>> Live from Boston, it's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back, everyone, to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillin. We have two guests for this session, we have Rebecca Shockley, she is executive consultant and IBM Global Business Services, and Alfred Essa, vice president analytics and R&D at McGraw-Hill Education. Rebecca and Alfred, thanks so much for coming on theCUBE. >> Thanks for having us. >> So I'm going to start with you, Rebecca. You're giving a speech tomorrow about the AI ladder, I know you haven't finished writing it-- >> Shh, don't tell. >> You're giving a speech about the AI ladder, what is the AI ladder? >> So, when we think about artificial intelligence, or augmented intelligence, it's very pervasive, we're starting to see it a lot more in organizations. But the AI ladder basically says that you need to build on a foundation of data, so that data and information architecture's your first rung, and with that data, then you can do analytics, next rung, move into machine learning once you're getting more comfortable, and that opens up the whole world of AI. And part of what we're seeing is organizations trying to jump to the top of the ladder or scramble up the ladder really quickly and then realize they need to come back down and do some foundational work with their data. I've been doing data and analytics with IBM for 21 years, and data governance is never fun. It's hard. And people would just as soon go do something else than do data governance, data security, data stewardship. Especially as we're seeing more business-side use of data. When I started my career, data was very much an IT thing, right. And part of my early career was basically just getting IT and business to communicate in a way that they were saying the same things. Well now you have a lot more self-service analytics, and business leaders, business executives, making software decisions and various decisions that impact the data, without necessarily understanding the ripples that their decisions can have throughout the data infrastructure, because that's not their forte. >> So what's the outcome, what's the result of this? >> Well, you start to see organizations, it's similar to what we saw when organizations first started making data lakes, right? The whole concept of a data lake, very exciting, interesting, getting all the data in together, whether it's virtual or physical. What ended up happening is without proper governance, without proper measures in place, you ended up with a data swamp instead of a data lake. Things got very messy very quickly, and instead of creating opportunities you were essentially creating problems. And so what we're advising clients, is you really have to make sure that you're focused on taking care of that first rung, right? Your data architecture, your information architecture, and treating the data with the respect as a strategic asset that it is, and making sure that you're dealing with that data in a proper manner, right? So, basically telling them, yes we understand that's fun up there, but come back down and deal with your foundation. And for a lot of organizations, they've never really stepped into data governance, because again, data isn't what they think makes the company run, right? So banks are bankers, not data people, but at the same time, how do you run a bank without data? >> Well exactly. And I want to bring you into this conversation, Alfred, as McGraw-Hill, a company that is climbing the ladder, in a more steady fashion. What's your approach? How do you think about bringing your teams of data scientists together to work to improve the company's bottom line, to enhance the customer experience? >> First I'd sort of like to start with laying some of the context of what we do. McGraw-Hill Education has been traditionally a textbook publisher, we've been around for over a hundred years, I started with the company over a hundred years ago. (all laughing) >> You've aged well. >> But we no longer think of ourselves as a textbook publisher. We're in the midst of a massive digital transformation. We started that journey over five years ago. So we think of ourselves as a software company. We're trying to create intelligent software based on smart data. But it's not just about software and AI and data, when it comes to education it's a tale of two cities. This is not just the U.S., but internationally. Used to be, we were born, went to school, got a job, raised a family, retired, and then we die. Well now, education is not episodic. People need to be educated, it's life-long learning. It's survival, but also flourishing. So that's created a massive problem and a challenge. It's a tale of two cities, by that I mean there's an incredible opportunity to apply technology, AI, we see a lot of potential in the new technologies. In that sense, it's the best of times. The worst of times is, we're faced with massive problems. There's a lot of inequity, we need to educate a people who have largely been neglected. That's the context. So I think in now answering your question about data science teams, first and foremost, we like to get people on the teams excited about the mission. It's like, what are we trying to achieve? What's the problem that we're trying to achieve? And I think the best employees, including data scientists, they like solving hard problems. And so, first thing that we try to do is, it's not what skills you have, but do you like solving really, really hard problems. And then taking it next step, I think the exciting thing about data science is it's an interdisciplinary field. It's not one skill, but you need to bring together a combination of skills. And then you also have to excel and have the ability to work in teams. >> You said that the AI has potential to improve the education process. Now, people have only so much capacity to learn, how can AI accelerate that process? >> Yeah, so if we stand back a little bit and look at the traditional model of education, there's nothing wrong with it but it was successful for a certain period of years, and it works for some people. But now the need for education is universal, and life long. So what our basic model, current model of education is lecture mode and testing. Now from a learning perspective, learning science perspective, all the research indicates that that doesn't work. It might work for a small group of people, but it's not universally applicable. What we're trying to do, and this is the promise of AI, it's not AI alone, but I think this is a big part of AI. What we can do is begin to customize and tailor the education to each individual's specific needs. And just to give you one quick example of that, different students come in with different levels of prior knowledge. Not everyone comes into a class, or a learning experience, knowing the same things. So what we can do with AI is determine, very, very precisely, just think of it as a brain scan, of what is it each student need to know at every given point in time, and then based on that we can determine also, this is where the models and algorithms are, what are you ready to learn next. And what you might be ready to learn next and what I might be ready to learn next is going to be very different. So our algorithms also help route delivery of information and knowledge at the right time to the right person, and so on. >> I mean, you're talking about these massive social challenges. Education as solving global inequity, and not every company has maybe such a high-minded purpose. But does it take that kind of mission, that kind of purpose, to unite employees? Both of you, I'm interested in your perspectives here. >> I don't think it takes, you know, a mission of solving global education. I do firmly agree with what Al said about people need a mission, they need to understand the outcome, and helping organizations see that outcome as being possible, gives them that rally point. So I don't disagree, I think everybody needs a mission to work towards but it doesn't have to be solving-- >> You want to extract that mission to a higher level, then. >> Exactly. >> Making the world a better place. >> Exactly, or at least your little corner of the world. Again what we're seeing, the difficulty is helping business leaders or consumers or whomever understand how data plays into that. You may have a goal of, we want better relationship with our customer, right? And at least folks of my age think that's a personal one-on-one kind of thing. Understanding who you are, I can find that much more quickly by looking at all your past transactions, and all of your past behaviors, and whether you clicked this or that. And you should expect that I remember things from one conversation to the next. And helping people understand that, you know, helping the folks who are doing the work, understand that the outcome will be that we can actually treat our customers the way that you want to be treated as a person, gives them that sense of purpose, and helps them connect the dots better. >> One of the big challenges that we hear CDOs face is getting buy-in, and what you're proposing about this new model really appending the old sage on the stage model, I mean, is there a lot of pushback? Is it difficult to get the buy-in and all stakeholders to be on the same page? >> Yeah, it is, I think it's doubly difficult. The way I think about it is, it's like a shift change in hockey, where you have one shift that's on the ice and another one that's about to come on the ice, that's a period of maximum vulnerability. That's where a lot of goals are scored, people get upset, start fighting. (all laughing) That's hockey. >> That's what you do. >> Organizations and companies are faced with the same challenge. It's not that they're resisting change. Many companies have been successful with one business model, while they're trying to bring in a new business model. Now you can't jettison the old business model because often that's paying the bills. That's the source of the revenue. So the real challenge is how are you going to balance out these two things at the same time? So that's doubly difficult, right. >> I want to ask you quickly, 'cause we have to end here, but there's a terrible shortage of cybersecurity professionals, data science professionals, the universities are simply not able to keep up with demand. Do you see the potential for AI to step in and fill that role? >> I don't think technology by itself will fill that role. I think there is a deficit of talented people. I think what's going to help fill that is getting people excited about really large problems that can be solved with this technology. I think, actually I think the talent is there, what I see is, I think we need to do a better job of bringing more women, other diverse groups, into the mix. There are a lot of barriers in diversity in bringing talented people. I think they're out there, I think we could do a much better job with that. >> Recruiting them, right. Alfred, Rebecca, thanks so much for coming on theCUBE, it was a pleasure. >> Thank you so much for having us. >> I'm Rebecca Knight, for Paul Gillin, we will have more from theCUBE's live coverage of the IBM CDO Summit here in Boston coming up in just a little bit.

