>> Welcome back to SuperCloud2. This is Dave Vellante, and we're here with Jack Greenfield. He's the Vice President of Enterprise Architecture and the Chief Architect for the global technology platform at Walmart. Jack, I want to thank you for coming on the program. Really appreciate your time. >> Glad to be here, Dave. Thanks for inviting me and appreciate the opportunity to chat with you. >> Yeah, it's our pleasure. Now we call what you've built a SuperCloud. That's our term, not yours, but how would you describe the Walmart Cloud Native Platform? >> So WCNP, as the acronym goes, is essentially an implementation of Kubernetes for the Walmart ecosystem. And what that means is that we've taken Kubernetes off the shelf as open source, and we have integrated it with a number of foundational services that provide other aspects of our computational environment. So Kubernetes off the shelf doesn't do everything. It does a lot. In particular the orchestration of containers, but it delegates through API a lot of key functions. So for example, secret management, traffic management, there's a need for telemetry and observability at a scale beyond what you get from raw Kubernetes. That is to say, harvesting the metrics that are coming out of Kubernetes and processing them, storing them in time series databases, dashboarding them, and so on. There's also an angle to Kubernetes that gets a lot of attention in the daily DevOps routine, that's not really part of the open source deliverable itself, and that is the DevOps sort of CICD pipeline-oriented lifecycle. And that is something else that we've added and integrated nicely. And then one more piece of this picture is that within a Kubernetes cluster, there's a function that is critical to allowing services to discover each other and integrate with each other securely and with proper configuration provided by the concept of a service mesh. So Istio, Linkerd, these are examples of service mesh technologies. And we have gone ahead and integrated actually those two. There's more than those two, but we've integrated those two with Kubernetes. So the net effect is that when a developer within Walmart is going to build an application, they don't have to think about all those other capabilities where they come from or how they're provided. Those are already present, and the way the CICD pipelines are set up, it's already sort of in the picture, and there are configuration points that they can take advantage of in the primary YAML and a couple of other pieces of config that we supply where they can tune it. But at the end of the day, it offloads an awful lot of work for them, having to stand up and operate those services, fail them over properly, and make them robust. All of that's provided for. >> Yeah, you know, developers often complain they spend too much time wrangling and doing things that aren't productive. So I wonder if you could talk about the high level business goals of the initiative in terms of the hardcore benefits. Was the real impetus to tap into best of breed cloud services? Were you trying to cut costs? Maybe gain negotiating leverage with the cloud guys? Resiliency, you know, I know was a major theme. Maybe you could give us a sense of kind of the anatomy of the decision making process that went in. >> Sure, and in the course of answering your question, I think I'm going to introduce the concept of our triplet architecture which we haven't yet touched on in the interview here. First off, just to sort of wrap up the motivation for WCNP itself which is kind of orthogonal to the triplet architecture. It can exist with or without it. Currently does exist with it, which is key, and I'll get to that in a moment. The key drivers, business drivers for WCNP were developer productivity by offloading the kinds of concerns that we've just discussed. Number two, improving resiliency, that is to say reducing opportunity for human error. One of the challenges you tend to run into in a large enterprise is what we call snowflakes, lots of gratuitously different workloads, projects, configurations to the extent that by developing and using WCNP and continuing to evolve it as we have, we end up with cookie cutter like consistency across our workloads which is super valuable when it comes to building tools or building services to automate operations that would otherwise be manual. When everything is pretty much done the same way, that becomes much simpler. Another key motivation for WCNP was the ability to abstract from the underlying cloud provider. And this is going to lead to a discussion of our triplet architecture. At the end of the day, when one works directly with an underlying cloud provider, one ends up taking a lot of dependencies on that particular cloud provider. Those dependencies can be valuable. For example, there are best of breed services like say Cloud Spanner offered by Google or say Cosmos DB offered by Microsoft that one wants to use and one is willing to take the dependency on the cloud provider to get that functionality because it's unique and valuable. On the other hand, one doesn't want to take dependencies on a cloud provider that don't add a lot of value. And with Kubernetes, we have the opportunity, and this is a large part of how Kubernetes was designed and why it is the way it is, we have the opportunity to sort of abstract from the underlying cloud provider for stateless workloads on compute. And so what this lets us do is build container-based applications that can run without change on different cloud provider infrastructure. So the same applications can run on WCNP over Azure, WCNP over GCP, or WCNP over the Walmart private cloud. And we have a private cloud. Our private cloud is OpenStack based and it gives us some significant cost advantages as well as control advantages. So to your point, in terms of business motivation, there's a key cost driver here, which is that we can use our own private cloud when it's advantageous and then use the public cloud provider capabilities when we need to. A key place with this comes into play is with elasticity. So while the private cloud is much more cost effective for us to run and use, it isn't as elastic as what the cloud providers offer, right? We don't have essentially unlimited scale. We have large scale, but the public cloud providers are elastic in the extreme which is a very powerful capability. So what we're able to do is burst, and we use this term bursting workloads into the public cloud from the private cloud to take advantage of the elasticity they offer and then fall back into the private cloud when the traffic load diminishes to the point where we don't need that elastic capability, elastic capacity at low cost. And this is a very important paradigm that I think is going to be very commonplace ultimately as the industry evolves. Private cloud is easier to operate and less expensive, and yet the public cloud provider capabilities are difficult to match. >> And the triplet, the tri is your on-prem private cloud and the two public clouds that you mentioned, is that right? >> That is correct. And we actually have an architecture in which we operate all three of those cloud platforms in close proximity with one another in three different major regions in the US. So we have east, west, and central. And in each of those regions, we have all three cloud providers. And the way it's configured, those data centers are within 10 milliseconds of each other, meaning that it's of negligible cost to interact between them. And this allows us to be fairly agnostic to where a particular workload is running. >> Does a human make that decision, Jack or is there some intelligence in the system that determines that? >> That's a really great question, Dave. And it's a great question because we're at the cusp of that transition. So currently humans make that decision. Humans choose to deploy workloads into a particular region and a particular provider within that region. That said, we're actively developing patterns and practices that will allow us to automate the placement of the workloads for a variety of criteria. For example, if in a particular region, a particular provider is heavily overloaded and is unable to provide the level of service that's expected through our SLAs, we could choose to fail workloads over from that cloud provider to a different one within the same region. But that's manual today. We do that, but people do it. Okay, we'd like to get to where that happens automatically. In the same way, we'd like to be able to automate the failovers, both for high availability and sort of the heavier disaster recovery model between, within a region between providers and even within a provider between the availability zones that are there, but also between regions for the sort of heavier disaster recovery or maintenance driven realignment of workload placement. Today, that's all manual. So we have people moving workloads from region A to region B or data center A to data center B. It's clean because of the abstraction. The workloads don't have to know or care, but there are latency considerations that come into play, and the humans have to be cognizant of those. And automating that can help ensure that we get the best performance and the best reliability. >> But you're developing the dataset to actually, I would imagine, be able to make those decisions in an automated fashion over time anyway. Is that a fair assumption? >> It is, and that's what we're actively developing right now. So if you were to look at us today, we have these nice abstractions and APIs in place, but people run that machine, if you will, moving toward a world where that machine is fully automated. >> What exactly are you abstracting? Is it sort of the deployment model or, you know, are you able to abstract, I'm just making this up like Azure functions and GCP functions so that you can sort of run them, you know, with a consistent experience. What exactly are you abstracting and how difficult was it to achieve that objective technically? >> that's a good question. What we're abstracting is the Kubernetes node construct. That is to say a cluster of Kubernetes nodes which are typically VMs, although they can run bare metal in certain contexts, is something that typically to stand up requires knowledge of the underlying cloud provider. So for example, with GCP, you would use GKE to set up a Kubernetes cluster, and in Azure, you'd use AKS. We are actually abstracting that aspect of things so that the developers standing up applications don't have to know what the underlying cluster management provider is. They don't have to know if it's GCP, AKS or our own Walmart private cloud. Now, in terms of functions like Azure functions that you've mentioned there, we haven't done that yet. That's another piece that we have sort of on our radar screen that, we'd like to get to is serverless approach, and the Knative work from Google and the Azure functions, those are things that we see good opportunity to use for a whole variety of use cases. But right now we're not doing much with that. We're strictly container based right now, and we do have some VMs that are running in sort of more of a traditional model. So our stateful workloads are primarily VM based, but for serverless, that's an opportunity for us to take some of these stateless workloads and turn them into cloud functions. >> Well, and that's another cost lever that you can pull down the road that's going to drop right to the bottom line. Do you see a day or maybe you're doing it today, but I'd be surprised, but where you build applications that actually span multiple clouds or is there, in your view, always going to be a direct one-to-one mapping between where an application runs and the specific cloud platform? >> That's a really great question. Well, yes and no. So today, application development teams choose a cloud provider to deploy to and a location to deploy to, and they have to get involved in moving an application like we talked about today. That said, the bursting capability that I mentioned previously is something that is a step in the direction of automatic migration. That is to say we're migrating workload to different locations automatically. Currently, the prototypes we've been developing and that we think are going to eventually make their way into production are leveraging Istio to assess the load incoming on a particular cluster and start shedding that load into a different location. Right now, the configuration of that is still manual, but there's another opportunity for automation there. And I think a key piece of this is that down the road, well, that's a, sort of a small step in the direction of an application being multi provider. We expect to see really an abstraction of the fact that there is a triplet even. So the workloads are moving around according to whatever the control plane decides is necessary based on a whole variety of inputs. And at that point, you will have true multi-cloud applications, applications that are distributed across the different providers and in a way that application developers don't have to think about. >> So Walmart's been a leader, Jack, in using data for competitive advantages for decades. It's kind of been a poster child for that. You've got a mountain of IP in the form of data, tools, applications best practices that until the cloud came out was all On Prem. But I'm really interested in this idea of building a Walmart ecosystem, which obviously you have. Do you see a day or maybe you're even doing it today where you take what we call the Walmart SuperCloud, WCNP in your words, and point or turn that toward an external world or your ecosystem, you know, supporting those partners or customers that could drive new revenue streams, you know directly from the platform? >> Great questions, Dave. So there's really two things to say here. The first is that with respect to data, our data workloads are primarily VM basis. I've mentioned before some VMware, some straight open stack. But the key here is that WCNP and Kubernetes are very powerful for stateless workloads, but for stateful workloads tend to be still climbing a bit of a growth curve in the industry. So our data workloads are not primarily based on WCNP. They're VM based. Now that said, there is opportunity to make some progress there, and we are looking at ways to move things into containers that are currently running in VMs which are stateful. The other question you asked is related to how we expose data to third parties and also functionality. Right now we do have in-house, for our own use, a very robust data architecture, and we have followed the sort of domain-oriented data architecture guidance from Martin Fowler. And we have data lakes in which we collect data from all the transactional systems and which we can then use and do use to build models which are then used in our applications. But right now we're not exposing the data directly to customers as a product. That's an interesting direction that's been talked about and may happen at some point, but right now that's internal. What we are exposing to customers is applications. So we're offering our global integrated fulfillment capabilities, our order picking and curbside pickup capabilities, and our cloud powered checkout capabilities to third parties. And this means we're standing up our own internal applications as externally facing SaaS applications which can serve our partners' customers. >> Yeah, of course, Martin Fowler really first introduced to the world Zhamak Dehghani's data mesh concept and this whole idea of data products and domain oriented thinking. Zhamak Dehghani, by the way, is a speaker at our event as well. Last question I had is edge, and how you think about the edge? You know, the stores are an edge. Are you putting resources there that sort of mirror this this triplet model? Or is it better to consolidate things in the cloud? I know there are trade-offs in terms of latency. How are you thinking about that? >> All really good questions. It's a challenging area as you can imagine because edges are subject to disconnection, right? Or reduced connection. So we do place the same architecture at the edge. So WCNP runs at the edge, and an application that's designed to run at WCNP can run at the edge. That said, there are a number of very specific considerations that come up when running at the edge, such as the possibility of disconnection or degraded connectivity. And so one of the challenges we have faced and have grappled with and done a good job of I think is dealing with the fact that applications go offline and come back online and have to reconnect and resynchronize, the sort of online offline capability is something that can be quite challenging. And we have a couple of application architectures that sort of form the two core sets of patterns that we use. One is an offline/online synchronization architecture where we discover that we've come back online, and we understand the differences between the online dataset and the offline dataset and how they have to be reconciled. The other is a message-based architecture. And here in our health and wellness domain, we've developed applications that are queue based. So they're essentially business processes that consist of multiple steps where each step has its own queue. And what that allows us to do is devote whatever bandwidth we do have to those pieces of the process that are most latency sensitive and allow the queue lengths to increase in parts of the process that are not latency sensitive, knowing that they will eventually catch up when the bandwidth is restored. And to put that in a little bit of context, we have fiber lengths to all of our locations, and we have I'll just use a round number, 10-ish thousand locations. It's larger than that, but that's the ballpark, and we have fiber to all of them, but when the fiber is disconnected, When the disconnection happens, we're able to fall back to 5G and to Starlink. Starlink is preferred. It's a higher bandwidth. 5G if that fails. But in each of those cases, the bandwidth drops significantly. And so the applications have to be intelligent about throttling back the traffic that isn't essential, so that it can push the essential traffic in those lower bandwidth scenarios. >> So much technology to support this amazing business which started in the early 1960s. Jack, unfortunately, we're out of time. I would love to have you back or some members of your team and drill into how you're using open source, but really thank you so much for explaining the approach that you've taken and participating in SuperCloud2. >> You're very welcome, Dave, and we're happy to come back and talk about other aspects of what we do. For example, we could talk more about the data lakes and the data mesh that we have in place. We could talk more about the directions we might go with serverless. So please look us up again. Happy to chat. >> I'm going to take you up on that, Jack. All right. This is Dave Vellante for John Furrier and the Cube community. Keep it right there for more action from SuperCloud2. (upbeat music)

>> Welcome back to SuperCloud2. This is Dave Vellante, and we're here with Jack Greenfield. He's the Vice President of Enterprise Architecture and the Chief Architect for the global technology platform at Walmart. Jack, I want to thank you for coming on the program. Really appreciate your time. >> Glad to be here, Dave. Thanks for inviting me and appreciate the opportunity to chat with you. >> Yeah, it's our pleasure. Now we call what you've built a SuperCloud. That's our term, not yours, but how would you describe the Walmart Cloud Native Platform? >> So WCNP, as the acronym goes, is essentially an implementation of Kubernetes for the Walmart ecosystem. And what that means is that we've taken Kubernetes off the shelf as open source, and we have integrated it with a number of foundational services that provide other aspects of our computational environment. So Kubernetes off the shelf doesn't do everything. It does a lot. In particular the orchestration of containers, but it delegates through API a lot of key functions. So for example, secret management, traffic management, there's a need for telemetry and observability at a scale beyond what you get from raw Kubernetes. That is to say, harvesting the metrics that are coming out of Kubernetes and processing them, storing them in time series databases, dashboarding them, and so on. There's also an angle to Kubernetes that gets a lot of attention in the daily DevOps routine, that's not really part of the open source deliverable itself, and that is the DevOps sort of CICD pipeline-oriented lifecycle. And that is something else that we've added and integrated nicely. And then one more piece of this picture is that within a Kubernetes cluster, there's a function that is critical to allowing services to discover each other and integrate with each other securely and with proper configuration provided by the concept of a service mesh. So Istio, Linkerd, these are examples of service mesh technologies. And we have gone ahead and integrated actually those two. There's more than those two, but we've integrated those two with Kubernetes. So the net effect is that when a developer within Walmart is going to build an application, they don't have to think about all those other capabilities where they come from or how they're provided. Those are already present, and the way the CICD pipelines are set up, it's already sort of in the picture, and there are configuration points that they can take advantage of in the primary YAML and a couple of other pieces of config that we supply where they can tune it. But at the end of the day, it offloads an awful lot of work for them, having to stand up and operate those services, fail them over properly, and make them robust. All of that's provided for. >> Yeah, you know, developers often complain they spend too much time wrangling and doing things that aren't productive. So I wonder if you could talk about the high level business goals of the initiative in terms of the hardcore benefits. Was the real impetus to tap into best of breed cloud services? Were you trying to cut costs? Maybe gain negotiating leverage with the cloud guys? Resiliency, you know, I know was a major theme. Maybe you could give us a sense of kind of the anatomy of the decision making process that went in. >> Sure, and in the course of answering your question, I think I'm going to introduce the concept of our triplet architecture which we haven't yet touched on in the interview here. First off, just to sort of wrap up the motivation for WCNP itself which is kind of orthogonal to the triplet architecture. It can exist with or without it. Currently does exist with it, which is key, and I'll get to that in a moment. The key drivers, business drivers for WCNP were developer productivity by offloading the kinds of concerns that we've just discussed. Number two, improving resiliency, that is to say reducing opportunity for human error. One of the challenges you tend to run into in a large enterprise is what we call snowflakes, lots of gratuitously different workloads, projects, configurations to the extent that by developing and using WCNP and continuing to evolve it as we have, we end up with cookie cutter like consistency across our workloads which is super valuable when it comes to building tools or building services to automate operations that would otherwise be manual. When everything is pretty much done the same way, that becomes much simpler. Another key motivation for WCNP was the ability to abstract from the underlying cloud provider. And this is going to lead to a discussion of our triplet architecture. At the end of the day, when one works directly with an underlying cloud provider, one ends up taking a lot of dependencies on that particular cloud provider. Those dependencies can be valuable. For example, there are best of breed services like say Cloud Spanner offered by Google or say Cosmos DB offered by Microsoft that one wants to use and one is willing to take the dependency on the cloud provider to get that functionality because it's unique and valuable. On the other hand, one doesn't want to take dependencies on a cloud provider that don't add a lot of value. And with Kubernetes, we have the opportunity, and this is a large part of how Kubernetes was designed and why it is the way it is, we have the opportunity to sort of abstract from the underlying cloud provider for stateless workloads on compute. And so what this lets us do is build container-based applications that can run without change on different cloud provider infrastructure. So the same applications can run on WCNP over Azure, WCNP over GCP, or WCNP over the Walmart private cloud. And we have a private cloud. Our private cloud is OpenStack based and it gives us some significant cost advantages as well as control advantages. So to your point, in terms of business motivation, there's a key cost driver here, which is that we can use our own private cloud when it's advantageous and then use the public cloud provider capabilities when we need to. A key place with this comes into play is with elasticity. So while the private cloud is much more cost effective for us to run and use, it isn't as elastic as what the cloud providers offer, right? We don't have essentially unlimited scale. We have large scale, but the public cloud providers are elastic in the extreme which is a very powerful capability. So what we're able to do is burst, and we use this term bursting workloads into the public cloud from the private cloud to take advantage of the elasticity they offer and then fall back into the private cloud when the traffic load diminishes to the point where we don't need that elastic capability, elastic capacity at low cost. And this is a very important paradigm that I think is going to be very commonplace ultimately as the industry evolves. Private cloud is easier to operate and less expensive, and yet the public cloud provider capabilities are difficult to match. >> And the triplet, the tri is your on-prem private cloud and the two public clouds that you mentioned, is that right? >> That is correct. And we actually have an architecture in which we operate all three of those cloud platforms in close proximity with one another in three different major regions in the US. So we have east, west, and central. And in each of those regions, we have all three cloud providers. And the way it's configured, those data centers are within 10 milliseconds of each other, meaning that it's of negligible cost to interact between them. And this allows us to be fairly agnostic to where a particular workload is running. >> Does a human make that decision, Jack or is there some intelligence in the system that determines that? >> That's a really great question, Dave. And it's a great question because we're at the cusp of that transition. So currently humans make that decision. Humans choose to deploy workloads into a particular region and a particular provider within that region. That said, we're actively developing patterns and practices that will allow us to automate the placement of the workloads for a variety of criteria. For example, if in a particular region, a particular provider is heavily overloaded and is unable to provide the level of service that's expected through our SLAs, we could choose to fail workloads over from that cloud provider to a different one within the same region. But that's manual today. We do that, but people do it. Okay, we'd like to get to where that happens automatically. In the same way, we'd like to be able to automate the failovers, both for high availability and sort of the heavier disaster recovery model between, within a region between providers and even within a provider between the availability zones that are there, but also between regions for the sort of heavier disaster recovery or maintenance driven realignment of workload placement. Today, that's all manual. So we have people moving workloads from region A to region B or data center A to data center B. It's clean because of the abstraction. The workloads don't have to know or care, but there are latency considerations that come into play, and the humans have to be cognizant of those. And automating that can help ensure that we get the best performance and the best reliability. >> But you're developing the dataset to actually, I would imagine, be able to make those decisions in an automated fashion over time anyway. Is that a fair assumption? >> It is, and that's what we're actively developing right now. So if you were to look at us today, we have these nice abstractions and APIs in place, but people run that machine, if you will, moving toward a world where that machine is fully automated. >> What exactly are you abstracting? Is it sort of the deployment model or, you know, are you able to abstract, I'm just making this up like Azure functions and GCP functions so that you can sort of run them, you know, with a consistent experience. What exactly are you abstracting and how difficult was it to achieve that objective technically? >> that's a good question. What we're abstracting is the Kubernetes node construct. That is to say a cluster of Kubernetes nodes which are typically VMs, although they can run bare metal in certain contexts, is something that typically to stand up requires knowledge of the underlying cloud provider. So for example, with GCP, you would use GKE to set up a Kubernetes cluster, and in Azure, you'd use AKS. We are actually abstracting that aspect of things so that the developers standing up applications don't have to know what the underlying cluster management provider is. They don't have to know if it's GCP, AKS or our own Walmart private cloud. Now, in terms of functions like Azure functions that you've mentioned there, we haven't done that yet. That's another piece that we have sort of on our radar screen that, we'd like to get to is serverless approach, and the Knative work from Google and the Azure functions, those are things that we see good opportunity to use for a whole variety of use cases. But right now we're not doing much with that. We're strictly container based right now, and we do have some VMs that are running in sort of more of a traditional model. So our stateful workloads are primarily VM based, but for serverless, that's an opportunity for us to take some of these stateless workloads and turn them into cloud functions. >> Well, and that's another cost lever that you can pull down the road that's going to drop right to the bottom line. Do you see a day or maybe you're doing it today, but I'd be surprised, but where you build applications that actually span multiple clouds or is there, in your view, always going to be a direct one-to-one mapping between where an application runs and the specific cloud platform? >> That's a really great question. Well, yes and no. So today, application development teams choose a cloud provider to deploy to and a location to deploy to, and they have to get involved in moving an application like we talked about today. That said, the bursting capability that I mentioned previously is something that is a step in the direction of automatic migration. That is to say we're migrating workload to different locations automatically. Currently, the prototypes we've been developing and that we think are going to eventually make their way into production are leveraging Istio to assess the load incoming on a particular cluster and start shedding that load into a different location. Right now, the configuration of that is still manual, but there's another opportunity for automation there. And I think a key piece of this is that down the road, well, that's a, sort of a small step in the direction of an application being multi provider. We expect to see really an abstraction of the fact that there is a triplet even. So the workloads are moving around according to whatever the control plane decides is necessary based on a whole variety of inputs. And at that point, you will have true multi-cloud applications, applications that are distributed across the different providers and in a way that application developers don't have to think about. >> So Walmart's been a leader, Jack, in using data for competitive advantages for decades. It's kind of been a poster child for that. You've got a mountain of IP in the form of data, tools, applications best practices that until the cloud came out was all On Prem. But I'm really interested in this idea of building a Walmart ecosystem, which obviously you have. Do you see a day or maybe you're even doing it today where you take what we call the Walmart SuperCloud, WCNP in your words, and point or turn that toward an external world or your ecosystem, you know, supporting those partners or customers that could drive new revenue streams, you know directly from the platform? >> Great question, Steve. So there's really two things to say here. The first is that with respect to data, our data workloads are primarily VM basis. I've mentioned before some VMware, some straight open stack. But the key here is that WCNP and Kubernetes are very powerful for stateless workloads, but for stateful workloads tend to be still climbing a bit of a growth curve in the industry. So our data workloads are not primarily based on WCNP. They're VM based. Now that said, there is opportunity to make some progress there, and we are looking at ways to move things into containers that are currently running in VMs which are stateful. The other question you asked is related to how we expose data to third parties and also functionality. Right now we do have in-house, for our own use, a very robust data architecture, and we have followed the sort of domain-oriented data architecture guidance from Martin Fowler. And we have data lakes in which we collect data from all the transactional systems and which we can then use and do use to build models which are then used in our applications. But right now we're not exposing the data directly to customers as a product. That's an interesting direction that's been talked about and may happen at some point, but right now that's internal. What we are exposing to customers is applications. So we're offering our global integrated fulfillment capabilities, our order picking and curbside pickup capabilities, and our cloud powered checkout capabilities to third parties. And this means we're standing up our own internal applications as externally facing SaaS applications which can serve our partners' customers. >> Yeah, of course, Martin Fowler really first introduced to the world Zhamak Dehghani's data mesh concept and this whole idea of data products and domain oriented thinking. Zhamak Dehghani, by the way, is a speaker at our event as well. Last question I had is edge, and how you think about the edge? You know, the stores are an edge. Are you putting resources there that sort of mirror this this triplet model? Or is it better to consolidate things in the cloud? I know there are trade-offs in terms of latency. How are you thinking about that? >> All really good questions. It's a challenging area as you can imagine because edges are subject to disconnection, right? Or reduced connection. So we do place the same architecture at the edge. So WCNP runs at the edge, and an application that's designed to run at WCNP can run at the edge. That said, there are a number of very specific considerations that come up when running at the edge, such as the possibility of disconnection or degraded connectivity. And so one of the challenges we have faced and have grappled with and done a good job of I think is dealing with the fact that applications go offline and come back online and have to reconnect and resynchronize, the sort of online offline capability is something that can be quite challenging. And we have a couple of application architectures that sort of form the two core sets of patterns that we use. One is an offline/online synchronization architecture where we discover that we've come back online, and we understand the differences between the online dataset and the offline dataset and how they have to be reconciled. The other is a message-based architecture. And here in our health and wellness domain, we've developed applications that are queue based. So they're essentially business processes that consist of multiple steps where each step has its own queue. And what that allows us to do is devote whatever bandwidth we do have to those pieces of the process that are most latency sensitive and allow the queue lengths to increase in parts of the process that are not latency sensitive, knowing that they will eventually catch up when the bandwidth is restored. And to put that in a little bit of context, we have fiber lengths to all of our locations, and we have I'll just use a round number, 10-ish thousand locations. It's larger than that, but that's the ballpark, and we have fiber to all of them, but when the fiber is disconnected, and it does get disconnected on a regular basis. In fact, I forget the exact number, but some several dozen locations get disconnected daily just by virtue of the fact that there's construction going on and things are happening in the real world. When the disconnection happens, we're able to fall back to 5G and to Starlink. Starlink is preferred. It's a higher bandwidth. 5G if that fails. But in each of those cases, the bandwidth drops significantly. And so the applications have to be intelligent about throttling back the traffic that isn't essential, so that it can push the essential traffic in those lower bandwidth scenarios. >> So much technology to support this amazing business which started in the early 1960s. Jack, unfortunately, we're out of time. I would love to have you back or some members of your team and drill into how you're using open source, but really thank you so much for explaining the approach that you've taken and participating in SuperCloud2. >> You're very welcome, Dave, and we're happy to come back and talk about other aspects of what we do. For example, we could talk more about the data lakes and the data mesh that we have in place. We could talk more about the directions we might go with serverless. So please look us up again. Happy to chat. >> I'm going to take you up on that, Jack. All right. This is Dave Vellante for John Furrier and the Cube community. Keep it right there for more action from SuperCloud2. (upbeat music)

