(upbeat soft intro music) >> Hey everyone. Welcome back to theCube's live coverage from Los Angeles of KubeCon and CloudNativeCon 2021. Lisa Martin with Dave Nicholson, Dave and I are pleased to welcome our next guest remotely. Steven Huels joins us, the senior director of Cloud Services at Red Hat. Steven, welcome to the program. >> Steven: Thanks, Lisa. Good to be here with you and Dave. >> Talk to me about where you're seeing traction from an AI/ML perspective? Like where are you seeing that traction? What are you seeing? Like it. >> It's a great starter question here, right? Like AI/ML is really being employed everywhere, right? Regardless of industry. So financial services, telco, governments, manufacturing, retail. Everyone at this point is finding a use for AI/ML. They're looking for ways to better take advantage of the data that they've been collecting for these years. It really, it wasn't all that long ago when we were talking to customers about Kubernetes and containers, you know, AI/ML really wasn't a core topic where they were looking to use a Kubernetes platform to address those types of workloads. But in the last couple of years, that's really skyrocketed. We're seeing a lot of interest from existing customers that are using Red Hat open shift, which is a Kubernetes based platform to take those AI/ML workloads and take them from what they've been doing for additionally, for experimentation, and really get them into production and start getting value out of them at the end of it. >> Is there a common theme, you mentioned a number of different verticals, telco, healthcare, financial services. Is there a common theme, that you're seeing among these organizations across verticals? >> ^There is. I mean, everyone has their own approach, like the type of technique that they're going to get the most value out of. But the common theme is really that everyone seems to have a really good handle on experimentation. They have a lot of very brig data scientists, model developers that are able to take their data and out of it, but where they're all looking to get, get our help or looking for help, is to put those models into production. So ML ops, right. So how do I take what's been built on, on somebody's machine and put that into production in a repeatable way. And then once it's in production, how do I monitor it? What am I looking for as triggers to indicate that I need to retrain and how do I iterate on this sequentially and rapidly applying what would really be traditional dev ops software development, life cycle methodologies to ML and AI models. >> So Steve, we're joining you from KubeCon live at the moment. What's, what's the connection with Kubernetes and how does Kubernetes enable machine learning artificial intelligence? How does it enable it and what are some of the special considerations to in mind? >> So the immediate connection for Red Hat, is Red Hat's open shift is basically an enterprise grade Kubernetics. And so the connection there is, is really how we're working with customers and how customers in general are looking to take advantage of all the benefits that you can get from the Kubernetes platform that they've been applying to their traditional software development over the years, right? The, the agility, the ability to scale up on demand, the ability to have shared resources, to make specialized hardware available to the individual communities. And they want to start applying those foundational elements to their AI/Ml practices. A lot of data science work traditionally was done with high powered monolithic machines and systems. They weren't necessarily shared across development communities. So connecting something that was built by a data scientist, to something that then a software developer was going to put into production was challenging. There wasn't a lot of repeatability in there. There wasn't a lot of scalability, there wasn't a lot of auditability and these are all things that we know we need when talking about analytics and AI/ML. There's a lot of scrutiny put on the auditability of what you put into production, something that's making decisions that impact on whether or not somebody gets a loan or whether or not somebody is granted access to systems or decisions that are made. And so that the connection there is really around taking advantage of what has proven itself in kubernetes to be a very effective development model and applying that to AI/ML and getting the benefits in being able to put these things into production. >> Dave: So, so Red Hat has been involved in enterprises for a long time. Are you seeing most of this from a Kubernetes perspective, being net new application environments or are these extensions of what we would call legacy or traditional environments. >> They tend to be net new, I guess, you know, it's, it's sort of, it's transitioned a little bit over time. When we first started talking to customers, there was desire to try to do all of this in a single Kubernetes cluster, right? How can I take the same environment that had been doing our, our software development, beef it up a little bit and have it applied to our data science environment. And over time, like Kubernetes advanced rights. So now you can actually add labels to different nodes and target workloads based on specialized machinery and hardware accelerators. And so that has shifted now toward coming up with specialized data science environments, but still connecting the clusters in that's something that's being built on that data science environment is essentially being deployed then through, through a model pipeline, into a software artifact that then makes its way into an application that that goes live. And, and really, I think that that's sensible, right? Because we're constantly seeing a lot of evolution in, in the types of accelerators, the types of frameworks, the types of libraries that are being made available to data scientists. And so you want the ability to extend your data science cluster to take advantage of those things and to give data scientists access to that those specialized environments. So they can try things out, determine if there's a better way to, to do what they're doing. And then when they find out there is, be able to rapidly roll that into your production environment. >> You mentioned the word acceleration, and that's one of the words that we talk about when we talk about 2020, and even 2021, the acceleration in digital transformation that was necessary really a year and a half ago, for companies to survive. And now to be able to pivot and thrive. What are you seeing in terms of customers appetites for, for adopting AI/ML based solutions? Has it accelerated as the pandemic has accelerated digital transformation. >> It's definitely accelerated. And I think, you know, the pandemic probably put more of a focus for businesses on where can they start to drive more value? How can they start to do more with less? And when you look at systems that are used for customer interactions, whether they're deflecting customer cases or providing next best action type recommendations, AI/ML fits the bill there perfectly. So when they were looking to optimize, Hey, where do we put our spend? What can help us accelerate and grow? Even in this virtual world we're living in, AI/ML really floated to the top there, that's definitely a theme that we've seen. >> Lisa: Is there a customer example that you think that you could mention that really articulates the value over that? >> You know, I think a lot of it, you know, we've published one specifically around HCA health care, and this had started actually before the pandemic, but I think especially, it's applicable because of the nature of what a pandemic is, where HCA was using AI/ML to essentially accelerate diagnosis of sepsis, right. They were using it for, for disease diagnoses. That same type of, of diagnosis was being applied to looking at COVID cases as well. And so there was one that we did in Canada with, it's called 'how's your flattening', which was basically being able to track and do some predictions around COVID cases in the Canadian provinces. And so that one's particularly, I guess, kind of close to home, given the nature of the pandemic, but even within Red Hat, we started applying a lot more attention to how we could help with customer support cases, right. Knowing that if folks were going to be out with any type of illness. We needed to be able to be able to handle that case, you know, workload without negatively impacting work-life balance for, for other associates. So we looked at how can we apply AI/ML to help, you know, maintain and increase the quality of customer service we were providing. >> it's a great use case. Did you have a keynote or a session, here at KubeCon CloudNative? >> I did. I did. And it really focused specifically on that