>> Commentator: Live from Orlando, Florida It's theCUBE. Covering Microsoft Ignite. Brought to you by Cohesity. >> Hello everyone and welcome back to theCUBE's live coverage of Microsoft Ignite 2019. We are theCUBE, we are here at the Cohesity booth in the middle of the show floor at the Orange County Convention Center. 26,000 people from around the globe here. It's a very exciting show. I'm your host, Rebecca Knight, along with my co-host, Stu Miniman. We are joined by Francesca Lazzeri. She is a Ph.D Machine Learning Scientist and Cloud Advocate at Microsoft. Thank you so much for coming on the show. >> Thank you for having me. I'm very excited to be here. >> Rebecca: Direct from Cambridge, so we're an all Boston table here. >> Exactly. >> I love it. I love it. >> We are in the most technology cluster, I think, in the world probably. >> So two words we're hearing a lot of here at the show, machine learning, deep learning, can you describe, define them for us here, and tell us the difference between machine learning and deep learning. >> Yeah, this is a great question and I have to say a lot of my customers ask me this question very, very often. Because I think right now there are many different terms such as deep learning as you said, machine learning, AI, that have been used more or less in the same way, but they are not really the same thing. So machine learning is portfolio, I would say, of algorithms, and when you say algorithms I mean really statistical models, that you can use to run some data analysis. So you can use these algorithms on your data, and these are going to produce what we call an output. Output are the results. So deep learning is just a type of machine learning, that has a different structure. We call it deep learning because there are many different layers, in a neural network, which is again a type of machine learning algorithm. And it's very interesting because it doesn't look at the linear relation within the different variables, but it looks at different ways to train itself, and learn something. So you have to think just about deep learning as a type of machine learning and then we have AI. AI is just on top of everything, AI is a way of building application on top of machine learning models and they run on top of machine learning algorithms. So it's a way, AI, of consuming intelligent models. >> Yeah, so Francesca, I know we're going to be talking to Jeffrey Stover tomorrow about a topic, responsible AI. Can you talk a little bit about how Microsoft is making sure that unintentional biases or challenges with data, leave the machine learning to do things, or have biases that we wouldn't want to otherwise. >> Yes, I think that Microsoft is actually investing a lot in responsible AI. Because I have to say, as a data scientist, as a machine learning scientist, I think that it's very important to understand what the model is doing and why it's give me analysis of a specific result. So, in my team, we have a tool kit, which is called, interpretability toolkit, and it's really a way to unpack machine learning models, so it's a way of opening machine learning models and understand what are the different relations between the different viables, the different data points, so it's an easy way through different type of this relation, that you can understand why your model is giving you specific results. So that you get that visibility, as a data scientist, but also as a final consumer, final users of these AI application. And I think that visibility is the most important thing to prevent unbias, sorry, bias application, and to make sure that our results are fair, for everybody. So there are some technical tools that we can use for sure. I can tell you, as a data scientist, that bias and unfairness starts with the data. You have to make sure that the data is representative enough of the population that you are targeting with your AI applications. But this sometimes is not possible. That's why it's important to create some services, some toolkits, that are going to allow you, again, as a data scientist, as a user, to understand what the AI application, or the machine learning model is doing. >> So what's the solution? If the problem, if the root of the problem is the data in the first place, how do we fix this? Because this is such an important issue in technology today. >> Yes, and so there are a few ways that you can use... So first of all I want to say that it's not a issue that you can really fix. I would say that, again, as a data scientist, there are a few things that you can do, in order to check that your AI application is doing a good job, in terms of fairness, again. And so these few steps are, as you said, the data. So most of the time, people, or customers, they just use their own data. Something that is very helpful is also looking at external type of data, and also make sure that, again, as I said, the pure data is representative enough of the entire population. So for example, if you are collecting data from a specific category of people, of a specific age, from a specific geography, you have to make sure that you understand that their results are not general results, are results that the machine learning algorithm learn from that target population. And so it's important again, to look at different type of data, different type of data sets, and use, if you can, also external data. And then, of course, this is just the first step. There's a second step, that you can always make sure that you check your model with a business expert, with data expert. So sometimes we have data scientists that work in siloes, they do not really communicate what they're doing. And I think that this is something that you need to change within your company, within your organization, you have to, always to make sure, that data scientists, machine learning scientists are working closely with data experts, business experts, and everybody's talking. Again, to make sure that we understand what we are doing. >> Okay, there were so many things announced at the show this week. In your space, what are some of the highlights of the things that people should be taking away from Microsoft Ignite. >> So I think that as your machine learning platform has been announcing a lot of updates, I love the product because I think it's a very dynamic product. There is, what we now call, the designer, which is a new version of the old Azure Machine Learning Studio. It's a drag and drop tool so it's a tool that is great for people who do not want to, code to match, or who are just getting started with machine learning. And you can really create end-to-end machine learning pipelines with these tools, in just a matter of a few minutes. The nice thing is that you can also deploy your machine learning models and this is going to create an API for you, and this API can be used by you, or by other developers in your company, to just call the model that you deployed. As I mentioned before, this is really the part where AI is arriving, and it's the part where you create application on top of your models. So this is a great announcement and we also created a algorithm cheat sheet, that is a really nice map that you can use to understand, based on your question, based on your data, what's the best machine learning algorithm, what's the best designer module that you can use to be build your end-to-end machine learning solution. So this, I would say, is my highlight. And then of course, in terms of Azure Machine Learning, there are other updates. We have the Azure Machine Learning python SDK, which is more for pro data scientists, who wants to create customized models, so models that they have to build from scratch. And for them it's very easy, because it's a python-based environment, where they can just build their models, train it, test it, deploy it. So when I say it's a very dynamic and flexible tool because it's really a tool on the pla- on the Cloud, that is targeting more business people, data analysts, but also pro data scientists and AI developers, so this is great to see and I'm very, very excited for that. >> So in addition to your work as a Cloud advocate at Microsoft, you are also a mentor to research and post-doc students at the Massachusetts Institute of Technology, MIT, so tell us a little more about that work in terms of what kind of mentorship do you provide and what your impressions are of this young generation, a young generation of scientists that's now coming up. >> Yes. So that's another wonderful question because one of the main goal of my team is actually working with a academic type of audience, and we started this about a year ago. So we are, again, a team of Cloud advocates, developers, data scientists, and we do not want to work only with big enterprises, but we want to work with academic type of institutions. So when I say academics, of course I mean, some of the best universities, like I've been working a lot with MIT in Cambridge, Massachusetts Institute of Technology, Harvard, and also now I've been working with the Columbia University, in New York. And with all of them, I work with both the PhD and post-doc students, and most of the time, what I try to help them with is changing their mindset. Because these are all brilliant students, that need just to understand how they can translate what they have learned doing their years of study, and also their technical skillset, in to the real world. And when I say the real world, I mean more like, building applications. So there is this sort of skill transfer that needs to be done and again, working with these brilliant people, I have to say, something that is easy to do, because sometimes they just need to work on a specific project that I create for them, so I give data to them and then we work together in a sort of lab environment, and we build end-to-end solutions. But from a knowledge perspective, from a, I would say, technical perspective, these are all excellent students, so