>> Narrator: From Berlin, Germany, it's The Cube. Covering DataWorks Summit Europe 2018. Brought to you by Hortonworks. >> Well, hello. Welcome to The Cube. I'm James Kobielus. I'm the Lead Analyst for Big Data Analytics in the Wikibon team of SiliconANGLE Media. We are here at DataWorks Summit 2018 in Berlin. Of course, it's hosted by a Hortonworks. We are in day one of two days of interviews with executives, with developers, with customers. And this morning the opening keynote, one of the speaker's was a customer of Hortonworks from Munich Re, the reinsurance company based of course in Munich, Germany. Andreas Kohlmaier, who's the the head of Data Engineering I believe, it was an excellent discussion you've built out of data lake. And the first thing I'd like to ask you Andreas is right now it's five weeks until GDPR, the general data protection regulation, goes into full force on May 25th. And of course it applies to the EU, to anybody who does business in the EU including companies based elsewhere, such as in the US, needs to start complying with GDPR in terms of protecting personal data. Give us a sense for how Munich Re is approaching the deadline, your level of readiness to comply with GDPR, and how your investment in your data lake serves as a foundation for that compliance. >> Absolutely. So thanks for the question. GDPR, of course, is the hot topic across all European organizations. And we actually pretty well prepared. We compiled all the processes and the necessary regulations and in fact we are now selling this also as a service product to our customers. This has been an interesting side effect because we have lots of other insurance companies and we started to think about why not offer this as a service to other insurance companies to help them prepare for GDPR. This is actually proving to be one of the exciting interesting things that can happen about GDPR. >> Maybe that would be your new line of business. You make more money doing that then. >> I'm not sure! (crosstalk) >> Well that's excellent! So you've learned a lot of lessons. So already so you're ready for May 25th? You have, okay, that's great. You're probably far ahead of I know a lot of U.S. based firms. We're, you know in our country and in other countries, we're still getting our heads around all the steps that are needed so you know many companies outside the EU may call on you guys for some consulting support. That's great! So give us a sense for your data lake. You discussed it this morning but can you give us a sense for the business justification for building it out? How you've rolled it out? What stage it's in? Who's using it for what? >> So absolutely. So one of the key things for us at Munich Re is the issue about complexity or data diversity as it was also called this morning. So we have so many different areas where we are doing business in and we have lots of experts in the different areas. And those people and I really have they are very knowledgeable in the area and now they also get access to new sources of information. So to give you a sense we have people for example that are really familiar with weather and climate change, also with satellites. We have captains for ships and pilots for aircraft. So we have lots of expertise in all the different areas. Why? Because we are taking those risks in our books. >> Those are big risks too. You're a reinsurance company so yeah. >> And these are actually complex risks where we really have people that really are experts on their field. So we have sometimes have people that have 20 years plus of experience in the area and then they change to the insurer to actually bring their expertise on the field also to the risk management side. And all those people, they now get an additional source of input which is the data that is now more or less readily available everywhere. So first of all, we are getting new data with the submissions and the risks that we are taking and there are also interesting open data sources to connect to so that those experts can actually bring their knowledge and their analytics to a new level by adding the layer of data and analytics to their existing knowledge. And this allows us, first of all, to understand the risks even better, to put a better price tag on that, and also to take up new risks that have not been possible to cover before. So one of the things is also in the media I think is that we are also now covering the Hyperloop once it's going to be built. So those kind of new things are only possible with data analytics. >> So you're a Hortonworks customer. Give us a sense for how you're using or deploying Hortonworks data platform or data plane service and whatnot inside of your data lake. It sounds like it's a big data catalog, is that a correct characterization? >> So one of the things that is key to us is actually finding the right information and connecting those different experts to each other. So this is why the data catalog plays a central role. Here we have selected Alation as a catalog tool to connect the different experts in the group. The data lake at the moment is an on-prem installation. We are thinking about moving parts of that workload to the cloud to actually save operation costs. >> On top of HTP. >> Yeah so Alation is actually as far as I know technically it's a separate server that indexes the hive tables on HTP. >> So essentially the catalog itself is provides visualization and correlation across disparate data sources that are managing your hadoop. >> Yeah, so the the catalog actually is a great way of connecting the experts together. So that's you know okay if we have people on one part of the group that are very knowledgeable about weather and they have great data about weather then we'd like to connect them for example to the guys that doing crop insurance for India so that they can use the weather data to improve the models for example for crop insurance in Asia. And there the data catalog helps us to connect those experts because you can first of all find the data sources and you can also see who is the expert on the data. You can then also call them up or ask them a question in the tool. So it's essentially a great way to share knowledge and to connect the different experts of the group. >> Okay, so it's also surfacing up human expertise. Okay, is it also serving as a way to find training datasets possibly to use to build machine learning models to do more complex analyses? Is that something that you're doing now or plan to do in the future? >> Yes, so we are doing some of course machine learning also deep learning projects. We are also just started a Center of Excellence for artificial intelligence to see okay how we can use deep learning and machine learning also to find different ways of pricing insurance lists for example and this of course for all those cases data is key and we really need people to get access to the right data. >> I have to ask you. One of the things I'm seeing, you mentioned Center of Excellence for AI. I'm seeing more companies consider, maybe not do it, consider establishing a office of the chief AI officer like reporting to the CEO. I'm not sure that that's a great idea for a lot of businesses but since an insurance company lives and dies by data and calculations and so forth, is that something that Munich Re is doing or considering in a C-Suite level officer of that sort responsible for this AI competency or no? >> Could be in the future. >> Okay. >> We sort of just started with the AI Center of Excellence. That is now reporting to our Chief Data Officer so it's not yet a C-Suite. >> Is the Center of Excellence for AI, is it simply like a training institute to provide some basic skill building or is there something more there? Do you do development? >> Actually they are trying out and developing ways on how we can use AI on deep learning for insurance. One of the core things of course is also about understanding natural language to structure the information that we are getting in PDFs and in documents but really also while using deep learning as a new way to build tariffs for the insurance industry. So that's one of the the core things to find and create new tariffs. And we also experimenting, haven't found the product yet there, whether or not we can use deep learning to create better tariffs. That could also then be one of the services, again we are providing to our customers, the insurance companies and they build that into their products. Something like yeah the algorithms is powered by Munich Re. >> Now your users of your data lake, these are expert quantitative analysts, right, for the most part? So you mentioned using natural language understanding AI capabilities. Is that something that you have a need to do in high volume as a reinsurance company? Take lots of source documents and be able to as it were identify the content and high volume and important you know not OCR but rather the actual build a graph of semantic graph of what's going on inside the document? >> I'm going to give you an example of the things that we are doing with natural language processing. And this one is about the energy business in the US. So we are actually taking up or seeing most of the risks that are related to oil and gas in the U.S. So all the refineries, all the larger stations, and the the petroleum tanks. They are all in our books and for each and every one of them we get a nice report on risks there with a couple of hundred of pages. And inside these reports there's also some paragraph written in where actually the refinery or the plants gets its supplies from and where it ships its products to. And thence we are seeing all those documents. That's in the scale of a couple of thousands so it's not really huge but all together a couple of hundred thousand pages. We use NLP and AI on those documents to extract the supply chain information out of it so in that way we can stitch together a more or less complete picture of the supply chain for oil and gas in the U.S. which helps us again to better understand that risk because supply chain breakdown is one of the major risk in the world nowadays. >> Andreas, this has been great! We can keep on going on. I'm totally fascinated by your use of AI but also your use of a data lake and I'm impressed by your ability to get your, as a company get your as we say in the U.S. get your GDPR ducks in a row and that's great. So it's been great to have you on The Cube. We are here at DataWorks Summit in Berlin. (techno music)

>>Good afternoon everyone, and welcome back to the Cube. I am Savannah Peterson here, coming to you from Detroit, Michigan. We're at Cuban Day three. Such a series of exciting interviews. We've done over 30, but this conversation is gonna be extra special, don't you think, John? >>Yeah, this is gonna be a good one. Griffon Labs is here with us. We're getting the conversation of what's going on in the industry management, watching the Kubernetes clusters. This is large scale conversations this week. It's gonna be a good one. >>Yeah. Yeah. I'm very excited. He's also got a fantastic Twitter handle, twitchy. H Please welcome Richie Hartman, who is the director of community here at Griffon. Richie, thank you so much for joining us. Thanks >>For having me. >>How's the show been for you? >>Busy. I, I mean, I, I, >>In >>A word, I have a ton of talks at at like maintain a thing and like the covering board searches at the TLC panel. I run forme day. So it's, it's been busy. It, yeah. Monday, I didn't have to run anything. That was quite nice. But there >>You, you have your hands in a lot. I'm not even gonna cover it. Looking at your bio, there's, there's so many different things that you're working on. I know that Grafana specifically had some announcements this week. Yeah, >>Yeah, yeah. We had quite a few, like the, the two largest ones is a, we now have a field Kubernetes integration on Grafana Cloud. So our, our approach is generally extremely open source first. So we try to push stuff into the exporters, like into the open source exporters, into mixes into things which are out there as open source for anyone to use. But that's little bit like a tool set, not a ready made solution. So when we talk integrations, we actually talk about things where you get this like one click experience, You log into your Grafana cloud, you click, I have a Kubernetes, which probably most of us have, and things just work like you in just the data. You have to write dashboards, you have to write alerts, you have to write everything to just get started with extremely opinionated dashboards, SLOs, alerts, again, all those things made by experts, so anyone can use them. And you don't have to reinvent the view for every single user. So that's the one. The other is, >>It's a big deal. >>Oh yeah, it is. Yeah. It is. It, we, we has, its heavily in integrations course. While, I mean, I don't have to convince anyone that perme is a DD factor standard in everything. Cloudnative. But again, it's, it's, it's sometimes a little bit hard to handle or a little bit not easy to get into. So, so smoothing this, this, this path onto onboarding yourself onto this stack and onto those types of solutions. Yes. Is what a lot of people need. Course, if you, if you look at the statistics from coupon, and we just heard this in the governing board session yesterday. Yeah. Like 60% of the people here are first time attendees. So there's a lot of people who just come into this thing and who need, like, this is your path. This is where you should be going. Or at least if you want to go, go there. This is how to get there. >>Here's your runway for takeoff. Yes. Yeah. I think that's a really good point. And I love that you, you had those numbers. I was curious. I, I had seen on Twitter, speaking of Twitter, I had seen, I had seen that, that there were a lot of people here coming for the first time. You're a community guy. Are we at an inflection point where this community is about to continue to scale? >>That's a very good question. Which I can't really answer. So I mean, >>Obviously I bet you're gonna try. >>I covid changed a few things. Yeah. Probably most people, >>A couple things. I mean, you know, casually, it's like such a gentle way of putting that, that was >>Beautiful. I'm gonna say yes, just to explode. All these new ERs are gonna learn Prometheus. They're gonna roll in with a open, open metrics, open telemetry. I love it, >>You know, But, but at the same time, like Cuban is, is ramping back up. But if you look at the, if you look at the registration numbers between Valencia Andro, it was more or less the same. Interesting. Which, so it didn't go onto this, onto this flu trajectory, which it was on like, up to, up to 2019. I expect this to take up again. But also with the economic situation, everything, I, I don't think >>It's, I think the jury's still out on hybrid. I think there's a lot, lot more hybrid. Let's see how the projects are gonna go. That's what I think it's gonna be the tell sign. How many people are in participating? How are the project's advancing? Some of the momentum, >>I mean, from the project level, Most of this is online anyway. Of course. That's how open source, right. I've been working for >>Ages. That's >>Cause you don't have any trouble budget or, or any office or, It's >>Always been that way. >>Yeah, precisely. So the projects are arguably spearheading this, this development and the, the online numbers. I I, I have some numbers in my head, but I'm, I'm not a hundred percent certain to, but they're higher for this time in Detroit than in volunteer as far somewhere. Cool. So that is growing and it's grown in parallel, which also is great. Cause it's much more accessible, much more inclusive. You don't have to have a budget of at least, let's say, I don't know, two to five k to, to fly over the pond and, and attend this thing. You can just do it from your home. So that is, that's a lot more inclusive. And I expect this to, to basically be a second more or less orthogonal growth, growth path. But the best thing about coupon is the hallway track. I'm just meeting people, talking to people and that kind of thing is not really possible with, >>It's, it's great to see people >>In person. No, and it makes such a difference. I mean, yeah. Even and interviewing people in person too. I mean, it does a, it's, it's, and, and this, this whole, I mean cncf, this whole community, every company here is community first. It's how these projects come to be. I think it's awesome. I feel like you got something you're saying to say, Johnny. >>Yeah. And I love some of the advancements. Rich Richie, we talked last time about, you know, open telemetry, open metrics. You're involved in dashboards. Yeah. One of the themes here is ease of use, simplicity, developer productivity. Where do you see the ease of use going from a project standpoint? For me, as you mentions everywhere, it's pretty much, it is, it's almost all corners of the world. Yep. And new people coming in. How, how are you making it easier? What's going on? Give us the update on that. >>So we also, funnily enough at precisely this topic in the TC panel just a few hours ago, about ease of use and about how to, how to make things easier to, to handle how developers currently, like if they just want to get into the cloud native seen, they have like, like we, we did some neck and math, like maybe 10 tools at least, which you have to be somewhat proficient in to just get started, which is honestly horrendous. Yeah. Course. Like with a server, I just had my survey install my thing and it runs, maybe I need a database, but that's roughly it. And this needs to change again. Like it's, it's nice that everything is, is un unraveled. And you have, you, you, you, you don't have those service boundaries which you had before. You can do all the horizontal scaling, you can do all the automatic scaling, all those things that they're super nice. But at the same time, this complexity, which used to be nicely compartmentalized, was deliberately broken up. And so it's becoming a lot harder to, to, like, we, we need to find new ways to compartmentalize this complexity back to, to human understandable levels again, in particular, as we keep onboarding new and new and new, new people, of course it's just not good use of anyone's time to, to just like learn the basics again and again and again. This is something which should be just compartmentalized and automated away. We're >>The three, We were talking to Matt Klein earlier and he was talking about as projects become mature and all over the place and have reach and and usage, you gotta work on the boring stuff. Yes. And when it's boring, that means you have success. Yes. But then you gotta work on the plumbing. What are some of the things that you guys are working on? Because people are relying on the product. >>Oh yeah. So for with my premises head on, the highlight feature is exponential or native or spars. Histograms. There's like three different names for one single concept. If you know Prometheus, you ha you currently have hard bucket boundaries where I say my latency is lower equal two seconds, one second, a hundred milliseconds, what have you. And I can put stuff into those histogram buckets accordingly to those predefined levels, which is extremely efficient, but like on the, on the code level. But it's not very nice for the humans course you need to understand your system before you're able to, to, to choose good cutoff points. And if you, if you, if you add new ones, that's completely fine. But if you want to actually change them, course you, you figured out that you made a fundamental mistake, you're going to have a break in the continue continuity of your observability data. And you cannot undo this in, into the past. So this is just gone native histograms. On the other hand, allow me to, to, okay, I'm not going to get get into the math, but basically you define a single formula, which there comes a good default. If you have good reasons, then you can change it. But if you don't, just don't talk, >>The people are in the math, Hit him up on Twitter. Twitter, h you'll get you that math. >>So the, >>The thing is people want the math, believe me. >>Oh >>Yeah. I mean we don't have time, but hit him up. Yeah. >>There's ProCon in two weeks in Munich and there will be whole talk about like the, the dirty details of all of the stuff. But the, the high level answer is it just does what people would expect it to do. And with very little overhead, you become, you get highly, highly or high resolution histograms, which is really important for a lot of use cases. But this is not just Prometheus with my open metrics head on the 2.0 feature, like the breaking highlight feature of Open Metrics 2.0 will be you guested precisely the same with my open telemetry head on. Low and behold the same underlying technology is being put or has been put into open telemetry. And we've worked for month and month and month and even longer between all different projects to, to assert that we have one single standard which is actually compatible with each other course. One of the worst things which you can have in the cloud ecosystem is if you have soly different things and they break in subtly wrong ways, like it's much better to just not work than to break in a way, which is just a little bit wrong. Of course you won't figure this out until it's too late. So we spent, like with all three hats, we spent insane amounts of time on making this happen and, and making this nice. >>Savannah, one of the things we have so much going on at Cube Con. I mean just you're unpacking like probably another day of cube. We can't go four days, but open time. >>I know, I know. I'm the same >>Open telemetry >>Challenge acceptance open. >>Sorry, we're gonna stay here. All the, They >>Shut the lights off on us last night. >>They literally gonna pull the plug on us. Yeah, yeah, yeah, yeah. They've done that before. It's not the first time we go until they kick us out. We love, love doing this. But Open telemetry is got a lot of news too. So that's, We haven't really talked much about that. >>We haven't at >>All. So there's a lot of stuff going on that, I won't call it boring. That's like code word's. That's cube talk for, for it's working. Yeah. So it's not bad, but there's a lot of stuff going on. Like open telemetry, open metrics, This is the stuff that matters cuz when you go in large scale, that's key. It's just what, missing all the, all the stuff. >>No, >>What are we missing? What are people missing? What's going on in the show that you think that's not actually being reported on? I mean it's a lot of high web assembly for instance got a lot >>Of high. Oh yeah, I was gonna say, I'm glad you're asking this because you, you've already mentioned about seven different hats that you wear. I can only imagine how many hats are actually in your hat cabinet. But you, you are someone with your, with your fingers in a lot of different things. So you can kind of give us a state of the union. Yeah. So go ahead. Let's talk about >>It. So I think you already hit a few good points. Ease of use is definitely one of them. And, and improving the developer experience and not having this like a value of pain. Yeah. That is one of the really big ones. It's going to be interesting cause it is boring. It is janitorial and it needs a different type of persona. A lot of, or maybe not most, but a large fraction of developers like the shiny stuff. And we could see this in Prometheus where like initially the people who contributed this the most where like those restless people who need to fix that one thing, this is impossible, are going to do it. Which changed over the years where the people who now contribute the most are off the janitorial. Like keep things boring, keep things running, still have substantial changes. But but not like more on the maintenance level. >>Yeah. The maintainers. I was just gonna bring that >>Up. Yeah. On the, on the keep things boring while still pushing 'em forward. Yeah. And the thing about ease of use is a lot of this is boring. A lot of this is strategy. A lot of this is toil. A lot of this takes lots of research also in areas where developers are not really good at, like UX for example, and ui like most software developers are really bad at those cause they just think differently from normal humans, I guess. >>So that's an interesting observation that you just made. I we could unpack that on a whole nother show as well. >>So the, the thing is this is going to be interesting for the open source scene course. This needs deliberate investment by companies who assign people to those projects and say, okay, fix that one thing or make it easier to use what have you. That is a lot easier with, with first party products and projects from companies cuz they can invest directly into the thing and they see much more of a value prop. It's, it's kind of normal by now to, to allow developers or even assigned developers onto open source projects. That's not so much the case for the tpms, for the architects, for the UX and your I people like for the documentation people that there's not as much awareness of that this is also driving value for everyone. Yes. And also there's not much as much. >>Yeah, that's a great point. This whole workflow production system of open source, which has grown and keeps growing and we'll keep growing. These be funded. And one of the things we were talking earlier in another session about is about the recession potentially we're hitting and the global issues, macroeconomics that might force some of these projects or companies not to get VC >>Funding. It's such a theme at the show. So, >>So to me, I said it's just not about VC funding. There's other funding mechanisms that's community oriented. There's companies participating, there's other meccas. Richie, if you could have your wishlist of how things could progress an open source, what would you want to see happen in terms of how it's, how things are funded, how things are executed. Cuz developers are going to run businesses. Cuz ultimately if you follow digital transformation to completion, it and developers aren't a department serving the business. They are the business. And that's coming fast. You know, what has to happen in your opinion, if you had the wish magic wand, what would you, what would you snap your fingers to make happen? >>If I had a magic wand that's very different from, from what is achievable. But let, let's >>Go with, Okay, go with the magic wand first. Cause we'll, we'll, we'll we'll riff on that. So >>I'm here for dreams. Yeah, yeah, >>Yeah. I mean I, I've been in open source for more than two, two decades, but now, and most of the open source is being driven forward by people who are not being paid for those. So for example, Gana is the first time I'm actually paid by a company to do my com community work. It's always been on the side. Of course I believe in it and I like doing it. I'm also not bad at it. And so I just kept doing it. But it was like at night on the weekends and everything. And to be honest, it's still at night and in the weekends, but the majority of it is during paid company time, which is awesome. Yeah. Most of the people who have driven this space forward are not in this position. They're doing it at night, they're doing it on the weekends. They're doing it out of dedication to a cause. Yeah. >>The commitment is insane. >>Yeah. At the same time you have companies mostly hyperscalers and either they have really big cloud offerings or they have really big advertisement business or both. And they're extracting a huge amount of value, which has been created in large part elsewhere. Like yes, they employ a ton of developers, but a lot of the technologies they built on and the shoulders of the giants they stand upon it are really poorly paid. And there are some efforts to like, I think the core foundation like which redistribute a little bit of money and such. But if I had my magic wand, everyone who is an open source and actually drives things forwards, get, I don't know, 20% of the value which they create just magically somehow. Yeah. >>Or, or other companies don't extract as much value and, and redistribute more like put more full-time engineers onto projects or whichever, like that would be the ideal state where the people who actually make the thing out of dedication are not more or less left on the sideline. Of course they're too dedicated to just say, Okay, I'm, I'm not doing this anymore. You figure this stuff out and let things tremble and falter. So I mean, it's like with nurses and such who, who just like, they, they know they have something which is important and they keep doing it. Of course they believe in it. >>I think this, I think this is an opportunity to start messaging this narrative because yeah, absolutely. Now we're at an inflection point where there's a big community, there is a shared responsibility in my opinion, to not spread the wealth, but make sure that it's equally balanced and, and the, and I think there's a way to do that. I don't know how yet, but I see that more than ever, it's not just come in, raid the kingdom, steal all the jewels, monetize it, and throw some token token money around. >>Well, in the burnout. Yeah, I mean I, the other thing that I'm thinking about too is it's, you know, it's, it's the, it's the financial aspect of this. It's the cognitive load. And I'm curious actually, when I ask you this question, how do you avoid burnout? You do a million different things and we're, you know, I'm sure the open source community that passion the >>Coach. Yeah. So it's just write code, >>It's, oh, my, my, my software engineering days are firmly over. I'm, I'm, I'm like, I'm the cat herer and the janitor and like this type of thing. I, I don't really write code anymore. >>It's how do you avoid burnout? >>So a i I didn't curse ahead burnout a few years ago. I was not nice, but that was still when I had like a full day job and that day job was super intense and on top I did all the things. Part of being honest, a lot of the people who do this are really dedicated and are really bad at setting boundaries between work >>And process. That's why I bring it up. Yeah. Literally why I bring it up. Yeah. >>I I I'm firmly in that area and I'm, I'm, I don't claim I have this fully figured out yet. It's also even more risky to some extent per like, it's, it's good if you're paid for this and you can do it during your work time. But on the other hand, if it's so nice and like if your hobby and your job are almost completely intersectional, it >>Becomes really, the lines are blurry. >>Yeah. And then yeah, like have work from home. You, you don't even commute anything or anymore. You just sit down at your computer and you just have fun doing your stuff and all of a sudden it's deep at night and you're still like, I want to keep going. >>Sounds like God, something cute. I >>Know. I was gonna say, I was like, passion is something we all have in common here on this. >>That's the key. That is the key point There is a, the, the passion project becomes the job. But now the contribution is interesting because now yeah, this ecosystem is, is has a commercial aspect. Again, this is the, this is the balance between commercialization and keeping that organic production system that's called open source. I mean, it's so fascinating and this is amazing. I want to continue that conversation. It's >>Awesome. Yeah. Yeah. This is, this is great. Richard, this entire conversation has been excellent. Thank you so much for joining us. How can people find you? I mean, I give em your Twitter handle, but if they wanna find out more about Grafana Prometheus and the 1700 things you do >>For grafana grafana.com, for Prometheus, promeus.io for my own stuff, GitHub slash richie age slash talks. Of course I track all my talks in there and like, I don't, I currently don't have a personal website cause I stop bothering, but my, like that repository is, is very, you find what I do over, like for example, the recording link will be uploaded to this GitHub. >>Yeah. Great. Follow. You also run a lot of events and a lot of community activity. Congratulations for you. Also, I talked about this last time, the largest IRC network on earth. You ran, built a data center from scratch. What happened? You done >>That? >>Haven't done a, he even built a cloud hyperscale compete with Amazon. That's the next one. Why don't you put that on the >>Plate? We'll be sure to feature whatever Richie does next year on the cube. >>I'm game. Yeah. >>Fantastic. On that note, Richie, again, thank you so much for being here, John, always a pleasure. Thank you. And thank you for tuning in to us here live from Detroit, Michigan on the cube. My name is Savannah Peterson and here's to hoping that you find balance in your life this weekend.