whole ML ops and model ops pipeline. It was called involving Kubernetes and bracing model ops. It was for a Kubernetes AI day. I believe it aired on Wednesday of this week. Tuesday, maybe. It all kind of condenses in the virtual world. >> Doesn't it? It does. >> So one of the questions that Lisa and I have for folks where we sit here, I don't know, was it year seven or so of the Dawn of Kubernetes, if I have that, right. Where do you think we are, in this, in this wave of adoption, coming from a Red Hat perspective, you have insight into, what's been going on in enterprises for the last 20 plus years. Where are we in this wave? >> That's a great question. Every time, like you, it's sort of that cresting wave sort of, of analogy, right? That when you get to top one wave, you notice the next wave it's even bigger. I think we've certainly gotten to the point where, where organizations have accepted that Kubernetes can, is applicable across all the workloads that they're looking to put in production. Now, the focus has shifted on optimizing those workloads, right? So what are the things that we need to run in our in-house data centers? What are things that we need, or can benefit from using commodity hardware from one of the hyperscalers, how do we connect those environments and more effectively target workloads? So if I look at where things are going to the future, right now, we see a lot of things being targeted based on cluster, right? We say, Hey, we have a data science cluster. It has characteristics because of X, Y, and Z. And we put all of our data science workloads into that cluster. In the future, I think we want to see more workload specific, type of categorization of workloads so that we're able to match available hardware with workloads rather than targeting a workload at a specific cluster. So a developer or data scientist can say, Hey, my particular algorithm here needs access to GPU acceleration and the following frameworks. And then it, the Kubernetes scheduler is able to determine of the available environments. What's the capacity, what are the available resources and match it up accordingly. So we get into a more dynamic environment where the developers and those that are actually building on top of these platforms actually have to know less and less about the clusters they're running on. It just have to know what types of resources they need access to. >> Lisa: So sort of democratizing that. Steve, thank you for joining Dave and me on the program tonight, talking about the traction that you're seeing with AI/ML, Kubernetes as an enabler, we appreciate your time. >> Thank you. >> Thanks Steve. >> For Dave Nicholson. I'm Lisa Martin. You're watching theCube live from Los Angeles KubeCon and CloudNativeCon 21. We'll be right back with our next guest. (subtle music playing) >> Lisa: I have been in the software and technology industry for over 12 years now. So I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCube. Being a host on the cube has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, Hey, I'm really interested in this. I love talking with customers...

from around the globe it's thecube with coverage of kubecon and cloudnativecon north america 2020 virtual brought to you by red hat the cloud native computing foundation and ecosystem partners hello and welcome back to the cube's coverage of kubecon plus cloud nativecon 2020 the virtual edition abinav joshi is here he's the senior product marketing manager for openshift at red hat and tom dean is the senior director of pro product management at cloudera gentlemen thanks for coming on thecube good to see you thank you very much for having us here hey guys i know you would be here it was great to have you and guys i know you're excited about the partnership and i definitely want to get in and talk about that but before we do i wonder if we could just set the tone you know what are you seeing in the market tom let's let's start with you i had a great deep dive a couple of weeks back with anupam singh and he brought me up to speed on what's new with cloudera but but one of the things we discussed was the accelerated importance of data putting data at the core of your digital business tom what are you seeing in the marketplace right now yeah absolutely so um overall we're still seeing a growing demand for uh storing and and processing massive massive amounts of data even in the past few months um where perhaps we see a little bit more variety is on by industry sector is on the propensity to adopt some of the latest and greatest uh technologies that are out there or that we we deliver to the market um so whether perhaps in the retail hospitality sector you may see a little bit more risk aversion around some of the latest tools then you you go to the healthcare industry as an example and you see we see a strong demand for our latest technologies uh with with everything that is that is going on um so overall um still a lot lots of demand around this space so abnormal i mean we just saw in ibm's earnings though the momentum of red hat you know growing in the mid teens and the explosion that we're seeing around containers and and obviously openshift is at the heart of that how the last nine months affected your customers priorities and what are you seeing yeah we've been a lot more busier like in the last few months because there's like a lot of use cases and if you look at the like a lot of the research and so on and we are seeing that from our customers as well that now the customers are actually speeding up the digital transformation right people say that okay kovac 19 has actually uh speeded up the digital transformation for a lot of our customers for the right reasons to be able to help the customers and so on so we are seeing a lot of attraction on like number of verticals and number of use cases beyond the traditional lab dev data analytics aiml messaging streaming edge and so on like lots of use cases in like a lot of different like industry verticals so there's a lot of momentum going on on openshift and the broader that portfolio as well yeah it's ironic the the timing of the pandemic but it sure underscores that this next 10 years is going to be a lot different than the last 10 years okay let's talk about some of the things that are new around data tom cloudera you guys have made a number of moves since acquiring hortonworks a little over two years ago what's new with uh with the cloudera data platform cdp sure so yes our latest therap uh platform is called cbp clara data platform last year we announced the public cloud version of cdp running on aws and then azure and what's new is just two months ago we announced the release of the version of this platform targeted at the data center and that's called cvp private cloud and really the focus of this platform this new version has been around solving some of the pain points that we see around agility or time to value and the ease of use of the platform and to give you some specific examples with our previous technology it could take a customer three months to provision a data warehouse if you include everything from obtaining the infrastructure to provisioning the warehouse loading the data setting security policies uh and fine-tuning the the software now with cbp private cloud we've been able to take those uh three months and turn it into three minutes so significant uh speed up in in that onboarding time and in time to valley and a key piece of this uh that enabled this this speed up was a revamping of the entire stack specifically the infrastructure and service services management layer and this is where the containerization of the platform comes in specifically kubernetes and red hat open shift that is a key piece of the puzzle that enables this uh order of magnitude uh improvement in time right uh now abner you think about uh red hat you think about cloudera of course hortonworks the stalwarts of of of open source you got kind of like birds of a feather how are red hat and cloudera partnering with each other you know what are the critical aspects of that relationship that people should be aware of yeah absolutely that's a very good question yeah so on the openshift side we've had a lot of momentum in the market and we have well over 2000 customers in terms of a lot of different verticals and the use cases that i talked about at the beginning of our conversation in terms of traditional and cloud native app dev databases data analytics like ai messaging and so on right and the value that you have with openshift and the containers kubernetes and devops like part of the solution being able to provide the agility flexibility scalability the cross cloud consistency like so all that that you see in a typical app dev world is directly applicable to fast track the data analytics and the ai projects as well and