it's really, I find myself in a position in which I'm mentoring them, I prepare them for their industry, because most of them, they want to become data scientist, machine learning scientist, but I have to say that I also learn a lot from them, because at the end of the day, when we build these solutions, it's really a way to build something, a project, an app together, and then we also see, the beauty of this is also that we also see how other people are using that to build something even better. So it's an amazing experience, and I feel very lucky that I'm in Cambridge, where, as you know, we have the best schools. >> Francesca, you've dug in some really interesting things, I'd love to get just a little bit, if you can share, about how machine learning is helping drive competitiveness and innovation in companies today, and any tips you have for companies, and how they can get involved even more. >> Yeah, absolutely. So I think that everything really start with the business problem because I think that, as we started this conversation, we were mentioning words such as deep learning, machine learning, AI, so it's, a lot of companies, they just want to do this because they think that they're missing something. So my first suggestion for them is really trying to understand what's the business question that they have, if there is a business problem that they can solve, if there is an operation that they can improve, so these are all interesting questions that they can ask themselves their themes. And then as soon as they have this question in mind, the second step is understand that, if they have the data, the right data, that are needed to support this process, that is going to help them with the business question. So after that, you understand that the data, I mean, if you understand, if you have the right data, they are the steppings, of course you have to understand if you have also external data, and if you have enough data, as we were saying, because this is very, very important as a first step, in your machine learning journey. And you know, it's important also, to be able to translate the business question in to a machine learning question. Like, for example, in the supervised learning, which is an area of machine learning, we have what is called the regression. Regression is a great type of model, that is great for, to answer questions such as, how many, how much? So if you are a retailer and you wanted to predict how much, how many sales of a specific product you're going to have in the next two weeks, so for example, the regression model, is going to be a good first find, first step for you to start your machine learning journey. So the translation of the business problem into a machine learning question, so it's a consequence in to a machine learning algorithm, is also very important. And then finally, I would say that you always have to make sure that you are able to deploy this machine learning model so that your environment is ready for the deployment and what we call the operizational part. Because this is really the moment in which we are going to allow the other people, meaning internal stake holders, other things in your company, to consume the machine learning model. That's the moment really in which you are going to add business value to your machine learning solution. So yeah, my suggestion for companies who want to start this journey is really to make sure that they have cleared these steps, because I think that if they have cleared these steps, then their team, their developers, their data scientists, are going to work together to build these end-to-end solutions. >> Francesca Lenzetti, thank you so much for coming on theCUBE, it was a pleasure having you. >> Thank you. Thank you. >> I'm Rebecca Knight, Stu Miniman. Stay tuned for more of theCUBE's live coverage of Microsoft Ignite. (upbeat music)

>> live from Boston, Massachusetts. It's the Cube covering A W s reinforce 2019. Brought to you by Amazon Web service is and its ecosystem partners. >> Well, welcome back. Everyone's Cube Live coverage here in Boston, Massachusetts, for AWS. Reinforce Amazon Web sources. First inaugural conference around security. It's not Osama. It's a branded event. Big time ecosystem developing. We have returning here. Cube Alumni Bill Jeff for VP of strategy and the partnerships that Iron Net Cyber Security Company. Welcome back. Thanks. General Keith Alexander, who was on a week and 1/2 ago. And it was public sector summit. Good to see you. Good >> to see you. Thanks for >> having my back, but I want to get into some of the Iran cyber communities. We had General Qi 1000. He was the original commander of the division. So important discussions that have around that. But don't get your take on the event. You guys, you're building a business. The minute cyber involved in public sector. This is commercial private partnership. Public relations coming together. Yeah. Your models are sharing so bringing public and private together important. >> Now that's exactly right. And it's really great to be here with eight of us were really close partner of AWS is we'll work with them our entire back in today. Runs on AWS really need opportunity. Get into the ecosystem, meet some of the folks that are working that we might work with my partner but to deliver a great product, right? And you're seeing a lot of people move to cloud, right? And so you know some of the big announcement that are happening here today. We're willing. We're looking to partner up with eight of us and be a first time provider for some key new Proactiv elves. AWS is launching in their own platform here today. So that's a really neat thing for us to be partnered up with this thing. Awesome organization. I'm doing some of >> the focus areas around reinforcing your party with Amazon shares for specifics. >> Yes. So I don't know whether they announced this capability where they're doing the announcement yesterday or today. So I forget which one so I'll leave that leave that leave that once pursued peace out. But the main thing is, they're announcing couple of new technology plays way our launch party with them on the civility place. So we're gonna be able to do what we were only wanted to do on Prem. We're gonna be able to do in the cloud with AWS in the cloud formation so that we'll deliver the same kind of guy that would deliver on prime customers inside their own cloud environments and their hybrid environment. So it's a it's a it's a sea change for us. The company, a sea change for a is delivering that new capability to their customers and really be able to defend a cloud network the way you would nonpregnant game changer >> described that value, if you would. >> Well, so you know, one of the key things about about a non pregnant where you could do you could look at all the flows coming past you. You look at all the data, look at in real time and develop behavior. Lana looks over. That's what we're doing our own prime customers today in the cloud with his world who looked a lox, right? And now, with the weight of your capability, we're gonna be able to integrate that and do a lot Maur the way we would in a in a in a normal sort of on Prem environment. So you really did love that. Really? Capability of scale >> Wagon is always killed. The predictive analytics, our visibility and what you could do. And too late. Exactly. Right. You guys solve that with this. What are some of the challenges that you see in cloud security that are different than on premise? Because that's the sea, So conversation we've been hearing. Sure, I know on premise. I didn't do it on premises for awhile. What's the difference between the challenge sets, the challenges and the opportunities they provide? >> Well, the opportunities air really neat, right? Because you've got that even they have a shared responsibility model, which is a little different than you officially have it. When it's on Prem, it's all yours essential. You own that responsibility and it is what it is in the cloud. Its share responsible to cloud provider the data holder. Right? But what's really cool about the cloud is you could deliver some really interesting Is that scale you do patch updates simultaneously, all your all your back end all your clients systems, even if depending how your provisioning cloud service is, you could deliver that update in real time. You have to worry about. I got to go to individual systems and update them, and some are updated. Summer passed. Some aren't right. Your servers are packed simultaneously. You take him down, you're bringing back up and they're ready to go, right? That's a really capability that for a sigh. So you're delivering this thing at scale. It's awesome now, So the challenge is right. It's a new environment so that you haven't dealt with before. A lot of times you feel the hybrid environment governed both an on Prem in sanitation and class sensation. Those have to talkto one another, right? And you might think about Well, how do I secure those those connections right now? And I think about spending money over here when I got all seduced to spend up here in the cloud. And that's gonna be a hard thing precisely to figure out, too. And so there are some challenges, but the great thing is, you got a whole ecosystem. Providers were one of them here in the AWS ecosystem. There are a lot here today, and you've got eight of us as a part of self who wants to make sure that they're super secure, but so are yours. Because if you have a problem in their cloud, that's a challenge. Them to market this other people. You talk about >> your story because your way interviews A couple weeks ago, you made a comment. I'm a recovering lawyer, kind of. You know, we all laughed, but you really start out in law, right? >> How did you end up here? Yeah, well, the truth is, I grew up sort of a technology or myself. My first computer is a trash 80 a trs 80 color computer. RadioShack four k of RAM on