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, its theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer event in San Francisco, Spring, 2018. About 100 people, predominantly practitioners, which is a pretty unique event. Not a lot of vendors, a couple of them around, but really a lot of people that are out in the wild doing this work. We're really excited to have a return guest. We last saw him at Spark Summit East 2017. Can you believe I keep all these shows straight? I do not. Alfred Essa, he is the VP, Analytics and R&D at McGraw-Hill Education. Alfred, great to see you again. >> Great being here, thank you. >> Absolutely, so last time we were talking it was Spark Summit, it was all about data in motion and data on the fly, and real-time analytics. You talked a lot about trying to apply these types of new-edge technologies and cutting-edge things to actually education. What a concept, to use artificial intelligence, a machine learning for people learning. Give us a quick update on that journey, how's it been progressing? >> Yeah, the journey progresses. We recently have a new CEO come on board, started two weeks ago. Nana Banerjee, very interesting background. PhD in mathematics and his area of expertise is Data Analytics. It just confirms the direction of McGraw-Hill Education that our future is deeply embedded in data and analytics. >> Right. It's funny, there's a often quoted kind of fact that if somebody came from a time machine from, let's just pick 1849, here in San Francisco, everything would look different except for Market Street and the schools. The way we get around is different. >> Right. >> The things we do to earn a living are different. The way we get around is different, but the schools are just slow to change. Education, ironically, has been slow to adopt new technology. You guys are trying to really change that paradigm and bring the best and latest in cutting edge to help people learn better. Why do you think it's taken education so long and must just see nothing but opportunity ahead for you. >> Yeah, I think the... It was sort of a paradox in the 70s and 80s when it came to IT. I think we have something similar going on. Economists noticed that we were investing lots and lots of money, billions of dollars, in information technology, but there were no productivity gains. So this was somewhat of a paradox. When, and why are we not seeing productivity gains based on those investments? It turned out that the productivity gains did appear and trail, and it was because just investment in technology in itself is not sufficient. You have to also have business process transformation. >> Jeff Frick: Right. >> So I think what we're seeing is, we are at that cusp where people recognize that technology can make a difference, but it's not technology alone. Faculty have to teach differently, students have to understand what they need to do. It's a similar business transformation in education that I think we're starting to see now occur. >> Yeah it's great, 'cause I think the old way is clearly not the way for the way forward. That's, I think, pretty clear. Let's dig into some of these topics, 'cause you're a super smart guy. One thing's talk about is this algorithmic transparency. A lot of stuff in the news going on, of course we have all the stuff with self-driving cars where there's these black box machine learning algorithms, and artificial intelligence, or augmented intelligence, bunch of stuff goes in and out pops either a chihuahua or a blueberry muffin. Sometimes it's hard to tell the difference. Really, it's important to open up the black box. To open up so you can at least explain to some level of, what was the method that took these inputs and derived this outpout. People don't necessarily want to open up the black box, so kind of what is the state that you're seeing? >> Yeah, so I think this is an area where not only is it necessary that we have algorithmic transparency, but I think those companies and organizations that are transparent, I think that will become a competitive advantage. That's how we view algorithms. Specifically, I think in the world of machine learning and artificial intelligence, there's skepticism, and that skepticism is justified. What are these machines? They're making decisions, making judgments. Just because it's a machine, doesn't mean it can't be biased. We know it can be. >> Right, right. >> I think there are techniques. For example, in the case of machine learning, what the machines learns, it learns the algorithm, and those rules are embedded in parameters. I sort of think of it as gears in the black box, or in the box. >> Jeff Frick: Right. >> What we should be able to do is allow our customers, academic researchers, users, to understand at whatever level they need to understand and want to understand >> Right. >> What the gears do and how they work. >> Jeff Frick: Right. >> Fundamental, I think for us, is we believe that the smarter our customers are and the smarter our users are, and one of the ways in which they can become smarter is understanding how these algorithms work. >> Jeff Frick: Right. >> We think that that will allow us to gain a greater market share. So what we see is that our customers are becoming smarter. They're asking more questions and I think this is just the beginning. >> Jeff Frick: Right. >> We definitely see this as an area that we want to distinguish ourselves. >> So how do you draw lines, right? Because there's a lot of big science underneath those algorithms. To different degrees, some of it might be relatively easy to explain as a simple formula, other stuff maybe is going into some crazy, statistical process that most layman, or business, or stakeholders may or may not understand. Is there a way you slice it? Is there kind of wars of magnitude in how much you expose, and the way you expose within that box? >> Yeah, I think there is a tension. The tension traditionally, I think organizations think of algorithms like they think of everything else, as intellectual property. We want to lock down our intellectual property, we don't want to expose that to our competitors. I think... I think that's... We do need to have intellectual property, however, I think many organizations get locked into a mental model, which I don't think is just the right one. I think we can, and we want our customers to understand how our algorithm works. We also collaborate quite a bit with academic researchers. We want validation from the academic research community that yeah, the stuff that you're building is in fact based on learning science. That it has warrant. That when you make claims that it works, yes, we can validate that. Now, where I think... Based on the research that we do, things that we publish, our collaboration with researchers, we are exposing and letting the world know how we do things. At the same time, it's very, very difficult to build an engineer, an architect, scalable solutions that implement those algorithms for millions of users. That's not trivial. >> Right, right, right. >> Even if we give away quite a bit of our secret sauce, it's not easy to implement that. >> Jeff Frick: Right. >> At the same time, I believe and we believe, that it's good to be chased by our competition. We're just going to go faster. Being more open also creates excitement and an ecosystem around our products and solutions, and it just makes us go faster. >> Right, which gives to another transition point, which would you talk about kind of the old mental model of closed IP systems, and we're seeing that just get crushed with open source. Not only open source movements around specific applications, and like, we saw you at Spark Summit, which is an open source project. Even within what you would think for sure has got to be core IP, like Facebook opening up their hardware spec for their data centers, again. I think what's interesting, 'cause you said the mental model. I love that because the ethos of open source, by rule, is that all the smartest people are not inside your four walls. >> Exactly. >> There's more of them outside the four walls regardless of how big your four walls are, so it's more of a significant mental shift to embrace, adopt, and engage that community from a much bigger accumulative brain power than trying to just trying to hire the smartest, and keep it all inside. How is that impacting your world, how's that impacting education, how can you bring that power to bear within your products? >> Yeah, I think... You were in effect quoting, I think it was Bill Joy saying, one of the founders of Sun Microsystems, they're always, you have smart people in your organization, there are always more smarter people outside your organization, right? How can we entice, lure, and collaborate with the best and the brightest? One of the ways we're doing that is around analytics, and data, and learning science. We've put together a advisory board of learning science researchers. These are the best and brightest learning science researcher, data scientists, learning scientists, they're on our advisory board and they help and set, give us guidance on our research portfolio. That research portfolio is, it's not blue sky research, we're on Google and Facebook, but it's very much applied research. We try to take the no-knowns in learning science and we go through a very quick iterative, innovative pipeline where we do research, move a subset of those to product validation, and then another subset of that to product development. This is under the guidance, and advice, and collaboration with the academic research community. >> Right, right. You guys are at an interesting spot, because people learn one way, and you've mentioned a couple times this interview, using good learning science is the way that people learn. Machines learn a completely different way because of the way they're built and what they do well, and what they don't do so well. Again, I joked before about the chihuahua and the blueberry muffin, which is still one of my favorite pictures, if you haven't seen it, go find it on the internet. You'll laugh and smile I promise. You guys are really trying to bring together the latter to really help the former. Where do those things intersect, where do they clash, how do you meld those two methodologies together? >> Yeah, it's a very interesting question. I think where they do overlap quite a bit is... in many ways machines learn the way we learn. What do I mean by that? Machine learning and deep learning, the way machines learn is... By making errors. There's something, a technical concept in machine learning called a loss function, or a cost function. It's basically the difference between your predicted output and ground truth, and then there's some sort of optimizer that says "Okay, you didn't quite get it right. "Try again." Make this adjustment. >> Get a little closer. >> That's how machines learn, they're making lots and lots of errors, and there's something behind the scenes called the optimizer, which is giving the machine feedback. That's how humans learn. It's by making errors and getting lots and lots of feedback. That's one of the things that's been absent in traditional schooling. You have a lecture mode, and then a test. >> Jeff Frick: Right. >> So what we're trying to do is incorporate what's called formative assessment, this is just feedback. Make errors, practice. You're not going to learn something, especially something that's complicated, the first time. You need to practice, practice, practice. Need lots and lots of feedback. That's very much how we learn and how machines learn. Now, the differences are, technologically and state of knowledge, machines can now do many things really well but there's still some things and many things, that humans are really good at. What we're trying to do is not have machines replace humans, but have augmented intelligence. Unify things that machines can do really well, bring that to bear in the case of learning, also insights that we provide. Instructors, advisors. I think this is the great promise now of combining the best of machine intelligence and human intelligence. >> Right, which is great. We had Gary Kasparov on and it comes up time and time again. The machine is not better than a person, but a machine and a person together are better than a person or a machine to really add that context. >> Yeah, and that dynamics of, how do you set up the context so that both are working in tandem in the combination. >> Right, right. Alright Alfred, I think we'll leave it there 'cause I think there's not a better lesson that we could extract from our time together. I thank you for taking a few minutes out of your day, and great to catch up again. >> Thank you very much. >> Alright, he's Alfred, I'm Jeff. You're watching theCUBE from the Corinium Chief Analytics Officer event in downtown San Francisco. Thanks for watching. (energetic music)

