>>Hey, we're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, it's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great >>To see you take it away. >>All right, guys. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing and flute and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution, things got interesting, right? You started to see automation, but that automation was done essentially programmed your robot to do something and did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different, right? >>Cause now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue, there we'll issue that, but it's important. Not for technology's sake, right? It's important because it actually drives very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, uh, companies and manufacturers moving to improve while its quality prompts still accounts for 20% of sales, right? So every fifth of what you meant are manufactured from a revenue perspective, do back quality issues that are costing you a lot planned downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of new spaces, we're not doing it just merely to implement technology. We're doing it to move these from members, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life with what like, right, but this is actually the business. The cloud area is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I say, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things are taking about time, but this, the ability to take these real-time actions or, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into an enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you could start to think about, you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we can put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one history sets data, you can build out those machine learning models. >>I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. Once you understand that you can actually then build out the smiles, you could deploy the models after the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, but schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. >>So, >>You know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is for SIA for ECA is the, um, is the, was, is the, um, the, uh, a supplier associated with Pooja central line out of France. They are huge, right? This is a multinational automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, um, they connected 2000 machines, right. Um, and they once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor the data firms coming in, you know, monitor the process. >>That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, fibrations pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision, wilding inspection. So let's take pictures of parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections beer. And so they both have those machine learning models. So they took that data. All this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case, a great example of how you can start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you wanted to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turn in the morning sessions and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're gonna, they're gonna hit? >>You know, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right. So, and it's unsafe, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. >>Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world for a long time, the silos, um, uh, you know, the silos, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid theme and you've kind of got this world, that's going toward an equilibrium. You've got the OT side, you know, pretty hardcore engineers. And we know, we know it. Uh, a lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space. And when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to it earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims, kick kickoff. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots by about warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning where simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start something with monitoring, get a lot of value, start, then bring together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases there's value to be had throughout. I >>Remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question that it kind of, um, goes back to one of the things I alluded earlier, we've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they've built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Patera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of industry 4.0, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to lead this discussion on the technology advances. I'd love to talk tech here, uh, are the key technology enablers, and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space, sorry, manufacturing in >>A factory space. Yeah. I knew what you meant in know in the manufacturing space. There's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can w we're finally being able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got back way capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, very much more quickly. Yep. We got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, for everybody who joined us. Thanks. Thanks for joining.

