>>from the Cube Studios in Palo Alto and Boston. It's the Cube covering empowering the autonomous enterprise brought to you by >>Oracle Consulting. Everybody, this is Dave Vellante. We've been covering the transformation of Oracle consulting and really, it's rebirth. And I'm here with Chris Fox, who's the group vice president for Enterprise Cloud Architects and chief technologist for the North America Tech Cloud at Oracle. Chris, thanks so much for coming on the Cube. >>Thanks too great to be here, >>So I love this title. You know, years ago, this thing is a cloud architect. Certainly there were chief technologist, but so you really that's those are your peeps, Is that right? >>That's right. That's right. That's really in my team. And I That's all we dio. So our focus is really helping our customers take this journey from when they were on premise. You really transforming with cloud? And when we think about Cloud, really, for us, it's a combination. It's it's our hybrid cloud, which happens to be on premise. And then, of course, the true public cloud, like most people, are familiar with so very exciting journey and frankly, of seeing just a lot of success for our customers. You know what I think we're seeing at Oracle, though? Because we're so connected with SAS. And then we're also connected with the traditional applications that have run the business for years. The legacy applications that have been, you know, servicing us for 20 years and then the cloud native developers. So with my team and I are constantly focused on now is things like digital transformation and really wiring up all three of these across. So if we think of, like a customer outcome like I want to have a package delivered to me from a retailer that actual process flow could touch a brand new cognitive site of e commerce it could touch essentially maybe a traditional application that used to be on Prem that's now in the cloud. And then it might even use new SAS application, maybe for maybe Herman process or delivery vehicle and scheduling. So when my team does, we actually connect all three. So what? I was mentioned, too. In my team and all of our customers, we have field service, all three of those constituents. And if you think about process flows, so I take a cloud. Native developer we help them become efficient. We take the person use to run in a traditional application, and we help them become more efficient. And then we have the SAS applications, which are now rolling out new features on a quarterly basis and the whole new delivery model. But the real key is connecting all three of these into your business process flow. That makes the customers life much more vision. >>So I want to get into this cloud conversations that you guys are using this term last mover advantage. I asked you last I was being last, You know, an advantage. But let me start there. >>People always say, You know, of course, we want to get out of the data center. We're going zero data center and how we say, Well, how are you going to handle that back office stuff, right? The stuff that's really big Frankie, um, doesn't handle just, you know, instances dying or things going away too easily. It needs predictable performance in the scale. It absolutely needs security. And ultimately, you know, a lot of these applications truly have relied on Oracle database. The Oracle database has its own specific characteristics that it means to run really well. So we actually looked at the cloud and we said, Let's take the first generation clouds but you're doing great But let's add the features that specifically a lot of times the Oracle workload needed in order to run very well and in a cost effective manner. So that's what we mean when we say last mover advantage, We said, Let's take the best of the clouds that are out there today. Let's look at the workloads that, frankly, Oracle runs and has been running for years. What are customers needed? And then let's build those features right into this, uh, this next version of the cloud we service the Enterprise. So our goal, honestly, which is interesting is even that first discussion we had about cloud, native and legacy applications and also the new SAS applications. We built a cloud that handles all three use cases at scale resiliently in very secure manner, and I don't know of any other cloud that's handling those three use cases all in. We'll call it the same pendency process. Oracle >>Mike witnesses. Why was it important for Oracle? And is it important for Oracle on its customers that have to participate in IAS and Pass and SAS. Why not just the last two layers of that? Um What does that mean from a strategic advantage standpoint? What does that do for >>you? Yeah, great question. So the number one reason why we needed to have all three was that we have so many customers to today are in a data center. They're running a lot of our workloads on premise, and they absolutely are trying to find a better way to deliver lower cost services to their customers. And so we couldn't just say, Let's just everyone needs to just become net new. Everyone just needs to ditch the old and go just a brand new alone. Too hard, too expensive at times. So we said, You know, let's kill us customers the ultimate amount of choice. So let's even go back against that developer conversation and SAS Um, if you didn't have eyes, we couldn't help customers achieve a zero data center strategy with their traditional applications will call it PeopleSoft or JD Edwards, Revisit Suite or even. There's some massive applications that are running on the Oracle cloud right now that are custom applications built on the Oracle database. What they want is, they said, Give me the lowest. Possibly a predictable performance. I as I'll run my app steer on this number two. Give me a platform service for database because, frankly, I don't really want to run your database. Like with all the manual effort. I want someone automate, patching scale up and down and all these types of features like should have given us. And then number three. You know, I do want SAS over time. So we spend a lot of time with our customers really saying, How do I take this traditional application, Run it on eyes and has and the number two Let's modernize it at scale. Maybe I want to start peeling off functionality and running in the cloud Native services right alongside, right? That's something again that we're doing at scale. And other people are having a hard time running these traditional workloads on Prem in the cloud. The second part is they say, you know, I've got this legacy traditional your api been servicing we well, or maybe a supply chain system ultimately want to get out of this. How do I get to SAS? You say Okay, here's the way to do this. First bring into the cloud running on IAS and pass and then selectively, I call it cloud slicing. Take a piece of functionality and put it into SAS. We're helping customers move to the cloud at scale. We're helping them do it at their rate, with whatever level of change they want. And when they're ready for SAS, we're ready for them. >>How does autonomous fit into this whole architecture Wait for that? That that description? I mean, it's a it's nuanced, but it's important. I'm sure you haven't discussed this conversation with a lot of cloud architects and chief technologist. They want to know this stuff. They want to know how it works. Um, you know, we will talk about what the business impact is, but but yeah, it's not about autonomous and where that fits. >>So the autonomous database, what we've done is really big. And look at all the runtime operations of an Oracle database. So tuning, patching, sparing all these different features and what we've done is taken the best of the Oracle database the best of something called Exit Data right, which we run in the cloud which really helps a lot of our customers. And then we wrapped it with a set of automation and security tools to help it. Really, uh, managing self tune itself. Patch itself scale up and down, independent between compute and storage. So why that's important, though, is that it? Really? Our goal is to help people run the Oracle databases they have for years, but with far less effort and then even not letting far less effort. Hopefully, you know a machine. Last man out of the equation we always talk about is your man plus machine is greater than man alone, so being assisted by, um, artificial intelligence and machine learning to perform those database operations, we should provide a better service to our customers. Far less paths are hoping goal is that people have been running Oracle databases, you know, How can we help them do it with far less effort and maybe spend more time on what the data can do for the organization? Right? Improve customer experience at Centra versus maybe like Hana Way. How do I spin up the table? It >>so talk about the business impact. So you go into customers, you talk to the the cloud Architects, the chief technologist. You pass that test now, you got to deliver the business impact. We're is Oracle Consulting fit with regard to that? And maybe you could talk about that where you were You guys want to take this thing? >>Yeah, absolutely. I mean, so you know, the cloud is a great set of technologies, but where Oracle Consulting is really helping us deliver is in, um, you know, one of the things I think that's been fantastic working with the Oracle consulting team is that, you know, Cloud is new for a lot of customers who've been running these environments for a number of years. There's always some fear and a little bit of trepidation saying, How do I learn this new cloud of the workloads? We're talking about David, like tier zero, tier one, tier two and all the way up to Dev and Test and, er, um, Oracle consulting. This really couple things in particular, Number one, they start with the end in mind, and number two that they start to do is they really help implement these systems. And, you know, there's a lot of different assurances that we have that we're going to get it done on time and better be under budget because ultimately, you know, again, that's a something is really paramount for us and then the third part of it. But sometimes a run book, right? We actually don't want to just live in our customer's environments. We want to help them understand how to run this new system. So training and change management. A lot of times, Oracle Consulting is helping with run books. We usually well, after doing it the first time. We'll sit back and say, Let the customer do in the next few times and essentially help them through the process. And our goal at that point is to leave only if the customer wants us to. But ultimately our goal is to implemented, get it to go live on time and then help the customer learn this journey to the cloud and without them. Frankly, uh, you know, I think these systems were sometimes too complex and difficult to do on your own. Maybe the first time, especially cause I could say they're closing the books. They might be running your entire supply chain. They run your entire HR system, whatever they might be, uh, too important, leading a chance. So they really help us with helping a customer become live and become very confident. Skilled. They could do themselves >>of the conversation. We have to leave it right there. But thanks so much for coming on the Cube and sharing your insights. Great stuff. >>Absolutely. Thanks for having me on. >>All right. You're welcome. And thank you for watching everybody. This is Dave Volante for the Cube. We are covering the oracle of North American Consulting. Transformation. And it's rebirth in this digital event. Keep it right there. We'll be right back.

>> Announcer: From the CUBE studios in Palo Alto and Boston, it's the CUBE, covering Empowering the Autonomous Enterprise brought to you by Oracle Consulting. >> Everybody welcome back to the CUBE, I'm Dave Vellante. We're covering the transformation of Oracle Consulting specifically focused on really what is, what I consider a rebirth from really staff augmentation to a much more strategic partner for customers. And with me to explore that a little bit is Sherry Lautenbach. She's the Senior Vice President of Cloud Key Accounts at Oracle, and we're also joined by Pat Mungovan, who's a Group VP for the North American Cloud Strategy also at Oracle. Folks welcome to the CUBE, thanks for comin' on. >> Thanks Dave. >> Yeah, thanks for havin' us. >> You're welcome. So Sherry, you're out talkin' to customers a lot, and I'm curious as to what that conversation is like specifically as it relates to consulting. Are you bringing Oracle Consulting now into the conversation? What's that conversation like? >> Absolutely, in fact every conversation we have relating to our cloud strategy, Oracle Consulting is part and parcel to that. And they are not staff augmentation, they are actually the digital transformation arm of what we do around cloud. So it's been really interesting to see what they've been able to do in terms of changing the narrative of what we do at Oracle, from just a software company to really transforming to a cloud provider. >> Strategy, obviously a fundamental part of any customer interaction, but what are you seeing? What underscores customer strategies? What are the business drivers for them right now? What are the catalysts that are driving their technology spending decision? >> I think a lot of it depends upon, especially in the times that we're in now, depends upon the industry that they're in, but most importantly, what we're seeing is right now is durability. So we want to make sure that the customers have, you know our Oracle customers and others, have an opportunity to have a disaster recovery, business continuity. In this stage right now it's less about expansion per se, unless they're in an industry that's uniquely positioned for that, and more about durability of the overall strategy. So when we look at that durability, we think about kind of two core missions. We think about sort of back office operations and continuity, and then we think about transformational revenue generation, and so when we partner with OCS, we want to make sure that we have both of those concepts in mind. >> You know we have a lot of talk about, in our community, about cloud first, and I think Oracle has sort of put forth the gauntlet of look we're leading now with cloud. You both have cloud in your title, but obviously being cloud first is more than that. Sherry, I wonder if you could talk about your customers in your cloud journey and share with us and kind of convince us that you are cloud first. >> I joined Oracle about 11 months ago, was in the industry for about 25 years, and I joined specifically because I believe in what Oracle is doing around this cloud journey. We are in our second generation of cloud capabilities and that's purposeful. And we do that because we realize that where cloud started and where we are today are two totally different things, and so we have capabilities around security, viability, extensions with autonomous that other cloud providers just simply don't have. And we built these from the ground up to ensure that we can run Oracle workloads, databases, and applications far better than any other cloud provider. So it's a super exciting time to be at Oracle, and it's absolutely fascinating what our customers are doing to adopt our technology. >> Pat, I want to ask you a sort of similar question, how fundamental is cloud to organization strategies, obviously everybody has a cloud strategy, but I'm specifically asking as it relates to mission critical workloads because, let's face it, that's been the hardest to move into the cloud. So when you're out talking to customers about their strategy, and obviously dovetailing it to Oracle's strategy, how do you align those? >> So first I think I would respond in the following way. When I think about our portfolio, I don't necessarily say cloud first, I say customer first, and I really want the customer to make a decision based upon a deployment model that makes sense for that particular customer, whether it's a regulated industry, or the public sector, or you know any sort of compliance considerations. So Oracle is one of the very few enterprise-class cloud Providers that has obviously on-premise capabilities as well, and so 99% of the cases that we see, with the exception of some of the sort of startup S and B type folks that are born in the cloud, we're dealing with the hybrid cloud model anyway. And so that's kind of the first order of priority is what's right for the customer and let's make sure that we get the appropriate deployment model for that customer. In terms of enterprise, essentially the workloads that we have, whether it's cloud or on prem, are enterprise workloads, and those are kind of separated into two buckets. One would be core mission, sort of the revenue generation side, and one would be mission critical, sort of the back office side. So Oracle is historically tremendous at the back office side, you know, running finance, running operations, running the supply chain, you know, doing those things that are mission critical. On the core mission side, that's really where we're starting to focus now, which is getting out into the revenue generation, the mission of the entity, with things like high performance computing and making sure that we have an ability to support our customers on both sides of the spectrum. >> All right Sherry, why are customers wanting to put mission critical workloads in the cloud? Is it the same sort of cloud agility and cost, et cetera, et cetera? I mean, why not just leave it on prem and keep it protected and maybe spend a little bit more? What's the driver for moving mission critical workloads into the cloud? >> Well, I think it's dependent upon what the initiatives are in the company, right? If they're looking for cost reduction are the looking for top line growth, are they looking for different capabilities around security that the cloud can provide? The great thing about what we do is we have optimized all of our workloads, both our database and our applications, into our cloud, so we're providing additional capabilities, but we're also saving a lot of money. So we say all the time that, you know, put us to the test, let us quantify what we would look like in the cloud with our workloads versus a competitor, and we will guarantee that we'll save you a lot of money. So I think that a lot of it has to do with one, it starts with essentially cost reduction but then they start seeing additional business value driven out of and back to Oracle Consulting. What Oracle Consulting provides in terms of the business value in the cloud is transformative for our customers. >> Talk about kind of how you lead in these customer conversations. >> Right, well normally our entry point is one, understanding what the business drivers are, right. It has to be a business led discussion. It really isn't a technology starter point, right? It really is around what business problems are you trying to solve and how can we help you solve them? And because we know your environments, we know what databases are deployed and where they're deployed, what Oracle applications you're leveraging to run your business, we can, I think, successfully position ourselves very, very competitively against other cloud providers. And I think that has been something that has resonated incredibly well with our customers and in fact, our largest customer. >> Yeah, so it seems like Oracle Consulting is an important ingredient as part of that strategy 'cause again, if it was, you know, five years ago, and it was just staff augmentation that's really not a compelling conversation to have with customers. But if you can come in with a mindset of strategic partner, you're bringing in Deloitte, we've been talking to some of their professionals about the Elevate Program with Oracle, that's a nice lever that you're, you can take advantage of. >> Absolutely, and in fact, we've seen that that is a huge opportunity for us because one, the partnership with Deloitte is incredibly strategic. But we also partner with other companies like Accenture and DXC and IBM candidly, and Oracle, Oracle Consulting is incredibly flexible in terms of what kind of partnerships and alignment they have with our customers and it's really based on what the customer preference is. >> It's not just about, you know, feature, function, speeds and feeds, maybe you could address that. And where does Oracle Consulting fit in that equation? >> We firmly believe that every customer is going to want to have a different option for what they do in the cloud and based on the provider. So we one, we've partnered with Microsoft, and we actually can interconnect our clouds together to provide that kind of flexibility to our customers, and Oracle Consulting is a key component of that. To engage our customers and talk about our Microsoft integration, our partnership, Oracle Consulting is the arm that does that work for us. So we are seeing them come up, come about in a much different way, and in a way that's differentiated between other consulting staff augmentation firms. >> I want to end on growth Pat, and maybe talk about everyone wants cloud, Cloud is the growth business. You look at Oracle's business, you know, everybody's business, this cloud is growing. Everything else is either hanging on or declining, so it's all about growth. How do you drive growth? What is cloud's role in terms of the growth strategy? And maybe add some color to that narrative. >> From a product perspective, I think we're sort of a luxury of riches around the autonomous capabilities with the (mumbles). So that's something that's incredibly unique to Oracle, you know, the autonomous database and all the autonomous services that we're rolling out. And that autonomous gets back to what we talked about earlier around security, around performance, around scalability and all these things so that ultimately we're positioning the capabilities of the future, but we're positioning them today. So we're a market leader in this space, you know, not only is the Oracle database, as you pointed out, the market leader, we're market leader in ERP Cloud and a bunch of the SAS series. But this autonomous segment of the market is crucial for us and crucial to our growth. >> Yeah, it really is an enabler, and what I've been saying is that it's almost compulsory for Oracle to participate and compete in the cloud because it gives you that automation and that scale. But you're talking about also setting up, you know, some future advantages of being able to take advantage of data, the combination of data, AI, and cloud is the new superpower within the industry. Sherry, I want to end on you. 11 months in at Oracle, let's say things work out great, you're here two, three, four years down the road, you look back, what does success look like? >> Success looks like every one of our customers moving to the Oracle cloud and seeing incredible business value from that partnering with Oracle Consulting. That's what my success criteria is. >> Guys, well listen, thanks for so much for coming on the CUBE where we've been tracking this transformation of Oracle Consulting. And one of the things that's very clear, is Oracle is obviously serious about cloud, but also seriously about bringing in new talent and new skill sets to really not only transform Oracle but help transform your customers. So thank you for your time, I really appreciate it. >> Thanks so much. >> Yep you bet, thank you. >> All right and thank you everybody for watching. This is Dave Vellante for the CUBE. We'll see you next time.

