>> Announcer: Live from Las Vegas, it's theCUBE covering Magento Imagine 2019, brought to you by Adobe. >> Hey, welcome back to Las Vegas. Lisa Martin with Jeff Frick. We're coming to you live from Magento Imagine 2019. Welcoming to theCUBE for the first time Gary Specter, the VP of Commerce, Sales and Customer Success at Adobe. Gary, welcome to theCUBE! >> Thank you, I'm thrilled to be here. >> So there's about 3,500 people here, you guys have, from 60-plus countries. >> Gary: That's right. >> I think 100 sessions, 150 speakers. People coming down from ceilings, up from the floor. >> Gary: And we're streaming live. >> First ever live stream, yes. >> On the general set, first ever. That's right. Someone tweeted out that there are 35,000 people watching. >> Marketing probably loved that and then had a heart attack at the same time. >> Yeah, I'm sure they did. Not exactly accurate but I'll take what I can get. >> Tell us about the event, the spirit of the event. This is kind of, yesterday evening things kicked off. What of some of the things you've hearing from customers, partners, developers? >> So, I think the thing that's really unique about Imagine is that it does involve partners, the community, developers, along with Magento and our customers and our prospects. And it makes it really different because the developer community and our partners are so passionate about Magento. And I think everybody feels really good about the marriage of Adobe and Magento. You had technologies that were very well aligned, not overlapping. It enables us to extend the capabilities of what we can do from both the Adobe side or the Magento side. I like to say that the color palette got a lot bigger, and I think there's a lot of excitement around that and what that means to all of these people, developers, partners, the ecosystem, customers, prospects. So the energy is really high. I think obviously people are, what's next? And what does this mean for Magento? And I think it means investment, I think it means a higher rate of agility and an expansion of what we do. Acceleration of our roadmap. So I think people are very, very positive. And this is my fourth Imagine, and it's really, I've never felt the energy higher than at this Imagine. So it's exciting for me. >> Gary, one of the interesting ways that you talked about community and everybody wants developer communities, right? And you guys also have open source as a passion. But you phrased it in a way I've never heard before, is that you like going to sleep at night knowing that there's a whole bunch of other CEOs betting their business-- >> That's right. >> On this platform. >> Yeah. >> And it's not just you guys, so it's a really different way to think about open source. We often think of the developers and there's smart people outside your four walls contributing code. But it's not often couched in terms of the business terms. >> No. >> If there's are other people betting their business, thinking about how are they gonna help grow your business by building their business on top of Magento. >> That's what drives the passion of the community. These people realize that there's a symbiotic relationship here. If Magento successful, the ability for them to be successful is very broad. And if Magento's not successful, then you have to ask yourselves did I make the right bet? So a lot of our tech partners have build these great solutions on top of Magento, and it's a partnership. And you don't have that anywhere else, and again, I sleep better at night, to your point. I don't know where you got that quote, but it's actually mine, it's phenomenal. >> No, no, I think I got it from your Argentina 2017 talk perhaps. >> Actually, it's true. I know that all of these tech partners, these CEOs, they have my back. I'd like them to know I have theirs. And I don't think Adobe has any, there's no reason or rhyme why that would ever change. I think Adobe will enhance it. And I think that's why there so much excitement here. >> Well, and it's really a validation and what we talked about before, the prior segment, was now to bring the marketing tools, and the AI and all the power that's in that big building in San Jose, free the commerce transaction, really, to your point, adds so much more horse power to the total solution. >> Like I said, color palette just got a lot bigger. There's so many more things that we can do and so many more colors we can use to create these great experience for our brands and our customers, that we could've done before but it was a lot of work, but now we've got all of the makings of a platform that will enable that and we're already pretty far along in taking the Adobe experience cloud and making that work. And I'm just really excited about the future and what this offers for our customers and our brands. >> We've heard a number of guests that talk about just what you were referring to a minute ago, and that was really this symbiosis of Adobe, the power that Adobe brings, the data that Adobe brings, along with Magento, So a new Adobe commerce buy was just launched a couple of months ago, at Adobe Summit powered by Magento Commerce, but you look at it as analytics, advertising, marketing, commerce, fundamentals for managing what is a changing and highly demand customer experience, 'cause we want more and more things accessible from right here. So some of the feedback from customers, partners, developers since that announcement and now going "Ahh, okay now I can actually touch and see and play with this two symbiosis machines coming together." >> Yeah, I think it's not a hard thing to get. I think when the acquisition first happened, there's a little let's wait and see and make sure they get it right. And I think what I feel today, or what people have given to me today is the feedback that they're believers. They know that we're gonna execute on this strategy, and this strategy is gonna allow us to extend our lead on our competitors, which in return, allows these brands and these commerce players to extend their lead on their competitors. >> Let's talk about the small/medium business folks for a minute. When the announcement was made last year, the intention, right after Imagine 2018 I believe, for Adobe to acquire Magento, and then right after they acquired Marketo, there was some concern for is Adobe gonna kind of shift what Magento has been doing, so successfully for so long, away from focusing on those smaller merchants to the enterprise folks. Yesterday and today, we heard some great, exciting announcements with what you guys are doing with Amazon Sales Channel, with Google Shopping, and it sounded like the small and medium business size folks were going "Yes, this is what we need." Talk to us a little bit about that. >> I mean, you mentioned two, along with PWA and some of the other things that we're doing. While these can be leveraged in the enterprise, they were built for the mid-market in the SMB space. And there is no doubt that Adobe and Magento both understand how important SMB and the mid-market is. And in fact, we've seen acceleration in the SMB space since the acquisition, from the Magento side of the house. And Adobe is fully committed and knows that there's market share there to be had. And the application or the business problems that we solve at the enterprise, are still applicable for the mid-market and the SMB space. They're handled in a little bit different of a manner, but they have same aspirations. And the solution's gonna be able, when you look across everything that you're gonna be able to do, it plays for both markets. And Adobe has an incredible opportunity to really drive market share in this mid-market. They don't have a big footprint there today. Even if you capture just a small portion of it, and its our plans to capture a large portion of it, but even a small portion of it is gonna make a big impact on Adobe. So I think that we will see acceleration in the mid-market and in the SMB space with what we're doing, what we're developing together, and the different types of products that we can offer to those markets that Adobe has in its broader portfolio. >> And of course on the enterprise side, what we don't see here that we saw at Adobe Summit a couple weeks back are some of the really big integrators who have huge practices built around and on top of the Adobe tool set that now you get to leverage. I'm sure you're pretty excited about as running field. There's, again, a whole nother group of people, not necessarily CEOs, but managing partners, who have bet their jobs, bet their livelihood, bet their practices on this, and now you getta take advantage of those resources as well. >> Absolutely, and I think that a lot of the large integrators and partners, I think everybody's starting to understand that commerce is very different now than it was five or 10 years ago, right? I call it bite small, chew fast. And HP is a great example, where they started in some of the smaller APAC countries and then went to Brazil, and they're looking at the US last, but they're taking it a step at a time. One country, one country, one country. And a lot of our big retailers or brands that wanna expand globally are doing the same things, or companies that have portfolios of brands, one at a time. Bite small, chew fast. Launch, be successful, launch, be successful. And I think the SIs, including the large partners, understand that and they're changing the way that they look at businesses holistically. So I think right time, right place. >> Yeah, we had Gillian Campbell from HP on right after her keynote this morning, and it was an interesting kinda POC program. And I said what was some of the market dynamics that identified APAC as the right market to start in. And part of that, I think, was that from a historical legacy perspective of using Magento on the HP Inc. side. But some of the things I found interesting to them was that leveraging the data to understand the cultural e-commerce differences snd how different cultures interact with different social media platforms or purchasing platforms differently, and how important it is to really understand those commerce patterns and start to drive conversions from there there and then go success, roll it out, rinse and repeat. >> And she nailed it right? I mean, buy online, pick up in store versus having it delivered to