>> Narrator: From the computer museum in Mountain View, California, it's theCUBE. Covering ACG Silicon Valley Grow Awards brought to you by ACG Silicon Valley. >> Hey welcome back everybody, Jeff Frick here with theCUBE, we're in Mountain View, California at the ACGSV awards, the grow awards, 14th annual. We've been coming for a couple of years, about 300 people celebrating, really, there's a lot of networking, it's an interesting organization. Check it out, we're excited to have our next guest, he's Nick O'Keefe, partner of Arnold and Porter. Nick, great to see you. >> Likewise, great seeing you, great to talk to you. >> So we were talking a little bit off camera, you came to Silicon Valley in 2000, and were saying you seen a lot of changes in those 18 years. >> Yeah it's phenomenal, it's epitomized by the great gathering that we have here today. As I was saying earlier, when I came, I worked in Silicon Alley. Silicon Valley was sort of a bigger version of Silicon Alley and it's just kept growing. You know, the practice between East Coast and West Coast is converged. I mean, there's some of the biggest most successful companies in the world are based here now, and some of the biggest deals. It's just incredible in a short period of time how that's happened. As I was saying earlier, you know, one of the things that really opened my mind, opened my eyes to how successful Silicon Valley is, is I opened up the Middle East offices to another law firm right around the time of the Great Recession. And it's seems like every country is trying to emulate Silicon Valley. We advised on how they can replicate it, what kind of laws they'd have to put in place, what kind of ecosystem they'd have to build. And there's just something really unique here that's really difficult to emulate in different countries-- >> Right because it's all industries. Right, all industries tend to aggregate and congregate around a usually a specific location, or one or two. You think of financial services in New York and London. Because you get the people, and those people leave and start new companies. You have the schools that drive people in their associates. It's tough, it's tough to replicate a whole ecosystem if you don't have all those components, and then, as it gels for a awhile, I think the barriers to entry become even higher. So, you get different versions of it, but really not the same. >> Yeah that's right, I mean, we have all the ingredients here, we have the great educational institutions, you know, Berkeley, Stanford. You have the financial institutions or the venture money. Very sophisticated population, it's just wonderful living here. Just so many smart people around, you can't just lift them up and put them somewhere else, they all have ties in the community. It's just very tough. What's interesting about financial services you mentioned, typically that's a New York-based practice, but with Fintech, you're seeing some of that migrate over here. Cryptocurrencies, a lot of that technology is being developed here, and that's really a convergence of financial services and tech, and Silicon Valley is the hub of that. >> Yeah, I really think that Stanford and Cal don't get enough credit. And Santa Clara and some of the other schools, but those two particularly, because they attract really great talent. They come, their weather's great, they've got a culture of innovation, they've got very nice connections with the local business community, so people don't leave. So you got this constant influx of smart people, and they stay, where a lot of other places, even great academic institutions don't necessarily have the business climate, the weather climate, or kind of the ecosystem to keep their brightest, it's there locally. So I think that's just a huge driver. >> Yeah absolutely, I completely agree. And there's, even if they don't stay, they still maintain their ties here. You know, people all over the world come to study here, as you're indicating. You know, I'm doing a deal currently with some Chinese people who did graduate research locally, and they formed a very successful start-up in China, where currently, we're doing a deal with. And the fact that Stanford, they couldn't be where they were if they hadn't gone through Stanford, and they develop ties with the region, and with the companies in the regions, so they're very much, sort of a diaspora of Silicon Valley, the way they've operated it. >> Right, what is your take on China? 'Cause to me, China's the big competitor. That's the one, I think, where there's the potential because they got a huge internal market, they're really good at fast following, and you look at Alibaba Cloud, and some of the big, big players over there. I think that's really where the biggest threat to the current US incumbents is going to come. >> It's very interesting, it's sort of two, two faceted. On the one hand, obviously, a huge population, and as the country develops, I mean, ultimately within the fairly near future, the Gross National Product is expected to overtake the US. But you have sort of a different culture, and they have the same challenges as everyone else does, this sort of replicating Silicon Valley, I don't think they'll ever take Silicon Valley, you know, take the crown away from them. And I think, what I'm seeing now in a couple of deals is, so the current administration is obviously trying to defend the US trade position, but it's having deleterious effects in that it's preventing Silicon Valley companies from growing and from doing deals. You know, a lot of the Chinese funds they're lucky to invest in the US, where there's currently some regulations that are expected to be proposed next month that could inhibit Chinese investment in the US. Now that's not good for Silicon Valley, so the attempt is to, sort of, protect the US economy, but, you know, I can see certain effects that are happening that are not helpful. It's interesting, there's sort of a symbiotic relationship between development here in the US, and development in other countries, and it's difficult to fight it 'cause you're going to have weird effects. You know, I think the US, it's just a unique country. You know, I think it'll always be unique, and I personally, I don't have a fear that China is going to somehow usurp the position the US occupies, or India, or other huge country, I'm just very polished on Silicon Valley, and the US generally. >> Yeah it is amazing 'cause I've been here a little longer than you, and it just, it just keeps reinventing, right? It's just wave after wave after wave, it was originally silicon and microprocessors, and then it's software, and then it's IOT. And now, you see all the automotive people have innovation centers here. So wave after wave after wave, just continues to come, and then we're going to have, you know, 5G, and it's this whole move to asymptomatically approaching zero cost of store, compute, and networking, and infinite, basically, amounts of those on tap. It really opens up a huge opportunity. >> It really does, yeah, and it's, a lot of it's going to come from here. >> Alright Nick, well thanks for taking a few minutes of your time, and stopping by. >> You bet, my pleasure. >> Alright he's Nick O'Keefe, I'm Jeff Frick, you're watching theCUBE, from the ACGSV awards, Grow Awards in Mountain View, California. Thanks for watching. (digital music)

(upbeat music) >> Hey, welcome back, everybody. Jeff Frick here with The Cube. We're at the RSA Convention in downtown San Francisco. 40,000 people talking security, trying to keep you safe. Keep your car safe, your nest safe, microwave safe, refrigerator safe. >> Everything safe. >> Oh my gosh. Jason Porter, VP, Security Solutions from AT&T, welcome. >> Very good, thanks for having me, Jeff. >> So what are your impressions of the show? This is a crazy event. >> It is crazy, I mean look at all the people. It's the crowds, it's a lot of fun. The best part is just walking the hallways, getting to connect with friends and network and really create new solutions to help our customers. >> It seems to be a reoccurring theme. Everybody sees everybody who's involved in this space is here today. >> Absolutely, yeah, for the next couple of days it's just all in all the time. >> AT&T, obviously, big network, you guys are carrying all this crazy IP traffic that's got good stuff and bad stuff, a lot of fast-moving parts, a ton more data flying through the system. What's kind of your step-back view of what's going on and how are you guys addressing new challenges with 5G and IoT and an ever-increasing amount of data-flow through the network? >> Absolutely, so you're right, at AT&T, we see a ton of traffic. We see 130 petabytes of traffic everyday across our network, so our threat-platform, we pull in five billion threat events every 10 minutes. So-- >> Wait, one more time. Five billion with a B? >> Five billion events every 10 minutes. >> Every 10 minutes. >> So, that's what our big data platform is analyzing with our data scientists and our math, so, lots of volume and activity going on. We have 200 million inpoints, all feeding that threat-platform as well. What are we seeing? We're seeing threats continuing to to grow. Obviously, everybody here at this show knows it, but give you some concrete examples, we've seen a 4,000% increase in IoT vulnerability scanning. IoT is something as a community, as a group here, we definitely need to go solve and that's why we launched our IoT Security Alliance last week. We formed an alliance with some big names out there, like Palo Alto Networks and IBM and Trustonic and others that really, we all have a passion in going out and solving IoT security. It's the number one barrier or concern for adopting IoT. >> You touched on all kinds of stuff there. >> A whole ton of stuff, sorry. >> Let's go to the big data. >> Yeah. >> What's interesting about big data and I always tell kids, right? Every coin has two sides. >> Absolutely. >> The bad part is you've got that much more data to sort through, but the good news is you can use a lot of those same tools. Obviously, it's not a guy sitting with a pager waiting for a red light to go off. >> That's right. >> Analyzing that. How has the big data tools helped you guys to be able to see the threats faster, to react to them faster? >> Yeah. >> To really be more proactive? >> That's a great point, so cyber security is a zero percent unemployment field, right? >> People, you can't get enough people to come work in Cyber security who have the right talent. We had to really evolve. A few years ago, we had to make a big shift that we were not going to just put platforms and people watching screens, looking for blinking red lights, right? We made the shift to a big data threat platform that's basically doing the work of identifying the threats without the people, so we're able to analyze at machine-speed instead of people-speed, which allows us to, as I said, get through many more events. >> Right. >> Much more quickly and allows us to eliminate false-positives and keep our people working really at that, looking at those new threats, those things that we want the people analyzing. >> Right, so the next thing you talked about is IoT. >> Yep. >> My favorite part of Iot is autonomous vehicles just cause I live in Palo Alto. >> Absolutely. >> We see the Google Cars and they're coming soon, right? >> Absolutely. >> But, now you're talking about moving in a 3,000 pound vehicle. >> Yeah. >> Potentially, somebody takes control, so security's so important for IoT. The good news for you guys, 5G's got to be a big part of it. >> Absolutely. >> Not necessarily just for security, but enablement, so you guys are right the heart of IoT. >> Yeah, we are, we have one of the largest IoT deployments in the world. We have the most connected devices and so, what we see is really a need for a layered approach to security. You mentioned 5G, 5G's certainly a part of getting capacity to that, but when you moved to IoT with connected cars and things, you move beyond data harm to physical harm for people and so we've got to be able to up our game and so a layered approach, securing that device, us putting malware detection, but even threat and monitoring what's going on between the hardware and the operating system and the user and then segmenting, say, in a car, telematics from infotainment right? You want to really segment the telematics so that the controls of driving and stopping that car are separate from the infotainment, the internet traffic, the video watching for my kids. >> Right, Spotify, or whatever, right, right right. >> Absolutely and so we do that through SMS, private SMS user groups, private APNs, VPNs, those kinds of things and then of course, you want to build that castle around your data. Your control unit that's managing that car. Make sure you do full UTM threat capabilities. Throw everything you can at that. We've even got some specialized solutions that we've built with some three-letter agencies to really monitor that control point. >> Right, then the last thing you touched on is really partnership. >> Okay. >> And coopetition. >> Yep. >> And sharing which has to be done at a scale that it wasn't before-- >> Absolutely. >> To keep up with the bad guys because apparently, they're sharing all their stuff amongst each other all the time. >> Yeah, absolutely. >> And here we are, 40,000 people, it's an eco-system. How is that evolving in terms of kind of the way that you share data that maybe you wouldn't have wanted to share before for the benefit of the whole? >> Yeah, so, our threat platform, we built it with that in mind with sharing, so it's all, it's surrounded by an API layer, so that we can actually extract data for our customers. Our customers can give us their date. It's interesting, I thought they would want to pull data, but our biggest customers said, no, you know what? We want your data scientists and your math looking at our environment too, so they wanted to push data, but speaking about alliances overall, it's got to be a community as you said. And our IoT Security Alliance is a great example of that. We've got some big suppliers in there, like Palo Alto, but we also have IBM. IBM and AT&T are two of the largest manage-security companies in the planet, so you would think competition, but we came together in this situation because we feel like IoT's one of those things we got to get right as a community. >> Right, right, all right, Jason. I'll give you the last words. >> Okay. >> 2017, we're just getting started, what are kind of your priorities for this year, what will we be talking about a year from now at RSA 2018? >> You're going to continue to hear more about attack types, different attack types, the expanding threats surface of IoT but I think you're going to continue to hear more about our critical infrastructure being targeted. You saw with the dying attack, you're starting to take out major pieces that are impacting people's lives and so you think about power grids and moving into some more critical infrastructure, I think that's going to be more and more the flavor of the day as you continue to progress through the year. >> All right, well hopefully you get good night's sleep. We want you working hard, we're all rooting for ya. >> Absolutely, we're all working on it >> All right, he's Jason Porter from AT&T. I'm Jeff Frick with The Cube. You're watching The Cube from RSA Conference San Francisco. Thanks for watching. (melodic music) (soothing beat)

(upbeat music) >> Hello and welcome to theCube's coverage of International Women's Day. I'm John Furrier, your host of "theCUBE." We've got great two remote guests coming into our Palo Alto Studios, some tech athletes, as we say, people that've been in the trenches, years of experience, Lena Smart, CISO at MongoDB, Cube alumni, and Tara Hernandez, VP of Developer Productivity at MongoDB as well. Thanks for coming in to this program and supporting our efforts today. Thanks so much. >> Thanks for having us. >> Yeah, everyone talk about the journey in tech, where it all started. Before we get there, talk about what you guys are doing at MongoDB specifically. MongoDB is kind of gone the next level as a platform. You have your own ecosystem, lot of developers, very technical crowd, but it's changing the business transformation. What do you guys do at Mongo? We'll start with you, Lena. >> So I'm the CISO, so all security goes through me. I like to say, well, I don't like to say, I'm described as the ones throat to choke. So anything to do with security basically starts and ends with me. We do have a fantastic Cloud engineering security team and a product security team, and they don't report directly to me, but obviously we have very close relationships. I like to keep that kind of church and state separate and I know I've spoken about that before. And we just recently set up a physical security team with an amazing gentleman who left the FBI and he came to join us after 26 years for the agency. So, really starting to look at the physical aspects of what we offer as well. >> I interviewed a CISO the other day and she said, "Every day is day zero for me." Kind of goofing on the Amazon Day one thing, but Tara, go ahead. Tara, go ahead. What's your role there, developer productivity? What are you focusing on? >> Sure. Developer productivity is kind of the latest description for things that we've described over the years as, you know, DevOps oriented engineering or platform engineering or build and release engineering development infrastructure. It's all part and parcel, which is how do we actually get our code from developer to customer, you know, and all the mechanics that go into that. It's been something I discovered from my first job way back in the early '90s at Borland. And the art has just evolved enormously ever since, so. >> Yeah, this is a very great conversation both of you guys, right in the middle of all the action and data infrastructures changing, exploding, and involving big time AI and data tsunami and security never stops. Well, let's get into, we'll talk about that later, but let's get into what motivated you guys to pursue a career in tech and what were some of the challenges that you faced along the way? >> I'll go first. The fact of the matter was I intended to be a double major in history and literature when I went off to university, but I was informed that I had to do a math or a science degree or else the university would not be paid for. At the time, UC Santa Cruz had a policy that called Open Access Computing. This is, you know, the late '80s, early '90s. And anybody at the university could get an email account and that was unusual at the time if you were, those of us who remember, you used to have to pay for that CompuServe or AOL or, there's another one, I forget what it was called, but if a student at Santa Cruz could have an email account. And because of that email account, I met people who were computer science majors and I'm like, "Okay, I'll try that." That seems good. And it was a little bit of a struggle for me, a lot I won't lie, but I can't complain with how it ended up. And certainly once I found my niche, which was development infrastructure, I found my true love and I've been doing it for almost 30 years now. >> Awesome. Great story. Can't wait to ask a few questions on that. We'll go back to that late '80s, early '90s. Lena, your journey, how you got into it. >> So slightly different start. I did not go to university. I had to leave school when I was 16, got a job, had to help support my family. Worked a bunch of various jobs till I was about 21 and then computers became more, I think, I wouldn't say they were ubiquitous, but they were certainly out there. And I'd also been saving up every penny I could earn to buy my own computer and bought an Amstrad 1640, 20 meg hard drive. It rocked. And kind of took that apart, put it back together again, and thought that could be money in this. And so basically just teaching myself about computers any job that I got. 'Cause most of my jobs were like clerical work and secretary at that point. But any job that had a computer in front of that, I would make it my business to go find the guy who did computing 'cause it was always a guy. And I would say, you know, I want to learn how these work. Let, you know, show me. And, you know, I would take my lunch hour and after work and anytime I could with these people and they were very kind with their time and I just kept learning, so yep. >> Yeah, those early days remind me of the inflection point we're going through now. This major C change coming. Back then, if you had a computer, you had to kind of be your own internal engineer to fix things. Remember back on the systems revolution, late '80s, Tara, when, you know, your career started, those were major inflection points. Now we're seeing a similar wave right now, security, infrastructure. It feels like it's going to a whole nother level. At Mongo, you guys certainly see this as well, with this AI surge coming in. A lot more action is coming in. And so there's a lot of parallels between these inflection points. How do you guys see this next wave of change? Obviously, the AI stuff's blowing everyone away. Oh, new user interface. It's been called the browser moment, the mobile iPhone moment, kind of for this generation. There's a lot of people out there who are watching that are young in their careers, what's your take on this? How would you talk to those folks around how important this wave is? >> It, you know, it's funny, I've been having this conversation quite a bit recently in part because, you know, to me AI in a lot of ways is very similar to, you know, back in the '90s when we were talking about bringing in the worldwide web to the forefront of the world, right. And we tended to think in terms of all the optimistic benefits that would come of it. You know, free passing of information, availability to anyone, anywhere. You just needed an internet connection, which back then of course meant a modem. >> John: Not everyone had though. >> Exactly. But what we found in the subsequent years is that human beings are what they are and we bring ourselves to whatever platforms that are there, right. And so, you know, as much as it was amazing to have this freely available HTML based internet experience, it also meant that the negatives came to the forefront quite quickly. And there were ramifications of that. And so to me, when I look at AI, we're already seeing the ramifications to that. Yes, are there these amazing, optimistic, wonderful things that can be done? Yes. >> Yeah. >> But we're also human and the bad stuff's going to come out too. And how do we- >> Yeah. >> How do we as an industry, as a community, you know, understand and mitigate those ramifications so that we can benefit more from the positive than the negative. So it is interesting that it comes kind of full circle in really interesting ways. >> Yeah. The underbelly takes place first, gets it in the early adopter mode. Normally industries with, you know, money involved arbitrage, no standards. But we've seen this movie before. Is there hope, Lena, that we can have a more secure environment? >> I would hope so. (Lena laughs) Although depressingly, we've been in this well for 30 years now and we're, at the end of the day, still telling people not to click links on emails. So yeah, that kind of still keeps me awake at night a wee bit. The whole thing about AI, I mean, it's, obviously I am not an expert by any stretch of the imagination in AI. I did read (indistinct) book recently about AI and that was kind of interesting. And I'm just trying to teach myself as much as I can about it to the extent of even buying the "Dummies Guide to AI." Just because, it's actually not a dummies guide. It's actually fairly interesting, but I'm always thinking about it from a security standpoint. So it's kind of my worst nightmare and the best thing that could ever happen in the same dream. You know, you've got this technology where I can ask it a question and you know, it spits out generally a reasonable answer. And my team are working on with Mark Porter our CTO and his team on almost like an incubation of AI link. What would it look like from MongoDB? What's the legal ramifications? 'Cause there will be legal ramifications even though it's the wild, wild west just now, I think. Regulation's going to catch up to us pretty quickly, I would think. >> John: Yeah, yeah. >> And so I think, you know, as long as companies have a seat at the table and governments perhaps don't become too dictatorial over this, then hopefully we'll be in a good place. But we'll see. I think it's a really interest, there's that curse, we're living in interesting times. I think that's where we are. >> It's interesting just to stay on this tech trend for a minute. The standards bodies are different now. Back in the old days there were, you know, IEEE standards, ITF standards. >> Tara: TPC. >> The developers are the new standard. I mean, now you're seeing open source completely different where it was in the '90s to here beginning, that was gen one, some say gen two, but I say gen one, now we're exploding with open source. You have kind of developers setting the standards. If developers like it in droves, it becomes defacto, which then kind of rolls into implementation. >> Yeah, I mean I think if you don't have developer input, and this is why I love working with Tara and her team so much is 'cause they get it. If we don't have input from developers, it's not going to get used. There's going to be ways of of working around it, especially when it comes to security. If they don't, you know, if you're a developer and you're sat at your screen and you don't want to do that particular thing, you're going to find a way around it. You're a smart person. >> Yeah. >> So. >> Developers on the front lines now versus, even back in the '90s, they're like, "Okay, consider the dev's, got a QA team." Everything was Waterfall, now it's Cloud, and developers are on the front lines of everything. Tara, I mean, this is where the standards are being met. What's your reaction to that? >> Well, I think it's outstanding. I mean, you know, like I was at Netscape and part of the crowd that released the browser as open source and we founded mozilla.org, right. And that was, you know, in many ways kind of the birth of the modern open source movement beyond what we used to have, what was basically free software foundation was sort of the only game in town. And I think it is so incredibly valuable. I want to emphasize, you know, and pile onto what Lena was saying, it's not just that the developers are having input on a sort of company by company basis. Open source to me is like a checks and balance, where it allows us as a broader community to be able to agree on and enforce certain standards in order to try and keep the technology platforms as accessible as possible. I think Kubernetes is a great example of that, right. If we didn't have Kubernetes, that would've really changed the nature of how we think about container orchestration. But even before that, Linux, right. Linux allowed us as an industry to end the Unix Wars and as someone who was on the front lines of that as well and having to support 42 different operating systems with our product, you know, that was a huge win. And it allowed us to stop arguing about operating systems and start arguing about software or not arguing, but developing it in positive ways. So with, you know, with Kubernetes, with container orchestration, we all agree, okay, that's just how we're going to orchestrate. Now we can build up this huge ecosystem, everybody gets taken along, right. And now it changes the game for what we're defining as business differentials, right. And so when we talk about crypto, that's a little bit harder, but certainly with AI, right, you know, what are the checks and balances that as an industry and as the developers around this, that we can in, you know, enforce to make sure that no one company or no one body is able to overly control how these things are managed, how it's defined. And I think that is only for the benefit in the industry as a whole, particularly when we think about the only other option is it gets regulated in ways that do not involve the people who actually know the details of what they're talking about. >> Regulated and or thrown away or bankrupt or- >> Driven underground. >> Yeah. >> Which would be even worse actually. >> Yeah, that's a really interesting, the checks and balances. I love that call out. And I was just talking with another interview part of the series around women being represented in the 51% ratio. Software is for everybody. So that we believe that open source movement around the collective intelligence of the participants in the industry and independent of gender, this is going to be the next wave. You're starting to see these videos really have impact because there are a lot more leaders now at the table in companies developing software systems and with AI, the aperture increases for applications. And this is the new dynamic. What's your guys view on this dynamic? How does this go forward in a positive way? Is there a certain trajectory you see? For women in the industry? >> I mean, I think some of the states are trying to, again, from the government angle, some of the states are trying to force women into the boardroom, for example, California, which can be no bad thing, but I don't know, sometimes I feel a bit iffy about all this kind of forced- >> John: Yeah. >> You know, making, I don't even know how to say it properly so you can cut this part of the interview. (John laughs) >> Tara: Well, and I think that they're >> I'll say it's not organic. >> No, and I think they're already pulling it out, right. It's already been challenged so they're in the process- >> Well, this is the open source angle, Tara, you are getting at it. The change agent is open, right? So to me, the history of the proven model is openness drives transparency drives progress. >> No, it's- >> If you believe that to be true, this could have another impact. >> Yeah, it's so interesting, right. Because if you look at McKinsey Consulting or Boston Consulting or some of the other, I'm blocking on all of the names. There has been a decade or more of research that shows that a non homogeneous employee base, be it gender or ethnicity or whatever, generates more revenue, right? There's dollar signs that can be attached to this, but it's not enough for all companies to want to invest in that way. And it's not enough for all, you know, venture firms or investment firms to grant that seed money or do those seed rounds. I think it's getting better very slowly, but socialization is a much harder thing to overcome over time. Particularly, when you're not just talking about one country like the United States in our case, but around the world. You know, tech centers now exist all over the world, including places that even 10 years ago we might not have expected like Nairobi, right. Which I think is amazing, but you have to factor in the cultural implications of that as well, right. So yes, the openness is important and we have, it's important that we have those voices, but I don't think it's a panacea solution, right. It's just one more piece. I think honestly that one of the most important opportunities has been with Cloud computing and Cloud's been around for a while. So why would I say that? It's because if you think about like everybody holds up the Steve Jobs, Steve Wozniak, back in the '70s, or Sergey and Larry for Google, you know, you had to have access to enough credit card limit to go to Fry's and buy your servers and then access to somebody like Susan Wojcicki to borrow the garage or whatever. But there was still a certain amount of upfrontness that you had to be able to commit to, whereas now, and we've, I think, seen a really good evidence of this being able to lease server resources by the second and have development platforms that you can do on your phone. I mean, for a while I think Africa, that the majority of development happened on mobile devices because there wasn't a sufficient supply chain of laptops yet. And that's no longer true now as far as I know. But like the power that that enables for people who would otherwise be underrepresented in our industry instantly opens it up, right? And so to me that's I think probably the biggest opportunity that we've seen from an industry on how to make more availability in underrepresented representation for entrepreneurship. >> Yeah. >> Something like AI, I think that's actually going to take us backwards if we're not careful. >> Yeah. >> Because of we're reinforcing that socialization. >> Well, also the bias. A lot of people commenting on the biases of the large language inherently built in are also problem. Lena, I want you to weigh on this too, because I think the skills question comes up here and I've been advocating that you don't need the pedigree, college pedigree, to get into a certain jobs, you mentioned Cloud computing. I mean, it's been around for you think a long time, but not really, really think about it. The ability to level up, okay, if you're going to join something new and half the jobs in cybersecurity are created in the past year, right? So, you have this what used to be a barrier, your degree, your pedigree, your certification would take years, would be a blocker. Now that's gone. >> Lena: Yeah, it's the opposite. >> That's, in fact, psychology. >> I think so, but the people who I, by and large, who I interview for jobs, they have, I think security people and also I work with our compliance folks and I can't forget them, but let's talk about security just now. I've always found a particular kind of mindset with security folks. We're very curious, not very good at following rules a lot of the time, and we'd love to teach others. I mean, that's one of the big things stem from the start of my career. People were always interested in teaching and I was interested in learning. So it was perfect. And I think also having, you know, strong women leaders at MongoDB allows other underrepresented groups to actually apply to the company 'cause they see that we're kind of talking the talk. And that's been important. I think it's really important. You know, you've got Tara and I on here today. There's obviously other senior women at MongoDB that you can talk to as well. There's a bunch of us. There's not a whole ton of us, but there's a bunch of us. And it's good. It's definitely growing. I've been there for four years now and I've seen a growth in women in senior leadership positions. And I think having that kind of track record of getting really good quality underrepresented candidates to not just interview, but come and join us, it's seen. And it's seen in the industry and people take notice and they're like, "Oh, okay, well if that person's working, you know, if Tara Hernandez is working there, I'm going to apply for that." And that in itself I think can really, you know, reap the rewards. But it's getting started. It's like how do you get your first strong female into that position or your first strong underrepresented person into that position? It's hard. I get it. If it was easy, we would've sold already. >> It's like anything. I want to see people like me, my friends in there. Am I going to be alone? Am I going to be of a group? It's a group psychology. Why wouldn't? So getting it out there is key. Is there skills that you think that people should pay attention to? One's come up as curiosity, learning. What are some of the best practices for folks trying to get into the tech field or that's in the tech field and advancing through? What advice are you guys- >> I mean, yeah, definitely, what I say to my team is within my budget, we try and give every at least one training course a year. And there's so much free stuff out there as well. But, you know, keep learning. And even if it's not right in your wheelhouse, don't pick about it. Don't, you know, take a look at what else could be out there that could interest you and then go for it. You know, what does it take you few minutes each night to read a book on something that might change your entire career? You know, be enthusiastic about the opportunities out there. And there's so many opportunities in security. Just so many. >> Tara, what's your advice for folks out there? Tons of stuff to taste, taste test, try things. >> Absolutely. I mean, I always say, you know, my primary qualifications for people, I'm looking for them to be smart and motivated, right. Because the industry changes so quickly. What we're doing now versus what we did even last year versus five years ago, you know, is completely different though themes are certainly the same. You know, we still have to code and we still have to compile that code or package the code and ship the code so, you know, how well can we adapt to these new things instead of creating floppy disks, which was my first job. Five and a quarters, even. The big ones. >> That's old school, OG. There it is. Well done. >> And now it's, you know, containers, you know, (indistinct) image containers. And so, you know, I've gotten a lot of really great success hiring boot campers, you know, career transitioners. Because they bring a lot experience in addition to the technical skills. I think the most important thing is to experiment and figuring out what do you like, because, you know, maybe you are really into security or maybe you're really into like deep level coding and you want to go back, you know, try to go to school to get a degree where you would actually want that level of learning. Or maybe you're a front end engineer, you want to be full stacked. Like there's so many different things, data science, right. Maybe you want to go learn R right. You know, I think it's like figure out what you like because once you find that, that in turn is going to energize you 'cause you're going to feel motivated. I think the worst thing you could do is try to force yourself to learn something that you really could not care less about. That's just the worst. You're going in handicapped. >> Yeah and there's choices now versus when we were breaking into the business. It was like, okay, you software engineer. They call it software engineering, that's all it was. You were that or you were in sales. Like, you know, some sort of systems engineer or sales and now it's,- >> I had never heard of my job when I was in school, right. I didn't even know it was a possibility. But there's so many different types of technical roles, you know, absolutely. >> It's so exciting. I wish I was young again. >> One of the- >> Me too. (Lena laughs) >> I don't. I like the age I am. So one of the things that I did to kind of harness that curiosity is we've set up a security champions programs. About 120, I guess, volunteers globally. And these are people from all different backgrounds and all genders, diversity groups, underrepresented groups, we feel are now represented within this champions program. And people basically give up about an hour or two of their time each week, with their supervisors permission, and we basically teach them different things about security. And we've now had seven full-time people move from different areas within MongoDB into my team as a result of that program. So, you know, monetarily and time, yeah, saved us both. But also we're showing people that there is a path, you know, if you start off in Tara's team, for example, doing X, you join the champions program, you're like, "You know, I'd really like to get into red teaming. That would be so cool." If it fits, then we make that happen. And that has been really important for me, especially to give, you know, the women in the underrepresented groups within MongoDB just that window into something they might never have seen otherwise. >> That's a great common fit is fit matters. Also that getting access to what you fit is also access to either mentoring or sponsorship or some sort of, at least some navigation. Like what's out there and not being afraid to like, you know, just ask. >> Yeah, we just actually kicked off our big mentor program last week, so I'm the executive sponsor of that. I know Tara is part of it, which is fantastic. >> We'll put a plug in for it. Go ahead. >> Yeah, no, it's amazing. There's, gosh, I don't even know the numbers anymore, but there's a lot of people involved in this and so much so that we've had to set up mentoring groups rather than one-on-one. And I think it was 45% of the mentors are actually male, which is quite incredible for a program called Mentor Her. And then what we want to do in the future is actually create a program called Mentor Them so that it's not, you know, not just on the female and so that we can live other groups represented and, you know, kind of break down those groups a wee bit more and have some more granularity in the offering. >> Tara, talk about mentoring and sponsorship. Open source has been there for a long time. People help each other. It's community-oriented. What's your view of how to work with mentors and sponsors if someone's moving through ranks? >> You know, one of the things that was really interesting, unfortunately, in some of the earliest open source communities is there was a lot of pervasive misogyny to be perfectly honest. >> Yeah. >> And one of the important adaptations that we made as an open source community was the idea, an introduction of code of conducts. And so when I'm talking to women who are thinking about expanding their skills, I encourage them to join open source communities to have opportunity, even if they're not getting paid for it, you know, to develop their skills to work with people to get those code reviews, right. I'm like, "Whatever you join, make sure they have a code of conduct and a good leadership team. It's very important." And there are plenty, right. And then that idea has come into, you know, conferences now. So now conferences have codes of contact, if there are any good, and maybe not all of them, but most of them, right. And the ideas of expanding that idea of intentional healthy culture. >> John: Yeah. >> As a business goal and business differentiator. I mean, I won't lie, when I was recruited to come to MongoDB, the culture that I was able to discern through talking to people, in addition to seeing that there was actually women in senior leadership roles like Lena, like Kayla Nelson, that was a huge win. And so it just builds on momentum. And so now, you know, those of us who are in that are now representing. And so that kind of reinforces, but it's all ties together, right. As the open source world goes, particularly for a company like MongoDB, which has an open source product, you know, and our community builds. You know, it's a good thing to be mindful of for us, how we interact with the community and you know, because that could also become an opportunity for recruiting. >> John: Yeah. >> Right. So we, in addition to people who might become advocates on Mongo's behalf in their own company as a solution for themselves, so. >> You guys had great successful company and great leadership there. I mean, I can't tell you how many times someone's told me "MongoDB doesn't scale. It's going to be dead next year." I mean, I was going back 10 years. It's like, just keeps getting better and better. You guys do a great job. So it's so fun to see the success of developers. Really appreciate you guys coming on the program. Final question, what are you guys excited about to end the segment? We'll give you guys the last word. Lena will start with you and Tara, you can wrap us up. What are you excited about? >> I'm excited to see what this year brings. I think with ChatGPT and its copycats, I think it'll be a very interesting year when it comes to AI and always in the lookout for the authentic deep fakes that we see coming out. So just trying to make people aware that this is a real thing. It's not just pretend. And then of course, our old friend ransomware, let's see where that's going to go. >> John: Yeah. >> And let's see where we get to and just genuine hygiene and housekeeping when it comes to security. >> Excellent. Tara. >> Ah, well for us, you know, we're always constantly trying to up our game from a security perspective in the software development life cycle. But also, you know, what can we do? You know, one interesting application of AI that maybe Google doesn't like to talk about is it is really cool as an addendum to search and you know, how we might incorporate that as far as our learning environment and developer productivity, and how can we enable our developers to be more efficient, productive in their day-to-day work. So, I don't know, there's all kinds of opportunities that we're looking at for how we might improve that process here at MongoDB and then maybe be able to share it with the world. One of the things I love about working at MongoDB is we get to use our own products, right. And so being able to have this interesting document database in order to put information and then maybe apply some sort of AI to get it out again, is something that we may well be looking at, if not this year, then certainly in the coming year. >> Awesome. Lena Smart, the chief information security officer. Tara Hernandez, vice president developer of productivity from MongoDB. Thank you so much for sharing here on International Women's Day. We're going to do this quarterly every year. We're going to do it and then we're going to do quarterly updates. Thank you so much for being part of this program. >> Thank you. >> Thanks for having us. >> Okay, this is theCube's coverage of International Women's Day. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Confidential computing is a technology that aims to enhance data privacy and security by providing encrypted computation on sensitive data and isolating data from apps in a fenced off enclave during processing. The concept of confidential computing is gaining popularity, especially in the cloud computing space where sensitive data is often stored and of course processed. However, there are some who view confidential computing as an unnecessary technology in a marketing ploy by cloud providers aimed at calming customers who are cloud phobic. