>> 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)

(upbeat music) >> Welcome back everyone to The Cubes coverage here in Seattle, Washington. For AWS's Marketplace Seller Conference. It's the big news within the Amazon partner network, combining with marketplace, forming the Amazon partner organization. Part of a big reorg as they grow to the next level, NextGen cloud, mid-game on the chessboard. Cube's got it covered. I'm John Furry, your host at Cube. Great guests here from Data bricks. Both cube alumni's. Jack Anderson, GM and VP of the Databricks partnership team for AWS. You handle that relationship and Joel Minick vice president of product and partner marketing. You guys have the keys to the kingdom with Databricks and AWS. Thanks for joining. Good to see you again. >> Thanks for having us back. >> Yeah, John, great to be here. >> So I feel like we're at Reinvent 2013. Small event, no stage, but there's a real shift happening with procurement. Obviously it's a no brainer on the micro, you know, people should be buying online. Self-service, Cloud Scale. But Amazon's got billions being sold through their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website, marketplace. Merge our partner organizations, have more synergy and frictionless experiences so everyone can make more money and customer's are going to be happier. >> Yeah, that's right. >> I mean, you're running relationship. You're in the middle of it. >> Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do, is to work with Amazon on these really, you know, unique use cases. >> You know, I interviewed Ali many times over the years. I remember many years ago, maybe six, seven years ago, we were talking. He's like, "we're all in on AWS." Obviously now the success of Databricks, you've got multiple clouds, see that. Customers have choice. But I remember the strategy early on. It was like, we're going to be deep. So this is, speaks volumes to the relationship you have. Years. Jack, take us through the relationship that Databricks has with AWS from a partner perspective. Joel, and from a product perspective. Because it's not like you guys are Johnny come lately, new to the scene. >> Right. >> You've been there, almost president creation of this wave. What's the relationship and how does it relate to what's going on today? >> So most people may not know that Databricks was born on AWS. We actually did our first $100 million of revenue on Amazon. And today we're obviously available on multiple clouds. But we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon, and marketplace broadens our reach. And so, we think of marketplace in three different aspects. We've got the marketplace private offer business, which we've been doing for a number of years. Matter of fact, we were driving well over a hundred percent year over year growth in private offers. And we have a nine figure business. So it's a very significant business. And when a customer uses a private offer, that private offer counts against their private pricing agreement with AWS. So they get pricing power against their private pricing. So it's really important it goes on their Amazon bill. In may we launched our pay as you go, on demand offering. And in five short months, we have well over a thousand subscribers. And what this does, is it really reduces the barriers to entry. It's low friction. So anybody in an enterprise or startup or public sector company can start to use Databricks on AWS, in a consumption based model, and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation, pilots, POCs. They're really learning the value of that first, use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offer, CPPO. Super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with Databricks on behalf of customers. >> So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data, coming in with the buyers you get. And obviously the integration piece all fitting in there. >> Exactly. >> Okay, so those are the offers, that's current, what's in marketplace today. Is that the products... What are people buying? >> Yeah. >> I mean, I guess what's the... Joel, what are people buying in the marketplace? And what does it mean for them? >> So fundamentally what they're buying is the ability to take silos out of their organization. And that is the problem that Databricks is out there to solve. Which is, when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data. And your teams are trying to use all of this data to solve really complicated problems. And as Databricks, as the Lakehouse Company, what we're helping customers do is, how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find, through the marketplace, those rapid adoption use cases where they can get rid of these data warehousing, data lake silos they've had in the past. Get their unstructured and structured data onto one data platform, an open data platform, that is no longer adherent to any proprietary formats and standards and something they can, very much, very easily, integrate into the rest of their data environment. Apply one common data governance layer on top of that. So that from the time they ingest that data, to the time they use that data, to the time they share that data, inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform, with that common governance solution, they'd being able to bring all of those use cases together. Across their real time streaming, their data engineering, their BI, their AI. All of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges they just couldn't solve before. A good example of this, you know, one of the world's now largest data streaming platforms runs on Databricks with AWS. And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses. That they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data, from what the customers are doing with their platform and the recommendations they want to push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex. But by building it on Databricks, they were able to release it in record time and have grown at a record pace to now be the number one platform. >> And this product, it's impacting product development. >> Absolutely. >> I mean, this is like the difference between lagging months of product development, to like days. >> Yes. >> Pretty much what you're getting at. >> Yes. >> So total agility. >> Mm-hmm. >> I got that. Okay, now, I'm a customer I want to buy in the marketplace, but you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from AWS's leadership, Chris, was up there speaking, and Mona was, "Hey, he's a CRO conference chief revenue officer" conversation. Which means someone's getting compensated. So, if I'm the sales rep at Databricks, what's my motion to the customer? Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Or, how do you handle it? >> Well, I'd add what Joel just talked about with, you know, with the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, it's the entire Data Bricks offering. And- >> The flagship, all the, the top stuff. >> Everything, the flagship, the complete offering. So it's not segmented. It's not a sub segment. >> Okay. >> It's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we view this two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend Databricks for the right situation. Same thing with Databricks, our sales force wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get Databricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars. You have $5 million of spend. You put that spend through the flywheel with AWS marketplace, and then you can use that in your negotiations with AWS to get better pricing overall. So that's how we view it. >> So customers are driving. This sounds like. >> Correct. For sure. >> So they're looking at this as saying, Hey, I'm going to just get purchasing power with all my relationships. Because it's a solution architectural market, right? >> Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider. If they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing power. >> Okay, Joel, we're going to date ourselves. At least I will. So back in the old days, (group laughter) It used to be, do a Barney deal with someone, Hey, let's go to market together. You got to get paper, you do a biz dev deal. And then you got to say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for Databricks, or any company is, to go find those partners and do deals, versus now Amazon is the center point for the customer. So you can still do those joint deals, but this seems to be flipping the script a little bit. >> Well, it is, but we still have vars and consulting partners that are doing implementation work. Very valuable work, advisory work, that can actually work with marketplace through the CPPO offering. So the marketplace allows multiple ways to procure your solution. >> So it doesn't change your business structure. It just makes it more efficient. >> That's correct. >> That's a great way to say it. >> Yeah, that's great. >> Okay. So, that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >> Yes. >> Absolutely. >> Economically. >> Economically, it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon. Especially when it comes back to co-selling, right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution. And our teams are working backwards from those use cases, you know, to collaborate and land them. >> Yeah. I want to get that out there. Go ahead, Joel. >> So one of the other things I might add to that too, you know, and why this is advantageous for companies like Databricks to work through the marketplace. Is it makes it so much easier for customers to deploy a solution. It's very, literally, one click through the marketplace to get Databricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. >> You know, it's interesting. I want to bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself, EDI back in the old days, you know, all that craziness. Now this is all the internet, basically through the console. I get the infrastructure side, you know, spin up and provision some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage. A little nuanced. Most enterprises aren't yet there of integrating tech, on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrators dilemma, not an innovator's dilemma. So like, I want to integrate. So now I have integration points with Databricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you got to build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or am I off because, no one's going to be buying software like they used to. They buy software to integrate it. >> Yeah, no- >> Because everything's integrated. >> I think AWS has done a great job at creating a partner ecosystem, right? To give customers the right tools for the right jobs. And those might be with third parties. Databricks is doing the same thing with our partner connect program, right? We've got customer partners like Five Tran and DBT that, you know, augment and enhance our platform. And so you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >> Yeah. It's almost like, you know, bundling and un bundling. I was talking about this with, with Dave Alante about Supercloud. Which is why wouldn't a customer want the best solution in their architecture? Period. In its class. If someone's got API security or an API gateway. Well, you know, I don't want to be forced to buy something because it's part of a suite. And that's where you see things get sub optimized. Where someone dominates a category and they have, oh, you got to buy my version of this. >> Joel and I were talking, we were actually saying, what's really important about Databricks, is that customers control the data, right? You want to comment on that? >> Yeah. I was going to say, you know, what you're pushing on there, we think is extraordinarily, you know, the way the market is going to go. Is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically, I think, really strong places, Databricks and AWS have lined up, is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the Lakehouse, one thing we've always been extremely committed to, as a company, is building the data platform on an open foundation. And we do that primarily through Delta Lake and making sure that, to Jack's point, with Databricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed Databricks to have the breadth of integrations that it has with all the other data tools out there. Because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >> When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what am I foreclosing if I go with something else that's not as open? What's the customer's downside as you think about what's around the corner in the industry? Because if you believe it's going to be open, open source, which I think open source software is the software industry, and integration is a big deal. Because software's going to be plentiful. >> Sure. >> Let's face it. It's a good time to be in software business. But Cloud's booming. So what's the downside, from your Databricks perspective? You see a buyer clicking on Databricks versus that alternative. What's potentially should they be a nervous about, down the road, if they go with a more proprietary or locked in approach? >> Yeah. >> Well, I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also, then, beholden to the pace at which that provider is able to innovate. >> Mm-hmm. >> And I think we've seen lots of times over history where, you know, a proprietary format may run ahead, for a while, on a lot of innovation. But as that market control begins to solidify, that desire to innovate begins to degrade. Whereas in the open formats- >> So extract rents versus innovation. (John laughs) >> Exactly. Yeah, exactly. >> I'll say it. >> But in the open world, you know, you have to continue to innovate. >> Yeah. >> And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you are always going to be at the forefront of what is the latest. >> You know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked with proprietary. >> Yeah. >> You know, SNA and IBM, deck net was digital. You know the rest. And then TCPIP was part of the open systems interconnect. >> Mm-hmm. >> Revolutionary (indistinct) a big part of that, as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. >> Yeah. >> But that helped inter operate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid-game, then you get the end game. You're not there at the end game yet at Cloud. But Cloud- >> There's, always some form of lock in, right? Andy Jazzy will address it, you know, when making a decision. But if you're going to make a decision you want to reduce- You don't want to be limited, right? So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now, is an AI driven business, right? And so it has to do with, can you get that data out of silos? Can you organize it and secure it? And then can you work with data scientists to feed those models? >> Yeah. >> In a very consistent manner. And so the tools of tomorrow will, to Joel's point, will be open and we want interoperability with those tools. >> And choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds, is that they have to compete directly too. Redshift competes directly with a lot of other stuff. But they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're going to be, they're onto it. This is the- >> To Amazon's credit by having these solutions that may compete with native services in marketplace, they are providing customers with choice, low price- >> And access to the core value. Which is the hardware- >> Exactly. >> Which is their platform. Okay. So I want to get you guys thought on something else I see emerging. This is, again, kind of Cube rumination moment. So on stage, Chris unpacked a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing, compensation, workflows, services behind the curtain. And one of those things he mentioned was, they talk about resellers or channel partners, depending upon what you talk about. We believe, Dave and I believe on the Cube, that the entire indirect sales channel of the industry is going to be disrupted radically. Because those players were selling hardware in the old days and software. That game is going to change. You mentioned you guys have a program, let me get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in. Which means that the old reseller channels are going to be rewritten. They're going to be refactored with this new kinds of access. Because you've got scale, you've got money and you've got product. And you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, a value added reseller or VAB or business. >> You've got to evolve. >> You got to, you got to be here. >> Yes. >> Yeah. >> How are you guys working with those partners? Because you say you have a product in your marketplace there. How do I make money if I'm a reseller with Databricks, with Amazon? Take me through that use case. >> Well I'll let Joel comment, but I think it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations. They need expertise from consulting and SI partners. If those consulting and SI partners happen to resell the solution as well. Well, that's another aspect of their business. But I really think it is the expertise that the partners bring to help customers get outcomes. >> Joel, channel big opportunity for Amazon to reimagine this. >> For sure. Yeah. And I think, you know, to your comment about how do resellers take advantage of that, I think what Jack was pushing on is spot on. Which is, it's becoming more and more about the expertise you bring to the table. And not just transacting the software. But now actually helping customers make the right choices. And we're seeing, you know, both SIs begin to be able to resell solutions and finding a lot of opportunity in that. >> Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's going to be the evolution that this goes. >> At the end of the day, it's about services, right? >> For sure. Yeah. >> I mean... >> You've got a great service. You're going to have high gross profits. >> Yeah >> Managed service provider business is alive and well, right? Because there are a number of customers that want that type of a service. >> I think that's going to be a really hot, hot button for you guys. I think being the way you guys are open, this channel, partner services model coming in, to the fold, really kind of makes for kind of that Supercloud like experience, where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on, within Databricks. >> For sure. >> On top of this ecosystem. How does that work? This is kind of like, hasn't been written up in business school and case studies yet. This is new. What is this? >> I think, you know, what it comes down to is, you're seeing ecosystems begin to evolve around the data platforms. And that's going to be one of the big, kind of, new horizons for us as we think about what drives ecosystems. It's going to be around, well, what's the data platform that I'm using? And then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around Databricks in its Lakehouse platform as well. As customers are looking at well, if I'm standing these Lakehouses up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >> I mean you think about ecosystem theory, we're living a whole nother dream. And I'm not kidding. It hasn't yet been written up and for business school case studies is that, we're now in a whole nother connective tissue, ecology thing happening. Where you have dependencies and value proposition. Economics, connectedness. So you have relationships in these ecosystems. >> And I think one of the great things about the relationships with these ecosystems, is that there's a high degree of overlap. >> Yeah. >> So you're seeing that, you know, the way that the cloud business is evolving, the ecosystem partners of Databricks, are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for you. >> Joel, Jack, I love it because you know what it means? The best ecosystem will win, if you keep it open. >> Sure, sure. >> You can see everything. If you're going to do it in the dark, you know, you don't know the outcome. I mean, this is really kind of what we're talking about. >> And John, can I just add that when I was at Amazon, we had a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? >> Yeah. >> And so there's a platform decision being made and that ecosystem is going to evolve around the platform. >> Yeah, and I totally agree. And the word innovation gets kicked around. That's why, you know, when we had our Supercloud panel, it was called the innovators dilemma, with a slash through it, called the integrater's dilemma. Innovation is the digital transformation. So- >> Absolutely. >> Like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating? If APIs are connective tissue, what's automation, what's the service messages look like? I mean, a whole nother set of, kind of thinking, goes on in these new ecosystems and these new products. >> And that thinking is, has been born in Delta Sharing, right? So the idea that you can have a multi-cloud implementation of Databricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud solution. >> Well, Databricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great growing ecosystem. And again, I think a shining example of what every enterprise is going to do. Build on top of something, operating model, get that operating model, driving revenue. >> Mm-hmm. >> Yeah. >> Whether, you're Goldman Sachs or capital one or XYZ corporation. >> S and P global, NASDAQ. >> Yeah. >> We've got, you know, the biggest verticals in the world are solving tough problems with Databricks. I think we'd be remiss because if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions. Whether it's the relationship we have with our engineering and service teams. Our marketing teams, you know, product development. And we're going to be at Reinvent. A big presence at Reinvent. We're looking forward to seeing you there, again. >> Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening. This NextGen Cloud is here. We're seeing this evolve, kind of new economics, new value propositions kind of scaling up. Producing more. So you guys are doing a great job. Thanks for coming on the Cube and taking the time. Joel, great to see you at the check. >> Thanks for having us, John. >> Okay. Cube coverage here. The world's changing as APN comes together with the marketplace for a new partner organization at Amazon web services. The Cube's got it covered. This should be a very big, growing ecosystem as this continues. Billions of being sold through the marketplace. And of course the buyers are happy as well. So we've got it all covered. I'm John Furry. your host of the cube. Thanks for watching. (upbeat music)

>>Welcome back everyone to the cubes coverage here in Seattle, Washington, AWS's marketplace seller conference. It's the big news within the Amazon partner network, combining with marketplaces, forming the Amazon partner organization, part of a big reorg as they grow the next level NextGen cloud mid-game on the chessboard. Cube's got cover. I'm John fur, host of Cub, a great guests here from data bricks, both cube alumnis, Jack Anderson, GM of the and VP of the data bricks partnership team. For ADOS, you handle that relationship and Joel Minick vice president of product and partner marketing. You guys are the, have the keys to the kingdom with data, bricks, and AWS. Thanks for joining. Thanks for good to see you again. Thanks for >>Having us back. Yeah, John, great to be here. >>So I feel like we're at reinvent 2013 small event, no stage, but there's a real shift happening with procurement. Obviously it makes it's a no brainer on the micro, you know, people should be buying online self-service cloud scale, but Amazon's got billions being sold to their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website marketplace merge our partner to have more synergy and friction, less experiences so everyone can make more money and customer's gonna be happier. >>Yeah, that's right. >>I mean, you're run relationship. You're in the middle of it. >>Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do is to work with Amazon on these really, you know, unique use cases. >>You know, I interviewed Ali many times over the years. I remember many years ago, I think six, maybe six, seven years ago, we were talking. He's like, we're all in ons. Obviously. Now the success of data bricks, you've got multiple clouds. See that customers have choice, but I remember the strategy early on. It was like, we're gonna be deep. So this is speaks volumes to the, the relationship you have years. Jack take us through the relationship that data bricks has with AWS from a, from a partner perspective, Joel, and from a product perspective, because it's not like you got to Johnny come lately new to the new, to the scene, right? We've been there almost president creation of this wave. What's the relationship and has it relate to what's going on today? >>So, so most people may not know that data bricks was born on AWS. We actually did our first 100 million of revenue on Amazon. And today we're obviously available on multiple clouds, but we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon and marketplace broadens our reach. And so we think of marketplace in three different aspects. We've got the marketplace, private offer business, which we've been doing for a number of years. Matter of fact, we we're driving well over a hundred percent year over year growth in private offers and we have a nine figure business. So it's a very significant business. And when a customer uses a private offer that private offer counts against their private pricing agreement with AWS. So they get pricing power against their, their private pricing. >>So it's really important. It goes on their Amazon bill in may. We launched our pay as you go on demand offering. And in five short months, we have well over a thousand subscribers. And what this does is it really reduces the barriers to entry it's low friction. So anybody in an enterprise or startup or public sector company can start to use data bricks on AWS and pay consumption based model and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation pilots, POCs, they're, they're really learning the value of that first use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offers C P O super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with data bricks on behalf of customers. >>So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data coming in with, with the buyers you go. And obviously the integration piece all fitting in there. Exactly. Exactly. Okay. So that's that those are the offers that's current and what's in marketplace today. Is that the products, what are, what are people buying? I mean, I guess what's the Joel, what are, what are people buying in the marketplace and what does it mean for >>Them? So fundamentally what they're buying is the ability to take silos out of their organization. And that's, that is the problem that data bricks is out there to solve, which is when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data, and your teams are trying to use all of this data to solve really complicated problems. And as data bricks as the lake house company, what we're helping customers do is how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find through the marketplace, those rapid adoption use cases where they can get rid of these data, warehousing data lake silos they've had in the past, get their unstructured and structured data onto one data platform and open data platform that is no longer adherent to any proprietary formats and standards and something. >>They can very much, very easily integrate into the rest of their data environment, apply one common data governance layer on top of that. So that from the time they ingest that data to the time they use that data to the time they share that data inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform with that common governance solution, they'd being able to bring all of those use cases together across their real time, streaming their data engineering, their BI, their AI, all of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges. They just couldn't solve before a good example of this, you know, one of the world's now largest data streaming platforms runs on data bricks with AWS. >>And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses, that they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data from what the customers are doing with their platform and the recommendations they wanna push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex, but by building it on data bricks, they were able to release it in record time and have grown at, at record pace >>To not be that's product platform that's impacting product development. Absolutely. I mean, this is like the difference between lagging months of product development to like days. Yes. Pretty much what you're getting at. Yeah. So total agility. I got that. Okay. Now I'm a customer I wanna buy in the marketplace, but I also, you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from a Davis's leadership, Chris was up there speaking and, and, and moment I was, Hey, he's a CRO conference, chief revenue officer conversation, which means someone's getting compensated. So if I'm the sales rep at data bricks, what's my motion to the customer. Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Is there or an audio lift? >>Well, I I'd add what Joel just talked about with, with, you know, what the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, the entire data bricks offering and >>The flagship, all the, the top, >>Everything, the flagship, the complete offering. So it's not, it's not segmented. It's not a sub segment. It's it's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we, we, we view this two, two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend data bricks for the right situation. Same thing with data bricks. Our Salesforce wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get data bricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars, you have $5 million of spend, you put that spend through the flywheel with AWS marketplace. And then you can use that in your negotiations with AWS to get better pricing overall. So that's how we, >>We do it. So customers are driving. This sounds like, correct. For sure. So they're looking at this as saying, Hey, I'm gonna just get purchasing power with all my relationships because it's a solution architectural market, right? >>Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider, if they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing >>Power, okay, Jill, we're gonna date ourselves. At least I will. So back in the old days, it used to be, do a Barney deal with someone, Hey, let's go to market together. You gotta get paper, you do a biz dev deal. And then you gotta say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for data bricks or any company is to go find those partners and do deals versus now Amazon is the center point for the customer so that you can still do those joint deals. But this seems to be flipping the script a little bit. >>Well, it is, but we still have VAs and consulting partners that are doing implementation work very valuable work advisory work that can actually work with marketplace through the C PPO offering. So the marketplace allows multiple ways to procure your >>Solution. So it doesn't change your business structure. It just makes it more efficient. That's >>Correct. >>That's a great way to say it. Yeah, >>That's great. So that's so that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >>Yes, >>Absolutely. Economically. Yeah. >>E economically it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon, especially when it comes back to co-selling right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution and our teams are working backwards from those use cases, you know, to collaborate, land them. >>Yeah. I want, I wanna get that out there. Go ahead, Joel. >>So one of the other things I might add to that too, you know, and why this is advantageous for, for companies like data bricks to, to work through the marketplace, is it makes it so much easier for customers to deploy a solution. It's, it's very, literally one click through the marketplace to get data bricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. You >>Know, it's interesting. I wanna bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself EDI back in the old days, you know, all that craziness. Now this is all the, all the internet, basically through the console, I get the infrastructure side, you know, spin up and provision. Some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage little nuance, most enterprises aren't yet there of integrating tech on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrator's dilemma, not an innovator's dilemma. So like, I want to integrate, so now I have integration points with data bricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you gotta build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or, or am I off because no, one's gonna be buying software. Like they used to, they buy software to integrate it. >>Yeah, >>No, I, cause everything's integrated. >>I think AWS has done a great job at creating a partner ecosystem, right. To give customers the right tools for the right jobs. And those might be with third parties, data bricks is doing the same thing with our partner connect program. Right. We've got customer, customer partners like five tra and D V T that, you know, augment and enhance our platform. And so you, you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >>Yeah. It's almost like, you know, bundling and unbundling. I was talking about this with, with Dave ante about Supercloud, which is why wouldn't a customer want the best solution in their architecture period. And it's class. If someone's got API security or an API gateway. Well, you know, I don't wanna be forced to buy something because it's part of a suite and that's where you see things get suboptimized where someone dominates a category and they have, oh, you gotta buy my version of this. Yeah. >>Joel, Joel. And that's Joel and I were talking, we're actually saying what what's really important about Databricks is that customers control the data. Right? You wanna comment on that? >>Yeah. I was say the, you know what you're pushing on there we think is extraordinarily, you know, the way the market is gonna go is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically I think really strong places, data, bricks, and AWS have lined up is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the lake house, one thing we've always been extremely committed to as a company is building the data platform on an open foundation. And we do that primarily through Delta lake and making sure that to Jack's point with data bricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed data bricks to have the breadth of integrations that it has with all the other data tools out there, because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >>When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what's the, what am I foreclosing? If I go with something else that's not as open what what's the customer's downside as you think about what's around the corner in the industry. Cuz if you believe it's gonna be open, open source, which I think opens our software is the software industry and integration is a big deal, cuz software's gonna be plentiful. Let's face it. It's a good time to be in software business, but cloud's booming. So what's the downside from your data bricks perspective, you see a buyer clicking on data bricks versus that alternative what's potentially is should they be a nervous about down the road if they go with a more proprietary or locked in approach? Well, >>I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also then beholden to the pace at which that provider is able to innovate. And I think we've seen lots of times over history where, you know, a proprietary format may run ahead for a while on a lot of innovation. But as that market control begins to solidify that desire to innovate begins to, to degrade, whereas in the open format. So >>Extract rents versus innovation. Exactly. >>Yeah, exactly. >>But >>I'll say it in the open world, you know, you have to continue to innovate. Yeah. And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you were always going to be at the forefront of what is the >>Latest, you know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked for proprietary. Yeah. You know, SNA at IBM deck net was digital, you know, the rest is, and then TCP, I P was part of the open systems, interconnect, revolutionary Oly, a big part of that as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. Yeah. But that helped interoperate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid game. Then you got the end game and we're not there. The end game yet cloud the cloud. >>There's, there's always some form of lock in, right. Andy jazzy will, will address it, you know, when making a decision. But if you're gonna make a decision you want to reduce as you don't wanna be limited. Right. So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now is an AI driven business. Right? And so it has to do with, can you get that data outta silos? Can you, can you organize it and secure it? And then can you work with data scientists to feed those models? Yeah. In a, in a very consistent manner. And so the tools of tomorrow will to Joel's point will be open and we want interoperability with those >>Tools and, and choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds is that they have to compete directly too. Redshift competes directly with a lot of other stuff, but they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're gonna be they're onto it. This is >>The Amazon's credit by having these, these solutions that may compete with native services in marketplace, they are providing customers with choice, low >>Price and access to the S and access to the core value. Exactly. Which the >>Hardware, which is their platform. Okay. So I wanna get you guys thought on something else. I, I see emerging, this is again kind of cube rumination moment. So on stage Chris unpacked, a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing compensation, workflows services behind the curtain. And one of the things he mentioned was they talk about resellers or channel partners, depending upon what you talk about. We believe Dave and I believe on the cube that the entire indirect sales channel of the industry is gonna be disrupted radically because those players were selling hardware in the old days and software, that game is gonna change. You know, you mentioned you guys have a program, want to get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in which means that the old reseller channels are gonna be rewritten. They're gonna be refactored with this new kinds of access. Cuz you've got scale, you've got money and you've got product and you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, value added reseller or V or business, >>You've gotta evolve. >>You gotta, you gotta be here. Yes. How are you guys working with those partners? Cuz you say you have a part in your marketplace there. How do I make money? If I'm a reseller with data bricks with eight Amazon, take me through that use case. >>Well I'll let Joel comment, but I think it's, it's, it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations, they need expertise from consulting and SI partners. If those consulting SI partners happen to resell the solution as well. Well, that's another aspect of their business, but I really think it is the expertise that the partners bring to help customers get outcomes. >>Joel, channel big opportunity for re re Amazon to reimagine this. >>For sure. Yeah. And I think, you know, to your comment about how to resellers take advantage of that, I think what Jack was pushing on is spot on, which is it's becoming more about more and more about the expertise you bring to the table and not just transacting the software, but now actually helping customers make the right choices. And we're seeing, you know, both SI begin to be able to resell solutions and finding a lot of opportunity in that. Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's gonna be the evolution that >>This gets at the end of the day. It's about services for sure, for sure. You've got a great service. You're gonna have high gross profits. And >>I think that the managed service provider business is alive and well, right? Because there are a number of customers that want that, that type of a service. >>I think that's gonna be a really hot, hot button for you guys. I think being the way you guys are open this channel partner services model coming in to the fold really kind of makes for kind of that super cloudlike experience where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on within data bricks for sure. On top of this ecosystem, how does that work? This is kinda like hasn't been written up in business school and case studies yet this is new. What is this? >>I think, you know, what it comes down to is you're seeing ecosystems begin to evolve around the data platforms and that's gonna be one of the big kind of new horizons for us as we think about what drives ecosystems it's going to be around. Well, what is the, what's the data platform that I'm using and then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around data bricks in its Lakehouse platform, as well as customers are looking at well, if I'm standing these Lakehouse up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >>I mean you think about ecosystem theory, we're living a whole nother dream and I'm, and I'm not kidding. It hasn't yet been written up and for business school case studies is that we're now in a whole nother connective tissue ecology thing happening where you have dependencies and value proposition economics connectedness. So you have relationships in these ecosystems. >>And I think one of the great things about relationships with these ecosystems is that there's a high degree of overlap. Yeah. So you're seeing that, you know, the way that the cloud business is evolving, the, the ecosystem partners of data bricks are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for >>You. Joel, Jack, I love it because you know what it means the best ecosystem will win. If you keep it open. Sure. You can see everything. If you're gonna do it in the dark, you know, you don't know the outcome. I mean, this is really kind we're talking about. >>And John, can I just add that when I was in Amazon, we had a, a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? Yeah. And so there's a platform decision being made and that ecosystem gonna evolve around the >>Platform. Yeah. And I totally agree. And, and, and the word innovation get kicks around. That's why, you know, when we had our super cloud panel was called the innovators dilemma with a slash through it called the integrated dilemma, innovation is the digital transformation. So absolutely like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating if APIs are the connective tissue, what's automation, what's the service message look like. I mean, a whole nother set of kind of thinking goes on and these new ecosystems and these new products >>And that, and that thinking is, has been born in Delta sharing. Right? So the idea that you can have a multi-cloud implementation of data bricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud >>Solution. Well, data bricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great grow ecosystem. And again, I think in a shining example of what every enterprise is going to do, build on top of something operating model, get that operating model, driving revenue. >>Yeah. >>Well we, whether whether you're Goldman Sachs or capital one or XYZ corporation >>S and P global NASDAQ, right. We've got, you know, these, the biggest verticals in the world are solving tough problems with data breaks. I think we'd be remiss cuz if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions, whether it's the relationship we have with our engineering and service teams. Yeah. Our marketing teams, you know, product development and we're gonna be at reinvent the big presence of reinvent. We're looking forward to seeing you there again. >>Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening this next gen cloud is here. We're seeing this evolve kind of new economics, new value propositions kind of scaling up, producing more so you guys are doing a great job. Thanks for coming on the Cuban, taking time. Chill. Great to see you at the check. Thanks for having us. Thanks. Going. Okay. Cube coverage here. The world's changing as APN comes to give the marketplace for a new partner organization at Amazon web services, the Cube's got a covered. This should be a very big growing ecosystem as this continues, billions of being sold through the marketplace. Of course the buyers are happy as well. So we've got it all covered. I'm John furry, your host of the cube. Thanks for watching.

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit 22 live from Caesars Forum in Las Vegas. I'm Lisa Martin with Dave Vellante. We've got a couple of guests joining us now. We're going to be talking about financial services. Rinesh Patel joins us, the Global Head of Financial Services for Snowflake, and Jack Berkowitz, Chief Data Officer at ADP. Guys, welcome to the program. >> Thanks, thanks for having us. >> Thanks for having us. >> Talk to us about what's going on in the financial services industry as a whole. Obviously, we've seen so much change in the last couple of years. What does the data experience look like for internal folks and of course, for those end user consumers and clients? >> So, one of the big things happening inside of the financial services industry is overcoming the COVID wait, right? A lot of banks, a lot of institutions like ours had a lot of stuff on-prem. And then the move to the Cloud allows us to have that flexibility to deal with it. And out of that is also all these new capabilities. So the machine learning revolution has really hit the services industry, right? And so it's affecting how our IT teams or our data teams are building applications. Also really affecting what the end consumers get out of them. And so there's all sorts of consumerization of the experience over the past couple of years much faster than we ever expected it to happen. >> Right, we have these expectations as consumers that bleed into our business lives that I can do transactions. It's going to be on the swipe in terms of checking authenticity, fraud detection, et cetera. And of course we don't want things to go back in terms of how brands are serving us. Talk about some of the things that you guys have put in place with Snowflake in the last couple of years, particularly at ADP. >> Yeah, so one of the big things that we've done, is, one of the things that we provide is compensation data. So we issue a thing called the National Employment Report that informs the world as to what's happening in the U.S. economy in terms of workers. And then we have compensation data on top of that. So the thing that we've been able to do with Snowflake is to lower the time that it takes us to process that and get that information out into the fingertips of people. And so people can use it to see what's changed in terms of with the worker changes, how much people are making. And they can get it very, very quickly. And we're able to do that with Snowflake now. Used to take us weeks, now it's in a matter of moments we can get that updated information out to people. >> Interesting. It helps with the talent war and- >> Helps in the talent war, helps people adjust, even where they're going to put supply chain in reaction to where people are migrating. We can have all of that inside of the Snowflake system and available almost instantaneously. >> You guys announced the Financial Data Cloud last year. What was that like? 'Cause I know we had Frank on early, he clearly was driving the verticalization of Snowflake if you will, which is kind of rare for a relatively new software company but what's that been like? Give us the update on where you're at and biggest vertical, right? >> Absolutely, it's been an exciting 12 months. We're a platform, but the journey and the vision is more. We're trying to bring together a fragmented ecosystem across financial services. The aim is really to bring together key customers, key data providers, key solution providers all across the different Clouds that exist to allow them to collaborate with data in a seamless way. To solve industry problems. To solve industry problems like ESG, to solve industry problems like quantitative research. And we're seeing a massive groundswell of customers coming to Snowflake, looking at the Financial Services Data Cloud now to actually solve business problems, business critical problems. That's really driving a lot of change in terms of how they operate, in terms of how they win customers, mitigate risk and so forth. >> Jack, I think, I feel like the only industry that's sometimes more complicated than security, is data. Maybe not, security's still maybe more fragmented- >> Well really the intersection of the two is a nightmare. >> And so as you look out on this ecosystem, how do you as the chief data officer, how do you and your organization, what process do you use to decide, okay, which of the, like a chef, which of these ingredients am I going to put together for my business. >> It's a great question, right? There's been explosion of companies. We kind of look at it in two ways. One is we want to make sure that the software and the data can interoperate because we don't want to be in the business of writing bridge code. So first thing is, is having the ecosystem so that the things are tested and can work together. The other area is, and it's important to us is understanding the risk profile of that company. We process about 20% of the U.S. payroll, another 25% of the taxes. And so there's a risk to us that we have an imperative to protect. So we're looking at those companies are they financed, what's their management team. What's the sales experience like, that's important to us. And so technology and the experience of the company coming together are super important to us. >> What's your purview as a chief data officer, I mean, a lot of CDOs that I know came out of the back office and it was a compliance or data quality. You come out of industry from a technology company. So you're sort of the modern... You're like the modern CDO. >> Thanks. Thanks. >> Dave: What's your role? >> I appreciate that. >> You know what I'm saying though? >> And for a while it was like, oh yeah, compliance. >> So I actually- >> And then all of a sudden, boom, big deal. >> Yeah, I really have two jobs. So I have that job with data governance but a lot of data security. But I also have a product development unit, a massive business in monetization of data or people analytics or these compensation benchmarks or helping people get mortgages. So providing that information, so that people can get their mortgage, or their bank loans, or all this other type of transactional data. *So it's both sides of that equation is my reading inside. >> You're responsible for building data products? >> That's right. >> Directly. >> That's right. I've got a massive team that builds data products. >> Okay. That's somewhat unique in your... >> I think it's where CDOs need to be. So we build data products. We build, and we assist as a hub to allow other business units to build analytics that help them either optimize their cost or increase their sales. And then we help with all that governance and communication, we don't want to divide it up. There's a continuum to it. >> And you're a peer of the CIO and the CISO? >> Yeah, exactly. They're my peers. I actually talk to them almost every day. So I've got the CIO as a peer. >> It's a team. >> I've got the security as a peer and we get things done together. >> Talk about the alignment with business. We've been talking a lot about alignment with the data folks, the business folks, the technical folks to identify the right solutions, to be able to govern data, to monetize it, to create data products. What does that... You mentioned a couple of your cohorts, but on the business side, who are some of those key folks? >> So we're like any other big, big organization. We have lots of different business units. So we work directly with either the operational team or the heads of those business units to divine analytic missions that they'll actually execute. And at the same time, we actually have a business unit that's all around data monetization. And so I work with them every single day. And so these business units will come together. I think the big thing for us is to define value and measure that value as we go. As long as we're measuring that value as we go, then we can continue to see improvements. And so, like I said, sometimes it's bottom line, sometimes it's top line, but we're involved. Data is actually a substrate of the company. It's not a side thing to the company. >> Yeah, you are. >> ADP. >> Yeah but if they say data first but you really are data first. >> Yeah. I mean, our CEO says- >> Data's your product. >> Data's our middle name. And it literally is. >> Well, so what do you do in the Snowflake financial services data Cloud? Are you monetizing? >> Yeah. >> What's the plan? >> Yeah, so we have clients. So part of our data monetization is actually providing aggregate and anonymized information that helps other clients make business decisions. So they'll take it into their analytics. So, supply chain optimization, where should we actually put the warehouses based on the population shifts? And so we're actually using the file distribution capabilities or the information distribution, no longer files, where we use Snowflake to actually be that data cloud for those clients. So the data just pops up for our other clients. >> I think the industry's existed a lot with the physical movement of data. When you physically move data, you also physically move the data management challenges. Where do you store it? How do you map it? How do you concord it? And ultimately data sharing is taking away that friction that exists. So it's easier to be able to make informed decisions with the data at hand across two counterparties. >> Yeah, and there's a benefit to us 'cause it lowers our friction. We can have a conversation and somebody can be... Obviously the contracts have to be signed, but once they get done, somebody's up and running on it within minutes. And where it used to be, as you were saying, the movement of data and loss of control, we never actually lose control of it. We know where it is. >> Or yeah, contracts signed, now you got to go through this long process of making sure everything's cool, or a lot of times it could slow down the sale. >> That's right. >> Let's see how that's going to... Let's do a little advanced work. Now you're working without a contract. Here, you can say, "Hey, we're in the Snowflake data cloud. It's governed, you're a part of the ecosystem." >> Yeah, and the ecosystem we announced, oh gee, I think it's probably almost a year and a half ago, a relationship with ICE, Intercontinental Exchange, where they're actually taking our information and their information and creating a new data product that they in turn sell. So you get this sort of combination. >> Absolutely. The ability to form partnerships and monetize data with your partners vastly increases as a consequence. >> Talk to us about the adoption of the financial services data cloud in the last what, maybe nine months or so, since it was announced? And also in terms of the its value proposition, how does the ADP use case articulate that? >> So, very much so. So in terms of momentum, we're a global organization, as you mentioned, we are verticalized. So we have increasingly more expertise and expertise experience now within financial services that allows us to really engage and accelerate our momentum with the top banks, with the biggest asset managers by AUM, insurance companies, sovereign wealth funds on Snowflake. And obviously those data providers and solution providers that we engage with. So the momentum's really there. We're really moving very, very fast in a great market because we've got great opportunity with the capabilities that we have. I mean, ADP is just one of many use cases that we're working with and collaborations that we're taking to market. So yeah, the opportunity to monetize data and help our partners monetize the data has vastly increased within this