>>From the cube studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a cute conversation. Hi, welcome back. I'm Stu middleman. And this is a cube conversation digging in with, talking about what they're doing to help people. Yeah. Really bringing some of the networking ideals to cloud native environment, both know in the cloud, in the data centers program, Krishna penny. He is the vice president of software. Thanks so much for joining us. Thank you so much for talking to me. Alright, so, so Krishna the pin Sandow team, uh, you know, very well known in the industry three, uh, you innovation. Yeah. Especially in the networking world. Give us a little bit about your background specifically, uh, how long you've been part of this team and, uh, you know, but, uh, you know, you and the team, you know? Yeah. >>And Sando. Yup. Um, so, uh, I'm VP of software in Sandow, um, before Penn Sarno, before founding concern, though, I worked in a few startups in CME networks, uh, newer systems and Greenfield networks, all those three startups have been acquired by Cisco. Um, um, my recent role before this, uh, uh, this, this company was a, it was VP of engineering and Cisco, uh, I was responsible for a product called ACA, which is course flagship SDN tonic. Mmm. So I mean, when, why did we find a phone, uh, Ben Sandoz? So when we were looking at the industry, uh, the last, uh, a few years, right? The few trends that are becoming clear. So obviously we have a lot of enterprise background. We were watching, you know, ECA being deployed in the enterprise data centers. One sore point for customers from operational point of view was installing service devices, network appliances, or storage appliances. >>So not only the operational complexity that this device is bringing, it's also, they don't give you the performance and bandwidth, uh, and PPS that you expect, but traffic, especially from East West. So that was one that was one major issue. And also, if you look at where the intelligence is going, has been, this has been the trend it's been going to the edge. The reason for that is the motors or switches or the devices in the middle. They cannot handle the scale. Yeah. I mean, the bandwidths are growing. The scale is growing. The stateful stuff is going in the network and the switches and the appliances not able to handle it. So you need something at the edge close to the application that can handle, uh, uh, this kind of, uh, services and bandwidth. And the third thing is obviously, you know, x86, okay. Even a few years back, you know, every two years, you know, you're getting more transistors. >>I mean, obviously the most lined it. And, uh, we know we know how that, that part is going. So the it's cycles are more valuable and we don't want to use them for this network services Mmm. Including SDN or firewalls or load balancer. So NBME, mutualization so looking at all these trends in the industry, you know, we thought there is a good, uh, good opportunity to do a domain specific processor for IO and build products around it. I mean, that's how we started Ben signed off. Yeah. So, so Krishna, it's always fascinating to watch. If you look at startups, they are often yeah. Okay. The time that they're in and the technologies that are available, you know, sometimes their ideas that, you know, cakes a few times and, you know, maturation of the technology and other times, you know, I'll hear teams and they're like, Oh, well we did this. >>And then, Oh, wow. There was this new innovation came out that I wish I had add that when I did this last time. So we do, a generation. Oh, wow. Talking about, you know, distributed architectures or, you know, well, over a decade spent a long time now, uh, in many ways I feel edge computing is just, you know, the latest discussion of this, but when it comes to, and you know, you've got software, uh, under, under your purview, um, what are some of the things that are available for that might not have been, you know, in your toolkit, you know, five years ago. Yeah. So the growth of open source software has been very helpful for us because we baked scale-out microservices. This controller, like the last time I don't, when we were building that, you know, we had to build our own consensus algorithm. >>We had to build our own dishwasher database for metrics and humans and logs. So right now, uh, we, I mean, we have, because of open source thing, we leverage CD elastic influx in all this open source technologies that you hear, uh, uh, since we want to leverage the Kubernetes ecosystem. No, that helped us a lot at the same time, if you think about it. Right. But even the software, which is not open source, close source thing, I'm maturing. Um, I mean, if you talk about SDN, you know, seven APS bank, it was like, you know, the end versions of doing off SDN, but now the industry standard is an ADPN, um, which is one of the core pieces of what we do we do as Dean solution with DVA. Um, so, you know, it's more of, you know, the industry's coming to a place where, you know, these are the standards and this is open source software that you could leverage and quickly innovate compared to building all of this from scratch, which will be a big effort for us stocked up, uh, to succeed and build it in time for your customer success. >>Yeah. And Krishna, I, you know, you talk about open forum, not only in the software, the hardware standards. Okay. Think about things, the open compute or the proliferation of, you know, GPS and, uh, everything along that, how was that impact? I did. So, I mean, it's a good thing you're talking about. For example, we were, we are looking in the future and OCP card, but I do know it's a good thing that SEP card goes into a HP server. It goes into a Dell software. Um, so pretty much, you know, we, we want to, I mean, see our goal is to enable this platform, uh, that what we built in, you know, all the use cases that customer could think of. Right. So in that way, hardware, standardization is a good thing for the industry. Um, and then same thing, if you go in how we program the AC, you know, we at about standards of this people, programming, it's an industry consortium led by a few people. >>Um, we want to make sure that, you know, we follow the standards for the customer who's coming