(slow upbeat music) >> Good afternoon and welcome back to Sin City. We're here at AWS reInvent with wall-to-wall coverage on theCUBE. My name is Savannah Peterson, joined with Dave Vellante, and very excited to have two exciting guests from Qlik and AARP with us. Molly and Samir, thank you so much for being here. Welcome to the show. >> Thank you for having us. >> Thank you for having us. >> How's it been so far for you, Molly? >> It's been a great show so far. We've got a big booth presence out here. We've had a lot of people coming by, doing demo stations and just really, really coming to the voice of the customer, so we've really enjoyed the event. >> Ah, love a good VOC conversation myself. How about for you, Samir? >> Oh, it's been great meeting a lot of product folks, meeting a lot of other people, trying to do similar things that we're doing, getting confirmation we're doing the right thing, and learning new things. And obviously, you know, here with Molly, it's been a highlight of my experience. >> What's the best thing you learned from your peers, this week? >> You know, some of the things, that we're all talking about, is how do we get data in the right place at the right time? And, you know, that's something that people are now starting to think about. >> Very hot topic. >> You know, doing it, and then not only getting it to the right place, but taking insights and taking action on it as it's getting there. So those are the conversations that are getting around, in the circle I've been hanging around with. >> You hearing the same thing at the booth or? >> Yeah, absolutely. >> And how are you guys responding? >> Well, I think, as a company, and the shifts in the market, people are really trying to determine what workloads belong in which Cloud, what belongs on-prem? And so talking about those realtime transformations, the integration points, the core systems they're coming from, and really how to unlock that data, is just really powerful and meaningful. So that's been a pretty consistent theme throughout the conference, and a lot of conversations that we have on a regular basis. >> I believe that, Molly, let's stick with you for a second. Just in case the audience isn't familiar, tell us a little more about Qlik. >> Yeah, so Qlik is a robust, end-to-end data pipeline. Starting with really looking at all of your source systems whether it's mainframe, SAP, relational database, kind of name your flavor as it's related to sources. Getting those sources over into the target landing spot whether it be Amazon, or other cloud players, or even if you're, if you're managing hybrid workloads. So that's kind of one piece of the end-to-end platform. And then the second piece is really having all that data, analytics ready, coming right through that real-time data pipeline, and really being able to use the data, to monetize the data, to make sense of the data. And then Qlik really does all that data preparation work underneath the visualization layer, which is where all the work happens. And then you get to see the output of that through the visualization of Qlik, which is, you know, the dashboards, the things that our people, people are used to seeing. >> I love that! So at AARP, what are you using Qlik for? What sort of dashboards are you pulling together? >> So when we started our journey to AWS, we knew that, you know, we're going to have our applications, they're distributed in the Cloud, but again, how do we get the data there, in the right place at the right time? So, as members are, taking action, they're calling into the call center, using our website, using our mobile apps. We want to want it to be able to take that information stream it, so we use Qlik, to take those changes when they happen as they happen, be able to stream it to Kafka and then push that data out to the applications that need it in the time that they needed it. So, instead of waiting for a batch job to happen overnight, we're able to now push this data in real time. And by doing that, we're able to personalize the engagement for our members. So if you come in, we know what you're doing, we can personalize the value that we put in front of you, and just make that engagement a lot more engaging for you. >> Yeah. >> And in the channel that you choose to want to come in with, right? Rather than a channel that we are trying to push to you. >> Everyone wants that personalized experience as we discussed, I love AARP, I've done a lot of work with AARP, I look forward to being a member, but in case the audience isn't familiar, you have the largest membership database of any company on Earth that I'm aware of. How many members does AARP have? >> We have nearly 38 million members, and 66,000 volunteers, and 2300 employees across every state in the United States. >> It's a perfect use case for Qlik, right? 'Cause you've been around for a while. You've got data in the million different places. You're trying to get, you've got a mainframe, right? You know, I hear Amazon's trying to put all the mainframes in the Cloud, but I'm guessing the business case isn't there for you. But you want the data that's coming out of that mainframe to be part of that data pipeline, right? So can you paint a picture, of how, what Molly was describing about the data pipeline, how that fits with AARP? >> Yeah, it's actually, it was a perfect use case. And you know, when we engaged with Qlik, what we wanted to be able to do is take that data in the mainframe, and get it distributed into the Cloud, accurately, securely, and make sure that we can track the lineage, and be able to say, hey, application A only needs name and address, application B needs, name, address, and payment. So we were able to do all of that within a couple of weeks, right? And getting that data out there, knowing that it's going to the right place, knowing it's secure, and knowing it's accurate, regardless of the application it goes to, we don't have to worry about seeking data across different applications. Now we know that there's a source of truth, and everything is done through the pipeline, and it's controlled in a way that, we can measure everything that's going through, how it's going through, and how it's being used by the applications, that are consuming it? >> So you've got the providence and the lineage of that data and that's what Qlik ensures, is that right? Is that your role or is that a partner role, combined? >> No, yes, that's absolutely Qlik's role. So for our new offering, Qlik Cloud data integration, it's a comprehensive solution, delivered as a service, delivers real time, automates, transformations, catalog and lineage, all extremely important. And in the case of Samir and AARP, they're trying to unlock the most valuable assets of their data in SAP and mainframe. And surprisingly, sometimes most valuable data in an organization is the hardest to actually get access to. >> Sure. >> So be, you know, just statistically, 70% of Fortune 500 companies still rely on mainframe. So when you think about that, and even when Samir and I are talking about it. >> That's a lot. >> Yeah. >> And that's a lot of scale, that's a lot of data. >> It's a lot of data. >> Yeah. >> So, you know, mainframe isn't a thing of the past. Companies are still relying on it. People have been saying that for years but when we're talking about getting the complex data out of there to really make something meaningful for AARP, we're really proud of the results, and the opportunity that we've been able to provide to really improve the member experience. And how people are able to consume AARP, and all the different offerings that they have? Kind of like you mentioned Savannah, and the way that you go about it. >> Well, it's also the high risk data. High value data, high risk data. You don't want to mess with it. You want to make sure that you've got that catalog to be able to say, okay, this is what we did with that data, this is where it came from. And then you essentially publish to other tools, analytic tools in the Cloud. Can you paint a picture of how that extends to the Cloud? >> Sure, so there's a couple of different things that we do with it. So once we get the data, into our streaming apps, we can publish it over to like our website. We can publish it to the call center, to mobile apps, to our data warehouse, where we can run analytics and AI on it. And then obviously a lot of our journeys, we use a journey orchestration tool, and we've built a CDP, a customer data platform, to get those insights in there, to drive, you know, personalization and experience. >> I'm smiling as you're talking, Samir, because I'm thinking of all the personalized experiences that my mother has with AARP, and it is so fun to learn about the technology that's serving that to her. >> Exactly. >> This segment actually becoming a bit more personal for me than I expected for a couple of reasons. So this is great. Molly, Qlik has been a part of the AWS ecosystem since the get go. How have things changed over the years? >> Yeah, so Qlik still remains the enterprise integration tool of choice for AWS especially- >> Let's call that a casual and just brag. >> Yeah. >> Because that's awesome. That's great, congratulations on that. >> Thank you for SAP and mainframe. So the relationship continues to evolve but we've been part of the ecosystem from since inception. So we look at, how we continue to evolve the partnership? And honestly, a lot of our customers landing spot is AWS. So the partnership evolves really on two fronts. One with Amazon itself, in a partnership lane, and two, with our customers, and what we're doing with them, and how we're able to really optimize what that looks like? And then secondly, earlier this year we announced an offering Amazon and Qlik, called Qlik Ramp, where we can come in and do, a half day architecture deep dive, look at SAP mainframe, and how they get to the Amazon landing spots, whether it's S3, Redshift, or EMR? So we got a lot of different things kind of going on in the Amazon ecosystem, whether it's customer forward and first, and how can we maximize the relationship spend et cetera, with Amazon. And then also how can we deliver, you know, kind of a shorter time to value throughout that process with something like a Qlik ramp, because we want to qualify, and solve customers needs, as equally as we want to you know, say when we're not the right fit. >> So data is a complicated- >> Love that honesty and transparency. >> Data is a complicated situation for most companies, right? And there's a lack of resource, lack of talent. There's hyper specialization. And you were just talking about the evolution of the Cloud and the relationship. How does automation fit into the equation? Are you able to automate a lot of that data integration through the pipeline? >> Yeah. >> Is it was a, what's your journey look like there? Were you resistant to that at first? 'Cause you got to trust the data. Take us through that. >> Yeah, so the first thing, we wanted to make sure is security right? We've got a lot of data, we're going to make sure privacy- >> Very personal data too. >> Exactly. And privacy and security is number one. So we want to make sure anything that we're doing with the data is secure, and it's not given out anywhere. In terms of automation, so what we've been able to do is being able to take these changes, and you know, in technology, the one thing you can guarantee is it's going to break. Network's going to go down, or a server goes down, a database goes down, and that's the only guarantee we have. And by using the product that we have today, we're able to take those outages, and minimize them because there's retry processes, there's ways of going back and saying, hey, I've missed this much data. How do we bring it back in? You don't want data to get out of sync because that causes downstream problems. >> Yeah. >> So all of that is done through the product, right? We don't have to worry about it. You know, we get notifications, but it's not like, oh, I've got to pay someone at two o'clock in the morning because the network's gone down and how's the data sync going to come back up, when it comes back up? All of that's done for us. >> Yeah, and just to add to that, automation, is a key component. I mean, the data engineering teams definitely see the value of automation and how we're able to deliver that. So, improving the experience but also the overall landscape of the environment is critical. >> Yeah, we've seen the stats, data scientists, data pro spend, you know, 80% of their time wrangling data, 20% of their time. >> Data preparation. >> You know extracting value from it. So. >> Yeah, it's so sad. It's such a waste of human capital, and you're obviously relieving that, and letting folks do their job more efficiently. >> The thing is too, you know, as I'm somebody who's love data you dive into the data, you get really excited then after a while you're like, Ugh! >> I'm still here. >> I'm slogging through this data. Taking a bath in it. >> But I think. >> I want to get to the insights. >> I think that world's changing a little bit. >> Yes, definitely. >> So as we're starting to get data that's coming through it's got high fidelity, and richness, right? So in the old days we'd put in a database, normalize it, and then, you know we'd go and do our magic, and hopefully, you know something comes out, and the least of frustration, you just spoke about. Well now, because it's moving in real time, and we can send the data to areas in the way we want it, and add automation, and machine learning on top of that, so that, now it becomes a commodity to massage that data into the in the format that you want it. Then you can concentrate on the value work, right? Which is really where people should be spending the time, rather than, oh, I've got to manipulate the data, make sure it's done in a consistent way, and then make sure it's compliant and done, the same way every single time. >> It may be too early to, you know quantify the business impact, but have you seen, for example, you know, what I was describing creates data silos. 'Cause nobody's going to use the data if it's not trusted. So what happens is it goes to a silo, they put a brick wall around it, and then, you know, they do their thing with it. They trust it for that one use case and then they don't share it. Has that begun to change as you've seen more integration that's automated and augmented? >> Absolutely. I mean, you know, if you're bringing in data and you're showing that it's consistent, and this is where governance and compliance comes in, right? So as long as you have a data catalog, you can make sure that this data's coming through with the lineage that you said is going to, here's the source, here's the target, here's who gets what they only need rather than giving them everything. And by being able to document that, in a way, that's automated rather than somebody going in, and running a report, it's key. Because that's where the trust comes in, rather than, oh, Samir has to go in and manipulate this stream so that, you know, Molly can get the reports she wants. Instead, hey, it's all going in there, the reports are coming out, they're audited, and that's where the trust factor comes. >> And that enables scale. >> Yeah. >> Cloud confidence and scale. Big topics of the show this week. >> Yep. >> It's been the whole thing. Molly, what's next for Qlik? >> Yeah, Qliks on a big journey. So we've released a lot of things most recently, Qlik Cloud data integration as a service, but we're just continuing to grow from a customer base, from a capabilities perspective. We also recently just became HIPAA compliant and went through some other services. >> Congratulations, that is not an easy process. >> Thank you, thank you. >> Yeah. >> And so for us it's really just about expanding and having, that same level of fidelity of the data, and really just getting all of that pushed out to the market so everybody really sees the full value of Qlik, and that we can make your data Qlik. And just for a minute, back to your earlier point. >> Beautiful pun drop there, Molly. Just going to see that. >> Thank you Savannah. >> Yeah. >> But back to your earlier point, just about the time that people are spending, when you're able to automate, and you're getting data delivered in real time, and operational systems are able to see that. 'Cause you're trying to create the least amount of disruption you can, right? 'Cause that's a critical part of the business. When you start to automate and relieve that burden then people have time to spend time on the real things. >> Right. >> Future forward, prescriptive analytics, machine learning, not data preparation, solving problems, fixing soft gaps. >> Staring a spreadsheet, yeah. >> Right? It's actually the full end-to-end pipeline. And so that's really where I feel like the power is unleashed. And as more sources and targets come to light, right? They're all over the showroom floor, so we don't have to mention any of 'em by name, but it's just continuing, to move into that world to have more SaaS integrations. And to be able to serve the customer, and meet them exactly where they're at, at the place that they want to be. And for Samir, and what we did in the transformation there, unlocking that data for mainframe and SAP, getting it into Qlik Cloud, has been a huge business driver for them. And so, because of partners like AWS and Samir and AARP, we're constantly evolving. And really trying to listen to the voice of the customer, to become better for all of you. >> Excellent. >> Love that community first attitude. Very clear that you both have it, both AARP and Qlik with that attitude. We have a new challenge this year to reInvent on theCUBE, little prompt here. >> Okay. >> We're going to put 30 seconds on the clock, although I'm not super crazy about watching the clock. So, feel comfortable with whatever however much time you need. >> Whatever works. >> Yeah, yeah, yeah, yeah, whatever works. But we're looking for equivocally, your Instagram reel, your hot take, your thought leadership, sizzle, with the key theme from this year's show. Molly, your smile is platinum and perfect. So I'm going to start with you. I feel like you've got this. >> Okay, great. >> Yeah. >> Just the closing statement is what you're looking for. >> Sure, yeah, sexy little sound bite. What do you, what's going to be your big takeaway from your experience here in Vegas this week? >> Yeah, so the experience at Vegas this week has been great but I think it's more than just the experience at Vegas, it's really the experience of the year, where we're at with the technology shift. And we're continuing to see, the need for Cloud, the move to Cloud, mixed workloads, hybrid workloads, unlocking core data, making sure that we're getting insights analytics, and value out of that. And really just working through that, kind of consistent evolution, which is exactly what it is. It's never, you never get to a point where, that's it, there's a bow on it, and it's perfect. It's continuously involving, evolving. >> Yeah. >> And I think that's the most important part that you have to take away. Samir's got his environment in a great place today but in six months, there may be some new things or transformations that he wants to look at, and we want to be there at the ready to work with him, roll up our sleeves, and kind of get into that. So the shift of the Cloud is here to stay. Qlik is a hundred percent here to stay. Here ready to serve our customers in any capacity that we can. And I think that's really my big takeaway from this week. And I've loved it, like this has been a great, this has been great with both of you. You both are super high energy. >> Aw, thank you. >> And Samir and I have had a great time over the event as well. >> Well, nailed it. You absolutely nailed it. All right, Samir, shoot your shot. >> So. >> Savannah. >> What I would say, I'm pretty, so. (laughing) >> I like to keep the smiles organic on stage, my perverse sense of humor, everyone just tolerates. >> Yeah, the one thing I think, I'm hearing a lot is, we have to look at data in motion. Streaming data is the way it's going to go. Whether it's customer data, operational data, it doesn't matter, right? We can't have these silos that you spoke about. Those days are gone, right? And if we really want to make a difference, and utilize all of the technology that's being built out there, all of the new features that were, you know, just in the keynotes. We can't have these separate silos, and the data has to go across, trusted data, it has to go across. The second thing I think we're all talking about is, we have to look at things differently. Unlearning the old is harder than learning the new. So we were just talking about event driven architecture. >> Understatement of the century. Sidebar, that was, yeah. >> So, you know, a lot of us techies are used to calling APIs. Well, now we have to push the data out, instead of pulling it. That just means retraining our brains, retraining our architects, retraining our developers, to think in a different way. And then the last thing I think I've learned is, us technology folks have put the customer first right? >> Yes, absolutely. >> What does a customer want? How do they want to feel when they engage with you? Because if we don't do that, none of this technology matters. And you know, we have to get away from the day where the IT guys go in the back black room, (laughing) coat up and then, you know, push something out, and don't think about what am I doing, and how am I impacting your mother? >> Yes, the end customer. It's no longer the person at the end of a terminal. Look at the green screen. >> And just one last thing. I think also it's fit for purpose transformations. And that's how we have to start thinking about how we're doing business. 'Cause there's a paradigm shift, right? From ETL to ELT, right? Extract, Load, Transform your data. And so as we're seeing that, I think it's really just about that fit for purpose, and looking at the transformations, the right transformations. And what's going to move the needle for the business. >> What a great closing note! Molly, Samir, thank you both for being here. >> Both: Thank you! >> This was a really fantastic chat, love where we took it. And thank all of you for tuning in to our live coverage from AWS reInvent here in fabulous Las Vegas, Nevada. I just want to give my mom a quick shout out, since she got a holler throughout this segment, as well as Stacy and all of my friends at AARP, I missed you all. My name's Savannah Peterson, joined with Dave Vellante. You're watching theCUBE. We are the technology leader in coverage for events like this. (slow upbeat music)

>>Hey everyone. Hello from Las Vegas. Lisa Martin here with you, and I'm on the show floor at Reinvent. But we have a very special program series that the Cube has been doing called Women of the Cloud. It's brought to you by aws and I'm so pleased to have an excellent panel of women leaders in technology and in cloud to talk about their tactical recommendations for you, what they see as found, where they've helped organizations be successful with cloud. Please welcome my three guests, Tara Kapor, president and Chief Revenue Officer, consulting and Digital Transformations, NTT Data. We have Rachel Mu, aws, head of North America, partner sales from aws, and Jimmy Barnes joins us as well, managing director, investment banking engineering at Goldman Sachs. It is so great to have you guys on this power panel. I love it. Thank you for joining me. >>Thank >>You. Let's start with you. Give us a little bit of, of your background at NTT Data and I, and I understand NTT has a big focus on women in technology and in stem. Talk to us a little bit about that and then we'll go around the table. >>Perfect, thank you. Thank you. So brand new role for me at Entity Data. I started three months back and it's a fascinating company. We are about 22 billion in size. We work across industries on multiple innovative use cases. So we are doing a ton of work on edge analytics in the cloud, and that's where we are here with aws. We are also doing a ton of work on the private 5G that we are rolling out and essentially building out industry-wide use cases across financial services, manufacturing, tech, et cetera. Lots of women identity. We essentially have women run cloud program today. We have a gal called Nore Hanson who is our practice leader for cloud. We have Matine who's Latifa, who's our AWS cloud leader. We have Molly Ward who leads up a solutions on the cloud. We have an amazing lady in Mona who leads up our marketing programs. So a fantastic plethora of diverse women driving amazing work identity on cloud. >>That's outstanding to hear because it's one of those things that you can't be what you can't see. Right. We all talk about that. Rachel, talk a little bit about your role and some of the focus that AWS has. I know they're big customer obsession, I'm sure obsessed with other things as well. >>Sure. So Rachel Muir, pleased to be here again. I think this will be my third time. So a big fan of the Cube. I'm fortunate enough to lead our North America partner and channel business, and I'll tell you, I've been at AWS for a little under two years, and honestly, it's been probably the best two years of my career. Just in terms of where the cloud is, where it's headed, the business outcomes that we can deliver with our customers and with our partners is absolutely remarkable. We get to, you know, make the impossible possible every day. So I'm, I'm thrilled to be here and I'm thrilled to, to be part of this inaugural Women of the Cloud panel. >>Oh, I'm prepared to have all three of you. One of the things that feedback, kind of pivoting off what, Rachel, one of the things that you said that one of our guests, some of several of our guests have said is that coming out of Adams keynote this morning, it just seems limitless what AWS can do and I love that it gives me kind of chills what they can do with cloud computing and technology, with its ecosystem of partners with its customers like Goldman Sachs. Jimmy, talk to us a little bit about you, your role at Goldman Sachs. You know, we think of Goldman Sachs is a, is a huge financial institution, but it's also a technology company. >>Yeah. I mean, since the age of 15 I've been super passionate about how we can use technology to transform business and simplify modernized business processes. And it's, I'm so thrilled that I have the opportunity to do that at Goldman Sachs as an engineer. I recently moved about two years ago into the investment banking business and it's, you know, it's best in class, one of the top companies in terms of mergers and acquisitions, IPOs, et cetera. But what surprised me is how technology enables all the businesses across the board. Right? And I get to be leading the digital platform for building out the digital platform for in the investment banking business where we're modernizing and transforming existing businesses. These are not new