space. >> When you think about... Oh go ahead, please. >> Yeah I was just going to say, and from our perspective, as we were getting into this, Snowflake was with us on the journey. And that's been a big deal. >> So when you think about data privacy, governance, et cetera, and public policy, it seems like you have, obviously you got things going on in Europe, and you got California, you have other states, there's increasing in complexity. You guys probably love that. (Dave laughs) More data warehouses, but where are we at with that whole? >> It's a great question. Privacy is... We hold some of the most critical information about people because that's our job to help people get paid. And we respect that as sort of our prime agenda. Part of it deals with the technology. How do you monitor, how do you see, make sure that you comply with all these regulations, but a lot of it has to do with the basic ethics of why you're doing and what you're doing. So we have a data and AI ethics board that meets and reviews our use cases. Make sure not only are we doing things properly to the regulation, but are these the types of products, are these the types of opportunities that we as a company want to stand behind on behalf of the consumers? Our company's been around 75 years. We talk about ourselves as a national asset. We have a trust relationship. We want to ensure that that trust relationship is never violated. >> Are you in a position where you can influence public policy and create more standards or framework. >> We actually are, right. We issue something every month called the National Employment Report. It actually tells you what's happening in the U.S. economy. We also issue it in some overseas countries like France. Because of that, we work a lot with various groups. And we can help shape, either data policy, we're involved in understanding although we don't necessarily want to be out in the front, but we want to learn about what's happening with federal trade commission, EOC, because at the end of the day we serve people, I always joke ADP, it's my grandfather's ADP. Well, it was actually my grandfather's ADP. (Dave laughs) He was a small businessman, and he used a ADP all those years ago. So we want to be part of that conversation because we want to continue to earn that trust every day. >> Well, plus your observation space is pretty wide. >> And you've got context and perspective on that that you can bring. >> We move somewhere between two, two and a half trillion dollars a year through our systems. And so we understand what's happening in the economy. >> What are some of the, oh sorry. >> Can your National Employment Report combined with a little Snowflake magic tell us what the hell's going to happen with this economy? >> It's really interesting you say that. Yeah, we actually can. >> Okay. (panelists laugh) >> I think when you think about the amount of data that we are working with, the types of partners that we're working with, the opportunities are infinite. They really, really are. >> So it's either a magic eight ball or it's a crystal ball, but you have it. >> We think- >> We've just uncovered that here on theCUBE. >> We think we have great partners. We have great data. We have a set of industry problems out there that we're working, collaboration with the community to be able to solve. >> What are some of the upcoming use cases Rinesh, that excite you, that are coming up in financial services- >> Great question. >> That snowflake is just going to knock out of the park. >> So look, I think there's a set of here and now problems that the industry faces, ESG's a good one. If you think about ESG, it means many different things from business ethics, to diversity, to your carbon footprint and every asset manager has to make sure they have now some form of green strategy that reflects the values of their investors. And every bank is looking to put in place sustainable lending to help their corporate customers transition. That's a big data problem. And so we're very much at the center of helping those organizations support those informed investors and help those corporates transition to a more sustainable landscape. >> Let me give you an example on Snowflake, we launched capabilities about diversity benchmarks. The first time in the industry companies can understand for their industry, their size, their location what their diversity profile looks like and their org chart profile looks like to differentiate or at least to understand are they doing the right things inside the business. The ability for banks to understand that and everything else, it's a big deal. And that was built on Snowflake. >> I think it's massive, especially in the context of the question around regulation 'cause we're seeing more and more disclosure agreements come out where regulators are making sure that there's no greenwashing taking place. So when you have really strong sources of data that are standardized, that allow that investment process to ingest that data, it does allow for a better outcome for investors. >> Real data, I mean, that diversity example they don't have to rely on a survey. >> It's not a survey. >> Anecdotes. >> It's coming right out of the transactional systems and it's updated, whenever those paychecks are run, whether it's weekly, whether it's biweekly or monthly, all that information gets updated and it's available. >> So it sounds like ADP is a facilitator of a lot of companies ESG initiatives, at least in part? >> Well, we partner with companies all the time. We have over 900,000 clients and all of them are... We've never spoken to a client who's not concerned about their people. And that's just good business. And so, yeah we're involved in that and we'll see where it goes over time now. >> I think there's tremendous opportunity if you think about the data that the ADP have in terms of diversity, in terms of gender pay gap. Huge, huge opportunity to incorporate that, as I said into the ESG principles and criteria. >> Good, 'cause that definitely is what needs to be addressed. (Lisa laughs) Guys thank you so much for joining Dave and me on the program, talking about Snowflake ADP, what you're doing together, and the massive potential that you're helping unlock with the value of data. We appreciate your insights and your time. >> Thank you for having us. >> Dave: Thanks guys. >> Thank you so much. >> For our guests, and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, live in Las Vegas at Snowflake Summit 22. Dave and I will be right back with our next guest. (upbeat music)

(upbeat theme music plays) >> Live from Atlanta, Georgia, it's theCube. Covering Citrix Synergy, Atlanta, 2019, brought to you by Citrix. >> Hi, welcome back to theCube. Lisa Martin with Keith Townsend, and we are live in Atlanta, Georgia for Citrix Synergy, 2019. We are pleased to welcome Jack Gold to The Cube, President and founder of Jack Gold & Associates. Jack, it's great to have you join Keith and me this afternoon. >> Thank you for having me. >> So, we had a great day. We've talked to eight or nine folks or so, lot's of really relevant exciting news from Citrix this morning. Talking about the employee experience as, and how I kind of interpreted it, as a catalyst for digital transformation, cultural transformation. You've been working with Citrix for a long time. I'd love to get your perspective on not just what you heard today from Citrix, and with Google and Microsoft, but in the last year or so since they've really kind of done a re-brand effort. What're your thoughts on that? >> Yeah, it's interesting from a Citrix perspective. Citrix, the old Citrix I guess I would put in quotes right, was always known as the VDI company. I've got, you know, the screen that will talk to the server, that will talk to whatever other apps I need it to talk to, and I can have a nice thin client sitting on my desktop and I don't have to spend a lot of money. And I also don't have to worry about if I'm going to bank people stealing stuff off the hard drive, or whatever. They've made a pretty significant transition that was the old work space, if you will. The modern work spaces which is where Citrix is really moving is one where, look we've all grown up with smart phones for the last ten or fifteen years, our kids don't know anything different. They're not going to deal with anything that's complex, anything where I have to log in and out of applications, anything where I have to switch between screens, this just doesn't make any sense for them. And so, what we're seeing Citrix do is move into an environment where, as I said, it's about the modern workspace, it's about being able to help me do my job not getting in my way of me doing my job, and that's really the transition. It's not just Citrix, the industry is moving in that direction as well, but Citrix is really at the forefront of making a lot of that work now. >> So, Jack, talk to us about the new promise of the new Citrix. The, if you remember me, it had to have be about seven years ago, I did a blog post of running Windows XP on your iPad. It was taking, you know, the then desktop solution and running it on your iPad. >> (Jack) Sure. >> And it was a cool trick. But we talked about, today, we would hope by today, that mobile technology would of forced companies to rewrite applications, for a mobile first experience. But that simply hasn't happened. So, presenting a bad application on to a mobile dot, to a mobile work station, or a mobile device, doesn't work. We end up packing in, trying it, and squeezing, and trying to get our work done, how is Citrix promising to change that experience, even versus their competitors? >> Sure. Well, first of all so two bad's don't make a good. Right. Having a bad app on a bad device doesn't make it good. >> (Keith) Right. >> Doesn't make it easy to use, doesn't help me get my job done. What we really are talking about, now, is the ability to build a workspace. Something where I sit and look at, that helps me get my job done, as opposed to getting in the way. Which means that, instead of having to punch fourteen different holes, or you know, icons and sit at my keyboard and type forty-eight different commands and do thirty-eight different log-ins as each one is different, and by the way I couldn't remember them so I just called the help desk in-between, and that's another half an hour of my time that I didn't want to, that I wasted. >> (laughs) Give me my word perfect templates. >> (Lisa laughs) >> (Jack) There you go, there you go, word perfect I remember that no so well. I remember it well not so nicely. What we're really trying to focus on now is user experience, right. What we're really trying to focus on is if, if you wanted to get your work done, I want to make it easy. Think about it as going to a grocery store. If you can't, if you've got a list of groceries and you can't find what you want in five minutes, you leave, you go somewhere else. You go to another grocery store where things are much easier to find. It's the same at work, or it should be the same at work. Now, that said, a lot of apps and organizations, especially big enterprises where they have, some can have literally thousands of apps, are not going away. The notion that everything is going to go into the modern workspace, where everything looks like a phone, it's a nice idea, it's properly not going to happen. Legacy apps will be legacy apps for a very long time, it's like mainframes are dead, guess what, they're still around. That said, that doesn't mean that you can't take some of those legacy apps and make them easier to use with the proper front-end. And that's really what Citrix is trying to do with the workspaces, and other's again, it's not just Citrix in this, we have to be fair there are lots people working in this space. But, if you can make the front-end workspace more attractive, easier to use, easier to navigate, even if I've got old, clunky stuff in the background. For me as a user, you can give me back fifteen, twenty, thirty minutes a day, an hour a day, that's really productivity. Look, if you're paying me a hundred dollars an hour, and you save me an hour a day you just made a hundred dollars every day that I'm working at that company, that sounds like a lot, but there are people who make that kind of money. Or even a fifty or twenty-five dollars, it all adds up. And so, what we're really doing is trying to move into an environment where if I can make you more productive but making things more easier for you to navigate, and getting in and our of applications more quickly, getting more information to me more quickly, which makes the overall organization more productive because I'm sharing more information with you, then that's a real win-win, and that's where I think Citrix is really trying to position itself, and doing a fairly good job at doing that. Clearly they don't have all of the components yet, but then no one does. This is an ongoing process. >> So, employee experience is table-stakes for any business, as we look at the modern workforce it's highly disrupted. >> (Jack) Yes. >> It's composed of five different generations. >> (Jack) Yes. >> Who have varying expertise with technology. It is also demanding because we're all consumers. >> (Jack) Yes. >> And so we have this expectation, or this, yeah I'd say expectation that I want to be able to go in and have this personalized experience. I don't want to have to become an expert in sales-force because I might need to understand, can I talk to that costumer and ask them to be a reference? How much time are you going to take? But this personalization is becoming more and more critical as we see this influence from the consumer side. >> Right. >> Were some of the things that you heard today from Citrix, what are your thoughts on how their going to be able to improve that more personalized employee experience? >> So people think of personalization, I think sometimes, too narrowly. For some people personalization is, you know, I've got my phone out, and I have the apps that I want on my phone and that's personalization. I think of it a little bit differently. We need to extend personalization. When I'm at work, what I want is not just the apps I want, clearly I want those, right, but also the ability, to get help with those apps as I need it, right. And so where Citrix is going is trying to put intelligence into the system, so that when I'm interacting with back-end solutions or my neighbors, or with teams collaboration, I get the assistance I need to make it easier for me to do that work. It's not just the apps, it's also help with the apps. And if we can do that, that's really what we want. We go, you know, if I have a problem with my laptop I'm going to come to you and say, hey, you did this yesterday what was the result, can you help me for five minutes? Five minutes is never five minutes, it's usually an hour and a half, but still. I'll come to you. Why can't I have an app on my desk that does the same thing? I'm having trouble. Help me. Fix it. Let me know what I'm doing wrong, or let me know how I can do it better. And that's where Citrix is trying to go with the analytics that they've got in place. Which is huge, I think they're underplaying that, because I think that the whole analytic space in making things easier for people to use, because in understanding where my problems are is huge, and that's going to pick up. The notion of having a nice pretty, pretty may not be the right word, but attractive at least, workspace for me to go in that doesn't get frustrated, frustration is a killer in productivity, as everyone knows. There are examples I've heard multiple people tell me now that they go out and hire, especially with millennials, that go out an hire twenty or thirty new employees, and half of them quit within a week because their systems are so bad that they get so frustrated that they're not going to work there. So, the notion of having a modern workspace where I get the applications that I need, I get the assistance I need, because of the analytics of that backend telling the systems what I need, and making it easier for me to do it. And then allowing me to be productive not just for myself, but for the organization, is where we all need to go and I think that Citrix is making some real progress going that way. >> (Keith) Well Jack, we're talking about products that haven't quite been released yet, so I'm trying to get a sense or, worth's the right built versus buy stage, in complexity Citrix should be? You know, I can make it apple pie by going out and picking the apple. >> (Jack laughs) Right. >> And making my own crust or, I can go buy filling, or I can just go buy any mince pie, stick it in the oven and warm it up. Three very different experiences. Three different layers of investment, and outcomes frankly. In this world, I can go hire application developers to write these many apps, to write these customizations, to write these integrations, but that's, I think that's akin to picking the apple and that just simply doesn't scale. But, also while any mince pie is okay every now and again, I want, you know, something of higher quality. Where do you think Citrix is on the kind of range of built versus buy with this intelligent experience? >> So built versus buy is a very interesting phenomenon. And it's interesting because a lot of it has to do with where you think you are right now in the world, right. You know you mention going out and getting developers and building your own, that's all well and good, it doesn't scale, and by the way in today's market you can't find them to begin with. So you often don't even have a choice. So that's number one. Number two is that there are companies out there that still think for competitive advantage that they have to do everything from scratch, like building your pie. Yes, you probably make the best pie in the world, but guess what, sometimes a good enough pie is good enough. Right, and if you're in business sometimes good enough is the only way you survive. It doesn't have to be a hundred percent perfect, ninety percent's okay too. People can deal with that. So that's the other piece. The third piece of it is, from an end-user perspective, right, if end-users are accustom to having an interaction in a certain way and the you go out and get developers that come in and do it, something completely different, which they're apt to do because each will have their own kind of flavor to it, then you just force them to learn one, two, three, four, five different interface interactions I'm not going to do that. I'm going to get frustrated as heck, and I'm going to go call the help desk or I'm going to go get my app and say go do this for me. Both of which are counterproductive to the company and to me. So, it really depends on where you are in the stage of where your company is, I would say built versus buy it's not a one or a zero. There's lots of shades of gray in between, it's also not all or nothing. So, some applications might be built internally, some you may want to buy externally, some you may have a hybrid, and the nice thing about where workspaces is going now is that you plug all of those into the same environment. That's really the ultimate goal, is to make it as easy and transparent for the organization as possible, and also for the user because the user ultimately is the end consumer. And if it's not good for the end consumer, it's not good for the company either. >> (Lisa) So delivering this great game-changing customer experience for this, as we talked about before this distributed modern work force that wants to be able to access mobile apps, Sass apps. >> (Jack) Right. >> Web apps from tablets, PC, phone, desktop. >> (Jack) Your car, your refrigerator >> Exactly. >> (Jack) Anything with a screen on it. >> Oh yeah, the refrigerators. Wherever you are, I think, okay people >> (Jack) Sure. >> We're people, and we are the biggest single security threat there is. >> (Jack laughs) >> So in your perspective, how is what Citrix is talking about balancing security as an essential component of this employee experience? >> So there are a few things, number one is a lot of companies think that if they limit the end user experience they're more secure. The truth of the matter is, yes, I mean if you don't let me get in to an app I can't steal application or information, or lose it somehow. But I also can't get my work done. So there's a balance between security and privacy which many companies don't talk about which is not exactly the same thing, there are two unique things, more and more privacy is becoming as big or bigger an issue than security, but you know we can get at that in a minute. But, the notion of security really relates to what I was talking about earlier which is analytics. If I know what you're suppose to be doing, you're here at Synergy. If someone just got your credentials and logged in from Los Angeles or New York or Chicago or Denver or wherever, I know it's not you. I can shut that thing down very quickly and not have to worry about them stealing information, also if you're, if I know you're not suppose to be in a certain version of SAP, you're not suppose to be doing some ERP system and you're in it, then again the analytics tells me that there's something going on, there's something anomalous going on that I need to investigate. So, having a system that protects because there's a kind of a front end to everything that's going on in the back end, and a realization of what's going on behind that screen gives me a much higher sense of security from a corporate perspective, it's not perfect there is no such thing as perfect security, but it's a lot better than just letting us kind of do our own thing, and loading, you know, semantic or McAfee or whatever on your PC. And that's where the industry ultimately has to go. That becomes part of the new modern workspace. It's not just about more productive it's about more secure. It's about more private. It's about not letting information escape that shouldn't be there to begin with. >> (Keith) So last question on data grabbing. Because we haven't talked about data and data is, you know, probably the most important thing in this topic. The importance of the (unintelligible) and Google announcement. You know, we, the yottabyte, the first time I've heard that term, yottabye of data that data's going to be spread across the world and this, this ideal of centralized compute and us being able to present, compute into data centers, no longer going to work, that we're going to have to, applications are going to be spread across the world. Where do you Citrix advancing that discipline of providing apps where they need to be with these relationships? >> So, it's an interesting phenomenon what we're going through right now, if you look back a couple of decades ago everything was centralized, people were centralized, they all work in one building, computing was centralized it was all in the data center, IT was centralized, it was all, you know, working around the servers. The Cloud is the opposite direction, although I would argue The Cloud isn't new, The Cloud is just time-share in a different environment, for us old people who remember the old IBM time-share computers. But everything is becoming distributed, data is distributed, people are distributed, applications are distributed, networks are distributed, you name it. The key critical factor for companies in keeping their productivity, keeping up the productivity is to make sure that the distributed environment doesn't get in the way of doing work. So you've got things like latency, if it takes me, if I'm in. (crowd cheers) >> They're having a party behind us. >> No, they agree with you! >> (Jack laughs) Yes, apparently. I, you know, if I'm here at Synergy but I have to work back at my offices near Boston, I can't wait five minutes for information to come back and forth, it's like the old days. Latency now has to be within five microseconds or people get frustrated, so that becomes a network issue, applications, same way, if I have to go to a data center, the data isn't local to my server here, it has to go to London, I'm not going to wait three minutes for it to come back like we use to, or ten minutes or an hour and a half. Or come back the next morning. You know, you want to book a flight on an airline, are you going to wait thirty minutes for them to find you a seat? You're going to go to another airline. So the whole notion of distributed means that it's very different now, even though it's distributed, everything is local. And by local, keeping it local means that you have to have latency below a certain point (crowd cheers) so that I don't realize that it's distributed, or I don't care that it's distributed. Yottabyte's of data means that we're going to have data everywhere, accessible all the time, and we're going to produce data like crazy. You know, a typical car, an autonomous car will produce a gigabyte of data every minute. Hundreds every, you know, hour. So, the amount of data is going to be fantastic that we have to deal with. Then, the big question becomes, okay so, I can't personally deal with all this data, it's impossible, I have to have the assistance, the intelligence within the system to go off and make something of that data so that I can actually interact with it in a meaningful fashion. That's where Citrix would like to go, that's where other's would like to go. They can't do it alone, because the problem is just too darn big. But, it will, we will get there, companies will get there eventually, not all of them perhaps, only the ones that are going to be successful long term are going to get there. >> Well, Jack, I wish we had more time to chat with you. This has, I just feel like going dot, dot, dot, to be continued. And I want to say, coincidence, I don't know, there were two rounds of applause when you talked about latency. (Keith laughs) >> There we go. They're just waiting for the bar to open, it's taking too long. >> (Lisa laughs) You think that's what it is? >> (Jack) Properly. >> All right well we'll get you over there, and thank-you again for joining Keith and me this afternoon. >> Thank-you very much. >> (Lisa) Our pleasure. For Keith Townsend, I'm Lisa Martin, you're watching theCube live from Citrix Synergy, 2019. Thanks for watching. (upbeat theme music plays)