your home, if you live in the middle of India, what's the reality of you getting that delivered in an hour? And if you look at country like Russia, which is very spread out, right, so there's not a high density outside of a lot of their major cities and you have a lot of the same issues. If you're gonna have it ship to your home, how long is it gonna take? It might be easier just to go pick it up in the store. And I think it's different in every region. And it's good to be able to have access to that data to get a good read on what are the things our customers want specifically to drive the experience they need within that region. >> Right, key for a company whether it's something the size of an HP Inc. or not, to be able to scale globally, but also have that sort of local market adaptation where you're able to react, understand the preferences in your markets, and deliver exactly what those consumers want. So having a tool like Magento as the power to enable that global scale regional adaptation, it's a driver. >> And I think you start to add complexity when you look at do they use their phone, do they use their computer? Do they use social networks and buy buttons? I have an interesting dynamic in my own house where I've got a 13-year-old, and the way that she would shop online is different than the way that my wife would shop online, which is very different from how I would shop online. I browse and go to the store. My wife uses her computer. My daughter shops on Pinterest, or Instagram, or Facebook. Very different journeys for the three of us, and we could be buying the same thing, and we're all gonna do it differently. So it crosses generations as well. >> So, Gary, it feels like kinda the dust has settled post-Adobe acquisition where everybody feels kinda comfortable, and it's been a year and everything didn't go bananas. So as you look forward now, after things have kinda settled, what are some of your priorities over the next year, If we sit down a year from now, what are you working on? >> I can tell you that for me, the biggest priority for me is to make sure that the mid-market and the SMB flywheel is effective, the way that we go to market, the way that we target that segment. And it's not that I'm not interested in the enterprise. I'm extremely interested in the enterprise. But we have a lot of people that are working on the enterprise. And Adobe doesn't have deep domain expertise around the mid-market. But with Marketo and Magento, you now do. So for me personally, I wanna make sure that that flywheel is well-run, it's well-oiled, it's set up for success, that operationally, the things that we do to drive market share in that segment run as effectively as the rest of Adobe on the enterprise side. It's a new sales motion for Adobe. But the good news is I think Adobe understands that. We understand that as a company, and I think over the next year, for me, that's where my focus is gonna be. >> So if we keep looking out to the next year, this is your fourth Magento Imagine. >> It is. >> Is there gonna be a Magento Imagine 2020? >> So I will tell you that there will be an Imagine 2020, and I will share details around that Wednesday. I've been asked to help close Imagine out, and when I do, I will be thrilled to announce our plans for Imagine 2020. >> So can folks watch that on the livestream tomorrow, Wednesday, that 15th? >> They can. >> Are you gonna be coming up from the floor, the ceiling? >> I think I'm probably just gonna dance on out. I have been invigorated, I love being here. Imagine is the one opportunity every year where I come out of this thing just feeling really good about the opportunities that we had ahead of us. And by Wednesday, although tired, I'm usually really happy to be going back and getting in the field with my teams and just driving opportunity. And I think we had an amazing one. >> Well, we'll be all watching. Is it imagine.magento.com to watch the livestream ? Or magento.imagine.com. go to to the Magento.com site, Wednesday tomorrow in the afternoon, you're gonna be able to hear more about what's to come next year. Gary, thank you so much for giving us time today. >> Thanks for having me, enjoy it. >> Our pleasure. >> It's great to meet you all. >> Excellent >> Thank you. >> For Jeff Frick, I'm Lisa Martin. Tou're watching theCUBE live from Magento Imagine 2019 from Vegas. Thanks for watching. (upbeat music)

>>Hey everybody. Welcome back. Good afternoon. Lisa Martin here with John Feer live in Detroit, Michigan. We are at Coon Cloud Native Con 2020s North America. John Thank is who. This is nearing the end of our second day of coverage and one of the things that has been breaking all day on this show is news. News. We have more news to >>Break next. Yeah, this next segment is a company we've been following. They got some news we're gonna get into. Managing Kubernetes life cycle has been a huge challenge when you've got large organizations, whether you're spinning up and scaling scale is the big story. Kubernetes is the center of the conversation. This next segment's gonna be great. It >>Is. We've got two guests from Specter Cloud here. Please welcome. It's CEO Chenery Fu and co-founder and it's c g a co-founder Sta Mallek. Guys, great to have you on the program. Thank >>You for having us. My pleasure. >>So Timary, what's going on? What's the big news? >>Yeah, so we just announced our Palace three this morning. So we add a bunch, a new functionality. So first of all we have a Nest cluster. So enable enterprise to easily provide Kubernete service even on top of their existing clusters. And secondly, we also support seamlessly migration for their existing cluster. We enable them to be able to migrate their cluster into our CNC for upstream Kubernete distro called Pallet extended Kubernetes, GX K without any downtime. And lastly, we also add a lot of focus on developer experience. Those additional capability enable developer to easily onboard and and deploy the application for. They have test and troubleshooting without, they have to have a steep Kubernetes lending curve. >>So big breaking news this morning, pallet 3.0. So you got the, you got the product. This is a big theme here. Developer productivity, ease of use is the top story here. As developers are gonna increase their code velocity cuz they're under a lot of pressure. This infrastructure's getting smarter. This is a big part of managing it. So the toil is now moving to the ops. Steves are now dev teams. Security, you gotta enable faster deployment of apps and code. This is what you guys solve while you getting this right. Is that, take us through that specific value proposition. What's the, what are the key things on in this news release? Yeah, >>You're exactly right. Right. So we basically provide our solution to platform engineering ship so that they can use our platform to enable Kubernetes service to serve their developers and their application ship. And then in the meantime, the developers will be able to easily use Kubernetes or without, They have to learn a lot of what Kubernetes specific things like. So maybe you can get in some >>Detail. Yeah. And absolutely the detail about it is there's a big separation between what operations team does and the development teams that are using the actual capabilities. The development teams don't necessarily to know the internals of Kubernetes. There's so much complexity when it comes, comes into it. How do I do things like deployment pause manifests just too much. So what our platform does, it makes it really simple for them to say, I have a containerized application, I wanna be able to model it. It's a really simple profile and from there, being able to say, I have a database service. I wanna attach to it. I have a specific service. Go run it behind the scenes. Does it run inside of a Nest cluster? Which we'll talk into a little bit. Does it run into a host cluster? Those are happen transparently for >>The developer. You know what I love about this? What you guys are doing in the news, it really points out what I love about DevOps. Because cloud, let's face a cloud early adopters, we're all the hardcore cloud folks as it goes mainstream. With Kubernetes, you start to see like words like platform engineering. I mean I love that term. That means as a platform, it's been around for a while. For people who are building their own stuff, that means it's gonna scale and enable people to enable value, build on top of it, move faster. This platform engineering is becoming now standard in enterprises. It wasn't like that before. What's your eyes reactions that, How do you see that evolving faster? Or do you believe that or what's your take on >>It? Yeah, so I think it's starting from the DevOps op team, right? That every application team, they all try to deploy and manage their application under their own ING infrastructure. But very soon all these each application team, they start realize they have to repeatedly do the same thing. So these will need to have a platform engineering team to basically bring some of common practice to >>That. >>And some people call them SREs like and that's really platform >>Engineering. It is, it is. I mean, you think about like Esther ability to deploy your applications at scale and monitoring and observability. I think what platform engineering does is codify all those best practices. Everything when it comes about how you monitor the actual applications. How do you do c i CD your backups? Instead of not having every single individual development team figuring how to do it themselves. Platform engineer is saying, why don't we actually build policy that we can provide as a service to different development teams so that they can operate their own applications at scale. >>So launching Pellet 3.0 today, you also had a launch in September, so just a few weeks ago. Talk about what these two announcements mean from Specter Cloud's perspective in terms of proof points, what you're delivering to the end users and the value that they're getting from that. >>Yeah, so our goal is really to help enterprise to deploy and around Kubernetes anywhere, right? Whether it's in cloud data center or even at Edge locations. So in September we also announce our HV two capabilities, which enable very easy deployment of Edge Kubernetes, right at at at any any location, like a