>>Welcome back to the cubes, continuous coverage of HP es latest Green Lake announcement firehose of innovation. We're seeing a >>cadence >>that HP is delivering in cloud services. Daniel Newman is here, he's the principal analyst at the tour, um, extraordinary research company. Daniel great to see you how you doing man. >>Dave Great to, great to be in person again six ft and safe. But it's good to be back. >>Yeah, it really is uh, been a blur. Right? So we're gonna talk about the pivot to cloud based services. We're seeing that everybody is sort of leaning in HP es all in. I want to talk about value and what this all means to investors. We talk about data, but let me start with the whole as a service move. As I said, everybody's doing it. You see it virtually every companies. Hp was certainly the first to say we're all in, It communicated very well to Wall Street. Everybody's in a debate. No, we were first. No, we were first, but you gotta evaluate based upon the actions that they're taking. How do you look at the trends in this space and how do you look at H. P. S performance? >>Yeah, I admired and Antonio's early pivot, you know, when he got on stage and he said, We're gonna move everything to as a service. I believe that was about two years ago now and the ambition was to have it by 2022. It immediately stood out to me because the momentum, the momentum was behind public cloud, you would have believed three years ago that every workload was going to be in the public cloud and unfortunately guys like us knew that wasn't true. But what we did know was the customers, the enterprise, we're all becoming very comfortable and preference was starting to be shown with that consumption of it meaning subscription based, moving from Capex to apex. That to me was a signal that the timing was right now. Once they got the timing right, it was really about how does this all happened right? It's not necessarily just, we're gonna flip a switch and we're going to start to offer everything as a subscription as a service. There's a lot of standing up those services, putting all that compute all that network, all that storage into a data center, making sure that you have a way to accurately price it and make it quickly consumable, which is something by the way I've admired over the past couple of years, watching the evolution of the software that HP has been rolling. Whether that's Green Lake Central as moral, is that, you know, whether that's kubernetes in the orchestration of hybrid cloud using containers or that's just the ability to spin up a single compute workload in a timely fashion. That's the attraction to public cloud. So, you know, take H P E and its strategy aside and what we have now is you have all of the traditional big iron I T O E M all moving in this direction concurrently. They all understand from both evaluation standpoint meeting Wall Street and also meeting the customer where they are, they have to step up. They had to, uh, whether that was what I was doing with apex Cisco with plus iBMS acquisition of red hat. All these companies were going from, you know, public to private, private to public and then of course you gotta go horizontal from edge to cloud as well. It's a lot to undertake Dave but it's an exciting time and knowing that hybrid is the answer the data is proving that it puts a lot of these companies in a good position to compete. >>Now you mentioned that is the customer preference for good reason. Right? That gives them more flexibility but there's also Wall Street's preference, right? You see that, you know, huge valuations companies like snowflake data, dog elastic. It's that annual recurring revenue that is appealing. They want that they want growth. We saw Q3 hp that did a beaten raise I think 1100 customers for green lake, they announced the orders were up well over 40%. I think revenue was up 30 30 plus percent. So those are the kind of metrics that Wall Street wants to see interestingly though Daniel of course the shift to an A. R. R. Model hurts the income statement but it makes it more predictable and that's what investors today want, what your thoughts. >>Absolutely. I had a chance to speak multiple times over the past few years with the leadership at HP. And it was the exact thing. David that I that I raised, I said you realize that it might be a sidestep or even a half a step backwards before you start to gain momentum. And the real problem with Wall Street is there's no patients. So you mentioned a couple of names like data dog and snowflake. These companies have exponential valuations to earnings because they don't earn anything yet. But most of the market is forward looking and the market tries to anticipate where growth is going to come and saAS companies tend to drive fast growth and fast multiples. This is also left for somewhat slow growth evaluation for companies like HP. Despite the fact that it's doing a lot of the right things you mentioned of course mid double digit growth in green lake, large customer growth numbers. You know, I believe you're serving a billion dollars in revenue or in subscription dollars. Um, fact check that on their >>way to a billion on their way to be honest. I think >>it's booked maybe over >>700 million in revenue that way. >>And so as all those, the confluence of all those events, the market has to be able to basically cherry pick though a part of the business. And I think that's been a little bit of a problem. Not just for HP, but just for all these companies that are, that are struggling with smaller multiples of their P. E ratios. This is true for Cisco? This is true for IBM this is true for for HP and I'll kind of close my thought here. But as the company continues to talk about green Lake and it continues to lean into this, this is the part that has to rise to the front front of the Wall Street investor of the business media to say that existing part of the business is stable, It's solid. They have great customers. However, concurrently the part of the business that is the future, the subscription part that attaches to the public cloud that is enabling companies to grow. That is where they're at. And that is why we see more value. There's a lot of value to unlock and it's because, you know, these small multiples and the business is heading in what I believe is the right >>direction. And HPV last quarter cited, they hit almost 35% gross margin, which is, which is a high mark, high water mark for them if you extract VM ware out of Dell there in the mid twenties. So these are two different businesses and I think that's a big reason why Dell's moving into the space. I almost think like the board conversation at HP was, hey, let's, let's not keep thinking about building boxes. Let's build services and let's add value to those services that are software based and then we can kind of control our own destiny as opposed to kind of intel getting all the margins and or M. D. Or whatever it is. So so that so how do you see as a service driving value for H. P. E. It's customers and ultimately what do they have to do to convince Wall street >>recurring revenue companies drive higher multiples? It's not even a debate and companies that have a large percentage of their business as recurring tend to drive much higher evaluation and tend to also be more beloved by shareholders. The performance of HP has been good, it's been solid, it's been in the right place especially given the circumstances of the pandemic and the impact of on prem it we all saw the explosion of SAS the explosion of cloud, you know, SAS and chips are hot, they're always hot. But everything that was sort of sandwiched in the middle became a little bit more murky throughout the pandemic times. And the ability for HP. And these companies that are in this space are operating to be able to bridge this gap. The companies have 25 or so percent of workload during the public cloud. That means the rest need services from companies like HP. So the tam is growing because the overall size of the workload, the volumes of data are all growing exponentially and that's an opportunity but the market wants to see fast growth. Dave I mean they're not going to accept the single digit overall growth if you want to get the kind of multiples of a, you know, even a Microsoft at a 40 or a sales force at 100. But HPV with its software is starting to play in those spaces where investors in the market maybe can start to recognize that it is undervalued. >>So we live in a data centric world, Antonio talks about this all the time and we're seeing HP makes some moves in terms of data data management, you see what they're doing with his moral and that's a big part of the software place. So to the extent that you can lean into that wave have a higher contribution from software, higher margin business obviously and a more predictable revenue stream. That seems to be the right direction in my view. Um it's gonna take some time to play out. They're not gonna overnight, you know, they don't have a green sheet of paper, they clean sheet of paper, they have a business that they have to manage and they have to service their customers. But to the extent that the majority of their business over time can become as a service, shouldn't that confer higher margins and and greater value to investors? Yeah, it's sticky >>for enterprise users when you move to that subscription model, it's not as easy as just lifting and shifting you build your entire business process around these investments in these technologies. Software. It's sticky, it's organizationally complex because where HP sits in the stack, where their analytic solutions and software help you more successfully deploy S. A. P type workloads. The entire company runs on that. So the involvement and the importance of the role that HP is playing is huge. The challenge for customers isn't as big customers get this, the enterprise users, the C I O. S. They get the importance Wall Street though it's a little harder for them sometimes to digest. Whereas they might be looking at something like a snowflake that you mentioned. That's fairly straightforward. Almost all of its revenue is pure subscription and it's looked at as 20 years in a perpetuity where people are still trying to wonder is HP gonna be sticky? Are these customers not only going to keep with HP but are they going to increase? Right. Is that net revenue expansion going to take place across the portfolio? And HP rolls out more services right. Started with storage and then it moves to compute and then it adds edge layer services. Are people going to buy the whole stack? Because that of course, also as we've seen with some of the bigger players can be an extremely attractive value proposition. >>Well, I also think as they move into cloud, HP has always been about optionality. So I feel as though with their day to play, for example, they can get deeper into data management but they can also partner with others, you're leaning into open source so that means you can expand your portfolio that's kind of what the cloud game is is you know, here's the cloud, we got all these different options, choose what you want, we'll manage it for you, charge you for that but we'll take away that headache. That's a good business, >>choose your own cloud adventure last week oracle reported. Um and I'm only pointing this out because you know, you look at the company and everybody was what's with their i as number? Why is it not big or smaller? Why don't we know right. But over the last couple of years we've realized that it's no longer little seeing big see little C which I would call infrastructure as a service no longer exists. Cloud is one big number. So H P E being in the cloud through its hybrid services, its software, its platform support is just as much about being in the cloud is a company that offers I. S. Or company that offers SAs however convincing the market that this is the case is the trick. We're starting to see companies because you you hear when IBM reports how their numbers are, you know, they're they're tying in all kinds of global business services and they're tying in you know, red hat numbers and they're telling in their public cloud numbers but what I'm saying is up to this point, a lot of these hybrid services are kind of not necessarily being bucket ID like this big sea of cloud but it really is the entire stack of of infrastructure platform software and then of course all those attached services for companies to deploy this that equal a cloud number. And so the subscription number grows. Green Lakes customer account grows. And I think convincing the street and everybody in between that this is a cloud number and not a on prem or a attached to the cloud number is going to really help boomer boom, the overall value that people see and what HP is doing. >>And I think not only H P E but I think others are I think finally they're starting to realize that wow, you know, we all know everything is not going to public cloud. We understand it's a hybrid world Public cloud spend a company's the hyper scale is collectively spent $100 billion dollars last year on Capex. That's like a gift to a company like HP that can connect the dots and create that abstraction layer that hides the underlying complexity. We'll take care of that for you will make everything cloud native. We can bring cloud native on prem and go out to the edge, which is like the Wild West that is a that's a trillion dollar opportunity that there's no limit to market potential for companies out there and HP specifically. >>Well the edges a massive opportunity and that's what I said, you know, a lot of us are and we do this ourselves as both analysts and sometimes media personalities is we like to debate how big the opportunity of cloud is. And of course there are some firms that try to market size this, but I actually think it's extraordinarily difficult to market sizes, especially because of the edge. You talk about data and analytics. I recently attended the a event. It's a car event in Munich and you just look at the amount of data that vehicles are going to be creating in the in the coming years. They're basically massive rolling data centers full of chips, compute networking storage. This is all going to take significant infrastructure investments at scale and it's creating this humongous opportunity at the edge and you look at five Gs impact and as we roll out five G it's scale. Every one of these things brings more data connects, more devices and all that intelligence needs infrastructure, It needs software, it needs services. So the overall tam Dave is going to continue to grow and I think if anything it tends to be underestimated because it's really hard to define just how big the data equation is actually going to be in the market. >>Digital changes the equation. It's not, it's no longer servers, storage, networking database, its cloud services that are enabling digital transformations. I'll give you one more >>thing that just crossed my mind. But I think is important is if you even look at the the S. G. And sustainability efforts that most companies are going to be taking the amount of investment in trying to capture, comprehend manage just the data and analytics to understand your footprint and understand how you are going to achieve carbon neutrality and how you're going to do this up and down. And I mean that's just one thing and of course that's a, I wouldn't call it table stakes at this point, the market expects every company to be making this kind of investment well, when you run a multi national global enterprise that has edge, that has data centers that has manufacturing facilities, there is just unbelievable requirements on technology. And again, we've got to connect that public cloud somehow. So we can't ignore the fact that those public cloud players are all addressing this, they're all bringing solutions out. But companies like HP, this is where their sweet spot is, and this is where I believe they're going to have to compete very aggressively and efficiently to show we are a great partner to the public cloud, but our legacy and our capabilities mean we understand this part of the business, we believe we're the right fit and trust me, the Azure and AWS are, they're not going to make this easy, they're going to be competitive but they're also going to going to be very cooperative >>well, and they're coming into the home court of the on prem vendors. So that's gonna be interesting to see how that plays out as an observer, as an analyst, what do you want to see from HP, Green Lake cloud services? What are the, what are the areas that you're gonna be watching that could serve as indicators of success and momentum? >>Well, we didn't even talk because we did talk about some of that, but we didn't even talk about aI and amount for instance, all this data itself has to be managed and processed. So the fact that you're getting to that data management at scale, the fact that you're building out orchestration for containers. Well this is because of that data delusion conundrum, whatever word we want to use for it. But the best companies in the world are going to find a way to extract more value from that data and that's going to be through the application of aI of ml of neural networks, deep learning and other important capabilities. Having a foot into that Dave is something I want to see HP and it already does, but I want to see the participation there. This is an area that I think public cloud is doing really well there. They really made big investments both with homegrown chips with partnering with the likes of videos and intel to, to offer a lot of enhancement acceleration, um Ml and AI services. I think this is gonna be an area that on prem and through hybrid offerings. We're gonna want to see the company compete. Uh and then of course, I think back to the one thing Dave, I'll just kind of wrap on this, is that that customer growth, I mean you talked about how to get evaluation, how to get the street up, people get excited about overall growth. They need to get that narrative carved out about green, like about the subscription growth, the service growth point next and all that stuff, but all that has to start to equate to overall growth. Um you know, I think it needs to be made at least single high digits, single overall percentage growth, especially because the whole portfolio supposed to be there. You know, companies get those big multiples are growing >>fast growth on, on that large of a base would get people's attention. You mentioned custom chips, H P >>E, you >>know, H P S H P S heritage and HP. They have chops in custom silicon. So be interesting to see if, if you know the future, you talk about ai inference at the edge, huge disruptive potential opportunities and I'm really curious as to see how that plays out because that is another trillion dollar market opportunity. Daniel, thanks so much for coming to the cubes. Great to have you looking forward to working with you in the future. >>Yeah, it's great to be here. And sorry, we didn't get to those chips earlier. We could have gone down a whole, another whole, another >>half hour. Great, great to talk to you. All right, thank you for watching everybody. This is the cubes, continuous coverage of HBs, Big Green Lake announcement. Keep it right there for more, great content. Mhm.

>>welcome back everyone. We're back to intermission. I'm hama in case you forgot and hear them with Brett and Peter. So what a great morning afternoon. We've had like we're now in the home stretch and you know, I really want to give a shout out to all of you who are sticking with us, especially if you're in different time zone than pacific. So I then jumped into the community rooms. The spanish won, the Brazilian won the french one. Everybody is just going strong. So again, so much so gratitude for that. Thank you for being so involved and really participating the chat rooms in the community. The chat windows in the community rooms are just going nuts. So it's, it's really good to see that. And as usual, Peter and brat had some great, very interactive panels and that was very exciting to watch. But you know, since they were on the panels, I decided to go and see some other things and I actually attended the last mile of container ization. That was, that was actually a very good session. We had a lot of good interactivity there. Yeah. And then while also talked about the container security in the cloud native world. So that was, I think that was your panel peter. That was, that was very exciting. And um, I want to share with everybody the numbers that we've been seeing for dr khan live. So as, as of, I'm sorry, said we need a drumroll. We do need a drum roll. Can you do a drum roll for me? No, no, no. >>Just a >>symbol. Okay, good. Go. Uh, we're at over 22,000 attendees um, today. So that's amazing. That's great. I love the sound effect. That's a great sound effect. The community rooms continue to be really engaged. We're still seeing hundreds of people in those rooms. So again shout out to everyone who is participating. And I felt again like a kid in a candy store didn't know which sessions to attend. They were all very interesting and you know, we're getting some good feedback on twitter. I want to read out some more tweets that we got and one in particular, I don't know whether to feel happy for this person or sad for this person, but it's uh well the initials are P. W. And he said that he was up at two am to watch the keynotes. So again, I'll let you decide whether you're it's a good thing or not, but we're happy to have you PW is awesome. Um as well. There was someone who said that these features are so needed. The things that dr announced this morning in the keynotes and that doctor has reacted to our pains and I think they mean has addressed their pain. So that was really gratifying to read. Yeah, really wonderful. That's some other countries that I didn't shout out before this just tells you what the breadth and scope of our community is. Indonesia, la paz Bolivia, Greece, Munich, Ukraine, oxford UK Australia Philippines. And there's just more and I'm going to do a special shadow to Montreal because that's where I'm from. So yes, applause for that. It was really great. And so I just want to thank all of you. Um, I want to encourage you when we talked about the power of community. Remember we're doing a fundraiser. So to combat Covid for Covid relief or actually all that money is going to go to UNICEF. Docker is contributing 10,000 and we're doing a go fund me. And the link is there on the screen. So please donate. You know, just $1. 1 person each of you donates $1. We would have raised over $22,000. So please please find it within you to contribute because again, our communities that are, that are the most effective are India and brazil, which are are very active doctor affinity. So please give back. I really appreciate that >>highlighted by the brazil. Yeah. >>You're going to brazil room and get them all to donate. Exactly. Um, also want to encourage, you know, if you're interested in participating in our, in our road map. Our public road map is on GIT hub. So it's get home dot com slash docker slash roadmap. And that's something that you can participate in and vote up features that you want to see. We love to get the community involved and participating in our, in our road map. So make sure to check that out. And I also want to note on that >>Hello can real quick. I'm sorry. Yeah, I talk about our road map all the time, but honestly folks out there are PMS are in their our ceo is in there that we do watch that. That is our roadmap is extremely, extremely important to us. So any features complaints, right, joining the conversation. That's a great way to get uh to interact with Docker in our products. Right. We we really highly valued the road map. Okay, back to your mama, sorry. >>Oh absolutely. And if you want to see us be even more responsive to what you need to participate in that road map discussion. That's really great. Um a couple of things coming up, just want to put the spotlight on. We have at 3 15 what's new with with desktop from our own ue cow. So I highly recommend that you attend that session and of course there's the Woman in tech live panel. So this is very exciting, moderated by yours truly and it has putting a spotlight on our women captains and our women developers. So that's very exciting. But I also hear that we're doing there's a session with jay frog coming up so peter, why don't you talk about that a little bit? >>Yeah, we have a session coming up from our partners from jay frog around devops patterns and anti patterns for continuous software updates. And another one that I'm extremely excited about is uh RM one talk from our very own Tony's from Docker. So if you have an M one and you're interested in multi arc architecture builds, check that out. It's gonna be a great, great talk. Um and then we have melissa McKay also from jay frog, talking about Docker and the container ecosystem and last but definitely not least. We'll check them all out there. Going to be great. But Brett is going to be doing I think the best panel that I'm gonna go watch and he made up a new word, it's called say this. I'm all about the trending new words today about this is gonna be awesome. Yeah. Yeah >>I'm going to have the battle bottle of the panels. >>Yeah. Yeah well mine's before years so we're not competing. So yeah we have we have two excellent panels in a row to finish off the day and just seven list is basically how to run, how can we run containers without managing servers? So it doesn't mean you don't actually have infrastructure just let's not manage service. Um Yeah and we we uh need to wrap it up and >>Uh before we do that I just want to um tell everyone that we actually have a promotion going on. So we um for those that sign up for a pro or team subscription, we're offering a 20% off so there's the U. R. L.. You can check out what the promotion is and this is for a new and returning users so you can use the promo code dr khan 21 all the information is on the website are really encourage you to check that out promotion for 20% off, join us for our panels. And we're doing a wrap up at five p.m. Where we'll have our own Ceo and that wrap up portion. Look forward to seeing there. All right, >>thank you too. All right everyone we'll see you on the next go around coming up next me and some other people awesome and Yeah. Mhm. Mhm. Yeah. >>Yeah. Yeah. Mhm. Is >>a really varied community. There's a lot of people with completely different backgrounds, completely different experience levels and completely different goals about how they want to use Docker. And I think that's really interesting. It's always easy to talk about the technology that I've used for so many years. I really love Doctor and I can find so many ways that it's useful and it's great to use in your day to day work clothes. I've >>used doctor for anything from um tracking airplanes with my son, which was a kind of cool project to more professional projects where we actually Built one of the first database as his services using docker even before it was 10 and I was released and we took it further and we start composing monitoring tools. We really start taking it to the next level. And we got to the point where I was trying to make everything in a container, I love to use >>doctor to make disposable project so I can download the project and it's been that up using Docker compose or something like that in a way that every developer that works in the project doesn't even need to know the underlying technology doesn't just need to run Docker compose up and the whole project is going to be up and running even if >>you're not using doctor and production, there are a lot of other ways that you can use doctor to make your life so much easier. As a developer, you can run your projects on your machine locally. Um as a tester you can actually launch test containers and be able to run um dependencies that your project requires. You can run real life versions so that um you're as close to production as possible. >>I was able to migrate most of the workloads from our on from uh to the cloud. Running complete IEDs inside a docker or running it or using it basically to replace their build scripts or using it to run not web applications but maybe compile c plus plus code or compile um projects that really just require some sort of consistency across their team, >>whether it be a web app or a database, I can control these all the same. That was really the power I saw within Doctors standardization and the portability >>doctor isn't the one that created containers uh and uh but it's the one that made it uh democratically possible, so everyone use it. And this effort has made the technology environment so much better for everyone that uses it, both for developers and for end users. So this >>past year has been quite interesting and I think we're all in the same boat here, so no one has, no one is an exception to this, but what we all learn from it is, you know, the community is very important and to lean on other people for help for assistance. >>Yeah, it's been really challenging of course, but I think the biggest and most obvious thing that I've learned both on a personal and a business perspective is just to be ready to adapt to change and don't be afraid of it either. I think it's worth noting that you should never really take it for granted that the paradigms of, you know, the world or technology or something like that aren't going to shift drastically and very, very quickly. >>I'm looking forward to what is coming down the pipe with doctor. What more are they going to throw our way in order to make our lives easier? >>It's very interesting to see the company grow and adapt the way it has. I mean it as well as the community, it's been very interesting to see, you know, how, you know, the return to develop our focus is now the main focus and I find that's very interesting because, you know, developers are the ones that really boost the doctor to where it is today. And if we keep on encouraging these developer innovation, we'll just see more tools being developed on top of Doctor in the future, and that's what I'm really excited to see with Doctor and the technology in the future.

>>Hello, and welcome. Very excited to see everybody here. DockerCon is going fantastic. Everybody's uh, engaging in the chat. It's awesome to see. My name is Peter McKee. I'm the head of developer relations here at Docker and Taber. Today. We're going to be talking about container first development now and in the future. But before we do that, a couple little housekeeping items, first of all, yes, we are live. So if you're in our session, you can go ahead and chat, ask us questions. We'd love to get all your questions and answer them. Um, if you come to the main page on the website and you do not see the chat, go ahead and click on the blue button and that'll die. Uh, deep dive you into our session and you can interact with the chat there. Okay. Without further ado, let's just jump right into it. Katie, how are you? Welcome. Do you mind telling everybody who you are and a little bit about yourself? >>Absolutely. Hello everyone. My name is Katie and currently I am the eco-system advocate at cloud native computing foundation or CNCF. My responsibility is to lead and represent the end-user community. So these are all the practitioners within the cloud native space that are vendor neutral. So they use cloud native technologies to build their services, but they don't sell it. So this is quite an important characteristic as well. My responsibility is to make sure to close the gap between these practitioners and the project maintainers, to make sure that there is a feedback loop around. Um, I have many roles within the community. I am on the advisory board for KIPP finishes, a sandbox project. I'm working with open UK to make sure that Elton standards are used fairly across data, hardware, and software. And I have been, uh, affiliated way if you'd asked me to make sure that, um, I'm distributing a cloud native fundamental scores to make cloud and they do a few bigger despite everyone. So looking forward to this panel and checking with everyone. >>Awesome. Yeah. Welcome. Glad to have you here. Johanna's how are you? Can you, uh, tell everybody a little bit about yourself and who you are? Yeah, sure. >>So hi everybody. My name is Johannes I'm one of the co-founders at get pot, which in case you don't know is an open-source and container based development platform, which is probably also the reason why you Peter reached out and invited me here. So pleasure to be here, looking forward to the discussion. Um, yeah, though it is already a bit later in Munich. Um, and actually my girlfriend had a remote cocktail class with her colleagues tonight and it took me some stamina to really say no to all the Moscow mules that were prepared just over there in my living room. Oh wow. >>You're way better than me. Yeah. Well welcome. Thanks for joining us. Jerome. How are you? Good to see you. Can you tell everybody who you are and a little bit about yourself? Hi, >>Sure. Yeah, so I'm, I, I used to work at Docker and some, for me would say I'm a container hipster because I was running containers in production before it for hype. Um, I worked at Docker before it was even called Docker. And then since 2018, I'm now a freelancer and doing training and consulting around Docker containers, Kubernetes, all these things. So I used to help folks do stuff with Docker when I was there and now I still have them with containers more generally speaking. So kind of, uh, how do we say same, same team, different company or something like that? Yeah. >>Yeah. Perfect. Yeah. Good to see you. I'm glad you're on. Uh, Jacob, how are you? Good to see you. Thanks for joining us. Good. Yeah. Thanks for having me tell, tell everybody a little bit about yourself who you are. >>Yeah. So, uh, I'm the creator of a tool called mutagen, which is an open source, uh, development tool for doing high performance file synchronization and, uh, network forwarding, uh, to enable remote development. And so I come from like a physics background where I was sort of always doing, uh, remote developments, you know, whether that was on a big central clusters or just like some sort of local machine that was a bit more powerful. And so I, after I graduated, I built this tool called mutagen, uh, for doing remote development. And then to my surprise, people just started using it to use, uh, with Docker containers. And, uh, that's kind of grown into its primary use case now. So I'm, yeah, I've gotten really involved with the Docker community and, uh, talked with a lot of great people and now I'm one of the Docker captains. So I get to talk with even more and, and join these events and yeah, but I'm, I'm kind of focused on doing remote development. Uh, cause I, you know, I like, I like having all my tools available on my local machine, but I also like being able to pull in a little bit more powerful hardware or uh, you know, maybe a software that I can't run locally. And so, uh, that's sort of my interest in, in Docker container. Yeah. Awesome. >>Awesome. We're going to come back to that for sure. But yeah. Thank you again. I really appreciate you all joining me and yeah. So, um, I've been thinking about container first development for a while and you know, what does that actually mean? So maybe, maybe we can define it in our own little way. So I, I just throw it out to the panel. When you think about container first development, what comes to mind? What w what, what are you kind of thinking about? Don't be shy. Go ahead. Jerome. You're never a loss of words >>To me. Like if I go back to the, kind of the first, uh, you know, training engagements we did back at Docker and kind of helping folks, uh, writing Dockerfiles to stop developing in containers. Um, often we were replacing, um, uh, set up with a bunch of Vagrant boxes and another, like the VMs and combinations of local things. And very often they liked it a lot and they were very soon, they wanted to really like develop in containers, like run this microservice. This piece of code is whatever, like run that in containers because that means they didn't have to maintain that thing on their own machine. So that's like five years ago. That's what it meant to me back then. However, today, if you, if you say, okay, you know, developing in containers, um, I'm thinking of course about things like get bought and, uh, I think it's called PR or something like that. >>Like this theme, maybe that thing with the ESCO, that's going to run in a container. And you, you have this vs code thing running in your browser. Well, obviously not in your browser, but in a container that you control from your browser and, and many other things like that, that I, I think that's what we, where we want to go today. Uh, and that's really interesting, um, from all kinds of perspectives, like Chevy pair pairing when we will not next to each other, but actually thousands of miles away, um, or having this little environment that they can put aside and come back to it later, without it having using resource in my machine. Um, I don't know, having this dev service running somewhere in the cloud without needing something like, it's at the rights that are like the, the possibilities are really endless. >>Yeah. Yeah. Perfect. Yeah. I'm, you know, a little while ago I was, I was torn, right. W do I spin up containers? Do I develop inside of my containers? Right. There's foul sinking issues. Um, you know, that we've been working on at Docker for a while, and Jacob is very, very familiar with those, right? Sometimes it, it becomes hard, but, and I, and I love developing in the cloud, but I also have this screaming, you know, fast machine sitting on my desktop that I think I should take advantage of. So I guess another question is, you know, should we be developing inside of containers? Is that a smart thing to do? Uh, I'd love to hear you guys' thoughts around that. >>You know, I think it's one of those things where it's, you know, for me container first development is really about, um, considering containers as sort of a first class citizen in, in terms of your development toolkit, right. I mean, there's not always that silver bullet, that's like the one thing you should use for everything. You know, you shouldn't, you shouldn't use containers if they're not fitting in or adding value to your workflow, but I think there's a lot of scenarios that are like, you know, super on super early on in the development process. Like as soon as you get the server kind of running and working and, you know, you're able to access it, you know, running on your local system. Uh that's I think that's when the value comes in to it to add containers to, you know, what you're doing or to your project. Right. I mean, for me, they're, um, they're more of a orchestrational tool, right? So if I don't have to have six different browser tabs open with like, you know, an API server running at one tab and a web server running in another tab and a database running in another tab, I can just kind of encapsulate those and, and use them as an automation thing. So I think, you know, even if you have a super powerful computer, I think there's still value in, um, using containers as, as a orchestrational mechanism. Yeah. Yeah, >>For sure. I think, I think one of the, one of my original aha moments with Docker was, oh, I can spin up different versions of a database locally and not have to install it and not have to configure it and everything, but, you know, it just ran inside of a container. And that, that was it. Although it's might seem simple to some people that's very, very powerful. Right. So I think being able to spin things up and containers very quickly is one of the super benefits. But yeah, I think, uh, developing in containers is, is hard right now, right. With, um, you know, and how do you do that? Right. Does anybody have any thoughts around, how do you go about that? Right. Should you use a container as just a development environment, so, you know, creating an image and then running it just with your dev tools in it, or do you just, uh, and maybe with an editor all inside of it, and it's just this process, that's almost like a VM. Um, yeah. So I'll just kick it back to the panel. I'd love to hear your thoughts on, you know, how do you set up and configure, uh, containers to develop in any thoughts around that? >>Maybe one step back again, to answer your question, what kind of container first development mean? I think it doesn't mean, um, by default that it has to be in the cloud, right? As you said, um, there are obvious benefits when it comes to the developer experience of containers, such as, I dunno, consistency, we have standardized tools dependencies for the dev side of things, but it also makes their dev environment more similar to all the pipeline that is somehow happening to the right, right. So CIC D all the way to production, it is security, right? Which also somehow comes with standardization. Um, but vulnerability scanning tools like sneak are doing a great job there. And, um, for us, it gets pod. One of the key reasons why we created get pod was literally creating this peace of mind for deaths. So from a developer's point of view, you do not need to take care anymore about all the hassle around setups and things that you will need to install. >>And locally, based on some outdated, REIT me on three operating systems in your company, everybody has something different and leading to these verbs in my machine situations, um, that really slow professional software developers down. Right. Um, back to your point, I mean, with good pod, we obviously have to package everything together in one container because