>> Announcer: From the Fairmont Hotel in the heart of Silicon Valley, it's The Cube. Covering when IOT met AI, the intelligence of things. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Rick here with The Cube. We're in downtown San Jose at the Fairmont Hotel at a little show called when IOT Met AI, the Intelligence of Things. Talking about big data, IOT, AI and how those things are all coming together with virtual reality, artificial intelligence, augmented reality, all the fun buzz words, but this is where it's actually happening and we're real excited to have a pioneer in this space. He's Jack McCauley. He was a co-founder at Occulus VR, now spending his time at UC Berkeley as an innovator in residence. Jack welcome. >> Thank you. >> So you've been watching this thing evolve, obviously Occulus, way out front in kind of the VR space and I think augmented a reality in some ways is even more exciting than just kind of pure virtual reality. >> Right. >> So what do you think as you see this thing develop from the early days when you first sat down and started putting this all together? >> Well, I come from a gaming background. That's what I did for 30 years. I worked in video game development, particularly in hardware and things, console hardware. >> That's right, you did the Guitar Hero. >> Guitar Hero. Yeah, that's right. >> We got that one at home. >> I built their guitars and designed and built their guitars for Activision. And when were part of Red Octane, which is a studio. I primarily worked in the studio, not the headquarters, but I did some of the IP work with them too, so, to your question, you know when you produce a product and put it on the market, you never really know how it's going to do. >> Jeff: Right. >> So we make, we made two developer kits, put them out there and they exceeded our expectations and that was very good. It means that there is a market for VR, there is. We produce a consumer version and sales are not what we expected for that particular product. That was designated towards PC gamers and hopefully console games. But what has done well is the mobile stuff has exceeded everyone's mildest expectations. I heard numbers, Gear VR, which is Occulus designed product for me, sold 7 million of those. That's a smash hit. Now, worldwide for phone mounted VR goggles, it's about 20 million and that's just in two years, so that's really intriguing. So, what has happened is it's shifted away from an expensive PC based rig with $700 or whatever it costs, plus $1,500 for the computer to something that costs $50 and you just stick your cell phone in it and that's what people, it doesn't give you the best experience, but that's what has sold and so if I were doing a start-up right now, I would not be working on PC stuff, I'd be working on mobile stuff. >> Jeff: Right. >> And the next thing I think, which will play out of this is, and I think you mentioned it prior to the interview, is the 360 cameras and Google has announced a camera that they're going to come out and it's for their VR 180 initiative, which allows you to see 180 video in stereo with a cell phone strapped to your face. And that's very intriguing. There's a couple of companies out there working on similar products. Lucid Cam, which is a start-up company here has a 180 camera that's very, very good and they have one coming out that's in 4K. They just launched their product. So to answer your question, it looks like what is going to happen is for VR, is that it's a cell phone strapped to your face and a camera somewhere else that you can view and experience. A concert. Imagine taking it to a sporting event where 5,000 people can view your video, 10,000 from your seat. That's very intriguing. >> Yeah, it's interesting I had my first kind of experience just not even 360 or live view, but I did a periscope from the YouTube concert here at Levi Stadium a couple of months ago, just to try it out, I'd never really done it and it was fascinating to watch the engagement of people on that application who had either seen them the prior week in Seattle or were anticipating them coming to the Rose Bowl, I think, you know, within a couple of days, and to have an interaction just based on my little, you know, mobile phone, I was able to find a rail so I had a pretty steady vantage point, but it was a fascinating, different way to experience media, as well as engagement, as well as kind of a crowd interaction beyond the people that happened to be kind of standing in a circle. >> You, what's intriguing about VR 180 is that anybody can film the concert and put the video on YouTube or stream it through their phone. And formerly it would require a $10,000 camera, a stereo camera set up professionally, but can you imagine though that a crowd, you know, sourced sort of thing where the media is sourced by the crowd and anyone can watch it with a mobile phone. That's what's happening, I think, and with Google's announcement, it even that reinforces my opinion anyways that that is where the market will be. It's live events, sporting events. >> Right, it's an experience, right? It all comes back to kind of experience. People are so much more experience drive these days than I think thing driven from everything from buying cars versus taking a new Uber and seeing it over and over and over again. People want the experience, but not necessarily, as the CEO of Zura said, the straps and straddles of ownership, let me have the fun, I don't necessarily want to own it. But I think the other thing that gets less talked about, get your opinion, is really the kind of combination of virtual reality plus the real world, augmented reality. We see the industrial internet of things all the time where, you know, you go take a walk on that factory before you put your goggles on and not only do you see what you see that's actually in front of you, but now you can start to see, it's almost like a heads up display, certain characteristics of the machinery and this and that are now driven from the database side back into the goggles, but now the richness of your observation has completely changed. >> Yes, and in some ways when you think of what Google did with Google Glass, not as well as we had liked. >> But for a first attempt. >> Yeah. They're way ahead of their time and there will come a time when, you know, Snap has their specs, right? Have you seen those? It's not augmented reality, but, there will come a time when you can probably have a manacle on your face and see the kinds of things you need to see if your driving a car for instance that, I mean, a heads up display or a projector projecting right into your retina. So, and, so I think that's the main thing for augmented reality. Will people, I mean, your Pokemon Go, that's kind of a AR game in a way. You look through your cell phone and the character stays fixed on the table or wherever you're looking for it. I mean that uses a mobile device to do that and I can imagine other applications that use a mobile device to do that and I'm aware of people working on things like that right now. >> So do you think that the breakthrough on the mobile versus the PC-based system was just good enough? In being able to just experience that so easily, you know, I mean, Google gave out hundreds and hundreds of thousands of the cardboard boxes, so wow. >> Yeah. Well, it didn't mean that Gear VR didn't move into the market, it did. You know, it did anyways, but to answer your question about AR, you know, I think that, you know, without having good locals, I mean the problem with wearing the Google Glass and the Google cardboard and Gear VR is it kind of makes you sick a little bit and nobody's working on the localization part. Like how to get rid of the nausea effect. I watched a video that was filmed with Lucid Cam at the Pride Parade in San Francisco and I put it on and somebody was moving with the crowd and I just felt nauseous, so that problem probably probably is one I would attempt to attack if I were going to build a company or something like that right now. >> But I wonder too, how much of that is kind of getting used to the format because people when they first put them on for sure, there's like, ah, but you know, if you settle in a little bit and our eyes are pretty forgiving, you get used to things pretty quickly. Your mind can get accustomed to it to a certain degree, but even I get nauseous and I don't get nauseous very easily. >> Okay, so you're title should just be tinkerer. I looked at your Twitter handle. You're building all kinds of fun stuff in your not a garage, but your big giant lab and you're working at Berkeley. What are some of the things that you can share that you see coming down the road that people aren't necessary thinking about that's going to take some of these technologies to the next level. >> I got one for you. So you've heard of autonomous vehicles, right? >> Jeff: Yep, yep. >> And you've heard of Hollow Lens, right. Hollow Lens is an augmented reality device you put on your had and it's got built in localization and it creates what's, it's uses what's know as SLAM or S-L-A-M to build a mesh of the world around you. And with that mesh, the next guy that comes into that virtual world that you mapped will be away ahead. In other words, the map will already exists and he'll modify upon that and the mesh always gets updated. Can you imagine getting that into a self-driving vehicle just for safety's sake, mapping out the road ahead of you, the vehicle ahead of you has already mapped the road for you and you're adding to the mesh and adjusting the mesh, so I think that that's, you know, as far as Hollow Lens is concerned and their localization system, that's going to be really relevant to self-driving cars. Now whether or not it'll be Microsoft's SLAM or somebody else's, I think that that's probably the best, that's the good thing that came out of Hollow Lens and that will bleed into the self-driving car market. It's a big data crunching number and in Jobs, he was actually looking at this a long time ago, like what can we do with self-driving vehicles and I think he had banned the idea because he realized he had a huge computing and data problem. That was 10 years ago. Things have changed. But I think that that's the thing that will possibly come out of, you know, this AR stuff is that localization is just going to be transported to other areas of technology and self-driving cars and so forth. >> I just love autonomous vehicles because everything gets distilled and applied into that application, which is a great application for people to see and understand it's so tangible. >> Yeah, it may change the way we think about cars and we may just not ever own a car. >> I think absolutely. The car industry, it's ownership, it's usage, it's frequency of usage, how they're used. It's not a steel cage anymore for safety as the crash rates go down significantly. I think there's a lot of changes. >> Yeah, you buy a car and it sits for 20 hours a day. >> Right. >> Unutilized. >> All right. Well, Jack I hope maybe I get a chance to come out and check out your lab one time because you're making all kinds of cool stuff. When's that car going to be done? >> I took it upon myself to remodel a house the same time I was doing that, but the car is moving ahead. In September I think I can get it started. Get the engine running and get the power train up and running. Right now I'm working on the electronics and we have an interesting feature on that car that we're going to do an announcement on later. >> Okay, we'll look out for that. We'll keep watching the Twitter. All right, thanks for taking a few minutes. All right, let's check with Cauley. I'm Jeff Rick. You're watching The Cube from When IOT Met AI, the Intelligence of Things in San Jose. We'll be right back after this short break. Thanks for watching. (technological jingle)

(upbeat music) [Narrator] Live from Las Vegas. It's the CUBE, covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. >> Welcome back everyone. We're live in Las Vegas here at the Mandalay Bay for Oracle's Modern Customer Experience conference, their second year. This is the CUBE, Silicon ANGLES flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier. My co-host Peter Burris, head of research at Wikibon.com. Our next guest is Jack Berkowitz who's the Vice President of Products and Data Science at Oracle. Well, great to have you on the CUBE. Thanks for coming on. >> Thanks a lot. >> Appreciate it. Love talking to the product guys, getting down and dirty on the products. So, AI is hot this year. It's everywhere. Everyone's got an AI in their product. What is the AI component in your product? >> Well, what we're working on is building truly adaptive experiences for people. So, we have a whole bunch of different techniques and technologies all of it comes together essentially to create a system that amplifies peoples capabilities. That's really the key thing. Two real important components. First of all, it's all about data. Everybody talks about it. Well, what we've put together is, in terms of consumers, is the largest collection of consumer data in the Oracle data cloud. So we take advantage of all that consumer data. We also have a lot of work going on with collecting business data, both Oracle originated data as well as partner data. We're bringing that all that together and it sets the context for the AI. Now on top of that we have not just the latest trends in terms of machine learning or neural networks or things like that, but we're borrowing concepts from advertising, borrowing concepts from hedge funds so that we can make a real-time system. It's all about real-time. >> You mentioned neural networks. A lot of stuff conceptually in computer science has been around literally for decades. What is, from your definition - obviously cloud creates a lot of data out there now, but what is AI these days? Because everyone now is seeing AI as a mainstream term. Even the word metadata, since Snowden's thing, is now a mainstream term. Who would have thought metadata and AI would be talked about at kitchen tables? >> Yeah. >> What is AI from your perspective? >> Yeah, from my perspective it's really about augmenting folks. It's really about helping people do things. So maybe we'll automate some very manual tasks out, right, that will free up people to have more time to do some other things. I don't think it's about replacing people. People are creative. We want to get people back to being creative and people are great at problem solving so let's get them that information. Let's get them aid so they can get back to it. >> And give them options. >> Give them options, exactly. Exactly. You know, if you can free up somebody from having to manipulate spreadsheets and all this other stuff so they can just get the answer and get on with things, people are happier. >> So Oracle is using first-person data and third-person data to build these capabilities, right? >> Jack: Yeah, exactly. >> How is that going to play out? How is Oracle going to go to a customer and say we will appropriately utilize this third-person data in a way that does not undermine your first-person rights or value proposition? >> That's a great question. So, privacy and respect has been sort of the principle we've been driving at here. So there's the mechanics of it. People can opt in. People can opt out. There's all the mechanics and the regulatory side of it but it's really about how do you use these things so that it doesn't feel creepy. How do you do this in a subtle way so that somebody accepts the fact that that's the case? And it's really about the benefit to the person as to whether or not they're willing to make that trade-off. A great example is Waze. Waze I use all the time to get around San Francisco traffic. You guys probably use it as well. Well, guess what? If you really think about it, Waze knows what time I leave the house in the morning, what time I come home. Uber knows that once a month I leave at 2:00 on a Sunday and come back a week later. So, as long as you think about that, I'm getting a benefit from Waze I'm happy to have that partnership with them in terms of my data and they respect it and so therefore it works. >> And that comes back to some of the broader concepts of modern customer experience. It is that quid pro quo that I'll take a little data from you to improve the service that I'm able to provide as measured by the increasing value customer experience that's provided. >> Yeah, that's right. I used to live in London and in London there's these stores where you can go in and that sales guy has been there for like twenty years and you just develop a relationship. He knows you. He knows your kids, and so sure enough, stationary store or whatever it is and he gives you that personal experience. That's a relationship that I've built. That really all we're trying to do with all of this. We're trying to create a situation where people can have relationships again. >> And he's prompted with history of knowing you, just give you a pleasant surprise or experience that makes you go wow. And that's data driven now. So how do you guys do that? Cause this is something that, you know, Mark Heard brought up in his keynote that every little experience in the world is a data touchpoint. >> Jack: Yeah. >> And digital, whatever you're doing, so how do you guys put that in motion for data because that means data's got to be freely available. >> Data's got to be freely available. One of the big things that we brought to bear with the Suite X is that the data is connected and the experiences are connected so really we're talking about adding that connected intelligence on top of that data. So, it's not just the data. In fact we talked about it last night. It's not just the data even from the CX systems from service, but even the feed of what inventory's going on in real-time. So I can tell somebody if something's broken, hey, tell you what. This store has it. You can go exchange it, in real-time. Instead of having to wait for a courier or things like that. So it is that data being connected and the fact that our third-party data, you know this consumer data, is actually connected as well. So we bring that in on the fly with the appropriate context so it just works. >> So one of the new things here is the adaptive intelligence positioning products. What is that and take a minute to explain the features of how that came to be and how it's different from the competition. >> Okay, great. So the products are very purposeful built apps that plug in and amplify Oracle cloud apps and you can actually put in a third-party capability if you happen to have it. So that's the capability and it's got the decision science and machine learning and the data. >> Peter: So give me an example of a product. >> So a product is adaptive intelligence offers which we were showing here. It gives product recommendations, gives promotions, gives content recommendations on websites but also in your email. If you go into the store you get the same stuff and we can then go and activate advertising campaigns to bring in more people based on those successful pick ups of products or promotions. Its a great example. Very constrained use case addressed? >> Peter: Fed by a lot of different data. >> Fed by a lot of different data. The reason why they're adaptive is because they happen in real-time. So this isn't a batch mode thing. We don't calculate it the day before. We don't calculate it a week before or every three hours. It's actually click by click for you, and for you, reacting and re-scoring and re-balancing. And so we can get a wisdom of the crowds going on and an individual reaction, click by click, interaction by interaction. >> This is an important point I think that's nuanced in the industry. You mentioned batch mode which talks about how things are processed and managed to real-time and the big data space is a huge transition whether you're looking at hadoop or in memory or at all the architectures out there from batch data lakes to data in motion they're calling it. >> Yeah, exactly. >> So now you have this free flowing scalable data layers, if you will, every where, so being adaptive means what? Being ready? Being ... >> Being ready is the fundamental principle to getting to being adaptive. Being adaptive is just like this conversation. Being able to adjust, right? And not giving you the same exact answer seven times in a row because you asked me the same question. >> Or if it's in some talking point database you'd pull up from a FAQ. >> Peter: So it adapts to context. >> It's all about adapting to context. If the concepts change, then the system will adopt that context and adapt it's response. >> That's right. And we were showing last night, even in the interaction, as more context is given, the system can then pick that up and spin and then give you what you need? >> The Omni Channel is a term that's not new but certainly is amplified by this because now you have a world certainly with multiple clouds available to customers but also data is everywhere. Data is everywhere and channels are everywhere. >> Data is everywhere. And being adaptive also means customizing something at a point and time >> Exactly. and you might not know what it is up until seconds or near real-time or actually real-time. >> Real time, right? Real human time. 100 milliseconds. 150 milliseconds, anywhere in the world, is what we're striving for. >> And that means knowing that in some database somewhere you checked into a hotel, The Four Seasons, doing a little check in the hotel and now, oh, you left your house on Uber. Oh, you're the CEO of Oracle. You're in a rental car. I'm going to give you a different experience. >> Jack: Yeah. >> Knowing you're a travel warrior, executive. That's kind of what Mark Heard was trying to get to yesterday. >> Yeah, that's what he's getting to. So it's a bit of a journey, right? This is not a sprint. So there's been all this press and you think, oh my god, if I don't have ... It's a journey. It's a bit of a marathon, but these are the experiences that are happening. >> I want to pick up on 150 milliseconds is quite the design point. I mean human beings are not able to register information faster than about 80 milliseconds. >> Jack: Yeah, yeah. So you're talking about two brain cycles coming back to that. >> Jack: Yeah. >> I mean it's an analogy but it's not a bad one. >> Jack: No. >> 150 milliseconds anywhere in the world. That is a supreme design point. >> And it is what we're shooting for. Obviously there's things about networks and everything that have to be worked through but yeah, that responsiveness, but you're seeing that responsiveness at some of the big consumer sites. You see that type of responsiveness. That's what we want to get to. >> So at the risk of getting too technical here, how does multiple cloud integration or hopping change that equation? Is this one of the reasons it's going to drive customers to a tighter relationship with Oracle because it's going to be easier to provide the 150 millisecond response inside the Oracle fabric? >> Yeah, you nailed it. And I don't want to take too many shots at my competitors, but I'm going to. We don't have to move data. I don't have to move my data from me to AWS to some place else, right, Blue Mix, whatever it happens to be. And because we don't have to move data, we can get that speed. And because it's behind the fabric, as you put it, we can get that speed. We have the ability to scale the data centers. We have the data centers located where we need them. Now your recommendations, if you happen to be here today, they're here. They may transition to Sydney if you're in Australia to be able to give you that speed but that is the notion to have that seamless experience for you, even for travelers. >> That's a gauntlet. You just threw down a gauntlet. >> Jack: I did. Yeah. >> And that's what we're going to go compete against. Because what we're competing is on the experience for people. We're not competing on who's got the better algorithm. We're competing on that experience to people and everything about that. >> So that also brings up the point of third-party data because to have that speed certainly you have advantages in your architecture but humans don't care about Oracle and on which server. They care about what's going on on their phone, on their mobile. >> Jack: That's right. >> Okay, so the user, that requires some integration. So it won't be 100 percent Oracle. There's some third-party. What's the architecture, philosophy, guiding principles around integrating third-party data for you guys. Because it's certainly part of the system. It's part of the product, but I don't think it's ... >> So there's third=party data which could be from data partners or Oracle originated data through our Oracle data cloud or the 1500 licensed data partners there and there's also third-party systems. So for example if somebody had Magento Commerce and they wanted to include that into our capability. On the third party systems, we actually have built this around an API architecture or infrastructure using REST and it's basically a challenge I gave my PMs. I said look, I want you to test against the Oracle cloud system. I want you to test against the Oracle on-prem system and I want you to find the leading third-party system. I don't care if it's sales force or anybody else and I want you to test against that and so as long as people can map to the REST APIs that we have, they can have inter-operation with their systems. >> I mean the architectural philosophy is to decouple and make highly cohesive elements and you guys are a big part of that with Oracle as a component. >> Jack: That's right. >> But I'm still going to need to get stuff from other places and so API is a strategy and microservices are all going to be involved with that. >> Yeah, and actually we deployed a full microservice architecture so behind the scenes on that offers one, 19 microservices interplaying and operating. >> But the reality is this is going to be one of the biggest challenges that answers faces is that how we bridge, or how we gateway, cloud services from a lot of different providers is a non-trivial challenge. >> Jack: That's right. >> I remember back early on in my career when we had all these mini computer companies and each one had their own proprietary network on the shop floor for doing cell controllers or finance or whatever it might be and when customers wanted to bring those things together the mini computer companies said, yeah, put a bridge in place. >> Yeah, exactly. >> And along came TCPIP and Cisco and said forget that. Throw them all out. It wasn't the microprocessor that couldn't stick to those mini computer companies. It was TCPIP. The challenge that we face here is how are we going to do something similar because we're not going to bridge these things. The latency and the speed, and you hit the key point, where is the data, is going to have an enormous impact on this. >> That's right. And again, the investments we have been making with the CX Cloud Suite will allow us to do that. Allow us to take advantage with a whole bunch of data right away and the integration with the ODCs, so we couldn't probably have done this two or three years ago because we weren't ready. We're ready now. And now we can start to build it. We can start to take it now up to the next level. >> And to his point about the road map and TCPIP was interesting. We're all historians here. We're old enough to remember those days, but TCPIP standardized the OSI model which was a fantasy of seven layers of open standards if you remember. >> Jack: Seven layers, yep, whew. >> Peter: See we still talk about it. >> What layer are you on? >> But at the time, the proprietary was IBM and DEC owned the network stacks so that essentially leveled off there so the high-water mark was operating at TCPIP. Is there an equivalent analog to that in this world because IF you can almost take what he said and say take it to the cloud and say look at some point in this whatever stack you want to call it, if it is a stack, there has to be a moment of coalescing around something for everybody. And then a point of differentiation. >> So yeah, and again I'm just going to go back - and that's a great question by the way and it's - I'm like thinking this through as I say it, but I'm going to go right back to what I said. It's about people. So if I coalesce the information around that person, whether that person is a consumer or that person's a sales guy or that person's working on inventory management or better yet disaster relief, which is all those things put together. It's about them and about what they need. So if I get that central object around people, around companies then I have something that I can coalesce and share a semantic on. So the semantic is another old seven layer word. I didn't want to say it today but I can have ... >> Disruptive enabler. >> So then what you're saying is that we need a stack, and I use that word prohibitively, but we need a way of characterizing layer seven application so that we have ... >> Or horizontal >> Either way. But the idea is that we need to get more into how the data gets handled and not just how the message gets handled. >> Jack: That's right. >> OSI's always focused on how the message got handled. Now we're focused on how the data gets handled given that messaging substraight and that is going to be the big challenge for the industry. >> Jack: Yeah. >> Well, certainly Larry Ellis is going to love this conversation, OSI, TCPIP, going old school right here. >> Jack: Like you said, we're all old and yeah, that's what we grew up in. >> Yeah, but this is definitely ... >> Hey, today's computers and today's notions are built on the shoulders of giants. >> Well the enabling that's happening is so disruptive it's going to be a 20 or 30 year innovation window and we're just at the beginning. So the final question I have for you Jack is summarize for the folks watching. What is the exciting things about the AI and the adaptive intelligence announcements and products that you guys are showing here and how does that go forward into the future without revealing any kind of secrets on Oracle like you're a public company. What's the bottom line? What's the exciting thing they should know about? >> I think the exciting thing is that they're going to be able to take advantage of these technologies, these techniques, all this stuff, without having to hire a thousand data scientists in a seven month program or seven year program to take advantage of it. They're going to be able to get up and running very, very quickly. They can experiment with it to be able to make sure that it's doing the right thing. From a CX company, they can get back to doing what they do which is building great product, building great promotions, building a great customer service experience. They don't have to worry about gee, what's our seven year plan for building AI capabilities? That's pretty exciting. It lets them get back to doing what they do which is to compete on their products. >> And I think the messaging of this show is really good because you talk about empowerment, the hero. It's kind of gimmicky but the truth is what cloud has shown in the world is you can offload some of those mundane stuff and really focus on the task at hand, being creative or building solutions, or whatever you're doing. >> Yeah. Mark was talking about it. You have this much money to spend, what's my decision to spend it on. Spend it on competing with your products. >> All right, Jack Berkowitz live here inside the CUBE here at Oracle's Modern Customer Experience, talking about the products, the data science, AI's hot. Great products. Thanks for joining us. Appreciate it. Welcome to the CUBE and good job sharing some great insight and the data here. I'm John Furrier with Peter Burris. We'll be back with more after this short break. (upbeat music)