retail stores restaurant, so on and so forth. So as you know, at Edge location, there's no cloud endpoint there. It's not easy to directly deploy and manage Kubernetes. And also at Edge location there's not, it's not as secure as as cloud or data center environment. So how to make the end to end system more secure, right? That it's temper proof, that is also very, very important. >>Right. Great, great take there. Thanks for explaining that. I gotta ask cuz I'm curious, what's the secret sauce? Is it nested clusters? What's, what's the core under the hood here on 3.0 that people should know about it's news? It's what's, what's the, what's that post important >>To? To be honest, it's about enabling developer velocity. Now how do you enable developer velocity? It's gonna be able for them to think about deploying applications without worrying about Kubernetes being able to build this application profiles. This NEA cluster that we're talking about enables them, they get access to it in complete cluster within seconds. They're essentially having access to be able to add any operations, any capabilities without having the ability to provision a cluster on inside of infrastructure. Whether it's Amazon, Google, or OnPrem. >>So, and you get the dev engine too, right? That that, that's a self-service provisioning in for environments. Is that, Yeah, >>So the dev engine itself are the capabilities that we offer to developers so that they can build these application profiles. What the application profiles, again they define aspects about, my application is gonna be a container, it's gonna be a database service, it's gonna be a helm chart. They define that entire structure inside of it. From there they can choose to say, I wanna deploy this. The target environment, whether it becomes an actual host cluster or a cluster itself is irrelevant to them. For them it's complete transparent. >>So transparency, enabling developer velocity. What's been some of the feedback so far? >>Oh, all developer love that. And also same for all >>The ops team. If it's easy and goods faster and the steps >>Win-win team. Yeah, Ops team, they need a consistency. They need a governance, they need visibility, but in the meantime, developers, they need the flexibility then theys or without a steep learning curve. So this really, >>So So I hear a lot of people say, I got a lot of sprawl, cluster sprawl. Yeah, let's get outta hand does, let's solve that. How do you guys solve that problem? Yeah, >>So the Neste cluster is a profit answer for that. So before you nest cluster, for a lot of enterprise to serving developers, they have to either create a very large TED cluster and then isolated by namespace, which not ideal for a lot of situation because name stay namespace is not a hard isolation and also a lot of global resource like CID and operator does not work in space. But the other way is you give each developer a separate, a separate ADE cluster, but that very quickly become too costly. Cause not every developer is working for four, seven, and half of the time your, your cluster is is a sit there idol and that costs a lot of money. So you cluster, you'll be able to basically do all these inside the your wholesale cluster, bring the >>Efficiency there. That is huge. Yeah. Saves a lot of time. Reduces the steps it takes. So I take, take a minute, my last question to you to explain what's in it for the developer, if they work with Spec Cloud, what is your value? What's the pitch? Not the sales pitch, but like what's the value pitch that >>You give them? Yeah, yeah. And the value for us is again, develop their number of different services and teams people are using today are so many, there are so many different languages or so many different libraries there so many different capabilities. It's too hard for developers to have to understand not only the internal development tools, but also the Kubernetes, the containers of technologies. There's too much for it. Our value prop is making it really easy for them to get access to all these different integrations and tooling without having to learn it. Right? And then being able to very easily say, I wanna deploy this into a cluster. Again, whether it's a Nest cluster or a host cluster. But the next layer on top of that is how do we also share those abilities with other teams. If I build my application profile, I'm developing an application, I should be able to share it with my team members. But Henry saying, Hey Tanner, why don't you also take a look at my app profile and let's build and collaborate together on that. So it's about collaboration and be able to move >>Really fast. I mean, more develops gotta be more productive. That's number one. Number one hit here. Great job. >>Exactly. Last question before we run out Time. Is this ga now? Can folks get their hands on it where >>Yes. Yeah. It is GA and available both as a, as a SaaS and also the store. >>Awesome guys, thank you so much for joining us. Congratulations on the announcement and the momentum that Specter Cloud is empowering itself with. We appreciate your insights on your time. >>Thank you. Thank you so much. Right, pleasure. >>Thanks for having us. For our guest and John Furrier, Lisa Martin here live in Michigan at Co con Cloud native PON 22. Our next guests join us in just a minute. So stick around.

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

>> Man: from around the globe, it's "theCUBE" with coverage of "Kubecon" and "CloudNativeCon Europe 2020", virtual. Brought to you by Red Hat, the cloud native computing foundation and ecosystem partners. >> Welcome back, I'm Stu Miniman, and this is "theCUBE's" coverage of KubeCon CloudNativeCon Europe 2020, the virtual edition of course, it, this ecosystem has been bustling, a lot of activity in the five years that we've been covering it with "theCUBE" we've watched very much the maturation of what's going on. Remember, in the early days, it was open source projects, companies pulling all the pieces together. Now, there's a lot more things to choose from lots of projects, not just Kubernetes, but all the other pieces, and still lots of new innovations and new startups coming into the space. So happy to welcome to the program, have two first time guests from Spectro Cloud, first of all, we have the co founder and CEO Tenry Fu, and also Tina Notle who's the Vice President of product, Tina and Tenry, thank you so much for joining us. >> Thank you for having us. >> Likewise. >> All right, so Tenry, as one of the co founders, I want to understand, you know, why Spectro Cloud? Why now, you know, many outsiders, would they have said for a while, you know, Kubernetes, it's just getting baked into all of the environment. They looked at all the platforms, whether you're talking, you know, Google and AWS or VMware, they all have their platforms, they all have their managed services offering. So help us understand, what your team does and how you differentiate from what's already existing. >> Absolutely yeah, so I actually used to work at VMware, I, and then, I saw clouds taking off right and then I left VMware, to start my first startup called CliQr Technologies, which focus on multicloud management. But at that time, really, multicloud management through a single pane of glass is obviously right, and then clicker later acquired by Cisco. So at Cisco, I kind of witness The Container and Kubernetes taking off, right? And it makes a lot of sense, right for the first time both the application workloads and infrastructure became truly portable across multiple environments, but also very interestingly at Cisco I observed there are many developer teams, right? That is adopting Kubernetes and everyone is doing a little bit different things, that because different teams, they have a different stack constructor requirements, like some for AI/ML, some, they need a different base OS, some they just don't want to have a different version, and a lot of existing solutions doesn't really provide this kind of flexibility to satisfy all the different needs, right? one size fit all, typically is a one size fit for nothing. So we asked ourselves, why can't we try to create a platform that will give people the flexibility, but not turning it into a DIY project, right, still have a full manageability, so that user don't need to worry about the upgrade, Day Two operations, governance so and so forth. >> Yeah to Tina, I know when I've looked at your product, it's discussed as layers, which my background's in networking. So I love seeing things visually and understanding the pieces as they lay out the stack. So maybe help us understand a little bit as to, you know, that the flexibility that you give and how it's not just the Paradox of Choice, just too many options out there and you know, developers left to create their own mess that they can't then support. (laughing) >> Yeah, so you know, as Tenry mentioned, offering folks flexibility without turning into a do it yourself, you know, hot mess is what we're what we're helping People do at Spectrol Cloud, the core of our solution, the core of the differentiation within our solution is around this concept of a cluster profile, and as you mentioned, cluster profile basically allows people to define in a layered fashion, what's part of their Kubernetes infrastructure stack? So at the bottom, you're talking, what's the base operating system? What's the version of Kubernetes, that's going to be part of clusters that uses profile? What's your networking and storage interface look like? And then on top of that, you have a number of optional layers. So again, you know, back to flexibility manageability, we give people options around what those other layers look like on top. They include everything from security, logging, monitoring, etc, just anything that you want to go ahead and kind of bake into a definition, a profile of what a cluster should look like in one of your deployed environments. >> All right, well, Want to make sure I understand when you talk about Kubernetes in there, can it be, you know, say VMware with Vsphere7, now has Kubernetes support. Red Hat open shift is an option, all of the cloud players have their, you know, AKS, EKS. And they're like, can I bake that Kubernetes in or are you taking a different approach? >> We're going with upstream vanilla Kubernetes today, that