(soft electronic music) >> Okay, we're back with Tim Yocum who is the Director of Engineering at InfluxData. Tim, welcome, good to see you. >> Good to see you, thanks for having me. >> You're really welcome. Listen, we've been covering opensource software on theCUBE for more than a decade and we've kind of watched the innovation from the big data ecosystem, the cloud is being built out on opensource, mobile, social platforms, key databases, and of course, InfluxDB. And InfluxData has been a big consumer and crontributor of opensource software. So my question to you is where have you seen the biggest bang for the buck from opensource software? >> So yeah, you know, Influx really, we thrive at the intersection of commercial services and opensource software, so OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services, templating engines. Our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants. And like you've mentioned, even better, we contribute a lot back to the projects that we use, as well as our own product InfluxDB. >> But I got to ask you, Tim, because one of the challenge that we've seen, in particular, you saw this in the heyday of Hadoop, the innovations come so fast and furious, and as a software company, you got to place bets, you got to commit people, and sometimes those bets can be risky and not pay off. So how have you managed this challenge? >> Oh, it moves fast, yeah. That's a benefit, though, because the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we tend to do is we fail fast and fail often; we try a lot of things. You know, you look at Kubernetes, for example. That ecosystem is driven by thousands of intelligent developers, engineers, builders. They're adding value every day, so we have to really keep up with that. And as the stack changes, we try different technologies, we try different methods. And at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's something that we just do every day. >> So we have a survey partner down in New York City called Enterprise Technology Research, ETR, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes, is one of the areas that is kind of, it's been off the charts and seen the most significant adoption and velocity particularly along with cloud, but really, Kubernetes is just, you know, still up and to the right consistently, even with the macro headwinds and all of the other stuff that we're sick of talking about. So what do you do with Kubernetes in the platform? >> Yeah, it's really central to our ability to run the product. When we first started out, we were just on AWS and the way we were running was a little bit like containers junior. Now we're running Kubernetes everywhere at AWS, Azure, Google cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code. So our developers can focus on delivering services not trying to learn the intricacies of Amazon, Azure, and Google, and figure out how to deliver services on those three clouds with all of their differences. >> Just a followup on that, is it now, so I presume it sounds like there's a PaaS layer there to allow you guys to have a consistent experience across clouds and out to the edge, wherever. Is that correct? >> Yeah, so we've basically built more or less platform engineering is this the new, hot phrase. Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that just gets all of the underlying infrastructure out of the way and lets them focus on delivering Influx cloud. >> And I know I'm taking a little bit of a tangent, but is that, I'll call it a PaaS layer, if I can use that term, are there specific attributes to InfluxDB or is it kind of just generally off-the-shelf PaaS? Is there any purpose built capability there that is value-add or is it pretty much generic? >> So we really build, we look at things with a build versus buy, through a build versus buy lens. Some things we want to leverage, cloud provider services, for instance, POSTGRES databases for metadata, perhaps. Get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can deliver on, that has consistency, that is all generated from code. that we can, as an SRE group, as an OPS team, that we can manage with very few people, really, and we can stamp out clusters across multiple regions in no time. >> So sometimes you build, sometimes you buy it. How do you make those decisions and what does that mean for the platform and for customers? >> Yeah, so what we're doing is, it's like everybody else will do. We're looking for trade-offs that make sense. We really want to protect our customers' data, so we look for services that support our own software with the most up-time reliability and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team and of course, for our customers; you don't even see that. But we don't want to try to reinvent the wheel, like I had mentioned with SQL datasource for metadata, perhaps. Let's build on top of what of these three large cloud providers have already perfected and we can then focus on our platform engineering and we can help our developers then focus on the InfluxData software, the Influx cloud software. >> So take it to the customer level. What does it mean for them, what's the value that they're going to get out of all these innovations that we've been talking about today, and what can they expect in the future? >> So first of all, people who use the OSS product are really going to be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across over four billion series keys that people have stored, so there's a proven ability to scale. Now in terms of the opensource software and how we've developed the platform, you're getting highly available, high cardinality time-series platform. We manage it and really, as I had mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in realtime. We deploy to our platform every day, repeatedly, all the time. And it's that continuous deployment that allow us to continue testing things in flight, rolling things out that change, new features, better ways of doing deployments, safer ways of doing deployments. All of that happens behind the scenes and like we had mentioned earllier, Kubernetes, I mean, that allows us to get that done. We couldn't do it without having that platform as a base layer for us to then put our software on. So we iterate quickly. When you're on the Influx cloud platform, you really are able to take advantage of new features immediately. We roll things out every day and as those things go into production, you have the ability to use them. And so in the then, we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let us do that for you. >> That makes sense. Are the innovations that we're talking about in the evolution of InfluxDB, do you see that as sort of a natural evolution for existing customers? Is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >> Yeah, it really is. It's a little bit of both. Any engineer will say, "Well it depends." So cloud-native technologies are really the hot thing, IoT, industrial IoT especially. People want to just shove tons of data out there and be able to do queries immediately and they don't want to manage infrastructure. What we've started to see are people that use the cloud service as their datastore backbone and then they use edge computing with our OSS product to ingest data from say, multiple production lines, and down-sample that data, send the rest of that data off to Influx cloud where the heavy processing takes place. So really, us being in all the different clouds and iterating on that, and being in all sorts of different regions, allows for people to really get out of the business of trying to manage that big data, have us take care of that. And, of course, as we change the platform, endusers benefit from that immediately. >> And so obviously you've taken away a lot of the heavy lifting for the infrastructure. Would you say the same things about security, especially as you go out to IoT at the edge? How should we be thinking about the value that you bring from a security perspective? >> We take security super seriously. It's built into our DNA. We do a lot of work to ensure that our platform is secure, that the data that we store is kept private. It's, of course, always a concern, you see in the news all the time, companies being compromised. That's something that you can have an entire team working on which we do, to make sure that the data that you have, whether it's in transit, whether it's at rest is always kept secure, is only viewable by you. You look at things like software bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software and we do that, you know, as we use new tools. That's something, that's just part of our jobs to make sure that the platform that we're running has fully vetted software. And you know, with opensource especially, that's a lot of work, and so it's definitely new territory. Supply chain attacks are definitely happening at a higher clip that they used to but that is really just part of a day in the life for folks like us that are building platforms. >> And that's key, especially when you start getting into the, you know, that we talk about IoT and the operations technologies, the engineers running that infrastrucutre. You know, historically, as you know, Tim, they would air gap everything; that's how they kept it safe. But that's not feasible anymore. Everything's-- >> Can't do that. >> connected now, right? And so you've got to have a partner that is, again, take away that heavy lifting to R&D so you can focus on some of the other activities. All right, give us the last word and the key takeaways from your perspective. >> Well, you know, from my perspective, I see it as a two-lane approach, with Influx, with any time-series data. You've got a lot of stuff that you're going to run on-prem. What you had mentioned, air gapping? Sure, there's plenty of need for that. But at the end of the day, people that don't want to run big datacenters, people that want to entrust their data to a company that's got a full platform set up for them that they can build on, send that data over to the cloud. The cloud is not going away. I think a more hybrid approach is where the future lives and that's what we're prepared for. >> Tim, really appreciate you coming to the program. Great stuff, good to see you. >> Thanks very much, appreciate it. >> Okay in a moment, I'll be back to wrap up today's session. You're watching theCUBE. (soft electronic music)

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

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

(upbeat music) >> Hey guys, welcome back to the show floor of KubeCon + CloudNativeCon '22 North America from Detroit, Michigan. Lisa Martin here with John Furrier. This is day one, John at theCUBE's coverage. >> CUBE's coverage. >> theCUBE's coverage of KubeCon. Try saying that five times fast. Day one, we have three wall-to-wall days. We've been talking about Kubernetes, containers, adoption, cloud adoption, app modernization all morning. We can't talk about those things without addressing security. >> Yeah, this segment we're going to hear container and Kubernetes security for modern application 'cause the enterprise are moving there. And this segment with Red Hat's going to be important because they are the leader in the enterprise when it comes to open source in Linux. So this is going to be a very fun segment. >> Very fun segment. Two guests from Red Hat join us. Please welcome Doron Caspin, Senior Principal Product Manager at Red Hat. Michael Foster joins us as well, Principal Product Marketing Manager and StackRox Community Lead at Red Hat. Guys, great to have you on the program. >> Thanks for having us. >> Thank you for having us. >> It's awesome. So Michael StackRox acquisition's been about a year. You got some news? >> Yeah, 18 months. >> Unpack that for us. >> It's been 18 months, yeah. So StackRox in 2017, originally we shifted to be the Kubernetes-native security platform. That was our goal, that was our vision. Red Hat obviously saw a lot of powerful, let's say, mission statement in that, and they bought us in 2021. Pre-acquisition we were looking to create a cloud service. Originally we ran on Kubernetes platforms, we had an operator and things like that. Now we are looking to basically bring customers in into our service preview for ACS as a cloud service. That's very exciting. Security conversation is top notch right now. It's an all time high. You can't go with anywhere without talking about security. And specifically in the code, we were talking before we came on camera, the software supply chain is real. It's not just about verification. Where do you guys see the challenges right now? Containers having, even scanning them is not good enough. First of all, you got to scan them and that may not be good enough. Where's the security challenges and where's the opportunity? >> I think a little bit of it is a new way of thinking. The speed of security is actually does make you secure. We want to keep our images up and fresh and updated and we also want to make sure that we're keeping the open source and the different images that we're bringing in secure. Doron, I know you have some things to say about that too. He's been working tirelessly on the cloud service. >> Yeah, I think that one thing, you need to trust your sources. Even if in the open source world, you don't want to copy paste libraries from the web. And most of our customers using third party vendors and getting images from different location, we need to trust our sources and we have a really good, even if you have really good scanning solution, you not always can trust it. You need to have a good solution for that. >> And you guys are having news, you're announcing the Red Hat Advanced Cluster Security Cloud Service. >> Yes. >> What is that? >> So we took StackRox and we took the opportunity to make it as a cloud services so customer can consume the product as a cloud services as a start offering and customer can buy it through for Amazon Marketplace and in the future Azure Marketplace. So customer can use it for the AKS and EKS and AKS and also of course OpenShift. So we are not specifically for OpenShift. We're not just OpenShift. We also provide support for EKS and AKS. So we provided the capability to secure the whole cloud posture. We know customer are not only OpenShift or not only EKS. We have both. We have free cloud or full cloud. So we have open. >> So it's not just OpenShift, it's Kubernetes, environments, all together. >> Doron: All together, yeah. >> Lisa: Meeting customers where they are. >> Yeah, exactly. And we focus on, we are not trying to boil the ocean or solve the whole cloud security posture. We try to solve the Kubernetes security cluster. It's very unique and very need unique solution for that. It's not just added value in our cloud security solution. We think it's something special for Kubernetes and this is what Red that is aiming to. To solve this issue. >> And the ACS platform really doesn't change at all. It's just how they're consuming it. It's a lot quicker in the cloud. Time to value is right there. As soon as you start up a Kubernetes cluster, you can get started with ACS cloud service and get going really quickly. >> I'm going to ask you guys a very simple question, but I heard it in the bar in the lobby last night. Practitioners talking and they were excited about the Red Hat opportunity. They actually asked a question, where do I go and get some free Red Hat to test some Kubernetes out and run helm or whatever. They want to play around. And do you guys have a program for someone to get start for free? >> Yeah, so the cloud service specifically, we're going to service preview. So if people sign up, they'll be able to test it out and give us feedback. That's what we're looking for. >> John: Is that a Sandbox or is that going to be in the cloud? >> They can run it in their own environment. So they can sign up. >> John: Free. >> Doron: Yeah, free. >> For the service preview. All we're asking for is for customer feedback. And I know it's actually getting busy there. It's starting December. So the quicker people are, the better. >> So my friend at the lobby I was talking to, I told you it was free. I gave you the sandbox, but check out your cloud too. >> And we also have the open source version so you can download it and use it. >> Yeah, people want to know how to get involved. I'm getting a lot more folks coming to Red Hat from the open source side that want to get their feet wet. That's been a lot of people rarely interested. That's a real testament to the product leadership. Congratulations. >> Yeah, thank you. >> So what are the key challenges that you have on your roadmap right now? You got the products out there, what's the current stake? Can you scope the adoption? Can you share where we're at? What people are doing specifically and the real challenges? >> I think one of the biggest challenges is talking with customers with a slightly, I don't want to say outdated, but an older approach to security. You hear things like malware pop up and it's like, well, really what we should be doing is keeping things into low and medium vulnerabilities, looking at the configuration, managing risk accordingly. Having disparate security tools or different teams doing various things, it's really hard to get a security picture of what's going on in the cluster. That's some of the biggest challenges that we talk with customers about. >> And in terms of resolving those challenges, you mentioned malware, we talk about ransomware. It's a household word these days. It's no longer, are we going to get hit? It's when? It's what's the severity? It's how often? How are you guys helping customers to dial down some of the risk that's inherent and only growing these days? >> Yeah, risk, it's a tough word to generalize, but our whole goal is to give you as much security information in a way that's consumable so that you can evaluate your risk, set policies, and then enforce them early on in the cluster or early on in the development pipeline so that your developers get the security information they need, hopefully asynchronously. That's the best way to do it. It's nice and quick, but yeah. I don't know if Doron you want to add to that? >> Yeah, so I think, yeah, we know that ransomware, again, it's a big world for everyone and we understand the area of the boundaries where we want to, what we want to protect. And we think it's about policies and where we enforce it. So, and if you can enforce it on, we know that as we discussed before that you can scan the image, but we never know what is in it until you really run it. So one of the thing that we we provide is runtime scanning. So you can scan and you can have policy in runtime. So enforce things in runtime. But even if one image got in a way and get to your cluster and run on somewhere, we can stop it in runtime. >> Yeah. And even with the runtime enforcement, the biggest thing we have to educate customers on is that's the last-ditch effort. We want to get these security controls as early as possible. That's where the value's going to be. So we don't want to be blocking things from getting to staging six weeks after developers have been working on a project. >> I want to get you guys thoughts on developer productivity. Had Docker CEO on earlier and since then I had a couple people messaging me. Love the vision of Docker, but Docker Hub has some legacy and it might not, has does something kind of adoption that some people think it does. Are people moving 'cause there times they want to have these their own places? No one place or maybe there is, or how do you guys see the movement of say Docker Hub to just using containers? I don't need to be Docker Hub. What's the vis-a-vis competition? >> I mean working with open source with Red Hat, you have to meet the developers where they are. If your tool isn't cutting it for developers, they're going to find a new tool and really they're the engine, the growth engine of a lot of these technologies. So again, if Docker, I don't want to speak about Docker or what they're doing specifically, but I know that they pretty much kicked off the container revolution and got this whole thing started. >> A lot of people are using your environment too. We're hearing a lot of uptake on the Red Hat side too. So, this is open source help, it all sorts stuff out in the end, like you said, but you guys are getting a lot of traction there. Can you share what's happening there? >> I think one of the biggest things from a developer experience that I've seen is the universal base image that people are using. I can speak from a security standpoint, it's awesome that you have a base image where you can make one change or one issue and it can impact a lot of different applications. That's one of the big benefits that I see in adoption. >> What are some of the business, I'm curious what some of the business outcomes are. You talked about faster time to value obviously being able to get security shifted left and from a control perspective. but what are some of the, if I'm a business, if I'm a telco or a healthcare organization or a financial organization, what are some of the top line benefits that this can bubble up to impact? >> I mean for me, with those two providers, compliance is a massive one. And just having an overall look at what's going on in your clusters, in your environments so that when audit time comes, you're prepared. You can get through that extremely quickly. And then as well, when something inevitably does happen, you can get a good image of all of like, let's say a Log4Shell happens, you know exactly what clusters are affected. The triage time is a lot quicker. Developers can get back to developing and then yeah, you can get through it. >> One thing that we see that customers compliance is huge. >> Yes. And we don't want to, the old way was that, okay, I will provision a cluster and I will do scans and find things, but I need to do for PCI DSS for example. Today the customer want to provision in advance a PCI DSS cluster. So you need to do the compliance before you provision the cluster and make all the configuration already baked for PCI DSS or HIPAA compliance or FedRAMP. And this is where we try to use our compliance, we have tools for compliance today on OpenShift and other clusters and other distribution, but you can do this in advance before you even provision the cluster. And we also have tools to enforce it after that, after your provision, but you have to do it again before and after to make it more feasible. >> Advanced cluster management and the compliance operator really help with that. That's why OpenShift Platform Plus as a bundle is so popular. Just being able to know that when a cluster gets provision, it's going to be in compliance with whatever the healthcare provider is using. And then you can automatically have ACS as well pop up so you know exactly what applications are running, you know it's in compliance. I mean that's the speed. >> You mentioned the word operator, I get triggering word now for me because operator role is changing significantly on this next wave coming because of the automation. They're operating, but they're also devs too. They're developing and composing. It's almost like a dashboard, Lego blocks. The operator's not just manually racking and stacking like the old days, I'm oversimplifying it, but the new operators running stuff, they got observability, they got coding, their servicing policy. There's a lot going on. There's a lot of knobs. Is it going to get simpler? How do you guys see the org structures changing to fill the gap on what should be a very simple, turn some knobs, operate at scale? >> Well, when StackRox originally got acquired, one of the first things we did was put ACS into an operator and it actually made the application life cycle so much easier. It was very easy in the console to go and say, Hey yeah, I want ACS my cluster, click it. It would get provisioned. New clusters would get provisioned automatically. So underneath it might get more complicated. But in terms of the application lifecycle, operators make things so much easier. >> And of course I saw, I was lucky enough with Lisa to see Project Wisdom in AnsibleFest. You going to say, Hey, Red Hat, spin up the clusters and just magically will be voice activated. Starting to see AI come in. So again, operations operator is got to dev vibe and an SRE vibe, but it's not that direct. Something's happening there. We're trying to put our finger on. What do you guys think is happening? What's the real? What's the action? What's transforming? >> That's a good question. I think in general, things just move to the developers all the time. I mean, we talk about shift left security, everything's always going that way. Developers how they're handing everything. I'm not sure exactly. Doron, do you have any thoughts on that. >> Doron, what's your reaction? You can just, it's okay, say what you want. >> So I spoke with one of our customers yesterday and they say that in the last years, we developed tons of code just to operate their infrastructure. That if developers, so five or six years ago when a developer wanted VM, it will take him a week to get a VM because they need all their approval and someone need to actually provision this VM on VMware. And today they automate all the way end-to-end and it take two minutes to get a VM for developer. So operators are becoming developers as you said, and they develop code and they make the infrastructure as code and infrastructure as operator to make it more easy for the business to run. >> And then also if you add in DataOps, AIOps, DataOps, Security Ops, that's the new IT. It seems to be the new IT is the stuff that's scaling, a lot of data's coming in, you got security. So all that's got to be brought in. How do you guys view that into the equation? >> Oh, I mean you become big generalists. I think there's a reason why those cloud security or cloud professional certificates are becoming so popular. You have to know a lot about all the different applications, be able to code it, automate it, like you said, hopefully everything as code. And then it also makes it easy for security tools to come in and look and examine where the vulnerabilities are when those things are as code. So because you're going and developing all this automation, you do become, let's say a generalist. >> We've been hearing on theCUBE here and we've been hearing the industry, burnout, associated with security professionals and some DataOps because the tsunami of data, tsunami of breaches, a lot of engineers getting called in the middle of the night. So that's not automated. So this got to get solved quickly, scaled up quickly. >> Yes. There's two part question there. I think in terms of the burnout aspect, you better send some love to your security team because they only get called when things get broken and when they're doing a great job you never hear about them. So I think that's one of the things, it's a thankless profession. From the second part, if you have the right tools in place so that when something does hit the fan and does break, then you can make an automated or a specific decision upstream to change that, then things become easy. It's when the tools aren't in place and you have desperate environments so that when a Log4Shell or something like that comes in, you're scrambling trying to figure out what clusters are where and where you're impacted. >> Point of attack, remediate fast. That seems to be the new move. >> Yeah. And you do need to know exactly what's going on in your clusters and how to remediate it quickly, how to get the most impact with one change. >> And that makes sense. The service area is expanding. More things are being pushed. So things will, whether it's a zero day vulnerability or just attack. >> Just mix, yeah. Customer automate their all of things, but it's good and bad. Some customer told us they, I think Spotify lost the whole a full zone because of one mistake of a customer because they automate everything and you make one mistake. >> It scale the failure really. >> Exactly. Scaled the failure really fast. >> That was actually few contact I think four years ago. They talked about it. It was a great learning experience. >> It worked double edge sword there. >> Yeah. So definitely we need to, again, scale automation, test automation way too, you need to hold the drills around data. >> Yeah, you have to know the impact. There's a lot of talk in the security space about what you can and can't automate. And by default when you install ACS, everything is non-enforced. You have to have an admission control. >> How are you guys seeing your customers? Obviously Red Hat's got a great customer base. How are they adopting to the managed service wave that's coming? People are liking the managed services now because they maybe have skills gap issues. So managed service is becoming a big part of the portfolio. What's your guys' take on the managed services piece? >> It's just time to value. You're developing a new application, you need to get it out there quick. If somebody, your competitor gets out there a month before you do, that's a huge market advantage. >> So you care how you got there. >> Exactly. And so we've had so much Kubernetes expertise over the last 10 or so, 10 plus year or well, Kubernetes for seven plus years at Red Hat, that why wouldn't you leverage that knowledge internally so you can get your application. >> Why change your toolchain and your workflows go faster and take advantage of the managed service because it's just about getting from point A to point B. >> Exactly. >> Well, in time to value is, you mentioned that it's not a trivial term, it's not a marketing term. There's a lot of impact that can be made. Organizations that can move faster, that can iterate faster, develop what their customers are looking for so that they have that competitive advantage. It's definitely not something that's trivial. >> Yeah. And working in marketing, whenever you get that new feature out and I can go and chat about it online, it's always awesome. You always get customers interests. >> Pushing new code, being secure. What's next for you guys? What's on the agenda? What's around the corner? We'll see a lot of Red Hat at re:Invent. Obviously your relationship with AWS as strong as a company. Multi-cloud is here. Supercloud as we've been saying. Supercloud is a thing. What's next for you guys? >> So we launch the cloud services and the idea that we will get feedback from customers. We are not going GA. We're not going to sell it for now. We want to get customers, we want to get feedback to make the product as best what we can sell and best we can give for our customers and get feedback. And when we go GA and we start selling this product, we will get the best product in the market. So this is our goal. We want to get the customer in the loop and get as much as feedback as we can. And also we working very closely with our customers, our existing customers to announce the product to add more and more features what the customer needs. It's all about supply chain. I don't like it, but we have to say, it's all about making things more automated and make things more easy for our customer to use to have security in the Kubernetes environment. >> So where can your customers go? Clearly, you've made a big impact on our viewers with your conversation today. Where are they going to be able to go to get their hands on the release? >> So you can find it on online. We have a website to sign up for this program. It's on my blog. We have a blog out there for ACS cloud services. You can just go there, sign up, and we will contact the customer. >> Yeah. And there's another way, if you ever want to get your hands on it and you can do it for free, Open Source StackRox. The product is open source completely. And I would love feedback in Slack channel. It's one of the, we also get a ton of feedback from people who aren't actually paying customers and they contribute upstream. So that's an awesome way to get started. But like you said, you go to, if you search ACS cloud service and service preview. Don't have to be a Red Hat customer. Just if you're running a CNCF compliant Kubernetes version. we'd love to hear from you. >> All open source, all out in the open. >> Yep. >> Getting it available to the customers, the non-customers, they hopefully pending customers. Guys, thank you so much for joining John and me talking about the new release, the evolution of StackRox in the last season of 18 months. Lot of good stuff here. I think you've done a great job of getting the audience excited about what you're releasing. Thank you for your time. >> Thank you. >> Thank you. >> For our guest and for John Furrier, Lisa Martin here in Detroit, KubeCon + CloudNativeCon North America. Coming to you live, we'll be back with our next guest in just a minute. (gentle music)

(upbeat music) >> Okay, we're back with Tim Yoakum, who is the Director of Engineering at Influx Data. Tim, welcome. Good to see you. >> Good to see you. Thanks for having me. >> You're really welcome. Listen, we've been covering open source software on the Cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem, the cloud is being built out on open source, mobile social platforms, key databases, and of course Influx DB, and Influx Data has been a big consumer and contributor of open source software. So my question to you is where have you seen the biggest bang for the buck from open source software? >> So, yeah, you know, Influx, really, we thrive at the intersection of commercial services and open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service, from our core storage engine technologies to web services, templating engines. Our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants. And like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product, Influx DB. >> You know, but I got to ask you, Tim, because one of the challenge that we've seen, in particular, you saw this in the heyday of Hadoop. The innovations come so fast and furious, and as a software company, you got to place bets, you got to, you know, commit people, and sometimes those bets can be risky and not pay off. How have you managed this challenge? >> Oh, it moves fast, yeah. That's a benefit though, because the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we tend to do is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example. That ecosystem is driven by thousands of intelligent developers, engineers, builders. They're adding value every day. So we have to really keep up with that. And as the stack changes, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's something that we just do every day. >> So we have a survey partner down in New York City called Enterprise Technology Research, ETR, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes, is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity, particularly, you know, along with cloud. But really Kubernetes is just, you know, still up and to the right consistently, even with, you know the macro headwinds and all of the other stuff that we're sick of talking about. So what are you doing with Kubernetes in the platform? >> Yeah, it's really central to our ability to run the product. When we first started out, we were just on AWS, and the way we were running was a little bit like containers junior. Now we're running Kubernetes everywhere, at AWS, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers, and we can manage that in code. So our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google, and figure out how to deliver services on those three clouds with all of their differences. >> Just a follow up on that, is it, now, so I presume it sounds like there's a PaaS layer there to allow you guys to have a consistent experience across clouds and up to the edge, you know, wherever. Is that, is that correct? >> Yeah, so we've basically built, more or less, platform engineering. This is the new hot phrase. You know, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on, and they only have to learn one way of deploying their application, managing their application. And so that just gets all of the underlying infrastructure out of the way and lets them focus on delivering Influx Cloud. >> Yeah, and I know I'm taking a little bit of a tangent, but is that, I'll call it a PaaS layer if I can use that term, are there specific attributes to Influx DB, or is it kind of just generally off the shelf PaaS? You know, is there any purpose built capability there that is value add, or is it pretty much generic? >> So we really build, we look at things with a build versus buy, through a build versus buy lens. Some things we want to leverage, cloud provider services for instance, Postgres databases for metadata perhaps, get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can deliver on, that has consistency, that is all generated from code that we can, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions in no time. >> So how, so sometimes you build, sometimes you buy it. How do you make those decisions, and what does that mean for the platform and for customers? >> Yeah, so what we're doing is, it's like everybody else will do. We're looking for trade offs that make sense. You know, we really want to protect our customers' data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers, you don't even see that, but we don't want to try to reinvent the wheel. Like I had had mentioned with SQL data storage for metadata perhaps. Let's build on top of what these three large cloud providers have already perfected, and we can then focus on our platform engineering, and we can have our developers then focus on the Influx Data software, Influx Cloud software. >> So take it to the customer level. What does it mean for them? What's the value that they're going to get out of all these innovations that we've been been talking about today? And what can they expect in the future? >> So first of all, people who use the OSS product are really going to be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you. But then you want to scale up. We have some 270 terabytes of data across over 4 billion series keys that people have stored. So there's a proven ability to scale. Now, in terms of the open source software, and how we've developed the platform, you're getting highly available, high cardinality time series platform. We manage it, and really as I mentioned earlier, we can keep up with the state of the art. We keep reinventing. We keep deploying things in real time. We deploy to our platform every day repeatedly, all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change, new features, better ways of doing deployments, safer ways of doing deployments. All of that happens behind the scenes. And we had mentioned earlier Kubernetes, I mean that allows us to get that done. We couldn't do it without having that platform as a base layer for us to then put our software on. So we iterate quickly. When you're on the Influx Cloud platform, you really are able to take advantage of new features immediately. We roll things out every day. And as those things go into production, you have the ability to use them. And so in the end, we want you to focus on getting actionable insights from your data instead of running infrastructure. You know, let us do that for you. >> And that makes sense, but so is the, are the innovations that we're talking about in the evolution of Influx DB, do you see that as sort of a natural evolution for existing customers? Is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >> Yeah, it really is. It's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are really the hot thing. IoT, industrial IoT especially, people want to just shove tons of data out there and be able to do queries immediately, and they don't want to manage infrastructure. What we've started to see are people that use the cloud service as their data store backbone, and then they use edge computing with our OSS product to ingest data from say multiple production lines and down-sample that data, send the rest of that data off to Influx Cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that, and being in all sorts of different regions allows for people to really get out of the business of trying to manage that big data, have us take care of that. And of course, as we change the platform, end users benefit from that immediately. >> And so obviously, taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IoT and the edge? How should we be thinking about the value that you bring from a security perspective? >> Yeah, we take security super seriously. It's built into our DNA. We do a lot of work to ensure that our platform is secure, that the data we store is kept private. It's of course always a concern. You see in the news all the time companies being compromised. You know, that's something that you can have an entire team working on, which we do, to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You look at things like software bill of materials. If you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that's just part of our jobs, to make sure that the platform that we're running has fully vetted software. And with open source especially, that's a lot of work. And so it's definitely new territory. Supply chain attacks are definitely happening at a higher clip than they used to. But that is really just part of a day in the life for folks like us that are building platforms. >> Yeah, and that's key. I mean, especially when you start getting into the, you know, we talk about IoT and the operations technologies, the engineers running that infrastructure. You know, historically, as you know, Tim, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >> Can't do that. >> connected now, right? And so you've got to have a partner that is, again, take away that heavy lifting to R and D so you can focus on some of the other activities. All right. Give us the last word and the key takeaways from your perspective. >> Well, you know, from my perspective, I see it as a a two lane approach. With Influx, with any any time series data, you know, you've got a lot of stuff that you're going to run on-prem. What you mentioned, air gaping, sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want to entrust their data to a company that's got a full platform set up for them that they can build on, send that data over to the cloud. The cloud is not going away. I think a more hybrid approach is where the future lives, and that's what we're prepared for. >> Tim, really appreciate you coming to the program. Great stuff. Good to see you. >> Thanks very much. Appreciate it. >> Okay, in a moment, I'll be back to wrap up today's session. You're watching the Cube. (gentle music)