>> Narrator: From theCUBE studios in Palo Alto in Boston, it's theCUBE! Covering empowering the autonomous enterprise. Brought to you by ORACLE Consulting. >> Back to theCUBE everybody, this is Dave Vellante. We've been covering the transformation of ORACLE Consulting, and really it's rebirth, and I'm here with Chris Fox, who's the Group Vice President for Enterprise Cloud Architects and Chief Technologist for the North America Tech Cloud at ORACLE. Chris, thanks so much for coming on theCUBE. >> Thanks Dave, glad to be here. >> So, I love this title. I mean, years ago, there was no such thing as a cloud architect. Certainly there were chief technologists, but so, you are really, those are your peeps, is that right? >> That's right, that's right. That's really my team and I, that's all we do. So, our focus is really helping our customers take this journey from when they were on-premise to really transforming with cloud, and when we think about cloud, really, for us, it's a combination. It's our hybrid cloud, which happens to be on-premise, and then, of course, the true public cloud, like most people are familiar with. So, very exciting journey and, frankly, I've seen just a lot of success for our customers. You know, Dave, what I think we're seeing at ORACLE though, because we're so connected with SaaS, and then we're also connected with the traditional applications that have run the business for years, the legacy applications that have been, you know, servicing us for 20 years, and then the cloud needed developers. So, what my team and I are constantly focused on now is things like digital transformation and really wiring up all three of these across. So, if we think of, like, a customer outcome like I want to have a package delivered to me from a retailer, that actual process flow could touch a brand new cloud-native site from eCommerce, it could touch, essentially, maybe a traditional application that used to be on-prem that's now on the cloud, and then it might even use a new SaaS application, maybe, for maybe a permit process or delivery vehicle and scheduling. So, what my team does, we actually connect all three. So, what I always mention to my team and all of our customers, we have to be able to service all three of those constituents and really think about process flows. So, I take the cloud-native developer, we help them become efficient. We take the person who's been running that traditional application and we help them become more efficient, and then we have the SaaS applications, which are now rolling out new features on a quarterly basis and it's a whole new delivery model, but the real key is connecting all three of these into a business process flow that makes the customer's life much more efficient. People always say, you know, Chris, we want to get out of the data center, we're going zero data center, and I always say, well, how are you going to handle that back office stuff? Right? The stuff that's really big, it's cranky, doesn't handle just, you know, instances dying or things going away too easily. It needs predictable performance, it needs scale, it absolutely needs security, and ultimately, you know, a lot of these applications truly have relied on an ORACLE database. The ORACLE database has its own specific characteristics that it needs to run really well. So, we actually looked at the cloud and we said, let's take the first generation clouds, which are doing great, but let's add the features that specifically, a lot of times, the ORACLE workload needed in order to run very well and in a cost effective manner. So, that's what we mean when we say last mover advantage. We said, let's take the best of the clouds that are out there today, let's look at the workloads that, frankly, ORACLE runs and has been running for years, what our customers needed, and then let's build those features right into this next version of the cloud which can service the enterprise. So, our goal, honestly, which is interesting, is even that first discussion we had about cloud-native and legacy applications and also the new SaaS applications, we built a cloud that handles all three use cases at scale, resiliently, in a very secure manner, and I don't know of any other cloud that's handling those three use cases all in, we'll call it the same tendency for us at ORACLE. >> My question is why was it important for ORACLE, and is it important for ORACLE and its customers, to participate in IaaS and PaaS and SaaS? Why not just the last two layers of that? What does that give you from a strategic advantage standpoint and what does that do for your customer? >> Yeah, great question. So, the number one reason why we needed to have all three was that we have so many customers who, today, are in a data center. They're running a lot of our workloads on-premise and they absolutely are trying to find a better way to deliver lower-cost services to their customers and so we couldn't just say, let's just, everyone needs to just become net new, everyone just needs to ditch the old and go just to brand-new alone. Too hard, too expensive, at times. So we said, you know, let's give us customers the ultimate amount of choice. So, let's even go back again to that developer conversation in SaaS. If you didn't have IaaS, we couldn't help customers achieve a zero data center strategy with their traditional application, we'll call it PeopleSoft or JD Edwards or E-Business Suite or even, there's some massive applications that are running on the ORACLE cloud right now that are custom applications built on the ORACLE database. What they want is they said, give me the lowest cost but yet predictable performance IaaS. I'll run my apps tier on this. Number two, give me a platform service for database, 'cause frankly, I don't really want to run your database, like, with all the menial effort. I want someone to automate patching, scale up and down, and all these types of features like the cloud should have given us. And then number three, I do want SaaS over time. So, we spend a lot of time with our customers really saying, how do I take this traditional application, run it on IaaS and PaaS, and then number two, let's modernize it at scale. Maybe I want to start peeling off functionality and running them as cloud-native services right alongside, right? That's something, again, that we're doing at scale and other people are having a hard time running these traditional workloads on-prem in the cloud. The second part is they say, you know, I've got this legacy traditional ERP. It's been servicing me well, or maybe a supply chain system. Ultimately I want to get out of this. How do I get to SaaS? And we say, okay, here's the way to do this. First, bring it to the cloud, run it on IaaS and PaaS, and then selectively, I call it cloud slicing, take a piece of functionality and put it into SaaS. We're helping customers move to the cloud at scale. We're helping 'em do it at their rate, with whatever level of change they want, and when they are ready for SaaS, we're ready for them. >> And how does autonomous fit into this whole architecture? Thank you, by the way, for that description. I mean, it's nuanced but it's important. I'm sure you're having this conversation with a lot of cloud architects and chief technologists. They want to know this stuff, and they want to know how it works. And then, obviously, we'll talk about what the business impact is, but talk about autonomous and where that fit. >> So, the autonomous database, what we've done is really taken a look at all the runtime operations of an ORACLE database, so tuning, patching, securing, all these different features, and what we've done is taken the best of the ORACLE database, the best of something called Exadata, right, which we run on the cloud, which really helps a lot of our customers, and then we've wrapped it with a set of automation and security tools to help it really manage itself, tune itself, patch itself, scale up and down independent between computant storage. So, why that's important though is that it really, our goal is to help people run the ORACLE database as they have for years but with far less effort, and then even not only far less effort, hopefully, you know, a machine plus man, kind of the equation we always talk about is man plus machine is greater than man alone. So, being assisted by artificial intelligence and machine learning to perform those database operations, we should provide a better service to our customers with far less cost. Our hope and goal is that people have been running ORACLE databases. How can we help them do it with far less effort, and maybe spend more time on what the data can do for the organization, right? Improve customer experience, etc. Versus maybe, like, how do I spin up (breaks up). >> So, let's talk about the business impact. So, you go into customers, you talk to the cloud architects, the chief technologists, you pass that test. Now you got to deliver the business impact. Where does ORACLE Consulting fit with regard to that? And maybe you could talk about where you guys want to take this thing. >> Yeah, absolutely. I mean, the cloud is great set of technologies, but where ORACLE Consulting is really helping us deliver is in the outcome. One of the things, I think, that's been fantastic working with the ORACLE Consulting team is that, you know, cloud is new. For a lot of customers who've been running these environments for a number of years, there's always some fear and a little bit of trepidation saying, how do I learn this new cloud? I mean, the workloads we're talking about, Dave, are like tier zero, tier one, tier two and, you know, all the way up to DEV and TEST and DR. ORACLE Consulting does really couple of things in particular. Number one, they start with the end in mind, and number two that they start to do, is they really help implement these systems and there's a lot of different assurances that we have that we're going to get it done on time and better be under budget, 'cause ultimately, again, that's something that's really paramount for us. And then the third part of it, a lot of times it's runbooks, right? We actually don't want to just live in our customers' environments. We want to help them understand how to run this new system, so in training and change management, a lot of times ORACLE Consulting is helping with runbooks. We usually will, after doing it the first time, we'll sit back and let the customer do it the next few times and essentially help them through the process, and our goal at that point is to leave. Only if the customer wants us to, but ultimately our goal is to implement it, get it to go live on time, and then help the customer learn this journey to the cloud. And without them, frankly, I think these systems are sometimes too complex and difficult to do on your own maybe the first time, especially 'cause like I say, they're closing the books. They might be running your entire supply chain. They run your entire HR system or whatever they might be. Too important to leave to chance. So, they really help us with helping the customer become live and become very confident and skilled 'cause they can do it themselves. >> Well Chris, we've covered the gamut. Loved the conversation. We'll have to leave it right there, but thanks so much for coming on theCUBE and sharing your insights. Great stuff. >> Absolutely, thanks Dave, and thanks for having me on. >> All right, you're welcome, and thank you for watching everybody. This is Dave Vellante for theCUBE. We are covering the ORACLE of North America Consulting transformation and its rebirth in this digital event. Keep it right there, we'll be right back.

>> Narrator: From theCUBE Studios in Palo Alto and Boston, it's theCUBE. Covering empowering the autonomous enterprise. Brought to you by Oracle Consulting. >> Welcome back to theCUBE. I'm Dave Valante. We're covering the transformation of Oracle Consulting, specifically focused on really what is what I consider a rebirth from really staff augmentation to a much more strategic partner for customers. And with me to explore that a little bit is Sherry Lautenbach. She's the Senior Vice President of Cloud Key Accounts at Oracle. And we're also joined by Pat Mongovin who's a group VP for the North American Cloud Strategy, also at Oracle. Folks, welcome to theCUBE. Thanks for coming on. >> Thanks Dave. >> Yep, thanks for having us. >> You're welcome. So Sherry, you're out talking to customers a lot, and I'm curious as to what that conversation is like, specifically as it relates to consulting. Are you bringing Oracle Consulting now into the conversation? What's that conversation like? >> Absolutely, in fact every conversation we have relating to our Cloud strategy, Oracle Consulting is part and parcel to that. They are not staff augmentation, they are actually the digital transformation arm of what we do around Cloud. So it's been really interesting to see what they've been able to do in terms of changing the narrative of what we do at Oracle from just a software company to really transforming to a Cloud provider. >> Strategy, obviously a fundamental part of any customer interaction. But what are you seeing? What underscores customer strategies? What are the business drivers for them right now? What are the catalysts that are driving their technology spending decision? >> Yeah, it's a great question Dave. I think a lot of it depends upon, especially in the times that we're in now, depends upon the industry that they're in. But, most importantly what we're seeing is right now is durability. So we want to make sure that the customers have our Oracle customers and others have an opportunity to have disaster recovery, business continuity. In this stage right now it's less about expansion per say, unless they're in an industry that's uniquely in a position for that and more about durability of the overall strategy. So when we look at that durability we think about kind of two core missions. We think about sort of back office operations and continuity and then we think about transformational revenue generation, and so when we partner with those, yes we want to make sure that we have both of those concepts in mind. >> You know we have a lot of talk about in our community about Cloud first. I think Oracle has sort of put forth the gauntlet of look, we're leading now with Cloud. You both have Cloud in your title, but obviously being Cloud first is more than that. Sherry, I wonder if you could talk about your customer's and your Cloud journey and share with us and kind of convince us that you are Cloud first. >> Sure, no that's a great question and in fact I joined Oracle about eleven months ago. I was in the industry for about 25 years and I joined specifically because I believe in what Oracle is doing around this Cloud journey. We are in our second generation of Cloud capabilities, and that's purposeful. And we do that because we've realized that where Cloud started and where we are today are two totally different things. And so we have capabilities around security, viability, extensions with autonomous, that other Cloud providers just simply don't have. And we've built these from the ground up to ensure that we can run Oracle workloads, databases and applications far better than any other Cloud provider. So it's a super exciting time to be at Oracle and it's absolutely fascinating what our customers are doing to adopt our technology. >> I want to ask you a sort of similar question. How fundamental is Cloud to organization strategies. Obviously everybody has a Cloud strategy. But I'm specifically asking as it relates to mission-critical workloads because let's face it. That's been the hardest to move into the Cloud. So, when you're out talking to customers about their strategy, and obviously dovetailing into Oracle's strategy, how do you align you know those two views? >> Yeah, it's actually a really fascinating question. So first, I think I would respond in the following way. When I think about our portfolio, I don't necessarily say Cloud first, I say customer first. And I really want the customer to make a decision based upon a deployment model that makes sense for that particular customer. Whether it's a regulated industry or the public sector or any sort of compliance considerations. So Oracle is one of the very few enterprise class Cloud providers that has, obviously, on-premise capabilities as well. And so, 99% of the cases that we see, with the exception of some of the sort of startup S&B-type folks, that are bored in the Cloud, we're dealing with the hybrid Cloud model anyway. And so that's kind of the first order of priorities, what's right for the customer and let's make sure that we get the appropriate deployment model for that customer. In terms of enterprise, essentially the workloads that we have. Whether it's Cloud or on-prem, are enterprise workloads. And those are kind of separated into two brackets. One would be for mission, sort of the revenue generation side, and one would be mission critical, sort of the back office. So Oracle is historically tremendous at the back office side, running finance, running operations, running the supply chain. Doing those things that are mission-critical. On the core mission side that's really where we're starting to focus now which is getting out into the revenue generation, the mission of the entity, with things like high-performance compute, and making sure that we have an ability to support our customers on both sides of the spectrum. >> Sherry, why are customers wanting to put mission-critical workloads in the Cloud? Is it the same sort of Cloud agility and cost, etc. I mean why not just leave it on-prem and keep it protected and maybe spend a little bit more? What's the driver for moving mission-critical workloads into the Cloud? >> Well, I think it's dependent upon what the initiatives are in the company, right? Are they looking for cost reduction, are they looking for top-line growth, are they looking for different capabilities around security that the Cloud can provide? The great thing about what we do is, we have optimized all of our workloads, both our databases and our applications into our Cloud. So we're providing additional capabilities but we're also saving a lot of money. So, we say all the time that you know put us to the test. Let us quantify what we would look like in the Cloud with our workloads versus a competitor. And we will guarantee that we will save you a lot of money. So I think that a lot of it has to do with one, it starts with potentially cost reduction, but then they start seeing additional business value driven out of and back to Oracle Consulting. What Oracle Consulting provides in terms of the business value in the Cloud is transformative for our customers. >> Talk about how you lead in these customer conversations. >> Right, well normally our entry point of one understanding what the business drivers are, right? It has to be a business-led discussion. It really isn't a technology starter point, right? It really is around what business problems are you trying to solve, and how can we help you solve them. And because we know your environments, we know what databases are employed and where they're deployed. What Oracle applications you're leveraging to run your business. We can, I think, successfully position ourselves very competitively against other Cloud providers. And I think that is then something that has resonated incredibly well with our customers, and in fact our largest customers. >> Yeah, so it seems like Oracle could solve things of an important ingredient as part of that strategy 'cause again, if it was five years ago and was just staff augmentation, that's really not a compelling conversation to have with customers. But if you can come in with a mindset of strategic partner, you're bringing in Deloitte, we've been talking to some of their professionals about the elevate program with Oracle. That is a nice lever that you can take advantage of. >> Absolutely, and in fact we've seen that, that is a huge opportunity for us because one, the partnership with Deloitte is incredibly strategic. We also partner with other companies like Accenture and DXC and IBM candidly. And Oracle Consulting is incredibly flexible in terms of what kind of partnerships and alignment they have with our customers, and it's really based on what the customer's preference is. >> Not just about feature or function, speeds and feeds, maybe you can address that, and where does Oracle Consulting fit in that equation? >> We firmly believe that every customer is going to want to have a different option for what they'll do in the Cloud and based on the provider. So we, one, we've partnered with Microsoft and we actually can interconnect our Clouds together to provide that kind of flexibility to our customers and consulting is a key component of that. So we engage our customers and talk about our Microsoft integration, our partnership or the consulting is the arm that does that work for us. So we are seeing them come about in a much different way, in a way that's differentiated between other consulting you know staff augmentation firms. >> I want to end on growth, Pat. Maybe talk about I mean Cloud, Cloud is the growth business, you look at Oracle's business, everybody's business. As Cloud is growing, everything else is either hanging on or declining, so it's all about growth. How do you drive growth, what is Cloud's role in terms of the growth strategy, and maybe add some color to that narrative. >> From a product perspective, I think we're sort of a luxury of riches around the autonomous capability which we haven't talked about. So that's something that's incredibly unique to Oracle. The autonomous database and all the autonomous services that we're rolling out. And that autonomous gives back to what we talked about earlier around security, around performance, around scalability and all these things. So that ultimately we're positioning the capabilities of the future but we're positioning them today. So we're a market leader in this space. We're not only is the Oracle database as you pointed out the market leader. We're a market leader in ERP Cloud and a bunch of the SaaS areas. But this autonomous segment of the market is crucial for us and crucial to our growth. >> Yeah it really is an enabler. I've been saying that it's almost compulsory for Oracle to participate and compete in the Cloud because it gives you that automation and that scale but you're talking about also setting up some future advantages of being able to take advantage of data, the combination of data, AI, and Cloud is the new superpower within the industry. Sherry, I want to end on you. Eleven months in at Oracle, let's say things work out great. You're here two, three, four years down the road, you look back. What does success look like? >> Success looks like every one of our customers moving to the Oracle Cloud and seeing incredible business value from that, partnering with Oracle Consulting. That's what my success criteria is. >> Guys, well listen. Thanks so much for coming on theCUBE where we've been tracking this transformation of Oracle Consulting and one of the things that's very clear as Oracle's obviously serious about Cloud but also serious about bringing in new talent and new skillsets, you're really not only transform Oracle but help transform your customers, so thank you for your time, really appreciate it. >> Thanks so much. >> Yep, you bet, thank you. >> All right, and thank you everybody for watching. This is Dave Valante for theCUBE. We'll see you next time. (bumper music)

>> Narrator: From theCUBE studios in Palo Alto in Boston, it's theCUBE! Covering empowering the autonomous enterprise. Brought to you by ORACLE Consulting. >> Back to theCUBE everybody, this is Dave Vellante. We've been covering the transformation of ORACLE Consulting, and really it's rebirth, and I'm here with Chris Fox, who's the Group Vice President for Enterprise Cloud Architects and Chief Technologist for the North America Tech Cloud at ORACLE. Chris, thanks so much for coming on theCUBE. >> Thanks Dave, glad to be here. >> So, I love this title. I mean, years ago, there was no such thing as a cloud architect. Certainly there were chief technologists, but so, you are really, those are your peeps, is that right? >> That's right, that's right. That's really my team and I, that's all we do. So, our focus is really helping our customers take this journey from when they were on-premise to really transforming with cloud, and when we think about cloud, really, for us, it's a combination. It's our hybrid cloud, which happens to be on-premise, and then, of course, the true public cloud, like most people are familiar with. So, very exciting journey and, frankly, I've seen just a lot of success for our customers. You know, Dave, what I think we're seeing at ORACLE though, because we're so connected with SaaS, and then we're also connected with the traditional applications that have run the business for years, the legacy applications that have been, you know, servicing us for 20 years, and then the cloud needed developers. So, what my team and I are constantly focused on now is things like digital transformation and really wiring up all three of these across. So, if we think of, like, a customer outcome like I want to have a package delivered to me from a retailer, that actual process flow could touch a brand new cloud-native site from eCommerce, it could touch, essentially, maybe a traditional application that used to be on-prem that's now on the cloud, and then it might even use a new SaaS application, maybe, for maybe a permit process or delivery vehicle and scheduling. So, what my team does, we actually connect all three. So, what I always mention to my team and all of our customers, we have to be able to service all three of those constituents and really think about process flows. So, I take the cloud-native developer, we help them become efficient. We take the person who's been running that traditional application and we help them become more efficient, and then we have the SaaS applications, which are now rolling out new features on a quarterly basis and it's a whole new delivery model, but the real key is connecting all three of these into a business process flow that makes the customer's life much more efficient. People always say, you know, Chris, we want to get out of the data center, we're going zero data center, and I always say, well, how are you going to handle that back office stuff? Right? The stuff that's really big, it's cranky, doesn't handle just, you know, instances dying or things going away too easily. It needs predictable performance, it needs scale, it absolutely needs security, and ultimately, you know, a lot of these applications truly have relied on an ORACLE database. The ORACLE database has its own specific characteristics that it needs to run really well. So, we actually looked at the cloud and we said, let's take the first generation clouds, which are doing great, but let's add the features that specifically, a lot of times, the ORACLE workload needed in order to run very well and in a cost effective manner. So, that's what we mean when we say last mover advantage. We said, let's take the best of the clouds that are out there today, let's look at the workloads that, frankly, ORACLE runs and has been running for years, what our customers needed, and then let's build those features right into this next version of the cloud which can service the enterprise. So, our goal, honestly, which is interesting, is even that first discussion we had about cloud-native and legacy applications and also the new SaaS applications, we built a cloud that handles all three use cases at scale, resiliently, in a very secure manner, and I don't know of any other cloud that's handling those three use cases all in, we'll call it the same tendency for us at ORACLE. >> My question is why was it important for ORACLE, and is it important for ORACLE and its customers, to participate in IaaS and PaaS and SaaS? Why not just the last two layers of that? What does that give you from a strategic advantage standpoint and what does that do for your customer? >> Yeah, great question. So, the number one reason why we needed to have all three was that we have so many customers who, today, are in a data center. They're running a lot of our workloads on-premise and they absolutely are trying to find a better way to deliver lower-cost services to their customers and so we couldn't just say, let's just, everyone needs to just become net new, everyone just needs to ditch the old and go just to brand-new alone. Too hard, too expensive, at times. So we said, you know, let's give us customers the ultimate amount of choice. So, let's even go back again to that developer conversation in SaaS. If you didn't have IaaS, we couldn't help customers achieve a zero data center strategy with their traditional application, we'll call it PeopleSoft or JD Edwards or E-Business Suite or even, there's some massive applications that are running on the ORACLE cloud right now that are custom applications built on the ORACLE database. What they want is they said, give me the lowest cost but yet predictable performance IaaS. I'll run my apps tier on this. Number two, give me a platform service for database, 'cause frankly, I don't really want to run your database, like, with all the menial effort. I want someone to automate patching, scale up and down, and all these types of features like the cloud should have given us. And then number three, I do want SaaS over time. So, we spend a lot of time with our customers really saying, how do I take this traditional application, run it on IaaS and PaaS, and then number two, let's modernize it at scale. Maybe I want to start peeling off functionality and running them as cloud-native services right alongside, right? That's something, again, that we're doing at scale and other people are having a hard time running these traditional workloads on-prem in the cloud. The second part is they say, you know, I've got this legacy traditional ERP. It's been servicing me well, or maybe a supply chain system. Ultimately I want to get out of this. How do I get to SaaS? And we say, okay, here's the way to do this. First, bring it to the cloud, run it on IaaS and PaaS, and then selectively, I call it cloud slicing, take a piece of functionality and put it into SaaS. We're helping customers move to the cloud at scale. We're helping 'em do it at their rate, with whatever level of change they want, and when they are ready for SaaS, we're ready for them. >> And how does autonomous fit into this whole architecture? Thank you, by the way, for that description. I mean, it's nuanced but it's important. I'm sure you're having this conversation with a lot of cloud architects and chief technologists. They want to know this stuff, and they want to know how it works. And then, obviously, we'll talk about what the business impact is, but talk about autonomous and where that fit. >> So, the autonomous database, what we've done is really taken a look at all the runtime operations of an ORACLE database, so tuning, patching, securing, all these different features, and what we've done is taken the best of the ORACLE database, the best of something called Exadata, right, which we run on the cloud, which really helps a lot of our customers, and then we've wrapped it with a set of automation and security tools to help it really manage itself, tune itself, patch itself, scale up and down independent between computant storage. So, why that's important though is that it really, our goal is to help people run the ORACLE database as they have for years but with far less effort, and then even not only far less effort, hopefully, you know, a machine plus man, kind of the equation we always talk about is man plus machine is greater than man alone. So, being assisted by artificial intelligence and machine learning to perform those database operations, we should provide a better service to our customers with far less cost. Our hope and goal is that people have been running ORACLE databases. How can we help them do it with far less effort, and maybe spend more time on what the data can do for the organization, right? Improve customer experience, etc. Versus maybe, like, how do I spin up (breaks up). >> So, let's talk about the business impact. So, you go into customers, you talk to the cloud architects, the chief technologists, you pass that test. Now you got to deliver the business impact. Where does ORACLE Consulting fit with regard to that? And maybe you could talk about where you guys want to take this thing. >> Yeah, absolutely. I mean, the cloud is great set of technologies, but where ORACLE Consulting is really helping us deliver is in the outcome. One of the things, I think, that's been fantastic working with the ORACLE Consulting team is that, you know, cloud is new. For a lot of customers who've been running these environments for a number of years, there's always some fear and a little bit of trepidation saying, how do I learn this new cloud? I mean, the workloads we're talking about, Dave, are like tier zero, tier one, tier two and, you know, all the way up to DEV and TEST and DR. ORACLE Consulting does really couple of things in particular. Number one, they start with the end in mind, and number two that they start to do, is they really help implement these systems and there's a lot of different assurances that we have that we're going to get it done on time and better be under budget, 'cause ultimately, again, that's something that's really paramount for us. And then the third part of it, a lot of times it's runbooks, right? We actually don't want to just live in our customers' environments. We want to help them understand how to run this new system, so in training and change management, a lot of times ORACLE Consulting is helping with runbooks. We usually will, after doing it the first time, we'll sit back and let the customer do it the next few times and essentially help them through the process, and our goal at that point is to leave. Only if the customer wants us to, but ultimately our goal is to implement it, get it to go live on time, and then help the customer learn this journey to the cloud. And without them, frankly, I think these systems are sometimes too complex and difficult to do on your own maybe the first time, especially 'cause like I say, they're closing the books. They might be running your entire supply chain. They run your entire HR system or whatever they might be. Too important to leave to chance. So, they really help us with helping the customer become live and become very confident and skilled 'cause they can do it themselves. >> Well Chris, we've covered the gamut. Loved the conversation. We'll have to leave it right there, but thanks so much for coming on theCUBE and sharing your insights. Great stuff. >> Absolutely, thanks Dave, and thanks for having me on. >> All right, you're welcome, and thank you for watching everybody. This is Dave Vellante for theCUBE. We are covering the ORACLE of North America Consulting transformation and its rebirth in this digital event. Keep it right there, we'll be right back.