board, right. We only >> a true TRS 80. Only when I know what you're saying. That >> it was a beautiful system, right? Way stored with sword programs on cassette tapes. Right? And when we operated from four Keita 16 k way were the talk of the Rainbow Computer Club in Santa Monica, California Game changer. It was a game here for 16. Warning in with 60 give onboard. Ram. I mean, this is this is what you gonna do. And so you know, I went from that and I in >> trouble or something, you got to go to law school like you're right >> I mean, you know, look, I mean, you know it. So my dad, that was a chemist, right? So he loved computers, love science. But he also had an unrequited political boners body. He grew up in East Africa, Tanzania. It was always thought that he might be a minister in government. The Socialist came to power. They they had to leave you at the end of the day. And he came to the states and doing chemistry, which is course studies. But he still loved politics. So he raised at NPR. So when I went to college, I studied political science. But I paid my way through college doing computer support, life sciences department at the last moment. And I ran 10 based. He came on climate through ceilings and pulled network cable do punch down blocks, a little bit of fibrous placing. So, you know, I was still a murderer >> writing software in the scythe. >> One major, major air. And that was when when the web first came out and we had links. Don't you remember? That was a text based browser, right? And I remember looking to see him like this is terrible. Who would use http slash I'm going back to go for gophers. Awesome. Well, turns out I was totally wrong about Mosaic and Netscape. After that, it was It was it was all hands on >> deck. You got a great career. Been involved a lot in the confluence of policy politics and tech, which is actually perfect skill set for the challenge we're dealing. So I gotta ask you, what are some of the most important conversations that should be on the table right now? Because there's been a lot of conversations going on around from this technology. I has been around for many decades. This has been a policy problem. It's been a societal problem. But now this really focus on acute focus on a lot of key things. What are some of the most important things that you think should be on the table for techies? For policymakers, for business people, for lawmakers? >> One. I think we've got to figure out how to get really technology knowledge into the hands of policymakers. Right. You see, you watch the Facebook hearings on Capitol Hill. I mean, it was a joke. It was concerning right? I mean, anybody with a technology background to be concerned about what they saw there, and it's not the lawmakers fault. I mean, you know, we've got to empower them with that. And so we got to take technologist, threw it out, how to get them to talk policy and get them up on the hill and in the administration talking to folks, right? And one of the big outcomes, I think, has to come out of that conversation. What do we do about national level cybersecurity, Right, because we assume today that it's the rule. The private sector provides cyber security for their own companies, but in no other circumstance to expect that when it's a nation state attacker, wait. We don't expect Target or Wal Mart or any other company. J. P. Morgan have surface to air missiles on the roofs of their warehouses or their buildings to Vegas Russian bear bombers. Why, that's the job of the government. But when it comes to cyberspace, we expect Private Cummings defending us everything from a script kiddie in his basement to the criminal hacker in Eastern Europe to the nation state, whether Russia, China, Iran or North Korea and these nation states have virtually a limited resource. Your armies did >> sophisticated RND technology, and it's powerful exactly like a nuclear weaponry kind of impact for digital. >> Exactly. And how can we expect prices comes to defend themselves? It's not. It's not a fair fight. And so the government has to have some role. The questions? What role? How did that consist with our values, our principles, right? And how do we ensure that the Internet remains free and open, while still is sure that the president is not is not hampered in doing its job out there. And I love this top way talk about >> a lot, sometimes the future of warfare. Yeah, and that's really what we're talking about. You go back to Stuxnet, which opened Pandora's box 2016 election hack where you had, you know, the Russians trying to control the mean control, the narrative. As you pointed out, that that one video we did control the belief system you control population without firing a shot. 20 twenties gonna be really interesting. And now you see the U. S. Retaliate to Iran in cyberspace, right? Allegedly. And I was saying that we had a conversation with Robert Gates a couple years ago and I asked him. I said, Should we be Maur taking more of an offensive posture? And he said, Well, we have more to lose than the other guys Glasshouse problem? Yeah, What are your thoughts on? >> Look, certainly we rely intimately, inherently on the cyber infrastructure that that sort of is at the core of our economy at the core of the world economy. Increasingly, today, that being said, because it's so important to us all the more reason why we can't let attacks go Unresponded to write. And so if you're being attacked in cyberspace, you have to respond at some level because if you don't, you'll just keep getting punched. It's like the kid on the playground, right? If the bully keeps punching him and nobody does anything, not not the not the school administration, not the kid himself. Well, then the boy's gonna keep doing what he's doing. And so it's not surprising that were being tested by Iran by North Korea, by Russia by China, and they're getting more more aggressive because when we don't punch back, that's gonna happen. Now we don't have to punch back in cyberspace, right? A common sort of fetish about Cyrus is a >> response to the issue is gonna respond to the bully in this case, your eggs. Exactly. Playground Exactly. We'll talk about the Iran. >> So So if I If I if I can't Yeah, the response could be Hey, we could do this. Let them know you could Yes. And it's a your move >> ate well, And this is the key is that it's not just responding, right. So Bob Gates or told you we can't we talk about what we're doing. And even in the latest series of alleged responses to Iran, the reason we keep saying alleged is the U. S has not publicly acknowledged it, but the word has gotten out. Well, of course, it's not a particularly effective deterrence if you do something, but nobody knows you did it right. You gotta let it out that you did it. And frankly, you gotta own it and say, Hey, look, that guy punch me, I punch it back in the teeth. So you better not come after me, right? We don't do that in part because these cables grew up in the intelligence community at N S. A and the like, and we're very sensitive about that But the truth is, you have to know about your highest and capabilities. You could talk about your abilities. You could say, Here are my red lines. If you cross him, I'm gonna punch you back. If you do that, then by the way, you've gotta punch back. They'll let red lines be crossed and then not respond. And then you're gonna talk about some level of capabilities. It can't all be secret. Can't all be classified. Where >> are we in this debate? Me first. Well, you're referring to the Thursday online attack against the intelligence Iranian intelligence community for the tanker and the drone strike that they got together. Drone take down for an arm in our surveillance drones. >> But where are we >> in this debate of having this conversation where the government should protect and serve its people? And that's the role. Because if a army rolled in fiscal army dropped on the shores of Manhattan, I don't think Citibank would be sending their people out the fight. Right? Right. So, like, this is really happening. >> Where are we >> on this? Like, is it just sitting there on the >> table? What's happening? What's amazing about it? Hi. This was getting it going well, that that's a Q. What's been amazing? It's been happening since 2012 2011 right? We know about the Las Vegas Sands attack right by Iran. We know about North Korea's. We know about all these. They're going on here in the United States against private sector companies, not against the government. And there's largely been no response. Now we've seen Congress get more active. Congress just last year passed to pass legislation that gave Cyber command the authority on the president's surgery defenses orders to take action against Russia, Iran, North Korea and China. If certain cyber has happened, that's a good thing, right to give it. I'll be giving the clear authority right, and it appears the president willing to make some steps in that direction, So that's a positive step. Now, on the back end, though, you talk about what we do to harden ourselves, if that's gonna happen, right, and the government isn't ready today to defend the nation, even though the Constitution is about providing for the common defense, and we know that the part of defense for long. For a long time since Secretary Panetta has said that it is our mission to defend the nation, right? But we know they're not fully doing that. How do they empower private sector defense and one of keys That has got to be Look, if you're the intelligence community or the U. S. Government, you're Clinton. Tremendous sense of Dad about what you're seeing in foreign space about what the enemy is doing, what they're preparing for. You have got to share that in real time at machine speed with industry. And if you're not doing that and you're still count on industry to be the first line defense, well, then you're not empowered. That defense. And if you're on a pair of the defense, how do