(upbeat music) >> Hey, everyone. Welcome to theCUBE's coverage of Women in Tech: International Women's Day, 2022. I'm your host, Lisa Martin. Erin Chu joins me next. Life Sciences Lead at AWS Open Data. Erin, welcome to the program. >> Thanks so much for having me, Lisa. Tell me a little bit about you and your role at AWS. >> I would love to. So I am a life sciences lead on the AWS Open Data team, and we are really in the business of democratizing access to data. We believe that if you make high quality, high impact data openly available in the cloud, that people can start innovate, make discoveries and do science faster with those data. So we have a number of specialists with expertise in different domains. Geospatial sciences, climate sustainability, statistical regulatory and then of course myself, the life sciences lead. >> So, you have a really interesting background. You're a veterinarian by training. You have a PhD, you've worked in mobile veterinary clinics, and also in an animal genomic startup, how did you make the change from the clinical side to working for a large international, one of the biggest companies in the world? >> Yeah, I love that question because so much of, I think, anybody's career path is serendipitous and circumstantial, right? But the fact is I was working in a mobile veterinary clinics while I was finishing up a PhD in molecular genomics. And at the same time was reached out to by a professor at Cornell who had started a little dog genomic startup. And he said, "Hey, we need a veterinarian who can talk to people and who understands the genomic side of things?" And I said, "Yeah, I'm your girl." And I came on full time with that startup towards the end of my PhD, signed on after I finished, came on on as their senior veterinary geneticist. Startups a great whirlwind. You end up learning a ton. You have a huge, deep learning curve. You're wearing every possible hat you can. And after a couple years there, I wondered what else I could do. And simply said, where else could I look for work? And how else could I grow? And I decided to try the larger tech world, because I said, this is a toolkit I don't have yet. So I'd like to try and see how I can do it, and here I am. >> And you, I was reading about you that you felt empowered by the notion that I have to trust my instincts. You look at careers in biology, you decided what directions you wanted to take but how did you kind of conjure that feeling of empowerment? >> Yeah, I have to see say I have an incredibly supportive team and in supportive manager, but a lot of it was simply because I've never been afraid to fail. The worst thing that someone can ever say to you is, no or that you didn't do that well. Once you come across that once in your life, it doesn't hurt so bad the second time around. And so, I was hired for a very specific data set that my team was helping to manage. And that does take up a good deal of my time, it still does, but I also had the freedom to say, "Hey, what are the trends in biology? I am an expert in this field. What do I know is coming around the corner? What do I know my researchers need?" And I was entrusted with that, this ability to say, "Hey, these are the decisions I think we should make." And I got to see those outcomes fairly quickly. So, my managers have always put a good deal of trust in me and I don't think I've let them down. >> I'm sure you haven't. Tell me a little bit about some of your mentors or sponsors that have helped guide you along the way and really kind of feel that empowerment that you already had. >> Absolutely. Well, the first and foremost mentor in has been my mother. So, in the spirit of International Women's Day, my mom is actually the first Asian engineer to ever reach executive level. Asian female engineer to ever reach executive level at IBM. And so, I spent my life seeing what my mother could do, and watching her just succeed. And I think very early it clear, she said, "What can't you do?" And that was kind of how I approached my entire life, is what can't I do, and what's the worst thing that will happen. You fail and then you try again. So she is absolutely my first mentor, and a role model to me and hopefully to women everywhere, honestly. I've had some amazing teachers and mentors. My professor who oversaw my PhD, Dr. Paul Soloway. He's currently still at Cornell, really just said, "What decisions do you want to make?" And, "I will support you in the best way I can." And we learned a lot together. I have a professor at Cornell who I still come back. I speak at her alternate careers in veterinary medicine because she just... And she was the one who told me, "Erin, you have a really high buoyancy factor. Don't lose that." And her name is Dr. Carolyn McDaniel. And she has just been such a positive force just saying, "What else could we do?" >> Well, that's- >> And, "Never let your degrees or your training say that this is what you have to do. Think of it as a starting point." >> That's a great point. We often, especially when we're little kids, many of us, you think of these very defined, doctor, lawyer, accountants, nurse instead of having something like you do and being able to go, what else can I do with this? How can I take this education, this information and the interest that I have and parlay it into something that really can kick the door wide open. And to your point, I love how your mom was saying, "What can't you do?" That's a message that everyone needs to hear. And there's an AWS Open Data Sponsorship Program. Talk to me a little bit about that. I'm always interested in