businesses. It's like sometimes I liken it to trying to change the train while it's moving, right? These are existing businesses, but now we get to modernize and transform on the cloud. Right. Not just efficiency for the business by efficiency for technologists as >>Well. Right, right. Sticking with you, Jimmy. I wanna understand, so you've been, you've been interested in tech since you were young. I only got into tech and accidentally as an adult. I'm curious about your career path, but talk to us about that. What are some of the recommendations that you would have for other women who might be looking at, I wanna be in technology, but I wanna work for some of the big companies and they don't think about the Goldman Sachs or some of the other companies like Walmart that are absolutely technology driven. What's your advice for those women who want to grow their career? >>I also, growing up, I was, I was interested in various things. I, I loved doing hair. I used to do my own hair and I used to do hair for other students at school and I was also interested in running an entertainment company. And I used actually go around performing and singing and dancing with a group of friends, especially at church. But what amazed me is when I landed my first job at a real estate agent and everything was being done manually on paper, I was like, wow, technology can bring transformation anywhere and everywhere. And so whilst I have a myriad of interest, there's so many ways that technology can be applied. There's so many different types of disciplines within technology. It's not, there's hands on, like I'm colder, I like to code, but they're product managers, there are business analysts, there are infrastructure specialist. They're a security specialist. And I think it's about pursuing your passion, right? Pursuing your passion and identifying which aspects of technology peak your interest. And then diving in. >>Love that. Diving in. Rachel, you're shaking your head. You definitely are in alignment with a lot of what >>Duties I am. So, you know, interesting enough, I actually started my career as a civil engineer and eventually made it into, into technology. So very similar. I saw in, you know, heavy highway construction how manual some of these processes were. And mind you, this was before the cloud. And I sat down and wrote a little computer program to automate a lot of these manual tasks. And for me it was about simplification of the customer journey and really figuring out how do you deliver value. You know, on fast forward, say 20 plus years, here I am with AWS who has got this amazing cloud platform with over 200 services. And when I think about what we do in tech, from business transformation to modernizing to helping customers think about how do they create new business models, I've really found, I've really found my sweet spot, and I'll say for anyone who wants to get into tech or even switch careers, there's just a couple words of advice that I have. And it's really two words, just start. >>Yes, >>That's it. Just start. Because sometimes later becomes never. And you know, fuel your passion, be curious, think about new things. Yes. And just >>Start, I love that. Just start, you should get t-shirts made with that. Tell me a little bit about some of your recommendations. Obviously just start is great when follow your passion. What would you say to those out there looking to plan the letter? >>So, you know, my, my story's a little bit like jus because I did not want to be in tech. You know, I wanted an easy life. I did well in school and I wanted to actually be an air hostess. And when I broke that to my father, you know, the standard Indian person, now he did, he, you know, he wanted me to go in and be an engineer. Okay? So I was actually push into computer engineering, graduated. But then really two things today, right? When I look back, really two pieces, two areas I believe, which are really important for success. One is, you know, we need to be competent. And the second is we need to be confident, right? Yes, yes. It's so much easier to be competent because a lot of us diverse women, diverse people tend to over rotate on knowing their technical skills, right? Knowing technical skills important, but you need to know how to potentially apply those to business, right? Be able to define a business roi. And I see Julie nodding because she wants people to come in and give her a business ROI for programs that you're executing at Goldman Sachs. I presume the more difficult part though is confidence. >>Absolutely. It's so hard, especially when, when we're younger, we don't know. Raise your hand because I guarantee you either half the people in the, in the room or on the zoom these days weren't listening or have the same question and are too afraid to ask because they don't have the confidence. That's right. Give me, let's pivot on confidence for a minute, Jim, and let's go back to how would you advise your younger self to find your confidence? >>That's, that's a tough one because I feel like even this older self is still finding exercise to, to be real. But I think it's about, I would say it's not praise. I think it's about praising yourself, like recognizing your accomplishments. When I think about my younger self, I think I, I like to focus more on what I didn't do or what I didn't accomplish, instead of majoring and focusing on all the accomplishments and the achievements and reminding myself of those day after day after day. And I think it's about celebrating your wins. >>I love that. Celebrating your wins. Do you agree, Rachel? >>I do. Here's the hard part, and I look around this table of amazing business leaders and I can guarantee that every single one of us sometime this year woke up and said, oh my gosh, I don't know how to do that. Oh >>Yeah. But >>What we haven't followed that by is, I don't know how to do that yet. Right. And here's the other thing I would tell my younger self is there will be days where every single one of us falls apart. There will be days when we feel like we failed at work. There will be days when you feel like you failed as a parent or you failed as a spouse. There'll be days where you have a kid in the middle of target screaming and crying while you're trying to close a big business deal and you just like, oh my gosh, is this really my life? But what I would tell my younger self is, look, the crying, the chaos, the second guessing yourself, the successes, every single one of those are milestones. And it's triumphant, it's tragic, but every single thing that we have been through is fiercely worthwhile. And it's what got us >>Here. Absolutely. Absolutely. Think of all the trials and tribulations and six and Zacks that got you to this table right now. Yep. So Terry, you brought up confidence. How would you advise the women out there won't say you're gonna know stuff. The women out there now that are watching those that are watching right there. Hi. How would you advise them to really find their, their ability to praise themselves, recognize all of the trials and the tribulations as milestones as Rachel said, and really give themselves a seat at the table, raise their hand regardless of who else is in the room? >>You know, it's a, it's a more complex question just because confidence stems from courage, right? Confidence also stems from the belief that you're going to be treated fairly right now in an organization for you to be treated fairly. You need to have, be surrounded by supporters that are going to promote your voice. And very often women don't invest enough in building that support system around them. Yeah. Right. We have mentors, and mentors are great because they come in and they advise us and they'll tell us what we need to go out and do. We really need a team of sponsors Yes. Who come in and support us in the moment in the business. Give us the informal channel because very often we are not plugged into the informal channel, right. So we don't get those special projects or assignments or even opportunities to prove that we can do the tough task. Yeah. So, you know, my, my advice would be to go out and build a network of sponsors. Yes. And if you don't have one, be a sponsor for someone else. That's right. I love that. Great way to win sponsorship is by extending it todos. >>And sometimes too, it's about, honestly, I didn't even know the difference between a mentor and a sponsor until a few years ago. And I started thinking, who are I? And then I started realizing who they were. That's right. And some of the conversations that we've had on the cube about women in technology, women of the cloud with some of the women leaders have said, build, and this is kind of like, sort of what you were saying, build your own personal board of directors. Yeah. And that, oh, it gives me chills. It's just, it's so important for, for not just women, but anybody, for everybody. But it's so important to do that. And if you, you think about LinkedIn as an example, you have a network, it's there, utilize it, figure out who your mentors are, who your sponsors are, who are gonna help you land the next thing, start building that reputation. But having that board of directors that you can kind of answer to or have some accountability towards, I think is hugely very >>Important. Yeah. >>Very important. I think, you know, just for, just for those that are listening, a really important distinction for me was mentors are people that you have that help you with, Hey, here's the situation that you were just in. They advise you on the situation. Sponsors are the people that stick up for you when you're not in the room to them. Right. Sponsors are the ones that say, Hey, I think so and so not only needs to have a seat at the table, but they need to build the table. And that's a really important delineation. Yeah. Between mentors and sponsors. And everybody's gotta have a sponsor both within their company and outside of their company. Someone that's advocating for them on their behalf when they don't even know it. Yeah. Yeah. >>I love that you said that. Build the table. It reminds me of a quote that I heard from Will I am, I know, very random. It was a podcast he did with Oprah Winfrey on ai. He's very into ai and I was doing a panel on ai, so I was doing a lot of research and he said, similar for Rachel to build the table, don't wait for a door to open. You go build a door. And I just thought, God, that is such brilliant advice. It is. It's hard to do. It is. Especially when, you know, the four of us in this room, there's a lot of women around here, but we are in an environment where we are the minority women of color are also the minority. What do you guys think where tech is in terms of de and I and really focusing on De and I as as really a very focused strategic initiative. Turner, what do you think? >>So, you know, I just, I, I spoke earlier about the women that we have at Entity Data, right? We have a fabulous team of women. And joining this team has been a moment of revelation for me coming in. I think to promote dni, we all need to start giving back, right? Yes. So today, I would love to announce that we at Entity would like to welcome all of you out there. You know, folks that have diverse ideas, you know, ISV, partners with diverse solutions, thought leaders out there who want to contribute into the ecosystem, right? Customers out there who want to work with companies that are socially responsible, right? We want to work with all of you, come back, reach out to us and be a part of the ecosystem because we can build this together, right? AWS has an amazing platform that gives us an opportunity to do things differently. Yes. Right. Entity data is building a women powered cloud team. And I want to really extend that out to everyone else to be a part this ecosystem, >>But a fantastic opportunity. You know, when we talk about diversity and inclusion and equity, it needs to be intentional for organization. It sounds very intentional at ntt. I know that that intention is definitely there at AWS as well. What are your thoughts on where tech is with respect to diversity? Even thought diversity? Because a lot of times we tend to go to our comfort zones. We do. And so we tend to start creating these circles of kind of like, you know, think tanks and they think alike people to go outside of that comfort zone. It's part of building the table, of building the, is the table and getting people from outside your comfort zone to come in and bring in diverse thought. Because can you imagine the potential of technology if we have true thought diversity in an organization? >>Right? It's, it's incredible. So one of the things that I always share with my team is we've got the opportunity to really change the outcome, right? As you know, you talked about Will I am I'm gonna talk about Bono from you too, right? One of, one of his favorite quotes is, we are the people we've been waiting for. Oh, I love that. And when you think about that, that is us. There is no one else that's gonna change the outcome and continue to deliver some of the business outcomes and the innovation that we are if we don't continue to raise our hand and we don't continue to, to inspire the next generation of leaders to do the same thing. And what I've found is when you start openly sharing what your innovation ideas are or how you're leveraging your engineering background, your stories and your successes, and, and frankly, some of your failures become the inspiration for someone you might not even know. Absolutely. And that's the, you know, that's the key. You're right. Inclusion, diversity, equity and accessibility, yes. Have to be at the forefront of every business decision. And I think too often companies think that, you know, inclusion, diversity, equity and accessibility is one thing, and business outcomes are another. And they're not. No, they are one in the same. You can't build business outcomes without also focusing on inclusion, diversity, equity, accessibility. That's the deliberate piece. >>And, and it has to be deliberate. Jimmy, I wanna ask you, we only have a couple of minutes left, but you're a woman in tech, you're a woman of color. What was that like for you? You, you were very intentional knowing when you were quite young. Yeah. What you wanted to do, but how have you navigated that? Because I can't imagine that was easy. >>It wasn't. I remember, I always tell the story and the, the two things that I really wanted to emphasize today when I thought about this panel is rep representation matters and showing up matters, right? And there's a statement, there's a flow, I don't know who it's attributed to, but be the change you want to see. And I remember walking through the doors of Goldman Sachs 15 years ago and not seeing a black female engineer leader, right? And at that point in time, I had a choice. I could be like, oh, there's no one look like, there's no one that looks like me. I don't belong here. Or I could do what I actually did and say, well, I'm gonna be that person. >>Good, >>Right? I'm going to be the chain. I'm going to show up and I am going to have a seat at the table so that other people behind me can also have a seat at the table. And I think that I've had the privilege to work for a company who has been inclusive, who has had the right support system, the right structures in place, so that I can be that person who is the first black woman tech fellow at Goldman Sachs, who is one of the first black females to be promoted up the rank as a, from analysts to managing director at the company. You know, that was not just because I determined that I belong here, but because the company ensure that I felt that I belong. >>Right. >>That's a great point. They ensure that you felt that. Yeah. You need to be able to feel that. Last question, we've only got about a minute left. 2023 is just around the corner. What comes to your mind, Jimmy will stick with you as you head into the new year. >>Sorry, can you repeat >>What comes to mind priorities for 2023 that you're excited about? >>I'm excited about the democratization of data. Yeah. I'm excited about a lot of the announcements today and I, I think there is a, a huge shift going on with this whole concept of marketplaces and data exchanges and data sharing. And I think both internally and externally, people are coming together more. Companies are coming together more to really de democratize and make data available. And data is power. But a lot of our businesses are running, running on insights, right? And we need to bring that data together and I'm really excited about the trends that's going on in cloud, in technology to actually bring the data sets together. >>Touro, what are you most excited about as we head to 2023? >>I think I'm really excited about the possibilities that entity data has right here, right now, city of Las Vegas, we've actually rolled out a smart city project. So saving citizens life, using data edge analytics, machine learning, being able to predict adverse incidents before they happen, and then being able to take remediation action, right? So that's technology actually working in real time to give us tangible results. We also sponsor the Incar races. Lots of work happening there in delivering amazing customer experience across the platform to millions of users real time. So I think I'm just excited about technology coming together, but while that's happening, I think we really need to be mindful at this time that we don't push our planet into per right. We need to be sustainable, we need to be responsible. >>Absolutely. Rachel, take us out. What are you most excited about going into 2023? >>So, you know, there are so many trends that are, that we could talk about, but I'll tell you at aws, you know, we're big. We, we impact the world. So we've gotta be really thoughtful and humble about what it is that we do. So for me, what I'm most excited about is, you know, one of our leadership principles is about, you know, with what broad responsibility brings, you know, you've got to impact sustainability and many of those other things. And for me, I think it's about waking up every day for our customers, for our partners, and for the younger generations. And being better, doing better, and making better for this planet and for, you know, the future generations to come. So >>I think your tag line just start applies to all of that. It does. It has been an absolute pleasure. And then really an honor to talk to you on the program. Thank you all for joining me, sharing your experiences, sharing what you've accomplished, your recommendations for those others who might be our same generation or older or younger. All really beautiful advice. Thank you so much for your time and your insights. We appreciate it. >>Thank you. Thank you. >>For my guests, I'm Lisa Martin. You're watching The Cube, the leader in live enterprise and emerging tech coverage. Thanks for watching.

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that part and we are gonna talk about it in grade length >>With one of our alumni. Moral morale to Molly is back DP and GM of Port Work's Peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful >>To be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of Kubernetes. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users. And dev users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want Selfer, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know that before we get into some more specifics, I want Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage are big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years. You had a great offering Stay right In >>Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just ad hot water and it's there. Yep. So the world of of it has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they want to be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, this is gonna be part of the headlines we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the, the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a serves all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back m dr and failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cuz you're a critical component. Storage is a service is a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where at Cube Con, after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all, all of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about how people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua or all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. E and, and ports and pure storage leading in the world of data management platforms >>There. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management perspective. >>Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kinda set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in to deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the Heco coworks and there's a platform engineering team. We are building that platform for them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO when they started Yep. They, they stayed on path. They didn't waiver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? Either GM cloud native business unit of a storage company that's transformed and transforming? >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey, you know, we're running so hard, you just take a step back. And we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fe and we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have, have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, it's, >>It's >>Right. All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people will continue to invest through it. The question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an entrepreneur, >>Which my, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. We're gonna grow by the way, in the next think >>It's core style. I think I'm, I'm more bullish. I think there's gonna be some, you know, weeding out of some overinvestment pre C or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these core platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in any industry to truly be data companies. Because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so you know, I mentioned our, as a service kind of platform, the global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward >>To it. Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, great stuff that you're achieving. Congratulations on that. Yeah. Great stuff >>Ahead and having fun. Let's not forget that, that's too life's too short to do. It is right. >>You're right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Thank you so much. It's pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Coan Cloud Native Con at 22. We'll be back after a short break.

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native, Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that, that part, and we're gonna talk about it in grade length >>With one of our alumni morale to Molly is back VP and GM of Port Work's peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful to >>Be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of es. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service, unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for the Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users and their users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want self serve, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know, that before we get into some more specifics, I want to Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage a big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years, You had a great offering Stay >>Right In Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just adho water and it's there. Yep. So the world of of IT has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they wanna be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, and this is gonna be part of the headlines, we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a services, all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to Yeah. Is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back DR. And failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cause you're a critical component. Storage is a service, it's a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where Atan after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all of of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about. Okay. How people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua are all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. And, and ports and pure storage leading in the world of data management >>Platforms there. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management >>Perspective. Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kind of set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in into deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the he of Coworks and there's a platform engineering team. We are building that platform for them, them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO and they started Yep. They, they stayed on path. They didn't waver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? You're a GM cloud native business unit of a storage company that's transformed and transforming. >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey to, you know, we're running so hard, you just take a step back and we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fee. And we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, >>It's >>Right. Never gonna stop prices, right? All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people who continue to invest through it, the question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an >>Entrepreneur. My, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. Yeah. We're gonna grow by the way, in the next, I think >>It's corn style. I think I'm, I'm more bullish. I think it's gonna be some, you know, weeding out of some overinvestment, pre covid or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these lower platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in, in the industry to truly be data companies because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a, a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so, you know, I mentioned our, as a service kind of platform. The global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on, which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance Dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward to it. >>Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, Great stuff that you are achieving. Congratulations on that. Great stuff >>Ahead and having fun. Let's not forget that that's too life's too short to do. It is. You're right. >>Right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Great. Thank you so much. It's a pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Cob Con Cloud native Con at 22. We'll be back after a short break.