you spend them to defend themselves against the nation? State threats? That's a real cry. So >> much tighter public private relationship. >> Absolutely, absolutely. And it doesn't have to be the government stand in the front lines of the U. S. Internet is, though, is that you could even determine the boundaries of the U. S. Internet. Right? Nobody wants an essay or something out there doing that, but you do want is if you're gonna put the private sector in the in the line of first defense. We gotta empower that defense if you're not doing that than the government isn't doing its job. And so we gonna talk about this for a long time. I worked on that first piece of information sharing legislation with the House chairman, intelligence Chairman Mike Rogers and Dutch Ruppersberger from Maryland, right congressman from both sides of the aisle, working together to get a fresh your decision done that got done in 2015. But that's just a first step. The government's got to be willing to share classified information, scaled speed. We're still not seeing that. Yeah, How >> do people get involved? I mean, like, I'm not a political person. I'm a moderate in the middle. But >> how do I How do people get involved? How does the technology industry not not the >> policy budgets and the top that goes on the top tech companies, how to tech workers or people who love Tad and our patriots and or want freedom get involved? What's the best approach? >> Well, that's a great question. I think part of is learning how to talk policy. How do we get in front policymakers? Right. And we're I run. I run a think tank on the side at the National Institute at George Mason University's Anton Scalia Law School Way have a program funded by the Hewlett Foundation who were bringing in technologists about 25 of them. Actually. Our next our second event. This Siri's is gonna be in Chicago this weekend. We're trained these technologies, these air data scientists, engineers and, like talk Paul's right. These are people who said We want to be involved. We just don't know how to get involved And so we're training him up. That's a small program. There's a great program called Tech Congress, also funded by the U. A. Foundation that places technologists in policy positions in Congress. That's really cool. There's a lot of work going on, but those are small things, right. We need to do this, its scale. And so you know, what I would say is that their technology out there want to get involved, reach out to us, let us know well with our partners to help you get your information and dad about what's going on. Get your voice heard there. A lot of organizations to that wanna get technologies involved. That's another opportunity to get in. Get in the building is a >> story that we want to help tell on be involved in David. I feel passion about this. Is a date a problem? So there's some real tech goodness in there. Absolutely. People like to solve hard problems, right? I mean, we got a couple days of them. You've got a big heart problems. It's also for all the people out there who are Dev Ops Cloud people who like to work on solving heart problems. >> We got a lot >> of them. Let's do it. So what's going on? Iron? Give us the update Could plug for the company. Keith Alexander found a great guy great guests having on the Cube. That would give the quick thanks >> so much. So, you know, way have done two rounds of funding about 110,000,000. All in so excited. We have partners like Kleiner Perkins Forge point C five all supporting us. And now it's all about We just got a new co CEO in Bill Welshman. See Scaler and duo. So he grew Z scaler. $1,000,000,000 valuation he came in to do Oh, you know, they always had a great great exit. Also, we got him. We got Sean Foster in from from From Industry also. So Bill and Sean came together. We're now making this business move more rapidly. We're moving to the mid market. We're moving to a cloud platform or aggressively and so exciting times and iron it. We're coming toe big and small companies near you. We've got the capability. We're bringing advanced, persistent defense to bear on his heart problems that were threat analytics. I collected defence. That's the key to our operation. We're excited >> to doing it. I call N S A is a service, but that's not politically correct. But this is the Cube, so >> Well, look, if you're not, if you want to defensive scale, right, you want to do that. You know, ECE knows how to do that key down here at the forefront of that when he was in >> the government. Well, you guys are certainly on the cutting edge, riding that wave of common societal change technology impact for good, for defence, for just betterment, not make making a quick buck. Well, you know, look, it's a good business model by the way to be in that business. >> I mean, It's on our business cards. And John Xander means it. Our business. I'd say the Michigan T knows that he really means that, right? Rather private sector. We're looking to help companies to do the right thing and protect the nation, right? You know, I protect themselves >> better. Well, our missions to turn the lights on. Get those voices out there. Thanks for coming on. Sharing the lights. Keep covers here. Day one of two days of coverage. Eight of us reinforce here in Boston. Stay with us for more Day one after this short break.

>> Announcer: Live from Boston, Massachusetts, it's theCUBE covering Red Hat Summit 2019. Brought to you by Red Hat. >> Welcome back to our coverage here on theCUBE of Red Hat Summit 2019. We're live in Boston right now, and I'm joined by a couple of award winning professionals. And we're looking forward to hearing what their story is because it's fascinating on both fronts. And also by DeLisa Alexander who has a great job title at Red Hat. Chief People Officer. I love that title. DeLisa, thanks for joining us. >> Thanks for having us. >> Also with us, Limor Fried who is the and founder and lead engineer of Adafruit and Saloni Garg who is an undgergrad student, third year student, at the LNM Institute of Technology. And that's in Jaipur, India. So Saloni, glad to have you with us. And Limor, a pleasure as well. >> Thank you. >> And you're all lit up. You've got things going on there, right? >> I'm glowing, we're gonna get all into that. >> We'll get into that later. First, let's talk about the award that, they're two women in open-source are our winners this year. On the community side, Limor won, on the academic side, Saloni won, so talk about the awards if you would, DeLisa. The process and really what you're trying to do with recognizing these kinds of achievements. >> Well, this is our fifth year for the Women in Open-Source Award. So after this period of time, I can tell you what we wanna do is make an impact by really fostering more diverse communities, particularly gender diverse in open-source. And so that's the whole goal. Five years into it, what we've discovered is that when you really focus on diversity and inclusion within a community, you actually can make an impact. And the thing that's so exciting this year is that our award winners are really evidence of that. >> So talk about the two categories then if you would please. You have community on one side, academics on the other. It appears to be pretty clear cut what you're hoping to achieve there by recognizing an active contributor, and then somebody who is in the wings and waiting for their moment. But go ahead and fill in a little bit about, >> Yeah, absolutely. >> Limor and Saloni too about, why are they here. >> Limor: Why am I here? >> Yes, well, really what we're trying to do is create role models for women and girls who would like to participate in technology but perhaps are not sure that that's the way that they can go. And they don't see people that are like them, so there's less a tendency to join into this type of community. So with the community award winner, we're looking at the professional who's been contributing to open-source for a period of time. And with our academic winner, we're looking to score more people who are in university to think about it. And, of course, the big idea is you'll all be looking at these women as people that will inspire you to potentially do more things with open-source and more things with technology. We've been hearing for many, many years that we definitely need to have more gender diversity in tech in general and in open-source. And Red Hat is kind of uniquely situated to focus on the open-source community, and so with our role as the open-source leader, we really feel like we need to make that commitment and to be able to foster that. >> Well, it makes perfect sense. Obviously. Great perfect sense. Saloni, if you would, let's talk first about your work. You've been involved in open-source for quite some time. I know you have a lot of really interesting projects that you're working on right now. We'll get to that in a bit, but just talk about, I guess, the attraction for you in terms of open-source and really kind of where that came from originally through your interest in stem education. >> Okay, so when I first came to college, I was really influenced to contribute to open-source by my seniors. They have already selected in programs like Google Summer of Code Outreach channel, so they actually felt empowered by open-source. So they encouraged me to join it too. I tried open-source, and I feel really, like, I'm a part of something bigger than myself. And I was helped greatly by my seniors, so I feel it's my duty to give it back to my juniors and to help them when they need it so that they can do wonders, yeah. >> Great. And Limor, for you, I know you founded the company. 100% female owned. You've got-- >> Yeah, 100% me. >> Yeah, right. 