hey welcome back everybody Jeff Rick here or the cube conversation and the SiliconANGLE cubes Palo Alto studio a little bit of a break and the crazy conference season so here to kind of fix the gear and take things up and sit down and in the context of a conversation outside of a show to really get the update and we're really excited to be joined here by two guests who are announcing a pretty exciting deal that's happening today is who you shoot a CTO of Hitachi Data scissors welcome again to you and jack mazzoni vice president storage networking at brocade welcome thank you so let's just jump right into it tell everybody what happened today and why this is big news yeah sure I'm gonna start it and so what we're announcing today is broke aids developed its its next-gen called gen-6 fibre channel switches and it's a whole portfolio and we're very excited that hitachi who resells the OEM are our product to to their customer base is launching on the state as well so we're very excited about that and it's big news right and so you'll be able to buy this product today from hitachi for all the great customers that we have out there today and that's the big announcement excellent and why is this important to your customers well this is just the next evolution in fibre channel I mean 32 gigabit per second don't you know we were just on 16 now not long ago and so it's going to open up a lot of bandwidth open up you know more workload being processed provide new opportunities for new applications as well so it's funny a lot a lot of talk on you know already it in the end of Moore's law we're going to be able to squeak out more compute power outage micro processors and stuff but it sounds like you guys are squeezing out a lot more performance on the network yeah I know absolutely and one thing that you know Hugh mentioned earlier which I thought was great is you know when you get performance like this in the network and you just get performance in general enables consolidation enables efficiencies right and when flash is able to go and allow you to do let's say the same or more amount of workloads with less course that's a good thing right and and the networks then got to be able to handle that and it's that kind of efficiencies that when we can jointly bring to our customers that allows them to then spend the time and think about how they transform their IT operations right into this digital transformation era into into enabling IT to be the strategic foundation to go drive the enterprise right and I know sometimes it's hard to to make that connection all the way from Genesis fibre channel to that that's how that connection goes so so it's it's been a proven winner to drive performance it saves money it saves it enables innovation and I think the commitment that brocade and Hitachi have had to to quality to the highest levels of reliability and customer service over the years has really been a cornerstone of our success it's been a great partnership I know our CEO likes to say we partner better than anybody in the industry and that's absolutely true and Hitachi has just been one of those fantastic partners for us for over a decade now so let's unpack that a little bit why is partnership so important and not only just specifically between the two companies here but you know we go to a ton of shows and every show now even if it's a specific vendor like the pentaho show is a whole ecosystem right nobody can do it alone anymore and there's a really kind of renewed focus around the ecosystem and and everyone kind of coming together at the end of the day provide solutions to customers that are going to solve problems so it's a very important piece of it yeah and I think you've a great example that is if it really is a requirement if you think about private cloud infrastructures and converge infrastructures you're bringing so many elements together to deliver a total solution and Hitachi UCP is a great example where you have you know great technologies from hitachi in there we've been able to participate some of our fibre channel as well as our IP storage switches we were able to participate with that and so really I think if you want to participate in a cloud type of architecture whether it's public private hybrid you're going to have to partner you're going to have to particles you may not have all the technologies you may not have all the specialties and customers could require that on at some some levels that's kind of my take on it yes I mean you know we could have tried to develop our own fibre channel switches but you know that would have taken a lot more effort and time and distraction from what our core competencies are you know I mean brocade has a conference season networking both IP and fibre channel networking so it doesn't make any sense for us to try to do that which is better to partner with that right so and the future is going to be all about partnering and and more toward open source to right and the other thing that we find over and over again is really the changing expectations of the way software performs and we hear it all the time you know that why doesn't the software at my work perform like the software on my phone and why isn't it faster and why isn't it more integrated with other sources of information so the demand for better faster stronger applications is only going to increase right nobody wants less data though he wants less performance buddy wats less latency and especially in kind of an API world where all these applications are now not just siloed stacks of applications but they're pulling data from all sorts of places the speed and latency really becomes critical yeah I know I absolutely right and that's why I think this announcement and the construct of the you know the all-flash data center and all the advancements happening with flash is so important it's that linkage and then what the applications can do once they take advantage that you know I always tell people it's like remember the first time your laptop went to a all flash disk remember that experience like via the old spinning disk you're booting up well go to windows right and then you with all flash it was like that emotional experience or how great it was right you take that level at an enterprise level right where you have thousands and thousands of thousands of disks running thousands of applications and now you bring in flash and then around the corner you ring in nvme it's amazing to think about what's going to be coming down the future right and we're very excited about our position it really being it that the the central point of if you will where all this information flow has to go through the network and you know whether it's fibre channel whether it's IP you know we're going to keep to our core values which is you know the highest levels of qualities resiliency bringing in the analytics and partner partner with the top quality companies in the world such as itachi and what was interesting about this release is you added a lot more than a tease a lot more manageability lot more reporting a lot more visibility you know one of the big themes obviously in big data is to move from you know reactive to predictive too prescriptive right and so to have the management layer to have kind of the extra amount of information that you can take advantage of because you've got excess capacity and the pipes if you will and better connectivity to the infrastructure enables a whole different layer of management is which is if you've talked about you and prior interviews you people have to manage a lot more right they're not managing individual boxes anymore now whole different scale man need to be able to automate that those management tools helps us to automate that the infrastructure management also the security part is very important you know the security that brocade brings them into their right into the switch itself right and the security is an interesting point right because that's again a consistent theme everywhere we go it's the old moat just doesn't work anymore the mode and the castle walls now you really have to have security baked in all over the place and the data layer the networking layer all over the place it was the interesting thing is if you think about fiber channel all right let's just offer five channels of technology it is fundamentally right more secure than Ethernet oh we love you know really a great IP portfolio and everything but if you think about Ethernet or excuse me fibre channel when you plug in it it's off by default well first it's a separate network all together right so that's that's one layer secure but it's an off by default meaning that just get you plug in doesn't mean you have access to anything you know you got to go through one man here go there's only going to go through some other stuff but some people say its complexity but but you're at least now actively saying how are the communication is going to happen thin within the this network or either that's really the opposite right because the benefit ethering you plug it in and hey everybody's connected right that's what you want but but when it comes to enterprise let's say storage applications that needs a really but is that the behavior you want that anything that just plugs into it all of a sudden now can connect and and that's one thing that gets kind of lost sometimes than the discussions and the monitor data centers and here I'm really glad that you brought it up and you dropped out of to Jeff it's it's we realize that we're adding more things into it right we're adding more capabilities for in-flight encryption you mentioned forward error correction so the other capabilities were built in it so we take security very seriously and and inherently I think that's another reason that the viability of fibre channel has remained for as long as it has been yeah and it just own it one more time you know kind of what are some of the specific benefits that came out of you two working together for this launch how are you really kind of taking advantage of each other's strengths to really provide a better solution today that people can go out and get well one of the things is they offer backward compelled compatibility we're with two generations right right and that enables us we have a lot of legacy things that we've got to bring forward they don't just rip everything out and put in all these stuff turn the data center off for the weekend is my bonus so you know it gives us that easy transition migration into these higher higher technology levels big one yeah a big one yeah and I think when when the in the porn part two is when we deliver our systems to itachi they test it with their latest and greatest storage they do this full you know systems total solution test so when a customer that brings it into the environment it's it's been fully tested completely NM by brocade and hitachi and then to Hugh's point it's it also then works its backward compatible with everything in the environment nothing's going to you know that nothing will ever break but you know so much time and effort is put into making sure everything runs to seamless as possible because again you have to think about the environments that we're in all right those from mission-critical big environments they got to solve some serious problems they're not up for science projects they're not for risk right yes they have to advance the technology but it's got it done be done in a way that a mitigator essence done a responsible way and that's where I think when you bring their storage and our network together as well as their servers right we have our technology part of their server solutions as well you get some very compelling solutions all right well congratulations to both of you and also to the team's I'm sure there was a lot of work that went up into this day and it's always a relief to get here so thanks for stopping by and sharing the story thank you absolutely right with you and Jack I'm chef Rick you're watching the Q's the cube conversation from Palo Alto thanks for watching we'll see you next time

>>From New York expecting the signal to nine. It's the cube covering sparks summit east brought to you by spark summit. Now your hosts, Dave Volante and George Gilbert >>Right here in Midtown at the Hilton hotel. This has sparked somebody and this is the cube. The cube goes out to the events. We extract the signal from the noise. Jack Norris is here. He's the CMO of Mapbox, long time cube, alum jackets. It's great to see you again. Hey, if you've been here since the beginning of this whole big data >>Meme and it might've started here, I don't know. I think we've yeah, >>I think you're right. I mean, it really did start it. I think in this building, it was our first big data show at the original, you know, uh, uh, Hadoop world. And, uh, and you guys, like I say, I've been there from the start. Uh, you were kind of impatient early on. You said, you know, we're just going to go build solutions and, uh, and ignore the noise and you built a really nice, nice business. Um, you guys have been growing, you're growing your Salesforce and, uh, and things are good and all of a sudden, boom, the spark thing comes in. So we're seeing the evolution. I remember saying to George and the early days of a dupe, we were geeking out talking to all the bits and bytes and then it turned into a business discussion. It's like we're back to the hardcore bits and bites. So give us the update from Matt bar's point of view, where are we in the whole big data space? >>Well, I think, um, I think it has transitioned. I mean, uh, if you look at the typical large fortune company, the web to Datto's, it's really, how do we best leverage our data and how do we leverage our data in that we can, we can make decisions much faster, right? That high-frequency decision-making process. Um, and typically that involves taking production data and analytics and joining them together so that you're actually impacting business as it happens and to do that effectively requires, um, innovations. So the exciting thing about spark is taking and, uh, and having a distributed compute engine, it's much easier to develop and, uh, in much faster. >>So in the remember the early days we'd be at these shows and the big question was, you know, can you take the humans out of the equation? It's like, no, no humans are the last mile. Um, is that, is that changing or would we still need that human interaction or, >>Um, humans are important part of the process, but increasingly if you can adjust and make, you know, small algorithmic decisions, um, and, and make those decisions at that kind of moment of truth, you got big impact, and I'll give you a few examples. So, um, ad platforms, you know, Rubicon project over a hundred billion ad auctions a day, you know, humans, part of that process in terms of setting that up and reviewing the process, but each, you know, each supply and demand decision, there is an automated decision optimizing that has a huge impact on the bottom line, um, fraud, uh, you know, credit card swiping that transaction and deciding is this fraudulent or not avoiding false positives, et cetera, a big leveraged item. So we're seeing things like that across manufacturing, across retail healthcare. And, um, it isn't about asking bigger questions or doing reports and looking back at, you know, what happened last week. It's more, how can I have an infrastructure in place that allows this organization to be agile? Because it's not the companies with the most data that's going to win. It's the companies that are the most agile and making intelligent. >>So it's so much data. Humans can ingest it any faster. I mean, we just, we can't keep up. So the world needs data scientists that needs trained developers. You've got some news I want to talk about on the training side, but even that we can only throw so many bodies at the problem. So it's really software. That's going to allow us to scale it. Software's hard. Software takes time. So we've seen a lot of the spend in the analytics, big data world on, on services. And obviously you guys and others have been working hard to shift it towards software. I want to come back to that training issue. We heard this morning about, uh, Databricks launched a move. They trained 20,000 people. That's a lot, but still long way to go. You guys are putting some investment into training. Talk about that news. Yeah. >>Yeah. Um, well it starts at the underlying software. If you can do things in the platform to make it much easier and do things that are hard to surround with services, like, uh, data protection, right? If you've lost data, it doesn't matter how many people you throw at it, you can't recover it. Right. So that's kind of the starting point you're gonna get fired. >>The, the, uh, the approach we've taken is, is to take, uh, a software product approach to the training as well. So we rolled out on demand training. So it's free, it's on demand. You work at your own pace. It's got different modules, there's some training associated with that, or some hands-on labs, if you will. Um, we launched that last January. So it's basically coming up the year anniversary. We recently celebrated, we trained 50,000 people, uh, on, on Hadoop and big data. Um, today we're announcing expansion on spark classes. We've got full curriculum around spark, including a certification. So you can get sparked certification through this, this map, our on demand training. Okay. >>Gotcha. You said something really, really intriguing that I want to dive into a little bit is where we were talking about the small decisions that can be made really, really fast for that a human in the loop human might have to train them, but it at runtime now where you said, it's not about asking bigger questions, it's finding faster answers, um, what had to change in your platform or in the underlying technology to make that possible. >>You know, um, there's a lot that into it. It's typically a series of functions, uh, a kind of breadth that needs to be brought to the problem as well as squeezing out latencies. So instead of, um, the traditional approach, which is different applications and different analytic techniques dictate a separate silo, a separate, you know, scheme of data. And you've got those all around the organization and data kind of travels, and you get an answer at the end of some period of time. Uh, it's converging that altogether into a single platform, squeezing out those latencies so that you can have an informed action at the speed of business, if you will. And, >>Um, let's say spark never came along. Would that be possible? >>Yes. Yes. Would you, how would you, so if you look at kind of the different architectures that are out there, there's typically deep analytics in terms of, you know, let's go look at the trends, you know, the last seven years, what happened. And then look, let's look at, um, doing actions on a streaming set, say for instance, storm, and then let's do a real time database operations. So you could do that with, with HBase or map RDB and all of that together. What spark has really done is made that whole development process just much easier and much more streamlined. And that's where a lot of the excitements happen. >>So you mentioned earlier, um, to, to use cases, ad tech and fraud detection. Um, and I want to ask you about those in the state of those. So ad tech obviously has come a long way, but it's still got a ways to go. I mean, you look at, I mean, who's making money on ads. Obviously Google will make tons of money. Everybody else is sorta chasing them Facebook making money. It's probably cause they didn't let Google in. Okay. So how will spark affect sort of that business? Uh, and, and what's map, R's sort of role in evolving that, you know, to the next level. >>So, so, um, there's, there's different kind of compute and the types of things you can do, um, on the data. I think increasingly we're seeing the kind of streaming analytics and making those decisions as the data arrives, right. And then there's the whole ecosystem in terms of how do you coordinate those flows of data? It's not just a simple, here's the origin, here's the destination. There's typically a complex data flow. Um, that's where we've kind of focused on map our streams, this huge publish and subscribe infrastructure so that you can get real-time data to the appropriate location and then do the right operations, a lot of that involved with spark, but not exclusively. >>Okay. And then on fraud detection, um, obviously come a long way. Sampling could have died. Yes. And now, but now we're getting too many false positives. You get the call and, you know, I mean, I get a lot of calls because we can buy so much equipment, but, um, but now what about the next level? What are you guys doing to take fraud detection to the next level? So that when I get on the plane in Boston and I land in London, it knows, um, is that a database problem? Is it an integration problem, a systems problem, and how, what role you guys play in solving that? >>Well, there's, there's, um, you know, there's, there's a lot of details and techniques that probably go, um, beyond, you know, what, what we'll share publicly or what are our customers talk about publicly? I think in general, it's the more data that you can apply to a problem. The more context, the better off you are, that's the way I kind of summarize it so that instead of a sampling or instead of a boy, that's a strange purchase over there, it's understanding, well, this is Dave Valenti and this is the full body of, of, uh, expenditures he's done, then the types of things and here's who he frequently purchases from. And here's kind of a transaction trend started in San Francisco, went to New York, et cetera. So in context it would make more sense. So >>Part of that is more data. And the other part of that is just better algorithms and better, better learnings and applying that on a continuous basis. How are your customers dealing with that, that constraint? I mean, if they got a, a hundred dollars to spend, yeah. They can only spend so much on, on each of those gathering more data, cleaning the data, they spent so much time getting it ready versus making their machine learning algorithms or whatever the other techniques to do. What are you seeing there as sort of best practice? It was probably varies. I'm sure, but give us some color on it. >>Um, I'll actually go back to Google and Google a letter last round, um, you know, excellent, excellent insights coming from Google. They wrote a paper called the unreasonable effectiveness of data and in it, they basically squarely addressed that problem. And given the choice to invest in either the complex model and algorithm or put more data at it, putting more data, had a huge impact. And, um, you know, my simple explanation is if you're sampling the data, you have to have a model that tries to recreate reality. If you're looking at all of the data, then the anomalies can, can pop up and be more apparent. And, um, the more context you can bring, the more data from other sources. So you get around, you know, a better picture of what's happening, the better off you are. And so that requires scale. It requires speed and requires different techniques that can be brought to bear, right? The database operation, here's a streaming operation, here's a deep, you know, file machine learning algorithm. >>So there's a lot of vendors in the sort of big data ecosystem are coming at spark from different angles and, um, are, are trying to add value to it and sort of bathe themselves in sort of the halo. Yep. Now you guys took some time upfront to build a converged platform so that you weren't trying to wrap your arms around 37 different projects. Can you tell us how having perhaps not anticipated spark how this converts platform allows you to add more value to it than other approaches? >>So, so we simplify, if you look at the Hadoop ecosystem, it's basically separated into the components for compute and management on top of the data layer, right? The Hadoop distributed file system. So how do you scale data? How do you protect it? It's very simply what's going on. Spark really does a great job at that top layer. Doesn't do anything about defining the underlying storage layer in the Hadoop community that underlying storage layer is a batch system. So you're trying to do, you know, micro batch kind of streaming operations on top of batch oriented data. What we addressed was to take that whole data layer, make it real time, make it random. Read-write converge enterprise storage together with Hadoop support and spark support on a single platform. And that's basically >>With the difference and to make an enterprise great. You guys were really the first to lead the lecture. You were, everybody started talking about attic price straight after you were kind of delivering it. So you've had a lead there. Do you feel like you still have a lead there, or is that the kind of thing where you sort of hit the top of the S-curve and start innovating elsewhere? >>NC state did a study, uh, just this past year, a recent study identified that only 25% of data corruption issues are identified and properly handled by the Hadoop distributed file system. 42% of those are silent. So there's a huge gap in terms of quote unquote enterprise grade features and what we think. >>Yes, silent data corruption has been a problem for decades now. And you're saying it's no different in the duke ecosystem, especially as, as mainstream businesses start to, uh, to adopt this what's happening in the valley. Uh, we're seeing, you know, in the wall street journal every day you read about down rounds, flat rounds, people can't get B rounds. Uh, you guys are funded, you know, you're growing, you're talking about investments, you know, what do you see? Do you, do you feel like you're achieving escape velocity? Um, maybe give us sort of an update on, uh, the state of the business. >>Yeah. I, I think the state of the business is best represented by the customers, right? And the customers kind of vote, right. They vote in terms of, you know, how well is this technology driving their business? So we've got a recent study, um, that kind of shows the, the returns that customers, um, are getting, uh, we've got a 1% chance, a 99% retention rate with our customers. We've got, uh, an expansion rate. That's, that's unbelievable. We've got multi-million dollar customers in, uh, in seven of the top verticals and nine out of the top $10 million customers. So we're seeing significant investments and more importantly, significant returns on the part of customers where they're not just doing a single application on the platform, but multiple >>Applications, Jack Norris map are always focused. Always a pleasure having you on the cube. Thanks very much for coming on. Appreciate it. Keep right there, buddy. We'll be back with our next guest is the cube we're live from spark somebody's right back. Okay.