sponsorship programs. >> Oh, thanks for asking. So the Open Data Sponsorship Program or the ODP since Open Data Sponsorship Program can be a little mouthful after you say it a few times, but the ODP is a program that AWS sponsors where we will actually cover at the cost of storage transfer and egress of high impact data sets in the cloud. Basically, we know that sometimes the barrier to getting into cloud can be very high for certain providers of gold standard data sets. And when I mean gold standard data sets, I mean like NASA Sentinel-2, or the National Institutes of Health Sequence Read Archive. These are invaluable data sets that are ingested by thousands if not millions of users every day. And what we want to do is lower that barrier to cloud and efficient distribution of those data to zero. So, the program is actually open to anybody. It can be a government entity, it can be a startup, it can be nonprofit. We want to understand more about your data and help you distribute it well in the cloud. >> So this is for any type of organization regardless of industry? >> That's right. >> So, you're really allowing more organizations... One of the things that we say often when we're talking on theCUBE is that every company these days is a data company, or it has to be. Every company has to be a tech company, whether we're talking about your grocery store or AWS, for example. So helping organizations to be able to take that data, understand it, and have those personal conversations that as consumers we expect is critical, but it's challenging for organizations that say, "Well, I came up in retail and now I've got to be a tech company." Talk to me about kind of empowering organizations to be able to use that data, to grow the organization, grow the business, but also to delight customers 'cause of course we are quite picky. >> You're so right. Data is power and it doesn't matter what you are selling or who you are serving. If you have the data about your product. And also to some degree, the data about who your consumers are, you can really tailor an experience. I always tell my colleagues that data is impersonal, right? You can look at bits and bites, numbers, structured columns and rows, but you can funnel data into a truly personal experience as long as you do you it right. And hopefully, when I work with my data providers I ask them, how do you want people to use your data? What are the caveats? How can we make these data easy to work with? But also easy to draw correct insights from. >> Right, that easy to use is critical because as you know the proliferation of data just continues and it will continue. If we think of experiences. I want to go back to your experience. What's been the biggest learning curve that you've had so far? >> Oh my gosh. So, the best part of being at a large company is that you're not in the same room or even like whatever the same slack channel as all of your colleagues, right? Coming from a startup or clinical space where quite literally you are in the same room as everybody 'cause there are less than 60 of you, you could just talk to the person who might be an internal stakeholder. You had that personal relationship, and frankly, like most of the time your views were very aligned. It was sell the product, get to MVP. Moving into larger tech, the steepest curve I had other than becoming very comfortable in the cloud, in all the services that AWS has to offer, were to manage those internal relationships. You have to understand who the stakeholders are. There typically many, many of them for any given project or a company that we're serving. And you have to make sure that you're all aligned internally, make sure that everyone gets what they need and that we reach that end to ultimately serve the customer together. >> Yeah, that communication and collaboration is key. And that's something that we've seen over the last two years, is how dependent we've all become on collaboration tools. But it is a different type of relationship. You're right. Going from a clinic where you're all in the same room or the same location to everyone being distributed globally. Relationship management there is key. It's one of my favorite things about being in tech is that, I think it's such a great community. It's a small community, and I think there's so there's so much opportunity there. If you're a good person, you manage those relationships and you learn how to work with different types of people. You'll always be successful. Talk to me about what you would say, if someone's saying, "Erin, I need some advice. I want to change industries or I want to take this background that I have, and use it in a different industry." What are the three pieces of advice that you would share? >> Oh, absolutely. So, the first thing that I always talk with my... I have quite a few colleagues who have approached me from all different parts of my life. And they've said, "Erin, how did you make the change? And how can I make a change?" And the first thing I say is let's look at your resume and define what your translational skills are. That is so big, right? It doesn't matter what you think you're a specialist in, it's how generalizable are those specialty skills and how can you show that to somebody who's looking at your resume. Let's call it a nontraditional resume. And the second is don't hesitate to ask question. Go for the informational interview. People want to tell you about how they've gotten to where they are and how you might be able to get there too. And so I say, get on LinkedIn and start asking questions. If one person says yes, and you