>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Good afternoon. Welcome back to the cubes coverage of AWS reinvent 19 from Las Vegas. I'm Lisa Martin. Co-host is Justin Warren, the founder and chief, endless at pivot nine. Justin, great to have you. Great to be here next to me in the hosting chair today. Always fun. Let's have a great conversation next. Shall we? All right, please be a couple of our guests have joined Justin and me. I've got Dan Hubbard to my love CEO of Lacework and Ilan Rabinovitch, the VP of product at Datadog. Guys, welcome. Our pleasure to be here. Love anytime we can talk about dogs, even if there's no relation to the actual technology. Two thumbs up for me. So, but let's go ahead. I know that you guys have both been on or your companies have, but give our audience, Dan, we'll start with you on a refresher and overview. Lacework what do you guys >>sure. Yeah. Lacework we wake up every morning with a goal of trying to help our customers secure their public cloud infrastructure and, or any type of cloud native technologies such as Kubernetes or containers or any microservices. So our security company for the cloud and cloud native technologies. >>Awesome. Any long, give us a refresher about Datadog, >>Datadog as a monitoring and analytics platform for your modern infrastructure and applications. So micro services, containers, cloud providers like AWS. We're here at reinvent. Our goal is to help teams collaborate and understand the health of their business and their applications and their infrastructure. >>So how do you guys work together? >>So we recently announced a partnership and an integration of the intelligence and the data of all the risks and the threats that at least work as identifying, um, being, sending those, uh, automatically inside of the Datadog platform. So we're, we're putting the data that from our platform, uh, directly into obviously the monitoring the metrics, uh, platform, uh, Datadog's. Yep. And so, uh, what we, when we, we were pulling, um, that intelligence from, from Lacework into our, um, into our platform for our new security monitoring platform. In addition to enriching it with metrics from our infrastructure and application monitoring. Um, we find that a lot of the, a lot of times the first signs that something's going wrong might be a change in how your infrastructure or your applications are performing or a request that came in. And so if we're able to marry the two together, it's just a much better to get, it's a better together story. >>Um, give people much, much clearer insights into what's going on. The security has been a really tricky thing to solve. Well, as long as I've been in computing, which is longer than I can remember, but, uh, walk us through what does this extra visibility actually provide to customers? One of the big issues that seems to be that security is just too hard. So how does this make security easier for customers? >> So one of the big trends that we're seeing is that security and infrastructure were in the past very separate groups. Silos didn't men, many of them didn't know each other or talk to each other. But dev ops has become becoming a unifying force of data intelligence and infrastructure. You know, it's infrastructure as code. It's a little bit different like AWS for example, but it still is infrastructure. And so the combination of security and infrastructure comes together. >>When you get dev ops, some people call it secure dev ops, dev, sec ops, dev ops, whatever you want to call it. But really bringing those two together is finally the first time really where there's a meaningful connection at the data level. It allows you to actually combine both. >> Exactly. And so as all of these teams are taking advantage of infrastructure as code and other DevOps best practices, the security teams are looking at this and saying, how do I get earlier in the cycle? How do I make sure that code is enforcing this? Some scaling, you know, I'm scaling with automation, scaling with code rather than with people. Uh, and then as, as they start to do that, they realize that the data that's in the security silo and that's an application or infrastructure silo, uh, is actually very relevant to one another. Right? If a crypto miner shows up on your systems, the first thing it's going to do is spike your CPU. Um, the, you know, something like Lacework will also, you know, will, will detect that as well if we both look at both of those signals with detective faster. >>Yeah. So go ahead Justin. Sorry. This is a bit of it. That's the reactive side of, of security, which is, you know, there's a threat happens and you react to that, but part of DevSecOps or whichever term you want to actually use, part of that is act to actually shift left and try to get rid of these security flows before they even happen in the code, which is a lot of software development. I like to say that the first 80% of software development is putting the bugs in and the second 90% is taking them out again. So how do you help developers actually remove all of the security vulnerabilities before they even make it into production code? Yeah, >>so just like metrics and monitoring allow you to look at the quality of your infrastructure are very early in the pipeline. A security needs to go there also. Um, and it's, it's really, there is no time. It's just a continuous cycle. Um, early, what we allow you to do is to look at your configuration and check to see if your configuration is changing in a way that is leaving you at risk or an exposure. What's particularly interesting about this partnership is that quite often security people don't know enough about the application or the infrastructure to know if it's a risk. It's actually the dev ops people then now, so security people when when we send an alert many times to security person, they scratch their heads and go, I don't know if this is good, bad, or indifferent. The dev ops people look at it and go, Oh yeah, this is definitely okay. >>Yeah, that's the way our infrastructure should work. This is the way our application should work. Or they say, Oh no, this is a big problem. Let's get security involved. So doing that early is really critical and again, >> it's all about breaking down. I mean if dev ops was all about breaking down silos between Devin operations and and other parts of the business, dev, sec ops or secure dev ops or whatever we want to call it, is just bringing more people into the fold and helping security join that party, um, and get at things earlier in the cycle so we can catch it before it, you know, before, before there's a breach that's in the news, >>right? To be able to be predictive, which is, and then prescriptive, which is about a lot of businesses would love to be able to be, I'd like to get your opinion, Dan, on how cloud >>native cloud and the tra, the transformation of cloud technologies is changing the conversation within the customer base. One of the things Andy Jassy said yesterday is that transformation has gotta be driven from the top down like true business transformation. So that you know, a company is an Uber I's for example. Are you seeing that? Are these, are these, for example, what you're talking about with enlightening the DevOps folks in the security folks bringing them together so that they can be more collaborative? Are you seeing that come from more of a top down approach in terms of how do we leverage our data better, make sure that we have security and are able to securely extract insights from the data? Or is it still kind of from both ends? It depends on the, >>but he, it's, it's very diverse. Uh, what we see a lot is in large, uh, large companies that are migrating to the cloud but weren't born in the cloud. Every company they're buying is a cloud native company. So they buy these new companies and they look, everyone looks at the new company goes, wow, that's amazing. They can move so fast. They, they are, you know, super forward thinking and they're pushing code and are more efficient than us. We want to do that also. So it just kind of breeds the innovation and the speed from an M and a perspective. You know, in the, in the cloud native side, what we see is, it depends on your tenure as a company when you really want to take security seriously. You know, usually B2B companies take it more seriously in B to C for example. But it's usually, it's when your customers start asking you how secure are you, is when people start paying attention. >>We would like it to be before that. Right? And it's not always, you know, before that. Yup. I mean, I think it's from both directions. It depends on the size of the company and the culture, but you can't dictate culture. Right? So, uh, and a lot of, a lot of this, a lot of these silos and a lot of these sort of, these camps and fiefdoms that start to exist within organizations that have caused these groups to be separate. Um, they weren't necessarily top down. It's just, you know, it's a, it's human to human interactions. And so you, you, you can't just walk in and say, you must now be collaborative. Um, the executives have to beat that drum and help people understand why that's important to the business. But the folks on the ground have to actually want to be at one, want to be friends, want to talk, want to collaborate on projects, want to pull people in earlier. >>Um, and once they have that human connection, it's a lot more successful. So you have to do both. Yeah. Well, I mean what we're seeing is as it becomes more distributed and security is more centralized, you run to problems. So the people that are getting it right or are distributing security as close to those teams, whether it's a scrum team, a weekly get together, you know, whatever it is to get that human interaction together because you don't understand the application and what people are working on. How are you going to understand the risks and the threats in the models. So distributing it is really key and it's important those security teams understand the business requirements as well. Sometimes the most secure answer isn't necessarily the answer that actually serves their customers. Sometimes some, and sometimes app teams don't understand the trade offs that security people may understand. So it has to be, it has to be a partnership. Yep. >>You mentioned called change is probably >>harder than anything else, especially if there's a legacy organization. And Dan, to your point, a lot of the acquisitions they're doing are a cloud native companies who are presumably much fresher, maybe have a younger workforce. That's hard to do. Ultimately though, what a business needs to look at is legacy business. There's probably somebody in my rear view mirror is a lot closer than I might think that is more agile, more nimble than we are, has great technology and the aptitude and the culture to be able to move faster. How do you see some of these enterprises that you work with together? Let's put them in the context of they're an AWS customer. How are you seeing these enterprise organizations that are adopting and acquiring cloud native businesses? How are they able to pivot at the speed they need to use cloud technology, understand the security issues that they can remediate and really take that data to what it should be, which is a business differentiator. >>Yeah, I mean, you know, a lot of the times you run into the dev ops people say security slows us down. They're getting in our way and security says developers are insecure that, you know, we're totally gonna get breached. So, um, you know, one of our mottoes is you got to move with speed and safety. Um, as soon as you get in the way of anything. You know, typically the developer and the application's going to win. So you got to figure out where to get involved in that. And really big companies, what we've seen that are very inquisitive is they're moving the security to a central governance role, um, and maybe have tooling and uh, you know, some specialty teams and then they're distributing security baked as deep into the development infrastructure as they can. And then they have groups which kind of work together, uh, you know, broadly across that. >>So you can structurally set it up that way I think. And if you have the incentives right now, you know, nobody's looking to create a security breach, there are a vulnerability there. Gold engine engineers and your employees have your best, the company's best intentions at heart, otherwise they wouldn't, they wouldn't work, you know, work there. So they're looking to do the right thing. You just have to make it easy for them with, and some that's tooling. Some of that's culture. Some of that's just starting the conversation, not the day of the release started, you know, start it when the, when the, when the, when the first line of code is being written, what would it take for us to solve this problem in a secure fashion? And then everybody was happy to work together. They just don't want to redo things. You know, the, the, the day before the launch should have to, you know, be slowed down. >>Well that technical debt becomes a real problem. Right? Yeah. I think one of the great things about, uh, you know, our technical, uh, partnership and integration here is security in the past has always been just very binary. Are we insecure, secure? That's it. We're actually, there's all kinds of nuances around it and that's what lends itself to metrics. If, you know, what are our metrics? How are we doing, what's our risk? What's our exposures? Is getting better over time? Is it worse over time? So there's always the doomsday scenario, but there's also the, what's happening over time and are we getting better at what we do? And metrics really lends itself to that. And that comes right back to that, to that, uh, you know, some of dev ops philosophies of continuous improvement and continuous learning, uh, you know, bringing that into the world of security is, is just as critical. >>So you, so you mentioned, you've mentioned culture, you mentioned transformation, you mentioned metrics. So three things very close to my heart. Uh, we keep hearing this security is becoming a board level conversation. So a lot of this is very technical and, and DevSecOps is down here with the technical people, but that structure of the organization that you referred to and, and changing that structure and setting the culture that tends to come from the top level. And we heard from Andy in the keynote yesterday that that is very, very important. So what are the sorts of conversations you're having with senior management and board level from what your products do together? What does that look like from the board's perspective? So learning to manage risk, looking at how are we doing, how much of what of what you do is actually available to the board for them to make their job easier. >>I think one of the exciting trends is that compliance is cool again, right complaints. It's never a cool thing, you know, flight's kind of a boring thing. The auditors come in once a year, you know, you get stuck with it and the way you go. Um, but now compliance is continuous. It's always running and it's more about risks and exposures and Mia adhering to compliance via the risks and exposures executives get, ER, it's very challenging to explain things like Kubernetes and pods and nodes and all this technical acronyms and mumbo jumbo that we live in every day, you know, in this world. But compliance is real. Are we PCI, SOC two NIST, are we, are we applying best standards and best practices? So the ability to pull that in either via a metrics dashboard or through measurable things over time, I think is really key. As part of that. >>And similarly as, as, as filter moving, you know, whether whether they're moving new application, existing applications from, uh, you know, legacy or on prem environment into the cloud or building something from scratch. Um, it's, you know, visibility on compliance is important. We can bring that into our dashboards, into our, into the tooling that executives can look at over time. But also just understanding, am I done with the migration? Is my application there? Um, taking this nebulous thing that is a cloud and making it a tangible asset that you can look at and see the health and progress on overtime and Datadog has significantly sped up. Many of our customers cloud migrations, um, they often get stuck in a sort of analysis paralysis. Are we, are we performing the same as we did in the data center? I don't know. Uh, are we as secure? Can we move this workload and tooling like Datadog, like Lacework and the two together helps them put that into something concrete that they can say, actually, yes, we're ready to go. >>Or no, there's these three things we need to do first, let's go do them. Um, it's really challenging if for, um, traditional security people and this new world order because it's very ephemeral. Things change all the time. You know, it used to be like, I got five racks, I got 22, you know, 2200 servers. These are the IPS and that's it. Now it's like, what time is it? I don't know what I have, you know? So I think visibility's key, you used to be able to have a server that you might've monitored throughout your tenure at a company. Now you probably can't monitor it through the tenure of your lunch. Yeah. Yeah. >>Last question for you guys is how much do you see a lift or an impact from something the capital one data >>breach that happened a few months ago? You talked about, you know, B2B being more on it in terms of B to C, but we S we see these breaches that and many generations that are alive today understand to some degree is that in terms of getting insight into where are all of our risks and vulnerabilities and needing to get that visibility on it, do you see some of these big breaches as, um, catalysts for businesses to go, Oh, we have a lot of stake here. We don't really, and try to understand what the heck's going on and what we own. >>I mean, security has a very bad reputation of fear, uncertainty and doubt. And, you know, I've been in the, in the industry for a long time. Um, that said, you know, those moments do, uh, get up very high. Um, especially somebody like capital one who, who's one of them, no one to be one of the most sophisticated cloud security organizations on the planet. Um, so it certainly piques people's interests. Um, you know, I think people get carried away maybe on the messaging side of things, but you know, in order for security market to get really big, you have to have a big it transformation trend. You have to have a very diverse attack surface and you have to have the beginnings of breach. If you don't have the beginnings of breach, you spent all your time convincing people there may be a problem. And because there is problems that are happening almost every weekend are getting published. >>Um, they know many of them are, are, are being acknowledged. Uh, you know, publicly it does help, you know, it definitely helps the conversation. You know, I don't think that there's a lot more, there are a lot more breaches in the news off to some extent because there's a lot more tech companies using going through these digital transmissions, having tech news. I don't know that this is cloud versus not cloud. What cloud does, however introduces new concepts and new workflows that security teams need to understand and that application teams, they understand. And so this is where the new breed of tooling and education comes in, is helping people be ready for that. Um, and yeah, of course anytime there's a headline on, you know, the big on any of the big news shows, of course the first thing we're going to do is say, well clearly there's a, they're going to bring on, they're going to bring on Dan or you know, you know, uh, one of our security experts or somebody in industry to talk about how you prevent that in the future. >>And so it, it does bring some attention in our way, but it's, uh, I think that's great. It's just finding people that what's important. And one of the conversations we have with our prospects is, uh, have you ever had a breach before? You know, they're always going to say no, of course. But then you ask, how do you know, how do you know? How do you really know that? And then let's walk through how you would actually find that out if you did know. And that's a very different conversation than, Oh, my traditional data center, I would know this way. So it's just very different. >>Interesting stuff, guys. Thank you for sharing with us and congratulations on the integration with Datadog and Lacework. We appreciate your time. Our pleasure for Justin Warren. I am Lisa Martin and you're watching the cube live from AWS, reinvent 19 from Vegas. Thanks for watching.

