(upbeat music) >> Hey, everyone. Welcome to theCUBE's coverage of Women in Tech: International Women's Day, 2022. I'm your host, Lisa Martin. Erin Chu joins me next. Life Sciences Lead at AWS Open Data. Erin, welcome to the program. >> Thanks so much for having me, Lisa. Tell me a little bit about you and your role at AWS. >> I would love to. So I am a life sciences lead on the AWS Open Data team, and we are really in the business of democratizing access to data. We believe that if you make high quality, high impact data openly available in the cloud, that people can start innovate, make discoveries and do science faster with those data. So we have a number of specialists with expertise in different domains. Geospatial sciences, climate sustainability, statistical regulatory and then of course myself, the life sciences lead. >> So, you have a really interesting background. You're a veterinarian by training. You have a PhD, you've worked in mobile veterinary clinics, and also in an animal genomic startup, how did you make the change from the clinical side to working for a large international, one of the biggest companies in the world? >> Yeah, I love that question because so much of, I think, anybody's career path is serendipitous and circumstantial, right? But the fact is I was working in a mobile veterinary clinics while I was finishing up a PhD in molecular genomics. And at the same time was reached out to by a professor at Cornell who had started a little dog genomic startup. And he said, "Hey, we need a veterinarian who can talk to people and who understands the genomic side of things?" And I said, "Yeah, I'm your girl." And I came on full time with that startup towards the end of my PhD, signed on after I finished, came on on as their senior veterinary geneticist. Startups a great whirlwind. You end up learning a ton. You have a huge, deep learning curve. You're wearing every possible hat you can. And after a couple years there, I wondered what else I could do. And simply said, where else could I look for work? And how else could I grow? And I decided to try the larger tech world, because I said, this is a toolkit I don't have yet. So I'd like to try and see how I can do it, and here I am. >> And you, I was reading about you that you felt empowered by the notion that I have to trust my instincts. You look at careers in biology, you decided what directions you wanted to take but how did you kind of conjure that feeling of empowerment? >> Yeah, I have to see say I have an incredibly supportive team and in supportive manager, but a lot of it was simply because I've never been afraid to fail. The worst thing that someone can ever say to you is, no or that you didn't do that well. Once you come across that once in your life, it doesn't hurt so bad the second time around. And so, I was hired for a very specific data set that my team was helping to manage. And that does take up a good deal of my time, it still does, but I also had the freedom to say, "Hey, what are the trends in biology? I am an expert in this field. What do I know is coming around the corner? What do I know my researchers need?" And I was entrusted with that, this ability to say, "Hey, these are the decisions I think we should make." And I got to see those outcomes fairly quickly. So, my managers have always put a good deal of trust in me and I don't think I've let them down. >> I'm sure you haven't. Tell me a little bit about some of your mentors or sponsors that have helped guide you along the way and really kind of feel that empowerment that you already had. >> Absolutely. Well, the first and foremost mentor in has been my mother. So, in the spirit of International Women's Day, my mom is actually the first Asian engineer to ever reach executive level. Asian female engineer to ever reach executive level at IBM. And so, I spent my life seeing what my mother could do, and watching her just succeed. And I think very early it clear, she said, "What can't you do?" And that was kind of how I approached my entire life, is what can't I do, and what's the worst thing that will happen. You fail and then you try again. So she is absolutely my first mentor, and a role model to me and hopefully to women everywhere, honestly. I've had some amazing teachers and mentors. My professor who oversaw my PhD, Dr. Paul Soloway. He's currently still at Cornell, really just said, "What decisions do you want to make?" And, "I will support you in the best way I can." And we learned a lot together. I have a professor at Cornell who I still come back. I speak at her alternate careers in veterinary medicine because she just... And she was the one who told me, "Erin, you have a really high buoyancy factor. Don't lose that." And her name is Dr. Carolyn McDaniel. And she has just been such a positive force just saying, "What else could we do?" >> Well, that's- >> And, "Never let your degrees or your training say that this is what you have to do. Think of it as a starting point." >> That's a great point. We often, especially when we're little kids, many of us, you think of these very defined, doctor, lawyer, accountants, nurse instead of having something like you do and being able to go, what else can I do with this? How can I take this education, this information and the interest that I have and parlay it into something that really can kick the door wide open. And to your point, I love how your mom was saying, "What can't you do?" That's a message that everyone needs to hear. And there's an AWS Open Data Sponsorship Program. Talk to me a little bit about that. I'm always interested in sponsorship programs. >> Oh, thanks for asking. So the Open Data Sponsorship Program or the ODP since Open Data Sponsorship Program can be a little mouthful after you say it a few times, but the ODP is a program that AWS sponsors where we will actually cover at the cost of storage transfer and egress of high impact data sets in the cloud. Basically, we know