get no responses I call that a success. Don't be afraid of not getting a response, that's okay. And the last thing, and I think this is the most important thing is to hold onto the things that make you happy no matter where you are in your life. It's important to realize you are more than your job. It is important to remember what makes you happy and try to hang on those. I am a gym rat. I admit that I am a gym rat. I'm in the gym five days a week. I have a horse. I go out to see him at least two or three a days. I know it's typical veterinarian, right? You just collect niches until you run out of things you want to pay for. But those are things that have been constant through 20 plus years of being in the workforce. And they've been what kept me going. Let's revise that in ten years. >> So critical because as we all know tech can be all consuming. It will take everything if you let it. So being able to have... We always talk about the balance. Well, the balance is hard. It's definitely a way to scale, right? It's going back and forth, but being able to hold onto the things that actually make you who you are, I think make you better at your job, probably more productive and happier. >> I agree. I totally agree. >> Another thing that you believe, which I love, this is an important message is that, if you look at a job, I like how you said earlier, the worst they can say is no. You have nothing to lose. And it's really true. As scary as that is same thing with raising your hand as you say, and I agree with you about that. Ask a question. It's not a dumb question. I guarantee you. If you're in a room or you're on a Zoom or even in a slack channel. A fair number of people probably have the same question. Be the one to raise your hand and say, "Maybe I missed this. Can you clarify this?" But you also think that you don't have to meet all the job requirements. If you see something that says, five years experience in this or 10 years in that or must have this degree or that degree, you're saying you don't have to meet all that criteria. >> I agree. Yeah, that's another big thing is that, I'll literally talk to people who are like, "Well, Erin, this job application, look at all these requirements and I can't fill these requirements." I'm like, "First of all, who says you can't?" Just because you don't have a certification, what has your work thus far done to reflect that? Yeah, you can meet that requirement, even if you don't have an official certification. But two, like what's the worst thing that happens. You don't get a call back from a recruiter. That's okay. I have so many friends who are afraid of failure, and I tell them, just fail once doesn't hurt. It never hurts as much as you think it's going to hurt. And then you just keep going. >> You keep going and you learn. But you've also brought up a great point about those transfer growth skills or those soft skills that are so important. Communication skills, for example. Relationship building skills that may not be in that written job description. So you may not think about actually there's a tremendous amount of importance that these skills have. That having this kind of breadth of background. I think is always so interesting we think about thought diversity, and if we're talking about women in tech. We know that the number of women in technical roles is is still pretty low, but there's so much data that shows that companies that have even 30% females on their executive staff are more performant and more profitable. So that thought diversity is important, but we need more women to be able to feel that empowerment I think that you feel. >> Yes. >> So when you think of International Women's Day with the theme of breaking the bias, what does that mean to you and where do you feel we are in terms of breaking the bias? >> Yeah, so it's interesting, I was just on a working group with some of my colleagues from our larger organization at AWS. And we were talking about, what are different kinds of bias and what our strategies to go ahead and combat them. The fact is we are all making progress and it has to be in one step at a time. I don't think that if we snapped our fingers, things would just go away. You have to take one step at a time. I also come at it from a data perspective, right? I'm a data person. I work with data. And like I said, data is, or data are, if you want to be correct. Data are impersonal, right? They are just statistics, their numbers, but you can use data to suddenly say, "Hey, where are the biases? And how can we fix them?" So I'm going to give you a great example. So my mother, again, a wonderful woman, a super amazing role model to me. She was diagnosed with breast cancer last year. And she being a smart lady, actually looked online. She went online on Google Scholar and PubMed Central. And she said, "May, look..." May is my little nickname. She goes, "Look at these numbers." She said, "My prognosis is terrible. Look at these numbers, how can you say that this is worth it. That chemotherapy is worth it." And I looked at it and I said, "Mom, I hate to break this to you. But this is a retrospective study of several thousand women from the Bavarian cancer registry." And you might guess I am not a Bavarian origin. I had a chat with her and I said, "Mom, let's look at the data. What are the data? And how can you take away stuff from this with the caveat that you may very well not have the same genetic background as some of the women or most of the women in this registry." There are biases. We know when we look at population sequencing, when we look at the people