100% you. >> It's my fault. >> Right. Well, I wasn't going to blame you. I'll credit you instead. >> Yeah, that's our big thing. We wanna change. Get blame to get credit. >> Right. It's all about credit. >> More positive. >> So 100 employees? Is that right? >> 100, 150, yep. >> Okay, talk a little bit about kind of the origin, the genesis of the company and where that came from and then your connection on the open-source side. >> Well, I, yeah, so I grew up actually in Boston. So I've lived here a very long time. >> You said like a block from here. Two blocks. >> I used to live, actually, yes, in South Station nearby. I used to live by the Griffin Book line, and so Wilson has a very strong open-source community, you know. Ephesoft is here. And, yeah, that's kind of the origins of a lot of this free software and open-source software community. And when I went to school, I ended up going to MIT, and the open-source software and open-source technology is kind of part of, like, the genetics there. There's actually this thinking that you wouldn't do it. It's kind of by default. People write code, you open-source, you release it. There's a culture of collaboration. Scientists, engineers, students, researchers. All working together and sharing code. And when I was in school, so I had to take Thesis. I really didn't wanna do it, and so instead, I started building, like, MP3 players and video games. Taking all the engineering that I was studying and, like, not doing the work I was supposed to be doing. But instead, I was having fun and building cool electronic parts, and I would publish these projects online. I had, like, a MediaLab webs page, and I would publish, you know, here's all the chips and the schematics and the layout. And people sort of started coming up with the idea of open-source hardware. Let's take the philosophy of open-source software where we release the source code. But, in here, you release CAD files, firmware, layouts, 3D models. And so I did that, and I was publishing here's how you make this, like, Lite-Brite toy for Burning Man or an MP3 player or a cell phone jammer. All these fun projects, and people would end up contacting me and saying, hey, these are really cool projects. I would like to build this project myself, but unlike software where you just, like, type in, like, make, config, and compile and all that. You actually have to buy parts, you have to get these physical things. And so they said, you know, could you sell me a kit, like a box, where we'd get it and take it home and be able to build it. And I was totally like, no, I'm busy. I have to, like, not write this thesis. >> That's not what I do. >> But eventually, I did write the thesis. And then I was really stuck because I'm like, now what do I do? So I ended up selling kits. So I sold the synthesizer kits and such, and I did an art fellowship and stuff. And then, eventually, I was kind of like, this is, I was doing, you know, it's, you kind of fall into business by accident because if you knew what you were getting into, you wouldn't do it in my opinion. So I ended up sort of developing that, and that was 13 years ago. And now we have 4,000 products in the store, you know. >> 4,000 products? >> Yeah, I know. Ridiculous, right? That's a lot. >> Yeah, who's doing that inventory, right? >> Well, we have a pretty intense inventory system that I'd love to talk to you about, but it's kind of boring. >> I'll bet you do. Now, I was reading something about an circuit playground express. >> Yes. >> Is that right? So is that what this is all about is-- >> Yes! I knew you'd ask, and that's why I wore this. >> So it's a, kind of, an exploratory circuit board of-- >> Yeah! It's open-source, open-source hardware, open-source software and firmware. And we had a lot of parents and teachers and educators and camp counselors come to us and say, we wanna teach physical computing. We wanna teach coding but with physical hardware because, you know, we all, all the tier coders, right? No, I don't know. But, eventually, you're like, I'm typing on the screen. And you want to take that and you wanna make it physical. You wanna bring it out into the world where there's a wearable or a cosplay or assistive technology, or you wanna make video games, that are, like, physical video games. And the problem that teachers had were the classrooms, a lot of these classrooms, they don't have a lot of money. So they said it has to be very low-cost. It has to be durable because these kids are, like, chewing on it and stuff, which is fun. And it also has to work on any computer, even extremely old computers. 'Cause a lot of these schools, they only have a budget every seven years to buy laptops. And so this actually becomes a very difficult technological problem. How do you design something that's $20 but can teach physical computing to anybody? From kids who are not even good at typing all the way to college students who wanna implement fast 48 transforms, and so we designed this hardware. It's open-source, and it's cool 'cause people are, like, remixing it and making improvements to it. It's open-source circuit playground, and I'm wearing it. And it's glowing, and I don't know. It's fun! It's got LEDs and sensors. And you can just alligator clip to it and make projects, and we've got schools from around the world learning how to code. And I think it's a much more fun experience than just typing at a computer. >> Absolutely. Yeah, Solani, on your side of the fence, so I obviously, in your education years if you will, not that we ever stop learning, but formally right now. But you're involved, among the many projects that you've been involved with, a smart vehicle. >> Yeah, I'm working on it. >> Project, right? So tell us a little bit about that and how open-source has come into play with what you're looking at in terms of, I assume, traffic and congestion and flows and those kinds of things. >> Yeah. So what we're working on is, basically, we'll be fitting cameras and Raspberry Pis on buses, college buses. And then they'll detect, like, they'll detect lane detection and traffic signal violation and will report the assigned people. If there's any breakage of law or any breakage of traffic signals, so that's what, basically, we are working on and how open-source comes into the play is that we actually knew nothing about OpenCV and all the technology that is before all this. So I looked up some open-source projects that had already the lump sum of all this, and I got to learn a lot about how things actually work on the code-based side. So that's how open-source actually helped me to make this project. >> And, ultimately, who do you report to on that? Or how is that data gonna become actionable or, I assume it can be. >> Yeah. >> At some point, right? I mean, who's your partner in that? Or who is the agency or the body that, you know, can most benefit from that? >> Yeah, so, currently, this is an academy project, and a classmate of mine has been working with me. And we are working on a faculty member. And so, basically, we have decided to expand this project and to use it as a government project. And we, authorities we'll be reporting to whenever there's a signal or law breakage is that the traffic police department will be notifying them in case of any signal breakage. >> So if there's an uptick in speeding or red light running in Jaipur, we know who to blame. >> Yeah. >> Right? >> Shouldn't have run a report. >> It's, Solani, why'd you do that to them, right? All right, ladies, if you would. And I'm gonna end with DeLisa, but I'd like to hear your thoughts about each other. Just about, as you look at the role of women in tech and the diversity that Red Hat is trying to encourage, Limor, what have you seen in Solani here over the last day, day and a half, that maybe you think will leave a lasting impression on you? >> I love Solani's energy and her passion, and I can just, she's has this emanated strength. I can just tell that nothing stops her from achieving what she wants. Like, she wants to, like, do this Raspberry Pi traffic camera. She's just gonna figure out what it takes to solve that problem. She's gonna use open-source software, hardware, whatever it takes. And she's just gonna achieve her goal. I totally sense that from her from the last few days we've been together. >> That's great. >> Thank you. >> Yeah! >> All right. Solani, your turn. For Limor. >> What I have done is just a fraction of what she has been doing. She's, like, inspiration. I look up to her, and I, also, I mean, I hope I start my own company someday. And she's really a role model and an inspiration for me. So yeah. >> Yeah, I think you've got a pretty good mentor there in that respect. And then, DeLisa, when you see young ladies like this who are, you know, their achievements are so impressive in their respects. What does that say to you about Red Hat, the direction of the program, and then the impact on young women that you're having? >> Well, the program has gotten so much more participation. So many people, 8,000 people actually voted to select our winners. And all of our finalists were so impressive. We have major contributors to open-source, and so, along with our finalists, our winners are people who are just role models. And I am just so impressed with them, and I think that every year, we're learning something different from each of the winners. And so, as they round down into a community, the things that they'll be able to mentor people on will just be exponentially increasing. And so it's really exciting. >> Fantastic. Well, thank you all. The three of you, the ladies. Congratulations on your recognition, your accomplishments. Well done. Safe travels back to New York and back to India as well, and I would look forward to hearing more about what you're up to down the road. I think this is not the last we're gonna hear from the two of you. >> Thank you for having us. >> And thank you for calling me a young lady. >> Absolutely. I mean, look at the source. Open-source, you might say. That was awful. All right, back with more Red Hat Summit 2019. We're live here on theCUBE in Boston. (gentle music)