>> Voiceover: Live from San Francisco, it's The Cube. Covering AWS Summit 2017. Brought to you by Amazon Web Services. >> Hi, welcome back to The Cube. We are live in San Francisco at the AWS Summit. We've had a great day so far. I'm Lisa Martin here with my co-host George Gilbert. We are very excited to be joined by Datadog. K Young the Director of Strategic Alliances from Datadog, welcome to The Cube. >> Thank you, hi. Glad to be here. >> So, tell us, besides loving your shirt, as I've already told you, tell us and our viewers a little bit about who Datadog is and what do you do. >> Alright, so Datadog does infrastructure monitoring and application performance monitoring. So what that means is we're able to not only look at your hosts and the resources they have available to them, meaning CPU and memory and that sort of thing, but also all the software that's running on top of it. So, if it's off the shelf software, like a database, like Postgres, or maybe it's EngineX, we understand over 200 different off-the-shelf types of software, integrate with them directly so all you have to do is turn on those integrations, and we can tell you whether those pieces of software are performing at the rate that they ought to, with a sufficiently low number of errors. That's the infrastructure monitoring side of things. Then application performance monitoring, is where you can actually trace execution of requests, individual requests, across different services, or microservices, and tell where time is being spent and track metadata so that in a forensic case, you can go back and determine, oh this type of call is producing a lot of errors. Oh, and those errors are coming from here, and then, you know, maybe a lot of time is being spent here, and then because Datadog also does infrastructure monitoring, drill down into, okay well, what's happening under the hood? Maybe we're having problems because our infrastructure itself is misbehaving in some way. >> You have some pretty big customers: Salesforce, Airbnb, Samsung. I was just reading yesterday, an article that was published, that you've been, Datadog, in the top five businesses profiled by IDC as the multi-cloud management vendors to look out for. So, some pretty big accolades, some pretty big customers. How long have you been in business? >> K Young: Since 2010. >> Lisa: 2010. And tell us about what you're doing with Amazon. >> What we're doing with Amazon. So, let's see, where to begin. Amazon, a lot of people come to Datadog when they have complex systems to manage, meaning highly dynamic, or high scale, or they've adopted Docker, and their infrastructure is changing frequently. More frequently than infrastructure used to change ten years ago. Because Datadog makes it easy or ... Easy, possible even, to make sense of what's happening, even as your infrastructure changes on an hourly basis. So, a lot of customers come to us around the time they're interested in using dynamic infrastructure. Sometimes that's on Amazon, and sometimes that's when you're On-Prem but you're adopting Docker, for example, or microservices. We get a lot of business on Amazon. I think it's fair to say Amazon loves us, because it makes it so much easier to use their service and to adopt their service. And we're sort of the defacto infrastructure monitoring service for Amazon. >> So, you talking about containers, microservices, hyperscale. Is there a break with earlier monitoring and management software that didn't handle the ephemeral nature of applications and infrastructure? Is that the change? >> Yeah, that's basically it. Ten years ago, you as an assistant administrator or operations person, would have known the names of every one of your servers, and you kind of treat them affectionately. "Oh, you know, old Roger is misbehaving again, we got to give it a reboot." These days you don't know, in many cases, how many servers you have, much less what's running on them. So, it used to be that you could set up monitoring where you say, "Okay, I need to look at these things. They should be doing these set of tasks." And you set it up and basically forget it for six months or a year. Now, what's happening on any given machine or what's inside of a container, is churning very, very frequently. And so, to make sense of that, you have to use tags. So to tag all of your infrastructure with what it's doing, maybe what environment it is, like if it's staging or production, whether it's in AWS or On-Prem. Maybe it's a part of a build. And then you can look at your infrastructure and its performance through those lenses. You don't have to think in advance, "Oh, I'm going to want to know what's happening in US-East-1 in production with build number 1180." You can just do that on the fly with Datadog. And that's the sort of thing that we make possible. It's necessary for modern applications and modern services, that really wasn't possible before. >> So, it sounds like it's fairly straightforward at the infrastructure level to know what metrics and events you want to collect, in the sense that, you know, CPU utilization, memory utilization and, you know, maybe even a database number of connections and query time, but as you move up at the application level, the things that you want to ask could become very different between apps. >> K Young: Yeah. >> And then very different across Cloud or On-Prem. >> Yeah, that's right. So, there's sort of two classes of different things you could want to ask. Datadog accepts totally custom metric, so we know about, as I said, 200 different technologies, and we can collect everything automatically. But then, you're going to have your own application and you're going to want to send us things that are specific to your business. We take those just as well. So, for example, I think we have one customer who tracks when cash register drawers open or close. You know, that's not built in, but they can send those metrics to us. They get graphed the same way. We can set alerts on it the same way. We can use sophisticated machine learning to make projections about how we expect those patterns to be in the future, and if the cash registers don't open at the right rate, we can let somebody know that something has gone wrong. So, we can collect any kind of metrics. Then on top of that, we've got application performance monitoring. Right, so that's where you've written custom code, and Datadog, since it's already running on all of your servers, can track requests as it moves from service to service, or between microservices, and recompile that request into a visualization that will show you everything that happened, how long it took, and allows you to drill in and get metadata about each thing. So, you can actually reconstruct where time is going or whether there are problems. >> Why don't I ask you about some of the trends? As I mentioned a minute ago reading that article, or the mention of Datadog by IDC as one of the top five multi-cloud management vendors. What are some of the trends that you were seeing with respect to hypercloud, multi-cloud? You know, we've heard some conversation today from AWS, but I'd love to get your feedback, as the Director of Strategic Initiatives, what are you seeing? >> So, the trend that ... I'm going to answer this, but the trend that we were seeing a few years ago was more and more people were adopting Cloud, period. And that's continued and continued and continued. 18 months ago, if you went and talked to a large financial services organization and you told them, we do monitoring. Okay, they're interested. Well, we run only in the Cloud, so you actually have to send your data to the Cloud. They'd show you the door very politely. And now, they say, "Oh well, we're going to the cloud, now, too." It's a great place to be. Now, we're seeing organizations of all sizes, all types, are in the Cloud. So, the next leading trend is containerization and microservices. So, we actually published a Docker adoption report. We've done it three times now. We refreshed it yesterday. We do it about every six months, and we take a look at all of the usage that we can see. Because we have this somewhat unique vantage point of being able to see tens of thousands of customer's usage, real usage, of infrastructure, and look at, okay, which percent are using Docker? When they use it, do they dabble with it? Do they fully adopt it? Do they eventually abandon it? What are they running on it? So, we published a very long report. Anyone who's interested can actually Google "Docker adoption" and we'll be the top hit there. We've got eight different fact that talk about how quickly it's being adopted. Docker adoption is really quite remarkable. We're seeing a 40% growth in true adoption, not just dabbling, since last year. At the same time, we've seen a more than 100% increase, a more than doubling, of the companies that use Docker, that are using orchestrators, like Kubernetes, to manage even more sophisticated and rapidly changing fleets of machines. And that's really meaningful, because orchestration with containers really enables microservices, which enables Devox, which enables people to move quickly with very little friction and own specific parts of a stack. >> Does that mean that their On-Prem operations are beginning to look more and more in terms of processes like the Clouds? That it's not just a VM, but they're actually orchestrating things? >> Yes, it does. And people will run orchestration on top of the Cloud, or they'll run it On-Prem. But yeah, it's exactly the same. It's the same idea. If you're On-Prem you have a physical machine, you're running several containers in it, and they can just be very fluid and dynamic. >> And then how does machine learning ... How do you fit machine learning into the, whether it's at the infrastructure level or at the application performance management level, do you run it and get a baseline of what's normal? Or ... >> So there's some very deep math behind what we do, so we're able to project where metrics ought to be in the future. Across any number of different categories or tags that you give us, it's important that we do that very accurately 'cause we don't have false positives in our alerts, meaning we don't want to wake people up unnecessarily. We also don't want to have false negatives, meaning we don't want not alert when we should have. So there's a lot of math that goes into that and we can take care of very complex periodicity even while trends are happening within metrics, and doing that at scale, so it happens in real time is a challenge, but one that we're very proud of our solution. >> So you've been able to really derive some differentiation in the market. One of the things I was also reading was that a lot of the business, I mentioned some of those great brands, is in the U.S. and your CIO has been quite vocal about wanting to change that. What's happened in the last year, maybe with big rounds of Fund-Me raise, that's going to help you get more global as even Amazon was talking about expansion and geographies this morning? >> Well so it's even been a while since we've raised money, a year and a half now, I guess, but the company is doing so well. It's a great place to be. The company's doing so well that we're just able to expand our operations and look bigger and bigger. Our two founders are actually French, or they were born in France, at any rate. And so we have a Paris office and we're moving pretty aggressively into Europe now. >> Lisa: Fantastic. >> One question on, again, the hybrid-cloud migration. Whether it's On-Prem to, say, Azure, or On-Prem to Azure and Amazon, would the use of Datadog make it easier for the customer to, essentially, run the same workloads on either of the Clouds? >> Absolutely. So we see a lot of people coming to Datadog at the moment when they need to move from pure On-Prem to maybe hybrid or maybe fully into the Cloud. Because you can set up Datadog to look at both those environments and understand the performance characteristics and then move over bytes of into the Cloud and make sure that nothing's falling apart and that everything is behaving exactly as you expect. >> And then how about for those who say, "Well, we want to be committed to two Clouds, because we don't want to be beholden." >> K Young: Right. >> Do you help with that? >> Yeah, we don't help with literally, like, data movement, which is sometimes one of the challenges. >> But in managing, it's sort of pane of glass? >> Yes, exactly. It's all one pane of glass and you can take ... Once metrics are in Datadog, it doesn't really matter where they came from, you can overlay requests per second or latency and frame Google's Cloud right alongside latency that you're seeing in AWS on the same graph or next to each other, but you can set alerts if they deviate too much from each other. >> So it's kind of an abstraction layer or at least a commonality that customers would be able to have those applications and different clouds from different providers and be able to see the performance of the application and the infrastructure. And so one last question for you, as we're getting ready up to wrap here, you know there's a lot of debate about hybrid-cloud and there's reports that say in the next few years, companies will have to be multi-cloud, just look at the Snap and IPO filing from a couple months ago. Big announcement. Two billion dollars over five years with Google. And then, revise that S1 filing to announce a billion dollar deal with Amazon. >> K Young: Yeah. >> So I'm just curious. Are you seeing that maybe with the enterprises, like a Snap, more and more that, by default, whether it's for redundancy of infrastructure operations, is that a trend that you're also seeing? That you're quite well-positioned to be able to facilitate? >> Yeah, we're definitely seeing ... You know, it's clear that Amazon is in the commanding position, for sure, but we are definitely seeing more and more interest in actual action and other Clouds as well. >> Fantastic. Well, we thank you first of all for being on the program today. Great. Congratulations on the success that you've had with Amazon, with others, and with the market differentiation. Congrats on expanding globally as well, and we look forward to having you back on the program. >> Right. Well, thanks very much for having me. >> Excellent. So K Young, Director of Strategic Alliances from Datadog. On behalf of K, my co-host George Gilbert, I'm Lisa Martin. You're watching The Cube live from the AWS Summit in San Francisco, but stick around 'cause we're going to be right back. (techno music) (dramatic music)

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

(bright music) >> Hi, everybody, this is Dave Vellante, welcome back to Supercloud 2. Last August, at the first Supercloud event, we invited the broader community to help further define Supercloud, we assessed its viability, and identified the critical elements and deployment models of the concept. The objectives here at Supercloud too are, first of all, to continue to tighten and test the concept, the second is, we want to get real world input from practitioners on the problems that they're facing and the viability of Supercloud in terms of applying it to their business. So on the program, we got companies like Walmart, Sachs, Western Union, Ionis Pharmaceuticals, NASDAQ, and others. And the third thing that we want to do is we want to drill into the intersection of cloud and data to project what the future looks like in the context of Supercloud. So in this segment, we want to explore the concept of data architectures and what's going to be required for Supercloud. And I'm pleased to welcome one of our Supercloud sponsors, ChaosSearch, Ed Walsh is the CEO of the company, with Thomas Hazel, who's the Founder, CTO, and Chief Scientist. Guys, good to see you again, thanks for coming into our Marlborough studio. >> Always great. >> Great to be here. >> Okay, so there's a little debate, I'm going to put you right in the spot. (Ed chuckling) A little debate going on in the community started by Bob Muglia, a former CEO of Snowflake, and he was at Microsoft for a long time, and he looked at the Supercloud definition, said, "I think you need to tighten it up a little bit." So, here's what he came up with. He said, "A Supercloud is a platform that provides a programmatically consistent set of services hosted on heterogeneous cloud providers." So he's calling it a platform, not an architecture, which was kind of interesting. And so presumably the platform owner is going to be responsible for the architecture, but Dr. Nelu Mihai, who's a computer scientist behind the Cloud of Clouds Project, he chimed in and responded with the following. He said, "Cloud is a programming paradigm supporting the entire lifecycle of applications with data and logic natively distributed. Supercloud is an open architecture that integrates heterogeneous clouds in an agnostic manner." So, Ed, words matter. Is this an architecture or is it a platform? >> Put us on the spot. So, I'm sure you have concepts, I would say it's an architectural or design principle. Listen, I look at Supercloud as a mega trend, just like cloud, just like data analytics. And some companies are using the principle, design principles, to literally get dramatically ahead of everyone else. I mean, things you couldn't possibly do if you didn't use cloud principles, right? So I think it's a Supercloud effect, you're able to do things you're not able to. So I think it's more a design principle, but if you do it right, you get dramatic effect as far as customer value. >> So the conversation that we were having with Muglia, and Tristan Handy of dbt Labs, was, I'll set it up as the following, and, Thomas, would love to get your thoughts, if you have a CRM, think about applications today, it's all about forms and codifying business processes, you type a bunch of stuff into Salesforce, and all the salespeople do it, and this machine generates a forecast. What if you have this new type of data app that pulls data from the transaction system, the e-commerce, the supply chain, the partner ecosystem, et cetera, and then, without humans, actually comes up with a plan. That's their vision. And Muglia was saying, in order to do that, you need to rethink data architectures and database architectures specifically, you need to get down to the level of how the data is stored on the disc. What are your thoughts on that? Well, first of all, I'm going to cop out, I think it's actually both. I do think it's a design principle, I think it's not open technology, but open APIs, open access, and you can build a platform on that design principle architecture. Now, I'm a database person, I love solving the database problems. >> I'm waited for you to launch into this. >> Yeah, so I mean, you know, Snowflake is a database, right? It's a distributed database. And we wanted to crack those codes, because, multi-region, multi-cloud, customers wanted access to their data, and their data is in a variety of forms, all these services that you're talked about. And so what I saw as a core principle was cloud object storage, everyone streams their data to cloud object storage. From there we said, well, how about we rethink database architecture, rethink file format, so that we can take each one of these services and bring them together, whether distributively or centrally, such that customers can access and get answers, whether it's operational data, whether it's business data, AKA search, or SQL, complex distributed joins. But we had to rethink the architecture. I like to say we're not a first generation, or a second, we're a third generation distributed database on pure, pure cloud storage, no caching, no SSDs. Why? Because all that availability, the cost of time, is a struggle, and cloud object storage, we think, is the answer. >> So when you're saying no caching, so when I think about how companies are solving some, you know, pretty hairy problems, take MySQL Heatwave, everybody thought Oracle was going to just forget about MySQL, well, they come out with Heatwave. And the way they solve problems, and you see their benchmarks against Amazon, "Oh, we crush everybody," is they put it all in memory. So you said no caching? You're not getting performance through caching? How is that true, and how are you getting performance? >> Well, so five, six years ago, right? When you realize that cloud object storage is going to be everywhere, and it's going to be a core foundational, if you will, fabric, what would you do? Well, a lot of times the second generation say, "We'll take it out of cloud storage, put in SSDs or something, and put into cache." And that adds a lot of time, adds a lot of costs. But I said, what if, what if we could actually make the first read hot, the first read distributed joins and searching? And so what we went out to do was said, we can't cache, because that's adds time, that adds cost. We have to make cloud object storage high performance, like it feels like a caching SSD. That's where our patents are, that's where our technology is, and we've spent many years working towards this. So, to me, if you can crack that code, a lot of these issues we're talking about, multi-region, multicloud, different services, everybody wants to send their data to the data lake, but then they move it out, we said, "Keep it right there." >> You nailed it, the data gravity. So, Bob's right, the data's coming in, and you need to get the data from everywhere, but you need an environment that you can deal with all that different schema, all the different type of technology, but also at scale. Bob's right, you cannot use memory or SSDs to cache that, that doesn't scale, it doesn't scale cost effectively. But if you could, and what you did, is you made object storage, S3 first, but object storage, the only persistence by doing that. And then we get performance, we should talk about it, it's literally, you know, hundreds of terabytes of queries, and it's done in seconds, it's done without memory caching. We have concepts of caching, but the only caching, the only persistence, is actually when we're doing caching, we're just keeping another side-eye track of things on the S3 itself. So we're using, actually, the object storage to be a database, which is kind of where Bob was saying, we agree, but that's what you started at, people thought you were crazy. >> And maybe make it live. Don't think of it as archival or temporary space, make it live, real time streaming, operational data. What we do is make it smart, we see the data coming in, we uniquely index it such that you can get your use cases, that are search, observability, security, or backend operational. But we don't have to have this, I dunno, static, fixed, siloed type of architecture technologies that were traditionally built prior to Supercloud thinking. >> And you don't have to move everything, essentially, you can do it wherever the data lands, whatever cloud across the globe, you're able to bring it together, you get the cost effectiveness, because the only persistence is the cheapest storage persistent layer you can buy. But the key thing is you cracked the code. >> We had to crack the code, right? That was the key thing. >> That's where the plans are. >> And then once you do that, then everything else gets easier to scale, your architecture, across regions, across cloud. >> Now, it's a general purpose database, as Bob was saying, but we use that database to solve a particular issue, which is around operational data, right? So, we agree with Bob's. >> Interesting. So this brings me to this concept of data, Jimata Gan is one of our speakers, you know, we talk about data fabric, which is a NetApp, originally NetApp concept, Gartner's kind of co-opted it. But so, the basic concept is, data lives everywhere, whether it's an S3 bucket, or a SQL database, or a data lake, it's just a node on the data mesh. So in your view, how does this fit in with Supercloud? Ed, you've said that you've built, essentially, an enabler for that, for the data mesh, I think you're an enabler for the Supercloud-like principles. This is a big, chewy opportunity, and it requires, you know, a team approach. There's got to be an ecosystem, there's not going to be one Supercloud to rule them all, so where does the ecosystem fit into the discussion, and where do you fit into the ecosystem? >> Right, so we agree completely, there's not one Supercloud in effect, but we use Supercloud principles to build our platform, and then, you know, the ecosystem's going to be built on leveraging what everyone else's secret powers are, right? So our power, our superpower, based upon what we built is, we deal with, if you're having any scale, or cost effective scale issues, with data, machine generated data, like business observability or security data, we are your force multiplier, we will take that in singularly, just let it, simply put it in your object storage wherever it sits, and we give you uniformity access to that using OpenAPI access, SQL, or you know, Elasticsearch API. So, that's what we do, that's our superpower. So I'll play it into data mesh, that's a perfect, we are a node on a data mesh, but I'll play it in the soup about how, the ecosystem, we see it kind of playing, and we talked about it in just in the last couple days, how we see this kind of possibly. Short term, our superpowers, we deal with this data that's coming at these environments, people, customers, building out observability or security environments, or vendors that are selling their own Supercloud, I do observability, the Datadogs of the world, dot dot dot, the Splunks of the world, dot dot dot, and security. So what we do is we fit in naturally. What we do is a cost effective scale, just land it anywhere in the world, we deal with ingest, and it's a cost effective, an order of magnitude, or two or three order magnitudes more cost effective. Allows them, their customers are asking them to do the impossible, "Give me fast monitoring alerting. I want it snappy, but I want it to keep two years of data, (laughs) and I want it cost effective." It doesn't work. They're good at the fast monitoring alerting, we're good at the long-term retention. And yet there's some gray area between those two, but one to one is actually cheaper, so we would partner. So the first ecosystem plays, who wants to have the ability to, really, all the data's in those same environments, the security observability players, they can literally, just through API, drag our data into their point to grab. We can make it seamless for customers. Right now, we make it helpful to customers. Your Datadog, we make a button, easy go from Datadog to us for logs, save you money. Same thing with Grafana. But you can also look at ecosystem, those same vendors, it used to be a year ago it was, you know, its all about how can you grow, like it's growth at all costs, now it's about cogs. So literally we can go an environment, you supply what your customer wants, but we can help with cogs. And one-on one in a partnership is better than you trying to build on your own. >> Thomas, you were saying you make the first read fast, so you think about Snowflake. Everybody wants to talk about Snowflake and Databricks. So, Snowflake, great, but you got to get the data in there. All right, so that's, can you help with that problem? >> I mean we want simple in, right? And if you have to have structure in, you're not simple. So the idea that you have a simple in, data lake, schema read type philosophy, but schema right type performance. And so what I wanted to do, what we have done, is have that simple lake, and stream that data real time, and those access points of Search or SQL, to go after whatever business case you need, security observability, warehouse integration. But the key thing is, how do I make that click, click, click answer, and do it quickly? And so what we want to do is, that first read has to be fast. Why? 