the average X value over some subset of the data base, for instance, where the subset of the data basis selected by apply some practical f So the public attribute exile The concrete, if we may be interested in it, would be, for instance, to look at, say, uh, individuals over the age of 40 who are focusing subscribers in the state of Wisconsin because they're more generous, setting off a tribute with the sums. Where are the up of the function Ethnic of Israel? One, but could be arbitrary. Wait. And the when FBIs the uproar license real one. This corresponds to this pressure from we'll be looking at this question from the point of your function. Encryption, where in function encryption We have encryption algorithm that they just put the database and outputs a cyber attacks with the key generation where them that this is simply a function and outputs a secret key and with the decryption algorithm that this is simple the separatists and the secret key outputs still actually with the sound. And we want the additional privacy guarantee that the site protection and the secret key should like no additional information beyond about the private values beyond what we learned from the attribute with that stuff. In this work, we construct function encryption scheme for actually with the sums for large class of programs corresponding toe aromatic question programs, which contains as a special case of bullion formula. Our scheme has the property that key generations independent off, and the site of database this'll means that we can generate secret keys without knowing a brewery the size off the database, and we can use the same secret key to decrypt database off any science containing any number of entries. Most of the encryption algorithm who's running time depends on end but otherwise independent off the complexity off the function at we achieve strong simulation base security against about the collusions, and we achieve security, understand assumptions over prime Auto buy dinner groups. Ah, construction is very simple. Um, position two steps. We start with the scheme for setting and equals toe one. So their scheme, which we did know superscript one that this is simple snz and description returned back in time, see? And then the second step, we amplify this species scheme from n equals one toe general arbitrary. And now, for this case and equals one, you can actually get the scheme by some simple tweaks. Toe briar works. So for this talk, we're going toe focus on the second steps, which is an amplification procedure that starts from n equals 212 general and >>without blowing up >>the site of secret key. So here's our first attempt at a subject and identification procedure. So this is a very natural construction, namely toe encrypt the entire database. We apply the basic scheme and each off the attribute value plastics. I see I And then secret key will basically be the secret key for the basic skin. Uh, to decrypt, we first applied a secret kid. They each of the individual anti production, the basic schemes to compute f of X, i C i and then some off these values. No, no, that correctness is very straightforward and for those from the off the underlying scheme. And we also accuse efficiency in the sense that the site of secret key only depends on F, and it's independent off the capital and the total number of innovation trees. The problem, however, is that we do not choose security in particular the this partial descriptions licked the individual ever besides the values words we should description should only like the Southeast values. To solve this problem, we basically will introduce additional randomize us to the skin. So during encryption who additionally big envelope scatters with some museum and we will apply the basis schemes and protection I together with I will see I the concatenation of the i n w I s the private very the secret keys similar to that from before, we have set tweak that we generate secret keeper function f that when we decrypt the basics type of tax return ever X icy blast W by see something we can do. And now if we summon all these values the W I can sell and then we get correctness unspeakable. And this additional randomize is from the w. I also guarantee that the partial descriptions don't leak additional information about the individual Apple Excellency. Nice. Now this works if we only get about one secret key. However, if we give up multiple secret keys, assist the case when we have collusions security no longer holds because we cannot reuse this randomizer w ice across multiple keys The fix this problem we will competition randomized w ice using a d this assumption concretely. During key generation, we will bigger random scatter arm the Children fresh for secret key and included in the exponents which we are, you know, by the square brackets and then the the secret key will be generated for a function f arm that when you saw that when you decrypt the individual stuff that you don't compute, emphasized the I rather you compute anxiety. I blast w items are Where are you are in the secret key and this computation is that India's for them. It's and toe to decrypt. We just need to multiply all these values together, which then basically induces a some India's opponents because, uh, toe get the final answer would need to do a group off the script lock so we only get efficient description. If the attribute With some life standpoint, it makes sense to me. All right, let's think about how we'll try to prove security. So consider the drug distribution of the partial descriptions, but we'll apply the devious assumption to replace W. I asked with uniformly random values, frustrated minders per secret key. Thanks, toa. Having a fresh are per security. And then we can a blessed testicle argument to essentially move each off the individual xz items toe the first time in the entire Siri's. This gives a security, >>however, if we try to carry this out Thesis, uh, argument out in the scheme, we need to somehow embed this and >>Eunice eventually be correspondent by tourists in tow. Either this type of tax or the secret key. >>Now let us know that we cannot embed >>and municipal will be into the secret key because the sight of secret cannot go through there. Now if we try to embed this annual, it's off and to be in the cyber attacks. It also doesn't work because we actually need, uh, and fresh units, Um, and to be her secretly query. And if you try to embed all this into the sack attacks the cyberattacks science will grow with number of secret key queries, which is something we cannot allow for. So to solve this problem, we're going toe instead, use a different strategy. We're going toe and what with the partial sums and and let this partial sums into the secret key across and introduced this aneurysm entropy, one unit at a time across and hybrid experiments in a bit more detail. Thistle. Again, it's the partial descriptions. We start by looking at the first two terms. We're >>gonna basically of like, the dishes before toe move that FX does that to turn from the second term to the custom. Now we look at the first and the term and again we can apply the delis assumption to the term toe move, uh, off extra. That three tools the custom on we can keep doing this over and over again. Moving the fourth f accepts for the four to the first summer and so on, so forth until we move everything to the question. This basically works. Each that will only need toe >>applied. The once per secret here introduced one you can eventually be. The main >>problem, however, is that we will now need in >>approval security to show that really assimilate itself that's indistinguishable while giving up assimilated secret key. >>This is the whole in >>general for the basic scheme, but we can work around this but essentially running two copies off the basic scheme that basically concludes the talk. So we show how to construct function encryption for attribute way that stands for automatic brushing programs in the what. We also discussed the extension toe a setting where the database is distributed across multiple clients, a couple of people problems. One is to achieve it up to security, and another is to get a scheme from lattice assumptions. Thank you very much.