>> Narrator: From theCUBE Studios in Palo Alto and Boston, it's theCUBE. Covering empowering the autonomous enterprise. Brought to you by Oracle Consulting. >> Welcome back to theCUBE. I'm Dave Valante. We're covering the transformation of Oracle Consulting, specifically focused on really what is what I consider a rebirth from really staff augmentation to a much more strategic partner for customers. And with me to explore that a little bit is Sherry Lautenbach. She's the Senior Vice President of Cloud Key Accounts at Oracle. And we're also joined by Pat Mongovin who's a group VP for the North American Cloud Strategy, also at Oracle. Folks, welcome to theCUBE. Thanks for coming on. >> Thanks Dave. >> Yep, thanks for having us. >> You're welcome. So Sherry, you're out talking to customers a lot, and I'm curious as to what that conversation is like, specifically as it relates to consulting. Are you bringing Oracle Consulting now into the conversation? What's that conversation like? >> Absolutely, in fact every conversation we have relating to our Cloud strategy, Oracle Consulting is part and parcel to that. They are not staff augmentation, they are actually the digital transformation arm of what we do around Cloud. So it's been really interesting to see what they've been able to do in terms of changing the narrative of what we do at Oracle from just a software company to really transforming to a Cloud provider. >> Strategy, obviously a fundamental part of any customer interaction. But what are you seeing? What underscores customer strategies? What are the business drivers for them right now? What are the catalysts that are driving their technology spending decision? >> Yeah, it's a great question Dave. I think a lot of it depends upon, especially in the times that we're in now, depends upon the industry that they're in. But, most importantly what we're seeing is right now is durability. So we want to make sure that the customers have our Oracle customers and others have an opportunity to have disaster recovery, business continuity. In this stage right now it's less about expansion per say, unless they're in an industry that's uniquely in a position for that and more about durability of the overall strategy. So when we look at that durability we think about kind of two core missions. We think about sort of back office operations and continuity and then we think about transformational revenue generation, and so when we partner with those, yes we want to make sure that we have both of those concepts in mind. >> You know we have a lot of talk about in our community about Cloud first. I think Oracle has sort of put forth the gauntlet of look, we're leading now with Cloud. You both have Cloud in your title, but obviously being Cloud first is more than that. Sherry, I wonder if you could talk about your customer's and your Cloud journey and share with us and kind of convince us that you are Cloud first. >> Sure, no that's a great question and in fact I joined Oracle about eleven months ago. I was in the industry for about 25 years and I joined specifically because I believe in what Oracle is doing around this Cloud journey. We are in our second generation of Cloud capabilities, and that's purposeful. And we do that because we've realized that where Cloud started and where we are today are two totally different things. And so we have capabilities around security, viability, extensions with autonomous, that other Cloud providers just simply don't have. And we've built these from the ground up to ensure that we can run Oracle workloads, databases and applications far better than any other Cloud provider. So it's a super exciting time to be at Oracle and it's absolutely fascinating what our customers are doing to adopt our technology. >> I want to ask you a sort of similar question. How fundamental is Cloud to organization strategies. Obviously everybody has a Cloud strategy. But I'm specifically asking as it relates to mission-critical workloads because let's face it. That's been the hardest to move into the Cloud. So, when you're out talking to customers about their strategy, and obviously dovetailing into Oracle's strategy, how do you align you know those two views? >> Yeah, it's actually a really fascinating question. So first, I think I would respond in the following way. When I think about our portfolio, I don't necessarily say Cloud first, I say customer first. And I really want the customer to make a decision based upon a deployment model that makes sense for that particular customer. Whether it's a regulated industry or the public sector or any sort of compliance considerations. So Oracle is one of the very few enterprise class Cloud providers that has, obviously, on-premise capabilities as well. And so, 99% of the cases that we see, with the exception of some of the sort of startup S&B-type folks, that are bored in the Cloud, we're dealing with the hybrid Cloud model anyway. And so that's kind of the first order of priorities, what's right for the customer and let's make sure that we get the appropriate deployment model for that customer. In terms of enterprise, essentially the workloads that we have. Whether it's Cloud or on-prem, are enterprise workloads. And those are kind of separated into two brackets. One would be for mission, sort of the revenue generation side, and one would be mission critical, sort of the back office. So Oracle is historically tremendous at the back office side, running finance, running operations, running the supply chain. Doing those things that are mission-critical. On the core mission side that's really where we're starting to focus now which is getting out into the revenue generation, the mission of the entity, with things like high-performance compute, and making sure that we have an ability to support our customers on both sides of the spectrum. >> Sherry, why are customers wanting to put mission-critical workloads in the Cloud? Is it the same sort of Cloud agility and cost, etc. I mean why not just leave it on-prem and keep it protected and maybe spend a little bit more? What's the driver for moving mission-critical workloads into the Cloud? >> Well, I think it's dependent upon what the initiatives are in the company, right? Are they looking for cost reduction, are they looking for top-line growth, are they looking for different capabilities around security that the Cloud can provide? The great thing about what we do is, we have optimized all of our workloads, both our databases and our applications into our Cloud. So we're providing additional capabilities but we're also saving a lot of money. So, we say all the time that you know put us to the test. Let us quantify what we would look like in the Cloud with our workloads versus a competitor. And we will guarantee that we will save you a lot of money. So I think that a lot of it has to do with one, it starts with potentially cost reduction, but then they start seeing additional business value driven out of and back to Oracle Consulting. What Oracle Consulting provides in terms of the business value in the Cloud is transformative for our customers. >> Talk about how you lead in these customer conversations. >> Right, well normally our entry point of one understanding what the business drivers are, right? It has to be a business-led discussion. It really isn't a technology starter point, right? It really is around what business problems are you trying to solve, and how can we help you solve them. And because we know your environments, we know what databases are employed and where they're deployed. What Oracle applications you're leveraging to run your business. We can, I think, successfully position ourselves very competitively against other Cloud providers. And I think that is then something that has resonated incredibly well with our customers, and in fact our largest customers. >> Yeah, so it seems like Oracle could solve things of an important ingredient as part of that strategy 'cause again, if it was five years ago and was just staff augmentation, that's really not a compelling conversation to have with customers. But if you can come in with a mindset of strategic partner, you're bringing in Deloitte, we've been talking to some of their professionals about the elevate program with Oracle. That is a nice lever that you can take advantage of. >> Absolutely, and in fact we've seen that, that is a huge opportunity for us because one, the partnership with Deloitte is incredibly strategic. We also partner with other companies like Accenture and DXC and IBM candidly. And Oracle Consulting is incredibly flexible in terms of what kind of partnerships and alignment they have with our customers, and it's really based on what the customer's preference is. >> Not just about feature or function, speeds and feeds, maybe you can address that, and where does Oracle Consulting fit in that equation? >> We firmly believe that every customer is going to want to have a different option for what they'll do in the Cloud and based on the provider. So we, one, we've partnered with Microsoft and we actually can interconnect our Clouds together to provide that kind of flexibility to our customers and consulting is a key component of that. So we engage our customers and talk about our Microsoft integration, our partnership or the consulting is the arm that does that work for us. So we are seeing them come about in a much different way, in a way that's differentiated between other consulting you know staff augmentation firms. >> I want to end on growth, Pat. Maybe talk about I mean Cloud, Cloud is the growth business, you look at Oracle's business, everybody's business. As Cloud is growing, everything else is either hanging on or declining, so it's all about growth. How do you drive growth, what is Cloud's role in terms of the growth strategy, and maybe add some color to that narrative. >> From a product perspective, I think we're sort of a luxury of riches around the autonomous capability which we haven't talked about. So that's something that's incredibly unique to Oracle. The autonomous database and all the autonomous services that we're rolling out. And that autonomous gives back to what we talked about earlier around security, around performance, around scalability and all these things. So that ultimately we're positioning the capabilities of the future but we're positioning them today. So we're a market leader in this space. We're not only is the Oracle database as you pointed out the market leader. We're a market leader in ERP Cloud and a bunch of the SaaS areas. But this autonomous segment of the market is crucial for us and crucial to our growth. >> Yeah it really is an enabler. I've been saying that it's almost compulsory for Oracle to participate and compete in the Cloud because it gives you that automation and that scale but you're talking about also setting up some future advantages of being able to take advantage of data, the combination of data, AI, and Cloud is the new superpower within the industry. Sherry, I want to end on you. Eleven months in at Oracle, let's say things work out great. You're here two, three, four years down the road, you look back. What does success look like? >> Success looks like every one of our customers moving to the Oracle Cloud and seeing incredible business value from that, partnering with Oracle Consulting. That's what my success criteria is. >> Guys, well listen. Thanks so much for coming on theCUBE where we've been tracking this transformation of Oracle Consulting and one of the things that's very clear as Oracle's obviously serious about Cloud but also serious about bringing in new talent and new skillsets, you're really not only transform Oracle but help transform your customers, so thank you for your time, really appreciate it. >> Thanks so much. >> Yep, you bet, thank you. >> All right, and thank you everybody for watching. This is Dave Valante for theCUBE. We'll see you next time. (bumper music)

>> Hi I'm Peter Burris and welcome to another Wikibon Action Item. (electronic music) One of the most pressing strategic issues that businesses face is how to get more value out of their data, In our opinion that's the essence of a digital business transformation, is the using of data as an asset to improve your operations and take better advantage of market opportunities. The problem of data though, it's shareable, it's copyable, it's reusable. It's easy to create derivative value out of it. One of the biggest misnomers in the digital business world is the notion that data is the new fuel or the new oil. It's not, You can only use oil once. You can apply it to a purpose and not multiple purposes. Data you can apply to a lot of purposes, which is why you are able to get such interesting and increasing returns to that asset if you use it appropriately. Now, this becomes especially important for technology companies that are attempting to provide digital business technologies or services or other capabilities to their customers. In the consumer world, it started to reach a head. Questions about Facebook's reuse of a person's data through an ad based business model is now starting to lead people to question the degree to which the information asymmetry about what I'm giving and how they're using it is really worth the value that I get out of Facebook, is something that consumers and certainly governments are starting to talk about. it's also one of the bases for GDPR, which is going to start enforcing significant fines in the next month or so. In the B2B world that question is going to become especially acute. Why? Because as we try to add intelligence to the services and the products that we are utilizing within digital business, some of that requires a degree of, or some sort of relationship where some amount of data is passed to improve the models and machine learning and AI that are associated with that intelligence. Now, some companies have come out and said flat out they're not going to reuse a customer's data. IBM being a good example of that. When Ginni Rometty at IBM Think said, we're not going to reuse our customer's data. The question for the panel here is, is that going to be a part of a differentiating value proposition in the marketplace? Are we going to see circumstances in which companies keep products and services low by reusing a client's data and others sustaining their experience and sustaining a trust model say they won't. How is that going to play out in front of customers? So joining me today here in the studio, David Floyer. >> Hi there. >> And on the remote lines we have Neil Raden, Jim Kobielus, George Gilbert, and Ralph Finos. Hey, guys. >> All: Hey. >> All right so... Neil, let me start with you. You've been in the BI world as a user, as a consultant, for many, many number of years. Help us understand the relationship between data, assets, ownership, and strategy. >> Oh, God. Well, I don't know that I've been in the BI world. Anyway, as a consultant when we would do a project for a company, there were very clear lines of what belong to us and what belong to the client. They were paying us generously. They would allow us to come in to their company and do things that they needed and in return we treated them with respect. We wouldn't take their data. We wouldn't take their data models that we built, for example, and sell them to another company. That's just, as far as I'm concerned, that's just theft. So if I'm housing another company's data because I'm a cloud provider or some sort of application provider and I say well, you know, I can use this data too. To me the analogy is, I'm a warehousing company and independently I go into the warehouse and I say, you know, these guys aren't moving their inventory fast enough, I think I'll sell some of it. It just isn't right. >> I think it's a great point. Jim Kobielus. As we think about the role that data, machine learning play, training models, delivering new classes of services, we don't have a clean answer right now. So what's your thought on how this is likely to play out? >> I agree totally with Neil, first of all. If it's somebody else's data, you don't own it, therefore you can't sell and you can't monetize it, clearly. But where you have derivative assets, like machine learning models that are derivative from data, it's the same phenomena, it's the same issue at a higher level. You can build and train, or should, your machine learning models only from data that you have legal access to. You own or you have license and so forth. So as you're building these derivative assets, first and foremost, make sure as you're populating your data lake, to build and to do the training, that you have clear ownership over the data. So with GDPR and so forth, we have to be doubly triply vigilant to make sure that we're not using data that we don't have authorized ownership or access to. That is critically important. And so, I get kind of queasy when I hear some people say we use blockchain to make... the sharing of training data more distributed and federated or whatever. It's like wait a second. That doesn't solve the issues of ownership. That makes it even more problematic. If you get this massive blockchain of data coming from hither and yon, who owns what? How do you know? Do you dare build any models whatsoever from any of that data? That's a huge gray area that nobody's really addressed yet. >> Yeah well, it might mean that the blockchain has been poorly designed. I think that we talked in one of the previous Action Items about the role that blockchain design's going to play. But moving aside from the blockchain, so it seems as though we generally agree that data is owned by somebody typically and that the ownership of it, as Neil said, means that you can't intercept it at some point in time just because it is easily copied and then generate rents on it yourself. David Floyer, what does that mean from a ongoing systems design and development standpoint? How are we going to assure, as Jim said, not only that we know what data is ours but make sure that we have the right protection strategies, in a sense, in place to make sure that the data as it moves, we have some influence and control over it. >> Well, my starting point is that AI and AI infused products are fueled by data. You need that data, and Jim and Neil have already talked about that. In my opinion, the most effective way of improving a company's products, whatever the products are, from manufacturing, agriculture, financial services, is to use AI infused capabilities. That is likely to give you the best return on your money and businesses need to focus on their own products. That's the first place you are trying to protect from anybody coming in. Businesses own that data. They own the data about your products, in use by your customers, use that data to improve your products with AI infused function and use it before your competition eats your lunch. >> But let's build on that. So we're not saying that, for example, if you're a storage system supplier, since that's a relatively easy one. You've got very, very fast SSDs. Very, very fast NVMe over Fabric. Great technology. You can collect data about how that system is working but that doesn't give you rights to then also collect data about how the customer's using the system. >> There is a line which you need to make sure that you are covering. For example, Call Home on a product, any product, whose data is that? You need to make sure that you can use that data. You have some sort of agreement with the customer and that's a win-win because you're using that data to improve the product, prove things about it. But that's very, very clear that you should have a contractual relationship, as Jim and Neil were pointing out. You need the right to use that data. It can't come beyond the hand. But you must get it because if you don't get it, you won't be able to improve your products. >> Now, we're talking here about technology products which have often very concrete and obvious ownership and people who are specifically responsible for administering them. But when we start getting into the IoT domain or in other places where the device is infused with intelligence and it might be collecting data that's not directly associated with its purpose, just by virtue of the nature of sensors that are out there and the whole concept of digital twin introduces some tension in all this. George Gilbert. Take us through what's been happening with the overall suppliers of technology that are related to digital twin building, designing, etc. How are they securing or making promises committing to their customers that they will not cross this data boundary as they improve the quality of their twins? >> Well, as you quoted Ginni Rometty starting out, she's saying IBM, unlike its competitors, will not take advantage and leverage and monetize your data. But it's a little more subtle than that and digital twins are just sort of another manifestation of industry-specific sort of solution development that we've done for decades. The differences, as Jim and David have pointed out, that with machine learning, it's not so much code that's at the heart of these digital twins, it's the machine learning models and the data is what informs those models. Now... So you don't want all your secret sauce to go from Mercedes Benz to BMW but at the same time the economics of industry solutions means that you do want some of the repeatability that we've always gotten from industry solutions. You might have parts that are just company specific. And so in IBM's case, if you really parse what they're saying, they take what they learn in terms of the models from the data when they're working with BMW, and some of that is going to go into the industry specific models that they're going to use when they're working with Mercedes-Benz. If you really, really sort of peel the onion back and ask them, it's not the models, it's not the features of the models, but it's the coefficients that weight the features or variables in the models that they will keep segregated by customer. So in other words, you get some of the benefits, the economic benefits of reuse across customers with similar expertise but you don't actually get all of the secret sauce. >> Now, Ralph Finos-- >> And I agree with George here. I think that's an interesting topic. That's one of the important points. It's not kosher to monetize data that you don't own but conceivably if you can abstract from that data at some higher level, like George's describing, in terms of weights and coefficients and so forth, in a neural network that's derivative from the model. At some point in the abstraction, you should be able to monetize. I mean, it's like a paraphrase of some copyrighted material. A paraphrase, I'm not a lawyer, but you can, you can sell a paraphrase because it's your own original work that's based obviously on your reading of Moby Dick or whatever it is you're paraphrasing. >> Yeah, I think-- >> Jim I-- >> Peter: Go ahead, Neil. >> I agree with that but there's a line. There was a guy who worked at Capital One, this was about ten years ago, and he was their chief statistician or whatever. This was before we had words like machine learning and data science, it was called statistics and predictive analytics. He left the company and formed his own company and rewrote and recoded all of the algorithms he had for about 20 different predictive models. Formed a company and then licensed that stuff to Sybase and Teradata and whatnot. Now, the question I have is, did that cross the line or didn't it? These were algorithms actually developed inside Capital One. Did he have the right to use those, even if he wrote new computer code to make them run in databases? So it's more than just data, I think. It's a, well, it's a marketplace and I think that if you own something someone should not be able to take it and make money on it. But that doesn't mean you can't make an agreement with them to do that, and I think we're going to see a lot of that. IMSN gets data on prescription drugs and IRI and Nielsen gets scanner data and they pay for it and then they add value to it and they resell it. So I think that's really the issue is the use has to be understood by all the parties and the compensation has to be appropriate to the use. >> All right, so Ralph Finos. As a guy who looks at market models and handles a lot of the fundamentals for how we do our forecasting, look at this from the standpoint of how people are going to make money because clearly what we're talking about sounds like is the idea that any derivative use is embedded in algorithms. Seeing how those contracts get set up and I got a comment on that in a second, but the promise, a number of years ago, is that people are going to start selling data willy-nilly as a basis for their economic, a way of capturing value out of their economic activities or their business activities, hasn't matured yet generally. Do we see like this brand new data economy, where everybody's selling data to each other, being the way that this all plays out? >> Yeah, I'm having a hard time imagining this as a marketplace. I think we pointed at the manufacturing industries, technology industries, where some of this makes some sense. But I think from a practitioner perspective, you're looking for variables that are meaningful that are in a form you can actually use to make prediction. That you understand what the the history and the validity of that of that data is. And in a lot of cases there's a lot of garbage out there that you can't use. And the notion of paying for something that ultimately you look at and say, oh crap, it's not, this isn't really helping me, is going to be... maybe not an insurmountable barrier but it's going to create some obstacles in the market for adoption of this kind of thought process. We have to think about the utility of the data that feeds your models. >> Yeah, I think there's going to be a lot, like there's going to be a lot of legal questions raised and I recommend that people go look at a recent SiliconANGLE article written by Mike Wheatley and edited by our Editor In Chief Robert Hof about Microsoft letting technology partners own right to joint innovations. This is a quite a difference. This is quite a change for Microsoft who used to send you, if you sent an email with an idea to them, you'd often get an email back saying oh, just to let you know any correspondence we have here is the property of Microsoft. So there clearly is tension in the model about how we're going to utilize data and enable derivative use and how we're going to share, how we're going to appropriate value and share in the returns of that. I think this is going to be an absolutely central feature of business models, certainly in the digital business world for quite some time. The last thing I'll note and then I'll get to the Action Items, the last thing I'll mention here is that one of the biggest challenges in whenever we start talking about how we set up businesses and institutionalize the work that's done, is to look at the nature of the assets and the scope of the assets and in circumstances where the asset is used by two parties and it's generating a high degree of value, as measured by the transactions against those assets, there's always going to be a tendency for one party to try to take ownership of it. One party that's able to generate greater returns than the other, almost always makes move to try to take more control out of that asset and that's the basis of governance. And so everybody talks about data governance as though it's like something that you worry about with your backup and restore. Well, that's important but this notion of data governance increasingly is going to become a feature of strategy and boardroom conversations about what it really means to create data assets, sustain those data assets, get value out of them, and how we determine whether or not the right balance is being struck between the value that we're getting out of our data and third parties are getting out of our data, including customers. So with that, let's do a quick Action Item. David Floyer, I'm looking at you. Why don't we start here. David Floyer, Action Item. >> So my Action Item is for businesses, you should focus. Focus on data about your products in use by your customers, to improve, help