>>Good afternoon everyone, and thank you so much for joining us. My name is Savannah Peterson, joined by my co-host Paul for the afternoon. Very excited. Oh, Savannah. Hello. I'm, I'm pumped for this. This is our first bit together. Exactly. >>It's gonna be fun. Yes. We have a great guest to kick off with. >>We absolutely do. We're at Supercomputing 2022 today, and very excited to talk to our next guest. We're gonna be talking about data at scale and data that really matters to us joining us. Kelly Gayer, thank you so much for being here and you are with tech. Tell everyone what TAC is. >>Tech is the Texas Advanced Computing Center at the University of Texas at Austin. And thank you so much for having me here. >>It is wonderful to have you. Your smile's contagious. And one of the themes that's come up a lot with all of our guests, and we just talked about it, is how good it is to be back in person, how good it is to be around our hardware, community tech. You did some very interesting research during the pandemic. Can you tell us about that? >>I can. I did. So when we realized sort of mid-March, we realized that, that this was really not normal times and the pandemic was statement. Yes. That pandemic was really gonna touch everyone. I think a lot of us at the center and me personally, we dropped everything to plug in and that's what we do. So UT's tagline is what starts here changes the world and tax tagline is powering discoveries that change the world. So we're all about impact, but I plugged in with the research group there at UT Austin, Dr. Lauren Myers, who's an epidemiologist, and just we figured out how to plug in and compute so that we could predict the spread of, of Covid 19. >>And you did that through the use of mobility data, cell phone signals. Tell us more about what exactly you were choreographing. >>Yeah, so that was really interesting. Safe graph during the pandemic made their mobility data. Typically it was used for marketing purposes to know who was going into Walmart. The offenses >>For advertising. >>Absolutely, yeah. They made all of their mobility data available for free to people who were doing research and plugging in trying to understand Covid. 19, I picked that data up and we used it as a proxy for human behavior. So we knew we had some idea, we got weekly mobility updates, but it was really mobility all day long, you know, anonymized. I didn't know who they were by cell phones across the US by census block group or zip code if we wanted to look at it that way. And we could see how people were moving around. We knew what their neighbor, their home neighborhoods were. We knew how they were traveling or not traveling. We knew where people were congregating, and we could get some idea of, of how people were behaving. Were they really, were they really locking down or were they moving in their neighborhoods or were they going outside of their neighborhoods? >>What a, what a fascinating window into our pandemic lives. So now that you were able to do this for this pandemic, as we look forward, what have you learned? How quickly could we forecast? What's the prognosis? >>Yeah, so we, we learned a tremendous amount. I think during the pandemic we were reacting, we were really trying. It was a, it was an interesting time as a scientist, we were reacting to things almost as if the earth was moving underneath us every single day. So it was something new every day. And I've told people since I've, I haven't, I haven't worked that hard since I was a graduate student. So it was really daylight to dark 24 7 for a long period of time because it was so important. And we knew, we, we knew we were, we were being a part of history and affecting something that was gonna make a difference for a really long time. And, and I think what we've learned is that indeed there is a lot of data being collected that we can use for good. We can really understand if we get organized and we get set up, we can use this data as a means of perhaps predicting our next pandemic or our next outbreak of whatever. It is almost like using it as a canary in the coal mine. There's a lot in human behavior we can use, given >>All the politicization of, of this last pandemic, knowing what we know now, making us better prepared in theory for the next one. How confident are you that at least in the US we will respond proactively and, and effectively when the next one comes around? >>Yeah, I mean, that's a, that's a great question and, and I certainly understand why you ask. I think in my experience as a scientist, certainly at tech, the more transparent you are with what you do and the more you explain things. Again, during the pandemic, things were shifting so rapidly we were reacting and doing the best that we could. And I think one thing we did right was we admitted where we felt uncertain. And that's important. You have to really be transparent to the general public. I, I don't know how well people are gonna react. I think if we have time to prepare, to communicate and always be really transparent about it. I think those are three factors that go into really increasing people's trust. >>I think you nailed it. And, and especially during times of chaos and disaster, you don't know who to trust or what to believe. And it sounds like, you know, providing a transparent source of truth is, is so critical. How do you protect the sensitive data that you're working with? I know it's a top priority for you and the team. >>It is, it is. And we, we've adopted the medical mantra, do no harm. So we have, we feel a great responsibility there. There's, you know, two things that you have to really keep in mind when you've got sensitive data. One is the physical protection of it. And so that's, that's governed by rule, federal rules, hipaa, ferpa, whatever, whatever kind of data that you have. So we certainly focus on the physical protection of it, but there's also sort of the ethical protection of it. What, what is the quote? There's lies, damn lies and statistics. >>Yes. Twain. >>Yeah. So you, you really have to be responsible with what you're doing with the data, how you're portraying the results. And again, I think it comes back to transparency is is basically if people are gonna reproduce what I did, I have to be really transparent with what I did. >>I, yeah, I think that's super important. And one of the themes with, with HPC that we've been talking about a lot too is, you know, do people trust ai? Do they trust all the data that's going into these systems? And I love that you just talked about the storytelling aspect of that, because there is a duty, it's not, you can cut data kind of however you want. I mean, I come from marketing background and we can massage it to, to do whatever we want. So in addition to being the deputy director at Tech, you are also the DEI officer. And diversity I know is important to you probably both as an individual, but also in the work that you're doing. Talk to us about that. >>Yeah, I mean, I, I very passionate about diversity, equity and inclusion in a sense of belongingness. I think that's one of the key aspects of it. Core >>Of community too. >>I got a computer science degree back in the eighties. I was akin to a unicorn in a, in an engineering computer science department. And, but I was really lucky in a couple of respects. I had a, I had a father that was into science that told me I could do anything I, I wanted to set my mind to do. So that was my whole life, was really having that support system. >>He was cheers to dad. >>Yeah. Oh yeah. And my mom as well, actually, you know, they were educators. I grew up, you know, in that respect, very, very privileged, but it was still really hard to make it. And I couldn't have told you back in that time why I made it and, and others didn't, why they dropped out. But I made it a mission probably back, gosh, maybe 10, 15 years ago, that I was really gonna do all that I could to change the needle. And it turns out that there are a number of things that you can do grassroots. There are certainly best practices. There are rules and there are things that you really, you know, best practices to follow to make people feel more included in an organization, to feel like they belong it, shared mission. But there are also clever things that you can do with programming to really engage students, to meet people and students where they are interested and where they are engaged. And I think that's what, that's what we've done over, you know, the course of our programming over the course of about maybe since 2016. We have built a lot of programming ATAC that really focuses on that as well, because I'm determined the needle is gonna change before it's all said and done. It just really has to. >>So what, what progress have you made and what goals have you set in