>> From every ISV to solve the problems. You want there to be tools in place that you can use, either open source tools or whatever it is that help you build it. And slowly over time, that building will become easier and easier. So my question to you was, where do you see you playing? Do you see yourself playing to ISVs as a set of tools, which will make their life a lot easier and provide that work? >> Absolutely. >> If they don't have, so they don't have to do it. Or you're providing this for the end users? Or both? >> So it's a progression. If you go to the ISVs first, you're doomed to starved before you have time for that other option. >> Yeah. >> Right? So it's a question of phase, the phasing of it. And also if you go directly to end users, you can demonstrate the power of it and get the attention of the ISVs. I believe that the ISVs, especially those with the biggest footprints and the most, you know, coveted estates, they have already made massive investments at trying to solve decentralization of their software stack. And I believe that they have used it as a hook to try to move to a software as a service model and rope people into leasing their infrastructure. So if you look at the clouds that have been propped up by Autodesk or by Adobe, or you name the company, they are building proprietary makeshift solutions for decentralizing or hybrid clouding. Or maybe they're not even doing that at all and all they're is saying hey, if you want to get location agnosticness, then what you should just, is just move into our cloud. >> Right. >> And then they try to solve on the background how to decentralize it between different regions so they can have decent offerings in each region. But those who are more advanced have already made larger investments and will be more averse to, you know, throwing that stuff away, all of their makeshift machinery away, and using a platform that gives them high performance parallel, low level file system access, while at the same time having metadata-driven, you know, policy-based, intent-based orchestration to manage the diffusion of data across a decentralized infrastructure. They are not going to be as open because they've made such an investment and they're going to look at how do they monetize it. So what we have found with like the movie studios who are using us already, many of the app they're using, many of those software offerings, the ISVs have their own cloud that offers that software for the cloud. But what we got when I asked about this, 'cause I was dealt specifically into this question because I'm very interested to know how we're going to make that leap from end user upstream into the ISVs where I believe we need to, and they said, look, we cannot use these software ISV-specific SAS clouds for two reasons. Number one is we lose control of the data. We're giving it to them. That's security and other issues. And here you're talking about we're doing work for Disney, we're doing work for Netflix, and they're not going to let us put our data on those software clouds, on those SAS clouds. Secondly, in any reasonable pipeline, the data is shared by many different applications. We need to be agnostic as to the application. 'Cause the inputs to one application, you know, the output for one application provides the input to the next, and it's not necessarily from the same vendor. So they need to have a data platform that lets them, you know, go from one software stack, and you know, to run it on another. Because they might do the rendering with this and yet, they do the editing with that, and you know, et cetera, et cetera. So I think the further you go up the stack in the structured data and dedicated applications for specific functions in specific verticals, the further up the stack you go, the harder it is to justify a SAS offering where you're basically telling the end users you need to park all your data with us and then you can run your application in our cloud and get this. That ultimately is a dead end path versus having the data be open and available to many applications across this supercloud layer. >> Okay, so-- >> Is that making any sense? >> Yes, so if I could just ask a clarifying question. So, if I had to take Snowflake as an example, I think they're doing exactly what you're saying is a dead end, put everything into our proprietary system and then we'll figure out how to distribute it. >> Yeah. >> And and I think if you're familiar with Zhamak Dehghaniis' data mesh concept. Are you? >> A little bit, yeah. >> But in her model, Snowflake, a Snowflake warehouse is just a node on the mesh and that mesh is-- >> That's right. >> Ultimately the supercloud and you're an enabler of that is what I'm hearing. >> That's right. What they're doing up at the structured level and what they're talking about at the structured level we're doing at the underlying, unstructured level, which by the way has implications for how you implement those distributed database things. In other words, implementing a Snowflake on top of Hammerspace would have made building stuff like in the first place easier. It would allow you to easily shift and run the database engine anywhere. You still have to solve how to shard and distribute at the transaction layer above, so I'm not saying we're a substitute for what you need to do at the app layer. By the way, there is another example of that and that's Microsoft Office, right? It's one thing to share that, to have a file share where you can share all the docs. It's something else to have Word and PowerPoint, Excel know how to allow people to be simultaneously editing the same doc. That's always going to happen in the app layer. But not all applications need that level of, you know, in-app decentralization. You know, many of them, many workflows are pipelined, especially the ones that are very data intensive where you're doing drug discovery or you're doing rendering, or you're doing machine learning training. These things are human in the loop with large stages of processing across tens of thousands of cores. And I think that kind of data processing pipeline is what we're focusing on first. Not so much the Microsoft Office or the Snowflake, you know, parking a relational database because that takes a lot of application layer stuff and that's what they're good at. >> Right. >> But I think... >> Go ahead, sorry. >> Later entrance in these markets will find Hammerspace as a way to accelerate their work so they can focus more narrowly on just the stuff that's app-specific, higher level sharing in the app. >> Yes, Snowflake founders-- >> I think it might be worth mentioning also, just keep this confidential guys, but one of our customers is Blue Origin. And one of the things that we have found is kind of the point of what you're talking about with our customers. They're needing to build this and since it's not commercially available or they don't know where to look for it to be commercially available, they're all building themselves. So this layer is needed. And Blue is just one of the examples of quite a few we're now talking to. And like manufacturing, HPC, research where they're out trying to solve this problem with their own scripting tools and things like that. And I just, I don't know if there's anything you want to add, David, but you know, but there's definitely a demand here and customers are trying to figure out how to solve it beyond what Hammerspace is doing. Like the need is so great that they're just putting developers on trying to do it themselves. >> Well, and you know, Snowflake founders, they didn't have a Hammerspace to lean on. But, one of the things that's interesting about supercloud is we feel as though industry clouds will emerge, that as part of company's digital transformations, they will, you know, every company's a software company, they'll begin to build their own clouds and they will be able to use a Hammerspace to do that. >> A super pass layer. >> Yes. It's really, I don't know if David's speaking, I don't want to speak over him, but we can't hear you. May be going through a bad... >> Well, a regional, regional talks that make that possible. And so they're doing these render farms and editing farms, and it's a cloud-specific to the types of workflows in the median entertainment world. Or clouds specifically to workflows in the chip design world or in the drug and bio and life sciences exploration world. There are large organizations that are kind of a blend of end users, like the Broad, which has their own kind of cloud where they're asking collaborators to come in and work with them. So it starts to even blur who's an end user versus an ISV. >> Yes. >> Right? When you start talking about the massive data is the main gravity is to having lots of people participate. >> Yep, and that's where the value is. And that's where the value is. And this is a megatrend that we see. And so it's really important for us to get to the point of what is and what is not a supercloud and, you know, that's where we're trying to evolve. >> Let's talk about this for a second 'cause I want to, I want to challenge you on something and it's something that I got challenged on and it has led me to thinking differently than I did at first, which Molly can attest to. Okay? So, we have been looking for a way to talk about the concept of cloud of utility computing, run anything anywhere that isn't addressed in today's realization of cloud. 'Cause today's cloud is not run anything anywhere, it's quite the opposite. You park your data in AWS and that's where you run stuff. And you pretty much have to. Same with with Azure. They're using data gravity to keep you captive there, just like the old infrastructure guys did. But now it's even worse because it's coupled back with the software to some degree, as well. And you have to use their storage, networking, and compute. It's not, I mean it fell back to the mainframe era. Anyhow, so I love the concept of supercloud. By the way, I was going to suggest that a better term might be hyper cloud since hyper speaks to the multidimensionality of it and the ability to be in a, you know, be in a different dimension, a different plane of existence kind of thing like hyperspace. But super and hyper are somewhat synonyms. I mean, you have hyper cars and you have super cars and blah, blah, blah. I happen to like hyper maybe also because it ties into the whole Hammerspace notion of a hyper-dimensional, you know, reality, having your data centers connected by a wormhole that is Hammerspace. But regardless, what I got challenged on is calling it something different at all versus simply saying, this is what cloud has always meant to be. This is the true cloud, this is real cloud, this is cloud. And I think back to what happened, you'll remember, at Fusion IO we talked about IO memory and we did that because people had a conceptualization of what an SSD was. And an SSD back then was low capacity, low endurance, made to go military, aerospace where things needed to be rugged but was completely useless in the data center. And we needed people to imagine this thing as being able to displace entire SAND, with the kind of capacity density, performance density, endurance. And so we talked IO memory, we could have said enterprise SSD, and that's what the industry now refers to for that concept. What will people be saying five and 10 years from now? Will they simply say, well this is cloud as it was always meant to be where you are truly able to run anything anywhere and have not only the same APIs, but you're same data available with high performance access, all forms of access, block file and object everywhere. So yeah. And I wonder, and this is just me throwing it out there, I wonder if, well, there's trade offs, right? Giving it a new moniker, supercloud, versus simply talking about how cloud is always intended to be and what it was meant to be, you know, the real cloud or true cloud, there are trade-offs. By putting a name on it and branding it, that lets people talk about it and understand they're talking about something different. But it also is that an affront to people who thought that that's what they already had. >> What's different, what's new? Yes, and so we've given a lot of thought to this. >> Right, it's like you. >> And it's because we've been asked that why does the industry need a new term, and we've tried to address some of that. But some of the inside baseball that we haven't shared is, you remember the Web 2.0, back then? >> Yep. >> Web 2.0 was the same thing. And I remember Tim Burners Lee saying, "Why do we need Web 2.0? "This is what the Web was always supposed to be." But the truth is-- >> I know, that was another perfect-- >> But the truth is it wasn't, number one. Number two, everybody hated the Web 2.0 term. John Furrier was actually in the middle of it all. And then it created this groundswell. So one of the things we wrote about is that supercloud is an evocative term that catalyzes debate and conversation, which is what we like, of course. And maybe that's self-serving. But yeah, HyperCloud, Metacloud, super, meaning, it's funny because super came from Latin supra, above, it was never the superlative. But the superlative was a convenient byproduct that caused a lot of friction and flack, which again, in the media business is like a perfect storm brewing. >> The bad thing to have to, and I think you do need to shake people out of their, the complacency of the limitations that they're used to. And I'll tell you what, the fact that you even have the terms hybrid cloud, multi-cloud, private cloud, edge computing, those are all just referring to the different boundaries that isolate the silo that is the current limited cloud. >> Right. >> So if I heard correctly, what just, in terms of us defining what is and what isn't in supercloud, you would say traditional applications which have to run in a certain place, in a certain cloud can't run anywhere else, would be the stuff that you would not put in as being addressed by supercloud. And over time, you would want to be able to run the data where you want to and in any of those concepts. >> Or even modern apps, right? Or even modern apps that are siloed in SAS within an individual cloud, right? >> So yeah, I guess it's twofold. Number one, if you're going at the high application layers, there's lots of ways that you can give the appearance of anything running anywhere. The ISV, the SAS vendor can engineer stuff to have the ability to serve with low enough latency to different geographies, right? So if you go too high up the stack, it kind of loses its meaning because there's lots of different ways to make due and give the appearance of omni-presence of the service. Okay? As you come down more towards the platform layer, it gets harder and harder to mask the fact that supercloud is something entirely different than just a good regionally-distributed SAS service. So I don't think you, I don't think you can distinguish supercloud if you go too high up the stack because it's just SAS, it's just a good SAS service where the SAS vendor has done the hard work to give you low latency access from different geographic regions. >> Yeah, so this is one of the hardest things, David. >> Common among them. >> Yeah, this is really an important point. This is one of the things I've had the most trouble with is why is this not just SAS? >> So you dilute your message when you go up to the SAS layer. If you were to focus most of this around the super pass layer, the how can you host applications and run them anywhere and not host this, not run a service, not have a service available everywhere. So how can you take any application, even applications that are written, you know, in a traditional legacy data center fashion and be able to run them anywhere and have them have their binaries and their datasets and the runtime environment and the infrastructure to start them and stop them? You know, the jobs, the, what the Kubernetes, the job scheduler? What we're really talking about here, what I think we're really talking about here is building the operating system for a decentralized cloud. What is the operating system, the operating environment for a decentralized cloud? Where you can, and that the main two functions of an operating system or an operating environment are the process scheduler, the thing that's scheduling what is running where and when and so forth, and the file system, right? The thing that's supplying a common view and access to data. So when we talk about this, I think that the strongest argument for supercloud is made when you go down to the platform layer and talk of it, talk about it as an operating environment on which you can run all forms of applications. >> Would you exclude--? >> Not a specific application that's been engineered as a SAS. (audio distortion) >> He'll come back. >> Are you there? >> Yeah, yeah, you just cut out for a minute. >> I lost your last statement when you broke up. >> We heard you, you said that not the specific application. So would you exclude Snowflake from supercloud? >> Frankly, I would. I would. Because, well, and this is kind of hard to do because Snowflake doesn't like to, Frank doesn't like to talk about Snowflake as a SAS service. It has a negative connotation. >> But it is. >> I know, we all know it is. We all know it is and because it is, yes, I would exclude them. >> I think I actually have him on camera. >> There's nothing in common. >> I think I have him on camera or maybe Benoit as saying, "Well, we are a SAS." I think it's Slootman. I think I said to Slootman, "I know you don't like to say you're a SAS." And I think he said, "Well, we are a SAS." >> Because again, if you go to the top of the application stack, there's any number of ways you can give it location agnostic function or you know, regional, local stuff. It's like let's solve the location problem by having me be your one location. How can it be decentralized if you're centralizing on (audio distortion)? >> Well, it's more decentralized than if it's all in one cloud. So let me actually, so the spectrum. So again, in the spirit of what is and what isn't, I think it's safe to say Hammerspace is supercloud. I think there's no debate there, right? Certainly among this crowd. And I think we can all agree that Dell, Dell Storage is not supercloud. Where it gets fuzzy is this Snowflake example or even, how about a, how about a Cohesity that instantiates its stack in different cloud regions in different clouds, and synchronizes, however magic sauce it does that. Is that a supercloud? I mean, so I'm cautious about having too strict of a definition 'cause then only-- >> Fair enough, fair enough. >> But I could use your help and thoughts on that. >> So I think we're talking about two different spectrums here. One is the spectrum of platform to application-specific. As you go up the application stack and it becomes this specific thing. Or you go up to the more and more structured where it's serving a specific application function where it's more of a SAS thing. I think it's harder to call a SAS service a supercloud. And I would argue that the reason there, and what you're lacking in the definition is to talk about it as general purpose. Okay? Now, that said, a data warehouse is general purpose at the structured data level. So you could make the argument for why Snowflake is a supercloud by saying that it is a general purpose platform for doing lots of different things. It's just one at a higher level up at the structured data level. So one spectrum is the high level going from platform to, you know, unstructured data to structured data to very application-specific, right? Like a specific, you know, CAD/CAM mechanical design cloud, like an Autodesk would want to give you their cloud for running, you know, and sharing CAD/CAM designs, doing your CAD/CAM anywhere stuff. Well, the other spectrum is how well does the purported supercloud technology actually live up to allowing you to run anything anywhere with not just the same APIs but with the local presence of data with the exact same runtime environment everywhere, and to be able to correctly manage how to get that runtime environment anywhere. So a Cohesity has some means of running things in different places and some means of coordinating what's where and of serving diff, you know, things in different places. I would argue that it is a very poor approximation of what Hammerspace does in providing the exact same file system with local high performance access everywhere with metadata ability to control where the data is actually instantiated so that you don't have to wait for it to get orchestrated. But even then when you do have to wait for it, it happens automatically and so it's still only a matter of, well, how quick is it? And on the other end of the spectrum is you could look at NetApp with Flexcache and say, "Is that supercloud?" And I would argue, well kind of because it allows you to run things in different places because it's a cache. But you know, it really isn't because it presumes some central silo from which you're cacheing stuff. So, you know, is it or isn't it? Well, it's on a spectrum of exactly how fully is it decoupling a runtime environment from specific locality? And I think a cache doesn't, it stretches a specific silo and makes it have some semblance of similar access in other places. But there's still a very big difference to the central silo, right? You can't turn off that central silo, for example. >> So it comes down to how specific you make the definition. And this is where it gets kind of really interesting. It's like cloud. Does IBM have a cloud? >> Exactly. >> I would say yes. Does it have the kind of quality that you would expect from a hyper-scale cloud? No. Or see if you could say the same thing about-- >> But that's a problem with choosing a name. That's the problem with choosing a name supercloud versus talking about the concept of cloud and how true up you are to that concept. >> For sure. >> Right? Because without getting a name, you don't have to draw, yeah. >> I'd like to explore one particular or bring them together. You made a very interesting observation that from a enterprise point of view, they want to safeguard their store, their data, and they want to make sure that they can have that data running in their own workflows, as well as, as other service providers providing services to them for that data. So, and in in particular, if you go back to, you go back to Snowflake. If Snowflake could provide the ability for you to have your data where you wanted, you were in charge of that, would that make Snowflake a supercloud? >> I'll tell you, in my mind, they would be closer to my conceptualization of supercloud if you can instantiate Snowflake as software on your own infrastructure, and pump your own data to Snowflake that's instantiated on your own infrastructure. The fact that it has to be on their infrastructure or that it's on their, that it's on their account in the cloud, that you're giving them the data and they're, that fundamentally goes against it to me. If they, you know, they would be a pure, a pure plate if they were a software defined thing where you could instantiate Snowflake machinery on the infrastructure of your choice and then put your data into that machinery and get all the benefits of Snowflake. >> So did you see--? >> In other words, if they were not a SAS service, but offered all of the similar benefits of being, you know, if it were a service that you could run on your own infrastructure. >> So did you see what they announced, that--? >> I hope that's making sense. >> It does, did you see what they announced at Dell? They basically announced the ability to take non-native Snowflake data, read it in from an object store on-prem, like a Dell object store. They do the same thing with Pure, read it in, running it in the cloud, and then push it back out. And I was saying to Dell, look, that's fine. Okay, that's interesting. You're taking a materialized view or an extended table, whatever you're doing, wouldn't it be more interesting if you could actually run the query locally with your compute? That would be an extension that would actually get my attention and extend that. >> That is what I'm talking about. That's what I'm talking about. And that's why I'm saying I think Hammerspace is more progressive on that front because with our technology, anybody who can instantiate a service, can make a service. And so I, so MSPs can use Hammerspace as a way to build a super pass layer and host their clients on their infrastructure in a cloud-like fashion. And their clients can have their own private data centers and the MSP or the public clouds, and Hammerspace can be instantiated, get this, by different parties in these different pieces of infrastructure and yet linked together to make a common file system across all of it. >> But this is data mesh. If I were HPE and Dell it's exactly what I'd be doing. I'd be working with Hammerspace to create my own data. I'd work with Databricks, Snowflake, and any other-- >> Data mesh is a good way to put it. Data mesh is a good way to put it. And this is at the lowest level of, you know, the underlying file system that's mountable by the operating system, consumed as a real file system. You can't get lower level than that. That's why this is the foundation for all of the other apps and structured data systems because you need to have a data mesh that can at least mesh the binary blob. >> Okay. >> That hold the binaries and that hold the datasets that those applications are running. >> So David, in the third week of January, we're doing supercloud 2 and I'm trying to convince John Furrier to make it a data slash data mesh edition. I'm slowly getting him to the knothole. I would very much, I mean you're in the Bay Area, I'd very much like you to be one of the headlines. As Zhamak Dehghaniis going to speak, she's the creator of Data Mesh, >> Sure. >> I'd love to have you come into our studio as well, for the live session. If you can't make it, we can pre-record. But you're right there, so I'll get you the dates. >> We'd love to, yeah. No, you can count on it. No, definitely. And you know, we don't typically talk about what we do as Data Mesh. We've been, you know, using global data environment. But, you know, under the covers, that's what the thing is. And so yeah, I think we can frame the discussion like that to line up with other, you know, with the other discussions. >> Yeah, and Data Mesh, of course, is one of those evocative names, but she has come up with some very well defined principles around decentralized data, data as products, self-serve infrastructure, automated governance, and and so forth, which I think your vision plugs right into. And she's brilliant. You'll love meeting her. >> Well, you know, and I think.. Oh, go ahead. Go ahead, Peter. >> Just like to work one other interface which I think is important. How do you see yourself and the open source? You talked about having an operating system. Obviously, Linux is the operating system at one level. How are you imagining that you would interface with cost community as part of this development? >> Well, it's funny you ask 'cause my CTO is the kernel maintainer of the storage networking stack. So how the Linux operating system perceives and consumes networked data at the file system level, the network file system stack is his purview. He owns that, he wrote most of it over the last decade that he's been the maintainer, but he's the gatekeeper of what goes in. And we have leveraged his abilities to enhance Linux to be able to use this decentralized data, in particular with decoupling the control plane driven by metadata from the data access path and the many storage systems on which the data gets accessed. So this factoring, this splitting of control plane from data path, metadata from data, was absolutely necessary to create a data mesh like we're talking about. And to be able to build this supercloud concept. And the highways on which the data runs and the client which knows how to talk to it is all open source. And we have, we've driven the NFS 4.2 spec. The newest NFS spec came from my team. And it was specifically the enhancements needed to be able to build a spanning file system, a data mesh at a file system level. Now that said, our file system itself and our server, our file server, our data orchestration, our data management stuff, that's all closed source, proprietary Hammerspace tech. But the highways on which the mesh connects are actually all open source and the client that knows how to consume it. So we would, honestly, I would welcome competitors using those same highways. They would be at a major disadvantage because we kind of built them, but it would still be very validating and I think only increase the potential adoption rate by more than whatever they might take of the market. So it'd actually be good to split the market with somebody else to come in and share those now super highways for how to mesh data at the file system level, you know, in here. So yeah, hopefully that answered your question. Does that answer the question about how we embrace the open source? >> Right, and there was one other, just that my last one is how do you enable something