that sometimes the barrier to getting into cloud can be very high for certain providers of gold standard data sets. And when I mean gold standard data sets, I mean like NASA Sentinel-2, or the National Institutes of Health Sequence Read Archive. These are invaluable data sets that are ingested by thousands if not millions of users every day. And what we want to do is lower that barrier to cloud and efficient distribution of those data to zero. So, the program is actually open to anybody. It can be a government entity, it can be a startup, it can be nonprofit. We want to understand more about your data and help you distribute it well in the cloud. >> So this is for any type of organization regardless of industry? >> That's right. >> So, you're really allowing more organizations... One of the things that we say often when we're talking on theCUBE is that every company these days is a data company, or it has to be. Every company has to be a tech company, whether we're talking about your grocery store or AWS, for example. So helping organizations to be able to take that data, understand it, and have those personal conversations that as consumers we expect is critical, but it's challenging for organizations that say, "Well, I came up in retail and now I've got to be a tech company." Talk to me about kind of empowering organizations to be able to use that data, to grow the organization, grow the business, but also to delight customers 'cause of course we are quite picky. >> You're so right. Data is power and it doesn't matter what you are selling or who you are serving. If you have the data about your product. And also to some degree, the data about who your consumers are, you can really tailor an experience. I always tell my colleagues that data is impersonal, right? You can look at bits and bites, numbers, structured columns and rows, but you can funnel data into a truly personal experience as long as you do you it right. And hopefully, when I work with my data providers I ask them, how do you want people to use your data? What are the caveats? How can we make these data easy to work with? But also easy to draw correct insights from. >> Right, that easy to use is critical because as you know the proliferation of data just continues and it will continue. If we think of experiences. I want to go back to your experience. What's been the biggest learning curve that you've had so far? >> Oh my gosh. So, the best part of being at a large company is that you're not in the same room or even like whatever the same slack channel as all of your colleagues, right? Coming from a startup or clinical space where quite literally you are in the same room as everybody 'cause there are less than 60 of you, you could just talk to the person who might be an internal stakeholder. You had that personal relationship, and frankly, like most of the time your views were very aligned. It was sell the product, get to MVP. Moving into larger tech, the steepest curve I had other than becoming very comfortable in the cloud, in all the services that AWS has to offer, were to manage those internal relationships. You have to understand who the stakeholders are. There typically many, many of them for any given project or a company that we're serving. And you have to make sure that you're all aligned internally, make sure that everyone gets what they need and that we reach that end to ultimately serve the customer together. >> Yeah, that communication and collaboration is key. And that's something that we've seen over the last two years, is how dependent we've all become on collaboration tools. But it is a different type of relationship. You're right. Going from a clinic where you're all in the same room or the same location to everyone being distributed globally. Relationship management there is key. It's one of my favorite things about being in tech is that, I think it's such a great community. It's a small community, and I think there's so there's so much opportunity there. If you're a good person, you manage those relationships and you learn how to work with different types of people. You'll always be successful. Talk to me about what you would say, if someone's saying, "Erin, I need some advice. I want to change industries or I want to take this background that I have, and use it in a different industry." What are the three pieces of advice that you would share? >> Oh, absolutely. So, the first thing that I always talk with my... I have quite a few colleagues who have approached me from all different parts of my life. And they've said, "Erin, how did you make the change? And how can I make a change?" And the first thing I say is let's look at your resume and define what your translational skills are. That is so big, right? It doesn't matter what you think you're a specialist in, it's how generalizable are those specialty skills and how can you show that to somebody who's looking at your resume. Let's call it a nontraditional resume. And the second is don't hesitate to ask question. Go for the informational interview. People want to tell you about how they've gotten to where they are and how you might be able to get there too. And so I say, get on LinkedIn and start asking questions. If one person says yes, and you get no responses I call that a success. Don't be afraid of not getting a response, that's okay. And the last thing, and I think this is the most important thing is to hold onto the things that make you happy no matter where you are in your life. It's important to realize you are more than your job. It is important to remember what makes you happy and try to hang on those. I am a gym rat. I admit that I am a gym rat. I'm in the gym five days a week. I have a horse. I go out to see him at least two or three a days. I know it's typical veterinarian, right? You just collect niches until you run out of things you want to pay for. But those are things that have been constant through 20 plus years of being in