>>Welcome back to the cubes. Continuous live coverage of AWS reinvent 2021 live from Las Vegas. It's I'm Lisa Martin. And it's so great to say that we are doing with AWS and its massive ecosystem of partners. One of the most important hybrid tech events of the year. We've two sets over a hundred guests to remote studios, lots going on. I've got an alumni back with me and a new guest. Please. Welcome back. Sabina. Jo said the GM of technology partners at AWS and Neil Fowler joins her is the GM of micro-focus AMC. And you're going to tell me what AMC stands for >>Application modernization and >>Connectivity. I love it. Awesome guys. It's great. It's great to see you again in person. Thank you for having us. It's great to have the buzz. I know it's gonna be a little bit hard to hear, but great to have. AWS has done a phenomenal job of getting everyone in here safely. I want to give them kudos to that. So being to talk to me with, it's been a while since I've seen you in person, but talk to me about your current role at AWS. What's going on? >>Yeah, so I'm the general manager for technology partnerships globally out of the Americas. We also help partners out of EMEA and APAC grow in the Americas. And one of the great examples of a successful partnership is micro-focus with their solutions across application modernization security, database services, mainframes. >>And so from your perspective, through your lens, how do you think they're performing as a partner? Yes. >>So, um, first of all, kudos to Neil and the entire micro-focus team. They have done a great job leaning in with a cloud first strategy with SAS solutions on AWS and these solutions help customers across application modernization, application, delivery, security, cyber resiliency, database services, and also it performance management. And we've been working with them now for a few years. And in fact, today we have actually 400 customer wins together regulations and then also eight digit annual recurring revenue. They have six active listings in marketplace and all of this is really helping customers move their workloads and modernize their workloads into AWS. >>We've seen that such an acceleration nail in the digital transformation cloud adoption. The pandemic has really been a forcing function for that. There are some silver linings, but talk to me about some of the things that you've seen at micro-focus the last 20 months or so. And how have you helped those 400 customers, you know, getting to that big ARR, how are you helping them with that acceleration? >>Well, I think as you're saying that there's lots of changes in the last 12 to 18 months, some of it brought on by the pandemic and the change in business in business to having to respond, deliver solutions more quickly to the market, as well as remote working. So optimizing and the economic environment of costs, but being there to be more dynamic, it really has caused businesses to have to do something different than just to be able to survive and serve their customers better. That was a >>Big thing that we saw in the very beginning. It was not survival mode. And then of course it wasn't too long when we started seeing those survivors really start to thrive. And you started seeing who were going to be the winners of tomorrow. Cause the thing is every company, these days is a data company. If it's not, it's going to be passed up by competitor, that's right there in the rear view mirror. >>For sure. And so we've got, you know, organizations, so running mainframes, you know, older applications, legacy applications, modernization, where are most industries in terms of adopting that, the mindset, first of all, that they need to change? Well, I think across the whole industry, I mean, it doesn't matter whether it's retail. I mean, if you think about airlines with when the, when the pandemic hit business went down to, unless they've got that elastic nature of flashy to respond to it, but everyone had to bring in new services, new offerings very quickly. So the ability to be able to innovate in their environments and bring more solutions to their customers in a really fast way, you know, they couldn't just sit there and work with what they had. They had to move forward just to be able to stay in the business, but also be able to reduce the costs out of what they're trying to do. So running and transforming at the same time. >>Absolutely. And so how can organizations integrate existing core applications with new technologies to really be able to thrive in today's dynamic market? >>We look at modernization overall. We think of it in sort of three different ways with application process and infrastructure. So with a move to cloud, that's the infrastructure modernization they've immediately got far more access to more scalable dynamic elastic, compute resources, as well as all the technology platforms they have around. And then if you look at the application size and that's where the Microfocus platform comes in, we can help customers actually move those applications forward in terms of making them available through API APIs, maybe as a journey to microservices and cloud native. But once that core business logic and that data is available, it can be integrated into artificial intelligence machine learning and actually rained out the whole solution. So the final part of that from the process modernization, if you, as they're