this area? >>Yeah, that, that's a great question. So, you know, at first I was a little bit reluctant to set concrete goals because I really didn't know what we could accomplish. I really wasn't sure what grassroots efforts was gonna be able to, you're >>So honest, you can tell how transparent you are with the data as well. That's >>Great. Yeah, I mean, if I really, most of the successful work that I've done is both a scientist and in the education and outreach space is really trust relationships. If I break that trust, I'm done. I'm no longer effective. So yeah, I am really transparent about it. But, but what we did was, you know, the first thing we did was we counted, you know, to the extent that we could, what does the current picture look like? Let's be honest about it. Start where we are. Yep. It was not a pretty picture. I mean, we knew that anecdotally it was not gonna be a great picture, but we put it out there and we leaned into it. We said, this is what it is. We, you know, I hesitated to say we're gonna look 10% better next year because I'm, I'm gonna be honest, I don't always know we're gonna do our best. >>The things that I think we did really well was that we stopped to take time to talk and find out what people were interested in. It's almost like being present and listening. My grandmother had a saying, you have two errors in one mouth for a reason, just respect the ratio. Oh, I love that. Yeah. And I think it's just been building relationships, building trust, really focusing on making a difference, making it a priority. And I think now what we're doing is we've been successful in pockets of people in the center and we are, we are getting everybody on board. There's, there's something everyone can do, >>But the problem you're addressing doesn't begin in college. It begins much, much, that's right. And there's been a lot of talk about STEM education, particularly for girls, how they're pushed out of the system early on. Also for, for people of color. Do you see meaningful progress being made there now after years of, of lip service? >>I do. I do. But it is, again, grassroots. We do have a, a, a researcher who was a former teacher at the center, Carol Fletcher, who is doing research and for CS for all we know that the workforce, so if you work from the current workforce, her projected workforce backwards, we know that digital skills of some kind are gonna be needed. We also know we have a, a, a shortage. There's debate on how large that shortage is, but about roughly about 1 million unmet jobs was projected in 2020. It hasn't gotten a lot better. We can work that problem backwards. So what we do there is a little, like a scatter shot approach. We know that people come in all forms, all shapes, all sizes. They get interested for all different kinds of reasons. We expanded our set of pathways so that we can get them where they can get on to the path all the way back K through 12, that's Carol's work. Rosie Gomez at the center is doing sort of the undergraduate space. We've got Don Hunter that does it, middle school, high school space. So we are working all parts of the problem. I am pretty passionate about what we consider opportunity youth people who never had the opportunity to go to college. Is there a way that we can skill them and get, get them engaged in some aspect and perhaps get them into this workforce. >>I love that you're starting off so young. So give us an example of one of those programs. What are you talking to kindergartners about when it comes to CS education? >>You know, I mean, gaming. Yes. Right. It's what everybody can wrap their head around. So most kids have had some sort of gaming device. You talk in the context, in the context of something they understand. I'm not gonna talk to them about high performance computing. It, it would go right over their heads. And I think, yeah, you know, I, I'll go back to something that you said Paul, about, you know, girls were pushed out. I don't know that girls are being pushed out. I think girls aren't interested and things that are being presented and I think they, I >>Think you're generous. >>Yeah. I mean, I was a young girl and I don't know why I stayed. Well, I do know why I stayed with it because I had a father that saw something in me and I had people at critical points in my life that saw something in me that I didn't see. But I think if we ch, if we change the way we teach it, maybe in your words they don't get pushed out or they, or they won't lose interest. There's, there's some sort of computing in everything we do. Well, >>Absolutely. There's also the bro culture, which begins at a very early >>Age. Yeah, that's a different problem. Yeah. That's just having boys in the classroom. Absolutely. You got >>It. That's a whole nother case. >>That's a whole other thing. >>Last question for you, when we are sitting here, well actually I've got, it's two parter, let's put it that way. Is there a tool or something you wish you could flick a magic wand that would make your job easier? Where you, you know, is there, can you identify the, the linchpin in the DEI challenge? Or is it all still prototyping and iterating to figure out the best fit? >>Yeah, that is a, that's a wonderful question. I can tell you what I get frustrated with is that, that >>Counts >>Is that I, I feel like a lot of people don't fully understand the level of effort and engagement it takes to do something meaningful. The >>Commitment to a program, >>The commitment to a program. Totally agree. It's, there is no one and done. No. And in fact, if I do that, I will lose them forever. They'll be, they will, they will be lost in the space forever. Rather. The engagement is really sort of time intensive. It's relationship intensive, but there's a lot of follow up too. And the, the amount of funding that goes into this space really is not, it, it, it's not equal to the amount of time and effort that it really takes. And I think, you know, I think what you work in this space, you realize that what you gain is, is really more of, it's, it really feels good to make a difference in somebody's life, but it's really hard to do on a shoer budget. So if I could kind of wave a magic wand, yes, I would increase understanding. I would get people to understand that it's all of our responsibility. Yes, everybody is needed to make the difference and I would increase the funding that goes to the programs. >>I think that's awesome, Kelly, thank you for that. You all heard that. More funding for diversity, equity, and inclusion. Please Paul, thank you for a fantastic interview, Kelly. Hopefully everyone is now inspired to check out tac perhaps become a, a Longhorn, hook 'em and, and come deal with some of the most important data that we have going through our systems and predicting the future of our pandemics. Ladies and gentlemen, thank you for joining us online. We are here in Dallas, Texas at Supercomputing. My name is Savannah Peterson and I look forward to seeing you for our next segment.

>>Hey everyone. Welcome to the AWS partner showcase season one, episode three women in tech. I'm your host. Lisa Martin. I've got two female rock stars joining me. Next Vera Reynolds is here engineering manager, telemetry at honeycomb, and one of our Cub alumni, Danielle GShock ISV PSA director a at AWS joins us as well. Ladies. It's great to have you talking about a very important topic today. >>Thanks for having us. Yeah, thanks for having me. Appreciate it. >>Of course, Vera, let's go ahead and start with you. Tell me about your background and tech. You're coming up on your 10th anniversary. Happy anniversary. >>Thank you. That's right. I can't believe it's been 10 years, but yeah, I started in tech in 2012. I was an engineer for most of that time. And just recently, as of March switched to engineering management here at honeycomb and, you know, throughout my career, I was very much interested in all the things, right. And it was a big FOMO as far as trying a few different companies and products. And I've done things from web development to mobile, to platforms. It would be apt to call me a generalist. And in the more recent years, I was sort of gravitating more towards developer tool space. And for me, that came in the form of cloud Foundry circle CI, and now honeycomb. I actually had my eye on honeycomb for a while before joining, I came across a blog post by charity majors. Who's one of our founders and she was actually talking about management and how to pursue that and whether or not it's right for your career. >>And so I was like, who is this person? I really like her found the company. They were pretty small at the time. So I was sort of keeping my eye on them. And then when the time came around for me to look again, I did a little bit more digging, found a lot of talks