the workforce. And they've been what kept me going. Let's revise that in ten years. >> So critical because as we all know tech can be all consuming. It will take everything if you let it. So being able to have... We always talk about the balance. Well, the balance is hard. It's definitely a way to scale, right? It's going back and forth, but being able to hold onto the things that actually make you who you are, I think make you better at your job, probably more productive and happier. >> I agree. I totally agree. >> Another thing that you believe, which I love, this is an important message is that, if you look at a job, I like how you said earlier, the worst they can say is no. You have nothing to lose. And it's really true. As scary as that is same thing with raising your hand as you say, and I agree with you about that. Ask a question. It's not a dumb question. I guarantee you. If you're in a room or you're on a Zoom or even in a slack channel. A fair number of people probably have the same question. Be the one to raise your hand and say, "Maybe I missed this. Can you clarify this?" But you also think that you don't have to meet all the job requirements. If you see something that says, five years experience in this or 10 years in that or must have this degree or that degree, you're saying you don't have to meet all that criteria. >> I agree. Yeah, that's another big thing is that, I'll literally talk to people who are like, "Well, Erin, this job application, look at all these requirements and I can't fill these requirements." I'm like, "First of all, who says you can't?" Just because you don't have a certification, what has your work thus far done to reflect that? Yeah, you can meet that requirement, even if you don't have an official certification. But two, like what's the worst thing that happens. You don't get a call back from a recruiter. That's okay. I have so many friends who are afraid of failure, and I tell them, just fail once doesn't hurt. It never hurts as much as you think it's going to hurt. And then you just keep going. >> You keep going and you learn. But you've also brought up a great point about those transfer growth skills or those soft skills that are so important. Communication skills, for example. Relationship building skills that may not be in that written job description. So you may not think about actually there's a tremendous amount of importance that these skills have. That having this kind of breadth of background. I think is always so interesting we think about thought diversity, and if we're talking about women in tech. We know that the number of women in technical roles is is still pretty low, but there's so much data that shows that companies that have even 30% females on their executive staff are more performant and more profitable. So that thought diversity is important, but we need more women to be able to feel that empowerment I think that you feel. >> Yes. >> So when you think of International Women's Day with the theme of breaking the bias, what does that mean to you and where do you feel we are in terms of breaking the bias? >> Yeah, so it's interesting, I was just on a working group with some of my colleagues from our larger organization at AWS. And we were talking about, what are different kinds of bias and what our strategies to go ahead and combat them. The fact is we are all making progress and it has to be in one step at a time. I don't think that if we snapped our fingers, things would just go away. You have to take one step at a time. I also come at it from a data perspective, right? I'm a data person. I work with data. And like I said, data is, or data are, if you want to be correct. Data are impersonal, right? They are just statistics, their numbers, but you can use data to suddenly say, "Hey, where are the biases? And how can we fix them?" So I'm going to give you a great example. So my mother, again, a wonderful woman, a super amazing role model to me. She was diagnosed with breast cancer last year. And she being a smart lady, actually looked online. She went online on Google Scholar and PubMed Central. And she said, "May, look..." May is my little nickname. She goes, "Look at these numbers." She said, "My prognosis is terrible. Look at these numbers, how can you say that this is worth it. That chemotherapy is worth it." And I looked at it and I said, "Mom, I hate to break this to you. But this is a retrospective study of several thousand women from the Bavarian cancer registry." And you might guess I am not a Bavarian origin. I had a chat with her and I said, "Mom, let's look at the data. What are the data? And how can you take away stuff from this with the caveat that you may very well not have the same genetic background as some of the women or most of the women in this registry." There are biases. We know when we look at population sequencing, when we look at the people who are sequenced, the people who put in medical survey information. There are not representations of certain ethnicities of certain sexes, of certain parts of the country. One of the things I really want to do in the next three years is say, how can we support people who are trying to increase representation and research so that every single woman gets the right care and can feel like they are themselves represented in what we call precision medicine or personalized care. >> Absolutely. >> That's a long story. >> It was a great story. >> That was a long answer to answer your question. >> You talked about how your mom was a great inspiration to you and it sounds like you've been quite a great inspiration to her as well. Was a delight talking with you, Erin. Congratulations on your success on being able to be one of those people that is helping to break the bias. We appreciate your time. >> Thanks, Lisa. >> My pleasure. For Erin Chu, I'm Lisa Martin. You're watching Women in Tech: International Women's Day, 2022. (upbeat music)