>>Live from New York city. It's cute. here's your host? Jeff Frick. >>Hi, Jeff Frick here with the Q we're on the ground at the USS Intrepid at the Hadoop on the Hudson party put on by Matt BARR. It's uh, I think it's the party of the night tonight here in big data week, New York city with strata cough, a dupe world, big data NYC. So Jack a great >>Venue. Yeah, it's excellent. Here. >>The place is filled. I'm just struck by the technology. There's a Gemini capsule over there, about 50 years old. It's about the size of a Volkswagen, I think would be much bigger. And to think that those guys went up into space with probably less technology than is on your four year old flip phone. Amazing. Yeah. >>Not, not much data at all. No. If >>You look at it, just kind of get that bounce on the gravity thing, which I never quite understood. So talk about you guys had some big news today. Once you give us a rundown on some of the announcements, >>We had two big announcements. One was incorporating the map RDB and our community edition that came out. We also reported results from our customers where the majority of customers reported less than a 12 month payback, uh, 65% of five X or greater return and 40%, 10 X or greater. And that included a subset of those customers that had experienced with other distributions. So kind of a Testament to when you get serious about Hadoop, you get serious with Mapbox >>And when they're getting those return on investments, we're always trying to explore where's the big, the big ROI, because it's really in value that's released for the customer. It's not necessarily because it's a cheaper way to do it, >>Right? So, so there are some costs that 63% was cost reduction that was driving it about 41% were top-line revenue projects. And about 23% were related to risk reduction and risk mitigation. And if you add those up, it's greater than a hundred percent because of many customers that are doing multiple applications. >>Great. So you've been coming to Hadoop world for longer than you would admit to me before we came on camera and, and the baseball playoffs are going on right now. I mean, we like to talk in sports analogy. So kind of where are we in, in kind of what inning are we in this adoption of big data and the duke specifically >>Early, early innings. Um, but, uh, what we've seen is the bases are loaded and we're up >>And it's it. And it seems to be we're way past now the POC stage. Now we're really getting in there for that. >>And the, the customer announcement, we did kind of shows how people are hitting it out of the park with Hadoop. And a lot of that is by impacting the operations, impacting the business as it happens. And that's coupling analytics plus this higher arrival rate data from a variety of sources and making adjustments so that you can impact revenue as businesses happening. You can mitigate risk as it's happening. It's not just reporting, looking back >>Function. Right, right. It's being able to react in real time, which is defined by, in time to do something about it. Right. Exactly. All right. Well, thanks for hosting a great party, Jack Norris. Here we are on the ground, uh, at the USS Intrepid at the Hadoop on the Hudson. Uh, uh, if you take a nice picture, tweet that in. I think they got some prizes. Hadoop Hudson is a hashtag Jeff Frick on the ground. You're watching the cube. Thanks. Big ship.

>>The queue at Hadoop summit, 2014 is brought to you by anchor sponsor Hortonworks. We do, I do. And headline sponsor when disco we make Hadoop invincible >>Okay. Welcome back. Everyone live here in Silicon valley in San Jose. This is a dupe summit. This is Silicon angle and Wiki bonds. The cube is our flagship program. We go out to the events and extract the signal to noise. I'm John barrier, the founder SiliconANGLE joins my cohost, Jeff Kelly, top big data analyst in the, in the community. Our next guest, Jack Norris, COO of map R security enterprise. That's the buzz of the show and it was the buzz of OpenStack summit. Another open source show. And here this year, you're just seeing move after, move at the moon, talking about a couple of critical issues. Enterprise grade Hadoop, Hortonworks announced a big acquisition when all in, as they said, and now cloud era follows suit with their news. Today, I, you sitting back saying, they're catching up to you guys. I mean, how do you look at that? I mean, cause you guys have that's the security stuff nailed down. So what Dan, >>You feel about that now? I think I'm, if you look at the kind of Hadoop market, it's definitely moving from a test experimental phase into a production phase. We've got tremendous customers across verticals that are doing some really interesting production use cases. And we recognized very early on that to really meet the needs of customers required some architectural innovation. So combining the open source ecosystem packages with some innovations underneath to really deliver high availability, data protection, disaster recovery features, security is part of that. But if you can't predict the PR protect the data, if you can't have multitenancy and separate workflows across the cluster, then it doesn't matter how secure it is. You know, you need those. >>I got to ask you a direct question since we're here at Hadoop summit, because we get this question all the time. Silicon lucky bond is so successful, but I just don't understand your business model without plates were free content and they have some underwriters. So you guys have been very successful yet. People aren't looking at map are as good at the quiet leader, like you doing your business, you're making money. Jeff. He had some numbers with us that in the Hindu community, about 20% are paying subscriptions. That's unlike your business model. So explain to the folks out there, the business model and specifically the traction because you have >>Customers. Yeah. Oh no, we've got, we've got over 500 paying customers. We've got at least $1 million customer in seven different verticals. So we've got breadth and depth and our business model is simple. We're an enterprise software company. That's looking at how to provide the best of open source as well as innovations underneath >>The most open distribution of Hadoop. But you add that value separately to that, right? So you're, it's not so much that you're proprietary at all. Right. Okay. >>You clarify that. Right. So if you look at, at this exciting ecosystem, Hadoop is fairly early in its life cycle. If it's a commoditization phase like Linux or, or relational database with my SQL open source, kind of equates the whole technology here at the beginning of this life cycle, early stages of the life cycle. There's some architectural innovations that are really required. If you look at Hadoop, it's an append only file system relying on Linux. And that really limits the types of operations. That types of use cases that you can do. What map ours done is provide some deep architectural innovations, provide complete read-write file systems to integrate data protection with snapshots and mirroring, et cetera. So there's a whole host of capabilities that make it easy to integrate enterprise secure and, and scale much better. Do you think, >>I feel like you were maybe a little early to the market in the sense that we heard Merv Adrian and his keynote this morning. Talk about, you know, it's about 10 years when you start to get these questions about security and governance and we're about nine years into Hadoop. Do you feel like maybe you guys were a little early and now you're at a tipping point, whereas these more, as more and more deployments get ready to go to production, this is going to be an area that's going to become increasingly important. >>I think, I think our timing has been spectacular because we, we kind of came out at a time when there was some customers that were really serious about Hadoop. We were able to work closely with them and prove our technology. And now as the market is just ramping, we're here with all of those features that they need. And what's a, what's an issue. Is that an incremental improvement to provide those kind of key features is not really possible if the underlying architecture isn't there and it's hard to provide, you know, online real-time capabilities in a underlying platform that's append only. So the, the HDFS layer written in Java, relying on the Linux file system is kind of the, the weak underbelly, if you will, of, of the ecosystem. There's a lot of, a lot of important developments happening yarn on top of it, a lot of really kind of exciting things. So we're actively participating in including Apache drill and on top of a complete read-write file system and integrated Hindu database. It just makes it all come to life. >>Yeah. I mean, those things on top are critical, but you know, it's, it's the underlying infrastructure that, you know, we asked, we keep on community about that. And what's the, what are the things that are really holding you back from Paducah and production and the, and the biggest challenge is they cited worth high availability, backup, and recovery and maintaining performance at scale. Those are the top three and that's kind of where Matt BARR has been focused, you know, since day one. >>So if you look at a major retailer, 2000 nodes and map bar 50 unique applications running on a single cluster on 10,000 jobs a day running on top of that, if you look at the Rubicon project, they recently went public a hundred million add actions, a hundred billion ad auctions a day. And on top of that platform, beats music that just got acquired for $3 billion. Basically it's the underlying map, our engine that allowed them to scale and personalize that music service. So there's a, there's a lot of proof points in terms of how quickly we scale the enterprise grade features that we provide and kind of the blending of deep predictive analytics in a batch environment with online capabilities. >>So I got to ask you about your go to market. I'll see Cloudera and Hortonworks have different business models. Just talk about that, but Cloudera got the massive funding. So you get this question all the time. What do you, how do you counter that army and the arms race? I think >>I just wrote an article in Forbes and he says cash is not a strategy. And I think that was, that was an excellent, excellent article. And he goes in and, you know, in this fast growing market, you know, an amount of money isn't necessarily translate to architectural innovations or speeding the development of that. This is a fairly fragmented ecosystem in terms of the stack that runs on top of it. There's no single application or single vendor that kind of drives value. So an acquisition strategy is >>So your field Salesforce has direct or indirect, both mixable. How do you handle the, because Cloudera has got feet on the street and every squirrel will find it, not if they're parked there, parking sales reps and SCS and all the enterprise accounts, you know, they're going to get the, squirrel's going to find a nut once in awhile. Yeah. And they're going to actually try to engage the clients. So, you know, I guess it is a strategy if they're deploying sales and marketing, right? So >>The beauty about that, and in fact, we're all in this together in terms of sharing an API and driving an ecosystem, it's not a fragmented market. You can start with one distribution and move to another, without recompiling or without doing any sort of changes. So it's a fairly open community. If this were a vendor lock-in or, you know, then spending money on brand, et cetera, would, would be important. Our focus is on the, so the sales execution of direct sales, yes, we have direct sales. We also have partners and it depends on the geographies as to what that percentage is. >>And John Schroeder on with the HP at fifth big data NYC has updated the HP relationship. >>Oh, excellent. In fact, we just launched our application gallery app gallery, make it very easy for administrators and developers and analysts to get access and understand what's available in the ecosystem. That's available directly on our website. And one of the featured applications there today is an integration with the map, our sandbox and HP Vertica. So you can get early access, try it and get the best of kind of enterprise grade SQL first, >>First Hadoop app store, basically. Yeah. If you want to call it that way. Right. So like >>Sure. Available, we launched with close to 30, 30 with, you know, a whole wave kind of following that. >>So talk a little bit about, you know, speaking of verdict and kind of the sequel on Hadoop. So, you know, there's a lot of talk about that. Some confusion about the different methods for applying SQL on predicts or map art takes an open approach. I know you'll support things like Impala from, from a competitor Cloudera, talk about that approach from a map arts perspective. >>So I guess our, our, our perspective is kind of unbiased open source. We don't try to pick and choose and dictate what's the right open source based on either our participation or some community involvement. And the reality is with multiple applications being run on the platform, there are different use cases that make difference, you know, make different sense. So whether it's a hive solution or, you know, drill drills available, or HP Vertica people have the choice. And it's part of, of a broad range of capabilities that you want to be able to run on the platform for your workflows, whether it's SQL access or a MapReduce or a spark framework shark, et cetera. >>So, yeah, I mean there is because there's so many different there's spark there's, you know, you can run HP Vertica, you've got Impala, you've got hive. And the stinger initiative is, is that whole kind of SQL on Hadoop ecosystem, still working itself out. Are we going to have this many options in a year or two years from now? Or are they complimentary and potentially, you know, each has its has its role. >>I think the major differences is kind of how it deals with the new data formats. Can it deal with self-describing data? Sources can leverage, Jason file does require a centralized metadata, and those are some of the perspectives and advantages say the Apache drill has to expand the data sets that are possible enabled data exploration without dependency on a, on an it administrator to define that, that metadata. >>So another, maybe not always as exciting, but taking workloads from existing systems, moving them to Hadoop is one of the ways that a lot of people get started with, to do whether associated transformation workloads or there's something in that vein. So I know you've announced a partnership with Syncsort and that's one of the things that they focus on is really making it as easy as possible to meet those. We'll talk a little bit about that partnership, why that makes sense for you and, and >>When your customer, I think it's a great proof point because we announced that partnership around mainframe offload, we have flipped comScore and experience in that, in that press release. And if you look at a workload on a mainframe going to duke, that that seems like that's a, that's really an oxymoron, but by having the capabilities that map R has and making that a system of record with that full high availability and that data protection, we're actually an option to offload from mainframe offload, from sand processing and provide a really cost effective, scalable alternative. And we've got customers that had, had tried to offload from the mainframe multiple times in the past, on successfully and have done it successfully with Mapbox. >>So talk a little bit more about kind of the broader partnership strategy. I mean, we're, we're here at Hadoop summit. Of course, Hortonworks talks a lot about their partnerships and kind of their reseller arrangements. Fedor. I seem to take a little bit more of a direct approach what's map R's approach to kind of partnering and, and as that relates to kind of resell arrangements and things like, >>I think the app gallery is probably a great proof point there. The strategy is, is an ecosystem approach. It's having a collection of tools and applications and management facilities as well as applications on top. So it's a very open strategy. We focus on making sure that we have open API APIs at that application layer, that it's very easy to get data in and out. And part of that architecture by presenting standard file system format, by allowing non Java applications to run directly on our platform to support standard database connections, ODBC, and JDBC, to provide database functionality. In addition to kind of this deep predictive analytics really it's about supporting the broadest set of applications on top of a single platform. What we're seeing in this kind of this, this modern architecture is data gravity matters. And the more processing you can do on a single platform, the better off you are, the more agile, the more competitive, right? >>So in terms of, so you're partnering with people like SAS, for example, to kind of bring some of the, some of the analytic capabilities into the platform. Can you kind of tell us a little bit about any >>Companies like SAS and revolution analytics and Skytree, and I mean, just a whole host of, of companies on the analytics side, as well as on the tools and visualization, et cetera. Yeah. >>Well, I mean, I, I bring up SAS because I think they, they get the fact that the, the whole data gravity situation is they've got it. They've got to go to where the data is and not have the data come to them. So, you know, I give them credit for kind of acknowledging that, that kind of big data truth ism, that it's >>All going to the data, not bringing the data >>To the computer. Jack talk about the success you had with the customers had some pretty impressive numbers talking about 500 customers, Merv agent. The garden was on with us earlier, essentially reiterating not mentioning that bar. He was just saying what you guys are doing is right where the puck is going. And some think the puck is not even there at the same rink, some other vendors. So I gotta give you props on that. So what I want you to talk about the success you have in specifically around where you're winning and where you're successful, you guys have struggled with, >>I need to improve on, yeah, there's a, there's a whole class of applications that I think Hadoop is enabling, which is about operations in analytics. It's taking this, this higher arrival rate machine generated data and doing analytics as it happens and then impacting the business. So whether it's fraud detection or recommendation engines, or, you know, supply chain applications using sensor data, it's happening very, very quickly. So a system that can tolerate and accept streaming data sources, it has real-time operations. That is 24 by seven and highly available is, is what really moves the needle. And that's the examples I used with, you know, add a Rubicon project and, you know, cable TV, >>The very outcome. What's the primary outcomes your clients want with your product? Is it stability? And the platform has enabled development. Is there a specific, is there an outcome that's consistent across all your wins? >>Well, the big picture, some of them are focused on revenues. Like how do we optimize revenue either? It's a new data source or it's a new application or it's existing application. We're exploding the dataset. Some of it's reducing costs. So they want to do things like a mainframe offload or data warehouse offload. And then there's some that are focused on risk mitigation. And if there's anything that they have in common it's, as they moved from kind of test and looked at production, it's the key capabilities that they have in enterprise systems today that they want to make sure they're in Hindu. So it's not, it's not anything new. It's just like, Hey, we've got SLS and I've got data protection policies, and I've got a disaster recovery procedure. And why can't I expect the same level of capabilities in Hindu that I have today in those other systems. >>It's a final question. Where are you guys heading this year? What's your key objectives. Obviously, you're getting these announcements as flurry of announcements, good success state of the company. How many employees were you guys at? Give us a quick update on the numbers. >>So, you know, we just reported this incredible momentum where we've tripled core growth year over year, we've added a tremendous amount of customers. We're over 500 now. So we're basically sticking to our knitting, focusing on the customers, elevating the proof points here. Some of the most significant customers we have in the telco and financial services and healthcare and, and retail area are, you know, view this as a strategic weapon view, this is a huge competitive advantage, and it's helping them impact their business. That's really spring our success. We've, you know, we're, we're growing at an incredible clip here and it's just, it's a great time to have made those calls and those investments early on and kind of reaping the benefits. >>It's. Now I've always said, when we, since the first Hadoop summit, when Hortonworks came out of Yahoo and this whole community kind of burst open, you had to duke world. Now Riley runs at it's a whole different vibe of itself. This was look at the developer vibe. So I got to ask you, and we would have been a big fan. I mean, everyone has enough beachhead to be successful, not about map arbors Hortonworks or cloud air. And this is why I always kind of smile when everyone goes, oh, Cloudera or Hortonworks. I mean, they're two different animals at this point. It would do different things. If you guys were over here, everyone has their quote, swim lanes or beachhead is not a lot of super competition. Do you think, or is it going to be this way for awhile? What's your fork at some? At what point do you see more competition? 10 years out? I mean, Merv was talking a 10 year horizon for innovation. >>I think that the more people learn and understand about Hadoop, the more they'll appreciate these kind of set of capabilities that matter in production and post-production, and it'll migrate earlier. And as we, you know, focus on more developer tools like our sandbox, so people can easily get experienced and understand kind of what map are, is. I think we'll start to see a lot more understanding and momentum. >>Awesome. Jack Norris here, inside the cube CMO, Matt BARR, a very successful enterprise grade, a duke player, a leader in the space. Thanks for coming on. We really appreciate it. Right back after the short break you're live in Silicon valley, I had dupe December, 2014, the right back.