about the product. And on the one hand, they really spoke to me as the solution. They talked about developers owning their coding in production and answering questions about what is happening, what are your users seeing? And I felt that pain, I got what they were trying to do. And also on the other hand, every talk I saw at the time was from an amazing woman, which I haven't seen before. So I came across charity majors again, Christine yen, who our other founder, and then Liz Frank Jones, who our principal developer advocate. And that really sealed the deal for me as far as wanting to work here. >>Yeah. Honeycomb is interesting. This is a female founded company. You're two leaders. You mentioned that you like the technology, but you were also attracted because you saw females in the leadership position. Talk to me a little bit about what that's like working for a female led organization at honeycomb. >>Yeah. You know, historically we have tried not to over index on that because there was this maybe fear or rareness of it taking away from our legitimacy as an engineering organization, from our success as a company. But I'm seeing that rhetoric shift recently because we believe that with great responsibility with great power comes great responsibility. And we're trying to be more intentional as far as using that attribute of our company. So I would say that for me, it was a choice between a few offers, right. And that was a selling point, for sure, because again, I've never experienced it and I've really seen how much they walk that walk. Even me being here and me moving into management, I think were both ways in which they really put a lot of trust and support in me. And so I it's been a great ride. >>Excellent. Sounds like it. Before we bring Danielle in to talk about the partnership. I do wanna have you here, talk to the audience a little bit about honeycomb, what technology it's delivering and what are its differentiators. >>Yeah, absolutely. So honeycomb is an observability tool that enables engineers to answer questions about the code that runs in production. And we work with a number of various customers. Some of them are Vanguard, slack. Hello, fresh. Just to name a couple. If you're not familiar with observability tooling, it's akin to traditional application performance monitoring, but we believe that observability is succeeding APM because APM tools were built at the time of monoliths and they just weren't designed to help us answer questions about complex distributed systems that we work with today, where things can go wrong anywhere in that chain. And you can't predict what you're gonna need to ask ahead of time. So some of the ways that we are different is our ability to store and query really rich data, which we believe is the key to understanding those complex systems. What I mean by rich data is something that has a lot of attributes. >>So for example, when an error happens, knowing who it happened to, which user ID, which I don't know region, they were in, what, what, what they were doing at the time and what was happening at the rest of your system. And our ingest engine is really fast. You can do it in as little as three seconds and we call data like this. I said, kind of rich data, contextual data. We refer it as having high ity and high dimensionality, which are big words. But at the end of the day, what that means is we can store and we can query this data and we can do it really fast. And to give you an example of how that looks for our customers, let's say you have a developer team who are using comb to understand and observe their system. And they get a report that a user is experiencing a slowdown or something's wrong. >>They can go into honeycomb and figure out that this only happens to users who are using a particular language pack with their app. And they operated their app last week, that it only happens when they are trying to upload a file. And so it's this level of granularity and being able to zoom in and out under data that allows you to understand what's happening, especially when you have an incident going on, right. Or your really important high profile customer is telling you that something's wrong. And we can do that. Even if everything else in your other tools looks fine, right? All of your dashboards are okay. You're not actually getting paged on it, but your customers are telling you that something's wrong. And we believe that's where we shine in helping you there. >>Excellent. It sounds like that's where you really shine that real time visibility is so critical these days. Danielle, Danielle, wanna bring you into the conversation. Talk to us a little bit about the honeycomb partnership from the AWS lens. >>Yeah. So excuse me, observability is obviously a very important segment in the cloud space, very important to AWS, because a lot of all of our customers, as they build their systems distributed, they need to be able to see where, where things are happening in the complex systems that they're building. And so honeycomb is a, is an advanced technology partner. They've been working with us for quite some time and they have a, their solution is listed on the marketplace. Definitely something that we see a lot of demand with our customers, and they have many integrations, which, you know, we've seen is key to success. Being able to work seamlessly with the rest of the services inside of the AWS platform. And I know that they've done some, some great things with people who are trying to develop games on top of AWS things in that area as well. And so very important partner in the observa observability market that we have. >>Vera a back to you, let's kind of unpack the partnership, the significance that honeycomb ha is getting from being partners with an organization as potent and pivotal as AWS. >>Yeah, absolutely. I Don know that this Predates me to some extent, but I Don know for a long time, AWS and honeycomb has really pushed the envelope together. And I think it's a beneficial relationship for both ends. There's kind of two ways of looking at it. On the one side, there is our own infrastructure. So honeycomb runs on AWS and actually one of our critical workloads that supports that fast query engine that I mentioned uses Lambda. And it does also in a pretty unorthodox way. So we've had a long standing conversation with the AWS team as far as drawing outside those lines and kind of figuring out how to use the technology in a way that works for us and hopefully will work for other customers of theirs as well. That also allows us to ask for early access for certain features when they become available. >>And then that way we can be sort of the Guinea pigs and try things out in a way that migrates our system and optimizes our own performance, but also allows again, other customers of AWS to follow in that path. And then the other side of that partnership is really supporting our customers who are both honeycomb users and AWS users, because it's, as you imagine, quite a big overlap, and there are certain ways in which we can allow our customers to more easily get their data from AWS to honeycomb. So for example, last year, we built a tool based on the new Lambda extension capability that allowed our users who run their applications in Lambdas to get that tele telemetry data out of their applications and into honeycomb and demand was win-win >>Excellent. So I'm hearing a lot of synergies from a technology perspective, you're sticking with you, and then Danielle will bring you in. Let's talk about how honeycomb supports D E and I across its organization. And how is that synergistic with AWS's approach Vera? >>Yeah, absolutely. So I sort of alluded to that hesitancy to over index on the women led aspect of ourselves. But again, a lot of things are shifting, we're growing a lot. And so we are recognizing that we need to be more intentional with our DEI initiatives, and we also notice that we can do better and we should do better. And to that end, we're doing a few things differently that are pretty recent initiatives. We are partnering with organizations that help us target specific communities that are underrepresented in tech. Some examples would be Africa, tech hu Latinas in tech among a number of others. And another initiative is DEI head start. That's something that is an internal practice that we started that includes reaching out to underrepresented applicants before any new job for honeycomb becomes live. So before we posted to LinkedIn, before it's even live on our job speech, and the idea there is to kind of balance our pipeline of applicants, which the hope is will lead to more diverse hires in the long term. >>That's a great focus there. Danielle, I know we've talked about this before, but for the audience, in terms of the context of the honeycomb