>>Hey everyone. Welcome back to the cubes day two coverage of VMware Explorer, 22 from San Francisco. Lisa Martin, back here with you with Dave Nicholson, we have a couple of guests from VMware. Joining us, please. Welcome Jay Workman, senior director, cloud partner, and alliances marketing, and Jeff Thompson, VP cloud provider sales at VMware guys. It's great to have you on the program. >>Ah, good to be here. Thanks for having us on. >>We're gonna be talking about a really interesting topic. Sovereign cloud. What is sovereign cloud? Jeff? Why is it important, but fundamentally, what is >>It? Yeah, well, we were just talking a second ago. Aren't we? And it's not about royalty. So yeah, data sovereignty is really becoming super important. It's about the regulation and control of data. So lots of countries now are being very careful and advising companies around where to place data and the jurisdictional controls mandate that personal data or otherwise has to be secured. We ask, we have to have access controls around it and privacy controls around it. So data sovereign clouds are clouds that have been built by our cloud providers in, in, in VMware that specifically satisfy the requirements of those jurisdictions and regulated industries. So we've built a, a little program around that. We launched it about a year ago and continuing to add cloud providers to that. >>Yeah, and I, I think it's also important just to build on what Jeff said is, is who can access that data is becoming increasingly important data is, is almost in it's. It is becoming a bit of a currency. There's a lot of value in data and securing that data is, is becoming over the years increasingly important. So it's, it's not like we built a problem or we created a solution for problem that didn't exist. It's gotten it's, it's been a problem for a while. It's getting exponentially bigger data is expanding and growing exponentially, and it's becoming increasingly important for organizations and companies to realize where my data sits, who can access it, what types of data needs to go and what type of clouds. And it's very, very aligned with multi-cloud because some data can sit in a, in a public cloud, which is fine, but some data needs to be secure. It needs to be resident within country. And so this is, this is what we're addressing through our partners. >>Yeah, I, yeah, I was just gonna add to that. I think there's a classification there there's data residency, and then there's data sovereignty. So residency is just about where is the data, which country is it in sovereignty is around who can access that data. And that's the critical aspect of, of data sovereignty who's got control and access to that data. And how do we make sure that all the controls are in place to make sure that only the right people can get access to that data? Yeah. >>So let's, let's sort of build from the ground up an example, and let's use Western Europe as an example, just because state to state in the United States, although California is about to adopt European standards for privacy in a, in a unique, in a unique, unique way, pick a country in, in Europe, I'm a service provider. I have an offering and that offering includes a stack of hardware and I'm running what we frequently refer to as the STDC or software defined data center stack. So I've got NEX and I've got vs N and I've got vSphere and I'm running and I have a cloud and you have all of the operational tools around that, and you can spin up VMs and render under applications there. And here we are within the borders of this country, what makes it a sovereign cloud at that? So at that point, is that a sovereign cloud or? >>No, not yet. Not it's close. I mean, you nailed, >>What's >>A secret sauce. You nailed the technology underpinning. So we've got 4,500 plus cloud provider partners around the world. Less than 10% of those partners are running the full STDC stack, which we've branded as VMware cloud verified. So the technology underpinning from our perspective is the starting point. Okay. For sovereignty. So they, they, they need that right. Technology. Okay. >>Verified is required for sovereign. Yes. >>Okay. Cloud verified is the required technology stack for sovereign. So they've got vSphere vs. A NSX in there. Okay. A lot of these partners are also offering a multitenant cloud with VMware cloud director on top of that, which is great. That's the starting point. But then we've, we've set a list of standards above and beyond that, in addition to the technology, they've gotta meet certain jurisdiction requirements, certain local compliance requirements and certifications. They've gotta be able to address the data re data residency requirements of their particular jurisdiction. So it's going above and beyond. But to your point, it does vary by country. >>Okay. So, so in this hypothetical example, this is this country. You a stand, I love it. When people talk about Stan, people talk about EMIA and you know, I, I love AMEA food. Isn't AIAN food. One. There's no such thing as a European until you have an Italian, a Britain, a German yep. In Florida arguing about how our beer and our coffee is terrible. Right. Right. Then they're all European. They go home and they don't like each other. Yeah. So, but let's just pretend that there's a thing called Europe. So this, so there's this, so we've got a border, we know residency, right. Because it physically is here. Yep. But what are the things in terms of sovereignty? So you're talking about a lot of kind of certification and validation, making sure that, that everything maps to those existing rules. So is, this is, this is a lot of this administrative and I mean, administrative in the, in the sort of state administrative terminology, >>I I'm let's build on your example. Yeah. So we were talking about food and obviously we know the best food in the world comes from England. >>Of course it does. Yeah. I, no doubt. I agree. I Don not get that. I do. I do do agree. Yeah. >>So UK cloud, fantastic partner for us. Okay. Whether they're one of our first sovereign cloud providers in the program. So UK cloud, they satisfied the requirements with the local UK government. They built out their cloud verified. They built out a stack specifically that enables them to satisfy the requirements of being a sovereign cloud provider. They have local data centers inside the UK. The data from the local government is placed into those data centers. And it's managed by UK people on UK soil so that they know the privacy, they know the security aspects, the compliance, all of that wrapped up on top of a secure SDDC platform. Okay. Satisfies the requirements of the UK government, that they are managing that data in a sovereign way that, that, that aligns to the jurisdictional control that they expect from a company like UK cloud. Well, >>I think to build on that, a UK cloud is an example of certain employees at UK, UK cloud will have certain levels of clearance from the UK government who can access and work on certain databases that are stored within UK cloud. So they're, they're addressing it from multiple fronts, not just with their hardware, software data center framework, but actually at the individual compliance level and individual security clearance level as to who can go in and work on that data. And it's not just a governmental, it's not a public sector thing. I mean, any highly regulated industry, healthcare, financial services, they're all gonna need this type of data protection and data sovereignty. >>Can this work in a hyperscaler? So you've got you, have, you have VMC AVS, right? GC V C >>O >>CVAs O CVS. Thank you. Can it be, can, can a sovereign cloud be created on top of physical infrastructure that is in one of those hyperscalers, >>From our perspective, it's not truly sovereign. If, if it's a United States based company operating in Germany, operating in the UK and a local customer or organization in Germany, or the UK wants to deploy workloads in that cloud, we wouldn't classify that as totally sovereign. Okay. Because by virtue of the cloud act in the United States, that gives the us government rights to request or potentially view some of that data. Yeah. Because it's, it's coming out of a us based operator data center sitting on foreign soil so that the us government has some overreach into that. And some of that data may actually be stored. Some of the metadata may reside back in the us and the customer may not know. So certain workloads would be ideally suited for that. But for something that needs to be truly sovereign and local data residency, that it wouldn't be a good fit. I think that >>Perspectives key thing, going back to residency versus sovereignty. Yeah. It can be, let's go to our UK example. It can be on a hyperscaler in the UK now it's resident in the UK, but some of the metadata, the profiling information could be accessible by the entity in the United States. For example, there now it's not sovereign anymore. So that's the key difference between a, what we view as a pro you know, a pure sovereign cloud play and then maybe a hyperscaler that's got more residency than sovereignty. >>Yeah. We talk a lot about partnerships. This seems to be a unique opportunity for a certain segment of partners yeah. To give that really is an opportunity for them to have a line of business established. That's unique from some of the hyperscale cloud providers. Yeah. Where, where sort of the, the modesty of your size might be an advantage if you're in a local. Yes. You're in Italy and you are a service provider. There sounds like a great fit, >>That's it? Yeah. You've always had the, the beauty of our program. We have 4,500 cloud providers and obviously not, all of them are able to provide a data, a sovereign cloud. We have 20 in the program today in, in the country. You you'd expect them to be in, you know, the UK, Italy, Italy, France, Germany, over in Asia Pacific. We have in Australia and New Zealand, Japan, and, and we have Canada and Latin America to, to dovetail, you know, the United States. But those are the people that have had these long term relationships with the local governments, with these regulated industries and providing those services for many, many years. It's just that now data sovereignty has become more important. And they're able to go that extra mile and say, Hey, we've been doing this pretty much, you know, for decades, but now we're gonna put a wrap and some branding around it and do these extra checks because we absolutely know that we can provide the sovereignty that's required. >>And that's been one of the beautiful things about the entire initiative is we're actually, we're learning a lot from our partners in these countries to Jeff's point have been doing this. They've been long time, VMware partners they've been doing sovereignty. And so collectively together, we're able to really establish a pretty robust framework from, from our perspective, what does data sovereignty mean? Why does it matter? And then that's gonna help us work with the customers, help them decide which workloads need to go and which type of cloud. And it dovetails very, very nicely into a multi-cloud that's a reality. So some of those workloads can sit in the public sector and the hyperscalers and some of 'em need to be sovereign. Yeah. So it's, it's a great solution for our customers >>When you're in customer conversations, especially as, you know, data sovereign to be is becomes a global problem. Where, who are you talking to? Are you talking to CIOs? Are you talking to chief data officers? I imagine this is a pretty senior level conversation. >>Yeah. I it's, I think it's all of the above. Really. It depends. Who's managing the data. What type of customer is it? What vertical market are they in? What compliance regulations are they are they beholden to as a, as an enterprise, depending on which country they're in and do they have a need for a public cloud, they may already be all localized, you know? So it really depends, but it, it could be any of those. It's generally I think a fair, fairly senior level conversation. And it's, it's, it's, it's consultancy, it's us understanding what their needs are working with our partners and figuring out what's the best solution for them. >>And I think going back to, they've probably having those conversations for a long time already. Yeah. Because they probably have had workloads in there for years, maybe even decades. It's just that now sovereignty has become, you know, a more popular, you know, requirements to satisfy. And so they've gone going back to, they've gone the extra mile with those as the trusted advisor with those people. They've all been working with for many, many years to do that work. >>And what sort of any examples you mentioned some of the highly regulated industries, healthcare, financial services, any customer come to mind that you think really articulates the value of what VMware's delivering through its service through its cloud provider program. That makes the obvious why VMware an obvious answer? >>Wow. I, I, I get there's, there's so many it's, it's actually, it's each of our different cloud providers. They bring their win wise to us. And we just have, we have a great library now of assets that are on our sovereign cloud website of those win wires. So it's many industries, many, many countries. So you can really pick, pick your, your choice. There. That's >>A good problem >>To have, >>To the example of UK cloud they're, they're really focused on the UK government. So some of them aren't gonna be referenced. Well, we may have indication of a major financial services company in Australia has deployed with AU cloud, one of our partners. So we we've also got some semi blind references like that. And, and to some degree, a lot of these are maintained as fairly private wins and whatnot for obvious security reasons, but, and we're building it and building that library up, >>You mentioned the number 4,500, a couple of times, you, you referencing VMware cloud provider partners or correct program partners. So VCP P yes. So 45, 4500 is the, kind of, is the, is the number, you know, >>That's the number >>Globally of our okay. >>Partners that are offering a commercial cloud service based at a minimum with vSphere and they're. And many of 'em have many more of our technologies. And we've got little under 10% of those that have the cloud verified designation that are running that full STDC, stack >>Somebody, somebody Talli up, all of that. And the argument has been made that, that rep that, that would mean that VMware cloud. And although some of it's on IAS from hyperscale cloud providers. Sure. But that, that rep, that means that VMware has the third or fourth largest cloud on the planet already right now. >>Right. Yep. >>Which is kind of interesting because yeah. If you go back to when, what 2016 or so when VMC was at least baned about yeah. Is that right? A lot of people were skeptical. I was skeptical very long history with VMware at the time. And I was skeptical. I I'm thinking, nah, it's not gonna work. Yeah. This is desperation. Sorry, pat. I love you. But it's desperation. Right. AWS, their attitude is in this transaction. Sure. Send us some customers we'll them. Yeah. Right. I very, very cynical about it. Completely proved me wrong. Obviously. Where did it go? Went from AWS to Azure to right. Yeah. To GCP, to Oracle, >>Oracle, Alibaba, >>Alibaba. Yep. Globally. >>We've got IBM. Yep. Right. >>Yeah. So along the way, it would be easy to look at that trajectory and say, okay, wow, hyperscale cloud. Yeah. Everything's consolidating great. There's gonna be five or six or 10 of these players. And that's it. And everybody else is out in the cold. Yeah. But it turns out that long tail, if you look at the chart of who the largest VCP P partners are, that long tail of the smaller ones seem to be carving out specialized yes. Niches where you can imagine now, at some point in the future, you sum up this long tail and it becomes larger than maybe one of the hyperscale cloud providers. Right. I don't think a lot of people predicted that. I think, I think people predicted the demise of VMware and frankly, a lot of people in the VMware ecosystem, just like they predicted the demise of the mainframe. Sure. The storage area network fill in the blank. I >>Mean, Jeff and I we've oh yeah. We've been on the, Jeff's been a little longer than I have, but we've been working together for 10 plus years on this. And we've, we've heard that many times. Yeah. Yeah. Our, our ecosystem has grown over the years. We've seen some consolidation, some M and a activity, but we're, we're not even actively recruiting partners and it's growing, we're focused on helping our partners gain more, share internally, gain, more share at wallet, but we're still getting organic growth in the program. Really. So it, it shows, I think that there is value in what we can offer them as a platform to build a cloud on. >>Yeah. What's been interesting is there's there's growth and there's some transition as well. Right? So there's been traditional cloud providers. Who've built a cloud in their data center, some sovereign, some not. And then there's other partners that are adopting VCP P because of our SA. So we've either converted some technology from product into SA or we've built net new SA or we've acquired companies that have been SA only. And now we have a bigger portfolio that service providers, cloud providers, managed service providers are all interested in. So you get resellers channel partners. Who've historically been doing ELAs and reselling to end customers. They're transitioning their business into doing recurring revenue and the only game in town where you really wanna do recurring revenues, VCP P. So our ecosystem is both growing because our cloud providers with their data center are doing more with our customers. And then we're adding more managed service providers because of our SA portfolio. And that, that, that combo, that one, two punch is creating a much bigger VCP P ecosystem overall. >>Yeah. >>Impressive. >>Do you think we have a better idea of what sovereign cloud means? Yes. I think we do. >>It's not Royal. >>It's all about royalty, >>All royalty. What are some of the things Jeff, as we look on the horizon, obviously seven to 10,000 people here at, at VMwares where people really excited to be back. They want to hear it from VMware. They wanna hear from its partner ecosystem, the community. What are some of the things that you think are on the horizon where sovereign cloud is concerned that are really opportunities yeah. For businesses to get it right. >>Yeah. We're in the early days of this, I think there's still a whole bunch of rules, regulatory laws that have not been defined yet. So I think there's gonna be some more learning. There's gonna be some top down guidance like Gaia X in Europe. That's the way that they're defining who gets access and control over what data and what's in. And what's out of that. So we're gonna get more of these Gaia X type things happening around the world, and they're all gonna be slightly different. Everyone's gonna have to understand what they are, how to interpret and then build something around them. So we need to stay on top of that, myself and Jay, to make sure that we've got the right cloud providers in the right space to capitalize on that, build out the sovereign cloud program over time and make sure that what they're building to support aligns with these different requirements that are out there across different countries. So it's an evolving landscape. That's >>Yeah. And one of the things too, we're also doing from a product perspective to better enable partners to, to address these sovereign cloud workloads is where we have, we have gaps maybe in our portfolio is we're partner partnering with some of our ISVs, like a, Konic like a Forex vem. So we can give our partners object storage or ransomware protection to add on to their sovereign cloud service, all accessible through our cloud director consult. So we're, we're enhancing the program that way. And to Jeff's point earlier, we've got 20 partners today. We're hoping to double that by the end of our fiscal year and, and just take a very methodical approach to growth of the program. >>Sounds great guys, early innings though. Thank you so much for joining Dave and me talking about what software and cloud is describing it to us, and also talking about the difference between that data residency and all the, all of the challenges and the, in the landscape that customers are facing. They can go turn to VMware and its ecosystem for that help. We appreciate your insights and your time. Guys. Thank >>You >>For >>Having us. Our >>Pleasure. Appreciate it >>For our guests and Dave Nicholson. I'm Lisa Martin. You've been watching the cube. This is the end of day, two coverage of VMware Explorer, 2022. Have a great rest of your day. We'll see you tomorrow.

(upbeat music) >> Good afternoon. theCUBE is live at VMware Explorer. Lisa Martin here in San Francisco with Dave Nicholson. This is our second day of coverage talking all things VMware and it's ecosystem. We're excited to welcome from DXC Technology, James Bion, Hybrid Cloud and Multi Cloud Offering manager to have a conversation next. Welcome to the program. >> Thank you very much. >> Welcome. >> Talk to us a little bit about before we get into the VMware partnership, what's new at DXC? What's going on? >> So DXC is really evolving and revitalizing into more of a cloud orientated company. So we're already driving change in our customers at the moment. We take them on that cloud journey, but we're taking them in the right way, in a structured mannered way. So we are really excited about it, we're kicking off our Cloud First type, Cloud Right sort of story and helping customers on that journey. >> Yesterday in the keynote, VMware was talking about customers are on this Cloud chaos phase, they want to get to Cloud Smart. You're saying they want to get to Cloud Right. Talk to us about what DXC Cloud Right is, what does it mean? What does it enable businesses to achieve? >> That's a very good question. So DXC has come up with this concept of Cloud Right, we looked at it from a services and outcome. So what do customers want to achieve? And how do we get it successfully? This is not a technology conversation, this is about putting the right workloads at the right place, at the right time, at the right cost to get the right value for your business. It's not about just doing it for the sake of doing it, okay. There's a lot of changes it's not technology only you've got to change how people operate. You've got to work through the organizational change. You need to ensure that you have the right security in place to maintain it. And it's about value, really about value proposition. So we don't just focus on cost, we focus on operations of it, we focus on security of it. We focus on ensuring the value proposition of it and putting not just for one Cloud, it's the right place. Big focus on Hybrid and Multi Cloud solutions in particular, we're very excited about what's happening with VMware Cloud on maybe AWS or et cetera because we see there a real dynamic change for our customers where they can transition across to the right Cloud services, at the right time, at the right place, but minimal disruption to the actual operation of their business. Very easy to move a workload into that place using the same skilled resources, the same tools, the same environment that you have had for many years, the same SLAs. Customers don't want a variance in their SLAs, they just want an outcome at a right price and the right time. >> Right, what are some of the things going on with the VMware partnership and anything you know, here we are at this the event called the theme is "The Center of the Multi Cloud Universe", which I keep saying sounds like a Marvel movie, I think there needs to be some superheroes here. But how is DXC working with VMware to help customers that are in Multi Cloud by default, not by design? >> That's a very good one. So DXC works jointly with VMware for more than a thousand clients out there. Wide diversity of different clients. We go to market together, we work collaboratively to put roadmaps in place for our clients, it's a unified team. On top of that, we have an extremely good VMware practice, joint working VMware team working directly with DXC dedicated resources and we deliver real value for clients. For example, we have a customer experience zone, we have a customer innovation zone so we can run proof of concepts on all the different VMware technologies for customers. If they want to try something different, try and push the boundaries a little bit with the VMware products, we can do that for them. But at the end of the day we deliver outcome based services. We are not there to deliver a piece of software, but a technology which show the customer the value of the service that they've been receiving within that. So we bring the VMware fantastic technologies in and then we bring the DXC managed services which we do so well and we look after our customers and do the right thing for our customers. >> So what does the go-to market strategy look like from a DXC perspective? We say that there are a finite number of strategic seats at the customer table. DXC has longstanding deep relationships with customers, so does VMware and probably over a shorter period of time, the Hyper scale Cloud Providers. How are you approaching these relationships with customers? Is it you bringing in your friends from the cloud? Is it the cloud bringing in their friend DXC? What does it look like? >> So we have relationships with all of them, but were agnostic. So we are the people who bring it all together into that unified platform and services that the customers expect. VMware will bring us certainly to the table and we'll bring VMware to the table. Equally, we work very collaboratively with all the cloud providers and we work in deals together. They bring us deals, we bring them deals. So it works extremely well from that perspective, but of course it's a multi-cloud world these days. We don't just deal with one cloud provider, we'll normally have all of the different services to find the right place for our customers. >> Now, one thing that that's been mentioned from DXC is this idea that Cloud First which has been sort of a mantra that scores you points if you're a CIO lately, maybe that's not the best way to wake up in the morning. Why not saying, Cloud First? >> So we have a lot of clients who who've tried that Cloud First journey and they've aggressively taken on migration of workloads. And now that they've settled in a few of those they're discovering maybe the ROI isn't quite what they expected it was going to be. That transformation takes a long time, a very long time. We've seen some of the numbers around averaging a hundred apps can take up to seven years to transition and transform, that's a long time. It makes you almost less agile by doing the transformation quite ironically. So DXC's Cloud Right program really helps you to ensure that you assess those workloads correctly, you target the ones that are going to give you the best business value, possibly the best return on investment using our Cloud and advisory practice to do that. And then obviously off the back of that we've got our migration teams and our run services and our application modernization factories and our application platforms for that. So DXC Cloud Right can certainly help our customers on that journey and get that sort of Hybrid Multi Cloud solution that suits their particular outcomes, not just one Cloud provider. >> So Cloud Right isn't just Cloud migration? >> No. >> People sometimes confuse digital transformation with Cloud migration. >> Correct. >> So to be clear Cloud Right and DXC has the ability to work with customers on not just, oh, here, this is how we box it up and ship it out, but what makes sense to box up and ship out. >> Correct, and it's all about that whole end to end life cycle. Remember, this is not just a technology conversation, this is an end to end business conversation. It's the outcomes are important, not the technology. That's why you have good partners like DXC who will help you on that technology journey. >> Let's talk about in the dynamics of the market the last couple of years, we saw so many customers in every industry race to the Cloud, race to digitally transform. You bring up a good point of people interchangeably talking about digital transformation, Cloud migration, but we saw the massive adoption of SaaS technologies. What are you seeing? Are you seeing customers in that sort of Cloud chaos as VMware calls it? That you're coming in with the Cloud Right approach saying, let's actually figure out, you may have done this because of the pandemic maybe it was accelerated, you needed to facilitate collaboration or whatnot, but actually this is the right approach. Are you seeing a lot of customers in that situation? >> We are certainly seeing some customers going into that chaos world. Some of them are still in the early stages of their journey and are taking a more cautious step towards in particular, the companies that would die on systems to be up available all the time. Others have gone too far, the other are in extreme are in the chaos world. And our Cloud Right program will certainly help them to pull their chaos back in, identify what workloads are potentially running in the wrong place, get the framework in place for ensuring that security and governance is in place. Ensuring that we don't have a cost spend blowout in particular, make sure that security is key to everything that we do and operations is key to everything we do. We have our own intelligent Platform X, it's called, our service management platform which is really the engine that sits behind our delivery mechanism. And that's got a whole lot of AI analytics engines in there to identify things and proactively identify workload placements, workload repairs, scripting, and hyper automation behind that too, to keep available here and there. And that's really some of our Cloud Right story, it's not just sorting out the mess, it's sorting out and then running it for you in the right way. >> So what does a typical, a customer engagement look like for a customer in that situation? >> So we would obviously engage our client right advisory team and they would come in and sit down with your application owners, sit down with the business units, identify what success needs to look like. They do all the discovery, they'll run it through our engines to identify what workloads are in the right place, should go to the right place. Just 'cause you can do something doesn't mean you should do something and that's an important thing. So we will come back with that and say, this is where I think your cloud roadmap journey should be. And obviously that takes an intuitive process, but we then can pick off the key topics early at the right time and that low hanging fruit that's really going to drive that value for the customer. >> And where are your customer conversations these days? I mean from a Cloud perspective, digital transformation, we're seeing everything escalate up the C-suite? Are you engaging the executives in this conversation so that they really want to facilitate, let's do things the right way that's the most efficient that allows us as a business to do what we're best at? >> So where we've seen programs fail is where we don't have executive leadership and brought in from day one. So if you don't have that executive and business driver and business leadership, then you're definitely not going to be successful. So to answer your question, yes, of course we are, but we also working directly with the IT departments as well. >> So you just brought up an insight executive alignment, critically important. Based on what you've experienced in the real world, contrast that with the sort of message to the world that we hear constantly about Cloud and IT, what would be the most shocking thing that you can share with us that people might not be aware of? It's like what shocks you the most about the disconnect between what everybody talks about and the reality on the ground? Don't name any names of anyone, but give us an example of the like, this is what's really going on. >> So, we certainly are seeing that big sort of move into Cloud quickly, okay. And then the big bill shock comes and just moving a workload across doesn't mean you're in Cloud, it's a transition and transformation to the SaaS and power services, it's where you get your true value out of cloud. So the concept that just 'cause it's in Cloud it's cheap is not always the case. Doing it right in Cloud is definitely going to have some cost value, but it's going to bring other additional values to their business. It's going to give them agility, it's going to give them resilience. So if you look at all three of those platforms cost, agility, and resilience and live across all three of those, then you're definitely going to get the best outcomes. And we've certainly seen some of those where they haven't taken all of those into consideration, quite often it's cost is what drives it, not the other two. And if you can't keep operations up working efficiently then you are in a lot of trouble. >> So Cloud wrong comes with sticker shock. >> It certainly does. >> What's on the horizon for DXC? >> We're certainly seeing a big drive towards apps modernization and certainly help our customers on that journey. DXC is definitely a Cloud company, may that be on Hybrid Cloud, Private Cloud, Public Cloud, DXC is certainly leading that edge and pushing it forward. >> Excellent, James, thank you so much for joining us on the program today talking about what Cloud Right is, the right approach, how you're helping customers really get to that right approach with the people, the processes, and the technology. We appreciate your time. >> Thank you very much. >> For our guest and Dave Nicholson, I'm Lisa Martin. You're watching theCUBE live from VMware Explorer, 2022. Our next guest joins us momentarily so don't change the channel. (upbeat music)