to run in every environment? And if we take the edge, for example, as being, as an environment which is much very, very compute heavy, but having a lot less capability, how do you do a hold? >> Perfect question. Perfect question. What we do today is a software appliance. We are using a Linux RHEL 8, RHEL 8 equivalent or a CentOS 8, or it's, you know, they're all roughly equivalent. But we have bundled and a software appliance which can be instantiated on bare metal hardware on any type of VM system from VMware to all of the different hypervisors in the Linux world, to even Nutanix and such. So it can run in any virtualized environment and it can run on any cloud instance, server instance in the cloud. And we have it packaged and deployable from the marketplaces within the different clouds. So you can literally spin it up at the click of an API in the cloud on instances in the cloud. So with all of these together, you can basically instantiate a Hammerspace set of machinery that can offer up this file system mesh. like we've been using the terminology we've been using now, anywhere. So it's like being able to take and spin up Snowflake and then just be able to install and run some VMs anywhere you want and boom, now you have a Snowflake service. And by the way, it is so complete that some of our customers, I would argue many aren't even using public clouds at all, they're using this just to run their own data centers in a cloud-like fashion, you know, where they have a data service that can span it all. >> Yeah and to Molly's first point, we would consider that, you know, cloud. Let me put you on the spot. If you had to describe conceptually without a chalkboard what an architectural diagram would look like for supercloud, what would you say? >> I would say it's to have the same runtime environment within every data center and defining that runtime environment as what it takes to schedule the execution of applications, so job scheduling, runtime stuff, and here we're talking Kubernetes, Slurm, other things that do job scheduling. We're talking about having a common way to, you know, instantiate compute resources. So a global compute environment, having a common compute environment where you can instantiate things that need computing. Okay? So that's the first part. And then the second is the data platform where you can have file block and object volumes, and have them available with the same APIs in each of these distributed data centers and have the exact same data omnipresent with the ability to control where the data is from one moment to the next, local, where all the data is instantiate. So my definition would be a common runtime environment that's bifurcate-- >> Oh. (attendees chuckling) We just lost them at the money slide. >> That's part of the magic makes people listen. We keep someone on pin and needles waiting. (attendees chuckling) >> That's good. >> Are you back, David? >> I'm on the edge of my seat. Common runtime environment. It was like... >> And just wait, there's more. >> But see, I'm maybe hyper-focused on the lower level of what it takes to host and run applications. And that's the stuff to schedule what resources they need to run and to get them going and to get them connected through to their persistence, you know, and their data. And to have that data available in all forms and have it be the same data everywhere. On top of that, you could then instantiate applications of different types, including relational databases, and data warehouses and such. And then you could say, now I've got, you know, now I've got these more application-level or structured data-level things. I tend to focus less on that structured data level and the application level and am more focused on what it takes to host any of them generically on that super pass layer. And I'll admit, I'm maybe hyper-focused on the pass layer and I think it's valid to include, you know, higher levels up the stack like the structured data level. But as soon as you go all the way up to like, you know, a very specific SAS service, I don't know that you would call that supercloud. >> Well, and that's the question, is there value? And Marianna Tessel from Intuit said, you know, we looked at it, we did it, and it just, it was actually negative value for us because connecting to all these separate clouds was a real pain in the neck. Didn't bring us any additional-- >> Well that's 'cause they don't have this pass layer underneath it so they can't even shop around, which actually makes it hard to stand up your own SAS service. And ultimately they end up having to build their own infrastructure. Like, you know, I think there's been examples like Netflix moving away from the cloud to their own infrastructure. Basically, if you're going to rent it for more than a few months, it makes sense to build it yourself, if it's at any kind of scale. >> Yeah, for certain components of that cloud. But if the Goldman Sachs came to you, David, and said, "Hey, we want to collaborate and we want to build "out a cloud and essentially build our SAS system "and we want to do that with Hammerspace, "and we want to tap the physical infrastructure "of not only our data centers but all the clouds," then that essentially would be a SAS, would it not? And wouldn't that be a Super SAS or a supercloud? >> Well, you know, what they may be using to build their service is a supercloud, but their service at the end of the day is just a SAS service with global reach. Right? >> Yeah. >> You know, look at, oh shoot. What's the name of the company that does? It has a cloud for doing bookkeeping and accounting. I forget their name, net something. NetSuite. >> NetSuite. NetSuite, yeah, Oracle. >> Yeah. >> Yep. >> Oracle acquired them, right? Is NetSuite a supercloud or is it just a SAS service? You know? I think under the covers you might ask are they using supercloud under the covers so that they can run their SAS service anywhere and be able to shop the venue, get elasticity, get all the benefits of cloud in the, to the benefit of their service that they're offering? But you know, folks who consume the service, they don't care because to them they're just connecting to some endpoint somewhere and they don't have to care. So the further up the stack you go, the more location-agnostic it is inherently anyway. >> And I think it's, paths is really the critical layer. We thought about IAS Plus and we thought about SAS Minus, you know, Heroku and hence, that's why we kind of got caught up and included it. But SAS, I admit, is the hardest one to crack. And so maybe we exclude that as a deployment model. >> That's right, and maybe coming down a level to saying but you can have a structured data supercloud, so you could still include, say, Snowflake. Because what Snowflake is doing is more general purpose. So it's about how general purpose it is. Is it hosting lots of other applications or is it the end application? Right? >> Yeah. >> So I would argue general purpose nature forces you to go further towards platform down-stack. And you really need that general purpose or else there is no real distinguishing. So if you want defensible turf to say supercloud is something different, I think it's important to not try to wrap your arms around SAS in the general sense. >> Yeah, and we've kind of not really gone, leaned hard into SAS, we've just included it as a deployment model, which, given the constraints that you just described for structured data would apply if it's general purpose. So David, super helpful. >> Had it sign. Define the SAS as including the hybrid model hold SAS. >> Yep. >> Okay, so with your permission, I'm going to add you to the list of contributors to the definition. I'm going to add-- >> Absolutely. >> I'm going to add this in. I'll share with Molly. >> Absolutely. >> We'll get on the calendar for the date. >> If Molly can share some specific language that we've been putting in that kind of goes to stuff we've been talking about, so. >> Oh, great. >> I think we can, we can share some written kind of concrete recommendations around this stuff, around the general purpose, nature, the common data thing and yeah. >> Okay. >> Really look forward to it and would be glad to be part of this thing. You said it's in February? >> It's in January, I'll let Molly know. >> Oh, January. >> What the date is. >> Excellent. >> Yeah, third week of January. Third week of January on a Tuesday, whatever that is. So yeah, we would welcome you in. But like I said, if it doesn't work for your schedule, we can prerecord something. But it would be awesome to have you in studio. >> I'm sure with this much notice we'll be able to get something. Let's make sure we have the dates communicated to Molly and she'll get my admin to set it up outside so that we have it. >> I'll get those today to you, Molly. Thank you. >> By the way, I am so, so pleased with being able to work with you guys on this. I think the industry needs it very bad. They need something to break them out of the box of their own mental constraints of what the cloud is versus what it's supposed to be. And obviously, the more we get people to question their reality and what is real, what are we really capable of today that then the more business that we're going to get. So we're excited to lend the hand behind this notion of supercloud and a super pass layer in whatever way we can. >> Awesome. >> Can I ask you whether your platforms include ARM as well as X86? >> So we have not done an ARM port yet. It has been entertained and won't be much of a stretch. >> Yeah, it's just a matter of time. >> Actually, entertained doing it on behalf of NVIDIA, but it will absolutely happen because ARM in the data center I think is a foregone conclusion. Well, it's already there in some cases, but not quite at volume. So definitely will be the case. And I'll tell you where this gets really interesting, discussion for another time, is back to my old friend, the SSD, and having SSDs that have enough brains on them to be part of that fabric. Directly. >> Interesting. Interesting. >> Very interesting. >> Directly attached to ethernet and able to create a data mesh global file system, that's going to be really fascinating. Got to run now. >> All right, hey, thanks you guys. Thanks David, thanks Molly. Great to catch up. Bye-bye. >> Bye >> Talk to you soon.

>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition at fraud waste and abuse per the government accountability office is broad as an attempt to obtain something about a value through unwelcomed misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal, uh, benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically for the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external perpetrators, again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, uh, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, those are broad stroke areas. What are the actual use cases that, um, agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use great, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment integrity. So fraud has its, um, uh, underpinnings in quite a few different government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that Chev is going to talk about later it's how do I look at more, that real, that streaming information? >>How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in essence, looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in, um, some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, uh, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific constituent, are there areas where we're seeing, uh, um, other aspects of a fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at, uh, simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. >>Um, and again, that really lends itself to a new opportunities. And on that, I'm going to turn it over to Shev to talk about, uh, the reference architecture for, uh, doing these baskets. >>Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection using, uh, Cloudera underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our data sets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so then comes clutter's platform and this reference architecture that needs to before you, so, uh, let's start on the left-hand side of this reference architecture with the collect phase. >>So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different porosities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with clutter data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL string builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or exploratory data analysis and visualization, uh, can all be enabled through clever visual patient technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutter's technology, right? And so, uh, the IRS is one of, uh, clutters customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually, uh, recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around, uh, how quad areas working in the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining Chevy and I today, we greatly appreciate your time and look forward to future >>Conversation..

>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud. Isn't an attempt to obtain something about value through unwelcome misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external, uh, perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically about 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from permit out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's a broad stroke areas. What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're gonna use focused specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of, um, unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has it it's, um, uh, underpinnings inquiry, like you different on government agencies and difficult, different analytical methods, and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models. We're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that shad is going to talk about later is how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the, the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like a constituent, are there areas where we're seeing, uh, >>Um, other >>Aspects of, of fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, uh, agent-based modeling techniques, where we're looking at simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to chef to talk about, uh, the reference architecture for, uh, doing these buckets. >>Thanks, Cindy. Um, so I'm gonna walk you through an example, reference architecture for fraud detection using, uh, Cloudera's underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. We need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, thinking, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on a patch NIFA in mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geolocation that's in that transaction data can be enriched with previously known locations of that very same individual. And all of that enriched data can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stricted to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So coffee is going to pretty much provide you with, uh, extremely fast resilient and fault tolerance storage. And it's also gonna give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone, uh, allowed that, you know, 17. So you can store that data in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL stream builder, which enables us to write, uh, streaming SQL jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks. And these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutters technology, right? And so, uh, the IRS is one of, uh, clutter's customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their spark based analytics and their machine learning, uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection, uh, looking at neural network analysis, time series information. So next steps we'd love to have additional conversation with you. You can also find on some additional information around, I have caught areas working in the, the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us Sheva and I today. We greatly appreciate your time and look forward to future progress. >>Good day, everyone. Thank you for joining me. I'm Sydney. Mike joined by Rick Taylor of Cloudera. Uh, we're here to talk about predictive maintenance for the public sector and how to increase assets, service, reliability on today's agenda. We'll talk specifically around how to optimize your equipment maintenance, how to reduce costs, asset failure with data and analytics. We'll go into a little more depth on, um, what type of data, the analytical methods that we're typically seeing used, um, the associated, uh, Brooke, we'll go over a case study as well as a reference architecture. So by basic definition, uh, predictive maintenance is about determining when an asset should be maintained and what specific maintenance activities need to be performed either based upon an assets of actual condition or state. It's also about predicting and preventing failures and performing maintenance on your time on your schedule to avoid costly unplanned downtime. >>McKinsey has looked at analyzing predictive maintenance costs across multiple industries and has identified that there's the opportunity to reduce overall predictive maintenance costs by roughly 50% with different types of analytical methods. So let's look at those three types of models. First, we've got our traditional type of method for maintenance, and that's really about our corrective maintenance, and that's when we're performing maintenance on an asset, um, after the equipment fails. But the challenges with that is we end up with unplanned. We end up with disruptions in our schedules, um, as well as reduced quality, um, around the performance of the asset. And then we started looking at preventive maintenance and preventative maintenance is really when we're performing maintenance on a set schedule. Um, the challenges with that is we're typically doing it regardless of the actual condition of the asset, um, which has resulted in unnecessary downtime and expense. Um, and specifically we're really now focused on pre uh, condition-based maintenance, which is looking at leveraging predictive maintenance techniques based upon actual conditions and real time events and processes. Um, within that we've seen organizations, um, and again, source from McKenzie have a 50% reduction in downtime, as well as an overall 40% reduction in maintenance costs. Again, this is really looking at things across multiple industries, but let's look at it in the context of the public sector and based upon some activity by the department of energy, um, several years ago, >>Um, they've really >>Looked at what does predictive maintenance mean to the public sector? What is the benefit, uh, looking at increasing return on investment of assets, reducing, uh, you know, reduction in downtime, um, as well as overall maintenance costs. So corrective or reactive based maintenance is really about performing once there's been a failure. Um, and then the movement towards, uh, preventative, which is based upon a set schedule or looking at predictive where we're monitoring real-time conditions. Um, and most importantly is now actually leveraging IOT and data and analytics to further reduce those overall downtimes. And there's a research report by the, uh, department of energy that goes into more specifics, um, on the opportunity within the public sector. So, Rick, let's talk a little bit about what are some of the challenges, uh, regarding data, uh, regarding predictive maintenance. >>Some of the challenges include having data silos, historically our government organizations and organizations in the commercial space as well, have multiple data silos. They've spun up over time. There are multiple business units and note, there's no single view of assets. And oftentimes there's redundant information stored in, in these silos of information. Uh, couple that with huge increases in data volume data growing exponentially, along with new types of data that we can ingest there's social media, there's semi and unstructured data sources and the real time data that we can now collect from the internet of things. And so the challenge is to collect all these assets together and begin to extract intelligence from them and insights and, and that in turn then fuels, uh, machine learning and, um, and, and what we call artificial intelligence, which enables predictive maintenance. Next slide. So >>Let's look specifically at, you know, the, the types of use cases and I'm going to Rick and I are going to focus on those use cases, where do we see predictive maintenance coming into the procurement facility, supply chain, operations and logistics. Um, we've got various level of maturity. So, you know, we're talking about predictive maintenance. We're also talking about, uh, using, uh, information, whether it be on a, um, a connected asset or a vehicle doing monitoring, uh, to also leveraging predictive maintenance on how do we bring about, uh, looking at data from connected warehouses facilities and buildings all bring on an opportunity to both increase the quality and effectiveness of the missions within the agencies to also looking at re uh, looking at cost efficiency, as well as looking at risk and safety and the types of data, um, you know, that Rick mentioned around, you know, the new types of information, some of those data elements that we typically have seen is looking at failure history. >>So when has that an asset or a machine or a component within a machine failed in the past? Uh, we've also looking at bringing together a maintenance history, looking at a specific machine. Are we getting error codes off of a machine or assets, uh, looking at when we've replaced certain components to looking at, um, how are we actually leveraging the assets? What were the operating conditions, uh, um, pulling off data from a sensor on that asset? Um, also looking at the, um, the features of an asset, whether it's, you know, engine size it's make and model, um, where's the asset located on to also looking at who's operated the asset, uh, you know, whether it be their certifications, what's their experience, um, how are they leveraging the assets and then also bringing in together, um, some of the, the pattern analysis that we've seen. So what are the operating limits? Um, are we getting service reliability? Are we getting a product recall information from the actual manufacturer? So, Rick, I know the data landscape has really changed. Let's, let's go over looking at some of those components. Sure. >>So this slide depicts sort of the, some of the inputs that inform a predictive maintenance program. So, as we've talked a little bit about the silos of information, the ERP system of record, perhaps the spares and the service history. So we want, what we want to do is combine that information with sensor data, whether it's a facility and equipment sensors, um, uh, or temperature and humidity, for example, all this stuff is then combined together, uh, and then use to develop machine learning models that better inform, uh, predictive maintenance, because we'll do need to keep, uh, to take into account the environmental factors that may cause additional wear and tear on the asset that we're monitoring. So here's some examples of private sector, uh, maintenance use cases that also have broad applicability across the government. For example, one of the busiest airports in Europe is running cloud era on Azure to capture secure and correlate sensor data collected from equipment within the airport, the people moving equipment more specifically, the escalators, the elevators, and the baggage carousels. >>The objective here is to prevent breakdowns and improve airport efficiency and passenger safety. Another example is a container shipping port. In this case, we use IOT data and machine learning, help customers recognize how their cargo handling equipment is performing in different weather conditions to understand how usage relates to failure rates and to detect anomalies and transport systems. These all improve for another example is Navistar Navistar, leading manufacturer of commercial trucks, buses, and military vehicles. Typically vehicle maintenance, as Cindy mentioned, is based on miles traveled or based on a schedule or a time since the last service. But these are only two of the thousands of data points that can signal the need for maintenance. And as it turns out, unscheduled maintenance and vehicle breakdowns account for a large share of the total cost for vehicle owner. So to help fleet owners move from a reactive approach to a more predictive model, Navistar built an IOT enabled remote diagnostics platform called on command. >>The platform brings in over 70 sensor data feeds for more than 375,000 connected vehicles. These include engine performance, trucks, speed, acceleration, cooling temperature, and break where this data is then correlated with other Navistar and third-party data sources, including weather geo location, vehicle usage, traffic warranty, and parts inventory information. So the platform then uses machine learning and advanced analytics to automatically detect problems early and predict maintenance requirements. So how does the fleet operator use this information? They can monitor truck health and performance from smartphones or tablets and prioritize needed repairs. Also, they can identify that the nearest service location that has the relevant parts, the train technicians and the available service space. So sort of wrapping up the, the benefits Navistar's helped fleet owners reduce maintenance by more than 30%. The same platform is also used to help school buses run safely. And on time, for example, one school district with 110 buses that travel over a million miles annually reduce the number of PTOs needed year over year, thanks to predictive insights delivered by this platform. >>So I'd like to take a moment and walk through the data. Life cycle is depicted in this diagram. So data ingest from the edge may include feeds from the factory floor or things like connected vehicles, whether they're trucks, aircraft, heavy equipment, cargo vessels, et cetera. Next, the data lands on a secure and governed data platform. Whereas combined with data from existing systems of record to provide additional insights, and this platform supports multiple analytic functions working together on the same data while maintaining strict security governance and control measures once processed the data is used to train machine learning models, which are then deployed into production, monitored, and retrained as needed to maintain accuracy. The process data is also typically placed in a data warehouse and use to support business intelligence, analytics, and dashboards. And in fact, this data lifecycle is representative of one of our government customers doing condition-based maintenance across a variety of aircraft. >>And the benefits they've discovered include less unscheduled maintenance and a reduction in mean man hours to repair increased maintenance efficiencies, improved aircraft availability, and the ability to avoid cascading component failures, which typically cost more in repair cost and downtime. Also, they're able to better forecast the requirements for replacement parts and consumables and last, and certainly very importantly, this leads to enhanced safety. This chart overlays the secure open source Cloudera platform used in support of the data life cycle. We've been discussing Cloudera data flow, the data ingest data movement and real time streaming data query capabilities. So data flow gives us the capability to bring data in from the asset of interest from the internet of things. While the data platform provides a secure governed data lake and visibility across the full machine learning life cycle eliminates silos and streamlines workflows across teams. The platform includes an integrated suite of secure analytic applications. And two that we're specifically calling out here are Cloudera machine learning, which supports the collaborative data science and machine learning environment, which facilitates machine learning and AI and the cloud era data warehouse, which supports the analytics and business intelligence, including those dashboards for leadership Cindy, over to you, Rick, >>Thank you. And I hope that, uh, Rick and I provided you some insights on how predictive maintenance condition-based maintenance is being used and can be used within your respective agency, bringing together, um, data sources that maybe you're having challenges with today. Uh, bringing that, uh, more real-time information in from a streaming perspective, blending that industrial IOT, as well as historical information together to help actually, uh, optimize maintenance and reduce costs within the, uh, each of your agencies, uh, to learn a little bit more about Cloudera, um, and our, what we're doing from a predictive maintenance please, uh, business@cloudera.com solutions slash public sector. And we look forward to scheduling a meeting with you, and on that, we appreciate your time today and thank you very much.