allows us to go ahead and provide what's newest within the ecosystem, and let people go ahead and have a really open, really open solution that's replying. >> Okay, so when I talk to, when you look out there, a lot of companies are saying how can I manage multiple clusters? So if you look at what Google, Microsoft and VMware, they're talking about, we can manage our clusters and we can also help you with those other clusters. How does that impact Tenry, your Solution, doesn't it need to be, it's just the upstream solution that I put into that cluster profile, or can I connect to, say a managed cloud solution? >> Yeah, so I think in terms the multi class management or the consistency is really the key, right. So through this class profile concept, not only it can be used as the initial template to deploy a cluster, but it can also use as a single source for choose, to drive the cluster Lifecycle Management income upgrade. So right now, as Tina mentioned, we primarily focus on upstream, so that we want to provide the maximum flexibility in terms of our end to end Kubernetes stack. But we do also have a plan, that down the road that we go into in Brownfield existing clusters. So that enterprise, existing investment to their Kubernete infrastructure can be under managed by us. >> Well there always reaches a time when the brand new technology gets called Brownfield. I think that's the first time I've heard something like, you know, EKS or the like, you know, referred to as Brownfield. Tina, you know, when I think back to my history with integrated solutions, obviously, if I have the various pieces, it should be easier for me to stay on the latest make upgrades, roll things forward or roll things back, but you know, what, give us if you could some of the, the key values of, you know, building these cluster profiles, what that enables for your customers. >> So the key around cluster profiles, we offer this policy based management, so you describe as an administrator, what it is that those clusters need to look like, right? And we've got, we adopt a declarative desired state, you know, management approach along what Kubernetes does itself, and so what you're able to get through adopting, utilize cluster profiles, is this guarantee that from deployment and then into day two as well, what you've described in this profile, winds up maintaining itself, it remains true of the clusters that have been deployed. So what it is that you require as far as the operating system, what is required as far as some configuration options, etc. So the profile itself winds up being ground source of truth and around what it is that you've got running at all these various locations, across clouds, across different clusters, etc. >> All right. Tenry, you mentioned that having things more standardized is going to help customers, absolutely, we saw that in data centers for a long time, and standardized, how do you help customers make sure that the configuration that they build are going to work, are going to be stable, if they make changes that they're not going to get things out of sync. Is there you know, interoperability matrix or some other ways that we're trying to make sure that customers, you know, stay on the rails, if you will. >> Absolutely right, So through our system, right, all the integration points, we carry the additional metadata, right to basically give the hint about compatibility, resource constraints, right, and also the upgradability, in terms of moving from one version to another. So this way, we can kind of give you some guidance, when they initially construct a class profile, what will work together nicely and then what will not, right. And then on top of that, when upgrading from one existing cluster to a new version of a class profile definition, then we can look at the environment, right to understand, right, if there's something that potentially incompatible will popping up right, so we call that pre pilot integration, check right and also post deployment, we also allow user to run additional conformance tests. So that make sure the cluster everything is actually is still acting as as it's supposed to be. >> Another way to explain that is that you know, the cluster profile concept has a lot of flexibility attached with to it, right? That's a lot of power, it can get you into trouble if you don't have the right safety nets and safety harnesses underneath you. So we have a multi layered approach to helping make sure that people are getting benefit out of that flexibility. >> Wonderful and I'm wondering did, when you've had more customers using this, is their shared information, and if there're community guidelines that help, you know, understand when it's going to be okay, hey, 1.19's out, we're looking at 1.20. You might want to do this or hey, if you're using this piece of networking, you might want to wait a little bit before you go to the next version. >> That's definitely the idea over time, folks that are engaging with us, are very interested in the fact that, because of the fact that we're SaaS management platform, SaaS space management platform today, that it offers them the opportunity to learn from their peers, if you will, right, and their peers experiences. On top of that, we also have the ability to watch just what's been going on in other deployments in the Kubernetes ecosystem and we can make sure that all that's available, as Tenry mentioned, you know, in the form of the metadata that's on top of those packs. >> All right, how about how do you price this solution? When I look out there, I talked about Kubernetes baked into all the platforms, oftentimes, it can be baked into ELA, It's part of, you know, my just general cloud spend from that platform. So how do you do the pricing and, you know, are you plugged into any of the cloud marketplaces yet? >> Yeah, so flexibility is really part of our DNA. So even for pricing, we want to provide the maximum flexibility to our customer. So unlike some traditional solution typically is priced based on number of pause, right, a year, or even number of nodes, right. So we actually price based on number of CPU cores of all workers node under management by hour. So what we call those, core hour under management, right, and then every thousand core hours at one unit, we call kilo core hours. So kind of similar to how electricity is consumed, right, so this way, based on these core hour consumption, we allow user to either pay as you go as amongst the on demand plan, or you can do an annual commitment. >> And we are in process on the marketplaces. >> Yeah. >> All right, how about, we talked about Kubernetes, I think service mesh are part of it. What in this Kube, kubecon cloud native con ecosystem, which projects are the most tied into what you're doing anything that specter cloud is particularly contributing to that you can share? >> Yeah, so our system is built on top of Kubernetes cluster API project. So we are one of the contributor to class API, we are actively adding additional functionality to enhance class API, especially by in some other VMware environment for some custom use case, such as static IP or some special placement behaviors, and also adding additional contribute on different cloud support. >> Yeah, and as far as things that we're watching, and clearly we're, we've seen a dramatic increase in the number of people on our customer front that are interested in actual deployment, of service mesh now. So that's something that you know, we're going to be more engaged in over time. And another one that we're hoping to see, check out more talks around Kubecon is AI ML, right? A lot of interest on the part of customers around AIML use cases. >> Yeah, absolutely edge and AI and ML. Definitely very hot topics to conversation this year at the, at the Europe show, expect that to continue. Tina, I'm wondering, do you have any customer examples, maybe even anonymized that could kind of just explain the key values that your customers are seeing using your solution? >> Yeah, sure, so we've got one of our earliest customers is a Canadian financial, who came to us because, they were looking to figure out how to manage consistently at scale, and they have the problem that Tenry described earlier, around, I've got different development teams, they have different needs, and you know, how do you satisfy all those guys without going crazy, right? They've got an AIML use case, that's a special snowflake they've got two separate teams in different groups that would like to be under an IT management umbrella. That's a convergence use case that they're looking at, so kind of a typical example of somebody that we think of is, you know, a really good set of people for us to be having conversations with. We've also been working with a telecom provider that it's in a similar, similar vein actually, there's an AIML, there are multiple teams of different infrastructure, and they want to be able to consistently manage it's a story that we're seeing over and over again, thankfully. >> Yeah, we also see right from I think, at individual group or team level, right. There are a lot of, kind of a product owner or data scientists that they really want to have a kind of an easy button to quickly be able to provision Kubernetes clusters that suit for their need, right. And a lot of these groups, their primary focus is really the application, right? It's not their interest to spend a lot of time and resource on Kubernete management, in terms of deploying update, or secure an operation. So through us, they can very easily spin up a Kubernetes cluster, whether it's for AIML or for developing experiment, they can very quickly do that But with the flexibility, because a lot of existing solution, they may limit the version of Kubernetes clusters, they may limit the what kind of integration they can do. >> Yeah, Tenry you, we talked a little bit earlier about, you know, potential integration down the road. I'm curious, just there's so many companies creating innovations out there, you know, say for example, one that I hear a lot of feedback on is AWS now has far gate support for their EKS offering. Is that Something down the line you should look at or do you have some guidance as to how customers should be thinking about that, and if they want that kind of functionality, how they would get that with a