in, uh, who wants to program it., it's good to have a standards based thing rather than doing something completely proprietary at the same time you're enabling innovations. And then those innovations here to push it back to the open source. That's what we trying to do with before. Yeah. Excellent. I've had some, some real good conversations about before. Um, and, and the way, uh, and Tondo is, is leveraging that, that may be a little bit differently. You know, you talk about standards and open source, oftentimes it's like, well, is there a differentiator there, there are certain parts of the ecosystem that you say, well, kind of been commodified. Mmm. Obviously you're taking a lot of different technologies, putting them together, uh, help, help share the uniqueness. Okay. And Tondo what differentiates, what you're doing from what was available in the market or that I couldn't just cobbled together, uh, you know, a bunch of open source hardware and software together. >>Yeah. I mean, if you look at a technologist, I think the networking that both of us are very familiar with that. If you want to build an SDN solution, or you can take a, well yes. Or you can use exhibit six and, you know, take some much in Silicon and cobble it together. But the problem is you will not get the performance and bandwidth that you're looking for. Okay. So let's say, you know, uh, if you want a high PPS solution or you want a high CPS solution, because the number of connections are going for your IOT use case or Fiji use case, right. If you, uh, to get that with an open source thing, without any assist, uh, from a domain specific processor, your performance will be low. So that is the, I mean, that's once an enterprise in the cloud use case state, as you know, you're trying to pack as many BMCs containers in one set of word, because, you know, you get charged. >>I mean, the customer, uh, the other customers make money based on that. Right? So you want to offload all of those things into a domain specific processor that what we've built, which we call the TSC, which will, um, which we'll, you know, do all the services at pretty much no cost to accept a six. I mean, it's to six, you'll be using zero cycles, a photo doing, you know, features like security groups or VPCs, or VPN, uh, or encryption or storage virtualization. Right. That's where that value comes in. I mean, if you count the TCO model using bunch of x86 codes or in a bunch of arm or AMD codes compared to what we do. Mmm. A TCO model works out great for our customers. I mean, that's why, you know, there's so much interest in a product. Excellent. I'm proud of you. Glad you brought up customers, Christina. >>One of the challenges I have seen over the years with networking is it tends to be, you know, a completely separate language that we speak there, you know, a lot of acronyms and protocols and, uh, you know, not necessarily passable to people outside of the silo of networking. I think back then, you know, SDN, uh, you know, people on the outside would be like, that stands for still does nothing, right? Like networking, uh, you know, mumbo jumbo there for people outside of networking. You know what I think about, you know, if I was going to the C suite of an enterprise customer, um, they don't necessarily care about those networking protocols. They care about the, you know, the business results and the product Liberty. How, how do you help explain what pen Sandow does to those that aren't, you know, steeped in the network, because the way I look at it, right? >>What is customer looking? But yeah, you're writing who doesn't need, what in cap you use customer is looking for is operational simplicity. And then he wants looking for security. They, it, you know, and if you look at it sometimes, you know, both like in orthogonal, if you make it very highly secure, but you make it like and does an operational procedure before you deploy a workload that doesn't work for the customer because in operational complexity increases tremendously. Right? So it, we are coming in, um, is that we want to simplify this for the customer. You know, this is a very simple way to deploy policies. There's a simple way to deploy your networking infrastructure. And in the way we do it is we don't care what your physical network is, uh, in some sense, right? So because we are close to the server, that's a very good advantage. >>We have, we have played the policies before, even the packet leaves the center, right? So in that way, he knows his fully secure environment and we, and you don't want to manage each one individually, we have this, okay, Rockwell PSM, which manages, you know, all this service from a central place. And it's easy to operationalize a fabric, whether you talk about upgrades or you talk about, you know, uh, deploying new services, it's all driven with rest API, and you can have a GUI, so you can do it a single place. And that's where, you know, a customer's value is rather than talking about, as you're talking about end caps or, you know, exactly the route to port. That is not the main thing that, I mean, they wake up every day, they wake up. Have you been thinking about it or do I have a security risk? >>And then how easy for me is to deploy new, uh, in a new services or bring up new data center. Right. Okay. Krishna, you're also spanning with your product, a few different worlds out. Yeah. You know, traditionally yeah. About, you know, an enterprise data center versus a hyperscale public cloud and ed sites, hi comes to mind very different skillset for management, you know, different types of okay. Appointments there. Mmm. You know, I understand right. You were going to, you know, play in all of those environments. So talk a little bit about that, please. How you do that and, you know, you know, where you sit