>>Hi, this is Cindy Mikey, vice president of industry solutions at Cloudera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and Shev we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud is an attempt to obtain something about a value through unwelcomed. Misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external are perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's broad stroke areas? What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has its, um, uh, underpinnings in quite a few different on government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to lock at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case, that shadow is going to talk about later it's how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So quite a different variety of data and the, the breadth, um, and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a sense, looking at a more extensive on data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be, um, investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like, uh, constituent, are there areas where we're seeing, uh, um, other aspects of, of fraud potentially being, uh, occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at simulation, Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, the public sector. >>Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to Chevy to talk about, uh, the reference architecture for doing these buckets. >>Sure. Yeah. Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection, using Cloudera as underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or anomalous behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create from normal behavior profiles and these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jace on or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to know downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin is going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer API APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transform data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on from machine learning, right? So the next step in the architecture is to leverage, uh, clutter SQL stream builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real-time. Uh, I'll, you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage Q2, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed, and we've our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, even deep learning techniques with neural networks. Uh, and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the X one, uh, scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. Uh, and this entire pipeline is powered by clutters technology. Uh, Cindy, next slide please. >>Right. And so, uh, the IRS is one of, uh, clutter as customers. That's leveraging our platform today and implementing a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, uh, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around how called areas working in federal government, by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us today. Uh, we greatly appreciate your time and look forward to future conversations. Thank you.

>>Individuals create developers, translate ideas to code, to create great applications and great applications. Touch everyone. A Docker. We know that collaboration is key to your innovation sharing ideas, working together. Launching the most secure applications. Docker is with you wherever your team innovates, whether it be robots or autonomous cars, we're doing research to save lives during a pandemic, revolutionizing, how to buy and sell goods online, or even going into the unknown frontiers of space. Docker is launching innovation everywhere. Join us on the journey to build, share, run the future. >>Hello and welcome to Docker con 2021. We're incredibly excited to have more than 80,000 of you join us today from all over the world. As it was last year, this year at DockerCon is 100% virtual and 100% free. So as to enable as many community members as possible to join us now, 100%. Virtual is also an acknowledgement of the continuing global pandemic in particular, the ongoing tragedies in India and Brazil, the Docker community is a global one. And on behalf of all Dr. Khan attendees, we are donating $10,000 to UNICEF support efforts to fight the virus in those countries. Now, even in those regions of the world where the pandemic is being brought under control, virtual first is the new normal. It's been a challenging transition. This includes our team here at Docker. And we know from talking with many of you that you and your developer teams are challenged by this as well. So to help application development teams better collaborate and ship faster, we've been working on some powerful new features and we thought it would be fun to start off with a demo of those. How about it? Want to have a look? All right. Then no further delay. I'd like to introduce Youi Cal and Ben, gosh, over to you and Ben >>Morning, Ben, thanks for jumping on real quick. >>Have you seen the email from Scott? The one about updates and the docs landing page Smith, the doc combat and more prominence. >>Yeah. I've got something working on my local machine. I haven't committed anything yet. I was thinking we could try, um, that new Docker dev environments feature. >>Yeah, that's cool. So if you hit the share button, what I should do is it will take all of your code and the dependencies and the image you're basing it on and wrap that up as one image for me. And I can then just monitor all my machines that have been one click, like, and then have it side by side, along with the changes I've been looking at as well, because I was also having a bit of a look and then I can really see how it differs to what I'm doing. Maybe I can combine it to do the best of both worlds. >>Sounds good. Uh, let me get that over to you, >>Wilson. Yeah. If you pay with the image name, I'll get that started up. >>All right. Sen send it over >>Cheesy. Okay, great. Let's have a quick look at what you he was doing then. So I've been messing around similar to do with the batter. I've got movie at the top here and I think it looks pretty cool. Let's just grab that image from you. Pick out that started on a dev environment. What this is doing. It's just going to grab the image down, which you can take all of the code, the dependencies only get brunches working on and I'll get that opened up in my idea. Ready to use. It's a here close. We can see our environment as my Molly image, just coming down there and I've got my new idea. >>We'll load this up and it'll just connect to my dev environment. There we go. It's connected to the container. So we're working all in the container here and now give it a moment. What we'll do is we'll see what changes you've been making as well on the code. So it's like she's been working on a landing page as well, and it looks like she's been changing the banner as well. So let's get this running. Let's see what she's actually doing and how it looks. We'll set up our checklist and then we'll see how that works. >>Great. So that's now rolling. So let's just have a look at what you use doing what changes she had made. Compare those to mine just jumped back into my dev container UI, see that I've got both of those running side by side with my changes and news changes. Okay. So she's put Molly up there rather than mobi or somebody had the same idea. So I think in a way I can make us both happy. So if we just jumped back into what we'll do, just add Molly and Moby and here I'll save that. And what we can see is, cause I'm just working within the container rather than having to do sort of rebuild of everything or serve, or just reload my content. No, that's straight the page. So what I can then do is I can come up with my browser here. Once that's all refreshed, refresh the page once hopefully, maybe twice, we should then be able to see your refresh it or should be able to see that we get Malia mobi come up. So there we go, got Molly mobi. So what we'll do now is we'll describe that state. It sends us our image and then we'll just create one of those to share with URI or share. And we'll get a link for that. I guess we'll send that back over to you. >>So I've had a look at what you were doing and I'm actually going to change. I think that might work for both of us. I wondered if you could take a look at it. If I send it over. >>Sounds good. Let me grab the link. >>Yeah, it's a dev environment link again. So if you just open that back in the doc dashboard, it should be able to open up the code that I've changed and then just run it in the same way you normally do. And that shouldn't interrupt what you're already working on because there'll be able to run side by side with your other brunch. You already got, >>Got it. Got it. Loading here. Well, that's great. It's Molly and movie together. I love it. I think we should ship it. >>Awesome. I guess it's chip it and get on with the rest of.com. Wasn't that cool. Thank you Joey. Thanks Ben. Everyone we'll have more of this later in the keynote. So stay tuned. Let's say earlier, we've all been challenged by this past year, whether the COVID pandemic, the complete evaporation of customer demand in many industries, unemployment or business bankruptcies, we all been touched in some way. And yet, even to miss these tragedies last year, we saw multiple sources of hope and inspiration. For example, in response to COVID we saw global communities, including the tech community rapidly innovate solutions for analyzing the spread of the virus, sequencing its genes and visualizing infection rates. In fact, if all in teams collaborating on solutions for COVID have created more than 1,400 publicly shareable images on Docker hub. As another example, we all witnessed the historic landing and exploration of Mars by the perseverance Rover and its ingenuity drone. >>Now what's common in these examples, these innovative and ambitious accomplishments were made possible not by any single individual, but by teams of individuals collaborating together. The power of teams is why we've made development teams central to Docker's mission to build tools and content development teams love to help them get their ideas from code to cloud as quickly as possible. One of the frictions we've seen that can slow down to them in teams is that the path from code to cloud can be a confusing one, riddle with multiple point products, tools, and images that need to be integrated and maintained an automated pipeline in order for teams to be productive. That's why a year and a half ago we refocused Docker on helping development teams make sense of all this specifically, our goal is to provide development teams with the trusted content, the sharing capabilities and the pipeline integrations with best of breed third-party tools to help teams ship faster in short, to provide a collaborative application development platform. >>Everything a team needs to build. Sharon run create applications. Now, as I noted earlier, it's been a challenging year for everyone on our planet and has been similar for us here at Docker. Our team had to adapt to working from home local lockdowns caused by the pandemic and other challenges. And despite all this together with our community and ecosystem partners, we accomplished many exciting milestones. For example, in open source together with the community and our partners, we open sourced or made major contributions to many projects, including OCI distribution and the composed plugins building on these open source projects. We had powerful new capabilities to the Docker product, both free and subscription. For example, support for WSL two and apple, Silicon and Docker, desktop and vulnerability scanning audit logs and image management and Docker hub. >>And finally delivering an easy to use well-integrated development experience with best of breed tools and content is only possible through close collaboration with our ecosystem partners. For example, this last year we had over 100 commercialized fees, join our Docker verified publisher program and over 200 open source projects, join our Docker sponsored open source program. As a result of these efforts, we've seen some exciting growth in the Docker community in the 12 months since last year's Docker con for example, the number of registered developers grew 80% to over 8 million. These developers created many new images increasing the total by 56% to almost 11 million. And the images in all these repositories were pulled by more than 13 million monthly active IP addresses totaling 13 billion pulls a month. Now while the growth is exciting by Docker, we're even more excited about the stories we hear from you and your development teams about how you're using Docker and its impact on your businesses. For example, cancer researchers and their bioinformatics development team at the Washington university school of medicine needed a way to quickly analyze their clinical trial results and then share the models, the data and the analysis with other researchers they use Docker because it gives them the ease of use choice of pipeline tools and speed of sharing so critical to their research. And most importantly to the lives of their patients stay tuned for another powerful customer story later in the keynote from Matt fall, VP of engineering at Oracle insights. >>So with this last year behind us, what's next for Docker, but challenge you this last year of force changes in how development teams work, but we felt for years to come. And what we've learned in our discussions with you will have long lasting impact on our product roadmap. One of the biggest takeaways from those discussions that you and your development team want to be quicker to adapt, to changes in your environment so you can ship faster. So what is DACA doing to help with this first trusted content to own the teams that can focus their energies on what is unique to their businesses and spend as little time as possible on undifferentiated work are able to adapt more quickly and ship faster in order to do so. They need to be able to trust other components that make up their app together with our partners. >>Docker is doubling down and providing development teams with trusted content and the tools they need to use it in their applications. Second, remote collaboration on a development team, asking a coworker to take a look at your code used to be as easy as swiveling their chair around, but given what's happened in the last year, that's no longer the case. So as you even been hinted in the demo at the beginning, you'll see us deliver more capabilities for remote collaboration within a development team. And we're enabling development team to quickly adapt to any team configuration all on prem hybrid, all work from home, helping them remain productive and focused on shipping third ecosystem integrations, those development teams that can quickly take advantage of innovations throughout the ecosystem. Instead of getting locked into a single monolithic pipeline, there'll be the ones able to deliver amps, which impact their businesses faster. >>So together with our ecosystem partners, we are investing in more integrations with best of breed tools, right? Integrated automated app pipelines. Furthermore, we'll be writing more public API APIs and SDKs to enable ecosystem partners and development teams to roll their own integrations. We'll be sharing more details about remote collaboration and ecosystem integrations. Later in the keynote, I'd like to take a moment to share with Docker and our partners are doing for trusted content, providing development teams, access to content. They can trust, allows them to focus their coding efforts on what's unique and differentiated to that end Docker and our partners are bringing more and more trusted content to Docker hub Docker official images are 160 images of popular upstream open source projects that serve as foundational building blocks for any application. These include operating systems, programming, languages, databases, and more. Furthermore, these are updated patch scan and certified frequently. So I said, no image is older than 30 days. >>Docker verified publisher images are published by more than 100 commercialized feeds. The image Rebos are explicitly designated verify. So the developers searching for components for their app know that the ISV is actively maintaining the image. Docker sponsored open source projects announced late last year features images for more than 200 open source communities. Docker sponsors these communities through providing free storage and networking resources and offering their community members unrestricted access repos for businesses allow businesses to update and share their apps privately within their organizations using role-based access control and user authentication. No, and finally, public repos for communities enable community projects to be freely shared with anonymous and authenticated users alike. >>And for all these different types of content, we provide services for both development teams and ISP, for example, vulnerability scanning and digital signing for enhanced security search and filtering for discoverability packaging and updating services and analytics about how these products are being used. All this trusted content, we make available to develop teams for them directly to discover poll and integrate into their applications. Our goal is to meet development teams where they live. So for those organizations that prefer to manage their internal distribution of trusted content, we've collaborated with leading container registry partners. We announced our partnership with J frog late last year. And today we're very pleased to announce our partnerships with Amazon and Miranda's for providing an integrated seamless experience for joint for our joint customers. Lastly, the container images themselves and this end to end flow are built on open industry standards, which provided all the teams with flexibility and choice trusted content enables development teams to rapidly build. >>As I let them focus on their unique differentiated features and use trusted building blocks for the rest. We'll be talking more about trusted content as well as remote collaboration and ecosystem integrations later in the keynote. Now ecosystem partners are not only integral to the Docker experience for development teams. They're also integral to a great DockerCon experience, but please join me in thanking our Dr. Kent on sponsors and checking out their talks throughout the day. I also want to thank some others first up Docker team. Like all of you this last year has been extremely challenging for us, but the Docker team rose to the challenge and worked together to continue shipping great product, the Docker community of captains, community leaders, and contributors with your welcoming newcomers, enthusiasm for Docker and open exchanges of best practices and ideas talker, wouldn't be Docker without you. And finally, our development team customers. >>You trust us to help you build apps. Your businesses rely on. We don't take that trust for granted. Thank you. In closing, we often hear about the tenant's developer capable of great individual feeds that can transform project. But I wonder if we, as an industry have perhaps gotten this wrong by putting so much emphasis on weight, on the individual as discussed at the beginning, great accomplishments like innovative responses to COVID-19 like landing on Mars are more often the results of individuals collaborating together as a team, which is why our mission here at Docker is delivered tools and content developers love to help their team succeed and become 10 X teams. Thanks again for joining us, we look forward to having a great DockerCon with you today, as well as a great year ahead of us. Thanks and be well. >>Hi, I'm Dana Lawson, VP of engineering here at get hub. And my job is to enable this rich interconnected community of builders and makers to build even more and hopefully have a great time doing it in order to enable the best platform for developers, which I know is something we are all passionate about. We need to partner across the ecosystem to ensure that developers can have a great experience across get hub and all the tools that they want to use. No matter what they are. My team works to build the tools and relationships to make that possible. I am so excited to join Scott on this virtual stage to talk about increasing developer velocity. So let's dive in now, I know this may be hard for some of you to believe, but as a former CIS admin, some 21 years ago, working on sense spark workstations, we've come such a long way for random scripts and desperate systems that we've stitched together to this whole inclusive developer workflow experience being a CIS admin. >>Then you were just one piece of the siloed experience, but I didn't want to just push code to production. So I created scripts that did it for me. I taught myself how to code. I was the model lazy CIS admin that got dangerous and having pushed a little too far. I realized that working in production and building features is really a team sport that we had the opportunity, all of us to be customer obsessed today. As developers, we can go beyond the traditional dev ops mindset. We can really focus on adding value to the customer experience by ensuring that we have work that contributes to increasing uptime via and SLS all while being agile and productive. We get there. When we move from a pass the Baton system to now having an interconnected developer workflow that increases velocity in every part of the cycle, we get to work better and smarter. >>And honestly, in a way that is so much more enjoyable because we automate away all the mundane and manual and boring tasks. So we get to focus on what really matters shipping, the things that humans get to use and love. Docker has been a big part of enabling this transformation. 10, 20 years ago, we had Tomcat containers, which are not Docker containers. And for y'all hearing this the first time go Google it. But that was the way we built our applications. We had to segment them on the server and give them resources. Today. We have Docker containers, these little mini Oasys and Docker images. You can do it multiple times in an orchestrated manner with the power of actions enabled and Docker. It's just so incredible what you can do. And by the way, I'm showing you actions in Docker, which I hope you use because both are great and free for open source. >>But the key takeaway is really the workflow and the automation, which you certainly can do with other tools. Okay, I'm going to show you just how easy this is, because believe me, if this is something I can learn and do anybody out there can, and in this demo, I'll show you about the basic components needed to create and use a package, Docker container actions. And like I said, you won't believe how awesome the combination of Docker and actions is because you can enable your workflow to do no matter what you're trying to do in this super baby example. We're so small. You could take like 10 seconds. Like I am here creating an action due to a simple task, like pushing a message to your logs. And the cool thing is you can use it on any the bit on this one. Like I said, we're going to use push. >>You can do, uh, even to order a pizza every time you roll into production, if you wanted, but at get hub, that'd be a lot of pizzas. And the funny thing is somebody out there is actually tried this and written that action. If you haven't used Docker and actions together, check out the docs on either get hub or Docker to get you started. And a huge shout out to all those doc writers out there. I built this demo today using those instructions. And if I can do it, I know you can too, but enough yapping let's get started to save some time. And since a lot of us are Docker and get hub nerds, I've already created a repo with a Docker file. So we're going to skip that step. Next. I'm going to create an action's Yammel file. And if you don't Yammer, you know, actions, the metadata defines my important log stuff to capture and the input and my time out per parameter to pass and puts to the Docker container, get up a build image from your Docker file and run the commands in a new container. >>Using the Sigma image. The cool thing is, is you can use any Docker image in any language for your actions. It doesn't matter if it's go or whatever in today's I'm going to use a shell script and an input variable to print my important log stuff to file. And like I said, you know me, I love me some. So let's see this action in a workflow. When an action is in a private repo, like the one I demonstrating today, the action can only be used in workflows in the same repository, but public actions can be used by workflows in any repository. So unfortunately you won't get access to the super awesome action, but don't worry in the Guild marketplace, there are over 8,000 actions available, especially the most important one, that pizza action. So go try it out. Now you can do this in a couple of ways, whether you're doing it in your preferred ID or for today's demo, I'm just going to use the gooey. I'm going to navigate to my actions tab as I've done here. And I'm going to in my workflow, select new work, hello, probably load some workflows to Claire to get you started, but I'm using the one I've copied. Like I said, the lazy developer I am in. I'm going to replace it with my action. >>That's it. So now we're going to go and we're going to start our commitment new file. Now, if we go over to our actions tab, we can see the workflow in progress in my repository. I just click the actions tab. And because they wrote the actions on push, we can watch the visualization under jobs and click the job to see the important stuff we're logging in the input stamp in the printed log. And we'll just wait for this to run. Hello, Mona and boom. Just like that. It runs automatically within our action. We told it to go run as soon as the files updated because we're doing it on push merge. That's right. Folks in just a few minutes, I built an action that writes an entry to a log file every time I push. So I don't have to do it manually. In essence, with automation, you can be kind to your future self and save time and effort to focus on what really matters. >>Imagine what I could do with even a little more time, probably order all y'all pieces. That is the power of the interconnected workflow. And it's amazing. And I hope you all go try it out, but why do we care about all of that? Just like in the demo, I took a manual task with both tape, which both takes time and it's easy to forget and automated it. So I don't have to think about it. And it's executed every time consistently. That means less time for me to worry about my human errors and mistakes, and more time to focus on actually building the cool stuff that people want. Obviously, automation, developer productivity, but what is even more important to me is the developer happiness tools like BS, code actions, Docker, Heroku, and many others reduce manual work, which allows us to focus on building things that are awesome. >>And to get into that wonderful state that we call flow. According to research by UC Irvine in Humboldt university in Germany, it takes an average of 23 minutes to enter optimal creative state. What we call the flow or to reenter it after distraction like your dog on your office store. So staying in flow is so critical to developer productivity and as a developer, it just feels good to be cranking away at something with deep focus. I certainly know that I love that feeling intuitive collaboration and automation features we built in to get hub help developer, Sam flow, allowing you and your team to do so much more, to bring the benefits of automation into perspective in our annual October's report by Dr. Nicole, Forsgren. One of my buddies here at get hub, took a look at the developer productivity in the stork year. You know what we found? >>We found that public GitHub repositories that use the Automational pull requests, merge those pull requests. 