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we revisit the notion of confidential computing, and to do so, we'll invite two Google experts to the show, but before we get there, let's summarize briefly. There's not a ton of ETR data on the topic of confidential computing. I mean, it's a technology that's deeply embedded into silicon and computing architectures. But at the highest level, security remains the number one priority being addressed by IT decision makers in the coming year as shown here. And this data is pretty much across the board by industry, by region, by size of company. I mean we dug into it and the only slight deviation from the mean is in financial services. The second and third most cited priorities, cloud migration and analytics, are noticeably closer to cybersecurity in financial services than in other sectors, likely because financial services has always been hyper security conscious, but security is still a clear number one priority in that sector. The idea behind confidential computing is to better address threat models for data in execution. Protecting data at rest and data and transit have long been a focus of security approaches, but more recently, silicon manufacturers have introduced architectures that separate data and applications from the host system. Arm, Intel, AMD, Nvidia and other suppliers are all on board, as are the big cloud players. Now the argument against confidential computing is that it narrowly focuses on memory encryption and it doesn't solve the biggest problems in security. Multiple system images updates different services and the entire code flow aren't directly addressed by memory encryption, rather to truly attack these problems, many believe that OSs need to be re-engineered with the attacker and hacker in mind. There are so many variables and at the end of the day, critics say the emphasis on confidential computing made by cloud providers is overstated and largely hype. This tweet from security researcher Rodrigo Branco sums up the sentiment of many skeptics. He says, "Confidential computing is mostly a marketing campaign for memory encryption. It's not driving the industry towards the hard open problems. It is selling an illusion." Okay. Nonetheless, encrypting data in use and fencing off key components of the system isn't a bad thing, especially if it comes with the package essentially for free. There has been a lack of standardization and interoperability between different confidential computing approaches. But the confidential computing consortium was established in 2019 ostensibly to accelerate the market and influence standards. Notably, AWS is not part of the consortium, likely because the politics of the consortium were probably a conundrum for AWS because the base technology defined by the the consortium is seen as limiting by AWS. This is my guess, not AWS's words, and but I think joining the consortium would validate a definition which AWS isn't aligned with. And two, it's got a lead with this Annapurna acquisition. This was way ahead with Arm integration and so it probably doesn't feel the need to validate its competitors. Anyway, one of the premier members of the confidential computing consortium is Google, along with many high profile names including Arm, Intel, Meta, Red Hat, Microsoft, and others. And we're pleased to welcome two experts on confidential computing from Google to unpack the topic, Nelly Porter is head of product for GCP confidential computing and encryption, and Dr. Patricia Florissi is the technical director for the office of the CTO at Google Cloud. Welcome Nelly and Patricia, great to have you. >> Great to be here. >> Thank you so much for having us. >> You're very welcome. Nelly, why don't you start and then Patricia, you can weigh in. Just tell the audience a little bit about each of your roles at Google Cloud. >> So I'll start, I'm owning a lot of interesting activities in Google and again security or infrastructure securities that I usually own. And we are talking about encryption and when encryption and confidential computing is a part of portfolio in additional areas that I contribute together with my team to Google and our customers is secure software supply chain. Because you need to trust your software. Is it operate in your confidential environment to have end-to-end story about if you believe that your software and your environment doing what you expect, it's my role. >> Got it. Okay. Patricia? >> Well, I am a technical director in the office of the CTO, OCTO for short, in Google Cloud. And we are a global team. We include former CTOs like myself and senior technologists from large corporations, institutions and a lot of success, we're startups as well. And we have two main goals. First, we walk side by side with some of our largest, more strategic or most strategical customers and we help them solve complex engineering technical problems. And second, we are devise Google and Google Cloud engineering and product management and tech on there, on emerging trends and technologies to guide the trajectory of our business. We are unique group, I think, because we have created this collaborative culture with our customers. And within OCTO, I spend a lot of time collaborating with customers and the industry at large on technologies that can address privacy, security, and sovereignty of data in general. >> Excellent. Thank you for that both of you. Let's get into it. So Nelly, what is confidential computing? From Google's perspective, how do you define it? >> Confidential computing is a tool and it's still one of the tools in our toolbox. And confidential computing is a way how we would help our customers to complete this very interesting end-to-end lifecycle of the data. And when customers bring in the data to cloud and want to protect it as they ingest it to the cloud, they protect it at rest when they store data in the cloud. But what was missing for many, many years is ability for us to continue protecting data and workloads of our customers when they running them. And again, because data is not brought to cloud to have huge graveyard, we need to ensure that this data is actually indexed. Again, there is some insights driven and drawn from this data. You have to process this data and confidential computing here to help. Now we have end to end protection of our customer's data when they bring the workloads and data to cloud, thanks to confidential computing. >> Thank you for that. Okay, we're going to get into the architecture a bit, but before we do, Patricia, why do you think this topic of confidential computing is such an important technology? Can you explain, do you think it's transformative for customers and if so, why? >> Yeah, I would maybe like to use one thought, one way, one intuition behind why confidential commuting matters, because at the end of the day, it reduces more and more the customer's thresh boundaries and the attack surface. That's about reducing that periphery, the boundary in which the customer needs to mind about trust and safety. And in a way, is a natural progression that you're using encryption to secure and protect the data. In the same way that we are encrypting data in transit and at rest, now we are also encrypting data while in use. And among other beneficials, I would say one of the most transformative ones is that organizations will be able to collaborate with each other and retain the confidentiality of the data. And that is across industry, even though it's highly focused on, I wouldn't say highly focused, but very beneficial for highly regulated industries. It applies to all of industries. And if you look at financing for example, where bankers are trying to detect fraud, and specifically double finance where you are, a customer is actually trying to get a finance on an asset, let's say a boat or a house, and then it goes to another bank and gets another finance on that asset. Now bankers would be able to collaborate and detect fraud while preserving confidentiality and privacy of the data. >> Interesting. And I want to understand that a little bit more but I'm going to push you a little bit on this, Nelly, if I can because there's a narrative out there that says confidential computing is a marketing ploy, I talked about this upfront, by cloud providers that are just trying to placate people that are scared of the cloud. And I'm presuming you don't agree with that, but I'd like you to weigh in here. The argument is confidential computing is just memory encryption and it doesn't address many other problems. It is over hyped by cloud providers. What do you say to that line of thinking? >> I absolutely disagree, as you can imagine, with this statement, but the most importantly is we mixing multiple concepts, I guess. And exactly as Patricia said, we need to look at the end-to-end story, not again the mechanism how confidential computing trying to again, execute and protect a customer's data and why it's so critically important because what confidential computing was able to do, it's in addition to isolate our tenants in multi-tenant environments the cloud covering to offer additional stronger isolation. They called it cryptographic isolation. It's why customers will have more trust to customers and to other customers, the tenant that's running on the same host but also us because they don't need to worry about against threats and more malicious attempts to penetrate the environment. So what confidential computing is helping us to offer our customers, stronger isolation between tenants in this multi-tenant environment, but also incredibly important, stronger isolation of our customers, so tenants from us. We also writing code, we also software providers will also make mistakes or have some zero days. Sometimes again us introduced, sometimes introduced by our adversaries. But what I'm trying to say by creating this cryptographic layer of isolation between us and our tenants and amongst those tenants, we're really providing meaningful security to our customers and eliminate some of the worries that they have running on multi-tenant spaces or even collaborating to gather this very sensitive data knowing that this particular protection is available to them. >> Okay, thank you. Appreciate that. And I think malicious code is often a threat model missed in these narratives. Operator access, yeah, maybe I trust my clouds provider, but if I can fence off your access even better, I'll sleep better at night. Separating a code from the data, everybody's, Arm, Intel, AMD, Nvidia, others, they're all doing it. I wonder if, Nelly, if we could stay with you and bring up the slide on the architecture. What's architecturally different with confidential computing versus how operating systems and VMs have worked traditionally. We're showing a slide here with some VMs, maybe you could take us through that. >> Absolutely. And Dave, the whole idea for Google and now industry way of dealing with confidential computing is to ensure that three main property is actually preserved. Customers don't need to change the code. They can operate on those VMs exactly as they would with normal non-confidential VMs, but to give them this opportunity of lift and shift or no changing their apps and performing and having very, very, very low latency and scale as any cloud can, something that Google actually pioneer in confidential computing. I think we need to open and explain how this magic was actually done. And as I said, it's again the whole entire system have to change to be able to provide this magic. And I would start with we have this concept of root of trust and root of trust where we will ensure that this machine, when the whole entire post has integrity guarantee, means nobody changing my code on the most low level of system. And we introduce this in 2017 called Titan. It was our specific ASIC, specific, again, inch by inch system on every single motherboard that we have that ensures that your low level former, your actually system code, your kernel, the most powerful system is actually proper configured and not changed, not tampered. We do it for everybody, confidential computing included. But for confidential computing, what we have to change, we bring in AMD, or again, future silicon vendors and we have to trust their former, their way to deal with our confidential environments. And that's why we have obligation to validate integrity, not only our software and our former but also former and software of our vendors, silicon vendors. So we actually, when we booting this machine, as you can see, we validate that integrity of all of the system is in place. It means nobody touching, nobody changing, nobody modifying it. But then we have this concept of AMD secure processor, it's special ASICs, best specific things that generate a key for every single VM that our customers will run or every single node in Kubernetes or every single worker thread in our Hadoop or Spark capability. We offer all of that. And those keys are not available to us. It's the best keys ever in encryption space because when we are talking about encryption, the first question that I'm receiving all the time, where's the key, who will have access to the key? Because if you have access to the key then it doesn't matter if you encrypted or not. So, but the case in confidential computing provides so revolutionary technology, us cloud providers, who don't have access to the keys. They sitting in the hardware and they head to memory controller. And it means when hypervisors that also know about these wonderful things saying I need to get access to the memories that this particular VM trying to get access to, they do not decrypt the data, they don't have access to the key because those keys are random, ephemeral and per VM, but the most importantly, in hardware not exportable. And it means now you would be able to have this very interesting role that customers or cloud providers will not be able to get access to your memory. And what we do, again, as you can see our customers don't need to change their applications, their VMs are running exactly as it should run and what you're running in VM, you actually see your memory in clear, it's not encrypted, but God forbid is trying somebody to do it outside of my confidential box. No, no, no, no, no, they would not be able to do it. Now you'll see cyber and it's exactly what combination of these multiple hardware pieces and software pieces have to do. So OS is also modified. And OS is modified such way to provide integrity. It means even OS that you're running in your VM box is not modifiable and you, as customer, can verify. But the most interesting thing, I guess, how to ensure the super performance of this environment because you can imagine, Dave, that encrypting and it's additional performance, additional time, additional latency. So we were able to mitigate all of that by providing incredibly interesting capability in the OS itself. So our customers will get no changes needed, fantastic performance and scales as they would expect from cloud providers like Google. >> Okay, thank you. Excellent. Appreciate that explanation. So, again, the narrative on this as well, you've already given me guarantees as a cloud provider that you don't have access to my data, but this gives another level of assurance, key management as they say is key. Now humans aren't managing the keys, the machines are managing them. So Patricia, my question to you is, in addition to, let's go pre confidential computing days, what are the sort of new guarantees that these hardware-based technologies are going to provide to customers? >> So if I am a customer, I am saying I now have full guarantee of confidentiality and integrity of the data and of the code. So if you look at code and data confidentiality, the customer cares and they want to know whether their systems are protected from outside or unauthorized access, and that recovered with Nelly, that it is. Confidential computing actually ensures that the applications and data internals remain secret, right? The code is actually looking at the data, the only the memory is decrypting the data with a key that is ephemeral and per VM and generated on demand. Then you have the second point where you have code and data integrity, and now customers want to know whether their data was corrupted, tampered with or impacted by outside actors. And what confidential computing ensures is that application internals are not tampered with. So the application, the workload as we call it, that is processing the data, it's also, it has not been tampered and preserves integrity. I would also say that this is all verifiable. So you have attestation and these attestation actually generates a log trail and the log trail guarantees that, provides a proof that it was preserved. And I think that the offer's also a guarantee of what we call ceiling, this idea that the secrets have been preserved and not tampered with, confidentiality and integrity of code and data. >> Got it. Okay, thank you. Nelly, you mentioned, I think I heard you say that the applications, it's transparent, you don't have to change the application, it just comes for free essentially. And we showed some various parts of the stack before. I'm curious as to what's affected, but really more importantly, what is specifically Google's value add? How do partners participate in this, the ecosystem, or maybe said another way, how does Google ensure the compatibility of confidential computing with existing systems and applications? >> And a fantastic question by the way. And it's very difficult and definitely complicated world because to be able to provide these guarantees, actually a lot of work was done by community. Google is very much operate in open, so again, our operating system, we working with operating system repository OSs, OS vendors to ensure that all capabilities that we need is part of the kernels, are part of the releases and it's available for customers to understand and even explore if they have fun to explore a lot of code. We have also modified together with our silicon vendors a kernel, host kernel to support this capability and it means working this community to ensure that all of those patches are there. We also worked with every single silicon vendor as you've seen, and that's what I probably feel that Google contributed quite a bit in this whole, we moved our industry, our community, our vendors to understand the value of easy to use confidential computing or removing barriers. And now I don't know if you noticed, Intel is pulling the lead and also announcing their trusted domain extension, very similar architecture. And no surprise, it's, again, a lot of work done with our partners to, again, convince, work with them and make this capability available. The same with Arm this year, actually last year, Arm announced their future design for confidential computing. It's called Confidential Computing Architecture. And it's also influenced very heavily with similar ideas by Google and industry overall. So it's a lot of work in confidential computing consortiums that we are doing, for example, simply to mention, to ensure interop, as you mentioned, between different confidential environments of cloud providers. They want to ensure that they can attest to each other because when you're communicating with different environments, you need to trust them. And if it's running on different cloud providers, you need to ensure that you can trust your receiver when you are sharing your sensitive data workloads or secret with them. So we coming as a community and we have this attestation sig, the, again, the community based systems that we want to build and influence and work with Arm and every other cloud providers to ensure that we can interrupt and it means it doesn't matter where confidential workloads will be hosted, but they can exchange the data in secure, verifiable and controlled by customers way. And to do it, we need to continue what we are doing, working open, again, and contribute with our ideas and ideas of our partners to this role to become what we see confidential computing has to become, it has to become utility. It doesn't need to be so special, but it's what we want it to become. >> Let's talk about, thank you for that explanation. Let's talk about data sovereignty because when you think about data sharing, you think about data sharing across the ecosystem and different regions and then of course data sovereignty comes up. Typically public policy lags, the technology industry and sometimes is problematic. I know there's a lot of discussions about exceptions, but Patricia, we have a graphic on data sovereignty. I'm interested in how confidential computing ensures that data sovereignty and privacy edicts are adhered to, even if they're out of alignment maybe with the pace of technology. One of the frequent examples is when you delete data, can you actually prove that data is deleted with a hundred percent certainty? You got to prove that and a lot of other issues. So looking at this slide, maybe you could take us through your thinking on data sovereignty. >> Perfect. So for us, data sovereignty is only one of the three pillars of digital sovereignty. And I don't want to give the impression that confidential computing addresses it all. That's why we want to step back and say, hey, digital sovereignty includes data sovereignty where we are giving you full control and ownership of the location, encryption and access to your data. Operational sovereignty where the goal is to give our Google Cloud customers full visibility and control over the provider operations, right? So if there are any updates on hardware, software stack, any operations, there is full transparency, full visibility. And then the third pillar is around software sovereignty where the customer wants to ensure that they can run their workloads without dependency on the provider's software. So they have sometimes is often referred as survivability, that you can actually survive if you are untethered to the cloud and that you can use open source. Now let's take a deep dive on data sovereignty, which by the way is one of my favorite topics. And we typically focus on saying, hey, we need to care about data residency. We care where the data resides because where the data is at rest or in processing, it typically abides to the jurisdiction, the regulations of the jurisdiction where the data resides. And others say, hey, let's focus on data protection. We want to ensure the confidentiality and integrity and availability of the data, which confidential computing is at the heart of that data protection. But it is yet another element that people typically don't talk about when talking about data sovereignty, which is the element of user control. And here, Dave, is about what happens to the data when I give you access to my data. And this reminds me of security two decades ago, even a decade ago, where we started the security movement by putting firewall protections and login accesses. But once you were in, you were able to do everything you wanted with the data. An insider had access to all the infrastructure, the data and the code. And that's similar because with data sovereignty we care about whether it resides, where, who is operating on the data. But the moment that the data is being processed, I need to trust that the processing of the data will abide by user control, by the policies that I put in place of how my data is going to be used. And if you look at a lot of the regulation today and a lot of the initiatives around the International Data Space Association, IDSA, and Gaia-X, there is a movement of saying the two parties, the provider of the data and the receiver of the data are going to agree on a contract that describes what my data can be used for. The challenge is to ensure that once the data crosses boundaries, that the data will be used for the purposes that it was intended and specified in the contract. And if you actually bring together, and this is the exciting part, confidential computing together with policy enforcement, now the policy enforcement can guarantee that the data is only processed within the confines of a confidential computing environment, that the workload is cryptographically verified that there is the workload that was meant to process the data and that the data will be only used when abiding to the confidentiality and integrity safety of the confidential computing environment. And that's why we believe confidential computing is one necessary and essential technology that will allow us to ensure data sovereignty, especially when it comes to user control. >> Thank you for that. I mean it was a deep dive, I mean brief, but really detailed. So I appreciate that, especially the verification of the enforcement. Last question, I met you two because as part of my year end prediction post, you guys sent in some predictions and I wasn't able to get to them in the predictions post. So I'm thrilled that you were able to make the time to come on the program. How widespread do you think the adoption of confidential computing will be in 23 and what's the maturity curve look like, this decade in your opinion? Maybe each of you could give us a brief answer. >> So my prediction in five, seven years, as I started, it'll become utility. It'll become TLS as of, again, 10 years ago we couldn't believe that websites will have certificates and we will support encrypted traffic. Now we do and it's become ubiquity. It's exactly where confidential computing is getting and heading, I don't know we deserve yet. It'll take a few years of maturity for us, but we will be there. >> Thank you. And Patricia, what's your prediction? >> I will double that and say, hey, in the future, in the very near future, you will not be able to afford not having it. I believe as digital sovereignty becomes evermore top of mind with sovereign states and also for multi national organizations and for organizations that want to collaborate with each other, confidential computing will become the norm. It'll become the default, if I say, mode of operation. I like to compare that today is inconceivable. If we talk to the young technologists, it's inconceivable to think that at some point in history, and I happen to be alive that we had data at rest that was not encrypted, data in transit that was not encrypted, and I think that will be inconceivable at some point in the near future that to have unencrypted data while in use. >> And plus I think the beauty of the this industry is because there's so much competition, this essentially comes for free. I want to thank you both for spending some time on Breaking Analysis. There's so much more we could cover. I hope you'll come back to share the progress that you're making in this area and we can double click on some of these topics. Really appreciate your time. >> Anytime. >> Thank you so much. >> In summary, while confidential computing is being touted by the cloud players as a promising technology for enhancing data privacy and security, there are also those, as we said, who remain skeptical. The truth probably lies somewhere in between and it will depend on the specific implementation and the use case as to how effective confidential computing will be. Look, as with any new tech, it's important to carefully evaluate the potential benefits, the drawbacks, and make informed decisions based on the specific requirements in the situation and the constraints of each individual customer. But the bottom line is silicon manufacturers are working with cloud providers and other system companies to include confidential computing into their architectures. Competition, in our view, will moderate price hikes. And at the end of the day, this is under the covers technology that essentially will come for free. So we'll take it. I want to thank our guests today, Nelly and Patricia from Google, and thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well out of our Boston studio, Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor-in-chief over at siliconangle.com. Does some great editing for us, thank you all. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com where you can get all the news. If you want to get in touch, you can email me at david.vellante@siliconangle.com or dm me @DVellante. And you can also comment on my LinkedIn post. Definitely you want to check out etr.ai for the best survey data in the enterprise tech business. I know we didn't hit on a lot today, but there's some amazing data and it's always being updated, so check that out. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Confidential computing is a technology that aims to enhance data privacy and security, by providing encrypted computation on sensitive data and isolating data, and apps that are fenced off enclave during processing. The concept of, I got to start over. I fucked that up, I'm sorry. That's not right, what I said was not right. On Dave in five, four, three. Confidential computing is a technology that aims to enhance data privacy and security by providing encrypted computation on sensitive data, isolating data from apps and a fenced off enclave during processing. The concept of confidential computing is gaining popularity, especially in the cloud computing space, where sensitive data is often stored and of course processed. However, there are some who view confidential computing as an unnecessary technology in a marketing ploy by cloud providers aimed at calming customers who are cloud phobic. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we revisit the notion of confidential computing, and to do so, we'll invite two Google experts to the show. But before we get there, let's summarize briefly. There's not a ton of ETR data on the topic of confidential computing, I mean, it's a technology that's deeply embedded into silicon and computing architectures. But at the highest level, security remains the number one priority being addressed by IT decision makers in the coming year as shown here. And this data is pretty much across the board by industry, by region, by size of company. I mean we dug into it and the only slight deviation from the mean is in financial services. The second and third most cited priorities, cloud migration and analytics are noticeably closer to cybersecurity in financial services than in other sectors, likely because financial services has always been hyper security conscious, but security is still a clear number one priority in that sector. The idea behind confidential computing is to better address threat models for data in execution. Protecting data at rest and data in transit have long been a focus of security approaches, but more recently, silicon manufacturers have introduced architectures that separate data and applications from the host system, ARM, Intel, AMD, Nvidia and other suppliers are all on board, as are the big cloud players. Now, the argument against confidential computing is that it narrowly focuses on memory encryption and it doesn't solve the biggest problems in security. Multiple system images, updates, different services and the entire code flow aren't directly addressed by memory encryption. Rather to truly attack these problems, many believe that OSs need to be re-engineered with the attacker and hacker in mind. There are so many variables and at the end of the day, critics say the emphasis on confidential computing made by cloud providers is overstated and largely hype. This tweet from security researcher Rodrigo Bronco, sums up the sentiment of many skeptics. He says, "Confidential computing is mostly a marketing campaign from memory encryption. It's not driving the industry towards the hard open problems. It is selling an illusion." Okay. Nonetheless, encrypting data in use and fencing off key components of the system isn't a bad thing, especially if it comes with the package essentially for free. There has been a lack of standardization and interoperability between different confidential computing approaches. But the confidential computing consortium was established in 2019 ostensibly to accelerate the market and influence standards. Notably, AWS is not part of the consortium, likely because the politics of the consortium were probably a conundrum for AWS because the base technology defined by the consortium is seen as limiting by AWS. This is my guess, not AWS' words. But I think joining the consortium would validate a definition which AWS isn't aligned with. And two, it's got to lead with this Annapurna acquisition. It was way ahead with ARM integration, and so it's probably doesn't feel the need to validate its competitors. Anyway, one of the premier members of the confidential computing consortium is Google, along with many high profile names, including Aem, Intel, Meta, Red Hat, Microsoft, and others. And we're pleased to welcome two experts on confidential computing from Google to unpack the topic. Nelly Porter is Head of Product for GCP Confidential Computing and Encryption and Dr. Patricia Florissi is the Technical Director for the Office of the CTO at Google Cloud. Welcome Nelly and Patricia, great to have you. >> Great to be here. >> Thank you so much for having us. >> You're very welcome. Nelly, why don't you start and then Patricia, you can weigh in. Just tell the audience a little bit about each of your roles at Google Cloud. >> So I'll start, I'm owning a lot of interesting activities in Google and again, security or infrastructure securities that I usually own. And we are talking about encryption, end-to-end encryption, and confidential computing is a part of portfolio. Additional areas that I contribute to get with my team to Google and our customers is secure software supply chain because you need to trust your software. Is it operate in your confidential environment to have end-to-end security, about if you believe that your software and your environment doing what you expect, it's my role. >> Got it. Okay, Patricia? >> Well, I am a Technical Director in the Office of the CTO, OCTO for short in Google Cloud. And we are a global team, we include former CTOs like myself and senior technologies from large corporations, institutions and a lot of success for startups as well. And we have two main goals, first, we walk side by side with some of our largest, more strategic or most strategical customers and we help them solve complex engineering technical problems. And second, we advice Google and Google Cloud Engineering, product management on emerging trends and technologies to guide the trajectory of our business. We are unique group, I think, because we have created this collaborative culture with our customers. And within OCTO I spend a lot of time collaborating with customers in the industry at large on technologies that can address privacy, security, and sovereignty of data in general. >> Excellent. Thank you for that both of you. Let's get into it. So Nelly, what is confidential computing from Google's perspective? How do you define it? >> Confidential computing is a tool and one of the tools in our toolbox. And confidential computing is a way how we would help our customers to complete this very interesting end-to-end lifecycle of the data. And when customers bring in the data to cloud and want to protect it as they ingest it to the cloud, they protect it at rest when they store data in the cloud. But what was missing for many, many years is ability for us to continue protecting data and workloads of our customers when they run them. And again, because data is not brought to cloud to have huge graveyard, we need to ensure that this data is actually indexed. Again, there is some insights driven and drawn from this data. You have to process this data and confidential computing here to help. Now we have end-to-end protection of our customer's data when they bring the workloads and data to cloud thanks to confidential computing. >> Thank you for that. Okay, we're going to get into the architecture a bit, but before we do Patricia, why do you think this topic of confidential computing is such an important technology? Can you explain? Do you think it's transformative for customers and if so, why? >> Yeah, I would maybe like to use one thought, one way, one intuition behind why confidential computing matters because at the end of the day, it reduces more and more the customer's thrush boundaries and the attack surface. That's about reducing that periphery, the boundary in which the customer needs to mind about trust and safety. And in a way is a natural progression that you're using encryption to secure and protect data in the same way that we are encrypting data in transit and at rest. Now, we are also encrypting data while in the use. And among other beneficials, I would say one of the most transformative ones is that organizations will be able to collaborate with each other and retain the confidentiality of the data. And that is across industry, even though it's highly focused on, I wouldn't say highly focused but very beneficial for highly regulated industries, it applies to all of industries. And if you look at financing for example, where bankers are trying to detect fraud and specifically double finance where a customer is actually trying to get a finance on an asset, let's say a boat or a house, and then it goes to another bank and gets another finance on that asset. Now bankers would be able to collaborate and detect fraud while preserving confidentiality and privacy of the data. >> Interesting and I want to understand that a little bit more but I got to push you a little bit on this, Nellie if I can, because there's a narrative out there that says confidential computing is a marketing ploy I talked about this up front, by cloud providers that are just trying to placate people that are scared of the cloud. And I'm presuming you don't agree with that, but I'd like you to weigh in here. The argument is confidential computing is just memory encryption, it doesn't address many other problems. It is over hyped by cloud providers. What do you say to that line of thinking? >> I absolutely disagree as you can imagine Dave, with this statement. But the most importantly is we mixing a multiple concepts I guess, and exactly as Patricia said, we need to look at the end-to-end story, not again, is a mechanism. How confidential computing trying to execute and protect customer's data and why it's so critically important. Because what confidential computing was able to do, it's in addition to isolate our tenants in multi-tenant environments the cloud offering to offer additional stronger isolation, they called it cryptographic isolation. It's why customers will have more trust to customers and to other customers, the tenants running on the same host but also us because they don't need to worry about against rats and more malicious attempts to penetrate the environment. So what confidential computing is helping us to offer our customers stronger isolation between tenants in this multi-tenant environment, but also incredibly important, stronger isolation of our customers to tenants from us. We also writing code, we also software providers, we also make mistakes or have some zero days. Sometimes again us introduce, sometimes introduced by our adversaries. But what I'm trying to say by creating this cryptographic layer of isolation between us and our tenants and among those tenants, we really providing meaningful security to our customers and eliminate some of the worries that they have running on multi-tenant spaces or even collaborating together with very sensitive data knowing that this particular protection is available to them. >> Okay, thank you. Appreciate that. And I think malicious code is often a threat model missed in these narratives. You know, operator access. Yeah, maybe I trust my cloud's provider, but if I can fence off your access even better, I'll sleep better at night separating a code from the data. Everybody's ARM, Intel, AMD, Nvidia and others, they're all doing it. I wonder if Nell, if we could stay with you and bring up the slide on the architecture. What's architecturally different with confidential computing versus how operating systems and VMs have worked traditionally? We're showing a slide here with some VMs, maybe you could take us through that. >> Absolutely, and Dave, the whole idea for Google and now industry way of dealing with confidential computing is to ensure that three main property is actually preserved. Customers don't need to change the code. They can operate in those VMs exactly as they would with normal non-confidential VMs. But to give them this opportunity of lift and shift though, no changing the apps and performing and having very, very, very low latency and scale as any cloud can, some things that Google actually pioneer in confidential computing. I think we need to open and explain how this magic was actually done, and as I said, it's again the whole entire system have to change to be able to provide this magic. And I would start with we have this concept of root of trust and root of trust where we will ensure that this machine within the whole entire host has integrity guarantee, means nobody changing my code on the most low level of system, and we introduce this in 2017 called Titan. So our specific ASIC, specific inch by inch system on every single motherboard that we have that ensures that your low level former, your actually system code, your kernel, the most powerful system is actually proper configured and not changed, not tempered. We do it for everybody, confidential computing included, but for confidential computing is what we have to change, we bring in AMD or future silicon vendors and we have to trust their former, their way to deal with our confidential environments. And that's why we have obligation to validate intelligent not only our software and our former but also former and software of our vendors, silicon vendors. So we actually, when we booting this machine as you can see, we validate that integrity of all of this system is in place. It means nobody touching, nobody changing, nobody modifying it. But then we have this concept of AMD Secure Processor, it's special ASIC best specific things that generate a key for every single VM that our customers will run or every single node in Kubernetes or every single worker thread in our Hadoop spark capability. We offer all of that and those keys are not available to us. It's the best case ever in encryption space because when we are talking about encryption, the first question that I'm receiving all the time, "Where's the key? Who will have access to the key?" because if you have access to the key then it doesn't matter if you encrypted or not. So, but the case in confidential computing why it's so revolutionary technology, us cloud providers who don't have access to the keys, they're sitting in the hardware and they fed to memory controller. And it means when hypervisors that also know about this wonderful things saying I need to get access to the memories, that this particular VM I'm trying to get access to. They do not decrypt the data, they don't have access to the key because those keys are random, ephemeral and per VM, but most importantly in hardware not exportable. And it means now you will be able to have this very interesting world that customers or cloud providers will not be able to get access to your memory. And what we do, again as you can see, our customers don't need to change their applications. Their VMs are running exactly as it should run. And what you've running in VM, you actually see your memory clear, it's not encrypted. But God forbid is trying somebody to do it outside of my confidential box, no, no, no, no, no, you will now be able to do it. Now, you'll see cyber test and it's exactly what combination of these multiple hardware pieces and software pieces have to do. So OS is also modified and OS is modified such way to provide integrity. It means even OS that you're running in your VM box is not modifiable and you as customer can verify. But the most interesting thing I guess how to ensure the super performance of this environment because you can imagine Dave, that's increasing and it's additional performance, additional time, additional latency. So we're able to mitigate all of that by providing incredibly interesting capability in the OS itself. So our customers will get no changes needed, fantastic performance and scales as they would expect from cloud providers like Google. >> Okay, thank you. Excellent, appreciate that explanation. So you know again, the narrative on this is, well, you've already given me guarantees as a cloud provider that you don't have access to my data, but this gives another level of assurance, key management as they say is key. Now humans aren't managing the keys, the machines are managing them. So Patricia, my question to you is in addition to, let's go pre-confidential computing days, what are the sort of new guarantees that these hardware based technologies are going to provide to customers? >> So if I am a customer, I am saying I now have full guarantee of confidentiality and integrity of the data and of the code. So if you look at code and data confidentiality, the customer cares and they want to know whether their systems are protected from outside or unauthorized access, and that we covered with Nelly that it is. Confidential computing actually ensures that the applications and data antennas remain secret. The code is actually looking at the data, only the memory is decrypting the data with a key that is ephemeral, and per VM, and generated on demand. Then you have the second point where you have code and data integrity and now customers want to know whether their data was corrupted, tempered with or impacted by outside actors. And what confidential computing ensures is that application internals are not tempered with. So the application, the workload as we call it, that is processing the data is also has not been tempered and preserves integrity. I would also say that this is all verifiable, so you have attestation and this attestation actually generates a log trail and the log trail guarantees that provides a proof that it was preserved. And I think that the offers also a guarantee of what we call sealing, this idea that the secrets have been preserved and not tempered with, confidentiality and integrity of code and data. >> Got it. Okay, thank you. Nelly, you mentioned, I think I heard you say that the applications is transparent, you don't have to change the application, it just comes for free essentially. And we showed some various parts of the stack before, I'm curious as to what's affected, but really more importantly, what is specifically Google's value add? How do partners participate in this, the ecosystem or maybe said another way, how does Google ensure the compatibility of confidential computing with existing systems and applications? >> And a fantastic question by the way, and it's very difficult and definitely complicated world because to be able to provide these guarantees, actually a lot of work was done by community. Google is very much operate and open. So again our operating system, we working this operating system repository OS is OS vendors to ensure that all capabilities that we need is part of the kernels are part of the releases and it's available for customers to understand and even explore if they have fun to explore a lot of code. We have also modified together with our silicon vendors kernel, host kernel to support this capability and it means working this community to ensure that all of those pages are there. We also worked with every single silicon vendor as you've seen, and it's what I probably feel that Google contributed quite a bit in this world. We moved our industry, our community, our vendors to understand the value of easy to use confidential computing or removing barriers. And now I don't know if you noticed Intel is following the lead and also announcing a trusted domain extension, very similar architecture and no surprise, it's a lot of work done with our partners to convince work with them and make this capability available. The same with ARM this year, actually last year, ARM announced future design for confidential computing, it's called confidential computing architecture. And it's also influenced very heavily with similar ideas by Google and industry overall. So it's a lot of work in confidential computing consortiums that we are doing, for example, simply to mention, to ensure interop as you mentioned, between different confidential environments of cloud providers. They want to ensure that they can attest to each other because when you're communicating with different environments, you need to trust them. And if it's running on different cloud providers, you need to ensure that you can trust your receiver when you sharing your sensitive data workloads or secret with them. So we coming as a community and we have this at Station Sig, the community-based systems that we want to build, and influence, and work with ARM and every other cloud providers to ensure that they can interop. And it means it doesn't matter where confidential workloads will be hosted, but they can exchange the data in secure, verifiable and controlled by customers really. And to do it, we need to continue what we are doing, working open and contribute with our ideas and ideas of our partners to this role to become what we see confidential computing has to become, it has to become utility. It doesn't need to be so special, but it's what what we've wanted to become. >> Let's talk about, thank you for that explanation. Let's talk about data sovereignty because when you think about data sharing, you think about data sharing across the ecosystem in different regions and then of course data sovereignty comes up, typically public policy, lags, the technology industry and sometimes it's problematic. I know there's a lot of discussions about exceptions but Patricia, we have a graphic on data sovereignty. I'm interested in how confidential computing ensures that data sovereignty and privacy edicts are adhered to, even if they're out of alignment maybe with the pace of technology. One of the frequent examples is when you delete data, can you actually prove the data is deleted with a hundred percent certainty, you got to prove that and a lot of other issues. So looking at this slide, maybe you could take us through your thinking on data sovereignty. >> Perfect. So for us, data sovereignty is only one of the three pillars of digital sovereignty. And I don't want to give the impression that confidential computing addresses it at all, that's why we want to step back and say, hey, digital sovereignty includes data sovereignty where we are giving you full control and ownership of the location, encryption and access to your data. Operational sovereignty where the goal is to give our Google Cloud customers full visibility and control over the provider operations, right? So if there are any updates on hardware, software stack, any operations, there is full transparency, full visibility. And then the third pillar is around software sovereignty, where the customer wants to ensure that they can run their workloads without dependency on the provider's software. So they have sometimes is often referred as survivability that you can actually survive if you are untethered to the cloud and that you can use open source. Now, let's take a deep dive on data sovereignty, which by the way is one of my favorite topics. And we typically focus on saying, hey, we need to care about data residency. We care where the data resides because where the data is at rest or in processing need to typically abides to the jurisdiction, the regulations of the jurisdiction where the data resides. And others say, hey, let's focus on data protection, we want to ensure the confidentiality, and integrity, and availability of the data, which confidential computing is at the heart of that data protection. But it is yet another element that people typically don't talk about when talking about data sovereignty, which is the element of user control. And here Dave, is about what happens to the data when I give you access to my data, and this reminds me of security two decades ago, even a decade ago, where we started the security movement by putting firewall protections and logging accesses. But once you were in, you were able to do everything you wanted with the data. An insider had access to all the infrastructure, the data, and the code. And that's similar because with data sovereignty, we care about whether it resides, who is operating on the data, but the moment that the data is being processed, I need to trust that the processing of the data we abide by user's control, by the policies that I put in place of how my data is going to be used. And if you look at a lot of the regulation today and a lot of the initiatives around the International Data Space Association, IDSA and Gaia-X, there is a movement of saying the two parties, the provider of the data and the receiver of the data going to agree on a contract that describes what my data can be used for. The challenge is to ensure that once the data crosses boundaries, that the data will be used for the purposes that it was intended and specified in the contract. And if you actually bring together, and this is the exciting part, confidential computing together with policy enforcement. Now, the policy enforcement can guarantee that the data is only processed within the confines of a confidential computing environment, that the workload is in cryptographically verified that there is the workload that was meant to process the data and that the data will be only used when abiding to the confidentiality and integrity safety of the confidential computing environment. And that's why we believe confidential computing is one necessary and essential technology that will allow us to ensure data sovereignty, especially when it comes to user's control. >> Thank you for that. I mean it was a deep dive, I mean brief, but really detailed. So I appreciate that, especially the verification of the enforcement. Last question, I met you two because as part of my year-end prediction post, you guys sent in some predictions and I wasn't able to get to them in the predictions post, so I'm thrilled that you were able to make the time to come on the program. How widespread do you think the adoption of confidential computing will be in '23 and what's the maturity curve look like this decade in your opinion? Maybe each of you could give us a brief answer. >> So my prediction in five, seven years as I started, it will become utility, it will become TLS. As of freakin' 10 years ago, we couldn't believe that websites will have certificates and we will support encrypted traffic. Now we do, and it's become ubiquity. It's exactly where our confidential computing is heeding and heading, I don't know we deserve yet. It'll take a few years of maturity for us, but we'll do that. >> Thank you. And Patricia, what's your prediction? >> I would double that and say, hey, in the very near future, you will not be able to afford not having it. I believe as digital sovereignty becomes ever more top of mind with sovereign states and also for multinational organizations, and for organizations that want to collaborate with each other, confidential computing will become the norm, it will become the default, if I say mode of operation. I like to compare that today is inconceivable if we talk to the young technologists, it's inconceivable to think that at some point in history and I happen to be alive, that we had data at rest that was non-encrypted, data in transit that was not encrypted. And I think that we'll be inconceivable at some point in the near future that to have unencrypted data while we use. >> You know, and plus I think the beauty of the this industry is because there's so much competition, this essentially comes for free. I want to thank you both for spending some time on Breaking Analysis, there's so much more we could cover. I hope you'll come back to share the progress that you're making in this area and we can double click on some of these topics. Really appreciate your time. >> Anytime. >> Thank you so much, yeah. >> In summary, while confidential computing is being touted by the cloud players as a promising technology for enhancing data privacy and security, there are also those as we said, who remain skeptical. The truth probably lies somewhere in between and it will depend on the specific implementation and the use case as to how effective confidential computing will be. Look as with any new tech, it's important to carefully evaluate the potential benefits, the drawbacks, and make informed decisions based on the specific requirements in the situation and the constraints of each individual customer. But the bottom line is silicon manufacturers are working with cloud providers and other system companies to include confidential computing into their architectures. Competition in our view will moderate price hikes and at the end of the day, this is under-the-covers technology that essentially will come for free, so we'll take it. I want to thank our guests today, Nelly and Patricia from Google. And thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well out of our Boston studio. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters, and Rob Hoof is our editor-in-chief over at siliconangle.com, does some great editing for us. Thank you all. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com where you can get all the news. If you want to get in touch, you can email me at david.vellante@siliconangle.com or DM me at D Vellante, and you can also comment on my LinkedIn post. Definitely you want to check out etr.ai for the best survey data in the enterprise tech business. I know we didn't hit on a lot today, but there's some amazing data and it's always being updated, so check that out. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (subtle music)