developing these applications with new tools, new ranges, in terms of where they can deploy on the AWS platform, they can automate the build deployment and operations so that all those existing applications and they were running on to contemporary platform with full access to the technologies that were available. >>That's fantastic and so necessary for businesses in any industry. So can you talk about some of the different business units of micro-focus? Are there any ones in particular that you want to call out? >>Yeah, so we work with them across all of their business units, but some of them that come to my mind is of course, Neil and team are doing a great job with application modernization and connectivity, really helping customers modernize the applications. And as customers are modernizing the applications, their cyber resiliency business unit is helping customers secure those applications. And then they also have their it operations management bridge product listed in marketplace. And then just since September are verdict a business unit launch Vertica accelerator on AWS. So I think they have a very holistic story to help customers >>On AWS. Talk to me a little bit, Neil, about cyber resiliency. We have seen such a dramatic change in cybersecurity in the threat landscape the last 20 months. I think I saw a stat recently that ransomware was up almost 11 X in the first half of 2021. Every, every day that companies had had a company, that data is gotta be secure. It's no longer a nice to have. That is a core requirement. How are you helping customers achieve that cyber? >>Well, the thing is, I mean, as you say, across the whole spectrum from cyber, from, from the identity access management through data encryption, through data protection, it's not, it's not a nice to actually say it's not a nice to have Kate take capability. You really have to have an integrated solution to be able to manage access control it, and also generating the events in terms of being able to, if anyone tries to get into the systems and log it because, you know, before, by the time you've discovered something it's too late, so you really need a combined solution for multi-factor authentication to really take it to that next level. >>Absolutely. Right. Once you've detected it, it's too late. And I mean, with ransomware as a service, cyber criminals are getting so much more sophisticated and also more brazen. There's so much money in it that the security front is, is I think even more interesting now than it's ever been. Talk to me about some joint customers and how you've helped them together with AWS with micro-focus achieve some of those key outcomes that you were talking about earlier. Well, I think >>Obviously with AWS as a platform has quite over a technology solutions going in, what we often find with our customers is a lots of, um, they're coming from an existing on-prem solution. So they need that hybrid model. So as part of taking that forward, been able to have that integrated solution that allows them to work both on-prem and as part of the cloud, most of it all being hooked up now, even that from even down to the, uh, as they're developing the applications now to do static code analysis, to help those applications be more secure with things like 40 pound demand, as well as integrating internet security platform for multifactor. So I think as you know, it's a combination of Brunel to bridge between all the different technologies, but have one single view of mail to protect the whole real estate, multiple layers for both external and internal threat. So that's, that's the other thing you also need to take into and can be able to protect all, all layers multi-layered approach. >>Absolutely. But you're right. The internal threats is something that we don't talk about as much, but that is obviously a substantial problem for organizations and most, if not any industries to be, to talk to me a little bit about, let's kind of get into the, the responsibilities that you have a little bit more in there. You've got responsibility for multiple solutions segments at AWS. You told me before we went live, you have 50 meetings this week. My goodness. And since day one, it taught all good. It's fun, fun. It is. Talk to me about AWS approach to partnering. What does it look like? What are some of the things that you think are really critical components? Yeah. >>So as you may have heard, we always start with the, at Amazon and AWS, we start with the customer. We work backwards when we are relaunching our products, our programs or services, you really go and ask the customers, what do you want us to develop? Where do you want us to focus the resources? It takes a lot of discipline to do that, but it's something that where we really want to walk the talk and we use the same approach with our partners when we started to work with micro-focus, we really kind of want to make sure that what we are working on together is what customers want, because we firmly believe that once you lay that foundation of that solution, you can scale your business a lot more quicker. Your story is a lot more simple and the customers are going to find a lot of value in what you are doing together. So it's really all about the customer for us. It is >>Absolutely critical, right? That's the whole point that the whole reason that we're here now, talk to me a little bit about maybe some