>> Announcer: Live from Barcelona, Spain, it's theCUBE! Covering Cisco Live 2018. Brought to you by Cisco, Veem, and theCUBE's ecosystem partners. >> Hey everyone, welcome back to the live CUBE coverage here in Barcelona, Spain for theCUBE's coverage of Cisco Live Europe 2018, kicking off the new year with the big event. I'm John Furrier with SiliconANGLE, cohost of theCUBE. Our next two guests, Alfred Manhart is a Senior Director Channel and System Integrators for NetApp, EMEA of Europe, Middle East and Africa, and Benjamin Laplane, EMEA Chief Sales and Solutions Officer with Outscale. You guys, welcome to theCUBE. >> Thank you. >> Hi. >> Love this partner segment. NetApp, you have a customer on, partner, and you guys have an interesting relationship. Would one of you like to talk about your relationship with Outscale, and why are you guys here? >> I think engaging not only with the typical resellers and distributors is pretty key for us. We engage with service providers and cloud providers from 2012, 2013 ongoing. It's mainly to be the foundation for the services they are going to market with, and Outscale is out of France, one of our predominant service providers we engage with on a local level. >> How has the channel changed, because as the cloud service providers, and cloud creates such great agility and speed. You can get products out faster, MVPs and those things can be very specialized. How has your go-to-market changed with the cloud, accelerated it, changed the makeup, what's NetApp- >> First of all, the market is demanding it, so some of our traditional players go the services way and some service providers go the typical, traditional way so engaging and broaden up the ecosystem was pretty critical for us. Different engagement models are needed because the customers require different kind of consumption models. >> Good leverage, sales model, always a good business. Benjamin, talk about what you guys do. I want to ask you some specific questions about your business, on how you guys are advising and implementing solutions with customers, but first, take a minute to explain your business. >> Outscale is a cloud service provider. We built the company in 2010 and we've been providing public cloud solution for worldwide, so implementing in the U.S., in Europe, and in Asia for the past five years now. The objective is to be able to provide sovereignty and reliable cloud solutions for our customers worldwide. It's based on NetApp and Cisco FlexPod architecture. >> So you guys actually have a cloud yourselves? >> Yeah, exactly. >> And you bring that to customers? >> Yeah for the past five years, what we've been doing is developing our own orchestration layer that allow us to actually use the whole FlexPod architecture to provide infrastructure as a service for our customers. What we've been doing for the past year is actually package all the technology that we've been developing for the past years into a unique solution, which is TINA On-Prem, which is a private cloud solution ready to be deployed wherever you need to. >> I'll get back to the FlexPod in a minute, but I want to drill down on this notion of serving the customers, because there's a thirst for customization and specialization, whether it's an application, or some regional challenge on the data, certainly you see that with GDPR, it's coming down like a freight train, like a ton of bricks on everybody. So there's design challenges that are now upon the customers. How are you guys bringing the customers' solutions to them? Is it rapid engagements, is it ongoing? What's your relationship with your customers? >> So if we talk specifically about GDPR, but I think it's true for most regulation that comes out, Outscale had the chance to be able to develop their software with security design first. That means that it's designed for security, but also for privacy, so that's kind of give us the edge when talking about regulation enforcement and also all the process that we put in place around infrastructure management that allows for us to provide the best services for our customer, always aligned with the regulation that comes out. >> What are the biggest challenges your customers face with the cloud? >> I think most of them, so things improved a lot for the past years, but the first thing was everyone wanted to do it because that was kind of the name, the thing that you want to go into, now it's more big data or AI. The idea behind this is a company knows that the cloud is not an option, they will go to the cloud, the question is how, and why and when and how. So we try to help all these companies to decide what's the best for public cloud or private cloud. >> Alfred and Benjamin, I want you guys both to answer this next question. We've been observing and reporting on theCUBE, and certainly Cisco's validated it, that everyone kind of has some cloud thing going on. Yeah I put an app in there, it might be low-hanging fruit, test dev, or something non-critical, but all the work and energy and money being spent is kind of getting their act together on-premise, because they got to get cloud operations going, move from the old operating model to cloud-ready on-premises, and then do some hybrid cloud. Do you guys see it the same way, and if so, what specifically are they doing on, is it DevOps, is it pure operational, what are your thoughts? Start with Benjamin. >> So from where I stand, what I can see is we've seen companies for the past year that went full public cloud, and then other company that always stay back and say, no, we won't go to the cloud and we kind of things going into a balance point where basically all companies now realize that they need to have a part of their infrastructure, such as private cloud, for security, politics, regulation sometimes. The other places to decide what's going to be the perimeter, they going to be allowed to put into the public cloud. That's why now we are more talking about hybrid between public and private cloud, and that's one of the first major design of the solution that we developed. >> Are you saying that you're seeing some customers move completely from on-premises to cloud, full migrations? >> No, I think what I've seen is people that have, so the cloud was not made for them, finally decided that maybe it could have been useful for some of their operations, so I don't think it's always like an all-in move. You need to decide where's it's going to be good, depending on the perimeter, the context, the data, the cre-dee-city of the data. >> Alfred, on-premise activity. >> Heavy on the one side. (laughing) On the other side, I think you talked about test dev. A lot of people play around with test dev, this is mainly on a local level, behind the scenes, but if it then goes to backup or a disaster recovery, it goes up the productive stack. They are more interested if it's really going well, if the data resides in their country, if all the legislations are held. We currently see getting out of the test dev, and on the other side we of course see a trend that the customers are forced by the software Windows to go to the cloud. So Microsoft is going cloud. SAP is also going cloud, so it's not only a market trend, it's also a trend from the software end that they are forced to do something, and they want to keep control of their data. That's why data's a little bit different from going to the cloud, it's computing with the apps. >> Data's a huge issue. So how are you guys using NetApp? Talk about the FlexPod, you mentioned that earlier. >> Outscale, we've been using NetApp for the past six years, something like that, which is a pretty long time compared to the lifetime of a company. The thing as far as the most important thing was to be able to provide the bridge services for our customers. Even if we abstract some of the features, some of the value of the NetApp that we buy, we just keep the value for ourself to be able to deliver more services, more value to the end customer. That's how we've been doing things. The second thing is also when you want to deploy private, on-prem solution, it's always better and it's more reassuring for the customer when you use and you partner with one of the leaders on the market, such as NetApp. >> So when I hear people use the term enterprise class architecture, what does that mean? Does that mean certain maybe arrays? Is it configuration, is it network? What is enterprise class architecture mean to you? >> For me it's two things. So the first thing you have the architecture, and you also have the hardware that you're going to use to apply to this architecture. The thing is, I was talking about reliability. I think that's one of the major things is how much maintenance is it going to require, how it's going to impact your permissions for the user or for the end customer, and when you see the architecture that we've deployed, it's everything is redundant, it's not fail-safe, it's failure-proof, which is even better because that means that you know things are going to fail at some point, and you can't even allow yourself to have a failure where you can't serve the service to your customer. >> What's the biggest thing that you've learned in doing the cloud migration, cloud service provider, with customers over the past two years? What's the big aha moment that you've had? >> I think that's when you realize that even if you have some pattern that you can recognize for a specific customer, or for a certain type of customer, you have no magic recipe. That means that you always need to take a step back, look at the problem of your customers and try to think what's the best for my customer, and how can I bring the right services to him so he can add value to his market and his business? >> Alfred, you mentioned regulation, so the question to Benjamin is how does the role of storage play in a world where data and sovereignty issues come into play? Does it change the strategy? What's goes on for the folks that are really trying to solve this problem? >> I think we see more and more movement where basically even the customer want more managed services. I think it's always important to give the customer the hands so he can do whatever he want with his data. We are here to support him, to give him the best advices, the best practices about data management, but at the end is he accountable and responsible for these data. So at the end I think it's just we need to give the right tools to our customers so they do exactly what they want to do with the data and they don't have hidden policies apply to their own data. For example, replication of your data for safety measures. Maybe they don't want it to be replicated abroad, they want it to stay on the territory, so that's kind of a thing that you need to rethink about and give the right tools to your customers. >> Alfred, what are the top use cases that you guys have seen at NetApp for cloud services providers, and just in general the partners, because they're on the front lines serving customers. They need to have low cost, high performance gear, great software, we heard reliability. What are the use cases now that you're seeing? Are they broader use cases, are they more narrow? What's your- >> So of course, when you come from a storage perspective, you mainly aim for the infrastructure and for the storage-related services, which we are not where we are stopping, because we are working with Cisco on this validated designs going up the stack, so if you are not going up the stack regarding different workloads, going after the IOT, going after the analytics, going after the application layer, we will fail. So having a fair balance of partner that can offer the services from bottom to the top, that's very important. Of course, use cases like intelligent business analytics, going after SAP, going after SAP HANA, going after Microsoft, this is obvious that the partners and the customers are going that way. >> Benjamin, talk about what it's like working with NetApp. You happy with them? Some things that they've done that you think other suppliers should adopt? What's the mode of support from NetApp, what's the overall experience like? >> I think I would describe it as a strong partnerships. They are our exclusive partner for the storage as Cisco can be on the other brinks of technology that we are using. We have a strong relationship, we have a booth on the on-stand today so that's one of the reason why we're here. We also pushing with them with the whole, we were talking about analytics, we are talking talking about big data also. We have a lot of use cases, pretty amazing use case in resales in Europe, and also we give them a lot of feedback about how we use the hardware, what could be improved, and I think that's the kind of communication that makes a strong partnerships and bring value to both sides. >> NetApp's a very engineering-oriented company, I know them very well living in Silicon Valley, so I give 'em props for that. Question for you is when you hear someone say data-driven storage, or data-driven analytics, what does that mean to you as a partner of a storage supplier? >> For us, it's another way to look at the way we're going to provide service to our customers in the years to come. I think that customers is going to expect more and more services, more and more value, from the service that we're going to provide them, whether it's going to be storage, computer network, or even security. I think that's always a good thing for us to have more tools to build new technology for tomorrow. >> Great, and NetApp's channels and partners, what's the message from NetApp these days to the partners? You're enabling them, obviously you help them make money obviously, but- >> I think the biggest challenge is that we drive the ecosystem in the right direction. If we just stick to the traditional players, we will not be successful, so we have to expand the ecosystem. Going up to different player that are currently probably not in our radar, going up to ISVs that help us to really embrace the data from a value perspective, so our biggest, let's say, message to the channel is don't stay where you currently are, develop the channel with ourself. >> And certainly the relationship with Cisco is blooming for NetApp. >> It is, it's probably since six years, we have now around 8,700 joint customers. We go up the stack, we talk about strategic engagements on a IT SP perspective, so it's going in the right direction. Very important. >> As your competitors get distracted, and do things or doing things, you guys eating their lunch? Is that, (laughs) you smiling? >> Eating their lunch is probably not the word. >> Maybe a little croissant. Breakfast, or was it dinner, what's going on? Are you eating the breakfast, lunch, or dinner of the competitors? >> Currently I would say in French, I think we are jointly engaging on a croissant perspective. (laughing) So we're heading in the right way. So these partnerships are very important. >> It's always a great, fun time. It's been fun watching the storage, been watching NetApp for many years, I remember when they went public back in the dot com A days, they still keep their roots. Great to see you having some great success. Congratulations on a great partnership. It's theCUBE live coverage, here with NetApp and their partner inside theCUBE here at Barcelona at Cisco Live 2018 in Europe. I'm John Furrier. We'll be back with more live coverage after this short break. (digital music)