otherwise, exactly the situation happens that you need to have five browser tabs open. So we try and leverage that. And I think a dev environment is not just the editor, right? So a dev environment includes your source code. It includes like a powerful shell. It includes file systems. It includes essentially all the tools you need in order to be productive databases and so on. And, um, yeah, we believe that should be encapsulated, um, um, in a container. >>Yeah. Awesome. Katie, you talked to a lot of end users, right. And you're talking to a lot of developers. What, what's your thoughts around container first development, right? Or, or what's the community out there screaming or screaming. It might be too to, uh, har you know, to, to two grand of the word. Right. But yeah, I love it. I love to hear what your, your thoughts. >>Absolutely. So I think when you're talking about continuing driven development, uh, the first thing that crosses my mind is the awareness of the infrastructure or the platform you're going to run your application on top of, because usually when you develop your application, you'd like to replicate as much as possible the production or even the staging environment to make sure that when you deploy your application, you have us little inconsistencies as possible, but at the same time, you minimize the risk for something to go wrong as well. So when it talking about the, the community, um, again, when you deploy applications and containers and Kubernetes, you have to use, you have awareness about, and probably apply some of the best practices, like introducing liveliness and readiness probes, to make sure that your application can restart in, in case it actually goes down or there's like a you're starving going CPU or something like that. >>So, uh, I think when it comes to deployment and development of an application, the main thing is to actually improve the end developer experience. I think there has been a lot of focus in the community to develop the tool, to actually give you the right tool to run application and production, but that doesn't necessarily, um, go back to how the end developer is actually enabling that application to run into that production system. So I think there has been, uh, this focus for the community identified now, and it's more, more, um, or trying to build momentum on enhancing the developer experience. And we've seen this going through many, uh, where we think production of many tools did what has been one of them, which actually we can have this portable, um, development environment if you choose so, and you can actually replicate them across different teams in different machines, which is actually quite handy. >>But at the same time, we had tools such as local composts has been a great tool to run locally. We have tool such as carefully, which is absolutely great to automatically dynamically upload any changes to how within your code. So I think all of these kinds of tools, they getting more matured. And again, this is going back to again, we need to enhance our developer experience coming back to what is the right way to do so. Um, I think it really depends on the environment you have in production, because there's going to define some of the structures with the tool and you're going to have internally, but at the same time, um, I'd like to say that, uh, it really depends on, on what trucks are developing. Uh, so it's, it's, I would like to personally, I would like to see a bit more diversification in this area because we might have this competitive solutions that is going to push us to towards a new edge. So this is like, what definitely developer experience. If we're talking about development, that's what we need to enhance. And that's what I see the momentum building at the moment. >>Yeah. Yeah. Awesome. Jerome, I saw you shaking your head there in agreement, or maybe not, but what's your thoughts? >>I was, uh, I was just reacting until 82. Uh, it depends thinking that when I, when I do training, that's probably the answer that I gave the most, uh, each time somebody asks, oh, should we do diesel? And I was also looking at some of the questions in the chat about, Hey, the, should we like have a negatory in the, in the container or something like that. And folks can have pretty strong opinions one way or the other, but as a ways, it kind of depends what we do. It also depends of the team that we're working with. Um, you, you could have teams, you know, with like small teams with folks with lots of experience and they all come with their own Feb tools and editorials and plugins. So you know that like you're gonna have PRI iMacs out of my cold dead hands or something like that. >>So of course, if you give them something else, they're going to be extremely unhappy or sad. On the other hand, you can have team with folks who, um, will be less opinionated on that. And even, I don't know, let's say suddenly you start working on some project with maybe a new programming language, or maybe you're targeting some embedded system or whatever, like something really new and different. And you come up with all the tools, even the ADE, the extensions, et cetera, folks will often be extremely happy in that case that you're kind of giving them a Dettol and an ADE, even if that's not what they usually would, uh, would use, um, because it will come with all of the, the, the nice stage, you know, the compression, the, um, the, the, the bigger, the, whatever, all these things. And I think there is also something interesting to do here with development in containers. >>Like, Hey, you're going to start working on this extremely complex target based on whatever. And this is a container that has everything to get started. Okay. Maybe it's not your favorites editor, but it has all the customization and the conserver and whatever. Um, so you can start working right away. And then maybe later you, we want to, you know, do that from the container in a way, and have your own Emacs, atom, sublime, vs code, et cetera, et cetera. Um, but I think it's great for containers here, as well as they reserve or particularly the opportunity. And I think like the, that, that's one thing where I see stuff like get blood being potentially super interesting. Um, it's hard for me to gauge because I confess I was never a huge ID kind of person had some time that gives me this weird feeling, like when I help someone to book some, some code and you know, that like with their super nice IDE and everything is set up, but they feel kind of lost. >>And then at some point I'm like, okay, let's, let's get VI and grep and let's navigate this code base. And that makes me feel a little bit, you know, as this kind of old code for movies where you have the old, like colorful guy who knows going food, but at the end ends up still being obsolete because, um, it's only a going for movies that whole good for masters and the winning right. In real life, we don't have conformance there's anymore mentioned. So, um, but part of me is like, yeah, I like having my old style of editor, but when, when the modern editorial modern ID comes with everything set up and configured, that's just awesome. That's I, um, it's one thing that I'm not very good at sitting up all these little things, but when somebody does it and I can use it, it's, it's just amazing. >>Yeah. Yeah. I agree. I'm I feel the same way too. Right. I like, I like the way I've I have my environment. I like the tools that I use. I like the way they're set up. And, but it's a big issue, right? If you're switching machines, like you said, if you're helping someone else out there, they're not there, your key bindings aren't there, you can't, you can't navigate their system. Right? Yeah. So I think, you know, talking about, uh, dev environments that, that Docker's coming out with, and we're, you know, there's a lot, there, there's a, it's super complex, all these things we're talking about. And I think we're taking the approach of let's do something, uh, well, first, right. And then we can add on to that. Right. Because I think, you know, setting up full, full developed environments is hard, right. Especially in the, the, um, cloud native world nowadays with microservices, do you run them on a repo? >>Do you not have a monitor repo? Maybe that would be interesting to talk about. I think, um, you know, I always start out with the mono repos, right. And you have all your services in there and maybe you're using one Docker file. And then, because that works fine. Cause everything is JavaScript and node. And then you throw a little Python in there and then you throw a little go and now you start breaking things out and then things get too complex there, you know, and you start pulling everything out into different, get repos and now, right. Not everything just fits into these little buckets. Right. So how do you guys think maybe moving forward, how do we attack that night? How do we attack these? Does separate programming languages and environments and kind of bring them all together. You know, we, we, I hesitate, we solve that with compose around about running, right about executing, uh, running your, your containers. But, uh, developing with containers is different than running containers. Right. It's a, it's a different way to think about it. So anyway, sorry, I'm rattling on a little bit, but yeah. Be interesting to look at a more complex, uh, setup right. Of, uh, of, you know, even just 10 microservices that are in different get repos and different languages. Right. Just some thoughts. And, um, I'm not sure we all have this flushed out yet, but I'd love to hear your, your, you guys' thoughts around that. >>Jacob, you, you, you, you look like you're getting ready to jump there. >>I didn't wanna interrupt, but, uh, I mean, I think for me the issue isn't even really like the language boundary or, or, um, you know, a sub repo boundary. I think it's really about, you know, the infrastructure, right? Because you have, you're moving to an era where you have these cloud services, which, you know, some of them like S3, you can, you can mock up locally, uh, or run something locally in a container. But at some point you're going to have like, you know, cloud specific hardware, right? Like you got TPS or something that maybe are forming some critical function in your, in your application. And you just can't really replicate that locally, but you still want to be able to develop against that in some capacity. So, you know, my, my feeling about where it's going to go is you'll end up having parts of your application running locally, but then you also have, uh, you know, containers or some other, uh, element that's sort of cohabitating with, uh, you know, either staging or, or testing or production services that you're, uh, that you're working with. >>So you can actually, um, you know, test against a really or realistic simulation or the actual, uh, surface that you're running against in production. Because I think it's just going to become untenable to keep emulating all of that stuff locally, or to have to like duplicate these, you know, and, you know, I guess you can argue about whether or not it's a good thing that, that everything's moving to these kind of more closed off cloud services, but, you know, the reality of situation is that's where it's going to go. And there's certain hardware that you're going to want in the cloud, especially if you're doing, you know, machine learning oriented stuff that there's just no way you're going to be able to run locally. Right. I mean, if you're, even if you're in a dev team where you have, um, maybe like a central machine where you've got like 10 or 20 GPU's in it, that's not something that you're going to be able to, to, to replicate locally. And so that's how I kind of see that, um, you know, containers easing that boundary between different application components is actually maybe more about co-location, um, or having different parts of your application run in different locations, on different hardware, you know, maybe someone on your laptop, maybe it's someone, you know, AWS or Azure or somewhere. Yeah. It'd be interesting >>To start seeing those boundaries blur right. Working local and working in the cloud. Um, and you might even, you might not even know where something is exactly is running right until you need to, you know, that's when you really care, but yeah. Uh, Johanas, what's your thoughts around that? I mean, I think we've, we've talked previously of, of, um, you know, hybrid kind of environments. Uh, but yeah. What, what's your thoughts around that? >>Um, so essentially, yeah, I think, I mean, we believe that the lines between cloud and local will also potentially blur, and it's actually not really about that distinction. It's just packaging your dev environment in a way and provisioning your dev environment in a way that you are what we call always ready to coat. So that literally, um, you, you have that for the, you described as, um, peace of mind that you can just start to be creative and start to be productive. And if that is a container potentially running locally and containers are at the moment. I think, you know, the vehicle that we use, um, two weeks ago, or one week ago actually stack blitz announced the web containers. So potentially some things, well, it's run in the browser at some point, but currently, you know, Docker, um, is the standard that enables you to do that. And what we think will happen is that these cloud-based or local, um, dev environments will be what we call a femoral. So it will be similar to CIS, um, that we are using right now. And it doesn't literally matter, um, where they are running at the end. It's just, um, to reduce friction as much as possible and decrease and yeah, yeah. Essentially, um, avoid or the hustle that is currently involved in setting up and also managing dev environments, um, going forward, which really slows down specifically larger teams. >>Yeah. Yeah. Um, I'm going to shift gears a little bit here. We have a question from the audience in chat, uh, and it's, I think it's a little bit two parts, but so far as I can see container first, uh, development, have the challenges of where to get safe images. Um, and I was going to answer it, but let me keep it, let me keep going, where to get safe images and instrumentation, um, and knowing where exactly the problem is happening, how do we provide instrument instrumentation to see exactly where a problem might be happening and why? So I think the gist of it is kind of, of everything is in a container and I'm sitting outside, you know, the general thought around containers is isolation, right. Um, so how do I get views into that? Um, whether debugging or, or, or just general problems going on. I think that's maybe a broader question around the, how you, you know, you have your local hosts and then you're running everything containers, and what's the interplay there. W what's your thoughts there? >>I tend to think that containers are underused interactively. I mean, I think in production, you have this mindset that there's sort of this isolated environment, but it's very, actually simple to drop into a shell inside of a container and use it like you would, you know, your terminal. Um, so if you want to install software that way, you know, through, through an image rather than through like Homebrew or something, uh, you can kind of treat containers in that way and you can get a very, um, you know, direct access to the, to the space in which those are running in. So I think, I think that's maybe the step one is just like getting rid of that mindset, that, that these are all, um, you know, these completely encapsulated environments that you can't interact with because it's actually quite easy to just Docker exec into a container and then use it interactively >>Yeah. A hundred percent. And maybe I'll pass, I'm going to pass this question. You drone, but maybe demystify containers a little bit when I talked about this on the last, uh, panel, um, because we have a question in the, in the chat around, what's the, you know, why, why containers now I have VMs, right? And I think there's a misunderstanding in the industry, uh, about what, what containers are, we think they're fair, packaged stuff. And I think Jacob was hitting on that of what's underneath the hood. So maybe drown, sorry, for a long way to set up a question of what, what, what makes up a container, what is a container >>Is a container? Well, I, I think, um, the sharpest and most accurate and most articulate definition, I was from Alice gold first, and I will probably misquote her, but she said something like containers are a bunch of capsulated processes, maybe running on a cookie on welfare system. I'm not sure about the exact definition, but I'm going to try and, uh, reconstitute that like containers are just processes that run on a Unix machine. And we just happen to put a bunch of, um, red tape or whatever around them so that they are kind of contained. Um, but then the beauty of it is that we can contend them as much, or as little as we want. We can go kind of only in and put some actual VM or something like firecracker around that to give some pretty strong angulation, uh, all we can also kind of decontam theorize some aspects, you know, you can have a container that's actually using the, um, the, um, the network namespace of the host. >>So that gives it an entire, you know, wire speed access to the, to the network of the host. Um, and so to me, that's what really interesting, of course there is all the thing about, oh, containers are lightweight and I can pack more of them and they start fast and the images can be small, yada yada, yada. But to me, um, with my background in infrastructure and building resilient, things like that, but I find really exciting is the ability to, you know, put the slider wherever I need it. Um, the, the, the ability to have these very light containers, all very heavily, very secure, very anything, and even the ability to have containers in containers. Uh, even if that sounds a little bit, a little bit gimmicky at first, like, oh, you know, like you, you did the Mimi, like, oh, I heard you like container. >>So I put Docker when you're on Docker. So you can run container for you, run containers. Um, but that's actually extremely convenient because, um, as soon as you stop building, especially something infrastructure related. So you challenge is how do you test that? Like, when we were doing.cloud, we're like, okay, uh, how do we provision? Um, you know, we've been, if you're Amazon, how do you provision the staging for us installed? How do you provision the whole region, Jen, which is actually staging? It kind of makes things complicated. And the fact that we have that we can have containers within containers. Uh, that's actually pretty powerful. Um, we're also moving to things where we have secure containers in containers now. So that's super interesting, like stuff like a SIS box, for instance. Um, when I saw that, that was really excited because, uh, one of the horrible things I did back in the days as Docker was privileged containers, precisely because we wanted to have Docker in Docker. >>And that was kind of opening Pandora's box. That's the right, uh, with the four, because privileged containers can do literally anything. They can completely wreck up the machine. Um, and so, but at the same time, they give you the ability to run VPNs and run Docker in Docker and all these cool things. You can run VM in containers, and then you can list things. So, um, but so when I saw that you could actually have kind of secure containers within containers, like, okay, there is something really powerful and interesting there. And I think for folks, well, precisely when you want to do development in containers, especially when you move that to the cloud, that kind of stuff becomes a really important and interesting because it's one thing to have my little dev thing on my local machine. It's another thing when I want to move that to a swarm or Kubernetes cluster, and then suddenly even like very quickly, I hit the wall, which is, oh, I need to have containers in my containers. Um, and then having a runtime, like that gets really intense. >>Interesting. Yeah, yeah, yeah. And I, and jumping back a bit, um, yeah, uh, like you said, drum at the, at the base of it, it containers just a, a process with, with some, uh, Abra, pardon me, operating constructs wrapped around it and see groups, namespaces those types of things. But I think it's very important to, for our discussion right. Of, uh, developers really understanding that, that this is just the process, just like a normal process when I spin up my local bash in my term. Uh, and I'm just interacting with that. And a lot of the things we talk about are more for production runtimes for securing containers for isolating them locally. I don't, I don't know. I'll throw the question out to the panel. Is that really relevant to us locally? Right. Do we want to pull out all of those restrictions? What are the benefits of containers for development, right. And maybe that's a soft question, but I'd still love to hear your thoughts. Maybe I'll kick it over to you, Katie, would you, would you kick us off a little bit with that? >>I'll try. Um, so I think when, again, I was actually thinking of the previous answers because maybe, maybe I could do a transition here. So, interesting, interesting about containers, a piece of trivia, um, the secrets and namespaces have been within the Linux kernel since 2008, I think, which just like more than 10 years ago, hover containers become popular in the last years. So I think it's, it's the technology, but it's about the organization adopting this technology. So I think why it got more popular now is because it became the business differentiator organizations started to think, how can I deliver value to my customers as quickly as possible? So I think that there should be this kind of two lane, um, kind of progress is the technology, but it's at the same time organization and cultural now are actually essential for us to develop, uh, our applications locally. >>Again, I think when it's a single application, if you have just one component, maybe it's easier for you to kind of run it locally, have a very simple testing environment. Sufficient is a container necessary, probably not. However, I think it's more important when you're thinking to the bigger picture. When we have an architecture that has myriads of microservices at the basis, when it's something that you have to expose, for example, an API, or you have to consume an API, these are kind of things where you might need to think about a lightweight set up within the containers, only local environment to make sure that you have at least a similar, um, environment or a configuration to make sure that you test some of the expected behavior. Um, I think the, the real kind of test you start from the, the dev cluster will like the dev environment. >>And then like for, for you to go to staging and production, you will get more clear into what exactly that, um, um, configuration should be in the end. However, at the same time, again, it's, it's more about, um, kind of understanding why you continue to see this, the thing, like, I don't say that you definitely need containers at all times, but there are situations when you have like, again, multiple services and you need to replicate them. It's just the place to, to, to work with these kind of, um, setups. So, um, yeah, really depends on what you're trying to develop here. Nothing very specific, unfortunately, but get your product and your requirements are going to define what you're going to work with. >>Yeah, no, I think that's a great answer, right. I think one of the best answers in, in software engineering and engineering in general as well, it depends. Right. It's things are very specific when we start getting down to the details, but yeah, generally speaking, you know, um, I think containers are good for development, but yeah, it depends, right. It really depends. Is it helping you then? Great. If it's hindering you then, okay. Maybe think what's, what's the hindrance, right. And are containers the right solution. I agree. 110% and, >>And everything. I would like absurd this too as well. When we, again, we're talking about the development team and now we have this culture where we have the platform and infrastructure team, and then you have your engineering team separately, especially when the regulations are going to be segregated. So, um, it's quite important to understand that there might be a, uh, a level of up-skilling required. So pushing for someone to use containers, because this is the right way for you to develop your application might be not, uh, might not be the most efficient way to actually develop a product because you need to spend some time to make sure that the, the engineering team has the skills to do so. So I think it's, it's, again, going back to my answers here is like, truly be aware of how you're trying to develop how you actually collaborate and having that awareness of your platform can be quite helpful in developing your, uh, your publication, the more importantly, having less, um, maybe blockers pushing it to a production system. >>Yeah, yeah. A hundred percent. Yeah. The, uh, the cultural issue is, is, um, within the organization, right. Is a very interesting thing. And it, and I would submit that it's very hard from top down, right. Pushing down tools and processes down to the dev team, man, we'll just, we'll just rebel. It usually comes from the bottom up. Right. What's working for us, we're going to do right. And whether we do it in the shadows and don't let it know, or, or we've conformed, right. Yeah. A hundred percent. Um, interesting. I would like to think a little bit in the future, right? Like, let's say, I don't know, two, three years from now, if, if y'all could wave a and I'm from Texas. So I say y'all, uh, if you all could wave a magic wand, what, what, what would that bring about right. What, what would, what would be the best scenario? And, and we just don't have to say containers. Right. But, you know, what's the best development environment and I'm going to kick it over to you, Jacob. Cause I think you hinted at some of that with some hybrid type of stuff, but, uh, yeah. Implies, they need to keep you awake. You're, you're, you're, uh, almost on the other side of the world for me, but yeah, please. >>Um, I think, you know, it's, it's interesting because you have this technology that you've been, that's been brought from production, so it's not, um, necessarily like the right or the normal basis for development. So I think there's going to be some sort of realignment or renormalization in terms of, uh, you know, what the, what the basis and the abstractions that we're using on a daily basis are right. Like images and containers as they exist now are really designed for, um, for production use cases. And, and in terms of like, even even the ergonomics of opening a shell inside a container, I think is something that's, um, you know, not as polished or not as smooth as it could be because they've come from production. And so I think it's important, like not to, not to have people look at, look at the technology as it exists now and say like, okay, this is slightly rough around the edges, or it wasn't designed for this use case and think, oh, there's, you know, there's never any way I could use this for, for my development of workflows. >>I think it's, you know, it's something Docker's exploring now with, uh, with the, uh, dev containers, you know, it's, it's a new, and it's an experimental paradigm and it may not be what the final picture looks like. As, you know, you were saying, there's going to be kind of a baseline and you'll add features to that or iterate on that. Um, but I think that's, what's interesting about it, right? Cause it's, there's not a lot of things as developers that you get to play with that, um, that are sort of the new technology. Like if you're talking about things you're building to ship, you want to kind of use tried and true components that, you know, are gonna, that are going to be reliable. But I think containers are that interesting point where it's like, this is an established technology, but it's also being used in a way now that's completely different than what it was designed for. And, and, you know, as hackers, I think that's kind of an interesting opportunity to play with it, but I think, I think that's, what's going to happen is you're just going to see kind of those production, um, designed, uh, knobs kind of sanded down or redesigned for, for development. So that's kind of where I see it going. >>Yeah. Yeah. And I think that's what I was trying to hint out earlier is like, um, yeah, just because all these things are there, does it actually mean we need them locally? Right. Do they make sense? I, I agree. A hundred percent, uh, anybody else drawn? What are your thoughts around that? And then, and then, uh, I'll probably just ask all of you. I'd love to hear each of your thoughts of the future. >>I had a thought was maybe unrelated, but I was kind of wondering if we would see something on the side of like energy efficiency in some way. Um, and maybe it's just because I've been thinking a lot about like climate change and things like that recently, and trying to reduce like the, uh, the energy use energy use and things like that. Perhaps it's also because I recently got a new laptop, which on paper is super awesome, but in practice, as soon as you try to have like two slack tabs and a zoom call, you know, it's super fast, both for 30 seconds. And after 30 seconds, it blows its thermal budget and it's like slows down to a crawl. And I started to think, Hmm, maybe, you know, like before we, we, we were thinking about, okay, I don't have that much CPU available. So you have to be kind of mindful about that. >>And now I wonder how are we going to get in something similar to that, but where you try to save CPU cycles, not just because you don't have that many CPU cycles, but more because you know, that you can't go super fast for super long when you are on one of these like small laptops or tablets or phones, like you have this demo budget to take into account. And, um, I wonder if, and how like, is there something where goaltenders can do some things here? I guess it can be really interesting if they can do some the equivalent of like Docker top and Docker stats. And if I could see, like how much what's are these containers using, I can already do that with power top on Linux, for instance, like process by process. So I'm thinking I could see what's the power usage of, of some containers. Um, and I wonder if down the line, is this going to be something useful or is this just silly because we can just masquerade CPU usage for, for Watson and forget about it. >>Yeah. Yeah. It was super, super interesting, uh, perspective for sure. I'm going to shut up because I want to, I want to give, make sure I give Johannes and Katie time. W w what are your thoughts of the future around, let's just say, you know, container development in general, right? You want, you want to start absolutely. Oh, honest, Nate. Johns wants more time. I say, I'll try not to. Beneficiate >>Expensive here, but, um, so one of the things that we've we've touched upon earlier in the panel was multicloud strategy. And I was reading one of the data reports from it was about the concept of Kubernetes from gamer Townsville. But what is working for you to see there is that more and more organizations are thinking about multicloud strategy, which means that you need to develop an application or need an infrastructure or a component, which will allow you to run this application bead on a public cloud bead, like locally in a data center and so forth. And here, when it comes to this kind of, uh, maybe problems we come across open standards, this is where we require something, which will allow us to execute our application or to run our platform in different environments. So when you're thinking about the application or development of the application, one of the things that, um, came out in 2019 at was the Oakland. >>Um, I wish it was Kybella, which is a, um, um, an open application model based application, which allows you to describe the way you would like your service to be executed in different environments. It doesn't need to be well developed specifically for communities. However, the open application model is specialized. So specialized tries to cover multiple platforms. You will be able to execute your application anywhere you want it to. So I think that that's actually quite important because it completely obstructs what is happening underneath it, completely obstructs notions, such as containers, uh, or processes is just, I want this application and I want to have this kind of behavior is so example of, to scale in this conditions or to, um, to be exposed for these, uh, end points and so forth. And everything that I would like to mention here is that maybe this transcends again, the, uh, the logistics of the application development, but it definitely will impact the way we run our applications. >>So one of the biggest, well, one of the new trends that is kind of gaining momentum now has been around Plaza. And this is again, something which is trying to present what we have the on containers. Again, it's focusing on the, it's kind of a cyclical, um, uh, action movement that we have here. When we moved from the VMs to containers, it was smaller footprint. We want like better execution, one, this agnosticism of the platforms. We have the same thing happening here with Watson, but again, it consents a new, um, uh, kind of, well, it teaches in you, uh, in new climax here, where again, we shrink the footprint of the cluster. We have a better isolation of all the services. We have a better trend, like portability of how services and so forth. So there is a great potential out there. And again, like why I'm saying this is some of these technologies are gonna define the way we're gonna do our development of the application on our local environment. >>That's why it's important to kind of maybe have an eye there and maybe see if some of those principles of some of those technologies we can bring internally as well. And just this, like a, a final thought here, um, security has been mentioned as well. Um, I think it's something which has been, uh, at the forefront, especially when it comes to containers, uh, especially when it comes to enterprise organizations and those who are regulated, which I feel come very comfortable to run their application within a VM where you have the full isolation, you can do what we have complete control of what's happening inside that compute. So, um, again, security has been at the forefront at the moment. So I know it has mentioned in the panel before. I'd like to mention that we have the security white paper, which has been published. We have the software supply chain, white paper as well, which twice to figure out or define some of these good practices as well, again, which you can already apply from your development environment and then propagate them to production. So I'm just going to leave, uh, all of these. That's all. >>That's awesome. And yeah, well, while is very, very interesting. I saw the other day that, um, and I forget who it was, maybe, maybe all can remember, um, you know, running, running the node, um, engine inside of, you know, in Walzem inside of a browser. Right. And, uh, at first glance I said, well, we already have a JavaScript execution engine. Right. And it's kind of like Docker and Docker. So you have, uh, you know, you have the browser, then, then you have blossom and then you have a node, you know, a JavaScript runtime. And, and I didn't understand was while I was, um, you know, actually executing is JavaScript and it's not, but yeah, it's super interesting, super powerful. I always felt that the browser was, uh, Java's what write once run anywhere kind of solution, right. That never came about, they were thinking of set top, uh, TV boxes and stuff like that, which is interesting. >>I don't know, you'll some of the history of Java, but yeah. Wasm is, is very, I'm not sure how to correctly pronounce it, but yeah, it's extremely interesting because of the isolation in that boxing. Right. And running powerful languages that were used to inside of a more isolated environment. Right. And it's almost, um, yeah, it's kind of, I think I've mentioned it before that the containers inside of containers, right. Um, yeah. So Johannes, hopefully I gave you enough time. I delayed, I delayed as much as I can. My friend, you better, you better just kidding. I'm just kidding, please, please. >>It was by the way, stack let's and they worked together with Google and with Russell, um, developing the web containers, it's called there's, it's quite interesting. The research they're doing there. Yeah. Yeah. I mean, what we believe and I, I also believe is that, um, yeah, probably somebody is doing to death environments, what Docker did to servers and at least that good part. We hope that somebody will be us. Um, so what we mean by that is that, um, we think today we are still somehow emotionally attached to our dev environments. Right. We give them names, we massage them over time, which can also have its benefits, but it's, they're still pets in some way. Right. And, um, we believe that, um, environments in the future, um, will be treated similar like servers today as automated resources that you can just spin up and close down whenever you need them. >>Right. And, um, this trend essentially that you also see in serverless, if you look at what kind of Netlify is doing a bit with preview environments, what were sellers doing? Um, there, um, we believe will also arrive at, um, at Steph environments. It probably won't be there tomorrow. So it will take some time because if there's also, you know, emotion involved into, in that, in that transition, but ultimately really believe that, um, provisioning dev environments also in the cloud allows you to leverage the power of the cloud and to essentially build all that stuff that you need in order to work in advance. Right? So that's literally either command or a button. So either, I don't know, a command that spins up your local views code and SSH into, into a container, or you do it in a browser, um, will be the way that professional development teams will develop in the future. Probably let's see in our direction of document, we say it's 2000 to 23. Let's see if that holds true. >>Okay. Can we, can, we let's know. Okay. Let's just say let's have a friendly bet. I don't know that's going to be closed now, but, um, yeah, I agree. I, you know, it's my thought around is it, it's hard, right? Th these are hard. And what problems do you tackle first, right? Do you tackle the day, one of, uh, you know, of development, right. I joined a team, Hey, here's your machine? And you have Docker installed and there you go, pull, pull down your environment. Right. Is that necessarily just an image? You know, what, what exactly is that sure. Containers are involved. Right. But that's, I mean, you, you've probably all gone through it. You joined a team, new project, even open-source project, right there. There's a huge hurdle just to get everything configured, to get everything installed, to get it up and running, um, you know, set aside all understanding the code base. >>Cause that's a different issue. Right. But just getting everything running locally and to your point earlier, Jacob of around, uh, recreating, local production cues and environments and, you know, GPS or anything like that, right. Is extremely hard. You can't do a lot of that locally. Right. So I think that's one of the things I'd love to see tackled. And I think that's where we're tackling in dev environments, uh, with Docker, but then now how do you become productive? Right. And where do we go from there? And, uh, and I would love to see this kind of hybrid and you guys have been all been talking about it where I can, yes. I have it configured everything locally on my nice, you know, apple notebook. Right. And then, you know, I go with the family and we go on vacation. I don't want to drag this 16 inch, you know, Mac laptop with me. >>And I want to take my nice iPad with the magic keyboard and all the bang stuff. Right. And I just want to fire up and I pick up where I left off. Right. And I keep coding and environment feels, you know, as much as it can that I'm still working at backup my desktop. I think those, those are very interesting to me. And I think reproducing, uh, the production running runtime environments as close as possible, uh, when I develop my, I think that's extremely powerful, extremely powerful. I think that's one of the hardest things, right. It's it's, uh, you know, we used to say, we, you debug in production. Right. We would launch, right. We would do, uh, as much performance testing as possible. But until you flip that switch on a big, on a big site, that's where you really understand what is going to break. >>Right. Well, awesome. I think we're just about at time. I really, really appreciate everybody joining me. Um, it's been a pleasure talking to all of you. We have to do this again. If I, uh, hopefully, you know, I I'm in here in America and we seem to be doing okay with COVID, but I know around the world, others are not. So my heart goes out to them, but I would love to be able to get out of here and come see all of you and meet you in person, maybe break some bread together. But, um, again, it was a pleasure talking to you all, and I really appreciate you taking the time. Have a good evening. Cool. >>Thanks for having us. Thanks for joining us. Yes.