>> Voiceover: From theCUBE's Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Welcome back to theCUBE's coverage of the AWS Startup Showcase. Next Big Thing in AI, Security and Life Sciences featuring Ahana for the AI Trek. I'm your host, John Furrier. Today, we're joined by two great guests, Steven Mih, Ahana CEO, and Sachin Nayyar, Securonix CEO. Gentlemen, thanks for coming on theCUBE. We're talking about the Next-Gen technologies on AI, Open Data Lakes, et cetera. Thanks for coming on. >> Thanks for having us, John. >> Thanks, John. >> What a great line up here. >> Sachin: Thanks, Steven. >> Great, great stuff. Sachin, let's get in and talk about your company, Securonix. What do you guys do? Take us through, I know you've got a slide to help us through this, I want to introduce your stuff first then jump in with Steven. >> Absolutely. Thanks again, Steven. Ahana team for having us on the show. So Securonix, we started the company in 2010. We are the leader in security analytics and response capability for the cybermarket. So basically, this is a category of solutions called SIEM, Security Incident and Event Management. We are the quadrant leaders in Gartner, we now have about 500 customers today and have been plugging away since 2010. Started the company just really focused on analytics using machine learning and an advanced analytics to really find the needle in the haystack, then moved from there to needle in the needle stack using more algorithms, analysis of analysis. And then kind of, I evolved the company to run on cloud and become sort of the biggest security data lake on cloud and provide all the analytics to help companies with their insider threat, cyber threat, cloud solutions, application threats, emerging internally and externally, and then response and have a great partnership with Ahana as well as with AWS. So looking forward to this session, thank you. >> Awesome. I can't wait to hear the news on that Next-Gen SIEM leadership. Steven, Ahana, talk about what's going on with you guys, give us the update, a lot of stuff happening. >> Yeah. Great to be here and thanks for that such, and we appreciate the partnership as well with both Securonix and AWS. Ahana is the open source company based on PrestoDB, which is a project that came out of Facebook and is widely used, one of the fastest growing projects in data analytics today. And we make a managed service for Presto easily on AWS, all cloud native. And we'll be talking about that more during the show. Really excited to be here. We believe in open source. We believe in all the challenges of having data in the cloud and making it easy to use. So thanks for having us again. >> And looking forward to digging into that managed service and why that's been so successful. Looking forward to that. Let's get into the Securonix Next-Gen SIEM leadership first. Let's share the journey towards what you guys are doing here. As the Open Data Lakes on AWS has been a hot topic, the success of data in the cloud, no doubt is on everyone's mind especially with the edge coming. It's just, I mean, just incredible growth. Take us through Sachin, what do you guys got going on? >> Absolutely. Thanks, John. We are hearing about cyber threats every day. No question about it. So in the past, what was happening is companies, what we have done as enterprise is put all of our eggs in the basket of solutions that were evaluating the network data. With cloud, obviously there is no more network data. Now we have moved into focusing on EDR, right thing to do on endpoint detection. But with that, we also need security analytics across on-premise and cloud. And your other solutions like your OT, IOT, your mobile, bringing it all together into a security data lake and then running purpose built analytics on top of that, and then having a response so we can prevent some of these things from happening or detect them in real time versus innovating for hours or weeks and months, which is is obviously too late. So with some of the recent events happening around colonial and others, we all know cybersecurity is on top of everybody's mind. First and foremost, I also want to. >> Steven: (indistinct) slide one and that's all based off on top of the data lake, right? >> Sachin: Yes, absolutely. Absolutely. So before we go into on Securonix, I also want to congratulate everything going on with the new cyber initiatives with our government and just really excited to see some of the things that the government is also doing in this space to bring, to have stronger regulation and bring together the government and the private sector. From a Securonix perspective, today, we have one third of the fortune 500 companies using our technology. In addition, there are hundreds of small and medium sized companies that rely on Securonix for their cyber protection. So what we do is, again, we are running the solution on cloud, and that is very important. It is not just important for hosting, but in the space of cybersecurity, you need to have a solution, which is not, so where we can update the threat models and we can use the intelligence or the Intel that we gather from our customers, partners, and industry experts and roll it out to our customers within seconds and minutes, because the game is real time in cybersecurity. And that you can only do in cloud where you have the complete telemetry and access to these environments. When we go on-premise traditionally, what you will see is customers are even thinking about pushing the threat models through their standard Dev test life cycle management, and which is just completely defeating the purpose. So in any event, Securonix on the cloud brings together all the data, then runs purpose-built analytics on it. Helps you find very few, we are today pulling in several million events per second from our customers, and we provide just a very small handful of events and reduce the false positives so that people can focus on them. Their security command