>> Announcer: Live from Berlin, Germany, it's The Cube covering NetApp Insight 2017. Brought to you by NetApp. >> Welcome back to The Cube's live coverage of NetApp Insight here in Berlin, Germany. I'm your host Rebecca Knight along with my co-host Peter Burris. We are join by Alfred Manhart. He is the Senior Direct Channel and System Integrator Ischemia for NetApp, and Lars Gobel, who is the Head of Strategy and Innovation for DARZ. Thanks so much for joining us. >> Thank you. >> Thank you for the invitation. >> So Manfred, I mean Alfred, before the cameras were rolling, you were talking a little bit about key partnerships and why they are so critical to helping NetApp manage the data and help it flow freely. Can you tell our viewers a little bit more about the partnerships aspect? >> So we have, of course, partnering with NetApp is a base of our strategy. It's not just a initiative. So partnering is key for us. And what we currently see is that the partner landscape has to change. The existing partner that what we are trying to help them to transform to the digital world change the world with data on one side and on the other side we need additional new partner that make the complex customer-oriented offering become reality. This is an example probably DARZ's staff anyhow, but they build up this kind of multiple partnerships to offer the customer-related offering and solution for the end customers. >> Great, great. So tell us how you fit in here Lars? I mean, as important of partnerships. >> So, we are in a situation that IT is getting more and more complex. And we also get into the position that the understand is now clear that not the company can internally are the best at every part. So, for example, Global Innovation Index makes analyzes with the outcome that everywhere where partnerships exists, the innovation is much higher. And today we talk over new business model, we talk over innovation, scalability, flexibility, and for these topics and all the for the new size of environments and also of the challenges the customers have. They need the best for every part of the solutions and we at DARZ, a full IT service provider, try to bring that together. So we offer from co-location housing over private co-hosting up to a public cloud and hyper cloud scenarios complete bandwidth. So we bring together Amazon Web Service and Microsoft Azure to realize one solution for the customer. >> So, every large enterprise is gonna have multiple relationships like the one that they have with you. And while you are helping to bring Amazon and Azure and others under the DARZ umbrella of services, there is gonna have to be something that connects them a little bit more deeply, right? That's probably gonna be data. >> Lars: Yeah. >> So tell us a little bit about that underlying fabric that's going to be required to ensure that data can be rendered in all of these different environments and sourced from all of these different environments according to the needs of business. What do you think? What will NetApp's role in that be? >> That's an interesting one. I think the world from a partnership perspective is even getting more complex, yeah? Instead of making everything as a single one st-- One initial shot, more technical, it's more outcome-based, longer-term based. So if you're not thinking that way, what should be my desired outcome of what-- How my world should look like in a year, in two years from now, you probably choose the wrong partner from the beginning. So this kind of being relevant and being prepared for the future, for all the challenges that are coming up, is very, very important. And data is a short-term issue and of course you have to consider what you want to do with data long term. That is the challenge to balance out the short-term benefits with the long-term objective you have. And thus makes the world more complex. >> So what do you look for in a partner? As you said, you could realize too late you chose the wrong partner from the beginning. But what are sort of the key characteristics and attributes that you want? >> OK, from our perspective we also, we do two things. On the one side, we concentrate on the existing partners and support them on their way to the new world. Yeah? Not all of them will make it. Yeah? And on the other side, we have an acquisition program in place, that we address the partner that are needed for the future and also expand the ecosystem with partners, which are probably we are not even aware of. Talking about coder partners, alliance partners, cloud partners we currently have not in our portfolio. So it's both, driving the existing channel ecosystem to the digital world and acquiring partners that are needed for the future. >> Great. Well Alfred, Lars, thank you so much for coming on the show. It's been great having you. >> Thank you >> Thank you very much for inviting us. >> I'm Rebecca Knight for Peter Burris, we will have more from NetApp Insight just after this. (upbeat music)

>> Announcer: Live from Boston, Massachusetts this is the CUBE covering Spark Summit East 2017 brought to you by Databricks. Now, here are your hosts Dave Vellante and George Gilbert. >> Welcome back to Boston everybody this is the CUBE. We're live here at Spark Summit East in the Hynes Convention Center. This is the CUBE, check out SiliconANGLE.com for all the news of the day. Check out Wikibon.com for all the research. I'm really excited about this session here. Al Essa is here, he's the vice president of analytics and R&D at McGraw-Hill Education. And I'm so excited because we always talk about digital transformations and transformations. We have an example of 150 year old company that has been, I'm sure, through many transformations. We're going to talk about a recent one. Al Essa, welcome to the CUBE, thanks for coming on. >> Thank you, pleasure to be here. >> So you heard my little narrative up front. You, obviously, have not been with the company for 150 years (laughs), you can't talk about all the transformations, but there's certainly one that's recent in the last couple of years, anyway which is digital. We know McGraw Hill is a print publisher, describe your business. >> Yeah, so McGraw Hill Education has been traditionally a print publisher, but beginning with our new CEO, David Levin, he joined the company about two years ago and now we call ourselves a learning science company. So it's no longer print publishing, it's smart digital and by smart digital we mean we're trying to transform education by applying principles of learning science. Basically what that means is we try to understand, how do people learn? And how they can learn better. So there are a number of domains, cognitive science, brain sciences, data science and we begin to try to understand what are the known knowns in these areas and then apply it to education. >> I think Marc Benioff said it first, at least the first I heard he said there were going to be way more Saas companies that come out of non-tech companies than tech companies. We're talking off camera, you're a software company. Describe that in some detail. >> Yeah, so being a software company is new for us, but we've moved pretty quickly. Our core competency has been really expert knowledge about education. We work with educators, subject matter experts, so for over a hundred years, we've created vetted content, assessments, and so on. So we have a great deal of domain expertise in education and now we're taking, sort of the new area of frontiers of knowledge, and cognitive science, brain sciences. How can learners learn better and applying that to software and models and algorithms. >> Okay, and there's a data component to this as well, right? >> So yeah, the way I think about it is we're a smart digital company, but smart digital is fueled by smart data. Data underlies everything that we do. Why? Because in order to strengthen learners, provide them with the optimal pathway, as well as instructors. We believe instructors are at the center of this new transformation. We need to provide immediate, real-time data to students and instructors on, how am I doing? How can I do better? This is the predictive component and then you're telling me, maybe I'm not on the best path. So what's my, "How can I do better?" the optimal path. So all of that is based on data. >> Okay, so that's, I mean, the major reason. Do you do any print anymore? Yes, we still do print, because there's still a huge need for print. So print's not going to go away. >> Right. Okay, I just wanted to clarify that. But what you described is largely a business model change, not largely, it is a business model change. But also the value proposition is changing. You're providing a new service, related, but new incremental value, right? >> Yeah, yeah. So the value proposition has changed, and here again, data is critical. Inquiring minds want to know. Our customers want to know, "All right, we're going to use your technology "and your products and solutions, "show us "rigorously, empirically, that it works." That's the bottom line question. Is it effective? Are the tools, products, solutions, not just ours, but are our products and solutions have a context. Is the instruction effective? Is it effective for everyone? So all that is reliant on data. >> So how much of a course, how much of the content in a course would you prepare? Is it now the entire courseware and you instrument the students interaction with it? And then, essentially you're selling the outcomes, the improved outcomes. >> Yeah, I think that's one way to think about it. Here's another model change, so this is not so much digital versus non-digital, but we've been a closed environment. You buy a textbook from us, all the material, the assessments is McGraw Hill Education. But now a fundamental part of our thinking as a software company is that we have to be an open company. Doesn't mean open as in free, but it's an open ecosystem, so one of the things that we believe in very much is standards. So there's a standard body in education called IMS Global. My boss, Stephen Laster, is on the board of IMS Global. So think of that as, this encompasses everything from different tools working together, interoperability tools, or interoperability standards, data standards for data exchange. So, we will always produce great content, great assessments, we have amazing platform and analytics capability, however, we don't believe all of our customers are going to want to use everything from McGraw Hill. So interoperability standards, data standards is vital to what we're doing. >> Can you explain in some detail this learning science company. Explain how we learn. We were talking off camera about sort of the three-- >> Yeah, so this is just one example. It's well known that memory decays exponentially, meaning when you see some item of knowledge for the first time, unless something happens, it goes into short-term memory and then it evaporates. One of the challenges in education is how can I acquire knowledge and retain knowledge? Now most of the techniques that we all use are not optimal. We cram right before an exam. We highlight things and that creates the illusion that we'll be able to recall it. But it's an illusion. Now, cognitive science and research in cognitive science tells us that there are optimal strategies for acquiring knowledge and recalling it. So three examples of that are effort for recall. If you have to actively recall some item of knowledge, that helps with the stickiness. Another is space practice. Practicing out your recall over multiple sessions. Another one is interleaving. So what we do is, we just recently came out with a product last week called, StudyWise. What we've done is taken those principles, written some algorithms, applies those algorithms into a mobile product. That's going to allow learners to optimize their acquisition and recall of knowledge. >> And you're using Spark to-- >> Yeah, we're using Spark and we're using Databricks. So I think what's important there is not just Spark as a technology, but it's an ecosystem, it's a set of technologies. And it has to be woven together into a workflow. Everything from building the model and algorithm, and those are always first approximations. We do the best we can, in terms of how we think the algorithm should work and then deploy that. So our data science team and learning science team builds the models, designs the models, but our IT team wants to make sure that it's part of a workflow. They don't want to have to deal with a new set of technologies, so essentially pressing the button goes into production and then it doesn't stop there, because as Studywise has gone on the market last week, now we're collecting data real-time as learners are interacting with our products. The results of their interactions is coming in to our research environment and we're analyzing that data, as a way of updating our models and tuning the models. >> So would it be fair to say that it was interesting when you talked about these new ways of learning. If I were to create an analogy to Legacy Enterprise apps, they standardize business transactions and the workflows that went with them. It's like you're picking out the best practices in learning, codifying them into an application. And you've opened it up so other platforms can take some or all and then you're taking live feedback from the models, but not just tuning the existing model, but actually adding learning to the model over time as you get a better sense for how effort of recall works or interleaving works. >> Yeah, I think that's exactly right. I do want to emphasize something, an aspect of what you just said is we believe, and it's not just we believe, the research in learning science shows that we can get the best, most significant learning gains when we place the instructor, the master teacher, at the center of learning. So, doing that, not just in isolation, but what we want to do is create a community of practitioners, master teachers. So think of the healthcare analogy. We have expert physicians, so when we have a new technique or even an old technique, What's working? What's not working? Let's look at the data. What we're also doing is instrumenting our tools so that we can surface these insights to the master practitioners or master teachers. George is trying this technique, that's working or not working, what adjustments do we need to make? So it's not just something has to happen with the learner. Maybe we need to adjust our curriculum. I have to change my teaching practices, my assessments. >> And the incentive for the master practitioners to collaborate is because that's just their nature? >> I think it is. So let's kind of stand back, I think the current paradigm of instruction is lecture mode. I want to impart knowledge, so I'm going to give a lecture. And then assessment is timed tests. In the educational, the jargon for that is summit of assessments, so lecture and tests. That's the dominant paradigm in education. All the research evidence says that doesn't work. (laughs) It doesn't work, but we still do it. >> For how many hundreds of years? >> Yeah. Well, it