'Cause then you're going to do all this siloing, layers, complexity. If your first read's not fast, you're at a disadvantage, particularly in cost. And nobody says I want less data, but everyone has to, whether they say we're going to shorten the window, we're going to use AI to choose, but in a security moment, when you don't have that answer, you're in trouble. And that's why we are this service, this Supercloud service, if you will, providing access, well-known search, well-known SQL type access, that if you just have one access point, you're at a disadvantage. >> We actually talked about Snowflake and BigQuery, and a different platform, Data Bricks. That's kind of where we see the phase two of ecosystem. One is easy, the low-hanging fruit is observability and security firms. But the next one is, what we do, our super power is dealing with this messy data that schema is changing like night and day. Pipelines are tough, and it's changing all the time, but you want these things fast, and it's big data around the world. That's the next point, just use us alongside, or inside, one of their platforms, and now we get the best of both worlds. Our superpower is keeping this messy data as a streaming, okay, not a batch thing, allow you to do that. So, that's the second one. And then to be honest, the third one, which plays you to Supercloud, it also plays perfectly in the data mesh, is if you really go to the ultimate thing, what we have done is made object storage, S3, GCS, and blob storage, we made it a database. Put, get, complex query with big joins. You know, so back to your original thing, and Muglia teed it up perfectly, we've done that. Now imagine if that's an ecosystem, who would want that? If it's, again, it's uniform available across all the regions, across all the clouds, and it's right next to where you are building a service, or a client's trying, that's where the ecosystem, I think people are going to use Superclouds for their superpowers. We're really good at this, allows that short term. I think the Snowflakes and the Data Bricks are the medium term, you know? And then I think eventually gets to, hey, listen if you can make object storage fast, you can just go after it with simple SQL queries, or elastic. Who would want that? I think that's where people are going to leverage it. It's not going to be one Supercloud, and we leverage the super clouds. >> Our viewpoint is smart object storage can be programmable, and so we agree with Bob, but we're not saying do it here, do it here. This core, fundamental layer across regions, across clouds, that everyone has? Simple in. Right now, it's hard to get data in for access for analysis. So we said, simply, we'll automate the entire process, give you API access across regions, across clouds. And again, how do you do a distributed join that's fast? How do you do a distributed join that doesn't cost you an arm or a leg? And how do you do it at scale? And that's where we've been focused. >> So prior, the cloud object store was a niche. >> Yeah. >> S3 obviously changed that. How standard is, essentially, object store across the different cloud platforms? Is that a problem for you? Is that an easy thing to solve? >> Well, let's talk about it. I mean we've fundamentally, yeah we've extracted it, but fundamentally, cloud object storage, put, get, and list. That's why it's so scalable, 'cause it doesn't have all these other components. That complexity is where we have moved up, and provide direct analytical API access. So because of its simplicity, and costs, and security, and reliability, it can scale naturally. I mean, really, distributed object storage is easy, it's put-get anywhere, now what we've done is we put a layer of intelligence, you know, call it smart object storage, where access is simple. So whether it's multi-region, do a query across, or multicloud, do a query across, or hunting, searching. >> We've had clients doing Amazon and Google, we have some Azure, but we see Amazon and Google more, and it's a consistent service across all of them. Just literally put your data in the bucket of choice, or folder of choice, click a couple buttons, literally click that to say "that's hot," and after that, it's hot, you can see it. But we're not moving data, the data gravity issue, that's the other. That it's already natively flowing to these pools of object storage across different regions and clouds. We don't move it, we index it right there, we're spinning up stateless compute, back to the Supercloud concept. But now that allows us to do all these other things, right? >> And it's no longer just cheap and deep object storage. Right? >> Yeah, we make it the same, like you have an analytic platform regardless of where you're at, you don't have to worry about that. Yeah, we deal with that, we deal with a stateless compute coming up -- >> And make it programmable. Be able to say, "I want this bucket to provide these answers." Right, that's really the hope, the vision. And the complexity to build the entire stack, and then connect them together, we said, the fabric is cloud storage, we just provide the intelligence on top. >> Let's bring it back to the customers, and one of the things we're exploring in Supercloud too is, you know, is Supercloud a solution looking for a problem? Is a multicloud really a problem? I mean, you hear, you know, a lot of the vendor marketing says, "Oh, it's a disaster, because it's all different across the clouds." And I talked to a lot of customers even as part of Supercloud too, they're like, "Well, I solved that problem by just going mono cloud." Well, but then you're not able to take advantage of a lot of the capabilities and the primitives that, you know, like Google's data, or you like Microsoft's simplicity, their RPA, whatever it is. So what are customers telling you, what are their near term problems that they're trying to solve today, and how are they thinking about the future? >> Listen, it's a real problem. I think it started, I think this is a a mega trend, just like cloud. Just, cloud data, and I always add, analytics, are the mega trends. If you're looking at those, if you're not considering using the Supercloud principles, in other words, leveraging what I have, abstracting it out, and getting the most out of that, and then build value on top, I think you're not going to be able to keep up, In fact, no way you're going to keep up with this data volume. It's a geometric challenge, and you're trying to do linear things. So clients aren't necessarily asking, hey, for Supercloud, but they're really saying, I need to have a better mechanism to simplify this and get value across it, and how do you abstract that out to do that? And that's where they're obviously, our conversations are more amazed what we're able to do, and what they're able to do with our platform, because if you think of what we've done, the S3, or GCS, or object storage, is they can't imagine the ingest, they can't imagine how easy, time to glass, one minute, no matter where it lands in the world, querying this in seconds for hundreds of terabytes squared. People are amazed, but that's kind of, so they're not asking for that, but they are amazed. And then when you start talking on it, if you're an enterprise person, you're building a big cloud data platform, or doing data or analytics, if you're not trying to leverage the public clouds, and somehow leverage all of them, and then build on top, then I think you're missing it. So they might not be asking for it, but they're doing it. >> And they're looking for a lens, you mentioned all these different services, how do I bring those together quickly? You know, our viewpoint, our service, is I have all these streams of data, create a lens where they want to go after it via search, go after via SQL, bring them together instantly, no e-tailing out, no define this table, put into this database. We said, let's have a service that creates a lens across all these streams, and then make those connections. I want to take my CRM with my Google AdWords, and maybe my Salesforce, how do I do analysis? Maybe I want to hunt first, maybe I want to join, maybe I want to add another stream to it. And so our viewpoint is, it's so natural to get into these lake platforms and then provide lenses to get that access. >> And they don't want it separate, they don't want something different here, and different there. They want it basically -- >> So this is our industry, right? If something new comes out, remember virtualization came out, "Oh my God, this is so great, it's going to solve all these problems." And all of a sudden it just got to be this big, more complex thing. Same thing with cloud, you know? It started out with S3, and then EC2, and now hundreds and hundreds of different services. So, it's a complex matter for a lot of people, and this creates problems for customers, especially when you got divisions that are using different clouds, and you're saying that the solution, or a solution for the part of the problem, is to really allow the data to stay in place on S3, use that standard, super simple, but then give it what, Ed, you've called superpower a couple of times, to make it fast, make it inexpensive, and allow you to do that across clouds. >> Yeah, yeah. >> I'll give you guys the last word on that. >> No, listen, I think, we think Supercloud allows you to do a lot more. And for us, data, everyone says more data, more problems, more budget issue, everyone knows more data is better, and we show you how to do it cost effectively at scale. And we couldn't have done it without the design principles of we're leveraging the Supercloud to get capabilities, and because we use super, just the object storage, we're able to get these capabilities of ingest, scale, cost effectiveness, and then we built on top of this. In the end, a database is a data platform that allows you to go after everything distributed, and to get one platform for analytics, no matter where it lands, that's where we think the Supercloud concepts are perfect, that's where our clients are seeing it, and we're kind of excited about it. >> Yeah a third generation database, Supercloud database, however we want to phrase it, and make it simple, but provide the value, and make it instant. >> Guys, thanks so much for coming into the studio today, I really thank you for your support of theCUBE, and theCUBE community, it allows us to provide events like this and free content. I really appreciate it. >> Oh, thank you. >> Thank you. >> All right, this is Dave Vellante for John Furrier in theCUBE community, thanks for being with us today. You're watching Supercloud 2, keep it right there for more thought provoking discussions around the future of cloud and data. (bright music)

>> From the Cube Studios in Palo Alto in Boston bringing you data-driven insights from the Cube and ETR. This is Breaking Analysis with Dave Vellante. >> The unraveling of market enthusiasm continued in Q4 of 2022 with the earnings reports from the US hyperscalers, the big three now all in. As we said earlier this year, even the cloud is an immune from the macro headwinds and the cracks in the armor that we saw from the data that we shared last summer, they're playing out into 2023. For the most part actuals are disappointing beyond expectations including our own. It turns out that our estimates for the big three hyperscaler's revenue missed by 1.2 billion or 2.7% lower than we had forecast from even our most recent November estimates. And we expect continued decelerating growth rates for the hyperscalers through the summer of 2023 and we don't think that's going to abate until comparisons get easier. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we share our view of what's happening in cloud markets not just for the hyperscalers but other firms that have hitched a ride on the cloud. And we'll share new ETR data that shows why these trends are playing out tactics that customers are employing to deal with their cost challenges and how long the pain is likely to last. You know, riding the cloud wave, it's a two-edged sword. Let's look at the players that have gone all in on or are exposed to both the positive and negative trends of cloud. Look the cloud has been a huge tailwind for so many companies like Snowflake and Databricks, Workday, Salesforce, Mongo's move with Atlas, Red Hats Cloud strategy with OpenShift and so forth. And you know, the flip side is because cloud is elastic what comes up can also go down very easily. Here's an XY graphic from ETR that shows spending momentum or net score on the vertical axis and market presence in the dataset on the horizontal axis provision or called overlap. This is data from the January 2023 survey and that the red dotted lines show the positions of several companies that we've highlighted going back to January 2021. So let's unpack this for a bit starting with the big three hyperscalers. The first point is AWS and Azure continue to solidify their moat relative to Google Cloud platform. And we're going to get into this in a moment, but Azure and AWS revenues are five to six times that of GCP for IaaS. And at those deltas, Google should be gaining ground much faster than the big two. The second point on Google is notice the red line on GCP relative to its starting point. While it appears to be gaining ground on the horizontal axis, its net score is now below that of AWS and Azure in the survey. So despite its significantly smaller size it's just not keeping pace with the leaders in terms of market momentum. Now looking at AWS and Microsoft, what we see is basically AWS is holding serve. As we know both Google and Microsoft benefit from including SaaS in their cloud numbers. So the fact that AWS hasn't seen a huge downward momentum relative to a January 2021 position is one positive in the data. And both companies are well above that magic 40% line on the Y-axis, anything above 40% we consider to be highly elevated. But the fact remains that they're down as are most of the names on this chart. So let's take a closer look. I want to start with Snowflake and Databricks. Snowflake, as we reported from several quarters back came down to Earth, it was up in the 80% range in the Y-axis here. And it's still highly elevated in the 60% range and it continues to move to the right, which is positive but as we'll address in a moment it's customers can dial down consumption just as in any cloud. Now, Databricks is really interesting. It's not a public company, it never made it to IPO during the sort of tech bubble. So we don't have the same level of transparency that we do with other companies that did make it through. But look at how much more prominent it is on the X-axis relative to January 2021. And it's net score is basically held up over that period of time. So that's a real positive for Databricks. Next, look at Workday and Salesforce. They've held up relatively well, both inching to the right and generally holding their net scores. Same from Mongo, which is the brown dot above its name that says Elastic, it says a little gets a little crowded which Elastic's actually the blue dot above it. But generally, SaaS is harder to dial down, Workday, Salesforce, Oracles, SaaS and others. So it's harder to dial down because commitments have been made in advance, they're kind of locked in. Now, one of the discussions from last summer was as Mongo, less discretionary than analytics i.e. Snowflake. And it's an interesting debate but maybe Snowflake customers, you know, they're also generally committed to a dollar amount. So over time the spending is going to be there. But in the short term, yeah maybe Snowflake customers can dial down. Now that highlighted dotted red line, that bolded one is Datadog and you can see it's made major strides on the X-axis but its net score has decelerated quite dramatically. Openshift's momentum in the survey has dropped although IBM just announced that OpenShift has a a billion dollar ARR and I suspect what's happening there is IBM consulting is bundling OpenShift into its modernization projects. It's got a, that sort of captive base if you will. And as such it's probably not as top of mind to the respondents but I'll bet you the developers are certainly aware of it. Now the other really notable call out here is CloudFlare, We've reported on them earlier. Cloudflare's net score has held up really well since January of 2021. It really hasn't seen the downdraft of some of these others, but it's making major major moves to the right gaining market presence. We really like how CloudFlare is performing. And the last comment is on Oracle which as you can see, despite its much, much lower net score continues to gain ground in the market and thrive from a profitability standpoint. But the data pretty clearly shows that there's a downdraft in the market. Okay, so what's happening here? Let's dig deeper into this data. Here's a graphic from the most recent ETR drill down asking customers that said they were going to cut spending what technique they're using to do so. Now, as we've previously reported, consolidating redundant vendors is by far the most cited approach but there's two key points we want to make here. One is reducing excess cloud resources. As you can see in the bars is the second most cited technique and it's up from the previous polling period. The second we're not showing, you know directly but we've got some red call outs there. Reducing cloud costs jumps to 29% and 28% respectively in financial services and tech telco. And it's much closer to second. It's basically neck and neck with consolidating redundant vendors in those two industries. So they're being really aggressive about optimizing cloud cost. Okay, so as we said, cloud is great 'cause you can dial it up but it's just as easy to dial down. We've identified six factors that customers tell us are affecting their cloud consumption and there are probably more, if you got more we'd love to hear them but these are the ones that are fairly prominent that have hit our radar. First, rising mortgage rates mean banks are processing fewer loans means less cloud. The crypto crash means less trading activity and that means less cloud resources. Third lower ad spend has led companies to reduce not only you know, their ad buying but also their frequency of running their analytics and their calculations. And they're also often using less data, maybe compressing the timeframe of the corpus down to a shorter time period. Also very prominent is down to the bottom left, using lower cost compute instances. For example, Graviton from AWS or AMD chips and tiering storage to cheaper S3 or deep archived tiers. And finally, optimizing based on better pricing plans. So customers are moving from, you know, smaller companies in particular moving maybe from on demand or other larger companies that are experimenting using on demand or they're moving to spot pricing or reserved instances or optimized savings plans. That all lowers cost and that means less cloud resource consumption and less cloud revenue. Now in the days when everything was on prem CFOs, what would they do? They would freeze CapEx and IT Pros would have to try to do more with less and often that meant a lot of manual tasks. With the cloud it's much easier to move things around. It still takes some thinking and some effort but it's dramatically simpler to do so. So you can get those savings a lot faster. Now of course the other huge factor is you can cut or you can freeze. And this graphic shows data from a recent ETR survey with 159 respondents and you can see the meaningful uptick in hiring freezes, freezing new IT deployments and layoffs. And as we've been reporting, this has been trending up since earlier last year. And note the call out, this is especially prominent in retail sectors, all three of these techniques jump up in retail and that's a bit of a concern because oftentimes consumer spending helps the economy make a softer landing out of a pullback. But this is a potential canary in the coal mine. If retail firms are pulling back it's because consumers aren't spending as much. And so we're keeping a close eye on that. So let's boil this down to the market data and what this all means. So in this graphic we show our estimates for Q4 IaaS revenues compared to the "actual" IaaS revenues. And we say quote because AWS is the only one that reports, you know clean revenue and IaaS, Azure and GCP don't report actuals. Why would they? Because it would make them look even, you know smaller relative to AWS. Rather, they bury the figures in overall cloud which includes their, you know G-Suite for Google and all the Microsoft SaaS. And then they give us little tidbits about in Microsoft's case, Azure, they give growth rates. Google gives kind of relative growth of GCP. So, and we use survey data and you know, other data to try to really pinpoint and we've been covering this for, I don't know, five or six years ever since the cloud really became a thing. But looking at the data, we had AWS growing at 25% this quarter and it came in at 20%. So a significant decline relative to our expectations. AWS announced that it exited December, actually, sorry it's January data showed about a 15% mid-teens growth rate. So that's, you know, something we're watching. Azure was two points off our forecast coming in at 38% growth. It said it exited December in the 35% growth range and it said that it's expecting five points of deceleration off of that. So think 30% for Azure. GCP came in three points off our expectation coming in 35% and Alibaba has yet to report but we've shaved a bid off that forecast based on some survey data and you know what maybe 9% is even still not enough. Now for the year, the big four hyperscalers generated almost 160 billion of revenue, but that was 7 billion lower than what what we expected coming into 2022. For 2023, we're expecting 21% growth for a total of 193.3 billion. And while it's, you know, lower, you know, significantly lower than historical expectations it's still four to five times the overall spending forecast that we just shared with you in our predictions post of between 4 and 5% for the overall market. We think AWS is going to come in in around 93 billion this year with Azure closing in at over 71 billion. This is, again, we're talking IaaS here. Now, despite Amazon focusing investors on the fact that AWS's absolute dollar growth is still larger than its competitors. By our estimates Azure will come in at more than 75% of AWS's forecasted revenue. That's a significant milestone. AWS is operating margins by the way declined significantly this past quarter, dropping from 30% of revenue to 24%, 30% the year earlier to 24%. Now that's still extremely healthy and we've seen wild fluctuations like this before so I don't get too freaked out about that. But I'll say this, Microsoft has a marginal cost advantage relative to AWS because one, it has a captive cloud on which to run its massive software estate. So it can just throw software at its own cloud and two software marginal costs. Marginal economics despite AWS's awesomeness in high degrees of automation, software is just a better business. Now the upshot for AWS is the ecosystem. AWS is essentially in our view positioning very smartly as a platform for data partners like Snowflake and Databricks, security partners like CrowdStrike and Okta and Palo Alto and many others and SaaS companies. You know, Microsoft is more competitive even though AWS does have competitive products. Now of course Amazon's competitive to retail companies so that's another factor but generally speaking for tech players, Amazon is a really thriving ecosystem that is a secret weapon in our view. AWS happy to spin the meter with its partners even though it sells competitive products, you know, more so in our view than other cloud players. Microsoft, of course is, don't forget is hyping now, we're hearing a lot OpenAI and ChatGPT we reported last week in our predictions post. How OpenAI is shot up in terms of market sentiment in ETR's emerging technology company surveys and people are moving to Azure to get OpenAI and get ChatGPT that is a an interesting lever. Amazon in our view has to have a response. They have lots of AI and they're going to have to make some moves there. Meanwhile, Google is emphasizing itself as an AI first company. In fact, Google spent at least five minutes of continuous dialogue, nonstop on its AI chops during its latest earnings call. So that's an area that we're watching very closely as the buzz around large language models continues. All right, let's wrap up with some assumptions for 2023. We think SaaS players are going to continue to be sticky. They're going to be somewhat insulated from all these downdrafts because they're so tied in and customers, you know they make the commitment up front, you've got the lock in. Now having said that, we do expect some backlash over time on the onerous and generally customer unfriendly pricing models of most large SaaS companies. But that's going to play out over a longer period of time. Now for cloud generally and the hyperscalers specifically we do expect accelerating growth rates into Q3 but the amplitude of the demand swings from this rubber band economy, we expect to continue to compress and become more predictable throughout the year. Estimates are coming down, CEOs we think are going to be more cautious when the market snaps back more cautious about hiring and spending and as such a perhaps we expect a more orderly return to growth which we think will slightly accelerate in Q4 as comps get easier. Now of course the big risk to these scenarios is of course the economy, the FED, consumer spending, inflation, supply chain, energy prices, wars, geopolitics, China relations, you know, all the usual stuff. But as always with our partners at ETR and the Cube community, we're here for you. We have the data and we'll be the first to report when we see a change at the margin. Okay, that's a wrap for today. I want to thank Alex Morrison who's on production and manages the podcast, Ken Schiffman as well out of our Boston studio getting this up on LinkedIn Live. Thank you for that. Kristen Martin also and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our Editor-in-Chief over at siliconangle.com. He does some great editing for us. Thank you all. Remember all these episodes are available as podcast. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com, at siliconangle.com where you can see all the data and you want to get in touch. Just all you can do is email me david.vellante@siliconangle.com or DM me @dvellante if you if you got something interesting, I'll respond. If you don't, it's either 'cause I'm swamped or it's just not tickling me. You can comment on our LinkedIn post as well. And please check out ETR.ai for the best survey data in the enterprise tech business. This is Dave Vellante for the Cube Insights powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle upbeat music)