>> 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)

>> Narrator: Live from Boston, Massachusetts, it's theCUBE covering OpenStack Summit 2017. Brought to you by the OpenStack Foundation, Red Hat, and additional ecosystem support. (techno music) >> And we're back. I'm Stu Miniman joined by my co-host, John Troyer. Happy to welcome to the program two of the keynote speakers this morning, worked on some of the container activity, Kendall Nelson, who's a Upstream Developer Advocate with the OpenStack Foundation. >> Yep. >> And John Griffith, who's a Principal Engineer from NetApp, excuse me, through the SolidFire acquisition. Thank you so much both for joining. >> Kendall Nelson: Yeah. Thank you. >> John Griffith: Thanks for havin' us. >> Stu Miniman: So you see-- >> Yeah. >> When we have any slip-ups when we're live, we just run through it. >> Run through it. >> Kendall, you ever heard of something like that happening? >> Kendall Nelson: Yeah. Yeah. That might've happened this morning a little bit. (laughs) >> So, you know, let's start with the keynote this morning. I tell ya, we're pretty impressed with the demos. Sometimes the demo gods don't always live up to expectations. >> Kendall Nelson: Yeah. >> But maybe share with our audience just a little bit about kind of the goals, what you were looking to accomplish. >> Yeah. Sure. So basically what we set out to do was once the ironic nodes were spun up, we wanted to set up a standalone cinder service and use Docker Compose to do that so that we could do an example of creating a volume and then attaching it to a local instance and kind of showing the multiple backend capabilities of Cinder, so... >> Yeah, so the idea was to show how easy it is to deploy Cinder. Right? So and then plug that into that Kubernetes deployment using a flex volume plugin and-- >> Stu Miniman: Yeah. >> Voila. >> It was funny. I saw some comments on Twitter that were like, "Well, maybe we're showing Management that it's not, you know, a wizard that you just click, click, click-- >> John Griffith: Right. >> Kendall Nelson: Yeah. >> "And everything's done." There is some complexity here. You do want to have some people that know what they're doing 'cause things can break. >> Kendall Nelson: Yeah. >> I love that the container stuff was called ironic. The bare metal was ironic because-- >> Kendall Nelson: Yeah. >> Right. When you think OpenStack at first, it was like, "Oh. This is virtualized infrastructure." And therefore when containers first came out, it was like, "Wait. It's shifting. It's going away from virtualization." John, you've been on Cinder. You helped start Cinder. >> Right. >> So maybe you could give us a little bit about historical view as to where that came from and where it's goin'. Yeah. >> Yeah. It's kind of interesting, 'cause it... You're absolutely right. There was a point where, in the beginning, where virtualization was everything. Right? Ironic actually, I think it really started more of a means to an end to figure out a better way to deploy OpenStack. And then what happened was, as people started to realize, "Oh, hey. Wait." You know, "This whole bare metal thing and running these cloud services on bare metal and bare metal clouds, this is a really cool thing. There's a lot of merit here." So then it kind of grew and took on its own thing after that. So it's pretty cool. There's a lot of options, a lot of choices, a lot of different ways to run a cloud now, so... >> Kendall Nelson: Yeah. >> You want to comment on that Kendall, or... >> Oh, no. Just there are definitely tons of ways you can run a cloud and open infrastructure is really interesting and growing. >> That has been one thing that we've noticed here at the show. So my first summit, so it was really interesting to me as an outsider, right, trying to perceive the shape of OpenStack. Right? Here the message has actually been very clear. We're no longer having to have a one winner... You know, one-size-fits-all kind of cloud world. Like we had that fight a couple of years ago. It's clear there's going to be multiple clouds, multiple places, multiple form factors, and it was very nice people... An acknowledgement of the ecosystem, that there's a whole open source ecosystem of containers and of other open source projects that have grown up all around OpenStack, so... But I want to talk a little bit about the... And the fact that containers and Kubernetes and that app layer is actually... Doesn't concern itself with the infrastructure so much so actually is a great fit for sitting on top of or... And adjacent to OpenStack. Can you all talk a little bit about the perception here that you see with the end users and cloud builders that are here at the show and how are they starting to use containers. Do they understand the way these two things fit together? >> Yeah. I think that we had a lot of talks submitted that were focused on containers, and I was just standing outside the room trying to get into a Women of OpenStack event, and the number of people that came pouring out that were interested in the container stack was amazing. And I definitely think people are getting more into that and using it with OpenStack is a growing direction in the community. There are couple new projects that are growing that are containers-focused, like... One just came into the projects, OpenStack Helm. And that's a AT&T effort to use... I think it's Kubernetes with OpenStack. So yeah, tons. >> So yeah, it's interesting. I think the last couple of years there's been a huge uptick in the interest of containers, and not just in containers of course, but actually bringing those together with OpenStack and actually running containers on OpenStack as the infrastructure. 'Cause to your point, what everybody wants to see, basically, is commoditized, automated and generic infrastructure. Right? And OpenStack does a really good job of that. And as people start to kind of realize that OpenStack isn't as hard and scary as it used to be... You know, 'cause for a few years there it was pretty difficult and scary. It's gotten a lot better. So deployment, maintaining, stuff like that, it's not so bad, so it's actually a really good solution to build containers on. >> Well, in fact, I mean, OpenStack has that history, right? So you've been solving a lot of problems. Right now the container world, both on the docker side and Kubernetes as well, you're dealing with storage drivers-- >> John Griffith: Yeah. >> Networking overlays-- >> Right. >> Multi-tenancy security, all those things that previous generations of technology have had to solve. And in fact, I mean, you know, right now, I'd say storage and storage interfaces actually are one of the interesting challenges that docker and Kubernetes and all that level of containers and container orchestration and spacing... I mean, it seems like... Has OpenStack already solved, in some way, it's already solved some of these problems with things like Cinder? >> Abso... Yeah. >> John Troyer: And possibly is there an application to containers directly? >> Absolutely. I mean, I think the thing about all of this... And there's a number of us from the OpenStack community on the Cinder side as well as the networking side, too-- >> Yeah. >> Because that's another one of those problem spaces. That are actually taking active roles and participating in the Kubernetes communities and the docker communities to try and kind of help with solving the problems over on that side, right? And moving forward. The fact is is storage is, it's kind of boring, but it's hard. Everybody thinks-- >> John Troyer: It's not boring. >> Yeah. >> It's really awesomely hard. Yeah. >> Everybody thinks it's, "Oh, I'll just do my own." It's actually a hard thing to get right, and you learn a lot over the last seven years of OpenStack. >> Yeah. >> We've learned a lot in production, and I think there's a lot to be learned from what we've done and how things could be going forward with other projects and new technologies to kind of learn from those lessons and make 'em better, so... >> Yeah. >> In terms of multicloud, hybrid cloud world that we're seeing, right? What do you see as the role of OpenStack in that kind of a multicloud deployments now? >> OpenStack can be used in a lot of different ways. It can be on top of containers or in containers. You can orchestrate containers with OpenStack. That's like the... Depending on the use case, you can plug and play a lot of different parts of it. On all the projects, we're trying to move to standalone sort of services, so that you can use them more easily with other technologies. >> Well, and part of your demo this morning, you were pulling out of a containerized repo somehow. So is that kind of a path forward for the mainline OpenStack core? >> So personally, I think it would be a pretty cool way to go forward, right? It would make things a lot easier, a lot simpler. And kind of to your point about hybrid cloud, the thing that's interesting is people have been talking about hybrid cloud for a long time. What's most interesting these days though is containers and things like Kubernetes and stuff, they're actually making hybrid cloud something that's really feasible and possible, right? Because now, if I'm running on a cloud provider, whether it's OpenStack, Amazon, Google, DigitalOcean, it doesn't matter anymore, right? Because all of that stuff in my app is encapsulated in the container. So hybrid cloud might actually become a reality, right? The one thing that's missing still (John Troyer laughs) is data, right? (Kendall Nelson laughs) Data gravity and that whole thing. So if we can figure that out, we've actually got somethin', I think. >> Interesting comment. You know, hybrid cloud a reality. I mean, we know the public cloud here, it's real. >> Yeah. >> With the Kubernetes piece, doesn't that kind of pull together some... Really enable some of that hybrid strategy for OpenStack, which I felt like two or three years ago it