>>Welcome back to NYC the Cube's coverage of Mongo DB 2022, a few thousand people here at least bigger than many people, perhaps expected, and a lot of buzz going on and we're gonna talk devs. I'm really excited to welcome back. Robbie Bellson who's the developer relations lead at Verizon and Ian Massingham. Who's the vice president of developer relations at Mongo DB Jens. Good to see you. Great >>To be here. >>Thanks having you. So Robbie, we just met a few weeks ago at the, the red hat summit in Boston and was blown away by what Verizon is doing in, in developer land. And of course, Ian, you know, Mongo it's rayon Detra is, is developers start there? Why is Mongo so developer friendly from your perspective? >>Well, it's been the ethos of MongoDB since day one. You know, back when we launched the first version of MongoDB back in 2009, we've always been about making developers lives easier. And then in 2016, we announced and released MongoDB Atlas, which is our cloud managed service for MongoDB, you know, starting with a small number of regions built on top of AWS and about 2,500 adoption events per week for MongoDB Atlas. After the first year today, MongoDB Atlas provides a managed service for MongoDB developers around the world. We're present in almost a hundred cloud regions across S DCP and Azure. And that adoption number is now running at about 25,000 developers a week. So, you know, the proof are in proof is really in the metrics. MongoDB is an incredibly popular platform for developers that wanna build data-centric applications. You just can't argue with the metrics really, >>You know, Ravi, sometimes there's an analyst who come up with these theories and one of the theories I've been spouting for a long time is that developers are gonna win the edge. And now to, to see you at Verizon building out this developer community was really exciting to me. So explain how you got this started with this journey. >>Absolutely. As you think about Verizon 5g edge or mobile edge computing portfolio, we knew from the start that developers would play a central role and not only consuming the service, but shaping the roadmap for what it means to build a 5g future. And so we started this journey back in late 20, 19 and fast forward to about a year ago with Mongo, we realized, well, wait a minute, you look at the core service offerings available at the edge. We didn't know really what to do with data. We wanted to figure it out. We wanted the vote of confidence from developers. So there I was in an apartment in Colorado racing, your open source Mongo against that in the region edge versus region, what would you see? And we saw tremendous performance improvements. It was so much faster. It's more than 40% faster for thousands and thousands of rights. And we said, well, wait a minute. There's something here. So what often starts is an organic developer, led intuition or hypothesis can really expand to a much broader go to market motion that really brings in the enterprise. And that's been our strategy from day one. Well, >>It's interesting. You talk about the performance. I, I just got off of a session talking about benchmarks in the financial services industry, you know, amazing numbers. And that's one of the hallmarks of, of Mongo is it can play in a lot of different places. So you guys both have developer relations in your title. Is that how you met some formal developer relations? >>We were a >>Program. >>Yeah, I would say that Verizon is one of the few customers that we also collaborate with on a developer relations effort. You know, it's in our mutual best interest to try to drive MongoDB consumption amongst developers using Verizon's 5g edge network and their platform. So of course we work together to help, to increase awareness of MongoDB amongst mobile developers that want to use that kind of technology. >>But so what's your story on this? >>I mean, as I, as I mentioned, everything starts with an organic developer discovery. It all started. I just cold messaged a developer advocate on Twitter and here we are at MongoDB world. It's amazing how things turn out. But one of the things that's really resonated with me as I was speaking with one of, one of your leads within your organization, they were mentioning that as Mongo DVIA developed over the years, the mantra really became, we wanna make software development easy. Yep. And that really stuck with me because from a network perspective, we wanna make networking easy. Developers are not gonna care about the internals of 5g network. In fact, they want us to abstract away those complexities so that they can focus on building their apps. So what better co-innovation opportunity than taking MongoDB, making software easy, and we make the network easy. >>So how do you think about the edge? How does you know variety? I mean, to me, you know, there's a lot of edge use cases, you know, think about the home Depot or lows. Okay, great. I can put like a little mini data center in there. That's cool. That's that's edge. Like, but when I think of Verizon, I mean, you got cell towers, you've got the far edge. How do you think about edge Robbie? >>Well, the edge is a, I believe a very ambiguous term by design. The edge is the device, the mobile device, an IOT device, right? It could be the radio towers that you mentioned. It could be in the Metro edge. The CDN, no one edge is better than the other. They're all just serving different use cases. So when we talk about the edge, we're focused on the mobile edge, which we believe is most conducive to B2B applications, a fleet of IOT devices that you can control a manufacturing plant, a fleet of ground and aerial robotics. And in doing so you can create a powerful compute mesh where you could have a private network and private mobile edge computing by way of say an AWS outpost and then public mobile edge computing by way of AWS wavelength. And why keep them separate. You could have a single compute mesh even with MongoDB. And this is something that we've been exploring. You can extend Atlas, take a cluster, leave it in the region and then use realm the mobile portfolio and spread it all across the edge. So you're creating that unified compute and data mesh together. >>So you're describing what we've been expecting is a new architecture emerging, and that's gonna probably bring new economics of new use cases, right? Where are we today in that first of all, is that a reasonable premise that this is a sort of a new architecture that's being built out and where are we in that build out? How, how do you think about the, the future of >>That? Absolutely. It's definitely early days. I think we're still trying to figure it out, but the architecture is definitely changing the idea to rip out a mobile device that was initially built and envisioned for the device and only for the device and say, well, wait a minute. Why can't it live at the edge? And ultimately become multi-tenant if that's the data volume that may be produced to each of those edge zones with hypothesis that was validated by developers that we continue to build out, but we recognize that we can't, we can't get that static. We gotta keep evolving. So one of our newest ideas as we think about, well, wait a minute, how can Mongo play in the 5g future? We started to get really clever with our 5g network APIs. And I, I think we talked about this briefly last time, 5g, programmability and network APIs have been talked about for a while, but developers haven't had a chance to really use them and our edge discovery service answering the question in this case of which database is the closest database, doesn't have to be invoked by the device anymore. You can take a thin client model and invoke it from the cloud using Atlas functions. So we're constantly permuting across the entire portfolio edge or otherwise for what it means to build at the edge. We've seen such tremendous results. >>So how does Mongo think about the edge and, and, and playing, you know, we've been wondering, okay, which database is actually gonna be positioned best for the edge? >>Well, I think if you've got an ultra low latency access network using data technology, that adds latency is probably not a great idea. So MongoDB since the very formative years of the company and product has been built with performance and scalability in mind, including things like in memory storage for the storage engine that we run as well. So really trying to match the performance characteristics of the data infrastructure with the evolution in the mobile network, I think is really fundamentally important. And that first principles build of MongoDB with performance and scalability in mind is actually really important here. >>So was that a lighter weight instance of, of Mongo or not >>Necessarily? No, not necessarily. No, no, not necessarily. We do have edge cashing with realm, the mobile databases Robbie's already mentioned, but the core database is designed from day one with those performance and scalability characteristics in mind, >>I've been playing around with this. This is kind of a, I get a lot of heat for this term, but super cloud. So super cloud, you might have data on Preem. You might have data in various clouds. You're gonna have data out at the edge. And, and you've got an abstraction that allows a developer to, to, to tap services without necessarily if, if he or she wants to go deep into the S great, but then there's a higher level of services that they can actually build for their customers. So is that a technical reality from a developer standpoint, in your view, >>We support that with the Mongo DB multi-cloud deployment model. So you can place Mongo DB, Atlas nodes in any one of the three hyperscalers that we mentioned, AWS, GCP or Azure, and you can distribute your data across nodes within a cluster that is spread across different cloud providers. So that kinds of an kind of answers the question about how you do data placement inside the MongoDB clustered environment that you run across the different providers. And then for the abstraction layer. When you say that I hear, you know, drivers ODMs the other intermediary software components that we provide to make developers more productive in manipulating data in MongoDB. This is one of the most interesting things about the technology. We're not forcing developers to learn a different dialect or language in order to interact with MongoDB. We meet them where they are by providing idiomatic interfaces to MongoDB in JavaScript in C sharp, in Python, in rust, in that in fact in 12 different pro programming languages that we support as a first party plus additional community contributed programming languages that the community have created drivers for ODMs for. So there's really that model that you've described in hypothesis exist in reality, using >>Those different Compli. It's not just a series of siloed instances in, >>In different it's the, it's the fabric essentially. Yeah. >>What, what does the Verizon developer look like? Where does that individual come from? We talked about this a little bit a few weeks ago, but I wonder if you could describe it. >>Absolutely. My view is that the Verizon or just mobile edge ecosystem in general for developers are present at this very conference. They're everywhere. They're building apps. And as Ian mentioned, those idiomatic interfaces, we need to take our network APIs, take the infrastructure that's being exposed and make sure that it's leveraging languages, frameworks, automation, tools, the likes of Terraform and beyond. We wanna meet developers where they are and build tools that are easy for them to use. And so you had talked about the super cloud. I often call it the cloud continuum. So we, we took it P abstraction by abstraction. We started with, will it work in one edge? Will it work in multiple edges, public and private? Will it work in all of the edges for a given region, public or private, will it work in multiple regions? Could it work in multi clouds? We've taken it piece by piece by piece and in doing so abstracting way, the complexity of the network, meaning developers, where they are providing those idiomatic interfaces to interact with our API. So think the edge discovery, but not in a silo within Atlas functions. So the way that we're able to converge portfolios, using tools that dev developers already use know and love just makes it that much easier. Do, >>Do you feel like I like the cloud continuum cause that's really what it is. The super cloud does the security model, how does the security model evolve with that? >>At least in the context of the mobile edge, the attack surface is a lot smaller because it's only for mobile traffic not to say that there couldn't be various configuration and human error that could be entertained by a given application experience, but it is a much more secure and also reliable environment from a failure domain perspective, there's more edge zones. So it's less conducive to a regionwide failure because there's so many more availability zones. And that goes hand in hand with security. Mm. >>Thoughts on security from your perspective, I mean, you added, you've made some announcements this week, the, the, the encryption component that you guys announced. >>Yeah. We, we issued a press release this morning about a capability called queryable encryption, which actually as we record this Mark Porter, our CTO is talking about in his keynote, and this is really the next generation of security for data stored within databases. So the trade off within field level encryption within databases has always been very hard, very, very rigid. Either you have keys stored within your database, which means that your memory, so your data is decrypted while it's resident in memory on your database engine. This allow, of course, allows you to perform query operations on that data. Or you have keys that are managed and stored in the client, which means the data is permanently OBS from the engine. And therefore you can't offload query capabilities to your data platform. You've gotta do everything in the client. So if you want 10 records, but you've got a million encrypted records, you have to pull a million encrypted records to the client, decrypt them all and see performance hit in there. Big performance hit what we've got with queryable encryption, which we announced today is the ability to keep data encrypted in memory in the engine, in the database, in the data platform, issue queries from the client, but use a technology called structural encryption to allow the database engine, to make decisions, operate queries, and find data without ever being able to see it without it ever being decrypted in the memory of the engine. So it's groundbreaking technology based on research in the field of structured encryption with a first commercial database provided to bring this to market. >>So how does the mobile edge developer think about that? I mean, you hear a lot about shifting left and not bolting on security. I mean, is this, is this an example of that? >>It certainly could be, but I think the mobile edge developer still stuck with how does this stuff even work? And I think we need to, we need to be mindful of that as we build out learning journeys. So one of my favorite moments with Mongo was an immersion day. We had hosted earlier last year where we, our, from an enterprise perspective, we're focused on BW BS, but there's nothing stopping us. You're building a B2C app based on the theme of the winner Olympics. At the time, you could take a picture of Sean White or of Nathan Chen and see that it was in fact that athlete and then overlaid on that web app was the number of medals they accrued with the little trumpeteer congratulating you for selecting that athlete. So I think it's important to build trust and drive education with developers with a more simple experience and then rapidly evolve overlaying the features that Ian just mentioned over time. >>I think one of the keys with cryptography is back to the familiar messaging for the cloud offloading heavy lifting. You actually need to make it difficult to impossible for developers to get this wrong, and you wanna make it as easy as possible for developers to deal with cryptography. And that of course is what we're trying to do with our driver technology combined with structure encryption, with query encryption. >>But Robbie, your point is lots of opportunity for education. I mean, I have to say the developers that I work with, it's, I'm, I'm in awe of how they solve problems and I, and the way they solve problems, if they don't know the answer, they figure out how to go get it. So how, how are your two communities and other communities, you know, how are they coming together to, to solve such problems and share whether it's best practices or how do I do this? >>Well, I'm not gonna lie in person. Events are a bunch of fun. And one of the easiest domain knowledge exchange opportunities, when you're all in person, you can ideate, you can whiteboard, you can brainstorm. And often those conversations are what leads to that infrastructure module that an immersion day features. And it's just amazing what in person events can do, but community groups of interest, whether it's a Twitch stream, whether it's a particular code sample, we rely heavily on digital means today to upscale the developer community, but also build on by, by means of a simple port request, introduce new features that maybe you weren't even thinking of before. >>Yeah. You know, that's a really important point because when you meet people face to face, you build a connection. And so if you ask a question, you're more likely perhaps to get an answer, or if one doesn't exist in a, in a search, you know, you, oh, Hey, we met at the, at the conference and let's collaborate on this guys. Congratulations on, on this brave new world. You're in a really interesting spot. You know, developers, developers, developers, as Steve bomber says screamed. And I was glad to see Dave was not screaming and jumping up and down on the stage like that, but, but the message still resonates. So thank you, definitely appreciate. All right, keep it right there. This is Dave ante for the cubes coverage of Mago DB world 2022 from New York city. We'll be right back.

(light music playing) >> Welcome back to VEEAMON 2022 in Las Vegas. We're at the Aria. This is theCUBE and we're covering two days of VEEAMON. We've done a number of VEEAMONs before, we did Miami, we did New Orleans, we did Chicago and we're, we're happy to be back live after two years of virtual VEEAMONs. I'm Dave Vellante. My co-host is David Nicholson. Eric Herzog is here. You think he's, Eric's been on theCUBE, I think more than any other guest, including Pat Gelsinger, who at one point was the number one guest. Eric Herzog, CMO of INFINIDAT great to see you again. >> Great, Dave, thank you. Love to be on theCUBE. And of course notice my Hawaiian shirt, except I now am supporting an INFINIDAT badge on it. (Dave laughs) Look at that. >> Is that part of the shirt or is that a clip-on? >> Ah, you know, one of those clip-ons but you know, it looks good. Looks good. >> Hey man, what are you doing at VEEAMON? I mean, you guys started this journey into data protection several years ago. I remember we were actually at one of their competitors' events when you first released it, but tell us what's going on with Veeam. >> So we do a ton of stuff with Veeam. We do custom integration. We got some integration on the snapshotting side, but we do everything and we have a purpose built backup appliance known as InfiniGuard. It works with Veeam. We also actually have some customers who use our regular primary storage device as a backup target. The InfiniGuard product will do the data reduction, the dedupe compression, et cetera. The standard product does not, it's just a standard high performance array. We will compress the data, but we have customers that do it either way. We have a couple customers that started with the InfiniBox and then transitioned to the InfiniGuard, realizing that why would you put it on regular storage? Why not go to something that's customized for it? So we do that. We do stuff in the field with them. We've been at all the VEEAMONs since the, since like, I think the second one was the first one we came to. We're doing the virtual one as well as the live one. So we've got a little booth inside, but we're also doing the virtual one today as well. So really strong work with Veeam, particularly at the field level with the sales guys and in the channel. >> So when INFINIDAT does something, you guys go hardcore, high end, fast recovery, you just, you know, reliable, that's kind of your brand. Do you see this movement into data protection as kind of an adjacency to your existing markets? Is it a land and expand strategy? Can you kind of explain the strategy there. >> Ah, so it's actually for us a little bit of a hybrid. So we have several accounts that started with InfiniBox and now have gone with the InfiniGuard. So they start with primary storage and go with secondary storage/modern data protection. But we also have, in fact, we just got a large PO from a Fortune 50, who was buying the InfiniGuard first and now is buying our InfiniBox. >> Both ways. Okay. >> All flash array. And, but they started with backup first and then moved to, so we've got them moving both directions. And of course, now that we have a full portfolio, our original product, the InfiniBox, which was a hybrid array, outperformed probably 80 to 85% of the all flash arrays, 'cause the way we use DRAM. And what's so known as our mural cash technology. So we could do very well, but there is about, you know, 15, 20% of the workloads we could not outperform the competition. So then we had an all flash array and purpose built backup. So we can do, you know, what I'll say is standard enterprise storage, high performance enterprise storage. And then of course, modern data protection with our partnerships such as what we do with Veeam and we've incorporated across the entire portfolio, intense cyber resilience technology. >> Why does the world, Eric, need another purpose built backup appliance? What do you guys bring that is filling a gap in the marketplace? >> Well, the first thing we brought was much higher performance. So when you look at the other purpose built backup appliances, it's been about our ability to have incredibly high performance. The second area has been CapEx and OpEx reduction. So for example, we have a cloud service provider who happens to be in South Africa. They had 14 purpose built backup appliances from someone else, seven in one data center and seven in another. Now they have two InfiniGuards, one in each data center handling all of their backup. You know, they're selling backup as a service. They happen to be using Veeam as well as one other backup company. So if you're the cloud provider from their perspective, they just dramatically reduce their CapEx and OpEx. And of course they've made it easier for them. So that's been a good story for us, that ability to consolidation, whether it be on primary storage or secondary storage. We have a very strong play with cloud providers, particularly those meeting them in small that have to compete with the hyperscalers right. They don't have the engineering of Amazon or Google, right? They can't compete with what the Azure guys have got, but because the way both the InfiniGuard and the InfiniBox work, they could dramatically consolidate workloads. We probably got 30 or 40 midsize and actually several members of the top 10 telcos use us. And when they do their clouds, both their internal cloud, but actually the clouds that are actually running the transmissions and the traffic, it actually runs on InfiniBox. One of them has close to 200 petabytes of InfiniBox and InfiniBox, all flash technology running one of the largest telcos on the planet in a cloud configuration. So all that's been very powerful for us in driving revenue. >> So phrases of the week have been air gap, logical air gap, immutable. Where does InfiniGuard fit into that universe? And what's the profile of the customer that's going to choose InfiniGuard as the target where they're immutable, Write Once Read Many, data is going to live. >> So we did, we announced our InfiniSafe technology first on the InfiniGuard, which actually earlier this year. So we have what I call the four legs of the stool of cyber resilience. One is immutable snapshots, but that's only part of it. Second is logical air gapping, and we can do both local and remote and we can provide and combine local with remote. So for example, what that air gap does is separate the management plane from the actual data plane. Okay. So in this case, the Veeam data backup sets. So the management cannot touch that immutable, can't change it, can't delete it. can't edit it. So management is separated once you start and say, I want to do an immutable snap of two petabytes of Veeam backup dataset. Then we just do that. And the air gap does it, but then you could take the local air gap because as you know, from inception to the end of an attack can be close to 300 days, which means there could be a fire. There could be a tornado, there could be a hurricane, there could be an earthquake. And in the primary data center, So you might as well have that air gap just as you would do- do a remote for disaster recovery and business continuity. Then we have the ability to create a fenced forensic environment to evaluate those backup data sets. And we can do that actually on the same device. That is the purpose built backup appliance. So when you look at the architectural, these are public from our competitors, including the guys that are in sort of Hopkinton/Austin, Texas. You can see that they show a minimum of two physical devices. And in many cases, a third, we can do that with one. So not only do we get the fence forensic environment, just like they do, but we do it with reduction, both CapEx and OpEx. Purpose built backup is very high performance. And then the last thing is our ability to recover. So some people talk about rapid recovery, I would say, they dunno what they're talking about. So when we launched the InfiniGuard with InfiniSafe, we did a live demo, 1.5 petabytes, a Veeam backup dataset. We recovered it in 12 minutes. So once you've identified and that's on the InfiniGuard. On the InfiniBox, once you've identified a good copy of data to do the recovery where you're free of malware ransomware, we can do the recovery in three to five seconds. >> Okay. >> So really, really quick. Actually want to double click on something because people talk about immutable copies, immutable snapshots in particular, what have the actual advances been? I mean, is this simply a setting that maybe we didn't set for retention at some time in the past, or if you had to engineer something net new into a system so to provide that logical air gap. >> So what's net new is the air gapping part. Immutable snapshots have been around, you know, before we were on screen, you talked about WORM, Write Once Read Many. Well, since I'm almost 70 years old, I actually know what that means. When you're 30 or 40 or 50, you probably don't even know what a WORM is. Okay. And the real use of immutable snapshots, it was to replace WORM which was an optical technology. And what was the primary usage? Regulatory and compliance, healthcare, finance and publicly traded companies that were worried about. The SEC or the EU or the Japanese finance ministry coming down on them because they're out of compliance and regulatory. That was the original use of immutable snap. Then people were, well, wait a second. Malware ransomware could attack me. And if I got something that's not changeable, that makes it tougher. So the real magic of immutability was now creating the air gap part. Immutability has been around, I'd say 25 years. I mean, WORMs sort of died back when I was at Mac store the first time. So that was 1990-ish is when WORMs sort of fell away. And there have been immutable snapshots from most of the major storage vendors, as well as a lot of the small vendors ever since they came out, it's kind of like a checkbox item because again, regulatory and compliance, you're going to sell to healthcare, finance, public trade. If you don't have the immutable snapshot, then they don't have their compliance and regulatory for SEC or tax purposes, right? With they ever end up in an audit, you got to produce data. And no one's using a WORM drive anymore to my knowledge. >> I remember the first storage conference I ever went to was in Monterey. It had me in the early 1980s, 84 maybe. And it was a optical disc drive conference. The Jim Porter of optical. >> Yep. (laughs) >> I forget what the guy's name was. And I remember somebody coming up to me, I think it was like Bob Payton rest his soul, super smart strategy guy said, this is never going to happen because of the cost and that's what it was. And now you've got that capability on flash, you know, hard disk, et cetera. >> Right. >> So the four pillars, immutability, the air gap, both local and remote, the fence forensics and the recovery speed. Right? >> Right. Pick up is one thing. Recovery is everything. Those are the four pillars, right? >> Those are the four things. >> And your contention is that those four things together differentiate you from the competition. You mentioned, you know, the big competition, but how unique is this in the marketplace, those capabilities and how difficult is it to replicate? >> So first of all, if someone really puts their engineering hat to it, it's not that hard to replicate. It takes a while. Particularly if you're doing an enterprise, for example, our solutions all have a hundred percent availability guarantee. That's hard to do. Most guys have seven nines. >> That's hard. >> We really will guarantee a hundred percent availability. We offer an SLA that's included when you buy. We don't charge extra for it. It's like if you want it, like you just get it. Second thing is really making sure on the recovery side is the hardest part, particularly on a purpose built backup appliance. So when you look at other people and you delve into their public material, press releases, white paper, support documentation. No one's talking about. Yeah, we can take a 1.5 petabyte Veeam backup data set and make it available in 12 minutes and 12 seconds, which was the exact time that we did on our live demo when we launched the product in February of 2022. No one's talking that. On primary storage, you're hearing some of the vendors such as my old employer that also who, also starts with an "I", talk about a recovery time of two to three hours once you have a known good copy. On primary storage, once we have a known good copy, we're talking three to five seconds for that copy to be available. So that's just sort of the power of the snapshot technology, how we manage our metadata and what we've done, which previous to cyber resiliency, we were known for our replication capability and our snapshot capability from an enterprise class data store. That's what people said. INFINIDAT really knows how to do the replication snapshot. I remember our founder was one of the technical founders of EMC for a product known as the Symmetric, which then became the DMAX, the VMAX and is now is the PowerMax. That was invented by the guy who founded INFINIDAT. So that team has the real chops at enterprise high-end storage to the global fortune 2000. And what are the key feature checkbox items they need that's in both the InfiniBox and also in the InfiniGuard. >> So the business case for cyber resiliency is changing. As Dave said, we've had a big dose last several months, you know, couple years actually, of the importance of cyber resiliency, given all the ransomware tax, et cetera. But it sounds like the business case is shifting really focused on avoiding that risk, avoiding that downtime time versus the cost. The cost is always important. I mean, you got a consolidation play here, right? >> Yeah, yeah. >> Dedupe, does dedupe come into play? >> So on the InfiniGuard we do both dedupe and compression. On the InfiniBox we only do compression. So we do have data reduction. It depends on which product you're using from a Veeam perspective. Most of that now is with the InfiniGuard. So you get the block level dedupe and you get compression. And if you can do both, depending on the data set, we do both. >> How does that affect recovery time? >> Yeah, good question. >> So it doesn't affect recovery times. >> Explain why. >> So first of all, when you're doing a backup data set, the final final recovery, you recovered the backup data set, whether it's Veeam or one of their competitors, you actually make it available to the backup administrator to do a full restore of a backup data set. Okay. So in that case, we get it ready and expose it to the Veeam admin or some other backup admin. And then they launch the Veeam software or the other software and do a restore. Okay. So it's really a two step process on the secondary storage model and actually three. First identifying a known good backup copy. Second then we recover, which is again 12, 13 minutes. And then the backup admin's got to do a, you know, a restore of the backup 'cause it's backup data set in the format of backup, which is different from every backup vendor. So we support that. We get it ready to go. And then whether it's a Veeam backup administrator and quite honestly, from our perspective, most of our customers in the global fortune 2000, 25% of the fortune 50 use INIFINIDAT products. 25% and we're a tiny company. So we must have some magic fairy dust that appeals to the biggest companies on the planet. But most of our customers in that area and actually say probably in the fortune 500 actually use two to three different backup packages. So we can support all those on a single InfiniGuard or multiples depending on how big their backup data sets. Our biggest InfiniGuard is 50 petabytes counting the data reduction technology. So we get that ready. On the InfiniBox, the recovery really is, you know, a couple of seconds and in that case, it's primary data in block format. So we just make that available. So on the InfiniBox, the recovery is once, well two. Identifying a known good copy, first step, then just doing recovery and it's available 'cause it's blocked data. >> And that recovery doesn't include movement of a whole bunch of data. It's essentially realignment of pointers to where the good data is. >> Right. >> Now in the InfiniBox as well as in InfiniGuard. >> No, it would be, So in the case of that, in the case of the InfiniGuard, it's a full recovery of a backup data set. >> Okay. >> So the backup software just launches and it sees, >> Okay. >> your backup one of Veeam and just starts doing a restore with the Veeam restoration technology. Okay? >> Okay. >> In the case of the block, as long as the physical InfiniBox, if that was the primary storage and then filter box is not damaged when you make it available, it's available right away to the apps. Now, if you had an issue with the app side or the physical server side, and now you're pointing new apps and you had to reload stuff on that side, you have to point it at that InfiniBox which has the data. And then you got to wait for the servers and the SAP or Oracle or Mongo, Cassandra to recognize, oh, this is my primary storage. So it depends on the physical configuration on the server side and the application perspective, how bad were the apps damaged? So let's take malware. Malware is even worse because you either destroying data or messing, playing with the app so that the app is now corrupted as well as the data is corrupted. So then it's going to take longer the block data's ready, the SAP workload. And if the SAP somehow was compromised, which is a malware thing, not a ransomware thing, they got to reload a good copy of SAP before it can see the data 'cause the malware attacked the application as well as the data. Ransomware doesn't do that. It just holds it for ransom and it encrypts. >> So this is exactly what we're talking about. When we talk about operational recovery and automation, Eric is addressing the reality that it doesn't just end at the line above some arbitrary storage box, you know, reaching up real recovery, reaches up into the application space and it's complicated. >> That's when you're actually recovered. >> Right. >> When the application- >> Well, think of it like a disaster. >> Okay. >> Yes, right. >> I'll knock on woods since I was born and still live in California. Dave too. Let's assume there's a massive earthquake in the bay area in LA. >> Let's not. >> Okay. Let's yes, but hypothetically and the data center's cat five. It doesn't matter what they're, they're all toast. Okay. Couple weeks later it's modern. You know, people figure out what to do and certain buildings don't fall down 'cause of the way earthquake standards are in California now. So there's data available. They move into temporary space. Okay. Data's sitting there in the Colorado data center and they could do a restore. Well, they can't do a restore. How many service did they need? Had they reloaded all of the application software to do a restoration. What happened to the people? If no one got injured, like in the 1989 earthquake in California, very few people got injured yet cost billions of dollars. But everyone was watching this San Francisco giants played in Oakland, >> I remember >> so no one was on the road. >> Al Michael's. >> Epic moment. >> Imagine it's in the middle of commute time in LA and San Francisco, hundreds of thousands of people. What if it's your data center team? Right? So there's a whole bunch around disaster recovery and business country that have nothing to do with the storage, the people, what your process. So I would argue that malware ransomware is a disaster and it's exactly the same thing. You know, you got the known good copy. You've got okay. You're sure that the SAP and Oracle, especially on the malware side, weren't compromised. On the ransomware side, you don't have to worry about that. And those things, you got to take a look at just as if it, I would argue malware and ransomware is a disaster and you need to have a process just like you would. If there was an earthquake, a fire or a flood in the data center, you need a similar process. That's slightly different, but the same thing, servers, people, software, the data itself. And when you have that all mapped out, that's how you do successful malware ransomeware recovery. It's a different type of disaster. >> It's absolutely a disaster. It comes down to business continuity and be able to transact business with as little disruption as possible. We heard today from the keynotes and then Jason Buffington came on about the preponderance of ransomware. Okay. We know that. But then the interesting stat was the percentage of customers that paid the ransom about a third weren't able to recover. And so 'cause you kind of had this feeling of all right, well, you know, see it on, you know, CNBC, should you pay the ransom or not? You know, pay the ransom. Okay. You'll get back. But no, it's not the case. You won't necessarily get back. So, you know, Veeam stated, Hey, our goal is to sort of eliminate that problem. Are you- You feel like you guys in a partnership can actually achieve that. >> Yes. >> So, and you have customers that have actually avoided, you know, been hit and were able to- >> We have people who won't publicly say they've been hit, but the way they talk about what they did, like in a meeting, they were hit and they were very thankful. >> (laughs) Yeah. >> And so that's been very good. I- >> So we got proof. >> Yes, we absolutely have proof. And quite honestly, with the recent legislation in the United States, malware and ransomware actually now is also regulatory and compliance. >> Yeah. >> Because the new law states mid-March that whether it's Herzog's bar and grill to bank of America or any large foreign company doing business in the US, you have to report to the United States federal government, any attack, same with the county school district with any local government, any agency, the federal government, as well as every company from the tiniest to the largest in the world that does, they're supposed to report it 'cause the government is trying to figure out how to fight it. Just the way if you don't report burglary, how they catch the burglars. >> Does your solution simplify testing in any way or reduce the risk of testing? >> Well, because the recovery is so rapid, we recommend that people do this on a regular basis. So for example, because the recovery is so quick, you can recover in 12 minutes while we do not practice, let's say once a month or once every couple weeks. And guess what? It also allows you to build a repository of known good copies. Remember when you get ransomeware, no one's going to come say, Hey, I'm Mr. Rans. I'm going to steal your stuff. It's all done surreptitiously. They're all James Bond on the sly who doesn't say "By the way, I'm James Bond". They are truly underneath the radar. And they're very slowly encrypting that data set. So guess what? Your primary data and your backup data that you don't want to be attacked can be attacked. So it's really about finding a known good copy. So if you're doing this on a regular basis, you can get an index of known good copies. >> Right. >> And then, you know, oh, I can go back to last Tuesday and you know that that's good. Otherwise you're literally testing Wednesday, Thursday, Friday, Saturday to try to find a known good copy, which delays the recovery process 'cause you really do have to test. They make sure it's good. >> If you increase that frequency, You're going to protect yourself. That's why I got to go. Thanks so much for coming on theCUBEs. Great to see you. >> Great. Thank you very much. I'll be wearing a different Hawaiian shirt next to. >> All right. That sounds good. >> All right, Eric Herzog, Eric Herzog on theCUBE, Dave Vallante for David Nicholson. We'll be right back at VEEAMON 2022. Right after this short break. (light music playing)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> The pandemic has changed the way we think about and predict the future. As we enter the third year of a global pandemic, we see the significant impact that it's had on technology strategy, spending patterns, and company fortunes Much has changed. And while many of these changes were forced reactions to a new abnormal, the trends that we've seen over the past 24 months have become more entrenched, and point to the way that's coming ahead in the technology business. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we welcome our partner and colleague and business friend, Erik Porter Bradley, as we deliver what's becoming an annual tradition for Erik and me, our predictions for Enterprise Technology in 2022 and beyond Erik, welcome. Thanks for taking some time out. >> Thank you, Dave. Luckily we did pretty well last year, so we were able to do this again. So hopefully we can keep that momentum going. >> Yeah, you know, I want to mention that, you know, we get a lot of inbound predictions from companies and PR firms that help shape our thinking. But one of the main objectives that we have is we try to make predictions that can be measured. That's why we use a lot of data. Now not all will necessarily fit that parameter, but if you've seen the grading of our 2021 predictions that Erik and I did, you'll see we do a pretty good job of trying to put forth prognostications that can be declared correct or not, you know, as black and white as possible. Now let's get right into it. Our first prediction, we're going to go run into spending, something that ETR surveys for quarterly. And we've reported extensively on this. We're calling for tech spending to increase somewhere around 8% in 2022, we can see there on the slide, Erik, we predicted spending last year would increase by 4% IDC. Last check was came in at five and a half percent. Gardner was somewhat higher, but in general, you know, not too bad, but looking ahead, we're seeing an acceleration from the ETR September surveys, as you can see in the yellow versus the blue bar in this chart, many of the SMBs that were hard hit by the pandemic are picking up spending again. And the ETR data is showing acceleration above the mean for industries like energy, utilities, retail, and services, and also, notably, in the Forbes largest 225 private companies. These are companies like Mars or Koch industries. They're predicting well above average spending for 2022. So Erik, please weigh in here. >> Yeah, a lot to bring up on this one, I'm going to be quick. So 1200 respondents on this, over a third of which were at the C-suite level. So really good data that we brought in, the usual bucket of, you know, fortune 500, global 2000 make up the meat of that median, but it's 8.3% and rising with momentum as we see. What's really interesting right now is that energy and utilities. This is usually like, you know, an orphan stock dividend type of play. You don't see them at the highest point of tech spending. And the reason why right now is really because this state of tech infrastructure in our energy infrastructure needs help. And it's obvious, remember the Florida municipality break reach last year? When they took over the water systems or they had the ability to? And this is a real issue, you know, there's bad nation state actors out there, and I'm no alarmist, but the energy and utility has to spend this money to keep up. It's really important. And then you also hit on the retail consumer. Obviously what's happened, the work from home shift created a shop from home shift, and the trends that are happening right now in retail. If you don't spend and keep up, you're not going to be around much longer. So I think the really two interesting things here to call out are energy utilities, usually a laggard in IT spend and it's leading, and also retail consumer, a lot of changes happening. >> Yeah. Great stuff. I mean, I recall when we entered the pandemic, really ETR was the first to emphasize the impact that work from home was going to have, so I really put a lot of weight on this data. Okay. Our next prediction is we're going to get into security, it's one of our favorite topics. And that is that the number one priority that needs to be addressed by organizations in 2022 is security and you can see, in this slide, the degree to which security is top of mind, relative to some other pretty important areas like cloud, productivity, data, and automation, and some others. Now people may say, "Oh, this is obvious." But I'm going to add some context here, Erik, and then bring you in. First, organizations, they don't have unlimited budgets. And there are a lot of competing priorities for dollars, especially with the digital transformation mandate. And depending on the size of the company, this data will vary. For example, while security is still number one at the largest public companies, and those are of course of the biggest spenders, it's not nearly as pronounced as it is on average, or in, for example, mid-sized companies and government agencies. And this is because midsized companies or smaller companies, they don't have the resources that larger companies do. Larger companies have done a better job of securing their infrastructure. So these mid-size firms are playing catch up and the data suggests cyber is even a bigger priority there, gaps that they have to fill, you know, going forward. And that's why we think there's going to be more demand for MSSPs, managed security service providers. And we may even see some IPO action there. And then of course, Erik, you and I have talked about events like the SolarWinds Hack, there's more ransomware attacks, other vulnerabilities. Just recently, like Log4j in December. All of this has heightened concerns. Now I want to talk a little bit more about how we measure this, you know, relatively, okay, it's an obvious prediction, but let's stick our necks out a little bit. And so in addition to the rise of managed security services, we're calling for M&A and/or IPOs, we've specified some names here on this chart, and we're also pointing to the digital supply chain as an area of emphasis. Again, Log4j really shone that under a light. And this is going to help the likes of Auth0, which is now Okta, SailPoint, which is called out on this chart, and some others. We're calling some winners in end point security. Erik, you're going to talk about sort of that lifecycle, that transformation that we're seeing, that migration to new endpoint technologies that are going to benefit from this reset refresh cycle. So Erik, weigh in here, let's talk about some of the elements of this prediction and some of the names on that chart. >> Yeah, certainly. I'm going to start right with Log4j top of mind. And the reason why is because we're seeing a real paradigm shift here where things are no longer being attacked at the network layer, they're being attacked at the application layer, and in the application stack itself. And that is a huge shift left. And that's taking in DevSecOps now as a real priority in 2022. That's a real paradigm shift over the last 20 years. That's not where attacks used to come from. And this is going to have a lot of changes. You called out a bunch of names in there that are, they're either going to work. I would add to that list Wiz. I would add Orca Security. Two names in our emerging technology study, in addition to the ones you added that are involved in cloud security and container security. These names are either going to get gobbled up. So the traditional legacy names are going to have to start writing checks and, you know, legacy is not fair, but they're in the data center, right? They're, on-prem, they're not cloud native. So these are the names that money is going to be flowing to. So they're either going to get gobbled up, or we're going to see some IPO's. And on the other thing I want to talk about too, is what you mentioned. We have CrowdStrike on that list, We have SentinalOne on the list. Everyone knows them. Our data was so strong on Tanium that we actually went positive for the first time just today, just this morning, where that was released. The trifecta of these are so important because of what you mentioned, under resourcing. We can't have security just tell us when something happens, it has to automate, and it has to respond. So in this next generation of EDR and XDR, an automated response has to happen because people are under-resourced, salaries are really high, there's a skill shortage out there. Security has to become responsive. It can't just monitor anymore. >> Yeah. Great. And we should call out too. So we named some names, Snyk, Aqua, Arctic Wolf, Lacework, Netskope, Illumio. These are all sort of IPO, or possibly even M&A candidates. All right. Our next prediction goes right to the way we work. Again, something that ETR has been on for awhile. We're calling for a major rethink in remote work for 2022. We had predicted last year that by the end of 2021, there'd be a larger return to the office with the norm being around a third of workers permanently remote. And of course the variants changed that equation and, you know, gave more time for people to think about this idea of hybrid work and that's really come in to focus. So we're predicting that is going to overtake fully remote as the dominant work model with only about a third of the workers back in the office full-time. And Erik, we expect a somewhat lower percentage to be fully remote. It's now sort of dipped under 30%, at around 29%, but it's still significantly higher than the historical average of around 15 to 16%. So still a major change, but this idea of hybrid and getting hybrid right, has really come into focus. Hasn't it? >> Yeah. It's here to stay. There's no doubt about it. We started this in March of 2020, as soon as the virus hit. This is the 10th iteration of the survey. No one, no one ever thought we'd see a number where only 34% of people were going to be in office permanently. That's a permanent number. They're expecting only a third of the workers to ever come back fully in office. And against that, there's 63% that are saying their permanent workforce is going to be either fully remote or hybrid. And this, I can't really explain how big of a paradigm shift this is. Since the start of the industrial revolution, people leave their house and go to work. Now they're saying that's not going to happen. The economic impact here is so broad, on so many different areas And, you know, the reason is like, why not? Right? The productivity increase is real. We're seeing the productivity increase. Enterprises are spending on collaboration tools, productivity tools, We're seeing an increased perception in productivity of their workforce. And the CFOs can cut down an expense item. I just don't see a reason why this would end, you know, I think it's going to continue. And I also want to point out these results, as high as they are, were before the Omicron wave hit us. I can only imagine what these results would have been if we had sent the survey out just two or three weeks later. >> Yeah. That's a great point. Okay. Next prediction, we're going to look at the supply chain, specifically in how it's affecting some of the hardware spending and cloud strategies in the future. So in this chart, ETRS buyers, have you experienced problems procuring hardware as a result of supply chain issues? And, you know, despite the fact that some companies are, you know, I would call out Dell, for example, doing really well in terms of delivering, you can see that in the numbers, it's pretty clear, there's been an impact. And that's not not an across the board, you know, thing where vendors are able to deliver, especially acute in PCs, but also pronounced in networking, also in firewall servers and storage. And what's interesting is how companies are responding and reacting. So first, you know, I'm going to call the laptop and PC demand staying well above pre-COVID norms. It had peaked in 2012. Pre-pandemic it kept dropping and dropping and dropping, in terms of, you know, unit volume, where the market was contracting. And we think can continue to grow this year in double digits in 2022. But what's interesting, Erik, is when you survey customers, is despite the difficulty they're having in procuring network hardware, there's as much of a migration away from existing networks to the cloud. You could probably comment on that. Their networks are more fossilized, but when it comes to firewalls and servers and storage, there's a much higher propensity to move to the cloud. 30% of customers that ETR surveyed will replace security appliances with cloud services and 41% and 34% respectively will move to cloud compute and storage in 2022. So cloud's relentless march on traditional on-prem models continues. Erik, what do you make of this data? Please weigh in on this prediction. >> As if we needed another reason to go to the cloud. Right here, here it is yet again. So this was added to the survey by client demand. They were asking about the procurement difficulties, the supply chain issues, and how it was impacting our community. So this is the first time we ran it. And it really was interesting to see, you know, the move there. And storage particularly I found interesting because it correlated with a huge jump that we saw on one of our vendor names, which was Rubrik, had the highest net score that it's ever had. So clearly we're seeing some correlation with some of these names that are there, you know, really well positioned to take storage, to take data into the cloud. So again, you didn't need another reason to, you know, hasten this digital transformation, but here we are, we have it yet again, and I don't see it slowing down anytime soon. >> You know, that's a really good point. I mean, it's not necessarily bad news for the... I mean, obviously you wish that it had no change, would be great, but things, you know, always going to change. So we'll talk about this a little bit later when we get into the Supercloud conversation, but this is an opportunity for people who embrace the cloud. So we'll come back to that. And I want to hang on cloud a bit and share some recent projections that we've made. The next prediction is the big four cloud players are going to surpass 167 billion, an IaaS and PaaS revenue in 2022. We track this. Observers of this program know that we try to create an apples to apples comparison between AWS, Azure, GCP and Alibaba in IaaS and PaaS. So we're calling for 38% revenue growth in 2022, which is astounding for such a massive market. You know, AWS is probably not going to hit a hundred billion dollar run rate, but they're going to be close this year. And we're going to get there by 2023, you know they're going to surpass that. Azure continues to close the gap. Now they're about two thirds of the size of AWS and Google, we think is going to surpass Alibaba and take the number three spot. Erik, anything you'd like to add here? >> Yeah, first of all, just on a sector level, we saw our sector, new survey net score on cloud jumped another 10%. It was already really high at 48. Went up to 53. This train is not slowing down anytime soon. And we even added an edge compute type of player, like CloudFlare into our cloud bucket this year. And it debuted with a net score of almost 60. So this is really an area that's expanding, not just the big three, but everywhere. We even saw Oracle and IBM jump up. So even they're having success, taking some of their on-prem customers and then selling them to their cloud services. This is a massive opportunity and it's not changing anytime soon, it's going to continue. >> And I think the operative word there is opportunity. So, you know, the next prediction is something that we've been having fun with and that's this Supercloud becomes a thing. Now, the reason I say we've been having fun is we put this concept of Supercloud out and it's become a bit of a controversy. First, you know, what the heck's the Supercloud right? It's sort of a buzz-wordy term, but there really is, we believe, a thing here. We think there needs to be a rethinking or at least an evolution of the term multi-cloud. And what we mean is that in our view, you know, multicloud from a vendor perspective was really cloud compatibility. It wasn't marketed that way, but that's what it was. Either a vendor would containerize its legacy stack, shove it into the cloud, or a company, you know, they'd do the work, they'd build a cloud native service on one of the big clouds and they did do it for AWS, and then Azure, and then Google. But there really wasn't much, if any, leverage across clouds. Now from a buyer perspective, we've always said multicloud was a symptom of multi-vendor, meaning I got different workloads, running in different clouds, or I bought a company and they run on Azure, and I do a lot of work on AWS, but generally it wasn't necessarily a prescribed strategy to build value on top of hyperscale infrastructure. There certainly was somewhat of a, you know, reducing lock-in and hedging the risk. But we're talking about something more here. We're talking about building value on top of the hyperscale gift of hundreds of billions of dollars in CapEx. So in addition, we're not just talking about transforming IT, which is what the last 10 years of cloud have been like. And, you know, doing work in the cloud because it's cheaper or simpler or more agile, all of those things. So that's beginning to change. And this chart shows some of the technology vendors that are leaning toward this Supercloud vision, in our view, building on top of the hyperscalers that are highlighted in red. Now, Jerry Chan at Greylock, they wrote a piece called Castles in the Cloud. It got our thinking going, and he and the team at Greylock, they're building out a database of all the cloud services and all the sub-markets in cloud. And that got us thinking that there's a higher level of abstraction coalescing in the market, where there's tight integration of services across clouds, but the underlying complexity is hidden, and there's an identical experience across clouds, and even, in my dreams, on-prem for some platforms, so what's new or new-ish and evolving are things like location independence, you've got to include the edge on that, metadata services to optimize locality of reference and data source awareness, governance, privacy, you know, application independent and dependent, actually, recovery across clouds. So we're seeing this evolve. And in our view, the two biggest things that are new are the technology is evolving, where you're seeing services truly integrate cross-cloud. And the other big change is digital transformation, where there's this new innovation curve developing, and it's not just about making your IT better. It's about SaaS-ifying and automating your entire company workflows. So Supercloud, it's not just a vendor thing to us. It's the evolution of, you know, the, the Marc Andreessen quote, "Every company will be a SaaS company." Every company will deliver capabilities that can be consumed as cloud services. So Erik, the chart shows spending momentum on the y-axis and net score, or presence in the ETR data center, or market share on the x-axis. We've talked about snowflake as the poster child for this concept where the vision is you're in their cloud and sharing data in that safe place. Maybe you could make some comments, you know, what do you think of this Supercloud concept and this change that we're sensing in the market? >> Well, I think you did a great job describing the concept. So maybe I'll support it a little bit on the vendor level and then kind of give examples of the ones that are doing it. You stole the lead there with Snowflake, right? There is no better example than what we've seen with what Snowflake can do. Cross-portability in the cloud, the ability to be able to be, you know, completely agnostic, but then build those services on top. They're better than anything they could offer. And it's not just there. I mean, you mentioned edge compute, that's a whole nother layer where this is coming in. And CloudFlare, the momentum there is out of control. I mean, this is a company that started off just doing CDN and trying to compete with Okta Mite. And now they're giving you a full soup to nuts with security and actual edge compute layer, but it's a fantastic company. What they're doing, it's another great example of what you're seeing here. I'm going to call out HashiCorp as well. They're more of an infrastructure services, a little bit more of an open-source freemium model, but what they're doing as well is completely cloud agnostic. It's dynamic. It doesn't care if you're in a container, it doesn't matter where you are. They recently IPO'd and they're down 25%, but their data looks so good across both of our emerging technology and TISA survey. It's certainly another name that's playing on this. And another one that we mentioned as well is Rubrik. If you need storage, compute, and in the cloud layer and you need to be agnostic to it, they're another one that's really playing in this space. So I think it's a great concept you're bringing up. I think it's one that's here to stay and there's certainly a lot of vendors that fit into what you're describing. >> Excellent. Thank you. All right, let's shift to data. The next prediction, it might be a little tough to measure. Before I said we're trying to be a little black and white here, but it relates to Data Mesh, which is, the ideas behind that term were created by Zhamak Dehghani of ThoughtWorks. And we see Data Mesh is really gaining momentum in 2022, but it's largely going to be, we think, confined to a more narrow scope. Now, the impetus for change in data architecture in many companies really stems from the fact that their Hadoop infrastructure really didn't solve their data problems and they struggle to get more value out of their data investments. Data Mesh prescribes a shift to a decentralized architecture in domain ownership of data and a shift to data product thinking, beyond data for analytics, but data products and services that can be monetized. Now this a very powerful in our view, but they're difficult for organizations to get their heads around and further decentralization creates the need for a self-service platform and federated data governance that can be automated. And not a lot of standards around this. So it's going to take some time. At our power panel a couple of weeks ago on data management, Tony Baer predicted a backlash on Data Mesh. And I don't think it's going to be so much of a backlash, but rather the adoption will be more limited. Most implementations we think are going to use a starting point of AWS and they'll enable domains to access and control their own data lakes. And while that is a very small slice of the Data Mesh vision, I think it's going to be a starting point. And the last thing I'll say is, this is going to take a decade to evolve, but I think it's the right direction. And whether it's a data lake or a data warehouse or a data hub or an S3 bucket, these are really, the concept is, they'll eventually just become nodes on the data mesh that are discoverable and access is governed. And so the idea is that the stranglehold that the data pipeline and process and hyper-specialized roles that they have on data agility is going to evolve. And decentralized architectures and the democratization of data will eventually become a norm for a lot of different use cases. And Erik, I wonder if you'd add anything to this. >> Yeah. There's a lot to add there. The first thing that jumped out to me was that that mention of the word backlash you said, and you said it's not really a backlash, but what it could be is these are new words trying to solve an old problem. And I do think sometimes the industry will notice that right away and maybe that'll be a little pushback. And the problems are what you already mentioned, right? We're trying to get to an area where we can have more assets in our data site, more deliverable, and more usable and relevant to the business. And you mentioned that as self-service with governance laid on top. And that's really what we're trying to get to. Now, there's a lot of ways you can get there. Data fabric is really the technical aspect and data mesh is really more about the people, the process, and the governance, but the two of those need to meet, in order to make that happen. And as far as tools, you know, there's even cataloging names like Informatica that play in this, right? Istio plays in this, Snowflake plays in this. So there's a lot of different tools that will support it. But I think you're right in calling out AWS, right? They have AWS Lake, they have AWS Glue. They have so much that's trying to drive this. But I think the really important thing to keep here is what you said. It's going to be a decade long journey. And by the way, we're on the shoulders of giants a decade ago that have even gotten us to this point to talk about these new words because this has been an ongoing type of issue, but ultimately, no matter which vendors you use, this is going to come down to your data governance plan and the data literacy in your business. This is really about workflows and people as much as it is tools. So, you know, the new term of data mesh is wonderful, but you still have to have the people and the governance and the processes in place to get there. >> Great, thank you for that, Erik. Some great points. All right, for the next prediction, we're going to shine the spotlight on two of our favorite topics, Snowflake and Databricks, and the prediction here is that, of course, Databricks is going to IPO this year, as expected. Everybody sort of expects that. And while, but the prediction really is, well, while these two companies are facing off already in the market, they're also going to compete with each other for M&A, especially as Databricks, you know, after the IPO, you're going to have, you know, more prominence and a war chest. So first, these companies, they're both looking pretty good, the same XY graph with spending velocity and presence and market share on the horizontal axis. And both Snowflake and Databricks are well above that magic 40% red dotted line, the elevated line, to us. And for context, we've included a few other firms. So you can see kind of what a good position these two companies are really in, especially, I mean, Snowflake, wow, it just keeps moving to the right on this horizontal picture, but maintaining the next net score in the Y axis. Amazing. So, but here's the thing, Databricks is using the term Lakehouse implying that it has the best of data lakes and data warehouses. And Snowflake has the vision of the data cloud and data sharing. And Snowflake, they've nailed analytics, and now they're moving into data science in the domain of Databricks. Databricks, on the other hand, has nailed data science and is moving into the domain of Snowflake, in the data warehouse and analytics space. But to really make this seamless, there has to be a semantic layer between these two worlds and they're either going to build it or buy it or both. And there are other areas like data clean rooms and privacy and data prep and governance and machine learning tooling and AI, all that stuff. So the prediction is they'll not only compete in the market, but they'll step up and in their competition for M&A, especially after the Databricks IPO. We've listed some target names here, like Atscale, you know, Iguazio, Infosum, Habu, Immuta, and I'm sure there are many, many others. Erik, you care to comment? >> Yeah. I remember a year ago when we were talking Snowflake when they first came out and you, and I said, "I'm shocked if they don't use this war chest of money" "and start going after more" "because we know Slootman, we have so much respect for him." "We've seen his playbook." And I'm actually a little bit surprised that here we are, at 12 months later, and he hasn't spent that money yet. So I think this prediction's just spot on. To talk a little bit about the data side, Snowflake is in rarefied air. It's all by itself. It is the number one net score in our entire TISA universe. It is absolutely incredible. There's almost no negative intentions. Global 2000 organizations are increasing their spend on it. We maintain our positive outlook. It's really just, you know, stands alone. Databricks, however, also has one of the highest overall net sentiments in the entire universe, not just its area. And this is the first time we're coming up positive on this name as well. It looks like it's not slowing down. Really interesting comment you made though that we normally hear from our end-user commentary in our panels and our interviews. Databricks is really more used for the data science side. The MLAI is where it's best positioned in our survey. So it might still have some catching up to do to really have that caliber of usability that you know Snowflake is seeing right now. That's snowflake having its own marketplace. There's just a lot more to Snowflake right now than there is Databricks. But I do think you're right. These two massive vendors are sort of heading towards a collision course, and it'll be very interesting to see how they deploy their cash. I think Snowflake, with their incredible management and leadership, probably will make the first move. >> Well, I think you're right on that. And by the way, I'll just add, you know, Databricks has basically said, hey, it's going to be easier for us to come from data lakes into data warehouse. I'm not sure I buy that. I think, again, that semantic layer is a missing ingredient. So it's going to be really interesting to see how this plays out. And to your point, you know, Snowflake's got the war chest, they got the momentum, they've got the public presence now since November, 2020. And so, you know, they're probably going to start making some aggressive moves. Anyway, next prediction is something, Erik, that you and I have talked about many, many times, and that is observability. I know it's one of your favorite topics. And we see this world screaming for more consolidation it's going all in on cloud native. These legacy stacks, they're fighting to stay relevant, but the direction is pretty clear. And the same XY graph lays out the players in the field, with some of the new entrants that we've also highlighted, like Observe and Honeycomb and ChaosSearch that we've talked about. Erik, we put a big red target around Splunk because everyone wants their gold. So please give us your thoughts. >> Oh man, I feel like I've been saying negative things about Splunk for too long. I've got a bad rap on this name. The Splunk shareholders come after me all the time. Listen, it really comes down to this. They're a fantastic company that was designed to do logging and monitoring and had some great tool sets around what you could do with it. But they were designed for the data center. They were designed for prem. The world we're in now is so dynamic. Everything I hear from our end user community is that all net new workloads will be going to cloud native players. It's that simple. So Splunk has entrenched. It's going to continue doing what it's doing and it does it really, really well. But if you're doing something new, the new workloads are going to be in a dynamic environment and that's going to go to the cloud native players. And in our data, it is extremely clear that that means Datadog and Elastic. They are by far number one and two in net score, increase rates, adoption rates. It's not even close. Even New Relic actually is starting to, you know, entrench itself really well. We saw New Relic's adoption's going up, which is super important because they went to that freemium model, you know, to try to get their little bit of an entrenched customer base and that's working as well. And then you made a great list here, of all the new entrants, but it goes beyond this. There's so many more. In our emerging technology survey, we're seeing Century, Catchpoint, Securonix, Lucid Works. There are so many options in this space. And let's not forget, the biggest data that we're seeing is with Grafana. And Grafana labs as yet to turn on their enterprise. Elastic did it, why can't Grafana labs do it? They have an enterprise stack. So when you look at how crowded this space is, there has to be consolidation. I recently hosted a panel and every single guy on that panel said, "Please give me a consolidation." Because they're the end users trying to actually deploy these and it's getting a little bit confusing. >> Great. Thank you for that. Okay. Last prediction. Erik, might be a little out of your wheelhouse, but you know, you might have some thoughts on it. And that's a hybrid events become the new digital model and a new category in 2022. You got these pure play digital or virtual events. They're going to take a back seat to in-person hybrids. The virtual experience will eventually give way to metaverse experiences and that's going to take some time, but the physical hybrid is going to drive it. And metaverse is ultimately going to define the virtual experience because the virtual experience today is not great. Nobody likes virtual. And hybrid is going to become the business model. Today's pure virtual experience has to evolve, you know, theCUBE first delivered hybrid mid last decade, but nobody really wanted it. We did Mobile World Congress last summer in Barcelona in an amazing hybrid model, which we're showing in some of the pictures here. Alex, if you don't mind bringing that back up. And every physical event that we're we're doing now has a hybrid and virtual component, including the pre-records. You can see in our studios, you see that the green screen. I don't know. Erik, what do you think about, you know, the Zoom fatigue and all this. I know you host regular events with your round tables, but what are your thoughts? >> Well, first of all, I think you and your company here have just done an amazing job on this. So that's really your expertise. I spent 20 years of my career hosting intimate wall street idea dinners. So I'm better at navigating a wine list than I am navigating a conference floor. But I will say that, you know, the trend just goes along with what we saw. If 35% are going to be fully remote. If 70% are going to be hybrid, then our events are going to be as well. I used to host round table dinners on, you know, one or two nights a week. Now those have gone virtual. They're now panels. They're now one-on-one interviews. You know, we do chats. We do submitted questions. We do what we can, but there's no reason that this is going to change anytime soon. I think you're spot on here. >> Yeah. Great. All right. So there you have it, Erik and I, Listen, we always love the feedback. Love to know what you think. Thank you, Erik, for your partnership, your collaboration, and love doing these predictions with you. >> Yeah. I always enjoy them too. And I'm actually happy. Last year you made us do a baker's dozen, so thanks for keeping it to 10 this year. >> (laughs) We've got a lot to say. I know, you know, we cut out. We didn't do much on crypto. We didn't really talk about SaaS. I mean, I got some thoughts there. We didn't really do much on containers and AI. >> You want to keep going? I've got another 10 for you. >> RPA...All right, we'll have you back and then let's do that. All right. All right. Don't forget, these episodes are all available as podcasts, wherever you listen, all you can do is search Breaking Analysis podcast. Check out ETR's website at etr.plus, they've got a new website out. It's the best data in the industry, and we publish a full report every week on wikibon.com and siliconangle.com. You can always reach out on email, David.Vellante@siliconangle.com I'm @DVellante on Twitter. Comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (mellow music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante predictions are all the rage at this time of year now on december 29th 2020 in collaboration with eric porter bradley of enterprise technology research etr we put forth our predictions for 2021 and the focus of our prognostications included tech spending remote work productivity apps cyber security ipos specs m a data architecture cloud hybrid cloud multi-cloud ai containers automation and semiconductors we covered a lot of ground now over the past several weeks we've been inundated with literally thousands of inbound emails pitching us on various predictions and trends in these and other areas here's my predictions folder and this is only a portion of the documents that i've received by email obviously printed them out killed a few trees sorry hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we're going to review briefly each of our predictions for this past year 2021 and suggest a grade as to how we did we're going to do this as a little warm up for our 2022 predictions which we'll be doing in the next over the next couple of weeks now before we dig in i want to make an observation many of the predictions that we received they were observations of trends and sometimes not really predictions or you know or not surprising we got a lot of self-serving marketing statements you know predictions in our view they should be measurable so you can look back and say okay did they get it right now granted there are gray areas so that's why we'll use a grading system today now there are also many really well done and thought-provoking predictions and there's an example of one that we received that is strong it's from equinix cio milan waglay who said within the decade data centers will be powered by a hundred percent renewable energy okay so you know that's clear and we can measure that but anyway thanks to all the pr folks who sent along like i said literally thousands of predictions we tried to read them all but the volume over the past week or so was just so overwhelming and we'll try to scan them before we do our 2022 predictions but today we want to do that warm up by evaluating how we did in 2021 so let's get started our first prediction was that tech spending would increase by four percent this year coming off of what we had thought was a contraction in 2020 and depending on which data you look at you know best case maybe was flat we definitely correctly called the continuation into 2022 of the remote work trend and the positive impact it would have on pcs and the like but we underestimated the shape of that rebound that that spend back curve idc has tech spending wrote this year at five and a half percent so we feel like while we called the bounce back it was more pronounced than we had thought in fact you know we think that idc number is probably going to go up even higher and we'll address that in our 2022 predictions so so we'll give ourselves a b minus here okay next prediction was remote worker trends become fossilized settling in at an average of 34 percent by year end 2021. so on average 34 of the workers would be remote by the end of this year now you know we made the call but we missed delta no we missed omacrom we said 34 remote which would be 2x the historical norms now the etr data suggests it was 52 in september and it's probably going to be somewhere in the 40 to 45 range by by the end of this month into december and the thing is 75 of the workforce is probably still working either fully remote or in a hybrid model and hybrid work is probably going to be the dominant trend and we're going to have to revisit that framework or how we think about this whole structure and we'll do that again in our 2022 predictions so we'll give ourselves a c on that one we'll take some credit for the permanence of the trend but the percentage was well off the mark you know thanks to the variance as well as some cultural shifts that whole hybrid notion okay so hey not really a great start for eric and me but we rebound with the next one the productivity increases we said seen in 2020 will lead organizations to double down on the successes and certain productivity apps will benefit so to measure this we said let's take a look at the most recent quarterly earnings and gauge the revenue growth year on year as an indicator docusign was up 42 smartsheet who we also called up was up 46 in revenue twilio up 65 zoom growth was 35 down from 325 confirming our layup call the zoom growth would moderate it had nowhere to go but down and microsoft teams has never been more ubiquitous has never seen greater adoption with hundreds of companies having a hundred thousand or more users and thousands of companies with ten thousand users or more so we really feel like we nailed this one so we're gonna give us give ourselves an a plus okay so now on to cyber it's an area that we've been making calls in for a couple of years now and we're really pleased looking back here we said permanent shifts in cso strategies are going to lead to share shifts in network security now we said to give you more detail maybe that sounds like an easy one but we said specifically identity cloud security and endpoint security would continue to benefit and we specifically named crowdstrike octa zscaler and a few others that are targeting their growth rates now gartner has the security market growing at 11 percent octa and zscaler revenues last quarter grew at 62 percent year over year crowdstrike 63 illumia we also called out they raised 225 million dollars on a 2.75 billion valuation on the strength of its growth that was in september now akamai acquired guardiocor for 600 million dollars another company we called out that they would do it they did that as a ransomware protection play and they paid a huge revenue multiple for the company and it seems the guys listed on the last line are all talking about subscriptions sas arr remaining performance obligations or rpo so we feel very good about this look back we'll take an a on this one no it's not an a plus because we're too conservative on the growth of octa crowdstrike and zscaler topping at 50 they they blew that away by another 10 points or so 10 to 15. but look pretty good call nonetheless okay again the next one you might feel like is a layup but not really so we said the increased tech spend would drive even more ipos spax and m a according to spac analytics ipos were up 109 this year the spac attack continued up 109 percent in 2021 on top of a record 2020 and according to kpmg m a dollar volume was up 19 okay you might say uh that was easy call but there was much more underneath this prediction we called out uipass ipo which was a lock but also said automation anywhere would go public uipath did aa didn't we did correctly call the hashicorp ipo we said they'd either get go ipo or get acquired and cloud flare grew revenue 219 percent last quarter but akamai was not acquired so the degree of difficulty on the overall prediction wasn't high but the automation anywhere in akamai events we made those calls that didn't happen and those were you know obviously tougher calls so we think this still deserves a b grade all right as you know data is one of our favorite subjects and we've reported extensively in the successes and failures of so-called big data we said next in the next prediction that in the 2020s 75 percent of large organizations will re-architect their big data platforms and we said this would occur you know in earnest over the next four to five years now again you may say duh dave but you have to evaluate the prediction based on the underlying comments here the jury is still out on things like snowflakes data cloud but we absolutely believe that it's the right direction but then you have then you have data bricks coming in taking a different approach they're coming at the problem from a data science angle trying to take on traditional bi and then you get snowflake coming from the analytics space and moving into ai and data science and you know we asked at aws aws re invent we asked benoit dejaville on the cube if there needs to be a semantic layer to bring these two worlds together and he said yes and that's what he claims snowflake is building meanwhile you got the big whales like oracle they continue to invest in their capabilities to try to eliminate data movement and then there's aws taking a totally different approach to data where it gives customers maximum optionality of offerings and database and other services and you can't forget microsoft and google so many customers might not take the steps that we predicted because they're comfortable where they are specifically we're talking about here a shift toward domain ownership and data product thinking and the reorganization of hyper-specialized technical teams many of the principles put forth by data mesh and we've said this change is going to take a number of years to play out four to five years so we start noticing in 2021 that that's clearly been the case as we reported on parts of jpmorgan chase uh rethinking its data architecture hellofresh and many others so this is still an incomplete the professor we'll give ourselves an incomplete on this one but we think it's trending in the right direction okay the next one is always fun discussion that's the battle to define hybrid and multi-cloud we said that's going to escalate in 2021 and we'll create bifurcated cio strategies now here we go aws sees the world as bringing its apis and primitives and model to the edge and the data center to aws is just another edge node and the company says that in still believes in the fullness of time that all data will be in the cloud however that's defined and aws awareness would say all this talk about hybrid of connecting on-prem to a cloud they would flat out say adam silipsky told us this that's not cloud is what he said then on the other side of the table you have the likes of cisco dell hpe etc saying hold on cloud is an operating model it's not a place and aws might say yeah and aws along with its customers is defining that operating model and these other guys would say no actually you're not we are with our customers and this battle 100 percent escalated in 2021 with the launch of apex by dell hp e double down on green lake cisco's as the service models and then of course oracle which actually announced a true same same public to on-prem hybrid capability two years before aws announced outpost and of course oracle's executing on that strategy in earnest in 2021 and the other nuance here is a concept that we introduced called super cloud which refers to the notion that look something like for example multi-cloud is not about running within a respective cloud it's not about cloud compatibility rather it's about abstracting the complexity of the underlying cloud primitives and building value on top of those cloud services on top of the investments in capex that the hyperscalers have made now some people didn't like the term super cloud maybe uber cloud would be a better term we're going to continue to use it to describe this capability we think it has meaning and we're seeing new examples like goldman sachs's financial cloud running on top of aws so a super cloud is not as an application or a suite of applications running on a single cloud now if those applications span multiple clouds like like snowflake is trying to do okay that's a service that could span multiple clouds or in the case of goldman sachs it's a portfolio of data tools and software that's made accessible as a service that floats on top of a single or even multiple clouds regardless we feel that this was a correct call given the evidence and we'll give ourselves an a minus taking points off for the somewhat anecdotal and observational measurement system that we apply to look back at this prediction okay the next prediction was we made was cloud containers ai and ml automation uh are gonna power that those big four are gonna power 2021 spending here's a graphic we use to predict that it plots survey data for the various technologies within the etr taxonomy net score or spending momentum on the vertical axis and market share or presence in the data set it's a pervasive measurement on the horizontal axis the one that matters here is the vertical that dotted line of 40 percent anything above that is considered highly elevated and these four areas have held served this year based on recent etr survey data that we're not showing here we'll we'll bring that into our 2022 prediction so this prediction came in correctly for the most recent survey data and that's our measurement system on this one so we're going to take an a for this one too now on the penelope ultimate prediction here we came back to automation saying that the automation mandate accelerates in 2021 uipath and automation anywhere we said would go public but microsoft remains a threat to these pure play rpa vendors well we gave ourselves a b on this one doubling down on automation anywhere going public you know that was wrong but we definitely saw this year companies leaning hard into automation and microsoft despite the fact that it doesn't have as feature rich a product and offering as uipath and automation anywhere microsoft remains a very large presence you know we spoke to a lot of customers at the uipath forward four event in october in las vegas physical event and they confirmed you know this is true but at the same time so they're using power automate from microsoft but also using in this case uipath so they've kind of confirmed that yeah it's not the same we use that for some of our productivity we're an azure customer it's easy for us but they're still leaning heavily and investing heavily into uipath and i think the same can be said for automation anywhere but autom but power automate shows up as a big time leader in the magic gartner magic quadrant so it can't be ignored but clearly the two leaders in rpa have a sizable product advantage relative to the legacy software players now if you look at the comment on pega systems they cooled off a bit as measured by their stock price their revenue grew 13 percent last quarter on a year-on-year basis but perhaps we overestimated the tailwind effect and the company's momentum so we'll take a b on this prediction correct call on the automation trend and the big software vendors piling in ibm et cetera but the chance we took on automation anywhere again was a miss so we'll dig ourselves on that and our last prediction for 2021 was 5g rollouts push new edge iot workloads and necessitate new system architectures now much of this prediction you can see in the underlying bullets here really related to the observation that arm was dominating at the edge it would find its way into the mainstream enterprise workloads and we've been asking a lot of the mainstream you know companies the oems you know what do you what do you see with with arm in the enterprise and they say yeah we don't see it yet but very clearly this came into focus in 2021 is aws announced graviton 3 now and new inference and new training silicon these are different types of workloads that are emerging in the enterprise these are all based on arm microsoft google alibaba oracle and others are now shipping or readying arm-based systems for the enterprise when you look at new storage network and security appliances and other systems they're very offering and often including arm-based processors to assist with the offloads and look intel is definitely under product under pressure as we've predicted many times not just in our predictions post even pat gelsinger has admitted this is a turnaround it's going to take at least five years that's kind of new and recent data that he's made public so we're going to take an a minus on this one we're going to take off some points for the fact that you know 5g rollouts in edge are evolving and this is a longer term trend but the underlying points that we made on this slide are still pretty solid now if we use the following scale where a plus is a hundred out of a hundred a minus is a 90 a b is an 85 a b minus is an 80 and a c is a 75 out of 100 and we exclude that incomplete prediction on data architectures we average out to an 87.8 so that's a solid b plus and so the professor in us said hey little yellow sticky good effort as most of the predictions could be quantified and or you know we tried to object objectively score them there were some layups in there so yeah maybe we'll try to take more risks uh you know or not you know we we we'll see we like winning and so you know you always have to couch some of these things with some obvious ones but but really try to give some detail underneath that's maybe non-obvious um and we'll try to keep it down in the legs we did this year to one or two multi-year predictions so what's next well eric bradley and i were working on our 2022 predictions we're going to release those in the next couple of weeks so stay tuned for that you know what do you think how did we do you know we're grading ourselves here love to know you know for we're off base on base we're too hard on ourselves too easy give us your feedback don't forget these episodes are all available as podcasts wherever you listen all you do is search breaking analysis podcast check out etr's website at etr dot plus remember we also publish a full report every week on wikibon.com and siliconangle.com you can always get in touch with email david.velante at siliconangle.com you can dm me at divalante or comment on our linkedin posts this is dave vellante for the cube insights powered by etr have a great week everybody stay safe be well we'll see you next time [Music] you