we've seen like a lot of customers and some of the ones that we can talk about in a public way like iix rbc bank hca healthcare boston children's bmw exxon mobil so all these organizations are being are able to leverage openshift to kind of speed up the ai projects and and help with the needs of the data engineers data scientists and uh and the app dev folks now from our perspective providing the best in class uh you say like experience for the customers at the platform level is key and we have to make sure that the tooling that the customers run on top of it uh gets the best in class the experience in terms of the day zero to day two uh management right and it's uh and and it's an ecosystem play for us and and and that's the way cloudera is the top isv in the space right when it comes to data analytics and ai and that was our key motivation to partner with cloudera in terms of bringing this joint solution to market and making sure that our customers are successful so the partnership is at all the different levels in the organization say both up and down as well as in the the engineering level the product management level the marketing level the sales level and at the support and services level as well so that way if you look at the customer journey in terms of selecting a solution uh putting it in place and then getting the value out of it so the partnership it actually spans across the entire spectrum yeah and tom you know i wonder if you could add anything there i mean it's not just about the public cloud with containers you're seeing obviously the acceleration of of cloud native principles on-prem in a hybrid you know across clouds it's sort of the linchpin containers really and kubernetes specifically linchpin to enable that what would you add to that discussion yeah as part of the partnership when we were looking for a vendor who could provide us that kubernetes layer we looked at our customer base and if you think about who clara is focused on we really go after that global the global 2000 firms out there these customers have very strict uh security requirements and they're often in these highly regulated uh industries and so when we looked at a customer's base uh we saw a lot of overlap and there was a natural good fit for us there but beyond that just our own technical evaluation of the solutions and also talking to uh to our own customers about who they do they see as a trusted platform that can provide enterprise grade uh features on on a kubernetes layer red hat had a clear leadership in in that front and that combined with our own uh long-standing relationship with our parent company ibm uh it made this partnership a natural good thing for us right and cloudera's always had a good relationship with ibm tom i want to stay with you if i can for a minute and talk about the specific joint solutions that you're providing with with red hat what are you guys bringing to customers in in terms of those solutions what's the business impact where's the value absolutely so the solution is called cbd or color data platform private cloud on red hat openshift and i'll describe three uh the three pillars that make up cbp uh first what we have is the five data analytic experiences and that is meant to cover the end to end data lifecycle in the first release we just came out two months ago we announced the availability of two of those five experiences we have data warehousing for bi analytics as well as machine learning and ai where we offer a collaborative data science data science tools for data scientists to come together do exploratory data analytics but also develop predictive models and push them to production going forward we'll be adding the remaining three uh experiences they include data engineering or transformations on uh on your data uh data flow for streaming analytics and ingest uh as well as operational database for uh real-time surveying of both structure and unstructured data so these five experiences have been re-banked right compared to our prior platform to target these specific use cases and simplify uh these data disciplines the second pillar that i'll talk about is the sdx or uh what what we call the shared data experience and what this is is the ability for these five experiences to have one global data set that they can all access with shared metadata security including fine grain permissions and a suite of governance tools that provide lineage provide auditing and business metadata so by having these shared data experiences our developers our users can build these multi-disciplinary workflows in a very straightforward way without having to create all this custom code and i can stitch you can stitch them together and the last pillar that i'll mention uh is the containerization of of the platform and because of containers because of kubernetes we're now able to offer that next level of agility isolation uh and infrastructure efficiency on the platform so give you a little bit more specific examples on the agility i mentioned going from three months to three minutes in terms of the speed up with i uh with uh containers we can now also give our users the ability to bring their own versions of their libraries and engines without colliding with another user who's sharing the platform that has been a big ask from our customers and last i'll mention infrastructure efficiency by re-architecting our services to running a microservices architecture we can now impact those servers in a much more efficient way we can also auto scale auto suspend bring all this as you mentioned bring all these cloud native concepts on premises and the end result of that is better infrastructure efficiency now our customers can do more with the same amount of hard work which overall uh reduces their their total spend on the solution so that's what we call cbp private cloud great thanks for that i mean wow we've seen really the evolution from the the wild west days of you know the early days of so-called big data ungoverned a lot of shadow data science uh maybe maybe not as efficient as as we'd like and but certainly today taking advantage of some of those capabilities dealing with the noisy neighbor problem enough i wonder if you could comment another question that i have is you know one of the things that jim whitehurst talked about when ibm acquired red hat was the scale that ibm could bring and what i always looked at in that context was ibm's deep expertise in vertical industries so i wonder what are some of the key industry verticals that you guys are targeting and succeeding in i mean yes there's the pandemic has some effects we talked about hospitality obviously airlines have to have to be careful and conserving cash but what are some of the interesting uh tailwinds that you're seeing by industry and some of the the more interesting and popular use cases yeah that's a very good question now in terms of the industry vertical so we are seeing the traction in like a number of verticals right and the top ones being the financial services like healthcare telco the automotive industry as well as the federal government are some of the key ones right and at the end of the day what what all the customers are looking at doing is be able to improve the experience of their customers with the digital services that they roll out right as part of the pandemic and so on as well and then being able to gain competitive edge right if you can have the services in your platform and make them kind of fresh and relevant and be able to update them on a regular basis that's kind of that's your differentiator these days right and then the next one is yeah if you do all this so you should be able to increase your revenue be able to save cost as well that's kind of a key one that you mentioned right that that a lot of the industries like the hospitality the airlines and so on are kind of working on saving cash right so if you can help them save the cost that's kind of key and then the last one is is being able to automate the business processes right because there's not like a lot of the manual processes so yeah if you can add in like a lot of automation that's all uh good for your business and then now if you look at the individual use cases in these different industry verticals what we're seeing that the use cases cannot vary from the industry to industry like if you look at the financial services the use cases like fraud detection being able to do the risk analysis and compliance being able to improve the customer support and so on are some of the key use cases the cyber security is coming up a lot as well because uh yeah nobody wants to be hacked and so and and so on yeah especially like in these times right and then moving on to healthcare and the life sciences right what we're seeing the use cases on being able to do the data-driven diagnostics and care and