foreign greetings from San Francisco thecube is live this is our second day of wall-to-wall coverage of VMware Explorer 2022. Lisa Martin and Dave Nicholson here we're going to be talking with some ladies from Rackspace next please welcome Adriana Bustamante VP of strategic alliances and Hannah Deuce director of strategic alliances from Rackspace it's great to have you on the program thank you so much for having us good afternoon good morning is it lunchtime already almost almost yes and it's great to be back in person we were just talking about the keynote yesterday that we were in and it was standing room only people are ready to be back they're ready to be hearing from VMware it's ecosystem its Partners it's Community yes talk to us Adriana about what Rackspace is doing with Dell and VMware particularly in the healthcare space sure no so for us Partnerships are a big foundation to how we operate as a company and um and I have the privilege of doing it for over over 16 years so we've been looking after the dell and VMware part partnership ourselves personally for the last three years but they've been long-standing partners for for us and and how do we go and drive more meaningful joint Solutions together so Rackspace you know been around since since 98 we've seen such an evolution of coming becoming more of this multi-cloud transformation agile Global partner and we have a lot of customers that fall in lots of different verticals from retail to public sector into Healthcare but we started noticing and what we're trying trying to drive as a company is how do we drive more specialized Solutions and because of the pandemic and because of post-pandemic and everyone really trying to to figure out what the new normal is addressing different clients we saw that need increasing and we wanted to Rally together with our most strategic alliances to do more Hannah talk about obviously the the pandemic created such problems for every industry but but Healthcare being front and center it still is talk about some of the challenges that Healthcare organizations are coming to Rackspace going help yeah common theme that we've heard from some of our large providers Healthcare Providers has been helped me do more with less which we're all trying to do as we navigate The New Normal but in that space we found the opportunity to really leverage some of our expertise long-term expertise and that the talent and the resource pool that we had to really help in a some of the challenges that are being faced at a resource shortage Talent shortage and so Rackspace is able to Leverage What what we've done for many many years and really tailor it to the outcomes that Health Care Providers are needing nowadays that more with less Mantra runs across the gamut but a lot of it's been helped me modernize helped me get to that next phase I can't I can't I don't have the resources to DIY it myself anymore I need to figure out a more robust business continuity program and so helping with business continuity Dr you know third copies of just all all this data that's growing so it's not just covered pandemic driven but it's that's definitely driving the the need and the requirement to modernize so much quicker it's interesting that you mentioned rackspace's history and expertise in doing things and moving that forward and leveraging that pivoting focusing on specific environments to create something net new we've seen a lot of that here if you go back 10 years I don't know if that's the perfect date to go back to but if you go back 10 years ago you think about VMware where would we have expected VMware to be in this era of cloud we may have thought of things very very differently differently Rackspace a Pioneer in creating off-premises hey we will do this for you didn't even really call it Cloud at the time right but it was Cloud yeah and so the ability for entities like Rackspace like VMware we had a NetApp talking to us about stuff they're doing in the cloud 10 years ago if you I would say no they'd be they'll be gone they'll be gone so it's really really cool to see Rackspace making this transition and uh you know being aware of everything that's going on and focusing on the best value proposition moving forward I mean am I am I you know do I sound like somebody who would who would fit into the Rackspace culture right now or do I not get it yes you sound like a rocker we'll make you an honorary record that's what we call a Rackspace employees yes you know what we've noticed too and is budgets are moving those decision makers are moving so again 10 years ago just like you said you would be talking to sometimes a completely different Persona than we do than we do today and we've seen a shift more towards that business value we have a really unique ability to bring business and Technical conversations together I did a lot of work in the past of working with a lot of CMO and and digital transformation companies and so helping bring it and business seeing the same and how healthcare because budgets are living in different places and even across the board with Rackspace people are trying to drive more business outcomes business driven Solutions so the technical becomes the back end and really the ingredients to make all of that all of that happen and that's what we're helping to solve and it's a lot it's very fast paced everyone wants to be agile now and so they're leaning on us more and more to drive more services so if you've seen Rackspace evolve we're driving more of that advisement and those transformation service type discussions where where our original history was DNA was very much always embedded in driving a great experience now they're just wanting more from us more services help us how help us figure out the how Adriana comment on the outcomes that you're helping Healthcare organizations achieve as as we as we it's such a relatable tangible topic Healthcare is Right everybody's everybody's got somebody who's sick or you've been sick or whatnot what are some of those outcomes that we can ex that customers can expect to achieve with Rackspace and VMware oh great great question so very much I can't mentioned earlier it's how do I modernize how do I optimize how do I take the biggest advantage of the budgets and the landscape that I have I want to get to the Cloud we need to help our patients and get access to that data is this ready to go into the cloud is this not ready to go into the cloud you know how do we how do we help make sure we're taking care of our patients we're keeping things secure and accessible you know what else do you think is coming up yeah and one specific one uh sequencing genetic sequencing and so we've had this come up from a few different types of providers whether it's medical devices that they may provide to their end clients and an outcome that they're looking for is how do we get how do we leverage um here's rip here's what we do but now we have so many more people we need to give this access to we need them to be able to have access to the sequencing that all of this is doing all of these different entities are doing and the outcome that they're trying to get to to is more collaboration so so that way we can speed up in the face of a pandemic we can speed up those resolutions we could speed up to you know whether it's a vaccine needed or something that's going to address the next thing that might be coming you know um so that's a specific one I've heard that from a handful of different different um clients that that we work with and so trying to give them a Consolidated not trying to we are able to deliver them a Consolidated place that their application and tooling can run in and then all of these other entities can safely and securely access this data to do what they're going to do in their own spaces and then hopefully it helps the betterment of of of us globally like as humans in the healthcare space we all benefit from this so leveraging the technology to really drive a valuable outcome helps us all so so and by the way I like trying to because it conveys the proper level of humility that we all need to bring to this because it's complicated and anybody who looks you in the eye it pretends like they know exactly how to do it you need to run from those people no it is and and look that's where our partners become so significant we we know we're Best in Class for specific things but we rely on our Partnerships with Dell and VMware to bring their expertise to bring their tried and true technology to help us all together collectively deliver something good technology for good technology for good it is inherently good and it's nice when it's used for goodness it's nice when it's yeah yeah talk about security for a second you know we've seen the threat landscape change dramatically obviously nobody wants to be the next breach ransomware becoming a household term it's now a matter of when we get a head not F where has security gone in terms of conversations with customers going help us ensure that what we're doing is delivering data access to the right folks that need it at the right time in real time in a secure fashion no uh that's another good question in hot and burning so you know I think if we think about past conversations it was that nice Insurance offering that seemed like it came at a high cost if you really need it I've never been breached before um I'll get it when I when I need it but exactly to your point it's the win and not the if so what we're finding and also working with a nice ecosystem of Partners as well from anywhere from Akamai to cloudflare to BT it's how do we help ensure that there is the security as Hannah mentioned that we're delivering the right data access to the right people and permissions you know we're able to help meet multitude of compliance and regulations obviously health care and other regulated space as well we look to make sure that from our side of the house from the infrastructure that we have the right building blocks to help them Reach those compliance needs obviously it's a mutual partnership in maintaining that compliance and that we're able to provide guidance and best practices on to make sure that the data is living in a secure place that the people that need access to it get it when they when they need it and monitor those permissions and back to your complexity comment so more and more complex as we are a global global provider so when you start to talk to our teams in the UK and our our you know clients there specializing um kind of that Sovereign Cloud mentality of hey we need to have um we need to have a cloud that is built for the specific needs that reside within Healthcare by region so it's not just even I mean you know we're we're homegrown out of San Antonio Texas so like we know the U.S and have spent time here but we've been Global for many years so we just get down into the into the nitty-gritty to customize what's needed within each region well Hannah is that part of the Rackspace value proposition at large moving forward because frankly look if I if I want if I want something generic I can I can swipe credit card and and fire up some Services sure um moving forward this is something that is going to more characterize the Rackspace experience and I and I understand that the hesitancy to say hey it's complicated it's like I don't want to hear that I want to hear that it's easy it's like well okay we'll make it easy for you yes but it's still complicated is that okay that's the honest that's that's the honest yeah that's why you need help right that's why we need to talk about that because people people have a legitimate question why Rackspace yep and we don't I don't want to put you on the spot but no yeah but why why Rackspace you've talked a little bit about it already but kind of encapsulate it oh gosh so good good question why Rackspace it's because you can stand up [Laughter] well you can you do it there's many different options out there um and if I had a PowerPoint slide I'd show you this like lovely web of options of directions that you could go and what is Rackspace value it's that we come in and simplify it because we've had experience with this this same use case whatever somebody is bringing forward to us is typically something we've dealt with at numerous times and so we're repeating and speeding up the ability to simplify the complex and to deliver something more simplified well it may be complex within us and we're like working to get it done the outcome that we're delivering is is faster it's less expensive than dedicating all the resources yourself to do it and go invest in all of that that we've already built up and then we're able to deliver it in a more simplified manner it's like the duck analogy the feet below the water yes exactly and a lot of expertise as well yes a lot talk a little bit about the solution that that Dell VMware Rackspace are delivering to customers sure so when we think about um Healthcare clouds or Cloud specific to the healthcare industry you know there's some major players within that space that you think epic we'll just use them as an example this can play out with others but we are building out a custom or we have a custom clouds able to host epic and then provide services up through the Epic help application through partnership so that is broadening the the market for us in the sense that we can tailor what the what that end and with that healthcare provider needs uh do they do they have the expertise to manage the application okay you do that and then we will build out a custom fit Cloud for that application oh and you need all the adjacent things that come with it too so then we have reference architecture you know built out already to to tailor to whatever all those other 40 80 90 hundreds of applications that need to come with that and then and then you start to think about Imaging platforms so we have Imaging platforms available for those specific needs whether it's MRIs and things like that and then the long-term retention that's needed with that so all of these pieces that build out a healthcare ecosystem and those needs we've built those we've built those out and provide those two to our clients yesterday VMware was talking about Cloud chaos yes and and it's true you talk about the complexity and Dave talks about it too like acknowledging yes this is a very complex thing to do yeah there's just so many moving parts so many Dynamics so many people involved or lack thereof people they they then talked about kind of this this the goal of getting customers from cloud chaos to Cloud smart how does that message resonate with Rackspace and how are you helping customers get from simplifying the chaos to eventually get to that cloud smart goal so a lot of it I I believe is with the power of our alliances and I was talking about this earlier we really believe in creating those powerful ecosystems and Jay McBain former for Forester analyst talks about you know the people are going to come ahead really are serve as that orchestration layer of bringing everybody together so if you look at all of that cloud chaos and all of the different logos and the webs and which decisions to make you know the ones that can help simplify that bring it all together like we're going to need a little bit of this like baking a cake in some ways we're going to need a little bit of sugar we'll need this technology this technology and whoever is able to put it together in a clean and seamless way and as Hannah said you know we have specific use cases in different verticals Healthcare specifically and talking from the Imaging and the Epic helping them get hospitals and different you know smaller clinics get to the edge so we have all of the building blocks to get them what they need and we can't do that without Partners but we help simplify those outcomes for those customers yep so there's where they're Cloud smart so then they're like I want I want to be agile I want to work on my cost I want to be able to leverage a multi-cloud fashion because some things may may inherently need to be on Azure some things we inherently need to be on VMware how do we make them feel like they still have that modernized platform and Technology but still give the secure and access that they need right yeah we like to think of it as are you multi-cloud by accident or multi-cloud by Design and help you get to that multi-cloud by Design and leveraging the right yeah the right tools the right places and Dell was talking about that just that at Dell Technologies world just a couple months ago that most most organizations are multi-cloud by default not designed are you seeing any customers that are are able or how are you able to help customers go from that we're here by default for whatever reason acquisition growth.oit line of business and go from that default to a more strategic multi-cloud approach yes it takes planning and commitment you know you really need the business leaders and the technical leaders bought in and saying this is what I'm gonna do because it is a journey because exactly right M A is like inherited four different tools you have databases that kind of look similar but they're a little bit different but they serve four different things so at Rackspace we're able to help assess and we sit down with their teams we have very amazing rock star expertise that will come in and sit with the customers and say what are we trying to drive for it let's get a good assessment of the landscape and let's figure out what are you trying to get towards in your journey and looking at what's the best fit for that application from where it is now to where it is where it wants to be because we saw a lot of customers move to the cloud very quickly you know they went Cloud native very fast some of it made sense retailers who had the spikiness that completely made sense we had some customers though that we've seen move certain workloads they've been in the public Cloud now for a couple years but it was a static website it doesn't make as much sense anymore for certain things so we're able to help navigate all of those choices for them so it's interesting you just you just said something sort of offhand about having experts having them come in so if I am a customer and I have some outcome I want to achieve yes the people that I'm going to be talking to from Rackspace or from Rackspace and the people from Rackspace who are going to be working with the actual people who are deploying infrastructure are also Rackspace people so the interesting contrast there between other circumstances oftentimes is you may have a Global Systems integrator with smart people representing what a cloud provider is doing the perception if they try to make people perceive that okay everybody is working in lockstep but often there are disconnects between what the real capabilities are and what's being advertised so is that I mean I I know it's like a leading question it's like softball get your bats out but I mean isn't that an advantage you've got a single you know the saying used to be uh one throat to show now it's one back to pack because it's kind of Contour friendly yeah yeah but talk about that is that a real Advantage it does it really helps us because again this is our our this is our expertise this is where we where we live we're really close to the infrastructure we're great at the advisement on it we can help with those ongoing and day two management and Opera in operations and what it feels like to grow and scale so we lay this out cleanly and and clearly as possible if this is where we're really good we can we can help you in these areas but we do work with system integrators as well and part of our partner Community because they're working on sometimes the bigger overall Transformations and then we're staying look we understand this multi-cloud but it helps us because in the end we're doing that end to end for for them customer knows this is Rackspace and on hand and we we really strive to be very transparent in what it is that we want to drive and outcomes so sometimes at the time where it's like we're gonna talk about a certain new technology Dell might bring some of their Architects to the table we will say here is Dell with us we're doing that actively in the healthcare space today and it's all coming together but you know at the end of the day this is what Rackspace is going to drive and deliver from an end to end and we tap those people when needed so you don't have to worry about picking up the phone to call Dell or VMware so if I had worded the hard-hitting journalist question the right way it would have elicited the same responses that yeah yeah it drives accountability at the end of the day because what we advised on what we said now we got to go deliver yeah and it's it's all the same the same organization driving accountability so from a customer perspective they're engaging Rackspace who will then bring in dell and VMware as needed as we find the solution exactly we have all of the certification I mean the team the team is great on getting all of the certs because we're getting to handling all of the level one level two level three business they know who to call they have their dedicated account teams they have engagement managers that help them Drive what those bigger conversations are and they don't have to worry about the experts because we either have it on hand or we'll pull them in as needed if it's the bat phone we need to call awesome ladies thank you so much for joining Dave and me today talking about what Rackspace is up to in the partner ecosystem space and specifically what you're doing to help Healthcare organizations transform and modernize we appreciate your insights and your thoughts yeah thank you for having us thank you pleasure for our guests and Dave Nicholson I'm Lisa Martin you're watching thecube live from VMware Explorer 2022 we'll be back after a short break foreign [Music]

>>Welcome back everyone to the cubes coverage here live in San Francisco for VMware Explorer. Formerly got it. World. We've been to every world since 2010. Now is VMware Explorer. I'm John furier host with Dave ante with Dave lium here. He's the chief cloud strategy officer at Deloitte. Welcome to the cube. Thanks for coming on. Appreciate your time. >>Thanks for having me. It's >>Epic keynote today on stage all seven minutes of your great seven minutes >>Performance discussion. Yes. Very, very, very, very quick to the order. I brought everybody up to speed and left. >>Well, Dave's great to have you on the cube one. We follow your work. We've been following for a long time. Thank you. A lot of web services, a lot of SOA, kind of in your background, kind of the old web services, AI, you know, samples, RSS, web services, all that good stuff. Now it's, it's now we're in kind of web services on steroids. Cloud came it's here. We're NextGen. You wrote a great story on Metacloud. You've been following the Supercloud with Dave. Does VMware have it right? >>Yeah, they do. Because I'll tell you what the market is turning toward. Anything that sit above and between the clouds. So things that don't exist in the hyperscaler, things that provide common services above the cloud providers are where the growth's gonna happen. We haven't really solved that problem yet. And so there's lots of operational aspects, security aspects, and the ability to have some sort of a brokering service that'll scale. So multi-cloud, which is their strategy here is not about cloud it's about things that exist in between cloud and making those things work. So getting to another layer of abstraction and automation to finally allow us to make use out of all these hyperscaler services that we're signing on today. Dave, >>Remember the old days back in the eighties, when we were young bucks coming into the business, the interoperability wave was coming. Remember that? Oh yeah, I got a deck mini computer. I got an IBM was gonna solve that unex. And then, you know, this other thing over here and lands and all and everything started getting into this whole, okay. Networking. Wasn't just coax. You started to see segment segments. Interoperability was a huge, what 10 year run. It feels like that's kind of like the vibe going on here. >>Yeah. We're not focused on having these things interop operate onto themselves. So what we're doing is putting a layer of things which allows them to interop operate. That's a different, that's a different problem to solve. And it's also solvable. We were talking about getting all these very distinct proprietary systems to communicate one to another and interate one to another. And that never really happened. Right? Cause you gotta get them to agree on interfaces and protocols. But if you put a layer above it, they can talk down to whatever native interfaces that are there and deal with the differences between the heterogeneity and abstract yourself in the complexity. And that's, that's kind of the different that works. The ability to kind of get everybody, you know, clunk their heads together and make them work together. That doesn't seem to scale couple >>And, and people gotta be motivated for that. Not many people might not >>Has me money. In other words has to be a business for them in doing so. >>A couple things I wanna follow up on from work, you know, this morning they used the term cloud chaos. When you talk to customers, you know, when they have multiple clouds, do they, are they saying to you, Hey, we have cloud chaos are, do they have cloud chaos? And they don't know it or do they not have cloud chaos? What's the mix. >>Yeah. I don't think the word chaos is used that much, but they do tell me they're hitting a complexity wall, which you do here out there as a term. So in other words, they're getting to a point where they can't scale operations to deal with a complexity and heterogeneity that they're, that they're bringing into the organization because using multiple clouds. So that is chaotic. So I guess that, you know, it is another way to name complexity. So there's so many services are moving from a thousand cloud services, under management to 3000 cloud services under management. They don't have the operational team, the skill, skill levels to do it. They don't have the tooling to do it. That's a wall. And you have to be able to figure out how to get beyond that wall to make those things work. So >>When, when we had our conversation about Metacloud and Supercloud, we we've, I think very much aligned in our thinking. And so now you've got this situation where you've got these abstraction layers, but, and there, but my question is, are we gonna have multiple abstraction layers? And will they talk to each other or are standards emerging? Will they be able to, >>No, we can't have multiple abstraction layers else. We just, we don't solve the problem. We go from complexity of exists at the native cloud levels to complexity of exists, that this thing we're dealing with to deal with complexity. So if you do that, we're screwing up. We have to go back and fix it. So ultimately this is about having common services, common security, layers, common operational layers, and things like that that are really reduced redundancy within the system. So instead of having a, you know, five different security layers and five different cloud providers, we're layering one and providing management and orchestration capabilities to make that happen. If we don't do that, we're not succeeding. >>What do you think about the marketplace? I know there's a lot of things going on that are happening around this. Wanna get your thoughts on obviously the industry dynamics, vendors preserving their future. And then you've got customers who have been leveraging the CapEx, goodness of say Amazon and then have to solve their whole distributed environment problem. So when you look at this, is it really solving? Is it is the order of operations first common layer abstraction because you know, it seems like the vendor, I won't say desperation move, but like their first move is we're gonna be the control plane or, you know, I think Cisco has a vision in their mind that no, no we're gonna have that management plane. I've heard a lot of people talking about, we're gonna be the management interface into something. How do you see that playing out? Because the order of operations to do the abstraction is to get consensus, right, right. First not competition. Right. So how do you see that? What's your reaction to that? And what's your observation. >>I think it's gonna be tough for the people who are supplying the underlying services to also be the orchestration and abstraction layers, because they're, they're kind of conflicted in making that happen. In other words, it's not in their best interest to make all these things work and interoperate one to another, but it's their best interest to provide, provide a service that everybody's going to leverage. So I see the layers here. I'm certainly the hyperscalers are gonna play in those layers and then they're welcome to play in those layers. They may come up with a solution that everybody picks, but ultimately it's about independence and your ability to have an objective way of, of allowing all these things to communicate together and driving this, driving this stuff together, to reduce the complexity again, to reduce. >>So a network box, for instance, maybe have hooks into it, but not try to dominate it >>Or that's right. Yeah, that's right. I think if you're trying to own everything and I get that a lot when I write about Supercloud and, and Metacloud they go, well, we're the Metacloud, we're the Supercloud you can't be other ones. That's a huge problem to solve. I know you don't have a solution for that. Okay. It's gonna be many different products to make that happen. And the reality is people who actually make that work are gonna have to be interdependent independent of the various underlying services. They're gonna, they can support them, but they really can't be them. They have to be an interate interop. They have to interoperate with those services. >>Do you, do you see like a w three C model, like the worldwide web consortium, remember that came out around 96, came to the us and MIT and then helped for some of those early standards in, in, in the internet, not DNS, but like the web, but DNS was already there and internet was already there, but like the web standards HTML kind of had, I think wasn't really hardcore get you in the headlock, but at least it was some sort of group that said, Hey, intellectually be honest, you see that happening in this area. >>I hope not. And here's >>Why not. >>Yeah. >>Here's, here's why the reality is is that when these consortiums come into play, it freezes the market. Everybody waits for the consortium to come up with some sort of a solution that's gonna save the world. And that solution never comes because you can't get these organizations through committee to figure out some sort of a technology stack that's gonna be working. So I'd rather see the market figure that out. Not a consortium when >>I, you mean the ecosystem, not some burning Bush. >>Yeah. Not some burning Bush. And it just hasn't worked. I mean, if it worked, it'd be great. And >>We had a, an event on August 9th, it was super cloud 22 and we had a security securing the super cloud panel. And one of my was a great conversation as you remember, John, but it was kind of depressing in that, like we're never gonna solve this problem. So what are you seeing in the security front? You know, it seems to like that's a main blocker to the Metacloud the Supercloud >>Yeah. The reality is you can't build all the security services in, in the Metacloud. You have to basically leverage the security services on the native cloud and leverage them as they exist. So this idea that we're gonna replace all of these security services with one layer of abstraction, that's gonna provide the services. So you don't need these underlying security systems that won't work. You have to leverage the native security systems, native governance, native operating interfaces, native APIs of all the various native clouds using the terms that they're looking to leverage. And that's the mistake. I think people are going to make, you don't need to replace something that's working. You just may need to make it easier to >>Use. Let's ask Dave about the, sort of the discussion that was on Twitter this morning. So when VMware announced their, you know, cross cloud services and, and the whole new Tansu one, three, and, and, and, and aria, there was a little chatter on Twitter basically saying, yeah, but VMware they'll never win the developers. And John came and said, well, hi, hang on. You know, if, if you've got open tools and you're embracing those, it's really about the ops and having standards on the op side. And so my question to you is, does VMware, that's >>Not exactly what I said, but close enough, >>Sorry. I mean, I'm paraphrasing. You can fine tune it, but, but does VMware have to win the developers or are they focused on kind of the right areas that whole, you know, op side of DevOps >>Focused on the op side, cuz that's the harder problem to solve. Developers are gonna use whatever tools they need to use to build these applications and roll them out. And they're gonna change all the time. In other words, they're gonna change the tools and technologies to do it in the supply chain. The ops problem is the harder problem to solve the ability to get these things working together and, and running at a certain point of reliability where the failure's not gonna be there. And I think that's gonna be the harder issue and doing that without complexity. >>Yeah. That's the multi-cloud challenge right there. I agree. The question I want to also pivot on that is, is that as we look at some of the reporting we've done and interviews, data and security really are hard areas. People are tune tuning up DevOps in the developer S booming, everyone's going fast, fast and loose. Shifting left, all that stuff's happening. Open source, booming Toga party. Everyone's partying ops is struggling to level up. So I guess the question is what's the order of operations from a customer. So a lot of customers have lifted and shift. The, some are going all in on say, AWS, yeah, I got a little hedge with Azure, but I'm not gonna do a full development team. As you talk to customers, cuz they're the ones deploying the clouds that want to get there, right? What's the order of operations to do it properly in your mind. And what's your advice as you look at as a strategy to, to do it, right? I mean, is there a playbook or some sort of situational, you know, sequence, >>Yes. One that works consistently is number one, you think about operations up front and if you can't solve operations, you have no business rolling out other applications and other databases that quite frankly can't be operated and that's how people are getting into trouble. So in other words, if you get into these very complex architectures, which is what a multicloud is, complex distributed system. Yeah. And you don't have an understanding of how you're gonna operationalize that system at scale, then you have no business in building the system. You have no business of going in a multicloud because you are going to run into that wall and it's gonna lead to a, an outage it's gonna lead to a breach or something that's gonna be company killing. >>So a lot of that's cultural, right. Having, having the cultural fortitude to say, we're gonna start there. We're gonna enforce these standards. >>That's what John CLE said. Yeah. CLE is famous line. >>Yeah, you're right. You're right. So, so, so what happens if the, if that as a consultant, if you, you probably have to insist on that first, right? Or, I mean, I don't know, you probably still do the engagement, but you, you're gonna be careful about promising an outcome aren't you, >>You're gonna have to insist on the fact they're gonna have to do some advanced planning and come up with a very rigorous way in which they're gonna roll it out. And the reality is if they're not doing that, then the advice would be you're gonna fail. So it's not a matter of when it's, when it's gonna happen. We're gonna, but at some point you're gonna fail either. Number one, you're gonna actually fail in some sort of a big disastrous event or more likely or not. You're gonna end up building something that's gonna cost you $10 million more a month to run and it's gonna be underoptimized. And is >>That effective when you, when you say that to a client or they say, okay, but, or do they say yes, you're >>Right. I view my role as a, someone like a doctor and a lawyer. You may not want to hear what I'm telling you. But the thing is, if I don't tell you the truth and I'm not doing my job as a trusted advisor. And so they'll never get anything but that from us, you know, as a firm and the reality is they can make their own decisions and will have to help them, whatever path they want to go. But we're making the warnings in place to make. >>And, and also also situationally it's IQ driven. Are they ready? What's their makeup. Are they have the kind of talent to execute. And there's a lot of unbeliev me. I totally think agree with on the op side, I think that's right on the money. The question I want to ask you is, okay, assume that someone has the right makeup of team. They got some badass people in there, coding away, DevOps, SREs, you name it. Everyone lined up platform teams, as they said today on stage, all that stuff. What's the CXO conversation at the boardroom that you, you have around business strategy. Cuz if you assume that cloud is here and you do things right and you get the right advisors in the next step is what does it transform my business into? Because you're talking about a fully digitalized business that converges it's not just, it helps you run an app back office with some terminal it's full blown business edge app business model innovation is it that the company becomes a cloud on their own and they have scale. And they're the super cloud of their category servicing a power law of second place, third place, SMB market. So I mean, Goldman Sachs could be the service provider cloud for financial services maybe. Or is that the dream? What, what's the dream for the, the, the CXO staff take us through the, >>What they're trying to do is get a level of automation with every able to leverage best breed technology to be as innovative as they possibly can. Using an architecture that's near a hundred percent optimized. It'll never be a hundred percent optimized. Therefore it's able to run, bring the best value to the business for the least amount of money. That's the big thing. If they want to become a cloud, that's, that's not a, not necessarily a good idea. If they're finance company be a finance company, just build these innovations around how to make a finance company be innovative and different for them. So they can be a disruptor without being disrupted. I see where see a lot of companies right now, they're gonna be exposed in the next 10 years because a lot of these smaller companies are able to weaponize technology to bring them to the next level, digital transformations, whatever, to create a business value. That's gonna be more compelling than the existing player >>Because they're on the CapEx back of Amazon or some technical innovation. Is that what the smaller guys, what's the, what's the lever that beats the >>It's the ability to use whatever technology you need to solve your issues. So in other words, I can use anything that exists on the cloud because it's part of the multi-cloud I'm I able to find the services that I need, the best AI system, the best database systems, the fastest transaction processing system, and assemble these syncs together to solve more innovative problems in my competitor. If I'm able to do that, I'm gonna win the game. So >>It's a buffet of technology. Pick your yes, your meal, come on, >>Case spray something, this operations, first thing in my head, remember Alan NA, when he came in the Cub and he said, listen, if you're gonna do cloud, you better change the operating model or you you're gonna make, you know, you'll drop millions to the bottom line. He was at CIO of Phillips at the time. You're not gonna drop billions. And it's all about, you know, the zeros, right? So do you find yourself in a lot of cases, sort of helping people rearchitect their operating model as a function of, of, of what cloud can, can enable? >>Yeah. Every, every engagement that we go into has operating model change op model changes, and typically it's gonna be major surgery. And so it's re reevaluating the skill sets, reevaluating, the operating model, reevaluating the culture. In fact, we have a team of people who come in and that's all they focus on. And so it used to be just kind of an afterthought. We'd put this together, oh, by the way, I think you need to do this and this and this. And here's what we recommend you do. But people who can go in and get cultural changes going get the operating models systems, going to get to the folks where they're gonna be successful with it. Reality. If you don't do that, you're gonna fail because you're not gonna have the ability to adapt to a cloud-based a cloud-based infrastructure. You can leverage this scale. >>David's like a masterclass here on the cube at VMware explore. Thanks for coming on. Thanks for spending the valuable time. Just what's going on in your world right now, take a quick minute to plug what's going on with you. What are you working on? What are you excited about? What what's happening, >>Loving life. I'm just running around doing, doing things like this, doing a lot of speaking, you know, still have the blog on in info world and have that for the last 12 years and just loving the fact that we're innovating and changing the world. And I'm trying to help as many people as I can, as quickly as I can. What's >>The coolest thing you've seen this year in terms of cloud kind of either weirdness coolness or something that made you fall outta your chair. Wow. That >>Was cool. I think the AI capabilities and application of AI, I'm just seeing use cases in there that we never would've thought about the ability to identify patterns that we couldn't identify in the past and do so for, for the good, I've been an AI analyst. It was my first job outta college and I'm 60 years old. So it's, it's matured enough where it actually impresses me. And so we're seeing applications >>Right now. That's NLP anymore. Is it? >>No, no, not list. That's what I was doing, but it's, we're able to take this technology to the next level and do, do a lot of good with it. And I think that's what just kind of blows me on the wall. >>Ah, I wish we had 20 more minutes, >>You know, one, one more masterclass sound bite. So we all kind of have kids in college, David and I both do young ones in college. If you're coming outta college, CS degree or any kind of smart degree, and you have the plethora of now what's coming tools and unlimited ways to kind of clean canvas up application, start something. What would you do if you were like 22? Right now, >>I would focus on being a multi-cloud architect. And I would learn a little about everything. Learn a little about at the various cloud providers. And I would focus on building complex distributed systems and architecting those systems. I would learn about how all these things kind of kind of run together. Don't learn a particular technology because that technology will ultimately go away. It'll be displaced by something else, learn holistically what the technologies is able to do and become the orchestrator of that technology. It's a harder problem to solve, but you'll get paid more for it. And it'll be more fun job. >>Just thinking big picture, big >>Picture, how everything comes together. True architecture >>Problems. All right, Dave is on the queue masterclass here on the cube. Bucha for Dave ante Explorer, 2022. Live back with our next segment. After this short break.