>>From around the globe. It's the cube with digital coverage of IBM. Think 20, 21 brought to you by IBM, everybody welcome back to the cubes. Continuous coverage of IBM think 20, 21, the virtual edition. My name is Dave Volante, and we're going to talk about observability, front and center for DevOps and developers. Things are really changing. We're going from monitoring and logs and metrics and just this mess. And now we're bringing in AI and machine intelligence and with us as Pablo Baron, who's the CTO of Instana, which is an IBM company that IBM acquired November of 2020 Pablo. Great to see you. Thanks for joining us from Munich. >>Thanks for having me. Thanks a lot. >>You're very welcome. So, you know, I always love to talk to founders and co-founders and try to understand sort of why they started their companies and congratulations on the exit. That's awesome. After, you know, five, five, I'm sure. Grinding, but relatively short years. Uh, why did you guys start in Stoneleigh and what were some of the trends that you saw and that you're seeing now in the observability space? >>Yeah, that's a very good question. So, um, the journey began, uh, as we worked in the company called code centric, the majority of the founders, and, uh, we actually specialized in troubleshooting, um, well, real hard customer performance problems. We used all different kinds of APM solutions for that. You know, we we've built expertise, uh, like, uh, collectively, maybe 300 years of the whole company. So we will go from one, um, adventure into the other and see customers suffer and to help them, you know, overcome this trouble. At some point we started seeing architectures, uh, coming up that were not well covered by the classic APM solutions. Like people went off to the suit, a suit, a suit of the virtualization, all in containers, you know, just dropping random, uh, workloads into container running this maybe in Cubanitos. Well, not, not actually not five, six ago but years ago, but you get the point we started with having continued containerization. >>And we've seen that a classic APM solution that is having the, you know, like machine oriented. And then, uh, some of them even counted by the number of CPU, et cetera, et cetera. The world very well suited for this plus all of the workloads are so dynamic. They keep coming and going. You cannot really, you know, place your agent there that is not adapting to change continuously. We've seen this coming and we really we've seen the trouble that we cannot really support the customers properly. So after looking around, we just said, Hey, uh, it's time to just implement the new one, right? This is, we started that adventure with the idea of a constant change to the AGL. If everything is containers with idea of everything goes towards cloud native people just, uh, run random, uh, um, workloads of all different versions that are linked all together that this whole microservices trend came up where people would just break down their model and resilience of, uh, literally very small components that could be deployed independently. Everything keeps changing all the time. The classic solution cannot keep up with it, >>Pick it up from there if I can. So it's interesting. Your timing is quite amazing because as you mentioned, it really wasn't cute Kubernetes when you started in the middle part of last decade, like containers have been around for a long time, but Coobernetti's, weren't that wasn't mainstream back then. So you had some foresight, uh, and, and the market has just come right into your vision, but, but maybe talk a little bit about the way APM used to work. It was, I started this talk about this. It was metrics, it was traces, it was logs. It was make your eyes bleed type of type of stuff. Um, and maybe you could talk about how, how you guys are different and how you're accommodating the rapid changes in the market today. >>Right? So, well, there is very, very many pieces to this. So first of all, we always have seen that the work that you should not be doing by hand, I mean, we already said that you should not be doing this and you shouldn't be automating as much as possible. We see this everywhere in the it industry that everything gets more and more automated and want to automate it through the whole continuous delivery cycle. Unfortunately, monitoring was the space that probably never was automated before installer came into place. So our idea was, Hey, just, just get rid of the unnecessary work because you keep people busy with stuff that they should not be doing, like manually watching dashboards, setting up agents, uh, with every single software change, like adopting configuration, et cetera, et cetera, et cetera, all of these things can be done automatically, you know, to very, very, very large extent. >>And that's what we did. We, we did this from the beginning, everything we approach, uh, we, we, we think twice about, uh, can we automate, you know, the maximum out of it. And only if we see that it's, it's, you know, too much in effort, et cetera, we will, we will problem in onto this, but otherwise we're not, we don't do this. And yet, you know, you can compromise the other, right? The other aspect is, so this is different to the classic APM world that is typically very expert heavy. The expert comes into, you know, into the project and really starts configuring, et cetera, et cetera, et cetera. This is, this is a totally different approach. The other approach is continuous change and, uh, you know, adapting to the continuous change container comes up. You need to know what this kind of workload, what kind of workload this thing is, how it is connected to all the others. >>And then at some point, probably it's gonna, it's gonna, you know, go through the change and get a new version, et cetera, et cetera. You need to capture this whole life cycle without really changing your monitoring system. Plus if you move your workloads from the classic monolith through microservices onto cause the need is you kind of trans transitioning, you know, it's a journey in this journey. You want to keep your business abstractions as stable as possible. The term application is nothing that you should be reconfiguring. Once you figured out what is payments in your system? This is a stable obstruction. It doesn't matter if you deliver it on containers. It doesn't matter if this is just a huge, you know, JVM that owns the whole box alone. It simply doesn't matter. So we, we decoupled everything infrastructure from everything logic and, uh, the foundation for this is what we call the dynamic graph. >>It's technically, it's pretty much a data structure. The regular route, the dispatcher would do no connections, uh, in, in, in multiple directions, from different nodes. But the point is that we actually decompose the whole it geography. This is the term I like to use because there is, there is no other it's infrastructure. It's typology. It is on the other hand, just, you know, same sides of the same thing. When you have a Linux process, it can be a JVM. It just, at the same time, it can be a problem with application. It's the same thing. I can give a different names and this different, you know, facets of this thing can be linked with everything else in a different way. So we're decomposing this from the beginning of the product, which allows us to, to have a very deep and hierarchical understanding of the problem when it appears so we can nail it, not down to a metric that probably doesn't make sense to any user, but really name the cause by look in this JVM, the drop wizard metric XYZ that is misbehaving. >>This indicates that this particular piece of technology is broken and here's how it's broken. So there's a built in explanation to a problem. So, um, the cloud, the classic APM, as I said, it is a very expert, heavy, um, uh, territory. We try to automate the expert. We have this guy called Stan. This is your, you know, kind of, uh, virtual dev ops engineer has AI in there. It has some, some artificial brain. It never sleeps. It observes all of the problems. It really is an amazing guy because nobody likes them because he always tells you what's broken. You don't need to invite them to the body and give them a raise. They're just there and conserving the system. >>I liked Stan. I liked Stan better than Fred. No offense to Fred, but Fred's is the guy in the lab coat that I have to call every time to help me fix my, and what you're describing is end to end visibility or observability, uh, in, in terms that the normal either normal people can understand, or certainly Stan can understand and can automate. And that kind of leads me to this notion of, of anti-patterns. Um, getting in software, we think of anti-patterns is, you know, you have software hairballs and software bloat. You've got stovepipe systems. You're, you're a data guy by background. And so you will understand, you know, stovepipe data systems, there's organizational examples of, of, of anti-patterns like micromanagement or over-analyze analysis by paralysis. If you will, how do anti-patterns fit into this world of observability? What do you see? >>Oh, there is many, I could write a whole book actually about that. Um, let, let me just list a few. So first of all, it is valid for any kind of automation. What you can automate, you should not be doing by hand. This is a very common pattern. People are just doing work by hand, just because the lazy where you know, like repetitive work or there is no kind of foundation to automate the, whatever, the reason, this is clearly an impact pattern. What we, what we also see in the monitoring space are very interesting things like normally since the problems in the observability and monitoring space are so hard, you would normally send your best people, watching rats want them to contribute to the business value rather than waste the time of serving charts. That's like 99% of them are marble. The other aspect of course, is what we also have seen is the other side of the spectrum where people just send total mobilizes into the, into the problem of ops observability and let them learn on the subject, which is also not a good thing, because you can not really, I mean, there are so many unknown unknowns for people who are not experts in this space. >>They will not catch the problem. You will go through pain, right? So it's not a learning project. It's not the research from a project. This is very essential to the operation of your business and to it. And there's many examples like that, >>Right? Yeah. So I want to end by just sort of connecting the dots. So this makes a lot of sense. And if you think about, you know, Auburn Christian said that IBM has got to win the architectural battle for hybrid cloud. And when I think of hybrid cloud, I think of on-prem connecting to public cloud, not only the IBM public cloud, but other public clouds going across clouds, going to the edge, bringing OpenShift and Kubernetes to the edge and developing new, supporting new workload. So as it is like the university keeps expanding and it gets more and more and more complicated. So to your point, humans are not going to be able to solve the classic performance problems in the classic way. Uh, they're going to need automation. So it really does fit well into IBM's hybrid cloud strategy, your, your thoughts, and I'll give you the last word. >>Yeah, totally. I mean IBM generally is of course, very far ahead in, in regards to AI and all these things, this desk, sorry, those could be combined within standard, very, very, you know, natively, right. We, we are prepared to automate using AI all of the, well, I would want to claim that all of the monitoring observability problems, of course there is manual work in some, uh, you know, in some cases you simply don't know what people want to observe, so you kind of need to give them names and that's what people come in, but this is more a creative work. Like you don't want to do the stupid work with people. It doesn't, you know, there is no, it doesn't make any sense. And IBM of course, um, requiring and Stan, I guess, you know, the foundation for all of the things that that used to be done by, by hand now fully automated, combined within starlet, combined with Watson AI ops. This is, this is huge. This is a real great story. Like the best research at the world meeting, uh, probably the best APM summit. >>That's great. Uh, Pablo really appreciate you taking us through and Stata and the trends and observability and what's going on at IBM and congratulations on your success. And thanks for hanging with us with all the craziness going on at your abode and, uh, really, it was a pleasure having you on. Thank you. Thanks a lot. Thank you for watching everybody. This is Dave Volante and the ongoing coverage of IBM. Think 2021. You're watching the cube.

>>from >>around the >>globe, it's the cube >>with digital coverage of >>IBM think 2021 >>brought to >>you by IBM >>everybody welcome back to the cubes, continuous coverage of IBM think 2021 the virtual edition, my name is Dave Volonte and we're gonna talk about observe ability front and center for devops and developers, things are really changing. We're going from monitoring and logs and metrics and just this mess and now we're bringing in a I and machine intelligence and with us is Pablo Baron, who is the Ceo of inst ana, which is an IBM company that IBM acquired november of 2020. Pablo great to see you. Thanks for joining us from Munich. >>Thanks for having me. Thanks a lot. >>You're very welcome. So you know, I always love to talk to founders and co founders and try to understand sort of why they started their companies and congratulations on the exit. That's awesome. After 55 I'm sure grinding but relatively short years. Why did you guys start in stana? And what were some of the trends that you saw in that you're seeing now in the observe ability space? >>Yeah, that's a very good question. So, um, the journey began ah, as we worked in the company called code centric, the majority of the founders and uh, we actually specialized in troubleshooting uh, well, real hard customer performance problems. We used all different kinds of A PM solutions for that. You know, we, we've built expertise like collectively maybe 300 years in the whole company. So we would go from one um, adventure into the other and see customers suffer and help them, you know, overcome this trouble. At some point we started seeing architectures coming up that were not well covered by the classic KPM sellers, like people went after this. Sudha, Sudha, Sudha virtualization all in containers, you know, just dropping random workloads into container running this maybe in cabinet as well. Not not actually not 56 ago, but years ago. But you get the point, we started with the heavy continues container ization and we've seen that a classic A PM solution that is heavily, you know, like machinery rented and and some of them you've encountered by the number of CPU etcetera etcetera. They were very well suited for this. Plus all of the workloads are so dynamic. They keep coming and going. You cannot really, you know, place your agent there that is not adopting to change continuously. We've seen this coming and we really, we've seen the trouble that we cannot really support the customers properly. So after looking around, we just said, hey, uh, I think it's time to just implement a new one. Right? So we started that adventure with the idea of a constant change, with the idea of everything is containers, with idea of everything goes towards glove needed. People just run random uh workloads of all different versions that are linked altogether than this. Whole microservices trend came up where people would just break down their monoliths and resilience of literally very small components that could be deployed independently. Everything keeps changing all the time. The classic solution cannot keep up with that. >>So let me pick it up from there if I can. So it's interesting. Your timing is quite amazing because as you mentioned, it really wasn't kubernetes when you started in the middle part of last decade. You know, containers have been around for a long time, but kubernetes weren't, it wasn't mainstream back then. So you had some foresight uh and and the market has just come right into your vision but but maybe talk a little bit about the way A. P. M. Used to work. It was, I started to talk about this. It was metrics, it was traces, it was logs, it was make your eyes bleed type of type of stuff. Um, and maybe you can talk about how you guys are different and how you're accommodating the rapid changes in the market today. >>Right? So well there is very, very many um cases this. So first of all we always have seen that the work that you should not be doing by hand. I mean we already said that you should not be doing this and you should be automating as much as possible. We see this everywhere in the industry that everything gets more and more automated. We want to animate through the whole continuous delivery cycle. Unfortunately monitoring was the space that probably never was automated before installing a came into place. So our idea was, hey, just just get rid of the unnecessary work because you keep people busy with stuff they should not be doing like manually watching dashboards, setting up agents with every single software change, like adopting configuration etcetera, etcetera, etcetera. All of these things can be done automatically, you know, to very, very, very large extent. And that's what we did. We did this from the beginning, everything we approached, we, we, we think twice about can we automate, you know, the maximum out of it And only if we see that it's, it's, you know, too much an effort, etcetera. We will, we will probably not do this, but otherwise we're not, we don't do the same thing. You know, you can compromise the other right? The other aspect is, so this is different to the classic A PM world that is typically very expert heavy. The expert comes into, you know, into the project and really starts configuring etcetera, etcetera etcetera. This is this is a totally different approach the other approaches continuous change and you know, adapting to the continuous change, container comes up, you need to know what this kind of workload, what kind of work load this thing is, how it is connected to all the others. And then at some point probably it's gonna it's gonna go through the change and get a new versions etcetera etcetera. You need to capture this whole life cycle without really changing your monitoring system. Plus, if you move your workloads from the classic Monolith, through microservices on to cuba needs, you kind of transitioning, you know, it's a journey and this journey, you want to keep your business abstractions as stable as possible. The term application is nothing that you should be reconfiguring. Once you figure out what is payment in your system. This is a stable abstraction. It doesn't matter if you deliver it on containers. Doesn't matter if this is just a huge JBM that owns the whole box alone. It simply doesn't matter. So we we decoupled everything infrastructure from everything logic and uh the foundation for this is what we call the dynamic ground. It technically is pretty much a data structure. Regular graph data structure with, you know, connections in multiple directions from different notes. But the point is that we actually decompose the whole, I teach geography. This is the term I like to use because there is, there is no other its infrastructure, its topology, it is on the other hand, just, you know, same sides of the same thing. When you have a limits process, it can be HIV m it's just at the same time, it can be approached with an application, it's the same thing and given different names and this different faces of this thing can be linked with everything else in a totally different way. So we're decomposing this from the beginning of the product which allows us to to have a very deep and hierarchical understanding of problems when it appears. So we can nail it not down to a metric. That probably doesn't make sense to any user but really name the cause by look in this J. V. M, the drop wizard metric exercise that is misbehaving. This indicates that this particular piece of technology is broken and here's how it's broken. So there's a built in explanation to a problem. So um the the classic eight pm as I said, it is a very expert heavy um, territory we try to automate the expert. We have this guy called stan this is your you know, kind of virtual devoPS engineer has a I in there. It has some artificial brain, it never sleeps, it observes all of the problems. It really is an amazing guy because nobody likes him because he always tells you what's broken. You don't need to invite them to the party and give them a raise just there and conserving your systems. >>I like stand, I like stand better than fred, no offense to fred but friends of the guy in the lab coat that I have to call every time to help me fix my problems and what you're describing is end to end visibility or observe ability in terms that norm either normal people can understand or certainly stand, can understand and can automate. And that kind of leads me to this notion of anti patterns um getting software, we think of anti patterns as you know you have software hairballs and software bloat, you've got stovepipe systems, your your data guy by background and so you will understand stovepiped data systems, there's organizational examples of of of anti patterns like micromanagement or over an analysis by paralysis. If you will, how do anti patterns fit into this world? Of observe ability? What do you see? >>Oh there's many, I could write a whole book actually about that. Um let me just list a few. So first of all it is valid for any kind of automation, what you can automate you should not be doing by hand, this is a very common entire pattern. People are just doing work by hand just because the lazy word, you know like repetitive work or there is no kind of foundation to automate that whatever the reason, this is clearly an anti pattern. What we, what we also see in the monitoring space are very interesting things like normally since the problems in the observe ability monitoring space is so hard, You normally send your best people watching grants who want them to contribute to the business value rather than waste the time observing charts that like 99 of them are normal. The other aspect, of course, is what we also have seen is the other side of the spectrum where people just send total mobilizes into the, into the problem of observe ability and let them learn on the subject. Which is also not a good thing because you cannot really I mean there are so many unknown unknowns for people who are not experts in the space. They will not catch the problem. You will go through pain, right? So it's not the learning project, that's not the research from a project. This is very essential to the operation of humor, business and humanity. And there's many examples like that, >>right? Yeah. So I want to end by just sort of connecting the dots so this makes a lot of sense. And if you think about, you know, Ivan Kushner said that IBM has got to win the architectural battle for hybrid cloud. And when I think of Hybrid cloud, I think of on prem connecting to public cloud, not only the IBM public cloud but other public clouds going across clouds going to the edge, bringing open shift and kubernetes to the edge and developing new supporting new workloads. So as I. T. Is like the university keeps expanding and it gets more and more and more complicated. So to your point humans are not going to be able to solve the classic performance problems in the classic way. Uh they're gonna need automation. So it really does fit well into iBMS hybrid cloud strategy, your, your thoughts and I'll give you the last word. >>Yeah, totally. I mean, I'm IBM generally is of course very far ahead in regards to research AI and all these things this death, sorry, those could be combined with an stand a very, very, you know, natively right. We we are prepared to automate using AI all of the well, I would want to claim that all of the monitoring observe ability problems. Of course, there is manual work in some, you know, in some cases you simply don't know what people want to observe. So you kind of need to give them names and that's where people come in. But this is more creative work. Like you don't want to do the stupid work with people. It doesn't, you know, there is no, it doesn't make any sense. And IBM of course, um requiring in stana gets, you know, the foundation for all of the things that used to be done by hand. Now, fully automated, combined within standard, combined with Watson, the ions, This is, this is huge. This is like a real great story, like the best research of the world eating. Uh, probably the best a PMC. >>That's great Pablo, really appreciate you taking us through Astana and the trends and observe ability and what's going on at IBM. And congratulations on your, your success and thanks for hanging with us with all the craziness going on at your abode. And uh really, it was a pleasure having you on. Thank you. >>Thanks a lot. >>All right, and thank you for watching everybody says Dave Volonte and our ongoing coverage of IBM, think 2021 you're watching the Cube? Yeah. Mhm

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. All right, you're continuing or we're continuing around the clock coverage and around the world coverage off a W s reinvent 2020 virtual conference This year, I'm guessing hundreds of thousands of folks are tuning in for coverage. And we have we have on the other end of the country a cube alarm. Stephen Towel, co founder and CTO of immunity. Stephen, welcome back to the show. >>Great. Great to be here. Thanks for having me again. I hope to match your enthusiasm. >>You know what is, uh, your co founder? I'm sure you could match the enthusiasm. Plus, we're talking about data governance. You You've been on the cute before, and you kind of laid the foundation for us last year. Talking about challenges around data access and data access control. I want to extend this conversation. I had a conversation with a CEO chief data officer a couple of years ago. He shared how his data analysts his the people that actually take the data and make business decisions or create outcomes to make business decisions spent 80% of their time wrangling the data just doing transformations. >>How's the >>Muda helping solve that problem? >>Yeah, great questions. So it's actually interesting. We're seeing a division of roles in these organizations where we have data engineering teams that are actually managing. Ah, lot of the prep work that goes into exposing data and releasing data analysts. Uh, and as part of their day to day job is to ensure that that data that they're released into the analyst is what they're allowed to see. Um and so we kind of see this, this problem of compliance getting in the way of analysts doing their own transformation. So it would be great if we didn't have to have a limited to just this small data engineering team to release the data. What we believe one of the rial issues behind that is that they are the ones that are trusted. They're the only ones that could see all the data in the clear. So it needs to be a very small subset of humans, so to speak, that can do this transformation work and release it. And that means that the data analyst downstream are hamstrung to a certain extent and bottlenecked by requesting these data engineers do some of this transformation work for them. Eso I think because, as you said, that's so critical to being able to analyze data, that bottleneck could could be a back breaker for organization. So we really think that to you need to tie transformation with compliance in order to streamline your analytics in your organization. >>So that has me curious. What does that actually look like? Because Because when I think of a data analyst, they're not always thinking about Well, who should have this data? They're trying to get the answer to the question Thio provide to the data engineer. What does that functionally looked like when that when you want to see that relationship of collaboration? >>Yeah, So we e think the beauty of a Muda and the beauty of governance solutions done right is that they should be invisible to the downstream analysts to a certain extent. So the data engineering team will takes on some requirements from their legal compliance. Seems such as you need a mask p I I or you need Thio. Hi. These kinds of rose from these kinds of analysts, depending on what the users doing. And we've just seen an explosion of different slices or different ways, you should dice up your data and what who's allowed to see what and not just about who they are, but what they're doing on DSO. You can kind of bake all these policies upfront on your data on a tool like Kamuda, and it will dynamically react based on who the analyst is and what they're doing to ensure that the right policies air being enforced. And we could do that in a way that when the analysts I mean, what we also see is just setting your policies on your data. Once up front, that's not the end of the story. Like a lot of people will tap themselves on the back and say, Look, we've got all our data protected appropriately, job done. But that's not really the case, because the analysts will start creating their own data products and they want to share that with other analysts. And so when you think about this, this becomes a very complex problem of okay. Before someone can share their data with anyone else, we need to understand what they were allowed to see eso being able to control the kind of this downstream flow of of transformations and feature engineering to ensure that Onley the right people, are seeing the things that they're allowed to see. But still, enabling analytics is really the challenges that that we saw that in Muda Thio, you know, help the the data teams create those initial policies at scale but also help the analytical teams build driven data products in a way that doesn't introduce data leaks. >>So as I think about the traditional ways in which we do this, we kind of, you know, take a data sad. Let's say, is the databases and we said, security rules etcetera on those data states. That's what you're painting to ISMM or of Dynamic. Has Muto approaching this problem from just a architectural direction? >>Yeah, great question. So I'm sure you've probably heard the term role based access control on, but it's been around forever where you basically aggregate your users in the roles, and then you build rules around those roles on gritty, much every legacy. Already, BMS manages data access this way. Um, what we're seeing now and I call it the private data era that we're now embarking on or have been embarking on for the past three years or so. Where consumers are more aware of their data, privacy and the needs they had their there's, you know, data regulations coming fast and furious with no end in sight. Um, we believe that this role based access control paradigm is just broken. We've got customers with thousands of roles that they're trying to manage Thio to, you know, slice up the data all the different ways that they need Thio. So instead, we we offer an accurate based access control solution and also policy based access control solution. We're. Instead, it's really about How do you dynamically enforced policy by separating who the user is from the policy that needs to be enforced and and having that execute at runtime? A good analogy to this is role based. Access control is like writing code without being able to use variables. You're writing the same block a code over and over again with slight changes based on the roll where actually based access control is, you're able to use variables and basically the policy gets decided at runtime based on who the user is and what they're doing. So >>that dynamic nature kind of lends itself to the public cloud. Were you seeing this applied in the world off a ws were here Reinvent so our customers using this with a W s >>So it all comes down to scalability so that the same reasons that used to separate storage from compute. You know, you get your storage in one place you could ephemera, lee, spin up, compute like EMR if you want. Um, you can use Athena against your storage in a server lis way that that kind of, um, freedom to choose whatever compute you want. Um, the same kind of concepts of apply with policy enforcement. You wanna separate your policy from your platform on that This private data era has has, you know, created this need just like you have to separate your compute from storage in the big data era. And this allows you to have a single plane of glass to enforce policy consistently, no matter what compute you're using or what a U s resource is you're using, um and so this gives our customers power to not only, um, you know, build the rules that they need to build and not have to do it uniquely her service in the U. S. But also proved to their legal and compliance teams that they're doing it correctly because, um, when when you do it this way, it really simplifies everything. And you have one place to go toe, understand how policies being enforced. And this really gives you the auditing and reporting around, um, be enforcement that you've been doing to put every one of these, that everything is being done correctly and that your data consumers can understand You know how your data is being protected. Their data is being protected. Um, and you could actually answer those questions when they come at you. >>So let's put this idea to the test a little bit. So I have the data engineer who kind of designs the security policy around the data or implements that policy using Kamuda Aziz dictated by the security and chief data officer of the organization. Then I have the analyst, and the analyst is just using the tools at their disposal. Let's say that one analyst wants to use AWS Lambda and another analysts wants to use our type database or analysis tools. You're telling me that Muda allows the flexibility for that analyst to use either tool within a W S. >>That's right, because we enforce policy at the data layer. Eso If you think about a Muda, it's really three layers policy authoring, which you touched on where those requirements get turned into real policies. Policy decision ing. So at query time we see who the user is, what they're doing on what policy has been defined to dynamically build that policy at run time and then enforcement, which is what you're getting at. The enforcement happens at the data layer, for example, we can enforce policies, natively and spark. So no matter how you're connecting to spark, that policy is going to get enforced appropriately. So we don't really care about what the clients Liz, because the enforcement is happening at the data or the compute layer is is a more accurate way todo to say it >>so. A practical reality off collaboration, especially around large data sets, is the ability to share data across organizations. How is immune hoping thio just make that barrier? Ah, little lower but ensuring security so that when I'm sharing data with, uh, analysts with within another firm. They're only seeing the data that they need to see, but we can effectively collaborate on those pieces of content. >>Yeah, I'm glad you asked this. I mean, this is like the, you know, the big finale, right? Like, this is what you get when you have this granularity on your own data ecosystem. It enables you to have that granularity now, when you want to share outside of your internal ecosystem. And so I think an important part about this is that when you think about governance, you can't necessarily have one God users so to speak, that has control over all tables and all policies. You really need segmentation of duty, where different parts of the organ hooking their own data build their own policies in a way where people can't step on each other and then this can expand this out. The third party data sharing where you can set different anonymous ation levels on your data when you're sharing an external the organization verse, if it's internal users and then someone else in your ord could share their data with you and then that also do that Third party. So it really enables and freeze these organizations Thio share with each other in ways that weren't possibly before. Because it happens in the day. The layer, um, these organizations can choose their own compute and still have the same policies being forced again. Going back to that consistency piece, um, it provides. Think of it is almost a authoritative way to share data in your organization. It doesn't have to be ad hoc. Oh, I have to share with this group over here. How should I do it? What policies should enforce. There's a single authoritative way to set policy and share your data. >>So the first thing that comes to my mind, especially when we give more power to the users, is when the auditors come and they say, You know what, Keith? I understand this is the policy, but prove it. How do we provide auditors with the evidence that you know, the we're implementing the policy that we designed and then two were ableto audit that policy? >>Yeah. Good question. So, um, I briefly spoke about this a little bit, but the when you author and define the policies in the Muda there immediately being enforced. So when you write something in our platform, um, it's not a glorified Wikipedia, right? It's actually turning those policies on and enforcing it at the data later. And because of that, any query that's coming through a Muda is going to be audited. But I think even more importantly, to be honest, we keep a history of how policy changes happening over time, too. So you could understand, you know, so and so changed the policy on this table versus other table, you know, got newly added, these people got dropped from it. So you get this rich history of not only who's touching what data and what data is important, but you're also getting a rich history off. Okay, how have we been treating this data from a policy perspective over time? How is it like what were my risk levels over the past year? With B six tables on? You can answer those kinds of questions as well. >>And then we're in the era of cloud. We expect to be able to consume these services via AP I via pay as you go type of thing. How is your relationship with AWS and how in the cutting. Ultimately, the customer. How do I consume a music? >>Yeah, so in Munich can pretty much be deployed anywhere. So obviously we're talking to us here. We have a SAS offering where you can spin up Muda pretrial and just be often running building policies and hooking up hooking our policy enforcement engine into your compute. Um, that runs in our, um you know, infrastructure. There's also a deployment model where you deploy immune it into your VPC s so it can run on your infrastructure. Behind your firewalls on DWI do not require any public Internet access at all for that to run. We don't do any kind of phone homing because, obviously, privacy company, we take this very seriously internally as well. We also have on premise deployments, um, again with zero connectivity air gapped environments. Eso. So we offer that kind of flexibility to our customers wherever they want immediate toe to be deployed. An important thing to remember their two is immediate. Does not actually store any data. We just store metadata and policy information. Um, so it's that also provides the customers some flexibility where if they want to use our SAS, they can simply go policy in there, and then the data still lives in their account. We're just kind of pushing policy down into that. Dynamically. >>So Stephen Towel co founder c t o of immunity. I don't think you have to worry about matching my energy level. I through some pretty tough questions at at you and you were ready there with all the answers. You wanna see more interesting conversations from around the world with founders, builders, AWS reinvent is all about builders and we're talking to the builders throughout this show. Visit us on the web. The Cube. You can engage with us on Twitter. Talk to you next episode off the Cube from AWS reinvent 2020.