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

(gentle music) >> Now welcome back to theCUBE, everyone. I'm John Walls, and once again, we're glad to have you here for AWS re:Invent 22. Our coverage continues here on Thursday, day three, of what has been a jam-packed week of tech and AWS, of course, has been the great host for this. It's now a pleasure to welcome in Anurag Gupta, who is the founder and CEO of Shoreline, joining us here as part of the AWS Global Showcase Startup Program, and Anurag, good to see you, sir. Thanks for joining us. >> Thank you so much. >> Tell us about Shoreline, about what you're up to. >> So we're a DevOps company. We're really focused on repairing issues. If you think about it, there are a ton DevOps companies and we all went to the cloud in order to gain faster innovation and by and large check. Then all of the things involved in getting things into production, artifact generation, testing, configuration management, deployment, also by and large, automated. Now pity the poor SRE who's getting the deluge of stuff on them, every week, every two days, sometimes multiple times a day, and it's complicated, right? Kubernetes, VMs, lots of services, multiple clouds, sometimes, and you know, they need to know a little bit about everything. And you know what, there are a ton of companies that actually help you with what we call Day-2 Ops. It's just that most of them help you with observability, telling you what's gone wrong, or incident management, routing something to someone. But you know, back when I was at AWS, I never got really that excited about one more dashboard to look at or one more like better ticket routing. What used to really excite me was having some issue extinguished forever. And if you think about it, like the first five minutes of an incident are detecting and routing. The next hour, two hours, is some human being going in and fixing it, so that feels like the big opportunity to reduce, so hopefully we can talk a little bit about different ways that one can do that. >> What about Day-2 Ops? Just tell me about how you define that. >> So I basically define it as once the software goes into a production, just making sure things stay up and are healthy and you're resilient and you don't get errors and all of those sorts of things because everything breaks sooner or later, you know, to a greater or lesser degree. >> Especially that SRE you're talking about, right? >> Yeah. >> So let's go back to that scenario. Yeah, you pity the poor soul because they do have to be a little expert in everything. >> Exactly. >> And that's really challenging and we all know that, that's really hard. So how do you go about trying to lighten that burden, then? >> So when you look at the numbers, about somewhere between 40% to even 95% of the alarms that fire, the alerts that fire, are false positives and that's crazy. Why is someone waking up just to deal with? >> It's a lot of wasted time, isn't it? >> A lot of wasted time. And you know, you're also training someone into what I call ClickOps, just to go in and click the button and resolve it and you don't actually know if it was the false positive or it's the rare real positive, and so that's a challenge, right? And so the first thing to do is to figure out where the false positives are. Like, let's say Datadog tells you that CPU is high and alarms. Is that a good thing or a bad thing? It's hard for them to tell, right? But you have to then introspect it into something precise like, oh, CPU is high, but response times are standard and the request rate is high. Okay, that's a good thing. I'm going to ignore this. Or CPU is high, but it kind of resolves itself, so I'm going to not wake anybody up. Or CPU is high and oh, it's the darn JVM starting to garbage collect again, so let me go and take a heap dump and give that to my dev team and then bounce the JVM and you know, without waking anybody up, or CPU is high, I have no idea what's going on. Now it's time to wake somebody up. You know, what you want to use humans for is the ability to think about novel stuff, not to do repetitive stuff, so that's the first step. The second step is, about 40% of what remains is repetitive and straightforward. So like a disk is full, I'd better clean up the garbage on the disk or maybe grow the disk. People shouldn't wake up to deal to grow a disk. And so for that, what you want to do is just have those sorts of things get automated away. One of the nice things about Shoreline is, is that we take the experience in what we build for one company, and if they're willing, provide it to everybody else. Our belief is, a central tenant is, if someone somewhere fixes something, everyone everywhere should gain the benefit because we all sit on the same three clouds, we all sit on the same set of database infrastructure, et cetera. We should all get the same benefits. Why do we have to scar our own backs rather than benefiting from somebody else's scar tissue, so that's the second thing. The third thing is, okay, let's say it's not straightforward, not something I've seen before, then in that case, what often happens is on average like eight people get involved. You know, it initially goes to L1 support or L1 ops and, but they don't necessarily know because, as you say, the environment's complex. And so, you know, they go into Slack and they say, "At here, can somebody help me with this?" And those things take a much longer time, so wouldn't it be better that if your best SRE is able to say, "Hey, check these 20 things and then run these actions." We could convert that into like a Jupyter Notebook where you could say the incident got fired I pre-populated all the diagnostics, and then I tell people very precisely, "If you see this, run this, et cetera." Like a wiki, but actually something you could run right in this product. And then, you know, last piece of the puzzle, the smaller piece, is sometimes new things happen and when something new happens, what you want is sort of the central tech of Shoreline, which is parallel distributed, real-time debugging. And so the ability to do, you know, execute a command across your fleet rather than individual boxes so that you can say something like, "I'm hearing that my credit card app is slow. For everything tagged as being part of my credit card app, please run for everything that's running over 90% CPU, please run a top command." And so, you know, then you can run in the same time on one host as you can on 30,000 and that helps a lot. So that's the core of what we do. People use us for all sorts of things, also preventative maintenance, you know, just the proactive regular things. You know, like your car, you do an oil change, well, you know, you need to rotate your certs, certificates. You need to make sure that, you know, there isn't drift in your configurations, there isn't drift in your software. There's also security elements to it, right? You want to make sure that you aren't getting weird inbound/outbound traffic across to ports you don't expect to be open. You don't want to have these processes running, you know, maybe something's bad. And so that's all the kind of weird anomaly detection that's easy to do if you run things in a distributed parallel way across everything. That's super hard to do if you have to go and Whac-A-Mole across one box after the next. >> Well, which leads to a question just in terms of setting priorities then, which is what you're talking about helping companies establish priorities, this hierarchy of level one warning, level two, level three, level four. Sounds like that should be a basic, right? But you're saying that's not, that's not really happening in the enterprise. >> Well, you know, I would say that if you hadn't automated deployments, you should do that first. If you haven't automated your testing pipeline, shame on you, you should do that like a year ago. But now it's time to help people in production because you've done that other work and people are suffering. You know, the crazy thing about the cloud is, is that companies spend about three times more on the human beings to operate their cloud infrastructure as on the cloud infrastructure itself. I've yet to hear anybody say that their cloud bill is too low, you know, so, you know, there's a clearer savings also available. And you know, back when I was at AWS, obviously I had to keep the lights on too, but you know, I had to do that, but it's kind of a tax on my engineers and I'd really spend, prefer to spend the head count on innovation, on doing things that delight my customers. You never delight your customers by keeping the lights on, you just avoid irritating them by turning 'em off, right? >> So why are companies so fixed in on spending so much time on manually repairing things and not looking for these kinds of little, much more elegant solution and cost-efficient, time-saving, so on so forth. >> Yeah, I think there just hasn't been very much in this space as yet because it's a hard, hard problem to solve. You know, automation's a little bit scary and that's the reality of it and the way you make it less scary is by proving it out, by doing the simple things first, like reducing the alert fatigue, you know, that's easy. You know, providing notebooks to people so that they can click things and do things in a straightforward way. That's pretty easy. The full automation, that's kind of the North Star, that's what we aspire to do. But you know, people get there over time and one of our customers had 700 instances of this particular incident solved for them last week. You imagine how many human beings would've been doing it otherwise, you know? >> Right. >> That's just one thing, you know? >> How many did it take the build a pyramid? How many decades did that take, right? You had an announcement this week. I don't think we've talked about that. >> No, yeah, so we just announced Incident Insights, which is a free product that lets people plug into initially PagerDuty and pretty soon the Opsgenie ServiceNow, et cetera. And what you can do is, is you give us an API key read-only and we will suck your PagerDuty data out. We apply some lightweight ML unsupervised learning, and in a couple of minutes, we categorize all of your incidents so that you can understand which are the ones that happen most often and are getting resolved really quickly. That's ClickOps, right? Those alarms shouldn't fire. Which are the ones that involve a lot of people? Those are good candidates to build a notebook. Which are the ones that happen again and again and again? Those are good candidates for automation. And so, I think one of the challenges people have is, is that they don't actually know what their teams are doing and so this is intended to provide them that visibility. One of our very first customers was doing the beta test for us on it. He used to tell us he had about 100 tickets, incidents a week. You know, he brought this tool in and he had 2,100 last week and was all, you know, like these false alarms, so while he's giving us- >> That was eye opening for him to see that, sure. >> And why he's, you know, looking at it, you know, he's just like filing Jiras to say, "Oh, change this threshold, cancel this alarm forever." You know, all of that kind of stuff. Before you get to do the fancy work, you got to clean your room before you get to do anything else, right? >> Right, right, dinner before dessert, basically. >> There you go. >> Hey, thanks for the insights on this and again the name of the new product, by the way, is... >> Incident Insights. >> Incident Insights. >> Totally free. >> Free. >> Yeah, it takes a couple of minutes to set up. Go to the website, Shoreline.io/insight and you can be up and running in a couple of minutes. >> Outstanding, again, the company is Shoreline. This is Anurag Gupta, and thank you for being with us. We appreciate it. >> Appreciate it, thank you. >> Glad to have to here on theCUBE. Back with more from AWA re:Invent 22. You're watching theCUBE, the leader in high-tech coverage. (gentle music)

(upbeat music) >> Good morning everybody. Welcome back to Las Vegas. This is day four of theCUBE's wall-to-wall coverage of our Super Bowl, aka AWS re:Invent 2022. I'm here with my co-host, Paul Gillin. My name is Dave Vellante. Sanjay Poonen is in the house, CEO and president of Cohesity. He's sitting in as our guest market watcher, market analyst, you know, deep expertise, new to the job at Cohesity. He was kind enough to sit in, and help us break down what's happening at re:Invent. But Paul, first thing, this morning we heard from Werner Vogels. He was basically given a masterclass on system design. It reminded me of mainframes years ago. When we used to, you know, bury through those IBM blue books and red books. You remember those Sanjay? That's how we- learned back then. >> Oh God, I remember those, Yeah. >> But it made me think, wow, now you know IBM's more of a systems design, nobody talks about IBM anymore. Everybody talks about Amazon. So you wonder, 20 years from now, you know what it's going to be. But >> Well- >> Werner's amazing. >> He pulled out a 24 year old document. >> Yup. >> That he had written early in Amazon's evolution about synchronous design or about essentially distributed architectures that turned out to be prophetic. >> His big thing was nature is asynchronous. So systems are asynchronous. Synchronous is an illusion. It's an abstraction. It's kind of interesting. But, you know- >> Yeah, I mean I've had synonyms for things. Timeless architecture. Werner's an absolute legend. I mean, when you think about folks who've had, you know, impact on technology, you think of people like Jony Ive in design. >> Dave: Yeah. >> You got to think about people like Werner in architecture and just the fact that Andy and the team have been able to keep him engaged that long... I pay attention to his keynote. Peter DeSantis has obviously been very, very influential. And then of course, you know, Adam did a good job, you know, watching from, you know, having watched since I was at the first AWS re:Invent conference, at time was President SAP and there was only a thousand people at this event, okay? Andy had me on stage. I think I was one of the first guest of any tech company in 2011. And to see now this become like, it's a mecca. It's a mother of all IT events, and watch sort of even the transition from Andy to Adam is very special. I got to catch some of Ruba's keynote. So while there's some new people in the mix here, this has become a force of nature. And the last time I was here was 2019, before Covid, watched the last two ones online. But it feels like, I don't know 'about what you guys think, it feels like it's back to 2019 levels. >> I was here in 2019. I feel like this was bigger than 2019 but some people have said that it's about the same. >> I think it was 60,000 versus 50,000. >> Yes. So close. >> It was a little bigger in 2019. But it feels like it's more active. >> And then last year, Sanjay, you weren't here but it was 25,000, which was amazing 'cause it was right in that little space between Omicron, before Omicron hit. But you know, let me ask you a question and this is really more of a question about Amazon's maturity and I know you've been following them since early days. But the way I get the question, number one question I get from people is how is Amazon AWS going to be different under Adam than it was under Andy? What do you think? >> I mean, Adam's not new because he was here before. In some senses he knows the Amazon culture from prior, when he was running sales and marketing prior. But then he took the time off and came back. I mean, this will always be, I think, somewhat Andy's baby, right? Because he was the... I, you know, sent him a text, "You should be really proud of what you accomplished", but you know, I think he also, I asked him when I saw him a few weeks ago "Are you going to come to re:Invent?" And he says, "No, I want to leave this to be Adam's show." And Adam's going to have a slightly different view. His keynotes are probably half the time. It's a little bit more vision. There was a lot more customer stories at the beginning of it. Taking you back to the inspirational pieces of it. I think you're going to see them probably pulling up the stack and not just focused in infrastructure. Many of their platform services are evolved. Many of their, even application services. I'm surprised when I talk to customers. Like Amazon Connect, their sort of call center type technologies, an app layer. It's getting a lot. I mean, I've talked to a couple of Fortune 500 companies that are moving off Ayer to Connect. I mean, it's happening and I did not know that. So it's, you know, I think as they move up the stack, the platform's gotten more... The data centric stack has gotten, and you know, in the area we're working with Cohesity, security, data protection, they're an investor in our company. So this is an important, you know, both... I think tech player and a partner for many companies like us. >> I wonder the, you know, the marketplace... there's been a big push on the marketplace by all the cloud companies last couple of years. Do you see that disrupting the way softwares, enterprise software is sold? >> Oh, for sure. I mean, you have to be a ostrich with your head in the sand to not see this wave happening. I mean, what's it? $150 billion worth of revenue. Even though the growth rates dipped a little bit the last quarter or so, it's still aggregatively between Amazon and Azure and Google, you know, 30% growth. And I think we're still in the second or third inning off a grand 1 trillion or 2 trillion of IT, shifting not all of it to the cloud, but significantly faster. So if you add up all of the big things of the on-premise world, they're, you know, they got to a certain size, their growth is stable, but stalling. These guys are growing significantly faster. And then if you add on top of them, platform companies the data companies, Snowflake, MongoDB, Databricks, you know, Datadog, and then apps companies on top of that. I think the move to the Cloud is inevitable. In SaaS companies, I don't know why you would ever implement a CRM solution on-prem. It's all gone to the Cloud. >> Oh, it is. >> That happened 15 years ago. I mean, begin within three, five years of the advent of Salesforce. And the same thing in HR. Why would you deploy a HR solution now? You've got Workday, you've got, you know, others that are so some of those apps markets are are just never coming back to an on-prem capability. >> Sanjay, I want to ask you, you built a reputation for being able to, you know, forecast accurately, hit your plan, you know, you hit your numbers, you're awesome operator. Even though you have a, you know, technology degree, which you know, that's a two-tool star, multi-tool star. But I call it the slingshot economy. This is like, I mean I've seen probably more downturns than anybody in here, you know, given... Well maybe, maybe- >> Maybe me. >> You and I both. I've never seen anything like this, where where visibility is so unpredictable. The economy is sling-shotting. It's like, oh, hurry up, go Covid, go, go go build, build, build supply, then pull back. And now going forward, now pulling back. Slootman said, you know, on the call, "Hey the guide, is the guide." He said, "we put it out there, We do our best to hit it." But you had CrowdStrike had issues you know, mid-market, ServiceNow. I saw McDermott on the other day on the, on the TV. I just want to pay, you know, buy from the guy. He's so (indistinct) >> But mixed, mixed results, Salesforce, you know, Octa now pre-announcing, hey, they're going to be, or announcing, you know, better visibility, forward guide. Elastic kind of got hit really hard. HPE and Dell actually doing really well in the enterprise. >> Yep. >> 'Course Dell getting killed in the client. But so what are you seeing out there? How, as an executive, do you deal with such poor visibility? >> I think, listen, what the last two or three years have taught us is, you know, with the supply chain crisis, with the surge that people thought you may need of, you know, spending potentially in the pandemic, you have to start off with your tech platform being 10 x better than everybody else. And differentiate, differentiate. 'Cause in a crowded market, but even in a market that's getting tougher, if you're not differentiating constantly through technology innovation, you're going to get left behind. So you named a few places, they're all technology innovators, but even if some of them are having challenges, and then I think you're constantly asking yourselves, how do you move from being a point product to a platform with more and more services where you're getting, you know, many of them moving really fast. In the case of Roe, I like him a lot. He's probably one of the most savvy operators, also that I respect. He calls these speedboats, and you know, his core platform started off with the firewall network security. But he's built now a very credible cloud security, cloud AI security business. And I think that's how you need to be thinking as a tech executive. I mean, if you got core, your core beachhead 10 x better than everybody else. And as you move to adjacencies in these new platforms, have you got now speedboats that are getting to a point where they are competitive advantage? Then as you think of the go-to-market perspective, it really depends on where you are as a company. For a company like our size, we need partners a lot more. Because if we're going to, you know, stand on the shoulders of giants like Isaac Newton said, "I see clearly because I stand on the shoulders giants." I need to really go and cultivate Amazon so they become our lead partner in cloud. And then appropriately Microsoft and Google where I need to. And security. Part of what we announced last week was, last month, yeah, last couple of weeks ago, was the data security alliance with the biggest security players. What was I trying to do with that? First time ever done in my industry was get Palo Alto, CrowdStrike, Wallace, Tenable, CyberArk, Splunk, all to build an alliance with me so I could stand on their shoulders with them helping me. If you're a bigger company, you're constantly asking yourself "how do you make sure you're getting your, like Amazon, their top hundred customers spending more with that?" So I think the the playbook evolves, and I'm watching some of these best companies through this time navigate through this. And I think leadership is going to be tested in enormously interesting ways. >> I'll say. I mean, Snowflake is really interesting because they... 67% growth, which is, I mean, that's best in class for a company that's $2 billion. And, but their guide was still, you know, pretty aggressive. You know, so it's like, do you, you know, when it when it's good times you go, "hey, we can we can guide conservatively and know we can beat it." But when you're not certain, you can't dial down too far 'cause your investors start to bail on you. It's a really tricky- >> But Dave, I think listen, at the end of the day, I mean every CEO should not be worried about the short term up and down in the stock price. You're building a long-term multi-billion dollar company. In the case of Frank, he has, I think I shot to a $10 billion, you know, analytics data warehousing data management company on the back of that platform, because he's eyeing the market that, not just Teradata occupies today, but now Oracle occupies or other databases, right? So his tam as it grows bigger, you're going to have some of these things, but that market's big. I think same with Palo Alto. I mean Datadog's another company, 75% growth. >> Yeah. >> At 20% margins, like almost rule of 95. >> Amazing. >> When they're going after, not just the observability market, they're eating up the sim market, security analytics, the APM market. So I think, you know, that's, you look at these case studies of companies who are going from point product to platforms and are steadily able to grow into new tams. You know, to me that's very inspiring. >> I get it. >> Sanjay: That's what I seek to do at our com. >> I get that it's a marathon, but you know, when you're at VMware, weren't you looking at the stock price every day just out of curiosity? I mean listen, you weren't micromanaging it. >> You do, but at the end of the day, and you certainly look at the days of earnings and so on so forth. >> Yeah. >> Because you want to create shareholder value. >> Yeah. >> I'm not saying that you should not but I think in obsession with that, you know, in a short term, >> Going to kill ya. >> Makes you, you know, sort of myopically focused on what may not be the right thing in the long term. Now in the long arc of time, if you're not creating shareholder value... Look at what happened to Steve Bomber. You needed Satya to come in to change things and he's created a lot of value. >> Dave: Yeah, big time. >> But I think in the short term, my comments were really on the quarter to quarter, but over a four a 12 quarter, if companies are growing and creating profitable growth, they're going to get the valuation they deserve. >> Dave: Yeah. >> Do you the... I want to ask you about something Arvind Krishna said in the previous IBM earnings call, that IT is deflationary and therefore it is resistant to the macroeconomic headwinds. So IT spending should actually thrive in a deflation, in a adverse economic climate. Do you think that's true? >> Not all forms of IT. I pay very close attention to surveys from, whether it's the industry analysts or the Morgan Stanleys, or Goldman Sachs. The financial analysts. And I think there's a gluc in