improve the quality of your products and fuse AI into those products as one of the most efficient ways of adding value to it. And do that before your competition has a chance to come in and get data that will stop you from doing that. >> George Gilbert, Action Item. >> I guess mine would be that... in most cases you you want to embrace some amount of reuse because of the economics involved from your joint development with a solution provider. But if others are going to get some benefit from sort of reusing some of the intellectual property that informs models that you build, make sure you negotiate with your vendor that any upgrades to those models, whether they're digital twins or in other forms, that there's a canonical version that can come back and be an upgraded path for you as well. >> Jim Kobielus, Action Item. >> My Action Item is for businesses to regard your data as a product that you monetize yourself. Or if you are unable to monetize it yourself, if there is a partner, like a supplier or a customer who can monetize that data, then negotiate the terms of that monetization in your your relationship and be vigilant on that so you get a piece of that stream. Even if the bulk of the work is done by your partner. >> Neil Raden, Action Item. >> It's all based on transparency. Your data is your data. No one else can take it without your consent. That doesn't mean that you can't get involved in relationships where there's an agreement to do that. But the problem is most agreements, especially when you look at a business consumer, are so onerous that nobody reads them and nobody understands them. So the person providing the data has to have an unequivocal right to sell it to you and the person buying it has to really understand what the limits are that they can do with it. >> Ralph Finos, Action Item. You're muted Ralph. But it was brilliant, whatever it was. >> Well it was and I really can't say much more than that. (Peter laughs) But I think from a practitioner perspective and I understand that from a manufacturing perspective how the value could be there. But as a practitioner if you're fishing for data out there that someone has that might look like something you can use, chances are it's not. And you need to be real careful about spending money to get data that you're not really clear is going to help you. >> Great. All right, thanks very much team. So here's our Action Item conclusion for today. The whole concept of digital business is predicated in the idea of using data assets in a differential way to better serve your markets and improve your operations. It's your data. Increasingly, that is going to be the base for differentiation. And any weak undertaking to allow that data to get out has the potential that someone else can, through their data science and their capabilities, re-engineer much of what you regard as your differentiation. We've had conversations with leading data scientists who say that if someone were to sell customer data into a open marketplace, that it would take about four days for a great data scientist to re-engineer almost everything about your customer base. So as a consequence, we have to tread lightly here as we think about what it means to release data into the wild. Ultimately, the challenge there for any business will be: how do I establish the appropriate governance and protections, not just looking at the technology but rather looking at the overall notion of the data assets. If you don't understand how to monetize your data and nonetheless enter into a partnership with somebody else, by definition that partner is going to generate greater value out of your data than you are. There's significant information asymmetries here. So it's something that, every company must undertake an understanding of how to generate value out of their data. We don't think that there's going to be a general-purpose marketplace for sharing data in a lot of ways. This is going to be a heavily contracted arrangement but it doesn't mean that we should not take great steps or important steps right now to start doing a better job of instrumenting our products and services so that we can start collecting data about our products and services because the path forward is going to demonstrate that we're going to be able to improve, dramatically improve the quality of the goods and services we sell by reducing the assets specificities for our customers by making them more intelligent and more programmable. Finally, is this going to be a feature of a differentiated business relationship through trust? We're open to that. Personally, I'll speak for myself, I think it will. I think that there is going to be an important element, ultimately, of being able to demonstrate to a customer base, to a marketplace, that you take privacy, data ownership, and intellectual property control of data assets seriously and that you are very, very specific, very transparent, in how you're going to use those in derivative business transactions. All right. So once again, David Floyer, thank you very much here in the studio. On the phone: Neil Raden, Ralph Finos, Jim Kobielus, and George Gilbert. This has been another Wikibon Action Item. (electronic music)

>>Okay, we're back. I'm Dave Valante with The Cube and you're watching Evolving Influx DB into the smart data platform made possible by influx data. Anna East Otis Georgio is here. She's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into realtime analytics. Anna is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IO X is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory, of course for speed. It's a kilo store, so it gives you compression efficiency, it's gonna give you faster query speeds, it gonna use store files and object storages. So you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOCs is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's lift tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import, super useful. Also, broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so a lot there. Now we talked to Brian about how you're using Rust and and which is not a new programming language and of course we had some drama around Russ during the pandemic with the Mozilla layoffs, but the formation of the Russ Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Rust was chosen because of his exceptional performance and rebi reliability. So while rust is synt tactically similar to c c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers and dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on card for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ, Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fixed race conditions to protect against buffering overflows and to ensure thread safe ay caching structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learned about the the new engine and the, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you're really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data and so much of the efficiency and performance of IOCs comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of illustrate why calmer data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then neighbor each other and when they neighbor each other in the storage format. This provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the min and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one times stamp and do that for every single row. So you're scanning across a ton more data and that's why row oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, calmer data fit framework. So that's where a lot of the advantages come >>From. Okay. So you've basically described like a traditional database, a row approach, but I've seen like a lot of traditional databases say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native it, is it not as effective as the, is the form not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. >>Yeah. Got it. So let's talk about Arrow data fusion. What is data fusion? I know it's written in rust, but what does it bring to to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as its in memory format. So the way that it helps influx DB IOx is that okay, it's great if you can write unlimited amount of cardinality into influx cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PDA's data frames as well and all of the machine learning tools associated with pandas. >>Okay. You're also leveraging par K in the platform course. We heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Par K and why is it important? >>Sure. So Par K is the calm oriented durable file format. So it's important because it'll enable bulk import and bulk export. It has compatibility with Python and pandas so it supports a broader ecosystem. Parque files also take very little disc disc space and they're faster to scan because again they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and these, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call it the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOCs and I really encourage if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and I just wanna learn more, then I would encourage you to go to the monthly tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel. Look for the influx D DB underscore IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about IOCs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how influx TB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and you guys super responsive, so really appreciate that. All right, thank you so much and East for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yokum. He's the director of engineering for Influx Data and we're gonna talk about how you update a SaaS engine while the plane is flying at 30,000 feet. You don't wanna miss this.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>Okay. We're back at the area in Las Vegas, Falcon 22. You're watching the cube. My name is Dave Valante. Michael cent is here. He's the chief technology officer at CrowdStrike. Michael. Good to see you. Thanks. Thanks >>For >>Having me. Yeah. So this is your first time I think, on the cube. It is, and, and it's really a pleasure. I've been following you, watching you very closely. You're, you know, quite prominent and, and, you know, very articulate. I loved your keynote talking about what is XDR. I think you guys are gonna do really well in that space, cuz you've got clarity of vision and execution. Talk about some of the announcements that you made this week, particularly interested in, in insight. XDR what's that all about? >>Yeah. So I've been talking about XDR for a while and trying to help push the right narrative. There's a lot of marketing in the industry with XDR. So we've been talking a lot about what it, what it means that the benefit that it provides from a technology perspective, what you need in the architecture. So we firmly believe it's a philosophy and we build all of our technology to work together, but it's bringing in third parties. And that was really a lot of the, the announcements. My keynote was to show everybody the work that we've been doing to bring in data from Zscaler and Proofpoint. And we talked about bringing in data from a whole range of different vendors, firewall vendors, and we've been doing XDR use cases for a long time. So a big part of our strategy is to make security easy. And we've been doing a lot of XDR use cases with our Falcon insight module. So the announcement that I made was to relaunch Falcon insight as insight XDR and it means all of our close to 20,000 customers have access to the product. >>So that gets bundled right in it's like SAS automatically part of the portfolio >>Log off on Friday, come back on Monday and you're good to go. >>And then, and you, you just, you just called out Zscaler and Proofpoint you, I think you also mentioned Palo Alto network, Cisco for net as well. You're pulling in telemetry from, yeah, >>We've got a, we got a long map of, of people that we're integrating with. We talked about Cisco, we talked about for drop and for net, we announced that we're gonna be pulling in telemetry from, from Palo and a range of other vendors, Microsoft and others. And that's what XDR is about. It's about first party and third party integration and making all of the telemetry work together. >>I was talking to George about this yesterday is I think there's a lot of confusion. Sometimes when you have the dogma of cloud native, you know, snowflake, same thing, no, we're not doing OnPrem. This is hybrid. People think that that you're excluding on-prem data, but you're not, you can ingest on-prem data, right? >>We absolutely are not excluding on-prem. We will support and, and secure every workload, whether it's on-prem or in the cloud, whether it's connected to the internet or offline, a lot of the, the indicators of attack and the, and the detection techniques that we have are on the sensor itself. So you don't have to be connected anywhere for that capability to work. You get the benefit when you connect to the cloud of the additional visibility, the additional protection, but the core capabilities on the sensor that we have >>Given that you guys started 11 years ago, plus two days now, and you had that dogma cloud cloud, first cloud cloud, only Nate cloud native. Was there ever a point where you're like, you know, boy, we might be missing some of the market, you know? And, and you, you, you held true to your principles. Two part question. Did you ever question that and by focusing all your resources on cloud, what, what has that given you? >>It's there's been a Eliza focus on having a, a native cloud platform. It's easy to say cloud native. And if you look at a lot of the vendors in the industry today, if you are a, a customer and you ask them, Hey, can you gimme an on-premise product? I'm not gonna buy your product. They've got an on premise product. The problem is when you have two different versions, you end up having compromise. You have to manage two code bases, impact to your engineering team. Their features are different customers. Ultimately are the ones that miss out because if I have the on-prem version or if the cloud version, I may not get the same capability for us, it's been very clear. It's been a laser focus to be a cloud and cloud only from day one. >>You've renamed humo. I gotta stop using humo. I guess it's not called log scale, Falcon, complete log scale. You're bringing together security and observability. Although you're not doing the full spectrum of observability, you're just sort of focusing on, you know, part of it. Can you explain that? >>Yeah. So first of all, we did rebrand and bring the homeo brand closer to a crowd strike by renaming it Falcon log scale. And just to be clear, it's not just the rebranding of the name. We've been spending a lot of time. We made that acquisition in March of, of last year, and we've been doing a lot of work on the technology. We built out long, the Falcon long term retention. We built a whole bunch of capability into the product. So now was the right time to rebrand it as Falcon log scale. And at the same time, we also announced Falcon complete log scale. And it's part of the complete franchise. And that's where customers can get the value and the benefit of log scale, but they don't have to set it up. They don't have to manage it. They leave that to us. >>So you get pretty much involved in, in the, the M and a activity. You talked on stage yesterday about reify and, and what's going on there. You guys got, obviously gotta, still do that. You, but you made investments this week. You announced investments in salt security, the API specialist, and, and also Vanta compliance automation. What's the thinking behind that, you know, explain actually the fund that you guys are sprinkling around as a strategic investor and why those companies. Yeah. >>So there's two, two parts that, that I'm involved in on that part of my team. One is the M and a team. And one is the Falcon fund side of the business. Obviously two very different things. The, the M and a part of CrowdStrike, we're always looking to see for every technology space that we want to get into, you know, what is the best option build by a partner? Sometimes it's built sometimes it's a, it's a hybrid approach of build and partner. Other times we go down the path of M and a, and I was super excited about reify, great company, great technology. And as you said, we made announcements to we're investing as part of the fund into, into van and salt. We, we, we are very blessed. We're very fortunate to have achieved a lot of success in a short period of time. And we think we've got an opportunity to help fledgling companies to help them guide through the process of setting up the company, helping them with engineering principles and guidelines, helping them with the go to market perspective. So the fund is really about that. It's finding the next cybersecurity company working closely together, and it's been a huge success. You had banter and salt on earlier, and there's so much excitement about what they do. >>Yeah. I mean, it's clear, clear, compliment to what you guys are doing. I want to ask you about your lightweight agent. There, there are other firms that say they have a lightweight agent too. You know, what, what makes your lightweight agent so different? So special? >>Yeah. I've never seen a PowerPoint presentation. That's wrong. It's very easy to, to say your lightweight agent is, is, you know, super lightweight. And many times when you look at them, they're, they're not lightweight. They take a lot of effort to install. They need reboots. If you've got security, that's part of the operating system. If you've got security that requires to reboot, you can't go to a bank and say, Hey, you've got a hundred thousand machines. We're gonna install all of this technology, but you've gotta reboot it once, twice, three times. So what ends up happening is you see deployment cycles that go on for 12 months. I've spoken to organizations here this week that said we had budgeted to roll out your product in 18 months because of what we experienced in the past. And we did it in seven weeks. That's a lightweight agent with no reboot. And then you look at the updates. You look at the CPU resource utilization. So again, very easy to say lightweight. I haven't seen anything like what we've built at crowd strike. >>How do you keep an agent lightweight when you're both acquiring in companies and adding modules? I think you're, you're over 20 modules now. How, how is it that the, the agent can remain so lightweight? >>So we spent a lot of time building out the agent cloud architecture that we have, the, the concept of our agent is very different. It's not collecting data, storing it, trying to sell, send it up. We have a smart agent with smart filtering built in. So we're very careful in terms of the data that we collect, but think of the aperture on a camera. You know, if you wanna let more light in you, you widen the aperture. It's the same as our, our agent. If we wanna bring in more telemetry, we, we widen that aperture. So we're very efficient on the network. And we collect data. When machine process runs, we collect that telemetry. We use it in different ways, but we collect once and reuse it many times. So it's the same agent for NextGen AV for EDR, for our spotlight vulnerability management module. And when we're looking at M M and a, so coming back to your, your question, we will look at technology. And if we can't bring that technology and incorporate it into the agent that we already have, we won't acquire it. Worst thing in security is complexity. When you give an organization, 1, 2, 3, 5 plus agents, and then they have 3, 4, 5 plus management consoles. It's too hard when they're under attack. >>Well, it's like my, my business partner co-host John furrier says is that as an industry, we tend to solve complexity with more complexity. And it's, that's problematic. Can you talk about your, your threat graph? Like, what is that? Is it a, is it a graph database? Is it a purpose built? Is it a time series, database, a combination? What, what is >>That? Yeah, it is a graph database. When we, when, when the company was started, obviously the vision was to crowdsource telemetry from so many machines from millions of devices around the world. And the thesis at the time was as that capability scales out, there's nothing commercially available that will be able to ingest all of that data. And today we are processing over 7 trillion events every single week. We, we can't go and get something off the shelf. So we've had to build the, the technology from the ground up. That's the first part. Secondly, there is a temporal element to this. There's a time element. And we, we have an ontology built where we track the relationship between all the telemetry that we get. The reason why I believe we stand alone in EDI is because of that time element, the relationship that we have, and we just have so much context that makes it easy for the threat hunter speed and, and ease of use is critical in cyber. >>So you see in data in the database world, everything's kind of converging with all this function, you know, 11 years ago, these were pretty rudimentary. I shouldn't say rudimentary, but immature markets they've come a long way. If you had to start, if, if those capabilities that are there today with graph databases and time series databases were available in, in 2010, would you have used off the shelf technology, or would you have still developed your >>Own? We would've done the same thing that we've done today. >>And, and why can you explain what that, what that is it a performance thing? Is it just control? >>Yeah, look, it, it, it's everything that I talked about before, the, the benefit that you get from the approach that we've taken and the scalability that the requirements that we need, we still today, there's nothing that we can, we can go and get off the shelf that can scale and give us the performance that we need that can give us the ability to, to have that relationship data, the ontology of, of what we have in the platform and the way that we inter operate with all of the different modules that just wouldn't exist. We wouldn't have that capability. And what you'd find is we'd be pretty much the same as every other vendor where they have on-prem solutions, they have hybrid hosted solutions. And when you have those trade offs, you see it in the product. >>Yeah. So the, the point is you're very focused on the purpose of your, your proprietary technology. You're not trying to serve the all things to all people. You used the term yesterday in your keynote, which it, it caught my attention. You used the term ground truth, and it has very specific meaning. Can you explain what you meant by what is ground truth, you know, in the world? And what, what, what does it mean to CrowdStrike? Yeah, >>I was talking about ground truth as it relates to the acquisition of reify and the big thing for us, we wanted to bring additional capability to the platform, to give our customers external and internal visibility of all their assets and all their vulnerabilities. What's important with us, with our agent is today, we give you a single source of truth. When we put that agent onto a device, we tell you everything about the hardware. We tell you everything about who's logged in. We tell you everything about the applications that are running the relationships between the, of the device and the application. We're not a CMDB. We feed CMDB with information that is instant, that is live. And when we look at reify, it broadens again, I'll use the same word. It broadens the aperture. It gives us more visibility around what's going on. So we're, we're super excited about that because having information about all of your assets, all of your users, the applications they use, whether they're vulnerable, how you need to protect them, having it at your finger fingertips, it's a game changer >>Contract, can CrowdStrike be a generational company. And what do you have to do to ensure that that outcome occurs? We, >>We, I think we absolutely are. And, and we're we're path paving a path to, you know, really continuing to build out that platform. I said, in my keynote that I think we're at an early innings. I, if you buy, for example, as a customer, our insight module, cuz you wanna start with EDR, you've got 21 modules to go yesterday. Today we, we talked about discover 2.0, we talked about discover for IOT. I talked about the, the repository acquisition, a whole range of technology built on that single cloud agent architecture. And we've heard the success stories here this week from customers that have just gotten so much benefit. They've rolled out one agent and they've turned off eight or nine from other security vendors. So absolutely we can be a generational company with what we're doing. What >>Are the blockers to customers turning on those additional modules? Cause not, not all customers are using our modules. Is it that they've made an investment in an alternative technology and they're sort of hugging onto it or are there other technical blockers? Yes. >>It many times it's the investment, right? So if you've made a, an investment in the company, you've got a year to go, you might wanna sweat that asset. But typically what we find is the benefit that we have. It's a very simple conversation. If we can give people a cost and a technology benefit, they're gonna make the transition to move. There's so many technical benefits. We talked about the single agent, but the actual features of the modules themselves. But the big thing for us is we've done over 4,700 business value assessments where we sit down with an organization and we look at what they have. We look at what their spend is. We look at their FTEs, we look at the security outcomes that they get. And then we come out with a model that shows them technology and business value. And that's what really drives them to make the switch. >>So the business value in that VVA is not just a, a reduction in expected loss. That's part of it, better security you're gonna, you know, be, be, be lower your risk. But you're saying it's also the labor associated with that. Yeah, >>Absolutely. It's it's how do you operationalize the solution? How many people do you need? How long does it take you to respond? You know, how do you interact with third parties with your suppliers is taking in all of that data. We've spent a long time building out that model and it's, it's proving to be very successful customers. Love it. Is >>That, is that sort of novel ROI thinking in the security business or I'm trying to think of, I mean, I know for years it would watch art. Coviello stand up at RSA and tell us how, how this year's worse than last year. And so, but, but, but I never really heard, you know, a strong business case that would resonate with the, with the P and L manager, other than, you know, we gotta do this or we're gonna get hacked and you're gonna be screwed. Is that new thinking? Or am I, did I just miss it? >>I don't know if I wanna size new thinking. I think what happened, what changed was 10, 15 years ago at a conference you'd stand up and everybody would tell you ransomwares up and fishing is up. And at the end of it, people are trying to work out. Is that good? Or is that bad? It went up 20% based off what that doesn't work anymore. Everyone, you know, got tired of that. And a few of us have been doing it for a while. I I'm, I'm sort of two and a half decades into this. And if you, if you try to use that model of scaring people, they switch off, they want to understand the benefit. You know, the break in the car is so you can go and stop safely when you need it. And I look at security the same way we want to accelerate the company. We want to help companies do their job, but security is there to make sure they don't get into trouble. >>Yeah. It's like having two security guards by your side, right? I mean, they're gonna help you get through the crowd and move forward. So Michael, thanks so much for coming to the cube. Thanks for having me your time. You're you're very welcome. All right. Keep it right there. After this short break, Dave ante will be back with the cube live coverage from Falcon 22 at the area in Las Vegas.