cultural alignment with AWS, that customer first customer obsession. It sounds like at Microfocus, very similar. >>Absolutely. I mean, the way that we always think about how we're building our products, it's all around customer centric innovation. So that aspect of trying to make sure that we can solve what the business, understanding what the customers are trying to do to then help develop, to deliver solutions that meet that and that combination of a, the way that we look at it from that infrastructure modernization and the range of technologies that are available and that relentless focus on making customer successful is so key. But we have to make sure that that collaboration works together to make sure that the solutions align and we're helping customers get there together >>In your customer conversations. I imagine they've changed quite a bit during the pandemic with so many things being escalated to the C-suite to the board. How have your, how important is that cultural alignment between AWS and Microfocus from your customer's perspective? Is it something that comes up fairly often? Well, >>It's, it's a, I think it, when you actually get a mismatching culture, it's more obvious. So don't think that necessarily people are looking for it to say, I need organizations, but if you're not thinking the same way, you're not behaving the same way and actually partnering. I think that partnering part of it is really important because you're both working together to come up with that desired outcome. So I think it's more, more obvious when it isn't a good match as opposed to what it looking for that particular site. But I think that's a really key aspect in the sense of working together to help that customer be successful. >>Right? That's a great point that you bring up, but it's probably more obvious when it isn't working than when it's beautifully aligned, falling into place and really focused on that customer. So what are some of the things that attendees can, can feel and see and learn at the micro-focus booth at this year's reinvent nail, >>As well as obviously the key Roundup application modernization, where we're looking at the mainframe modernization on the site, we've got the full range of the Microsoft booth in terms of cyber resilience, as well as our, uh, item, my top, uh, it operations management or ADM portfolios. So we've got a lot of technologies which we can learn about in the booth interactive as well as all by experts to understand how we can do all these things and work together as part of the AWS platform to be able to deliver those solutions. >>Excellent. I'm sure there will be plethora of, of knowledge shared at the booth there. Last question, Neil, for you, talk to me about the vision going forward with the partnership. What are some of the things that you're looking forward to as we end 2021 and go into hopefully what is a better year, 2022? >>You know, one of the key things, you know, especially range, no one might, my passionate areas is helping our customers really look in terms of building the platform of the future. We can help solve their customer the problems today, but we're really trying to create that innovation platform to going through. So again, that combination of the technologies that we can bring to help our customers and the breadth and the investment that AWS continue making in the platform, those two combinations really helps us help our customers, not just solve today's problems, who really move into the forward to be the platform for innovation for the next decade. >>And that's really critical that that future ready state that is so undefined most of the time, I mean, none of us saw the pandemic coming, all right. That was a complete shock, but to be able to partner together, to help your customers really set up the foundation to be innovative as things happen that we can't even predict is really critical. So congratulations on your 400 customer wins your eight digit ARR. That's fantastic. Yes, we thank you so much for joining us on the queue, talking about the Microfocus AWS partnership and all of the successes that you guys have had. Great job. And I hope that you have cough drops and a lot of water this week. Sabina. I hope you do too guys. Thanks for joining me. Pleasure for my is I'm Lisa Martin. You're watching the cube, the global leader in live tech coverage.

>> Narrator: Live from New York City, it's theCUBE. Covering IBM Data Science For All. Brought to you by IBM. >> Welcome back to New York here on theCUBE. Along with Dave Vellante, I'm John Walls. We're Data Science For All, IBM's two day event, and we'll be here all day long wrapping up again with that panel discussion from four to five here Eastern Time, so be sure to stick around all day here on theCUBE. Joining us now is Vikram Murali, who is a program director at IBM, and Vikram thank for joining us here on theCUBE. Good to see you. >> Good to see you too. Thanks for having me. >> You bet. So, among your primary responsibilities, The Data Science Experience. So first off, if you would, share with our viewers a little bit about that. You know, the primary mission. You've had two fairly significant announcements. Updates, if you will, here over the past month or so, so share some information about that too if you would. >> Sure, so my team, we build The Data Science Experience, and our goal is for us to enable data