certain sectors that will get pulled back. Traditional view is when the economies are growing people spend on the top line, front office stuff, sales, marketing. If you go and look at just the cloud 100 companies, which are the hottest private companies, and maybe with the public market companies, there's way too many companies focused on sales and marketing. Way too many. I think during a downsizing and recession, that's going to probably shrink some, because they were all built for the 2009 to 2021 era, where it was all about the top line. Okay, maybe there's now a proposition for companies who are focused on cost optimization, supply chain visibility. Security's been intangible, that I think is going to continue to an investment. So I tell, listen, if you are a tech investor or if you're an operator, pay attention to CIO priorities. And right now, in our business at Cohesity, part of the reason we've embraced things like ransomware protection, there is a big focus on security. And you know, by intelligently being a management and a security company around data, I do believe we'll continue to be extremely relevant to CIO budgets. There's a ransomware, 20 ransomware attempts every second. So things of that kind make you relevant in a bank. You have to stay relevant to a buying pattern or else you lose momentum. >> But I think what's happening now is actually IT spending's pretty good. I mean, I track this stuff pretty closely. It's just that expectations were so high and now you're seeing earnings estimates come down and so, okay, and then you, yeah, you've got the, you know the inflationary factors and your discounted cash flows but the market's actually pretty good. >> Yeah. >> You know, relative to other downturns that if this is not a... We're not actually not in a downturn. >> Yeah. >> Not yet anyway. It may be. >> There's a valuation there. >> You have to prepare. >> Not sales. >> Yeah, that's right. >> When I was on CNBC, I said "listen, it's a little bit like that story of Joseph. Seven years of feast, seven years of famine." You have to prepare for potentially your worst. And if it's not the worst, you're in good shape. So will it be a recession 2023? Maybe. You know, high interest rates, inflation, war in Russia, Ukraine, maybe things do get bad. But if you belt tightening, if you're focused in operational excellence, if it's not a recession, you're pleasantly surprised. If it is one, you're prepared for it. >> All right. I'm going to put you in the spot and ask you for predictions. Expert analysis on the World Cup. What do you think? Give us the breakdown. (group laughs) >> As my... I wish India was in the World Cup, but you can't get enough Indians at all to play soccer well enough, but we're not, >> You play cricket, though. >> I'm a US man first. I would love to see one of Brazil, or Argentina. And as a Messi person, I don't know if you'll get that, but it would be really special for Messi to lead, to end his career like Maradonna winning a World Cup. I don't know if that'll happen. I'm probably going to go one of the Latin American countries, if the US doesn't make it far enough. But first loyalty to the US team, and then after one of the Latin American countries. >> And you think one of the Latin American countries is best bet to win or? >> I don't know. It's hard to tell. They're all... What happens now at this stage >> So close, right? >> is anybody could win. >> Yeah. You just have lots of shots of gold. I'm a big soccer fan. It could, I mean, I don't know if the US is favored to win, but if they get far enough, you get to the finals, anybody could win. >> I think they get Netherlands next, right? >> That's tough. >> Really tough. >> But... The European teams are good too, but I would like to see US go far enough, and then I'd like to see Latin America with team one of Argentina, or Brazil. That's my prediction. >> I know you're a big Cricket fan. Are you able to follow Cricket the way you like? >> At god unearthly times the night because they're in Australia, right? >> Oh yeah. >> Yeah. >> I watched the T-20 World Cup, select games of it. Yeah, you know, I'm not rapidly following every single game but the World Cup games, I catch you. >> Yeah, it's good. >> It's good. I mean, I love every sport. American football, soccer. >> That's great. >> You get into basketball now, I mean, I hope the Warriors come back strong. Hey, how about the Warriors Celtics? What do we think? We do it again? >> Well- >> This year. >> I'll tell you what- >> As a Boston Celtics- >> I would love that. I actually still, I have to pay off some folks from Palo Alto office with some bets still. We are seeing unprecedented NBA performance this year. >> Yeah. >> It's amazing. You look at the stats, it's like nothing. I know it's early. Like nothing we've ever seen before. So it's exciting. >> Well, always a pleasure talking to you guys. >> Great to have you on. >> Thanks for having me. >> Thank you. Love the expert analysis. >> Sanjay Poonen. Dave Vellante. Keep it right there. re:Invent 2022, day four. We're winding up in Las Vegas. We'll be right back. You're watching theCUBE, the leader in enterprise and emerging tech coverage. (lighthearted soft music)

(upbeat music) (background crowd chatter) >> Hello, fantastic cloud community and welcome back to Las Vegas where we are live from the show floor at AWS re:Invent. My name is Savannah Peterson. Joined for the first time. >> Yeah, Doobie. >> VIP, I know. >> All right, let's do this. >> Thanks for having me Dave, I really appreciate it. >> I appreciate you doing all the hard work. >> Yeah. (laughs) >> You, know. >> I don't know about that. We wouldn't be here without you and all these wonderful stories that all the businesses have. >> Well, when I host with John it's hard for me to get a word in edgewise. I'm just kidding, John. (Savannah laughing) >> Shocking, I've never want that experience. >> We're like knocking each other, trying to, we're elbowing. No, it's my turn to speak, (Savannah laughing) so I'm sure we're going to work great together. I'm really looking forward to it. >> Me too Dave, I feel very lucky to be here and I feel very lucky to introduce our guest this afternoon, Clint Sharp, welcome to the show. You are with Cribl. Yeah, how does it feel to be on the show floor today? >> It's amazing to be back at any conference in person and this one is just electric, I mean, there's like a ton of people here love the booth. We're having like a lot of activity. It's been really, really exciting to be here. >> So you're a re:Ieinvent alumni? Have you been here before? You're a Cube alumni. We're going to have an OG conversation about observability, I'm looking forward to it. Just in case folks haven't been watching theCUBE for the last nine years that you've been on it. I know you've been with a few different companies during that time period. Love that you've been with us since 2013. Give us the elevator pitch for Cribl. >> Yeah, so Cribl is an observability company which we're going to talk about today. Our flagship product is a telemetry router. So it just really helps you get data into the right places. We're very specifically in the observability and security markets, so we sell to those buyers and we help them work with logs and metrics and open telemetry, lots of different types of data to get it into the right systems. >> Why did observability all of a sudden become such a hot thing? >> Savannah: Such a hot topic. >> Right, I mean it just came on the scene so quickly and now it's obviously a very crowded space. So why now, and how do you guys differentiate from the crowd? >> Yeah, sure, so I think it's really a post-digital transformation thing Dave, when I think about how I interact with organizations you know, 20 years ago when I started this business I called up American Airlines when things weren't working and now everything's all done digitally, right? I rarely ever interact with a human being and yet if I go on one of these apps and I get a bad experience, switching is just as easy as booking another airline or changing banks or changing telecommunications providers. So companies really need an ability to dive into this data at very high fidelity to understand what Dave's experience with their service or their applications are. And for the same reasons on the security side, we need very, very high fidelity data in order to understand whether malicious actors are working their way around inside of the enterprise. And so that's really changed the tooling that we had, which, in prior years, it was really hard to ask arbitrary questions of that data. You really had to deal with whatever the vendor gave you or you know, whatever the tool came with. And observability is really an evolution, allowing people to ask and answer questions of their data that they really weren't planning in advance. >> Dave: Like what kind of questions are people asking? >> Yeah sure so what is Dave's performance with this application? I see that a malicious actor has made their way on the inside of my network. Where did they go? What did they do? What files did they access? What network connections did they open? And the scale of machine data of this machine to machine communication is so much larger than what you tend to see with like human generated data, transactional data, that we really need different systems to deal with that type of data. >> And what would you say is your secret sauce? Like some people come at it, some search, some come at it from security. What's your sort of superpower as Lisa likes to say? >> Yeah, so we're a customer's first company. And so one of the things I think that we've done incredibly well is go look at the market and look for problems that are not being solved by other vendors. And so when we created this category of an observability pipeline, nobody was really marketing an observability pipeline at that time. And really the problem that customers had is they have data from a lot of different sources and they need to get it to a lot of different destinations. And a lot of that data is not particularly valuable. And in fact, one of the things that we like to say about this class of data is that it's really not valuable until it is, right? And so if I have a security breach, if I have an outage and I need to start pouring through this data suddenly the data is very, very valuable. And so customers need a lot of different places to store this data. I might want that data in a logging system. I might want that data in a metric system. I might want that data in a distributed tracing system. I might want that data in a data lake. In fact AWS just announced their security data lake product today. >> Big topic all day. >> Yeah, I mean like you can see that the industry is going in this way. People want to be able to store massively greater quantities of data than they can cost effectively do today. >> Let's talk about that just a little bit. The tension between data growth, like you said it's not valuable until it is or until it's providing context, whether that be good or bad. Let's talk about the tension between data growth and budget growth. How are you seeing that translate in your customers? >> Yeah, well so data's growing in a 25% CAGR per IDC which means we're going to have two and a half times the data in five years. And when you talk to CISOs and CIOs and you ask them, is your budget growing at a 25% CAGR, absolutely not, under no circumstances am I going to have, you know, that much more money. So what got us to 2022 is not going to get us to 2032. And so we really need different approaches for managing this data at scale. And that's where you're starting to see things like the AWS security data lake, Snowflake is moving into this space. You're seeing a lot of different people kind of moving into the database for security and observability type of data. You also have lots of other companies that are competing in broad spectrum observability, companies like Splunk or companies like Datadog. And these guys are all doing it from a data-first approach. I'm going to bring a lot of data into these platforms and give users the ability to work with that data to understand the performance and security of their applications. >> Okay, so carry that through, and you guys are different how? >> Yeah, so we are this pipeline that's sitting in the middle of all these solutions. We don't care whether your data was originally intended for some other tool. We're going to help you in a vendor-neutral way get that data wherever you need to get it. And that gives them the ability to control cost because they can put the right data in the right place. If it's data that's not going to be frequently accessed let's put it in a data lake, the cheapest place we can possibly put that data to rest. Or if I want to put it into my security tool maybe not all of the data that's coming from my vendor, my vendor has to put all the data in their records because who knows what it's going to be used for. But I only use half or a quarter of that information for security. And so what if I just put the paired down results in my more expensive storage but I kept full fidelity data somewhere else. >> Okay so you're observing the observability platforms basically, okay. >> Clint: We're routing that data. >> And then creating- >> It's meta observability. >> Right, observability pipeline. When I think a data pipeline, I think of highly specialized individuals, there's a data analyst, there's a data scientist, there's a quality engineer, you know, etc, et cetera. Do you have specific roles in your customer base that look at different parts of that pipeline and can you describe that? >> Yeah, absolutely, so one of the things I think that we do different is we sell very specifically to the security tooling vendors. And so in that case we are, or not to the vendors, but to the customers themselves. So generally they have a team inside of that organization which is managing their security tooling and their operational tooling. And so we're building tooling very specifically for them, for the types of data they work with for the volumes and scale of data that they work with. And that is giving, and no other vendor is really focusing on them. There's a lot of general purpose data people in the world and we're really the only ones that are focusing very specifically on observability and security data. >> So the announcement today, the security data lake that you were talking about, it's based on the Open Cybersecurity Framework, which I think AWS put forth, right? And said, okay, everybody come on. [Savannah] Yeah, yeah they did. >> So, right, all right. So what are your thoughts on that? You know, how does it fit with your strategy, you know. >> Yeah, so we are again a customer's first neutral company. So if OCSF gains traction, which we hope it does then we'll absolutely help customers get data into that format. But we're kind of this universal adapter so we can take data from other vendors, proprietary schemas, maybe you're coming from one of the other send vendors and you want to translate that to OCSF to use it with the security data lake. We can provide customers the ability to change and reshape that data to fit into any schema from any vendor so that we're really giving security data lake customers the ability to adapt the legacy, the stuff that they have that they can't get rid of 'cause they've had it for 10 years, 20 years and nothing inside of an enterprise ever goes away. That stuff stays forever. >> Legacy. >> Well legacy is working right? I mean somebody's actually, you know, making money on top of this thing. >> We never get rid of stuff. >> No, (laughing) we just added the toolkit. It's like all the old cell phones we have, it's everything. I mean we even do it as individual users and consumers. It's all a part of our little personal library. >> So what's happened in the field company momentum? >> Yeah let's talk trends too. >> Yeah so the company's growing crazily fast. We're north of 400 employees and we're only a hundred and something, you know, a year ago. So you can kind of see we're tripling you know, year over year. >> Savannah: Casual, especially right now in a lot of companies are feeling that scale back. >> Yeah so obviously we're keeping our eye closely on the macro conditions, but we see such a huge opportunity because we're a value player in this space that there's a real flight to value in enterprises right now. They're looking for projects that are going to pay themselves back and we've always had this value prop, we're going to come give you a lot of capabilities but we're probably going to save you money at the same time. And so that's just really resonating incredibly well with enterprises today and giving us an opportunity to continue to grow in the face of some challenging headwinds from a macro perspective. >> Well, so, okay, so people think okay, security is immune from the macro. It's not, I mean- >> Nothing, really. >> No segment is immune. CrowdStrike announced today the CrowdStrike rocket ship's still growing AR 50%, but you know, stocks down, I don't know, 20% right now after our- >> Logically doesn't make- >> Okay stuff happens, but still, you know, it's interesting, the macro, because it was like, to me it's like a slingshot, right? Everybody was like, wow, pandemic, shut down. All of a sudden, oh wow, need tech, boom. >> Savannah: Yeah, digitally transformed today. >> It's like, okay, tap the brakes. You know, when you're driving down the highway and you get that slingshotting effect and I feel like that's what's going on now. So, the premise is that the real leaders, those guys with the best tech that really understand the customers are going to, you know, get through this. What are your customers telling you in terms of, you know they're spending patterns, how they're trying to maybe consolidate vendors and how does that affect you guys? >> Yeah, for sure, I mean, I think, obviously, back to that flight to value, they're looking for vendors who are aligned with their interests. So, you know, as their budgets are getting pressure, what vendors are helping them provide the same capabilities they had to provide to the business before especially from a security perspective 'cause they're going to get cut along with everybody else. If a larger organization is trimming budgets across, security's going to get cut along with everybody else. So is IT operations. And so since they're being asked to do more with less that's you know, really where we're coming in and trying to provide them value. But certainly we're seeing a lot of pressure from IT departments, security departments all over in terms of being able to live and do more with less. >> Yeah, I mean, Celip's got a great quote today. "If you're looking to tighten your belt the cloud is the place to do it." I mean, it's probably true. >> Absolutely, elastic scalability in this, you know, our new search product is based off of AWS Lambda and it gives you truly elastic scalability. These changes in architectures are what's going to allow, it's not that cloud is cheaper, it's that cloud gives you on-demand scalability that allows you to truly control the compute that you're spending. And so as a customer of AWS, like this is giving us capabilities to offer products that are scalable and cost effective in ways that we just have not been able to do in the cloud. >> So what does that mean for the customer that you're using serverless using Lambda? What does that mean for them in terms of what they don't have to do that they maybe had to previously? >> It offers us the ability to try to charge them like a truly cloud native vendor. So in our cloud product we sell a credit model whereby which you deduct credits for usage. So if you're streaming data, you pay for gigabytes. If you're searching data then you're paying for CPU consumption, and so it allows us to charge them only for what they're consuming which means we don't have to manage a whole fleet of servers, and eventually, well we go to managing our own compute quite possibly as we start to get to scale at certain customers. But Lambda allowed us to not have to launch that way, not have to run a bunch of infrastructure. And we've been able to align our charging model with something that we think is the most customer friendly which is true consumption, pay for what you consume. >> So for example, you're saying you don't have to configure the EC2 Instance or figure out the memory sizing, you don't have to worry about any of that. You just basically say go, it figures that out and you can focus on upstream, is that right? >> Yep, and we're able to not only from a cost perspective also from a people perspective, it's allowed us velocity that we did not have before, which is we can go and prototype and build significantly faster because we're not having to worry, you know, in our mature products we use EC2 like everybody else does, right? And so as we're launching new products it's allowed us to iterate much faster and will we eventually go back to running our own compute, who knows, maybe, but it's allowed us a lot faster velocity than we were able to get before. >> I like what I've heard you discuss a lot is the agility and adaptability. We're going to be moving and evolving, choosing different providers. You're very outspoken about being vendor agnostic and I think that's actually a really unique and interesting play because we don't know what the future holds. So we're doing a new game on that note here on theCUBE, new game, new challenge, I suppose I would call it to think of this as your 30 second thought leadership highlight reel, a sizzle of the most important topic or conversation that's happening theme here at the show this year. >> Yeah, I mean, for me, as I think, as we're looking, especially like security data lake, et cetera, it's giving customers ownership of their data. And I think that once you, and I'm a big fan of this concept of open observability, and security should be the same way which is, I should not be locking you in as a vendor into my platform. Data should be stored in open formats that can be analyzed by multiple places. And you've seen this with AWS's announcement, data stored in open formats the same way other vendors store that. And so if you want to plug out AWS and you want to bring somebody else in to analyze your security lake, then great. And as we move into our analysis product, our search product, we'll be able to search data in the security data lake or data that's raw in S3. And we're really just trying to give customers back control over their future so that they don't have to maintain a relationship with a particular vendor. They're always getting the best. And that competition fuels really great product. And I'm really excited for the next 10 years of our industry as we're able to start competing on experiences and giving customers the best products, the customer wins. And I'm really excited about the customer winning. >> Yeah, so customer focused, I love it. What a great note to end on. That was very exciting, very customer focused. So, yo Clint, I have really enjoyed talking to you. Thanks. >> Thanks Clint. >> Thanks so much, it's been a pleasure being on. >> Thanks for enhancing our observability over here, I feel like I'll be looking at things a little bit differently after this conversation. And thank all of you for tuning in to our wonderful afternoon of continuous live coverage here at AWS re:Ieinvent in fabulous Las Vegas, Nevada with Dave Vellante. I'm Savannah Peterson. We're theCUBE, the leading source for high tech coverage. (bright music)