(bright upbeat music) >> Welcome back, everyone, to the "Supercloud 22." I'm John Furrier, host of "The Cube." This is the Cloud-erati panel, the distinguished experts who have been there from day one, watching the cloud grow, from building clouds, and all open source stuff as well. Just great stuff. Good friends of "The Cube," and great to introduce back on "The Cube," Adrian Cockcroft, formerly with Netflix, formerly AWS, retired, now commentating here in "The Cube," as well as other events. Great to see you back out there, Adrian. Lori MacVittie, Cloud Evangelist with F5, also wrote a great blog post on supercloud, as well as Dave Vellante as well, setting up the supercloud conversation, which we're going to get into, and Chris Hoff, who's the CTO and CSO of LastPass who's been building clouds, and we know him from "The Cube" before with security and cloud commentary. Welcome, all, back to "The Cube" and supercloud. >> Thanks, John. >> Hi. >> All right, Lori, we'll start with you to get things going. I want to try to sit back, as you guys are awesome experts, and involved from building, and in the trenches, on the front lines, and Adrian's coming out of retirement, but Lori, you wrote the post setting the table on supercloud. Let's start with you. What is supercloud? What is it evolving into? What is the north star, from your perspective? >> Well, I don't think there's a north star yet. I think that's one of the reasons I wrote it, because I had a clear picture of this in my mind, but over the past, I don't know, three, four years, I keep seeing, in research, my own and others', complexity, multi-cloud. "We can't manage it. They're all different. "We have trouble. What's going on? "We can't do anything right." And so digging into it, you start looking into, "Well, what do you mean by complexity?" Well, security. Migration, visibility, performance. The same old problems we've always had. And so, supercloud is a concept that is supposed to overlay all of the clouds and normalize it. That's really what we're talking about, is yet another abstraction layer that would provide some consistency that would allow you to do the same security and monitor things correctly. Cornell University actually put out a definition way back in 2016. And they said, "It's an architecture that enables migration "across different zones or providers," and I think that's important, "and provides interfaces to everything, "makes it consistent, and normalizes the network," basically brings it all together, but it also extends to private clouds. Sometimes we forget about that piece of it, and I think that's important in this, so that all your clouds look the same. So supercloud, big layer on top, makes everything wonderful. It's unicorns again. >> It's interesting. We had multiple perspectives. (mumbles) was like Snowflake, who built on top of AWS. Jerry Chan, who we heard from earlier today, Greylock Penn's "Castles in the Cloud" saying, "Hey, you can have a moat, "you can build an advantage and have differentiation," so startups are starting to build on clouds, that's the native cloud view, and then, of course, they get success and they go to all the other clouds 'cause they got customers in the ecosystem, but it seems that all the cloud players, Chris, you commented before we came on today, is that they're all fighting for the customer's workloads on their infrastructure. "Come bring your stuff over to here, "and we'll make it run better." And all your developers are going to be good. Is there a problem? I mean, or is this something else happening here? Is there a real problem? >> Well, I think the north star's over there, by the way, Lori. (laughing) >> Oh, there it is. >> Right there. The supercloud north star. So indeed I think there are opportunities. Whether you call them problems or not, John, I think is to be determined. Most companies have, especially if they're a large enterprise, whether or not they've got an investment in private cloud or not, have spent time really trying to optimize their engineering and workload placement on a single cloud. And that, regardless of your choice, as we take the big three, whether it's Amazon, Google, or Microsoft, each of them have their pros and cons for various types of workloads. And so you'll see a lot of folks optimizing for a particular cloud, and it takes a huge effort up and down the stack to just get a single cloud right. That doesn't take into consideration integrations with software as a service, instantiated, oftentimes, on top of infrastructure of the service that you need to supplement where the obstruction layer ends in infrastructure of the service. You've seen most IS players starting to now move up-chain, as we predicted years ago, to platform as a service, but platforms of various types. So I definitely see it as an opportunity. Previous employers have had multiple clouds, but they were very specifically optimized for the types of workloads, for example, in, let's say, AWS versus GCP, based on the need for different types and optimized compute platforms that each of those providers ran. We never, in that particular case, thought about necessarily running the same workloads across both clouds, because they had different pricing models, different security models, et cetera. And so the challenge is really coming down to the fact that, what is the cost benefit analysis of thinking about multi-cloud when you can potentially engineer the resiliency or redundancy, all the in-season "ilities" that you might need to factor into your deployments on a single cloud, if they are investing at the pace in which they are? So I think it's an opportunity, and it's one that continues to evolve, but this just reminds me, your comments remind me, of when we were talking about OpenStack versus AWS. "Oh, if there were only APIs that existed "that everybody could use," and you saw how that went. So I think that the challenge there is, what is the impetus for a singular cloud provider, any of the big three, deciding that they're going to abstract to a single abstraction layer and not be able to differentiate from the competitors? >> Yeah, and that differentiation's going to be big. I mean, assume that the clouds aren't going to stay still like AWS and just not stop innovating. We see the devs are doing great, Adrian, open source is bigger and better than ever, but now that's been commercialized into enterprise. It's an ops problem. So to Chris's point, the cost benefit analysis is interesting, because do companies have to spin up multiple operations teams, each with specialized training and tooling for the clouds that they're using, and does that open up a can of worms, or is that a good thing? I mean, can you design for this? I mean, is there an architecture or taxonomy that makes it work, or is it just the cart before the horse, the solution before the problem? >> Yeah, well, I think that if you look at any large vendor... Sorry, large customer, they've got a bit of everything already. If you're big enough, you've bought something from everybody at some point. So then you're trying to rationalize that, and trying to make it make sense. And I think there's two ways of looking at multi-cloud or supercloud, and one is that the... And practically, people go best of breed. They say, "Okay, I'm going to get my email "from Google or Microsoft. "I'm going to run my applications on AWS. "Maybe I'm going to do some AI machine learning on Google, "'cause those are the strengths of the platforms." So people tend to go where the strength is. So that's multi-cloud, 'cause you're using multiple clouds, and you still have to move data and make sure they're all working together. But then what Lori's talking about is trying to make them all look the same and trying to get all the security architectures to be the same and put this magical layer, this unicorn magical layer that, "Let's make them all look the same." And this is something that the CIOs have wanted for years, and they keep trying to buy it, and you can sell it, but the trouble is it's really hard to deliver. And I think, when I go back to some old friends of ours at Enstratius who had... And back in the early days of cloud, said, "Well, we'll just do an API that abstracts "all the cloud APIs into one layer." Enstratius ended up being sold to Dell a few years ago, and the problem they had was that... They didn't have any problem selling it. The problem they had was, a year later, when it came up for renewal, the developers all done end runs around it were ignoring it, and the CIOs weren't seeing usage. So you can sell it, but can you actually implement it and make it work well enough that it actually becomes part of your core architecture without, from an operations point of view, without having the developers going directly to their favorite APIs around them? And I'm not sure that you can really lock an organization down enough to get them onto a layer like that. So that's the way I see it. >> You just defined- >> You just defined shadow shadow IT. (laughing) That's pretty- (crosstalk) >> Shadow shadow IT, yeah. >> Yeah, shadow shadow it. >> Yeah. >> Yeah. >> I mean, this brings up the question, I mean, is there really a problem? I mean, I guess we'll just jump to it. What is supercloud? If you can have the magic outcome, what is it? Enstratius rendered in with automation? The security issues? Kubernetes is hot. What is the supercloud dream? I guess that's the question. >> I think it's got easier than it was five, 10 years ago. Kubernetes gives you a bunch of APIs that are common across lots of different areas, things like Snowflake or MongoDB Atlas. There are SaaS-based services, which are across multiple clouds from vendors that you've picked. So it's easier to build things which are more portable, but I still don't think it's easy to build this magic API that makes them all look the same. And I think that you're going to have leaky abstractions and security being... Getting the security right's going to be really much more complex than people think. >> What about specialty superclouds, Chris? What's your view on that? >> Yeah, I think what Adrian is alluding to, those leaky abstractions, are interesting, especially from the security perspective, 'cause I think what you see is if you were to happen to be able to thin slice across a set of specific types of workloads, there is a high probability given today that, at least on two of the three major clouds, you could get SaaS providers that sit on those same infrastructure of the service clouds for you, string them together, and have a service that technically is abstracted enough from the things you care about to work on one, two, or three, maybe not all of them, but most SaaS providers in the security space, or identity space, data space, for example, coexist on at least Microsoft and AWS, if not all three, with Google. And so you could technically abstract a service to the point that you let that level of abstract... Like Lori said, no computer science problem could not be... So, no computer science problem can't be solved with more layers of abstraction or misdirection... Or redirection. And in that particular case, if you happen to pick the right vendors that run on all three clouds, you could possibly get close. But then what that really talks about is then, if you built your seven-layer dip model, then you really have specialty superclouds spanning across infrastructure of the service clouds. One for your identity apps, one for data and data layers, to normalize that, one for security, but at what cost? Because you're going to be charged not for that service as a whole, but based on compute resources, based on how these vendors charge across each cloud. So again, that cost-benefit ratio might start being something that is rather imposing from a budgetary perspective. >> Lori, weigh in on this, because the enterprise people love to solve complexity with more complexity. Here, we need to go the other way. It's a commodity. So there has to be a better way. >> I think I'm hearing two fundamental assumptions. One, that a supercloud would force the existing big three to implement some sort of equal API. Don't agree with that. There's no business case for that. There's no reason that could compel them to do that. Otherwise, we would've convinced them to do that, what? 10, 15 years ago when we said we need to be interoperable. So it's not going to happen there. They don't have a good reason to do that. There's no business justification for that. The other presumption, I think, is that we would... That it's more about the services, the differentiated services, that are offered by all of these particular providers, as opposed to treating the core IaaS as the commodity it is. It's compute, it's some storage, it's some networking. Look at that piece. Now, pull those together by... And it's not OpenStack. That's not the answer, it wasn't the answer, it's not the answer now, but something that can actually pull those together and abstract it at a different layer. So cloud providers don't have to change, 'cause they're not going to change, but if someone else were to build that architecture to say, "all right, I'm going to treat all of this compute "so you can run your workloads," as Chris pointed out, "in the best place possible. "And we'll help you do that "by being able to provide those cost benefit analysis, "'What's the best performance, what are you doing,' "And then provide that as a layer." So I think that's really where supercloud is going, 'cause I think that's what a lot of the market actually wants in terms of where they want to run their workloads, because we're seeing that they want to run workloads at the edge, "a lot closer to me," which is yet another factor that we have to consider, and how are you going to be moving individual workloads around? That's the holy grail. Let's move individual workloads to where they're the best performance, the security, cost optimized, and then one layer up. >> Yeah, I think so- >> John Considine, who ultimately ran CloudSwitch, that sold to Verizon, as well as Tom Gillis, who built Bracket, are both rolling in their graves, 'cause what you just described was exactly that. (Lori laughing) Well, they're not even dead yet, so I can't say they're rolling in their graves. Sorry, Tom. Sorry, John. >> Well, how do hyperscalers keep their advantage with all this? I mean, to that point. >> Native services and managed services on top of it. Look how many flavors of managed Kubernetes you have. So you have a choice. Roll your own, or go with a managed service, and then differentiate based on the ability to take away and simplify some of that complexity. Doesn't mean it's more secure necessarily, but I do think we're seeing opportunities where those guys are fighting tooth and nail to keep you on a singular cloud, even though, to Lori's point, I agree, I don't think it's about standardized APIs, 'cause I think that's never going to happen. I do think, though, that SaaS-y supercloud model that we were talking about, layering SaaS that happens to span all the three infrastructure of the service are probably more in line with what Lori was talking about. But I do think that portability of workload is given to you today within lots of ways. But again, how much do you manage, and how much performance do you give up by running additional abstraction layers? And how much security do you give up by having to roll your own and manage that? Because the whole point was, in many cases... Cloud is using other people's computers, so in many cases, I want to manage as little of it as I possibly can. >> I like this whole SaaS angle, because if you had the old days, you're on Amazon Web Services, hey, if you build a SaaS application that runs on Amazon, you're all great, you're born in the cloud, just like that generations of startups. Great. Now when you have this super pass layer, as Dave Vellante was riffing on his analysis, and Lori, you were getting into this pass layer that's kind of like SaaS-y, what's the SaaS equation look like? Because that, to me, sounds like a supercloud version of saying, "I have a workload that runs on all the clouds equally." I just don't think that's ever going to happen. I agree with you, Chris, on that one. But I do see that you can have an abstraction that says, "Hey, I don't really want to get in the weeds. "I don't want to spend a lot of ops time on this. "I just want it to run effectively, and magic happens," or, as you said, some layer there. How does that work? How do you see this super pass layer, if anything, enabling a different SaaS game? >> I think you hit on it there. The last like 10 or so years, we've been all focused on developers and developer productivity, and it's all about the developer experience, and it's got to be good for them, 'cause they're the kings. And I think the next 10 years are going to be very focused on operations, because once you start scaling out, it's not about developers. They can deliver fast or slow, it doesn't matter, but if you can't scale it out, then you've got a real problem. So I think that's an important part of it, is really, what is the ops experience, and what is the best way to get those costs down? And this would serve that purpose if it was done right, which, we can argue about whether that's possible or not, but I don't have to implement it, so I can say it's possible. >> Well, are we going to be getting into infrastructure as code moves into "everything is code," security, data, (laughs) applications is code? I mean, "blank" is code, fill in the blank. (Lori laughing) >> Yeah, we're seeing more of that with things like CDK and Pulumi, where you are actually coding up using a real language rather than the death by YAML or whatever. How much YAML can you take? But actually having a real language so you're not trying to do things in parsing languages. So I think that's an interesting trend. You're getting some interesting templates, and I like what... I mean, the counterexample is that if you just go deep on one vendor, then maybe you can go faster and it is simpler. And one of my favorite vendor... Favorite customers right now that I've been talking to is Liberty Mutual. Went very deep and serverless first on AWS. They're just doing everything there, and they're using CDK Patterns to do it, and they're going extremely fast. There's a book coming out called "The Value Flywheel" by Dave Anderson, it's coming out in a few months, to just detail what they're doing, but that's the counterargument. If you could pick one vendor, you can go faster, you can get that vendor to do more for you, and maybe get a bigger discount so you're not splitting your discounts across vendors. So that's one aspect of it. But I think, fundamentally, you're going to find the CIOs and the ops people generally don't like sitting on one vendor. And if that single vendor is a horizontal platform that's trying to make all the clouds look the same, now you're locked into whatever that platform was. You've still got a platform there. There's still something. So I think that's always going to be something that the CIOs want, but the developers are always going to just pick whatever the best tool for building the thing is. And a analogy here is that the developers are dating and getting married, and then the operations people are running the family and getting divorced. And all the bad parts of that cycle are in the divorce end of it. You're trying to get out of a vendor, there's lawyers, it's just a big mess. >> Who's the lawyer in this example? (crosstalk) >> Well... (laughing) >> Great example. (crosstalk) >> That's why ops people don't like lock-in, because they're the ones trying to unlock. They aren't the ones doing the lock-in. They're the ones unlocking, when developers, if you separate the two, are the ones who are going, picking, having the fun part of it, going, trying a new thing. So they're chasing a shiny object, and then the ops people are trying to untangle themselves from the remains of that shiny object a few years later. So- >> Aren't we- >> One way of fixing that is to push it all together and make it more DevOps-y. >> Yeah, that's right. >> But that's trying to put all the responsibilities in one place, like more continuous improvement, but... >> Chris, what's your reaction to that? Because you're- >> No, that's exactly what I was going to bring up, yeah, John. And 'cause we keep saying "devs," "dev," and "ops" and I've heard somewhere you can glue those two things together. Heck, you could even include "sec" in the middle of it, and "DevSecOps." So what's interesting about what Adrian's saying though, too, is I think this has a lot to do with how you structure your engineering teams and how you think about development versus operations and security. So I'm building out a team now that very much makes use of, thanks to my brilliant VP of Engineering, a "Team Topologies" approach, which is a very streamlined and product oriented way of thinking about, for example, in engineering, if you think about team structures, you might have people that build the front end, build the middle tier, and the back end, and then you have a product that needs to make use of all three components in some form. So just from getting stuff done, their ability then has to tie to three different groups, versus building a team that's streamlined that ends up having front end, middleware, and backend folks that understand and share standards but are able to uncork the velocity that's required to do that. So if you think about that, and not just from an engineering development perspective, but then you couple in operations as a foundational layer that services them with embedded capabilities, we're putting engineers and operations teams embedded in those streamlined teams so that they can run at the velocity that they need to, they can do continuous integration, they can do continuous deployment. And then we added CS, which is continuously secure, continuous security. So instead of having giant, centralized teams, we're thinking there's a core team, for example, a foundational team, that services platform, makes sure all the trains are running on time, that we're doing what we need to do foundationally to make the environments fully dev and operator and security people functional. But then ultimately, we don't have these big, monolithic teams that get into turf wars. So, to Adrian's point about, the operators don't like to be paned in, well, they actually have a say, ultimately, in how they architect, deploy, manage, plan, build, and operate those systems. But at the same point in time, we're all looking at that problem across those teams and go... Like if one streamline team says, "I really want to go run on Azure, "because I like their services better," the reality is the foundational team has a larger vote versus opinion on whether or not, functionally, we can satisfy all of the requirements of the other team. Now, they may make a fantastic business case and we play rock, paper, scissors, and we do that. Right now, that hasn't really happened. We look at the balance of AWS, we are picking SaaS-y, supercloud vendors that will, by the way, happen to run on three platforms, if we so choose to expand there. So we have a similar interface, similar capability, similar processes, but we've made the choice at LastPass to go all in on AWS currently, with respect to how we deliver our products, for all the reasons we just talked about. But I do think that operations model and how you build your teams is extremely important. >> Yeah, and to that point- >> And has the- (crosstalk) >> The vendors themselves need optionality to the customer, what you're saying. So, "I'm going to go fast, "but I need to have that optionality." I guess the question I have for you guys is, what is today's trade-off? So if the decision point today is... First of all, I love the go-fast model on one cloud. I think that's my favorite when I look at all this, and then with the option, knowing that I'm going to have the option to go to multiple clouds. But everybody wants lock-in on the vendor side. Is that scale, is that data advantage? I mean, so the lock-in's a good question, and then also the trade-offs. What do people have to do today to go on a supercloud journey to have an ideal architecture and taxonomy, and what's the right trade-offs today? >> I think that the- Sorry, just put a comment and then let Lori get a word in, but there's a lot of... A lot of the market here is you're building a product, and that product is a SaaS product, and it needs to run somewhere. And the customers that you're going to... To get the full market, you need to go across multiple suppliers, most people doing AWS and Azure, and then with Google occasionally for some people. But that, I think, has become the pattern that most of the large SaaS platforms that you'd want to build out of, 'cause that's the fast way of getting something that's going to be stable at scale, it's got functionality, you'd have to go invest in building it and running it. Those platforms are just multi-cloud platforms, they're running across them. So Snowflake, for example, has to figure out how to make their stuff work on more than one cloud. I mean, they started on one, but they're going across clouds. And I think that that is just the way it's going to be, because you're not going to get a broad enough view into the market, because there isn't a single... AWS doesn't have 100% of the market. It's maybe a bit more than them, but Azure has got a pretty solid set of markets where it is strong, and it's market by market. So in some areas, different people in some places in the world, and different vertical markets, you'll find different preferences. And if you want to be across all of them with your data product, or whatever your SaaS product is, you're just going to have to figure this out. So in some sense, the supercloud story plays best with those SaaS providers like the Snowflakes of this world, I think. >> Lori? >> Yeah, I think the SaaS product... Identity, whatever, you're going to have specialized. SaaS, superclouds. We already see that emerging. Identity is becoming like this big SaaS play that crosses all clouds. It's not just for one. So you get an evolution going on where, yes, I mean, every vendor who provides some kind of specific functionality is going to have to build out and be multi-cloud, as it were. It's got to work equally across them. And the challenge, then, for them is to make it simple for both operators and, if required, dev. And maybe that's the other lesson moving forward. You can build something that is heaven for ops, but if the developers won't use it, well, then you're not going to get it adopted. But if you make it heaven for the developers, the ops team may not be able to keep it secure, keep everything. So maybe we have to start focusing on both, make it friendly for both, at least. Maybe it won't be the perfect experience, but gee, at least make it usable for both sides of the equation so that everyone can actually work in concert, like Chris was saying. A more comprehensive, cohesive approach to delivery and deployment. >> All right, well, wrapping up here, I want to just get one final comment from you guys, if you don't mind. What does supercloud look like in five years? What's the Nirvana, what's the steady state of supercloud in five to 10 years? Or say 10 years, make it easier. (crosstalk) Five to 10 years. Chris, we'll start with you. >> Wow. >> Supercloud, what's it look like? >> Geez. A magic pane, a single pane of glass. (laughs) >> Yeah, I think- >> Single glass of pain. >> Yeah, a single glass of pain. Thank you. You stole my line. Well, not mine, but that's the one I was going to use. Yeah, I think what is really fascinating is