being able to do the discovery of drugs being able to say track kobit 19 and be able to tell that okay uh which of my like hospital is going to be full when and what kind of ppe am i going to need at my uh the the sites and so on so that way i can yeah and mobilize like as needed are some of the key ones that we are seeing on the healthcare side uh and then in terms of the automotive industry right that's where being able to speed up the autonomous driving initiatives uh being able to do uh the auto warranty pricing based on the history of the drivers and so on and then being able to save on the insurance cost is a big one that we are seeing as well for the insurance industries and then but more like manufacturing right being able to do the quality assurance uh at the shop floor being able to do the predictive maintenance on machinery and also be able to do the robotics process automation so like lots of use cases that customers are prioritizing but it's very verticalized it kind of varies from the vertical to a vertical but at the end of the day yeah it's all about like improving the customer experience the revenue saving cost and and being able to automate the business processes yeah that's great thank you for that i mean we we heard a lot about automation we were covering ansible fest i mean just think about fraud how much you know fraud detection has changed in the last 10 years it used to be you know so slow you'd have to go go through your financial statements to find fraud and now it's instantaneous cyber security is critical because the adversaries are very capable healthcare is a space where you know it's ripe for change and now of course with the pandemic things are changing very rapidly automotive another one an industry that really hasn't hadn't seen much disruption and now you're seeing with a number of things autonomous vehicles and you know basically software on wheels and insurance great example even manufacturing you're seeing you know a real sea change there so thank you for that description you know very often in the cube we like to look at joint engineering solutions that's a gauge of the substance of a partnership you know sometimes you see these barney deals you know there's a press release i love you you love me okay see you but but so i wonder if you guys could talk about specific engineering that you're doing tom maybe you could start sure yeah so on the on the engineering and product side um we've um for cbp private cloud we've we've changed our uh internal development and testing to run all on uh openshift uh internally uh and as part of that we we have a direct line to red hat engineering to help us solve any issues that that uh we run into so in the initial release we start with support of openshift43 we're just wrapping up uh testing of and we'll begin with openshift46 very soon on another aspect of their partnership is on being able to update our images to account for any security vulnerabilities that are coming up so with the guidance and help from red hat we've been we've standardized our docker images on ubi or the universal based image and that allows us to automatically get many of these security fixes uh into our into our software um the last point that i mentioned here is that it's not just about providing kubernetes uh red hat helps us with the end to end uh solution so there is also the for example bringing a docker registry into the picture or providing a secure vault for storing uh all the secrets so all these uh all these pieces combined make up the uh a strong complete solution actually the last thing i'll mention is is a support aspect which is critical to our customers in this model our customers can bring support tickets to cluberra but as soon as we determine that it may be an issue that uh related to red hat or openshift where we can use their help we have that direct line of communication uh and automated systems in the back end to resolve those support tickets uh quickly for our customers so those are some of the examples of what we're doing on the technical side great thank you uh enough we're out of time but i wonder if we could just close here i mean when we look at our survey data with our data partner etr we see containers container orchestration container management generally and again kubernetes specifically is the the number one area of investment for companies that has the most momentum in terms of where they're putting their efforts it's it's it's right up there and even ahead of ai and machine learning and even ahead of cloud which is obviously larger maybe more mature but i wonder if you can add anything and bring us home with this segment yeah absolutely and i think uh so uh one thing i want to add is like in terms of the engineering level right we also have like between cloudera and red hat the partnership and the sales and the go to market levels as well because once you build the uh the integration it yeah it has to be built out in the customer environments as well right so that's where we have the alignment um at the marketing level as well as the sales level so that way we can like jointly go in and do the customer workshops and make sure the solutions are getting deployed the right way right uh and also we have a partnership at the professional services level as well right where um the experts from both the orgs are kind of hand in hand to help the customers right and then at the end of the day if you need help with support and that's what tom talked about that we have the experts on the support side as well yeah and then so to wrap things up right uh so all the industry research and the customer conversation that we are having are kind of indicating that the organizations are actually increasing the focus on digital uh transformation with the data and ai being a key part of it and that's where this strategic partnership between cloudera and and red hat is going to play a big role to help our mutual customers uh through that our transition and be able to achieve the key goals that they set for their business great well guys thanks so much for taking us through the partnership and the integration work that you guys are doing with customers a great discussion really appreciate your time yeah thanks a lot dave really appreciate it really enjoyed the conversation all right keep it right there everybody you're watching thecube's coverage of cubecon plus cloud nativecon north america the virtual edition keep it right there we'll be right back

>> Announcer: From around the globe, it's theCUBE, with digital coverage of PegaWorld iNspire, brought to you by Pegasystems. >> Hi everybody, welcome back. This is Dave Vellante, and you're watching theCUBE's coverage of PegaWorld iNspire 2020. Kerim Akgonul is here. He's the senior vice president of product at Pega, Pegasystems. Kerim, great to see you. Thanks for coming on. >> Hi Dave. Thanks for having me. Yeah, I mean I wish we were face-to-face at your big show, but this is going to have to do. A little different this year doing the virtual event. You're used to a big stage, big audience, lots of clapping and buzz. How's it been for you, this virtual pivot? >> It's been different, it's definitely been different, especially since the last few years we had it in Vegas, so it was a big Vegas show. Now we're in my living room. Not the same vibe, but nevertheless we have a lot of new products and new stories to tell, new experiences to share with the clients, so we're focusing on those aspects. >> Yeah, I'm excited to get into that, but I mean your whole raison d'être is you guys build for change, and obviously we've been thrown this curve ball, more than a curve ball, knuckle ball. Maybe talk about what you're seeing your customers do in terms of being able to rapidly adapt to this new abnormal. >> Yeah, so we've seen, obviously, across the globe, right, not just with Pega, not with just our clients, we've seen a tremendous amount of change. We've seen change in how we work, how we communicate, how we collaborate, how we get into meetings, and a lot of our clients, of course, had to quickly adjust to these recent changes as well in these last couple of months, and in many cases they had to make technology choices, and we're pretty excited that basically Pega technology has been on that top shelf of technologies that our clients chose to leverage in this time of crisis. They chose to use the technology to better engage across their organizational work that they do. They use the Pega technology to actually digitize how a lot of the work that gets done in their organization. They use it as a COVID-19 response. They use it to engage directly with the consumers, so it's been on, as I said, the top shelf of technologies that they had to leverage to adjust and transform, so it's been very busy, Dave. >> Obviously a lot of companies have