(gentle music) >> Good morning from Las Vegas. It's theCUBE live at AWS Reinvent 2022 with tons of thousands of people today. Really kicks off the event. Big keynote that I think is probably just wrapping up. Lisa Martin here with Dave Vellante. Dave, this is going to be an action packed week on theCUBE no doubt. We talked with so many different companies. Every company's a software company these days but we're also seeing a lot of companies leaving software that can help them operate more efficiently in the background. >> Yeah, well some things haven't changed at Reinvent. A lot of people here, you know, back to 2019 highs and I think we exceeded those two hour keynotes. Peter DeSantis last night talking about new Graviton instances and then Adam Selipsky doing the typical two hour keynote. But what was different he was a lot more poetic than we used to hear from Andy Jassy, right? He was talking about the universe as an analogy for data. >> I loved that. >> Talked about ocean exploration as for the security piece and then exploring into the Antarctic for, you know, better chips, you know? So yeah, I think he did a good job there. I think a lot of people might not love it but I thought it was very well done. >> I thought so too. We're having kicking off a great day of live content for you all day today. We've got Eleanor Dorfman joining us, the sales leader at Retool. Eleanor, welcome to theCUBE. It's great to have you. >> Thank you so much for having me. >> So let's talk a little bit about Retool. I was looking on your LinkedIn page. I love the tagline, build custom internal tools best. >> Eleanor: Yep. >> Talk to us a little bit about the company you recently raised, series C two. Give us the backstory. >> Yeah, so the company was founded in 2017 by two co-founders who are best friends from college. They actually set out to build a FinTech company, a payments company. And as they were building that, they needed to build a ton of custom operations software that goes with that. If you're going to be managing people's money, you need to be able to do refunds. You need to be able to look up accounts, you need to be able to detect fraud, you need to do know your customer operations. And as they were building the sort of operations software that supports the business, they realized that there were patterns to all of it and that the same components were used at and again. And had the insight that that was actually probably a better direction to go in than recreating Venmo, which was I think the original idea. And that actually this is a problem every company has because every company needs operations engineering and operations software to run their business. And so they pivoted and started building Retool which is a platform for building custom operations software or internal tools. >> Dave: Good pivot. >> In hindsight, actually probably in the moment as well, was a good pivot. >> But you know, when you talk about some of those things, refunds, fraud, you know, KYC, you know, you think of operations software, you think of it as just internal, but all those things are customer facing. >> Eleanor: Yep. >> Right so, are we seeing as sort of this new era? Is that a trend that you guys, your founders saw that hey, these internal operations can be pointed at customers to support what, a better customer service, maybe even generate revenue, subscriptions? >> I think it's a direction we're actually heading now but we're just starting to scratch the surface of that. The focus for the last five years has very much been on this operations software and sort of changing the economics of developing it and making it easy and fast to productize workflows that were previously being done in spreadsheets or hacky workarounds and make it easier for companies to prioritize those so they can run their business more efficiently. >> And where are you having your customer conversations these days? Thinking of operations software in the background, but to Dave's point, it ends up being part of the customer experience. So where are you having your customer conversations, target audience, who's that persona? >> Mainly developers. So we're working almost exclusively with developer teams who have backlogs and backlogs of internal tools requests to build that sales teams are building manual forecasts. Support teams are in 19 different tools. Their supply chain teams are using seven different spreadsheets to do demand forecasting or freight forwarding or things like that. But they've never been able to be prioritized to the top of the list because customer facing software, revenue generating software, always takes prioritization. And in this economic environment, which is challenging for many companies right now, it's important to be able to do more with less and maximize the productivity especially of high value employees like engineers and developers. >> So what would you say the biggest business outcomes are? If the developer is really the focus, productivity is the- >> Productivity. It's for both, I would say. Developer productivity and being able to maximize your sort of R and D and maximize the productivity of your engineers and take away some of the very boring parts of the job. But, so I would say developer productivity, but then also the tools and the software that they're building are very powerful for end users. So I would say efficiency and productivity across your business. >> Across the business. >> I mean historically, you know, operations is where we focused IT and code. How much of the code out there is dedicated to sort of operations versus that customer facing? >> So I think it would actually be, it's kind of surprising. We have run a few surveys on this sort of, we call them the state of engineering time, and focusing on what developers are spending their time on. And a third of all code that is being written today is actually for this internal operations software. >> Interesting. And do you guys have news at the show? Are you announcing anything interesting or? >> Yeah, so our focus historically, you sort of gave away with one of your early questions, but our focus has always been on this operations, this building web applications on building UIs on top of databases and APIs and doing that incredibly fast and being able to do it all in one place and integrate with as any data source that you need. We abstract away access authentication deployment and you build applications for your internal teams. But recently, we've launched two new products. We're actually supporting more external use cases and more customer facing use cases as well as automating CRON jobs, ETL jobs alerting with the new retail workflows product. So we're expanding the scope of operations software from web applications to also internal operations like CRON jobs and ETL jobs. >> Explain that. Explain the scourge of CRON jobs to the audience. >> Yeah, so operations software businesses run on operations software. It's interesting, zooming out, it's actually something you said earlier as well. Every company has become a software company. So when you think about software, you tend to think about here. Very cool software that people are selling. And software that you use as a consumer. But Coca-Cola for example, has hundreds of software engineers that are building tools to make the business run for forecasting, for demand gen, for their warehouse distribution and monitoring inventory. And there's two types of that. There's the applications that they build and then the operations that have to run behind that. Maybe a workflow that is detecting how many bottles of Coca-Cola are in every warehouse and sending a notification to the right person when they're out or when they, a refill is very strong, but you know when you need a refill. So it does that, it takes those tasks, those jobs that run in the background and enables you to customize them and build them very rapidly in a code first way. >> So some of the notes that you guys provided say that there's over 500 million software apps that are going to be built in the next few years alone. That's tremendous. How much of that is operation software? >> I mean I think at least a third of that, if not more. To the point where every company is being forced to maximize their resources today and operational efficiency is the way to do that. And so it can become a competitive advantage when you can take the things that humans are doing in spreadsheets with 19 open tabs and automate that. That saves hours a day. That's a significant, significant driver of efficiency and productivity for a business >> It does, and there's direct correlation to the customer experience. The use experience. >> Almost certainly. When you think about building support tooling, I was web chat, chatting on the with Gogo wifi support on my flight over here and they asked for my order number and I sent it and they looked up my account and that's a custom piece of software they were using to look up the account, create a new account for me, and restore my second wifi purchase. And so when you think about it, you're actually, even just as a consumer, interacting with this custom software on the day time. And that's because that's what companies use to have a good customer experience and have an efficient business. >> And what's the relationship with AWS? You guys started, I think you said 2017, so you obviously started in the cloud, but I'm particularly interested in from a seller perspective, what that's like. Working with Amazon, how's that affected your business? >> Yeah, I mean so we're built on AWS, so we're customers and big fans. And obviously like from a selling perspective, we have a ton of integrations with AWS so we're able to integrate directly into all the different AWS products that people are using for databases, for data warehouses, for deployment configurations, for monitoring, for security, for observability, we can basically fit into your existing AWS stack in order to make it as seamless integration with your software so that building in Retool is just as seamless as building it on your own, just much, much faster. >> So in your world, I know you wanted to but, in your world is it more analytics? is it more transactional, sort of? Is it both? >> It's all of the above. And I think what's, over Thanksgiving, I was asked a lot to explain what Retool did with people who were like, we just got our first iPhone. And so I tried to explain with an example because I have yet to stumble on the perfect metaphor. But the example I typically use is DoorDash is a customer of ours. And for about three years, and three years ago, they had a problem. They had no way of turning off delivery in certain zip codes during storms. Which as someone who has had orders canceled during a storm, it's an incredibly frustrating experience. And the way it worked is that they had operation team members manually submitting requests to engineers to say there's a storm in this zip code and an engineer would run a manual task. This didn't scale with Doordash as they were opening in new countries all over the world that have very different weather patterns. And so they looked, they had one, they were sort of confronted with a choice. They could buy a piece of software out of the box. There is not a startup that does this yet. They could build it by hand, which would mean scoping the requirements designing a UI, building authentication, building access controls, putting it into a, putting it into a sprint, assigning an engineer. This would've taken months and months. And then it would take just as long to iterate on it or they could use Retool. So they used Retool, they built this app, it saved, I think they were saying up to two years of engineering time for this one application because of how quickly it was. And since then they've built, I think 50 or 60 more automating away other tasks like that that were one out of spreadsheets or in Jira or in Slack notifications or an email saying, "Hey, could you please do this thing? There's a storm." And so now they use us for dozens and dozens of operations like that. >> A lot of automation and of course a lot of customer delight on the other end of the spectrum as you were talking about. It is frustrating when you don't get that order but it's also the company needs to be able to have the the tools in place to automate to be able to react quickly. >> Eleanor: Exactly. >> Because the consumers are, as we know, quite demanding. I wanted to ask you, I mentioned the tagline in the beginning, build custom internal tools fast. You just gave us a great example of DoorDash. Huge business outcomes they're achieving but how fast are we talking? How fast can the average developer build these internal tools? >> Well, we've been doing a fun thing at our booth where we ask people what a problem is and build a tool for them while we're there. So for something lightweight, you can build it in 10 minutes. For something a little more complex, it can take up to a few weeks depending on what the requirements are. But we all have people who will be on a call with us introducing them to our software for the first time and they'll start telling us about their problems and in the background we'll be building it and then at the end we're like, is this what you meant? And they're like, we'd like to add that to our cart. And obviously, it's a platform so you can't do that. But we've been able to build applications on a call before while people are telling us what they need. >> So fast is fast. >> I would say very fast, yeah. >> Now how do you price? >> Right now, we have a couple different plans. We actually have a motion where you can sign up on our website and get started. So we have a free plan, we've got plans for startups, and then we've got plans all the way up to the enterprise. >> Right. And that's a subscription pricing kind of thing? >> Subscription model, yes. >> So I get a subscription to the platform and then what? Is there also a consumption component? >> Exactly. So there's a consumption component as well. So there's access to the platform and then you can build as many applications as you need. Or build as many workflows. >> When you're having customer conversations with prospects, what do you define as Retool's superpowers? You're the sales leader. What are some of those key superpowers that you think really differentiate Retool? >> I do think, well, the sales team first and foremost, but that's not a fair answer. I would say that people are a bit differentiator though. We have a lot of very talented people who are have a ton of domain expertise and care a ton about the customer outcomes, which I do actually think is a little more rare than it should be. But we're one of the only products out there that's built with a developer first mindset, a varied code first mindset, built to integrate with your software development life cycle but also built with the security and robustness that enterprise companies require. So it's able to take an enterprise grade software with a developer first approach while still having a ton of agility and nimbleness which is what people are really craving as the earth keeps moving around them. So I would say that's something that really sets us apart from the field. >> And then talk about some of the what developers are saying, some of the feedback, some of the responses, and maybe even, I know we're just on day one of the show, but any feedback from the booth so far? >> We've had a few people swing by our booth and show us their Retool apps, which is incredibly cool. That's my absolute favorite thing is encountering a Retool application in the wild which happens a lot more than I would've thought, which I shouldn't say, but is incredibly rewarding. But people love it. It's the reason I joined is I'd never heard someone have a product that customers talked about the way they talk about Retool because Retool enables them to do things. For some folks who use it, it enables them to do something they previously couldn't do. So it gives them super powers in their job and to triple their impact. And then for others, it just makes things so fast. And it's a very delightful experience. It's very much built by developers, for developers. And so it's built with a developer's first mindset. And so I think it's quite fun to build in Retool. Even I can build and Retool, though not well. And then it's extremely impactful and people are able to really impact their business and delight their coworkers which I think can be really meaningful. >> Absolutely. Delighting the coworkers directly relates to delighting the customers. >> Eleanor: Exactly. >> Those customer experience, employee experience, they're like this. >> Eleanor: Exactly. >> They go hand in hand and the employee experience has to be outstanding to be able to delight those customers, to reduce churn, to increase revenue- >> Eleanor: Exactly. >> And for brand reputation. >> And it also, I think there is something as someone who is customer facing, when my coworkers and developers I work with build tools that enable me to do my job better and feel better about my own performance and my ability to impact the customer experience, it's just this incredibly virtuous cycle. >> So Retool.com is where folks can go to learn more and also try that subscription that you said was free for up to five users. >> Yes, exactly. >> All right. I guess my last question, well couple questions for you. What are some of the things that excited you that you heard from Adam Selipsky this morning? Anything from the keynote that stood out in terms of- >> Dave: Did you listen to the keynote? >> I did not. I had customer calls this morning. >> Okay, so they're bringing- >> East coast time, east coast time. >> One of the things that will excite you I think is they're connecting, making it easier to connect their databases. >> Eleanor: That would very much exciting. >> Aurora and Redshift, right? Okay. And they're making it easier to share data. I dunno if it goes across regions, but they're doing better integration. >> Amazing. >> Right? And you guys are integrating with those tools, right? Those data platforms. So that to me was a big thing for you guys. >> It is also and what a big thing Retool does is you can build a UI layer for your application on top of every single data source. And you hear, it's funny, you hear people talk about the 360 degree review of the customer so much. This is another, it's not our primary value proposition, but it is certainly another way to get there is if you have data from their desk tickets from in Redshift, you have data from Stripe, from their payments, you have data from Twilio from their text messages, you have data from DataDog where they're having your observability where you can notice analytics issues. You can actually just use Retool to build an app that sits on top of that so that you can give your support team, your sales team, your account management team, customer service team, all of the data that they need on their customers. And then you can build workflows so that you can do automated customer engagement reports. I did a Slack every week that shows what our top customers are doing with the product and that's built using all of our automation software as well. >> The integration is so important, as you just articulated, because every, you know, we say every company's a software company these days. Every company's a data company. But also, the data democratization that needs to happen to be able for lines of business so that data moves out of certain locked in functions and enables lines of business to use it. To get that visibility that you were just talking about is really going to be a competitive advantage for those that survive and thrive and grow in this market. >> It's able to, I think it's first it's visibility, but then it's action. And I think that's what Retool does very uniquely as well is it can take and unite the data from all the places, takes it out of the black box, puts it in front of the teams, and then enables them to act on it safely and securely. So not only can you see who might be fraudulent, you can flag them as fraud. Not only can you see who's actually in danger, you can click a button and send them an email and set up a meeting. You can set up an approval workflow to bring in an exec for engagement. You can update a password for someone in one place where you can see that they're having issues and not have to go somewhere else to update the password. So I think that's the key is that Retool can unlock the data visibility and then the action that you need to serve your customers. >> That's a great point. It's all about the actions, the insights that those actions can be acted upon. Last question for you. If you had a billboard that you could put any message that you want on Retool, what would it say? What's the big aha? This is why Retool is so great. >> I mean, I think the big thing about Retool is it's changing the economics of software development. It takes something that previously would've been below the line and that wouldn't get prioritized because it wasn't customer facing and makes it possible. And so I would say one of two billboards if I could be a little bit greedy, one would be Retool changed the economics of software development and one would be build operations software at the speed of thought. >> I love that. You're granted two billboards. >> Eleanor: Thank you. >> Those are both outstanding. Eleanor, it's been such a pleasure having you on the program. Thank you for talking to us about Retool. >> Eleanor: Thank you. >> Operations software and the massive impact that automating it can make for developers, businesses alike, all the way to the top line. We appreciate your insights. >> Thank you so much. >> For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live, emerging, and enterprise tech coverage. (gentle music)

from the cube studios in Palo Alto in Boston bringing you data-driven insights from the cube and ETR this is breaking analysis with Dave vellante the ascendancy of AWS under the leadership of Andy jassy was marked by a tsunami of data and corresponding cloud services to leverage that data now those Services they mainly came in the form of Primitives I.E basic building blocks that were used by developers to create more sophisticated capabilities AWS in the 2020s being led by CEO Adam solipski will be marked by four high-level Trends in our opinion one A Rush of data that will dwarf anything we've previously seen two a doubling or even tripling down on the basic elements of cloud compute storage database security Etc three a greater emphasis on end-to-end integration of AWS services to simplify and accelerate customer adoption of cloud and four significantly deeper business integration of cloud Beyond it as an underlying element of organizational operations hello and welcome to this week's wikibon Cube insights powered by ETR in this breaking analysis we extract and analyze nuggets from John furrier's annual sit-down with the CEO of AWS we'll share data from ETR and other sources to set the context for the market and competition in cloud and we'll give you our glimpse of what to expect at re invent in 2022. now before we get into the core of our analysis Alibaba has announced earnings they always announced after the big three you know a month later and we've updated our Q3 slash November hyperscale Computing forecast for the year as seen here and we're going to spend a lot of time on this as most of you have seen the bulk of it already but suffice to say alibaba's cloud business is hitting that same macro Trend that we're seeing across the board but a more substantial slowdown than we expected and more substantial than its peers they're facing China headwinds they've been restructuring its Cloud business and it's led to significantly slower growth uh in in the you know low double digits as opposed to where we had it at 15 this puts our year-end estimates for 2022 Revenue at 161 billion still a healthy 34 growth with AWS surpassing 80 billion in 2022 Revenue now on a related note one of the big themes in Cloud that we've been reporting on is how customers are optimizing their Cloud spend it's a technique that they use and when the economy looks a little shaky and here's a graphic that we pulled from aws's website which shows the various pricing plans at a high level as you know they're much more granular than that and more sophisticated but Simplicity we'll just keep it here basically there are four levels first one here is on demand I.E pay by the drink now we're going to jump down to what we've labeled as number two spot instances that's like the right place at the right time I can use that extra capacity in the moment the third is reserved instances or RIS where I pay up front to get a discount and the fourth is sort of optimized savings plans where customers commit to a one or three year term and for a better price now you'll notice we labeled the choices in a different order than AWS presented them on its website and that's because we believe that the order that we chose is the natural progression for customers this started on demand they maybe experiment with spot instances they move to reserve instances when the cloud bill becomes too onerous and if you're large enough you lock in for one or three years okay the interesting thing is the order in which AWS presents them we believe that on-demand accounts for the majority of AWS customer spending now if you think about it those on-demand customers they're also at risk customers yeah sure there's some switching costs like egress and learning curve but many customers they have multiple clouds and they've got experience and so they're kind of already up to a learning curve and if you're not married to AWS with a longer term commitment there's less friction to switch now AWS here presents the most attractive plan from a financial perspective second after on demand and it's also the plan that makes the greatest commitment from a lock-in standpoint now In fairness to AWS it's also true that there is a trend towards subscription-based pricing and we have some data on that this chart is from an ETR drill down survey the end is 300. pay attention to the bars on the right the left side is sort of busy but the pink is subscription and you can see the trend upward the light blue is consumption based or on demand based pricing and you can see there's a steady Trend toward subscription now we'll dig into this in a later episode of Breaking analysis but we'll share with you a little some tidbits with the data that ETR provides you can select which segment is and pass or you can go up the stack Etc but so when you choose is and paths 44 of customers either prefer or are required to use on-demand pricing whereas around 40 percent of customers say they either prefer or are required to use subscription pricing again that's for is so now the further mu you move up the stack the more prominent subscription pricing becomes often with sixty percent or more for the software-based offerings that require or prefer subscription and interestingly cyber security tracks along with software at around 60 percent that that prefer subscription it's likely because as with software you're not shutting down your cyber protection on demand all right let's get into the expectations for reinvent and we're going to start with an observation in data in this 2018 book seeing digital author David michella made the point that whereas most companies apply data on the periphery of their business kind of as an add-on function successful data companies like Google and Amazon and Facebook have placed data at the core of their operations they've operationalized data and they apply machine intelligence to that foundational element why is this the fact is it's not easy to do what the internet Giants have done very very sophisticated engineering and and and cultural discipline and this brings us to reinvent 2022 in the future of cloud machine learning and AI will increasingly be infused into applications we believe the data stack and the application stack are coming together as organizations build data apps and data products data expertise is moving from the domain of Highly specialized individuals to Everyday business people and we are just at the cusp of this trend this will in our view be a massive theme of not only re invent 22 but of cloud in the 2020s the vision of data mesh We Believe jamachtagani's principles will be realized in this decade now what we'd like to do now is share with you a glimpse of the thinking of Adam solipsky from his sit down with John Furrier each year John has a one-on-one conversation with the CEO of AWS AWS he's been doing this for years and the outcome is a better understanding of the directional thinking of the leader of the number one Cloud platform so we're now going to share some direct quotes I'm going to run through them with some commentary and then bring in some ETR data to analyze the market implications here we go this is from solipsky quote I.T in general and data are moving from departments into becoming intrinsic parts of how businesses function okay we're talking here about deeper business integration let's go on to the next one quote in time we'll stop talking about people who have the word analyst we inserted data he meant data data analyst in their title rather will have hundreds of millions of people who analyze data as part of their day-to-day job most of whom will not have the word analyst anywhere in their title we're talking about graphic designers and pizza shop owners and product managers and data scientists as well he threw that in I'm going to come back to that very interesting so he's talking about here about democratizing data operationalizing data next quote customers need to be able to take an end-to-end integrated view of their entire data Journey from ingestion to storage to harmonizing the data to being able to query it doing business Intelligence and human-based Analysis and being able to collaborate and share data and we've been putting together we being Amazon together