was like, "No, no, no. Don't do public cloud. >> John Griffith: Yeah. >> "It's expensive and (laughter) hard or something. "And yeah, infrastructure's easy and free, right?" (laughter) Wait, no. I think I missed that somewhere. (laughter) But yeah, it feels like you're right at the space that enables some of those hybrid and multicloud capabilities. >> Well, and the thing that's interesting is if you look at things like Swarm and Kubernetes and stuff like that, right? One of the first things that they all build are cloud providers, whether OpenStack, AWS, they're all in there, right? So for Swarm, it's pretty awesome. I did a demo about a year ago of using Amazon and using OpenStack, right? And running the exact same workloads the exact same way with the exact same tools, all from Docker machine and Swarm. It was fantastic, and now you can do that with Kubernetes. I mean, now that's just... There's nothing impressive. It's just normal, right? (Kendall Nelson laughs) That's what you do. (laughs) >> I love the demos this morning because they actually were, they were CLI. They were command-line driven, right? >> Kendall Nelson: Yeah. >> I felt at some conferences, you see kind of wizards and GUIs and things like that, but here they-- >> Yeah. >> They blew up the terminal and you were typing. It looked like you were actually typing. >> Kendall Nelson: Oh, yeah. (laughter) >> John Griffith: She was. >> And I actually like the other demo that went on this morning too, where they... The interop demo, right? >> Mm-hmm. >> John Troyer: They spun up 15 different OpenStack clouds-- >> Yeah. >> From different providers on the fly, right there, and then hooked up a CockroachDB, a huge cluster with all of them, right? >> Kendall Nelson: Yeah. >> Can you maybe talk... I just described it, but can you maybe talk a little bit about... That seemed actually super cool and surprising that that would happen that... You could script all that that it could real-time on stage. >> Yeah. I don't know if you, like, noticed, but after our little flub-up (laughs) some of the people during the interop challenge, they would raise their hand like, "Oh, yeah. I'm ready." And then there were some people that didn't raise their hands. Like, I'm sure things went wrong (John Troyer laughs) and with other people, too. So it was kind of interesting to see that it's really happening. There are people succeeding and not quite gettin' there and it definitely is all on the fly, for sure. >> Well, we talked yesterday to CTO Red Hat, and he was talking same thing. No, it's simpler, but you're still making a complicated distributed computing system. >> Kendall Nelson: Oh, definitely. >> Right? There are a lot of... This is not a... There are a lot of moving parts here. >> Kendall Nelson: Yeah. >> Yeah. >> Well, it's funny, 'cause I've been around for a while, right? So I remember what it was like to actually build these things on your own. (laughs) Right? And this is way better, (laughter) so-- >> So it gets your seal of approval? We have reached a point of-- >> Yeah. >> Of usability and maintainability? >> Yeah, and it's just going to keep gettin' better, right? You know, like the interop challenge, the thing that's awesome there is, so they use Ansible, and they talk to 20 different clouds and-- >> Kendall Nelson: Yeah. >> And it works. I mean, it's awesome. It's great. >> Kendall Nelson: Yeah. >> So I guess I'm hearing containers didn't kill OpenStack, as a matter of fact, it might enable the next generation-- >> Kendall Nelson: Yeah. >> Of what's going on, so-- >> John Griffith: Yeah. >> How about serverless? When do we get to see that in here? I actually was lookin' real quick. There's a Functions as a Service session that somebody's doing, but any commentary as to where that fits into OpenStack? >> Go ahead. (laughs) >> So I'm kind of mixed on the serverless stuff, especially in a... In a public cloud, I get it, 'cause then I just call it somebody else's server, right? >> Stu Miniman: Yeah. >> In a private context, it's something that I haven't really quite wrapped my head around yet. I think it's going to happen. I mean, there's no doubt about it. >> Kendall Nelson: Yeah. >> I just don't know exactly what that looks like for me. I'm more interested right now in figuring out how to do awesome storage in things like Kubernetes and stuff like that, and then once we get past that, then I'll start thinking about serverless. >> Yeah. >> Yeah. >> 'Cause where I guess I see is... At like an IoT edge use case where I'm leveraging a container architecture that's serverless driven, that's where-- >> Yeah. >> It kind of fits, and sometimes that seems to be an extension of the public cloud, rather than... To the edge of the public cloud rather than the data center driven-- >> John Griffith: Yeah. >> But yeah. >> Well, that's kind of interesting, actually, because in that context, I do have some experience with some folks that are deploying that model now, and what they're doing is they're doing a mini OpenStack deployment on the edge-- >> Stu Miniman: Yep. >> And using Cinder and Instance and everything else, and then pushing, and as soon as they push that out to the public, they destroy what they had, and they start over, right? And so it's really... It's actually really interesting. And the economics, depending on the scale and everything else, you start adding it up, it's phenomenal, so... >> Well, you two are both plugged into the user community, the hands-on community. What's the mood of the community this year? Like I said, my first year, everybody seems engaged. I've just run in randomly to people that are spinning up their first clouds right now in 2017. So it seems like there's a lot of people here for the first time excited to get started. What do you think the mood of the user community is like? >> I think it's pretty good. I actually... So at the beginning of the week, I helped to run the OpenStack Upstream Institute, which is teaching people how to contribute to the Upstream Community. And there were a fair amount of users there. There are normally a lot of operators and then just a set of devs, and it seemed like there were a lot more operators and users looking that weren't originally interested in contributing Upstream that are now looking into those things. And at our... We had a presence at DockerCon, actually. We had a booth there, and there were a ton of users that were coming and talking to us, and like, "How can I use OpenStack with containers?" So it's, like, getting more interest with every day and growing rapidly, so... >> That's great. >> Yeah. >> All right. Well, want to thank both of you for joining us. I think this went flawless on the interview. (laughter) And yeah, thanks so much. >> Yeah. >> All these things happen... Live is forgiving, as we say on theCUBE and absolutely going forward. So thanks so much for joining us. >> John Griffith: Thank you. John and I will be back with more coverage here from the OpenStack Summit in Boston. You're watching theCUBE. (funky techno music)

>> Narrator: Live from Boston, Massachusetts, this is theCUBE covering Spark Summit East 2017. Brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Everybody the euphoria is still palpable here, we're in downtown Boston at the Hynes Convention Center. For Spark Summit East, #SparkSummit, my co-host and I, George Gilbert, will be unpacking what's going on for the next two days. George, it's good to be working with you again. >> Likewise. >> I always like working with my man, George Gilbert. We go deep, George goes deeper. Fantastic action going on here in Boston, actually quite a good crowd here, it was packed this morning in the keynotes. The rave is streaming. Everybody's talking about streaming. Let's sort of go back a little bit though George. When Spark first came onto the scene, you saw these projects coming out of Berkeley, it was the hope of bringing real-timeness to big data, dealing with some of the memory constraints that we found going from batch to real-time interactive and now streaming, you're going to talk about that a lot. Then you had IBM come in and put a lot of dough behind Spark, basically giving it a stamp, IBM's imprimatur-- >> George: Yeah. >> Much in the same way it did with Lynx-- >> George: Yeah. >> Kind of elbowing it's way in-- >> George: Yeah. >> The marketplace and sort of gaining a foothold. Many people at the time thought that Hadoop needed Spark more than Spark needed Hadoop. A lot of people thought that Spark was going to replace Hadoop. Where are we today? What's the state of big data? >> Okay so to set some context, when Hadoop V1, classic Hadoop came out it was file system, commodity file system, keep everything really cheap, don't have to worry about shared storage, which is very expensive and the processing model, the execution of munging through data was map produced. We're all familiar with those-- >> Dave: Complicated but dirt cheap. >> Yes. >> Dave: Relative to a traditional data warehouse. >> Yes. >> Don't buy a big Oracle Unix box or Lynx box, buy this new file system and figure out how to make it work and you'll save a ton of money. >> Yeah, but unlike the traditional RDBMS', it wasn't really that great for doing interactive business intelligence and things like that. It was really good for big batch jobs that would run overnight or periods of hours, things like that. The irony is when Matei Zaharia, the co-creator of Spark or actually the creator and co-founder of Databricks, which is steward of Spark. When he created the language and the execution environment, his objective was to do a better MapReduce than Radue, than MapReduce, make it faster, take advantage of memory, but he did such a good job of it, that he was able to extend it to be a uniform engine not just for MapReduce type batch stuff, but for streaming stuff. >> Dave: So originally they start out thinking that if I get this right-- >> Yeah. >> It was sort of a