(plane engine roars) (upbeat music) >> They call me Dr. Goh. I'm Senior Vice President and Chief Technology Officer of AI at Hewlett Packard Enterprise. And today I'm in Munich, Germany. Home to one and a half million people. Munich is famous for everything from BMW, to beer, to breathtaking architecture and festive markets. The Bavarian capital is the beating heart of Germany's automobile industry. Over 50,000 of its residents work in automotive engineering, and to date, Munich allocated around 30 million euros to boost electric vehicles and infrastructure for them. (upbeat music) >> Hello, everyone, my name is Dr. Jerome Baudry. I am a professor at the University of Alabama in Huntsville. Our mission is to use a computational resources to accelerate the discovery of drugs that will be useful and efficient against the COVID-19 virus. On the one hand, there is this terrible crisis. And on the other hand, there is this absolutely unique and rare global effort to fight it. And that I think is a is a very positive thing. I am working with the Cray HPE machine called Sentinel. This machine is so amazing that it can actually mimic the screening of hundreds of thousands, almost millions of chemicals a day. What we take weeks, if not months, or years, we can do in a matter of a few days. And it's really the key to accelerating the discovery of new drugs, new pharmaceuticals. We are all in this together, thank you. (upbeat music) >> Hello, everyone. I'm so pleased to be here to interview Dr. Jerome Baudry, of the University of Alabama in Huntsville. >> Hello, Dr. Goh, I'm very happy to be meeting with you here, today. I have a lot of questions for you as well. And I'm looking forward to this conversation between us. >> Yes, yes, and I've got lots of COVID-19 and computational science questions lined up for you too Jerome. Yeah, so let's interview each other, then. >> Absolutely, let's do that, let's interview each other. I've got many questions for you. And , we have a lot in common and yet a lot of things we are addressing from a different point of view. So I'm very much looking forward to your ideas and insights. >> Yeah, especially now, with COVID-19, many of us will have to pivot a lot of our research and development work, to address the most current issues. I watch your video and I've seen that you're very much focused on drug discovery using super computing. The central notebook you did, I'm very excited about that. Can you tell us a bit more about how that works, yeah? >> Yes, I'd be happy to in fact, I watch your video as well manufacturing, and it's actually quite surprisingly close, what we do with drugs, and with what other people do with planes or cars or assembly lanes. we are calculating forces, on molecules, on drug candidates, when they hit parts of the viruses. And we essentially try to identify what small molecules will hit the viruses or its components, the hardest to mess with its function in a way. And that's not very different from what you're doing. What you are describing people in the industry or in the transportation industry are doing. So that's our problem, so to speak, is to deal with a lot of small molecules. Guy creating a lot of forces. That's not a main problem, our main problem is to make intelligent choices about what calculates, what kind of data should we incorporate in our calculations? And what kind of data should we give to the people who are going to do the testing? And that's really something I would like you to do to help us understand better. How do you see artificial intelligence, helping us, putting our hands on the right data to start with, in order to produce the right data and accuracy. >> Yeah, that's that's a great question. And it is a question that we've been pondering in our strategy as a company a lot recently. Because more and more now we realize that the data is being generated at the far out edge. By edge. I mean, something that's outside of the cloud and data center, right? Like, for example, a more recent COVID-19 work, doing a lot of cryo electron microscope work, right? To try and get high resolution pictures of the virus and at different angles, so creating lots of movies under electron microscope to try and create a 3D model of the virus. And we realize that's the edge, right, because that's where the microscope is, away from the data center. And massive amounts of data is generated, terabytes and terabytes of data per day generated. And we had to develop means, a workflow means to get that data off the microscope and provide pre-processing and processing, so that they can achieve results without delay. So we learned quite a few lessons there, right, especially trying to get the edge to be more intelligent, to deal with the onslaught of data coming in, from these devices. >> That's fantastic that you're saying that and that you're using this very example of cryo-EM, because that's the kind of data that feeds our computations. And indeed, we have found that it is very, very difficult to get the right cryo-EM data to us. Now we've been working with HPE supercomputer Sentinel, as you may know, for our COVID-19 work. So we have a lot of computational power. But we will be even faster and better, frankly, if we knew what kind of cryo-EM data to focus on. In fact, most of our discussions are based on not so much how to compute the forces of the molecules, which we do quite well on an HP supercomputer. But again, what cryo-EM 3D dimensional space to look at. And it's becoming almost a bottleneck. >> Have access to that. >> And we spend a lot of time, do you envision a point where AI will be able to help us, to make this kind of code almost live or at least as close to live as possible, as that that comes from the edge? How to pack it and not triage it, but prioritize it for the best possible computations on supercomputers? >> What a visionary question and desire, right? Like exactly the vision we have, right? Of course, the ultimate vision, you aim for the best, and that will be a real time stream of processed data coming off the microscope straight, providing your need, right? We are not there. Before this, we are far from there, right? But that's the aim, the ability to push more and more intelligence forward, so that by the time the data reaches you, it is what you need, right, without any further processing. And a lot of AI is applied there, particularly in cryo-EM where they do particle picking, right, they do a lot of active pictures and movies of the virus. And then what they do is, they rotate the virus a little bit, right? And then to try and figure out in all the different images in the movies, to try and pick the particles in there. And this is very much image processing that AI is very good at. So many different stages, application is made. The key thing, is to deal with the data that is flowing at this at this speed, and to get the data to you in the right form, that in time. So yes, that's the desire, right? >> It will be a game changer, really. You'll be able to get things in a matter of weeks, instead of a matter of years to the colleague who will be doing the best day. If the AI can help me learn from a calculation that didn't exactly turn out the way we want it to be, that will be very, very helpful. I can see, I can envision AI being able to, live AI to be able to really revolutionize all the process, not only from the discovery, but all the way to the clinical, to the patient, to the hospital. >> Well, that's a great point. In fact, I caught on to your term live AI. That's actually what we are trying to achieve. Although I have not used that term before. Perhaps I'll borrow it for next time. >> Oh please, by all means. >> You see, yes, we have done, I've been doing also recent work on gene expression data. So a vaccine, clinical trial, they have the blood, they get the blood from the volunteers after the first day. And then to run very, very fast AI analytics on the gene expression data that the one, the transcription data, before translation to emit amino acid. The transcription data is enormous. We're talking 30,000, 60,000 different items, transcripts, and how to use that high dimensional data to predict on day one, whether this volunteer will get an adverse event or will have a good antibody outcome, right? For efficacy. So yes, how to do it so quickly, right? To get the blood, go through an SA, right, get the transcript, and then run the analytics and AI to produce an outcome. So that's exactly what we're trying to achieve, yeah. Yes, I always emphasize that, ultimately, the doctor makes that decision. Yeah, AI only suggests based on the data, this is the likely outcome based on all the previous data that the machine has learned from, yeah. >> Oh, I agree, we wouldn't want the machine to decide the fate of the patient, but to assist the doctor or nurse making the decision that will be invaluable? And are you aware of any kind of industry that already is using this kind of live AI? And then, is there anything in, I don't know in sport or crowd control? Or is there any kind of industry? I will be curious to see who is ahead of us in terms of making this kind of a minute based decisions using AI? Yes, in fact, this is very pertinent question. We as In fact, COVID-19, lots of effort working on it, right? But now, industries and different countries are starting to work on returning to work, right, returning to their offices, returning to the factories, returning to the manufacturing plants, but yet, the employers need to reassure the employees that things, appropriate measures are taken for safety, but yet maintain privacy, right? So our Aruba organization actually developed a solution called contact location tracing inside buildings, inside factories, right? Why they built this, and needed a lot of machine learning methods in there to do very, very well, as you say, live AI right? To offer a solution? Well, let me describe the problem. The problem is, in certain countries, and certain states, certain cities where regulations require that, if someone is ill, right, you actually have to go in and disinfect the area person has been to, is a requirement. But if you don't know precisely where the ill person has been to, you actually disinfect the whole factory. And if you have that, if you do that, it becomes impractical and cost prohibitive for the company to keep operating profitably. So what they are doing today with Aruba is, that they carry this Bluetooth Low Energy tag, which is a quarter size, right? The reason they do that is, so that they extract the tag from the person, and then the system tracks, everybody, all the employees. We have one company, there's 10,000 employees, right? Tracks everybody with the tag. And if there is a person ill, immediately a floor plan is brought up with hotspots. And then you just targeted the cleaning services there. The same thing, contact tracing is also produced automatically, you could say, anybody that is come in contact with this person within two meters, and more than 15 minutes, right? It comes up the list. And we, privacy is our focused here. There's a separation between the tech and the person, on only restricted people are allowed to see the association. And then things like washrooms and all that are not tracked here. So yes, live AI, trying to make very, very quick decisions, right, because this affects people. >> Another question I have for you, if you have a minute, actually has to be the same thing. Though, it's more a question about hardware, about computer hardware purify may. We're having, we're spending a lot of time computing on number crunching giant machines, like Sentinel, for instance, which is a dream to use, but it's very good at something but when we pulled it off, also spent a lot of time moving back and forth, so data from clouds from storage, from AI processing, to the computing cycles back and forth, back and forth, did you envision an architecture, that will kind of, combine the hardware needed for a massively parallel calculations, kind of we are doing. And also very large storage, fast IO to be more AI friendly, so to speak. You see on the horizon, some kind of, I would say you need some machine, maybe it's to be determined, to be ambitious at times but something that, when the AI ahead plan in terms of passing the vector to the massively parallel side, yeah, that makes sense? >> Makes a lot of sense. And you ask it I know, because it is a tough problem to solve, as we always say, computation, right, is growing capability enormously. But bandwidth, you have to pay for, latency you sweat for, right? >> That's a very good >> So moving data is ultimately going to be the problem. >> It is. >> Yeah, and we've move the data a lot of times, right, >> You move back and forth, so many times >> Back and forth, back and forth, from the edge that's where you try to pre-process it, before you put it in storage, yeah. But then once it arrives in storage, you move it to memory to do some work and bring it back and move it memory again, right, and then that's what HPC, and then you put it back into storage, and then the AI comes in you, you do the learning, the other way around also. So lots of back and forth, right. So tough problem to solve. But more and more, we are looking at a new architecture, right? Currently, this architecture was built for the AI side first, but we're now looking and see how we can expand that. And this is that's the reason why we announced HPE Ezmeral Data Fabric. What it does is that, it takes care of the data, all the way from the edge point of view, the minute it is ingested at the edge, it is incorporated in the global namespace. So that eventually where the data arrives, lands at geographically one, or lands at, temperature, hot data, warm data or cold data, regardless of eventually where it lands at, this Data Fabric checks everything, from in a global namespace, in a unified way. So that's the first step. So that data is not seen as in different places, different pieces, it is a unified view of all the data, the minute that it does, Just start from the edge. >> I think it's important that we communicate that AI is purposed for good, A lot of sci-fi movies, unfortunately, showcase some psychotic computers or teams of evil scientists who want to take over the world. But how can we communicate better that it's a tool for a change, a tool for good? >> So key differences are I always point out is that, at least we have still judgment relative to the machine. And part of the reason we still have judgment is because our brain, logical center is automatically connected to our emotional center. So whatever our logic say is tempered by emotion, and whatever our emotion wants to act, wants to do, right, is tempered by our logic, right? But then AI machine is, many call them, artificial specific intelligence. They are just focused on that decision making and are not connected to other more culturally sensitive or emotionally sensitive type networks. They are focus networks. Although there are people trying to build them, right. That's this power, reason why with judgment, I always use the phrase, right, what's correct, is not always the right thing to do. There is a difference, right? We need to be there to be the last Judge of what's right, right? >> Yeah. >> So that says one of the the big thing, the other one, I bring up is that humans are different from machines, generally, in a sense that, we are highly subtractive. We, filter, right? Well, machine is highly accumulative today. So an AI machine they accumulate to bring in lots of data and tune the network, but our brains a few people realize, we've been working with brain researchers in our work, right? Between three and 30 years old, our brain actually goes through a pruning process of our connections. So for those of us like me after 30 it's done right. (laughs) >> Wait till you reach my age. >> Keep the brain active, because it prunes away connections you don't use, to try and conserve energy, right? I always say, remind our engineers about this point, about prunings because of energy efficiency, right? A slice of pizza drives our brain for three hours. (laughs) That's why, sometimes when I get need to get my engineers to work longer, I just offer them pizza, three more hours, >> Pizza is universal solution to our problems, absolutely. Food Indeed, indeed. There is always a need for a human consciousness. It's not just a logic, it's not like Mr. Spock in "Star Trek," who always speaks about logic but forgets the humanity aspect of it. >> Yes, yes, The connection between the the logic centers and emotional centers, >> You said it very well. Yeah, yeah and the thing is, sleep researchers are saying that when you don't get enough REM sleep, this connection is weakened. Therefore, therefore your decision making gets affected if you don't get enough sleep. So I was thinking, people do alcohol test breathalyzer test before they are allowed to operate sensitive or make sensitive decisions. Perhaps in the future, you have to check whether you have enough REM sleep before, >> It is. This COVID-19 crisis obviously problematic, and I wish it never happened, but there is something that I never experienced before is, how people are talking to each other, people like you and me, we have a lot in common. But I hear more about the industry outside of my field. And I talk a lot to people, like cryo-EM people or gene expression people, I would have gotten the data before and process it. Now, we have a dialogue across the board in all aspects of industry, science, and society. And I think that could be something wonderful that we should keep after we finally fix this bug. >> Yes. yes, yes. >> Right? >> Yes, that's that's a great point. In fact, it's something I've been thinking about, right, for employees, things have changed, because of COVID-19. But very likely, the change will continue, yeah? >> Right. Yes, yes, because there are a few positive outcomes. COVID-19 is a tough outcome. But there positive side of things, like communicating in this way, effectively. So we were part of the consortium that developed a natural language processing system in AI system that would allow you scientists to do, I can say, with the link to that website, allows you to do a query. So say, tell me the latest on the binding energy between the Sasko B2 virus like protein and the AC receptor. And then you will, it will give you a list of 10 answers, yeah? And give you a link to the papers that say, they say those answers. If you key that in today to NLP, you see 315 points -13.7 kcal per mole, which is right, I think the general consensus answer, and see a few that are highly out of out of range, right? And then when you go further, you realize those are the earlier papers. So I think this NLP system will be useful. (both chattering) I'm sorry, I didn't mean to interrupt, but I mentioned yesterday about it, because I have used that, and it's a game changer indeed, it is amazing, indeed. Many times by using this kind of intelligent conceptual, analyzes a very direct use, that indeed you guys are developing, I have found connections between facts, between clinical or pharmaceutical aspects of COVID-19. That I wasn't really aware of. So a it's a tool for creativity as well, I find it, it builds something. It just doesn't analyze what has been done, but it creates the connections, it creates a network of knowledge and intelligence. >> That's why three to 30 years old, when it stops pruning. >> I know, I know. (laughs) But our children are amazing, in that respect, they see things that we don't see anymore. they make connections that we don't necessarily think of, because we're used to seeing a certain way. And the eyes of a child, are bringing always something new, which I think is what AI could potentially bring here. So look, this is fascinating, really. >> Yes, yes, difference between filtering subtractive and the machine being accumulative. That's why I believe, the two working together, can have a stronger outcome if used properly. >> Absolutely. And I think that's how AI will be a force for good indeed. Obviously see, seems that we would have missed that would end up being very important. Well, we are very interested in or in our quest for drug discovery against COVID-19, we have been quite successful so far. We have accelerated the process by an order of magnitude. So we're having molecules that are being tested against the virus, otherwise, it would have taken maybe three or four years to get to that point. So first thing, we have been very fast. But we are very interested in natural products, that chemicals that come from plants, essentially. We found a way to mine, I don't want to say explore it, but leverage, that knowledge of hundreds of years of people documenting in a very historical way of what plants do against what diseases in different parts of the world. So that really has been a, not only very useful in our work, but a fantastic bridge to our common human history, basically. And second, yes, plants have chemicals. And of course we love chemicals. Every living cell has chemicals. The chemicals that are in plants, have been fine tuned by evolution to actually have some biological function. They are not there just to look good. They have a role in the cell. And if we're trying to come up with a new growth from scratch, which is also something we want to do, of course, then we have to engineer a function that evolution hasn't already found a solution to, for in plants, so in a way, it's also artificial intelligence. We have natural solutions to our problems, why don't we try to find them and see their work in ourselves, we're going to, and this is certainly have to reinvent the wheel each time. >> Hundreds of millions of years of evolution, >> Hundreds of millions of years. >> Many iterations, >> Yes, ending millions of different plants with all kinds of chemical diversity. So we have a lot of that, at our disposal here. If only we find the right way to analyze them, and bring them to our supercomputers, then we will, we will really leverage this humongus amount of knowledge. Instead of having to reinvent the wheel each time we want to take a car, we'll find that there are cars whose wheels already that we should be borrowing instead of, building one each time. Most of the keys are out there, if we can find them, They' re at our disposal. >> Yeah, nature has done the work after hundreds of millions of years. >> Yes. (chattering) Is to figure out, which is it, yeah? Exactly, exactly hence the importance of biodiversity. >> Yeah, I think this is related to the Knowledge Graph, right? Where, yes, to objects and the linking parameter, right? And then you have hundreds of millions of these right? A chemical to an outcome and the link to it, right? >> Yes, that's exactly what it is, absolutely the kind of things we're pursuing very much, so absolutely. >> Not only only building the graph, but building the dynamics of the graph, In the future, if you eat too much Creme Brulee, or if you don't run enough, or if you sleep, well, then your cells, will have different connections on this graph of the ages, will interact with that molecule in a different way than if you had more sleep or didn't eat that much Creme Brulee or exercise a bit more, >> So insightful, Dr. Baudry. Your, span of knowledge, right, impressed me. And it's such fascinating talking to you. (chattering) Hopefully next time, when we get together, we'll have a bit of Creme Brulee together. >> Yes, let's find out scientifically what it does, we have to do double blind and try three times to make sure we get the right statistics. >> Three phases, three clinical trial phases, right? >> It's been a pleasure talking to you. I like we agreed, you knows this, for all that COVID-19 problems, the way that people talk to each other is, I think the things that I want to keep in this in our post COVID-19 world. I appreciate very much your insight and it's very encouraging the way you see things. So let's make it happen. >> We will work together Dr.Baudry, hope to see you soon, in person. >> Indeed in person, yes. Thank you. >> Thank you, good talking to you.