a broad Suite of tools from database to analytics to business intelligence to help customers with that and this last statement it's true Amazon has a lot of tools and you know they're beginning to become more and more integrated but again under jassy there was not a lot of emphasis on that end-to-end integrated view we believe it's clear from these statements that solipsky's customer interactions are leading him to underscore that the time has come for this capability okay continuing quote if you have data in one place you shouldn't have to move it every time you want to analyze that data couldn't agree more it would be much better if you could leave that data in place avoid all the ETL which has become a nasty three-letter word more and more we're building capabilities where you can query that data in place end quote okay this we see a lot in the marketplace Oracle with mySQL Heatwave the entire Trend toward converge database snowflake [ __ ] extending their platforms into transaction and analytics respectively and so forth a lot of the partners are are doing things as well in that vein let's go into the next quote the other phenomenon is infusing machine learning into all those capabilities yes the comments from the michelleographic come into play here infusing Ai and machine intelligence everywhere next one quote it's not a data Cloud it's not a separate Cloud it's a series of broad but integrated capabilities to help you manage the end-to-end life cycle of your data there you go we AWS are the cloud we're going to come back to that in a moment as well next set of comments around data very interesting here quote data governance is a huge issue really what customers need is to find the right balance of their organization between access to data and control and if you provide too much access then you're nervous that your data is going to end up in places that it shouldn't shouldn't be viewed by people who shouldn't be viewing it and you feel like you lack security around that data and by the way what happens then is people overreact and they lock it down so that almost nobody can see it it's those handcuffs there's data and asset are reliability we've talked about that for years okay very well put by solipsky but this is a gap in our in our view within AWS today and we're we're hoping that they close it at reinvent it's not easy to share data in a safe way within AWS today outside of your organization so we're going to look for that at re invent 2022. now all this leads to the following statement by solipsky quote data clean room is a really interesting area and I think there's a lot of different Industries in which clean rooms are applicable I think that clean rooms are an interesting way of enabling multiple parties to share and collaborate on the data while completely respecting each party's rights and their privacy mandate okay again this is a gap currently within AWS today in our view and we know snowflake is well down this path and databricks with Delta sharing is also on this curve so AWS has to address this and demonstrate this end-to-end data integration and the ability to safely share data in our view now let's bring in some ETR spending data to put some context around these comments with reference points in the form of AWS itself and its competitors and partners here's a chart from ETR that shows Net score or spending momentum on the x-axis an overlap or pervasiveness in the survey um sorry let me go back up the net scores on the y-axis and overlap or pervasiveness in the survey is on the x-axis so spending momentum by pervasiveness okay or should have share within the data set the table that's inserted there with the Reds and the greens that informs us to how the dots are positioned so it's Net score and then the shared ends are how the plots are determined now we've filtered the data on the three big data segments analytics database and machine learning slash Ai and we've only selected one company with fewer than 100 ends in the survey and that's databricks you'll see why in a moment the red dotted line indicates highly elevated customer spend at 40 percent now as usual snowflake outperforms all players on the y-axis with a Net score of 63 percent off the charts all three big U.S cloud players are above that line with Microsoft and AWS dominating the x-axis so very impressive that they have such spending momentum and they're so large and you see a number of other emerging data players like rafana and datadog mongodbs there in the mix and then more established players data players like Splunk and Tableau now you got Cisco who's gonna you know it's a it's a it's a adjacent to their core networking business but they're definitely into you know the analytics business then the really established players in data like Informatica IBM and Oracle all with strong presence but you'll notice in the red from the momentum standpoint now what you're going to see in a moment is we put red highlights around databricks Snowflake and AWS why let's bring that back up and we'll explain so there's no way let's bring that back up Alex if you would there's no way AWS is going to hit the brakes on innovating at the base service level what we call Primitives earlier solipsky told Furrier as much in their sit down that AWS will serve the technical user and data science Community the traditional domain of data bricks and at the same time address the end-to-end integration data sharing and business line requirements that snowflake is positioned to serve now people often ask Snowflake and databricks how will you compete with the likes of AWS and we know the answer focus on data exclusively they have their multi-cloud plays perhaps the more interesting question is how will AWS compete with the likes of Specialists like Snowflake and data bricks and the answer is depicted here in this chart AWS is going to serve both the technical and developer communities and the data science audience and through end-to-end Integrations and future services that simplify the data Journey they're going to serve the business lines as well but the Nuance is in all the other dots in the hundreds or hundreds of thousands that are not shown here and that's the AWS ecosystem you can see AWS has earned the status of the number one Cloud platform that everyone wants to partner with as they say it has over a hundred thousand partners and that ecosystem combined with these capabilities that we're discussing well perhaps behind in areas like data sharing and integrated governance can wildly succeed by offering the capabilities and leveraging its ecosystem now for their part the snowflakes of the world have to stay focused on the mission build the best products possible and develop their own ecosystems to compete and attract the Mind share of both developers and business users and that's why it's so interesting to hear solipski basically say it's not a separate Cloud it's a set of integrated Services well snowflake is in our view building a super cloud on top of AWS Azure and Google when great products meet great sales and marketing good things can happen so this will be really fun to watch what AWS announces in this area at re invent all right one other topic that solipsky talked about was the correlation between serverless and container adoption and you know I don't know if this gets into there certainly their hybrid place maybe it starts to get into their multi-cloud we'll see but we have some data on this so again we're talking about the correlation between serverless and container adoption but before we get into that let's go back to 2017 and listen to what Andy jassy said on the cube about serverless play the clip very very earliest days of AWS Jeff used to say a lot if I were starting Amazon today I'd have built it on top of AWS we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point I think the same thing is true here with Lambda which is I think if Amazon were starting today it's a given they would build it on the cloud and I think we with a lot of the applications that comprise Amazon's consumer business we would build those on on our serverless capabilities now we still have plenty of capabilities and features and functionality we need to add to to Lambda and our various serverless services so that may not be true from the get-go right now but I think if you look at the hundreds of thousands of customers who are building on top of Lambda and lots of real applications you know finra has built a good chunk of their market watch application on top of Lambda and Thompson Reuters has built you know one of their key analytics apps like people are building real serious things on top of Lambda and the pace of iteration you'll see there will increase as well and I really believe that to be true over the next year or two so years ago when Jesse gave a road map that serverless was going to be a key developer platform going forward and so lipsky referenced the correlation between serverless and containers in the Furrier sit down so we wanted to test that within the ETR data set now here's a screen grab of The View across 1300 respondents from the October ETR survey and what we've done here is we've isolated on the cloud computing segment okay so you can see right there cloud computing segment now we've taken the functions from Google AWS Lambda and Microsoft Azure functions all the serverless offerings and we've got Net score on the vertical axis we've got presence in the data set oh by the way 440 by the way is highly elevated remember that and then we've got on the horizontal axis we have the presence in the data center overlap okay that's relative to each other so remember 40 all these guys are above that 40 mark okay so you see that now what we're going to do this is just for serverless and what we're going to do is we're going to turn on containers to see the correlation and see what happens so watch what happens when we click on container boom everything moves to the right you can see all three move to the right Google drops a little bit but all the others now the the filtered end drops as well so you don't have as many people that are aggressively leaning into both but all three move to the right so watch again containers off and then containers on containers off containers on so you can see a really major correlation between containers and serverless okay so to get a better understanding of what that means I call my friend and former Cube co-host Stu miniman what he said was people generally used to think of VMS containers and serverless as distinctly different architectures but the lines are beginning to blur serverless makes things simpler for developers who don't want to worry about underlying infrastructure as solipsky and the data from ETR indicate serverless and containers are coming together but as Stu and I discussed there's a spectrum where on the left you have kind of native Cloud VMS in the middle you got AWS fargate and in the rightmost anchor is Lambda AWS Lambda now traditionally in the cloud if you wanted to use containers developers would have to build a container image they have to select and deploy the ec2 images that they or instances that they wanted to use they have to allocate a certain amount of memory and then fence off the apps in a virtual machine and then run the ec2 instances against the apps and then pay for all those ec2 resources now with AWS fargate you can run containerized apps with less infrastructure management but you still have some you know things that you can you can you can do with the with the infrastructure so with fargate what you do is you'd build the container images then you'd allocate your memory and compute resources then run the app and pay for the resources only when they're used so fargate lets you control the runtime environment while at the same time simplifying the infrastructure management you gotta you don't have to worry about isolating the app and other stuff like choosing server types and patching AWS does all that for you then there's Lambda with Lambda you don't have to worry about any of the underlying server infrastructure you're just running code AS functions so the developer spends their time worrying about the applications and the functions that you're calling the point is there's a movement and we saw in the data towards simplifying the development environment and allowing the cloud vendor AWS in this case to do more of the underlying management now some folks will still want to turn knobs and dials but increasingly we're going to see more higher level service adoption now re invent is always a fire hose of content so let's do a rapid rundown of what to expect we talked about operate optimizing data and the organization we talked about Cloud optimization there'll be a lot of talk on the show floor about best practices and customer sharing data solipsky is leading AWS into the next phase of growth and that means moving beyond I.T transformation into deeper business integration and organizational transformation not just digital transformation organizational transformation so he's leading a multi-vector strategy serving the traditional peeps who want fine-grained access to core services so we'll see continued Innovation compute storage AI Etc and simplification through integration and horizontal apps further up to stack Amazon connect is an example that's often cited now as we've reported many times databricks is moving from its stronghold realm of data science into business intelligence and analytics where snowflake is coming from its data analytics stronghold and moving into the world of data science AWS is going down a path of snowflake meet data bricks with an underlying cloud is and pass layer that puts these three companies on a very interesting trajectory and you can expect AWS to go right after the data sharing opportunity and in doing so it will have to address data governance they go hand in hand okay price performance that is a topic that will never go away and it's something that we haven't mentioned today silicon it's a it's an area we've covered extensively on breaking analysis from Nitro to graviton to the AWS acquisition of Annapurna its secret weapon new special specialized capabilities like inferential and trainium we'd expect something more at re invent maybe new graviton instances David floyer our colleague said he's expecting at some point a complete system on a chip SOC from AWS and maybe an arm-based server to eventually include high-speed cxl connections to devices and memories all to address next-gen applications data intensive applications with low power requirements and lower cost overall now of course every year Swami gives his usual update on machine learning and AI building on Amazon's years of sagemaker innovation perhaps a focus on conversational AI or a better support for vision and maybe better integration across Amazon's portfolio of you know large language models uh neural networks generative AI really infusing AI everywhere of course security always high on the list that reinvent and and Amazon even has reinforce a conference dedicated to it uh to security now here we'd like to see more on supply chain security and perhaps how AWS can help there as well as tooling to make the cio's life easier but the key so far is AWS is much more partner friendly in the security space than say for instance Microsoft traditionally so firms like OCTA and crowdstrike in Palo Alto have plenty of room to play in the AWS ecosystem we'd expect of course to hear something about ESG it's an important topic and hopefully how not only AWS is helping the environment that's important but also how they help customers save money and drive inclusion and diversity again very important topics and finally come back to it reinvent is an ecosystem event it's the Super Bowl of tech events and the ecosystem will be out in full force every tech company on the planet will have a presence and the cube will be featuring many of the partners from the serial floor as well as AWS execs and of course our own independent analysis so you'll definitely want to tune into thecube.net and check out our re invent coverage we start Monday evening and then we go wall to wall through Thursday hopefully my voice will come back we have three sets at the show and our entire team will be there so please reach out or stop by and say hello all right we're going to leave it there for today many thanks to Stu miniman and David floyer for the input to today's episode of course John Furrier for extracting the signal from the noise and a sit down with Adam solipski thanks to Alex Meyerson who was on production and manages the podcast Ken schiffman as well Kristen Martin and Cheryl Knight helped get the word out on social and of course in our newsletters Rob hoef is our editor-in-chief over at siliconangle does some great editing thank thanks to all of you remember all these episodes are available as podcasts wherever you listen you can pop in the headphones go for a walk just search breaking analysis podcast I published each week on wikibon.com at siliconangle.com or you can email me at david.valante at siliconangle.com or DM me at di vallante or please comment on our LinkedIn posts and do check out etr.ai for the best survey data in the Enterprise Tech business this is Dave vellante for the cube insights powered by ETR thanks for watching we'll see it reinvent or we'll see you next time on breaking analysis [Music]

>>Good morning and welcome back to the Cube where we are excited to be broadcasting live all week from Detroit to Michigan at Cuban slash cloud Native con. Depending on who you're asking, Lisa, it's day two things are buzzing. How are you feeling? >>Good, excited. Ready for day two, ready to have more great conversations to see how this community is expanding, how it's evolving, and how it's really supporting it itself. >>Yeah, Yeah. This is a very supportive community. Something we talked a lot about. And speaking of community, we've got some very bold and brave folks over here. We've got this CTO and the head of product from Storm Forge, and they are on a mission to automate Kubernetes. Now automatic and Kubernetes are not words that go in the same sentence very often, so please welcome Patrick and Yasmin. Thank you both for being here. Hello. How you doing? >>Thanks for having us. >>Thanks for having us. >>Talk about what you guys are doing. Cause as you said, Kubernetes auto spelling is anything but auto. >>Yeah. >>The, what are some of the challenges? How do you help >>Eliminate this? Yeah, so the mission at Storm Forge is primarily automatic resource configuration and optimization essentially. So we started as a machine learning company first. And it's kind of an interesting story cuz we're one of those startups that has pivoted a few times. And so we were running our machine learning workloads. Most >>Have, I think, >>Right? Yeah. Yeah. We were, we started out running our machine learning workloads and moving them into Kubernetes. And then we weren't quite sure how to correctly adjust and size our containers. And so our ML team, we've got three PhDs and applied mathematics. They said, Well, hang on, we could write an algorithm for that. And so they did. And then, Oh, I love this. Yeah. And then we said, Well holy cow, that's actually really useful. I wonder if other people would like that. And that's kind of where we got our start. >>You solved your own problem and then you built a business >>Around it. Yeah, exactly. >>That is fantastic. Is, is that driving product development at Storm Forge still? That kind of attitude? >>I mean that kind of attitude definitely drives product development, but we're, you know, balancing that with what the users are, the challenges that they have, especially at large scale. We deal with a lot of large enterprises and for us as a startup, we can relate to the problems that come with Kubernetes when you're trying to scale it. But when you're talking about the scale of some of these larger enterprises, it's just a different mentality. So we're trying to balance that of how we take that input into how we build our product. Talk >>About that, like the, the end user input and how you're taking that in, because of course it's only going to be a, you know, more of a symbiotic relationship when that customer feedback is taken and >>Acted on. Yeah, totally. And for us, because we use machine learning, it's a lot of building confidence with our users. So making sure that they understand how we look at the data, how we come up with the recommendations, and actually deploy those changes in their environment. There's a lot of trust that needs to be built there. So being able to go back to our users and say, Okay, we're presenting you this type of data, give us your feedback and building it alongside them has helped a lot in these >>Relationships. Absolutely. You said the word trust, and that's something that we talk about at every >>Show. I was gonna jump on that too. It's >>Not, Yeah, it's not a buzzword. It's not, It shouldn't be. Yeah. It really should be, I wanna say lived and breathed, but that's probably grammatically incorrect. >>We're not a gram show. It's okay darling. Yeah, thank >>You. It should be truly embodied. >>Yeah. And I, I think it's, it's not even unique to just what we do, but across tech in general, right? Like when I talk about SRE and building SRE teams, one of the things I mentioned is you have to build that trust first. And with machine learning, I think it can be really difficult too for a couple different reasons. Like one, it tends to be a black box if it's actually true machine learning. Totally. Which ours is. But the other piece that we run into. Yeah. And the other piece we run into though is, is what I was an executive at United Health Group before I joined Storm Forge. And I would get companies that would come to me and try to sell me machine learning and I would kind of look at it and say, Well no, that's just a basic decision tree. Or like, that's a super basic whole winter forecast, right? Like that's not actually machine learning. And that's one of the things that we actually find ourselves kind of battling a little bit when we talk about what we do in building that trust. >>Talk a little bit about the latest release as you guys had a very active September. Here we are. And towards the, I think end of October. Yeah. What are some of the, the new things that have come out? New integrations, new partnerships. Give us a scoop on that. >>Yeah, well I guess I'll start and then I'll probably hand it over to you. But like the, the big thing for us is we talked about automating Kubernetes in the very beginning, right? Like Kubernetes has got a vpa it's >>A wild sentence anyway. Yeah, yeah. >>It it >>Has. We're not gonna get over at the whole show. Yeah. >>It as a VPA built in, it has an HPA built in and, and when you look at the data and even when you read the documentation from Google, it explicitly says never the two should meet. Right. Because you'll end up thrashing and they'll fight each other. Well the big release we just announced is with our machine learning, we can now do both. And so we vertically scale your pods to the correct up. Yeah. >>Follow status. I love that. >>Yeah, we can, we can scale your pods to the correct size and still allow you to enable the HPA and we'll make recommendations for your scaling points and your thresholds on the HPA as well so that they can work together to really truly maximize your efficiency that without sacrificing your performance and your reliability of the applications that you're running. That >>Sounds like a massive differentiator for >>Storm launch, which I would say it is. Yeah. I think as far as I know, we're the first in the industry that can do this. Yeah. >>And >>From very singularity vibes too. You know, the machines are learning, teaching themselves and doing it all automatically. Yep. Gets me very >>Excited. >>Yeah, absolutely. And from a customer demand perspective, what's the feedback been? Yeah, it's been a few >>Weeks. Yeah, it's been really great actually. And a lot of why we went down this path was user driven because they're doing horizontal scale and they want to be able to vertically size as they're scaling. So if you put yourself in the shoes of someone that's configuring Kubernetes, you're usually guessing on what you're setting your CPU requests and limits do. But horizontal scale makes sense. You're either adding more things or removing more things. And so once they actually are scaled out as a large environment and they have to rethink, how am I gonna resize this now? It's just not possible. It's so many thousands of settings across all the different environments and you're only thinking about CPU memory, You're not thinking about a lot of things. It's just, but once you scale that out, it's a big challenge. So they came to us and said, Okay, you're doing, cuz we were doing vertical scaling before and now we enable vertical and horizontal. And so they came to us and said, I love what you're doing about right sizing, but we wanna be able to do this while also horizontally scaling. And so the way that our software works is we give you the recommendations for what the setting should be and then allow Kubernetes to continue to add and remove replicas as needed. So it's not like we're going in and making changes to Kubernetes, but we make changes to the configuration settings so that it's the most optimal from a resource perspective. >>Efficiency has been a real big theme of the show. Yeah. And it's clear that that's a focus for you. Everyone here wants to do more faster Of course. And innovation, that's the thing to do that sometimes we need partners. You just announced an integration with Datadog. Tell us about that. Yeah, >>Absolutely. Yeah. So the way our platform works is we need data of course, right? So they're, they're a great partner for us and we use them both as an input and an output. So we pull in metrics from Datadog to provide recommendations and we'll actually display all those within the Datadog portal. Cause we have a lot of users that are like, Look, Datadog's my single pane of glass and I hate using that word, but they get all their insights there. They can see their recommendations and then actually go deploy those. Whether they wanna automatically have the recommendations deployed or go in and actually push a button. >>So give me an example of a customer that is using the, the new release and some of the business outcomes they're achieving. I imagine one of the things that you're enabling is just closing that ES skills gap. But from a business level perspective, how are they gaining like competitive advantages to be able to get products to market faster, for example? >>Yeah, so one of the customers that was actually part of our press release and launch and spoke about us at a webinar, they are a SaaS