(upbeat music) >> Welcome back, everyone to CUBE Supercloud 22. I'm John Furrier, your host. Got a great influencer, Cloud Cloud RRT segment with Sarbjeet Johal, Cloud influencer, Cloud economist, Cloud consultant, Cloud advisor. Sarbjeet, welcome back, CUBE alumni. Good to see you. >> Thanks John and nice to be here. >> Now, what's your title? Cloud consultant? Analyst? >> Consultant, actually. Yeah, I'm launching my own business right now formally, soon. It's in stealth mode right now, we'll be (inaudible) >> Well, I'll just call you a Cloud guru, Cloud influencer. You've been great, friend of theCUBE. Really powerful on social. You share a lot of content. You're digging into all the trends. Supercloud is a thing, it's getting a lot of traction. We introduced that concept last reinvent. We were riffing before that. As we kind of were seeing the structural change that is now Supercloud, it really is kind of the destination or outcome of what we're seeing with hybrid cloud as a steady state into the what's now, they call multicloud, which is kind of awkward. It feels like it's default. Like multicloud, multi-vendor, but Supercloud has much more of a comprehensive abstraction around it. What's your thoughts? >> As you said, as Dave says that too, the Supercloud has that abstraction built into it. It's built on top of cloud, right? So it's being built on top of the CapEx which is being spent by likes of AWS and Azure and Google Cloud, and many others, right? So it's leveraging that infrastructure and building software stack on top of that, which is a platform. I see that as a platform being built on top of infrastructure as code. It's another platform which is not native to the cloud providers. So it's like a kind of cross-Cloud platform. That's what I said. >> Yeah, VMware calls it that cloud-cross cloud. I'm not a big fan of the name but I get what you're saying. We had a segment on earlier with Adrian Cockcroft, Laurie McVety and Chris Wolf, all part of the Cloud RRT like ourselves, and you've involved in Cloud from day one. Remember the OpenStack days Early Cloud, AWS, when they started we saw the trajectory and we saw the change. And I think the OpenStack in those early days were tell signs because you saw the movement of API first but Amazon just grew so fast. And then Azure now is catching up, their CapEx is so large that companies like Snowflake's like, "Why should I build my own? "I just sit on top of AWS, "move fast on one native cloud, then figure it out." Seems to be one of the playbooks of the Supercloud. >> Yeah, that is true. And there are reasons behind that. And I think number one reason is the skills gravity. What I call it, the developers and/or operators are trained on one set of APIs. And I've said that many times, to out compete your competition you have to out educate the market. And we know which cloud has done that. We know what traditional vendor has done that, in '90s it was Microsoft, they had VBS number one language and they were winning. So in the cloud era, it's AWS, their marketing efforts, their go-to market strategy, the micro nature of the releasing the micro sort of features, if you will, almost every week there's a new feature. So they have got it. And other two are trying to mimic that and they're having low trouble light. >> Yeah and I think GCP has been struggling compared to the three and native cloud on native as you're right, completely successful. As you're caught up and you see the Microsoft, I think is a a great selling point around multiple clouds. And the question that's on the table here is do you stay with the native cloud or you jump right to multicloud? Now multicloud by default is kind of what I see happening. We've been debating this, I'd love to get your thoughts because, Microsoft has a huge install base. They've converted to Office 365. They even throw SQL databases in there to kind of give it a little extra bump on the earnings but I've been super critical on their numbers. I think their shares are, there's clearly overstating their share, in my opinion, compared to AWS is a need of cloud, Azure though is catching up. So you have customers that are happy with Microsoft, that are going to run their apps on Azure. So if a customer has Azure and Microsoft that's technically multiple clouds. >> Yeah, true. >> And it's not a strategy, it's just an outcome. >> Yeah, I see Microsoft cloud as friendly to the internal developers. Internal developers of enterprises. but AWS is a lot more ISV friendly which is the software shops friendly. So that's what they do. They just build software and give it to somebody else. But if you're in-house developer and you have been a Microsoft shop for a long time, which enterprise haven't been that, right? So Microsoft is well entrenched into the enterprise. We know that, right? >> Yeah. >> For a long time. >> Yeah and the old joke was developers love code and just go with a lock in and then ops people don't want lock in because they want choice. So you have the DevOps movement that's been successful and they get DevSecOps. The real focus to me, I think, is the operating teams because the ops side is really with the pressure vis-a-vis. I want to get your reaction because we're seeing kind of the script flip. DevOps worked, infrastructure's code has worked. We don't yet see security as code yet. And you have things like cloud native services which is all developer, goodness. So I think the developers are doing fine. Give 'em a thumbs up and open source's booming. So they're shifting left, CI/CD pipeline. You have some issues around repo, monolithic repos, but devs are doing fine. It's the ops that are now have to level up because that seems to be a hotspot. What's your take? What's your reaction to that? Do you agree? And if you say you agree, why? >> Yeah, I think devs are doing fine because some of the devs are going into ops. Like the whole movement behind DevOps culture is that devs and ops is one team. The people who are building that application they're also operating that as well. But that's very foreign and few in enterprise space. We know that, right? Big companies like Google, Microsoft, Amazon, Twitter, those guys can do that. They're very tech savvy shops. But when it comes to, if you go down from there to the second tier of enterprises, they are having hard time with that. Once you create software, I've said that, I sound like a broken record here. So once you create piece of software, you want to operate it. You're not always creating it. Especially when it's inhouse software development. It's not your core sort of competency to. You're not giving that software to somebody else or they're not multiple tenants of that software. You are the only user of that software as a company, or maybe maximum to your employees and partners. But that's where it stops. So there are those differences and when it comes to ops, we have to still differentiate the ops of the big companies, which are tech companies, pure tech companies and ops of the traditional enterprise. And you are right, the ops of the traditional enterprise are having tough time to cope up with the changing nature of things. And because they have to run the old traditional stacks whatever they happen to have, SAP, Oracle, financial, whatnot, right? Thousands of applications, they have to run that. And they have to learn on top of that, new scripting languages to operate the new stack, if you will. >> So for ops teams do they have to spin up operating teams for every cloud specialized tooling, there's consequences to that. >> Yeah. There's economics involved, the process, if you are learning three cloud APIs and most probably you will end up spending a lot more time and money on that. Number one, number two, there are a lot more problems which can arise from that, because of the differences in how the APIs work. The rule says if you pick one primary cloud and then you're focused on that, and most of your workloads are there, and then you go to the secondary cloud number two or three on as need basis. I think that's the right approach. >> Well, I want to get your take on something that I'm observing. And again, maybe it's because I'm old school, been around the IT block for a while. I'm observing the multi-vendors kind of as Dave calls the calisthenics, they're out in the market, trying to push their wears and convincing everyone to run their workloads on their infrastructure. multicloud to me sounds like multi-vendor. And I think there might not be a problem yet today so I want to get your reaction to my thoughts. I see the vendors pushing hard on multicloud because they don't have a native cloud. I mean, IBM ultimately will probably end up being a SaaS application on top of one of the CapEx hyperscale, some say, but I think the playbook today for customers is to stay on one native cloud, run cloud native hybrid go in on OneCloud and go fast. Then get success and then go multiple clouds. versus having a multicloud set of services out of the gate. Because if you're VMware you'd love to have cross cloud abstraction layer but that's lock in too. So what's your lock in? Success in the marketplace or vendor access? >> It's tricky actually. I've said that many times, that you don't wake up in the morning and say like, we're going to do multicloud. Nobody does that by choice. So it falls into your lab because of mostly because of what MNA is. And sometimes because of the price to performance ratio is better somewhere else for certain kind of workloads. That's like foreign few, to be honest with you. That's part of my read is, that being a developer an operator of many sort of systems, if you will. And the third tier which we talked about during the VMworld, I think 2019 that you want vendor diversity, just in case one vendor goes down or it's broken up by feds or something, and you want another vendor, maybe for price negotiation tactics, or- >> That's an op mentality. >> Yeah, yeah. >> And that's true, they want choice. They want to get locked in. >> You want choice because, and also like things can go wrong with the provider. We know that, we focus on top three cloud providers and we sort of assume that they'll be there for next 10 years or so at least. >> And what's also true is not everyone can do everything. >> Yeah, exactly. So you have to pick the provider based on all these sort of three sets of high level criteria, if you will. And I think the multicloud should be your last choice. Like you should not be gearing up for that by default but it should be by design, as Chuck said. >> Okay, so I need to ask you what does Supercloud in my opinion, look like five, 10 years out? What's the outcome of a good Supercloud structure? What's it look like? Where did it come from? How did it get there? What's your take? >> I think Supercloud is getting born in the absence of having standards around cloud. That's what it is. Because we don't have standards, we long, or we want the services at different cloud providers, Which have same APIs and there's less learning curve or almost zero learning curve for our developers and operators to learn that stuff. Snowflake is one example and VMware Stack is available at different cloud providers. That's sort of infrastructure as a service example if you will. And snowflake is a sort of data warehouse example and they're going down the stack. Well, they're trying to expand. So there are many examples like that. What was the question again? >> Is Supercloud 10 years out? What does it look like? What's the components? >> Yeah, I think the Supercloud 10 years out will expand because we will expand the software stack faster than the hardware stack and hardware stack will be expanding of course, with the custom chips and all that. There was the huge event yesterday was happening from AWS. >> Yeah, the Silicon. >> Silicon Day. And that's an eyeopening sort of movement and the whole technology consumption, if you will. >> And yeah, the differentiation with the chips with supply chain kind of herding right now, we think it's going to be a forcing function for more cloud adoption. Because if you can't buy networking gear you going to go to the cloud. >> Yeah, so Supercloud to me in 10 years, it will be bigger, better in the likes of HashiCorp. Actually, I think we need likes of HashiCorp on the infrastructure as a service side. I think they will be part of the Supercloud. They are kind of sitting on the side right now kind of a good vendor lost in transition kind of thing. That sort of thing. >> It's like Kubernetes, we'll just close out here. We'll make a statement. Is Kubernetes a developer thing or an infrastructure thing? It's an ops thing. I mean, people are coming out and saying Kubernetes is not a developer issue. >> It's ops thing. >> It's an ops thing. It's in operation, it's under the hood. So you, again, this infrastructure's a service integrating this super pass layer as Dave Vellante and Wikibon call it. >> Yeah, it's ops thing, actually, which enables developers to get that the Azure service, like you can deploy your software in sort of different format containers, and then you don't care like what VMs are those? And, but Serverless is the sort of arising as well. It was hard for a while now it's like the lull state, but I think Serverless will be better in next three to five years on. >> Well, certainly the hyperscale is like AWS and Azure and others have had great CapEx and investments. They need to stay ahead, in your opinion, final question, how do they stay ahead? 'Cause, AWS is not going to stand still nor will Azure, they're pedaling as fast as they can. Google's trying to figure out where they fit in. Are they going to be a real cloud or a software stack? Same with Oracle. To me, it's really, the big race is now with AWS and Azure's nipping at their heels. Hyperscale, what do they need to do to differentiate going forward? >> I think they are in a limbo. They, on one side, they don't want to compete with their customers who are sitting on top of them, likes of Snowflake and others, right? And VMware as well. But at the same time, they have to keep expanding and keep innovating. And they're debating within their themselves. Like, should we compete with these guys? Should we launch similar sort of features and functionality? Or should we keep it open? And what I have heard as of now that internally at AWS, especially, they're thinking about keeping it open and letting people sort of (inaudible)- >> And you see them buying some the Cerner with Oracle that bought Cerner, Amazon bought a healthcare company. I think the likes of MongoDB, Snowflake, Databricks, are perfect examples of what we'll see I think on the AWS side. Azure, I'm not so sure, they like to have a little bit more control at the top of the stack with the SaaS, but I think Databricks has been so successful open source, Snowflake, a little bit more proprietary and closed than Databricks. They're doing well is on top of data, and MongoDB has got great success. All of these things compete with AWS higher level services. So, that advantage of those companies not having the CapEx investment and then going multiple clouds on other ecosystems that's a path of customers. Stay one, go fast, get traction, then go. >> That's huge. Actually the last sort comment I want to make is that, Also, that you guys include this in the definition of Supercloud, the likes of Capital One and Soner sort of vendors, right? So they are verticals, Capital One is in this financial vertical, and then Soner which Oracle bar they are in this healthcare vertical. And remember in the beginning of the cloud and when the cloud was just getting born. We used to say that we will have the community clouds which will be serving different verticals. >> Specialty clouds. >> Specialty clouds, community clouds. And actually that is happening now at very sort of small level. But I think it will start happening at a bigger level. The Goldman Sachs and others are trying to build these services on the financial front risk management and whatnot. I think that will be- >> Well, what's interesting, which you're bringing up a great discussion. We were having discussions around these vertical clouds like Goldman Sachs Capital One, Liberty Mutual. They're going all in on one native cloud then going into multiple clouds after, but then there's also the specialty clouds around functionality, app identity, data security. So you have multiple 3D dimensional clouds here. You can have a specialty cloud just on identity. I mean, identity on Amazon is different than Azure. Huge issue. >> Yeah, I think at some point we have to distinguish these things, which are being built on top of these infrastructure as a service, in past with a platform, a service, which is very close to infrastructure service, like the lines are blurred, we have to distinguish these two things from these Superclouds. Actually, what we are calling Supercloud maybe there'll be better term, better name, but we are all industry path actually, including myself and you or everybody else. Like we tend to mix these things up. I think we have to separate these things a little bit to make things (inaudible) >> Yeah, I think that's what the super path thing's about because you think about the next generation SaaS has to be solved by innovations of the infrastructure services, to your point about HashiCorp and others. So it's not as clear as infrastructure platform, SaaS. There's going to be a lot of interplay between this levels of services. >> Yeah, we are in this flasker situation a lot of developers are lost. A lot of operators are lost in this transition and it's just like our economies right now. Like I was reading at CNBC today, and here's sort of headline that people are having hard time understanding what state the economy is in. And so same is true with our technology economy. Like we don't know what state we are in. It's kind of it's in the transition phase right now. >> Well we're definitely in a bad economy relative to the consumer market. I've said on theCUBE publicly, Dave has as well, not as aggressive. I think the tech is still in a boom. I don't think there's tech bubble at all that's bursting, I think, the digital transformation from post COVID is going to continue. And this is the first recession downturn where the hyperscalers have been in market, delivering the economic value, almost like they're pumping on all cylinders and going to the next level. Go back to 2008, Amazon web services, where were they? They were just emerging out. So the cloud economic impact has not been factored into the global GDP relationship. I think all the firms that are looking at GDP growth and tech spend as a correlation, are completely missing the boat on the fact that cloud economics and digital transformation is a big part of the new economics. So refactoring business models this is continuing and it's just the early days. >> Yeah, I have said that many times that cloud works good in the bad economy and cloud works great in the good economy. Do you know why? Because there are different type of workloads in the good economy. A lot of experimentation, innovative solutions go into the cloud. You can do experimentation that you have extra money now, but in the bad economy you don't want to spend the CapEx because don't have money. Money is expensive at that point. And then you want to keep working and you don't need (inaudible) >> I think inflation's a big factor too right now. Well, Sarbjeet, great to see you. Thanks for coming into our studio for our stage performance for Supercloud 22, this is a pilot episode that we're going to get a consortium of experts Cloud RRT like yourselves, in the conversation to discuss what the architecture is. What is a taxonomy? What are the key building blocks and what things need to be in place for Supercloud capability? Because it's clear that if without standards, without defacto standards, we're at this tipping point where if it all comes together, not all one company can do everything. Customers want choice, but they also want to go fast too. So DevOps is working. It's going the next level. We see this as Supercloud. So thank you so much for your participation. >> Thanks for having me. And I'm looking forward to listen to the other sessions (inaudible) >> We're going to take it on A stickers. We'll take it on the internet. I'm John Furrier, stay tuned for more Supercloud 22 coverage, here at the Palo Alto studios in one minute. (bright music)