solution like yours? >> Yeah, actually, we really share the same vision as AWS, right. So we believe, ultimately is the infrastructure really should be transparent to application developers, right, and it should be boundary-less. So our goal is not only manage Kubernetes, across multiple environment, but eventually we will be able to link all these cluster together, to make them acting as a single infrastructure. So developers, they can still use their familiar Kubernetes interface to deploy and manage their application, but without worrying about the how infrastructure underneath is operated or managed, right. So this in a way will eventually become kind of a phallic model, but across multiple cluster and multiple clouds. >> Alright, Tina, if maybe if you could give us the final takeaway, people attending Kubecon, cloud native con, what's the one thing that if you know they have a problem, they should be coming to Spectro cloud to hear more about? >> Yeah, sure so what Spectrol cloud aims to do is help enterprises not have to trade off between flexibility and control of their infrastructure, and manageability of use that stuff's that's the main, the main thing that we would like people to remember. >> All right, well Tenry and Tina, thank you so much for sharing with our community a little bit about Specter Cloud great talking to you and look forward to hearing more in the future. >> Thanks so much. >> Thank you too. >> All right, and stay tuned more coverage from Kubecon Cloud Native Con 2020. I'm Stu MiniMan and thank you, for watching "theCUBE." (light music)

>>live from Atlanta, Georgia. It's the Q covering answerable Best 2019. Brought to you by Red Hat. >>Welcome back. Everyone keeps live coverage of answerable fast here in Atlanta. Georgia John for my coach do Minutemen were here. Stephanie chairs to the vice president of general manager of the rail business unit. Red Hat. Great to see you. Nice to see you, too. You have all your three year career. IBM now Invincible. Back, Back in the fold. >>Yeah. >>So last time we chatted at Red Hat Summit Rail. Eight. How's it going? What's the update? >>Yeah, so we launched. Related some. It was a huge opportunity for arrested Sort of Show it off to the world. A couple of key things we really wanted to do There was make sure that we showed up the red hat portfolio. It wasn't just a product launch. It was really a portfolio. Lunch feedback so far on relate has been great. We have a lot of adopters on their early. It's still pretty early days. When you think about it, it's been about a little over 445 months. So, um, still early days the feedback has been good. You know it's actually interesting when you run a subscription based software model, because customers can choose to go to eight when they need those features and when they assess those features and they can pick and choose how they go. But we have a lot of folks who have areas of relate that they're testing the feature function off. >>I saw a tweet you had on your Twitter feed 28 years old, still growing up, still cool. >>Yeah, >>I mean 28 years old, The world's an adult now >>know Lennox is running. The enterprise is now, and now it's about how do you bring new innovation in when we launched Relate. We focused really on two sectors. One was, how do we help you run your business more efficiently? And then how do we help you grow your business with innovation? One of the key things we did, which is probably the one that stuck with me the most, was we actually partnered with the Redhead Management Organization and we pulled in the capability of what's called insights into the product itself. So all carbon subscription 678 all include insights, which is a rules based engine built upon the data that we have from, you know, over 15 years of helping customers run large scale Lennox deployments. And we leverage that data in order to bring that directly to customers. And that's been huge for us. And it's not only it's a first step into getting into answerable. >>I want to get your thoughts on We're here and Ansel Fest ate one of our two day coverage. The Red Hat announced the answer Automation platform. I'll see. That's the news. Why is this show so important in your mind? I mean, you see the internal. You've seen the history of the industry's a lot of technology changes happening in the modern enterprises. Now, as things become modernized both public sector and commercial, what's the most important thing happening? Why is this as well fest so important this year? >>To me, it comes down to, I'd say, kind of two key things. Management and automation are becoming one of the key decision makers that we see in our customers, and that's really driven by. They need to be efficient with what they have running today, and they need to be able to scale and grow into innovation. platform. So management and automation is a core critical decision point. I think the other aspect is, you know, Lennox started out 28 years ago proving to the world how open source development drives innovation. And that's what you see here. A danceable fest. This is the community coming together to drive innovation, super modular, able to provide impact right from everything from how you run your legacy systems to how you bring security to it into how do you bring new applications and deploy them in a safe and consistent way? It spans the whole gambit. >>So, Stephanie, you know, there's so much change going on in the industry you talked about, you know what's happening in Relate. I actually saw a couple of hello world T shirts which were given out at Summit in Boston this year, maybe help tie together how answerable fits into this. How does it help customers, you know, take advantage of the latest technology and and and move their companies along to be able to take advantage of some of the new features? >>Yeah, and so I really believe, of course, that unopened hybrid cloud, which is our vision of where people want to go, You need Lennox. So Lenox sits at the foundation. But to really deploy it in in a reasonable way in a Safeway in an efficient way, you need management on automation. So we've started on this journey. When we launched, we announced its summit that we brought in insights and that was our first step included in we've seen incredible uptick. So, um, when we launch, we've seen 87% increase since May in the number of systems that are linked in, we're seeing 33% more increase in coverage of rules based and 152% increase in customers who are using it. What that does is it creates a community of people using and getting value from it, but also giving value back because the more data we have, the better the rules get. So one interesting thing at the end of May, the engineering team they worked with all the customers that currently have insights. Lincoln and they did a scan for Specter meltdown, which, of course, everyone knows about in the industry with the customers who had systems hooked up, they found 100 and 76,000 customer systems that were vulnerable to Spector meltdown. What we did was we had unanswerable playbook that could re mediate that problem. We proactively alerted those customers. So now you start to see problems get identified with something like insights. Now you bring an answerable and answerable tower. You can effectively decide. So I want to re mediate. I can re mediate automatically. I can schedule that remediation for what's best for my company. So, you know, we've tied these three things together kind of in the stepwise function. In fact, if you have a real subscription, you've hooked up to insights. If insights finds an issue, there's a fix it and with answerable, creates a playbook. Now I can use that playbook and answerable tower so really ties through nicely through the whole portfolio to be able to to do everything in feeling. >>It also creates collaboration to these playbooks can be portable, move across the organization, do it once. That's the automation pieces that >>yeah, absolutely. So now we're seeing automation. How do you look at it across multiple teams within an organization so you could have a tower, a tower admin be able to set rules and boundaries for teams, I can have an array l writes. I t operations person be able to create playbooks for the security protocols. How do I set up a system being able to do things repeatedly and consistently brings a whole lot of value and security and efficiency? >>One of the powers of answerable is that it can live in a header Ji. In this environment, you got your windows environment. You know, I've talked of'em where customers that are using it and, of course, in cloud help help us understand kind of the realm. You know why rail plus answerable is, you know, an optimal solution for customers in those header ingenious environment. And what would love I heard a little bit in the keynote about kind of the road map where it's going. Maybe you can talk to about where those would fit together. >>Yeah, perfect and e think your comment about Header genius World is is Keith. That is the way we live, And folks will have to live in a head or a genius, a cz far as the eye can see. And I think that's part of the value, right to bring choice when you look at what we do with rail because of the close collaboration we have between my team and Theo team. That in the management bu around insights are engineering team is actively building rules so we can bring added value from the sense of we have our red Hat engineers who build rail creating rules to mitigate things, to help things with migration. So us develop well, Aden adoption. We put in in place upgrades, of course, in the product. But also there's a whole set of rules curated, supported by red hat that help you upgrade to relate from a prior version. So it's the tight engineering collaboration that we can bring. But to your point, it's, you know, we want to make sure that answerable and answerable tower and the rules that are set up bring added value to rebel and make that simple. But it does have to be in a head of a genius world. I'm gonna live with neighbors in any data center. Of course, >>what one of the pieces of the announcement talked about collections, eyes there, anything specific from from your team that it should be pointed out about from a collections in the platform announcement. >>So I think I think his collection starts