ultimately, to answer that question, I would reflect on market consolidation and market dynamics that happens even in the SaaS space. So we will see SaaS companies combining in focal areas to be able to leverage the positions, let's say, in the identity space that somebody has built to provide a set of compelling services that help abstract that identity problem or that security problem or that instrumentation and observability problem. So take your favorite vendors today. I think what we'll end up seeing is more consolidation in SaaS offerings that run on top of infrastructure of the service offerings to where a supercloud might look like something I described before. You have the combination of your favorite interoperable identity, observability, security, orchestration platforms run across them. They're sold as a stack, whether it be co-branded by an enterprise vendor that sells all of that and manages it for you or not. But I do think that... You talked about, I think you said, "Is this an innovator's dilemma?" No, I think it's an integrator's dilemma, as it has always ultimately been. As soon as you get from Genesis to Bespoke Build to product to then commoditization, the cycle starts anew. And I think we've gotten past commoditization, and we're looking at niche areas. So I see just the evolution, not necessarily a revolution, of what we're dealing with today as we see more consolidation in the marketplace. >> Lori, what's your take? Five years, 10 years, what does supercloud look like? >> Part of me wants to take the pie in the sky unicorn approach. "No, it will be beautiful. "One button, and things will happen," but I've seen this cycle many times before, and that's not going to happen. And I think Chris has got it pretty close to what I see already evolving. Those different kinds of super services, basically. And that's really what we're talking about. We call them SaaS, but they're... X is a service. Everything is a service, and it's really a supercloud that can run anywhere, but it presents a different interface, because, well, it's easier. And I think that's where we're going to go, and that's just going to get more refined. And yes, a lot of consolidation, especially on the observability side, but that's also starting to consume the security side, which is really interesting to watch. So that could be a little different supercloud coming on there that's really focused on specific types of security, at least, that we'll layer across, and then we'll just hook them all together. It's an API first world, and it seems like that's going to be our standard for the next while of how we integrate everything. So superclouds or APIs. >> Awesome. Adrian... Adrian, take us home. >> Yeah, sure. >> What's your- I think, and just picking up on Lori's point that these are web services, meaning that you can just call them from anywhere, they don't have to run everything in one place, they can stitch it together, and that's really meant... It's somewhat composable. So in practice, people are going to be composable. Can they compose their applications on multiple platforms? But I think the interesting thing here is what the vendors do, and what I'm seeing is vendors running software on other vendors. So you have Google building platforms that, then, they will support on AWS and Azure and vice versa. You've got AWS's distro of Kubernetes, which they now give you as a distro so you can run it on another platform. So I think that trend's going to continue, and it's going to be, possibly, you pick, say, an AWS or a Google software stack, but you don't run it all on AWS, you run it in multiple places. Yeah, and then the other thing is the third tier, second, third tier vendors, like, I mean, what's IBM doing? I think in five years time, IBM is going to be a SaaS vendor running on the other clouds. I mean, they're already halfway there. To be a bit more controversial, I guess it's always fun to... Like I don't work for a corporate entity now. No one tells me what I can say. >> Bring it on. >> How long can Google keep losing a billion dollars a quarter? They've either got to figure out how to make money out of this thing, or they'll end up basically being a software stack on another cloud platform as their, likely, actual way they can make money on it. Because you've got to... And maybe Oracle, is that a viable cloud platform that... You've got to get to some level of viability. And I think the second, third tier of vendors in five, 10 years are going to be running on the primary platform. And I think, just the other final thing that's really driving this right now. If you try and place an order right now for a piece of equipment for your data center, key pieces of equipment are a year out. It's like trying to buy a new fridge from like Sub-Zero or something like that. And it's like, it's a year. You got to wait for these things. Any high quality piece of equipment. So you go to deploy in your data center, and it's like, "I can't get stuff in my data center. "Like, the key pieces I need, I can't deploy a whole system. "We didn't get bits and pieces of it." So people are going to be cobbling together, or they're going, "No, this is going to cloud, because the cloud vendors "have a much stronger supply chain to just be able "to give you the system you need. "They've got the capacity." So I think we're going to see some pandemic and supply chain induced forced cloud migrations, just because you can't build stuff anymore outside the- >> We got to accelerate supercloud, 'cause they have the supply. They are the chain. >> That's super smart. That's the benefit of going last. So I'm going to scoop in real quick. I can't believe we can call this "Web3 Supercloud," because none of us said "Web3." Don't forget DAO. (crosstalk) (indistinct) You have blockchain, blockchain superclouds. I mean, there's some very interesting distributed computing stuff there, but we'll have to do- >> (crosstalk) We're going to call that the "Cubeverse." The "Cubeverse" is coming. >> Oh, the "Cubeverse." All right. >> We will be... >> That's very meta. >> In the metaverse, Cubeverse soon. >> "Stupor cloud," perhaps. But anyway, great points, Adrian and Lori. Loved it. >> Chris, great to see you. Adrian, Lori, thanks for coming on. We've known each other for a long time. You guys are part of the cloud-erati, the group that has been in there from day one, and watched it evolve, and you get the scar tissue to prove it, and the experience. So thank you so much for sharing your commentary. We'll roll this up and make it open to everybody as additional content. We'll call this the "outtakes," the longer version. But really appreciate your time, thank you. >> Thank you. >> Thanks so much. >> Okay, we'll be back with more "Supercloud 22" right after this. (bright upbeat music)

(upbeat music) >> With cloud computing, programmable infrastructure, open source momentum with things like Terraform and software defined everything, people have been asking, "Does hardware still matter?" The obvious answer is software has to run on something but why does hardware still matter specifically? What customer value is there in advanced hardware architectures and what are some of the less frequently discussed nuances of hardware that make software run better and businesses run more efficiently and securely at scale. Welcome to the Cube's ongoing series where we explore the importance of hardware its evolution over the decades and its future outlook with me is longtime cubilam, Shannon Champion. Who's the vice president of Product Marketing at Dell Technologies. Welcome Shannon. >> Thank you. Glad to be here. >> Yeah, it's always great to collaborate with you. Shannon, you've had a pretty impressive career. You've got this killer combination of you have an engineering degree, multiple engineering degrees actually combined with business education. You've worked as a semiconductor engineer, a quality engineer, product manager, product marketing exec, et cetera. And you now have responsibility for a variety of hardware and software led infrastructure at Dell. How have you seen hardware evolve over the years? >> Well, first of all, thank you. I appreciate that intro Dave. Yeah, it's been a fun journey. I think there's two things. I think there's a product led evolution and then there's customer evolution. And obviously those go hand in hand. If you think about the technology from a hardware perspective, it's become more advanced, more specialized and the diversification of chip architectures is really what's driving that. It's gone from general purpose CPUs to GPUs, to specialty processors like, DPUs and purpose-built accelerators. And with all that specialization, obviously more and more software is required to really knit it together. We believe Dell is uniquely positioned to do that. >> Awesome. So I want to just come right out and ask you, you know, with cloud and software defined and hyper-converged why specifically does hardware still matter? >> Well, if you know anything about Dell, you know we are driven by customer first mindset. So I'm going to go back to that customer evolution I talked about and from a customer perspective, purchase decisions used to be more about feature function, Like how much compute memory storage can you pack in and get the best performance characteristics. Of course, people still care about this and almost every customer, if you look at the widespread surveys that have been done in the industry projections are still going to be making data center infrastructure purchases for the foreseeable future, but more and more, these sort of like traditional hardware capabilities are table stakes. And what customers are making purchase decisions on are the software driven capabilities that provide the differentiating capabilities to allow them to do more with less. So with that sort of comes a refocusing of where IT adds value for their organizations. We know maintaining and managing the infrastructure is not what differentiates companies and makes them stand out from the crowd. So that's what this whole notion of IT Transformation is all about. Our customers are pulling us into a broader set of problems and their purchase criteria is moving away from hardware feature function to differentiated solution and software value decision making with more focus on how they can drive business value beyond the infrastructure. So it's really the combination of hardware with software that optimizes and delivers the best outcomes and the tighter the link we can create between them the more seamless the experience for customers. >> Gotcha and I mean, this is more important than ever with the push toward digital transformation. And everybody's trying to get digital right. Now thinking about Dell as a company and its broader strategy, the majority of revenue comes from what most people would think of as hardware but as Jeff Clark often points out, the vast majority of engineers are software engineers. Can you explain how that dynamic works and what role hardware plays in that equation? >> Yeah, totally. So if you think about IT transformation infrastructure is the enabler of that transformation, but infrastructure needs to be smarter, easier, more automated, more secure. And that's done with software and our software engineering focus is nothing new. I think Dave, we were together five years ago talking about the latest version of HCI on the 14th generation of power edge servers. And at that time we were talking about how our hardware platform engineers were working with the software engineers to design in software defined storage capabilities within the power edge platform. So, you know, we, that we are not new to this. We've been looking at ways we can use software to exploit the underlying hardware features and capabilities and do that in a differentiated way because it delivers value for customers. And honestly, they're willing to pay a premium for that. >> Yeah. I remember that well, 14G now 15G, soon we're going to be talking about 16G. Can you give me an example of where hardware differentiation has created value for your customers beyond, you know what a straight software only solution running on generic white boxes might bring? >> Yeah, I have a couple of examples. The first is easily VxRail, right? VxRail, our jointly engineered HCI system with VMware. It provides full stack integration of hardware and software for that consistent operations in VMware environments. And when you think about evolution of infrastructure VxRail is actually a cool story. When it was introduced six years ago its scalability and performance, you know had it be rapidly adopted mainly in the data center but customer demands have evolved and they wanted to extend that operational efficiency to a broader and broader set of workloads. Not only in the data center, but in the cloud at the edge. So VxRail grew and its portfolio today has maximum flexibility. You can choose the best platform to meet performance, storage, graphics, IO, cost requirements a range of processor types and NVMe drives and graphics cards. So it really is the most configurable HCI system to meet any workload demand. And we recently introduced some new node types. That's hardware based, right? VxRail dynamic nodes and satellite nodes and our customers and partners are really excited about these, the dynamic nodes, as you know add the capability to scale compute and storage independently and extend to primary storage like power store and the satellite nodes are single nodes for the edge. So that's all hardware stuff, but the secret to VxRail really is more about the software. So I'm going to go back there. The VxRail HCI system software is what makes VxRail more seamless and simple than any other HCI system. And when managing your environment is easier and more automated and your workloads can stay up and running, leveraging that intelligent life cycle management customers pay attention. So again, it's that combination of hardware and software and for VxRail customers it's how we're delivering that truly curated experience like we like to call it that they can't get anywhere else. >> Awesome. So last question. Anything else you want to bring into the discussion before we close? >> Yeah. Two things, actually I have another good example of hardware differentiation and how it creates value for customers. And this one is based upon PowerStore. So PowerStore inline data reduction uses Intel quick assist technology and it performs hardware accelerated compression. So it's basically handling data reduction in hardware. We offload the compute intensive workloads of compression and conserve the CPU cycles for storage IO tasks that save application and storage processing time, cycles and costs. So it's a more consistent way to do storage efficiency and leverage power storage advance inline compression and it's always on, and it doesn't compromise performance of other services. So, with PowerStore using this hardware differentiated approach to inline data reduction, customers get an average four to one data reduction across all their workloads, don't compromise performance or services. And honestly, a lot of times we see them achieving up to 20 to 1 or more depending on the data type. So yeah, I just wanted to throw out that other example. >> Great. >> The last thing I'll say is we just launched a trifecta storage innovation at Dell Technologies World. We have over 500 new high value software enhancements that bring out the best in our storage hardware platforms and that's across PowerStore, PowerMax and PowerFlex. So I encourage folks to go check that out and you know obviously let us know what you think. >> Yeah. We can put a link to those in the show notes. And I was there at Dell Tech World. It was actually quite amazing. Shannon, thanks so much for coming on and sharing your insights really appreciate it. >> My pleasure. >> All right. And thank you for watching this Cube conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

>>Welcome everyone to this cube conversation featuring unit Q. I'm your host, Lisa Martin. And we are excited to be joined by Christian Vickle, the founder and CEO of unit Q Christian. Thank you so much for joining me today. >>Thank you so much, Lisa pleasure to be here. >>Let's talk a little bit about unit Q. You guys were founded in 2018, so pretty recent. What is it that unit Q does. And what were some of the gaps in the market that led you to founding the company? >>Yep. So me and my co-founder Nick, we're actually doing our second company now is the unit Q is number two, and our first company was called scout years ago. We were back ES wicks and it was very different from unit Q. It's a social network for meeting people. And it was really during that experience where we saw the impact that quality of the experience quality of the product can have on your growth trajectory and the challenges we faced. How do we test everything before we ship it? And in reality, a modern company will have, let's say, 20 languages supported you support Android, Iowas, web big screen, small screen, you have 20 plus integrations and you have lots of different devices out there that might run your binary a little differently. So who is the ultimate test group of all of these different permutation and that's the end user. >>And we, we saw the, the big gap in the market, sort of the dream platform for us was unit queue. So if, if this would've existed back in the day, we would've been a, a happy purchaser and customer, and it really comes down to how do we, how do we harness the power of user feedback? You know, the end user, that's testing your product every single day in all different configurations. And then they're telling you that, Hey, something didn't work for me. I got double build or the passive recent link didn't work, or I couldn't, you know, when music, when the ad is finished playing on, on my app, the music doesn't resume. So how do we capture those signals into something that the company and different teams can align on? So that's where, you know, unit Q the, the vision here is to build a quality company, to help other companies build higher quality products. >>So really empowering companies to take a data driven approach to product quality. I was looking on your website and noticed that Pandora is one of your customers, but talk to me a little bit about a customer example that you think really articulates the value of what Q unit he was delivering. >>Right? So maybe we should just go back one little step and talk about what is quality. And I think quality is something that is, is a bit subjective. It's something that we live and breathe every day. It's something that can be formed in an instant first impressions. Last it's something that can be built over time that, Hey, I'm using this product and it's just not working for me. Maybe it's missing features. Maybe there are performance related bots. Maybe there is there's even fulfillment related issues. Like we work with Uber and hello, fresh and, and other types of more hybrid type companies in addition to the Pandoras and, and Pinterest and, and Spotify, and these more digital, only products, but the, the end users I'm producing this data, the reporting, what is working and not working out there in many different channels. So they will leave app produce. >>They will write into support. They might engage with a chat support bot. They will post stuff on Reddit on Twitter. They will comment on Facebook ads. So like this data is dispersed everywhere. The end user is not gonna fill out a perfect bug report in a form somewhere that gets filed into gr like they're, they're producing this content everywhere in different languages. So the first value of what we do is to just ingest all of that data. So all the entire surface area of use of feedback, we ingest into a machine and then we clean the data. We normalize it, and then we translate everything into English. And it was actually a surprise to us when we started this company, that there are quite a few companies out there that they're only looking at feedback in English. So what about my Spanish speaking users? What about my French speaking users? >>And when, when, when that is done, like when all of that data is, is need to organized, we extract signals from that around what is impacting the user experience right now. So we break these, all of this data down into something called quality monitors. So quality monitor is basically a topic which can be again, passive reset, link noting, or really anything that that's impacting the end user. And the important part here is that we need to have specific actionable data. For instance, if I tell you, Hey, Lisa music stops playing is a growing trend that our users are reporting. You will tell me, well, what can I do with that? Like what specifically is breaking? So we deploy up to 1500 unique quality monitors per customer. So we can then alert different teams inside of the organization of like, Hey, something broke and you should take a look at it. >>So it's really breaking down data silos within the company. It aligns cross-functional teams to agree on what should be fixed next. Cause there's typically a lot of confusion, you know, marketing, they might say, Hey, we want this fixed engineering. They're like, well, I can't reproduce, or that's not a high priority for us. The support teams might also have stuff that they want to get fixed. And what we've seen is that these teams, they struggle to communicate. So how do we align them around the single source of truth? And I think that's for unit two is early identification of stuff. That's not working in production and it's also aligning the teams so they can quickly triage and say, yes, we gotta fix this right before it snowballs into something. We say, you know, we wanna, we wanna cap catch issues before you go into crisis PR mode, right? So we want to get this, we wanna address it early in the cycle. >>Talk to me about when you're in customer conversations, Christian, the MarTech landscape is competitive. There's nearly 10,000 different solutions out there, and it's growing really quickly quality monitors that you just described is that one of the key things that, that you talk to customers about, that's a differentiator for unit Q. >>Yeah. So I mean, it, it, it comes down to, as you're building your product, right, you, you have, you have a few different options. One is to build new features and we need to build new features and innovate and, and, and that's all great. We also need to make sure that the foundation of the product is working and that we keep improving quality and what, what we see with, with basically every customer that we work with, that, that when quality goes up, it's supercharges the growth machine. So quality goes up, you're gonna see less support tickets. You're gonna see less one star reviews, less one star reviews is of course good for making the store front convert better. You know, I, I want install a 4.5 star app, not a 3.9 star app. We also see that sentiment. So for those who are interested in getting that NPS score up for the next time we measure it, we see that quality is of course a very important piece of that. >>And maybe even more importantly, so sort of inside of the product machine, the different conversion steps, let's say sign up to activate it to coming back in second day, 30 day, 90 day, and so forth. We see a dramatic impact on how quality sort of moves that up and down the retention function, if you will. So it, it really, if you think about a modern company, like the product is sort of the center of the existence of the company, and if the product performs really well, then you can spend more money in marketing because it converts really good. You can hire more engineers, you can hire, you can hire more support people and so forth. So it's, it's really cool to see that when quality improves its supercharges, everything else I think for marketing it's how do you know if you're spending into a broken product or not? >>And I, and I, I feel like marketing has, they have their insights, but it's, it's not deep enough where they can go to engineering and say, Hey, these 10 issues are impacting my MPS score and they're impacting my conversion and I would love for you to fix it. And when you can bring tangible impact, when you can bring real data to, to engineering and product, they move on it cause they also wanna help build the company. And, and so I think that's, that's how we stand out from the more traditional MarTech, because we need to fix the core of, of sort of this growth engine, which is the quality of the product >>Quality of the product. And obviously that's directly related to the customer experience. And we know these days, one of the things I think that's been in short supply the last couple of years is patience. We know when customers are unhappy with the product or service, and you talked about it a minute ago, they're gonna go right to, to Reddit or other sources to complain about that. So being able to, for uniq, to help companies to improve the customer experience, isn't I think table stakes for businesses it's mission critical these days. Yeah, >>It is mission critical. So if you look at the, let's say that we were gonna start a, a music app. Okay. So how do we, how do we compete as a music app? Well, if you, if you were to analyze all different music apps out there, they have more or less the same features app. Like they, the feature differentiation is minimal. And, and if you launch a new cool feature than your competitor will probably copy that pretty quickly as well. So competing with features is really hard. What about content? Well, I'm gonna get the same content on Spotify as apple SD. So competing with content is also really hard. What about price? So it turns out you'll pay 9 99 a month for music, but there's no, there's no 1 99. It's gonna be 9 99. So quality of the experience is one of the like last vectors or areas where you can actually compete. >>And we see consistently that if you' beating your competition on quality, you will do better. Like the best companies out there also have the highest quality experience. So it's, it's been, you know, for us at our last company, measuring quality was something that was very hard. How do we talk about it? And when we started this company, I went out and talked to a bunch of CEOs and product leaders and board members. And I said, how do you talk about quality in a board meeting? And they were, they said, well, we don't, we don't have any metrics. So actually the first thing we did was to define a metrics. We have, we have this thing called this unit Q score, which is on our website as well, where we can base it's like the credit score. So you can see your score between zero and a hundred. >>And if your score is 100, it means that we're finding no quality issues in the public domain. If your score is 90, it means that 10% of the data we look at refers to a quality issue. And the definition of a quality issue is quite simple. It is when the user experience doesn't match the user expectation. There is a gap in between, and we've actually indexed the 5,000 largest apps out there. So we're then looking at all the public review. So on our website, you can go in and, and look up the unit Q score for the 5,000 largest products. And we republish these every night. So it's an operational metric that changes all the time. >>Hugely impactful. Christian, thank you so much for joining me today, talking to the audience about unit Q, how you're turning qualitative feedback into pretty significant product improvements for your customers. We appreciate your insights. >>Thank you, Lisa, have a great day. >>You as well, per Christian Lin, I'm Lisa Martin. You're watching a cube conversation.