was okay if we needed to train and educate a handful of people. But now, everyone needs to be educated and it's lifelong learning rate, so that paradigm doesn't work. And the research evidence is overwhelming that it doesn't work. We have to change our paradigm where the new paradigm, and this is again based on research, is differentiated instruction. Different learners are at different stages in their learning and depending on what you need to know, I'm at a different stage. So, we need assessments. Assessments are not punitive, they're not tests. They help us determine what kind of knowledge, what kind of information each learner needs to know. And the instructor helps with the differentiated instruction. >> It's an alignment. >> It's an alignment, yeah. Really to take it to the next stage, the master practitioners, if they are armed with the right data, they can begin to compare. All right, practices this way of teaching for these types of students works well, these are the adjustments that we need to make. >> So, bringing it down to earth with Spark, these models of how to teach, or perhaps how to differentiate the instruction, how to do differentiated assessments, these are the Spark models. >> Yeah, these are the Spark models. So let's kind of stand back and see what's different about traditional analytics or business intelligence and the new analytics enabled by Spark, and so on. First, traditional analytics, the questions that you need to be able to answer are defined beforehand. And then they're implemented in schemas in a data warehouse. In the new order of things, I have questions that I need to ask and they just arise right now. I'm not going to anticipate all the questions that I might want to be able to ask. So, we have to be enable the ability to ask new questions and be able to receive answers immediately. Second, the feedback loop, traditional analytics is a batch mode. Overnight, data warehouse gets updated. Imagine you're flying an airplane, you're the pilot, a new weather system emerges. You can't wait a week or six months to get a report. I have to have corrective course. I have to re-navigate and find a new course. So, the same way, a student encounters difficulty, tell me what I need to do, what course correction do I need to apply? The data has to come in real-time. The models have to run real-time. And if it's at scale, then we have to have parallel processing and then the updates, the round trip, data back to the instructor or the student has to be essentially real-time or near real-time. Spark is one of the technologies that's enabling that. >> The way you got here is kind of interesting. You used to be CIO, got that big Yale brain (laughs) working for you. You're not a developer, I presume, is that right? >> No. >> How did you end up in this role? >> I think it's really a passion for education and I think this is at McGraw Hill. So I'm a first generation college student, I went to public school in Los Angeles. I had a lot of great breaks, I had great teachers who inspired me. So I think first, it's education, but I think we have a major, major problem that we need to solve. So if we look at... So I spent five years with the Minnesota state colleges and university system, most of the colleges, community colleges are open access institutions. So let me just give you a quick statistic. 70% of students who enter community colleges are not prepared in math and english. So seven out of 10 students need remediation. Of the seven out of 10 students who need remediation, only 15% not 5-0, one-five succeed to the next level. This is a national tragedy. >> And that's at the community college level? >> That's at the community college level. We're talking about millions of students who are not making it past the first gate. And they go away thinking they've failed, they incurred debt, their life is now stuck. So this is playing itself out, not to tens of thousands of students, but hundreds of thousands of students annually. So, we've got to solve this problem. I think it's not technology, but reshaping the paradigm of how we think about education. >> It is a national disaster, because often times that's the only affordable route for folks and they are taking on debt, thinking okay, this is a gateway. Al, we have to leave it there. Awesome segment, thanks very much for coming to the CUBE, really appreciate it. >> Thank you very much. >> All right, you're welcome. Keep it right there, my buddy, George and I will be back with our next guest. This is the CUBE, we're live from Boston. Be right back. (techno music) >> Narrator: Since the dawn of the cloud

>>From around the globe. It's the view with digital coverage of Docker con live 2020 brought to you by Docker and its ecosystem partners. >>LeBron. Welcome back to DockerCon 2020 hashtag Docker 20 this is the cube and Dockers coverage of Docker con 20 I'm Sean for you and the Palo Alto studios with our quarantine crew. We've got a great interview segment here in big news around developer workflow code to cloud. We've got Amanda silver corporate vice president, product for developer tools at Microsoft and Scott Johnson, the CEO of Docker. Scott had a great keynote talking about this relationship news has hit about the extension of the Microsoft partnership. So congratulations Amanda. Welcome to the cube. >>Thanks for having me. >>Amanda, tell us a bit about what your role is at Microsoft. You guys are well known in the developer community to develop an ecosystem when even when I was in college going way back, very modern. Now cloud is, is the key code to cloud. That's the theme. Tell us about your role at Microsoft. >>Yeah. So I basically run the product, uh, product design and user research team that works on our developer tools that Microsoft and so that includes the visual studio product as well as visual studio code. Um, that's become pretty popular in the last few years, but it also includes things like the.net runtime and the TypeScript programming language as well as all of our Azure tooling. >>What's your thoughts on the relationship with Docker? I'll show you the news extension of an existing relationship. Microsoft's got a lot of tools. You've got a lot of things you guys are doing, bringing the cloud to every business. Tell us about your thoughts on this relationship with Donker. >>Yeah, well we're very excited about the partnership for sure. Um, you know, our goal is really to make sure that Azure is a fantastic place where all developers can kind of bring their code and they feel welcome. They feel natural. Uh, we really see a unique opportunity to make the experience really great for Docker, for the Docker community by creating more integrated and seamless experience across Docker, desktop windows and visual studio. And we really appreciate how, how Docker is kind of, you know, supported our windows ecosystem to run in Docker as well. >>Scott, this relationship and an extension with Microsoft is really, uh, I think impressive and also notable because Microsoft's got so many, so many tools out there and they have so successful with Azure. You guys have been so successful with your developer community, but this also is reflective of the new Docker. Uh, could you share your thoughts on how this partnership with Microsoft extending the way it is with the growth of the cloud is a reflection of the new Docker? >>Yeah, absolutely. John's great question. One of the things that we've really been focused on since November is fully embracing the ecosystem and all the partnerships and all the possibilities of that ecosystem. And part of that is just reality. That we're a smaller company now and we can't do it all, nor should we do it all. Part of us. The reality that developers love voice and no one's gonna change their minds on choice. And third is just acknowledging that there's so much creativity and so much energy. The four walls of Docker that we'd be building, not the big advantage of that and welcome it and embrace it and provide that as a phenomenal experience part of Alfred's. So this is a great example of that. The sneak partnership we announced last week is a grant to have that and you're going to see many more of uh, partnerships like this going forward that are reflective of exactly this point. >>You've been a visionary on the product side of the interviewed before. Also deploying is more important than ever. That whole workflow, simplifying, it's not getting complex. People want choice, building code, managing code, deploying code. This has been a big focus of yours. Can you just share your thoughts on where Microsoft comes in because they got stuff too. You've got stuff, it all works together. What's your thoughts? >>Right? So it needs to work together, right? Because developers want to focus on their app. They don't want to focus on duct taping and springing together different siloed pools, right? So you can see in the demo and you'll see in, uh, demonstrations later throughout the conference. Just the seamless experience that a developer gets in the document man line inter-operating with visual studio code with the Docker command line and then deploying to Azure and what's what's wonderful about the partnership is that both parties put real engineering effort and design effort into making it a great experience. So a lot of the complexities around the figuration around default settings around uh, security, user management, all of that is abstracted out and taken away from the developer so they can focus on applications and getting those applications deployed to the proudest quickly as possible. Getting their app from code to cloud is the wok word or the or the call to action for this partnership. And we think we really hit it out of the park with the integration that you saw, >>Great validation and a critical part of the workflow. You guys have been part of Amanda, we're living in a time we're doing these remote interviews. The coven crisis has shown the productivity gains of working at home and working in sheltering in place, but also as highlighted, the focus of developers mainly who have also worked at home. They've kind of used to this. Do you see the rigs? I saw her at Microsoft build some amazing rigs from the studio. So these guys streaming their code demos. This is, um, a Cambrin explosion of new kinds of productivity. And yet the world's getting more complex at scale. This is what cloud does. What's your thoughts on this? Cause the tooling is more tools than ever, right? So I still gotta deploy code. It's gotta be more agile. It's gotta be faster. It's gotta be at scale. This is what you guys believe in. What's your thinking on all these tooling and abstraction layers and the end of the day, don't you still got to do their job? >>Yeah, well, absolutely. And now, even more than ever. I mean, I think we've, we've certainly seen over the past few months, uh, uh, a more rapid acceleration of digital transformation. And it's really happened in the past few years. Uh, you know, paper processes are now becoming digit digital processes. All of a sudden, you know, everybody needs to work and learn from home. And so there's just this rapid acceleration to kind of move everything to support our new remote lifestyle. Um, but even more so, you know, we now have remote development teams actually working from home as well in a variety of different kinds of, uh, environments. Whether they're using their own personal machine to connect to their infrastructure or they're using a work issued machine. You know, it's more important than ever that developers are productive, but they are productive as a team. Right? Software is a team sport. >>We all need to be able to work together and to be able to collaborate. And one of the most important aspects of agility for developers is consistency. And, uh, what Docker really enables is, uh, with, with containerization is to make the infrastructure consistent and repeatable so that as developers are moving through the life cycle from their local, local dev desktop and developing on their local desktop to a test environment and to staging and to production, it's really, it's infrastructure of or, or developers as well as operations. And so it's that, that infrastructure that's completely customizable for what the developer's operating system of choices, what their app stack is, all of those dependencies kind of running together. And so that's what really enables developers to be really agile and have a really, really fast iteration cycle but also to have that consistency across all of their development team. And you know, we, we now need to think about things like how are we actually going to bring on interns for the summer, uh, and make sure that they can actually set up their developer boxes in a consistent way that we can actually support them. And things like Docker really helped with that >>As your container instances and a visual studio cloud that you guys have has had great success. Um, there's a mix and match formula here. At the end of the day, developers want to ship the code. What's the message that you guys are sending here with this? Because I think productivity is one, simplification is the other, but as developers on the front lines and they're shipping in real time, this is a big part of the value proposition that you guys are bringing to the table. >>Yeah, I mean the, the core message is that any developer and their code is welcome, uh, and that we really want to support them and power them and increase their velocity and the impact that they can have. Um, and so, you know, having things like the fact that the Docker CLI is natively integrated into the Azure experience, uh, is a really important aspect of making sure that developers are feeling welcome and feeling comfortable. Um, and now that the Docker CLI tools are, that are part of Docker desktop, have access to native commands that work well with Azure container instances. Uh, Azure container instances, if anybody's on familiar with that, uh, is the simplest and fastest way to kind of set up containers and Azure. And, and so we believe that developers have really been looking for a really simple way to kind of get containers on Azure. And now we that really consistent experience across our service services and our tools and visual studio code and visual studio extensions make full use of Docker desktop and the Docker CLI so that they can get that combination of the productivity and the power that they're looking for. And in fact, we've, we've