partnership, the focus at AWS for D E and I is really significant, unpack that a little bit for us. >>Well, let me just bring it back to just how we think about it with the companies that we work with, but also in, in terms of, you know, what we want to be able to do, excuse me, it's very important for us to, you know, build products that reflect the customers that we have. And I think, you know, working with a company like honeycomb that is looking to differentiate in a space by, by bringing in, you know, the experiences of many different types of people I genuinely believe. And I'm sure Vera also believes that by having those diverse perspectives, that we're able to then build better products for our customers. And, you know, it's one of, one of our leadership principles is, is rooted in this. I write a lot, it asks for us to seek out diverse perspectives and you can't really do that if everybody kind of looks the same and thinks the same and has the same background. So I think that is where our de and I, you know, I thought process is rooted and, you know, companies like honeycomb that give customers choice and differentiate and help them to do what they need to do in their unique environments is super important. So >>The, the importance of thought diversity cannot be underscored enough. It's something that is, can be pivotal to organizations. And it's very nice to hear that that's so fundamental to both companies, Barry, I wanna go back to you for a second. You, I think you mentioned this, the DEI head start program, that's an internal program at honeycomb. Can you shed a little bit of light on that? >>Yeah, that's right. And I actually am in the process of hiring a first engineer for my team. So I'm learning a lot of these things firsthand and how it works is we try to make sure to pre-load our pipeline of applicants for any new job opening we have with diverse candidates to the best of our abilities. And that can involve partnering with the organizations that I mentioned or reaching out to our internal network and make sure that we give those applicants a head start, so to speak. >>Excellent. I like that. Danielle, before we close, I wanna get a little bit of, of your background. We've got various background in tech, she's celebrating her 10th anniversary. Give me a, a short kind of description of the journey that you've navigated through being a female in technology. >>Yeah, thanks so much. I really appreciate being able to share this. So I started as a software engineer back actually in the late nineties during the, the first.com bubble and have, have spent quite a long time actually as an individual contributor, probably working in software engineering teams up through 2014 at a minimum until I joined AWS as a customer facing solutions architect. I do think spending a lot of time, hands on, definitely helped me with some of the imposter syndrome issues that folks suffer from not to say I don't at all, but it, it certainly helped with that. And I've been leading teen at AWS since 2015. So it's really been a great ride. And like I said, I'm very happy to see all of our engineering teams change as far as their composition. And I'm, I'm grateful to be part of it. >>It's pretty great to be able to witness that composition change for the better last question for each of you. And we're almost out of time and Danielle, I'm gonna stick with you. What's your advice, your recommendations for women who either are thinking about getting into tech or those who may be in tech, maybe they're in individual contributor positions, and they're not sure if they should apply for that senior leadership position. What do you advise them to do? >>I mean, definitely for the individual contributors, tech tech is a great career direction and you will always be able to find women like you, you have to maybe just work a little bit harder to join, have community in that. But then as a leader, representation is very important and we can bring more women into tech by having more leaders. So that's my, you just have to take the lead, >>Take the lead. Love that various same question for you. What's your advice and recommendations for those maybe future female leaders in tech? >>Yeah, absolutely. Danielle mentioned imposter syndrome and I think we all struggle with it from time to time, no matter how many years it's been. And I think for me, for me, the advice would be if you're starting out, don't be afraid to ask questions and don't be afraid to kind of show a bit, a little bit of ignorance because we've all been there. And I think it's on all of us to remember what it's like to not know how things work. And on the flip side of that, if you are a more senior IC or in a leadership role, also being able to model just saying, I don't know how this works and going and figuring out answers together because that was a really powerful shift for me early in my career is just to feel like I can say that I don't know something. >>I totally agree. I've been in that same situation where just ask the question because you I'm guaranteed. There's a million outta people in the room that probably has the, have the same question and because an imposter syndrome don't wanna admit, I don't understand that. Can we back up, but I agree with you. I think that is one of the best things. Raise your hand and ask a question, ladies. Thank you so much for joining me talking about honeycomb and AWS, what you're doing together from a technology perspective and the focus efforts that each company has on D E and I, we appreciate your insights. >>Thank you so much for having us talking to >>My pleasure. Likewise, for my guests, I'm Lisa Martin. You're watching the AWS partner showcase women in.

>>Hello from Las Vegas. It's the cube live at AWS reinvent 2021, Lisa Martin and Dave Nicholson here. We're in our fourth day, Dave, we have two live sets of the kid. There's a dueling set right across from us, kind of like dueling pianos, only a little bit louder. We have had about a hundred guests on the program at AWS reinvent this year. And we're pleased to welcome back. One of our alumni, Richard Potter joins us the CEO of peak. Richard. Welcome back to the cube. >>Great to be here. Talk to >>Us. So we haven't seen you in a couple of years. Talk to us about what's going on at pink. I know there's some news. >>Yeah, yeah. Loads of things going on at peak. I mean, we've been growing really quick. So since the last time you saw us, which was yeah, in London a few years ago, uh, we've grown to be the, sort of essentially the global leader in decision intelligence systems. Um, us as an AI company, we specialize in putting artificial intelligence right into the heart of how companies run their businesses and make their day-to-day decisions, which is why we call it decision intelligence. We think it's the biggest thing in software and, uh, probably the biggest new category of software. Um, we will see this decade. So it's super exciting to be in that position and great to be back chatting to you guys on the cube. When were you based founded? We were founded in 2016. Uh, and, uh, yeah. And you can probably tell by my accent English company headquartered in Manchester, but we're global. Now we have operations in India. We have a couple of development centers in India. We have a growing customer base in Asia and a growing customer base in the U S as well. Uh, so yeah, we're kind of international, but born out of, uh, Northern English roots. >>I like it. Talk to me about back in 2016, what were some of the gaps in the market that you saw from a, because you know, as, as here we are in almost 20, 22, every company is a data company. They have to be being able to extract intelligence timely hard. What gaps did you see back in 2016 >>Back then a read on the market was really simple, which was the companies that are going to harness data to run themselves well, we'll win, but the most companies were struggling to make that change to be data-driven. So our rich was, you know, as founders, there's three of us who started the business was trying to explore that problem. Like what, what, what stops companies running on data? And there's loads of reasons, right? Tech ones, uh, skills, ones, even just like business people using data in their day-to-day decision-making rather than say their gut-feel, which I think is also a data-driven decision. They just don't understand that necessarily. Uh, so we really honed in on that problem and we grew quite quickly to be the leading business in that sort of applied data space in the UK, you know, a market leader in, uh, helping companies perform better with data. And over time that has taken us on this journey to be the sort of global leader in decision intelligence, which is really cool. But the itch we were scratching was that, Hey, you