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante recent survey data from etr shows that enterprise tech spending is tracking with projected u.s gdp growth at six to seven percent this year many markers continue to point the way to a strong recovery including hiring trends and the loosening of frozen it project budgets however skills shortages are blocking progress at some companies which bodes well for an increased reliance on external i.t services moreover while there's much to talk about well there's much talk about the rotation out of work from home plays and stocks such as video conferencing vdi and other remote worker tech we see organizations still trying to figure out the ideal balance between funding headquarter investments that have been neglected and getting hybrid work right in particular the talent gap combined with a digital mandate means companies face some tough decisions as to how to fund the future while serving existing customers and transforming culturally hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we welcome back eric porter bradley of etr who will share fresh data perspectives and insights from the latest survey data eric great to see you welcome thank you very much dave always good to see you and happy to be on the show again okay we're going to share some macro data and then we're going to dig into some highlights from etr's most recent march covid survey and also the latest april data so eric the first chart that we want to show it shows cio and it buyer responses to expected i.t spend for each quarter of 2021 versus 2020. and you can see here a steady quarterly improvement eric what are the key takeaways from your perspective sure well first of all for everyone out there this particular survey had a record-setting number of uh participation we had uh 1 500 i.t decision makers participate and we had over half of the fortune 500 and over a fifth of the global 1000. so it was a really good survey this is the seventh iteration of the covet impact survey specifically and this is going to transition to an over large macro survey going forward so we could continue it and you're 100 right what we've been tracking here since uh march of last year was how is spending being impacted because of covid where is it shifting and what we're seeing now finally is that there is a real re-acceleration in spend i know we've been a little bit more cautious than some of the other peers out there that just early on slapped an eight or a nine percent number but what we're seeing is right now it's at a midpoint of over six uh about six point seven percent and that is accelerating so uh we are still hopeful that that will continue uh really that spending is going to be in the second half of the year as you can see on the left part of this chart that we're looking at uh it was about 1.7 versus 3 for q1 spending year over year so that is starting to accelerate through the back half you know i think it's prudent to be be cautious relative because normally you'd say okay tech is going to grow a couple of points higher than gdp but it's it's really so hard to predict this year okay the next chart is here that we want to show you is we ask respondents to indicate what strategies they're employing in the short term as a result of coronavirus and you can see a few things that i'll call out and then i'll ask eric to chime in first there's been no meaningful change of course no surprise in tactics like remote work and halting travel however we're seeing very positive trends in other areas trending downward like hiring freezes and freezing i.t deployments downward trend in layoffs and we also see an increase in the acceleration of new i.t deployments and in hiring eric what are your key takeaways well first of all i think it's important to point out here that uh we're also capturing that people believe remote work productivity is still increasing now the trajectory might be coming down a little bit but that is really key i think to the backdrop of what's happening here so people have a perception that productivity of remote work is better than hybrid work and that's from the i.t decision makers themselves um but what we're seeing here is that uh most importantly these organizations are citing plans to increase hiring and that's something that i think is really important to point out it's showing a real thawing and to your point in right in the beginning of the intro uh we are seeing deployments stabilize versus prior survey levels which means early on they had no plans to launch new tech deployments then they said nope we're going to start and now that's stalling and i think it's exactly right what you said is there's an i.t skills shortage so people want to continue to do i.t deployments because they have to support work from home and a hybrid back return to the office but they just don't have the skills to do so and i think that's really probably the most important takeaway from this chart um is that stalling and to really ask why it's stalling yeah so we're going to get into that for sure and and i think that's a really key point is that that that accelerating it deployments is some it looks like it's hit a wall in the survey and so but before before we get deep into the skills let's let's take a look at this next chart and we're asking people here how a return to the new normal if you will and back to offices is going to change spending with on-prem architectures and applications and so the first two bars they're cloud-friendly if you add them up at 63 percent of the respondents say that either they'll stay in the cloud for the most part or they're going to lower the on-prem spend when they go back to the office the next three bars are on-prem friendly if you add those up as 29 percent of the respondents say their on-prem spend is going to bounce back to pre-covert levels or actually increase and of course 12 percent of that number by the way say they they've never altered their on-prem spend so eric no surprise but this bodes well for cloud but but it it isn't it also a positive for on-prem this we've had this dual funding premise meaning cloud continues to grow but neglected data center spend also gets a boost what's your thoughts you know really it's interesting it's people are spending on all fronts you and i were talking in a prep it's like you know we're we're in battle and i've got naval i've got you know air i've got land uh i've got to spend on cloud and digital transformation but i also have to spend for on-prem uh the hybrid work is here and it needs to be supported so this spending is going to increase you know when you look at this chart you're going to see though that roughly 36 percent of all respondents say that their spending is going to remain mostly on cloud so this you know that is still the clear direction uh digital transformation is still happening covid accelerated it greatly um you know you and i as journalists and researchers already know this is where the puck is going uh but spend has always lagged a little bit behind because it just takes some time to get there you know inversely 27 said that their on-prem spending will decrease so when you look at those two i still think that the trend is the friend for cloud spending uh even though yes they do have to continue spending on hybrid some of it's been neglected there are refresh cycles coming up so overall it just points to more and more spending right now it really does seem to be a very strong backdrop for it growth so i want to talk a little bit about the etr taxonomy before we bring up the next chart we get a lot of questions about this and of course when you do a massive survey like you're doing you have to have consistency for time series so you have to really think through what that what the buckets look like if you will so this next chart takes a look at the etr taxonomy and it breaks it down into simple to understand terms so the green is the portion of spending on a vendor's tech within a category that is accelerating and the red is the portion that is decelerating so eric what are the key messages in this data well first of all dave thank you so much for pointing that out we used to do uh just what we call a next a net score it's a proprietary formula that we use to determine the overall velocity of spending some people found it confusing um our data scientists decided to break this sector breakdown into what you said which is really more of a mode analysis in that sector how many of the vendors are increasing versus decreasing so again i just appreciate you bringing that up and allowing us to explain the the the reasoning behind our analysis there but what we're seeing here uh goes back to something you and i did last year when we did our predictions and that was that it services and consulting was going to have a true rebound in 2021 and that's what this is showing right here so in this chart you're going to see that consulting and services are really continuing their recovery uh 2020 had a lot of declines and they have the biggest sector over year-over-year acceleration sector-wise the other thing to point out in this which we'll get to again later is that the inverse analysis is true for video conferencing uh we will get to that so i'm going to leave a little bit of ammunition behind for that one but what we're seeing here is it consulting services being the real favorable and video conferencing uh having a little bit more trouble great okay and then let's let's take a look at that services piece and this next chart really is a drill down into that space and emphasizes eric what you were just talking about and we saw this in ibm's earnings where still more than 60 percent of ibm's business comes from services and the company beat earnings you know in part due to services outperforming expectations i think it had a somewhat easier compare and some of this pen-up demand that we've been talking about bodes well for ibm and in other services companies it's not just ibm right eric no it's not but again i'm going to point out that you and i did point out ibm in our in our predictions one we did in late december so it is nice to see one of the reasons we don't have a more favorable rating on ibm at the moment is because they are in the the process of spinning out uh this large unit and so there's a little bit of you know corporate action there that keeps us off on the sideline but i would also want to point out here uh tata infosys and cognizant because they're seeing year-over-year acceleration in both it consulting and outsourced i t services so we break those down separately and those are the three names that are seeing acceleration in both of those so again a tata emphasis and cognizant are all looking pretty well positioned as well so we've been talking a little bit about this skill shortage and this is what's i think so hard for for forecasters um is that you know on the one hand there's a lot of pent up demand you know it's like scott gottlieb said it's like woodstock coming out of the covid uh but on the other hand if you have a talent gap you've got to rely on external services so there's a learning curve there's a ramp up it's an external company and so it takes time to put those together so so this data that we're going to show you next uh is is really important in my view and ties what we're saying we're saying at the top it asks respondents to comment on their staffing plans the light blue is we're increasing staff the gray is no change in the magenta or whatever whatever color that is that sort of purplish color anyway that color is is decreasing and the picture is very positive across the board full-time staff offshoring contract employees outsourced professional services all up trending upwards and this eric is more evidence of the services bounce back yeah it certainly is david and what happened is when we caught this trend we decided to go one level deeper and say all right we're seeing this but we need to know why and that's what we always try to do here data will tell you what's happening it doesn't always tell you why and that's one of the things that etr really tries to dig in with through the insights interviews panels and also going direct with these more custom survey questions uh so in this instance i think the real takeaway is that 30 of the respondents said that their outsourced and managed services are going to increase over the next three months that's really powerful that's a large portion of organizations in a very short time period so we're capturing that this acceleration is happening right now and it will be happening in real time and i don't see it slowing down you and i are speaking about we have to you know increase cloud spend we have to increase hybrid spend there are refresh cycles coming up and there's just a real skill shortage so this is a long-term setup that bodes very well for it services and consulting you know eric when i came out of college i somebody told me read read read read as much as you can and and so i would and they said read the wall street journal every day and i so i did it and i would read the tech magazines and back then it was all paper and what happens is you begin to connect the dots and so the reason i bring that up is because i've now been had taken a bath in the etr data for the better part of two years and i'm beginning to be able to connect the dots you know the data is not always predictive but many many times it is and so this next data gets into the fun stuff where we name names a lot of times people don't like it because the marketing people and organizations say well the data's wrong of course that's the first thing they do is attack the data but you and i know we've made some really great calls work from home for sure you're talking about the services bounce back uh we certainly saw the rise of crowdstrike octa zscaler well before people were talking about that same thing with video conferencing and so so anyway this is the fun stuff and it looks at positive versus negative sentiment on on companies so first how does etr derive this data and how should we interpret it and what are some of your takeaways [Music] sure first of all how we derive the data or systematic um survey responses that we do on a quarterly basis and we standardize those responses to allow for time series analysis so we can do trend analysis as well we do find that our data because it's talking about forward-looking spending intentions is really more predictive because we're talking about things that might be happening six months three months in the future not things that a lot of other competitors and research peers are looking at things that already happened uh they're looking in the past etr really likes to look into the future and our surveys are set up to do so so thank you for that question it's an enjoyable lead-in but to get to the fun stuff like you said uh what we do here is we put ratings on the data sets i do want to put the caveat out there that our spending intentions really only captures top-line revenue it is not indicative of profit margin or any other line items so this is only going to be viewed as what we are rating the data set itself not the company um you know that's not what we're in the game of doing so i think that's very important for the marketing and the vendors out there themselves when they when they take a look at this we're just talking about what we can control which is our data we're going to talk about a few of the names here on this highlighted vendors list one we're going to go back to that you and i spoke about i guess about six months ago or maybe even earlier which was the observability space um you and i were noticing that it was getting very crowded a lot of new entrants um there was a lot of acquisition from more of the legacy or standard entrance players in the space and that is continuing so i think in a minute we're going to move into that observability space but what we're seeing there is that it's becoming incredibly crowded and we're possibly seeing signs of them cannibalizing each other uh we're also going to move on a little bit into video conferencing where we're capturing some spend deceleration and then ultimately we're going to get into a little bit of a storage refresh cycle and talk about that but yeah these are the highlighted vendors for april um we usually do this once a quarter and they do change based on the data but they're not usually whipsawed around the data doesn't move that quickly yeah so you can see the some of the big names on the left-hand side some of the sas companies that have momentum obviously servicenow has been doing very very well we've talked a lot about snowflake octa crowdstrike z scalar in all very positive as well as you know several others i i guess i'd add some some things i mean i think if thinking about the next decade it's it's cloud which is not going to be like the same cloud as last decade a lot of machine learning and deep learning and ai and the cloud is extending to the edge in the data center data obviously very important data is decentralized and distributed so data architectures are changing a lot of opportunities to connect across clouds and actually create abstraction layers and then something that we've been covering a lot is processor performance is actually accelerating relative to moore's law it's probably instead of doubling every two years it's quadrupling every two years and so that is a huge factor especially as it relates to powering ai and ai inferencing at the edge this is a whole new territory custom silicon is is really becoming in vogue uh and so we're something that we're watching very very closely yeah i completely completely agree on that and i do think that the the next version of cloud will be very different another thing to point out on that too is you can't do anything that you're talking about without collecting the data and and organizations are extremely serious about that now it seems it doesn't matter what industry they're in every company is a data company and that also bodes well for the storage call we do believe that there is going to just be a huge increase in the need for storage um and yes hopefully that'll become portable across multi-cloud and hybrid as well now as eric said the the etr data's it's it's really focused on that top line spend so if you look at the uh on on the right side of that chart you saw you know netapp was kind of negative was very negative right but there's a company that's in in transformation now they've lowered expectations and they've recently beat expectations that's why the stock has been doing better but but at the macro from a spending standpoint it's still challenged so you have big footprint companies like netapp and oracle is another one oracle's stock is at an all-time high but the spending relative to sort of previous cycles or relative to you know like for instance snowflake much much smaller not as high growth but they're managing expectations they're managing their transition they're managing profitability zoom is another one zoom looking looking negative but you know zoom's got to use its market cap now to to transform and increase its tam uh and then splunk is another one we're going to talk about splunk is in transition it acquired signal fx it just brought on this week teresa carlson who was the head of aws public sector she's the president and head of sales so they've got a go to market challenge and they brought in teresa carlson to really solve that but but splunk has been trending downward we called that you know several quarters ago eric and so i want to bring up the data on splunk and this is splunk eric in analytics and it's not trending in the right direction the green is accelerating span the red is and the bars is decelerating spend the top blue line is spending velocity or net score and the yellow line is market share or pervasiveness in the data set your thoughts yeah first i want to go back is a great point dave about our data versus a disconnect from an equity analysis perspective i used to be an equity analyst that is not what we do here and you you may the main word you said is expectations right stocks will trade on how they do compared to the expectations that are set uh whether that's buy side expectations sell side expectations or management's guidance themselves we have no business in tracking any of that what we are talking about is top line acceleration or deceleration so uh that was a great point to make and i do think it's an important one for all of our listeners out there now uh to move to splunk yes i've been capturing a lot of negative commentary on splunk even before the data turned so this has been about a year-long uh you know our analysis and review on this name and i'm dating myself here but i know you and i are both rock and roll fans so i'm gonna point out a led zeppelin song and movie and say that the song remains the same for splunk we are just seeing uh you know recent spending intentions are taking yet another step down both from prior survey levels from year ago levels uh this we're looking at in the analytics sector and spending intentions are decelerating across every single customer group if we went to one of our other slide analysis um on the etr plus platform and you do by customer sub sample in analytics it's dropping in every single vertical it doesn't matter which one uh it's really not looking good unfortunately and you had mentioned this as an analytics and i do believe the next slide is an information security yeah let's bring that up and it's unfortunately it's not doing much better so this is specifically fortune 500 accounts and information security uh you know there's deep pockets in the fortune 500 but from what we're hearing in all the insights and interviews and panels that i personally moderate for etr people are upset they didn't like the the strong tactics that splunk has used on them in the past they didn't like the ingestion model pricing the inflexibility and when alternatives came along people are willing to look at the alternatives and that's what we're seeing in both analytics and big data and also for their sim in security yeah so i think again i i point to teresa carlson she's got a big job but she's very capable she's gonna she's gonna meet with a lot of customers she's a go to market pro she's gonna have to listen hard and i think you're gonna you're gonna see some changes there um okay so there's more sorry there's more bad news on splunk so bring this up is is is net score for splunk in elastic accounts uh this is for analytics so there's 106 elastic accounts that uh in the data set that also have splunk and it's trending downward for splunk that's why it's green for elastic and eric the important call out from etr here is how splunk's performance in elastic accounts compares with its performance overall the elk stack which obviously elastic is a big part of that is causing pain for splunk as is data dog and you mentioned the pricing issue uh is it is it just well is it pricing in your assessment or is it more fundamental you know it's multi-level based on the commentary we get from our itdms that take the survey so yes you did a great job with this analysis what we're looking at is uh the spending within shared accounts so if i have splunk already how am i spending i'm sorry if i have elastic already how is my spending on splunk and what you're seeing here is it's down to about a 12 net score whereas splunk overall has a 32 net score among all of its customers so what you're seeing there is there is definitely a drain that's happening where elastic is draining spend from splunk and usage from them uh the reason we used elastic here is because all observabilities the whole sector seems to be decelerating splunk is decelerating the most but elastic is the only one that's actually showing resiliency so that's why we decided to choose these two but you pointed out yes it's also datadog datadog is cloud native uh they're more devops oriented they tend to be viewed as having technological lead as compared to splunk so that's a really good point a dynatrace also is expanding their abilities and splunk has been making a lot of acquisitions to push their cloud services they are also changing their pricing model right they're they're trying to make things a little bit more flexible moving off ingestion um and moving towards uh you know consumption so they are trying and the new hires you know i'm not gonna bet against them because the one thing that splunk has going for them is their market share in our survey they're still very well entrenched so they do have a lot of accounts they have their foothold so if they can find a way to make these changes then they you know will be able to change themselves but the one thing i got to say across the whole sector is competition is increasing and it does appear based on commentary and data that they're starting to cannibalize themselves it really seems pretty hard to get away from that and you know there are startups in the observability space too that are going to be you know even more disruptive i think i think i want to key on the pricing for a moment and i've been pretty vocal about this i think the the old sas pricing model where essentially you essentially lock in for a year or two years or three years pay up front or maybe pay quarterly if you're lucky that's a one-way street and i think it's it's a flawed model i like what snowflake's doing i like what datadog's doing look at what stripe is doing look what twilio is doing these are cons you mentioned it because it's consumption based pricing and if you've got a great product put it out there and you know damn the torpedoes and i think that is a game changer i i look at for instance hpe with green lake i look at dell with apex they're trying to mimic that model you know they're there and apply it to to infrastructure it's much harder with infrastructure because you got to deploy physical infrastructure but but that is a model that i think is going to change and i think all of the traditional sas pricing is going to is going to come under disruption over the next you know better part of the decades but anyway uh let's move on we've we've been covering the the apm space uh pretty extensively application performance management and this chart lines up some of the big players here comparing net score or spending momentum from the april 20th survey the gray is is um is sorry the the the gray is the april 20th survey the blue is jan 21 and the yellow is april 21. and not only are elastic and data dog doing well relative to splunk eric but everything is down from last year so this space as you point out is undergoing a transformation yeah the pressures are real and it's you know it's sort of that perfect storm where it's not only the data that's telling us that but also the direct feedback we get from the community uh pretty much all the interviews i do i've done a few panels specifically on this topic for anyone who wants to you know dive a little bit deeper we've had some experts talk about this space and there really is no denying that there is a deceleration in spend and it's happening because that spend is getting spread out among different vendors people are using you know a data dog for certain aspects they're using elastic where they can because it's cheaper they're using splunk because they have to but because it's so expensive they're cutting some of the things that they're putting into splunk which is dangerous particularly on the security side if i have to decide what to put in and whatnot that's not really the right way to have security hygiene um so you know this space is just getting crowded there's disruptive vendors coming from the emerging space as well and what you're seeing here is the only bit of positivity is elastic on a survey over survey basis with a slight slight uptick everywhere else year over year and survey over survey it's showing declines it's just hard to ignore and then you've got dynatrace who based on the the interviews you do in the venn you're you know one on one or one on five you know the private interviews that i've been invited to dynatrace gets very high scores uh for their road map you've got new relic which has been struggling you know financially but they've got a purpose built they've got a really good product and a purpose-built database just for this apm space and then of course you've got cisco with appd which is a strong business for them and then as you mentioned you've got startups coming in you've got chaos search which ed walsh is now running you know leave the data in place in aws and really interesting model honeycomb it's going to be really disruptive jeremy burton's company observed so this space is it's becoming jump ball yeah there's a great line that came out of one of them and that was that the lines are blurring it used to be that you knew exactly that app dynamics what they were doing it was apm only or it was logging and monitoring only and a lot of what i'm hearing from the itdm experts is that the lines are blurring amongst all of these names they all have functionality that kind of crosses over each other and the other interesting thing is it used to be application versus infrastructure monitoring but as you know infrastructure is becoming code more and more and more and as infrastructure becomes code there's really no difference between application and infrastructure monitoring so we're seeing a convergence and a blurring of the lines in this space which really doesn't bode well and a great point about new relic their tech gets good remarks uh i just don't know if their enterprise level service and sales is up to snuff right now um as one of my experts said a cto of a very large public online hospitality company essentially said that he would be shocked that within 18 months if all of these players are still uh standalone that there needs to be some m a or convergence in this space okay now we're going to call out some of the data that that really has jumped out to etr in the latest survey and some of the names that are getting the most queries from etr clients which are many of which are investor clients so let's start by having a look at one of the most important and prominent work from home names zoom uh let's let's look at this eric is the ride over for zoom oh i've been saying it for a little bit of a time now actually i do believe it is um i will get into it but again pointing out great dave uh the reason we're presenting today splunk elastic and zoom are they are the most viewed on the etr plus platform uh trailing behind that only slightly is f5 i decided not to bring f5 to the table today because we don't have a rating on the data set um so then i went one deep one below that and it's pure so the reason we're presenting these to you today is that these are the ones that our clients and our community are most interested in which is hopefully going to gain interest to your viewers as well so to get to zoom um yeah i call zoom the pandec pandemic bull market baby uh this was really just one that had a meteoric ride you look back january in 2020 the stock was at 60 and 10 months later it was like like 580. that's in 10 months um that's cooled down a little bit uh into the mid 300s and i believe that cooling down should continue and the reason why is because we are seeing a huge deceleration in our spending intentions uh they're hitting all-time lows it's really just a very ugly data set um more importantly than the spending intentions for the first time we're seeing customer growth in our survey flattened in the past we could we knew that the the deceleration and spend was happening but meanwhile their new customer growth was accelerating so it was kind of hard to really make any call based on that this is the first time we're seeing flattening customer growth trajectory and that uh in tandem with just dominance from microsoft in every sector they're involved in i don't care if it's ip telephony productivity apps or the core video conferencing microsoft is just dominating so there's really just no way to ignore this anymore the data and the commentary state that zoom is facing some headwinds well plus you've pointed out to me that a lot of your private conversations with buyers says that hey we're we're using the freebie version of zoom you know we're not paying them and so in that combined with teams i mean it's it's uh it's i think you know look zoom has to figure it out they they've got to they've got to figure out how to use their elevated market cap to transform and expand their tan um but let's let's move on here's the data on pure storage and we've highlighted a number of times this company is showing elevated spending intentions um pure announces earnings in in may ibm uh just announced storage what uh it was way down actually so sort of still pure more positive but i'll comment on a moment but what does this data tell you eric yeah you know right now we started seeing this data last survey in january and that was the first time we really went positive on the data set itself and it's just really uh continuing so we're seeing the strongest year-over-year acceleration in the entire survey um which is a really good spot to be pure is also a leading position in among its sector peers and the other thing that was pretty interesting from the data set is among all storage players pure has the highest positive public cloud correlation so what we can do is we can see which respondents are accelerating their public cloud spend and then cross-reference that with their storage spend and pure is best positioned so as you and i both know uh you know digital transformation cloud spending is increasing you need to be aligned with that and among all storage uh sector peers uh pure is best positioned in all of those in spending intentions and uh adoptions and also public cloud correlation so yet again just another really strong data set and i have an anecdote about why this might be happening because when i saw the date i started asking in my interviews what's going on here and there was one particular person he was a director of cloud operations for a very large public tech company now they have hybrid um but their data center is in colo so they don't own and build their own physical building he pointed out that doran kovid his company wanted to increase storage but he couldn't get into his colo center due to covert restrictions they weren't allowed you had so 250 000 square feet right but you're only allowed to have six people in there so it's pretty hard to get to your rack and get work done he said he would buy storage but then the cola would say hey you got to get it out of here it's not even allowed to sit here we don't want it in our facility so he has all this pent up demand in tandem with pent up demand we have a refresh cycle the ssd you know depreciation uh you know cycle is ending uh you know ssds are moving on and we're starting to see uh new technology in that space nvme sorry for technology increasing in that space so we have pent up demand and we have new technology and that's really leading to a refresh cycle and this particular itdm that i spoke to and many of his peers think this has a long tailwind that uh storage could be a good sector for some time to come that's really interesting thank you for that that extra metadata and i want to do a little deeper dive on on storage so here's a look at storage in the the industry in context and some of the competitive i mean it's been a tough market for the reasons that we've highlighted cloud has been eating away that flash headroom it used to be you'd buy storage to get you know more spindles and more performance and you were sort of forced to buy more flash gave more headroom but it's interesting what you're saying about the depreciation cycle so that's good news so etr combines just for people's benefit here combines primary and secondary storage into a single category so you have companies like pure and netapp which are really pure play you know primary storage companies largely in the sector along with veeam cohesity and rubric which are kind of secondary data or data protection so my my quick thoughts here are that pure is elevated and remains what i call the one-eyed man in the land of the blind but that's positive tailwinds there so that's good news rubric is very elevated but down it's a big it's big competitor cohesity is way off its highs and i have to say to me veeam is like the steady eddy consistent player here they just really continue to do well in the data protection business and and the highs are steady the lows are steady dell is also notable they've been struggling in storage their isg business which comprises service and storage it's been soft during covid and and during even you know this new product rollout so it's notable with this new mid-range they have in particular the uptick in dell this survey because dell so large a small uptick can be very good for dell hpe has a big announcement next month in storage so that might improve based on a product cycle of course the nimble brand continues to do well ibm as i said just announced a very soft quarter you know down double digits again uh and there in a product cycle shift and netapp is that looks bad in the etr data from a spending momentum standpoint but their management team is transforming the company into a cloud play which eric is why it was interesting that pure has the greatest momentum in in cloud accounts so that is sort of striking to me i would have thought it would be netapp so that's something that we want to pay attention to but i do like a lot of what netapp is doing uh and other than pure they're the only big kind of pure play in primary storage so long winded uh uh intro there eric but anything you'd add no actually i appreciate it was long winded i i'm going to be honest with you storage is not my uh my best sector as far as a researcher and analyst goes uh but i actually think a lot of what you said is spot on um you know we do capture a lot of large organizations spend uh we don't capture much mid and small so i think when you're talking about these large large players like netapp and um you know not looking so good all i would state is that we are capturing really big organizations spending attention so these are names that should be doing better to be quite honest uh in those accounts and you know at least according to our data we're not seeing it and it's long-term depression as you can see uh you know netapp now has a negative spending velocity in this analysis so you know i can go dig around a little bit more but right now the names that i'm hearing are pure cohesity uh um i'm hearing a little bit about hitachi trying to reinvent themselves in the space but you know i'll take a wait-and-see approach on that one but uh pure and cohesity are the ones i'm hearing a lot from our community so storage is transforming to cloud as a service you're seeing things like apex and in green lake from dell and hpe and container storage little so not really a lot of people paying attention to it but pure about a company called portworx which really specializes in container storage and there's many startups there they're trying to really change the way david flynn has a startup in that space he's the guy who started fusion i o so a lot a lot of transformations happening here okay i know it's been a long segment we have to summarize and then let me go through a summary and then i'll give you the last word eric so tech spending appears to be tracking us gdp at six to seven percent this talent shortage could be a blocker to accelerating i.t deployments and that's kind of good news actually for for services companies digital transformation you know it's it remains a priority and that bodes well not only for services but automation uipath went public this week we we profiled that you know extensively that went public last wednesday um organizations they've i said at the top face some tough decisions on how to allocate resources you know running the business growing the business transforming the business and we're seeing a bifurcation of spending and some residual effects on vendors and that remains a theme that we're watching eric your final thoughts yeah i'm going to go back quickly to just the overall macro spending because there's one thing i think is interesting to point out and we're seeing a real acceleration among mid and small so it seems like early on in the covid recovery or kovitz spending it was the deep pockets that moved first right fortune 500 knew they had to support remote work they started spending first round that in the fortune 500 we're only seeing about five percent spent but when you get into mid and small organizations that's creeping up to eight nine so i just think it's important to point out that they're playing catch-up right now uh also would point out that this is heavily skewed to north america spending we're seeing laggards in emea they just don't seem to be spending as much they're in a very different place in their recovery and uh you know i do think that it's important to point that out um lastly i also want to mention i know you do such a great job on following a lot of the disruptive vendors that you just pointed out pure doing container storage we also have another bi-annual survey that we do called emerging technology and that's for the private names that's going to be launching in may for everyone out there who's interested in not only the disruptive vendors but also private equity players uh keep an eye out for that we do that twice a year and that's growing in its respondents as well and then lastly one comment because you mentioned the uipath ipo it was really hard for us to sit on the sidelines and not put some sort of rating on their data set but ultimately um the data was muted unfortunately and when you're seeing this kind of hype into an ipo like we saw with snowflake the data was resoundingly strong we had no choice but to listen to what the data said for snowflake despite the hype um we didn't see that for uipath and we wanted to and i'm not making a large call there but i do think it's interesting to juxtapose the two that when snowflake was heading to its ipo the data was resoundingly positive and for uipath we just didn't see that thank you for that and eric thanks for coming on today it's really a pleasure to have you and uh so really appreciate the the uh collaboration and look forward to doing more of these we enjoy the partnership greatly dave we're very very happy to have you in the etr family and looking forward to doing a lot lot more with you in the future ditto okay that's it for today remember these episodes are all available as podcasts wherever you listen all you got to do is search breaking analysis podcast and please subscribe to the series check out etr's website it's etr dot plus we also publish a full report every week on wikibon.com at siliconangle.com you can email me david.velante at siliconangle.com you can dm me on twitter at dvalante or comment on our linkedin post i could see you in clubhouse this is dave vellante for eric porter bradley for the cube insights powered by etr have a great week stay safe be well and we'll see you next time