1.2 times faster. And the number of pooled merged pull requests increased by 1.3 times, that is 34% more poor requests merged. And other words, automation can con can dramatically increase, but the speed and quantity of work completed in any role, just like an open source development, you'll work more efficiently with greater impact when you invest the bulk of your time in the work that adds the most value and eliminate or outsource the rest because you don't need to do it, make the machines by elaborate by leveraging automation in their workflows teams, minimize manual work and reclaim that time for innovation and maintain that state of flow with development and collaboration. More importantly, their work is more enjoyable because they're not wasting the time doing the things that the machines or robots can do for them. >>And I remember what I said at the beginning. Many of us want to be efficient, heck even lazy. So why would I spend my time doing something I can automate? Now you can read more about this research behind the art behind this at October set, get hub.com, which also includes a lot of other cool info about the open source ecosystem and how it's evolving. Speaking of the open source ecosystem we at get hub are so honored to be the home of more than 65 million developers who build software together for everywhere across the globe. Today, we're seeing software development taking shape as the world's largest team sport, where development teams collaborate, build and ship products. It's no longer a solo effort like it was for me. You don't have to take my word for it. Check out this globe. This globe shows real data. Every speck of light you see here represents a contribution to an open source project, somewhere on earth. >>These arts reach across continents, cultures, and other divides. It's distributed collaboration at its finest. 20 years ago, we had no concept of dev ops, SecOps and lots, or the new ops that are going to be happening. But today's development and ops teams are connected like ever before. This is only going to continue to evolve at a rapid pace, especially as we continue to empower the next hundred million developers, automation helps us focus on what's important and to greatly accelerate innovation. Just this past year, we saw some of the most groundbreaking technological advancements and achievements I'll say ever, including critical COVID-19 vaccine trials, as well as the first power flight on Mars. This past month, these breakthroughs were only possible because of the interconnected collaborative open source communities on get hub and the amazing tools and workflows that empower us all to create and innovate. Let's continue building, integrating, and automating. So we collectively can give developers the experience. They deserve all of the automation and beautiful eye UIs that we can muster so they can continue to build the things that truly do change the world. Thank you again for having me today, Dr. Khan, it has been a pleasure to be here with all you nerds. >>Hello. I'm Justin. Komack lovely to see you here. Talking to developers, their world is getting much more complex. Developers are being asked to do everything security ops on goal data analysis, all being put on the rockers. Software's eating the world. Of course, and this all make sense in that view, but they need help. One team. I told you it's shifted all our.net apps to run on Linux from windows, but their developers found the complexity of Docker files based on the Linux shell scripts really difficult has helped make these things easier for your teams. Your ones collaborate more in a virtual world, but you've asked us to make this simpler and more lightweight. You, the developers have asked for a paved road experience. You want things to just work with a simple options to be there, but it's not just the paved road. You also want to be able to go off-road and do interesting and different things. >>Use different components, experiments, innovate as well. We'll always offer you both those choices at different times. Different developers want different things. It may shift for ones the other paved road or off road. Sometimes you want reliability, dependability in the zone for day to day work, but sometimes you have to do something new, incorporate new things in your pipeline, build applications for new places. Then you knew those off-road abilities too. So you can really get under the hood and go and build something weird and wonderful and amazing. That gives you new options. Talk as an independent choice. We don't own the roads. We're not pushing you into any technology choices because we own them. We're really supporting and driving open standards, such as ISEI working opensource with the CNCF. We want to help you get your applications from your laptops, the clouds, and beyond, even into space. >>Let's talk about the key focus areas, that frame, what DACA is doing going forward. These are simplicity, sharing, flexibility, trusted content and care supply chain compared to building where the underlying kernel primitives like namespaces and Seagraves the original Docker CLI was just amazing Docker engine. It's a magical experience for everyone. It really brought those innovations and put them in a world where anyone would use that, but that's not enough. We need to continue to innovate. And it was trying to get more done faster all the time. And there's a lot more we can do. We're here to take complexity away from deeply complicated underlying things and give developers tools that are just amazing and magical. One of the area we haven't done enough and make things magical enough that we're really planning around now is that, you know, Docker images, uh, they're the key parts of your application, but you know, how do I do something with an image? How do I, where do I attach volumes with this image? What's the API. Whereas the SDK for this image, how do I find an example or docs in an API driven world? Every bit of software should have an API and an API description. And our vision is that every container should have this API description and the ability for you to understand how to use it. And it's all a seamless thing from, you know, from your code to the cloud local and remote, you can, you can use containers in this amazing and exciting way. >>One thing I really noticed in the last year is that companies that started off remote fast have constant collaboration. They have zoom calls, apron all day terminals, shattering that always working together. Other teams are really trying to learn how to do this style because they didn't start like that. We used to walk around to other people's desks or share services on the local office network. And it's very difficult to do that anymore. You want sharing to be really simple, lightweight, and informal. Let me try your container or just maybe let's collaborate on this together. Um, you know, fast collaboration on the analysts, fast iteration, fast working together, and he wants to share more. You want to share how to develop environments, not just an image. And we all work by seeing something someone else in our team is doing saying, how can I do that too? I can, I want to make that sharing really, really easy. Ben's going to talk about this more in the interest of one minute. >>We know how you're excited by apple. Silicon and gravis are not excited because there's a new architecture, but excited because it's faster, cooler, cheaper, better, and offers new possibilities. The M one support was the most asked for thing on our public roadmap, EFA, and we listened and share that we see really exciting possibilities, usership arm applications, all the way from desktop to production. We know that you all use different clouds and different bases have deployed to, um, you know, we work with AWS and Azure and Google and more, um, and we want to help you ship on prime as well. And we know that you use huge number of languages and the containers help build applications that use different languages for different parts of the application or for different applications, right? You can choose the best tool. You have JavaScript hat or everywhere go. And re-ask Python for data and ML, perhaps getting excited about WebAssembly after hearing about a cube con, you know, there's all sorts of things. >>So we need to make that as easier. We've been running the whole month of Python on the blog, and we're doing a month of JavaScript because we had one specific support about how do I best put this language into production of that language into production. That detail is important for you. GPS have been difficult to use. We've added GPS suppose in desktop for windows, but we know there's a lot more to do to make the, how multi architecture, multi hardware, multi accelerator world work better and also securely. Um, so there's a lot more work to do to support you in all these things you want to do. >>How do we start building a tenor has applications, but it turns out we're using existing images as components. I couldn't assist survey earlier this year, almost half of container image usage was public images rather than private images. And this is growing rapidly. Almost all software has open source components and maybe 85% of the average application is open source code. And what you're doing is taking whole container images as modules in your application. And this was always the model with Docker compose. And it's a model that you're already et cetera, writing you trust Docker, official images. We know that they might go to 25% of poles on Docker hub and Docker hub provides you the widest choice and the best support that trusted content. We're talking to people about how to make this more helpful. We know, for example, that winter 69 four is just showing us as support, but the image doesn't yet tell you that we're working with canonical to improve messaging from specific images about left lifecycle and support. >>We know that you need more images, regularly updated free of vulnerabilities, easy to use and discover, and Donnie and Marie neuro, going to talk about that more this last year, the solar winds attack has been in the, in the news. A lot, the software you're using and trusting could be compromised and might be all over your organization. We need to reduce the risk of using vital open-source components. We're seeing more software supply chain attacks being targeted as the supply chain, because it's often an easier place to attack and production software. We need to be able to use this external code safely. We need to, everyone needs to start from trusted sources like photography images. They need to scan for known vulnerabilities using Docker scan that we built in partnership with sneak and lost DockerCon last year, we need just keep updating base images and dependencies, and we'll, we're going to help you have the control and understanding about your images that you need to do this. >>And there's more, we're also working on the nursery V2 project in the CNCF to revamp container signings, or you can tell way or software comes from we're working on tooling to make updates easier, and to help you understand and manage all the principals carrier you're using security is a growing concern for all of us. It's really important. And we're going to help you work with security. We can't achieve all our dreams, whether that's space travel or amazing developer products ever see without deep partnerships with our community to cloud is RA and the cloud providers aware most of you ship your occasion production and simple routes that take your work and deploy it easily. Reliably and securely are really important. Just get into production simply and easily and securely. And we've done a bunch of work on that. And, um, but we know there's more to do. >>The CNCF on the open source cloud native community are an amazing ecosystem of creators and lovely people creating an amazing strong community and supporting a huge amount of innovation has its roots in the container ecosystem and his dreams beyond that much of the innovation is focused around operate experience so far, but developer experience is really a growing concern in that community as well. And we're really excited to work on that. We also uses appraiser tool. Then we know you do, and we know that you want it to be easier to use in your environment. We just shifted Docker hub to work on, um, Kubernetes fully. And, um, we're also using many of the other projects are Argo from atheists. We're spending a lot of time working with Microsoft, Amazon right now on getting natural UV to ready to ship in the next few. That's a really detailed piece of collaboration we've been working on for a long term. Long time is really important for our community as the scarcity of the container containers and, um, getting content for you, working together makes us stronger. Our community is made up of all of you have. Um, it's always amazing to be reminded of that as a huge open source community that we already proud to work with. It's an amazing amount of innovation that you're all creating and where perhaps it, what with you and share with you as well. Thank you very much. And thank you for being here. >>Really excited to talk to you today and share more about what Docker is doing to help make you faster, make your team faster and turn your application delivery into something that makes you a 10 X team. What we're hearing from you, the developers using Docker everyday fits across three common themes that we hear consistently over and over. We hear that your time is super important. It's critical, and you want to move faster. You want your tools to get out of your way, and instead to enable you to accelerate and focus on the things you want to be doing. And part of that is that finding great content, great application components that you can incorporate into your apps to move faster is really hard. It's hard to discover. It's hard to find high quality content that you can trust that, you know, passes your test and your configuration needs. >>And it's hard to create good content as well. And you're looking for more safety, more guardrails to help guide you along that way so that you can focus on creating value for your company. Secondly, you're telling us that it's a really far to collaborate effectively with your team and you want to do more, to work more effectively together to help your tools become more and more seamless to help you stay in sync, both with yourself across all of your development environments, as well as with your teammates so that you can more effectively collaborate together. Review each other's work, maintain things and keep them in sync. And finally, you want your applications to run consistently in every single environment, whether that's your local development environment, a cloud-based development environment, your CGI pipeline, or the cloud for production, and you want that micro service to provide that consistent experience everywhere you go so that you have similar tools, similar environments, and you don't need to worry about things getting in your way, but instead things make it easy for you to focus on what you wanna do and what Docker is doing to help solve all of these problems for you and your colleagues is creating a collaborative app dev platform. >>And this collaborative application development platform consists of multiple different pieces. I'm not going to walk through all of them today, but the overall view is that we're providing all the tooling you need from the development environment, to the container images, to the collaboration services, to the pipelines and integrations that enable you to focus on making your applications amazing and changing the world. If we start zooming on a one of those aspects, collaboration we hear from developers regularly is that they're challenged in synchronizing their own setups across environments. They want to be able to duplicate the setup of their teammates. Look, then they can easily get up and running with the same applications, the same tooling, the same version of the same libraries, the same frameworks. And they want to know if their applications are good before they're ready to share them in an official space. >>They want to collaborate on things before they're done, rather than feeling like they have to officially published something before they can effectively share it with others to work on it, to solve this. We're thrilled today to announce Docker, dev environments, Docker, dev environments, transform how your team collaborates. They make creating, sharing standardized development environments. As simple as a Docker poll, they make it easy to review your colleagues work without affecting your own work. And they increase the reproducibility of your own work and decreased production issues in doing so because you've got consistent environments all the way through. Now, I'm going to pass it off to our principal product manager, Ben Gotch to walk you through more detail on Docker dev environments. >>Hi, I'm Ben. I work as a principal program manager at DACA. One of the areas that doc has been looking at to see what's hard today for developers is sharing changes that you make from the inner loop where the inner loop is a better development, where you write code, test it, build it, run it, and ultimately get feedback on those changes before you merge them and try and actually ship them out to production. Most amount of us build this flow and get there still leaves a lot of challenges. People need to jump between branches to look at each other's work. Independence. Dependencies can be different when you're doing that and doing this in this new hybrid wall of work. Isn't any easier either the ability to just save someone, Hey, come and check this out. It's become much harder. People can't come and sit down at your desk or take your laptop away for 10 minutes to just grab and look at what you're doing. >>A lot of the reason that development is hard when you're remote, is that looking at changes and what's going on requires more than just code requires all the dependencies and everything you've got set up and that complete context of your development environment, to understand what you're doing and solving this in a remote first world is hard. We wanted to look at how we could make this better. Let's do that in a way that let you keep working the way you do today. Didn't want you to have to use a browser. We didn't want you to have to use a new idea. And we wanted to do this in a way that was application centric. We wanted to let you work with all the rest of the application already using C for all the services and all those dependencies you need as part of that. And with that, we're excited to talk more about docket developer environments, dev environments are new part of the Docker experience that makes it easier you to get started with your whole inner leap, working inside a container, then able to share and collaborate more than just the code. >>We want it to enable you to share your whole modern development environment, your whole setup from DACA, with your team on any operating system, we'll be launching a limited beta of dev environments in the coming month. And a GA dev environments will be ID agnostic and supporting composts. This means you'll be able to use an extend your existing composed files to create your own development environment in whatever idea, working in dev environments designed to be local. First, they work with Docker desktop and say your existing ID, and let you share that whole inner loop, that whole development context, all of your teammates in just one collect. This means if you want to get feedback on the working progress change or the PR it's as simple as opening another idea instance, and looking at what your team is working on because we're using compose. You can just extend your existing oppose file when you're already working with, to actually create this whole application and have it all working in the context of the rest of the services. >>So it's actually the whole environment you're working with module one service that doesn't really understand what it's doing alone. And with that, let's jump into a quick demo. So you can see here, two dev environments up and running. First one here is the same container dev environment. So if I want to go into that, let's see what's going on in the various code button here. If that one open, I can get straight into my application to start making changes inside that dev container. And I've got all my dependencies in here, so I can just run that straight in that second application I have here is one that's opened up in compose, and I can see that I've also got my backend, my front end and my database. So I've got all my services running here. So if I want, I can open one or more of these in a dev environment, meaning that that container has the context that dev environment has the context of the whole application. >>So I can get back into and connect to all the other services that I need to test this application properly, all of them, one unit. And then when I've made my changes and I'm ready to share, I can hit my share button type in the refund them on to share that too. And then give that image to someone to get going, pick that up and just start working with that code and all my dependencies, simple as putting an image, looking ahead, we're going to be expanding development environments, more of your dependencies for the whole developer worst space. We want to look at backing up and letting you share your volumes to make data science and database setups more repeatable and going. I'm still all of this under a single workspace for your team containing images, your dev environments, your volumes, and more we've really want to allow you to create a fully portable Linux development environment. >>So everyone you're working with on any operating system, as I said, our MVP we're coming next month. And that was for vs code using their dev container primitive and more support for other ideas. We'll follow to find out more about what's happening and what's coming up next in the future of this. And to actually get a bit of a deeper dive in the experience. Can we check out the talk I'm doing with Georgie and girl later on today? Thank you, Ben, amazing story about how Docker is helping to make developer teams more collaborative. Now I'd like to talk more about applications while the dev environment is like the workbench around what you're building. The application itself has all the different components, libraries, and frameworks, and other code that make up the application itself. And we hear developers saying all the time things like, how do they know if their images are good? >>How do they know if they're secure? How do they know if they're minimal? How do they make great images and great Docker files and how do they keep their images secure? And up-to-date on every one of those ties into how do I create more trust? How do I know that I'm building high quality applications to enable you to do this even more effectively than today? We are pleased to announce the DACA verified polisher program. This broadens trusted content by extending beyond Docker official images, to give you more and more trusted building blocks that you can incorporate into your applications. It gives you confidence that you're getting what you expect because Docker verifies every single one of these publishers to make sure they are who they say they are. This improves our secure supply chain story. And finally it simplifies your discovery of the best building blocks by making it easy for you to find things that you know, you can trust so that you can incorporate them into your applications and move on and on the right. You can see some examples of the publishers that are involved in Docker, official images and our Docker verified publisher program. Now I'm pleased to introduce you to marina. Kubicki our senior product manager who will walk you through more about what we're doing to create a better experience for you around trust. >>Thank you, Dani, >>Mario Andretti, who is a famous Italian sports car driver. One said that if everything feels under control, you're just not driving. You're