know, there's something in this, we think companies that do this and do it well are gonna win, but no one's doing it. So why is that? And then, and then we've built software that effectively responds to that opportunity. >>You mentioned harnessing data. Yeah. How do you balance the harnessing of data successfully with being harnessed by data? Because, because if you're talking about the concept of Dai yeah. Who's making the decision. If the machine is making the decision, I better trust it. Why should I trust it? So how do you, how do you strike that balance to get people to trust what you're doing? The work you're doing for them behind the scenes? Yeah, >>I think it's, it's really important that humans trust the machines that they're working alongside. And I think that's the big change we're seeing, right? So this is a new industrial revolution, the intelligence era that we're in, but all previous industrial revolutions have all amplified human potential. They've amplified like a physical potential, whether it was, you know, machinery, steam, power and so on, or computers have amplified our cognitive capability, but humans have always controlled those machines. If you think about it now in the intelligence era, our machines can think with us, they can think alongside us. So we have to learn how to, as people, how to co-exist with those machines and then let those machines amplify us and essentially make us superhuman and what we do. And that's a part of the challenge we face at peak as to how do we make, how do we humanize that? >>How do we make it such that everyone trusts the machine? Uh, and we always have that human in the loop is the way we think about it. Uh, decision intelligence empowers us to be awesome at our jobs, make the great decisions all the time. If we trust the machine so much that we just want it to make the decision for us, we can let it, but we're always in control and we're in control of how it thinks and what it does. And it's our job as a software company to build software that lets you understand why that recommendation or that decision is being suggested to you. So I think, I think the coexistence of our machines alongside people in a new way that a human to machine interface is going to completely change with artificial intelligence and decision intelligence and, and us as people we're going to have to relearn how we, how we work with our technology. >>You just mentioned a couple of really good words in terms of, of the people, part of people, process and technologies, amplify and empower. Those are two things that stuck out at me is that's what you're giving people in any, whether they're an operations or finance or marketing, it's the amplification to do their jobs, empowering them to do their jobs with data that will help make them more skilled and better able to make decisions that benefit themselves, the company. >>That's exactly right. Yeah, because if you, if you redact doing business to its basics, it's, it's actually just making decisions, right. Companies are make great decisions. They win and those decisions could be anything, you know, they could be product decisions, they could be pricing decisions, operational supply chain decisions, but it's a sequence of decisions that creates value for my company. And so that's why I believe this technology is so empowering because as people we're, we're actually great at making those decisions. What we're not great at is making those decisions 24 by seven really, really quickly, very consistently. So, you know, humans are awesome at forecasting. They're awesome at choosing pricing that would appeal to other people, but alongside this technology, we can have machines that do a lot of that thinking for us, speed us up and help us make more, um, quick, great consistently awesome decisions. And then that just makes us great at our jobs. If you're a marketeer or in finance or in supply chain, you, you become awesome. And I think that that, that empowerment is key to the sort of humanization of AI in business. And actually that's what it means in practice. It isn't AI coming for peoples' jobs or replacing jobs. It's it's AI helping us all be gray. And our companies grow faster with wider profit margins when we do that, which creates more jobs for people, which is really cool. >>So, um, we talk about people trusting machines to do things for them. Uh, it's, it's not necessarily a new concept. We just sort of take some of those things for granted. Um, I trust my refrigerator at home to measure the internal temperature and make adjustments as necessary. Turn the compressor on, turn the compressor off. And I'm sorry, I you're from England refrigerators, this thing, it's a box. We use it to refrigerate our beer, which I took to make it >>Cold, which I know. >>So it's kind of a, you know, got to love those cliches, but so can you give us an example of a situation where a customer is trusting something that it's gotten from DEI from peak, where if you, as the CEO heard that anecdotal story, you would be absolutely delighted. >>Well, I think the earth is loads of great examples of that. So, um, the reason we call it decision intelligence decision intelligence is because it's the, it's applying AI into the active decision making, right? Uh, artificial intelligence or machine learning is making a prediction or a categorization over a huge data set. Right? But that on its own is kind of useless. You need to take that prediction that forward looking view and then effectively infuse it with business logic constraints and like knowledge of how your company works to give you a recommendation. Right? So let's just say I'm a marketeer and I'm trying to work out who I should send a particular offer to on black Friday over email, or even not even over email over any channel. When, if I, if I was CEO and I heard one of my teams say, Hey, what I've done is I've used the decision intelligence platform to tell me who buy, who are my customers that are in market for X type of products at why kind of price and what channels do they like to be communicated to over? >>Uh, I would think that's awesome. And then that market here, we're typically infuse that message with the sort of language and content that would appeal to that customer. But they're using the artificial intelligence to be super targeted and really like deliver the message to that person in the way they want to consume it, which creates a really enjoyable experience as a customer. You don't feel spammed or you don't feel like it's effectively used. You feel like you're having a direct one-to-one personal communication with the brand or retailer. That's talking to you, which in itself creates loyalty and like increases the lifetime value of that relationship, which is great for the retailer. But I think using AI for those kinds of decisions is essentially like a great example of like amplifying the human potential of a marketing team for this. >>Absolutely. Because what we expect as consumers, regardless of what the product or service is, is that we want brands to know who we are, what we want. Don't if I just bought a tent on Amazon, don't show me more tests, show me other things that go with it. I want you to know that. And so we have this expectation that brands when whatever industry they're in, no, oh, Richard bought this. >>Exactly, exactly. So, and I think that it starts to really jar. Now you've got some retailers and brands doing this really well, and you get really enjoyable, uh, communications at the frequency you want with the offers and the promotions that were irrelevant to you. When you just start to get trapped, you know, effectively stalked around the internet for something you've already bought, it becomes really jarring and frustrating. And then that actually creates a negative brand effect for that particular brand. So it's super important that these retailers, CPG com everyone really moves to this way of thinking and tries to have a direct. And that's the beauty of AI and decision intelligence. I think for retail, if we get into retail specifically, it allows us to treat every individual customer individually because we can use the machine to make decisions on a per customer basis. And then our marketing can be amplified by that. Whereas in the past, we bucketed customers into groups and just treated them all the same, which does create a rather impersonal experience. >>Yeah. Which can be a negative for a brand, as you mentioned, but give them the ability to treat people individually, but at scale, and in real time, one of the things we learned in the pandemic is that real-time data access isn't no is not a nice to have. It's an essential one of the themes too, that Dave and I have been talking about the last few days is that we're hearing at re-invent