>> From theCUBE studios in Palo Alto and Boston, Bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Recent survey data from ETR shows that enterprise tech spending is tracking with projected US GDP growth at six to 7% this year. Many markers continue to point the way to a strong recovery, including hiring trends and the loosening of frozen IT Project budgets. However skills shortages are blocking progress at some companies which bodes well for an increased reliance on external IT services. Moreover, while there's much talk about the rotation out of work from home plays and stocks such as video conferencing, VDI, and other remote worker tech, we see organizations still trying to figure out the ideal balance between funding headquarter investments that have been neglected and getting hybrid work right. In particular, the talent gap combined with a digital mandate, means companies face some tough decisions as to how to fund the future while serving existing customers and transforming culturally. Hello everyone, and welcome to this week's Wikibon CUBE's Insights powered by ETR. In this "Breaking Analysis", we welcome back Erik Porter Bradley of ETR who will share fresh data, perspectives and insights from the latest survey data. Erik, great to see you. Welcome. >> Thank you very much, Dave. Always good to see you and happy to be on the show again. >> Okay, we're going to share some macro data and then we're going to dig into some highlights from ETR's most recent March COVID survey and also the latest April data. So Erik, the first chart that we want to show, it shows CIO and IT buyer responses to expected IT spend for each quarter of 2021 versus 2020, and you can see here a steady quarterly improvement. Erik, what are the key takeaways, from your perspective? >> Sure, well, first of all, for everyone out there, this particular survey had a record-setting number of participation. We had a 1,500 IT decision makers participate and we had over half of the Fortune 500 and over a fifth of the Global 1000. So it was a really good survey. This is seventh iteration of the COVID Impact Survey specifically, and this is going to transition to an overlarge macro survey going forward so we can continue it. And you're 100% right, what we've been tracking here since March of last year was, how is spending being impacted because of COVID? Where is it shifting? And what we're seeing now finally is that there is a real re-acceleration in spend. I know we've been a little bit more cautious than some of the other peers out there that just early on slapped an eight or a 9% number, but what we're seeing is right now, it's at a midpoint of over six, about 6.7% and that is accelerating. So, we are still hopeful that that will continue, and really, that spending is going to be in the second half of the year. As you can see on the left part of this chart that we're looking at, it was about 1.7% versus 3% for Q1 spending year-over-year. So that is starting to accelerate through the back half. >> I think it's prudent to be cautious (indistinct) 'cause normally you'd say, okay, tech is going to grow a couple of points higher than GDP, but it's really so hard to predict this year. Okay, the next chart here that we want to show you is we asked respondents to indicate what strategies they're employing in the short term as a result of coronavirus and you can see a few things that I'll call out and then I'll ask Erik to chime in. First, there's been no meaningful change of course, no surprise in tactics like remote work and holding travel, however, we're seeing very positive trends in other areas trending downward, like hiring freezes and freezing IT deployments, a downward trend in layoffs, and we also see an increase in the acceleration of new IT deployments and in hiring. Erik, what are your key takeaways? >> Well, first of all, I think it's important to point out here that we're also capturing that people believe remote work productivity is still increasing. Now, the trajectory might be coming down a little bit, but that is really key, I think, to the backdrop of what's happening here. So people have a perception that productivity of remote work is better than hybrid work and that's from the IT decision makers themselves, but what we're seeing here is that, most importantly, these organizations are citing plans to increase hiring, and that's something that I think is really important to point out. It's showing a real following, and to your point right in the beginning of the intro, we are seeing deployments stabilize versus prior survey levels, which means early on, they had no plans to launch new tech deployments, then they said, "Nope, we're going to start." and now that stalling, and I think it's exactly right, what you said, is there's an IT skills shortage. So people want to continue to do IT deployments 'cause they have to support work from home and a hybrid back return to the office, but they just don't have the skills to do so, and I think that's really probably the most important takeaway from this chart, is that stalling and to really ask why it's stalling. >> Yeah, so we're going to get into that for sure, and I think that's a really key point, is that accelerating IT deployments, it looks like it's hit a wall in the survey, but before we get deep into the skills, let's take a look at this next chart, and we're asking people here how our return to the new normal, if you will, and back to offices is going to change spending with on-prem architectures and applications. And so the first two bars, they're Cloud-friendly, if you add them up, it's 63% of the respondents, say that either they'll stay in the Cloud for the most part, or they're going to lower their on-prem spend when they go back to the office. The next three bars are on-prem friendly. If you add those up it's 29% of the respondents say their on-prem spend is going to bounce back to pre-COVID levels or actually increase, and of course, 12% of that number, by the way, say they've never altered their on-prem spend. So Erik, no surprise, but this bodes well for Cloud, but isn't it also a positive for on-prem? We've had this dual funding premise, meaning Cloud continues to grow, but neglected data center spend also gets a boost. What's your thoughts? >> Really, it's interesting. It's people are spending on all fronts. You and I were talking in the prep, it's like we're in battle and I've got naval, I've got air, I've got land, I've got to spend on Cloud and digital transformation, but I also have to spend for on-prem. The hybrid work is here and it needs to be supported. So this is spending is going to increase. When you look at this chart, you're going to see though, that roughly 36% of all respondents say that their spending is going to remain mostly on Cloud. So that is still the clear direction, digital transformation is still happening, COVID accelerated it greatly, you and I, as journalists and researchers already know this is where the puck is going, but spend has always lagged a little bit behind 'cause it just takes some time to get there. Inversely, 27% said that their on-prem spending will decrease. So when you look at those two, I still think that the trend is the friend for Cloud spending, even though, yes, they do have to continue spending on hybrid, some of it's been neglected, there are refresh cycles coming up, so, overall it just points to more and more spending right now. It really does seem to be a very strong backdrop for IT growth. >> So I want to talk a little bit about the ETR taxonomy before we bring up the next chart. We get a lot of questions about this, and of course, when you do a massive survey like you're doing, you have to have consistency for time series, so you have to really think through what the buckets look like, if you will. So this next chart takes a look at the ETR taxonomy and it breaks it down into simple-to-understand terms. So the green is the portion of spending on a vendor's tech within a category that is accelerating, and the red is the portion that is decelerating. So Erik, what are the key messages in this data? >> Well, first of all, Dave, thank you so much for pointing that out. We used to do, just what we call a Net score. It's a proprietary formula that we use to determine the overall velocity of spending. Some people found it confusing. Our data scientists decided to break this sector, break down into what you said, which is really more of a mode analysis. In that sector, how many of the vendors are increasing versus decreasing? So again, I just appreciate you bringing that up and allowing us to explain the reasoning behind our analysis there. But what we're seeing here goes back to something you and I did last year when we did our predictions, and that was that IT services and consulting was going to have a true rebound in 2021, and that's what this is showing right here. So in this chart, you're going to see that consulting and services are really continuing their recovery, 2020 had a lot of the clients and they have the biggest sector year-over-year acceleration sector wise. The other thing to point out on this, which we'll get to again later, is that the inverse analysis is true for video conferencing. We will get to that, so I'm going to leave a little bit of ammunition behind for that one, but what we're seeing here is IT consulting services being the real favorable and video conferencing having a little bit more trouble. >> Great, okay, and then let's take a look at that services piece, and this next chart really is a drill down into that space and emphasizes, Erik, what you were just talking about. And we saw this in IBM's earnings, where still more than 60% of IBM's business comes from services and the company beat earnings, in part, due to services outperforming expectations, I think it had a somewhat easier compare and some of this pent-up demand that we've been talking about bodes well for IBM and other services companies, it's not just IBM, right, Erik? >> No, it's not, but again, I'm going to point out that you and I did point out IBM in our predictions when we did in late December, so, it is nice to see. One of the reasons we don't have a more favorable rating on IBM at the moment is because they are in the process of spinning out this large unit, and so there's a little bit of a corporate action there that keeps us off on the sideline. But I would also want to point out here, Tata, Infosys and Cognizant 'cause they're seeing year-over-year acceleration in both IT consulting and outsourced IT services. So we break those down separately and those are the three names that are seeing acceleration in both of those. So again, at the Tata, Infosys and Cognizant are all looking pretty well positioned as well. >> So we've been talking a little bit about this skills shortage, and this is what's, I think, so hard for forecasters, is that in the one hand, There's a lot of pent up demand, Scott Gottlieb said it's like Woodstock coming out of the COVID, but on the other hand, if you have a talent gap, you've got to rely on external services. So there's a learning curve, there's a ramp up, it's an external company, and so it takes time to put those together. So this data that we're going to show you next, is really important in my view and ties what we were saying at the top. It asks respondents to comment on their staffing plans. The light blue is "We're increasing staff", the gray is "No change" and the magenta or whatever, whatever color that is that sort of purplish color, anyway, that color is decreasing, and the picture is very positive across the board. Full-time staff, offshoring, contract employees, outsourced professional services, all up trending upwards, and this Erik is more evidence of the services bounce back. >> Yeah, it's certainly, yes, David, and what happened is when we caught this trend, we decided to go one level deeper and say, all right, we're seeing this, but we need to know why, and that's what we always try to do here. Data will tell you what's happening, it doesn't always tell you why, and that's one of the things that ETR really tries to dig in with through the insights, interviews panels, and also going direct with these more custom survey questions. So in this instance, I think the real takeaway is that 30% of the respondents said that their outsourced and managed services are going to increase over the next three months. That's really powerful, that's a large portion of organizations in a very short time period. So we're capturing that this acceleration is happening right now and it will be happening in real time, and I don't see it slowing down. You and I are speaking about we have to increase Cloud spend, we have to increase hybrid spend, there are refresh cycles coming up, and there's just a real skills shortage. So this is a long-term setup that bodes very well for IT services and consulting. >> You know, Erik, when I came out of college, somebody told me, "Read, read, read, read as much as you can." And then they said, "Read the Wall Street Journal every day." and so I did it, and I would read the tech magazines and back then it was all paper, and what happens is you begin to connect the dots. And so the reason I bring that up is because I've now taken a bath in the ETR data for the better part of two years and I'm beginning to be able to connect the dots. The data is not always predictive, but many, many times it is. And so this next data gets into the fun stuff where we name names. A lot of times people don't like it because they're either marketing people at organizations, say, "Well, data's wrong." because that's the first thing they do, is attack the data. But you and I know, we've made some really great calls, work from home, for sure, you're talking about the services bounce back. We certainly saw the rise of CrowdStrike, Okta, Zscaler, well before people were talking about that, same thing with video conferencing. And so, anyway, this is the fun stuff and it looks at positive versus negative sentiment on companies. So first, how does ETR derive this data and how should we interpret it, and what are some of your takeaways? >> Sure, first of all, how we derive the data, are systematic survey responses that we do on a quarterly basis, and we standardize those responses to allow for time series analysis so we can do trend analysis as well. We do find that our data, because it's talking about forward-looking spending intentions, is really more predictive because we're talking about things that might be happening six months, three months in the future, not things that a lot of other competitors and research peers are looking at things that already happened, they're looking in the past, ETR really likes to look into the future and our surveys are set up to do so. So thank you for that question, It's a enjoyable lead in, but to get to the fun stuff, like you said, what we do here is we put ratings on the datasets. I do want to put the caveat out there that our spending intentions really only captures top-line revenue. It is not indicative of profit margin or any other line items, so this is only to be viewed as what we are rating the data set itself, not the company, that's not what we're in the game of doing. So I think that's very important for the marketing and the vendors out there themselves when they take a look at this. We're just talking about what we can control, which is our data. We're going to talk about a few of the names here on this highlighted vendors list. One, we're going to go back to that you and I spoke about, I guess, about six months ago, or maybe even earlier, which was the observability space. You and I were noticing that it was getting very crowded, a lot of new entrants, there was a lot of acquisition from more of the legacy or standard players in the space, and that is continuing. So I think in a minute, we're going to move into that observability space, but what we're seeing there is that it's becoming incredibly crowded and we're possibly seeing signs of them cannibalizing each other. We're also going to move on a little bit into video conferencing, where we're capturing some spend deceleration, and then ultimately, we're going to get into a little bit of a storage refresh cycle and talk about that. But yeah, these are the highlighted vendors for April, we usually do this once a quarter and they do change based on the data, but they're not usually whipsawed around, the data doesn't move that quickly. >> Yeah, so you can see some of the big names in the left-hand side, some of the SAS companies that have momentum. Obviously, ServiceNow has been doing very, very well. We've talked a lot about Snowflake, Okta, CrowdStrike, Zscaler, all very positive, as well as several others. I guess I'd add some things. I mean, I think if thinking about the next decade, it's Cloud, which is not going to be like the same Cloud as the last decade, a lot of machine learning and deep learning and AI and the Cloud is extending to the edge and the data center. Data, obviously, very important, data is decentralized and distributed, so data architectures are changing. A lot of opportunities to connect across Clouds and actually create abstraction layers, and then something that we've been covering a lot is processor performance is actually accelerating relative to Moore's law. It's probably instead of doubling every two years, it's quadrupling every two years, and so that is a huge factor, especially as it relates to powering AI and AI inferencing at the edge. This is a whole new territory, custom Silicon is really becoming in vogue and so something that we're watching very, very closely. >> Yeah, I completely, agree on that and I do think that the next version of Cloud will be very different. Another thing to point out on that too, is you can't do anything that you're talking about without collecting the data and organizations are extremely serious about that now. It seems it doesn't matter what industry they're in, every company is a data company, and that also bodes well for the storage goal. We do believe that there is going to just be a huge increase in the need for storage, and yes, hopefully that'll become portable across multi-Cloud and hybrid as well. >> Now, as Erik said, the ETR data, it's really focused on that top-line spend. So if you look on the right side of that chart, you saw NetApp was kind of negative, was very negative, right? But it is a company that's in transformation now, they've lowered expectations and they've recently beat expectations, that's why the stock has been doing better, but at the macro, from a spending standpoint, it's still stout challenged. So you have big footprint companies like NetApp and Oracle is another one. Oracle's stock is at an all time high, but the spending relative to sort of previous cycles are relative to, like for instance, Snowflake, much, much smaller, not as high growth, but they're managing expectations, they're managing their transition, they're managing profitability. Zoom is another one, Zoom looking negative, but Zoom's got to use its market cap now to transform and increase its TAM. And then Splunk is another one we're going to talk about. Splunk is in transition, it acquired SignalFX, It just brought on this week, Teresa Carlson, who was the head of AWS Public Sector. She's the president and head of sales, so they've got a go-to-market challenge and they brought in Teresa Carlson to really solve that, but Splunk has been trending downward, we called that several quarters ago, Erik, and so I want to bring up the data on Splunk, and this is Splunk, Erik, in analytics, and it's not trending in the right direction. The green is accelerating spend, the red is in the bars is decelerating spend, the top blue line is spending velocity or Net score, and the yellow line is market share or pervasiveness in the dataset. Your thoughts. >> Yeah, first I want to go back. There's a great point, Dave, about our data versus a disconnect from an equity analysis perspective. I used to be an equity analyst, that is not what we do here. And the main word you said is expectations, right? Stocks will trade on how they do compare to the expectations that are set, whether that's buy-side expectations, sell-side expectations or management's guidance themselves. We have no business in tracking any of that, what we are talking about is the top-line acceleration or deceleration. So, that was a great point to make, and I do think it's an important one for all of our listeners out there. Now, to move to Splunk, yes, I've been capturing a lot of negative commentary on Splunk even before the data turns. So this has been a about a year-long, our analysis and review on this name and I'm dating myself here, but I know you and I are both rock and roll fans, so I'm going to point out a Led Zeppelin song and movie, and say that the song remains the same for Splunk. We are just seeing recent spending attentions are taking yet another step down, both from prior survey levels, from year ago levels. This, we're looking at in the analytics sector and spending intentions are decelerating across every single group, and we went to one of our other slide analysis on the ETR+ platform, and you do by customer sub-sample, in analytics, it's dropping in every single vertical. It doesn't matter which one. it's really not looking good, unfortunately, and you had mentioned this is an analytics and I do believe the next slide is an information security. >> Yeah, let's bring that up. >> And unfortunately it's not doing much better. So this is specifically Fortune 500 accounts and information security. There's deep pockets in the Fortune 500, but from what we're hearing in all the insights and interviews and panels that I personally moderate for ETR, people are upset, that they didn't like the strong tactics that Splunk has used on them in the past, they didn't like the ingestion model pricing, the inflexibility, and when alternatives came along, people are willing to look at the alternatives, and that's what we're seeing in both analytics and big data and also for their SIM and security. >> Yeah, so I think again, I pointed Teresa Carlson. She's got a big job, but she's very capable. She's going to meet with a lot of customers, she's a go-to-market pro, she's going to to have to listen hard, and I think you're going to see some changes there. Okay, so sorry, there's more bad news on Splunk. So (indistinct) bring this up is Net score for Splunk and Elastic accounts. This is for analytics, so there's 106 Elastic accounts in the dataset that also have Splunk and it's trending downward for Splunk, that's why it's green for Elastic. And Erik, the important call out from ETR here is how Splunk's performance in Elastic accounts compares with its performance overall. The ELK stack, which obviously Elastic is a big part of that, is causing pain for Splunk, as is Datadog, and you mentioned the pricing issue, well, is it pricing in your assessment or is it more fundamental? >> It's multi-level based on the commentary we get from our ITDMs teams that take the survey. So yes, you did a great job with this analysis. What we're looking at is the spending within shared accounts. So if I have Splunk already, how am I spending? I'm sorry if I have Elastic already, how am I spending on Splunk? And what you're seeing here is it's down to about a 12% Net score, whereas Splunk overall, has a 32% Net score among all of its customers. So what you're seeing there is there is definitely a drain that's happening where Elastic is draining spend from Splunk and usage from them. The reason we used Elastic here is because all observabilities, the whole sector seems to be decelerating. Splunk is decelerating the most, but Elastic is the only one that's actually showing resiliency, so that's why we decided to choose these two, but you pointed out, yes, it's also Datadog. Datadog is Cloud native. They're more dev ops-oriented. They tend to be viewed as having technological lead as compared to Splunk. So a really good point. Dynatrace also is expanding their abilities and Splunk has been making a lot of acquisitions to push their Cloud services, they are also changing their pricing model, right? They're trying to make things a little bit more flexible, moving off ingestion and moving towards consumption. So they are trying, and the new hires, I'm not going to bet against them because the one thing that Splunk has going for them is their market share in our survey, they're still very well entrenched. So they do have a lot of accounts, they have their foothold. So if they can find a way to make these changes, then they will be able to change themselves, but the one thing I got to say across the whole sector is competition is increasing, and it does appear based on commentary and data that they're starting to cannibalize themselves. It really seems pretty hard to get away from that, and you know there are startups in the observability space too that are going to be even more disruptive. >> I think I want to key on the pricing for a moment, and I've been pretty vocal about this. I think the old SAS pricing model where you essentially lock in for a year or two years or three years, pay up front, or maybe pay quarterly if you're lucky, that's a one-way street and I think it's a flawed model. I like what Snowflake's doing, I like what Datadog's doing, look at what Stripe is doing, look at what Twilio is doing, you mentioned it, it's consumption-based pricing, and if you've got a great product, put it out there and damn, the torpedoes, and I think that is a game changer. I look at, for instance, HPE with GreenLake, I look at Dell with Apex, they're trying to mimic that model and apply it to infrastructure, it's much harder with infrastructure 'cause you've got to deploy physical infrastructure, but that is a model that I think is going to change, and I think all of the traditional SAS pricing is going to come under disruption over the next better part of the decades, but anyway, let's move on. We've been covering the APM space pretty extensively, application performance management, and this chart lines up some of the big players here. Comparing Net score or spending momentum from the April 20th survey, the gray is, sorry, the gray is the April 20th survey, the blue is Jan 21 and the yellow is April 21, and not only are Elastic and Datadog doing well relative to Splunk, Erik, but everything is down from last year. So this space, as you point out, is undergoing a transformation. >> Yeah, the pressures are real and it's sort of that perfect storm where it's not only the data that's telling us that, but also the direct feedback we get from the community. Pretty much all the interviews I do, I've done a few panels specifically on this topic, for anyone who wants to dive a little bit deeper. We've had some experts talk about this space and there really is no denying that there is a deceleration in spend and it's happening because that spend is getting spread out among different vendors. People are using a Datadog for certain aspects, they are using Elastic where they can 'cause it's cheaper. They're using Splunk because they have to, but because it's so expensive, they're cutting some of the things that they're putting into Splunk, which is dangerous, particularly on the security side. If I have to decide what to put in and whatnot, that's not really the right way to have security hygiene. So this space is just getting crowded, there's disruptive vendors coming from the emerging space as well, and what you're seeing here is the only bit of positivity is Elastic on a survey-over-survey basis with a slight, slight uptick. Everywhere else, year-over-year and survey-over-survey, it's showing declines, it's just hard to ignore. >> And then you've got Dynatrace who, based on the interviews you do in the (indistinct), one-on-one, or one-on-five, the private interviews that I've been invited to, Dynatrace gets very high scores for their roadmap. You've got New Relic, which has been struggling financially, but they've got a really good product and a purpose-built database just for this APM space, and then of course, you've got Cisco with AppD, which is a strong business for them, and then as you mentioned, you've got startups coming in, you got ChaosSearch, which Ed Walsh is now running, leave the data in place in AWS and really interesting model, Honeycomb is getting really disruptive, Jeremy Burton's company, Observed. So this space is it's becoming jumped ball. >> Yeah, there's a great line that came out of one of them, and that was that the lines are blurring. It used to be that you knew exactly that AppDynamics, what they were doing, it was APM only, or it was logging and monitoring only, and a lot of what I'm hearing from the ITDM experts is that the lines are blurring amongst all of these names. They all have functionality that kind of crosses over each other. And the other interesting thing is it used to be application versus infrastructure monitoring, but as you know, infrastructure is becoming code more and more and more, and as infrastructure becomes code, there's really no difference between application and infrastructure monitoring. So we're seeing a convergence and a blurring of the lines in this space, which really doesn't bode well, and a great point about New Relic, their tech gets good remarks. I just don't know if their enterprise level service and sales is up to snuff right now. As one of my experts said, a CTO of a very large public online hospitality company essentially said that he would be shocked that within 18 months if all of these players are still standalone, that there needs to be some M and A or convergence in this space. >> Okay, now we're going to call out some of the data that really has jumped out to ETR in the latest survey, and some of the names that are getting the most queries from ETR clients, many of which are investor clients. So let's start by having a look at one of the most important and prominent work from home names, Zoom. Let's look at this. Erik is the ride over for Zoom? >> Ah, I've been saying it for a little bit of a time now actually. I do believe it is, and we'll get into it, but again, pointing out, great, Dave, the reason we're presenting today Splunk, Elastic and Zoom, they are the most viewed on the ETR+ platform. Trailing behind that only slightly is F5, I decided not to bring F5 to the table today 'cause we don't have a rating on the data set. So then I went one deep, one below that and it's pure. So the reason we're presenting these to you today is that these are the ones that our clients and our community are most interested in, which is hopefully going to gain interest to your viewers as well. So to get to Zoom, yeah, I call Zoom the pandemic bull market baby. This was really just one that had a meteoric ride. You look back, January in 2020, the stock was at $60 and 10 months later, it was like 580, that's in 10 months. That's cooled down a little bit into the mid-300s, and I believe that cooling down should continue, and the reason why is because we are seeing huge deceleration in our spending intentions. They're hitting all-time lows, it's really just a very ugly dataset. More importantly than the spending intentions, for the first time, we're seeing customer growth in our survey flatten. In the past, we knew that the deceleration of spend was happening, but meanwhile, their new customer growth was accelerating, so it was kind of hard to really make any call based on that. This is the first time we're seeing flattening customer growth trajectory, and that in tandem with just dominance from Microsoft in every sector they're involved in, I don't care if it's IP telephony, productivity apps or the core video conferencing, Microsoft is just dominating. So there's really just no way to ignore this anymore. The data and the commentary state that Zoom is facing some headwinds. >> Well, plus you've pointed out to me that a lot of your private conversations with buyers says that, "Hey, we're, we're using the freebie version of Zoom, and we're not paying them." And that combined with Teams, I mean, it's... I think, look, Zoom, they've got to figure out how to use their elevated market cap to transform and expand their TAM, but let's move on. Here's the data on Pure Storage and we've highlighted a number of times this company is showing elevated spending intentions. Pure announced it's earnings in May, IBM just announced storage, it was way down actually. So still, Pure, more positive, but I'll on that comment in a moment, but what does this data tell you, Erik? >> Yeah, right now we started seeing this data last survey in January, and that was the first time we really went positive on the data set itself, and it's just really continuing. So we're seeing the strongest year-over-year acceleration in the entire survey, which is a really good spot to be. Pure is also a leading position among its sector peers, and the other thing that was pretty interesting from the data set is among all storage players, Pure has the highest positive public Cloud correlation. So what we can do is we can see which respondents are accelerating their public Cloud spend and then cross-reference that with their storage spend and Pure is best positioned. So as you and I both know, digital transformation Cloud spending is increasing, you need to be aligned with that. And among all storage sector peers, Pure is best positioned in all of those, in spending intentions and adoptions and also public Cloud correlation. So yet again, to start another really strong dataset, and I have an anecdote about why this might be happening, because when I saw the data, I started asking in my interviews, what's going on here? And there was one particular person, he was a director of Cloud operations for a very large public tech company. Now, they have hybrid, but their data center is in colo, So they don't own and build their own physical building. He pointed out that during COVID, his company wanted to increase storage, but he couldn't get into his colo center due to COVID restrictions. They weren't allowed. You had 250,000 square feet, right, but you're only allowed to have six people in there. So it's pretty hard to get to your rack and get work done. He said he would buy storage, but then the colo would say, "Hey, you got to get it out of here. It's not even allowed to sit here. We don't want it in our facility." So he has all this pent up demand. In tandem with pent up demand, we have a refresh cycle. The SSD depreciation cycle is ending. SSDs are moving on and we're starting to see a new technology in that space, NVMe sorry, technology increasing in that space. So we have pent up demand and we have new technology and that's really leading to a refresh cycle, and this particular ITDM that I spoke to and many of his peers think this has a long tailwind that storage could be a good sector for some time to come. >> That's really interesting, thank you for that extra metadata. And I want to do a little deeper dive on storage. So here's a look at storage in the industry in context and some of the competitive. I mean, it's been a tough market for the reasons that we've highlighted, Cloud has been eating away that flash headroom. It used to be you'd buy storage to get more spindles and more performance and we're sort of forced to buy more, flash, gave more headroom, but it's interesting what you're saying about the depreciation cycle. So that's good news. So ETR combines, just for people's benefit here, combines primary and secondary storage into a single category. So you have companies like Pure and NetApp, which are really pure play primary storage companies, largely in the sector, along with Veeam, Cohesity and Rubrik, which are kind of secondary data or data protection. So my quick thoughts here that Pure is elevated and remains what I call the one-eyed man in the land of the blind, but that's positive tailwinds there, so that's good news. Rubrik is very elevated but down, it's big competitor, Cohesity is way off its highs, and I have to say to me, Veeam is like the Steady Eddy consistent player here. They just really continue to do well in the data protection business, and the highs are steady, the lows are steady. Dell is also notable, they've been struggling in storage. Their ISG business, which comprises servers and storage, it's been softer in COVID, and during even this new product rollout, so it's notable with this new mid range they have in particular, the uptick in Dell, this survey, because Dell is so large, a small uptick can be very good for Dell. HPE has a big announcement next month in storage, so that might improve based on a product cycle. Of course, the Nimble brand continues to do well, IBM, as I said, just announced a very soft quarter, down double digits again, and they're in a product cycle shift. And NetApp, it looks bad in the ETR data from a spending momentum standpoint, but their management team is transforming the company into a Cloud play, which Erik is why it was interesting that Pure has the greatest momentum in Cloud accounts, so that is sort of striking to me. I would have thought it would be NetApp, so that's something that we want to pay attention to, but I do like a lot of what NetApp is doing, and other than Pure, they're the only big kind of pure play in primary storage. So long-winded, intro there, Erik, but anything you'd add? >> No, actually I appreciate it as long-winded. I'm going to be honest with you, storage is not my best sector as far as a researcher and analyst goes, but I actually think that a lot of what you said is spot on. We do capture a lot of large organizations spend, we don't capture much mid and small, so I think when you're talking about these large, large players like NetApp not looking so good, all I would state is that we are capturing really big organization spending attention, so these are names that should be doing better to be quite honest, in those accounts, and at least according to our data, we're not seeing it in. It's longterm depression, as you can see, NetApp now has a negative spending velocity in this analysis. So, I can go dig around a little bit more, but right now the names that I'm hearing are Pure, Cohesity. I'm hearing a little bit about Hitachi trying to reinvent themselves in the space, but I'll take a wait-and-see approach on that one, but pure Cohesity are the ones I'm hearing a lot from our community. >> So storage is transforming to Cloud as a service. You've seen things like Apex in GreenLake from Dell and HPE and container storage. A little, so not really a lot of people paying attention to it, but Pure bought a company called Portworx which really specializes in container storage, and there's many startups there, they're trying to really change the way. David Flynn, has a startup in that space, he's the guy who started Fusion-io. So a lot of transformations happening here. Okay, I know it's been a long segment, we have to summarize, and let me go through a summary and then I'll give you the last word, Erik. So tech spending appears to be tracking US GDP at 6 to 7%. This talent shortage could be a blocker to accelerating IT deployments, so that's kind of good news actually for services companies. Digital transformation, it remains a priority, and that bodes, well, not only for services, but automation. UiPath went public this week, we profiled that extensively, that went public last Wednesday. Organizations that sit at the top face some tough decisions on how to allocate resources. They're running the business, growing the business, transforming the business, and we're seeing a bifurcation of spending and some residual effects on vendors, and that remains a theme that we're watching. Erik, your final thoughts. >> Yeah, I'm going to go back quickly to just the overall macro spending, 'cause there's one thing I think is interesting to point out and we're seeing a real acceleration among mid and small. So it seems like early on in the COVID recovery or COVID spending, it was the deep pockets that moved first, right? Fortune 500 knew they had to support remote work, they started spending first. Around that in the Fortune 500, we're only seeing about 5% spend, but when you get into mid and small organizations, that's creeping up to eight, nine. So I just think it's important to point out that they're playing catch up right now. I also would point out that this is heavily skewed to North America spending. We're seeing laggards in EMEA, they just don't seem to be spending as much. They're in a very different place in their recovery, and I do think that it's important to point that out. Lastly, I also want to mention, I know you do such a great job on following a lot of the disruptive vendors that you just pointed out, with Pure doing container storage, we also have another bi-annual survey that we do called Emerging Technology, and that's for the private names. That's going to be launching in May, for everyone out there who's interested in not only the disruptive vendors, but also private equity players. Keep an eye out for that. We do that twice a year and that's growing in its respondents as well. And then lastly, one comment, because you mentioned the UiPath IPO, it was really hard for us to sit on the sidelines and not put some sort of rating on their dataset, but ultimately, the data was muted, unfortunately, and when you're seeing this kind of hype into an IPO like we saw with Snowflake, the data was resoundingly strong. We had no choice, but to listen to what the data said for Snowflake, despite the hype. We didn't see that for UiPath and we wanted to, and I'm not making a large call there, but I do think it's interesting to juxtapose the two, that when snowflake was heading to its IPO, the data was resoundingly positive, and for UiPath, we just didn't see that. >> Thank you for that, and Erik, thanks for coming on today. It's really a pleasure to have you, and so really appreciate the collaboration and look forward to doing more of these. >> Yeah, we enjoy the partnership greatly, Dave. We're very happy to have you on the ETR family and looking forward to doing a lot, lot more with you in the future. >> Ditto. Okay, that's it for today. Remember, these episodes are all available as podcasts wherever you listen. All you have to do is search "Breaking Analysis" podcast, and please subscribe to the series. Check out ETR website it's etr.plus. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me, david.vellante@siliconangle.com, you can DM me on Twitter @dvellante or comment on our LinkedIn posts. I could see you in Clubhouse. This is Dave Vellante for Erik Porter Bradley for the CUBE Insights powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (bright music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Hi. I'm still Minuteman. And welcome back to the Cube on Cloud. Talking about really important topics is toe how developers we're changing how they build their applications where they live. Of course. Long discussion we've had for a number of years, you know? How do things change in hybrid environment? We've been talking for years. Public cloud and Private Cloud and really excited for this session. We're gonna talk about how edge environment and ai impact that. So happy to walk back. One of our cube alumni, Simon Crosby, is currently the chief technology officer with swim. Got plenty of viewpoints on AI the edge and knows the developer world. Well, Simon, welcome back. Thanks so much for joining us. >>Thank you, sir, for having me. >>All right. So let let let's start start for a second. Let's talk about developers, you know, used to be, you know, for for years we talked about, you know, what's the level of abstraction we get? Does it sit? You know, you know, do I put it on bare metal? Do I virtualized it? Do I contain Arise it. Do I make it serve? Ellis? Ah, lot of those things. You know that the app developer doesn't want to even think about. But location matters a whole lot when we're talking about things like a I where do I have all my data? That I could do my training? Where do I actually have to do the processing? And, of course, edge. Just changes by orders of magnitude, Some of the things like Leighton see, and where data lives and everything like that. So with that as a set up would love to get just your framework as to what you're hearing from developers and what will gettinto Some of the solutions that that you and your team are helping them toe do their jobs >>where you're up to lights to the data onslaught is very riel. Companies that I deal with are facing more and more real time data from products from their infrastructure from their partners, whatever it happens to be, and they need to make decisions rapidly. And the problem that they're facing is that traditional ways of processing that data or to so so perhaps the big data approach which by now is a bit old. It's been long in the tooth, Um, where you stored it and then you analyze it later is problematic. First of all, data streams of boundless so you don't really know winter analyze. But second, you can't store all. And so the story and analyze approach has to change and swim is trying to do something about this by adopting a process off. Analyze um, on the fly. So as dead is generate as you receive events, you don't bother Saw them. You you analyze them, and then if you have tow you still the data. But you you need to analyze as you receive data. Andre react immediately to be able to generate reasonable insights or predictions that can drive commerce and decisions in the real world. >>Yeah, absolutely. I remember back, you know, the early days of big data, you know, real time got thrown around a little, but it was usually I need to react fast enough toe. Make sure we don't, you know, lose the customer, we react toe something. But it was we gather all the data and let's move compute to the data. Uh, today is you talk about real time streams are so important. We've been talking about observe ability for last couple of years to just really understand the systems and the outputs More than, uh, looking back historically at where things were waiting for alerts. So could you give us some examples, if you would, Is toe You know that those streams, you know what is so important about being able to interact and leverage that data when you need it? And, boy, it's great if we can if we can use it then and not have to store it and think about it later. Obviously, there's some benefits there because >>every product nowadays has a CPU, right? And so there's more and more data and just let me give you an example. Um, swim processes real time data from more than 100 million mobile devices in real time, Um, in for a mobile operator. And what we're doing there is We're optimizing connection quality between devices and the network. Now that volume of data is more than four petabytes per day. Okay, now there is simply no way you could ever store that and analyze it later. The interesting thing about this is that if you adopt and analyze. And then if you really have to store architecture, you get to take advantage of Muslim. So you're running at CPU memory speeds instead of a disc speed, and so that gives you a million fold speed up. And it also means you don't have the Leighton see problem off reaching out to her boat storage, dead base or whatever. And so that reduces cost so we can do it all about 10% of the infrastructure that they previously had for her do style implementation. >>So maybe would help if we just explain when we say edge, people think of a lot of different things. Is it? You know, on I o. T device sitting out into the edge Are we talking about the telecom edge? We're watching a WS for years, you know, Spider out their services and into various environment. So what when you talk about the type of solutions you're doing and what your customers have is that the Telkom edges that the, you know, actual device edge, you know, where where does processing happen and where do these, you know, services that that work on it live? >>Uh, so I think the right way to think about edges. Where can you reasonably process the data? And it obviously makes sense to process data at the first opportunity you have. But much data is encrypted between the original device. Say Onda. The application and so edge as a place doesn't make as much sense as edge as an opportunity to decrypt and analyze data in the clear. So is computing is not so much a place in my view as the first opportunity you have to process state in the clear and to make sense of it. And then edge makes sense in terms of Leighton, see, by locating compute as close as possible to the sources of data, um, to reduce latency and maximize your ability to get insights. You know, Andre return to uses in, you know, quickly. So edge for me often is the cloud >>excellent. One of the other things I I think about back from, you know, the big data days or even earlier It was that how long it took to get from the raw data to processing that data, to be able to getting some insight and then being able to take action. Uh, it sure sounds like we're trying to collapse That completely. Is that you know, how do we do that? You know, Can we actually, you know, build the system so that we can, you know, in that real time continuous model that you talk about, You know? So what character movements? One >>of the wonderful things about cloud computing is that two major abstractions really served us on. Those are rest which expect this computing and databases and rest means in the old server can do the job for me. And then the database is just a napi I call away. The problem with that is that it's desperately slow. So when I say desperately slow, I mean, it's probably thrown away the last 10 years, Um, was law. Just think about this way. Your CPU runs at gigahertz and the network runs at milliseconds. So by definition, every time you reach out to a data store, you're going a million times slower than your Cebu. That's terrible. It's absolutely tragic. Okay, so a model which is much more effective is to have and in memory, computing architecture er in which you engage in state will computation. So instead of having to reach out to a database every time to update the database and whatever you know, store something and then fetch it again a few moments later when the next event