>> From theCUBE Studios in Palo Alto and Boston, bringing you data driven insights from theCUBE in ETR, this is Breaking Analysis with Dave Vellante. >> The introduction in socialization of data mesh has caused practitioners, business technology executives, and technologists to pause, and ask some probing questions about the organization of their data teams, their data strategies, future investments, and their current architectural approaches. Some in the technology community have embraced the concept, others have twisted the definition, while still others remain oblivious to the momentum building around data mesh. Here we are in the early days of data mesh adoption. Organizations that have taken the plunge will tell you that aligning stakeholders is a non-trivial effort, but necessary to break through the limitations that monolithic data architectures and highly specialized teams have imposed over frustrated business and domain leaders. However, practical data mesh examples often lie in the eyes of the implementer, and may not strictly adhere to the principles of data mesh. Now, part of the problem is lack of open technologies and standards that can accelerate adoption and reduce friction, and that's what we're going to talk about today. Some of the key technology and architecture questions around data mesh. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR, and in this Breaking Analysis, we welcome back the founder of data mesh and director of Emerging Technologies at Thoughtworks, Zhamak Dehghani. Hello, Zhamak. Thanks for being here today. >> Hi Dave, thank you for having me back. It's always a delight to connect and have a conversation. Thank you. >> Great, looking forward to it. Okay, so before we get into it in the technology details, I just want to quickly share some data from our friends at ETR. You know, despite the importance of data initiative since the pandemic, CIOs and IT organizations have had to juggle of course, a few other priorities, this is why in the survey data, cyber and cloud computing are rated as two most important priorities. Analytics and machine learning, and AI, which are kind of data topics, still make the top of the list, well ahead of many other categories. And look, a sound data architecture and strategy is fundamental to digital transformations, and much of the past two years, as we've often said, has been like a forced march into digital. So while organizations are moving forward, they really have to think hard about the data architecture decisions that they make, because it's going to impact them, Zhamak, for years to come, isn't it? >> Yes, absolutely. I mean, we are moving really from, slowly moving from reason based logical algorithmic to model based computation and decision making, where we exploit the patterns and signals within the data. So data becomes a very important ingredient, of not only decision making, and analytics and discovering trends, but also the features and applications that we build for the future. So we can't really ignore it, and as we see, some of the existing challenges around getting value from data is not necessarily that no longer is access to computation, is actually access to trustworthy, reliable data at scale. >> Yeah, and you see these domains coming together with the cloud and obviously it has to be secure and trusted, and that's why we're here today talking about data mesh. So let's get into it. Zhamak, first, your new book is out, 'Data Mesh: Delivering Data-Driven Value at Scale' just recently published, so congratulations on getting that done, awesome. Now in a recent presentation, you pulled excerpts from the book and we're going to talk through some of the technology and architectural considerations. Just quickly for the audience, four principles of data mesh. Domain driven ownership, data as product, self-served data platform and federated computational governance. So I want to start with self-serve platform and some of the data that you shared recently. You say that, "Data mesh serves autonomous domain oriented teams versus existing platforms, which serve a centralized team." Can you elaborate? >> Sure. I mean the role of the platform is to lower the cognitive load for domain teams, for people who are focusing on the business outcomes, the technologies that are building the applications, to really lower the cognitive load for them, to be able to work with data. Whether they are building analytics, automated decision making, intelligent modeling. They need to be able to get access to data and use it. So the role of the platform, I guess, just stepping back for a moment is to empower and enable these teams. Data mesh by definition is a scale out model. It's a decentralized model that wants to give autonomy to cross-functional teams. So it is core requires a set of tools that work really well in that decentralized model. When we look at the existing platforms, they try to achieve this similar outcome, right? Lower the cognitive load, give the tools to data practitioners, to manage data at scale because today centralized teams, really their job, the centralized data teams, their job isn't really directly aligned with a one or two or different, you know, business units and business outcomes in terms of getting value from data. Their job is manage the data and make the data available for then those cross-functional teams or business units to use the data. So the platforms they've been given are really centralized around or tuned to work with this structure as a team, structure of centralized team. Although on the surface, it seems that why not? Why can't I use my, you know, cloud storage or computation or data warehouse in a decentralized way? You should be able to, but some changes need to happen to those online platforms. As an example, some cloud providers simply have hard limits on the number of like account storage, storage accounts that you can have. Because they never envisaged you have hundreds of lakes. They envisage one or two, maybe 10 lakes, right. They envisage really centralizing data, not decentralizing data. So I think we see a shift in thinking about enabling autonomous independent teams versus a centralized team. >> So just a follow up if I may, we could be here for a while. But so this assumes that you've sorted out the organizational considerations? That you've defined all the, what a data product is and a sub product. And people will say, of course we use the term monolithic as a pejorative, let's face it. But the data warehouse crowd will say, "Well, that's what data march did. So we got that covered." But Europe... The primest of data mesh, if I understand it is whether it's a data march or a data mart or a data warehouse, or a data lake or whatever, a snowflake warehouse, it's a node on the mesh. Okay. So don't build your organization around the technology, let the technology serve the organization is that-- >> That's a perfect way of putting it, exactly. I mean, for a very long time, when we look at decomposition of complexity, we've looked at decomposition of complexity around technology, right? So we have technology and that's maybe a good segue to actually the next item on that list that we looked at. Oh, I need to decompose based on whether I want to have access to raw data and put it on the lake. Whether I want to have access to model data and put it on the warehouse. You know I need to have a team in the middle to move the data around. And then try to figure organization into that model. So data mesh really inverses that, and as you said, is look at the organizational structure first. Then scale boundaries around which your organization and operation can scale. And then the second layer look at the technology and how you decompose it. >> Okay. So let's go to that next point and talk about how you serve and manage autonomous interoperable data products. Where code, data policy you say is treated as one unit. Whereas your contention is existing platforms of course have independent management and dashboards for catalogs or storage, et cetera. Maybe we double click on that a bit. >> Yeah. So if you think about that functional, or technical decomposition, right? Of concerns, that's one way, that's a very valid way of decomposing, complexity and concerns. And then build solutions, independent solutions to address them. That's what we see in the technology landscape today. We will see technologies that are taking care of your management of data, bring your data under some sort of a control and modeling. You'll see technology that moves that data around, will perform various transformations and computations on it. And then you see technology that tries to overlay some level of meaning. Metadata, understandability, discovery was the end policy, right? So that's where your data processing kind of pipeline technologies versus data warehouse, storage, lake technologies, and then the governance come to play. And over time, we decomposed and we compose, right? Deconstruct and reconstruct back this together. But, right now that's where we stand. I think for data mesh really to become a reality, as in independent sources of data and teams can responsibly share data in a way that can be understood right then and there can impose policies, right then when the data gets accessed in that source and in a resilient manner, like in a way that data changes structure of the data or changes to the scheme of the data, doesn't have those downstream down times. We've got to think about this new nucleus or new units of data sharing. And we need to really bring back transformation and governing data and the data itself together around these decentralized nodes on the mesh. So that's another, I guess, deconstruction and reconstruction that needs to happen around the technology to formulate ourselves around the domains. And again the data and the logic of the data itself, the meaning of the data itself. >> Great. Got it. And we're going to talk more about the importance of data sharing and the implications. But the third point deals with how operational, analytical technologies are constructed. You've got an app DevStack, you've got a data stack. You've made the point many times actually that we've contextualized our operational systems, but not our data systems, they remain separate. Maybe you could elaborate on this point. >> Yes. I think this is, again, has a historical background and beginning. For a really long time, applications have dealt with features and the logic of running the business and encapsulating the data and the state that they need to run that feature or run that business function. And then we had for anything analytical driven, which required access data across these applications and across the longer dimension of time around different subjects within the organization. This analytical data, we had made a decision that, "Okay, let's leave those applications aside. Let's leave those databases aside. We'll extract the data out and we'll load it, or we'll transform it and put it under the analytical kind of a data stack and then downstream from it, we will have analytical data users, the data analysts, the data sciences and the, you know, the portfolio of users that are growing use that data stack. And that led to this really separation of dual stack with point to point integration. So applications went down the path of transactional databases or urban document store, but using APIs for communicating and then we've gone to, you know, lake storage or data warehouse on the other side. If we are moving and that again, enforces the silo of data versus app, right? So if we are moving to the world that our missions that are ambitions around making applications, more intelligent. Making them data driven. These two worlds need to come closer. As in ML Analytics gets embedded into those app applications themselves. And the data sharing, as a very essential ingredient of that, gets embedded and gets closer, becomes closer to those applications. So, if you are looking at this now cross-functional, app data, based team, right? Business team, then the technology stacks can't be so segregated, right? There has to be a continuum of experience from app delivery, to sharing of the data, to using that data, to embed models back into those applications. And that continuum of experience requires well integrated technologies. I'll give you an example, which actually in some sense, we are somewhat moving to that direction. But if we are talking about data sharing or data modeling and applications use one set of APIs, you know, HTTP compliant, GraQL or RAC APIs. And on the other hand, you have proprietary SQL, like connect to my database and run SQL. Like those are very two different models of representing and accessing data. So we kind of have to harmonize or integrate those two worlds a bit more closely to achieve that domain oriented cross-functional teams. >> Yeah. We are going to talk about some of the gaps later and actually you look at them as opportunities, more than barriers. But they are barriers, but they're opportunities for more innovation. Let's go on to the fourth one. The next point, it deals with the roles that the platform serves. Data mesh proposes that domain experts own the data and take responsibility for it end to end and are served by the technology. Kind of, we referenced that before. Whereas your contention is that today, data systems are really designed for specialists. I think you use the term hyper specialists a lot. I love that term. And the generalist are kind of passive bystanders waiting in line for the technical teams to serve them. >> Yes. I mean, if you think about the, again, the intention behind data mesh was creating a responsible data sharing model that scales out. And I challenge any organization that has a scaled ambitions around data or usage of data that relies on small pockets of very expensive specialists resources, right? So we have no choice, but upscaling cross-scaling. The majority population of our technologists, we often call them generalists, right? That's a short hand for people that can really move from one technology to another technology. Sometimes we call them pandric people sometimes we call them T-shaped people. But regardless, like we need to have ability to really mobilize our generalists. And we had to do that at Thoughtworks. We serve a lot of our clients and like many other organizations, we are also challenged with hiring specialists. So we have tested the model of having a few specialists, really conveying and translating the knowledge to generalists and bring them forward. And of course, platform is a big enabler of that. Like what is the language of using the technology? What are the APIs that delight that generalist experience? This doesn't mean no code, low code. We have to throw away in to good engineering practices. And I think good software engineering practices remain to exist. Of course, they get adopted to the world of data to build resilient you know, sustainable solutions, but specialty, especially around kind of proprietary technology is going to be a hard one to scale. >> Okay. I'm definitely going to come back and pick your brain on that one. And, you know, your point about scale out in the examples, the practical examples of companies that have implemented data mesh that I've talked to. I think in all cases, you know, there's only a handful that I've really gone deep with, but it was their hadoop instances, their clusters wouldn't scale, they couldn't scale the business and around it. So that's really a key point of a common pattern that we've seen now. I think in all cases, they went to like the data lake model and AWS. And so that maybe has some violation of the principles, but we'll come back to that. But so let me go on to the next one. Of course, data mesh leans heavily, toward this concept of decentralization, to support domain ownership over the centralized approaches. And we certainly see this, the public cloud players, database companies as key actors here with very large install bases, pushing a centralized approach. So I guess my question is, how realistic is this next point where you have decentralized technologies ruling the roost? >> I think if you look at the history of places, in our industry where decentralization has succeeded, they heavily relied on standardization of connectivity with, you know, across different components of technology. And I think right now you are right. The way we get value from data relies on collection. At the end of the day, collection of data. Whether you have a deep learning machinery model that you're training, or you have, you know, reports to generate. Regardless, the model is bring your data to a place that you can collect it, so that we can use it. And that leads to a naturally set of technologies that try to operate as a full stack integrated proprietary with no intention of, you know, opening, data for sharing. Now, conversely, if you think about internet itself, web itself, microservices, even at the enterprise level, not at the planetary level, they succeeded as decentralized technologies to a large degree because of their emphasis on open net and openness and sharing, right. API sharing. We don't talk about, in the API worlds, like we don't say, you know, "I will build a platform to manage your logical applications." Maybe to a degree but we actually moved away from that. We say, "I'll build a platform that opens around applications to manage your APIs, manage your interfaces." Right? Give you access to API. So I think the shift needs to... That definition of decentralized there means really composable, open pieces of the technology that can play nicely with each other, rather than a full stack, all have control of your data yet being somewhat decentralized within the boundary of my platform. That's just simply not going to scale if data needs to come from different platforms, different locations, different geographical locations, it needs to rethink. >> Okay, thank you. And then the final point is, is data mesh favors technologies that are domain agnostic versus those that are domain aware. And I wonder if you could help me square the circle cause it's nuanced and I'm kind of a 100 level student of your work. But you have said for example, that the data teams lack context of the domain and so help us understand what you mean here in this case. >> Sure. Absolutely. So as you said, we want to take... Data mesh tries to give autonomy and decision making power and responsibility to people that have the context of those domains, right? The people that are really familiar with different business domains and naturally the data that that domain needs, or that naturally the data that domains shares. So if the intention of the platform is really to give the power to people with most relevant and timely context, the platform itself naturally becomes as a shared component, becomes domain agnostic to a large degree. Of course those domains can still... The platform is a (chuckles) fairly overloaded world. As in, if you think about it as a set of technology that abstracts complexity and allows building the next level solutions on top, those domains may have their own set of platforms that are very much doing agnostic. But as a generalized shareable set of technologies or tools that allows us share data. So that piece of technology needs to relinquish the knowledge of the context to the domain teams and actually becomes domain agnostic. >> Got it. Okay. Makes sense. All right. Let's shift gears here. Talk about some of the gaps and some of the standards that are needed. You and I have talked about this a little bit before, but this digs deeper. What types of standards are needed? Maybe you could walk us through this graphic, please. >> Sure. So what I'm trying to depict here is that if we imagine a world that data can be shared from many different locations, for a variety of analytical use cases, naturally the boundary of what we call a node on the mesh will encapsulates internally a fair few pieces. It's not just the boundary of that, not on the mesh, is the data itself that it's controlling and updating and maintaining. It's of course a computation and the code that's responsible for that data. And then the policies that continue to govern that data as long as that data exists. So if that's the boundary, then if we shift that focus from implementation details, that we can leave that for later, what becomes really important is the scene or the APIs and interfaces that this node exposes. And I think that's where the work that needs to be done and the standards that are missing. And we want the scene and those interfaces be open because that allows, you know, different organizations with different boundaries of trust to share data. Not only to share data to kind of move that data to yes, another location, to share the data in a way that distributed workloads, distributed analytics, distributed machine learning model can happen on the data where it is. So if you follow that line of thinking around the centralization and connection of data versus collection of data, I think the very, very important piece of it that needs really deep thinking, and I don't claim that I have done that, is how do we share data responsibly and sustainably, right? That is not brittle. If you think about it today, the ways we share data, one of the very common ways is around, I'll give you a JDC endpoint, or I give you an endpoint to your, you know, database of choice. And now as technology, whereas a user actually, you can now have access to the schema of the underlying data and then run various queries or SQL queries on it. That's very simple and easy to get started with. That's why SQL is an evergreen, you know, standard or semi standard, pseudo standard that we all use. But it's also very brittle, because we are dependent on a underlying schema and formatting of the data that's been designed to tell the computer how to store and manage the data. So I think that the data sharing APIs of the future really need to think about removing this brittle dependencies, think about sharing, not only the data, but what we call metadata, I suppose. Additional set of characteristics that is always shared along with data to make the data usage, I suppose ethical and also friendly for the users and also, I think we have to... That data sharing API, the other element of it, is to allow kind of computation to run where the data exists. So if you think about SQL again, as a simple primitive example of computation, when we select and when we filter and when we join, the computation is happening on that data. So maybe there is a next level of articulating, distributed computational data that simply trains models, right? Your language primitives change in a way to allow sophisticated analytical workloads run on the data more responsibly with policies and access control and force. So I think that output port that I mentioned simply is about next generation data sharing, responsible data sharing APIs. Suitable for decentralized analytical workloads. >> So I'm not trying to bait you here, but I have a follow up as well. So you schema, for all its good creates constraints. No schema on right, that didn't work, cause it was just a free for all and it created the data swamps. But now you have technology companies trying to solve that problem. Take Snowflake for example, you know, enabling, data sharing. But it is within its proprietary environment. Certainly Databricks doing something, you know, trying to come at it from its angle, bringing some of the best to data warehouse, with the data science. Is your contention that those remain sort of proprietary and defacto standards? And then what we need is more open standards? Maybe you could comment. >> Sure. I think the two points one is, as you mentioned. Open standards that allow... Actually make the underlying platform invisible. I mean my litmus test for a technology provider to say, "I'm a data mesh," (laughs) kind of compliant is, "Is your platform invisible?" As in, can I replace it with another and yet get the similar data sharing experience that I need? So part of it is that. Part of it is open standards, they're not really proprietary. The other angle for kind of sharing data across different platforms so that you know, we don't get stuck with one technology or another is around APIs. It is around code that is protecting that internal schema. So where we are on the curve of evolution of technology, right now we are exposing the internal structure of the data. That is designed to optimize certain modes of access. We're exposing that to the end client and application APIs, right? So the APIs that use the data today are very much aware that this database was optimized for machine learning workloads. Hence you will deal with a columnar storage of the file versus this other API is optimized for a very different, report type access, relational access and is optimized around roles. I think that should become irrelevant in the API sharing of the future. Because as a user, I shouldn't care how this data is internally optimized, right? The language primitive that I'm using should be really agnostic to the machine optimization underneath that. And if we did that, perhaps this war between warehouse or lake or the other will become actually irrelevant. So we're optimizing for that human best human experience, as opposed to the best machine experience. We still have to do that but we have to make that invisible. Make that an implementation concern. So that's another angle of what should... If we daydream together, the best experience and resilient experience in terms of data usage than these APIs with diagnostics to the internal storage structure. >> Great, thank you for that. We've wrapped our ankles now on the controversy, so we might as well wade all the way in, I can't let you go without addressing some of this. Which you've catalyzed, which I, by the way, I see as a sign of progress. So this gentleman, Paul Andrew is an architect and he gave a presentation I think last night. And he teased it as quote, "The theory from Zhamak Dehghani versus the practical experience of a technical architect, AKA me," meaning him. And Zhamak, you were quick to shoot back that data mesh is not theory, it's based on practice. And some practices are experimental. Some are more baked and data mesh really avoids by design, the specificity of vendor or technology. Perhaps you intend to frame your post as a technology or vendor specific, specific implementation. So touche, that was excellent. (Zhamak laughs) Now you don't need me to defend you, but I will anyway. You spent 14 plus years as a software engineer and the better part of a decade consulting with some of the most technically advanced companies in the world. But I'm going to push you a little bit here and say, some of this tension is of your own making because you purposefully don't talk about technologies and vendors. Sometimes doing so it's instructive for us neophytes. So, why don't you ever like use specific examples of technology for frames of reference? >> Yes. My role is pushes to the next level. So, you know everybody picks their fights, pick their battles. My role in this battle is to push us to think beyond what's available today. Of course, that's my public persona. On a day to day basis, actually I work with clients and existing technology and I think at Thoughtworks we have