who are sequenced, the people who put in medical survey information. There are not representations of certain ethnicities of certain sexes, of certain parts of the country. One of the things I really want to do in the next three years is say, how can we support people who are trying to increase representation and research so that every single woman gets the right care and can feel like they are themselves represented in what we call precision medicine or personalized care. >> Absolutely. >> That's a long story. >> It was a great story. >> That was a long answer to answer your question. >> You talked about how your mom was a great inspiration to you and it sounds like you've been quite a great inspiration to her as well. Was a delight talking with you, Erin. Congratulations on your success on being able to be one of those people that is helping to break the bias. We appreciate your time. >> Thanks, Lisa. >> My pleasure. For Erin Chu, I'm Lisa Martin. You're watching Women in Tech: International Women's Day, 2022. (upbeat music)

>> Announcer: Coverage DataWorks Summit Europe 2017 brought to you by Hortonworks. >> Welcome back everyone. Live here in Munich, Germany for theCUBE'S special presentation of Hortonworks Hadoop Summit now called DataWorks 2017. I'm John Furrier, my co-host Dave Vellante, our next guest is Shaun Connolly, Vice President of Corporate Strategy, Chief Strategy Officer. Shaun great to see you again. >> Thanks for having me guys. Always a pleasure. >> Super exciting. Obviously we always pontificating on the status of Hadoop and Hadoop is dead, long live Hadoop, but runs in demise is greatly over-exaggerated, but reality is is that no major shifts in the trends other than the fact that the amplification with AI and machine learning has upleveled the narrative to mainstream around data, big data has been written on on gen one on Hadoop, DevOps, culture, open-source. Starting with Hadoop you guys certainly have been way out in front of all the trends. How you guys have been rolling out the products. But it's now with IoT and AI as that sizzle, the future self driving cars, smart cities, you're starting to really see demand for comprehensive solutions that involve data-centric thinking. Okay, said one. Two, open-source continues to dominate MuleSoft went public, you guys went public years ago, Cloudera filed their S-1. A crop of public companies that are open-source, haven't seen that since Red Hat. >> Exactly. 99 is when Red Hat went public. >> Data-centric, big megatrend with open-source powering it, you couldn't be happier for the stars lining up. >> Yeah, well we definitely placed our bets on that. We went public in 2014 and it's nice to see that graduating class of Taal and MuleSoft, Cloudera coming out. That just I think helps socializes movement that enterprise open-source, whether it's for on-prem or powering cloud solutions pushed out to the edge, and technologies that are relevant in IoT. That's the wave. We had a panel earlier today where Dahl Jeppe from Centric of British Gas, was talking about his ... The digitization of energy and virtual power plant notions. He can't achieve that without open-source powering and fueling that. >> And the thing about it is is just kind of ... For me personally being my age in this generation of computer industry since I was 19, to see the open-source go mainstream the way it is, is even gets better every time, but it really is the thousandth flower bloom strategy. Throwing the seeds out there of innovation. I want to ask you as a strategy question, you guys from a performance standpoint, I would say kind of got hammered in the public market. Cloudera's valuation privately is 4.1 billion, you guys are close to 700 million. Certainly Cloudera's going to get a haircut looks like. The public market is based on the multiples from Dave and I's intro, but there's so much value being created. Where's the value for you guys as you look at the horizon? You're talking about white spaces that are really developing with use cases that are creating value. The practitioners in the field creating value, real value for customers. >> So you covered some of the trends, but I'll translate em into how the customers are deploying. Cloud computing and IoT are somewhat related. One is a centralization, the other is decentralization, so it actually calls for a connected data architecture as we refer to it. We're working with a variety of IoT-related use cases. Coca-Cola, East Japan spoke at Tokyo Summit about beverage replenishment analytics. Getting vending machine analytics from vending machines even on Mount Fuji. And optimizing their flow-through of inventory in just-in-time delivery. That's an IoT-related to run on Azure. It's a cloud-related story and it's a big data analytics story that's actually driving better margins for the business and actually better revenues cuz they're getting the inventory where it needs to be so people can buy it. Those are really interesting use cases that we're seeing being deployed and it's at this convergence of IoT cloud and big data. Ultimately that leads to AI, but I think that's what we're seeing the rise of. >> Can you help us understand that sort of value chain. You've got the edge, you got the cloud, you need something in-between, you're calling it connected data platform. How do you guys participate in that value chain? >> When we went public our primary workhorse platform was Hortonworks Data Platform. We had first class cloud services with Azure HDInsight and Hortonworks Data Cloud for AWS, curated cloud services pay-as-you-go, and Hortonworks