>> (upbeat music) >> production: From around the globe, it's the Cube covering Google cloud Next on-Air 20. (Upbeat music) >> Welcome back. I'm Stu Miniman and this is the CUBE coverage of Google cloud Next on Air 20. Of course, the nine week distributed all online program that Google cloud is doing and going to be talking about, of course, multi-cloud, Google of course had a big piece in multi-cloud. When they took what was originally Borg, They built Kubernetes. They made that open source and gave that to the CNCF and one of Google's partners and a leader in that space is of course, Red Hat. Happy to welcome to the program Sathish Balakrishnan, he is the Vice President of hosted platforms at Red Hat. Sathish, thanks so much for joining us. >> Thank you. It's great to be here with you on Google Cloud Native insights. >> Alright. So I, I tied it up, of course, you know, we talk about, you know, the hybrid multicloud and open, you know, two companies. I probably think of the most and that I've probably said the most about the open cloud are Google and Red Hat. So maybe if we could start just, uh, you hosted platforms, help us understand what that is. And, uh, what was the relationship between Red Hat and the Open Shift team and Google cloud? >> Absolutely. Great question. And I think Google has been an amazing partner for us. I think we have a lot of things going on with them upstream in the community. I think, you know, we've been with Google and the Kubernetes project since the beginning and you know, like the second biggest contributor to Kubernetes. So we have great relationships upstream. We also made Red Hat Enterprise Linux as well as Open Shift available on Google. So we have customers using both our offerings as well as our other offerings on Google cloud as well. And more recently with the hosted our offerings. You know, we actually manage Open Shift on multiple clouds. We relaunched our Open Shift dedicated offering on Google cloud back at Red Hat Summit. There's a lot of interest for the offering. We had back offered the offering in 2017 with Open Shift Three and we just relaunched this with Open Shift Four and we received considerable interest for the Google cloud Open Shift dedicated offering. >> Yeah, Sathish maybe it makes sense if we talk about kind of the maturation of open source solutions, managed services has seen really tremendous growth, something we've seen, especially if we were talking about in the cloud space. Maybe if you could just walk us through a little bit out that, you know, what are you hearing from customers? How does Red Hat think about managed solutions? >> Absolutely. Stu, I think it was a good question, right? I think, uh, as we say, the customers are looking at, you know, multiple infrastructure footprints, Be iteither the public cloud or on-prem. They'll start looking at, you know, if I go to the cloud, you know, there's this concept of, I want something to be managed. So what Open Shift is doing is in Open Shift, as you know it's Red Hat's hybrid cloud platform and with Open Shift, all the things that we strive to do is to enable the vision of the Open Hybrid Cloud. Uh, so, but Open Hybrid Cloud, it's all about choice, So we want to make sure the customers have both the managed as well as the self managed option. Uh, so if you really look at it, you know, Red Hat has multiple offerings from a managed standpoint. One as you know, we have Open Shift dedicated, which runs from AWS and Google. And, you know, we just have, as I mentioned earlier. We relaunched our Google service at Red Hat Summit back in May. So that's actually getting a lot of traction. We also have joint offerings with Azure that we announced a couple of years back and, there's a lot of interest for that offering as well as the new offering that we announced post-summit, the Amazon-Red Hat Open Shift, which basically is another native offering that we have on Amazon. If you really look at, having, having spoken about these offerings, if you really look at Red Hat's evolution as a managed service provider in the public cloud, we've been doing this since 2011. You know, that's kind of surprising for a lot of people, but you know, we've been doing Open Shift online, which is kind of a multi-tenant parcel multi-talent CaaS solution 2011. And we are one of the earliest providers of managed kubernetes, you know, along with Google Kubernetes engine GKE, we are our Open Shift dedicated offering back in 2015. So we've been doing Kubernetes managed since, Open Shift 3.1. So that's actually, you know, we have a lot of experience with management of Kubernetes and, you know, the devolution of Open Shift we've now made it available and pretty much all the clouds. So that customers have that exact same experience that they can get any one cloud across all clouds, as well as on-prem. Managed service customers now have a choice of a self managed Open Shift or completely managed Open Shift. >> Yeah. You mentioned the choice and one of the challenges we have right now is there's really the paradox of choice. If you look in the Kubernetes space, you know, there are dozens of offerings. Of course, every cloud provider has their offerings. You know, Google's got GKE, they have Anthos, uh, they, they have management tools around there. You, you talked a bit about the, you know, the experience and all the customers you have, the, you know, there's one of the fighters talks about, there's no compression algorithm for experience. So, you know, what is Red Hat Open Shift? What really differentiates in the market place from, you know, so many of the other offerings, either from the public high providers, some of the new startups, that we should know. >> Yeah. I think that's an interesting question, right? I think all Google traders start with it's complete open source and, you know, we are a complete open source company. So there is no proprietary software that we put into Open Shift. Open Shift, basically, even though it has, you know, OC command, it basically has CPR. So you can actually use native Google networks as you choose on any Google network offering that you have be it GKE, EKS or any of the other things that are out there. So that's why I think there are such things with google networks and providers and Red Hat does not believe in open provider. It completely believes in open source. We have everything that we is open source. From an it standpoint, the value prop for Red Hat has always been the value of the subscription, but we actually make sure that, you know, Google network is taken from an upstream product. It's basically completed productized and available for the enterprise to consume. But that right, when we have the managed offering, we provide a lot more benefits to it, right? The benefits are right. We actually have customer zero for Open Shift. So what does that mean? Right. We will not release Open Shift if we can't run open Shift dedicated or any of their (indistinct) out Open Shift for them is under that Open Shift. Really really well. So you won't get a software version out there. The second thing is we actually run a lot of workloads, but then Red Hat that are dependent on our managed or open shift off. So for example, our billing systems, all of those internal things that are important for Red Hat run on managed Open Shift, for example, managed Open Shift. So those are the important services for Red Hat and we have to make sure that those things are running really, really well. So we provide that second layer of enterprise today. Then having put Open Shift online, out that in public. We have 4 million applications and a million developers that use them. So that means, I've been putting it out there in the internet and, you know, there's security hosts that are constantly being booked that are being plugged in. So that's another benefit that you get from having a product that's a managed service, but it also is something that enterprises can now use it. From an Open Shift standpoint, the real difference is we add a lot of other things on top of google network without compromising the google network safety. That basically helps customers not have to worry about how they're going to get the CIC pipeline or how they have to do a bunch of in Cobra Net as an outside as the inside. Then you have technologies like Store Street Metrics kind of really help customers not to obstruct the way the containerization led from that. So those are some of the benefits that we provide with Open Shift. >> Yeah. So, so, so Sathish, as it's said, there's lots of options when it comes to Kubernetes, even from a Red Hat offering, you've got different competing models there. If I look inside your portfolio, if it's something that I want to put on my infrastructure, if I haven't read the Open Shift container platform, is that significantly different from the managed platform. Maybe give us a little compare contrast, you know. What do I have to do as a customer? Is the code base the same? Can I do, you know, hybrid environments between them and you know, what does that mean? >> It's a smart questions. It's a really, really good question that you asked. So we actually, you know, as I've said, we add a lot of things on top of google network to make it really fast, but do you want to use the cast, you can use the desktop. So one of the things we've found, but you know, what we've done with our managed offering is we actually take Open Shift container platform and we manage that. So we make sure that you get like a completely managed source, you know. They'll be managed, the patching of the worker nodes and other things, which is, again, another difference that we have with the native Cobra Net of services. We actually give plush that admin functionality to customers that basically allows them to choose all the options that they need from an Open Shift container platform. So from a core base, it's exactly the same thing. The only thing is, it's a little bit opinionated. It to start off when we deploy the cluster for the customer and then the customer, if they want, they can choose how to customize it. So what this really does is it takes away any of the challenges the customer may have with like how to install and provision a cluster, which we've already simplified a lot of the open shift, but with the managed the Open Shift, it's actually just a click of it. >> Great. Sathish Well, I've got the trillion dollar question for you. One of the things we've been looking at for years of course, is, you know, what do I keep in my data center? What do I move to the cloud? How do I modernize it? We understand it's a complex and nuanced solution, but you talk to a lot of customers. So I, you know, here in 2020, what's the trends? What are some of the pieces that you're seeing some change and movement that, you know, might not have been the case a year ago? >> I think, you know, this is an interesting question and it's an evolving question, right? And it's something that if you ask like 10 people you'll get real answers, but I'm trying to generalize what I've seen just from all the customer conversations I've been involved. I think one thing is very clear, right? I think that the world is right as much as anybody may want to say that I'm going to go to a single cloud or I'm going to just be on prem. It is inevitable that you're going to basically end up with multiple infrastructure footprint. It's either multicloud or it's on Prem versus a single cloud or on prem versus multiple cloud. So the main thing is that, we've been noticing as, what customers are saying in a whole. How do I make sure that my developers are not confused by all these difference than one? How do I give them a consistent way to develop and build their applications? Not really worry about, what is the infrastructure. What is the footprint that they're actually servicing? So that's kind of really, really important. And in terms of, you know, things that, you know, we've seen customers, you know, I think you always start with compliance requirements and data regulations. Back there you got to figure it out. What compliance do I need? And as the infrastructure or the platform that I'm going to go to meet the compliance requirements that I have, and what are the data regulations? You know, what is the data I'm going to be setting? Is it going to meet the data submitted rules that my country or my geo has? I got to make sure I worry about that. And then I got to figure out if I'm going to basically more to the cloud from the data center or from one cloud to another cloud. I might just be doing a lift or shift. Am I doing a transformation? What is it that I really worry about? In addition to the transformation, they got to figure it out, or I need to do that. Do I not need to do that? And then, you know, we've got to figure out what your data going to set? What your database going to look in? And do you need to connect to some legacy system that you have on prem? Or how do you go? How do you have to figure that out and give them all of these complexities? This is really, really common for any large enterprise that has like an enterprise ID for that multi-cloud. That's basically in multiple geographies, servicing millions of customers. So that has a lot of experience doing all these things. We have open innovation labs, which are really, really awesome experience for customers. Whether they take a small project, they figured out how to change things. Not only learn how to change things from a technology standpoint, but also learn how to culturally change things, because a lot of these things. So it's not just moving from one infrastructure to another, but also learning how to do things differently. Then we have things like the container adoption programmer, which is like, how do you take a big legacy monolith application? How do you containerize it? How do you make it micro services? How do you make sure that you're leveraging the real benefits that you're going to get out of moving to the cloud or moving to a container platform? And then we have a bunch of other things like, how do you get started with Open Shift and all of that? So we've had a lot of experience with like our 2,400 plus customers doing this kind of really heavy workload migration and lifting. So the customers really get the benefits that they see out of Open Shift. >> Yeah. So Sathish, if I think about Google, specifically talking about Google cloud, one of the main reasons we hear customers using Google is to have access to the data services. They have the AI services they have. So how does that tie into what we were just talking about? If I, if I use Open Shift and you know. I'm living in Google cloud, can, can I access all of those cloud native services? Are there any nuances things I need to think about to be able to really unleash that innovation of the platform that I'm tying into? >> Yeah, absolutely not. Right. I think it's a great question. And I think customers are always wondering about. Hey, if I use Open Shift, am I going to be locked out of using the cloud services? And if anything run out as antilock. We want to make sure that you can use the best services that you need for your enterprise, like the strategy as well as for applications. So with that, right. And we've developed the operator framework, which I think Google has been a very early supporter of. They've built a lot of operators around their services. So you can develop those operators to monitor the life cycle of these services, right from Open Shift. So you can actually connect to an AI service if you want. That's absolutely fine. You can connect the database services as well. And you can leverage all of those things while your application runs on Open Shift from Google cloud. Also I think that done us right. We recognize that, when you're talking about the open hybrid cloud, you got to make sure that customers can actually leverage services that are the same across different clouds. So when you can actually leverage the Google services from On Prem as well, if you choose to have localized services. We have a large catalog of operators that we have in our operator hub, as well as in the Red Hat marketplace that you can actually go and leverage from third party, third party ISV, so that you're basically having the same consistent experience if you choose to. But based on the consistent experience, that's not tied to a cloud. You can do that as well. But we would like for customers to use any service that they want, right from Open Shift without any restrictions. >> Yeah. One of the other things we've heard a lot from Google over the last year or so has been, you know, just helping customers, especially for those mission, critical business, critical applications, things like SAP. You talked a bit about databases. What advice would you give customers these days? They're, they're looking at, you know, increasing or moving forward in their cloud journeys. >> I think it sounds as an interesting question because I think customers really have to look at, you know, what is the ID and technology strategy? What are the different initiatives to have? Is it digital transformation? Is it cloud native development? Is it just containerization or they have an overarching theme over? They've got to really figure that out and I'm sure they're looking at it. They know which one is the higher priority when all of them are interrelated and in some ways. They also got to figure out how they going to expand to new business. Because I think as we said, right, ID is basically what is driving personal software is eating the load. Software services are editing them. So you got to figure out, what are your business needs? Do you need to be more agile? Do you need to enter new businesses? You know, those are kind of important things. For example, BMW is a great example, they use Open Shift container platform as well as they use Open Shift dedicated, you know. They are like a hundred hundred plus year old car, guess, you know what they're trying to do. They're actually now becoming connected car infrastructure. That's the main thing that they're trying to build so that they can actually service the cars in any job. So in one shoe, they came from a car manufacturing company to now focus on being a SAS, an Edge and IOT company. If you really look at the cars as like the internet of things on an edge computer and what does that use case require? That use case cannot anymore have just one data center in Munich, they have to basically build a global platform of data centers or they can really easily go to the cloud. And then they need to make sure that that application double close when they're starting to run on multiple clouds, multiple geographies, they have the same abstraction layer so that they can actually apply things fast. Develop fast. They don't have to worry about the infrastructure frequently. And that's basically why they started using Open Shift. And don't know why they're big supporters of Open Shift. And then I think it's the right mission for their use. So I think it really depends on, you know, what the customer is looking for, but irrespective of what they're looking for, I think Open Shift nicely fits in because what it does, is it provides you that commonality across all infrastructure footprints. It gives you all the productivity gains and it allows you to connect to any service that you want anywhere because we are agnostic to that and as well as we bring a whole lot of services from Red Hat marketplace so you can actually leverage your status. >> Well, Sathish Balakrishnan, thank you so much for the updates. Great to hear about the progress you've got with your customers. And thank you for joining us on the Google cloud Next On Air Event. >> Thank you Stu. It's been great talking to you and look forward to seeing you in person one day. >> Alright. I'm Stu Miniman. And thank you as always for watching the Cube. (upbeat music) (upbeat music)

everybody welcome to this digital coverage of the verdict of big data conference you're watching the cube and my name is Dave Galante it's my pleasure to invite in Ben white who's the senior database engineer at Domo been great to see you man thanks for coming on great to be here and here you know as I said you know earlier when we were off camera I really was hoping I could meet you face to face and in Boston this year but hey I'll take it and you know our community really wants to hear from experts like yourself but let's start with with domo is the company share with us what Domo does and what your role is there well if Parker can go straight to the official what Domo does is we provide we process data at bi to scale with we provide VI leverage a cloud scale in record time and so what that means is that you know we are a business operating system where we provide a number of analytical abilities to companies of all sizes but we do that at cloud scale and so I think that difference is quite a bit so a lot of your work if I understand it and just in terms of understanding with Domo does--is there's a lot of pressure in terms of being real-time it's not like you sometimes don't know what's coming at you so it's AD Hoch I wonder if you could sort of talk about that confirm that and maybe add a little color to it yeah absolutely absolutely that's probably the biggest challenge it is to being the operating Domo is that it is an ad hoc environment and certainly what that means is that you've got analysts and executives that are able to submit their own queries without very with very few limitations so from an engineering standpoint the challenge in that of course is that you don't have this predictable dashboard to plan for when it comes to performance planning and so it definitely presents some challenges for us that we've done some pretty unique things I think to address those right sounds like your background fits well with that I understand here if people have called you a database whisperer and an envelope pusher what does that mean to do a DBA in this in this day and age well the whisperer part is probably a lost art in the sense that it's not really sustainable right the idea that you know whatever it is I'm able to do with the database it has to be repeatable and so that's really what analytics comes in right and that's where pushing the envelope comes in in a little right away that's what vertical comes in with this open architecture and so as a person who has a reputation for saying I understand this is what our limitations should be but I think we can do more having a platform like vertical is such an open architecture kinda lets you push those limits by the bit I mean I've always felt like you know vertical when I first saw the Stonebreaker architecture and doctors some of the early founders I always felt like it was the Ferrari of databases certainly at the time and it sounds like you guys use it in that in that regard but talk a little bit more about how you use Vertica why in a ym ppy Vertica you know why why can't you do this with our DBMS educate us a little bit on some of the basics but for us it was part of what I mentioned when we start and we talked about the very nature of the demo platform where there's a an incredible amount of resiliency required and so Vertica the NPP platform of course allows us to build individual database clusters that can perform best for the workload that may be assigned to them so the the open the expandable the the the ability to grow vertically as your base grow those are all important factors when you're losing early on right without a real idea of how growth would be or what it would look like if you were kind of doing that something to the dark you looked at the vertical platforming you can see well as I grow I can kind of feel with this right I can do some some unique things with the platform in terms of this poking architecture that will allow me to not have to make all my decisions today right about Harlem so you're using Vertica I know at least in part you you working with AWS as well can you describe sort of your environment that you give anything on Prem is everything in the cloud what's your setup sure we have a hybrid cloud environment where we have a significant presence in public files in our own private cloud and so yeah having said that we certainly have a really an extensive presence I will say an AWS and so they're definitely the partner of our when it comes to providing the databases the server power that we need to operator but from the standpoint of engineering and architecting a database what was some of the challenges that you faced when you had to create that hybrid architecture what did you face and how did you overcome that well you know some of the there are some things we need faced in terms of wine and made it easy that Vertica and AWS have their own they play well together we'll say that and so vertical is designed to reprise I'm gonna AWS and so that part of it the care of itself not our own private cloud and being able to connect that because our public clouds has been a part of our own engineering ability and again I don't want to make a little light of it it's certainly not impossible and so we've some of the challenges though this pertains to the database really were in their early days that you mentioned when we talked a little bit earlier about marathas most recent Eon mode and I'm sure you'll get to that but when I think of our early challenges some of the early challenges were the architecture of enterprise mode when I talk about all of these this idea that we could have unique databases or database clusters of different sizes so this elasticity that's really if you know that the enterprise architecture that's not necessarily dandified architecture so we added this Munich things I think to overcome that right early to get around the rigidness though enterprise yeah I mean I hear you right Enterprise is complex and and you like when things are hardened and fossilized but in your ad hoc environment that's not what you needed so talking more about Aeon mode what what is e on mode for you and how do you apply it what are some of the challenges and opportunities there that you found um so the opportunities were certainly in its elastic architecture the ability to separate the storage immediately meant that for some of the unique data paths that we wanted to take right we could do that fairly quickly certainly we could expand databases right quickly but more importantly now you could reduce because previously in the past right when I mention the Enterprise Architect with the idea of growing a database in itself has its pain right as far as the time it takes to speed the data in that but to read to then think about taking that database back down no Innova though all of us under the eon right you had this elasticity where you could kind of start to think about auto scaling where you go up and down and maybe used to save some money or maybe you could improve performance or maybe in needham and at a time when the customers needed most in a real way right so it was definitely a game in that regard I always have to talk to the customers because I get to you know I hear from the vendor what they say and I think they sort of validate it so you know Vertica talks a lot about separating compute and storage they're not the only one from an architectural standpoint to do that but Vertica stresses that they're the only one that does that with a hybrid architecture they can do it off ram they can do it in the cloud from your experience well first of all is it true you may or may not know it is that advantageous to you and if so why well first of all it's certainly true earlier in some of the original beta ethnic for the arm prim GI mode stuff we I was able to participate in it and be aware of it so it's certainly a reality day I'm it's actually supported on pure spirit with flash played and it's time quite impressive you know for who who that who that will be for tough one a Spartacus question that they're probably still answering but I think obviously some enterprise users that probably have some hybrid cloud right they have some architecture they have some hardware that their sales want to make you so we certainly would probably fit into one of their you know their market segments that they would say we might be the wants to look at on pram er mo begin the the beauty of it is the elasticity right that the idea that you could have this and so a lot of times so I want to go back real quick to separating them and you know we start by separating it and I like to think of it maybe more as like decoupling because a new in a true way it's not necessary separated there's ultimately you bring the compute and the doors back together but to be able to typically couple it quickly replace knows bring in those that's certainly fits I think what we were trying to do in building this Emma I'll me let the ecosystem that could respond to a unknown or of a customer demand I see thank you for that clarification because you're right it's really not separating its decoupling in it that's important because you can scale them independently but you still need compute and you still need storage to run you your workloads but from a cost standpoint you're not to buy it in in chunks you can you can't buy granular segments for whatever your workload requires is that is that the correct understanding yeah and to be able to the ability to be able to reuse compute throw it in a scenario of AWS or even in the scenario your on-prem solution you've got this data that's safest here and ask for your in your storage but then the compute that you have you can reuse that right you could have a scenario that you have some query that needs more analytic more firepower more memory more what have you that you haven't so you can kind of move to the next important right that's maybe more important then and I grow them separately can I can I borrow it can I borrow that computer use for my perfect give it back type of thing and you can do that when you're so easily a couple different ooh all right and likewise if you have a down period where customers aren't using it you'd like to be able to not use that if you no longer require if you'd like to give it back go in it open the door to a lot of those things that allow performance and cross the spark to meet up we're going to ask you a question winsome pure a couple times are you using pure flash blade on-prem is that correct that is the solution that is supported that is supported by Vertica for the on print so at this point we were we have been discuss with them about some our own PLC's for that time before again we back to the idea of how do we see ourselves using it and so we've certainly discussed the feasibility of bringing it in and give it a job but that's not something we're Oh happily all right now then what is Domo for Domo tell us about that we really started this this idea even in the company where we say you know we should be using Domo in our everyday business the sales folks the marketing folks right everybody we're gonna use Domo it's a business platform for us in the engineering team it was kind of like well if we use Domo say for instance to be better at the database engineers now we've pointed Domo edits tell fried verdict is running Domo in the background for some degree and then we turn around and say hey Domo how can we better at running you and so it became this kind of cool thing we played with where we're now able to put some dumb methods together where we can actually do their eye we can monitor using our platform it's really good at processing large amounts of data and spitting out useful analytics right we take those analytics out make recommendation changes that the day so now you've got still more for Domo happening it allows us to sit at home and and work now even when we have to even before we had to well you know look look at us here right it couldn't mean in Boston physically we're now meeting remote you're you're on a hot spot because you got some weather and your satellite internet and in Atlanta and we're having a great conversation so so we're here with with Ben white who's the senior database engineer at Domo I want to ask you about some of the envelope-pushing that you've done around autonomous you hear that that word thrown around a lot means a lot of things to a lot of different people how do you look at autonomous and how does it fit with Eon and some of the other things that you're doing you know I'm a tall amidst the idea of economy is something that I don't even know that I'm I have already ready to define and so even in my discussion I often mention it as a road to it exactly where it is it's hard to pin down because there's always this idea how much trust do you give right to the system or how much how much is truly autonomous how much authority is being intervened by us the engineers so I do hate on using it but on this road towards autonomy when we look at what would how we're using Domo and even what that really means to vertical because in a lot of my examples and a lot of the things that we've engineered a demo work designs maybe over something I thought was a limitation day and so many times Oh as we've done that verdict is kind of met us like right after we've kind of engineered our architecture stuff than we thought it felt on our side Vertica has some released it kinda addresses it so the autonomy idea and the idea that we could analyzed metadata make recommendations and then execute those recommendations without intervention is that road to autonomy and once the databases start able to do that you can see in our ad-hoc environment how that would be pretty pretty useful where with literally millions of queries every hour trying to figure out what's the best you know probably for years I felt like I I T folks sometimes we really did not want that automation they wanted the knobs to turn but but I wonder if you comment I feel as though the level of complexity now with cloud with on-prem with you know hybrid multi clouds the scale the speed the real-time it just gets the pace is just too much for for humans and so it's almost like you know the industries is gonna have to capitulate to the Machine and then really trust the machine but I'm sitting I'm still sensing from you a little bit of hesitation there but light at the end of the tunnel I wonder if you could comment sure I think that in the light of the tunnel is even in recent months in recent we've really began incorporating more machine learning in artificial intelligence to the model right and back to where we're saying it so I do feel they were getting close for too finding conditions that we don't know about because right now our system is kind of a rule rules based system where we've said well these are the things that we should be looking for these are the things that we think are a problem to mature to the point where the database is recognized and anomalies and taken on at imagining saying these are problems you didn't know happen and that's kind of the next step right identifying the things you didn't know and that's where that's the path we're on now and that's probably more exciting even then kind of nailing down all the things you think you know and to figure out what we don't know yet so I want to close with I know you're a prominent member of the respected member of the Vertica a customer advisory board you know without divulging anything confidential to me what are the kinds of things that you want Vertica to do going forward I think some of the end a in database autonomy the ability to take some of the recommendations that we know we can derive from the metadata that already exists in the platform and start to execute some of the recommendation another thing we talk about and I'm gonna pretty open about talking to it is talking about it is the new version of the database designer I think it's something that I'm sure they're working on lightweight something that can give us that's database design without the overhead those are two things I think as they nail or particularly the database designer as they respect that they'll really have all the components in place to do in based economy and I think that's just some victory where they're headed yeah nice well Ben listen I really appreciate you coming on your a thought leader be very open open-minded verdict is you know really open community I mean they've always been quite transparent in terms of where they're going it's just awesome to have guys like you on the cube to share with our community so thank you so much and hopefully we can meet face to face currently absolutely will you stay safe in Boston I'm one of my favorite towns and so no doubt when this when the doors get back open I'll be from coming down or coming I'm gonna do work take care all right and thank you for watching everybody Villante with a cube we're here covering the virtual Vertica of big data conference you [Music]

>>Live from San Diego, California. It's the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem. >>Welcome back to the queue bumps to men. And my cohost is John Troyer and you're watching the cube here at CubeCon, cloud-native con 2019 in beautiful and sunny San Diego today. Happy to welcome to the program a first time guest, Tom Willkie, who's vice president of product ECRO funnel labs. Thank you. Thank you so much for joining us. All right, so it's on your tee shirt. We've been hearing, uh, customers talking about it and the like, but, uh, why don't you introduce the company to our audience in a, where you fit in this broad landscape, uh, here at the CNCF show. Thank you. Yes. So Grafana is probably the most popular open source project for dashboarding and visualization. Um, started off focused on time series data on metrics, um, but really recently has branched out into log analysis and tracing and, and all, all of the kinds of aspects of your observability stack. >>Alright, so really big, uh, you know, broad topic there. Uh, we know many of the companies in that space. Uh, there's been many acquisitions, uh, you know, uh, recently in this, um, where, where do you fit in your system? I saw like databases, like a big focus, uh, when, when I, when I look at the company website, uh, bring us inside a little bit. Yeah. As a product to the offering. The customers most, um, >> most, most vendors in this space will sell you a monitoring product that includes the time series database normally includes visualization and some agent as well where pharma Lampson Griffon open source projects, very focused on the visualization aspects. So we are data source agnostic and we have back ends for more than 60 different data sources. So if you want to bring together data from let's say Datadog and combine it with some open source monitoring from, you can do that with. >>Uh, you can, you can have the dashboards and the individual panels in that dashboard combined data from multiple different data sources and we're pretty much the only game in town for that. You can, you can think of it like Tableau allows you to plug into a whole bunch of different databases for your BI with that. But for monitoring and for metrics. Well, so Tom, maybe let's, before we get into the exit products and more of the service and the, and the conference here, let's talk a little well on the front page of your website, you use the Oh 11, why word? So we've said where it's like monitoring here we use words like management, we use words like ops. Observability is a hot topic in the space and for people in a space that has some nuances. And so can you just maybe let the viewers and us know a little bit about what, how the space is looking at this and how you all feel about observability and what everybody here who's running some cloud native apps needs to actually function in production. >>Yeah. So I think, um, you can't talk about observability without either being pro or, or for, um, uh, the three pillars, right? So people talk about metrics, logs and traces. Um, I think what people miss here is that it's more about the experience for the developer, you know, Gruffalo and what we're trying to achieve is all about giving engineers and developers the tools they need to understand what their applications and their infrastructure doing, right? So we're not actually particularly picky about which pillars you use and which products you use to implement those pillars. But what we want to do is provide you with an experience that allows you to bring it all into a single, a single user interface and allows you to seamlessly move between the different sources of data and, and hopefully, uh, combine them in your analysis and in your root cause of any particular incident. >>And that for me is what observability means. It's about helping you understand the behavior of your application in particular. I mean, I'm, I'm a, I'm a software engineer by trade. I'm still on call. I still get paged at 3:00 AM occasionally. And, and having the right tools at 3:00 AM to allow me to as quickly as possible, figure out what happened and then dive into a fix. That's what we're about over funnel labs. All right. So Tom, one of the things we always need to understand and show here. There's the project and there's the company. Yep. Help us just kind of understand, you know, definitely a difference. The products, the, the, the mission of the company and how that fits with the project. So the Gruffalo project predates the company and it was started by taco. Um, he, you know, he saw a spot for like needing a much better kind of graphical editing of dashboards and making, making the kind of metrics way more accessible to your average human. >>Um, the final lab started really to focus on the it and, uh, monitoring observability use cases of profanity and, but the project itself is much broader than that. We see a lot of use cases in industrial, in IOT, even in BI as well. But Grafana labs is a company we're focused on the monitoring side of things. We're focused on the observability. So we also offer, we mean, like most companies, we have an enterprise version of. It has a few data sources for commercial vendors. So if you want to, you want to get your data dog or your Splunk into Grafana, then there's a commercial auction for that. But we also offer a hosted observability platform called Grafana clown. And this is where we take the best open source projects, the best tools that we think you need as an engineer to understand your applications and we host them for you and we operate them for you. >>We scale them, we upgrade them, we fix bugs, we sacrifice the clouds predominantly are hosted from atheists, our hosted graphite and our hosted Loki, our log aggregation system, um, all combined and brought together with uh, with the Gruffalo frontend. So yeah, like two products, a bunch of open source projects for final labs, employees, four of the promethium maintainers. And I'm one of the promethium maintainers. Um, we am employee graphite maintainers. Obviously a lot of Gryffindor maintainers, but also Loki. Um, I'm trying to think, like there's just so many open source projects. We, uh, we get involved with that. Really it's about synthesizing, uh, an observability platform out of those. And that's what we offer as a product. So you recently had an announcement that Loki is now GA. can you talk just a little bit about Loki and aggregation and logs and what Loki does? >>Yeah, I'd love to. Yeah. Um, a year ago in Seattle actually we announced the Loki project. Um, it was super early. I mean I just basically been finishing the code on the plane over and we announced it and no one I think could have predicted the response we had. Um, everyone was so keen and so hungry for alternative to traditional log aggregation systems. Um, so it's been a year and we've learned a hell of a lot. We've had so much feedback from the community. We've built a whole team internally around, around Loki. We now offer a hosted version of it and we've been running it in production now for over a year, um, doing some really great scale on it and we think it's ready for other people to do the same. One of the things we hear, especially at shows like this is I really, I really, you know, developers and the grassroots adopters come to us, say, we really love Loki. >>We really love what you're doing with it. Um, but my boss won't let me use it until it goes to be one. And so really yesterday we announced it's Don V. one, we think it's stable. We're not going to change any of the APS on you. We, uh, we would love you to use it and uh, and put it into production. All right. Uh, we'd like to hear a little bit more about the business side of things. So, um, I believe there was some news around funding, uh, uh, you know, how many people you have, how many, you know, can you parse for us, you know, how many customers have the projects versus how many customers have, uh, you know, the company's products. Well, we don't, we don't call them customers of the projects that users, yes, yes, we, uh, but I'm from a company where we have hundreds of customers. >>Um, I don't believe we make our revenue figures public and, uh, so I'm probably not going to dive into them, but I know, I know the CEO stands up at our, our yearly conference and, and discloses, you know, what our revenue the last year was. So I'll refer you to that. Um, the funding announcement, that was about a month ago. We, uh, we raised a great round from Lightspeed, um, 24 million I believe. Um, and we're gonna use that to really invest in the community, really invest in our projects and, and build a bit more of a commercial function. Um, the company is now about 110 people. I think, um, it's growing so quickly. I joined 18 months ago and we were 30 people and so we've almost quadrupled in size in, in the last year and a half. Um, so keeping up is quite a challenge. Uh, the two projects, uh, products I've already touched on a few hundred customers and I think we're, you know, we're really happy with the growth. >>We've been, uh, we've never had any institutional funding before this. The company is about five years old. So we've been growing based on organic revenue and, and, and, and, you know, barely profitable, uh, but reinvesting that into the company and, and it's, yeah, it's going really well. We're also one of the, I mean it's not that unique I guess, but we're remote first. We have a more than 50% of our employees work from home. I work from my basement in London. We have a few tiny like offices, one in Stockholm and one in New York, but, but we're really keen to hire the best people wherever they are. Um, and we invest a lot in travel. Uh, we invest a lot in, um, the, the right tools and getting the whole company together to really make that work. Actually a really fun place to work. What time? >>We're S we're still in the business here and I don't know how much time you've spent at the booth this year, but I don't, can you compare, I mean, we've been talking about the growth of this community and the growth of this conference. Can you compare say this year to last year, the, the people coming up, their maturity, the maturity of their production, et cetera. Are they, are they ready to buy? Are they still kicking? Are they still wondering what this Cooper Cooper need easy things is, you know, where, where is everybody this year and how does that, how has it changed? Yeah, and that's a good question where we're definitely seeing people with a lot more sophisticated questions. The, the, the conversations we're having at the booth are a lot longer than they've been in previous years. The um, you know, in particular people now know what key is. We only announced it a year ago and gonna have a lot of people asking us very detailed questions about what scale they can run it at. >>Um, otherwise, yeah, I think there is starting to be a bit more commercial intent at the conference, some few more buying decisions being made here. It's still predominantly a community oriented conference and I think the, the, I don't want that to go away. Like, that's one of the things that makes it attractive to me. And, and I bring my whole team here and that's one of the things that makes it attractive to them. But there is a little bit more, I'm a little more sales activity going on for sure. Any updates to the, to the tracing and monitoring observability stories of the projects here at CNCF this year since you as you're part of the promethium project? >> Yes. So we actually, we had the promethium conference in Munich two weeks ago and after each committee conference, the maintainers like to get together and kind of plan out the next six months of the project. >>So we started to talk about um, adding support for things like exemplars into Prometheus's. This is where each histogram bucket, you can associate an example trace that goes, that contributed towards that, that history and that latency. And then you can build nice user interfaces around that. So you can very quickly move from a latency graph to example traces that caused that. Um, so that's one of the things we're looking to do in Prometheus. And of course Jaeger graduated just a week ago. I think. Um, we're big users of Jaeger internally at for final amps. And actually on our booth right now, uh, we're showing a demo of how we're integrating, um, visualization of distributed tracing, integral foreigner. So you can, you know, using the same approach we do with metrics where we support multiple backends, we're going to support Yeager, we're going to support Zipkin, we're going to support as many open source tracing projects as we can with the Grafana UI experience and being able to seamlessly kind of switch between different data sources, metrics all the way to logs all the way to traces within one UI. >>And without ever having to copy and paste your query and make mistakes and kind of translate it in your head. Right. >> Tom, give us a little bit, look forward. Uh, you know, a lot of activities as the thing's going to, you know, graduating and pulling things together. So what should your users be looking for kind of over the next six to 12 months? >> That's a great question. Yeah, I think we do a yearly release cycle for foreigners. So the next one we're, we're aiming towards is for seven, like for me to find a seven's going to be all about tracing. So I really want to see the demo we're doing. I want to see that turned into like production ready code support for multiple different data sources, support for things like exemplars, which we're not showing yet. Um, I want to see all of that done in Grafana in the next year and we've also massively been flushing out the logging story. >>I'm with Loki, we've been adding support for uh, extracting metrics from the logs and I really think that's kind of where we're going to drive Loki forward in the future. And that really helps with systems that aren't really exposing metrics like legacy systems where the only kind of output you get from them is the logs. Um, beyond that. Yeah, I mean the welds are kind of oyster. I think I'm really keen to see the development of open telemetry and um, we've just starting to get involved to that project ourselves. Um, I'm really interested to kind of talk to people about what they need out of a tracing system. We, we see people asking for a hosted tracing systems. Um, but, but IMO is very much like pick the best open source ones. I don't think that's, that's emerged yet. I don't think people know which is the best one yet. >>So we're going to get involved in all of them. See which one's a C, which one's a community kind of coalesces around and maybe start offering a hosted version of that. >> You know, our final thing is, uh, you know, what advice do you have for users? Obviously, you know, you like the open source thing, but you know, they're hearing about observability everywhere there are, you know, the, the whole APM market is moving this direction. There's acquisitions as we talked about earlier. Um, there's so many moving pieces and a lot of different viewpoints out there. So just, you know, from a user, how do you know, how will things ma, what makes their lives easier and what advice would you give them? Yeah, no, definitely. I think a lot of vendors will tell you like to pick a, pick a vendor who's going to help you with this journey. >>Like I would say like, pick a vendor you trust who can help you make those decisions. Like find someone impartial who's gonna not make, not try and persuade you to buy their product. So we would, uh, you know, I would encourage you to try things out to dog food and to really like invest in experimentation. There's a lot going on in, uh, in, in the observability world and in the cloud native world. And you've got to, you've got to try it and see what fits. Like we embrace this, uh, composability of the, uh, of the observatory of, of the observability ecosystem. So like, try and find which, which choices work best for you. Like I, uh, whenever, whenever I talk to him, you still have to lick all the cupcakes in 2019. I think. I mean, I would, it depends on your level of kind of maturity, right? >>And sophistication. Like, I think if, uh, if, if this is really important to you, you should go down that approach. You should try them all. If this is not one of your core competencies that may be going with a vendor that helps you is a better approach. But, but I'm, I come from the open source world and, uh, you know, I like to see the, um, the whole ecosystem and all the different players and all the different, new and exciting ways to solve these problems. Um, so I'm, I'm always going to encourage people to have a play and try things out. All right, Tom, final word, Loki. Explain to us, uh, you know, when you're coming up with it, how you ended, uh, are you the God of mischief? Well, so the official line is the Loki is the, um, is the North mythology equivalent of Prometheus's, uh, in Greek mythology and, and lochia logging project is, is, is Prometheus's inspired logging. So we've tried to take the operational model from, from atheists, the query language from, from atheists and, and the kind of a cost efficiency from, from atheists and apply it to logs. Um, but I will admit to being a big fan of the Marvel movies. All right, Tom Willkie. Thank you so much for sharing the updates on, on the labs. Uh, we definitely look forward to hearing updates from you and thank you. All right, for, for John Troyer, I'm Stu Madmen back with more coverage here from San Diego. Thank you for watching. Thank you for watching the cube.