arrives. You keep state in memory and you compute on the fly as data arrives and that way you get a million times speed up. You also end up with this tremendous cost direction because you don't end up with as many instances having to compute by comparison. So let me give you a quick example. If you go to a traffic dots from the AI, you can see, um, the real time state off the traffic infrastructure in Palo Alto. And, um, each one of those, um intersections is predicting its own future. Now, the volume of data from just a few 100 lights in Palo Alto is about four terabyte today. And sure, you can deal with this in AWS Lambda. There are lots and lots of servers up there. But the problem is that the end to end per event leighton see, is about 100 milliseconds. And you know, if I'm dealing with 30,000 events a second, that's just too much so solving that problem with a stateless architectures is extraordinarily expensive. You know, more than $5000 a month. Where is the staple architectural? Which you could think of as an evolution all for, uh, you know, something reactive or the actor model, Um, get you, You know, something like 1/10 of the cost. Okay, so cloud is fabulous for things that need to scale wide, but a state formal is required for dealing with things which update you rapidly or regularly about their changes in state. >>Yeah, absolutely. I You know, I think about if we were talking, I mentioned before AI training models often, if you look at something like autonomous vehicles, the massive amounts of data that it needs to process, you know, has to happen in the public cloud. Um, but then that gets pushed back down to the end device. In this case, it's a car because it needs to be able to react in real time and get fed at a regular update. The new training algorithms that that it has there. Um what are you saying? You know, we >>were reviews on on this training approach and the science in general, and that is that there aren't enough the scientists or no smart people to train these algorithms, deploy them to the edge and so on. And so there is an alternative worldview, which is a much simpler one, and that is that relatively simple algorithms deployed at scale to staple representatives. Their school, you know, digital twins off things, um, can deliver enormous improvements in behavior. Um, as things learn for themselves. So the way I think the at least this edge world gets smaller is that relatively simple models off things will learn for themselves for their own futures based on what they can see and and then react. And so this idea that we have lots and lots of very scientists dealing with vast amounts of information in the cloud, Um, it's suitable for certain algorithms, but it doesn't work for the vast majority of our applications. >>So where are we with the state of what the developers need to think about? You mentioned that there's compute in most devices. That's true, but you know they need some special in video chip set out there. Are there certain programming languages that that you're seeing more prevalent? Yeah, you know, interoperability. Give us a little bit of toe, you know, some tips and tricks for for those developing >>super so number one a staple architectures is fundamental and sure react is well known. Andi, there are, For example, on er lang swim is another. So I'm going to use some language. And I would encourage you to look at Cem O s or G to go from play there. A staple architecture, ER which allows actors small, concurrent objects to Stapley evolve their own state based on updates from the real world is fundamental. But the way in swim, we use data to build these models. So, um, these little agents for things we call them Web agents because the object I'd is a your I, um they staple evolved by processing their own real world data safely representing it. And then they do this wonderful thing, which is build a model on the fly, and they build a model by linking to things that they're related to. So a knit section would link to all of its sensors. But it would also licked all of its neighbors because the neighbors and linking is like a sub in pubs up and it allows that Web agent then to continually analyze, learn and predict on the fly. And so every one of these concurrent objects is doing this job off and analyzing its own raw data and then predicting from that and streaming the results so and swim you get stream board data in. And what streams out is predictions. Predictions about the future state off the infrastructure, and that's a very powerful staple approach, which can run all the memory. No stories required, by the way. It's still persistence. If you lose the no, you can just come back up and carry on. But there's no need to store huge amounts of raw data if you don't need it. And let me just be clear. The volumes of raw data from the real world are staggering, right? So for Porter by today from Palo Alto. But Las Vegas, about 60 terabytes today from the traffic lights, Um, no more than 100 million mobile devices is is tens of petabytes per day, which is just too much the store. >>Well, Simon, you'd mentioned that we we have a shortage when it comes to data scientists and the people that could be involved in those things. How about from the developer side? Do most enterprises that you're talking to? Do they have the skill set? Is the ecosystem mature enough for the company take involved? Or what do we need to do? Looking forward, toa help companies be able to take advantage of this opportunity. >>Yeah, So there is a huge change in terms of, I guess just cloud native skills. Um, and this is exacerbated. The more you get out into, I guess what you could think of as traditional kind of companies, all of whom have tons and tons of data sources. So we need to make it easy and swim tries to do this by effectively using skills of people already have Java or JavaScript and giving them easy ways to develop, deploy and then run applications without thinking about them. So instead of finding developers to notions of place and where databases are and all that sort of stuff, if they can write simple, object oriented programs about things like intersections and push buttons, a pedestrian lights, and in road loops and so on and simply relate basic objects in their world to each other, then we let data build the model by essentially creating these little concurrent objects for each thing, and they will then link to each other and solve the problem. We end up solving a huge problem for developers to which is that they don't need to acquire complicated cloud native skill sets to get to work. >>Well, absolutely. Simon, that's something we've been trying to do for a long time. Is to truly simplify things. I wanna let you have the final word. Uh, if you look out there, uh, the opportunity that challenge in the space, what final takeaways would would you get our audience? >>So very simple. If you adopt a staple competing Achter should like swim, you get to go a million times faster. The applications always have an answer. They analyze, learn and predict on the fly, and they go million times faster. They use 10% less. No. So 10% off the infrastructure of a store than analyze approach. And it's the way of the future. >>Simon Crosby. Thanks so much for sharing. Great having you on the program. >>Thank you too. >>And thank you for joining. I'm stew Minuteman. Thank you. As always for watching the cube. Yeah,

>> Hi, my name is Andy Clemenko. I'm a Senior Solutions Engineer at StackRox. Thanks for joining us today for my talk on labels, labels, labels. Obviously, you can reach me at all the socials. Before we get started, I like to point you to my GitHub repo, you can go to andyc.info/dc20, and it'll take you to my GitHub page where I've got all of this documentation, socials. Before we get started, I like to point you to my GitHub repo, you can go to andyc.info/dc20, (upbeat music) >> Hi, my name is Andy Clemenko. I'm a Senior Solutions Engineer at StackRox. Thanks for joining us today for my talk on labels, labels, labels. Obviously, you can reach me at all the socials. Before we get started, I like to point you to my GitHub repo, you can go to andyc.info/dc20, and it'll take you to my GitHub page where I've got all of this documentation, I've got the Keynote file there. YAMLs, I've got Dockerfiles, Compose files, all that good stuff. If you want to follow along, great, if not go back and review later, kind of fun. So let me tell you a little bit about myself. I am a former DOD contractor. This is my seventh DockerCon. I've spoken, I had the pleasure to speak at a few of them, one even in Europe. I was even a Docker employee for quite a number of years, providing solutions to the federal government and customers around containers and all things Docker. So I've been doing this a little while. One of the things that I always found interesting was the lack of understanding around labels. So why labels, right? Well, as a former DOD contractor, I had built out a large registry. And the question I constantly got was, where did this image come from? How did you get it? What's in it? Where did it come from? How did it get here? And one of the things we did to kind of alleviate some of those questions was we established a baseline set of labels. Labels really are designed to provide as much metadata around the image as possible. I ask everyone in attendance, when was the last time you pulled an image and had 100% confidence, you knew what was inside it, where it was built, how it was built, when it was built, you probably didn't, right? The last thing we obviously want is a container fire, like our image on the screen. And one kind of interesting way we can kind of prevent that is through the use of labels. We can use labels to address security, address some of the simplicity on how to run these images. So think of it, kind of like self documenting, Think of it also as an audit trail, image provenance, things like that. These are some interesting concepts that we can definitely mandate as we move forward. What is a label, right? Specifically what is the Schema? It's just a key-value. All right? It's any key and pretty much any value. What if we could dump in all kinds of information? What if we could encode things and store it in there? And I've got a fun little demo to show you about that. Let's start off with some of the simple keys, right? Author, date, description, version. Some of the basic information around the image. That would be pretty useful, right? What about specific labels for CI? What about a, where's the version control? Where's the source, right? Whether it's Git, whether it's GitLab, whether it's GitHub, whether it's Gitosis, right? Even SPN, who cares? Where are the source files that built, where's the Docker file that built this image? What's the commit number? That might be interesting in terms of tracking the resulting image to a person or to a commit, hopefully then to a person. How is it built? What if you wanted to play with it and do a git clone of the repo and then build the Docker file on your own? Having a label specifically dedicated on how to build this image might be interesting for development work. Where it was built, and obviously what build number, right? These kind of all, not only talk about continuous integration, CI but also start to talk about security. Specifically what server built it. The version control number, the version number, the commit number, again, how it was built. What's the specific build number? What was that job number in, say, Jenkins or GitLab? What if we could take it a step further? What if we could actually apply policy enforcement in the build pipeline, looking specifically for some of these specific labels? I've got a good example of, in my demo of a policy enforcement. So let's look at some sample labels. Now originally, this idea came out of label-schema.org. And then it was a modified to opencontainers, org.opencontainers.image. There is a link in my GitHub page that links to the full reference. But these are some of the labels that I like to use, just as kind of like a standardization. So obviously, Author's, an email address, so now the image is attributable to a person, that's always kind of good for security and reliability. Where's the source? Where's the version control that has the source, the Docker file and all the assets? How it was built, build number, build server the commit, we talked about, when it was created, a simple description. A fun one I like adding in is the healthZendpoint. Now obviously, the health check directive should be in the Docker file. But if you've got other systems that want to ping your applications, why not declare it and make it queryable? Image version, obviously, that's simple declarative And then a title. And then I've got the two fun ones. Remember, I talked about what if we could encode some fun things? Hypothetically, what if we could encode the Compose file of how to build the stack in the first image itself? And conversely the Kubernetes? Well, actually, you can and I have a demo to show you how to kind of take advantage of that. So how do we create labels? And really creating labels as a function of build time okay? You can't really add labels to an image after the fact. The way you do add labels is either through the Docker file, which I'm a big fan of, because it's declarative. It's in version control. It's kind of irrefutable, especially if you're tracking that commit number in a label. You can extend it from being a static kind of declaration to more a dynamic with build arguments. And I can show you, I'll show you in a little while how you can use a build argument at build time to pass in that variable. And then obviously, if you did it by hand, you could do a docker build--label key equals value. I'm not a big fan of the third one, I love the first one and obviously the second one. Being dynamic we can take advantage of some of the variables coming out of version control. Or I should say, some of the variables coming out of our CI system. And that way, it self documents effectively at build time, which is kind of cool. How do we view labels? Well, there's two major ways to view labels. The first one is obviously a docker pull and docker inspect. You can pull the image locally, you can inspect it, you can obviously, it's going to output as JSON. So you going to use something like JQ to crack it open and look at the individual labels. Another one which I found recently was Skopeo from Red Hat. This allows you to actually query the registry server. So you don't even have to pull the image initially. This can be really useful if you're on a really small development workstation, and you're trying to talk to a Kubernetes cluster and wanting to deploy apps kind of in a very simple manner. Okay? And this was that use case, right? Using Kubernetes, the Kubernetes demo. One of the interesting things about this is that you can base64 encode almost anything, push it in as text into a label and then base64 decode it, and then use it. So in this case, in my demo, I'll show you how we can actually use a kubectl apply piped from the base64 decode from the label itself from skopeo talking to the registry. And what's interesting about this kind of technique is you don't need to store Helm charts. You don't need to learn another language for your declarative automation, right? You don't need all this extra levels of abstraction inherently, if you use it as a label with a kubectl apply, It's just built in. It's kind of like the kiss approach to a certain extent. It does require some encoding when you actually build the image, but to me, it doesn't seem that hard. Okay, let's take a look at a demo. And what I'm going to do for my demo, before we actually get started is here's my repo. Here's a, let me actually go to the actual full repo. So here's the repo, right? And I've got my Jenkins pipeline 'cause I'm using Jenkins for this demo. And in my demo flask, I've got the Docker file. I've got my compose and my Kubernetes YAML. So let's take a look at the Docker file, right? So it's a simple Alpine image. The org statements are the build time arguments that are passed in. Label, so again, I'm using the org.opencontainers.image.blank, for most of them. There's a typo there. Let's see if you can find it, I'll show you it later. My source, build date, build number, commit. Build number and get commit are derived from the Jenkins itself, which is nice. I can just take advantage of existing URLs. I don't have to create anything crazy. And again, I've got my actual Docker build command. Now this is just a label on how to build it. And then here's my simple Python, APK upgrade, remove the package manager, kind of some security stuff, health check getting Python through, okay? Let's take a look at the Jenkins pipeline real quick. So here is my Jenkins pipeline and I have four major stages, four stages, I have built. And here in build, what I do is I actually do the Git clone. And then I do my docker build. From there, I actually tell the Jenkins StackRox plugin. So that's what I'm using for my security scanning. So go ahead and scan, basically, I'm staging it to scan the image. I'm pushing it to Hub, okay? Where I can see the, basically I'm pushing the image up to Hub so such that my StackRox security scanner can go ahead and scan the image. I'm kicking off the scan itself. And then if everything's successful, I'm pushing it to prod. Now what I'm doing is I'm just using the same image with two tags, pre-prod and prod. This is not exactly ideal, in your environment, you probably want to use separate registries and non-prod and a production registry, but for demonstration purposes, I think this is okay. So let's go over to my Jenkins and I've got a deliberate failure. And I'll show you why there's a reason for that. And let's go down. Let's look at my, so I have a StackRox report. Let's look at my report. And it says image required, required image label alert, right? Request that the maintainer, add the required label to the image, so we're missing a label, okay? One of the things we can do is let's flip over, and let's look at Skopeo. Right? I'm going to do this just the easy way. So instead of looking at org.zdocker, opencontainers.image.authors. Okay, see here it says build signature? That was the typo, we didn't actually pass in. So if we go back to our repo, we didn't pass in the the build time argument, we just passed in the word. So let's fix that real quick. That's the Docker file. Let's go ahead and put our dollar sign in their. First day with the fingers you going to love it. And let's go ahead and commit that. Okay? So now that that's committed, we can go back to Jenkins, and we can actually do another build. And there's number 12. And as you can see, I've been playing with this for a little bit today. And while that's running, come on, we can go ahead and look at the Console output. Okay, so there's our image. And again, look at all the build arguments that we're passing into the build statement. So we're passing in the date and the date gets derived on the command line. With the build arguments, there's the base64 encoded of the Compose file. Here's the base64 encoding of the Kubernetes YAML. We do the build. And then let's go down to the bottom layer exists and successful. So here's where we can see no system policy violations profound marking stack regimes security plugin, build step as successful, okay? So we're actually able to do policy enforcement that that image exists, that that label sorry, exists in the image. And again, we can look at the security report and there's no policy violations and no vulnerabilities. So that's pretty good for security, right? We can now enforce and mandate use of certain labels within our images. And let's flip back over to Skopeo, and let's go ahead and look at it. So we're looking at the prod version again. And there's it is in my email address. And that validated that that was valid for that policy. So that's kind of cool. Now, let's take it a step further. What if, let's go ahead and take a look at all of the image, all the labels for a second, let me remove the dash org, make it pretty. Okay? So we have all of our image labels. Again, author's build, commit number, look at the commit number. It was built today build number 12. We saw that right? Delete, build 12. So that's kind of cool dynamic labels. Name, healthz, right? But what we're looking for is we're going to look at the org.zdockerketers label. So let's go look at the label real quick. Okay, well that doesn't really help us because it's encoded but let's base64 dash D, let's decode it. And I need to put the dash r in there 'cause it doesn't like, there we go. So there's my Kubernetes YAML. So why can't we simply kubectl apply dash f? Let's just apply it from standard end. So now we've actually used that label. From the image that we've queried with skopeo, from a remote registry to deploy locally to our Kubernetes cluster. So let's go ahead and look everything's up and running, perfect. So what does that look like, right? So luckily, I'm using traefik for Ingress 'cause I love it. And I've got an object in my Kubernetes YAML called flask.doctor.life. That's my Ingress object for traefik. I can go to flask.docker.life. And I can hit refresh. Obviously, I'm not a very good web designer 'cause the background image in the text. We can go ahead and refresh it a couple times we've got Redis storing a hit counter. We can see that our server name is roundrobing. Okay? That's kind of cool. So let's kind of recap a little bit about my demo environment. So my demo environment, I'm using DigitalOcean, Ubuntu 19.10 Vms. I'm using K3s instead of full Kubernetes either full Rancher, full Open Shift or Docker Enterprise. I think K3s has some really interesting advantages on the development side and it's kind of intended for IoT but it works really well and it deploys super easy. I'm using traefik for Ingress. I love traefik. I may or may not be a traefik ambassador. I'm using Jenkins for CI. And I'm using StackRox for image scanning and policy enforcement. One of the things to think about though, especially in terms of labels is none of this demo stack is required. You can be in any cloud, you can be in CentOs, you can be in any Kubernetes. You can even be in swarm, if you wanted to, or Docker compose. Any Ingress, any CI system, Jenkins, circle, GitLab, it doesn't matter. And pretty much any scanning. One of the things that I think is kind of nice about at least StackRox is that we do a lot more than just image scanning, right? With the policy enforcement things like that. I guess that's kind of a shameless plug. But again, any of this stack is completely replaceable, with any comparative product in that category. So I'd like to, again, point you guys to the andyc.infodc20, that's take you right to the GitHub repo. You can reach out to me at any of the socials @clemenko or andy@stackrox.com. And thank you for attending. I hope you learned something fun about labels. And hopefully you guys can standardize labels in your organization and really kind of take your images and the image provenance to a new level. Thanks for watching. (upbeat music) >> Narrator: Live from Las Vegas It's theCUBE. Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel along with it's ecosystem partners. >> Okay, welcome back everyone theCUBE's live coverage of AWS re:Invent 2019. This is theCUBE's 7th year covering Amazon re:Invent. It's their 8th year of the conference. I want to just shout out to Intel for their sponsorship for these two amazing sets. Without their support we wouldn't be able to bring our mission of great content to you. I'm John Furrier. Stu Miniman. We're here with the chief of AWS, the chief executive officer Andy Jassy. Tech athlete in and of himself three hour Keynotes. Welcome to theCUBE again, great to see you. >> Great to be here, thanks for having me guys. >> Congratulations on a great show a lot of great buzz. >> Andy: Thank you. >> A lot of good stuff. Your Keynote was phenomenal. You get right into it, you giddy up right into it as you say, three hours, thirty announcements. You guys do a lot, but what I liked, the new addition, the last year and this year is the band; house band. They're pretty good. >> Andy: They're good right? >> They hit the queen notes, so that keeps it balanced. So we're going to work on getting a band for theCUBE. >> Awesome. >> So if I have to ask you, what's your walk up song, what would it be? >> There's so many choices, it depends on what kind of mood I'm in. But, uh, maybe Times Like These by the Foo Fighters. >> John: Alright. >> These are unusual times right now. >> Foo Fighters playing at the Amazon Intersect Show. >> Yes they are. >> Good plug Andy. >> Headlining. >> Very clever >> Always getting a good plug in there. >> My very favorite band. Well congratulations on the Intersect you got a lot going on. Intersect is a music festival, I'll get to that in a second But, I think the big news for me is two things, obviously we had a one-on-one exclusive interview and you laid out, essentially what looks like was going to be your Keynote, and it was. Transformation- >> Andy: Thank you for the practice. (Laughter) >> John: I'm glad to practice, use me anytime. >> Yeah. >> And I like to appreciate the comments on Jedi on the record, that was great. But I think the transformation story's a very real one, but the NFL news you guys just announced, to me, was so much fun and relevant. You had the Commissioner of NFL on stage with you talking about a strategic partnership. That is as top down, aggressive goal as you could get to have Rodger Goodell fly to a tech conference to sit with you and then bring his team talk about the deal. >> Well, ya know, we've been partners with the NFL for a while with the Next Gen Stats that they use on all their telecasts and one of the things I really like about Roger is that he's very curious and very interested in technology and the first couple times I spoke with him he asked me so many questions about ways the NFL might be able to use the Cloud and digital transformation to transform their various experiences and he's always said if you have a creative idea or something you think that could change the world for us, just call me he said or text me or email me and I'll call you back within 24 hours. And so, we've spent the better part of the last year talking about a lot of really interesting, strategic ways that they can evolve their experience both for fans, as well as their players and the Player Health and Safety Initiative, it's so important in sports and particularly important with the NFL given the nature of the sport and they've always had a focus on it, but what you can do with computer vision and machine learning algorithms and then building a digital athlete which is really like a digital twin of each athlete so you understand, what does it look like when they're healthy and compare that when it looks like they may not be healthy and be able to simulate all kinds of different combinations of player hits and angles and different plays so that you could try to predict injuries and predict the right equipment you need before there's a problem can be really transformational so we're super excited about it. >> Did you guys come up with the idea or was it a collaboration between them? >> It was really a collaboration. I mean they, look, they are very focused on players safety and health and it's a big deal for their- you know, they have two main constituents the players and fans and they care deeply about the players and it's a-it's a hard problem in a sport like Football, I mean, you watch it. >> Yeah, and I got to say it does point out the use cases of what you guys are promoting heavily at the show here of the SageMaker Studio, which was a big part of your Keynote, where they have all this data. >> Andy: Right. >> And they're data hoarders, they hoard data but the manual process of going through the data was a killer problem. This is consistent with a lot of the enterprises that are out there, they have more data than they even know. So this seems to be a big part of the strategy. How do you get the customers to actually wake up to the fact that they got all this data and how do you tie that together? >> I think in almost every company they know they have a lot of data. And there are always pockets of people who want to do something with it. But, when you're going to make these really big leaps forward; these transformations, the things like Volkswagen is doing where they're reinventing their factories and their manufacturing process or the NFL where they're going to radically transform how they do players uh, health and safety. It starts top down and if the senior leader isn't convicted about wanting to take that leap forward and trying something different and organizing the data differently and organizing the team differently and using machine learning and getting help from us and building algorithms and building some muscle inside the company it just doesn't happen because it's not in the normal machinery of what most companies do. And so it always, almost always, starts top down. Sometimes it can be the Commissioner or CEO sometimes it can be the CIO but it has to be senior level conviction or it doesn't get off the ground. >> And the business model impact has to be real. For NFL, they know concussions, hurting their youth pipe-lining, this is a huge issue for them. This is their business model. >> They lose even more players to lower extremity injuries. And so just the notion of trying to be able to predict injuries and, you know, the impact it can have on rules and the impact it can have on the equipment they use, it's a huge game changer when they look at the next 10 to 20 years. >> Alright, love geeking out on the NFL but Andy, you know- >> No more NFL talk? >> Off camera how about we talk? >> Nobody talks about the Giants being 2 and 10. >> Stu: We're both Patriots fans here. >> People bring up the undefeated season. >> So Andy- >> Everybody's a Patriot's fan now. (Laughter) >> It's fascinating to watch uh, you and your three hour uh, Keynote, uh Werner in his you know, architectural discussion, really showed how AWS is really extending its reach, you know, it's not just a place. For a few years people have been talking about you know, Cloud is an operational model its not a destination or a location but, I felt it really was laid out is you talked about Breadth and Depth and Werner really talked about you know, Architectural differentiation. People talk about Cloud, but there are very-there are a lot of differences between the vision for where things are going. Help us understand why, I mean, Amazon's vision is still a bit different from what other people talk about where this whole Cloud expansion, journey, put ever what tag or label you want on it but you know, the control plane and the technology that you're building and where you see that going. >> Well I think that, we've talked about this a couple times we have two macro types of customers. We have those that really want to get at the low level building blocks and stitch them together creatively however they see fit to create whatever's in their-in their heads. And then we have the second segment of customers that say look, I'm willing to give up some of that flexibility in exchange for getting 80% of the way there much faster. In an abstraction that's different from those low level building blocks. And both segments of builders we want to serve and serve well and so we've built very significant offerings in both areas. I think when you look at microservices um, you know, some of it has to do with the fact that we have this very strongly held belief born out of several years of Amazon where you know, the first 7 or 8 years of Amazon's consumer business we basically jumbled together all of the parts of our technology in moving really quickly and when we wanted to move quickly where you had to impact multiple internal development teams it was so long because it was this big ball, this big monolithic piece. And we got religion about that in trying to move faster in the consumer business and having to tease those pieces apart. And it really was a lot of impetus behind conceiving AWS where it was these low level, very flexible building blocks that6 don't try and make all the decisions for customers they get to make them themselves. And some of the microservices that you saw Werner talking about just, you know, for instance, what we-what we did with Nitro or even what we did with Firecracker those are very much about us relentlessly working to continue to uh, tease apart the different components. And even things that look like low level building blocks over time, you build more and more features and all of the sudden you realize they have a lot of things that are combined together that you wished weren't that slow you down and so, Nitro was a completely re imagining of our Hypervisor and Virtualization layer to allow us, both to let customers have better performance but also to let us move faster and have a better security story for our customers. >> I got to ask you the question around transformation because I think that all points, all the data points, you got all the references, Goldman Sachs on stage at the Keynote, Cerner, I mean healthcare just is an amazing example because I mean, that's demonstrating real value there there's no excuse. I talked to someone who wouldn't be named last night, in and around the area said, the CIA has a cost bar like this a cost-a budget like this but the demand for mission based apps is going up exponentially, so there's need for the Cloud. And so, you see more and more of that. What is your top down, aggressive goals to fill that solution base because you're also a very transformational thinker; what is your-what is your aggressive top down goals for your organization because you're serving a market with trillions of dollars of spend that's shifting, that's on the table. >> Yeah. >> A lot of competition now sees it too, they're going to go after it. But at the end of the day you have customers that have a demand for things, apps. >> Andy: Yeah. >> And not a lot of budget increase at the same time. This is a huge dynamic. >> Yeah. >> John: What's your goals? >> You know I think that at a high level our top down aggressive goals are that we want every single customer who uses our platform to have an outstanding customer experience. And we want that outstanding customer experience in part is that their operational performance and their security are outstanding, but also that it allows them to build, uh, build projects and initiatives that change their customer experience and allow them to be a sustainable successful business over a long period of time. And then, we also really want to be the technology infrastructure platform under all the applications that people build. And we're realistic, we know that you know, the market segments we address with infrastructure, software, hardware, and data center services globally are trillions of dollars in the long term and it won't only be us, but we have that goal of wanting to serve every application and that requires not just the security operational premise but also a lot of functionality and a lot of capability. We have by far the most amount of capability out there and yet I would tell you, we have 3 to 5 years of items on our roadmap that customers want us to add. And that's just what we know today. >> And Andy, underneath the covers you've been going through some transformation. When we talked a couple of years ago, about how serverless is impacting things I've heard that that's actually, in many ways, glue behind the two pizza teams to work between organizations. Talk about how the internal transformations are happening. How that impacts your discussions with customers that are going through that transformation. >> Well, I mean, there's a lot of- a lot of the technology we build comes from things that we're doing ourselves you know? And that we're learning ourselves. It's kind of how we started thinking about microservices, serverless too, we saw the need, you know, we would have we would build all these functions that when some kind of object came into an object store we would spin up, compute, all those tasks would take like, 3 or 4 hundred milliseconds then we'd spin it back down and yet, we'd have to keep a cluster up in multiple availability zones because we needed that fault tolerance and it was- we just said this is wasteful and, that's part of how we came up with Lambda and you know, when we were thinking about Lambda people understandably said, well if we build Lambda and we build this serverless adventure in computing a lot of people were keeping clusters of instances aren't going to use them anymore it's going to lead to less absolute revenue for us. But we, we have learned this lesson over the last 20 years at Amazon which is, if it's something that's good for customers you're much better off cannibalizing yourself and doing the right thing for customers and being part of shaping something. And I think if you look at the history of technology you always build things and people say well, that's going to cannibalize this and people are going to spend less money, what really ends up happening is they spend less money per unit of compute but it allows them to do so much more that they ultimately, long term, end up being more significant customers. >> I mean, you are like beating the drum all the time. Customers, what they say, we encompass the roadmap, I got that you guys have that playbook down, that's been really successful for you. >> Andy: Yeah. >> Two years ago you told me machine learning was really important to you because your customers told you. What's the next traunch of importance for customers? What's on top of mind now, as you, look at- >> Andy: Yeah. >> This re:Invent kind of coming to a close, Replay's tonight, you had conversations, you're a tech athlete, you're running around, doing speeches, talking to customers. What's that next hill from if it's machine learning today- >> There's so much I mean, (weird background noise) >> It's not a soup question (Laughter) And I think we're still in the very early days of machine learning it's not like most companies have mastered it yet even though they're using it much more then they did in the past. But, you know, I think machine learning for sure I think the Edge for sure, I think that um, we're optimistic about Quantum Computing even though I think it'll be a few years before it's really broadly useful. We're very um, enthusiastic about robotics. I think the amount of functions that are going to be done by these- >> Yeah. >> robotic applications are much more expansive than people realize. It doesn't mean humans won't have jobs, they're just going to work on things that are more value added. We're believers in augmented virtual reality, we're big believers in what's going to happen with Voice. And I'm also uh, I think sometimes people get bored you know, I think you're even bored with machine learning already >> Not yet. >> People get bored with the things you've heard about but, I think just what we've done with the Chips you know, in terms of giving people 40% better price performance in the latest generation of X86 processors. It's pretty unbelievable in the difference in what people are going to be able to do. Or just look at big data I mean, big data, we haven't gotten through big data where people have totally solved it. The amount of data that companies want to store, process, analyze, is exponentially larger than it was a few years ago and it will, I think, exponentially increase again in the next few years. You need different tools and services. >> Well I think we're not bored with machine learning we're excited to get started because we have all this data from the video and you guys got SageMaker. >> Andy: Yeah. >> We call it the stairway to machine learning heaven. >> Andy: Yeah. >> You start with the data, move up, knock- >> You guys are very sophisticated with what you do with technology and machine learning and there's so much I mean, we're just kind of, again, in such early innings. And I think that, it was so- before SageMaker, it was so hard for everyday developers and data scientists to build models but the combination of SageMaker and what's happened with thousands of companies standardizing on it the last two years, plus now SageMaker studio, giant leap forward. >> Well, we hope to use the data to transform our experience with our audience. And we're on Amazon Cloud so we really appreciate that. >> Andy: Yeah. >> And appreciate your support- >> Andy: Yeah, of course. >> John: With Amazon and get that machine learning going a little faster for us, that would be better. >> If you have requests I'm interested, yeah. >> So Andy, you talked about that you've got the customers that are builders and the customers that need simplification. Traditionally when you get into the, you know, the heart of the majority of adoption of something you really need to simplify that environment. But when I think about the successful enterprise of the future, they need to be builders. how'l I normally would've said enterprise want to pay for solutions because they don't have the skill set but, if they're going to succeed in this new economy they need to go through that transformation >> Andy: Yeah. >> That you talk to, so, I mean, are we in just a total new era when we look back will this be different than some of these previous waves? >> It's a really good question Stu, and I don't think there's a simple answer to it. I think that a lot of enterprises in some ways, I think wish that they could just skip the low level building blocks and only operate at that higher level abstraction. That's why people were so excited by things like, SageMaker, or CodeGuru, or Kendra, or Contact Lens, these are all services that allow them to just send us data and then run it on our models and get back the answers. But I think one of the big trends that we see with enterprises is that they are taking more and more of their development in house and they are wanting to operate more and more like startups. I think that they admire what companies like AirBnB and Pintrest and Slack and Robinhood and a whole bunch of those companies, Stripe, have done and so when, you know, I think you go through these phases and eras where there are waves of success at different companies and then others want to follow that success and replicate it. And so, we see more and more enterprises saying we need to take back a lot of that development in house. And as they do that, and as they add more developers those developers in most cases like to deal with the building blocks. And they have a lot of ideas on how they can creatively stich them together. >> Yeah, on that point, I want to just quickly ask you on Amazon versus other Clouds because you made a comment to me in our interview about how hard it is to provide a service to other people. And it's hard to have a service that you're using yourself and turn that around and the most quoted line of my story was, the compression algorithm- there's no compression algorithm for experience. Which to me, is the diseconomies of scale for taking shortcuts. >> Andy: Yeah. And so I think this is a really interesting point, just add some color commentary because I think this is a fundamental difference between AWS and others because you guys have a trajectory over the years of serving, at scale, customers wherever they are, whatever they want to do, now you got microservices. >> Yeah. >> John: It's even more complex. That's hard. >> Yeah. >> John: Talk about that. >> I think there are a few elements to that notion of there's no compression algorithm for experience and I think the first thing to know about AWS which is different is, we just come from a different heritage and a different background. We ran a business for a long time that was our sole business that was a consumer retail business that was very low margin. And so, we had to operate at very large scale given how many people were using us but also, we had to run infrastructure services deep in the stack, compute storage and database, and reliable scalable data centers at very low cost and margins. And so, when you look at our business it actually, today, I mean its, its a higher margin business in our retail business, its a lower margin business in software companies but at real scale, it's a high volume, relatively low margin business. And the way that you have to operate to be successful with those businesses and the things you have to think about and that DNA come from the type of operators we have to be in our consumer retail business. And there's nobody else in our space that does that. So, you know, the way that we think about costs, the way we think about innovation in the data center, um, and I also think the way that we operate services and how long we've been operating services as a company its a very different mindset than operating package software. Then you look at when uh, you think about some of the uh, issues in very large scale Cloud, you can't learn some of those lessons until you get to different elbows of the curve and scale. And so what I was telling you is, its really different to run your own platform for your own users where you get to tell them exactly how its going to be done. But that's not the way the real world works. I mean, we have millions of external customers who use us from every imaginable country and location whenever they want, without any warning, for lots of different use cases, and they have lots of design patterns and we don't get to tell them what to do. And so operating a Cloud like that, at a scale that's several times larger than the next few providers combined is a very different endeavor and a very different operating rigor. >> Well you got to keep raising the bar you guys do a great job, really impressed again. Another tsunami of announcements. In fact, you had to spill the beans earlier with Quantum the day before the event. Tight schedule. I got to ask you about the musical festival because, I think this is a very cool innovation. It's the inaugural Intersect conference. >> Yes. >> John: Which is not part of Replay, >> Yes. >> John: Which is the concert tonight. Its a whole new thing, big music act, you're a big music buff, your daughter's an artist. Why did you do this? What's the purpose? What's your goal? >> Yeah, it's an experiment. I think that what's happened is that re:Invent has gotten so big, we have 65 thousand people here, that to do the party, which we do every year, its like a 35-40 thousand person concert now. Which means you have to have a location that has multiple stages and, you know, we thought about it last year and when we were watching it and we said, we're kind of throwing, like, a 4 hour music festival right now. There's multiple stages, and its quite expensive to set up that set for a party and we said well, maybe we don't have to spend all that money for 4 hours and then rip it apart because actually the rent to keep those locations for another two days is much smaller than the cost of actually building multiple stages and so we thought we would try it this year. We're very passionate about music as a business and I think we-I think our customers feel like we've thrown a pretty good music party the last few years and we thought we would try it at a larger scale as an experiment. And if you look at the economics- >> At the headliners real quick. >> The Foo Fighters are headlining on Saturday night, Anderson Paak and the Free Nationals, Brandi Carlile, Shawn Mullins, um, Willy Porter, its a good set. Friday night its Beck and Kacey Musgraves so it's a really great set of um, about thirty artists and we're hopeful that if we can build a great experience that people will want to attend that we can do it at scale and it might be something that both pays for itself and maybe, helps pay for re:Invent too overtime and you know, I think that we're also thinking about it as not just a music concert and festival the reason we named it Intersect is that we want an intersection of music genres and people and ethnicities and age groups and art and technology all there together and this will be the first year we try it, its an experiment and we're really excited about it. >> Well I'm gone, congratulations on all your success and I want to thank you we've been 7 years here at re:Invent we've been documenting the history. You got two sets now, one set upstairs. So appreciate you. >> theCUBE is part of re:Invent, you know, you guys really are apart of the event and we really appreciate your coming here and I know people appreciate the content you create as well. >> And we just launched CUBE365 on Amazon Marketplace built on AWS so thanks for letting us- >> Very cool >> John: Build on the platform. appreciate it. >> Thanks for having me guys, I appreciate it. >> Andy Jassy the CEO of AWS here inside theCUBE, it's our 7th year covering and documenting the thunderous innovation that Amazon's doing they're really doing amazing work building out the new technologies here in the Cloud computing world. I'm John Furrier, Stu Miniman, be right back with more after this short break. (Outro music)