been hit, and some industries have been very hard hit in the shutdown, but I want to pick a couple of examples. Let's start with healthcare. I mean they've been hit like no other, front lines. Do you have some examples that you can share, or any example in healthcare, how they pivoted? I mean have they been able to even spend time on anything that's not emergency? Maybe you could share some of your experiences there. >> Absolutely. Actually a lot of the healthcare organizations that we're working with, the front line workers, obviously, the way that they engage has changed quite a bit, but also the people that work in the corporate, in the back office, in the technology, they have changed as well as they had to really respond to the changes in the scale of their operations, changes in how they engage with their customers, with the other organizations that they work with, and how they operated their processes. We did have one of the customers that I talk about, HCA, one of the Pega customers, they basically implemented a Pega solution just in a couple of days, and rolled it out into production just a couple of days to keep track of their employees, the volunteers that basically work with them, to keep track of people who are impacted by COVID-19, and they have about 200,000 people that they need to manage the availability in the schedules, and they decided to use Pega technology to be able to manage that across the enterprise, which has been a great experience for us working with them. >> So Kerim, how would that work? So they're an existing Pega customer, they spun up a new module, they sort of developed it themselves. You guys helped them. Describe how that sort of became real. >> Sure, so we actually have a couple of different examples of these types of applications that went live in the last couple of months, from the healthcare organizations, we had it from some organizations in the telecommunications industry, we had state governments and different public sector companies. It works differently for each one of them, but it all starts with really having somebody, having a clear idea on exactly what they want to actually do. What do they want to keep track of? What do they want to operate? What do they want to be able to actually get done? And having somebody to have that vision and being able to articulate that in the Pega construct to automate it to define the process, to define what they're going to keep track of, to define the journeys of those things that they're going to keep track of, and a lot of the clients that have centers of excellence in their organizations with Pega experts, some of our clients work with our great set of partners who have come up with ideas and brought them into these organizations, and we also get pulled into a couple of these implementations, and like you said, Dave, we always talk about being built for change, and this is a time of crisis. This is a time of change, and Pega's technology is perfectly structured to be able to get things quickly done and up and running, but what it really needed at all times is somebody to actually have the vision and the ability to make a decision and go execute on it. And we know that the people are there. We know the technology is there, and that's how a lot of the results got done. >> Yeah, very fast decisions had to get made. Another example is we've been tracking the telecom space, and the whole work-from-home pivot has really put stress on distributed networks, the traditional corporate networks. Now everybody's at home. We've all experienced this, whether video calls, et cetera. The kids are at home, at school, sometimes gaming, so the internet, it didn't blow up, luckily, but still major change in the telco industry. >> Absolutely. How lucky we are to actually have access to all this technology, to all this internet capacity, and yeah, it's been a big change. Obviously the demand on their business has increased quite a bit in the telecommunications industry. One of our clients that basically had contact centers in other countries where the agents actually didn't have an opportunity to go into the contact center, and they couldn't actually enter the building. They weren't even allowed to be on the streets, out on the streets, so what they did, and while this is happening, right, while basically the agents are not able to go to work, at the same time the volumes are increasing through the roof, right? There's a tremendous amount of urgency and higher levels of volumes of requests coming in from the end customers, the end consumers coming in, right? It's basically a perfect storm of things happening, so what our clients have done is a couple of things. One, they created new sets of processes, and they created an army of volunteers from within the business to be able to respond to customer requests from home, and two, they really completely ramped up the pace of taking processes and making them self-service available on the mobile apps, on the website, on the IVR, because customers, consumers have a sense of urgency. They need an answer. They need something to get done quickly, and they want to be able to avoid waiting on line for four hours, right? We saw that, we saw a lot of the websites that says, "Hey, if you call our contact center," some companies put up these messages, "it's going to be so many hours." So our clients were able to take the processes that they have defined for their contact center agents and actually pushed them to self-service channels like the mobile channel, like the web self-service channel, as well as chat and chat bot channels, to be able to get the answers that the consumers need quickly and get their work done, respond to them quickly while in this time of amazing change. >> Yeah, so that enables scaling. Self-service is critical. Yeah, I want to ask you about digital transformation. It's a theme of PegaWorld iNspire. There's been a lot of talk the last three, four years about digital transformation. Frankly, a lot of lip service. I think it was Satya Nadella said we've accelerated. We've pulled two years of digital transformation into two months, but again, you guys are all about digital and digitizing processes, so kind of I want to know if you can talk about that theme of the show, kind of what it means to you and your client. >> I think it's been amazing. I think, like you said, there's been a lot of talk about it in several years, and there have been lots of initiatives, but I think it was missing the urgency that it needed to be able to get moving and get things done. We have had so many discussions. So many people have talked about what do we need to do, do we need to do it now, can we basically wait? Long meetings and long delays on making decisions to actually move forward, and this just basically changed all that, right? There's no more the question of do we need to go through a digital transformation? Everybody knows it's a yes. We had to do it, no question about it. There's no more question of can we do it. Yep, we know we can do it. Do we have the technology, do we have the people? Yep, got it. All that is in place. Now really the thing that we're seeing people succeed in is the ability to make a decision to move forward, to move forward aggressively, and having now proven that the people and the technology is there, and that they can get done, and it really basically requires decisiveness and leadership. >> Yeah, I think the word you use, 'urgency,' because there was a lot of complacency leading up to this, but the good news was there was also a lot of experimentation going on. So COVID obviously accelerated that urgency. Anna Gleiss from Siemens is an example of somebody who spoke during your keynote. Big industrial exposed with a huge supply chain, which for years some of that's been really opaque, and digitize that, now you get greater transparency. What were the key learnings from her discussion? >> Right, so Anna and the team have done a spectacular job, and like I say, they didn't need a worldwide pandemic to get going, and they basically approached theirs systematically with a great plan, and what they basically were able to do is really do that, another thing that people have done a lot of lip service in the past is IT and business collaboration. They actually executed brilliantly from that perspective where the IT organization, technology organization sort of delivered, on top of the Pega platform delivered a platform to be able to manage all the technical aspects of business applications that all the processes that seems needed, and in different departments and different divisions were able to leverage those assets and be able to quickly get applications up and running, and being able to dramatically increase the speed of innovation while at the same time dramatically reducing the cost of getting these things