>>I from Midtown Manhattan, the cute quiet coverage of big data NYC Civicon angled, Wiki bonds production made possible by Hortonworks. We do hairdo and lamb disco and new made invincible. And now your hosts, John furrier and Volante >>Hi buddy. We're back. This is Dave Volante with Jeff Kelly with Wiki bond. And this is the cube Silicon angle's continuous production. We're here at big data NYC right across the street from the Hilton where strata comp and a dupe world is going on. We've got a multi-time cube guest, Jack Norris, the CMO of map bars here, Jack. Welcome back to the cube first. So by the way, thank you so much for the support. As you know, we're across the street here at the Warwick hotel map, our, you guys have always been so generous supporting the cube. We can't thank you enough for that. So really appreciate it. Thank you. So we were able to listen to your keynote yesterday. It was, we, we, we weren't broadcasting, you know, head to head yesterday and had an opportunity to hear your keynote. So, first of all, how did that go? I want to ask you some questions about it. >>It, it was a really well-received and I think people were kind of clamoring to try to separate the myths from, from reality on, on Hadoop, >>We had three myths that you talked about, you know, one related to the distraction. I'd like to get into some of those. So what was the, the first myth was around the, the, the, the district distribution battle. So take us through that. >>So, you know, th the impression that it's a knock-down drag-out competitive battle across Hadoop distributions was the first myth. And the reality is that all of the distribution share the same open source Apache code. And this is one of the first markets that's really, really created, or the first open-source technologies it's really created a market. I mean, look, what's happened here with this whole, this whole big data and Hadoop, but given that early stage, there's the requirement to really combine that open source code with additional innovations to meet customer needs. And so what you see is you see those aggregators that are taken open source, you see others that are taking the open source, and then adding maybe management utility, couple of, of, you know, different applications on top. And then our approach at map R is we're taking the open source with those management innovations, doing some development, the open source community with things like Apache drill, and then really focusing on the underlying architecture, the data platform and providing innovations at that layer. So >>Actually sort of the three major destroys that we talk about all the time. You know, you guys, Hortonworks and Hadoop, you guys have been consistent the whole time as has Hortonworks, right? Cloud era basically put out a post recently saying, Hey, kind of going in a different direction, sort of what I call the tapped out of the Hadoop distro, you know, piece of it. But so there's a lot of discussion around it. You're putting forth the, Hey, it's not an internet seen war, but does it matter is my question? >>Well, I think if you take a step back, the Hadoop ecosystem is incredibly strong growing very, very quickly, fastest growing big data technology, one of the top 10 technologies overall. And I think it's because we are sharing the same API. It is possible for customers to learn on one, develop and move seamlessly to another. And, you know, in the keynote, I talked about the difference between the no SQL market, which is, you know, there is no consensus there and, and customers have to figure out not only what's the right word workload, but what's the technology that's actually going to have some staying power, right? >>That's a powerful comment. Amazon turn the data center and into an API, or you as the duke community is essentially turning data, access into an API. And that is a very powerful and leverageable concept. Okay. Your second myth was around the whole, no SQL yes. Piece of it. You help you put up a slide. I thought I read Jeff Kelly's reports. And I thought, I thought I knew them all, but there were a couple in there that I didn't recognize as you probably knew them all, but so take us through myth. Number two >>Too. I'm sure we missed some >>There wasn't room on the slide for anymore. >>The, yeah, it's basically about the consensus. There is no real consensus. There's no common API. There's no ability to move applications seamlessly across no SQL solutions. If you look at one no SQL solution, and that's, HBase a big inherent advantage because it's integrated with Hindu, you know, this whole trend is about compute and data together. So if you've got a no sequel solution, that's on that same, you know, massive data store, you know, big leg up. And, and then we got into the, well, if you've got HBase, it's included in all the distributions and all the distribution share the same open source, then obviously it must run the same across all distributions. And there, we shared some pretty interesting data to show the difference. When you, when you do architectural differences and innovations underneath that you can dramatically change the performance of, of not only MapReduce, but of no SQL. Yes. >>Okay. So not all no SQL is created equally. Not all HBase is created equally as essentially what you're saying there. Now the third piece was to dupe is enterprise ready, right? Yeah. So you guys were first to say, well, we have a Hadoop platform that's enterprise ready way ahead on that. Got criticized a lot for going down that path shrugged and said, okay, we'll just keep doing business with customers. And you've been again, very clear and consistent on that. So talk about the third myth >>And that's, you know, is, is Hadoop ready for prime time? And I think the way to combat that myth is by customer examples and showing the tremendous success that customers are enjoying with Hadoop. And, you know, we, we don't have time on the cube here to go through all of them, but, you know, I like to point out 90 billion auctions a day with Rubicon, they've surpassed Google in terms of ad reach. They're doing that on Mapbox 1.7 trillion events a month with comScore that's on, on map bar. You look in, in traditional enterprise, you know, a single retailer with over 2000 nodes of Hadoop. I mean, it's a key part of their merchandising and retail operations, and combining all sorts of, of data feeds and all sorts of use cases there, financial services over a thousand nodes of risk medication, personalized offers streamlining their operations. I mean, it's, it's dramatic. And then, you know, we shared some of the more, more interesting ones, esoteric ones like garbage and whiskey and weather prediction. >>There was consider these, we even as diverse and eclectic as they are, they consider these mission critical application. >>Oh, absolutely. No it it's. And I think that's the difference because what we're talking about is not Hadoop as this cash, right? This temporary processing, where we can do, you know, some interesting batch analytics and then take that and put that someplace else. And yes, there are applications like that, but companies soon realized that if I'm going to use this as a key part of my operations, and it's about data on compute, then I want a consistent permanent store. I want a system of record. So all of the SLS and high availability and data protection features that they expect in their enterprise applications should be present in Hadoop, right? That's where we focus. Let's run down a couple of those. >>What are some of the key capabilities that you need in an enterprise enterprise grade platform? That map bar is >>Well, let's, let's take, let's take business continuity cause that's important if you're really going to trust data there. And you know, one of the big drivers as you expand data is how much am I going to spend on it? And if you look at a large investment bank, $270 million of their budget, not total, but incremental to address the additional capacity, there's a big emphasis for let's look at a better way to do that. So instead of spending $15,000 a terabyte, if you can spend a few hundred dollars a terabyte, that's a huge, huge advantage. And that's the focus of Hindu, but to do that, well, then the features that are in this enterprise storage have to be present. And we're talking about, you know, mirroring and not a copy table function, but replication, that's how that's how organizations do it, right. If you're going to recovery and recovery, you know, you can't back up a petabyte of information through a copy function, right? You have to do a snapshot and the snapshots have to be consistent, right. And, and we're not saying anything that, you know, an enterprise administrator doesn't know, there is some confusion when you're more on the developer side as to what these features are and the difference between a fuzzy snapshot and a point in time, consistent snaps. >>Got it. So let's talk a little bit about the, the enterprise data hub, this, this concept that Michael Wilson with clutter introduced yesterday. Tell us a little bit about your take on, on, on Mike's I guess, definition and, and essentially I think trying to name the category of kind of what Hadoop can do and what, and where it sits in the architecture. Did you agree with his, his, >>Yeah. I mean, if you look at, at that description, it's about I'm taking important data and I'm putting it in a dupe and I'm combining a lot of different data sources and it's been referred to as a data lake and a data reservoir and a data ocean. I mean, we've heard a lot of terms. We worked with an outside consultant that was originally an architect at Terre data. It's been about eight months, almost a year ago now where he defined it and enterprise data hub. And it's it's, he went through kind of the list of requirements. And once you move from a transitory to a permanent store, then that becomes an enterprise data hub. And an enterprise data hub can be used to select and process information, maybe it's ETL and serve some downstream applications. It can also be useful to do analysis directly on it, to, you know, to serve different business functions. But the system requirements that he established for that I think are absolutely true. And it's, you have to have the full data protection. You have to have the full disaster recovery. You have to have the full high availability because this is going to be important data serving the organization. If it's data that you can lose, if it's data that you, you don't really care about having highly available, then it's a very narrow use case that that data hub serves. >>So you're saying the enterprise data hub isn't ready for prime time. >>No, I'm saying that there, there are requirements. And we have companies today that have deployed an enterprise data hub and they are quite successful with it. And, you know, the quotes are the ETL functions that they're doing on that hub are 10 times faster and it's 10 times cheaper than what they're seeing. >>Soundbite, Dave, >>I agree, but it's nuanced. Right. And so, you know, the customers cause a lot of vendors, right? They're all saying the same thing to the customers, right? So you've got your messaging that you've, you know, you've proven out over the last several years and then the entire market starts to use the same terminology. So it is, this is why I, like, I think this, what is, what are those >>Things? We're in a little bit of this, this kind of marketing fog here in the relative early stages. I think the best response there is customer proof points. And I think some education in the very beginning, you know, when they're in development and test, it's really important to understand, you know, what is Hadoop and what can I use it for and what data source am I going to leverage? I think the features that we're talking about really start to show up as you deploy in production. And as you expand its use in production and there we've enjoyed tremendous success, >>But he would argue that you have a lead in this space. I wouldn't, I don't think you would either the space being robustness enterprise ready, mission criticality is your lead increasing, decreasing staying the same. >>What's your sense? Well, it's hard cause there's no, you know, th th there's no external service that's out there, you know, interviewing every customer and, and giving numbers. I do know that we passed 500 paying customers. I do know that we've got significant deployments and you can measure those in terms of number of nodes, you know, in the thousands of nodes, you can measure those in terms of use cases. So we've got, you know, one company they've passed 20 different use cases on the same cluster. I think that's an interesting proof point. We're scaling in terms of the number of, of people in an organization that are trained in leveraging the data in map are again in the, in the thousands. So, you know, I think this market is so big and so dynamic that this isn't about, you know, one company success at the expense of everyone. Else's zero sum game. I think, you know, we're all here kind of raising this, this boat and focusing on this paradigm shift, but when it comes to production success, that's our focus. And I think that's where we've, we've proven that >>One thing I'm really want to get your opinion on, you know, as, as to do matures and some of the innovations you guys are doing and, and making the platform, you know, basically a multi application platform, you can do more things with Hadoop. And we've been talking about this on the cube, is that as that happens, you're going to start you as an industry. You're going to start bumping up against the EDW vendors and some of the other database vendors in the traditional world. And you're now you're doing some of the things that those, those tools can do now, you know, two years ago, it was very much just, this is all very complimentary Hadoop and your EDW. There's no overlap. We're gonna all play nice. But increasingly we're seeing that there is an overlap. How do you view that? Is that, and what is your relationship with those, with those EDW vendors and, and what are you hearing from customers when you go into a customer? Okay. >>So, I mean, there's a, there's a lot in that question. I think the F the first comment though, is don't look at Hadoop through this single data warehouse lens. And if you look at, at trying to use Hadoop to completely replace an enterprise data warehouse where there's, here's a few decades of experience, there, there are many organizations that have a lot of activities that are based in that data warehouse. And that's where we're seeing a data warehouse offload that is complimentary, but it gives organizations this lever to say, well, I'm going to control the fill rate, and I'm going to take some of the data that's no longer, you know, really active and put that on Hadoop and really change my ability to manage the costs in a data warehouse environment. The other thing that's interesting is that the types of applications that duper doing, I think are creating a new class it's about operations and analytics, kind of combined together, taking high arrival rate data and making very quick micro changes to optimize whether that's fraud detection or recommendation engines, or taking sensor data and predictive analytics for, for maintenance, et cetera. There is just a tremendous number of, of applications. In some cases, leveraging a new data source in some cases, doing new applications, but it's just opening things up. And, and I think organizations are moving to be very data-driven and Hadoop is at the center of that. >>And you control the field, right? That's another really good soundbites. And, and these that, you mentioned this high arrival rate data, this fraud detection, predictive analytics, maintenance, these are things that you're doing today with >>Navarre right? Yeah, >>Absolutely. Great. All right, Jack. Well, listen, always a pleasure. Thanks very much for coming by. Great to see you again. All right. Keep it right there about Uber, right back with our next guest. This is the cube we're live from the big apple.