is every company has to be a data company. Yep. Talk to me about with that in mind, are you talking to more chief data officers, chief digital officers, where are your customer conversations as we've we're in this explosion of data? >>It's a great question though. So if every company has to be a data company and a company that's powered by AI, that means you have to be talking to everyone really. So your chief data, chief chief information officers, chief data officers, CEO, CFOs, and every sort of head of business, head of line of business, it's really important. So what we do at peak is as a decision intelligence platform, peak itself, unifies everything you need in one cloud platform, into a single software product that gives you all the infrastructure for your technical teams to process data for your data scientists to create the intelligence, but then it gives you a place to work for your business teams. So unifies your whole business around a platform. And then that means our conversations. As you know, as the provider of that technology are with technical teams, they're with business teams, they're with business leaders because it has to permeate everything. So I think it's, I think that's the future companies will have to effectively run alongside they'll create their own intelligence, basically on a dedicated platform like peek. And that intelligence will then be distributed across the whole business, um, with w w you know, in the way we do it. So I think it's really cool and exciting. Yeah. >>Let let's say hypothetically, now this is something that would never happen, but just hypothetically say I'm an American goes to England to take over coaching, a British soccer, soccer, or football. Okay. I sounds crazy, but how would I, how would I use peak and Dai and BI to help improve my winning percentage if I cared about winning? Because it's possible that I would, I I'm really only interested in the personal development of my, of my team as individuals, but, but, but what would in athletics? Is that something that is a, >>I think possible? Yeah, for sure. I mean, you're seeing an explosion of data science and analytics and AI techniques being used in sport. Right. I mean, peak we're very much focused on the commercial application of AI with our platform. So we, we work with, uh, commercial businesses and so on, but in that space, yeah, absolutely. I mean, there's, if you think about it, what do you need to create that intelligence? You need data and you can see it on the back of every players share. They've got the little devices that are gathering data in training in matches, constantly monitored. Those data points, feed algorithms. Those algorithms can show us if a player is fatigued, you know, where they are, or they can even show us, uh, deep learning techniques can help us see patterns of play and understand like how should we better set our teams up? How should we get players to interact in for, you know, on a soccer field? Um, and yeah, and you're seeing premier league clubs use those sort of techniques all the time. We don't do that at peak, but yeah, I mean, I think, uh, I think those sort of things are readily available now for, uh, those kinds of clubs to do that kind of stuff. >>I think Dave is angling to be a consultant on Ted last. So I think what I'm hearing last question for you, you guys are from an AWS relationship perspective. Richard, you guys were announced just yesterday, you're named by AWS as an ISB partner, APN partner of the year for 2021 for UK. And I, congratulations. Talk to us a little bit about that. >>Yeah, it was really, I kind of, yeah, it's super exciting for us. It's a great recognition. Obviously they give one of those awards out every year, uh, as a global company, it's nice to have that sort of stamp of approval that AWS sees us as their independent software vendor partner of the year. It's a, it's a great recognition for us because we come from a heritage of, uh, starting peak as a consulting company, actually just to do whatever it took to help our customers be successful. And in doing that, we had an idea for a software platform. Uh, we got some venture funding to do that, and we've turned into a, you know, we became a software company a couple of years after we founded, uh, and to get to this point now a few years later where AWS are recognizing us as their software vendor partner of the year is, um, a huge team. Fantastic. It's a huge Testament to, uh, to our engineering teams and the, and the, and the technical teams at peak that we've built something so impactful. Yeah, >>Absolutely. That validation is really, really critical. And last question in our last 30 seconds or so what are some of the things on the roadmap that you're excited for for, for peak for 20 22, 22 >>Is going to be a huge year for us. Cause I think it's the year that, uh, our platform goes out there into the wild, into the mainstream. So we made a couple of big announcements in the last few weeks. Uh, we've launched some new products on the pig platform. So there's three big platform, product sets. Now, one very much geared around creating your AI ready data set. That's called doc, uh, one that's very much geared around creating your intelligence, which is factory. And then an area where our business like the business teams of our customers go to work, which is called work actually. So those three big feature sets are going to be available from January. And the platform is being totally opened up as a self-serve platform for anyone anywhere to build upon. So I think it's a huge moment for decision intelligence. Garner is saying decision intelligence is the big tech trend of next year. And we feel as the market leader, we've got the platform that can help everyone get on, get on that trend really. So I think we're really looking forward to 2022 and what it brings. And, um, we think that our platform and our company is in a great shape to help more and more businesses take that leap into being powered by decision Intel. >>It sounds exciting, Richard, so we'll have to follow up with you next year and see what's going on. We appreciate you joining us on the cube, talking about peep, what you're doing, your relationship with AWS and how impactful decision intelligence can be for everybody. We appreciate it. Thanks for Dave Nicholson. I'm Lisa Martin. You're watching the cube, the global leader in live tech coverage.

>> Voiceover: From around the globe, it's theCUBE, with coverage of Kubecon and CloudNativeCon, North America, 2020, virtual brought to you by Red Hat, the Cloud Native Computing Foundation and Ecosystem Partners. >> Welcome back to theCUBE's coverage, virtual coverage of Kubecon and CloudNativeCon 2020. We're not in person this year, normally we're there in person. We have to do remote because of the pandemic, but hey, it opens up more conversations. And this is theCUBE virtual. I'm John Furrier, your host. And you'll see a lot of interviews. We've got some great guests, Talking to the leaders, the developers, the end users, as well as the vendors with the CNCF, we got two great guests, Priyanka Sharma, the General Manager of the CNCF, great to see you and Stephen Augustus OSS Engineer at VMware. He's also the KubeCon co-chair back on the cube. Thanks for coming on folks. I appreciate it. >> Thank you for having us. So, thanks for coming on, actually, remote and virtual. We're doing a lot of interviews, we're getting some perspectives, people are chatting in Slack. It's still got the hallway vibe feel, a lot of talks, a lot of action, keynotes happening, but I think the big story for me, and I would like to talk about, I want to get your perspective is this new working group that's out there. So I know there's some news around it. Could you take a minute to explain kind of what this is all about? >> Sure. I'll give a little bit of context for those who may have missed my keynote which... very bad. (Priyanka laughs) As I announced, I'm so proud to be working with the likes of Stephen Augustus here, and a bunch of other folks from different companies, different open source projects, et cetera, to bring inclusive naming to code. I think it's been a forever issue. Quite frankly. We've had many problematic terms in software out there. The most obvious one being master-slave. That really shouldn't be there. That have no place in an inclusive world, inclusive software, inclusive community with the help of amazing people like Stephen, folks from IBM, Red Hat, and many, many others. We came together because while there's a lot of positive enthusiasm and excitement for people to make the changes that are necessary to make the community welcome for all, there's a lot of different work streams happening. And we really wanted to make sure there is a centralized place for guidelines