done and running them. So basically they built that environment where IT provided the technical aspects as a service to business applications so that they can quickly get things done, automate their processes, and deliver tremendous amount of operational efficiency into the organization. >> Now Kerim, of course, is the head of products. I want to get into some of the product discussion, some of the hard news that you have at PegaWorld. This notion of the Pega Process Fabric, I mean the metaphor is very strong. You think about digital, you think about a fabric. But what do we need to know about the Pega Process Fabric? >> Dave, it's a great solution that I believe corporations, especially enterprises, need to be able to make their staff more effective, streamline their work, getting them to a world where they don't have to personally navigate through dozens of different applications just to achieve an outcome, because whenever you basically have a situation where an employee of an enterprise has to jump through six, 10, 12 different applications just to be able to get something done for the customer, there's a tremendous amount of efficiency that's lost, there's a tremendous amount of training that's required to be able to actually get people to be able to manage all these, working across all these applications, and of course it's very easy to make mistakes. And whenever you have an environment that's built out like that, it inevitably gets exposed to the customers, and they basically, their experiences realize that there's a lot of jumping around. The Process Fabric is around bringing an experience to the users that is basically a single experience, even though work is coming from many different applications in the organization, right? You talk to any enterprise in anywhere in the world, and you basically name any enterprise software company, and they'll tell you, "Yeah, we got that." They have it. >> Yeah. >> They have Microsoft, they have Salesforce, they have ServiceNow, they have Pega, they have it, and users, employees have to juggle through all of these systems to be able to actually get their work done. The job of Process Fabric is to actually bring all these tasks, bring all this work that the workers, and then on behalf of the customers, have to get done, and weave them together into a single experience so that they don't have to jump around. There's much more efficiency. Get work done fast, and the organization then also has control around how the work is prioritized across different systems. How the work is managed through how it gets assigned, how to handle key customers and be able to see all the work that we're doing on behalf of them across all the different systems, and be able to actually bring a home all of these efforts and provide that experience to the user. >> So Kerim, what's the secret sauce there? Is it a combination of using APIs to those applications, and machine intelligence, and machine learning? >> There's a little bit of many things. The key is, one, we basically come with standard connectivity to standard enterprise solutions. We come prepackaged with connectivity to Pega environments within the organizations, as we have many customers that have deployed dozens of different Pega applications. We come with a standard open API approach to be able to provide connectivity, and then we use our decisioning capabilities and process capabilities to manage the prioritization, to be able to manage the routing and the experience for the end users. >> Okay, and the prioritization is something that's determined by business rules, is that correct? Or how does that all work? >> Absolutely. Absolutely, so the idea is to be able to leverage the business rules capabilities of the Pega platform to be able to handle the prioritization and the routing and sort of collating things together that are associated with the same work streams and for the same customers. >> When Alan Trefler started Pega it was right around the time I started in the industry and AI was the hot buzzword, and it took a while to get here, but it feels pretty real right now. How do you look at machine intelligence and the role that it plays? You've used the term real realtime AI. >> Right. >> What do you mean by that, and what's so special about your AI? >> Well, our realtime AI is real, so that's one of the main specialties, but look, there's a lot basically technology out there. There's a lot of great technology out there with great use cases that can look at historical sets of data and be able to actually generate predictive models from them, and those are great. Those are very, very valuable. But we believe that especially when we're directly engaging with customers, that is not enough. That you need actually realtime, real realtime AI. Let me give you an example. If you are basically running some predictive models against a set of customer data, say basically in January and February and using them in March, you will not get the right results that are basically for each individual customer, because things have changed dramatically between February and March. You couldn't make decisions about a customer based on what happened in their activity in January based on what's today. One of our telecom... One of our, I'm sorry, banking clients, for example, used their customer data in the UK, NatWest, used their customer data and identified people that work for the National Health Services and provided realtime programs that are specifically tailored for them, right, so that's basically being able to actually leverage the power of AI and be able to change how you engage with customers. They looked at customer data who might be at financial risk due to the crisis and actually changed programs and payment programs for them, because things have changed dramatically in the timeframe. Our AI leverages predictive models based on historical data, which is great, but actually also adds on top of it the ability to evaluate realtime data based on the real context of the end customer at this point in time, at this point on their experience on the website, on the IVR, on the mobile app, and be able to determine the best way to engage with that customer at that moment in time, and be able to deliver that one-to-one personalized experience. And this has been basically one of the major capabilities of Pega technology. That's how we differentiate in the marketplace in our ability to actually drive the AI capabilities in realtime interactions. >> Wonder if I could ask you about one of the trends in the marketplace, and you're seeing it in the equity markets, these private equity robotic process automation. People, I think, sometimes misunderstand you, and I've said, I've reported a number of times that RPA's just a small part of what you guys do, but at the same time you're seeing a lot of energy in the marketplace, money, billions of dollars, billions, yeah, have poured in. How do you look at RPA? Where does it fit in the Pega platform? >> Yeah, so RPA's absolutely a part of the overall journey. We look at things from an end-to-end automation perspective, essentially we need to do something for a customer, on behalf of a customer, to get an outcome delivered to a customer, and there's a process associated with it. And this process is frequently going to touch through a bunch of different systems. And some of these systems it's going to touch are old. They've been around for a very, very long time. They're a pain point for a lot of organizations. What RPA does really well is it basically lets you put a robotic process, essentially, a process that runs on the desktop and to be able to sort of execute that process inside that old system automatically. And that saves time and saves money, and there's basically a clear ROI associated with it, but it doesn't eliminate that old technology. It just puts, essentially, a veneer in front of it so that the end user doesn't have to key into some old application. It just does it on their behalf. We think that's a part of an end-to-end process automation, and as you go through different steps you might have to execute these robotic process automations, but it's not digital transformation. You're not really transforming it, right? You are basically eliminating that pain point for time being, and it will become a problem maybe for the next person that has to deal with it. We believe that robotic process automation is a great way to automate stuff, but each one of those elements need to go through that transformation as a part of the modernization, digital transformation journey. >> So it's that systems view that you would stress, and obviously you've always taken a systems view. You've got a platform that is an end-to-end platform. That's really what you mean by the end-to-end is that systems view, correct? >> Well, what we mean, really, by