(inspiring music) >> Announcer: theCUBE presents HPE Discover 2022. Brought to you by HPE. >> Welcome back to theCUBE's coverage of HPE Discover 2022, from the Venetian in Las Vegas, the first Discover since 2019. I really think this is my 14th Discover, when you include HP, when you include Europe. And I got to say this Discover, I think has more energy than any one that I've ever seen, about 8,000 people here. Really excited to have one of HPE's longstanding partners, Veeam CTO, Danny Allen is here, joined by David Harvey, Vice President of Strategic Alliances at Veeam. Guys, good to see you again. It was just earlier, let's see, last month, we were together out here. >> Yeah, just a few weeks ago. It's fantastic to be back and what it's telling us, technology industry is coming back. >> And the events business, of course, is coming back, which we love. I think the expectations were cautious. You saw it at VeeamON, a little more than you expected, a lot of great energy. A lot of people, 'cause it was last month, it was their first time out, >> Yes. >> in two years. Here, I think people have started to go out more, but still, an energy that's palpable. >> You can definitely feel it. Last night, I think I went to four consecutive events and everyone's out having those discussions and having conversations, it's good to be back. >> You guys hosted the Storage party last night, which is epic. I left at midnight, I took a picture, it was still packed. I said, okay, time to go, nothing good happens after midnight kids. David, talk about the alliance with HPE, how it's evolved, and where you see it going? >> I appreciate it, and certainly this, as you said, has been a big alliance for us. Over 10 years or so, fantastic integrations across the board. And you touched on 2019 Discover. We launched with GreenLake at that event, we were one of the launch partners, and we've seen fantastic growth. Overall, what we're excited about, is that continuation of the movement of the customer's buying patterns in line with HPE's portfolio and in line with Veeam. We continue to be with all their primary, secondary storage, we continue to be a spearhead position with GreenLake, which we're really excited about. And we're also really excited to hear from HPE, unfortunately under NDA, some of their future stuff they're investing in, which is a really nice invigoration for what they're doing for their portfolio. And we see that being a big deal for us over the next 24 months. >> Your relationship with HPE predates the HP, HPE split. >> Mmm. >> Yes. >> But it was weird, because they had Data Protector, and that was a quasi-competitor, or really not, but it was a competitor, a legacy competitor, of what you guys have, kind of modern data protection I think is the tagline, if I got it right. Post the split, that was an S-curve moment, wasn't it, in terms of the partnership? >> It really was. If you go back 10 years, we did our first integration sending data to StoreOnce and we had some blueprints around that. But now, if you look what we have, we have integrations on the primary side, so, 3PAR, Primera, Nimble, all their top-tier storage, we can manage the snapshots. We have integration on the target side. We integrate with Catalyst in the movement of data and the management of data. And, as David alluded to, we integrate with GreenLake. So, customers who want to take this as a consumption model, we integrate with that. And so it's been, like you said, the strongest relationship that we have on the technology alliance side. >> So, V12, you announced at VeeamON. What does that mean for HPE customers, the relationship? Maybe you guys could both talk about that. >> Technology side, to touch on a few things that we're doing with them, ransomware has been a huge issue. Security's been a big theme, obviously, at the conference, >> Dave: Yeah, you bet. and one of the things we're doing in V12 is adding immutability for both StoreOnce and StoreEver. So, we take the features that our partners have, immutability being big in the security space, and we integrate that fully into the product. So a customer checks a box and says, hey, I want to make sure that the data is secure. >> Yeah, and also, it's another signification about the relationship. Every single release we've done has had HPE at the heart of it, and the same thing is being said with V12. And it shows to our customers, the continual commitment. Relationships come and go. They're hard, and the great news is, 10 years has proven that we get through good times and tricky situations, and we both continue to invest, et cetera. And I think there's a lot of peace of mind and the revenue figures prove that, which is what we're really excited about. >> Yeah I want to come back to that, but just to follow up, Danny, on that immutability, that's a feature that you check? It's service within GreenLake, or within Veeam? How does that all work? >> We have immutability now depending on the target. We introduced the ability to send data, for example, into S3 two years ago, and make it immutable when you send it to an S3 or S3 compatible environment. We added, in Version 11, the ability to take a Linux repository and make it, and harden it, essentially make it immutable. But what we're doing now is taking our partner systems like StoreOnce, like StoreEver, and when we send data there, we take advantage of an API flag or whatever it happens to be, that it makes the data, when it's written to that system, can't be deleted, can't be encrypted. Now, what does that mean for a customer? Well, we do all the hard work in the back end, it's just a check box. They say, I want to make it immutable, and we manage how long it's immutable. Because if you made everything immutable forever, that's hugely expensive, right? So, it's all about, how long is that immutable before you age it out and make sure the new data coming in is immutable. >> Dave: It's like an insurance policy, you have that overlap. >> Yes. >> Right, okay. And then David, you mentioned the revenue, Lou bears that out. I got the IDC guys comin' on later on today. I'll ask 'em about that, if that's their swim lane. But you guys are basically a statistical tie, with Dell for number one? Am I getting that right? And you're growing at a faster rate, I believe, it's hard to tell 'cause I don't think Dell reports on the pace of its growth within data protection. You guys obviously do, but is that right? It's a statistical tie, is it? >> Yeah, hundred percent. >> Yeah, statistical tie for first place, which we're super excited about. When I joined Veeam, I think we were in fifth place, but we've been in the leader's quadrant of the Gartner Magic- >> Cause and effect there or? (panelists laughing) >> No, I don't think so. >> Dave: Ha, I think maybe. >> We've been on a great trajectory. But statistical tie for first place, greatest growth sequentially, and year-over-year, of all of the data protection vendors. And that's a testament not just to the technology that we're doing, but partnerships with HPE, because you never do this, the value of a technology is not that technology alone, it's the value of that technology within the ecosystem. And so that's why we're here at HPE Discover. It's our joint technology solutions that we're delivering. >> What are your thoughts or what are you seeing in the field on As-a-service? Because of course, the messaging is all about As-a-service, you'd think, oh, a hundred percent of everything is going to be As-a-service. A lot of customers, they don't mind CapEx, they got good, balance sheet, and they're like, hey, we'll take care of this, and, we're going to build our own little internal cloud. But, what are you seeing in the market in terms of As-a-service, versus, just traditional licensing models? >> Certainly, there's a mix between the two. What I'd say, is that sources that are already As-a-service, think Microsoft 365, think AWS, Azure, GCP, the cloud providers. There's a natural tendency for the customer to want the data protection As-a-service, as well for those. But if you talk about what's on premises, customers who have big data centers deployed, they're not yet, the pendulum has not shifted for that to be data protection As-a-service. But we were early to this game ourselves. We have 10,000, what we call, Veeam Cloud Service Providers, that are offering data protection As-a-service, whether it be on premises, so they're remotely managing it, or cloud hosted, doing data protection for that. >> So, you don't care. You're providing the technology, and then your customers are actually choosing the delivery model. Is that correct? >> A hundred percent, and if you think about what GreenLake is doing for example, that started off as being a financial model, but now they're getting into that services delivery. And what we want to do is enable them to deliver it, As-a-service, not just the financial model, but the outcome for the customer. And so our technology, it's not just do backup, it's do backup for a multi-tenant, multi-customer environment that does all of the multi-tenancy and billing and charge back as part of that service. >> Okay, so you guys don't report on this, but I'm going to ask the question anyway. You're number one now, let's call you, let's declare number one, 'cause we're well past that last reporting and you're growin' faster. So go another quarter, you're now number one, so you're the largest. Do you spend more on R&D in data protection than any other company? >> Yes, I'm quite certain that we do. Now, we have an unfair advantage because we have 450,000 customers. I don't think there's any other data protection company out there, the size and scope and scale, that we have. But we've been expanding, our largest R&D operation center's in Prague, it's in Czech Republic, but we've been expanding that. Last year it grew 40% year on year in R&D, so big investment in that space. You can see this just through our product space. Five years ago, we did data protection of VMware only, and now we do all the virtual environments, all the physical environments, all the major cloud environments, Kubernetes, Microsoft 365, we're launching Salesforce. We announced that at VeeamON last month and it will be coming out in Q3. All of that is coming from our R&D investments. >> A lot of people expect that when a company like Insight, a PE company, purchases a company like Veeam, that one of the things they'll dial down is R&D. That did not happen in this case. >> No, they very much treat us as a growth company. We had 22% year-over-year growth in 2020, and 25% year-over-year last year. The growth has been tremendous, they continue to give us the freedom. Now, I expect they'll want returns like that continuously, but we have been delivering, they have been investing. >> One of my favorite conversations of the year was our supercloud conversation, which was awesome, thank you for doing that with me. But that's clearly an area of focus, what we call supercloud, and you don't use that term, I know, you do sometimes, but it's not your marketing, I get that. But that is an R&D intensive effort, is it not? To create that common experience. And you see HPE, attempting to do that as well, across all these different estates. >> A hundred percent. We focus on three things, I always say, our differentiators, simplicity, flexibility, and reliability. Making it simple for the customers is not an easy thing to do. Making that checkbox for immutability? We have to do a lot behind the scenes to make it simple. Same thing on flexibility. We don't care if they're using 3PAR, Primera, Nimble, whatever you want to choose as the primary storage, we will take that out of your hands and make it really easy. You mentioned supercloud. We don't care what the cloud infrastructure, it can be on GreenLake, it can be on AWS, can be on Azure, it can be on GCP, it can be on IBM cloud. It is a lot of effort on our part to abstract the cloud infrastructure, but we do that on behalf of our customers to take away that complexity, it's part of our platform. >> Quick follow-up, and then I want to ask a question of David. I like talking to you guys because you don't care where it is, right? You're truly agnostic to it all. I'm trying to figure out this repatriation thing, cause I hear a lot of hey, Dave, you should look into repatriation that's happened all over the place, and I see pockets of it. What are you seeing in terms of repatriation? Have customers over-rotated to the cloud and now they're pullin' back a little bit? Or is it, as I'm claiming, in pockets? What's your visibility on that? >> Three things I see happening. There's the customers who lifted up their data center, moved it into the cloud and they get the first bill. >> (chuckling) Okay. >> And they will repatriate, there's no question. If I talk to those customers who simply lifted up and moved it over because the CIO told them to, they're moving it back on premises. But a second thing that we see is people moving it over, with tweaks. So they'll take their SQL server database and they'll move it into RDS, they'll change some things. And then you have people who are building cloud-native, they're never coming back on premises, they are building it for the cloud environment. So, we see all three of those. We only really see repatriation on that first scenario, when they get that first bill. >> And when you look at the numbers, I think it gets lost, 'cause you see the cloud is growing so fast. So David, what are the conversations like? You had several events last night, The Veeam party, slash Storage party, from HPE. What are you hearing from your alliance partners and the customers at the event. >> I think Danny touched on that point, it's about philosophy of evolution. And I think at the end of the day, whether we're seeing it with our GSI alliances we've got out there, or with the big enterprise conversations we're having with HPE, it's about understanding which workloads they want to move. In our mind, the customers are getting much smarter in making that decision, rather than experimenting. They're really taking a really solid look. And the work we're doing with the GSIs on workplace modernization, data center transformation, they're really having that investment work up front on the workloads, to be able to say, this works for me, for my personality and my company. And so, to the point about movement, it's more about decisive decision at the start, and not feeling like the remit is, I have to do one thing or another, it's about looking at that workflow position. And that's what we've seen with the revenue part as well. We've seen our movement to GreenLake tremendously grow in the last 18 months to two years. And from our GSI work as well, we're seeing the types of conversations really focus on that workload, compared to, hey, I just need a backup solution, and that's really exciting. >> Are you having specific conversations about security, or is it a data protection conversation still, (David chuckles) that's an adjacency to security? >> That's a great question. And I think it's a complex one, because if you come to a company like Veeam, we are there, and you touched on it before, we provide a solution when something has happened with security. We're not doing intrusion detection, we're not doing that barrier position at the end of it, but it's part of an end-to-end assumption. And I don't think that at this particular point, I started in security with RSA and Check Point, it was about layers of protection. Now it's layers of protection, and the inevitability that at some point something will happen, so about the recovery. So the exciting conversations we're having, especially with the big enterprises, is not about the fear factor, it's about, at some point something's going to occur. Speed of recovery is the conversation. And so for us, and your question is, are they talking to us about security, or more, the continuity position? And that's where the synergy's getting a lot simpler, rather than a hard demark between security and backup. >> Yeah, when you look at the stock market, everything's been hit, but security, with the exception of Okta, 'cause it got that weird benign hack, but security, generally, is an area that CIOs have said, hey, we can't really dial that back. We can maybe, some other discretionary stuff, we'll steal and prioritize. But security seems to be, and I think data protection is now part of that discussion. You're not a security company. We've seen some of your competitors actually pivot to become security companies. You're not doing that, but it's very clearly an adjacency, don't you think? >> It's an adjacency, and it's a new conversation that we're having with the Chief Information Security Officer. I had a meeting an hour ago with a customer who was hit by ransomware, and they got the call at 2:00 AM in the morning, after the ransomware they recovered their entire portfolio within 36 hours, from backups. Didn't even contact Veeam, I found out during this meeting. But that is clearly something that the Chief Information Security Officer wants to know about. It's part of his purview, is the recovery of that data. >> And they didn't pay the ransom? >> And they did not pay the ransom, not a penny. >> Ahh, we love those stories. Guys, thanks so much for coming on theCUBE. Congratulations on all the success. Love when you guys come on, and it was such a fun event at VeeamON. Great event here, and your presence is, was seen. The Veeam green is everywhere, so appreciate your time. >> Thank you. >> Thanks, Dave. >> Okay, and thank you for watching. This is Dave Vellante for John Furrier and Lisa Martin. We'll be back right after this short break. You're watching theCUBE's coverage of HPE Discover 2022, from Las Vegas. (inspiring music)

(upbeat music) >> Welcome back, everyone, to theCUBE's coverage here live on the floor at AWS re:MARS 2022. I'm John Furrier, host for theCUBE. Great event, machine learning, automation, robotics, space, that's MARS. It's part of the re-series of events, re:Invent's the big event at the end of the year, re:Inforce, security, re:MARS, really intersection of the future of space, industrial, automation, which is very heavily DevOps machine learning, of course, machine learning, which is AI. We have Luis Ceze here, who's the CEO co-founder of OctoML. Welcome to theCUBE. >> Thank you very much for having me in the show, John. >> So we've been following you guys. You guys are a growing startup funded by Madrona Venture Capital, one of your backers. You guys are here at the show. This is a, I would say small show relative what it's going to be, but a lot of robotics, a lot of space, a lot of industrial kind of edge, but machine learning is the centerpiece of this trend. You guys are in the middle of it. Tell us your story. >> Absolutely, yeah. So our mission is to make machine learning sustainable and accessible to everyone. So I say sustainable because it means we're going to make it faster and more efficient. You know, use less human effort, and accessible to everyone, accessible to as many developers as possible, and also accessible in any device. So, we started from an open source project that began at University of Washington, where I'm a professor there. And several of the co-founders were PhD students there. We started with this open source project called Apache TVM that had actually contributions and collaborations from Amazon and a bunch of other big tech companies. And that allows you to get a machine learning model and run on any hardware, like run on CPUs, GPUs, various GPUs, accelerators, and so on. It was the kernel of our company and the project's been around for about six years or so. Company is about three years old. And we grew from Apache TVM into a whole platform that essentially supports any model on any hardware cloud and edge. >> So is the thesis that, when it first started, that you want to be agnostic on platform? >> Agnostic on hardware, that's right. >> Hardware, hardware. >> Yeah. >> What was it like back then? What kind of hardware were you talking about back then? Cause a lot's changed, certainly on the silicon side. >> Luis: Absolutely, yeah. >> So take me through the journey, 'cause I could see the progression. I'm connecting the dots here. >> So once upon a time, yeah, no... (both chuckling) >> I walked in the snow with my bare feet. >> You have to be careful because if you wake up the professor in me, then you're going to be here for two hours, you know. >> Fast forward. >> The average version here is that, clearly machine learning has shown to actually solve real interesting, high value problems. And where machine learning runs in the end, it becomes code that runs on different hardware, right? And when we started Apache TVM, which stands for tensor virtual machine, at that time it was just beginning to start using GPUs for machine learning, we already saw that, with a bunch of machine learning models popping up and CPUs and GPU's starting to be used for machine learning, it was clear that it come opportunity to run on everywhere. >> And GPU's were coming fast. >> GPUs were coming and huge diversity of CPUs, of GPU's and accelerators now, and the ecosystem and the system software that maps models to hardware is still very fragmented today. So hardware vendors have their own specific stacks. So Nvidia has its own software stack, and so does Intel, AMD. And honestly, I mean, I hope I'm not being, you know, too controversial here to say that it kind of of looks like the mainframe era. We had tight coupling between hardware and software. You know, if you bought IBM hardware, you had to buy IBM OS and IBM database, IBM applications, it all tightly coupled. And if you want to use IBM software, you had to buy IBM hardware. So that's kind of like what machine learning systems look like today. If you buy a certain big name GPU, you've got to use their software. Even if you use their software, which is pretty good, you have to buy their GPUs, right? So, but you know, we wanted to help peel away the model and the software infrastructure from the hardware to give people choice, ability to run the models where it best suit them. Right? So that includes picking the best instance in the cloud, that's going to give you the right, you know, cost properties, performance properties, or might want to run it on the edge. You might run it on an accelerator. >> What year was that roughly, when you were going this? >> We started that project in 2015, 2016 >> Yeah. So that was pre-conventional wisdom. I think TensorFlow wasn't even around yet. >> Luis: No, it wasn't. >> It was, I'm thinking like 2017 or so. >> Luis: Right. So that was the beginning of, okay, this is opportunity. AWS, I don't think they had released some of the nitro stuff that the Hamilton was working on. So, they were already kind of going that way. It's kind of like converging. >> Luis: Yeah. >> The space was happening, exploding. >> Right. And the way that was dealt with, and to this day, you know, to a large extent as well is by backing machine learning models with a bunch of hardware specific libraries. And we were some of the first ones to say, like, know what, let's take a compilation approach, take a model and compile it to very efficient code for that specific hardware. And what underpins all of that is using machine learning for machine learning code optimization. Right? But it was way back when. We can talk about where we are today. >> No, let's fast forward. >> That's the beginning of the open source project. >> But that was a fundamental belief, worldview there. I mean, you have a world real view that was logical when you compare to the mainframe, but not obvious to the machine learning community. Okay, good call, check. Now let's fast forward, okay. Evolution, we'll go through the speed of the years. More chips are coming, you got GPUs, and seeing what's going on in AWS. Wow! Now it's booming. Now I got unlimited processors, I got silicon on chips, I got, everywhere >> Yeah. And what's interesting is that the ecosystem got even more complex, in fact. Because now you have, there's a cross product between machine learning models, frameworks like TensorFlow, PyTorch, Keras, and like that and so on, and then hardware targets. So how do you navigate that? What we want here, our vision is to say, folks should focus, people should focus on making the machine learning models do what they want to do that solves a value, like solves a problem of high value to them. Right? So another deployment should be completely automatic. Today, it's very, very manual to a large extent. So once you're serious about deploying machine learning model, you got a good understanding where you're going to deploy it, how you're going to deploy it, and then, you know, pick out the right libraries and compilers, and we automated the whole thing in our platform. This is why you see the tagline, the booth is right there, like bringing DevOps agility for machine learning, because our mission is to make that fully transparent. >> Well, I think that, first of all, I use that line here, cause I'm looking at it here on live on camera. People can't see, but it's like, I use it on a couple couple of my interviews because the word agility is very interesting because that's kind of the test on any kind of approach these days. Agility could be, and I talked to the robotics guys, just having their product be more agile. I talked to Pepsi here just before you came on, they had this large scale data environment because they built an architecture, but that fostered agility. So again, this is an architectural concept, it's a systems' view of agility being the output, and removing dependencies, which I think what you guys were trying to do. >> Only part of what we do. Right? So agility means a bunch of things. First, you know-- >> Yeah explain. >> Today it takes a couple months to get a model from, when the model's ready, to production, why not turn that in two hours. Agile, literally, physically agile, in terms of walk off time. Right? And then the other thing is give you flexibility to choose where your model should run. So, in our deployment, between the demo and the platform expansion that we announced yesterday, you know, we give the ability of getting your model and, you know, get it compiled, get it optimized for any instance in the cloud and automatically move it around. Today, that's not the case. You have to pick one instance and that's what you do. And then you might auto scale with that one instance. So we give the agility of actually running and scaling the model the way you want, and the way it gives you the right SLAs. >> Yeah, I think Swami was mentioning that, not specifically that use case for you, but that use case generally, that scale being moving things around, making them faster, not having to do that integration work. >> Scale, and run the models where they need to run. Like some day you want to have a large scale deployment in the cloud. You're going to have models in the edge for various reasons because speed of light is limited. We cannot make lights faster. So, you know, got to have some, that's a physics there you cannot change. There's privacy reasons. You want to keep data locally, not send it around to run the model locally. So anyways, and giving the flexibility. >> Let me jump in real quick. I want to ask this specific question because you made me think of something. So we're just having a data mesh conversation. And one of the comments that's come out of a few of these data as code conversations is data's the product now. So if you can move data to the edge, which everyone's talking about, you know, why move data if you don't have to, but I can move a machine learning algorithm to the edge. Cause it's costly to move data. I can move computer, everyone knows that. But now I can move machine learning to anywhere else and not worry about integrating on the fly. So the model is the code. >> It is the product. >> Yeah. And since you said, the model is the code, okay, now we're talking even more here. So machine learning models today are not treated as code, by the way. So do not have any of the typical properties of code that you can, whenever you write a piece of code, you run a code, you don't know, you don't even think what is a CPU, we don't think where it runs, what kind of CPU it runs, what kind of instance it runs. But with machine learning model, you do. So what we are doing and created this fully transparent automated way of allowing you to treat your machine learning models if you were a regular function that you call and then a function could run anywhere. >> Yeah. >> Right. >> That's why-- >> That's better. >> Bringing DevOps agility-- >> That's better. >> Yeah. And you can use existing-- >> That's better, because I can run it on the Artemis too, in space. >> You could, yeah. >> If they have the hardware. (both laugh) >> And that allows you to run your existing, continue to use your existing DevOps infrastructure and your existing people. >> So I have to ask you, cause since you're a professor, this is like a masterclass on theCube. Thank you for coming on. Professor. (Luis laughing) I'm a hardware guy. I'm building hardware for Boston Dynamics, Spot, the dog, that's the diversity in hardware, it's tends to be purpose driven. I got a spaceship, I'm going to have hardware on there. >> Luis: Right. >> It's generally viewed in the community here, that everyone I talk to and other communities, open source is going to drive all software. That's a check. But the scale and integration is super important. And they're also recognizing that hardware is really about the software. And they even said on stage, here. Hardware is not about the hardware, it's about the software. So if you believe that to be true, then your model checks all the boxes. Are people getting this? >> I think they're starting to. Here is why, right. A lot of companies that were hardware first, that thought about software too late, aren't making it. Right? There's a large number of hardware companies, AI chip companies that aren't making it. Probably some of them that won't make it, unfortunately just because they started thinking about software too late. I'm so glad to see a lot of the early, I hope I'm not just doing our own horn here, but Apache TVM, the infrastructure that we built to map models to different hardware, it's very flexible. So we see a lot of emerging chip companies like SiMa.ai's been doing fantastic work, and they use Apache TVM to map algorithms to their hardware. And there's a bunch of others that are also using Apache TVM. That's because you have, you know, an opening infrastructure that keeps it up to date with all the machine learning frameworks and models and allows you to extend to the chips that you want. So these companies pay attention that early, gives them a much higher fighting chance, I'd say. >> Well, first of all, not only are you backable by the VCs cause you have pedigree, you're a professor, you're smart, and you get good recruiting-- >> Luis: I don't know about the smart part. >> And you get good recruiting for PhDs out of University of Washington, which is not too shabby computer science department. But they want to make money. The VCs want to make money. >> Right. >> So you have to make money. So what's the pitch? What's the business model? >> Yeah. Absolutely. >> Share us what you're thinking there. >> Yeah. The value of using our solution is shorter time to value for your model from months to hours. Second, you shrink operator, op-packs, because you don't need a specialized expensive team. Talk about expensive, expensive engineers who can understand machine learning hardware and software engineering to deploy models. You don't need those teams if you use this automated solution, right? Then you reduce that. And also, in the process of actually getting a model and getting specialized to the hardware, making hardware aware, we're talking about a very significant performance improvement that leads to lower cost of deployment in the cloud. We're talking about very significant reduction in costs in cloud deployment. And also enabling new applications on the edge that weren't possible before. It creates, you know, latent value opportunities. Right? So, that's the high level value pitch. But how do we make money? Well, we charge for access to the platform. Right? >> Usage. Consumption. >> Yeah, and value based. Yeah, so it's consumption and value based. So depends on the scale of the deployment. If you're going to deploy machine learning model at a larger scale, chances are that it produces a lot of value. So then we'll capture some of that value in our pricing scale. >> So, you have direct sales force then to work those deals. >> Exactly. >> Got it. How many customers do you have? Just curious. >> So we started, the SaaS platform just launched now. So we started onboarding customers. We've been building this for a while. We have a bunch of, you know, partners that we can talk about openly, like, you know, revenue generating partners, that's fair to say. We work closely with Qualcomm to enable Snapdragon on TVM and hence our platform. We're close with AMD as well, enabling AMD hardware on the platform. We've been working closely with two hyperscaler cloud providers that-- >> I wonder who they are. >> I don't know who they are, right. >> Both start with the letter A. >> And they're both here, right. What is that? >> They both start with the letter A. >> Oh, that's right. >> I won't give it away. (laughing) >> Don't give it away. >> One has three, one has four. (both laugh) >> I'm guessing, by the way. >> Then we have customers in the, actually, early customers have been using the platform from the beginning in the consumer electronics space, in Japan, you know, self driving car technology, as well. As well as some AI first companies that actually, whose core value, the core business come from AI models. >> So, serious, serious customers. They got deep tech chops. They're integrating, they see this as a strategic part of their architecture. >> That's what I call AI native, exactly. But now there's, we have several enterprise customers in line now, we've been talking to. Of course, because now we launched the platform, now we started onboarding and exploring how we're going to serve it to these customers. But it's pretty clear that our technology can solve a lot of other pain points right now. And we're going to work with them as early customers to go and refine them. >> So, do you sell to the little guys, like us? Will we be customers if we wanted to be? >> You could, absolutely, yeah. >> What we have to do, have machine learning folks on staff? >> So, here's what you're going to have to do. Since you can see the booth, others can't. No, but they can certainly, you can try our demo. >> OctoML. >> And you should look at the transparent AI app that's compiled and optimized with our flow, and deployed and built with our flow. That allows you to get your image and do style transfer. You know, you can get you and a pineapple and see how you look like with a pineapple texture. >> We got a lot of transcript and video data. >> Right. Yeah. Right, exactly. So, you can use that. Then there's a very clear-- >> But I could use it. You're not blocking me from using it. Everyone's, it's pretty much democratized. >> You can try the demo, and then you can request access to the platform. >> But you get a lot of more serious deeper customers. But you can serve anybody, what you're saying. >> Luis: We can serve anybody, yeah. >> All right, so what's the vision going forward? Let me ask this. When did people start getting the epiphany of removing the machine learning from the hardware? Was it recently, a couple years ago? >> Well, on the research side, we helped start that trend a while ago. I don't need to repeat that. But I think the vision that's important here, I want the audience here to take away is that, there's a lot of progress being made in creating machine learning models. So, there's fantastic tools to deal with training data, and creating the models, and so on. And now there's a bunch of models that can solve real problems there. The question is, how do you very easily integrate that into your intelligent applications? Madrona Venture Group has been very vocal and investing heavily in intelligent applications both and user applications as well as enablers. So we say an enable of that because it's so easy to use our flow to get a model integrated into your application. Now, any regular software developer can integrate that. And that's just the beginning, right? Because, you know, now we have CI/CD integration to keep your models up to date, to continue to integrate, and then there's more downstream support for other features that you normally have in regular software development. >> I've been thinking about this for a long, long, time. And I think this whole code, no one thinks about code. Like, I write code, I'm deploying it. I think this idea of machine learning as code independent of other dependencies is really amazing. It's so obvious now that you say it. What's the choices now? Let's just say that, I buy it, I love it, I'm using it. Now what do I got to do if I want to deploy it? Do I have to pick processors? Are there verified platforms that you support? Is there a short list? Is there every piece of hardware? >> We actually can help you. I hope we're not saying we can do everything in the world here, but we can help you with that. So, here's how. When you have them all in the platform you can actually see how this model runs on any instance of any cloud, by the way. So we support all the three major cloud providers. And then you can make decisions. For example, if you care about latency, your model has to run on, at most 50 milliseconds, because you're going to have interactivity. And then, after that, you don't care if it's faster. All you care is that, is it going to run cheap enough. So we can help you navigate. And also going to make it automatic. >> It's like tire kicking in the dealer showroom. >> Right. >> You can test everything out, you can see the simulation. Are they simulations, or are they real tests? >> Oh, no, we run all in real hardware. So, we have, as I said, we support any instances of any of the major clouds. We actually run on the cloud. But we also support a select number of edge devices today, like ARMs and Nvidia Jetsons. And we have the OctoML cloud, which is a bunch of racks with a bunch Raspberry Pis and Nvidia Jetsons, and very soon, a bunch of mobile phones there too that can actually run the real hardware, and validate it, and test it out, so you can see that your model runs performant and economically enough in the cloud. And it can run on the edge devices-- >> You're a machine learning as a service. Would that be an accurate? >> That's part of it, because we're not doing the machine learning model itself. You come with a model and we make it deployable and make it ready to deploy. So, here's why it's important. Let me try. There's a large number of really interesting companies that do API models, as in API as a service. You have an NLP model, you have computer vision models, where you call an API and then point in the cloud. You send an image and you got a description, for example. But it is using a third party. Now, if you want to have your model on your infrastructure but having the same convenience as an API you can use our service. So, today, chances are that, if you have a model that you know that you want to do, there might not be an API for it, we actually automatically create the API for you. >> Okay, so that's why I get the DevOps agility for machine learning is a better description. Cause it's not, you're not providing the service. You're providing the service of deploying it like DevOps infrastructure as code. You're now ML as code. >> It's your model, your API, your infrastructure, but all of the convenience of having it ready to go, fully automatic, hands off. >> Cause I think what's interesting about this is that it brings the craftsmanship back to machine learning. Cause it's a craft. I mean, let's face it. >> Yeah. I want human brains, which are very precious resources, to focus on building those models, that is going to solve business problems. I don't want these very smart human brains figuring out how to scrub this into actually getting run the right way. This should be automatic. That's why we use machine learning, for machine learning to solve that. >> Here's an idea for you. We should write a book called, The Lean Machine Learning. Cause the lean startup was all about DevOps. >> Luis: We call machine leaning. No, that's not it going to work. (laughs) >> Remember when iteration was the big mantra. Oh, yeah, iterate. You know, that was from DevOps. >> Yeah, that's right. >> This code allowed for standing up stuff fast, double down, we all know the history, what it turned out. That was a good value for developers. >> I could really agree. If you don't mind me building on that point. You know, something we see as OctoML, but we also see at Madrona as well. Seeing that there's a trend towards best in breed for each one of the stages of getting a model deployed. From the data aspect of creating the data, and then to the model creation aspect, to the model deployment, and even model monitoring. Right? We develop integrations with all the major pieces of the ecosystem, such that you can integrate, say with model monitoring to go and monitor how a model is doing. Just like you monitor how code is doing in deployment in the cloud. >> It's evolution. I think it's a great step. And again, I love the analogy to the mainstream. I lived during those days. I remember the monolithic propriety, and then, you know, OSI model kind of blew it. But that OSI stack never went full stack, and it only stopped at TCP/IP. So, I think the same thing's going on here. You see some scalability around it to try to uncouple it, free it. >> Absolutely. And sustainability and accessibility to make it run faster and make it run on any deice that you want by any developer. So, that's the tagline. >> Luis Ceze, thanks for coming on. Professor. >> Thank you. >> I didn't know you were a professor. That's great to have you on. It was a masterclass in DevOps agility for machine learning. Thanks for coming on. Appreciate it. >> Thank you very much. Thank you. >> Congratulations, again. All right. OctoML here on theCube. Really important. Uncoupling the machine learning from the hardware specifically. That's only going to make space faster and safer, and more reliable. And that's where the whole theme of re:MARS is. Let's see how they fit in. I'm John for theCube. Thanks for watching. More coverage after this short break. >> Luis: Thank you. (gentle music)