given the talk we gave a case study talk with a colleague of mine and I intentionally got him to talk about (indistinct) I want to talk about the technology that we use to implement data mesh. And the reason I haven't really embraced, in my conversations, the specific technology. One is, I feel the technology solutions we're using today are still not ready for the vision. I mean, we have to be in this transitional step, no matter what we have to be pragmatic, of course, and practical, I suppose. And use the existing vendors that exist and I wholeheartedly embrace that, but that's just not my role, to show that. I've gone through this transformation once before in my life. When microservices happened, we were building microservices like architectures with technology that wasn't ready for it. Big application, web application servers that were designed to run these giant monolithic applications. And now we're trying to run little microservices onto them. And the tail was riding the dock, the environmental complexity of running these services was consuming so much of our effort that we couldn't really pay attention to that business logic, the business value. And that's where we are today. The complexity of integrating existing technologies is really overwhelmingly, capturing a lot of our attention and cost and effort, money and effort as opposed to really focusing on the data product themselves. So it's just that's the role I have, but it doesn't mean that, you know, we have to rebuild the world. We've got to do with what we have in this transitional phase until the new generation, I guess, technologies come around and reshape our landscape of tools. >> Well, impressive public discipline. Your point about microservice is interesting because a lot of those early microservices, weren't so micro and for the naysayers look past this, not prologue, but Thoughtworks was really early on in the whole concept of microservices. So be very excited to see how this plays out. But now there was some other good comments. There was one from a gentleman who said the most interesting aspects of data mesh are organizational. And that's how my colleague Sanji Mohan frames data mesh versus data fabric. You know, I'm not sure, I think we've sort of scratched the surface today that data today, data mesh is more. And I still think data fabric is what NetApp defined as software defined storage infrastructure that can serve on-prem and public cloud workloads back whatever, 2016. But the point you make in the thread that we're showing you here is that you're warning, and you referenced this earlier, that the segregating different modes of access will lead to fragmentation. And we don't want to repeat the mistakes of the past. >> Yes, there are comments around. Again going back to that original conversation that we have got this at a macro level. We've got this tendency to decompose complexity based on technical solutions. And, you know, the conversation could be, "Oh, I do batch or you do a stream and we are different."' They create these bifurcations in our decisions based on the technology where I do events and you do tables, right? So that sort of segregation of modes of access causes accidental complexity that we keep dealing with. Because every time in this tree, you create a new branch, you create new kind of new set of tools and then somehow need to be point to point integrated. You create new specialization around that. So the least number of branches that we have, and think about really about the continuum of experiences that we need to create and technologies that simplify, that continuum experience. So one of the things, for example, give you a past experience. I was really excited around the papers and the work that came around on Apache Beam, and generally flow based programming and stream processing. Because basically they were saying whether you are doing batch or whether you're doing streaming, it's all one stream. And sometimes the window of time, narrows and sometimes the window of time over which you're computing, widens and at the end of today, is you are just getting... Doing the stream processing. So it is those sort of notions that simplify and create continuum of experience. I think resonate with me personally, more than creating these tribal fights of this type versus that mode of access. So that's why data mesh naturally selects kind of this multimodal access to support end users, right? The persona of end users. >> Okay. So the last topic I want to hit, this whole discussion, the topic of data mesh it's highly nuanced, it's new, and people are going to shoehorn data mesh into their respective views of the world. And we talked about lake houses and there's three buckets. And of course, the gentleman from LinkedIn with Azure, Microsoft has a data mesh community. See you're going to have to enlist some serious army of enforcers to adjudicate. And I wrote some of the stuff down. I mean, it's interesting. Monte Carlo has a data mesh calculator. Starburst is leaning in, chaos. Search sees themselves as an enabler. Oracle and Snowflake both use the term data mesh. And then of course you've got big practitioners J-P-M-C, we've talked to Intuit, Orlando, HelloFresh has been on, Netflix has this event based sort of streaming implementation. So my question is, how realistic is it that the clarity of your vision can be implemented and not polluted by really rich technology companies and others? (Zhamak laughs) >> Is it even possible, right? Is it even possible? That's a yes. That's why I practice then. This is why I should practice things. Cause I think, it's going to be hard. What I'm hopeful, is that the socio-technical, Leveling Data mentioned that this is a socio-technical concern or solution, not just a technology solution. Hopefully always brings us back to, you know, the reality that vendors try to sell you safe oil that solves all of your problems. (chuckles) All of your data mesh problems. It's just going to cause more problem down the track. So we'll see, time will tell Dave and I count on you as one of those members of, (laughs) you know, folks that will continue to share their platform. To go back to the roots, as why in the first place? I mean, I dedicated a whole part of the book to 'Why?' Because we get, as you said, we get carried away with vendors and technology solution try to ride a wave. And in that story, we forget the reason for which we even making this change and we are going to spend all of this resources. So hopefully we can always come back to that. >> Yeah. And I think we can. I think you have really given this some deep thought and as we pointed out, this was based on practical knowledge and experience. And look, we've been trying to solve this data problem for a long, long time. You've not only articulated it well, but you've come up with solutions. So Zhamak, thank you so much. We're going to leave it there and I'd love to have you back. >> Thank you for the conversation. I really enjoyed it. And thank you for sharing your platform to talk about data mesh. >> Yeah, you bet. All right. And I want to thank my colleague, Stephanie Chan, who helps research topics for us. Alex Myerson is on production and Kristen Martin, Cheryl Knight and Rob Hoff on editorial. Remember all these episodes are available as podcasts, wherever you listen. And all you got to do is search Breaking Analysis Podcast. Check out ETR's website at etr.ai for all the data. And we publish a full report every week on wikibon.com, siliconangle.com. You can reach me by email david.vellante@siliconangle.com or DM me @dvellante. Hit us up on our LinkedIn post. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (bright music)

(upbeat music) >> Hello, welcome to theCUBE's presentation with Unstoppable Domains. It's a showcase we're featuring all the best content in Web 3 and with unstoppable showcase, I'm John Furrier, your host of theCUBE. We got a great guest here, Matt Mickiewicz who's the Chief Revenue Officer of Unstoppable Domains. Matt, welcome to the showcase, appreciate it. >> Thank you for having me. >> So the theme of this segment is the potential of the Web 3 marketplace with Unstoppable Domains. You're the Chief Revenue Officer, you guys have a very interesting concept that's going extremely well, congratulations. But you're using NFTs for access and domains, Of course through the metaverse is huge. People want their own domains, but it's not just like real estate in the sense of a website. It's bigger than that it's a lot going on. So take us through what is the value proposition and what is the product? >> Absolutely, so for the past 20 years, most of us have been interacting on the internet using usernames issued to us by big corporations like Facebook, Google, Twitter, TikTok, Snapchat, et cetera. Whenever we get these usernames for free it's because we and our data are the product. As some of the recent leaks in the media have shown incentive individual in companies are not always aligned. And most importantly individuals are not in control of their own digital identity and the data, which means they can economically benefit from the value they create online. Think of Twitter as a two-sided marketplace with 0% revenue share back to its creators. We're now having in the creator economy and we believe that individuals should see the economic rewards of what they do and create online. That's what we are trying to do in** support of domains is provide user own and control identity to four and a half billion internet users. >> It's interesting to see change that's happening with Web3 and just in cultural terms, users are expecting to be part of the creator the personality of the company, there's this almost this intermediation of the middle man whether it's an ad network or a gatekeeper of any kind people going direct, right? So if I'm an artist, I can go direct to my fans. >> Exactly, so Web3 really shifts the power away from a aggregators. Aggregators and marketplaces have been some of the best business models for the last 20 years onto the internet. But Web3 is going to dramatically change all over the next decade. Bring more power back in the hands of consumers. >> What type of companies do you guys work with and partner with that we see out there? Give us some examples of the kinds of companies you're doing business with end partnering with. >> Yeah, so let's talk about use cases first actually. Was the big use case that we identified initially for NFT domain names was around cryptocurrency transfers. Anyone who's ever bought cryptocurrency and tried to transfer it between accounts or wallets is familiar with these awkwardly long hexa decimal strings of random numbers and letters, or even if you make a single type of money is lost forever. That's a pretty scary experience that exists today. That 2 trillion asset dollar as a class with 250 million users. So the first set of partners that we worked on integrating with, we're actually crypto wallets and exchanges. So we will allow users to do is replace all their long hexa decimal wallet addresses with a single human readable name, like John.NFT or MattMickiewicz.crypto to allow for simple crypto transfers. >> And how do the exchange work with you guys on that is it a plugin, is it co-locating code together? What's the relationship between exchanges and Unstoppable Domains? >> Yeah, absolutely great question. So exchanges actually have to do a little bit of engineering list to work with us and they can do that by either using our resolution libraries or using one of our APIs in order to look up an Unstoppable Domain and figure out all the wallet addresses that's associated with that name. So today we work with dozens of the world's top exchanges and wallets ranging from OKX to Coinbase wallet, to Trust wallet, to bread wallet, and many many others. >> I got to ask you on the wallet side, is that a requirement in terms of having specific code and are the wallets that you work well with? Explain the wallet dynamic between Unstoppable Domains and wallets. >> Yeah, so wallets all have this huge usability problem for their users because every single cryptocurrency held by every single one of their users has a different hexadecimal wallet address. And once again every user is subject to the same human fallacies and errors where if they make a single type their money can be lost forever. So what we enable these wallets to do is to make crypto transfer simple and less scary than the current status quo by giving the users an Unstoppable name that they can use to attach to all the wallet addresses on the back end. So companies like Trust Wallet for example, which has 10 million user or Coinbase Wallet. When you go to the crypto transfer fields, there you can just type in an unstoppable name It'll correctly route the currency to the right person, to the right wallet, without any chance for human error. >> When these big waves coming out I got to ask this question, 'cause a lot of people in the mainstream are getting into it now. It reminds me of the web wave that hit the big thing was how many people are coming online, was one of the key metrics and how many web pages are being developed was another metric, which meant that people were building out webpages. And it's hard to look back and think, wow, that was actually a KPI. So internet users and webpages where the two proxies 'cause then search engines came out and everything else happened. So I got to ask you, there are people watching, they're seeing it on commercials on TV, they're seeing it everywhere stadiums are named after crypto companies. So, the bottom line is people want to know how NFT domains take the fear out of working with crypto and sending crypto. >> Yeah, absolutely, so imagine we had to navigate the web using IP addresses rather than typing in Google.com. You'd have to type in a random string of numbers that you'd had to memorize. That would be super painful for users and internet wouldn't have gotten to where it is today with almost 5 billion people online. The history of computer networks we have human readable naming systems built on top in every single instance, it's almost crazy that we got to a $2 trillion asset class with 250 million users worldwide. 13 years after the Satoshi white paper, without a human readable naming system other Unstoppable Domains in a few of our competitors, that's a fundamental problem that we need to solve in order to go from 250 million crypto users in 2022 to 5 billion crypto users a decade from now. >> And just to point out, not to look back and maybe make a correlation but I will, if you look at the naming system of DNS, what it did to IP addresses, that's one major innovation that enabled the web. Then you look at what keyword navigation has done on top of DNS, what that did for the industry, and that basically birthed Google keywords basically ads. So that's trillions and trillions of dollars. Again, now shifting to you guys, is that how you see it? Obviously it's decentralized, so what's different? Okay, I get, so if you compare here Google was successful, keyword advertising industry for the last of 25 years or 20 years. >> What's different now is? >> yeah >> Yeah, what's different now is the technology inflection points. So Blockchains have evolved to a point where they enable high throughput high transaction volume and true decentralized ownership. The NFTs standard, which is only a couple years old, has taken off massively around trading of profile pictures like CryptoPunks and the Bored Apes Yacht Club where the use cases extend much more than just a cool JPEG that goes up in value two or three X year over year. There is a true use case here around ownership of identity ownership over data, a decentralized login authentication and permission data sharing. One of the sad things that happened on the internet the last decade really was, that the platforms built out have now allowed developers to build on top of them in a trustless comissionless way. Developers who built applications on top of them, the early monopolies in the last decade, got the rules changed on them. APIs cut off, new fees instituted. That's not going to happen in Web3 because all permission list. Once an NFT is minted, it's custody in a user's own wallet, we cannot take the way it will continue to exist in eternity, regardless of what happens to Unstoppable Domains, which gives developers a lot more confidence in building new products for the Web3 identity standard that we're building out. >> You know what's amazing is that's a whole another generational shift. I've always been a big fan of abstractions when innovation is needed when there are problems that need to be solved, messes to be cleaned up, a good abstraction layer on top of new architecture is really, really phenomenal. I guess the key question for I have for you is, theCUBE we have all this video where's our NFT how should we implement NFTs? >> There's a couple different ways you could think about it, you could do proof of attendance protocol NFTs, which are really interesting way for users to show that they were at particular event. So just in the same way that people collect T-shirts from conferences, people will be collecting NFTs to show they were attending in person cultural moments or that they were part of an event online or offline. You could do NFTs for our employees to show that they were at your company during certain periods of the company's growth. So think of replacing their resume with a cryptographically secure resume like this on the Blockchain and perpetuity. Now more than half of all resumes contain lies, which is a pretty gnarly problem as a hiring manager that we constantly have to sort through. There's where that this can impact that side of the market as well. >> That's awesome, and I think this is a use case for everything we appreciate that. And of course we can have the most favorite cube moments, it can be a cube host NFT at Board Apes out there. Why not have a board cube host going on and then.. >> We're an auction for charity and OpenSea. >> All right, great stuff, now let's get into some of the cool tech nerd stuff, which is really the login piece which I think is fascinating. The having NFTs be a login mechanism is another great innovation, okay. So this is cool, 'cause it's like think of it as one click NFTs, if you will. What's the response been on this login with Unstoppable for that product? What's some of the use cases, can you get some examples of the momentum intraction? >> Yeah, absolutely, so we launched a product less than 90 days ago and we already have 90 committed or integrated partners live today with a login product. And this replaces login with Google, login with Facebook with a way that it's user owned and user controlled. And over time people will be attaching additional information back to their NFT domain name, such as their reputation, their history, things they've done online and be able to permission to share that with applications that they interact with in order to gain rewards. Once you own all of your data, and you can choose who you shared with . Companies will incentivize you to share data. For example, imagine you just buy a new house and you have 3000 square feet to furnish. If you could tell that fact and prove it, to a company like Wayfair, would they be incentivized to give you discounts? We're spending 10, 20, $30,000 and you'll do all of your purchasing there rather than spread across other e-commerce retailers. For sure they would, but right now when you go to that website, you're just another random email address. They have no idea who you are, what you've done, what your credit score is, whether you're a new house buyer or not. But if you could permission to share that using a log and installable product, I mean the web would just be much much different. >> And I think one of the things too, as these, I call them analog old school companies, old guard companies as referred to in theCUBE talk here. But we always call that old guard as the people who aren't innovating. You could think about companies having more community too, because if you have more sharing and you have this marketplace concept and you have these new dynamics of how people are working together, sharing will provide more or transparency but yet security on identity. Therefore things are going to be happening organically. That's a community dynamic what's your view on that? And what's your reaction. >> Communities are such an important part of Web3 and the cryptos ecosystem in general. People are very tightly knit, they all support each other. There there's a huge amount of collaboration in this space because we're all trying to onboard the next billion users into the ecosystem. And we know we have some fundamental challenges and problems to solve, whether it's complex wallet addresses, whether it's the lack of portable data sharing, whether it's just simple education, right? I'm sure, tens of million of people have gone to crypto for the first time during this year's Super Bowl based on some of those awesome ads they ran. >> Yeah, love the QR code, that's a direct response. I remember when the QR codes been around for a long time. I remember in the late 90's, it was a device at red QR code that did navigation to a webpage. So I mean, QR codes are super cool, great way to get, and we all using it too with the pandemic to ordering food. So I think QR codes are here to stay, in fact, we should have a QR code on all of our images here on the screen too. So we'll work on that, but I got to ask you on the project side, now let's get into the devs and kind of the applications, the users that are adopting unstoppable and this new way of things. Why are they gravitating towards this login concept? Can you give some examples and give some color commentary to why are these D-application, distributed application, dApps guys and gals programming with you guys? >> Yeah, they all believe that the potential for what we're trying to create around user own controlled identity. Where the only company in the market right now with a product that's live and working today. There's been a lot of promises made, and we're the first ones to actually delivered. So companies like Cook Finance for example, are seeing the benefit of being able to have their users, go through a simple process to check in and authenticate into the application using your NFT domain name rather than having to create an email address and password combination as a login, which inevitably leads to problems such as lost passwords, password resets, all those fun things that we used to deal with on a daily basis. >> Okay, so now I got to ask you the kind of partnerships you guys are looking at doing. I can only imagine the old school days you had a registry and you had registrars, you had a sales mechanism. I noticed you guys are selling NFT kind of like domain names on your website. Is that a kind of a current situation, is that going to be ongoing? How do you envision your business model evolving and what kind of partnerships do you see coming along? >> Yeah, absolutely, so we're working with a lot of different companies from browsers to exchanges, to wallets, to individual NFT projects, to more recently even exploring partnership opportunities with fashion brands for example. Monetarily, market is moving so so fast. And what we're trying to essentially do here is create the standard naming system for Web3. So a big part of that for us will be working with partners like blockchain.com and with Circle, who's behind the USDC coin on creating registry such as .blockchain and .coin and making those available to tens of millions and ultimately hundreds of millions and billions of users worldwide. We want an Unstoppable domain name to be the first asset that every user in crypto gets even before they buy their Bitcoin, Ethereum or Dogecoin. >> It makes a lot of sense to abstract the way the long hexa desal stream we all know, that we all write down, put in a safe, hopefully we don't forget about it. I always say, make sure you tell someone where your address is. So in case something happens, you don't lose all that crypto. All good stuff. I got to ask this the question around the ecosystem. Okay, can you share your view and vision of either yourself or the company when you have this kind of new market, you have all kinds of, we meant the web was a good example, right? Web pages, you need to web develop and tools. You had HTML by hand, then you had all these tools. So you had tools and platforms and things kind of came well grew together. How is the Web3 stakeholder ecosystem space evolving? What are some of the white spaces? What are some of the clearly defined areas that are developing? >> Yeah, I mean, we've seen explosion in new smart contract blockchains in the past couple of years, actually going live, which is really interesting because they support a huge number of different use cases, different trade offs on each. We recently partnered and moved over a primary infrastructure to Polygon, which is a leading EVM compatible smart chain, which allows us to provide free gas fees to users for minting and managing their domain name. So we're trying to move all obstacles around user adoption. Here you'll need to have Ethereum in your wallet in order to be an Unstoppable Domains customer or user, you don't have to worry about paying transaction fees every time you want to update the wallet addresses associated with your domain name. We want to make this really big and accessible for everybody. And that means driving down costs as much as possible. >> Yeah, it's a whole nother wave. It's a wave that's built on the shoulders of others. It's a shift in infrastructure, new capabilities, new applications. I think it's a great thing you guys do in the naming system, makes a lot of sense. It abstraction layer creates that ease of use, it simplifies things, makes things easier. I mean was the promise of these abstraction layer. Final question, if I want to get involved, say we want to do a CUBE NFT with Unstoppable, how do we work with you? How do we engage? Can you give a quick plug on what companies can do to engage with you guys on a business level? >> Yeah, absolutely, so we're looking to partner with wallet exchanges, browsers and companies who are in the crypto space already and realize they have a huge problem around usability with crypto transfers and wallet addresses. Additionally, we're looking to partner with decentralized applications as well as Web2 companies who perhaps want to offer logging with Unstoppable domain functionality. In addition to, or in replacement of the login with Google and login with Facebook buttons that we all know and love. And we're looking to work with fashion brands and companies in the sports sector who perhaps want to claim their Unstoppable name, free of charge from us. I might add in order to use that on Twitter or in other marketing materials that they may have out there in the world to signal that they're not only forward looking, but that they're supportive of this huge waves that we're all riding at the moment. >> Matt, great insight, chief revenue officer, Unstoppable Domains. Thanks for coming on the showcase, theCUBE and Unstoppable Domains share in the insights. Thanks for coming on. >> Thank you. >> Okay, this CUBE's coverage here with the Unstoppable Domain showcase. I'm John Furrier, your host, thanks for watching. (upbeat music)