scientist, in their path, to gain insights into data using data science techniques, mission learning, the latest and greatest open source especially, and be able to do collaboration with fellow data scientist, with data engineers, business analyst, and it's all about freedom. Giving freedom to data scientist to pick the tool of their choice, and program and code in the language of their choice. So that's the mission of Data Science Experience, when we started this. The two releases, that you mentioned, that we had in the last 45 days. There was one in September and then there was one on October 30th. Both of these releases are very significant in the mission learning space especially. We now support Scikit-Learn, XGBoost, TensorFlow libraries in Data Science Experience. We have deep integration with Horton Data Platform, which is keymark of our partnership with Hortonworks. Something that we announced back in the summer, and this last release of Data Science Experience, two days back, specifically can do authentication with Technotes with Hadoop. So now our Hadoop customers, our Horton Data Platform customers, can leverage all the goodies that we have in Data Science Experience. It's more deeply integrated with our Hadoop based environments. >> A lot of people ask me, "Okay, when IBM announces a product like Data Science Experience... You know, IBM has a lot of products in its portfolio. Are they just sort of cobbling together? You know? So exulting older products, and putting a skin on them? Or are they developing them from scratch?" How can you help us understand that? >> That's a great question, and I hear that a lot from our customers as well. Data Science Experience started off as a design first methodology. And what I mean by that is we are using IBM design to lead the charge here along with the product and development. And we are actually talking to customers, to data scientist, to data engineers, to enterprises, and we are trying to find out what problems they have in data science today and how we can best address them. So it's not about taking older products and just re-skinning them, but Data Science Experience, for example, it started of as a brand new product: completely new slate with completely new code. Now, IBM has done data science and mission learning for a very long time. We have a lot of assets like SPSS Modeler and Stats, and digital optimization. And we are re-investing in those products, and we are investing in such a way, and doing product research in such a way, not to make the old fit with the new, but in a way where it fits into the realm of collaboration. How can data scientist leverage our existing products with open source, and how we can do collaboration. So it's not just re-skinning, but it's building ground up. >> So this is really important because you say architecturally it's built from the ground up. Because, you know, given enough time and enough money, you know, smart people, you can make anything work. So the reason why this is important is you mentioned, for instance, TensorFlow. You know that down the road there's going to be some other tooling, some other open source project that's going to take hold, and your customers are going to say, "I want that." You've got to then integrate that, or you have to choose whether or not to. If it's a super heavy lift, you might not be able to do it, or do it in time to hit the market. If you architected your system to be able to accommodate that. Future proof is the term everybody uses, so have you done? How have you done that? I'm sure API's are involved, but maybe you could add some color. >> Sure. So we are and our Data Science Experience and mission learning... It is a microservices based architecture, so we are completely dockerized, and we use Kubernetes under the covers for container dockerstration. And all these are tools that are used in The Valley, across different companies, and also in products across IBM as well. So some of these legacy products that you mentioned, we are actually using some of these newer methodologies to re-architect them, and we are dockerizing them, and the microservice architecture actually helps us address issues that we have today as well as be open to development and taking newer methodologies and frameworks into consideration that may not exist today. So the microservices architecture, for example, TensorFlow is something that you brought in. So we can just pin up a docker container just for TensorFlow and attach it to our existing Data Science Experience, and it just works. Same thing with other frameworks like XGBoost, and Kross, and Scikit-Learn, all these are frameworks and libraries that are coming up in open source within the last, I would say, a year, two years, three years timeframe. Previously, integrating them into our product would have been a nightmare. We would have had to re-architect our product every time something came, but now with the microservice architecture it is very easy for us to continue with those. >> We were just talking to Daniel Hernandez a little bit about the Hortonworks relationship at high level. One of the things that I've... I mean, I've been following Hortonworks since day one when Yahoo kind of spun them out. And know those guys pretty well. And they always make a big deal out of when they do partnerships, it's deep engineering integration. And so they're very proud of that, so