>>Everyone wants a cloud operating model. Since the introduction of the modern cloud. Last decade, the entire technology landscape has changed. We've learned a lot from the hyperscalers, especially from AWS. Now, one thing is certain in the technology business. It's so competitive. Then if a faster, better, cheaper idea comes along, the industry will move quickly to adopt it. They'll add their unique value and then they'll bring solutions to the market. And that's precisely what's happening throughout the technology industry because of cloud. And one of the best examples is Amazon's nitro. That's AWS has custom built hypervisor that delivers on the promise of more efficiently using resources and expanding things like processor, optionality for customers. It's a secret weapon for Amazon. As, as we, as we wrote last year, every infrastructure company needs something like nitro to compete. Why do we say this? Well, Wiki Bon our research arm estimates that nearly 30% of CPU cores in the data center are wasted. >>They're doing work that they weren't designed to do well, specifically offloading networking, storage, and security tasks. So if you can eliminate that waste, you can recapture dollars that drop right to the bottom line. That's why every company needs a nitro like solution. As a result of these developments, customers are rethinking networks and how they utilize precious compute resources. They can't, or won't put everything into the public cloud for many reasons. That's one of the tailwinds for tier two cloud service providers and why they're growing so fast. They give options to customers that don't want to keep investing in building out their own data centers, and they don't want to migrate all their workloads to the public cloud. So these providers and on-prem customers, they want to be more like hyperscalers, right? They want to be more agile and they do that. They're distributing, networking and security functions and pushing them closer to the applications. >>Now, at the same time, they're unifying their view of the network. So it can be less fragmented, manage more efficiently with more automation and better visibility. How are they doing this? Well, that's what we're going to talk about today. Welcome to changing the game for cloud networking made possible by pluribus networks. My name is Dave Vellante and today on this special cube presentation, John furrier, and I are going to explore these issues in detail. We'll dig into new solutions being created by pluribus and Nvidia to specifically address offloading, wasted resources, accelerating performance, isolating data, and making networks more secure all while unifying the network experience. We're going to start on the west coast and our Palo Alto studios, where John will talk to Mike of pluribus and AMI, but Donnie of Nvidia, then we'll bring on Alessandra Bobby airy of pluribus and Pete Lummus from Nvidia to take a deeper dive into the technology. And then we're gonna bring it back here to our east coast studio and get the independent analyst perspective from Bob Liberte of the enterprise strategy group. We hope you enjoy the program. Okay, let's do this over to John >>Okay. Let's kick things off. We're here at my cafe. One of the TMO and pluribus networks and NAMI by Dani VP of networking, marketing, and developer ecosystem at Nvidia. Great to have you welcome folks. >>Thank you. Thanks. >>So let's get into the, the problem situation with cloud unified network. What problems are out there? What challenges do cloud operators have Mike let's get into it. >>Yeah, it really, you know, the challenges we're looking at are for non hyperscalers that's enterprises, governments, um, tier two service providers, cloud service providers, and the first mandate for them is to become as agile as a hyperscaler. So they need to be able to deploy services and security policies. And second, they need to be able to abstract the complexity of the network and define things in software while it's accelerated in hardware. Um, really ultimately they need a single operating model everywhere. And then the second thing is they need to distribute networking and security services out to the edge of the host. Um, we're seeing a growth in cyber attacks. Um, it's, it's not slowing down. It's only getting worse and, you know, solving for this security problem across clouds is absolutely critical. And the way to do it is to move security out to the host. >>Okay. With that goal in mind, what's the pluribus vision. How does this tie together? >>Yeah. So, um, basically what we see is, uh, that this demands a new architecture and that new architecture has four tenants. The first tenant is unified and simplified cloud networks. If you look at cloud networks today, there's, there's sort of like discreet bespoke cloud networks, you know, per hypervisor, per private cloud edge cloud public cloud. Each of the public clouds have different networks that needs to be unified. You know, if we want these folks to be able to be agile, they need to be able to issue a single command or instantiate a security policy across all those locations with one command and not have to go to each one. The second is like I mentioned, distributed security, um, distributed security without compromise, extended out to the host is absolutely critical. So micro-segmentation and distributed firewalls, but it doesn't stop there. They also need pervasive visibility. >>You know, it's, it's, it's sort of like with security, you really can't see you can't protect what you can't see. So you need visibility everywhere. The problem is visibility to date has been very expensive. Folks have had to basically build a separate overlay network of taps, packet brokers, tap aggregation infrastructure that really needs to be built into this unified network I'm talking about. And the last thing is automation. All of this needs to be SDN enabled. So this is related to my comment about abstraction abstract, the complexity of all of these discreet networks, physic whatever's down there in the physical layer. Yeah. I don't want to see it. I want to abstract it. I wanted to find things in software, but I do want to leverage the power of hardware to accelerate that. So that's the fourth tenant is SDN automation. >>Mike, we've been talking on the cube a lot about this architectural shift and customers are looking at this. This is a big part of everyone who's looking at cloud operations next gen, how do we get there? How do customers get this vision realized? >>That's a great question. And I appreciate the tee up. I mean, we're, we're here today for that reason. We're introducing two things today. Um, the first is a unified cloud networking vision, and that is a vision of where pluribus is headed with our partners like Nvidia longterm. Um, and that is about, uh, deploying a common operating model, SDN enabled SDN, automated hardware, accelerated across all clouds. Um, and whether that's underlying overlay switch or server, um, hype, any hypervisor infrastructure containers, any workload doesn't matter. So that's ultimately where we want to get. And that's what we talked about earlier. Um, the first step in that vision is what we call the unified cloud fabric. And this is the next generation of our adaptive cloud fabric. Um, and what's nice about this is we're not starting from scratch. We have a, a, an award-winning adaptive cloud fabric product that is deployed globally. Um, and in particular, uh, we're very proud of the fact that it's deployed in over a hundred tier one mobile operators as the network fabric for their 4g and 5g virtualized cores. We know how to build carrier grade, uh, networking infrastructure, what we're doing now, um, to realize this next generation unified cloud fabric is we're extending from the switch to this Nvidia Bluefield to DPU. We know there's a, >>Hold that up real quick. That's a good, that's a good prop. That's the blue field and video. >>It's the Nvidia Bluefield two DPU data processing unit. And, um, uh, you know, what we're doing, uh, fundamentally is extending our SDN automated fabric, the unified cloud fabric out to the host, but it does take processing power. So we knew that we didn't want to do, we didn't want to implement that running on the CPU, which is what some other companies do because it consumes revenue generating CPU's from the application. So a DPU is a perfect way to implement this. And we knew that Nvidia was the leader with this blue field too. And so that is the first that's, that's the first step in the getting into realizing this vision. >>I mean, Nvidia has always been powering some great workloads of GPU. Now you've got DPU networking and then video is here. What is the relationship with clothes? How did that come together? Tell us the story. >>Yeah. So, you know, we've been working with pluribus for quite some time. I think the last several months was really when it came to fruition and, uh, what pluribus is trying to build and what Nvidia has. So we have, you know, this concept of a Bluefield data processing unit, which if you think about it, conceptually does really three things, offload, accelerate an isolate. So offload your workloads from your CPU to your data processing unit infrastructure workloads that is, uh, accelerate. So there's a bunch of acceleration engines. So you can run infrastructure workloads much faster than you would otherwise, and then isolation. So you have this nice security isolation between the data processing unit and your other CPU environment. And so you can run completely isolated workloads directly on the data processing unit. So we introduced this, you know, a couple of years ago, and with pluribus, you know, we've been talking to the pluribus team for quite some months now. >>And I think really the combination of what pluribus is trying to build and what they've developed around this unified cloud fabric, uh, is fits really nicely with the DPU and running that on the DPU and extending it really from your physical switch, all the way to your host environment, specifically on the data processing unit. So if you think about what's happening as you add data processing units to your environment. So every server we believe over time is going to have data processing units. So now you'll have to manage that complexity from the physical network layer to the host layer. And so what pluribus is really trying to do is extending the network fabric from the host, from the switch to the host, and really have that single pane of glass for network operators to be able to configure provision, manage all of the complexity of the network environment. >>So that's really how the partnership truly started. And so it started really with extending the network fabric, and now we're also working with them on security. So, you know, if you sort of take that concept of isolation and security isolation, what pluribus has within their fabric is the concept of micro-segmentation. And so now you can take that extended to the data processing unit and really have, um, isolated micro-segmentation workloads, whether it's bare metal cloud native environments, whether it's virtualized environments, whether it's public cloud, private cloud hybrid cloud. So it really is a magical partnership between the two companies with their unified cloud fabric running on, on the DPU. >>You know, what I love about this conversation is it reminds me of when you have these changing markets, the product gets pulled out of the market and, and you guys step up and create these new solutions. And I think this is a great example. So I have to ask you, how do you guys differentiate what sets this apart for customers with what's in it for the customer? >>Yeah. So I mentioned, you know, three things in terms of the value of what the Bluefield brings, right? There's offloading, accelerating, isolating, that's sort of the key core tenants of Bluefield. Um, so that, you know, if you sort of think about what, um, what Bluefields, what we've done, you know, in terms of the differentiation, we're really a robust platform for innovation. So we introduced Bluefield to, uh, last year, we're introducing Bluefield three, which is our next generation of Bluefields, you know, we'll have five X, the arm compute capacity. It will have 400 gig line rate acceleration, four X better crypto acceleration. So it will be remarkably better than the previous generation. And we'll continue to innovate and add, uh, chips to our portfolio every, every 18 months to two years. Um, so that's sort of one of the key areas of differentiation. The other is the, if you look at Nvidia and, and you know, what we're sort of known for is really known for our AI artificial intelligence and our artificial intelligence software, as well as our GPU. >>So you look at artificial intelligence and the combination of artificial intelligence plus data processing. This really creates the, you know, faster, more efficient, secure AI systems from the core of your data center, all the way out to the edge. And so with Nvidia, we really have these converged accelerators where we've combined the GPU, which does all your AI processing with your data processing with the DPU. So we have this convergence really nice convergence of that area. And I would say the third area is really around our developer environment. So, you know, one of the key, one of our key motivations at Nvidia is really to have our partner ecosystem, embrace our technology and build solutions around our technology. So if you look at what we've done with the DPU, with credit and an SDK, which is an open SDK called Doka, and it's an open SDK for our partners to really build and develop solutions using Bluefield and using all these accelerated libraries that we expose through Doka. And so part of our differentiation is really building this open ecosystem for our partners to take advantage and build solutions around our technology. >>You know, what's exciting is when I hear you talk, it's like you realize that there's no one general purpose network anymore. Everyone has their own super environment Supercloud or these new capabilities. They can really craft their own, I'd say, custom environment at scale with easy tools. Right. And it's all kind of, again, this is the new architecture Mike, you were talking about, how does customers run this effectively? Cost-effectively and how do people migrate? >>Yeah, I, I think that is the key question, right? So we've got this beautiful architecture. You, you know, Amazon nitro is a, is a good example of, of a smart NIC architecture that has been successfully deployed, but enterprises and serve tier two service providers and tier one service providers and governments are not Amazon, right? So they need to migrate there and they need this architecture to be cost-effective. And, and that's, that's super key. I mean, the reality is deep user moving fast, but they're not going to be, um, deployed everywhere on day one. Some servers will have DPS right away, some servers will have use and a year or two. And then there are devices that may never have DPS, right. IOT gateways, or legacy servers, even mainframes. Um, so that's the beauty of a solution that creates a fabric across both the switch and the DPU, right. >>Um, and by leveraging the Nvidia Bluefield DPU, what we really like about it is it's open. Um, and that drives, uh, cost efficiencies. And then, um, uh, you know, with this, with this, our architectural approach effectively, you get a unified solution across switch and DPU workload independent doesn't matter what hypervisor it is, integrated visibility, integrated security, and that can, uh, create tremendous cost efficiencies and, and really extract a lot of the expense from, from a capital perspective out of the network, as well as from an operational perspective, because now I have an SDN automated solution where I'm literally issuing a command to deploy a network service or to create or deploy our security policy and is deployed everywhere, automatically saving the oppor, the network operations team and the security operations team time. >>All right. So let me rewind that because that's super important. Get the unified cloud architecture, I'm the customer guy, but it's implemented, what's the value again, take, take me through the value to me. I have a unified environment. What's the value. >>Yeah. So I mean, the value is effectively, um, that, so there's a few pieces of value. The first piece of value is, um, I'm creating this clean D mark. I'm taking networking to the host. And like I mentioned, we're not running it on the CPU. So in implementations that run networking on the CPU, there's some conflict between the dev ops team who owned the server and the NetApps team who own the network because they're installing software on the, on the CPU stealing cycles from what should be revenue generating. Uh CPU's. So now by, by terminating the networking on the DPU, we click create this real clean DMARC. So the dev ops folks are happy because they don't necessarily have the skills to manage network and they don't necessarily want to spend the time managing networking. They've got their network counterparts who are also happy the NetApps team, because they want to control the networking. >>And now we've got this clean DMARC where the DevOps folks get the services they need and the NetApp folks get the control and agility they need. So that's a huge value. Um, the next piece of value is distributed security. This is essential. I mentioned earlier, you know, put pushing out micro-segmentation and distributed firewall, basically at the application level, right, where I create these small, small segments on an by application basis. So if a bad actor does penetrate the perimeter firewall, they're contained once they get inside. Cause the worst thing is a bad actor, penetrates a perimeter firewall and can go wherever they want and wreak havoc. Right? And so that's why this, this is so essential. Um, and the next benefit obviously is this unified networking operating model, right? Having, uh, uh, uh, an operating model across switch and server underlay and overlay, workload agnostic, making the life of the NetApps teams much easier so they can focus their time on really strategy instead of spending an afternoon, deploying a single villain, for example. >>Awesome. And I think also from my standpoint, I mean, perimeter security is pretty much, I mean, they're out there, it gets the firewall still out there exists, but pretty much they're being breached all the time, the perimeter. So you have to have this new security model. And I think the other thing that you mentioned, the separation between dev ops is cool because the infrastructure is code is about making the developers be agile and build security in from day one. So this policy aspect is, is huge. Um, new control points. I think you guys have a new architecture that enables the security to be handled more flexible. >>Right. >>That seems to be the killer feature here, >>Right? Yeah. If you look at the data processing unit, I think one of the great things about sort of this new architecture, it's really the foundation for zero trust it's. So like you talked about the perimeter is getting breached. And so now each and every compute node has to be protected. And I think that's sort of what you see with the partnership between pluribus and Nvidia is the DPU is really the foundation of zero trust. And pluribus is really building on that vision with, uh, allowing sort of micro-segmentation and being able to protect each and every compute node as well as the underlying network. >>This is super exciting. This is an illustration of how the market's evolving architectures are being reshaped and refactored for cloud scale and all this new goodness with data. So I gotta ask how you guys go into market together. Michael, start with you. What's the relationship look like in the go to market with an Nvidia? >>Sure. Um, I mean, we're, you know, we're super excited about the partnership, obviously we're here together. Um, we think we've got a really good solution for the market, so we're jointly marketing it. Um, uh, you know, obviously we appreciate that Nvidia is open. Um, that's, that's sort of in our DNA, we're about open networking. They've got other ISV who are gonna run on Bluefield too. We're probably going to run on other DPS in the, in the future, but right now, um, we're, we feel like we're partnered with the number one, uh, provider of DPS in the world and, uh, super excited about, uh, making a splash with it. >>I'm in get the hot product. >>Yeah. So Bluefield too, as I mentioned was GA last year, we're introducing, uh, well, we now also have the converged accelerator. So I talked about artificial intelligence or artificial intelligence with the Bluefield DPU, all of that put together on a converged accelerator. The nice thing there is you can either run those workloads. So if you have an artificial intelligence workload and an infrastructure workload, you can warn them separately on the same platform or you can actually use, uh, you can actually run artificial intelligence applications on the Bluefield itself. So that's what the converged accelerator really brings to the table. Uh, so that's available now. Then we have Bluefield three, which will be available late this year. And I talked about sort of, you know, uh, how much better that next generation of Bluefield is in comparison to Bluefield two. So we will see Bluefield three shipping later on this year, and then our software stack, which I talked about, which is called Doka we're on our second version are Doka one dot two. >>We're releasing Doka one dot three, uh, in about two months from now. And so that's really our open ecosystem framework. So allow you to program the Bluefields. So we have all of our acceleration libraries, um, security libraries, that's all packed into this STK called Doka. And it really gives that simplicity to our partners to be able to develop on top of Bluefield. So as we add new generations of Bluefield, you know, next, next year, we'll have, you know, another version and so on and so forth Doka is really that unified unified layer that allows, um, Bluefield to be both forwards compatible and backwards compatible. So partners only really have to think about writing to that SDK once, and then it automatically works with future generations of Bluefields. So that's sort of the nice thing around, um, around Doka. And then in terms of our go to market model, we're working with every, every major OEM. So, uh, later on this year, you'll see, you know, major server manufacturers, uh, releasing Bluefield enabled servers. So, um, more to come >>Awesome, save money, make it easier, more capabilities, more workload power. This is the future of, of cloud operations. >>Yeah. And, and, and, uh, one thing I'll add is, um, we are, um, we have a number of customers as you'll hear in the next segment, um, that are already signed up and we'll be working with us for our, uh, early field trial starting late April early may. Um, we are accepting registrations. You can go to www.pluribusnetworks.com/e F T a. If you're interested in signing up for, um, uh, being part of our field trial and providing feedback on the product, >>Awesome innovation and network. Thanks so much for sharing the news. Really appreciate it. Thanks so much. Okay. In a moment, we'll be back to look deeper in the product, the integration security zero trust use cases. You're watching the cube, the leader in enterprise tech coverage, >>Cloud networking is complex and fragmented slowing down your business. How can you simplify and unify your cloud networks to increase agility and business velocity? >>Pluribus unified cloud networking provides a unified simplify and agile network fabric across all clouds. It brings the simplicity of a public cloud operation model to private clouds, dramatically reducing complexity and improving agility, availability, and security. Now enterprises and service providers can increase their business philosophy and delight customers in the distributed multi-cloud era. We achieve this with a new approach to cloud networking, pluribus unified cloud fabric. This open vendor, independent network fabric, unifies, networking, and security across distributed clouds. The first step is extending the fabric to servers equipped with data processing units, unifying the fabric across switches and servers, and it doesn't stop there. The fabric is unified across underlay and overlay networks and across all workloads and virtualization environments. The unified cloud fabric is optimized for seamless migration to this new distributed architecture, leveraging the power of the DPU for application level micro-segmentation distributed fireball and encryption while still supporting those servers and devices that are not equipped with a DPU. Ultimately the unified cloud fabric extends seamlessly across distributed clouds, including central regional at edge private clouds and public clouds. The unified cloud fabric is a comprehensive network solution. That includes everything you need for clouds, networking built in SDN automation, distributed security without compromises, pervasive wire speed, visibility and application insight available on your choice of open networking switches and DP use all at the lowest total cost of ownership. The end result is a dramatically simplified unified cloud networking architecture that unifies your distributed clouds and frees your business to move at cloud speed, >>To learn more, visit www.pluribusnetworks.com. >>Okay. We're back I'm John ferry with the cube, and we're going to go deeper into a deep dive into unified cloud networking solution from Clovis and Nvidia. And we'll examine some of the use cases with Alessandra Burberry, VP of product management and pullovers networks and Pete Bloomberg who's director of technical marketing and video remotely guys. Thanks for coming on. Appreciate it. >>Yeah. >>So deep dive, let's get into the what and how Alexandra we heard earlier about the pluribus Nvidia partnership and the solution you're working together on what is it? >>Yeah. First let's talk about the water. What are we really integrating with the Nvidia Bluefield, the DPO technology, uh, plugable says, um, uh, there's been shipping, uh, in, uh, in volume, uh, in multiple mission critical networks. So this advisor one network operating systems, it runs today on a merchant silicone switches and effectively it's a standard open network operating system for data center. Um, and the novelty about this system that integrates a distributed control plane for, at water made effective in SDN overlay. This automation is a completely open and interoperable and extensible to other type of clouds is not enclosed them. And this is actually what we're now porting to the Nvidia DPO. >>Awesome. So how does it integrate into Nvidia hardware and specifically how has pluribus integrating its software with the Nvidia hardware? >>Yeah, I think, uh, we leverage some of the interesting properties of the Bluefield, the DPO hardware, which allows actually to integrate, uh, um, uh, our software, our network operating system in a manner which is completely isolated and independent from the guest operating system. So the first byproduct of this approach is that whatever we do at the network level on the DPU card that is completely agnostic to the hypervisor layer or OSTP layer running on, uh, on the host even more, um, uh, we can also independently manage this network, know that the switch on a Neek effectively, um, uh, managed completely independently from the host. You don't have to go through the network operating system, running on x86 to control this network node. So you throw yet the experience effectively of a top of rack for virtual machine or a top of rack for, uh, Kubernetes bots, where instead of, uh, um, if you allow me with the analogy instead of connecting a server knee directly to a switchboard, now you're connecting a VM virtual interface to a virtual interface on the switch on an ache. >>And, uh, also as part of this integration, we, uh, put a lot of effort, a lot of emphasis in, uh, accelerating the entire, uh, data plane for networking and security. So we are taking advantage of the DACA, uh, Nvidia DACA API to program the accelerators. And these accomplished two things with that. Number one, uh, you, uh, have much greater performance, much better performance. They're running the same network services on an x86 CPU. And second, this gives you the ability to free up, I would say around 20, 25% of the server capacity to be devoted either to, uh, additional workloads to run your cloud applications, or perhaps you can actually shrink the power footprint and compute footprint of your data center by 20%, if you want to run the same number of compute workloads. So great efficiencies in the overall approach, >>And this is completely independent of the server CPU, right? >>Absolutely. There is zero code from running on the x86, and this is what we think this enables a very clean demarcation between computer and network. >>So Pete, I gotta get, I gotta get you in here. We heard that, uh, the DPU is enabled cleaner separation of dev ops and net ops. Can you explain why that's important because everyone's talking DevSecOps right now, you've got net ops, net, net sec ops, this separation. Why is this clean separation important? >>Yeah, I think it's a, you know, it's a pragmatic solution in my opinion. Um, you know, we wish the world was all kind of rainbows and unicorns, but it's a little, a little messier than that. And I think a lot of the dev ops stuff and that, uh, mentality and philosophy, there's a natural fit there. Right? You have applications running on servers. So you're talking about developers with those applications integrating with the operators of those servers. Well, the network has always been this other thing and the network operators have always had a very different approach to things than compute operators. And, you know, I think that we, we in the networking industry have gotten closer together, but there's still a gap there's still some distance. And I think in that distance, isn't going to be closed. And so, you know, again, it comes down to pragmatism and I think, you know, one of my favorite phrases is look good fences, make good neighbors. And that's what this is. >>Yeah. That's a great point because dev ops has become kind of the calling card for cloud, right. But dev ops is as simply infrastructure as code and infrastructure is networking, right? So if infrastructure is code, you know, you're talking about, you know, that part of the stack under the covers under the hood, if you will, this is super important distinction. And this is where the innovation is. Can you elaborate on how you see that? Because this is really where the action is right now. >>Yeah, exactly. And I think that's where, um, one from, from the policy, the security that the zero trust aspect of this, right? If you get it wrong on that network side, all of a sudden you, you can totally open up that those capabilities. And so security is part of that. But the other part is thinking about this at scale, right? So we're taking one top of rack switch and adding, you know, up to 48 servers per rack. And so that ability to automate, orchestrate and manage at scale becomes absolutely critical. >>I'll Sandra, this is really the why we're talking about here, and this is scale. And again, getting it right. If you don't get it right, you're going to be really kind of up, you know what you know, so this is a huge deal. Networking matters, security matters, automation matters, dev ops, net ops, all coming together, clean separation, um, help us understand how this joint solution with Nvidia fits into the pluribus unified cloud networking vision, because this is what people are talking about and working on right now. >>Yeah, absolutely. So I think here with this solution, we're attacking two major problems in cloud networking. One is, uh, operation of, uh, cloud networking. And the second is a distributing security services in the cloud infrastructure. First, let me talk about the first water. We really unifying. If we're unifying something, something must be at least fragmented or this jointed and the, what is this joint that is actually the network in the cloud. If you look holistically, how networking is deployed in the cloud, you have your physical fabric infrastructure, right? Your switches and routers, you'll build your IP clause fabric leaf in spine typologies. This is actually a well understood the problem. I, I would say, um, there are multiple vendors, uh, uh, with, uh, um, uh, let's say similar technologies, um, very well standardized, whether you will understood, um, and almost a commodity, I would say building an IP fabric these days, but this is not the place where you deploy most of your services in the cloud, particularly from a security standpoint, two services are actually now moved into the compute layer where you actually were called builders, have to instrument the, a separate, uh, network virtualization layer, where they deploy segmentation and security closer to the workloads. >>And this is where the complication arise. These high value part of the cloud network is where you have a plethora of options that they don't talk to each other. And they are very dependent on the kind of hypervisor or compute solution you choose. Um, for example, the networking API to be between an GSXI environment or an hyper V or a Zen are completely disjointed. You have multiple orchestration layers. And when, and then when you throw in also Kubernetes in this, in this, in this type of architecture, uh, you're introducing yet another level of networking. And when Kubernetes runs on top of VMs, which is a prevalent approach, you actually just stacking up multiple networks on the compute layer that they eventually run on the physical fabric infrastructure. Those are all ships in the nights effectively, right? They operate as completely disjointed. And we're trying to attack this problem first with the notion of a unified fabric, which is independent from any workloads, whether it's this fabric spans on a switch, which can be con connected to a bare metal workload, or can span all the way inside the DPU, uh, where, um, you have, uh, your multi hypervisor compute environment. >>It's one API, one common network control plane, and one common set of segmentation services for the network. That's probably the number one, >>You know, it's interesting you, man, I hear you talking, I hear one network month, different operating models reminds me of the old serverless days. You know, there's still servers, but they call it serverless. Is there going to be a term network list? Because at the end of the day, it should be one network, not multiple operating models. This, this is a problem that you guys are working on. Is that right? I mean, I'm not, I'm just joking server listen network list, but the idea is it should be one thing. >>Yeah, it's effectively. What we're trying to do is we are trying to recompose this fragmentation in terms of network operation, across physical networking and server networking server networking is where the majority of the problems are because of the, uh, as much as you have standardized the ways of building, uh, physical networks and cloud fabrics with IP protocols and internet, you don't have that kind of, uh, uh, sort of, uh, um, um, uh, operational efficiency, uh, at the server layer. And, uh, this is what we're trying to attack first. The, with this technology, the second aspect we're trying to attack is are we distribute the security services throughout the infrastructure, more efficiently, whether it's micro-segmentation is a stateful firewall services, or even encryption. Those are all capabilities enabled by the blue field, uh, uh, the Butte technology and, uh, uh, we can actually integrate those capabilities directly into the nettle Fabrica, uh, limiting dramatically, at least for east-west traffic, the sprawl of, uh, security appliances, whether virtual or physical, that is typically the way the people today, uh, segment and secure the traffic in the cloud. >>Awesome. Pete, all kidding aside about network lists and serverless kind of fun, fun play on words there, the network is one thing it's basically distributed computing, right? So I love to get your thoughts about this distributed security with zero trust as the driver for this architecture you guys are doing. Can you share in more detail the depth of why DPU based approach is better than alternatives? >>Yeah, I think what's, what's beautiful and kind of what the DPU brings. That's new to this model is a completely isolated compute environment inside. So, you know, it's the, uh, yo dog, I heard you like a server, so I put a server inside your server. Uh, and so we provide, uh, you know, armed CPU's memory and network accelerators inside, and that is completely isolated from the host. So the server, the, the actual x86 host just thinks it has a regular Nick in there, but you actually have this full control plane thing. It's just like taking your top of rack switch and shoving it inside of your compute node. And so you have not only the separation, um, within the data plane, but you have this complete control plane separation. So you have this element that the network team can now control and manage, but we're taking all of the functions we used to do at the top of rack switch, and we're just shooting them now. >>And, you know, as time has gone on we've, we've struggled to put more and more and more into that network edge. And the reality is the network edge is the compute layer, not the top of rack switch layer. And so that provides this phenomenal enforcement point for security and policy. And I think outside of today's solutions around virtual firewalls, um, the other option is centralized appliances. And even if you can get one that can scale large enough, the question is, can you afford it? And so what we end up doing is we kind of hope that of aliens good enough, or we hope that if the excellent tunnel is good enough and we can actually apply more advanced techniques there because we can't physically, you know, financially afford that appliance to see all of the traffic. And now that we have a distributed model with this accelerator, we could do it. >>So what's the what's in it for the customer. I real quick, cause I think this is interesting point. You mentioned policy, everyone in networking knows policy is just a great thing and it adds, you hear it being talked about up the stack as well. When you start getting to orchestrating microservices and whatnot, all that good stuff going on there, containers and whatnot and modern applications. What's the benefit to the customers with this approach? Because what I heard was more scale, more edge deployment, flexibility, relative to security policies and application enablement. I mean, is that what what's the customer get out of this architecture? What's the enablement. >>It comes down to, uh, taking again the capabilities that were in that top of rack switch and asserting them down. So that makes simplicity smaller blast radiuses for failure, smaller failure domains, maintenance on the networks, and the systems become easier. Your ability to integrate across workloads becomes infinitely easier. Um, and again, you know, we always want to kind of separate each one of those layers. So just as in say, a VX land network, my leaf and spine don't have to be tightly coupled together. I can now do this at a different layer. And so you can run a DPU with any networking in the core there. And so you get this extreme flexibility. You can start small, you can scale large. Um, you know, to me, the, the possibilities are endless. Yes, >>It's a great security control plan. Really flexibility is key. And, and also being situationally aware of any kind of threats or new vectors or whatever's happening in the network. Alessandra, this is huge upside, right? You've already identified some successes with some customers on your early field trials. What are they doing and why are they attracted to the solution? >>Yeah, I think the response from customers has been, uh, the most, uh, encouraging and, uh, exciting, uh, for, uh, for us to, uh, to sort of continue and work and develop this product. And we have actually learned a lot in the process. Um, we talked to tier two tier three cloud providers. Uh, we talked to, uh, SP um, software Tyco type of networks, uh, as well as a large enterprise customers, um, in, uh, one particular case. Um, uh, one, uh, I think, um, let me, let me call out a couple of examples here, just to give you a flavor. Uh, there is a service provider, a cloud provider, uh, in Asia who is actually managing a cloud, uh, where they are offering services based on multiple hypervisors. They are native services based on Zen, but they also are on ramp into the cloud, uh, workloads based on, uh, ESI and, uh, uh, and KVM, depending on what the customer picks from the piece on the menu. >>And they have the problem of now orchestrating through their orchestrate or integrating with the Zen center with vSphere, uh, with, uh, open stack to coordinate these multiple environments and in the process to provide security, they actually deploy virtual appliances everywhere, which has a lot of costs, complication, and eats up into the server CPU. The problem is that they saw in this technology, they call it actually game changing is actually to remove all this complexity of in a single network and distribute the micro-segmentation service directly into the fabric. And overall, they're hoping to get out of it, uh, uh, tremendous, uh, um, opics, uh, benefit and overall, um, uh, operational simplification for the cloud infrastructure. That's one potent a use case. Uh, another, uh, large enterprise customer global enterprise customer, uh, is running, uh, both ESI and hyper V in that environment. And they don't have a solution to do micro-segmentation consistently across hypervisors. >>So again, micro-segmentation is a huge driver security looks like it's a recurring theme, uh, talking to most of these customers and in the Tyco space, um, uh, we're working with a few types of customers on the CFT program, uh, where the main goal is actually to our Monet's network operation. They typically handle all the VNF search with their own homegrown DPDK stack. This is overly complex. It is frankly also as low and inefficient, and then they have a physical network to manage the, the idea of having again, one network, uh, to coordinate the provision in our cloud services between the, the take of VNF, uh, and, uh, the rest of the infrastructure, uh, is extremely powerful on top of the offloading capability of the, by the bluefin DPOs. Those are just some examples. >>That was a great use case, a lot more potential. I see that with the unified cloud networking, great stuff, feed, shout out to you guys at Nvidia had been following your success for a long time and continuing to innovate as cloud scales and pluribus here with the unified networking, kind of bring it to the next level. Great stuff. Great to have you guys on. And again, software keeps driving the innovation again, networking is just a part of it, and it's the key solution. So I got to ask both of you to wrap this up. How can cloud operators who are interested in, in this, uh, new architecture and solution, uh, learn more because this is an architectural shift. People are working on this problem. They're trying to think about multiple clouds of trying to think about unification around the network and giving more security, more flexibility, uh, to their teams. How can people learn more? >>Yeah, so, uh, all Sandra and I have a talk at the upcoming Nvidia GTC conference. Um, so that's the week of March 21st through 24th. Um, you can go and register for free and video.com/at GTC. Um, you can also watch recorded sessions if you ended up watching us on YouTube a little bit after the fact. Um, and we're going to dive a little bit more into the specifics and the details and what we're providing in the solution. >>Alexandra, how can people learn more? >>Yeah, absolutely. People can go to the pluribus, a website, www boost networks.com/eft, and they can fill up the form and, uh, they will contact durables to either know more or to know more and actually to sign up for the actual early field trial program, which starts at the end of April. >>Okay. Well, we'll leave it there. Thanks. You both for joining. Appreciate it up next. You're going to hear an independent analyst perspective and review some of the research from the enterprise strategy group ESG. I'm John ferry with the >>Cube. Thanks for watching. >>Okay. We've heard from the folks at networks and Nvidia about their effort to transform cloud networking and unify bespoke infrastructure. Now let's get the perspective from an independent analyst and to do so. We welcome in ESG, senior analysts, Bob LA Liberte, Bob. Good to see you. Thanks for coming into our east coast studios. >>Oh, thanks for having me. It's great to be >>Here. Yeah. So this, this idea of unified cloud networking approach, how serious is it? What's what's driving it. >>Yeah, there's certainly a lot of drivers behind it, but probably the first and foremost is the fact that application environments are becoming a lot more distributed, right? So the, it pendulum tends to swing back and forth. And we're definitely on one that's swinging from consolidated to distributed. And so applications are being deployed in multiple private data centers, multiple public cloud locations, edge locations. And as a result of that, what you're seeing is a lot of complexity. So organizations are having to deal with this highly disparate environment. They have to secure it. They have to ensure connectivity to it and all that's driving up complexity. In fact, when we asked in one of our last surveys and last year about network complexity, more than half 54% came out and said, Hey, our network environment is now either more or significantly more complex than it used to be. >>And as a result of that, what you're seeing is it's really impacting agility. So everyone's moving to these modern application environments, distributing them across areas so they can improve agility yet it's creating more complexity. So a little bit counter to the fact and, you know, really counter to their overarching digital transformation initiatives. From what we've seen, you know, nine out of 10 organizations today are either beginning in process or have a mature digital transformation process or initiative, but their top goals, when you look at them, it probably shouldn't be a surprise. The number one goal is driving operational efficiency. So it makes sense. I've distributed my environment to create agility, but I've created a lot of complexity. So now I need these tools that are going to help me drive operational efficiency, drive better experience. >>I mean, I love how you bring in the data yesterday. Does a great job with that. Uh, questions is, is it about just unifying existing networks or is there sort of a need to rethink kind of a do-over network, how networks are built? >>Yeah, that's a, that's a really good point because certainly unifying networks helps right. Driving any kind of operational efficiency helps. But in this particular case, because we've made the transition to new application architectures and the impact that's having as well, it's really about changing and bringing in new frameworks and new network architectures to accommodate those new application architectures. And by that, what I'm talking about is the fact that these new modern application architectures, microservices, containers are driving a lot more east west traffic. So in the old days, it used to be easier in north south coming out of the server, one application per server, things like that. Right now you've got hundreds, if not thousands of microservices communicating with each other users communicating to them. So there's a lot more traffic and a lot of it's taking place within the servers themselves. The other issue that you starting to see as well from that security perspective, when we were all consolidated, we had those perimeter based legacy, you know, castle and moat security architectures, but that doesn't work anymore when the applications aren't in the castle, right. >>When everything's spread out that that no longer happens. So we're absolutely seeing, um, organizations trying to, trying to make a shift. And, and I think much, like if you think about the shift that we're seeing with all the remote workers and the sassy framework to enable a secure framework there, this it's almost the same thing. We're seeing this distributed services framework come up to support the applications better within the data centers, within the cloud data centers, so that you can drive that security closer to those applications and make sure they're, they're fully protected. Uh, and that's really driving a lot of the, you know, the zero trust stuff you hear, right? So never trust, always verify, making sure that everything is, is, is really secure micro-segmentation is another big area. So ensuring that these applications, when they're connected to each other, they're, they're fully segmented out. And that's again, because if someone does get a breach, if they are in your data center, you want to limit the blast radius, you want to limit the amount of damage that's done. So that by doing that, it really makes it a lot harder for them to see everything that's in there. >>You know, you mentioned zero trust. It used to be a buzzword, and now it's like become a mandate. And I love the mode analogy. You know, you build a moat to protect the queen and the castle, the Queens left the castles, it's just distributed. So how should we think about this, this pluribus and Nvidia solution. There's a spectrum, help us understand that you've got appliances, you've got pure software solutions. You've got what pluribus is doing with Nvidia, help us understand that. >>Yeah, absolutely. I think as organizations recognize the need to distribute their services to closer to the applications, they're trying different models. So from a legacy approach, you know, from a security perspective, they've got these centralized firewalls that they're deploying within their data centers. The hard part for that is if you want all this traffic to be secured, you're actually sending it out of the server up through the rack, usually to in different location in the data center and back. So with the need for agility, with the need for performance, right, that adds a lot of latency. Plus when you start needing to scale, that means adding more and more network connections, more and more appliances. So it can get very costly as well as impacting the performance. The other way that organizations are seeking to solve this problem is by taking the software itself and deploying it on the servers. Okay. So that's a, it's a great approach, right? It brings it really close to the applications, but the things you start running into there, there's a couple of things. One is that you start seeing that the DevOps team start taking on that networking and security responsibility, which they >>Don't want to >>Do, they don't want to do right. And the operations teams loses a little bit of visibility into that. Um, plus when you load the software onto the server, you're taking up precious CPU cycles. So if you're really wanting your applications to perform at an optimized state, having additional software on there, isn't going to, isn't going to do it. So, you know, when we think about all those types of things, right, and certainly the other side effects of that is the impact of the performance, but there's also a cost. So if you have to buy more servers because your CPU's are being utilized, right, and you have hundreds or thousands of servers, right, those costs are going to add up. So what, what Nvidia and pluribus have done by working together is to be able to take some of those services and be able to deploy them onto a smart Nick, right? >>To be able to deploy the DPU based smart SMARTNICK into the servers themselves. And then pluribus has come in and said, we're going to unify create that unified fabric across the networking space, into those networking services all the way down to the server. So the benefits of having that are pretty clear in that you're offloading that capability from the server. So your CPU's are optimized. You're saving a lot of money. You're not having to go outside of the server and go to a different rack somewhere else in the data center. So your performance is going to be optimized as well. You're not going to incur any latency hit for every trip round trip to the, to the firewall and back. So I think all those things are really important. Plus the fact that you're going to see from a, an organizational aspect, we talked about the dev ops and net ops teams. The network operations teams now can work with the security teams to establish the security policies and the networking policies. So that they've dev ops teams. Don't have to worry about that. So essentially they just create the guardrails and let the dev op team run. Cause that's what they want. They want that agility and speed. >>Yeah. Your point about CPU cycles is key. I mean, it's estimated that 25 to 30% of CPU cycles in the data center are wasted. The cores are wasted doing storage offload or, or networking or security offload. And, you know, I've said many times everybody needs a nitro like Amazon nugget, but you can't go, you can only buy Amazon nitro if you go into AWS. Right. Everybody needs a nitro. So is that how we should think about this? >>Yeah. That's a great analogy to think about this. Um, and I think I would take it a step further because it's, it's almost the opposite end of the spectrum because pluribus and video are doing this in a very open way. And so pluribus has always been a proponent of open networking. And so what they're trying to do is extend that now to these distributed services. So leverage working with Nvidia, who's also open as well, being able to bring that to bear so that organizations can not only take advantage of these distributed services, but also that unified networking fabric, that unified cloud fabric across that environment from the server across the switches, the other key piece of what pluribus is doing, because they've been doing this for a while now, and they've been doing it with the older application environments and the older server environments, they're able to provide that unified networking experience across a host of different types of servers and platforms. So you can have not only the modern application supported, but also the legacy environments, um, you know, bare metal. You could go any type of virtualization, you can run containers, et cetera. So a wide gambit of different technologies hosting those applications supported by a unified cloud fabric from pluribus. >>So what does that mean for the customer? I don't have to rip and replace my whole infrastructure, right? >>Yeah. Well, think what it does for, again, from that operational efficiency, when you're going from a legacy environment to that modern environment, it helps with the migration helps you accelerate that migration because you're not switching different management systems to accomplish that. You've got the same unified networking fabric that you've been working with to enable you to run your legacy as well as transfer over to those modern applications. Okay. >>So your people are comfortable with the skillsets, et cetera. All right. I'll give you the last word. Give us the bottom line here. >>So yeah, I think obviously with all the modern applications that are coming out, the distributed application environments, it's really posing a lot of risk on these organizations to be able to get not only security, but also visibility into those environments. And so organizations have to find solutions. As I said, at the beginning, they're looking to drive operational efficiency. So getting operational efficiency from a unified cloud networking solution, that it goes from the server across the servers to multiple different environments, right in different cloud environments is certainly going to help organizations drive that operational efficiency. It's going to help them save money for visibility, for security and even open networking. So a great opportunity for organizations, especially large enterprises, cloud providers who are trying to build that hyperscaler like environment. You mentioned the nitro card, right? This is a great way to do it with an open solution. >>Bob, thanks so much for, for coming in and sharing your insights. Appreciate it. >>You're welcome. Thanks. >>Thanks for watching the program today. Remember all these videos are available on demand@thekey.net. You can check out all the news from today@siliconangle.com and of course, pluribus networks.com many thanks diplomas for making this program possible and sponsoring the cube. This is Dave Volante. Thanks for watching. Be well, we'll see you next time.