not driving fast enough. Maya Andretti is not a software developer and a software developers. We know that no matter how fast we need to go in order to drive the innovation that we're working on, we can never allow our applications to spin out of control and a Docker. As we continue talking to our, to the developers, what we're realizing is that in order to reach that speed, the developers are the, the, the development community is looking for the building blocks and the tools that will, they will enable them to drive at the speed that they need to go and have the trust in those building blocks. And in those tools that they will be able to maintain control over their applications. So as we think about some of the things that we can do to, to address those concerns, uh, we're realizing that we can pursue them in a number of different venues, including creating reliable content, including creating partnerships that expands the options for the reliable content. >>Um, in order to, in a we're looking at creating integrations, no link security tools, talk about the reliable content. The first thing that comes to mind are the Docker official images, which is a program that we launched several years ago. And this is a set of curated, actively maintained, open source images that, uh, include, uh, operating systems and databases and programming languages. And it would become immensely popular for, for, for creating the base layers of, of the images of, of the different images, images, and applications. And would we realizing that, uh, many developers are, instead of creating something from scratch, basically start with one of the official images for their basis, and then build on top of that. And this program has become so popular that it now makes up a quarter of all of the, uh, Docker poles, which essentially ends up being several billion pulse every single month. >>As we look beyond what we can do for the open source. Uh, we're very ability on the open source, uh, spectrum. We are very excited to announce that we're launching the Docker verified publishers program, which is continuing providing the trust around the content, but now working with, uh, some of the industry leaders, uh, in multiple, in multiple verticals across the entire technology technical spec, it costs entire, uh, high tech in order to provide you with more options of the images that you can use for building your applications. And it still comes back to trust that when you are searching for content in Docker hub, and you see the verified publisher badge, you know, that this is, this is the content that, that is part of the, that comes from one of our partners. And you're not running the risk of pulling the malicious image from an employee master source. >>As we look beyond what we can do for, for providing the reliable content, we're also looking at some of the tools and the infrastructure that we can do, uh, to create a security around the content that you're creating. So last year at the last ad, the last year's DockerCon, we announced partnership with sneak. And later on last year, we launched our DACA, desktop and Docker hub vulnerability scans that allow you the options of writing scans in them along multiple points in your dev cycle. And in addition to providing you with information on the vulnerability on, on the vulnerabilities, in, in your code, uh, it also provides you with a guidance on how to re remediate those vulnerabilities. But as we look beyond the vulnerability scans, we're also looking at some of the other things that we can do, you know, to, to, to, uh, further ensure that the integrity and the security around your images, your images, and with that, uh, later on this year, we're looking to, uh, launch the scope, personal access tokens, and instead of talking about them, I will simply show you what they look like. >>So if you can see here, this is my page in Docker hub, where I've created a four, uh, tokens, uh, read-write delete, read, write, read only in public read in public creeper read only. So, uh, earlier today I went in and I, I logged in, uh, with my read only token. And when you see, when I'm going to pull an image, it's going to allow me to pull an image, not a problem success. And then when I do the next step, I'm going to ask to push an image into the same repo. Uh, would you see is that it's going to give me an error message saying that they access is denied, uh, because there is an additional authentication required. So these are the things that we're looking to add to our roadmap. As we continue thinking about the things that we can do to provide, um, to provide additional building blocks, content, building blocks, uh, and, and, and tools to build the trust so that our DACA developer and skinned code faster than Mario Andretti could ever imagine. Uh, thank you to >>Thank you, marina. It's amazing what you can do to improve the trusted content so that you can accelerate your development more and move more quickly, move more collaboratively and build upon the great work of others. Finally, we hear over and over as that developers are working on their applications that they're looking for, environments that are consistent, that are the same as production, and that they want their applications to really run anywhere, any environment, any architecture, any cloud one great example is the recent announcement of apple Silicon. We heard from developers on uproar that they needed Docker to be available for that architecture before they could add those to it and be successful. And we listened. And based on that, we are pleased to share with you Docker, desktop on apple Silicon. This enables you to run your apps consistently anywhere, whether that's developing on your team's latest dev hardware, deploying an ARM-based cloud environments and having a consistent architecture across your development and production or using multi-year architecture support, which enables your whole team to collaborate on its application, using private repositories on Docker hub, and thrilled to introduce you to Hughie cower, senior director for product management, who will walk you through more of what we're doing to create a great developer experience. >>Senior director of product management at Docker. And I'd like to jump straight into a demo. This is the Mac mini with the apple Silicon processor. And I want to show you how you can now do an end-to-end arm workflow from my M one Mac mini to raspberry PI. As you can see, we have vs code and Docker desktop installed on a, my, the Mac mini. I have a small example here, and I have a raspberry PI three with an led strip, and I want to turn those LEDs into a moving rainbow. This Dockerfile here, builds the application. We build the image with the Docker, build X command to make the image compatible for all raspberry pies with the arm. 64. Part of this build is built with the native power of the M one chip. I also add the push option to easily share the image with my team so they can give it a try to now Dr. >>Creates the local image with the application and uploads it to Docker hub after we've built and pushed the image. We can go to Docker hub and see the new image on Docker hub. You can also explore a variety of images that are compatible with arm processors. Now let's go to the raspberry PI. I have Docker already installed and it's running Ubuntu 64 bit with the Docker run command. I can run the application and let's see what will happen from there. You can see Docker is downloading the image automatically from Docker hub and when it's running, if it's works right, there are some nice colors. And with that, if we have an end-to-end workflow for arm, where continuing to invest into providing you a great developer experience, that's easy to install. Easy to get started with. As you saw in the demo, if you're interested in the new Mac, mini are interested in developing for our platforms in general, we've got you covered with the same experience you've come to expect from Docker with over 95,000 arm images on hub, including many Docker official images. >>We think you'll find what you're looking for. Thank you again to the community that helped us to test the tech previews. We're so delighted to hear when folks say that the new Docker desktop for apple Silicon, it just works for them, but that's not all we've been working on. As Dani mentioned, consistency of developer experience across environments is so important. We're introducing composed V2 that makes compose a first-class citizen in the Docker CLI you no longer need to install a separate composed biter in order to use composed, deploying to production is simpler than ever with the new compose integration that enables you to deploy directly to Amazon ECS or Azure ACI with the same methods you use to run your application locally. If you're interested in running slightly different services, when you're debugging versus testing or, um, just general development, you can manage that all in one place with the new composed service to hear more about what's new and Docker desktop, please join me in the three 15 breakout session this afternoon. >>And now I'd love to tell you a bit more about bill decks and convince you to try it. If you haven't already it's our next gen build command, and it's no longer experimental as shown in the demo with built X, you'll be able to do multi architecture builds, share those builds with your team and the community on Docker hub. With build X, you can speed up your build processes with remote caches or build all the targets in your composed file in parallel with build X bake. And there's so much more if you're using Docker, desktop or Docker, CE you can use build X checkout tonus is talk this afternoon at three 45 to learn more about build X. And with that, I hope everyone has a great Dr. Khan and back over to you, Donnie. >>Thank you UA. It's amazing to hear about what we're doing to create a better developer experience and make sure that Docker works everywhere you need to work. Finally, I'd like to wrap up by showing you everything that we've announced today and everything that we've done recently to make your lives better and give you more and more for the single price of your Docker subscription. We've announced the Docker verified publisher program we've announced scoped personal access tokens to make it easier for you to have a secure CCI pipeline. We've announced Docker dev environments to improve your collaboration with your team. Uh, we shared with you Docker, desktop and apple Silicon, to make sure that, you know, Docker runs everywhere. You need it to run. And we've announced Docker compose version two, finally making it a first-class citizen amongst all the other great Docker tools. And we've done so much more recently as well from audit logs to advanced image management, to compose service profiles, to improve where you can run Docker more easily. >>Finally, as we look forward, where we're headed in the upcoming year is continuing to invest in these themes of helping you build, share, and run modern apps more effectively. We're going to be doing more to help you create a secure supply chain with which only grows more and more important as time goes on. We're going to be optimizing your update experience to make sure that you can easily understand the current state of your application, all its components and keep them all current without worrying about breaking everything as you're doing. So we're going to make it easier for you to synchronize your work. Using cloud sync features. We're going to improve collaboration through dev environments and beyond, and we're going to do make it easy for you to run your microservice in your environments without worrying about things like architecture or differences between those environments. Thank you so much. I'm thrilled about what we're able to do to help make your lives better. And now you're going to be hearing from one of our customers about what they're doing to launch their business with Docker >>I'm Matt Falk, I'm the head of engineering and orbital insight. And today I want to talk to you a little bit about data from space. So who am I like many of you, I'm a software developer and a software developer about seven companies so far, and now I'm a head of engineering. So I spend most of my time doing meetings, but occasionally I'll still spend time doing design discussions, doing code reviews. And in my free time, I still like to dabble on things like project oiler. So who's Oberlin site. What do we do? Portal insight is a large data supplier and analytics provider where we take data geospatial data anywhere on the planet, any overhead sensor, and translate that into insights for the end customer. So specifically we have a suite of high performance, artificial intelligence and machine learning analytics that run on this geospatial data. >>And we build them to specifically determine natural and human service level activity anywhere on the planet. What that really means is we take any type of data associated with a latitude and longitude and we identify patterns so that we can, so we can detect anomalies. And that's everything that we do is all about identifying those patterns to detect anomalies. So more specifically, what type of problems do we solve? So supply chain intelligence, this is one of the use cases that we we'd like to talk about a lot. It's one of our main primary verticals that we go after right now. And as Scott mentioned earlier, this had a huge impact last year when COVID hit. So specifically supply chain intelligence is all about identifying movement patterns to and from operating facilities to identify changes in those supply chains. How do we do this? So for us, we can do things where we track the movement of trucks. >>So identifying trucks, moving from one location to another in aggregate, same thing we can do with foot traffic. We can do the same thing for looking at aggregate groups of people moving from one location to another and analyzing their patterns of life. We can look at two different locations to determine how people are moving from one location to another, or going back and forth. All of this is extremely valuable for detecting how a supply chain operates and then identifying the changes to that supply chain. As I said last year with COVID, everything changed in particular supply chains changed incredibly, and it was hugely important for customers to know where their goods or their products are coming from and where they were going, where there were disruptions in their supply chain and how that's affecting their overall supply and demand. So to use our platform, our suite of tools, you can start to gain a much better picture of where your suppliers or your distributors are going from coming from or going to. >>So what's our team look like? So my team is currently about 50 engineers. Um, we're spread into four different teams and the teams are structured like this. So the first team that we have is infrastructure engineering and this team largely deals with deploying our Dockers using Kubernetes. So this team is all about taking Dockers, built by other teams, sometimes building the Dockers themselves and putting them into our production system, our platform engineering team, they produce these microservices. So they produce microservice, Docker images. They develop and test with them locally. Their entire environments are dockerized. They produce these doctors, hand them over to him for infrastructure engineering to be deployed. Similarly, our product engineering team does the same thing. They develop and test with Dr. Locally. They also produce a suite of Docker images that the infrastructure team can then deploy. And lastly, we have our R and D team, and this team specifically produces machine learning algorithms using Nvidia Docker collectively, we've actually built 381 Docker repositories and 14 million. >>We've had 14 million Docker pools over the lifetime of the company, just a few stats about us. Um, but what I'm really getting to here is you can see actually doctors becoming almost a form of communication between these teams. So one of the paradigms in software engineering that you're probably familiar with encapsulation, it's really helpful for a lot of software engineering problems to break the problem down, isolate the different pieces of it and start building interfaces between the code. This allows you to scale different pieces of the platform or different pieces of your code in different ways that allows you to scale up certain pieces and keep others at a smaller level so that you can meet customer demands. And for us, one of the things that we can largely do now is use Dockers as that interface. So instead of having an entire platform where all teams are talking to each other, and everything's kind of, mishmashed in a monolithic application, we can now say this team is only able to talk to this team by passing over a particular Docker image that defines the interface of what needs to be built before it passes to the team and really allows us to scalp our development and be much more efficient. >>Also, I'd like to say we are hiring. Um, so we have a number of open roles. We have about 30 open roles in our engineering team that we're looking to fill by the end of this year. So if any of this sounds really interesting to you, please reach out after the presentation. >>So what does our platform do? Really? Our platform allows you to answer any geospatial question, and we do this at three different inputs. So first off, where do you want to look? So we did this as what we call an AOI or an area of interest larger. You can think of this as a polygon drawn on the map. So we have a curated data set of almost 4 million AOIs, which you can go and you can search and use for your analysis, but you're also free to build your own. Second question is what you want to look for. We do this with the more interesting part of our platform of our machine learning and AI capabilities. So we have a suite of algorithms that automatically allow you to identify trucks, buildings, hundreds of different types of aircraft, different types of land use, how many people are moving from one location to another different locations that people in a particular area are moving to or coming from all of these different analyses or all these different analytics are available at the click of a button, and then determine what you want to look for. >>Lastly, you determine when you want to find what you're looking for. So that's just, uh, you know, do you want to look for the next three hours? Do you want to look for the last week? Do you want to look every month for the past two, whatever the time cadence is, you decide that you hit go and out pops a time series, and that time series tells you specifically where you want it to look what you want it to look for and how many, or what percentage of the thing you're looking for appears in that area. Again, we do all of this to work towards patterns. So we use all this data to produce a time series from there. We can look at it, determine the patterns, and then specifically identify the anomalies. As I mentioned with supply chain, this is extremely valuable to identify where things change. So we can answer these questions, looking at a particular operating facility, looking at particular, what is happening with the level of activity is at that operating facility where people are coming from, where they're going to, after visiting that particular facility and identify when and where that changes here, you can just see it's a picture of our platform. It's actually showing all the devices in Manhattan, um, over a period of time. And it's more of a heat map view. So you can actually see the hotspots in the area. >>So really the, and this is the heart of the talk, but what happened in 2020? So for men, you know, like many of you, 2020 was a difficult year COVID hit. And that changed a lot of what we're doing, not from an engineering perspective, but also from an entire company perspective for us, the motivation really became to make sure that we were lowering our costs and increasing innovation simultaneously. Now those two things often compete with each other. A lot of times you want to increase innovation, that's going to increase your costs, but the challenge last year was how to do both simultaneously. So here's a few stats for you from our team. In Q1 of last year, we were spending almost $600,000 per month on compute costs prior to COVID happening. That wasn't hugely a concern for us. It was a lot of money, but it wasn't as critical as it was last year when we really needed to be much more efficient. >>Second one is flexibility for us. We were deployed on a single cloud environment while we were cloud thought ready, and that was great. We want it to be more flexible. We want it to be on more cloud environments so that we could reach more customers. And also eventually get onto class side networks, extending the base of our customers as well from a custom analytics perspective. This is where we get into our traction. So last year, over the entire year, we computed 54,000 custom analytics for different users. We wanted to make sure that this number was steadily increasing despite us trying to lower our costs. So we didn't want the lowering cost to come as the sacrifice of our user base. Lastly, of particular percentage here that I'll say definitely needs to be improved is 75% of our projects never fail. So this is where we start to get into a bit of stability of our platform. >>Now I'm not saying that 25% of our projects fail the way we measure this is if you have a particular project or computation that runs every day and any one of those runs sale account, that is a failure because from an end-user perspective, that's an issue. So this is something that we know we needed to improve on and we needed to grow and make our platform more stable. I'm going to something that we really focused on last year. So where are we now? So now coming out of the COVID valley, we are starting to soar again. Um, we had, uh, back in April of last year, we had the entire engineering team. We actually paused all development for about four weeks. You had everyone focused on reducing our compute costs in the cloud. We got it down to 200 K over the period of a few months. >>And for the next 12 months, we hit that number every month. This is huge for us. This is extremely important. Like I said, in the COVID time period where costs and operating efficiency was everything. So for us to do that, that was a huge accomplishment last year and something we'll keep going forward. One thing I would actually like to really highlight here, two is what allowed us to do that. So first off, being in the cloud, being able to migrate things like that, that was one thing. And we were able to use there's different cloud services in a more particular, in a more efficient way. We had a very detailed tracking of how we were spending things. We increased our data retention policies. We optimized our processing. However, one additional piece was switching to new technologies on, in particular, we migrated to get lab CICB. >>Um, and this is something that the costs we use Docker was extremely, extremely easy. We didn't have to go build new new code containers or repositories or change our code in order to do this. We were simply able to migrate the containers over and start using a new CIC so much. In fact, that we were able to do that migration with three engineers in just two weeks from a cloud environment and flexibility standpoint, we're now operating in two different clouds. We were able to last night, I've over the last nine months to operate in the second cloud environment. And again, this is something that Docker helped with incredibly. Um, we didn't have to go and build all new interfaces to all new, different services or all different tools in the next cloud provider. All we had to do was build a base cloud infrastructure that ups agnostic the way, all the different details of the cloud provider. >>And then our doctors just worked. We can move them to another environment up and running, and our platform was ready to go from a traction perspective. We're about a third of the way through the year. At this point, we've already exceeded the amount of customer analytics we produce last year. And this is thanks to a ton more albums, that whole suite of new analytics that we've been able to build over the past 12 months and we'll continue to build going forward. So this is really, really great outcome for us because we were able to show that our costs are staying down, but our analytics and our customer traction, honestly, from a stability perspective, we improved from 75% to 86%, not quite yet 99 or three nines or four nines, but we are getting there. Um, and this is actually thanks to really containerizing and modularizing different pieces of our platform so that we could scale up in different areas. This allowed us to increase that stability. This piece of the code works over here, toxin an interface to the rest of the system. We can scale this piece up separately from the rest of the system, and that allows us much more easily identify issues in the system, fix those and then correct the system overall. So basically this is a summary of where we were last year, where we are now and how much more successful we are now because of the issues that we went through last year and largely brought on by COVID. >>But that this is just a screenshot of the, our, our solution actually working on supply chain. So this is in particular, it is showing traceability of a distribution warehouse in salt lake city. It's right in the center of the screen here. You can see the nice kind of orange red center. That's a distribution warehouse and all the lines outside of that, all the dots outside of that are showing where people are, where trucks are moving from that location. So this is really helpful for supply chain companies because they can start to identify where their suppliers are, are coming from or where their distributors are going to. So with that, I want to say, thanks again for following along and enjoy the rest of DockerCon.