I want to come on to test that a little bit. Can you share with our audience the kind of integrations you've done? What you've brought to the table? What Hortonworks brought to the table? >> Yes, so Data Science Experience today can work side by side with Horton Data Platform, HDP. And we could have actually made that work about two, three months back, but, as part of our partnership that was announced back in June, we set up drawing engineering teams. We have multiple touch points every day. We call it co-development, and they have put resources in. We have put resources in, and today, especially with the release that came out on October 30th, Data Science Experience can authenticate using secure notes. That I previously mentioned, and that was a direct example of our partnership with Hortonworks. So that is phase one. Phase two and phase three is going to be deeper integration, so we are planning on making Data Science Experience and a body management pact. And so a Hortonworks customer, if you have HDP already installed, you don't have to install DSX separately. It's going to be a management pack. You just spin it up. And the third phase is going to be... We're going to be using YARN for resource management. YARN is very good a resource management. And for infrastructure as a service for data scientist, we can actually delegate that work to YARN. So, Hortonworks, they are putting resources into YARN, doubling down actually. And they are making changes to YARN where it will act as the resource manager not only for the Hadoop and Spark workloads, but also for Data Science Experience workloads. So that is the level of deep engineering that we are engaged with Hortonworks. >> YARN stands for yet another resource negotiator. There you go for... >> John: Thank you. >> The trivia of the day. (laughing) Okay, so... But of course, Hortonworks are big on committers. And obviously a big committer to YARN. Probably wouldn't have YARN without Hortonworks. So you mentioned that's kind of what they're bringing to the table, and you guys primarily are focused on the integration as well as some other IBM IP? >> That is true as well as the notes piece that I mentioned. We have a notes commenter. We have multiple notes commenters on our side, and that helps us as well. So all the notes is part of the HDP package. We need knowledge on our side to work with Hortonworks developers to make sure that we are contributing and making end roads into Data Science Experience. That way the integration becomes a lot more easier. And from an IBM IP perspective... So Data Science Experience already comes with a lot of packages and libraries that are open source, but IBM research has worked on a lot of these libraries. I'll give you a few examples: Brunel and PixieDust is something that our developers love. These are visualization libraries that were actually cooked up by IBM research and the open sourced. And these are prepackaged into Data Science Experience, so there is IBM IP involved and there are a lot of algorithms, mission learning algorithms, that we put in there. So that comes right out of the package. >> And you guys, the development teams, are really both in The Valley? Is that right? Or are you really distributed around the world? >> Yeah, so we are. The Data Science Experience development team is in North America between The Valley and Toronto. The Hortonworks team, they are situated about eight miles from where we are in The Valley, so there's a lot of synergy. We work very closely with them, and that's what we see in the product. >> I mean, what impact does that have? Is it... You know, you hear today, "Oh, yeah. We're a virtual organization. We have people all over the world: Eastern Europe, Brazil." How much of an impact is that? To have people so physically proximate? >> I think it has major impact. I mean IBM is a global organization, so we do have teams around the world, and we work very well. With the invent of IP telephoning, and screen-shares, and so on, yes we work. But it really helps being in the same timezone, especially working with a partner just eight miles or ten miles a way. We have a lot of interaction with them and that really helps. >> Dave: Yeah. Body language? >> Yeah. >> Yeah. You talked about problems. You talked about issues. You know, customers. What are they now? Before it was like, "First off, I want to get more data." Now they've got more data. Is it figuring out what to do with it? Finding it? Having it available? Having it accessible? Making sense of it? I mean what's the barrier right now? >> The barrier, I think for data scientist... The number one barrier continues to be data. There's a lot of data out there. Lot of data being generated, and the data is dirty. It's not clean. So number one problem that data scientist have is how do I get to clean data, and how do I access data. There are so many data repositories, data lakes, and data swamps out there. Data scientist, they don't want to be in the business of finding out how do I access data. They want to have instant access to data, and-- >> Well if you would let me interrupt you. >> Yeah? >> You say it's dirty. Give me an example. >> So it's not structured data, so data scientist-- >> John: So unstructured versus structured? >> Unstructured versus structured. And if you look at all the social media feeds that are being generated, the amount of data that is