end-to-end is a customer comes in and they have a need, and we basically get them what they come in here for, and whatever is in between, whatever processes, and systems, and integrations, and technologies that sit in between, that's sort of the second part of the story. The main important part is work that needs to get done, we get the work done. And we will do anything in between. We'll do integrations, we'll do routing, we will do automation, we'll do business rules, we'll do AI, we'll do robotic process automation, anything that is necessary to basically drive that outcome, drive efficiency, faster response times, and better customer experience. >> Okay, so those are the key metrics. You just answered that other question. Last question, then, is we've got uncertain times. We've talked the gamut of digital transformation, but what advice would you give to customers given this uncertainty? How should they be best prepared? >> I think it's most important, really, to pay attention to the end consumers, and look at it from a perspective of empathy. What is the end consumer worried about right now? What is difficult for them? What is it that they need from your organization given their current circumstances, and make sure the experience that your corporation provides to them is the right experience. This is, I think, a time for a lot of corporations to build some incredible loyalty with their end customers, with the consumers. This is an amazing opportunity to basically have great engagement and to be able to have people realize that yeah, they were there for me. It was a good experience, it was an easy experience, it was a seamless experience, and I would mostly emphasize on that empathy factor. Make sure that we understand what's going through, what's happening in their lives, what they need, and when they engage with the corporation make sure that we provide a seamless experience to them. >> I think that's a great point. We're not going back to the customer experiences of the 2010s. We're entering a new decade, and Kerim, thanks so much for your insights and coming on theCUBE to share them. >> My pleasure, thanks for having me. >> You're welcome, and thank you for watching, everybody. You're watching theCUBE's coverage of PegaWorld iNspire 2020. Be right back right after this short break. (smooth music)

>> Announcer: Live, from Boston, Massachusets, it's theCUBE covering Red Hat Summit, 2019. Brought to you by Red Hat. >> Well, welcome back here in Boston. We're at the BCEC as we are starting to wrap up our coverage here of day two of the Red Hat Summit, 2019. Along with Stu Miniman, I'm John Walls, and we're now joined by Ashesh Badani, who is the senior vice president of Cloud Platforms at Red Hat. Been a big day for you, hasn't it Mr. Badani? >> It sure has, thanks for having me back on! >> You bet! All right, so OpenShift 4, we saw the unveiling, your baby gets introduced to the world. What's the reaction been between this morning and this afternoon in terms of people, what they're asking you about, what they're most curious about, and maybe what their best reaction is. >> Yeah, so it's not necessarily a surprise for the folks who have been following OpenShift closely, we put the beta out for a little while, so that's the good news, but let me roll back just a little. >> John: Sure >> I think another part of the news that was really important for us is our announcement of a milestone that we crossed, which is a thousand customers, right? And it was at this very summit and theCUBE definitely knows this well, right, because they've been talking for a while. At this very Summit in 2015, four years ago, that we launched OpenShift Version 3. Right and so, you know you fast forward four years, right, and now the diversity of cases that we see, you know, spanning, established apps, cloud native apps, we heard Exxon talking about AIML data signs that they're putting on the platform, in a variety of different industries, is amazing. And I think the way OpenShift 4 has come along for us, is us having the opportunity to learn what have all these customers been doing well, and what else do we need to do on the platform to make that experience a better one. How do we reimagine enterprise kubernetes, to take it to the next level. And I think that's what we're introducing to the industry. >> Ashesh I think back four years ago, kubernetes was not something that was on the tip of the tongues of most people here. Congratulations on 1,000. >> Thank you. >> I hear what, 100, 150, new customers every quarter is the current rate there, but what I've really enjoyed, talked to a CIO and they're like okay, we're talking about digital transformation, we're talking about how we're modernizing all of our environments, and OpenShift is the platform that we do it. So, talk a little bit, from a customer's standpoint, the speeds, the feeds, the technical pieces, but that outcome, what is it an enabler of for your customers? >> Yeah, so excellent points Stu, we've seen whole sale complete digital transformations underway with our customers. So whether it's Deutsche Bank, who came and talked about running thousands of containers now, moving a whole bunch of workload onto the platform, which is incredible to see. Whether it's a customer like Volkswagen, who talking yesterday, if you caught that, about building an autonomous, self-driving, sets of technologies on the platform. What we're seeing is not just what we thought we would only see in the beginning which is one built, cloud native apps, and digital apps, and so on. Or, more nice existing apps, and bring them on the platform. But also, technologies that are making a fundamental difference, and I'll call one out. So I'm a judge for The Innovation Awards, we do this every year, I have been for many years, I love it, it's one of my favorite parts of the show. This year, we had one entry, which is one of the winners, which is HCA, which is a healthcare provider, talking about how they've been using the OpenShift platform as a means to make a fundamental difference in patients' lives. And when I say fundamental difference, actually saving lives. And you'll hear more about their story, but what they've done, is be able to say, look how can we detect early warning signals, faster than we have been, take some AI technology, and correlate against that, and see how we can reduce sepsis within patients. It's a very personal story for me, my mother died of sepsis. And the fact that they've been able to do this, and I think they're reporting they've already saved dozens of lives based on this. That's when you know, the things that you're doing are making a real difference, making a real transformation, not just in an actual customers' lives, but in users and people around the world. >> You were saying earlier too, Ashesh, about looking at what customers are doing and then trying to improve upon that experience, and give them a more effective experience, whatever the right adjective might be, in terms of what you're doing with 4. If you had to look at it, and say okay, these are the two or three pillars of this where I think we've made the biggest improvement or the biggest change, what would those be? >> Yes, so, one is to look at the world as it is in some sense, which is what a customer's doing. Customers weren't deployed to hybrid cloud, right? They want choice, they want independence with regard to which environments are rented on, whether it's physical, virtual, private, or any public cloud. Customers want one platform, to say I want to run these next generation, cloud native, market service based applications, along with my established stateful applications. Customers want a platform for innovation, right? So for example, we have customers that say, look, I really need a modern platform because I want to recruit the next generation of developers from colleges, if I don't give them the ability to play with Go, or Python, or new databases, they're gonna go to some Silicon Valley company, and I'm going to deplete my pool of talent that I need to compete, right? 