>>live from Orlando, Florida It's the cue covering Microsoft Ignite Brought to you by Cho He City Welcome >>back, everyone to the Cubes Live coverage of Microsoft IC Night. 26,000 people were here. The cube, the middle of the show floor. It's an exciting time. I'm your host. Rebecca Night, along with my co host, Stew Minutemen. We're joined by Barbara Homans. She is the director. Global ecosystem strategy and micro ecosystem lead at HP Thank you so much for coming on the Cube direct from Munich. Yes, Rebecca. Glad to be here. So you have You have two Rolls Global Ecosystem Strategy and Michael Microsoft's ecosystem lead. Explain how those work and how they there is synergy between those two roles. Yeah, I mean, I started >>off with the Microsoft role, but what we figured out is that actually, the world is much bigger than just one alliance, and that's why we call ourselves the Ecosystem. So it's all about driving alliances from different partner speed as I speed Eyes V's or also smaller partners in different segments and build a whole ecosystem play. That's what I'm attempting to do. >>So how do HB and Microsoft worked together. So we've >>seen partnering for 30 years strong, strong relationship with Microsoft and really nice to see. Also today, you know some of the H p e solutions on stage and even deepening our partnership. We have several areas. Probably 34 I can talk about in the next few minutes on how we work together with Microsoft specifically. >>Yeah. So? So Barbara, You know, I think most of us remember back, you know, early if you're talking about windows and office and you know HP here what's now part of HP Inc? Not sure. As many people know about all of the places that H p e Partners, obviously on the server side, it makes sense. But Azure is something. And the Azure arc announcement Help us understand, you know, Azure stack and beyond. Where? HP. Ethan with Microsoft on the Enterprise side. >>Perfect. Absolutely. We have still in Microsoft. Oh, am business where we have actually service attached with licenses. That's not going away rights. We absolutely. It's a strong business class. We work very closely around sequel with Microsoft, and that's also worried this whole azure arc announcement fits in. But it's more than just a sequel right with this as your arc. For me, it's a announcement around deepening relationships. Both. We're interested in a hybrid strategy. I really like Thio here from Satya today. How important hybrid is for Microsoft and this announcement as your ark. That's in public preview now, right? Well, give somewhat details on that. So we'd love to work with customers on that we actually our part of the public review and if anyone is interested, love to hear from customers. Please come to me, Barbara Holman's and we'll hook you up and get into the program. It's really about the hybrid piece, right that we both worked >>in Barbara H. P. E. If my understanding plays on both sides of it, it's not just in the data center with some gear there, but as you said, there's a sequel. The application side, you know, hybrid HP, you know, plays across the board, >>Indeed, So I don't know if you know about HB is actually a expert MSP partner for Azure. We got that last year. We're very proud of what I think we're one of 50 world by its partners. That also means we can actually offer Manage Service's Migration Service is helping people to move to an azure based clout. And that actually came partially because off our position off CTP Cloud Technology Partners, but also read pixie in the UK, and there are no old part off our point. Next service is group, and so as such, we have numerous customers were actually helped into the public cloud. Help them to find the right place. Because if you don't know if you've seen the video from Eric Poodle, that was part of the announcement today as well around as your ark, this is all about finding the right mix off your applications, and this is where we work together and a perfect fit. >>What are some of the biggest challenges you're seeing from your cut from your customers in terms of how you might, how Azure Arc might be the solution for them >>so as your ark? It's hard to say at this >>stage, because I just really don't work for Michael >>Self. So, yeah, we have to ask these people. But again, what I understand division is really that way will be able to manage hybrid environments in a in a better way, and again, this is what HP You know, we have a lot off our tour, of course, but we also announce that our hardware, all of that, will be available as a service within the next two for years. So we're moving in that direction in addition to Azure. And I think this will help customers to take adventures in the end. But it's hard to say Right, So you on this. This is very new. At this stage, the odds are right >>and this is a Microsoft show, not on HP show, but I I read somewhere that you had done a talk. Fear no cloud with H. P m. Our company's afraid. I mean, how would you describe the atmosphere with the companies that you work with? I worked >>in the cloud space, but for the last 10 years or longer, you know, it was on different parts off the industry there and from the early adoption. Really. People looking into you know, should I trust my data in this specific with this cloud provider or which applications am I gonna move? And I think today people have lost the fear a little bit, but they still don't know what to put where and there's applications, you do not want to move in a cloud. There's others that you for your specific company, you don't want to move, and another company may do that. And that's what we're trying to help them, right? So don't you don't have to fear the cloud you can. Actually, we can help you to adopt it at your pace in your way and so that you take most of the advantage out of it. >>But Barbara would love to hear any color you could give from the joint HP, EA and Microsoft customers very much. The announcement today feels like it completely. It's an update on the hybrid message, but A B and Microsoft have been working together on solutions like Azure Stack for a number of years. So what? What's working well today? What do you think you know? This will mean down the road a CZ. Some of these solutions start start to mature even further. >>Maybe moving to another area that HB and Microsoft worked very well together is around the modern workplace practice, and in there we just had a really nice win with Portia thing, actually in Austria, but planning to roll this out no further than that, and h b E's team has helped them to move from the current applicator from the current environment. Thio up two dates. Microsoft 3 65 Environment There's em OD in the UK and it's fast twice if I can talk about M. O D on stage here and they said yes, another customer that we should help to move to a Microsoft 3 65 environment. So there's numerous customers that trust HP with Microsoft in moving their their information to the to the clouds. Yeah, that's one example Asha Stack we have. You know, there's several customers that hard won about ashes. Takis. Difficult to talk about the customers because a lot of them are in the government sector on. So you know, there's a few that we can talk about, but they're mostly service providers, but the really big names, unfortunately, we can talk about because of the conference shit Confidentiality. Yeah, >>trust is one of the things that we keep hearing so much of it about at this conference. Satya Nadella talked about it on the main stage this morning in terms of the relationship that you have and HP standing in the technology world. How do you feel trust with customers? And how do you make sure you are maintaining that? That bond of trust and also the reputation of being a trustworthy partner? >>Yeah, I think I love you know, I love Saturdays, Point on trust because that actually makes the difference between you. Just deliver hardware and you walk away. And this is probably coming back to Azure stack Hop, as it's called now, right? You know, we've been told actually by Microsoft that we've accomplished with the customers from a delivery from a You know, we don't just walk away and say Good luck with the equipment you're on your own really helped them thio and make sure it's working for them. So for me, that's the key that you can come back to a customer afterwards and the customer will actually have you in your office again. >>Well, Barbara, I think back for most of my career what one of the hallmarks of an H. P e solution Was that the turnkey offering we know from, you know, ordering through delivery through, you know, up and running. HP has been streamlining that you know, I think back my entire career cloud has been not necessarily the simplest solutions out there. So maybe give us directionally. How does HPD partner with Microsoft on dhe your customers toe make? I would easier as WeII go through this journey >>S O s aside. Whereas your expert MSP partner a such we have done several of course trainings with Microsoft. We make sure that our people are educated on it way have, you know, with red pixy in the UK it's now part of point next, but I love to say the name because people really associate still with this a specific, strong and trustworthy team. You really build up a very good practice with Microsoft. There's, you know, local deal clinics where we really work in the specific deal. Steal by deal on how we can make it better for the customer. So a lot off local engagement. But for me, that all happens in country. Write me at a global level. I can only help them and steered a little bit. But that's also for me trust. It's a person to person relationship that happens in country. >>And would you say there are big differences country to country in terms of how willingly trust you and and and then how long it takes to build that relationship. >>So I'm gonna get in >>trouble now with some of the country. >>No, I you know the >>somewhere, even your CEO. >>You know, it's no, I mean you and I personally lift in Canada for a while, and so for me, it's some people are harder, you know, you need to get to know them. But then trust is even deeper then some of the others. But I have to say, it's all we're I mean, we're, I would say, from all those who look at h p were really a global company, right? And from this goes from Japan, Thio South Pacific too. You know, many countries in Asia will be very successful with ashes, stack specifically and always in Europe, the Middle East, all the way to North America, South America. So, I mean, that's the nice thing about HPD, I would say for the customers as well that they really get a global view on DA, a global company that can trust. >>So you're here, Ed ignite from Germany. What are the kinds of conversations you're having. And what do you think you're gonna take back with you when you go back to the office next week? So the other piece >>and we have ah, quite big. Both hear it at the event, right? We have a very nice edge line 8000 with us, which is kind of a ruggedized us or a smaller version. It's kindof almost my hand back, kind of to carry along, which has caught a lot of interest from the customers. So just standing there, watching the customers, asking, What is it? Can you tell me more about it? Rest is, you know, I love the bus and I love the actually part of the Microsoft Advisory Council for inspired, which is the partner event, right? But I love the bus to see here what's what's going on and always like to see how other people what they do, what they what they do at these events and then just Microsoft. I think it's wonderful, wonderful company. The inspiration. The story today was just into end a great story with great customer stories as well. So she does to the Microsoft team. Well done. >>Congratulations. Your gear was highlighted in the keynote this morning, so I'm sure that's driving a lot of traffic through for people Thio CC the latest. >>I would >>hope Superdome flex was there and then the actual stick. Both of them were there. So we worked hard for that. Thank you, Michael Self, for giving us the opportunity to be present and the keynote today. Well, >>thank you so much for coming on the Cube. It was a pleasure having you on Barbara. >>Thank you, Rebecca. Thank you. Stupid. >>I'm Rebecca Knight. First to minimum. Stay tuned for more of cubes. Live coverage of Microsoft ignite.

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCube! (funky electronic music) Now, here's your host, Dave Vellante! >> Welcome, everybody, to this Cube conversation here in our Marlborough offices. I am very excited today, I spent a number of years at IDC, which, of course, is owned by IDG. And there's a new book out, relatively new, called Future Forward: Leadership Lessons from Patrick McGovern, the Visionary Who Circled the Globe and Built a Technology Media Empire. And it's a great book, lotta stories that I didn't know, many that I did know, and the author of that book, Glenn Rifkin, is here to talk about not only Pat McGovern but also some of the lessons that he put forth to help us as entrepreneurs and leaders apply to create better businesses and change the world. Glenn, thanks so much for comin' on theCube. >> Thank you, Dave, great to see ya. >> So let me start with, why did you write this book? >> Well, a couple reasons. The main reason was Patrick McGovern III, Pat's son, came to me at the end of 2016 and said, "My father had died in 2014 and I feel like his legacy deserves a book, and many people told me you were the guy to do it." So the background on that I, myself, worked at IDG back in the 1980s, I was an editor at Computerworld, got to know Pat during that time, did some work for him after I left Computerworld, on a one-on-one basis. Then I would see him over the years, interview him for the New York Times or other magazines, and every time I'd see Pat, I'd end our conversation by saying, "Pat, when are we gonna do your book?" And he would laugh, and he would say, "I'm not ready to do that yet, there's just still too much to do." And so it became sort of an inside joke for us, but I always really did wanna write this book about him because I felt he deserved a book. He was just one of these game-changing pioneers in the tech industry. >> He really was, of course, the book was even more meaningful for me, we, you and I started right in the same time, 1983-- >> Yeah. >> And by that time, IDG was almost 20 years old and it was quite a powerhouse then, but boy, we saw, really the ascendancy of IDG as a brand and, you know, the book reviews on, you know, the back covers are tech elite: Benioff wrote the forward, Mark Benioff, you had Bill Gates in there, Walter Isaacson was in there, Guy Kawasaki, Bob Metcalfe, George Colony-- >> Right. >> Who actually worked for a little stint at IDC for a while. John Markoff of The New York Times, so, you know, the elite of tech really sort of blessed this book and it was really a lot to do with Pat McGovern, right? >> Oh, absolutely, I think that the people on the inside understood how important he was to the history of the tech industry. He was not, you know, a household name, first of all, you didn't think of Steve Jobs, Bill Gates, and then Pat McGovern, however, those who are in the know realize that he was as important in his own way as they were. Because somebody had to chronicle this story, somebody had to share the story of the evolution of this amazing information technology and how it changed the world. And Pat was never a front-of-the-TV-camera guy-- >> Right. >> He was a guy who put his people forward, he put his products forward, for sure, which is why IDG, as a corporate name, you know, most people don't know what that means, but people did know Macworld, people did know PCWorld, they knew IDC, they knew Computerworld for sure. So that was Pat's view of the world, he didn't care whether he had the spotlight on him or not. >> When you listen to leaders like Reed Hoffman or Eric Schmidt talk about, you know, great companies and how to build great companies, they always come back to culture. >> Yup. >> The book opens with a scene of, and we all, that I usually remember this, well, we're just hangin' around, waitin' for Pat to come in and hand out what was then called the Christmas bonus-- >> Right. >> Back when that wasn't politically incorrect to say. Now, of course, it's the holiday bonus. But it was, it was the Christmas bonus time and Pat was coming around and he was gonna personally hand a bonus, which was a substantial bonus, to every single employee at the company. I mean, and he did that, really, literally, forever. >> Forever, yeah. >> Throughout his career. >> Yeah, it was unheard of, CEOs just didn't do that and still don't do that, you were lucky, you got a message on the, you know, in the lunchroom from the CEO, "Good work, troops! Keep up the good work!" Pat just had a really different view of the culture of this company, as you know from having been there, and I know. It was very familial, there was a sense that we were all in this together, and it really was important for him to let every employee know that. The idea that he went to every desk in every office for IDG around the United States, when we were there in the '80s there were probably 5,000 employees in the US, he had to devote substantial amount-- >> Weeks and weeks! >> Weeks at a time to come to every building and do this, but year after year he insisted on doing it, his assistant at the time, Mary Dolaher told me she wanted to sign the cards, the Christmas cards, and he insisted that he ensign every one of them personally. This was the kind of view he had of how you keep employees happy, if your employees are happy, the customers are gonna be happy, and you're gonna make a lot of money. And that's what he did. >> And it wasn't just that. He had this awesome holiday party that you described, which was epic, and during the party, they would actually take pictures of every single person at the party and then they would load the carousel, you remember the 35-mm. carousel, and then, you know, toward the end of the evening, they would play that and everybody was transfixed 'cause they wanted to see their, the picture of themselves! >> Yeah, yeah. (laughs) >> I mean, it was ge-- and to actually pull that off in the 1980s was not trivial! Today, it would be a piece of cake. And then there was the IDG update, you know, the Good News memos, there was the 10-year lunch, the 20-year trips around the world, there were a lot of really rich benefits that, you know, in and of themselves maybe not a huge deal, but that was the culture that he set. >> Yeah, there was no question that if you talked to anybody who worked in this company over, say, the last 50 years, you were gonna get the same kind of stories. I've been kind of amazed, I'm going around, you know, marketing the book, talking about the book at various events, and the deep affection for this guy that still holds five years after he died, it's just remarkable. You don't really see that with the CEO class, there's a couple, you know, Steve Jobs left a great legacy of creativity, he was not a wonderful guy to his employees, but Pat McGovern, people loved this guy, and they st-- I would be signing books and somebody'd say, "Oh, I've been at IDG for 27 years and I remember all of this," and "I've been there 33 years," and there's a real longevity to this impact that he had on people. >> Now, the book was just, it was not just sort of a biography on McGovern, it was really about lessons from a leader and an entrepreneur and a media mogul who grew this great company in this culture that we can apply, you know, as business people and business leaders. Just to give you a sense of what Pat McGovern did, he really didn't take any outside capital, he did a little bit of, you know, public offering with IDG Books, but, really, you know, no outside capital, it was completely self-funded. He built a $3.8 billion empire, 300 publications, 280 million readers, and I think it was almost 100 or maybe even more, 100 countries. And so, that's an-- like you were, used the word remarkable, that is a remarkable achievement for a self-funded company. >> Yeah, Pat had a very clear vision of how, first of all, Pat had a photographic memory and if you were a manager in the company, you got a chance to sit in meetings with Pat and if you didn't know the numbers better than he did, which was a tough challenge, you were in trouble! 'Cause he knew everything, and so, he was really a numbers-focused guy and he understood that, you know, his best way to make profit was to not be looking for outside funding, not to have to share the wealth with investors, that you could do this yourself if you ran it tightly, you know, I called it in the book a 'loose-tight organization,' loose meaning he was a deep believer in decentralization, that every market needed its own leadership because they knew the market, you know, in Austria or in Russia or wherever, better than you would know it from a headquarters in Boston, but you also needed that tightness, a firm grip on the finances, you needed to know what was going on with each of the budgets or you were gonna end up in big trouble, which a lot of companies find themselves in. >> Well, and, you know, having worked there, I mean, essentially, if you made your numbers and did so ethically, and if you just kind of followed some of the corporate rules, which we'll talk about, he kind of left you alone. You know, you could, you could pretty much do whatever you wanted, you could stay in any hotel, you really couldn't fly first class, and we'll maybe talk about that-- >> Right. >> But he was a complex man, I mean, he was obviously wealthy, he was a billionaire, he was very generous, but at the same time he was frugal, you know, he drove, you know, a little, a car that was, you know, unremarkable, and we had buy him a car. He flew coach, and I remember one time, I was at a United flight, and I was, I had upgraded, you know, using my miles, and I sat down and right there was Lore McGovern, and we both looked at each other and said right at the same time, "I upgraded!" (laughs) Because Pat never flew up front, but he would always fly with a stack of newspapers in the seat next to him. >> Yeah, well, woe to, you were lucky he wasn't on the plane and spotted you as he was walking past you into coach, because he was not real forgiving when he saw people, people would hide and, you know, try to avoid him at all cost. And, I mean, he was a big man, Pat was 6'3", you know, 250 lbs. at least, built like a linebacker, so he didn't fit into coach that well, and he wasn't flying, you know, the shuttle to New York, he was flyin' to Beijing, he was flyin' to Moscow, he was going all over the world, squeezing himself into these seats. Now, you know, full disclosure, as he got older and had, like, probably 10 million air miles at his disposal, he would upgrade too, occasionally, for those long-haul flights, just 'cause he wanted to be fresh when he would get off the plane. But, yeah, these are legends about Pat that his frugality was just pure legend in the company, he owned this, you know, several versions of that dark blue suit, and that's what you would see him in. He would never deviate from that. And, but, he had his patterns, but he understood the impact those patterns had on his employees and on his customers. >> I wanna get into some of the lessons, because, really, this is what the book is all about, the heart of it. And you mentioned, you know, one, and we're gonna tell from others, but you really gotta stay close to the customer, that was one of the 10 corporate values, and you remember, he used to go to the meetings and he'd sometimes randomly ask people to recite, "What's number eight?" (laughs) And you'd be like, oh, you'd have your cheat sheet there. And so, so, just to give you a sense, this man was an entrepreneur, he started the company in 1964 with a database that he kind of pre-sold, he was kind of the sell, design, build type of mentality, he would pre-sold this thing, and then he started Computerworld in 1967, so it was really only a few years after he launched the company that he started the Computerworld, and other than Data Nation, there was nothing there, huge pent-up demand for that type of publication, and he caught lightning in a bottle, and that's really how he funded, you know, the growth. >> Yeah, oh, no question. Computerworld became, you know, the bible of the industry, it became a cash cow for IDG, you know, but at the time, it's so easy to look in hindsight and say, oh, well, obviously. But when Pat was doing this, one little-known fact is he was an editor at a publication called Computers and Automation that was based in Newton, Massachusetts and he kept that job even after he started IDC, which was the original company in 1964. It was gonna be a research company, and it was doing great, he was seeing the build-up, but it wasn't 'til '67 when he started Computerworld, that he said, "Okay, now this is gonna be a full-time gig for me," and he left the other publication for good. But, you know, he was sorta hedging his bets there for a little while. >> And that's where he really gained respect for what we'll call the 'Chinese Wallet,' the, you know, editorial versus advertising. We're gonna talk about that some more. So I mentioned, 1967, Computerworld. So he launched in 1964, by 1971, he was goin' to Japan, we're gonna talk about the China Stories as well, so, he named the company International Data Corp, where he was at a little spot in Newton, Mass.-- >> Right, right. >> So, he had a vision. You said in your book, you mention, how did this gentleman get it so right for so long? And that really leads to some of the leadership lessons, and one of them in the book was, sort of, have a mission, have a vision, and really, Pat was always talking about information, about information technology, in fact, when Wine for Dummies came out, it kind of created a little friction, that was really off the center. >> Or Wine for Dummies, or Sex for Dummies! >> Yeah, Sex for Dummies, boy, yeah! >> With, that's right, Ruth Westheimer-- >> Dr. Ruth Westheimer. >> But generally speaking, Glenn, he was on that mark, he really didn't deviate from that vision. >> Yeah, no, it was very crucial to the development of the company that he got people to, you know, buy into that mission, because the mission was everything. And he understood, you know, he had the numbers, but he also saw what was happening out there, from the 1960s, when IBM mainframes filled a room, and, you know, only the high priests of data centers could touch them. He had a vision for, you know, what was coming next and he started to understand that there would be many facets to this information about information technology, it wasn't gonna be boring, if anything, it was gonna be the story of our age and he was gonna stick to it and sell it. >> And, you know, timing is everything, but so is, you know, Pat was a workaholic and had an amazing mind, but one of the things I learned from the book, and you said this, Pat Kenealy mentioned it, all American industrial and social revolutions have had a media company linked to them, Crane and automobiles, Penton and energy, McGraw-Hill and aerospace, Annenberg, of course, and TV, and in technology, it was IDG. >> Yeah, he, like I said earlier, he really was a key figure in the development of this industry and it was, you know, one of the key things about that, a lot publications that came and went made the mistake of being platform or, you know, vertical market specific. And if that market changed, and it was inevitably gonna change in high tech, you were done. He never, you know, he never married himself to some specific technology cycle. His idea was the audience was not gonna change, the audience was gonna have to roll with this, so, the company, IDG, would produce publications that got that, you know, Computerworld was actually a little bit late to the PC game, but eventually got into it and we tracked the different cycles, you know, things in tech move in sine waves, they come and go. And Pat never was, you know, flustered by that, he could handle any kind of changes from the mainframes down to the smartphone when it came. And so, that kind of flexibility, and ability to adjust to markets, really was unprecedented in that particular part of the market. >> One of the other lessons in the book, I call it 'nation-building,' and Pat shared with you that, look, that you shared, actually, with your readers, if you wanna do it right, you've gotta be on the ground, you've gotta be there. And the China story is one that I didn't know about how Pat kind of talked his way into China, tell us, give us a little summary of that story. >> Sure, I love that story because it's so Pat. It was 1978, Pat was in Tokyo on a business trip, one of his many business trips, and he was gonna be flying to Moscow for a trade show. And he got a flight that was gonna make a stopover in Beijing, which in those days was called Peking, and was not open to Americans. There were no US and China diplomatic relations then. But Pat had it in mind that he was going to get off that plane in Beijing and see what he could see. So that meant that he had to leave the flight when it landed in Beijing and talk his way through the customs as they were in China at the time with folks in the, wherever, the Quonset hut that served for the airport, speaking no English, and him speaking no Chinese, he somehow convinced these folks to give him a day pass, 'cause he kept saying to them, "I'm only in transit, it's okay!" (laughs) Like, he wasn't coming, you know, to spy on them on them or anything. So here's this massive American businessman in his dark suit, and he somehow gets into downtown Beijing, which at the time was mostly bicycles, very few cars, there were camels walking down the street, they'd come with traders from Mongolia. The people were still wearing the drab outfits from the Mao era, and Pat just spent the whole day wandering around the city, just soaking it in. He was that kind of a world traveler. He loved different cultures, mostly eastern cultures, and he would pop his head into bookstores. And what he saw were people just clamoring to get their hands on anything, a newspaper, a magazine, and it just, it didn't take long for the light bulb to go on and said, this is a market we need to play in. >> He was fascinated with China, I, you know, as an employee and a business P&L manager, I never understood it, I said, you know, the per capita spending on IT in China was like a dollar, you know? >> Right. >> And I remember my lunch with him, my 10-year lunch, he said, "Yeah, but, you know, there's gonna be a huge opportunity there, and yeah, I don't know how we're gonna get the money out, maybe we'll buy a bunch of tea and ship it over, but I'm not worried about that." And, of course, he meets Hugo Shong, which is a huge player in the book, and the home run out of China was, of course, the venture capital, which he started before there was even a stock market, really, to exit in China. >> Right, yeah. No, he was really a visionary, I mean, that word gets tossed around maybe more than it should, but Pat was a bonafide visionary and he saw things in China that were developing that others didn't see, including, for example, his own board, who told him he was crazy because in 1980, he went back to China without telling them and within days he had a meeting with the ministry of technology and set up a joint venture, cost IDG $250,000, and six months later, the first issue of China Computerworld was being published and within a couple of years it was the biggest publication in China. He said, told me at some point that $250,0000 investment turned into $85 million and when he got home, that first trip, the board was furious, they said, "How can you do business with the commies? You're gonna ruin our brand!" And Pat said, "Just, you know, stick with me on this one, you're gonna see." And the venture capital story was just an offshoot, he saw the opportunity in the early '90s, that venture in China could in fact be a huge market, why not help build it? And that's what he did. >> What's your take on, so, IDG sold to, basically, Chinese investors. >> Yeah. >> It's kind of bittersweet, but in the same time, it's symbolic given Pat's love for China and the Chinese people. There's been a little bit of criticism about that, I know that the US government required IDC to spin out its supercomputer division because of concerns there. I'm always teasing Michael Dow that at the next IDG board meeting, those Lenovo numbers, they're gonna look kinda law. (laughs) But what are your, what's your, what are your thoughts on that, in terms of, you know, people criticize China in terms of IP protections, etc. What would Pat have said to that, do you think? >> You know, Pat made 130 trips to China in his life, that's, we calculated at some point that just the air time in planes would have been something like three and a half to four years of his life on planes going to China and back. I think Pat would, today, acknowledge, as he did then, that China has issues, there's not, you can't be that naive. He got that. But he also understood that these were people, at the end of the day, who were thirsty and hungry for information and that they were gonna be a player in the world economy at some point, and that it was crucial for IDG to be at the forefront of that, not just play later, but let's get in early, let's lead the parade. And I think that, you know, some part of him would have been okay with the sale of the company to this conglomerate there, called China Oceanwide. Clearly controversial, I mean, but once Pat died, everyone knew that the company was never gonna be the same with the leader who had been at the helm for 50 years, it was gonna be a tough transition for whoever took over. And I think, you know, it's hard to say, certainly there's criticism of things going on with China. China's gonna be the hot topic page one of the New York Times almost every single day for a long time to come. I think Pat would have said, this was appropriate given my love of China, the kind of return on investment he got from China, I think he would have been okay with it. >> Yeah, and to invoke the Ben Franklin maxim, "Trading partners seldom wage war," and so, you know, I think Pat would have probably looked at it that way, but, huge home run, I mean, I think he was early on into Baidu and Alibaba and Tencent and amazing story. I wanna talk about decentralization because that was always something that was just on our minds as employees of IDG, it was keep the corporate staff lean, have a flat organization, if you had eight, 10, 12 direct reports, that was okay, Pat really meant it when he said, "You're the CEO of your own business!" Whether that business was, you