microbatch leveraging memory more effectively and then it extended beyond-- >> The microbatch is their current way to address the streaming stuff. >> Dave: Okay. >> It takes MapReduce, which would be big long running jobs, and they can slice them up and so each little slice turns into an element in the stream. >> Dave: Okay, so the point it was improvement upon these big long batch jobs-- >> George: Yeah. >> They're making it batch to interactive in real-time, so let's go back to big data for a moment here. >> George: Yeah. >> Big data was the hottest topic in the world three or four years ago and now it's sort of waned as a buzz word, but big data is now becoming more mainstream. We've talked about that a lot. A lot of people think it's done. Is big data done? >> George: Not it's more that it's sort of-- it's boring for us, kind of pundits, to talk about because it's becoming part of the fabric. The use cases are what's interesting. It started out as a way to collect all data into this really cheap storage repository and then once you did that, this was the data you couldn't afford to put into your terra data, data warehouse at 25,000 per terabyte or with running costs a multiple of that. Here you put all your data in here, your data scientists and data engineers started munging with the data, you started taking workloads off your data warehouse, like ETL things that didn't belong there. Now people are beginning to experiment with business intelligence sort of exploration and reporting on Hadoop, so taking more workloads off the data warehouse. The limitations, there are limitations there that will get solved by putting MPP SQL back-ends on it, but the next step after that. So we're working on that step, but the one that comes after that is make it easier for data scientists to use this data, to create predictive models-- [Dave] Okay, so I often joke that the ROI on big data was reduction on investment and lowering the denominator-- >> George: Yeah. >> In the expense equation, which I think it's fair to say that big data and Hadoop succeeded in achieving that, but then the question becomes, what's the real business impact. Clearly big data has not, except in some edge cases and there are a number of edge cases and examples, but it's not yet anyway lived up to the promise of real-time, affecting outcomes before, you know taking the human out of the decision, bringing transaction and analytics together. Now we're hearing a lot of that talk around AI and machine learning, of course, IoT is the next big thing, that's where streaming fits in. Is it same line new bottle? Or is it sort of the evolution of the data meme? >> George: It's an evolution, but it's not just a technology evolution to make it work. When we've been talking about big data as efficiency, like low cost, cost reduction for the existing type of infrastructure, but when it starts going into machine learning you're doing applications that are more strategic and more top line focused. That means your c-level execs actually have to get involved because they have to talk about the strategic objectives, like growth versus profitability or which markets you want to target first. >> So has Spark been a headwind or tailwind to Hadoop? >> I think it's very much been a tailwind because it simplified a lot of things that took many, many engines in Hadoop. That's something that Matei, creator of Spark, has been talking about for awhile. >> Dave: Okay something I learned today and actually I had heard this before, but the way I phrased it in my tweet, Genomiocs is kicking Moore's Law's ass. >> George: Yeah. >> That the price performance of sequencing a gene improves three x every year to what is essentially a doubling every 18 months for Moore's Law. The amount of data that's being created is just enormous, I think we heard from Broad Institute that they create 17 terabytes a day-- >> George: Yeah. >> As compared to YouTube, which is 24 terabytes a day. >> And then a few years it will be-- >> It will be dwarfing YouTube >> Yeah. >> Of course Twitter you couldn't even see-- >> Yeah. >> So what do you make of that? Is that just the fun fact, is that a new use case, is that really where this whole market is headed? >> It's not a fun fact because we've been hearing for years and years about this study about data doubling every 18 to 24 months, that's coming from the legacy storage guys who can only double their capacity every 18 to 24 months. The reality is that when we take what was analog data and we make it digitally accessible, the only thing that's preventing us from capturing all this data is the cost to acquire and manage it. The available data is growing much, much faster than 40% every 18 months. >> Dave: So what you're saying is that-- I mean this industry has marched to the cadence of Moore's Law for decades and what you're saying is that linear curve is actually reshaping and it's becoming exponential. >> George: For data-- >> Yes. >> George: So the pressure is on for compute, which is now the bottleneck to get clever and clever about how to process it-- >> So that says innovation has to come from elsewhere, not just Moore's Law. It's got to come from a combination of-- Thomas Friedman talks a lot about Moore's Law being one of the fundamentals, but there are others. >> George: Right. >> So from a data perspective, what are those combinatorial effects that are going to drive innovation forward? >> George: There was a big meetup for Spark last night and the focus was this new database called SnappyData that spun out of Pivotal and it's being mentored by Paul Maritz, ex-head of Development in Microsoft in the 90s and former head of VMWare. The interesting thing about this database, and we'll start seeing it in others, is you don't necessarily want to be able to query and analyze petabytes at once, it will take too long, sort of like munging through data of that size on Hadoop took too long. You can do things that approximate the answer and get it much faster. We're going to see more tricks like that. >> Dave: It's interesting you mention Maritz, I heard a lot of messaging this morning that talked about essentially real-time analysis and being able to make decisions on data that you've never seen before and actually affect outcomes. This narrative I first heard from Maritz many, many years ago when they launched Pivotal. He launched Pivotal to be this platform for building big data apps and now you're seeing Databricks and others sort of usurp that messaging and actually seeming to be at the center of that trend. What's going on there? >> I think there's two, what would you call it, two centers of gravity and our CTO David Floyer talks about this. The edge is becoming more intelligent because there's a huge bandwidth and latency gap between these smart devices at the edge, whether the smart device is like a car or a drone or just a bunch of sensors on a turbine. Those things need to analyze and respond in near real-time or hard real-time, like how to tune themselves, things like that, but they also have to send a lot of data back to the cloud to learn about how these things evolve. In other words it would be like sending the data to the cloud to figure out how the weather patterns are changing. >> Dave: Um,humm. >> That's the analogy. You need them both. >> Dave: Okay. >> So Spark right now is really good in the cloud, but they're doing work so that they can take a lighter weight version and put at the edge. We've also seen Amazon put some stuff at the edge and Azure as well. >> Dave: I want you to comment. We're going to talk about this later, we have a-- George and I are going to do a two-part series at this event. We're going to talk about the state of the market and then we're going to release our big data, in a glimpse to our big data numbers, our Spark forecast, our streaming forecast-- I say I mention streaming because that is-- we talk about batch, we talk about interactive/real-time, you know you're at a terminal-- anybody who's as old as I am remembers that. But now you're talking about streaming. Streaming is a new workload type, you call these things continuous apps, like streams of events coming into a call center, for example, >> George: Yeah. >> As one example that you used. Add some color to that. Talk about that new workload type and the roll of streaming, and really potentially how it fits into IoT. >> Okay, so for the last 60 years, since the birth of digital computing, we've had either one of two workloads, they were either batch, which is jobs that ran offline, you put your punch cards in and sometime later the answer comes out. Or we've had interactive, which is originally it was green screens and now we have PCs and mobile devices. The third one coming up now is continuous or streaming data that you act on in near real-time. It's not that those apps will replace the previous ones, it's that you'll have apps that have continuous processing, batch processing, interactive as a mix. An example would be today all the information about how your applications and data center infrastructure are operating, that's a lot of streams of data that Splunk first, took amat and did very well with-- so that you're looking in real-time and able to figure out if something goes wrong. That type of stuff, all the coulometry from your data center, that is a training wheel for Internet things, where you've got lots of stuff out at the edge. >> Dave: It's interesting you mention Splunk, Splunk doesn't actually use the big data term in its marketing, but they actually are big data and they are streaming. They're actually not talking about it, they're just doing it, but anyway-- Alright George, great thanks for that overview. We're going to break now, bring back our first guest, Arun Murthy, coming in from Hortonworks, co-founder at Hortonworks, so keep it right there everybody. This is theCUBE we're live from Spark Summit East, #SparkSummit, we'll be right back. (upbeat music)