integrated these as a design point since very early on in our partnership when we've been partnering with, with Docker for quite a while. >>Amanda, I want to ask you about the, the, the, the tool chain. We've heard about workflows, making it simpler, bottom line, from a developer standpoint, what's the bottom line for me? What does this mean to me? Uh, every day developer out there? >>Um, I, I mean, I really think it means you know, your productivity on your terms. Um, and so, you know, Microsoft has been a developer company since the very, very beginning with, you know, bill Gates and, and, uh, GW basic. Um, and it's actually similar for Docker, right? They really have a developer first point of view, uh, which certainly speaks to my heart. And so one of the things that we're really trying to do with, with Docker is to make sure that we can create a workflow that's super productive at every stage of the developer experience, no matter which stack they're actually targeting, whether there's targeting node or Python or.net and C-sharp or Java. Uh, we really want to make sure that we have a super simple experience that you can actually initiate all of these commands, create, you know, Docker container images and use the compose Docker compose files. >>Um, and then, you know, just kind of do that consistently as you're deploying it all the way up into your infrastructure in Azure. And the other thing that we really want to make sure is that that even post deployment, you can actually inspect and diagnose these containers and images without having to leave the tool. Um, so we, we also think about the process of writing the code, but also the process of kind of managing the code and remediating issues that might come up in production. And so, you know, we really want you to be able to look at containers up in the Azure. Uh, up that are deployed into Azure and make sure that they're running and healthy and that if there, if something's wrong, that you can actually open up a shell and be in an interactive mode and be able to look at the logs from those containers and even inspect when to see environment variables or other details. >>Yeah, that's awesome. You know, writing code, managing code, and then you've got to deploy, right? So what I've been loving about the, the past generation of agile is deployment's been fast to deploy all the time. Scott, this brings up that the ease of use, but you want to actually leverage automation. This is the trend that you want to get in. You want, you don't want, you want to make it easy to write code, manage code. But during the deployment phase, that's a big innovation. That's the last point. Making that better and stronger. What's your thoughts on simplifying that? >>So that was a big part of this partnership, John, that the Docker in Microsoft embarked on and as you saw from the demo and the keynote, um, all within the man line, the developers able to do in two simple commands, deploy an app, uh, defining compose from the desktop to Azure and there's a whole slew of automation and pre-configured smart defaults or sane defaults that have gone on behind the scenes and that took a lot of hardcore engineering work on part of Docker and Microsoft together to simplify that and make that easy and that, that goes exactly to your point. We just like the simpler you can make it more, you can abstract a way to kind of underlying plumbing and infrastructure. The faster devs can get there. Their application from code to cloud. >>Scott, you've been a product CEO, you've been a product person, a CEO, but you have a product background. You've been involved with the relationship with Microsoft for a long time. What's the state of the market right now? I mean, obviously Microsoft has evolved. Look at just the performance corporate performance. The shift to the cloud has been phenomenal. Now developers getting more empowered, there's more demand for the pressure to put on developers to do more and more, more creativity. So you've seen this evolve, this relationship, what does it mean? >>Yeah, it's honestly a wonderful question, John. And I want to thank Amanda and the entire Microsoft team for being long standing partners with us on this journey. So it's might not be known to everyone on today's, uh, day's event. But Microsoft came to the very first Docker con event, uh, way back in June, 2014 and I had the privilege of, of reading them and welcoming them and they're, they were full on ready to see what all the excitement about Docker was about and really embrace it. And you mentioned kind of openness and Microsoft's growth over that, uh, over time in that dimension. And we think kind of Docker together with Microsoft have really shown what an open developer community can do. And that started back in 2014 and then we embarked on an open source collaboration around the Docker command line of the Docker engine, bringing that Docker engine from Linux and now moving it to windows applications. And so all of a sudden the promise of right ones and use the same primitives, the same formats, the same fan lines, uh, as you can with Linux onto windows applications. We brought that promise to the market and it's been an ongoing journey together with Microsoft of open standards based, developer facing friendliness, ease of use, fast time to deploy. And this, this partnership that we announced yesterday and we highlighted at the keynote is just another example of that ongoing relationship laser like focused on developer productivity and helping teams build great apps. >>Why do you like Azure in the cloud for Docker? Can you share why? >>Well, it's as Amanda has been sharing, it's super focused on what are the needs of developers to help them continue to stay focused on their apps and not have their cognitive load burdened by other aspects of getting their apps to the cloud. And Azure, phenomenal job of simplifying and providing sane defaults out of the box. And as we've been talking about, it's also very open to partner like the one we've announced >>Yesterday and highlighted, you know, but >>Uh, make it just easy for development teams to choose their tools and build their apps and deploy them onto Azure. It's possible. So, uh, it's, it's a phenomenal plan, one for developers and we're very excited and proud of partner with Microsoft on it. >>Amanda, on your side, I see DACA has got millions of developers. You guys got millions of developers even more. How do you see the developers in Microsoft side engaging with Docker desktop and Docker hub? Where does it all fit? >>I think it's a great question. I mean, I mentioned earlier how the Docker context can help individuals and teams kind of work in their environments work. Let me try that over. I mentioned earlier how I, how I see Docker context really improving the way that individuals and teams work with their environments and making sure that they're consistent. But I think this really comes together as we work with Docker desktop and Docker hub. Uh, when developers sign into Docker hub from Docker desktop, everything kind of lights up. And so they can see all of the images in their repositories and they can also see the cloud environments they're running them in. And so, you know, once you sign into the hub, you can see all the contexts that map to the logical environments that they have access to like dev and QA and maybe staging. And another use case that's really important is that, you know, we can access the same integration environment. >>So, so I could have, you know, microservices that I've been working on, but I can also see microservices that my, my teammates and their logs, uh, from the services that they've been working on, which I think is really, really great and certainly helps with, with team productivity. The other thing too is that this also really helps with hybrid cloud deployments, right? Where, you know, you might have some on premises, uh, hosted containers and you might have some that's hosted in a public cloud. And so you can see all of those things, uh, through your Docker hub. >>Well, I got to say I love the code to cloud tagline. I think that's very relevant and, and catchy. Um, and I think, I guess to me what I'm seeing, and I'd love to get your thoughts, Amanda, on this, as you oversee a key part of Microsoft's business that's important for developers, just the vibe and people are amped up right now. I know people are tense and anxiety with the covert 19 crisis, but I think people are generally agreeing that this is going to be a massive inflection point for just more headroom needed for developers to accelerate their value on the front lines. What's your personal take on this and you've seen these ways before, but now in this time, what are you most excited about? What are you optimist about? What's your view on the opportunities? Can you share your thoughts? Because people are going to get back to work or they're working now remotely, but when we go back to hybrid world, they're going to be jamming on projects. >>Yeah, for sure. But I mean, people are jamming on projects right now. And I think that, you know, in a lot of ways, uh, developers are our first responders in, you know, in that they are, developers are always trying to support somebody else, right? We're trying to somebody else's workflow and you know, so we have examples of people who are, uh, creating new remote systems to be able to, uh, schedule meetings in hospitals or the doctors who are actually the first, first responders taking care of patients. But at the end of the day, it's the developer who's actually creating that solution, right? And so we're being called the duty right now. Um, and so we need to make sure that we're actually there to support the needs of our users and that we're, we're basically cranking on code as fast as we can. Uh, and to be able to do that, we have to make sure that every developer is empowered and they can move quickly, but also that they can collaborate freely. And so, uh, I think that, you know, Docker hub Docker kind of helps you ensure that you have that consistency, but you also have that connection to the infrastructure that's hosted by your, your organization. >>I think you nailed that amazing insight. And I think that's, you know, the current situation in the community matters because there's a lot of um, frontline work being done to your point. But then we've got to rebuild. The modernization is happening as well coming out of this. So there's going to be that and there's a lot of comradery going on and massive community involvement. I'm seeing more of, you know, the empathy, but also now there's going to be the building, the creation, the new creation. So Scott, this is going to call for more simplicity and to abstract away the complexities. This is the core issue. >>Well that's exactly right and it is time to build, right? Um, and we're going to build our way out of this. Um, and it is the community that's responding. And so in some sense, Microsoft and Docker are there to support that, that community energy and give them the tools to go. And identify and have an impact as quickly as possible. We have referenced in the keynote, um, completely bottoms up organic adoption of Docker desktop and Docker hub in racing to provide solutions against the COBIT 19 virus. Right? It's a, it's a war against this pandemic that is heavily dependent on applications and data and there's over 200 projects, community projects on Docker hub today where you've got uh, cools and containers and data analysis all in service to the photo at 19 battle that's being fought. And then as you said, John, as we, as we get through this, the other side, there's entire industries that are completely rethinking their approach that were largely offline before that. Now see the imperative and the importance of going online and that tectonic shift nearly overnight of offline to online behavior and commerce and social and go on down the list that requires new application development. And I'm very pleased about this partnership is that together we're giving developers the tools to really take advantage of that opportunity and go and build our way out of it. >>Well, Scott, congratulations on a great extended partnership with Microsoft and the Docker brand. You know, I'm a big fan of from day one. I know you guys have pivoted on a new trajectory which is very community oriented, very open source, very open. So congratulations on that Amanda. Thanks for spending the time to come on. I'll give you the final word. Take a minute to talk about what's new at Microsoft. For the folks that know Microsoft, know they have a developer mindset from day one cloud is exploding code to cloud. What's the update? What's the new narrative? What should people know about Microsoft with developer community? Can you share from some, some, some uh, data for the folks that aren't in the community or might want to join with folks in the community who want to get an update? >>Yeah, it's a, it's a great, great kind of question. I mean, you know, right now I think we are all really focused on making sure that we can empower developers throughout the world and that includes both those who are building solutions for their organizations today. But also I think we're going to end up with a ton of new developers over this next period who are really entering the workforce and uh, and learning to create, you know, digital solutions overall. There's a massive developer shortage across the world. Um, there's so much opportunity for developers to kind of, you know, address a lot of the needs that we're seeing out of organizations again across the world. Um, and so I think it's just a really exciting time to be a developer. Uh, and you know, my, my uh, my only hope is that basically we're, we're building tools that actually enable them to solve problems. >>Awesome insight and thank you so much for your time code to cloud developers are cranking away that the first responders are going to take care of business and then continue to build out the modern applications. And when you have a crisis like this, people cut right through the noise and get right to the tools that matter. So thanks for sharing the Microsoft Docker partnership and the things that you guys are working on together. Thanks for your time. Okay. This is the cubes coverage. We are Docker con 2020 digital is the cube virtual. I'm Sean for bringing all the action. More coverage. Stay with us for more Docker con virtual. After this short break.