in, in that overall discussion. Yes. So, I mean, a number one rule inside a company is we are driven by customers and obviously not customer success is our success. So, but given said that, right. What we try to do is that we try to build a platform that is kind of, you know, programmable obviously starting from, you know, before that we talked about earlier, but it's also from a software point of view, it's kind of plugable right. >>So when we build a software, for example, at cloud customers, and they use BSC, they use the same set of age KPI's or GSP CRS, TPS that DSC provides their controller. But when we ship the same, uh, platform, what enterprise customers, we built our own controller and we use the same DC APS. So the way we are trying to do is things is fully leverage yeah. In what we do for enterprise customers and cloud customers. Mmm. We don't try to reinvent the wheel. Uh, obviously at the same time, if you look at the highest level constructs from a network perspective, right. Uh, audience, for his perspective, what are you trying to do? You're trying to provide connectivity, but you're trying to avoid isolation and you're trying to provide security. Uh, so all these constructs we encapsulated in APA is a, which, you know, uh, in some, I, some, some mostly like cloud, like APS and those APIs are, are used, but cloud customers and enterprise customers, and the software is built in a way of it. >>Any layer is, can be removed on any layer. It can be hard, right? Because it's not interested. We don't want to be multiple different offers for different customers. Right. Then we will not scale. So the idea when we started the software architecture, is that how we make it pluggable and how will you make the program will that customer says, I don't want this piece of it. You can put them third party piece on it and still integrate, uh, at a, at a common layer with using. Yeah. Yeah. Well, you know, Krishna, you know, I have a little bit of appreciation where some of the hard work, what your team has been doing, you know, a couple of years in stealth, but, you know, really accelerating from, uh, you know, the announcement coming out of stealth, uh, at the end of 2019. Yeah. Just about half a year, your GA with a major OEM of HPE, definitely a lot of work that needs to be done. >>It brings us to, you know, what, what are you most proud about from the work that your team's doing? Uh, you know, we don't need to hear any, you know, major horror stories, but, you know, there always are some of them, you know, not holes or challenges that, uh, you know, often get hidden yeah. Behind the curtain. Okay. I mean, personally, I'm most proud of the team that we've made. Um, so, uh, you know, obviously, you know, uh, our executors have it good track record of disrupting the market multiple times, but I'm most proud of the team because the team is not just worried about that., uh, that, uh, even delegate is senior technologist and they're great leaders, but they're also worried about the customer problem, right? So it's always about, you know, getting the right mix, awfully not execution combined with technology is when you succeed, that is what I'm most proud of. >>You know, we have a team with, and Cletus running all these projects independently, um, and then releasing almost we have at least every week, if you look at all our customers, right. And then, you know, being a small company doing that is a, Hmm, it's pretty challenging in a way. But we did, we came up with methodologists where we fully believe in automation, everything is automated. And whenever we release software, we run through the full set of automation. So then we are confident that customer is getting good quality code. Uh, it's not like, you know, we cooked up something and that they should be ready and they need to upgrade to the software. That's I think that's the key part. If you want to succeed in this day and age, uh, developing the features at the velocity that you would want to develop and still support all these customers at the same time. >>Okay. Well, congratulations on that, Christian. All right. Final question. I have for you give us a little bit of guidance going forward, you know, often when we see a company out and we, you know, to try to say, Oh, well, this is what company does. You've got a very flexible architecture, lot of different types of solutions, what kind of markets or services might we be looking at a firm, uh, you know, download down the road a little ways. So I think we have a long journey. So we have a platform right now. We already, uh, I mean, we have a very baby, we are shipping. Mmm Mmm. The platforms are really shipping in a storage provider. Uh, we are integrating with the premier clouds, public clouds and, you know, enterprise market, you know, we already deployed a distributed firewall. Some of the customers divert is weird firewall. >>So, you know, uh, so if you take this platform, it can be extendable to add in all the services that you see in data centers on clubs, right. But primarily we are driven from a customer perspective and customer priority point of view. Mmm. So BMW will go is even try to add more ed services. We'll try to add more storage features. Mmm. And then we, we are also this initial interest in service provider market. What we can do for Fiji and IOT, uh, because we have the flexible platform. We have the, see, you know, how to apply this platform, this new application, that's where it probably will go into church. All right. Well, Krishna not a penny vice president of software with Ben Tondo. Thank you so much for joining us. Thank you, sir. It was great talking to you. All right. Be sure to check out the cube.net. You can find lots of interviews from Penn Sundo I'm Stu Miniman and thank you. We're watching the cute.