>>Ash it's, you know, what will that mean to my investment? And the announcement fusion IO is that, you know, we're 25 times faster on read intensive HBase applications. The combination. So as organizations are deploying Hadoop, and they're looking at technology changes coming down the pike, they can rest assured that they'll be able to take advantage of those in a much more aggressive fashion with map R than, than other distribution. >>Jack, how I got to ask you, we were talking last night at the Hadoop summit, kind of the kickoff party and, you know, everyone was there. All the top execs were there and all the developers, you know, we were in the queue. I think, I think that either Dave or myself coined the term, the big three of big data, you guys ROMs cloud Cloudera map R and Hortonworks, really at the, at the beginning of the key players early on and Charles from Cloudera was just recently on. And, and he's like, oh no, this, this enterprise grade stuff has been kicked around. It's been there from the beginning. You guys have been there from the beginning and Matt BARR has never, ever waffled on your, on your messaging. You've always been very clear. Hey, we're going to take a dupe open source a dupe and turn it into an enterprise grade product. Right. So that's clear, right? That's, that's, that's a great, that's a great, so what's your take on this because now enterprise grade is kind of there, I guess, the buzz around getting the, like the folks that have crossed the chasm implemented. So what can you comment on that about one enterprise grade, the reality of it, certainly from your perspective, you haven't been any but others. And then those folks that are now rolling it out for the first time, what can you share with them around? What does it mean to be enterprise grade? >>So enterprise grade is more about the customer experience than, than a marketing claim. And, you know, by enterprise grade, what we're talking about are some of the capabilities and features that they've grown to expect in their, their other enterprise applications. So, you know, the ability to meet full S SLA is full ha recovery from multiple failures, rolling upgrades, data protection was consistent snapshots business continuity with mirroring the ability to share a cluster across multiple groups and have, you know, volumes. I mean, there's a, there's a host of features that fall under the umbrella enterprise grade. And when you move from no support for any of those features to support to a few of them, I don't think that's going to, to ha it's more like moving to low availability. And, and there's just a lot of differences in terms of when we say enterprise grade with those features mean versus w what we view as kind of an incomplete story. So >>What do you, what do you mean by low availability? Well, I mean, it's tongue in cheek. It's nice. It's a good term. It's really saying, you know, just available when you sometimes is that what you mean? Is this not true availability? I mean, availability is 99.9%. Right? >>Right. So if you've got a, an ha solution that can't recover from multiple failures, that's downtime. If you've got an HBase application that's running online and you have data that goes down and it takes 10 to 30 minutes to have the region servers recover it from another place in the distribution, that's downtime. If you have snapshots that aren't consistent across the cluster, that doesn't provide data protection, there's no point in time recovery for, for a cluster. So, you know, there's a lot of details underneath that, but what it, what it amounts to is, do you have interruptions? Do you have downtime? Do you have the potential for losing data? And our answer is you need a series of features that are hardened and proven to deliver that. >>What about recoverability? You mentioned that you guys have done a lot of work in that area with snapshotting, that's kind of being kicked around, are our folks addressing, what are the comp what's your competition doing in those areas of recoverability just mentioned availability. Okay, got that. Recoverability security, compliance, and usability. Those are the areas that seem to be the hot focus areas what's going on in the energy. How would you give them the grade, the letter grade, if you will, candidly, compared to what you guys offer? Well, the, >>The first of all, it's take recoverability. You know, one of the tenants is you have a point in time recovery, the ability to restore to a previous point that's consistent across the cluster. And right now there's, there's no point in time recovery for, for HDFS, for the files. And there's no point in time recovery for HBase tables. So there's snapshot support. It's being talked about in the open source community with respect to snapshots, but it's being referred to in the JIRAs as fuzzy snapshots and really compared to copy table. >>So, Jack, I want to turn the conversation to the, kind of the topic we've talked about before kind of the open versus a proprietary that, that whole debate we've, we've, we've heard about that. We talked about that before here on the cube. So just kind of reiterate for us your take. I mean, we, we hear perhaps because of the show we're at, there's a lot of talk about the open source nature of Hadoop and some of the purists, as you might call them are saying, it's gotta be open a hundred percent Patrick compatible, et cetera. And then there's others that are taking a different approach, explain your approach and why you think that's the key way to make, to really spur adoption of a dupe and make it >>W w we're we're a part of the community we're, we've got, you know, commitment going on. We've, you know, pioneered and pushed a patchy drill, but we have done innovations as well. And I think that those innovations are really required to support and extend the, the whole ecosystem. So canonical distributes RN, three D distribution. We've got, you know, all our, our packages are, are available on get hub and, and open source. So it's not, it's not a binary debate. And I think the, the point being that there's companies that have jumped ahead and now that Peloton is, is, you know, pedaling faster and, and we'll, we'll catch up. We'll streamline. I think the difference is we rearchitected. So we're basically in a race car and, you know, are, are racing ahead with, with enterprise grade features that are required. And there's a lot of work that still needs to be done, needs to be accomplished before that full rearchitecture is, is in place. >>Well, I mean, I think for me, the proof is really in the pudding when you, when it comes to talk about customers that are doing real things and real production, grade mission, critical applications that they're running. And to me that shows the successor or relative success of a given approach. So I know you guys are working with companies like ancestry.com, live nation and Quicken loans. Maybe you could, could you walk us through a couple of those scenarios? Let's take ancestry.com. Obviously they've got a huge amount of data based on the kind of geological information, where do you guys do >>With them? Yeah, so they've got, I mean, they've got the world's largest family genealogy services available on the web. So there's a massive amount of data that they make accessible and, and, you know, ability for, for analysis. And then they've rolled out new features and new applications. One of which is to ship a kit out, have people spit in a tube, returned back and they do DNA matching and reveal additional details. So really some really fabulous leading edge things that are being done with, with the use of, of Hadoop. >>Interesting. So talk about when you went to, to work with them, what were some of their key requirements? Was it around, it was more around the enterprise enterprise, grade security and uptime kind of equation, or was it more around some of the analytics? What, what, what's the kind of the killer use case for them? >>It's kind of, you know, it's, it's hard with a specific company or even, you know, to generalize across companies. Cause they're really three main areas in terms of ease of use and administration dependability, which includes the full ha and then, and then performance. And in some cases, it's, it's just one of those that kind of drives it. And it's used to justify, in other cases, it's kind of a collection. The ease of use is being able to use a cluster, not only as Hadoop, but to access it and treat it like enterprise storage. So it's a complete POSIX compliance file system underneath that allows the, the mounting and access and updates and using it in dynamic read-write. So what that means from an application level, it's, it's faster, it's much easier to administer and it's much easier and reliable for developers to, to utilize. >>I got to ask you about the marketing question cause I see, you know, map our, you guys have done a good job of marketing. Certainly we want to be thankful to you guys is supporting the cube in the past and you guys have been great supporters of our mission, but now the ecosystem's evolving a lot more competition. Claudia mentioned those eight companies they're tracking in quote Hadoop, and certainly Jeff and I, and, and SiliconANGLE by look at there's a lot more because Hadoop washing has been going on now for the term Hadoop watching me and jumping in and doing Hadoop, slapping that onto an existing solution. It's not been happening full, full, full bore for a year. At least what's the next for you guys to break above the noise? Obviously the communities are very active projects are coming online. You guys have your mission in the enterprise. What's the strategy for you guys going forward is more of the same and anything new even share. >>Yeah, I, I, I think as far as breaking above the noise, it will be our customers, their success and their use cases that really put the spotlight on what the differences are in terms of, of, you know, using a big data platform. And I think what, what companies will start to realize is I'd rather analogy between supply chain and the big, the big revolution in supply chain was focusing on inventory at each stage in the supply chain. And how do you reduce that inventory level and how do you speed the, the flow of goods and the agility of a company for competitive advantage. And I think we're going to view data the same way. So companies instead of raw data that they're copying and moving across different silos, if they're able to process data in place and send small results sets, they're going to be faster, more agile and more competitive. >>And that puts the spotlight on what data platform is out there that can support a broad set of applications and it can have the broadest set of functionality. So, you know, what we're delivering is a mission grade, you know, enterprise grade mission, critical support platform that supports MapReduce and does that high performance provides NFS POSIX access. So you can use it like a file system integrates, you know, enterprise grade, no SQL applications. So now you can do, you know, high-speed consistent performance, real time operations in addition to batch streaming, integrated search, et cetera. So it's, it's really exciting to provide that platform and have organizations transform what they're doing. >>How's the feedback on with Ted Dunning? I haven't seen a lot of buzz on the Twittersphere is getting positive feedback here. He's a, a tech athlete. He's a guru, he's an expert. He's got his hands in all the pies. He's a scientist type. What's he up to? What's his, what's his role within Mapa and he's obviously playing in the open-source community. What's he up to these days, >>Chief application architect, he's on the leading edge of my house. So machine learning, so, you know, sharing insights there, he was speaking at the storm meetup two nights ago and sharing how you can integrate long running batch, predictive analytics with real-time streaming and how the use of snapshots really that, that easy and possible. He travels the world and is helping organizations understand how they can take some very complex, long running processes and really simplify and shorten those >>Chance to meet him in New York city had last had duke world at a, at a, a party and great guy, fantastic geek, and certainly is doing a great work and shout out to Ted. Congratulations, continue up that support. How's everyone else doing? How's John and Treevis doing how's the team at map are we're pedaling as best as you can growing >>Really quickly. No, we're just shifting gears. Would it be on pedaling >>Engine? >>Yeah. Give us an update on the company in terms of how the growth and kind of where you guys are moving that. >>Yeah. We're, we're expanding worldwide, you know, just this, you know, last few months we've opened up offices and in London and Munich and Paris, we're expanding in Asia, Japan and Korea. So w our, our sales and services and engineering, and basically across the whole company continues to expand rapidly. Some really great, interesting partnerships and, and a lot of growth Natalie's we add customers, but it's, it's nice to see customers that continue to really grow their use of map are within their organization, both in terms of amount of data that they're analyzing and the number of applications that they're bringing to bear on the platform. >>Well, that a little bit, because I think, you know, one of the, one of the trends we do see is when a company brings in big data, big data platform, and they might start experiment experimenting with it, build an application. And then maybe in the, maybe in the marketing department, then the sales guys see it and they say, well, maybe we can do something with that. How is that typically the kind of the experience you're seeing and how do you support companies that want to start expanding beyond those initial use cases to support other departments, potentially even other physical locations around the world? How do you, how do you kind of, >>That's been the beauty of that is if you have a platform that can support those new applications. So if you know, mission critical workloads are not an issue, if you support volumes so that you can logically separate makes it much easier, which we have. So one of our customers Zions bank, they brought in Matt BARR to do fraud detection. And pretty soon the fact that they were able to collect all of that data, they had other departments coming to them and saying, Hey, we'd like to use that to do analysis on because we're not getting that data from our existing system. >>Yeah. They come in and you're sitting on a goldmine, there are use cases. And you also mentioned kind of, as you're expanding internationally, what's your take on the international market for big data to do specifically is, is the U S kind of a leaps and bounds ahead of the rest of the world in terms of adoption of the technology. What are you seeing out there in terms of where, where the rest of the, >>I wouldn't say leaps and bounds, and I think internationally, they're able to maybe skip some of the experimental steps. So we're seeing, we're seeing deployment of class financial services and telecom, and it's, it's fairly broad recruit technologies there. The largest provider of recruiting services, indeed.com is one of their subsidiaries they're doing a lot with, with Hadoop and map are specifically, so it's, it's, it's been, it's been expanding rapidly. Fantastic. >>I also, you know, when you think about Europe, what's going on with Google and some of the, the privacy concerns even here, or I should say, is there, are there different regulatory environments you've got to navigate when you're talking about data and how you use data when you're starting to expand to other, other locales? >>Yeah. There's typically by vertical, there's different, different requirements, HIPAA and healthcare, and basal to, and financial services. And so all of those, and it, it, it basically, it's the same theme of when you're bringing Hadoop into an organization and into a data center, the same sorts of concerns and requirements and privacy that you're applying in other areas will be applied on Hindu. >>I'm now kind of turning back to the technology. You mentioned Apache drill. I'd love to get an update on kind of where, where that stands. You know, it's put, then put that into context for people. We hear a lot about the SQL and Hadoop question here, where does drill fit into that, into that equation? >>Well, the, the, you know, there's a lot of different approaches to provide SQL access. A lot of that is driven by how do you, how do you leverage some of the talent and organization that, you know, speak SQL? So there's developments with respect to hive, you know, there's other projects out there. Apache drill is an open source project, getting a lot of community involvement. And the design center there is pretty interesting. It started from the beginning as an open source project. And two main differences. One was in looking at supporting SQL it's, let's do full ANSI SQL. So it's full 2003 ANSI, sequel, not a SQL like, and that'll support the greatest number of applications and, you know, avoid a lot of support and, and issues. And the second design center is let's support a broad set of data sources. So nested sources like Jason scheme on discovery, and basically fitting it into an enterprise environment, which sometimes is kinda messy and can get messy as acquisitions happen, et cetera. So it's complimentary, it's about, you know, enabling interactive, low latency queries. >>Jack, I want to give you the final word. We are out of time. Thanks for coming on the cube. Really preached. Great to see you again, keep alumni, but final word. And we'll end the segment here on the cube is your quick thoughts on what's happening here at Hadoop world. What is this show about? Share with the audience? What's the vibe, the summary quick soundbite on Hadoop. >>I think I'll go back to how we started. It's not, if you used to do putz, how you use to do and, you know, look at not only the first application, but what it's going to look like in multiple applications and pay attention to what enterprise grade means. >>Okay. They were secure. We got a more coverage coming, Jack Norris with map R I'll say one of the big three original, big three, still on the, on the list in our mind, and the market's mind with a unique approach to Hadoop and the mid-June great. This is the cube I'm Jennifer with Jeff Kelly. We'll be right back after this short break, >>Let's settle the PR program out there and fighting gap tech news right there. Plenty of the attack was that providing a new gadget. Let's talk about the latest game name, but just the.

>>Okay. We're back here inside the cube, our flagship program about the events and extract the signal from the noise. This is strata conference. O'Reilly media is a big data event. We're talking about Hadoop analytics, data platforms, and big is come into the enterprise from the front door. As we heard them yesterday. I'm John Frey with Dave Volante, wiki.org. And we're here with Jack Norris, our cube alumni, and a favorite guest here. You're a in charge executive at map. Our, you guys are leading the charge with this use of a dupe. Welcome back to the cube. Thank you. Okay, so what's, let's chat about what's going on. What's your take on all the big news out here for the distributions. I'll the big power moose. You guys have a relationship with EMC. Okay. Exclusive relationship with those guys. Intel's got a distribution Horton versus with Microsoft, a lot of things going on. So this is your wheelhouse. So what's your take on the Hadoop action here? >>Well, I think there's an article in Forbes where I think they, they said it best. This is showing that map bars had the right strategy all along. And what we're seeing is, is basically there's a fairly low bar to taking a patchy Hadoop and providing a distribution. And so we're seeing a lot of new entrance in the market and there's, there's a lot of options. If you want to try Hadoop and experiment and get started. And then there's production class Hadoop, which includes enterprise data protection, snapshots mirrors, ability to integrate. And that's basically map R so start and test and dev with, with a lot of options and then move into production, class >>Mapbox. So break it down for the folks out there who are tipping the toe in the water and hearing all the noise. Cause it's right now, the noise level is very high, right? With the, with the recent announcements. But you guys have been doing business obviously for many years in this area. So when people say, Hey, I want to get a Hadoop distribution with enterprise. What, what should they be looking for? Okay. Because it's not that easy to kind of swing through the noise. So could you share with the folks out there, what, what to look for in like the, the table stakes, the check boxes? Cause there's a lot of claims. There's a lot of noise is this. And that is a lot of different options. Some teams have more committers or no committers than others, so that's all noise, but let's what are the key things that customers need to know? So I think there's, miling, >>There's three areas. All right. One is kind of how it integrates into your enterprise. And with Hadoop, you have the Hadoop distributed file system API. That's how you interact. Well, if you're able to also use standard tools that can use standard file and database access, it makes it much, much easier. So map ours unique and supporting NFS and making that happen. That's a, that's a big difference. The second is on dependability and there's high availability capabilities and then there's data protection. So I'll focus on snapshots as an example, you've got data replicated and Hindu. That's great. But if you have a user error, an application error, that's replicated just as quickly. So having the ability to recover and double-edged in time. Yeah. So if I can say, Hey, I made a mistake. Can I go back two minutes earlier with snapshots that makes it possible map ours, unique and snapshot support. And then finally, there's there's disaster recovery mirroring where you can go across clusters, mirror, what's going on across the land and being able to recover in the case of a disaster where you lose a whole cluster or use a whole >>Section and that's not available in >>Other, those aren't available either. That's >>NFS, >>Snapshots has been on the JIRA list for over five years. >>Yeah. Okay. So I wonder >>If I could find that and then there's third. Cause I said three and almost said two, the third is performance and scale and, but >>That'd be for >>Integration, dependability and speed. >>Okay. So dependability Jr's part of the VR snapshots. MDR. Okay. So let's talk about the performance because you guys had asked a Google's a big partner of you guys. So we should, we just had them on the cube strata. So you have to have a record setting. Do you have a record setting? EMC take that. Well, you work with DMC. So let me talk about the performance real quick. Then we'll talk about some of the EMC conversations, but performance, you have a variety of diverse performance benchmarks, Google you have within the enterprise. Can you talk about those? >>So, so what we announced this week was the minute sort world record. So minutes or runs across technologies is just, how can you, you know, how much data can you sort in 60 seconds? And if you look back at, at the previous record that was done in the labs with Microsoft with special purpose software, and they did 1.4 terabytes Hadoop hasn't been used since 2009, it's been several years because it's got features in there that work against performance. Things like checkpointing and logging because it assumes you've got long running MapReduce jobs. So we set the record with our distribution of Hadoop. So we have kind of one hand tied behind our back, given that technology. Secondly, we sent it in the cloud, which is the other hand tied behind our back because it's a, it's a virtualized environment. So we set the record with just with your legs And a 1.5 terabytes in 60 seconds. Very proud of that. >>Well, that's interesting because we've been doing a lot of labs testing, Dave and I and our teams on cost. Right. So, yeah. And it's an interesting benchmark because you always don't look at the nuance, the cost to compare a cloud performance versus bare metal. Most people don't factor into setup, cost of deployment. Exactly. So can you just quickly talk about that and how significant of an order of magnitude of your customer? >>So the, the previous Hadoop record took 3,400 servers about 27,000 cores, 13, 13,000, almost 14,000 discs and did 600 gigs, actually a little less than that at 5 78. And on Google, we did it with 2020 100 virtual instances, 8,000 cores did 1.5 terabytes >>And costs. You spin up the Google versus >>Basically if you look at that and you assume conservatively 4,000 per server, it's $13.8 million worth of hardware previously. And the cost to do that run on Google was $20 and 33 cents. >>Well, you got to discount. I mean, come on a partner mean it really costs that much. I mean, they that's what they would charge for it. Actually >>We are map artist's case on that minute. If you look at the Asheville charges to be 1200, >>Okay. It's not six millions, so millions to thousands. Yep. Okay. That's impressive. We'll have to go look at the numbers. Like we're going to look at GreenPlum's numbers in the next couple of weeks when talking about the Google relationship and men were that the up way with that was that >>Very excited about it. We're actually deployed throughout the cloud. We've got multiple partners Google's in limited preview. So we've got a number of customers kind of, you know, testing that and, and doing some really interesting things. >>So we monitor the data center market. I'll see with our proprietary tool that you know about the viewfinder and crowd spots and thing is that the data center verticals interesting, right? If you look at the sentiment analysis of what the conversation is on, on just the Twitter data, it's Facebook, apple, these companies. And when we dig into the numbers, it's not so much the companies, it's the fact that their data center operations are significantly being looked at as the leading indicator for where CEO's are going. So I want to ask you in your conversations with your customers, what are the conversations around moving to the cloud and where are they on that transition? Because we hear, yeah, one of the cloud for all the benefits you were mentioning, but Google and Facebook, these are the gold standards as, as architecture necessarily a cut and paste architecture, but they see the benefits that they're doing. So what are your conversations with your enterprise customers around the cloud cloud architecture and what other features besides replication and disaster recovery, are they, are they looking at >>Well, it's basically work, workload driven and dataset driven. So data that's already in the cloud are kind of a natural first step is, well, why don't I do the analysis there as well? So things like Google earth and digital advertising data, that's real interesting candidates for that also periodic workload. So if they have workloads that need to spin up and spin down, the, the cloud works, works really well for that. And in some cases it's driven by their own environments. They've got data centers that are approaching capacity and they need to kind of do offloads and then looking at the, at the cloud because it's easy to get up running quickly and uses an alternative. >>I want to do come back to one of your three sort of value props here, particularly the dependability piece and specifically the snapshot. So somebody asked me one time, how do you know a couple of years ago, how do you back up a petabyte as he could do this thing? And then his answer was, well, you don't know. So I want to, I want to ask you how your customers are protecting and, and, and, and what you guys are bringing to the table. >>So snapshots is not a bolt on feature. It's basically a low level feature based on the underlying data architecture. So when we architected that from the beginning, snapshots was, was a, was a core feature. And if you use a technique called redirect on, right, you're not copying the data, right? So you can do efficient, you can do a petabyte snapshot, you know, basically almost instantaneously because you're tracking the pointers of the latest blocks that have been written. So if, if the data change rate is, is basically, data's not changing, you can snapshot every minute and not have any additional storage overhead. >>Right. Okay. And, and so you can set that. So you, you map, map, our technologies will allow them to set that, dial that up, dial it down and switches. >>So we support logical volumes. So you can set policies at that volume and you can say, well, this volume is critical data. And then I can set policies. Well, critical data is every minute. And then I can change what the definition of critical data is. Maybe it's every five minutes, et cetera. So you can set up these different policies at volumes and have snapshots happen independently for each. >>Can you do that by workload or dataset or by application or whatever I get essentially provided as a service, as opposed to kind of a one size fits all approach. >>Exactly. And that, that also corresponds to user access, administrative privileges, you know, other features and policies within the, within the cluster. >>How about the, you know, this whole trend toward bringing SQL into, into Hadoop. What's, what's your take on that? And what's your angle? >>So interactive, SQL's an important aspect because you've got so many people trained in the organization and, and leverage, you know, sequel, but it's one of many use cases that needs to run across a big data platform. So there's a range of big data analytics, batch analytics, interactive capabilities with sequel, database operations, no sequel search streaming, all those are kind of functions that need to run across a platform. So it's a piece, but it's not the big driver, because what we've seen is that there's higher rival rate of machine generated data and machine generated response to respond to those for digital advertising, for recommendation engines for fraud detection can really move the needle for an organization, have huge swings and profitability >>And the ball down the field big time. Yeah. And >>Having an interactive piece with a kind of a human element involved, it doesn't really scale and work on a 24 by seven basis. >>Jack final question, we're over now by a minute. But when I ask a one party question, obviously, very competitive landscape right now in terms of competitiveness, the stakes are higher because the demand in the market market opportunities is massive. What's map ours business strategy going forward, no change in direction. Is it going to be same old, same old. You guys have any new things going down and you see the marketplace. >>We've got a huge lead when it comes to kind of mission critical enterprise grade features. And our focus is one platform. So the ability to support enterprise Hadoop, enterprise HBase and provide those full capabilities for ease of use for dependability, for performance. And, you know, we've seen a lot of companies test on one distribution and switch to map are and will continue to help that in the future. >>Well, we, we will, we will say we've been covering this big data space now going on four years now, Dave and I, and we've watched all the players pivot a few times. You guys have not, you guys have been true to your mission from day one and that we know where you stand. No one, everyone knows where you stand enterprise grade. It's a good strategy. I think everyone's putting that on their label now. So enterprise grade Washington, we call it a congratulations map art and said the cube. We'll be right back with our next guest here on day three wall-to-wall coverage at O'Reilly media. When do our news, our next from 12 to one, we'll be right back after this short break.