>> Narrator: Live from Austin, Texas. It's theCUBE, covering KubeCon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux Foundation, and theCUBE's ecosystem partners. >> Hey, welcome back everyone, we're here live at theCUBE in Austin, Texas for KubeCon 2017, 2nd annual conference of the Kubernetes Conference, I'm John Furrier, here with my co-host, Stu Miniman, Ben Sigelman, who's the CEO of LightStep, welcome to theCUBE. >> Thank you so much. >> So you're also involved in open tracer, all this stuff with service mesh, really instrumental tech work going on right now. >> Mmhmm, yep. >> With this KubernetesCon, I mean Kubernetes has been successful. People are now learning for, the first time in mainstream, but it's really galvanized the community. At many levels, and I haven't seen this much action and so fast, up and down the stack. You know, you got the infrastructure plumbing guys, and you got the app plumbing guys all building really, really fast. What's the state of the union? Give us a peak of what's happening, what's solid, what's foundational? What are the building blocks that are being built on and what's the current task of jobs being worked on projects and what not? >> Yeah, and that's a great question. I was, emerged my hotel room yesterday just to get on the elevator and Kelsey Hightower emerged from his hotel room, turns out two doors down from me, and we're walking to the elevator together, I'm like, "Hey! You know, so, what's your big announcement?" He's so good on stage, he's a brilliant communicator, and he's like, you know, honestly, the big news right now, is that actually there's not that much news from a release standpoint about Kubernetes, which is actually a really big deal. It's gotten to the point where it's feature set is actually appropriate and somewhat stable. And now we finally are at the point where it's, I think, it has a really natural architecture for plugins and extensions and now we can build this entire ecosystem around it, instead of building around something that's a bit of a moving target. I think it's incredible how, it is truly incredible, to see this conference over the last couple of years. >> So Pete's foundational elements are in place. >> Yeah. >> That's his, kind of his... >> Yeah, exactly. And it's incredible to see how much of, not just a commercial ecosystem, but a technology ecosystem, that's built around those primitives, and so I think those really are the right primitives, to democratize the pieces that should be democratized, and to centralize the pieces that should be centralized. So to me, this year is really about going a level up in the stack, and delivering value that's beyond, you know, the container, Kubernetes level, and that's what a lot of the projects that I'm excited about are doing. >> Yeah, so Ben, and that leads right into one of the things that we've been talking about all week here, service meshes. >> Ben: Yeah. >> So, you gave a keynote yesterday, maybe give our audience a little bit about service meshes, servibility, and there's something about a pigeon? >> (laughs) Yeah that was very funny. Just the reference about the pigeon, the first slide in my talk was a picture of a murmuration of starlings, this beautiful cloud of birds moving in harmony, and while I was waxing on about how this represented microservices, an actual bird flew above me on stage. There was a pigeon trapped in this room `(laughter) and so everyone started laughing, I didn't know what was so funny, I'm like... >> Jeez. What a great demo. >> ...like what did I do wrong? Do I have a note on my back or something? And then the hilarious thing is the second slide was actually the operational experience of deploying this sort of microservice technology is actually very difficult, and so it was this slide from Alfred Hitchcock's "The Birds," with these birds attacking this poor child. And so, and the bird is still circling around above me. It was perfect stagecraft, I wish I had tried to do it, it would have been amazing to take credit for arranging an actual live animal as part of my presentation. But in terms of the actual material in the presentation, which may be less entertaining than the bird flying around my head, but the material of the presentation is something I feel very strongly about, and I alluded to this a moment ago, I think that containers are incredibly important, I think Kubernetes is incredibly important, and I am extraordinarily confident that in ten years, they're going to be everywhere. That said, they're not something an application developer really should care that deeply about as part of their job of writing business logic for the service that they are maintaining and developing. That shouldn't be a layer that they care about. And there are a lot of really, really important problems that crop up at the application layer. At Google, the way we addressed this, was by having not a monolithic architecture, but a monolithic software repository where everyone developed the same code base, but one of the things that I thought was interesting was being at Google, if you wanted to deploy an application, even something that just printed out 'Hello, world' or something, it was like a 150 megabyte binary, because there's so much stuff that was crammed in to level 7, user level stuff, and that was right for Google, it's not really the best architecture for a lot of enterprises out there and I think what's so cool about service mesh, is that it's taken a bunch of really, genuinely hard computer science problems, like service discovery, connection, and load balancing, and reconnection, health checks, security and authentication, observability and tracing, these are really hard things to do well, and it's factored them off into a side car that you can run alongside ordinary applications that were not even developed with that in mind and take advantage of these application level, level 7 primitives. We've had people who are trying to build solutions for any number of managerial and monitoring tasks at the container level, where often that stuff is completely obscured. Like by the time you're at the kernel that you can't see any of this stuff. If you're up at level 7 in the service mesh, you have easy access to application level data, which makes everything a lot more elegant and straightforward for developers, so it's like, to me, it's this single point of integration that removes a bunch of hard computer science problems from ordinary application development. >> And so people were stuffing containers basically and trying to overdrive that. Makes total sense architecturally and I want you to take a step back and kind of unpack that a little bit. We didn't get here by accident. We got here through real hard work, I mean people were out there building from open-source large-scale systems. >> Yeah. >> Uber, Lyft, there's a handful of other examples. What was the driver around this, because you're talking about a really elegant architecture that allows for solving a problem for the guys that solve their own problems. Thousands, hundreds of thousands of transactions, services, millions of transactions per second. >> Yup. >> So this was not like "Hey, let's just design a new system!" It was some scar tissue. >> Yeah. >> How does that connect to like, reality now for, whether it's a start-up saying "Hey, you know, we're a couple of years old, we're on AWS, and we're growing, and I want to add more value, but I don't want to relearn machine learning, I want to build on all this stuff and create business value from my enterprise, growing an enterprise. Or, big enterprises, trying to be cloud enabled. So that's, how should someone think about that? And what specifically was the problem that was solved? >> Yes. Well, I'm an obsessive person, I'll admit that. And I'm personally obsessed with performance, and so when I think about this, I actually think about profiling the engineers who are building this stuff. You have developers, let's profile them, like what are they spending their time on? 'Cause that's really a precious resource right now, right? It's like, it's hard to even hire people fast enough, right? So if you think about profiling people, you have folks that are spending a lot of time trying to get their services communicated properly, to authenticate, to observe these systems, in a way that's sane. And so it's only natural you try to factor that out and make that factored out. You try to amortize the cost of solving that problem across your entire organization. And I think that you've seen people who've been at other companies, and want to recreate something like what they had at Google or Facebook or Twitter or what have you, but they want to do it in a way that meshes with their existing systems. I'm actually not surprised that super, super young companies that are starting with the true green field code base, move in this direction. What has been interesting to me, and although I shouldn't say surprising, this is actually very rational, but you also have companies that are much larger, and we, LightStep has, we have customers that are running a mainframe, alongside legacy Java VMs, alongside microservices, and they're all working in concert to the service application requests from end users. And these things need to talk to each other, and I think what's actually really fun for me, Google gets a lot of credit for building things the right way, I don't know if that's accurate for not, but it's really funny 'cause the problem is actually a lot more interesting outside of Google, because you have to integrate with a much larger surface area and the thing that's so exciting to me about a lot of the technologies that are really taking off here, is that they're designed for that kind of heterogeneity, certainly I've talked about service mesh a million times already here, open tracing also exists specifically because of heterogeneity, we didn't need open tracing at Google because everything was perfectly factored, so it was unnecessary. Outside of Google, it's necessary to have a common API to describe transactions as they propagate, because otherwise, you can't make sense of anything that's happening in your application. This sort of heterogeneity has encouraged projects that standardize at the right layer, and I think those are the ones that are proliferating. >> What is service mesh about now? I mean, how would you describe it, I mean, how would you define, in the world of Kubernetes, in the world we're talking about, for someone just getting, tech person, just getting started. What's the hubbub about with service mesh? What is it? >> Well, I mean, I think at the most basic level, it's something that sits in between any two processes that are communicating in your system, and it sits in between them at a layer where you can observe the application itself. Like, you're able to access application levels, security information application level, primitives like, you know, the particular path you're hitting for any HTP requests, something like that. It's something that sits in between at that layer. Because microservices, you know, I've seen Lyft up close 'cause they're also a customer for LightStep, and to see Envoy deployed at their company is really instructive. It's amazing, I mean it's really amazing. They went from having no integration with our product to having 100% integration with our product by flipping a configuration bit to on, you know. Actually it wasn't even on, they could do it by percentage, I mean, they can roll these things out with perfect, perfect precision. And, I mean, it's an incredibly powerful