being generated, it's all unstructured data. So we need to clean up the data, and the algorithms need structured data or data in a particular format. And data scientist don't want to spend too much time in cleaning up that data. And access to data, as I mentioned. And that's where Data Science Experience comes in. Out of the box we have so many connectors available. It's very easy for customers to bring in their own connectors as well, and you have instant access to data. And as part of our partnership with Hortonworks, you don't have to bring data into Data Science Experience. The data is becoming so big. You want to leave it where it is. Instead, push analytics down to where it is. And you can do that. We can connect to remote Spark. We can push analytics down through remote Spark. All of that is possible today with Data Science Experience. The second thing that I hear from data scientist is all the open source libraries. Every day there's a new one. It's a boon and a bane as well, and the problem with that is the open source community is very vibrant, and there a lot of data science competitions, mission learning competitions that are helping move this community forward. And it's a good thing. The bad thing is data scientist like to work in silos on their laptop. How do you, from an enterprise perspective... How do you take that, and how do you move it? Scale it to an enterprise level? And that's where Data Science Experience comes in because now we provide all the tools. The tools of your choice: open source or proprietary. You have it in here, and you can easily collaborate. You can do all the work that you need with open source packages, and libraries, bring your own, and as well as collaborate with other data scientist in the enterprise. >> So, you're talking about dirty data. I mean, with Hadoop and no schema on, right? We kind of knew this problem was coming. So technology sort of got us into this problem. Can technology help us get out of it? I mean, from an architectural standpoint. When you think about dirty data, can you architect things in to help? >> Yes. So, if you look at the mission learning pipeline, the pipeline starts with ingesting data and then cleansing or cleaning that data. And then you go into creating a model, training, picking a classifier, and so on. So we have tools built into Data Science Experience, and we're working on tools, that will be coming up and down our roadmap, which will help data scientist do that themselves. I mean, they don't have to be really in depth coders or developers to do that. Python is very powerful. You can do a lot of data wrangling in Python itself, so we are enabling data scientist to do that within the platform, within Data Science Experience. >> If I look at sort of the demographics of the development teams. We were talking about Hortonworks and you guys collaborating. What are they like? I mean people picture IBM, you know like this 100 plus year old company. What's the persona of the developers in your team? >> The persona? I would say we have a very young, agile development team, and by that I mean... So we've had six releases this year in Data Science Experience. Just for the on premises side of the product, and the cloud side of the product it's got huge delivery. We have releases coming out faster than we can code. And it's not just re-architecting it every time, but it's about adding features, giving features that our customers are asking for, and not making them wait for three months, six months, one year. So our releases are becoming a lot more frequent, and customers are loving it. And that is, in part, because of the team. The team is able to evolve. We are very agile, and we have an awesome team. That's all. It's an amazing team. >> But six releases in... >> Yes. We had immediate release in April, and since then we've had about five revisions of the release where we add lot more features to our existing releases. A lot more packages, libraries, functionality, and so on. >> So you know what monster you're creating now don't you? I mean, you know? (laughing) >> I know, we are setting expectation. >> You still have two months left in 2017. >> We do. >> We do not make frame release cycles. >> They are not, and that's the advantage of the microservices architecture. I mean, when you upgrade, a customer upgrades, right? They don't have to bring that entire system down to upgrade. You can target one particular part, one particular microservice. You componentize it, and just upgrade that particular microservice. It's become very simple, so... >> Well some of those microservices aren't so micro. >> Vikram: Yeah. Not. Yeah, so it's a balance. >> You're growing, but yeah. >> It's a balance you have to keep. Making sure that you componentize it in such a way that when you're doing an upgrade, it effects just one small piece of it, and you don't have to take everything down. >> Dave: Right. >> But, yeah, I agree with you. >> Well, it's been a busy year for you. To say the least, and I'm sure 2017-2018 is not going to slow down. So continue success. >> Vikram: Thank you. >> Wish you well with that. Vikram, thanks for being with us here on theCUBE. >> Thank you. Thanks for having me. >> You bet. >> Back with Data Science For All. Here in New York City, IBM. Coming up here on theCUBE right after this. >> Cameraman: You guys are clear. >> John: All right. That was great.