DataFlow, I call as our connective tissue, it manages all of your data motion, it's a data logistics platform, it's like FedEx for data delivery. It goes all the way out to the edge. There's a little component called Minify, mini and ify, which does secure intelligent analytics at the edge and transmission. These smart manufacturing lines, you're gathering the data, you're doing analytics on the manufacturing lines, and then you're bringing the historical stuff into the data center where you can do historical analytics across manufacturing lines. Those are the use cases that are connect the data archives-- >> Dave: A subset of that data comes back, right? >> A subset of the data, yep. The key events of that data it may not be full of-- >> 10%, half, 90%? >> It depends if you have operational events that you want to store, sometimes you may want to bring full fidelity of that data so you can do ... As you manufacture stuff and when it got deployed and you're seeing issues in the field, like Western Digital Hard Drives, that failure's in the field, they want that data full fidelity to connect the data architecture and analytics around that data. You need to ... One of the terms I use is in the new world, you need to play it where it lies. If it's out at the edge, you need to play it there. If it makes a stop in the cloud, you need to play it there. If it comes into the data center, you also need to play it there. >> So a couple years ago, you and I were doing a panel at our Big Data NYC event and I used the term "profitless prosperity," I got the hairy eyeball from you, but nonetheless, we talked about you guys as a steward of the industry, you have to invest in open-source projects. And it's expensive. I mean HDFS itself, YARN, Tez, you guys lead a lot of those initiatives. >> Shaun: With the community, yeah, but we-- >> With the community yeah, but you provided contributions and co-leadership let's say. You're there at the front of the pack. How do we project it forward without making forward-looking statements, but how does this industry become a cashflow positive industry? >> Public companies since end of 2014, the markets turned beginning at 2016 towards, prior to that high growth with some losses was palatable, losses were not palatable. That his us, Splunk, Tableau most of the IT sector. That's just the nature of the public markets. As more public open-source, data-driven companies will come in I think it will better educate the market of the value. There's only so much I can do to control the stock price. What I can from a business perspective is hit key measures from a path to profitability. The end of Q4 2016, we hit what we call the just-to-even or breakeven, which is a stepping stone. On our earnings call at the end of 2016 we ended with 185 million in revenue for the year. Only five years into this journey, so that's a hard revenue growth pace and we basically stated in Q3 or Q4 of 17, we will hit operating cashflow neutrality. So we are operating business-- >> John: But you guys also hit a 100 million at record pace too, I believe. >> Yeah, in four years. So revenue is one thing, but operating margins, like if you look at our margins on our subscription business for instance, we've got 84% margin on that. It's a really nice margin business. We can make that better margins, but that's a software margin. >> You know what's ironic, we were talking about Red Hat off camera. Here's Red Hat kicking butt, really hitting all cylinders, three billion dollars in bookings, one would think, okay hey I can maybe project forth some of these open-source companies. Maybe the flip side of this, oh wow we want it now. To your point, the market kind of flipped, but you would think that Red Hat is an indicator of how an open-source model can work. >> By the way Red Hat went public in 99, so it was a different trajectory, like you know I charted their trajectory out. Oracle's trajectory was different. They didn't even in inflation adjusted dollars they didn't hit a 100 million in four years, I think it was seven or eight years or what have you. Salesforce did it in five. So these SaaS models and these subscription models and the cloud services, which is an area that's near and dear to my heart. >> John: Goes faster. >> You get multiple revenue streams across different products. We're a multi-products cloud service company. Not just a single platform. >> So we were actually teasing this out on our-- >> And that's how you grow the business, and that's how Red Hat did it. >> Well I want to get your thoughts on this while we're just kind of ripping live here because Dave and I were talking on our intro segment about the business model and how there's some camouflage out there, at least from my standpoint. One of the main areas that I was kind of pointing at and trying to poke at and want to get your reaction to is in the classic enterprise go-to-market, you have sales force expansive, you guys pay handsomely for that today. Incubating that market, getting the profitability for it is a good thing, but there's also channels, VARs, ISVs, and so on. You guys have an open-source channel that kind of not as a VAR or an ISV, these are entrepreneurs and or businesses themselves. There's got to be a monetization shift there for you guys in the subscription business certainly. When you look at these partners, they're co-developing, they're in open-source, you can almost see the dots connecting. Is this new ecosystem, there's always been an ecosystem, but now that you have kind of a monetization inherently in a pure open distribution model. >> It