'Cause digital transformation is about taking existing companies, and making them digitally enabled. Going forward, what we're also seeing is the ability for us to say well maybe the experience we've given existing customers can be improved. How do we for example, give them a platform, that's more autonomous in nature, more self-driving in nature, that can heal itself, based on for example, there's a critical update that's required that we can send over the air to them. How can we bring greater automation into the platform? It's all of those ideas that we've got based on how customers are using it today, is what we're bringing to bear, going forward. >> Ashesh, one of the errors we have trying to help customers parse through the language is, everybody's talking about platforms, if you look at the public clouds, everybody's all in on kubernetes, a few weeks ago, we were at the Google Cloud event, talked to Red Hat there, there's Anthos, there's OpenShift, look at Azure, we Satya Nadella up on stage, and you're like, okay they've got their own kubernetes platform, but I've got OpenShift fully integrated there. >> Ashesh: Yeah. >> Can you help is kinda understand how those fit together because it's an interesting and changing dynamic. >> Well it's a very Silicon Valley buzzword, right? Everyone wants a platform, everyone wants to build a platform, Facebook's a platform, Uber's a platform, Airbnb is, everything's seeming a platform, right? What I really want to focus on more is in regard to, we want to be able to give folks literally an abstraction level, an ability for companies to say I want to embrace digital transformation. Before we get there, someone's like what's digital transformation, I don't even understand what that means anymore. My simple definition is basically flipping the table. Typically companies spend 80% on maintenance, 20% innovation, how do we flip that? So they're spending 80% innovation, 20% maintenance. So if we're still thinking in those terms, let me give you a way to develop those applications, spend more time and energy on innovation, and then allow for you to take advantage of what I'll call a pool of resources. Compute, network, and storage. Across the environment that you have in place. Some of which you might own, some of which some third parties might provide for you, and some of which you get from public cloud. And take advantage of innovation that's being done outside. Innovative services that come from either public cloud providers, or ISPs, or separate providers, and then be able to do that innovated rapid fashion, you know, develop, deploy, iterate quickly. So to me that is really fundamentally what we're trying to provide customers, and it takes different forms, internal packaging. >> Maybe you can explain to me, the Azure OpenStack seems different than some of the other partnerships. Two years ago, when we were sitting in this building, we talked to you about AWS with OpenShift in that partnership, so what's differentiated and special about the Azure OpenStack integration. >> Yeah, so the Azure partnership, it's a good question because we've now taken our partnering with the public cloud providers to the next level, if you will. With Azure there's a few things in play, first it's a jointly offered managed service from Red Hat and Microsoft, where we're both supporting it together. So in the case of OpenShift and AWS, that's you know OpenShift directly to the ring of service, in this case, it's right out of Microsoft, working close together to make that happen. It's a native service to Azure, so if you saw in the keynote, you could use a command line to call OpenShift directly integrate into the Azure command line. It's available within the interface of Microsoft-Azure. So it feels like a native service, you can take advantages of other Azure services, and bring those to bear, so obviously increases developer experience from that perspective. We also inherit all the compliances, certifications, that Microsoft-Azure has, as well, for that service, as well as all the availability requirements that they put out there, so it's much more closely integrated together, much better developer experience, native to Azure, and then the ability for the Microsoft sales team to go out and sell it to their customers in conjunction. >> You talk a lot about different partnerships, and bringing this collaborative, open-mindset to each and every relationship, how hard is that to do? Because you have your of way of doing things and it's worked very well, and yet, you go out and you have these new partnerships or extensions of partnerships, and not everybody with whom you work does things the same way, and so, everybody's gotta be malleable to a certain extent, but just in terms of being that flexible all the time, what does that do for you? >> So, we take that for granted sometimes, the way we work. And I don't mean to say that to be boastful, or arrogant, in any fashion. I had an interview earlier today, and the reporter said why don't you put on your page, that you're 100% open source? And I said we never put that on our page because that's just how we work, we assume that, we assume everyone knows that about us, and we're going forward. And he says, well, I don't know, perhaps there's others that don't know. And he's right. The world's changing, we're expanding our opportunities in front of folks. In the same way we've only and always known, we used to collaborate with others in the community, before we fully embraced OpenStack, there were certain projects that Red Hat was investing in that were Red Hat driven, and we say maybe there wasn't as much community around it, we're gonna go down and embrace and fully parse an OpenStack community. Same's the case, for example, in kubernetes too. It's not necessarily a project that we created on our own, in conjunction with Google, and many others in the community. And so that's something that's part of our DNA, I'm not sure we're doing anything different, in engaging with communities, just how we work. >> So, Ashesh, I know your team's busy doing a lot of things. We've been hearing about what sessions are overflowing, down in the expo floor, so why don't you give us some visibility. But there was one specific one I wondered if you could start with. >> Ashesh: Sure. >> So down on the expo floor, it's a containerized environment and it has something to do with puppies, and therefor how does that connect with OpenShift 4 if we can start there. >> That's a tough one, you're gonna have to go and ask the puppies how to make a difference in the world. (laughing) >> John: So we go from kubernetes to canines, (laughing) that's what we're doing here. >> I do believe they're comfort dogs, but there was coding and some of the other stuff, so give us a little bit of the walk around, the expo flow, the breakouts and the like, in some of the hot areas, that your team's working on. >> Fair enough, fair enough. Maybe not puppies, but maybe we're trying to herd cats, close enough, right? >> John: Safer terrain. >> The amount of interest, the number of sessions, with OpenShift, or container based technologies, cloud based technologies, it's tremendous to see that. So regardless if whether you see the breakouts that are in place, the customer sessions, I think we've got over 100 customers, I think. Who are presenting on all aspects of their journey. So to me, that's remarkable. Lots of interest in our road map going forward, which is great to see, standing room only for OpenShift 4 and where we're taking that. Other technology that's interesting, the work, for example, we're doing in serverless. We announced an OpenSource collaboration with Mircrosoft, something called KEDA, the Kubernetes eventually. Our scaling project, so interesting how customers can kind of engage around that as well. And then the partner ecosystem, you can walk around and see just a plethora of ISVs, we're all looking to build operators, or have operators and are certifying operators within our ecosystem. And then it's ways for us to expose that to our joint customers. >> We're gonna cut you loose, and let you go, the floor's gonna be open for a few minutes, those puppies are just down behind Stu, we'll let you go check that out. >> Alright, thanks, I hear you can adopt them if you want to, as well. >> Before we let you go see the comfort dogs, 1,000 customers, where do you see, when we come back a year from now, where you are, where you wanna see it go, show us a little bit looking forward. >> So there's been some news around Red Hat that has probably happened over the last few months, the people are hearing this, I look at that as a great opportunity for us to expand our reach into markets, both in terms of industries perhaps we haven't necessarily gone into, that other companies have been. Perhaps we say it's manufacturing, perhaps this is the opportunity for us to cross the chasm, have a lot more trained consultants who can help get more customers on the journey, so I fully expect our reach increasing over a period time. And then you'll see, if you will, iterations of OpenShift 4 and the progress we've made against that, and hopefully many more success stories on the stage. >> Alright, looking forward to catching up next year, if not sooner. >> Ashesh: Okay, excellent. >> John: And congratulations on today, and best of luck down the road. >> Thanks again for having me. >> And good to see you! >> Ashesh: Yeah, likewise! >> Back with more on theCube, you are watching our coverage live, here from Red Hat Summit, 2019, in Boston, Massachusetts. (upbeat techno music)