and discussion for everybody in a very non...pan-organizational kind of way. And so that's the working group that John is talking about. With that said, Stephen, I think you can do the best justice to speak to the overall initiative. >> Yeah, absolutely. So I think that's to Priyanka's point, there are lots of people who are interested in this work and again, lots of work where this is already happening, which is very exciting to say, but as any good engineer, I think that's it's important to not duplicate your work. It's important to recognize the efforts that are happening elsewhere and work towards bringing people together. So part of this is providing, being able to provide a forum for discussion for a variety of companies, for a variety of associations that... and foundations that are involved in inclusive naming efforts. And then to also provide a framework for walking people through how we evaluate language and how we make these kinds of changes. As an example, for Kubernetes, we started off the Kubernetes working group naming and the hope for the working group naming was that it was going to evolve into hopefully an effort like this, where we could bring a lot of people on and not just talk about Kubernetes. So since we formed that back in, I want to say, June-ish, we've done some work on about of providing a language evaluation framework, providing templates for recommendations, providing a workflow for moving from just a suggestion into kind of actuating those ideas right and removing that language where it gets tricky and code is thinking about, thinking about, say a Kubernetes API. And in fact that we have API deprecation policies. And that's something that we have to if offensive language is in one of our APIs, we have to work through our deprecation policy to get that done. So lots of moving parts, I'm very excited about the overall effort. >> Yeah, I mean, your mind can explode if you just think about all the complications involved, but I think this is super important. I think the world has voted on this, I think it's pretty obvious and Priyanka, you hit some of the key top-line points, inclusive software. This is kind of the high order bit, but when you get down to it, it's hard as hell to do, because if you want to get ne new namings and/or changing namings accepted by the community and code owners, you're dealing with two things, a polarizing environment around the world today, and two, the hassles involved, which includes duplicate efforts. So you've got kind of a juggling act going on between two forces. So it's a hard problem. So how are you tackling this? Because it's certainly the right thing to do. There's no debate there. How do you make it happen? How do you go in without kind of blowing things up, if you will? And do it in a way that's elegant and clean and accept it. 'Cause that's the... end of the day, it's acceptance and putting it code owners. >> Absolutely. I think so, as you said, we live in a polarizing environment right now. Most of us here though know that this is the right thing to do. Team CloudNative is for everyone. And that is the biggest takeaway I hope people get from our work in this initiative. Open source belongs to everybody and it was built for the problems of today. That's why I've been working on this. Now, when it goes into actual execution, as you said, there are many moving parts, Stephen and the Kubernetes working group, is our shining example and a really good blueprint for many folks to utilize. In addition to that, we have to bring in diverse organizations. It's not just open source projects. It's not just companies. It's also standards organizations. It's also folks who think about language in books, who have literally done PhDs in this subject. And then there are folks who are really struggling through making the changes today and tomorrow and giving them hope and excitement. So that at the end of this journey, not only do you know you've done the right thing, but you'd be recognized for it. And more people will be encouraged by your own experience. So we and the LF have been thinking at it from a holistic perspective, let's bring in the standards bodies, let's bring in the vendors, let's bring in the open source projects, give them guidelines and blueprints that we are lucky that our projects are able to generate, combine it with learnings from other people, because many people are doing great work so that there is one cohesive place where people can go and learn from each other. Eventually, what we hope to do is also have like a recognition program so that it's like, hey, this open source project did this. They are now certified X or there's like an awards program. They're still figuring that piece out, but more to come on that space. That's my part. But Stephen can tell you about all the heavy lifting that they've been doing. >> Before we get to Steve, I just want to say congratulations to you. That's great leadership. And I think you're taking a pragmatic approach and you putting the stake in the ground. And that's the number one thing, and I want to take my hat off to you guys and Priyanka, thank you for that leadership. All right, Stephen, let's talk about how this gets done because you guys open sources is what it's all about is about the people, it's about building on the successes of others, standing on the shoulders of others, you guys are used to sitting in rooms now virtually and squabbling over things like, code reviews and you got governing bodies. This is not a new thing in collaboration. So this is also a collaboration test. What are you seeing as the playbook to get this going? Can you share your insights into what the Kubernetes group's doing and how you see this. What are the few first few steps you see happening? So people can either understand it, understand the context and get involved? >> So I think it comes down to a lot of it is scope, right? So as a new contributor, as a current contributor, maybe you are one of those language experts, that is interested in getting involved as a co-chair myself for SIG Release. A lot of the things that we do, we have to consider scope. If we make this change, how is it affecting an end user? And maybe you work in contributor experience. Maybe you work in release, maybe you work in architecture. But you may not have the entire scope that you need to make a change. So I think that first it's amazing to see all of the thought that has gone through making certain changes, like discussing master and slave, discussing how we name control plane members, doing the... having the discussion around a whitelist and blacklist. What's hard about it is, is when people start making those changes. We've already seen several instances of an invigorated contributor, and maybe the new contributor coming in and starting to kind of like search and replace words. And it... I wish it was that simple, it's a discussion that has to be heard, you need buy-in from the code owners, if it's an API that you're touching, it's a conversation that you need to have with the SIG Architecture, as well as say SIG Docs. If it's something that's happening in Release, then it's a easier 'cause you can come and talk to me, but, overall I think it's getting people to the point where they can clearly understand how a change affects the community. So we kind of in this language evaluation framework, we have this idea of like first, second and third order concerns. And as you go through those concerns, there are like diminishing impacts of potential harm that a piece of language might be causing to people. So first order concerns are the ones that we want to eliminate immediately. And the ones that we commonly hear this discussion framed around. So master-slave and whitelist, blacklist. So those are ones that we know that are kind of like on the track to be removed. The next portion of that it's kind of like understanding what it means to provide a recommendation and who actually approves the recommendation. Because this group is, we have several language aficionados in this group, but we are by now means experts. And we also want to make sure that we do not make decisions entirely for the community. So, discussing that workflow from a turning a recommendation into actuating a solution for that is something that we would also do with the steering committee. So Kubernetes kind of like top governing body. Making sure that the decision is made from the top level and kind of filtered out to all of the places where people may own code or documentation around it is I think is really the biggest thing. And having a framework to make it easy to make, do those evaluations, is what we've been craving and now have. >> Well, congratulations. That's awesome. I think it's always... it's easier said than done. I mean, it's a