to starts to grow on. Git brings out sort of the the simplicity of being pulled. It pulled playbooks and rolls on and pull that all in tow. One spot. We'll be looking at key scenarios that we pulled together that mean the most Terrell customers. Migration, of course, is one. We have other spaces, of course. Where we work with key ecosystem partners, of course, ASAP, Hana, running on rail has been a big focus for us in partnership with S A P. We have a playbook for installing ASAP Hana on Well, so this collaboration will continue to grow. I think collections offers a huge opportunity for a simpler experience to be able to kind of do a automated solution, if you will kind of on your floor >>automation for all. That's the theme here. >>That's what I >>want to get your thoughts on. The comment you made about analytical analytics keep it goes inside rail. This seems to be a key area for insights. Tying the two things together so kind of cohesive. But decoupled. I see how that works. What kind of analytical cables are you guys serving up today and what's coming around the corner because environments are changing. Hybrid and multi cloud are part of what everyone's talking about. Take care of the on premises. First, take care of the public cloud. Now, hybrids now on operating model has to look the same. This is a key thing. What kind of new capabilities of analytics do you see? >>Yes, that's it. So let me step you through that a little bit because because your point is exactly right. Our goal is to provide a single experience that can be on Prem or off Prem and provides value across both, as as you choose to deploy. So insights, which is the analytics engine that we use built upon our data. You can have that on Prem with. Well, you can have it off from with well, in the public cloud. So where we have data coming in from customers who are running well on the public cloud, so that provides a single view. So if you if you see a security vulnerability, you can skin your entire environment, Which is great. Um, I mentioned earlier. The more people we have participating, the more value comes so new rules are being created. So as a subscription model, you get more value as you go. And you can see the automation analytics that was announced today as part of the platform. So that brings analytics capabilities to, you know, first to be able to see what who's running what, how much value they're getting out of analytics, that the presentation by J. P. Morgan Chase was really compelling to see the value that automation is delivering to them. For a company to be ableto look at that in a dashboard with analytics automation, that's huge value, they can decide. Do we need to leverage it here more? Do we need to bring it value value here? Now you combine those two together, right? It's it, And being informed is the best. >>I want to get your reaction way Make common. Are opening student in our opening segment around the J. P. Morgan comment, you know, hours, two minutes, days, two minutes, depending on what the configurations. Automation is a wonderful thing. Where pro automation, as you know, we think it's gonna be huge category, but we took, um ah survey inside our community. We asked our practitioners in our community members about automation, and then they came back with the following. I want to get your reaction. Four. Major benefits. Automation focused efforts allows for better results. Efficiency. Security is a key driver in all this. You mentioned that automation drives job satisfaction, and then finally, the infrastructure Dev ops folks are getting re skilled up the stack as the software distraction. Those are the four main points of why automation is impacting enterprise. Do you agree with that? You make comments on some of those points? >>No, I do. I agree. I think skills is one thing that we've seen over and over again. Skills is skills. His key. We see it in Lennox. We have to help, right? Bridge window skills in tow. Lennox skills. I think automation that helps with skills development helps not only individuals but helps the company. I think the 2nd 2nd piece that you mentioned about job satisfaction at the end of the day, all of us want to have impact. And when you can leverage automation for one individual toe, have impact that that is much broader than they could do before with manual tasks. That's just that's just >>you know, Stew and I were talking also about the one of the key note keywords that kept on coming out and the keynote was scales scales, driving a lot of change in the industry at many levels. Certainly, software automation drives more value. When you have scale because you scaling more stuff, you can manually configure his stuff. A scale software certainly is gonna be a big part of that. But the role of cloud providers, the big cloud providers see IBM, Amazon, all the big enterprises like Microsoft. They're traveling massive scale. So there's a huge change in the open source community around how to deal with scale. This is a big topic of conversation. What's your thoughts on this? Sending general opinions on how the scales change in the open source equation. Is it more towards platforms, less tools, vice versa? Is there any trends? You see? >>I think it's interesting because I think when I think a scale, I think both volume right or quantity as the hyper scale ours do. I think also it's about complexity. I think I think the public clouds have great volume that they have to deal with in numbers of systems, but they have the ability to customize leveraging development teams and leveraging open source software they can customize. They can customize all the way down to the servers and the processor chips. As we know for most folks, right, they scale. But when they scale across on Prem in off from its adding complexity for them. And I think automation has value both in solving volume issues around scale, but also in complexity issues around scale. So even you know mid size businesses if they want a leverage on Prem, an off ramp to them, that's complexity scale. And I think automation has a huge amount of value to >>bring that abstracts away. The complexity automated, absolutely prized job satisfaction but also benefits of efficiency >>absolutely intimately. The greatest value of efficiency is now. There's more time to bring an innovation right. It's a zoo, Stephanie. >>Last thing I wondering, What feedback are you hearing from customers? You know, one of the things that struck me we're talking about the J. P. Morgan is they made great progress. But he said they had about a year of working with security of the cyber, the control groups to help get them through that knothole of allowing them toe really deploy automation. So, you know, usually something like answerable. You think? Oh, I can get a team. Let me get it going. But, oh, wait, no, Hold on. Corporate needs to make its way through. What is that something you hear generally? Is that a large enterprise thing? You know what? What are you hearing from customers that you're >>talking? I think I think we see it more and more, and it came up in the discussions today. The technical aspect is one aspect. The sort of cultural or the ability to pull it in is a whole separate aspect. And you think that technology from all of us who are engineers, we think, Well, that's the tough bit. But actually, the culture bit is just it's hard. One thing that that I see over and over again is the way cos air structured has a big impact. The more silo the teams are, do they have a way to communicate because fixing that so that you, when you bring in automation, it has that ability to sort of drive more ubiquitous value across. But if you're not structured toe leverage that it's really hard if your I T ops guys don't talk to the application folks bringing that value is very hard, so I think it is kind of going along in parallel right. The technical capabilities is one aspect. How you get your organization structure to reap the benefits is another aspect, and it's a journey. That's that's really what I see from folks. It is a journey. And, um, I think it's inspiring to see the stories here when they come back and talk about it. But to me the most, the greatest thing about it's just start right. Just start wherever you are and and our goal is to try and help on ramps for folks wherever their journey is, >>is a graft over people's careers and certainly the modernization of the enterprise and public sector and governments from how they procure technology to how they deploy and consume it is radically changing very quickly. By the way too scale on these things were happening. I've got to get your take on. I want to get your expert opinion on this because you have been in the industry of some of the different experiences. The cloud one Datta was the era of compute storage startups started Airbnb start all these companies examples of cloud scale. But now, as we start to get into the impact to businesses in the enterprise with hybrid multi cloud, there's a cloud. 2.0 equation again mentioned Observe Ability was just network management at White Space. Small category. Which company going public? It's important now kind of subsystem of cloud 2.0, automation seems to feel the same way we believe. What's your definition of cloud to point of cloud? One daughter was simply stand up some storage and compete. Use the public cloud and cloud to point is enterprise. What does that mean to you? What? How would you describe cloud to point? >>So my view is Cloud one Dato was all about capability. Cloud to Dato is all about experience, and that is bringing a whole do way that we look at every product in the stack, right? It has to be a seamless, simple experience, and that's where automation and management comes in in spades. Because all of that stuff you needed incapability having it be secure, having it be reliable, resilient. All of that still has to be there. But now you now you need the experience or to me, it's all about the experience and how you pull that together. And that's why we're hoping. You know, I'm thrilled here to be a danceable fast cause. The more I can work with the teams that are doing answerable and insights and the management aspect in the automation, it'll make the rail experience better >>than people think it's. Software drives it all. Absolutely. Adam, Thanks for sharing your insights on the case. Appreciate you coming back on and great to see you. >>Great to be here. Good to see >>you. Coverage here in Atlanta. I'm John for Stupid Men Cube coverage here and answerable Fest Maur coverage. After the short break, we'll be right back. >>Um