product and deal with really bursty workloads. And so their cloud costs have been growing 40% year over year. And their platform engineering team is basically enabled to provide the automation for developers and in their environment, but also to reduce those costs. So they want to, it's that trade off of resiliency and cost performance. And so they came to us and said, Look, we know we're over provisioned, but we don't know how to tackle that problem without throwing tons of humans at the problem. And so we worked with them and just on a single app found 60% savings and we're working now to kind of deploy that across their entire production workload. But that allows them to then go back and get more out of the, the budget that they already have and they can kind of reallocate that in other areas, >>Right? So there can be chop line and bottom >>Line impact. Yeah. And I, I think there's some really direct impact to the carbon emissions of an organization as well. That's a good point. When you can reduce your compute consumption by 60%. >>I love this. We haven't talked about this at all during the show. Yeah. And I'm really glad that you brought this up. All of the things that power this use energy. Yeah. >>What is it like seven to 8% of all electricity in the world is consumed by data centers. Like it's crazy. Yeah. Yeah. And so like that's wild. Yeah. Yeah. So being able to make a reduction in impact there too, especially with organizations that are trying to sign green pledges and everything else. >>It's hard. Yeah. ESG initiatives are huge. >>Absolut, >>It's >>A whole lot. A lot of companies have ESG initiatives where they can't even go out and do an RFP with a business, Right. If they don't have an actual active starting, impactful ESG program. Yes. Yeah. >>And the RFPs that we have to fill out, we have to tell them how they'll help. >>Yeah. Yes. It's so, yeah, I mean I was really struck when I looked on your website and I saw 54% average cost reduction for Yeah. For your cloud operations. I hadn't even thought about it from a power perspective. Yeah. I mean, imagine if we cut that to 3% of the world's power grid. That is just, that is very compelling. Speaking of compelling and exciting future things, talk to us about what's next? What's got you pumped for 2023 and and what lies >>Ahead? Oh man. Well that seems like a product conversation for sure. >>Well, we're super excited about extending what we do to other platforms, other metrics. So we optimize a lot right now around CPU and memory, but we can also give people insights into, you know, limiting kills, limiting CPU throttling, so extending the metrics. And when you look at hba and horizontal scale today, most of it is done with cpu, but there are some organizations out there that are scaling on custom metrics. So being able to take in more data to provide more recommendations and kind of extend what we can do from an optimization standpoint. >>That's, yeah, that's cool. And what house you most excited on the show floor? Anything? Anything that you've seen? Any keynotes? >>There's, Well, I haven't had a lot of time to go to the keynotes unfortunately, but it's, >>Well, I'm shock you've been busy or something, right? Much your time here. >>I can't imagine why. But no, there's, it's really interesting to see all the vendors that are popping up around Kubernetes focus specifically with security is always something that's really interesting to me. And automating CICD and how they continue to dive into that automation devs, SEC ops continues to be a big thing for a lot of organizations. Yeah. Yeah. >>I I do, I think it's interesting when we marry, Were you guys here last year? >>I was not here. >>No. So at, at the smaller version of this in Los Angeles. Yeah. I, I was really struck because there was still a conversation of whether or not we were all in on Kubernetes as, as kind of a community and a society this year. And I'm curious if you feel this way too. Everyone feels committed. Yeah. Yeah. I I I feel like there's no question that Kubernetes is the tool that we are gonna be using. >>Yeah. I I think so. And I think a lot of that is actually being unlocked by some of these vendors that are being partners and helping people get the most outta Kubernetes, you know, especially at the larger enterprise organizations. Like they want to do it, but the skills gap is a very real problem. Right. And so figuring out, like Jasmine talked about figuring out how do we, you know, optimize or set up the correct settings without throwing thousands of humans at it. Never mind the fact you'll never find a thousand people that wanna do that all day every day. >>I was gonna, It's a fold endeavor for those >>People study, right? Yeah. And, and being able to close some of those gaps, whether it's optimization, security, DevOps, C I C D. As we get more of those partners like I just talked about on the floor, then you see more and more enterprises being more open to leaning into Kubernetes a little bit. >>Yeah. Yeah. We've seen, we've had some great conversations the last day and, and today as well with organizations that are history companies like Ford Motor Companies for >>Example. Yeah. Right. >>Just right behind us. One of their EVs and, and it's, they're becoming technology companies that happen to do cars or home >>Here. I had a nice job with 'em this morning. Yes. With that storyline, honestly. >>Yes. That when we now have such a different lens into these organizations, how they're using technologies, advanced technologies, Kubernetes, et cetera, to really become data companies. Yeah. Because they have to be, well the consumers on the other end expect a Home Depot or a Ford or whomever or your bank Yeah. To know who you are. I want the information right here whenever I need it so I can do the transaction I need and I want you to also deliver me information that is relevant to me. Yeah. Because there, there's no patience anymore. Yeah. >>And we partner with a lot of big FinTech companies and it's, it's very much that. It's like how do we continue to optimize? But then as they look at transitioning off of older organizations and capabilities, whether that's, they have a physical data center that's racked to the gills and they can't do anything about that, so they wanna move to cloud or they're just dipping their toe into even private cloud with Kubernetes in their own instances. A lot of it is how do we do this right? Like how do we lean in and, Yeah. >>Yeah. Well I think you said it really well that the debate seems to be over in terms of do we go in on Kubernetes? That that was a theme that I think we felt that yesterday, even on on day one of the keynotes. The community seems to be just craving more. I think that was another thing that we felt yesterday was all of the contributors and the collaborators, people want to be able to help drive this community forward because it's, it's a flywheel of symbiosis for all of the vendors here. The maintainers and, and really businesses in any industry can benefit. >>Yeah. It's super validating. I mean if you just look at the floor, there's like 20 different booths that talk about cost reporting for Kubernetes. So not only have people moved, but now they're dealing with those challenges at scale. And I think for us it's very validating because there's so many vendors that are looking into the reporting of this and showing you the problem that you have. And then where we can help is, okay, now you know, you have a problem, here's how we can fix it for you. >>Yeah. Yeah. That, that sort of dealing with challenges at scale that you set, I think that's also what we're hearing. Yeah. And seeing and feeling on the show floor. >>Yeah, absolutely. >>What can folks see and, and touch and feel in your booth? >>We have some demos there you can play around with the product. We're giving away a Lego set so we've let >>Gotta gets >>Are right now we're gonna have to get some Lego, We do a swag segment at the end of the day every day. Now we've >>Some cool socks. >>Yep. Socks are hot. Let's, let's actually talk about scale internally as our closing question. What's going on at Storm Forge? If someone's watching right now, they're excited. Are you hiring? We are hiring. Yeah. How can they stalk you? What's the >>School? Absolutely. So you can check us out on Storm forge.io. We're certainly hiring across the engineering organization. We're hiring across the UX a product organization. We're dealing, like I said, we've got some really big customers that we're, we're working through with some really fun challenges. And we're looking to continue to build on what we do and do new innovative things like especially cuz like I said, we are a machine learning organization first. And so for me it's like how do I collect all the data that I can and then let's find out what's interesting in there that we can help people with. Whether that's cpu, memory, custom metrics, like as said, preventing kills, driving availability, reliability, What can we do to, to kind of make a little bit more transparent the stuff that's going on underneath the covers in Kubernetes for the decision makers in these organizations. >>Yes. Transparency is a goal of >>Many. >>Yeah, absolutely. Well, and you mentioned fun. If this conversation is any representation, it would be very fun to be working on both of your teams. We, we have a lot of fun Ya. Patrick, thank you so much for joining. Thanks for having us, Lisa, As usual, thanks for being here with me. My pleasure. And thank you to all of you for turning into the Cubes live show from Detroit. My name's Savannah Peterson and we'll be back in a few.

>> From theCUBE Studios in Palo Alto and Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> The technology spending outlook is not pretty and very much unpredictable right now. The negative sentiment is of course being driven by the macroeconomic factors in earnings forecasts that have been coming down all year in an environment of rising interest rates. And what's worse, is many people think earnings estimates are still too high. But it's understandable why there's so much uncertainty. I mean, technology is still booming, digital transformations are happening in earnest, leading companies have momentum and they got cash runways. And moreover, the CEOs of these leading companies are still really optimistic. But strong guidance in an environment of uncertainty is somewhat risky. Hello and welcome to this week's Wikibon CUBE Insights Powered by ETR. In this breaking analysis, we share takeaways from ETR'S latest spending survey, which was released to their private clients on October 21st. Today, we're going to review the macro spending data. We're going to share where CIOs think their cloud spend is headed. We're going to look at the actions that organizations are taking to manage uncertainty and then review some of the technology companies that have the most positive and negative outlooks in the ETR data set. Let's first look at the sample makeup from the latest ETR survey. ETR captured more than 1300 respondents in this latest survey. Its highest figure for the year and the quality and seniority of respondents just keeps going up each time we dig into the data. We've got large contributions as you can see here from sea level executives in a broad industry focus. Now the survey is still North America centric with 20% of the respondents coming from overseas and there is a bias toward larger organizations. And nonetheless, we're still talking well over 400 respondents coming from SMBs. Now ETR for those of you who don't know, conducts a quarterly spending intention survey and they also do periodic drilldowns. So just by the way of review, let's take a look at the expectations in the latest drilldown survey for IT spending. Before we look at the broader technology spending intentions survey data, followers of this program know that we reported on this a couple of weeks ago, spending expectations that peaked last December at 8.3% are now down to 5.5% with a slight uptick expected for next year as shown here. Now one CIO in the ETR community said these figures could be understated because of inflation. Now that's an interesting comment. Real GDP in the US is forecast to be around 1.5% in 2022. So these figures are significantly ahead of that. Nominal GDP is forecast to be significantly higher than what is shown in that slide. It was over 9% in June for example. And one would interpret that survey respondents are talking about real dollars which reflects inflationary factors in IT spend. So you might say, well if nominal GDP is in the high single digits this means that IT spending is below GDP which is usually not the case. But the flip side of that is technology tends to be deflationary because prices come down over time on a per unit basis, so this would be a normal and even positive trend. But it's mixed right now with prices on hard to find hardware, they're holding more firms. Software, you know, software tends to be driven by lock in and competition and switching costs. So you have those countervailing factors. Services can be inflationary, especially now as wages rise but certain sectors like laptops and semis and NAND are seeing less demand and maybe even some oversupply. So the way to look at this data is on a relative basis. In other words, IT buyers are reporting 280 basis point drop in spending sentiment from the end of last year. Now, something that we haven't shared from the latest drilldown survey which we will now is how IT bar buyers are thinking about cloud adoption. This chart shows responses from 419 IT execs from that drilldown and depicts the percentage of workloads their organizations have in the cloud today and what the expectation is through years from now. And you can see it's 27% today and it's nearly 50% in three years. Now the nuance is if you look at the question, that ETRS, it's they asked about IaaS and PaaS, which to some could include on-prem. Now, let me come back to that. In particular, financial services, IT, telco and retail and services industry cited expectations for the future for three years out that we're well above the average of the mean adoption levels. Regardless of how you interpret this data there's most certainly plenty of public cloud in the numbers. And whether you believe cloud is an operating environment or a place out there in the cloud, there's plenty of room for workloads to move into a cloud model well beyond mid this decade. So you know, as ho hum as we've been toward recent as-a-service models announced from the likes of HPE with GreenLake and Dell with APEX, the timing of those offerings may be pretty good actually. Now let's expand on some of the data that we showed a couple weeks ago. This chart shows responses from 282 execs on actions their organizations are taking over the next three months. And the Deltas are quite traumatic from the early part of this charter than the left hand side. The brown line is hiring freezes, the black line is freezing IT projects, and the green line is hiring increases and that red line is layoffs. And we put a box around the sort of general area of the isolation economy timeframe. And you can see the wild swings on this chart. By mid last summer, people were kickstarting things and more hiring was going on and the black line shows IT project freezes, you know, came way down. And now, or on the way back up as our hiring freezes. So we're seeing these wild swings in organizational actions and strategies which underscores the lack of predictability. As with supply chains around the world, this is likely due to the fact that organizations, pre pandemic they were optimized for efficiency, not a lot of waste rather than business resilience. Meaning, you know, there's again not a lot of fluff in the system or if there was it got flushed out during the pandemic. And so the need for productivity and automation is becoming increasingly important, especially as actions that solely rely on headcount changes are very, very difficult to manage. Now, let's dig into some of the vendor commentary and take a look at some of the names that have momentum and some of the others possibly facing headwinds. Here's a list of companies that stand out in the ETR survey. Snowflake, once again leads the pack with a positive spending outlook. HashiCorp, CrowdStrike, Databricks, Freshworks and ServiceNow, they round out the top six. Microsoft, they seem to always be in the mix, as do a number of other security and related companies including CyberArk, Zscaler, CloudFlare, Elastic, Datadog, Fortinet, Tenable and to a certain extent Akamai, you can kind of put them sort of in that group. You know, CDN, they got to worry about security. Everybody worries about security, but especially the CDNs. Now the other software names that are highlighted here include Workday and Salesforce. On the negative side, you can see Dynatrace saw some negatives in the latest survey especially around its analytics business. Security is generally holding up better than other sectors but it's still seeing greater levels of pressure than it had previously. So lower spend. And defections relative to its observability peers, that's really for Dynatrace. Now the other one that was somewhat surprising is IBM. You see the IBM was sort of in that negative realm here but IBM reported an outstanding quarter this past week with double digit revenue growth, strong momentum in software, consulting, mainframes and other infrastructure like storage. It's benefiting from the Kyndryl restructuring and it's on track IBM to deliver 10 billion in free cash flow this year. Red Hat is performing exceedingly well and growing in the very high teens. And so look, IBM is in the midst of a major transformation and it seems like a company that is really focused now with hybrid cloud being powered by Red Hat and consulting and a decade plus of AI investments finally paying off. Now the other big thing we'll add is, IBM was once an outstanding acquire of companies and it seems to be really getting its act together on the M&A front. Yes, Red Hat was a big pill to swallow but IBM has done a number of smaller acquisitions, I think seven this year. Like for example, Turbonomic, which is starting to pay off. Arvind Krishna has the company focused once again. And he and Jim J. Kavanaugh, IBM CFO, seem to be very confident on the guidance that they're giving in their business. So that's a real positive in our view for the industry. Okay, the last thing we'd like to do is take 12 of the companies from the previous chart and plot them in context. Now these companies don't necessarily compete with each other, some do. But they are standouts in the ETR survey and in the market. What we're showing here is a view that we like to often show, it's net score or spending velocity on the vertical axis. And it's a measure, that's a measure of the net percentage of customers that are spending more on a particular platform. So ETR asks, are you spending more or less? They subtract less from the mores. I mean I'm simplifying, but that's what net score is. Now in the horizontal axis, that is a measure of overlap which is which measures presence or pervasiveness in the dataset. So bigger the better. We've inserted a table that informs how the dots in the companies are positioned. These companies are all in the green in terms of net score. And that right most column in the table insert is indicative of their presence in the dataset, the end. So higher, again, is better for both columns. Two other notes, the red dotted line there you see at 40%. Anything over that indicates an highly elevated spending momentum for a given platform. And we purposefully took Microsoft out of the mix in this chart because it skews the data due to its large size. Everybody else would cluster on the left and Microsoft would be all alone in the right. So we take them out. Now as we noted earlier, Snowflake once again leads with a net score of 64%, well above the 40% line. Having said that, while adoption rates for Snowflake remains strong the company's spending velocity in the survey has come down to Earth. And many more customers are shifting from where they were last year and the year before in growth mode i.e. spending more year to year with Snowflake to now shifting more toward flat spending. So a plus or minus 5%. So that puts pressure on Snowflake's net score, just based on the math as to how ETR calculates, its proprietary net score methodology. So Snowflake is by no means insulated completely to the macro factors. And this was seen especially in the data in the Fortune 500 cut of the survey for Snowflake. We didn't show that here, just giving you anecdotal commentary from the survey which is backed up by data. So, it showed steeper declines in the Fortune 500 momentum. But overall, Snowflake, very impressive. Now what's more, note the position of Streamlit relative to Databricks. Streamlit is an open source python framework for developing data driven, data science oriented apps. And it's ironic that it's net score and shared in is almost identical to those of data bricks, as the aspirations of Snowflake and Databricks are beginning to collide. Now, however, the Databricks net score has held up very well over the past year and is in the 92nd percentile of its machine learning and AI peers. And while it's seeing some softness, like Snowflake in the Fortune 500, Databricks has steadily moved to the right on the X axis over the last several surveys even though it was unable to get to the public markets and do an IPO during the lockdown tech bubble. Let's come back to the chart. ServiceNow is impressive because it's well above the 40% mark and it has 437 shared in on this cut, the largest of any company that we chose to plot here. The only real negative on ServiceNow is, more large customers are keeping spending levels flat. That's putting a little bit pressure on its net score, but that's just conservatives. It's kind of like Snowflakes, you know, same thing but in a larger scale. But it's defections, the ServiceNow as in Snowflake as well. It's defections remain very, very low, really low churn below 2% for ServiceNow, in fact, within the dataset. Now it's interesting to also see Freshworks hit the list. You can see them as one of the few ITSM vendors that has momentum and can potentially take on ServiceNow. Workday, on this chart, it's the other big app player that's above the 40% line and we're only showing Workday HCM, FYI, in this graphic. It's Workday Financials, that offering, is below the 40% line just for reference. Now let's talk about CrowdStrike. We attended Falcon last month, CrowdStrike's user conference and we're very impressed with the product visio, the company's execution, it's growing partnerships. And you can see in this graphic, the ETR survey data confirms the company's stellar performance with a net score at 50%, well above the 40% mark. And importantly, more than 300 mentions. That's second only to ServiceNow, amongst the 12 companies that we've chosen to highlight here. Only Microsoft, which is not shown here, has a higher net score in the security space than CrowdStrike. And when it comes to presence, CrowdStrike now has caught up to Splunk in terms of pervasion in the survey. Now CyberArk and Zscaler are the other two security firms that are right at that 40% red dotted line. CyberArk for names with over a hundred citations in the security sector, is only behind Microsoft and CrowdStrike. Zscaler for its part in the survey is seeing strong momentum in the Fortune 500, unlike what we said for Snowflake. And its pervasion on the X-axis has been steadily increasing. Again, not that Snowflake and CrowdStrike compete with each other but they're too prominent names and it's just interesting to compare peers and business models. Cloudflare, Elastic and Datadog are slightly below the 40% mark but they made the sort of top 12 that we showed to highlight here and they continue to have positive sentiment in the survey. So, what are the big takeaways from this latest survey, this really quick snapshot that we've taken. As you know, over the next several weeks we're going to dig into it more and more. As we've previously reported, the tide is going out and it's taking virtually all the tech ships with it. But in many ways the current market is a story of heightened expectations coming down to Earth, miscalculations about the economic patterns and the swings and imperfect visibility. Leading Barclays analyst, Ramo Limchao ask the question to guide or not to guide in a recent research note he wrote. His point being, should companies guide or should they be more cautious? Many companies, if not most companies, are actually giving guidance. Indeed, when companies like Oracle and IBM are emphatic about their near term outlook and their visibility, it gives one confidence. On the other hand, reasonable people are asking, will the red hot valuations that we saw over the last two years from the likes of Snowflake, CrowdStrike, MongoDB, Okta, Zscaler, and others. Will they return? Or are we in for a long, drawn out, sideways exercise before we see sustained momentum? And to that uncertainty, we add elections and public policy. It's very hard to predict right now. I'm sorry to be like a two-handed lawyer, you know. On the one hand, on the other hand. But that's just the way it is. Let's just say for our part, we think that once it's clear that interest rates are on their way back down and we'll stabilize it under 4% and we have clarity on the direction of inflation, wages, unemployment and geopolitics, the wild swings and sentiment will subside. But when that happens is anyone's guess. If I had to peg, I'd say 18 months, which puts us at least into the spring of 2024. What's your prediction? You know, it's almost that time of year. Let's hear it. Please keep in touch and let us know what you think. Okay, that's it for now. Many thanks to Alex Myerson. He is on production and he manages the podcast for us. Ken Schiffman as well is our newest addition to the Boston Studio. Kristin Martin and Cheryl Knight, they help get the word out on social media and in our newsletters. And Rob Hoff is our EIC, editor-in-chief over at SiliconANGLE. He does some wonderful editing for us. Thank you all. Remember all these episodes, they are available as podcasts. Wherever you listen, just search breaking analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me at david.vellante@siliconangle.com or DM me @dvellante. Or feel free to comment on our LinkedIn posts. And please do check out etr.ai. They've got the best survey data in the enterprise tech business. If you haven't checked that out, you should. It'll give you an advantage. This is Dave Vellante for theCUBE Insights Powered by ETR. Thanks for watching. Be well and we'll see you next time on Breaking Analysis. (soft upbeat music)