forces you to collaborate. IBM was on stage talking about our system certified on the Power Systems. Many may look at IBM as competitive, we view them as a partner. Amazon, some may view them as a competitor with us, they've been a great partner in our for AWS. So it forces you to think about how do you collaborate around deeply engineered systems and value and we get great revenue streams that are pulled through that they can sell into the market to their ecosystems. >> How do you vision monetizing the partners? Let's just say Dave and I start this epic idea and we create some connective tissue with your orchestrator called the Data Platform you have and we start making some serious bang. We make a billion dollars. Do you get paid on that if it's open-source? I mean would we be more subscriptions? I'm trying to see how the tide comes in, whose boats float on the rising tide of the innovation in these white spaces. >> Platform thinking is you provide the platform. You provide the platform for 10x value that rides atop that platform. That's how the model works. So if you're riding atop the platform, I expect you and that ecosystem to drive at least 10x above and beyond what I would make as a platform provider in that space. >> So you expect some contributions? >> That's how it works. You need a thousand flowers to be running on the platform. >> You saw that with VMware. They hit 10x and ultimately got to 15 or 16, 17x. >> Shaun: Exactly. >> I think they don't talk about it anymore. I think it's probably trading the other way. >> You know my days at JBoss Red Hat it was somewhere between 15 to 20x. That was the value that was created on top of the platforms. >> What about the ... I want to ask you about the forking of the Hadoop distros. I mean there was a time when everybody was announcing Hadoop distros. John Furrier announced SiliconANGLE was announcing Hadoop distro. So we saw consolidation, and then you guys announced the ODP, then the ODPI initiative, but there seems to be a bit of a forking in Hadoop distros. Is that a fair statement? Unfair? >> I think if you look at how the Linux market played out. You have clearly Red Hat, you had Conicho Ubuntu, you had SUSE. You're always going to have curated platforms for different purposes. We have a strong opinion and a strong focus in the area of IoT, fast analytic data from the edge, and a centralized platform with HDP in the cloud and on-prem. Others in the market Cloudera is running sort of a different play where they're curating different elements and investing in different elements. Doesn't make either one bad or good, we are just going after the markets slightly differently. The other point I'll make there is in 2014 if you looked at the then chart diagrams, there was a lot of overlap. Now if you draw the areas of focus, there's a lot of white space that we're going after that they aren't going after, and they're going after other places and other new vendors are going after others. With the market dynamics of IoT, cloud and AI, you're going to see folks chase the market opportunities. >> Is that dispersity not a problem for customers now or is it challenging? >> There has to be a core level of interoperability and that's one of the reasons why we're collaborating with folks in the ODPI, as an example. There's still when it comes to some of the core components, there has to be a level of predictability, because if you're an ISV riding atop, you're slowed down by death by infinite certification and choices. So ultimately it has to come down to just a much more sane approach to what you can rely on. >> When you guys announced ODP, then ODPI, the extension, Mike Olson wrote a blog saying it's not necessary, people came out against it. Now we're three years in looking back. Was he right or not? >> I think ODPI take away this year, there's more than we can do above and beyond the Hadoop platform. It's expanded to include SQL and other things recently, so there's been some movement on this spec, but frankly you talk to John Mertic at ODPI, you talk to SAS and others, I think we want to be a bit more aggressive in the areas that we go after and try and drive there from a standardization perspective. >> We had Wei Wang on earlier-- >> Shaun: There's more we can do and there's more we should do. >> We had Wei on with Microsoft at our Big Data SV event a couple weeks ago. Talk about the Microsoft relationship with you guys. It seems to be doing very well. Comments on that. >> Microsoft was one of the two companies we chose to partner with early on, so and 2011, 2012 Microsoft and Teradata were the two. Microsoft was how do I democratize and make this technology easy for people. That's manifest itself as Azure Cloud Service, Azure HDInsight-- >> Which is growing like crazy. >> Which is globally deployed and we just had another update. It's fundamentally changed our engineering and delivering model. This latest release was a cloud first delivery model, so one of the things that we're proud of is the interactive SQL and the LLAP technology that's in HDP, that went out through Azure HDInsight what works data cloud first. Then it certified in HDP 2.6 and it went power at the same time. It's that cadence of delivery and cloud first delivery model. We couldn't do it without a partnership with Microsoft. I think we've really learned what it takes-- >> If you look at Microsoft at that time. I remember interviewing you on theCUBE. Microsoft was trading something like $26 a share at that time, around their low point. Now the stock is performing really well. Stockinnetel very cloud oriented-- >> Shaun: They're very open-source. >> They're very open-source and friendly they've been donating a lot to the OCP, to the data center piece. Extremely different Microsoft, so you slipped into that beautiful spot, reacted on that growth. >> I think as one of the stalwarts of enterprise software providers, I think they've done a really great job of bending the curve towards cloud and still having a mixed portfolio, but in sending a field, and sending a channel, and selling cloud and growing that revenue stream, that's nontrivial, that's hard. >> They know the enterprise sales motions too. I want to ask you how that's going over all within Hortonworks. What are some of the conversations that you're involved in with customers today? Again we were saying in our opening segment, it's on YouTube if you're not watching, but the customers is the forcing function right now. They're really putting the pressure one the suppliers, you're one of them, to get tight, reduce friction, lower costs of ownership, get into the cloud, flywheel. And so you see a lot-- >> I'll throw in another aspect some of the more late majority adopters traditionally, over and over right here by 2025 they want to power down the data center and have more things running in the public cloud, if not most everything. That's another eight years or what have you, so it's still a journey, but this journey to making that an imperative because of the operational, because of the agility, because of better predictability, ease of use. That's fundamental. >> As you get into the connected tissue, I love that example, with Kubernetes containers, you've got developers, a big open-source participant and you got all the stuff you have, you just start to see some coalescing around the cloud native. How do you guys look at that conversation? >> I view container platforms, whether they're container services that are running one on cloud or what have you, as the new lightweight rail that everything will ride atop. The cloud currently plays a key role in that, I think that's going to be the defacto way. In particularly if you go cloud first models, particularly for delivery. You need that packaging notion and you need the agility of updates that that's going to provide. I think Red Hat as a partner has been doing great things on hardening that, making it secure. There's others in the ecosystem as well as the cloud providers. All three cloud providers actually are investing in it. >> John: So it's good for your business? >> It removes friction of deployment ... And I ride atop that new rail. It can't get here soon enough from my perspective. >> So I want to ask about clouds. You were talking about the Microsoft shift, personally I think Microsoft realized holy cow, we could actaully make a lot of money if we're selling hardware services. We can make more money if we're selling the full stack. It was sort of an epiphany and so Amazon seems to be doing the same thing. You mentioned earlier you know Amazon is a great partner, even though a lot of people look at them as a competitor, it seems like Amazon, Azure etc., they're building out their own big data stack and offering it as a service. People say that's a threat to you guys, is it a threat or is it a tailwind, is it it is what it is? >> This is why I bring up industry-wide we always have waves of centralization, decentralization. They're playing out simultaneously right now with cloud and IoT. The fact of the matter is that you're going to have multiple clouds on-prem data and data at the edge. That's the problem I am looking to facilitate and solve. I don't view them as competitors, I view them as partners because we need to collaborate because there's a value chain of the flow of the data and some of it's going to be running through and on those platforms. >> The cloud's not going to solve the edge problem. Too expensive. It's just physics. >> So I think that's where things need to go. I think that's why we talk about this notion of connected data. I don't talk hybrid cloud computing, that's for compute. I talk about how do you connect to your data, how do you know where your data is and are you getting the right value out of the data by playing it where it lies. >> I think IoT has been a great sweet trend for the big data industry. It really accelerates the value proposition of the cloud too because now you have a connected network, you can have your cake and eat it too. Central and distributed. >> There's different dynamics in the US versus Europe, as an example. US definitely we're seeing a cloud adoption that's independent of IoT. Here in Europe, I would argue the smart mobility initiatives, the smart manufacturing initiatives, and the connected grid initiatives are bringing cloud in, so it's IoT and cloud and that's opening up the cloud opportunity here. >> Interesting. So on a prospects for Hortonworks cashflow positive Q4 you guys have made a public statement, any other thoughts you want to share. >> Just continue to grow the business, focus on these customer use cases, get them to talk about them at things like DataWorks Summit, and then the more the merrier, the more data-oriented open-source driven companies that can graduate in the public markets, I think is awesome. I think it will just help the industry. >> Operating in the open, with full transparency-- >> Shaun: On the business and the code. (laughter) >> Welcome to the party baby. This is theCUBE here at DataWorks 2017 in Munich, Germany. Live coverage, I'm John Furrier with Dave Vellante. Stay with us. More great coverage coming after this short break. (upbeat music)