thing to be able to have that kind of leverage over an entire architecture and that didn't require all their developers to redeploy. This system required the service mesh to redeploy, so you make these sorts of changes without touching application CSCD stuff, you can do all these infrastructural level changes independently from application pushes-- >> All right, >> And that's very powerful. >> So, so hold on, I know Stu wants to get a question in, but let's stop there for a second. Compare and contrast what the old way would have been. >> Stu: Yeah. What would it have taken to do this similar concept that full team had met, assuming they had another architecture. >> I've seen, I mean, you know- >> John: Months, weeks, redeploys... >> So, you know, the model that I've seen at Google where would we make changes to software that was linked into every application would go out with the next release, we would make that change in some central place, I'd say 50% of the services would be deployed within a week, 90% within two weeks, but to get to 99% would take over a year, and so the issue is if you need a change that's going to cut across your entire system, it is not feasible to wait for people to redeploy because there are going to be services that are not being maintained by human beings anymore, and no one's about to volunteer for that chore- >> John: It's a nightmare basically. >> Of reintegrating, taking in months of code changes, making sure it still works and deploys. >> Yeah, they're going to quit right there. I mean, no one wants that. >> It's infeasible. >> Yeah, it's not feasible. >> Ben, I wanted you to be able to share a little bit about founding LightStep, you know what's kind of the need in the market, and what you're seeing from your early customers. >> Sure, LightStep is, it has a pretty simple mission. We aim to deliver insights about very complex production software, which is commonplace at this point. Anyone who's building a meaningful business is building meaningful production software, and that means it's complicated. So that's what we want to do. The way that we're doing that with our first product, LightStep XPM, is by delivering root cause analysis for the symptoms that are of most interest to these businesses, regardless of their application or architecture, as I said earlier, we have customers that run mainframes as well as microservices at the same time, multi-cloud, it doesn't matter. We follow transactions across these distributed services and use those to explain behaviors that they're puzzling over and help them with performance analysis and root cause analysis. >> And what's the relationship between the open source projects and... >> That's a great question. It's not a normal open core model. Open tracing is really an API project that's designed to ease integration with any number of vendors, and open tracing is supported by LightStep of course, but also by Jaeger, and CNCF, it's compatible with Zipkin, it's supported by New Relic and Datadog, I'll give a shoutout to some competitors. We're all in this together in the sense that I think we see that we all have a much bigger market as things like open tracing proliferate, and make it easier to actually observe your own system. I would love to compete in the playing field of solutions and not worry so much about integration, so open tracing is an integration project, it's not our core technology. Our core IP is something that's very powerful, that's designed to absorb a lot of information about these distributed systems and deliver value about that. >> And when I look at your website, and see kind of some of your early customers, I mean, jump out, you know, Lyft, Twilio, Digital Ocean, I mean, these are not kind of your typical companies, is it, you know, fully kind of cloud-native, you know, horn of the web, type companies? >> I'm really glad you asked that. No. >> Stu: Yeah. >> I mean, most of our customers at this point are, have actually never seen a full microservice deployment, certainly not at one of customers. It's always a combination of a monolith in the middle and microservices on the outside, but a lot of our customers are more traditional enterprises that we haven't put on our website for logo rights reasons, but they get a lot value out of the solution, I would say even more value in some cases because they're dealing with a greater diversity of technology generations they need to cut across. >> Yeah, I want to go back. You mentioned the time for people these days and you talk about developers and people building, the fight for talent is huge out there. What are you seeing in your customers? Is that something that you help? How's kind of that interaction? >> Yeah absolutely, I mean, I think, Digital Ocean says they're saving, I think 1000 engineer hours a month or something like that on LightStep. It's a huge timesaver for people who are trying to get to the bottom of issues. So it's a labor issue, but also root cause analysis, I mean, every second counts. Seconds cost hundreds of thousands of dollars for some of our customers for any big outage, and so we help people get those, Twilio's addressing the instance 92% faster after using LightStep, so it's a big change to their root cause analysis. >> Yeah, there was a great quote I saw that said, "When something goes wrong, it used to be you knew, now it turned into a murder mystery." >> Yeah. (laughter) >> Tell the story of why did you start the company. Was there an itch you were scratching? You saying, "Hey, you know, I've seen this movie before, I want to get out there, help customers, I mean, I heard, your mission is really straightforward, clean, good positioning. Why start the company? What was the rationale? What was the motivation? >> That's a very easy one for me. I mean, the reason I left Google was not necessarily to start a company per se, it was that I wanted to have as much of an impact on the industry as I could, I wanted to see things, not just make money and siphon cash away from companies, but actually to change the way that software is built. And the first act for us, this product, is a way for us to kind of get into the tendril, get our system deep into the fabric of an application, and from that point, I'd like to see LightStep really change the way people build software. I think people right now, it's almost like everyone's programming an assembly. Like we're all trying to operate this level that's totally inappropriate, and I'd love to see LightStep be a part of this story for making the industry move up the value chain and really focus on building applications, and that's what I want to see us do. >> You know, we've been saying, first, we have a similar mission along our media business, but one of the things we're seeing, we go to all the shows, sometimes it's like, why is theCUBE covering, you know, Node.js, or why are you covering Hadoop in 2010, why are you, because we see it early, we get in early, as I said, we can see the innovation, we like it, but I got to tell you, we've been seeing recently, I've been seeing it specifically, we see a huge renaissance in software development companies. >> Yeah, for sure. >> And my piece is, I want to test this with you because I think this is going to change the culture, certainly in Silicon Valley and around the world. Certainly with open source is exponentially growing, you know, Zemlin puts that stat up pretty clear. All software development models was crafty and built a product you QA and you'd ship it, it either worked or it didn't work, put some art to it, around ownership, and then AdJail derisked that risk, but you can get it to the market quicker, and you listen to the data, you learn from the data, but it kind of took the craft out of it. You know what I'm saying, almost we're coding and we're iterating, we're on a treadmill, which is good. But now, with what we're seeing here, is that you're getting back to extracting away, to your point, all these services you don't need to worry about anymore. I could actually focus all of my attention on the artisan aspect of the solution. Not UX, love UX design, not that kind of art, but something about software art. What's your reaction to that? Do you see that coming? Because if this continues, we're going to have a whole class of software developers just essentially painting software art, if you will. >> Yeah. >> I mean, that potentially is a scenario. Your thoughts. >> Yes, I agree with that scenario being feasible. I think it's probably more than a couple of weeks away, but I'm really excited about it. I think you're right on the money, I think a lot of the changes that we're seeing allow people to operate more independently and that's what motivates the transitions to microservice in the first place, it wasn't just to rewrite everyone's software for fun, it was because we want everyone to be able to be independent of each other and operate in that mode. The thing that I think is exciting about that vision which I would echo is a lot of the primitives that we see in the marketplace right now allow developers to focus on the semantics of application and the requirements of application which is where all of the interesting stuff is, and what we all get excited about. And I think we do see a lot of the, this number of people here right now, that investment as a community in allowing developers to focus on the logic and nothing more is really tremendous and exciting to me. >> How has community changed? I know you believe in community. Community's more important than ever now, in this new model, 'cause there's so much leverage going on with the software. How important is community and how is it changing and how should it evolve to handle all this awesome growth? >> Yeah I do have some thoughts about that. It's definitely important, I mean no one's going to deny that. I think one of the biggest challenges that I think about anyway in this sphere, has to do with, I referred to this earlier, it's important to figure out what problem you're solving with the community aspect of things, like with open tracing we thought really hard about this, like are we going to focus on, like, the bits and bytes and the wire protocols, or on the part that really needs to be standardized. I think community makes sense when standards are appropriate and standard interfaces are appropriate. I'm actually a little bit skeptical of community driven solutions where it's, you're delivering the entire package as a community because it ends up intersecting in ways that are complex I think with business motivations. I think the most successful projects are areas where the community really must collaborate, which usually has something to do with standardization. Those are the areas where I'm most excited. And then you actually literally, I was talking with Ken Goldberg yesterday, and they intentionally carved out areas for vendors to play, because they don't want to kind of meddle in that are. It's actually better not to meddle in that area. It's actually better- >> It's like microservices, you put the vendors over there and you put core commuters over there. Ben Sigelman, thanks for coming on theCUBE, I appreciate it. Congratulations on LightStep and the success and your talks here. Early community exploding, cloud native is not only a movement, it's clear to everyone, cloud and data and software and open source is making it happen, easier, accelerating velocity. It's theCUBE, doing our part, bringing you the data, here in Texas, I'm John Furrier, with Stu Miniman. We're back with more live coverage after this short break. >> Thank you. (techno music)