(upbeat music) >> Announcer: From the CUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE Conversation. >> Hey, welcome back everybody Jeffrey Frick with theCUBE. We are getting through the COVID crisis. It continues and impacting the summer. I can't believe the summer's almost over, but there's a whole lot of things going on in terms of privacy and contact tracing and this kind of this feeling that there's this conflict between kind of personal identification and your personal privacy versus the public good around things like contact tracing. And I was in a session last week with two really fantastic experts. I wanted to bring them on the show and we're really excited to have back for I don't even know how many times Michelle has been on Michelle Dennedy, She is the former chief privacy officer at Cisco and now she's running the CEO of Identity, Michelle great to see you. >> Good to see you always Jeff >> Yeah and for the first time Dr. Ann Cavoukian and she is the executive director Global Privacy & Security By Design Center. Joining us from Toronto, worked with the government and is not short on opinions about privacy. (laughing) Ann good to see you. >> Hi Jeff thank you >> Yes, so let's jump into it cause I think one of the fundamental issues that we keep hearing is this zero-sum game. And I know and it's a big topic for you that there seems to be this trade off this either or and specifically let's just go to contact tracing. Cause that's a hot topic right now with COVID. I hear that it's like you're telling everybody where I'm going and you're sharing that with all these other people. How is this even a conversation and where do I get to choose whether I want to participate or not? >> You can't have people traced and tracked and surveil. You simply can't have it and it can't be an either or win lose model. You have to get rid of that data. Zero-sum game where only one person can win and the other one loses and it sums to a total of zero. Get rid of that, that's so yesterday. You have to have both groups winning positive sum. Meaning yes, you need public health and public safety and you need privacy. It's not one versus the other. We can do both and that's what we insist upon. So the contact term tracing app that was developed in Canada was based on the Apple Google framework, which is actually called exposure notification. It's totally privacy protective individuals choose to voluntarily download this app. And no personal information is collected whatsoever. No names, no geolocation data, nothing. It's simply notifies you. If you've been exposed to someone who is COVID-19 positive, and then you can decide on what action you wish to take. Do you want to go get tested? Do you want to go to your family doctor, whatever the decision lies with you, you have total control and that's what privacy is all about. >> Jeffrey: But what about the person who was sick? Who's feeding the top into that process and is the sick person that you're no notifying they obviously their personal information is part of that transaction. >> what the COVID alerts that we developed based on the Apple Google framework. It builds on manual contact tracing, which also take place the two to compliment each other. So the manual contact tracing is when individuals go get to get tested and they're tested as positive. So healthcare nurses will speak to that individual and say, please tell us who you've been in contact with recently, family, friends, et cetera. So the two work together and by working together, we will combat this in a much more effective manner. >> Jeffrey: So shifting over to you Michelle, you know, there's PIN and a lot of conversations all the time about personal identifiable information but right. But then medical has this whole nother class of kind of privacy restrictions and level of care. And I find it really interesting that on one hand, you know, we were trying to do the contract tracing on another hand if you know, my wife works in a public school. If they find out that one of the kids in this class has been exposed to COVID somehow they can't necessarily tell the teacher because of HIPAA restriction. So I wonder if you could share your thoughts on this kind of crossover between privacy and health information when it gets into this kind of public crisis and this inherent conflict for the public right to know and should the teacher be able to be told and it's not a really clean line with a simple answer, I don't think. >> No and Jeff, and you're also layering, you know, when you're talking about student data, you layering another layer of legal restriction. And I think what you're putting your thumb on is something that's really critical. When you talk about privacy engineering, privacy by design and ethics engineering. You can't simply start with the legal premise. So is it lawful to share HIPAA covered data. A child telling mommy I don't feel well not HIPAA covered. A child seeing a doctor for medical services and finding some sort of infection or illness covered, right? So figuring out the origin of the exact same zero one. Am I ill or not, all depends on context. So you have to first figure out, first of all let's tackle the moral issues. Have we decided that it is a moral imperative to expose certain types of data. And I separate that from ethics intentionally and with apologies to true ethicists. The moral imperative is sort of the things we find are so wrong. We don't want a list of kids who are sick or conversely once the tipping point goes the list of kids who are well. So then they are called out that's the moral choice. The ethical choice is just because you can should you, and that's a much longer conversation. Then you get to the legal imperative. Are you allowed to based on the past mistakes that we made. That's what every piece of litigation or legislation is particularly in a common law construct in the US. It's very important to understand that civil law countries like the European theater. They try to prospectively legislate for things that might go wrong. The construct is thinner in a common law economy where you do, you use test cases in the courts of law. That's why we are such a litigious society has its own baggage. But you have to now look at is that legal structure attempting to cover past harms that are so bad that we've decided as a society to punish them, is this a preventative law? And then you finally get to what I say is stage four for every evaluation is isn't viable, are the protections that you have to put on top of these restrictions. So dire that they either cannot be maintained because of culture process or cash or it just doesn't make sense anymore. So does it, is it better to just feel someone's forehead for illness rather than giving a blood assay, having it sent away for three weeks and then maybe blah, blah, blah, blah, blah, blah. >> Right. >> You have to look at this as a system problem solving issue. >> So I want to look at it in the context of, again kind of this increased level of politicization and or, you know, kind of exposure outside of what's pretty closed. And I want to bring up AIDS and the porn industry very frankly right? Where people behaving in the behavior of the business risk a life threatening disease of which I still don't think it as a virus. So you know why, cause suddenly, you know, we can track for that and that's okay to track for that. And there's a legitimate reason to versus all of the other potential medical conditions that I may or may not have that are not necessarily brought to bear within coming to work. And we might be seeing this very soon. As you said, if people are wanting our temperatures, as we come in the door to check for symptoms. How does that play with privacy and healthcare? It's still fascinates me that certain things is kind of pop out into their own little bucket of regulation. I'm wondering if you could share your thoughts on that Ann. >> You know, whenever you make it privacy versus fill in the blank, especially in the context of healthcare. You end up turning it to a lose lose as opposed to even a win lose. Because you will have fewer people wanting to allow themselves to be tested, to be brought forward for fear of where that information may land. If it lands in the hands of your employer for example or your whoever owns your house if you're in renting, et cetera. It creates enormous problems. So regardless of what you may think of the benefits of that model. History has shown that it doesn't work well that people end up shying away from being tested or seeking treatment or any of those things. Even now with the contact tracing apps that have been developed. If you look globally the contact tracing apps for COVID-19. They have failed the ones that identify individuals in the UK, in Australia, in Western Canada that's how it started out. And they've completely dropped them because they don't work. People shy away from them. They don't use them. So they've gotten rid of that. They've replaced it with the, an app based on the Apple Google framework, which is the one that protects privacy and will encourage people to come forward and seek to be tested. If there's a problem in Germany. Germany is one of the largest privacy data protection countries in the world. Their privacy people are highly trusted in Germany. Germany based their app on the Apple Google framework. About a month ago they released it. And within 24 hours they had 6.5 million people download the app. >> Right. >> Because there is such trust there unlike the rest of the world where there's very little trust and we have to be very careful of the trust deficit. Because we want to encourage people to seek out these apps so they can attempt to be tested if there's a problem, but they're not going to use them. They're just going to shy away from them. If there is such a problem. And in fact I'll never forget. I did an interview about a month ago, three weeks ago in the US on a major major radio station that has like 54 million people followers. And I was telling them about the COVID alert the Canadian contact tracing app, actually it's called exposure notification app, which was built on the Apple Google framework. And people in hoard said they wouldn't trust anyone with it in the US. They just wouldn't trust it. So you see there's such a trust deficit. That's what we have to be careful to avoid. >> So I want to hold on the trust for just a second, but I want to go back to you Michelle and talk about the lessons that we can learn post 9/11. So the other thing right and keep going back to this over and over. It's not a zero-sum game. It's not a zero-sum game and yet that's the way it's often positioned as a way to break down existing barriers. So if you go back to 9/11 probably the highest profile thing being the Patriot Act, you know, where laws are put in place to protect us from terrorism that are going to do things that were not normally allowed to be done. I bet without checking real exhaustively that most of those things are still in place. You know, cause a lot of times laws are written. They don't go away for a long time. What can we learn from what happened after 9/11 and the Patriot Act and what should be really scared of, or careful of or wary of using that as a framework for what's happening now around COVID and privacy. >> It's a perfect, it's not even an analogy because we're feeling the shadows of the Patriot Act. Even now today, we had an agreement from the United States with the European community until recently called the Privacy Shield. And it was basically if companies and organizations that were, that fell under the Federal Trade Commissions jurisdiction, there's a bit of layering legal process here. But if they did and they agreed to supply enough protection to data about people who were present in the European Union to the same or better level than the Europeans would. Then that information could pass through this Privacy Shield unencumbered to and from the United States. That was challenged and taken down. I don't know if it's a month ago or if it's still March it's COVID time, but very recently on basis that the US government can overly and some would say indifferent nations, improperly look at European data based on some of these Patriot Act, FISA courts and other intrusive mechanisms that absolutely do apply if we were under the jurisdiction of the United States. So now companies and private actors are in the position of having to somehow prove that they will mechanize their systems and their processes to be immune from their own government intrusion before they can do digital trade with other parts of the world. We haven't yet seen the commercial disruption that will take place. So the unintended consequence of saying rather than owning the answers or the observations and the intelligence that we got out of the actual 9/11 report, which said we had the information we needed. We did not share enough between the agencies and we didn't have the decision making activity and will to take action in that particular instance. Rather than sticking to that knowledge. Instead we stuck to the Patriot Act, which was all but I believe to Congress people. When I mean, you see the hot mess. That is the US right now. When everyone but two people in the room vote for something on the quick. There's probably some sort of a psychological gun to your head. That's probably well thought out thing. We fight each other. That's part of being an American dammit. So I think having these laws that say, you've got to have this one solution because the boogeyman is coming or COVID is coming or terrorists or child pornographers are coming. There's not one solution. So you really have to break this down into an engineering problem and I don't mean technology when I say engineering. I mean looking at the culture, how much trust do you have? Who is the trusted entity? Do we trust Microsoft more than we trust the US government right now? Maybe that might be your contact. How you're going to build people, process and technology not to avoid a bad thing, but to achieve a positive objective because if you're not achieving that positive objective of understanding that safe to move about without masks on, for example, stop, just stop. >> Right, right. My favorite analogy Jeff, and I think I've said this to you in the past is we don't sit around and debate the merits of viscosity of water to protect concrete holes. We have to make sure that when you lead them to the concrete hole, there's enough water in the hole. No, you're building a swimming pool. What kind of a swimming pool do you want? Is it commercial, Is it toddlers? Is it (indistinct), then you build in correlation, protection and da da da da. But if you start looking at every problem as how to avoid hitting a concrete hole. You're really going to miss the opportunity to build and solve the problem that you want and avoid the risk that you do not want. >> Right right, and I want to go back to you on the trust thing. You got an interesting competent in that other show, talking about working for the government and not working directly for the people are voted in power, but for the kind of the larger bureaucracy and agency. I mean, the Edelman Trust Barometer is really interesting. They come out every year. I think it's their 20th year. And they break down kind of like media, government and business. And who do you trust and who do you not trust? What what's so fascinating about the time we're in today is even within the government, the direction that's coming out is completely diametrically opposed oftentimes between the Fed, the state and the local. So what does kind of this breakdown of trust when you're getting two different opinions from the same basic kind of authority due to people's ability or desire to want to participate and actually share the stuff that maybe or maybe not might get reshared. >> It leaves you with no confidence. Basically, you can't take confidence in any of this. And when I was privacy commissioner. I served for three terms, each term that was a different government, different political power in place. And before they had become the government, they were all for privacy and data protection believed in and all that. And then once they became the government all that changed and all of a sudden they wanted to control everyone's information and they wanted to be in power. No, I don't trust government. You know, people often point to the private sector as being the group you should distrust in terms of privacy. I say no, not at all. To me far worse is actually the government because everyone thinks they're there to do good job and trust them. You can't trust. You have to always look under the hood. I always say trust but verify. So unfortunately we have to be vigilant in terms of the protections we seek for privacy both with private sector and with the government, especially with the government and different levels of government. We need to ensure that people's privacy remains intact. It's preserved now and well into the future. You can't give up on it because there's some emergency a pandemic, a terrorist incident whatever of course we have to address those issues. But you have to insist upon people's privacy being preserved. Privacy forms the foundation of our freedom. You cannot have free and open societies without a solid foundation of privacy. So I'm just encouraging everyone. Don't take anything at face value, just because the government tells you something. It doesn't mean it's so always look under the hood and let us ensure the privacy is strongly protected. See emergencies come and go. The pandemic will end. What cannot end is our privacy and our freedom. >> So this is a little dark in here, but we're going to lighten it up a little bit because there's, as Michelle said, you know, if you think about building a pool versus putting up filling a hole, you know, you can take proactive steps. And there's a lot of conversation about proactive steps and I pulled Ann your thing Privacy by Design, The 7 Foundational Principles. I have the guys pull up a slide. But I think what's really interesting here is, is you're very, very specific prescriptive, proactive, right? Proactive, not reactive. Privacy is the default setting. You know, don't have to read the ULAs and I'm not going to read the, all the words we'll share it. People can find it. But what I wanted to focus on is there is an opportunity to get ahead of the curve, but you just have to be a little bit more thoughtful. >> That's right, and Privacy By Design it's a model of prevention, much like a medical model of prevention where you try to prevent the harms from arising, not just deal with them after the facts through regulatory compliance. Of course we have privacy laws and that's very important, but they usually kick in after there's been a data breach or privacy infraction. So when I was privacy commissioner obviously those laws were intact and we had to follow them, but I wanted something better. I wanted to prevent the privacy harms from arising, just like a medical model of prevention. So that's a Privacy By Design is intended to do is instantiate, embed much needed privacy protective measures into your policies, into your procedures bake it into the code so that it has a constant presence and can prevent the harms from arising. >> Jeffrey: Right right. One of the things I know you love to talk about Michelle is compliance, right? And is compliance enough. I know you like to talk about the law. And I think one of the topics that came up on your guys' prior conversation is, you know, will there be a national law, right? GDPR went through on the European side last year, the California Protection Act. A lot of people think that might become the model for more of a national type of rule. But I tell you, when you watch some of the hearings in DC, you know, I'm sure 90% of these people still print their emails and have their staff hand them to them. I mean, it's really scary that said, you know, regulation always does kind of lag probably when it needs to be put in place because people maybe abuse or go places they shouldn't go. So I wonder if you could share your thoughts on where you think legislation is going to going and how should people kind of see that kind of playing out over the next several years, I guess. >> Yeah, it's such a good question Jeff. And it's like, you know, I think even the guys in Vegas are having trouble with setting the high laws on this. Cameron said in I think it was December of 2019, which was like 15 years ago now that in the first quarter of 2020, we would see a federal law. And I participated in a hearing at the Senate banking committee, again, November, October and in the before times. I'm talking about the same thing and here we are. Will we have a comprehensive, reasonable, privacy law in the United States before the end of this president's term. No, we will not. I can say that with just such faith and fidelity. (laughing) But what does that mean? And I think Katie Porter who I'm starting to just love, she's the Congresswoman who's famous for pulling on her white board and just saying, stop fudging the numbers. Let's talk about the numbers. There's about a, what she calls the 20% legislative flip phone a caucus. So there are 20% or more on both sides of the aisle of people in the US who are in the position of writing our laws. who are still on flip phones and aren't using smart phones and other kinds of technologies. There's a generation gap. And as much as I can kind of chuckle at that a little bit and wink, wink, nudge, nudge, isn't that cute. Because you know, my dad, as you know, is very very technical and he's a senior citizen. This is hard. I hope he doesn't see that but... (laughing) But then it's not old versus young. It's not let's get a whole new group and crop and start over again. What it is instead and this is, you know, as my constant tome sort of anti compliance. I'm not anti compliance. You got to put your underwear on before your pants or it's just really hard. (laughing) And I would love to see anyone who is capable of putting their underwater on afterwards. After you've made the decision of following the process. That is so basic. It comes down to, do you want the data that describes or is donated or observed about human beings. Whether it's performance of your employees. People you would love to entice onto your show to be a guest. People you'd like to listen and consume your content. People you want to meet. People you want to marry. Private data as Ann says, does the form the foundation of our freedom, but it also forms the foundation of our commerce. So that compliance, if you have stacked the deck proactively with an ethics that people can understand and agree with and have a choice about and feel like they have some integrity. Then you will start to see the acceleration factor of privacy being something that belongs on your balance sheet. What kind of data is high quality, high nutrition in the right context. And once you've got that, you're in good shape. >> I'm laughing at privacy on the balance sheet. We just had a big conversation about data on the balance sheets. It's a whole, that's a whole another topic. So we can go for days. I have Pages and pages of notes here. But unfortunately I know we've got some time restrictions. And so, and I want to give you the last word as you look forward. You've been in this for a while. You've been in it from the private side, as well as the government side. And you mentioned lots of other scary things, kind of on the horizon. Like the kick of surveillance creep, which there's all kinds of interesting stuff. You know, what advice do you give to citizens. What advice do you give to leaders in the public sector about framing the privacy conversation >> I always want to start by telling them don't frame privacy as a negative. It's not a negative. It's something that can build so much. If you're a business, you can gain a competitive advantage by strongly protecting your customer's privacy because then it will build such loyalty and you'll gain a competitive advantage. You make it work for you. As a government you want your citizens to have faith in the government. You want to encourage them to understand that as a government you respect their privacy. Privacy is highly contextual. It's only the individual who can make determinations relating to the disclosure of his or her personal information. So make sure you build that trust both as a government and as a business, private sector entity and gain from that. It's not a negative at all, make it work for you, make it work for your citizens, for your customers, make it a plus a win win that will give you the best returns. >> Isn't it nice when doing the right thing actually provides better business outcomes too. It's like diversity of opinion and women on boards. And kind of things- >> I love that. we cover these days. >> Well ladies, thank you very very much for your time. I know you've got a hard stop, so I'm going to cut you loose or else we would go for probably another hour and a half, but thank you so much for your time. Thank you for continuing to beat the drum out there and look forward to our next conversation. Hopefully in the not too distant future. >> My pleasure Jeff. Thank you so much. >> Thank you. >> Thank you too. >> All right She's Michelle. >> She's Ann. I'm Jeff. You're watching theCUBE. Thanks for watching. We'll see you next time. (upbeat music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation [Music] hi buddy this is Dave Volante and welcome to this cute conversation you know the we've been following a company called Activia for quite some time now they they've really popularized the concept of copy data management really innovative Boston based Waltham based company and with me Brian Regan who's the chief marketing officer and all 40 who's the newly minted chief revenue officer of actifi Oh guys great to see you I wish we were face to face that you're you're you're June event but this will have to do yeah you bet yeah so you know Brian you've been on the cube a bunch I'm gonna start with Paul if that's okay Paul you know just let's talk a little bit about your your background you've you've done a number of stance at a variety of companies you know big companies like IBM and others as well what attracted you to Activia in all honesty I've been a software guy and candidly a data specific leader for many many years and so IT infrastructure particularly associated around data has always been sort of my forte for fun on words there and and so Activia was just smack dab in the middle of that right and so when I was looking for my next adventure you know I had an opportunity to to meet with a shower CEO and Founder and describe and discuss kind of what activity was all about and candidly the the number of connections that we had that were the same a lot of our OEM relationships with people that I actually worked with and for and some that worked for me historically so it was almost this perfect world right and I'm a Boston guy so it was in my in my old backyard and it was just a perfect yeah it was a perfect match for what I was looking for which was really a small growth company that was trying to you know get to the next level that had compelling technology in a space that I was super familiar with and understanding and articulate the value proposition well as we're saying in Boston Paulie we got to get you back here I know I pack my cock let's talk about the let's talk about the climate right now I mean nobody expected this of course I mean it's funny I was I saw ash and an event in in Boston last fall we were talking like hey what do you expected for next year yeah a little bit of softening but you know nobody expected this sort of Black Swan but you guys I just got your press release you put out you had a good you had a good quarter you had a record first quarter um what's going on in the marketplace how you guys doing yeah well I think that today more than than ever businesses are realizing that data is what is actually going to carry them through this crisis and that data whether it's changing the nature of how companies interact with their customers how they manage through their supply chain and in frankly how they take care of their employees is all very data-centric and so businesses that are protecting that data that are helping businesses get faster access to that data and ultimately give them choice as to where they manage that data on-premises in the cloud and hybrid configuration those are the businesses that are really going to be top of a CIOs mind I think our q1 is a demonstration that customers voted with their wallets in their confidence in ectopy Oh has an important part of their data supplied nopal I want to come back to you first of all your your other people know you're next you're next Army Ranger so thank you for your service that's awesome you know I was talking to Frank's lute man we interviewed me other day and he was sharing with me sort of how he manages and and he says the other managed by a playbook he's a situational manager and that's something that he learned in the military well this is weird this is a situation okay and that really is kind of how you're trained and and of course we've never seen anything like this but you're trained to deal with things that you've never seen before so how are you seeing organizations generally actifi Oh specifically going to manage through this process what are some of the moves that you're advising recommending give us some insight there yeah so I'm it's really interesting it's a it's funny that you mentioned my military background I was just having this discussion with one of my leaders the other day that you know one of the things that they trained for in the military is the eventualities of chaos right and so when you when you do an exercise they we will literally tap the leader on the shoulder and say okay you're now dead and without that person being allowed to speak they take a knee and the unit has to go on and so what happens is you you learn by muscle memory like how to react in time suffice it or and you know this is a classic example of leadership and crisis and so um so it's just it's just interesting like so to me you have a playbook I think everybody needs to start with a playbook and then start with a plan I can't remember if it was Mike Tyson but one of them one of my famous quotes was you know let you know plan is good until somebody punches you in the face that's the reality of what just happened the business across the globe is it just got punched in the face and so you got a playbook that you rely on and then you have to remain nimble and creative and candidly opportunistic and from a leadership perspective I think you can't lose your confidence right so I've watched some of my friends and of what some other businesses crippled in the midst of this and I'm because they're afraid instead of instead of looking at this in my first commentary that our first staff meeting Brian if I remember it was this okay so what makes active feel great in disembark like not why is it not great right and so we didn't get scared we jumped right into it we you know we adjusted our playbook a little bit and candidly we just had a record quarter and we just down here the honestly date we took down deals in every single geography around the globe to include Italy I mean so it was insane it was really fun okay so this wasn't just one monster deal that gave you that record Porter is really a broad-based the demand yeah so if you you know if you dug underneath the covers you would see that we had the largest number of transactions ever in the first quarter we had the largest average selling price in the first quarter ever we had the largest contribution from our panel partners and our OEM partners ever and we had the highest number ever and so it was a it was really a nice truly balanced performance across the globe and across the size of deal sets and candidly across industries interesting I mean you use the term opportunistic and and I think you're right on I mean you obviously you don't want to be chasing ambulances at the same time you know we've talked to a lot of CEOs and essentially what they're doing and I'd like to get your feedback on this Brian you you you're kind of reassessing the ideal profile of a customer you're reassessing your value proposition in the context of the current pandemic and and I noticed that you guys in your press release talked about cyber resiliency you talked about digital initiatives you know data center transformations etc so maybe you could talk a little bit about that Brian did you do those things how did you do those things what kind of pace were you guys at how did you do it remotely with everybody working from home give us some color on that sure and you know Ashley if you were here you would probably remind us that Activia was born in the midst of the 2008 financial crisis so we we have essentially been bookended by two black swans over the last decade the and the lessons we learned in 2008 are every bit as as relevant today everything starts with cost containment in hospital and in protection of the business and so cio is in the midst of this shock to the system I think we're very much looking at what are the absolutely vital critical initiatives and what is a nice to have and I'm going to pause on my step and invest entirely in the critical mission and the critical initiatives tended to be around getting people safely working for remotely getting people safe access to their systems and their applications in their data and then ultimately it also became about protecting the systems from malicious individuals and state actors up unfortunately as we've seen in other times of crisis this is when crime and cyber crime particularly tends to spike particularly against industries that don't have the strong safeguards in place to to really ensure the resiliency their applications so we very much went a little bit back to the 2008 playbook around helping people get control of their costs helping people continue to do the things they need to do at a much more infrastructure light manner but also really emphasize the fact that if you are under attack or if you are concerned that you're infected but you don't know when you know instant access to data and a time machine that can take you back and forth to those points in time is something that is incredibly valuable so so let's >> cyber resiliency so specifically what is aekta video doing for its customers from a product standpoint capabilities maybe it's part of the the 10 see announcement as well but but can you can you give us some specifics on where you fit in let's take that use case cyber resiliency yeah absolutely so I think there's there's a staff of capabilities when it comes to cyber resiliency at the lowest level you need a time machine because most people don't know when they're in fact and so the ability to go back in time test the recoverability of data test the validity of the data is step one step two is once you've found the clean point being able to resume operations being able to resume the applications operation instantly or very rapidly is the next phase and that's something that Activia was founded on this notion of instant access to data and then the third phase and this is really where our partnerships really shine is you probably want to go back and mitigate that risk you want to go back and clean that system you want to go back and find the infection and eliminate it and that's where our partnership with IBM freezing resiliency services and their cyber incident recovery solution which takes the activity of platform and then rappers and a complete managed services around it so they can help the customer not only get their their systems and applications back on their feet but clean the systems and allow them to resume operations normally on a much safer and more stable okay so so that's interesting so Paul Paul was it kind of new adoptions was it was it increases from existing customers kind of a combination and you talk to that yeah totally so like ironically to really come clean we are the metrics that we had in the first quarter were very similar through the metrics that we see historically so the mix need our existing customer base and then our new customer acquisition were very similar to our historical metrics which candidly we were a little surprised by we anticipated um that the majority of our business would come from that safe harbor of your existing customer base but candidly we had a really nice split which was great which meant that you know a value proposition was resonating not only with our existing customer base where you would expect it but also in in any of our new customers as well who had been evaluating us that either accelerated or or just continue down the path of adoption during the time frame of Koba 19 across industries I would say that again um there was there were there were some industries I would say that pushed pause and so the ones that you can imagine that accelerated during during this past period were the ones you would think of right so financial institutions primarily as well as some some of the medical so some of those transactions healthcare and medical they accelerated along with financial institutions and then I would say that that we did have some industries that push pause and you can probably guess what some of those are a majority of those were the ones that we're dealing with the small and mid-sized businesses or consumer facing businesses things like retail stuff like that where we typically do have a pretty nice residence in a really nice value proposition but there were there were definitely some transactions that we saw basically just pause like we're going to come back but overall the yeah the feedback was just in general it felt like any other quarter and it felt like just pretty normal as strange as that sounds because I know speaking to a lot of my friends and gear companies your software companies they didn't have that experience but we did pretty well that's interesting I mean you're right I mean certain industries Airlines I'm interviewing a cio of major resort next week you know really interested to hear how they're you know dealing with this but those those are obviously depressed and they've dialed everything down but but we've we were one of the first to report that work from home pivot it didn't it didn't you know buffer the decline in IT spending that were expecting to be down you know maybe as much as 5% this year but it definitely offset it what about cloud we're seeing elevated levels in cloud demand guys you know have offerings there what are you seeing in cloud guys you want that yeah I'll start and then fall please please weigh in I think that'd be the move to the cloud that we've been witnessing and the acceleration of the MOOC table that we've been whipped over the past several years probably ramped up in intensity over the last two months The Improv been on the you know 18 to 24 month road map have all of a sudden been accelerated into maybe this year but in terms of the wholesale you know everything moves to cloud and I abandoned my on-premises estate I I don't think we've seen that quite yet I think the the world is still hybrid when it comes to cloud although I do think that the beneficiaries of this are probably the the non number one or number two cloud providers but the rest of the hyper scalers who are fighting for market share because now they have an opportunity to perhaps google for example a strategic partner of ours has a you know a huge offering when it comes to enabling work home and remote work so leveraging that as a platform and then extending into their enterprise offerings I think gives them a wedge that the you know Amazon might not have so this it's an acceleration of interest but I think it's just a continuation of the trend of seeing four years yeah and I would add a little bit if the you know IBM held their think conference this past week I don't know if you had an opportunity to participate there one of our OEM partners and oh yeah because you know when our the CEO presented his kind of opening his opening remarks it was really about digital transformation and he really he really kind of put it down to two things and said you know any business that's trying to transform is either talking about hybrid cloud but they're talking about AI and machine learning and that's kind of it right and so every digital business is talking in one of those categories and so when I look 2q1 it's interesting that we really didn't see anything other than as brian talked about all the cloud business which is some version of an acceleration but outside of that the customers that are in those industries that are in position to accelerate and double down during this opportunity didn't so and those that did not you know kind of just peeled back a little bit but overall I still I would agree with with ibm's assessment of the market that you know those are kind of the two hot spots and have a cloud is hot and the good news is we've got a nice guy operating Molloy yeah Arvind Krista talked about the the in and it has it maybe not I think but he talked earlier in his remarks on the earnings call just in Publix Davis that IBM must win the battle the architectural battle the hybrid cloud and also that he wants to lead with a more technical sell essentially which is submitted to me those those two things are great news for you guys obviously you know Red Hat is the linchpin of that I want to ask you guys about your your conference data-driven so we were there last year it was a great really great intimate event of course you know you hand up the physical events anymore so you've pushed to September you're going all digital would give us the update on on that program we're um we're eager to have the cube participate in our September event so I'm sure we'll be talking more about that in the coming weeks but awesome we love it we exactly so you can tell Frank to put that so we we've been participating in some of the other conferences I think most notably last week learning a lot and and really trying to cherry pick the best ideas and the best tactics for putting on a digital event I think that as we look to September and as we look to put on a really rich digital event one of the things that is I think first and foremost in our minds is we want to actually produce more on-demand digital content particularly from a technology standpoint our technology sessions last year were oversubscribed the digital format allows people to stream whenever they can and frankly as many sessions as they as they might so I think we can be far more efficient in terms of delivering technical content or the users of our technology and then we're also eager to have as we've done with data driven in the years past our customers tell the story of how they're using data and this year certainly I think we're going to hear a lot of stories about in particular how they use data during this incredible you know crisis and and hopefully renewal from crisis well one of my favorite interviews last year your show is the the guys from draft King so hopefully they'll be back on it will have some football to talk about let's hope I mean I want it I want to end with just sort of this notion of you know we've been so tactical the last eight weeks right I'm you guys too I'm sure just making sure you're there for customers making sure your employees are ok but as we start to think about coming out of this you know into a post probe Adaro it looks like it's gonna be with us for a while but we're getting back the you know quasi opening so I'm hearing you know hybrid is here to stay we agree for sure cyber resiliency is very interesting I think you know one of the things we've said is that that companies may sub optimize near-term profitability to make sure that they've got the flexibility and resilience business resiliency in place you know that's obviously something that is I think good news for you guys but but I'll start with Paul and then maybe Brian you can bring us home how do you see this sort of emergence from this lockdown and into the post ghovat era yeah so this is a really interesting topic for me in fact I've had many discussions over the last couple weeks with some of our investors as well as our executive staff and so my personal belief is that the way buying and selling has occurred for IT specifically at the enterprise level is about to go through a transformation no different than we watched the transformation of SAS businesses when you basically replace the cold-calling salesperson with an inside and you know inbound marketing kind of effort followed up with SDR and vdr because what we're finding is that our clients now are able to meet more frequently because we don't have the friction of airplane ride or or physical building to go through and so like that that whole thing has been removed from the sales process and so it's interesting to me that one of the things that I'm starting to see is that the amount of activity that our sales organization is doing and the amount of physical calls that were going on they happen to be online however you couple that with the cost savings of not traveling around the globe and not being in offices and and I really think that those companies that embrace this new model are gonna find ways to penetrate more customers in a less expensive way and I do believe that the professional sales enterprise salesperson of tomorrow is gonna look at then it looks today and so I'm super excited to be in a company that is smack dab in the middle of selling to enterprise clients and and watching us learn together how we're gonna buy sell and market to each other in this post public way because I I'm the only thing I really do know it's just not gonna be the way it used to be what is it gonna look like I think all of us are placing bets and I don't think anybody has the answer yet but it's gonna look different for sure they're very very thoughtful comments and so Brian you know our thinking is the differentiation and the war yes it gets one in digital how is that affecting you know sort of your marketing and your thing around that we we fortunately decided coming into 2020 our fiscal 21 that we were actually going to overweight digital anyway we felt that it was far more effective we were seeing far better conversion rates we saw you know way better ROI in terms of very targeted tentative digital campaigns or general-purpose ABM type of efforts so our strategy had essentially been set and and what this provided us is the opportunity to essentially redirect all of the other funds individually so you know we have essentially a two-pronged marketing you know attack Frank now which is you know digital creating inbounds and B DRS that are calling on those in bounds that are created digital and so it's a you know it's going to be a really interesting transition back when physical events if and when they do actually come back into form you know how much we decide to actually go back into that that been I think that you know to someone to some extent we've talked about this in the past II you know the physical events and the the sheer spectacle and this year you know audacity of having to spend a million dollars just to break through that was an unsustainable model and so I think this is this is hastening perhaps the decline or demise of really silly marketing expense and getting back to telling telling customers what they need to know to help their an assist their buying journey in their investigation journey into a new technology I mean the IT world is hybrid and I think the events world is also going to be hybrid to me nice intimate events you know they're gonna live on but they're also gonna have a major digital component to them I'm very excited that you know we're a lot of learnings now in digital especially around events and by September the a lot of the the bugs are gonna be worked out you know we've been going to it so it feels like 24/7 but really excited to have you guys on thanks so much really looking forward to working with you in in September it's data-driven so guys thanks a lot for coming on the cube oh my gosh thank you Dave so nice it's so nice to be here thank you alright pleasure you did thank you everybody thank you and thanks for watching this is Dave Volante for the cube and we'll see you next time [Music]