>>From Miami beach, Florida. It's the queue covering a cryonics global cyber summit 2019 brought to you by Acronis. >>Okay. Welcome back everyone. This is the cubes coverage here at the Chronis global cyber summit 2019 I'm John furrier, host to the cube. We're Miami beach at the Fontainebleau hotel with a second day. Excited to have this next guest on Christian Garcia, senior vice president of finance and administration at the chauffeur housing ShipIt housing Institute of technology. Did they get it right? Almost right. housing. welcome back. Welcome to the cube. Good to see you. Good to see you. Thanks for having me here. This is a really cool story because you guys are doing something very entrepreneurial, right, with education, right. Okay. Inspired by the founder of a Chronis. Exactly as well. He's got. He's made a lot of money in his day, so he's doing some good things with it. Um, but this is an interesting opportunity for you to take a minute to explain what this Institute stands for. >>It's sit for short. >> Yeah, so sat actually as a name Schaffhausen Institute of technology. So we are actually starting up a university in Schaffhausen in Schaffhausen. These a beautiful tiny CD in Switzerland, 30 minutes or 30 minutes from the Zurich airport, which is the biggest airport in Switzerland, uh, close to Germany at the border with Germany. And uh, so that's kind of your, in the center of Europe and that's where we plan to have our main campus. Now let me tell you this story. How about the vision about target, his vision on these, on this project? Um, he, he said that, you know, uh, he needs to have skills in 10 to 15 years time that nowadays at the institutions that do not do not, do not bring, um, there is the need of computer scientists that are not enough computer scientists and we are having emergent technologies and these is something that provides us with tremendous opportunities, which we cannot even imagine nowadays what type of opportunities and to be on the forefront there. >>That's why we want to found these are, we have founded the Schaffhausen Institute of technology. >> Chef housing is a technology just for share. The day was just two months ago, couple months ago. It was two months ago where we, where we have started up the legal structure and now we are really laying the foundation. We have to find some that are kind of secured for for the next 12 to 18 months. And um, we are, you know, defining the strategic advisory board. We are setting up the curriculum for our students. And so it's everything up and running and to be defined. So risk is right at the creation present at creation. We are talking about this as a, this is the origination story. Exactly. Of the shelf house in Institute of technology. Exactly. What's the vision? >>I mean obviously getting skills for jobs that are our century, our time that's having been teaching in universities and before I get back. But is it about being open and what's the vision is just Switzerland is going to be global. Can you just share, what do you guys are thinking? >>Sure, absolutely. So basically what we are trying to do is to design a curriculum in um, computer science and physics because we think that computer science or present the software in physics represents the hardware. And these two things need to be combined in a entrepreneurial mindset or with an entrepreneurial mindset, which means that we also want to foster the transformation process and the anti entrepreneurship. Now, let me go back to the software path. Uh, our curriculum will cover, um, software engineering, cybersecurity. That's why we are here today. Uh, the curriculum we also cover, um, on the physics part. On the hardware part, we'll cover, uh, quantum technologies, uh, quantum physics and also new materials. Um, and these will be kind of the foundation that will build the curriculum for students, computer scientists to have physics and physics to have computer science in their curriculum so that at some point in time they can come together and to research together. >>This is the digital transformation that we're talking about. The, the intersection and the confluence of physical reality. A world that we live in, whether it's a baseball game or a soccer match to the digital culture, they're not mutually exclusive anymore and they're together. And then the impact is profound. I can only imagine. IOT, industrial, IOT, airplanes, cars, electricity, electronic batteries, all these things, correct. It's software and digital. And physical material. Exactly that you guys are thinking. >>Exactly. Exactly that and actually also considering the industry, talking to the industry, talking to chief information technology officers around the world to understand what they need are and what type of they believe of skills are needed in in 10 to 15 years time. And that's what we want to build up now to get >>well you guys car gotta go, you gotta go faster because there's jobs now. There's thousands of jobs right now in cybersecurity. There's thousands and thousands of jobs for provision and cloud computing. Amazon educate. We talked to them all the time. They just can't get the word out fast enough that Hey, if you're unemployed there's no excuse for being unemployed. Write down there's so many new jobs. But because someone didn't go to the linear school and exactly know go step by step over the years and now you can level up very quickly. Exactly for certification. But you guys are taking a much more bigger idea around real kind of masters level. Is that what it is? Undergraduate masters level? What's the level of, actually we, we, we are starting >>out with this university and we have already students that are at our or with our partner universities currently in Singapore with NUS. And we then move to Karnak and Molly here in the U S um, in order to have it, we'll do a degree. So that's a unique opportunity to already start up with some presence, uh, in, in education. And uh, you ultimately, they will be then acquired. So we hope by, by, by, by the industry and the were terrific. Elon Musk is in there somewhere innovating with who knows what's next out there and he's around. And next Sergei is out there too. A exactly. Exactly. So just look at our, at our home page, look at the curriculum, which we are currently defining now. Eh, that would be, that would be great on sit.org take me through how it works. I know you're just starting, but as you guys look at the world, I mean, first of all, I can see, I can see the attractiveness of a dual degree. >>Yeah. Because most kids get bored in college. They're freelancing anyway. They're learning on their own. I get that. But I can S so I want, so as you guys start building it out, what's going on? What's going, how's it work? What are you guys doing? You're recruiting tickets through the, the factory of work that needs to get done, if you will. What's the workflows look like? What's happening right now? So currently, I mean, we are talking about the university because we, we have students and we will have students and we weren't to have the best talents, uh, globally available. And that's why we are building institution that attracts those talents. And these is kind of the first priority to have, do I have the talents to get the tens to get students come to, to, to sit? And obviously the second part is he said, well, talking to the CEOs and Tito was in to understand what are the needs in 10 to 15 years as an outcome of this digital transformation. >>I mean, the world is computerized. Uh, as you just mentioned before, there are not enough computer scientists currently available. So four out of five companies in Switzerland direction also globally are lacking. Uh, of computer scientists and they understand, you know, at what the digital transformation means. And that's something that we really try to understand as well to build it up the curriculum. What's the timeline of starting with students? Is you right away? Do you have a location? Is there a building, I mean, give us a timeline. When did classes start? When you start bringing people in? Is it happening now? I mean, absolutely. So, so actually currently we are, we are hunting at, at uh, at some campus locations, looking at some campus locations, each a thousand where our main campus will be, will be located. Um, at the, at the, at the same time we are really building buildings structure. >>So we are appointing the strategic advisory board will be, we twill direct, eh, the curriculum of the university. Um, and, and which is represented already by, uh, very, um, great scientists. One of them, the president of the strategic advisory board being professor Dr. Noble selloff, which is a Nobel prize winner. And which actually brings in that, that new ma new material, um, science in our physics curriculum. So that's another thing that we are currently trying to do to build up that governance appropriate components. And third element that we are looking at is also to attract uh, industries and companies that sponsor the students. And that's actually an attractive ecosystem that we are trying to build up to combine science education and also entrepreneurship in business. In order to foster that, which means that we are looking at the campus, we are setting up a research center and I'm talking about two or three years down the line, the research center and then also a tech park where we can commercialize the innovation that the science green Springs in. >>So all in all we really aim to have a closed ecosystem and self sustaining ecosystem. Hopefully that we are going to establish. It's a really big idea. Congratulations. It's bold. It's and it's relevant. Absolutely. So I got to ask you the question, how do you finance all this? Who's paying for it? So tell us how do we get funded? It's very important. Otherwise we pull in, start up with such a tremendous pace. Uh, actually the vision is, is from Sergei Velo self, uh, founder and CEO of Acronis. Um, he, he's, Hey has actually secured the initial founding of the institution and now really we need to have more partners on board in order to make this self sustaining education edge educational system system as sustainable as you are going to be tuition base or scholarship based. Have you guys thought about that? Um, in terms of students it would be tuition-based ah, that's a classical classical model or at least at least in Switzerland and obviously to get the industry sponsoring students in order to also down the line employee them later on. >>That would be the idea situation. Nice vision for Sergei and nice gesture. But you've got to look at what his business is doing. They created a category called cyber protection. Extending the benefit to him is more candidates know physics edge. So why not? This is a great vision. Absolutely the win-win. Absolutely. And we all believe in that the entire, um, you know, stand up team believe in that vision. That's where we are here and building up this institution. Well when you need to go global will be in Silicon Valley and waiting for you guys to come there and collaborate with us there. I hope. I hope that because we want to compliment each other. As I mentioned, computer scientists, our need is globally and obviously also in the Silicon Valley and why not? I think the collaboration aspect is going to be a big part of the growth as you guys get >>settled in on the the first use case in Shevon housing. Exactly. You know, and get that built out, but I think with digital technologies, I think there'll be a great collaboration, bring some good talent in as faculty and advisors and exactly get the flywheel going except congratulations. Thanks for coming on. The key, the education game is changing with modernization of a global impact of technology for good. You're seeing the landscape of innovation hit education. This is another great example of it. Super proud. The interview. Thanks for coming on and sharing the insights. The world continues to evolve. Of course, the cube is, they're watching every turn. I'm John Feria here in Miami beach for the Crohn's global cyber summit. 2019 deck with more coverage after this short break.

>> Announcer: From Boston, Massachusetts, it's theCUBE! Covering Cloud Foundry Summit 2018. Brought to you by the Cloud Foundry Foundation. >> I'm Stu Minamin and this is theCUBE's coverage of Cloud Foundry Summit 2018. Here in beautiful Boston, Massachusetts. Happy to welcome back to the program Chip Childers, who is the CTO of the Cloud Foundry Foundation. Chip, you started off this morning saying the runners this morning got a taste of the Boston Marathon. >> They did, they did! >> It's raining, it's cold, it's miserable. >> Yesterday was beautiful. >> At least there was less wind. >> Yesterday was absolutely beautiful. So we kicked off the summit, beautiful sun, but then we had our Fun Run this morning. >> As a local, I do apologize for the weather. Normally April's a great time. We want more tech coverage here in the area. More tech shows. We're in the center of a great tech hub, here in the Boston Seaport. We've talked to a couple of Boston startups, you know, here at the show. And, you know, great ecosystem if you go there. Thank you for bringing your show here. >> Absolutely, happy to be here. >> All right, so, last time we caught up was year ago at the show. And I think it was, what, 213 working days or something? I think Molly said >> Something like that Something like that yeah. >> The good thing is in our industry, nothings changing, we can talk about the same stuff as last year. >> Leisurely pace >> No concern, let's just sit back and you know, talk about our favorite pop culture references. Chip what's hot on your plate? And what are you hearing from the users in the community? >> Sure. So this year the theme Our events team came up with a very fun pun, which is Running at Scale. It means two things. One, the Boston Marathon was on Monday, but two it really does represent the stories that we're getting from our users, the customers, and the distributions, those that use the open source directly. So not only are we seeing a broadening of adoption across new organizations, but they're getting really deep into using it. We filled a survey, user survey, just did our second run of it. In fact we didn't have this data back in Santa Clara last year. So it's been less than a year since the 2017 one. And what we found was that there was a 21 point swing in those companies that were using Cloud Foundry with more than 50 developers, alright. So 50 developers and higher When you really talk to the interesting, large scale Fortune 500 companies, they're talking thousands of developers, that are working on the platform, being productive, and that truly is kind of what this event is about for us. >> I grew up around the infrastructure stuff, and scale means a lot of things to a lot of people, but had a great discussion with Dr. Nick, just before talking about how if you were to build your kind of utopian environment You look at some of the hyper-scale companies, the Facebooks and Googles of the world, and thing is they're such a scale that if they don't have good automation, and don't have you know really the distributive architectures that we're all talking about and things like that, there's no way that they could run their businesses. >> Yeah and the reality is a lot of the businesses that aren't Google, aren't Facebook, they have to be able to think about that level of scale. To me it really boils down to three basic principles, and to me this is the best definition of what Cloud native means. Whether you're talking about a platform, whether you're talking about how you design your applications, it's simple patterns, highly automated, which can be scaled with ease, right? And through that you can do really amazing things with software, but it has to be easily scaled, it has be easily managed, and you do that through the simplicity of the patterns that you apply. >> Yeah, and being simple is difficult. >> Yes >> How much we have arguments in the industry it's like well, let's throw an abstraction layer in there, do an overlay or underlay, but you know really building kind of distributed systems, is a little bit different. >> It is a little bit different. So one of the things that the Cloud Foundry ecosystem has, is a rich history of iterating towards a better and better developer experience. At its heart, the Cloud Foundry ecosystem of distribution, and tools, and the different products we have, they're all about helping the developer be a better developer in the context of their organization. So we've been iterating on that experience and just doing incremental constant improvement and change and we're very proud of that productivity, right? And that's really what drive these organizations to say look, this is a platform that is operated very easily with small teams. I think you've spoken with a couple companies, and if you ever ask them hot many operators do you have to handle thousands of engineers, tens of thousands of applications, they say, well, maybe ten. >> The T-Mobile example is >> Great example >> Ten to fifteen operators with 17000 developers so >> Chip: Yep, yep >> It's funny cause I remember we used to talk about you know in the enterprise how many servers can a single admin handle and then if you go to the hyper-scale ones it was three orders magnitude different. But in the hyper-scale ones they didn't really have server people, they had people that brought in servers, and people threw them in the wood chipper when they were done >> Chip: Absolutely >> And they didn't manage them. It was the old cattle versus pets analogy that we talked about in the other room, It's just totally different mindsets is how we think about this. I love, For me, it was in the enterprise you know, we harden the hardware, we think about this, and in the software world it's you know, No no, I built it in the application layer, because One of my favorite lines I use is you know, Hardware will eventually fail, and software will eventually work right? >> Absolutely. I think that's the difference between, So application centric thinking leads you to Necessarily, you have to have infrastructure to run it right? My favorite thing is this whole server-less term is absolutely ridiculous if anybody understands it, but there's a little bit behind it, which is, in fact I'd argue Cloud Foundry's fundamentally server-less because when you push code into it, you don't care what operating system's underneath it, right? All you care about is the fact that you've written some code in Java or in Nojass or in Ruby, you're handing it to a platform it deals with all of the details of building a container image, scaling it, managing it, pulling independencies, you don't care what underlying operating systems there, and then that ten person platform operations team, in the Cloud Foundry world, they have the benefit of upstream projects actually producing the operating system image that they can consume, within hours of major vulnerabilities being announced. >> I love actually, at this show you've got a containers and server-less track >> We do >> And I'm an infrastructure guy by background and when we went to virtualization we went little bit up the stack, I don't think about servers I'm trying to get closer to that application. Love you to comment on is Cloud Foundry helps gives some stability and control at that infrastructure level, but it still involved with infrastructure, from in my own data center, >> Chip: Yep >> or hosted data center or I know what could I'm on. When I start going up to like server-less, I'm a little bit higher up the stack, and that's why they can live together, >> Yeah, yeah >> And its closer tied to the application than it is to the infrastructure, so maybe you can tease that out for us a little. >> Yeah, so I think one of the main things that we've heard from the user community and this is actually coming from users of a number of the different distributions. They're saying, look there are roughly, today, roughly two different modes that we care about, cloud native application workloads. And this might expand to include functions and service but predominantly there's two. There's the custom software that we write, which the past experience is great for, and then there's the ISV delivered software, which today increasingly the medium of software delivery is becoming the container image, whether it's an OCI container, whether it's a Docker image, ISV ships software as container images, and you need a great place to land that, so those two abstractions, that paths, just hand the system your code, or the container service just hand it a container image, both of them work really well together, and part of what we're trying to do as a community, a technical community, is we're evolving those integrations so that we can work really well with the Kubernetes ecosystem. There are different options for how these things might be stacked, depending on the vendor that you're talking to, I think mostly that's immaterial to the customers, I think mostly the customers care about having those two experiences be unified from their developer or app owner prospective. >> When you come to this show, there's more than just Cloud Foundry. There's a lot of other projects >> Chip: For sure >> That are coming on to the space Gives us a little viewpoint as to how the foundation looks at this. What's the charter which it fits under Linux foundation There's so many different pieces, Some kind of bleed into what the CNCF is doing, and just try to help map out >> Chip: Yeah how some of these pieces and it's this great toolbox that we've talked about in open source. I love like the zip car guy got up and he's like, I use all the peripheral stuff, and none of the core stuff >> Right >> And that's okay >> Absolutely, that's the fun of open source. So there's a couple ways to look at this. So first, the open source communities collectively. There's a lot of innovations going on in this space, obviously What the Cloud Foundry ecosystem generally does, historically has done, and will continue to do, is that we are focused on the user needs, first and foremost. And what our technical project teams do is they look at what's available in the broader open source ecosystem. They adopt and integrate what makes sense, where we have to build something ourselves, simply because there isn't an equivalent, or it's necessary for technical reasons. We'll build that software. But our architecture has changed many times. In fact, since 2015, right. It hasn't been that many years, as you said, we move slow in this industry (Stu laughs) We've changed this architecture constantly. The upstream projects releasing at minimum of twice a month. That's a pretty high velocity. And it's a big coordinated release. So we're going to continue to evolve the architecture, to bring in new components, this is where CNCF relates. We've integrated Envoy, which is a CNCF project. We're now bringing in Kubernetes, in a couple of different ways. We're working closely with Istio, which is not a CNCF project, yet. But it looks like it might head that way. Service mesh capabilities, We were an early adopter of the container networking interface. Another Linux foundation effort was the open container initiative, right. Seeded from some code from Docker, again one of the earliest platforms to adopt that, outside of Docker. So we really look at the entire spectrum of open source software as a rich market of componentry that can be brought together. And we bring it together so that all these great users that you're talking to, can go along this journey, and think of it almost as a rationalization of the innovative chaos that's occurring. So we rationalize that. Our job is to rationalize our distributions, use that rationalization, and then all of the users get to take advantage of new things that come up. But also we take what gets integrated very seriously, because it has to reach a point of maturity. T-Mobile again, running their whole business on Cloud Foundry. Comcast, running their whole business on Cloud Foundry. US Air Force, fundamentally running their air traffic control, right, how do they get fuel to the jets, on Cloud Foundry. So we take that seriously. And so it's this combination of, harvesting innovation from where we can harvest it, bring it all together, be very thoughtful about how we bring it together, and then the distributions get the advantage of saying, here's a stable core that's going to evolve and take us into the future. >> Chip I've loved the discussion with real customers, doing digital transformation. What that means for them. How they're moving their business forward. Want to give you the final word, for those that couldn't come to the show, I know a lot of the stuffs online, there's a lot of information out there, anything particular do you want to call out, or say hey this is cool, interesting, or exciting you that you'd want to point to. >> Yeah, I actually. There are a lot of things but the one thing that I'll point to is as a US citizen, I'm particularly proud of some of the work that's happening in the US Government. Through 18F, with cloud.gov as an example, but if I step back even further, Cloud Foundry is serving as a vehicle for collaboration across multiple nations right now. We're seeing Australia, we're seeing the United Kingdom, Netherlands, Canada, South Korea, all of these national governments, are trying to figure out how to change citizen engagement to follow the lead of the startups, which are the internet companies, at the same time that these large Fortune 500 companies, are also trying to digitally transform. Governments are trying to do the same thing. So we had a, we're almost done for the day here, but there was almost a full day track of governments talking about their use of the tech, talking about that same digital transformation journey. So to me that's actually really inspiring to see that happen >> Alright well Chip Childers. He was a dancing stick figure >> Chip: I was in the keynote this morning, but here with us on theCUBE. Thank you so much for joining once again, and thank you to the foundation for helping us bring this program to our audience. >> Chip: We're happy to have you here. >> I'm Stu Miniman, and this is theCUBE. Thanks for watching (bright popping music)