system when you have systems and code, it's like, there's always consequences in systems architecture, you know that you do in large scales OSS. You guys know what that means. And I think the low hanging fruit, obviously master, slave, blacklist, whitelist, that's just got to get done. I mean, to me, if that just doesn't get done, that's just like a stake in the ground that must happen. But I think this idea of it takes a village, kind of is a play here. People just buy into it. That so it's a little bit of a PR thing going on too, for get buy-in, this is again a classic, getting people on board, Priyanka, isn't it? It's the obvious and then there's like, okay, let's just do this. And then what's the framework? What's the process? What's the scope? >> Yeah, absolutely agree. And many people are midway through the journey. That's one of the big challenges. Some people are on different phases of the journey, and that was one of the big reasons we started this working group, because we want to be able to provide a place of conversation for people at different stages. So we get align now rather than a year later, where everybody has their own terms as replacements and nothing works. And maybe the downstream projects that are affected, like who knows, right? It can go pretty bad. And it's very complex and it's large-scale opensource or coasters, anywhere, large software. And so because team CloudNative belongs to everyone because open source belongs to everyone. We got up, get people on the same page. For those who are eager to learn more, as I said in my keynote, please do join the two sessions that we have planned. One is going to happen, which is about inclusive naming in general, it's an hour and a half session happening on Thursday. I'm pretty sure. And there we will talk about all the various artists who are involved. Everybody will have a seat at the table and we'll have documentation and a presentation to share on how we recommend the all move, move together as an ecosystem, and then second is a presentation by Celeste in the Kubernetes working group about how Kubernetes specifically has done naming. And I feel like Stephen, you and your peers have done such amazing work that many can benefit from it. >> Well, I think engineers, you got two things going to work in for you, which is one, it's a mission. And that's... There's certainly societal benefits for this code, code is for the people. Love that, that's always been the marching orders, but also engineers are efficient. If you have duplicate efforts. I mean, it's like you think about people just doing it on their own, why not do it now, do it together, more efficient, fixing bugs over stuff, you could have solved now. I mean, this is a huge issue. So totally believe it. I know we got to go, but I want to get the news and Priyanka, you guys had some new stuff coming out from the CNCF, new things, survey, certifications, all kinds of new reports. Give us the quick highlights on the news. >> Yes, absolutely. So much news. So many talking points. Well, and that's a good thing, why? Because the CloudNative Ecosystem is thriving. There is so many people doing so much awesome stuff that I have a lot to share with you. And what does that tell us about our spirit? It tells you about the spirit of resilience. You heard about that briefly in the conversation we just had with Stephen about our working group to align various parties and initiatives together, to bring inclusive naming to code. It's about resilience because we did not get demoralized. We did not say, "oh, it's a pandemic. I can't meet anyone. So this isn't happening." No, we kept going. And that is happening in inclusive naming that is happening in the CloudNative series we're doing, that's happening in the new members that are joining, as you may have seen Volcano Engine just joined as platinum member and that's super exciting. They come from China. They're part of the larger organization that builds Tik Tok, which is pretty cool as a frequent bruiser I can say that, in addition, on a more serious note, security is really key and as I was talking to someone just minutes ago, security is not something that's a fad. Security is something that as we keep innovating, as cloud native keeps being the ground zero, for all future innovation, it keeps evolving. The problems keep getting more complex and we have to keep solving them. So in that spirit, we in CNCF see it as our job, our duty, to enable the ecosystem to be better conversant in the security needs of our code. So to that end, we are launching the CKS program, which is a certification for our Kubernetes security specialist. And it's been in the works for awhile as many of you may know, and today we are able to accept registrations. So that's a really exciting piece of news, I recommend you go ahead and do that as part of the KubeCon registration folks have a discount to get started, and I think they should do it now because as I said, the security problems keep getting worse, keep getting more complicated. And this is a great baseline for folks to start when they are thinking about this. it's also a great boon for any company out there, whether they're end users, vendors, it's all sometimes a blurry line between the two, which is all healthy. Everybody needs developers who are security conversant I would say, and this certification help you helps you achieve that. So send all of your people to go take it. So that's sort of the announcements. Then other things I would like to share are as you go, sorry, were you saying something? >> No. Go ahead. >> No, as you know, we talked about the whole thing of team CloudNative is for everyone. Open source is for everyone And I'm really proud that CNCF has offered over 1000 diversity scholarships since 2016 to traditionally under-representing our marginalized groups. And I think that is so nice, and, but just the very, very beginning. As we grow into 2021, you will see more and more of these initiatives. Every member I talked to was so excited that we put our money where our mouth is, and we support people with scholarships, mentorships, and this is only going to grow. And it just so like at almost 17%, the CNCF mentors in our program are women. So for folks who are looking for that inspiration, for folks who want to see someone who looks like them in these places, they have more diverse people to look up to. And so overall, I think our DEI focus is something I'm very proud of and something you may hear about in other news items. And then finally, I would like to say is that CloudNative continues to grow. The cloud native wave is strong. The 2.0 for team CloudNative is going very well. For the CloudNative annual survey, 2020, we found an astonishing number of places where CloudNative technologies are in production. You heard some stories that I told in my keynote of people using multiple CNCF projects together. And these are amazing and users who have this running in production. So our ecosystem has matured. And today I can tell you that Kubernetes is used in production, by at about 83% of the places out there. And this is up by 5% from 78% last year. And just so much strength in this ecosystem. I mean, now at 92% of people are using containers. So at this point we are ubiquitous. And as you've heard from us in various times, our 70 plus project portfolio shows that we are the ground zero of innovation in cloud native. So if you asked me to summarize the news, it's number one, team CloudNative and open source is for everyone. Number two, we take pride in our diversity and over 1000 scholarships have been given out since 2016 to recipients from underrepresented groups. Number three, this is the home base for innovation with 83% of folks using Kubernetes in production and 70 plus projects that deliver a wide variety of support to enterprises as they modernize their software and utilize containers. >> Awesome. That was a great summary. First of all, you're a great host. You should be hosting theCUBE with us. Great keynote, love the virtual events that you guys have been doing, love the innovation. I think I would just say just from my perspective and being from there from the beginning is it's always been inclusive and the experience of the events and the community have been top-notch. People squabble, people talk, people have conversations, but at the end of the day, it is a great community and it's fun, memorable, and people are accepting, it's a great job. Stephen, good job as co-chair this year. Well done. Congratulations. >> Thank you very much. >> Okay. Thanks for coming on, I appreciate it. >> Take it easy. >> Okay, this is theCUBE virtual, we wish we were there in person, but we're not, we're remote. This is the virtual Cube. I'm John Furrier, your host. Thanks for watching. (upbeat music)