>> Live from Washington D.C. It's theCUBE, covering the AWS Public Sector Summit 2018. Brought to you by Amazon Web Services, and its eco-system partners. (upbeat music) >> Welcome back to the nation's capitol. I'm Stu Miniman and you're watching theCUBE's coverage of AWS Public Sector Summit 2018. Joining me for the wrap-up of day one, John Furrier, Dave Vellante. So John, thanks for bringing us down. So you were here last year. We've interviewed Teresa Carlson a number of times at Reinvent, but we've got to start with you. Since you were here last year, watching this explode. I said, this reminds me of Reinvent three years ago, how big it is, 14,500 people, wow. >> Yeah, so you're right on. This is definitely a Reinvent kind of vibe, in a way to describe what happened with Amazon Reinvent, their annual conference which we were at the 2nd year, 2013, and have been every year. Reinvent got bigger every year, and just became more prominent, and the solutions scaled, the number of announcements, as we know Amazon today is packed, it's bigger than ever. The public sector market, which is defined as government, education, and global public sector countries like Bahrain and other countries, are really the target. They have unique requirements. So what's happening is that that market is being disrupted, and there's been similar moments in the public sector here in the United States, as well known. The fail of the website that Obama. You know, the health care sight was one. The government initiatives that have been going on. The government is not modern and people are frustrated. The IT workers are living in cages, they're strapped in. It's like, not good. The tooling's old, old client server, old vendors like Oracle and IBM and others that are trying to keep that business, and they're not modernizing. So, this modernization wave has hit the public sector across the board, and what's happening is they can actually build newer systems faster, and get lower cost, more efficiency, done faster. And this is disrupting not only their business model, but how they buy technology, the role of the supplier in that piece of the equation, and also just overall faster innovation. So, this is driving it. The shocker of all of it is the security conversation has been up leveled, meaning it's not a real issue. Certainly the security is a real issue, but in terms of a barrier that stops everything, that's not the case anymore. The CIA is really the most notable that came on and said the worst day in cloud security is better than anything we got working today. So that's a really interesting thing and the Department of Defense Jedi project is billions of dollars that would have gone to say, an Oracle, IBM, and all the incumbents, or, beltway bandits, as they've been called. Those days are over. So that to me is a really exciting thing for the country. But, Amazon is running the tables too. So again, this year, more of the same, bigger. Big agencies. Small partners and big, all riding the wave of growth. And, it's a new operating model, and again, we'll predict it here in theCUBE, as we always say, and then we'll be right again. This is going to be a special market for Amazon going forward. >> I think government market is definitely a microcosm of the overall marketplace as John said. It's very bureaucratic, they're slower to move, you got to regime change every four or eight years, so a lot of turnover. It's really hard to get. Okay, we're going to go with strategy, cause the strategy as they start stop, it's a near to mid term strategies are affected in the government. Obviously, there's a greater focus on security. Cloud addresses a lot of those. We certainly heard that from the CIA. I don't think you can talk about cloud and federal, without talking about that milestone CIA deal. That really was a watershed moment. It was a wake up call to the old guard. IBM, as you might recall, tried to fight the government, because the CIA awarded the contract to Amazon. IBM lost that case, they were eviscerated by the judge. It forced IBM to go out and pay two billion dollars for software. It was years later that Oracle really got in. So, Amazon, to an earlier guest's point, has a huge lead. The estimate was five to 10 years, I heard, over some of the legacy suppliers. Interesting, not sure exactly where Microsoft fits in there. Stu, I'd love to get your thoughts. The thing about cloud that we've, John, you talk about being right, for years, we've talked about the economics of cloud, the scale of cloud, the marginal economics, looking much more like software. That's clearly been to Amazon's advantage. And, they're mopping the floor with guys who can't keep pace. And so, that's played out in a big way, and this seems to be a winner take all market. Or, a few companies take all market. >> Yeah, the thing that I actually wanted to comment on that's really interesting to dig in here, is if you talk about application modernization. Yes, it is super challenging, and it's not happening overnight, but, have heard universities, non profits, they're moving. It's not just mobility, moving to the web, but talking about how they are decoupling and creating cloud native microservices environments. So, was talking to a large, government healthcare organization that was super excited to show me how he was going to take his really old application, and start pulling together services at a time. And, he's like, I've got 130 services. And here's how I'll stick a router in here and I'll start pulling them off to the cloud. Talked to a big university and said, how are they going from, my data center, which I'm out of power, I'm out of capacity. I'm going to use the VMWare thing, but over time, I'm moving to containers, I'm moving to serverless. That modernization, we know it's not moving all of it to the public cloud, but that migration is happening. It is challenging and as I've said many times in many of these Amazon shows, Dave and John, it's the companies that come here. They're the ones that are trying cool stuff. They're are able to play in some of these environments and they make progress. So, the thing that really excites me too, is when you hear government agencies that are doing innovative, cool things. It's like, how do I leverage my data and give back to the communities I serve. Help charities, help our communities, and do it in cost effective ways. >> Stu, I got to say, Dave, Theresa Carlson just came by theCUBE, we gave her a wave. She's the CEO of Public Sector, as I call her, she's the chief, she's in charge. Andy Jackson's the CEO of AWS, but again, public sector's almost its own little pocket of AWS. Her leadership, I think, is a real driving force of why it was successful so fast. Theresa Carleson is hard charging, she knows the government game. She's super nice, but she can fight. And she motivates her team. But she listens to the customers, and she takes advantage of that Amazon vibe, which is solve a problem, lower prices, make things go faster, that's the flywheel of the culture. And she brings it to a whole nother level. She's brought together a group of people that are succeeding with her. She leans on her partners, so partners are making money. She's bringing in cloud native kind of culture. I mean, CrowdStrike, you can't get any better than seeing guys like CrowdStrike raise 200 million dollars, Dave, today announced, worth over three billion dollars, because they built their system to work for cloud scale. CloudChecker, another company. Purpose built for the cloud and is extremely successful because they're not trying to retrofit an enterprise technology and make it cloudified. They actually built it for the cloud. This, to me, is a signal of what has to happen on successful deployments, from a customer standpoint. And I think that's what attracting the customers and they will change their operations 'cause the benefits are multifold and they're pretty big. Financially, operationally, culturally, it's disruptive. So I think that's a key point. >> Yeah, and I think again, this a microcosm of the larger AWS, which is a microcosm of the larger Amazon, but, some of the things we heard today, some of the benchmarks and milestones from Theresa on the keynote. 60 consultancies that she put up on the slide, 200 ISVs ans SAAS companies, 950 third-party software providers, this is all GovCloud. And then Aurora now in GovCloud, which is, you know, you see here, it lags. >> Database. >> Amazon and Specter, you've heard a lot about database. Amazon and Specter, which manages security configurations. We heard about the intent to go forward with the VMWare partnership, the VMWare cloud in GovCloud. So, a little bit behind where you see the Amazon web services in commercial. But, taking basically the same strategy as John said. The requirements are different. I also think, Stu and John, it's important to point out just the progress of AWS. We're talking about tracking to 22 billion dollars this year. They're growing still at 15 percent, that massive number. 