know, IDC, big company, or a manager at IDC, where you might have, you know, done tens of millions of dollars, but you felt like a CEO, you were encouraged to try new things, you were encouraged to fail, and fail fast. Their arch nemesis of IDG was Ziff Davis, they were a command and control, sort of Bill Ziff, CMP to a certain extent was kind of the same way out of Manhasset, totally different philosophies and I think Pat never, ever even came close to wavering from that decentralization philosophy, did he? >> No, no, I mean, I think that the story that he told me that I found fascinating was, he didn't have an epiphany that decentralization would be the mechanism for success, it was more that he had started traveling, and when he'd come back to his office, the memos and requests and papers to sign were stacked up two feet high. And he realized that he was holding up the company because he wasn't there to do this and that at some point, he couldn't do it all, it was gonna be too big for that, and that's when the light came on and said this decentralization concept really makes sense for us, if we're gonna be an international company, which clearly was his mission from the beginning, we have to say the people on the ground in those markets are the people who are gonna make the decisions because we can't make 'em from Boston. And I talked to many people who, were, you know, did a trip to Europe, met the folks in London, met the folks in Munich, and they said to a person, you know, it was so ahead of its time, today it just seems obvious, but in the 1960s, early '70s, it was really not a, you know, a regular leadership tenet in most companies. The command and control that you talked about was the way that you did business. >> And, you know, they both worked, but, you know, from a cultural standpoint, clearly IDG and IDC have had staying power, and he had the three-quarter rule, you talked about it in your book, if you missed your numbers three quarters in a row, you were in trouble. >> Right. >> You know, one quarter, hey, let's talk, two quarters, we maybe make some changes, three quarters, you're gone. >> Right. >> And so, as I said, if you were makin' your numbers, you had wide latitude. One of the things you didn't have latitude on was I'll call it 'pay to play,' you know, crossing that line between editorial and advertising. And Pat would, I remember I was at a meeting one time, I'm sorry to tell these stories, but-- >> That's okay. (laughs) >> But we were at an offsite meeting at a woods meeting and, you know, they give you a exercise, go off and tell us what the customer wants. Bill Laberis, who's the editor-in-chief at Computerworld at the time, said, "Who's the customer?" And Pat said, "That's a great question! To the publisher, it's the advertiser. To you, Bill, and the editorial staff, it's the reader. And both are equally important." And Pat would never allow the editorial to be compromised by the advertiser. >> Yeah, no, he, there was a clear barrier between church and state in that company and he, you know, consistently backed editorial on that issue because, you know, keep in mind when we started then, and I was, you know, a journalist hoping to, you know, change the world, the trade press then was considered, like, a little below the mainstream business press. The trade press had a reputation for being a little too cozy with the advertisers, so, and Pat said early on, "We can't do that, because everything we have, our product is built, the brand is built on integrity. And if the reader doesn't believe that what we're reporting is actually true and factual and unbiased, we're gonna lose to the advertisers in the long run anyway." So he was clear that that had to be the case and time and again, there would be conflict that would come up, it was just, as you just described it, the publishers, the sales guys, they wanted to bring in money, and if it, you know, occasionally, hey, we could nudge the editor of this particular publication, "Take it a little bit easier on this vendor because they're gonna advertise big with us," Pat just would always back the editor and say, "That's not gonna happen." And it caused, you know, friction for sure, but he was unwavering in his support. >> Well, it's interesting because, you know, Macworld, I think, is an interesting case study because there were sort of some backroom dealings and Pat maneuvered to be able to get the Macworld, you know, brand, the license for that. >> Right. >> But it caused friction between Steve Jobs and the writers of Macworld, they would write something that Steve Jobs, who was a control freak, couldn't control! >> Yeah. (laughs) >> And he regretted giving IDG the license. >> Yeah, yeah, he once said that was the worst decision he ever made was to give the license to Pat to, you know, Macworlld was published on the day that Mac was introduced in 1984, that was the deal that they had and it was, what Jobs forgot was how important it was to the development of that product to have a whole magazine devoted to it on day one, and a really good magazine that, you know, a lot of people still lament the glory days of Macworld. But yeah, he was, he and Steve Jobs did not get along, and I think that almost says a lot more about Jobs because Pat pretty much got along with everybody. >> That church and state dynamic seems to be changing, across the industry, I mean, in tech journalism, there aren't any more tech journalists in the United States, I mean, I'm overstating that, but there are far fewer than there were when we were at IDG. You're seeing all kinds of publications and media companies struggling, you know, Kara Swisher, who's the greatest journalist, and Walt Mossberg, in the tech industry, try to make it, you know, on their own, and they couldn't. So, those lines are somewhat blurring, not that Kara Swisher is blurring those lines, she's, you know, I think, very, very solid in that regard, but it seems like the business model is changing. As an observer of the markets, what do you think's happening in the publishing world? >> Well, I, you know, as a journalist, I'm sort of aghast at what's goin' on these days, a lot of my, I've been around a long time, and seeing former colleagues who are no longer in journalism because the jobs just started drying up is, it's a scary prospect, you know, unlike being the enemy of the people, the first amendment is pretty important to the future of the democracy, so to see these, you know, cutbacks and newspapers going out of business is difficult. At the same time, the internet was inevitable and it was going to change that dynamic dramatically, so how does that play out? Well, the problem is, anybody can post anything they want on social media and call it news, and the challenge is to maintain some level of integrity in the kind of reporting that you do, and it's more important now than ever, so I think that, you know, somebody like Pat would be an important figure if he was still around, in trying to keep that going. >> Well, Facebook and Google have cut the heart out of, you know, a lot of the business models of many media companies, and you're seeing sort of a pendulum swing back to nonprofits, which, I understand, speaking of folks back in the mid to early 1900s, nonprofits were the way in which, you know, journalism got funded, you know, maybe it's billionaires buying things like the Washington Post that help fund it, but clearly the model's shifting and it's somewhat unclear, you know, what's happening there. I wanted to talk about another lesson, which, Pat was the head cheerleader. So, I remember, it was kind of just after we started, the Computerworld's 20th anniversary, and they hired the marching band and they walked Pat and Mary Dolaher walked from 5 Speen Street, you know, IDG headquarters, they walked to Computerworld, which was up Old, I guess Old Connecticut Path, or maybe it was-- >> It was actually on Route 30-- >> Route 30 at the time, yeah. And Pat was dressed up as the drum major and Mary as well, (laughs) and he would do crazy things like that, he'd jump out of a plane with IDG is number one again, he'd post a, you know, a flag in Antarctica, IDG is number one again! It was just a, it was an amazing dynamic that he had, always cheering people on. >> Yeah, he was, he was, when he called himself the CEO, the Chief Encouragement Officer, you mentioned earlier the Good News notes. Everyone who worked there, at some point received this 8x10" piece of paper with a rainbow logo on it and it said, "Good News!" And there was a personal note from Pat McGovern, out of the blue, totally unexpected, to thank you and congratulate you on some bit of work, whatever it was, if you were a reporter, some article you wrote, if you were a sales guy, a sale that you made, and people all over the world would get these from him and put them up in their cubicles because it was like a badge of honor to have them, and people, I still have 'em, (laughs) you know, in a folder somewhere. And he was just unrelenting in supporting the people who worked there, and it was, the impact of that is something you can't put a price tag on, it's just, it stays with people for all their lives, people who have left there and gone on to four or five different jobs always think fondly back to the days at IDG and having, knowing that the CEO had your back in that manner. >> The legend of, and the legacy of Patrick J. McGovern is not just in IDG and IDC, which you were interested in in your book, I mean, you weren't at IDC, I was, and I was started when I saw the sort of downturn and then now it's very, very successful company, you know, whatever, $3-400 million, throwin' off a lot of profits, just to decide, I worked for every single CEO at IDC with the exception of Pat McGovern, and now, Kirk Campbell, the current CEO, is moving on Crawford del Prete's moving into the role of president, it's just a matter of time before he gets CEO, so I will, and I hired Crawford-- >> Oh, you did? (laughs) >> So, I've worked for and/or hired every CEO of IDC except for Pat McGovern, so, but, the legacy goes beyond IDG and IDC, great brands. The McGovern Brain Institute, 350 million, is that right? >> That's right. >> He dedicated to studying, you know, the human brain, he and Lore, very much involved. >> Yup. >> Typical of Pat, he wasn't just, "Hey, here's the check," and disappear. He was goin' in, "Hey, I have some ideas"-- >> Oh yeah. >> Talk about that a little. >> Yeah, well, this was a guy who spent his whole life fascinated by the human brain and the impact technology would have on the human brain, so when he had enough money, he and Lore, in 2000, gave a $350 million gift to MIT to create the McGovern Institute for Brain Research. At the time, the largest academic gift ever given to any university. And, as you said, Pat wasn't a guy who was gonna write a check and leave and wave goodbye. Pat was involved from day one. He and Lore would come and sit in day-long seminars listening to researchers talk about about the most esoteric research going on, and he would take notes, and he wasn't a brain scientist, but he wanted to know more, and he would talk to researchers, he would send Good News notes to them, just like he did with IDG, and it had same impact. People said, "This guy is a serious supporter here, he's not just showin' up with a checkbook." Bob Desimone, who's the director of the Brain Institute, just marveled at this guy's energy level, that he would come in and for days, just sit there and listen and take it all in. And it just, it was an indicator of what kind of person he was, this insatiable curiosity to learn more and more about the world. And he wanted his legacy to be this intersection of technology and brain research, he felt that this institute could cure all sorts of brain-related diseases, Alzheimer's, Parkinson's, etc. And it would then just make a better future for mankind, and as corny as that might sound, that was really the motivator for Pat McGovern. >> Well, it's funny that you mention the word corny, 'cause a lot of people saw Pat as somewhat corny, but, as you got to know him, you're like, wow, he really means this, he loves his company, the company was his extended family. When Pat met his untimely demise, we held a crowd chat, crowdchat.net/thankspat, and there's a voting mechanism in there, and the number one vote was from Paul Gillen, who posted, "Leo Durocher said that nice guys finish last, Pat McGovern proved that wrong." >> Yeah. >> And I think that's very true and, again, awesome legacy. What number book is this for you? You've written a lot of books. >> This is number 13. >> 13, well, congratulations, lucky 13. >> Thank you. >> The book is Fast Forward-- >> Future Forward. >> I'm sorry, Future Forward! (laughs) Future Forward by Glenn Rifkin. Check out, there's a link in the YouTube down below, check that out and there's some additional information there. Glenn, congratulations on getting the book done, and thanks so much for-- >> Thank you for having me, this is great, really enjoyed it. It's always good to chat with another former IDGer who gets it. (laughs) >> Brought back a lot of memories, so, again, thanks for writing the book. All right, thanks for watching, everybody, we'll see you next time. This is Dave Vellante. You're watchin' theCube. (electronic music)

>> Hello everyone. Welcome to this Cube conversation here in Palo Alto. On John Furrier, co host of the Cube. I'm here. Aaron Kalb is the co founder and VP of design and Alation. Great to see them on some fresh funding news. Aaron, Thanks for coming. And spend the time. Good to see you again. >> Good to see you, John. Thanks for having me >> So big news. You guys got a very big round of financing because you go to the next level. A startup. Certainly coming out that start up phase and growth phase super exciting news. You guys doing some very innovative things around, date around community around people and really kind of cracking the code on this humanization democratization of data, but actually helping businesses. I want to talk about it with you. First. Give us the update on the financing, the amount what it means to the company. A lot of cash. >> Yeah. So we're very excited to have raised a fifty million dollar round. Sapphire led the round, and we also had, you know, re ups from all of our existing investors. And, you know, as as a co founder, he always had big dreams for growth. And it's just validating tohave. Ah, a community of investors who can see the future, too, as well as our great community of over one hundred customers now who want to build this data democratized future with us. >> We've been following you guys since the founding obviously watching you guys great use of capital. Fifty million's a lot of capital, so obviously validation check. Good, good job. But now you go to a whole other level growth. What's the capital gonna be deployed for? What's going on with company where you guys I and in terms of innovation, what's the key focus? >> It's a great question. So you know, obviously we have revenue from our customers. But getting this extra infusion from VC lets us just supercharge our development. It's growth. It's going to more customers, both domestically and abroad, goingto a broader user base. And we're Enterprise-wide Adoption within those customers, as well as innovation in the core product, new technology, great design and futures. that are really going to change the organization's access and use data to make better decisions? >> What was the key Learnings As you guys went into this round of funding outside the validation to get through due diligence, all that good stuff. But you guys have made some successful milestones. What was the key? Notable accomplishments that Alation hit to kind of hit this trigger point here for the fifty million? >> Yeah, I'm glad you asked about that. I think that the key thing that's changed it's enabled this. This next phase is that the data catalog market has really come into its own right. In the beginning, in the early days, we were knocking on doors, trying to say, You know, we don't even know it was going to be called data catalog in our first few months. And even though we had the technology, we said, Hey, we got this thing and we know it's useful. Please buy it. Please want it. And the question was, you know, what's the data catalog by what I ever even look at that? And it's just turned a corner. Now, you know, Thanks. In part of things like Gartner telling companies you know, in the next year by twenty twenty, if you have a data catalog, you're goingto see twice the ROI from your existing data investments than if you don't your stories like that are making companies say? Of course, you want to data catalog. It just turned out a dime. Now they're asking, Which data catalog should we get? Why is yours the best in this change of the market maturing? I think it's the biggest change we've seen >> with one thing that we've observed. I want to get your reaction to This is that I'll stay with cloud computing economics, a phenomenally C scale data data science working the cloud. We see great success there. Now there's multiple clouds, multi clouds, a big trend, but also the validation that it's not just all cloud anymore. The on premises activity steel is relevant, although it might have a cloud. Operations really kind of changes the role of data. You mentioned the data catalogue kind of being kind of having a common mainstream visibility from the analysts like Gardner and others on Wiki Bond as well. It makes data the center of the innovation. Now you have data challenges around. Okay, where's the data deployed? Where my using the data? Because data scientists want ease of data, they want quality data. They want to make sure their their algorithm, whether it's machine learning component or software actually running a good data. So data effectiveness is now part of the operations of most businesses. What's your reaction to that? Which your thoughts. Is that how you see it? Is there something different there? What's going on with the whole date at the center? >> Absolutely hit on two key themes for us. One of that idea of the center and the other is your point about data quality and data trust. So, so centrality, we think, is really essential. You know, we're seeing cataloging technology crop up more and more. A lot of people were coming out with catalogs or catalog kind of add ons to their products. But what our customers really tell us is they want the data catalog to be the hub, that one stop shop where they go to to access any data, wherever it lives, whether it's in the cloud or on Prem, whether it's in a relational database or a file system, so is one of Alations key. Differentiators early on was being that central index, much like Google is out of the front page to the Internet, even though it's linking to ad pages all over the place. And the other thing in terms of that data quality and data trustworthiness has been a differentiator, and this was something that was part of our technology when we launched that we didn't put the label out till later. Is this idea of Behavior IO, that's kind of looking at previous human behavior to influence future human behavior to be better. And there's another place we really took some inspiration from Google and Terry Winograd at Stanford before that, you know, he observed. You know, if you remember back before Google search sucked, frankly, right, the results on top are not the most development were not the most trustworthy. And the reason was those algorithms were based on saying, how often does your key word appear in that website? Built, in other words, and so you'd get results on top. That might just not be very good. Or even that were created by spammers who put in a lot of words to get SEO and and, you know, that isn't the best result for you on what Google did was turned that around with page rank and say, Let's use the signals that other people are getting behind about the pages they find valuable to get the best result on top. And Alation is the exact same thing our patented proprietary behavior technology lets us say Who's using this data? How were they using it? Is it reputable? And that enables us to get the right data and transfer the data in front of decision makers. >> And you call that Behavioral IO >> Behavior IO, that's right. >> I mean, certainly remember Google algorithmic search was pooh poohed. It first had to be a portal. Everyone kind of my age. You can't remember those those days and the results were key word stuff by spammer's. But algorithmic search accelerated the quality. So I got to ask you the behavioral Io to kind of impact a little bit. Go a little deeper. What does that mean for customers? Because now I'll see as people start thinking, OK, I need to catalogue my data because now I need to have replication, all kinds of least technical things that are going on around integrity of the data. But why Behavioral Aya? What's the angle on that? What's the impact of the customer? Why is this important? Absolutely so. >> Might have to work through an example, you know we joke about. You might be looking around in your SharePoint drive and find an Excel file called Q three Numbers final. Underscore final. Okay, that seems that'S inject the final numbers, and then you see next to it when it says underscore final underscore, final underscore finalist. Okay, well, is that one final? And it turns out what Data says about itself is less reliable than what other people say about the data. Same thing with Google that if everyone's linking with Wikipedia Page, that's a more reliable page than one that just has, you know, paid for a higher placement, Right? So what a means an organization is with Alation will tell you. You know, this is the data table that was refreshed yesterday and that the CFO and everybody in this department is using every day. That's a really strong signal. That's trustworthy data, as opposed to something that was only used once a year ago. >> So relevance is key there. >> Absolutely. It's relevant. And trustworthiness. We find both all right, indicated more strongly by who's using it and how than by the data itself. >> Are you seeing adoption with data scientist and people who were wrangling date or data analysts that if the date is not high quality, they abandoned. The usage is they're getting kind of stats around that are because that we're hearing a lot of Hey, you know, that I'm not going to really work on the data. But I'm not going to do all the heavy lifting on the front end the data qualities, not there. >> Absolutely. We see a really cool upward spiral. So in Alation, we have a mix of manual, human curated metadata, you know, data stewards and that a curator saying, this is endorsed data. It's a certified data. This is applicable for this context. But we also do this automatic behavior. Io. We parse the query logs. These logs were, you know, put there for audit on debugging purposes. But we were mining that for behavioral insight, and we'll show them side by side on what we see is overtime on day one. There's no manual curation. But as that curation gets added in, we see a strong correlation between the best highest quality data and the most used data. And we also see an upward spiral where, if on day one. People are using data that isn't trustworthy that stale or miscalculated as soon as Ah, an Alation steward slaps a deprecation or a warning on the data asset because of technology like trust check talking about last time I was here, that technology, that's the O part of behavior IO We then stop the future behavior from being on bad data, and we see an upward spiral where suddenly the bad sata is no longer being used and everyone's guided put the pound. >> One thing I'm really impressed with you guys on is you have a great management team and overall team with mixed disciplines. Okay, I think last night about your role, Stanford and the human side of the world. But you have to search analogy, which is interesting because you have search folks. You got hardcore data data geeks all working together. And if you think about Discovery and navigation, which is the Google parent, I need to find a Web page and go, Go, go to it. You guys were in that same business of helping people discover data and act on it or take action. Same kind of paradigm, so explain some customer impact anecdotes. People who bought Alation, what your service and offering and what happened after and what was it like before? We talk about some of that? And because I think you're onto something pretty big here with this discovery. Actionable data perspective. >> Yeah, well, one of our values, it Alation, is that we measure our success through customer impact, you know, not do financing or other other milestones that we are excited about them. So I I would love to talk about our customers. One example of a business impact is an example that our champion at Safeway Albertsons describes where, after safe, it was acquired by Albertson's. They've been sort of pioneers of sort of digital, ah, loyalty and engagement. And there was a move to kind of stop that in its tracks and switch should just mailing people big books of coupons that of customizing, you know, deals for you based on your buying behavior. And they talked about getting a thirty x  ROI on the dollars they've spent on Alation by basically proving the value of their program and kind of maximizing their relationship with their customers. But the stories they're even more exciting to me, then just business impacts in dollars and cents when we can leave a positive impact on people's lives with data. There's a few examples of that Munich reinsurance, the biggest being sure and also a primary ensure in Europe, had some coverage and Forbes about the way that they use Alation, other data tools to be able to help people get back on their feet more quickly after, ah, earthquakes and other natural disasters. And similarly, there's a piece in The Wall Street Journal about how Pfizer is able to create diagnostics and treatments for rare diseases where it wouldn't have been a good ROI even invest in those if they didn't get that increased efficient CNN analytics from Alation on the other data. >> So it's not just one little vertical. It's kind of mean data is horizontally. Scaleable is not like one. Industry is going to leverage Alation, >> Absolutely so you know, I mentioned just now. Insurance and health care and retail were also in tech were in basically every vertical you can imagine and even multiple sectors. You know, I've been focusing on industry, but there's another case that you can read about at the city of San Diego were there. They're doing an open data initiative, enabling people to figure out everything from where parking is easiest, the hardest to anything else. >> The behavioral Io. And it's all about context and behavior, role of data and all this. It's kind of fundamental to businesses. >> That's right. It's all about taking everything about how people using data today and driving people to be even more data driven, more accurate, better able to satisfy their curiosity and be more rational in >> the future. So if I'm a from a potential customer and I heard a rAlation, get the buzz out there, why would I need you? What air? Some signals that would indicate that I should call Alation. What's some of that Corvette? What's the pitch? >> Yeah, it's a great question. No, I sometimes joke with the team that you know every five minutes another enterprise reaches that point where they can't do it the old way anymore. And the needle ations. And the reason for that is that data is growing exponentially and people can only grow at most, you know, linearly. So I compare it a bit again to the days of of Yahoo When the Internet was small, you make a table of contents for it. But as there came to be trillions of red pages, you needed an automatic index with pay drink to make sense of it. So I would say, once you find that your analytics team has spread out and they're spending, you know eighty percent of their time calling up other people to find where development data is, you're asked to Your point is this data high quality show even spend my time on it? You know that's probably not money is well spent with these highly paid people spending other times scrounging If you switch from scrounging to finding understanding and trusting their data for quick and accurate analysis, give us >> a call. So basically the pitches, if you want to be like Yahoo, do it the old way. We know what happened. Yeah, you want to be like Google, two algorithmic and have data >> God rAlation, and you'll be around for a while very well. After that, maybe the one see that that's my words. >> And and that's part of turning that corner. I think in the beginning we were trying to tell people this could be a nice toe have. And now customers are coming to us realizing it's a must have to stay a relevant, you know, And if you've made all these investments in data infrastructure and data people, but you can't connect the dots is you said, between the human side and the tech side that money's all wasted and you're going to not be able to compete against your competitors and impact of customers what you want. >> Well, Eric, congratulations. Certainly is the co founder. It's great success. And how hard is that you start ups? You guys worked hard and again. Why following you guys? Been interesting to see that growth and this innovation involved in creative, A lot of energy. You guys do a good job. So final question, talk about the secret sauce of Alation. What's the key innovation formula? And now that you got the funding where you're going to double down on, where's the innovation going to come next? So the innovation formula and where the innovation, the future, >> absolutely innovation has been critical for us to get here on our customers didn't just buy the exciting features with behavioral and trust. Check that we had but also are buying into the idea that we're going to continue to be the leaders and to innovate. Andi, we're going to do that. So I think the secret sauce which we've had in the past, we're going to continue to innovate in this vein, is to be really conscious of water computers great at and what humans uniquely good at what you humans like doing and trying to have the human and computers work together to really help the human achieve their goals. Right? So, Doctor, the Google example. You know, there's a bunch of systems for collaboratively ranking things, but it takes work to, you know, write a review on the upper Amazon. Google had the insight that we could leverage people are already doing and make it about it. Out of that, we're going to continue to do that. >> The other kind of innovation you'll see is bringing Alation to a wider and wider audience, with less and less technical skill needed. So I came from Syria Apple, and the idea is you have to learn a programming language to Queria database. You could just speak in English. That helps you ask answer questions like What's the weather today? Imagine taking that same kind of experience of seamless integration to the more important questions enterprises are asking. >> We'll have to tap your expertise is we want to have an app called the Cube Syria, which is a cube. What's the innovation in Silicon Valley and have it just spit out a video on the kidding? Final question just to double down on that piece, because I think the human interactions a big part of what you're saying I've always loved that about with your vision is. But this points to a major problems. Seeing whether it's, you know, media, the news cycle These days, people are challenging the efficacy of finding the research and the real deep research on the media. So I was seeing scale on data scale is a huge challenge. You mentioned the growth of data. Computers can scale things, but the knowledge and the curation kind of dynamic of packaging it, finding it, acting on it. It's kind of where you guys are hitting. Talk about that tie name, my getting that right and set is that important? Because, you know, certainly scale is table stakes these days. >> That is super insightful John, because I think human cognition and human thought excuse me, is the bottleneck four being data driven right we have on the Internet trillions of Web pages, you know, more than the Library of Alexandria a hundred times over, and we have in databases millions of columns and trillions of rose. But for that to actually impact the business and impact the world in a positive way, it's got to go through a person who could understand it. And so, in the same way that Google became the mechanism by which the Internet becomes accessible, we think that Alation for organizations is becoming the way that data can become actionable. And the other thing I would say is, you know, in this age of alternative facts and mistrust of data, you know, we've sort of realizing the just having more information out there doesn't actually make people wiser and better able to reason. It can actually be a lot of noise that muddies the signal and confuses people. So we think Alation by also using human computer interaction to help separate the signal from the noise and the quality from the garbage can help stop the garbage in garbage out and make people more rational and more curious and have more trust than what there. Hearing understanding >> build that Paige rang kind of metaphor is interesting because the human gestures, whether it's work or engaging on the data, is a signal tube, not just algorithmic meta data extraction. >> Absolutely anything you do with data and any tool, even outside of Alation. Alation will capture that and use it to guide future behavior for you and your appears to be better and smarter. >> Fifty million dollars. Where's this all going to lead to wins the next innovation. What do you guys see? The future for rAlation? >> Well, you know, I, uh I was just thinking before the show I used to be an apple kind of in the golden Age when Apple was really innovative. And there was the joke where they released something new and say, Redman, start your photocopier. So in this interview, I'm going to be a little close to the chest about the specifics, but we're releasing. But I will tell you we have a room that we're really excited about to go to a broader and broader audience that impactor customers more fully >> well you feel free to say one more thing? >> Yeah. I think the secret to the future is Aaron. Thanks for coming on. >> Really preachy. Congratulations on the funding. He has got a very innovative formula. Good luck. And we'll be following you guys. Thanks, but come on, keep commerce. Thanks so much. Eric Kalb, co founder and VP of designing Alation. Interesting formula. Great. Successful. Former great innovation. Alation. Check him out. I'm Jennifer here in Palo Alto for cube conversation. Thanks for watching.