>>mhm. Okay, we're back. I'm John. Fully with the Cuban. We're going to go deeper into a deep dive into unified cloud networking solution from Pluribus and NVIDIA. And we'll examine some of the use cases with Alexandra Barberry, VP of product Management and Pluribus Networks. And Pete Lambasts, the director of technical market and video. Remotely guys, thanks for coming on. Appreciate it. >>I think >>so. Deep dive. Let's get into the what and how Alexandra, we heard earlier about the pluribus and video partnership in the solution you're working together on. What is it? >>Yeah. First, let's talk about the what? What are we really integrating with the NVIDIA Bluefield deep You Technology pluribus says, uh, has been shipping, uh, in volume in multiple mission critical networks. So this adviser, one network operating systems it runs today on merchant silicon switches and effectively, it's a standard based open network computing system for data centre. Um, and the novelty about this operating system is that it integrates a distributed the control plane for Atwater made effective in STN overlay. This automation is completely open and interoperable, and extensible to other type of clouds is nothing closed and this is actually what we're now porting to the NVIDIA GPU. >>Awesome. So how does it integrate into video hardware? And specifically, how is plural is integrating its software within video hardware? >>Yeah, I think we leverage some of the interesting properties of the blue field the GPU hardware, which allows actually to integrate, um, our soft our network operating system in a manner which is completely isolated and independent from the guest operating system. So the first byproduct of this approach is that whatever we do at the network level on the GPU card is completely agnostic to the hyper visor layer or OS layer running on on the host even more. Um, uh, we can also independently manage this network. Note this switch on a nick effectively, uh, managed completely independently from the host. You don't have to go through the network operating system running on X 86 to control this network node. So you truly have the experience effectively of a top of rack for virtual machine or a top of rack for kubernetes spots. Where instead of, uh, um, if you allow me with analogy instead of connecting a server nique directly to a switchboard now you're connecting a VM virtual interface to a virtual interface on the switch on a nick. And also as part of this integration, we, uh, put a lot of effort, a lot of emphasis in accelerating the entire day to play in for networking and security. So we are taking advantage of the DACA, uh, video DACA api to programme the accelerators and this your accomplished two things with that number one, you, uh, have much greater performance, much better performance than running the same network services on an X 86 CPU. And second, this gives you the ability to free up. I would say around 2025% of the server capacity to be devoted either to additional war close to run your cloud applications. Or perhaps you can actually shrink the power footprint and compute footprint of your data centre by 20% if you want to run. The same number of computer work was so great efficiencies in the overall approach. >>And this is completely independent of the server CPU, right? >>Absolutely. There is zero quote from pluribus running on the X 86 this is what why we think this enables a very clean demarcation between computer and network. >>So, Pete, I gotta get I gotta get you in here. We heard that the GPUS enable cleaner separation of devops and net ops. Can you explain why that's important? Because everybody's talking. Def SEC ops, right now you've got Net ops. Net net SEC ops, this separation. Why is this clean separation important? >>Yeah, I think it's, uh, you know, it's a pragmatic solution, in my opinion, Um, you know, we wish the world was all kind of rainbows and unicorns, but it's a little a little messier than that. And I think a lot of the devops stuff in that, uh, mentality and philosophy. There's a natural fit there, right? You have applications running on servers. So you're talking about developers with those applications integrating with the operators of those servers? Well, the network has always been this other thing, and the network operators have always had a very different approach to things than compute operators. And, you know, I think that we we in the networking industry have gotten closer together. But there's still a gap. There's still some distance, and I think in that distance isn't going to be closed and So again it comes down to pragmatism. And I think, you know, one of my favourite phrases is look, good fences make good neighbours. And that's what this is. Yeah, >>it's a great point because devops has become kind of the calling card for cloud. Right? But devops is a simply infrastructure as code infrastructure is networking, right? So if infrastructure as code, you know, you're talking about, you know, that part of the stack under the covers under the hood, if you will. This is super important distinction. And this is where the innovation is. Can you elaborate on how you see that? Because this is really where the action is right now. >>Yeah, exactly. And I think that's where one from from the policy, the security, the zero trust aspect of this right. If you get it wrong on that network side, all of a sudden, you you can totally open up that those capabilities and so security is part of that. But the other part is thinking about this at scale, right. So we're taking one top of rack switch and adding, you know, up to 48 servers per rack, and so that ability to automate orchestrate and manage its scale becomes absolutely critical. >>Alexandra, this is really the why we're talking about here. And this is scale and again getting it right. If you don't get it right, you're gonna be really kind of up. You know what you know. So this is a huge deal. Networking matters. Security matters. Automation matters. DEVOPS. Net ops all coming together. Clean separation. Help us understand how this joint solution within video gets into the pluribus unified cloud networking vision. Because this is what people are talking about and working on right now. >>Yeah, absolutely. So I think here with this solution, we're talking to major problems in cloud networking. One is the operation of cloud networking, and the second is distributing security services in the cloud infrastructure. First, let me talk about first. What are we really unifying? If you really find something, something must be at least fragmented or disjointed. And what is this? Joint is actually the network in the cloud. If you look holistically how networking is deployed in the cloud, you have your physical fabric infrastructure, right? Your switches and routers. You build your I P clause fabric leaf and spine to apologies. this is actually well understood the problem. I would say, um, there are multiple vendors with a similar technologies. Very well, standardised. Very well understood. Um, and almost a commodity, I would say building an I P fabric these days. But this is not the place where you deploy most of your services in the cloud, particularly from a security standpoint. Those services are actually now moved into the compute layer where you actually were called. Builders have to instrument a separate network virtualisation layer, where they deploy segmentation and security closer to the workloads. And this is where the complication arise. This high value part of the cloud network is where you have a plethora of options, that they don't talk to each other, and they are very dependent on the kind of hyper visor or compute solution you choose. Um, for example, the networking API between an SX I environment or and hyper V or a Zen are completely disjointed. You have multiple orchestration layers and when and then when you throw in Also kubernetes in this In this in this type of architecture, uh, you're introducing yet another level of networking, and when you burn it, it runs on top of the M s, which is a prevalent approach. You actually just stuck in multiple networks on the compute layer that they eventually run on the physical fabric infrastructure. Those are all ships in the night effectively, right? They operate as completely disjointed. And we're trying to attack this problem first with the notion of a unified fabric, which is independent from any work clothes. Uh, whether it's this fabric spans on a switch which can become connected to a bare metal workload or can spend all the way inside the deep You where you have your multi hypervisors computer environment. It's one a P I one common network control plane and one common set of segmentation services for the network. That's probably number one. >>You know, it's interesting you I hear you talking. I hear one network different operating models reminds me the old server list days. You know there's still servers, but they called server list. Is there going to be a term network list? Because at the end of the, it should be one network, not multiple operating models. This this is like a problem that you guys are working on. Is that right? I mean, I'm not I'm just joking. Server, Listen, network list. But the idea is it should be one thing. >>Yeah, it's effectively. What we're trying to do is we're trying to recompose this fragmentation in terms of network operations across physical networking and server networking. Server networking is where the majority of the problems are because of the as much as you have standardised the ways of building, uh, physical networks and cloud fabrics with high people articles on the Internet. And you don't have that kind of, uh, sort of, uh, operational efficiency at the server layer. And this is what we're trying to attack first with this technology. The second aspect we're trying to attack is how we distribute the security services throughout the infrastructure more efficiently. Whether it's micro segmentation is a state, full firewall services or even encryption, those are all capabilities enabled by the blue field deep you technology and, uh, we can actually integrate those capabilities directly into the network fabric. Limiting dramatically, at least for is to have traffic, the sprawl of security appliances with a virtual or physical that is typically the way people today segment and secured the traffic in the >>cloud. All kidding aside about network. Listen, Civil is kind of fun. Fun play on words There the network is one thing is basically distributed computing, right? So I love to get your thoughts about this Distributed security with zero trust as the driver for this architecture you guys are doing. Can you share in more detail the depth of why DPU based approach is better than alternatives? >>Yeah, I think. What's what's beautiful and kind of what the deep you brings that's new to this model is completely isolated. Compute environment inside. So you know, it's the yo dog. I heard you like a server, So I put a server inside your server. Uh, and so we provide, you know, arm CPUs, memory and network accelerators inside, and that is completely isolated from the host. So the server, the the actual X 86 host just thinks it has a regular nick in there. But you actually have this full control plane thing. It's just like taking your top of rack, switch and shovel. Get inside of your compute node. And so you have not only the separation, um, within the data plane, but you have this complete control plane separation. So you have this element that the network team can now control and manage. But we're taking all of the functions we used to do at the top of rack Switch, and we distribute them now. And, you know, as time has gone on, we've we've struggled to put more and more and more into that network edge. And the reality is the network edge is the compute layer, not the top of rack switch layer. And so that provides this phenomenal enforcement point for security and policy. And I think outside of today's solutions around virtual firewalls, um, the other option is centralised appliances. And even if you can get one that can scale large enough, the question is, can you afford it? And so what we end up doing is we kind of hope that if aliens good enough or we hope that if you excellent tunnel is good enough, and we can actually apply more advanced techniques there because we can't physically, financially afford that appliance to see all of the traffic, and now that we have a distributed model with this accelerator, we could do it. >>So what's the what's in it for the customer real quick. I think this is an interesting point. You mentioned policy. Everyone in networking those policies just a great thing. And it has. You hear it being talked about up the stack as well. When you start getting to orchestrate microservices and what not all that good stuff going on their containers and whatnot and modern applications. What's the benefit to the customers with this approach? Because what I heard was more scale, more edge deployment, flexibility relative to security policies and application. Enablement. I mean, is that what what's the customer get out of this architecture? What's the enablement? >>It comes down to taking again the capabilities that were that top of rack switch and distracting them down. So that makes simplicity smaller. Blast Radius is for failure, smaller failure domains, maintenance on the networks and the systems become easier. Your ability to integrate across workloads becomes infinitely easier. Um, and again, you know, we always want to kind of separate each one of those layers. So, just as in, say, a Vieques land network, my leaf and spine don't have to be tightly coupled together. I can now do this at a different layer and so you can run a deep You with any networking in the core there. And so you get this extreme flexibility, you can start small. You can scale large. Um, you know, to me that the possibilities are endless. >>It's a great security control Playing really flexibility is key, and and also being situationally aware of any kind of threats or new vectors or whatever is happening in the network. Alexandra, this is huge Upside, right? You've already identified some, uh, successes with some customers on your early field trials. What are they doing? And why are they attracted? The solution? >>Yeah, I think the response from customer has been the most encouraging and exciting for for us to, uh, to sort of continuing work and develop this product. And we have actually learned a lot in the process. Um, we talked to three or two or three cloud providers. We talked to s P um, sort of telco type of networks, uh, as well as enter large enterprise customers. Um, in one particular case, um uh, one, I think. Let me let me call out a couple of examples here just to give you a flavour. There is a service provider, a cloud provider in Asia who is actually managing a cloud where they are offering services based on multiple hypervisors their native services based on Zen. But they also, um, ramp into the cloud workloads based on SX I and N K P M. Depending on what the customer picks from the piece from the menu. And they have the problem of now orchestrating through the orchestrate or integrating with Zen Centre with this fear with open stock to coordinate this multiple environments and in the process to provide security, they actually deploy virtual appliances everywhere, which has a lot of cost complication, and it's up into the service of you the promise that they saw in this technology they call it. Actually, game changing is actually to remove all this complexity, even a single network, and distribute the micro segmentation service directly into the fabric. And overall, they're hoping to get out of it. Tremendous OPEC's benefit and overall operational simplification for the cloud infrastructure. That's one important use case, um, another large enterprise customer, a global enterprise customer is running both Essex I and I purvey in their environment, and they don't have a solution to do micro segmentation consistently across Hypervisors. So again, micro segmentation is a huge driver. Security looks like it's a recurring theme talking to most of these customers and in the telco space. Um, uh, we're working with a few telco customers on the CFT programme, uh, where the main goal is actually to Arman Eyes Network operation. They typically handle all the V NFC with their own homegrown DPD K stock. This is overly complex. It is, frankly, also slow and inefficient. And then they have a physical network to manage the idea of having again one network to coordinate the provisioning of cloud services between the take of the NFC. Uh, the rest of the infrastructure is extremely powerful on top of the offloading capability. After by the blue fill the pews. Those are just some examples. >>There's a great use case, a lot more potential. I see that with the unified cloud networking. Great stuff shout out to you guys that NVIDIA, you've been following your success for a long time and continuing to innovate his cloud scales and pluribus here with unified networking. Kind of bringing the next level great stuff. Great to have you guys on and again, software keeps, uh, driving the innovation again. Networking is just part of it, and it's the key solution. So I got to ask both of you to wrap this up. How can cloud operators who are interested in in this new architecture and solution learn more? Because this is an architectural ship. People are working on this problem. They're trying to think about multiple clouds are trying to think about unification around the network and giving more security more flexibility to their teams. How can people learn more? >>And so, uh, Alexandra and I have a talk at the upcoming NVIDIA GTC conference, so it's the week of March 21st through 24th. Um, you can go and register for free and video dot com slash gtc. Um, you can also watch recorded sessions if you end up watching this on YouTube a little bit after the fact, Um, and we're going to dive a little bit more into the specifics and the details and what we're providing a solution >>as Alexandra. How can people learn more? >>Yeah, so that people can go to the pluribus website www pluribus networks dot com slash e. F t and they can fill up the form and, uh, they will contact Pluribus to either no more or to know more and actually to sign up for the actual early field trial programme. Which starts at the end of it. >>Okay, well, we'll leave it there. Thank you both for joining. Appreciate it up. Next, you're going to hear an independent analyst perspective and review some of the research from the Enterprise Strategy Group E s G. I'm John Ferry with the Cube. Thanks for watching. Mhm. Mhm.