26 percent operating income. Their operating income is growing at 54 percent a year. So, just to compare Amazon web services to other so called infrastructure providers, HPE's operating income is eight percent, IBM's is nine percent, VMWare, which is a software company, is at 19 percent, Amazon's at 26 percent. It's Cisco level of profitability. Only companies like Oracle and Microsoft are showing better operating income. This is that marginal economics, that we've talked about for years. And Amazon is crushing it, just in terms of the economic model. >> Yeah, and they bring in the public sector. Can you imagine that disruption for that incumbent mindset of these government kind of agencies that have been the frog in boiling water for so many years around IT. It's like Boom, what a wake up call. If you know IT, you know what it's like. Older tools, huge budget cycles, massive amounts of technology trends in terms of time to value. I mean, Stu, you've seen this buoy before. >> Yeah, absolutely, and it's interesting. Some of the things we heard is there's challenge in the government sometimes, moving from capex to opex. The way that government is used to buying is they buy out of the GSA catalog, they are making that move. We actually had on the federal CTO for Cohesity, came from the GSA, and he said we're making progress as an industry on this. Dave, you mentioned a whole lot of stats here. I mean, year after year, Q1 Amazon was up 49 percent revenue growth. So, you know, you always hear on the news, it's like, oh well, market share is shifting. Amazon is still growing at such a phenomenal pace, and in the GovCloud, one of the things I thought Kind of interesting that gets overlooked is the GovCloud is about five years, no it launched in August of 2011, so it's coming up on seven years. It's actually based out of the West Coast. They have GovCloud, US East is coming later this year. And we talked in the VMWare interview that we did today about why some of the lag and you need to go through the certification and you need to make sure there's extra security levels. Because, there's not only GovCloud, then they've got the secret region, the top secret region, so special things that we need to make sure that you're FedRAMP compliant and all these things. Amazon is hitting it hard, and definitely winning in this space. >> Yeah, and they have a competitive advantage, I mean, they're running the table, literally. Because no body else has secret cloud, right? So, Amazon, Google, others, they don't have what the spec requires on these big agencies, like the DOD. So, it's not a sole source deal. And we saw the press that President Trump had dinner with Safra Catz, the CEO of Oracle. And, that Amazon, that people are crying foul. Making a multicloud, multivendor kind of, be fair, you know fairness. Amazon's not asking for sole source, they're just saying we're responding to the bid. And, we're the only ones that actually can do it. You know, John Wood, the CEO of Telos, said it best on theCUBE today, Amazon is well down the road, five years advantage over any cloud, five years he said. >> There's no compression algorithm for experience, right? >> Right, right, but this is a real conundrum for the government buyers, the citizens, and the vendors. So, typically, let's face it, technology, IBM, HPE, Oracle, Dell, they can all pretty much do the same thing. Granted, they got software, Cisco, whatever. They got their different spaces, but head to head, they all pretty much can do what the RFP requires. But what you just pointed out John, is Amazon's the only one that can do a lot of this stuff, and so, when they say, okay let's make it fair, what they're really saying is, let's revert back to the mean. Is that the right thing for the citizens? That's the kind of question that's on the table now. As a citizen, do you want the government pushing the envelope... >> That's what he said from CrowdStrike, why go backwards? >> Right, right, but that's essentially what the old guard is saying. Come back to us, make it fair, is that unfair? >> You're too successful, let the competition catch up, so it can be fair. No, they've got to match up the value proposition. And that fundamentally is going to put the feet to fire of government and it's going to be a real critical tell sign on how much teeth to the mission that the government modernization plan is. If that mission to be modernizing government has teeth, they will stay in the course. Now, if they have the way to catch up, that's great. I can already hear it on Twitter, John, you don't really know what you're talking about. Microsoft's right there. Okay, you can say you're doing cloud, but as they teach you in business school, there's diseconomies of scale, to try to match a trajectory of an experienced cloud vendor. Stu, you just mentioned that, let's explore that. If I want to match Amazon's years of experience, I can say I'm up there with all these services, but you can't just match that overnight. There's diseconomies of scale, reverse proxies, technical debt, all kinds of stuff. So, Microsoft, although looking good on paper, is under serious pressure and those diseconomies of scales creates more risk. That more risk is more downtime. They just saw 11 hours of downtime on Microsoft Azure in Europe, 11 hours. That's massive, it's not like, oh, something just happened for a day. >> Here's the behind the scenes narrative that you hear from certain factions. Is, hey, we hire people, let's say I'm talkin' about Microsoft, we hire people out of Amazon too, we know where they're at. We think we've narrowed that lead down to six months. You and I have both heard that. When you talk to people on the other side of the table, it's like, no way, there's no way. We're movin' faster, in fact, our lead is extended. So, the proof is in the pudding. In the results that you see in the marketplace. >> Well, and just to build on that, the customers. Amazon has the customers, you talk to anybody that's in these agencies, you know, like any industry, they're all moving around. Not only the federal, but, I had a great interview with Nutanix this morning, he said this was the best collection of state and local government that I ever had. It's like I got to meet all my customers in person last year when they came here. So, the fed kind of sets the bar, and then state, local, education, they all learn there. So, as you said, John, Theresa and her team have really built a flywheel of customers, and those customers, they understand the product. They're going deeper on that. >> But look, Microsoft has success where it has a software state. Clearly there are a lot of Microsoft customers in the government, and they're going to do very well there. But it's really different. We're talkin' about the inventor, essentially, of infrastructure as a service in Public Cloud and Amazon with a clean sheet of paper. >> Microsoft, Google and the others, they have to catch up. So, really if you look at, let's compare and contrast. Amazon, first mover, they did the heavy lifting up front. They win the CIA deal three, four years ago. Now they're going to win the DOD deal and more. So, they've got the boiler plate, and they've got scale, economies of scale. Microsoft's got to catch up, so, they've got diseconomies of scale. Google is kind of backing out, we heard. Some Google employees revolting cause they don't want to work on these AI projects for drones or what not. But, Google's approach is not tryin' to match Amazon speed for speed, they're thing is they have leverage. Their Android, their security, the data. So, Google's staying much more pragmatic. And they're humble, they're saying, look, we're not tryin' to match Amazon. But we're going to have a badass cloud from a Google perspective. Microsoft hasn't yet said that, they just try to level up. I think if Microsoft takes that approach, they will do well. >> Well, you got to give Microsoft a lot of credit, obviously for the transformation that's occurred. Again it's still tied to the company's software estate, in my view anyway. >> All right Stu, what's your impression, what's your take? >> So, John, like every Amazon show I've been to, I'm impressed, it set a high bar. We go to a lot of shows and not only are there more people here, but the quality of people, the energy, the passion, the discussion of innovation and change, is just super impressive. >> You and I cover cloud data pretty deep. We go to all the shows, obviously the Lennox Foundation and Amazon Reinvent, and others. Does the Public Sector have that vibe in your opinion? What's your sense of it? >> Oh, yeah, no, I've already had a couple of conversations about Kubernetes and Lambda, you know, more serverless conversations at this show than almost any show I go to, other than probably KubeCon or the Serverless conf. So, no, advanced users, these are not the ones, a couple of years ago, oh I'm checking what this is. No, no, no, they're in, they're deep, they're using. >> Yeah, I notice also, near the press room, they had the certification stickers, now levels of certifications. So, they're just movin' the ball down the field at Amazon. Dave, I want to go to you and ask you what your impression is. Obviously, you know, we've done shows like HPE Reinvent, which we didn't do this year. That's goin' down its own path. We've got other shows. >> HPE Discover you mean. >> What did I say? >> You said Reinvent. >> Okay, every year they break. >> There's two ends of the spectrum. >> You know, there's is going to try to transform. What's your take of this show, Public Sector? What's your view? >> Well, first of all, it's packed. And, the ecosystem here is really robust. I mean, you see the consultancies, you see every technology vendor, I mean, it's quite amazing. They got to figure out the logistics, right? I've never seen a line so long. The line to get into registration was longer than Disney lines this morning. I mean, really, it was amazing. >> It's a Disneyland for Public Sector. >> It really is, and people are excited here. I think you were touching upon it before. They've sort of been hit with this bureaucratic, you know, cemented infrastructure. And now, it's like they're takin' the gloves off and they're really excited. >> Stu and Dave, I really got to say, I'm not a big federal person, over the years in my career but my general impression over the past couple years, diggin' in here, is that most of the people in the agency want to do a good job. I saw that last year, it's like, these are real innovators. And finally they can break away, right, and do some real, good. Not do shadow IT, do it legit with a cloud. So, good stuff. Guys, thanks for commentating, Stu? >> Yeah, so let me bring it on home. I just want to say, this goes up in a podcast, if you go to your favorite podcast player and look for theCUBE Insights, you'll find this as well as the key analysis from our team from all of the shows. Of course, as always, go to theCube dot net to get all the research. If you want the exclusive, more detail on Theresa Carlson, just search John Ferrier in Forbes and you'll find that article. This is the end of Day One of two days live coverage from AWS Public Sector. Of course, theCUBE dot net, come find us, we've got stickers if you're at the show. For Dave Vellante, John Furrier, I'm Stu Miniman. And as always, thanks so much for watching theCUBE. (techno music)