center can focus on that and then configure response actions on top of that. So we can take action for known issues and have intelligence in all the layers. So that's kind of what the Securonix is focused on. >> Steven, he just brought up, probably the most important story in technology right now. That's ransomware more than, first of all, cybersecurity in general, but ransomware, he mentioned some of the government efforts. Some are saying that the ransomware marketplace is bigger than some governments, nation state governments. There's a business model behind it. It's highly active. It's dominating the scene and it's a real threat. This is the new world we're living in, cloud creates the refactoring capabilities. We're hearing that story here with Securonix. How does Presto and Securonix work together? Because I'm connecting the dots here in real time. I think you're going to go there. So take us through because this is like the most important topic happening. >> Yeah. So as Sachin said, there's all this data that needs to go into the cloud and it's all moving to the cloud. And there's a massive amounts of data and hundreds of terabytes, petabytes of data that's moving into the data lakes and that's the S3-based data lakes, which are the easiest, cheapest, commodified place to put all this data. But in order to deliver the results that Sachin's company is driving, which is intelligence on when there's a ransomware or possibility, you need to have analytics on them. And so Presto is the open source project that is a open source SQL query engine for data lakes and other data sources. It was created by Facebook as part of the Linux foundation, something called Presto foundation. And it was built to replace the complicated Hadoop stack in order to then drive analytics at very lightning fast queries on large, large sets of data. And so Presto fits in with this Open Data Lake analytics movement, which has made Presto one of the fastest growing projects out there. >> What is an Open Data Lake? Real quick for the audience who wants to learn on what it means. Does is it means it's open source in the Linux foundation or open meaning it's open to multiple applications? What does that even mean? >> Yeah. Open Data Lake analytics means that you're, first of all, your data lake has open formats. So it is made up of say something called the ORC or Parquet. And these are formats that any engine can be used against. That's really great, instead of having locked in data types. Data lakes can have all different types of data. It can have unstructured, semi-structured data. It's not just the structured data, which is typically in your data warehouses. There's a lot more data going into the Open Data Lake. And then you can, based on what workload you're looking to get benefit from, the insights come from that, and actually slide two covers this pictorially. If you look on the left here on slide two, the Open Data Lake is where all the data is pulling. And Presto is the layer in between that and the insights which are driven by the visualization, reporting, dashboarding, BI tools or applications like in Securonix case. And so analytics are now being driven by every company for not just industries of security, but it's also for every industry out there, retail, e-commerce, you name it. There's a healthcare, financials, all are looking at driving more analytics for their SaaSified applications as well as for their own internal analysts, data scientists, and folks that are trying to be more data-driven. >> All right. Let's talk about the relationship now with where Presto fits in with Securonix because I get the open data layer. I see value in that. I get also what we're talking about the cloud and being faster with the datasets. So how does, Sachin' Securonix and Ahana fit in together? >> Yeah. Great question. So I'll tell you, we have two customers. I'll give you an example. We have two fortune 10 customers. One has moved most of their operations to the cloud and another customer which is in the process, early stage. The data, the amount of data that we are getting from the customer who's moved fully to the cloud is 20 times, 20 times more than the customer who's in the early stages of moving to the cloud. That is because the ability to add this level of telemetry in the cloud, in this case, it happens to be AWS, Office 365, Salesforce and several other rescalers across several other cloud technologies. But the level of logging that we are able to get the telemetry is unbelievable. So what it does is it allows us to analyze more, protect the customers better, protect them in real time, but there is a cost and scale factor to that. So like I said, when you are trying to pull in billions of events per day from a customer billions of events per day, what the customers are looking for is all of that data goes in, all of data gets enriched so that it makes sense to a normal analyst and all of that data is available for search, sometimes 90 days, sometimes 12 months. And then all of that data is available to be brought back into a searchable format for up to seven years. So think about the amount of data we are dealing with here and we have to provide a solution for this problem at a price that is affordable to the customer and that a medium-sized company as well as a large organization can afford. So after a lot of our analysis on this and again, Securonix is focused on cyber, bringing in the data, analyzing it, so after a lot of our analysis, we zeroed in on S3 as the core bucket where this data needs to be stored because the price point, the reliability, and all the other functions available on top of that. And with that, with S3, we've created a great partnership with AWS as well as with Snowflake that is providing this, from a data lake perspective, a bigger data lake, enterprise data lake perspective. So now for us to be able to provide customers the ability to search that data. So data comes in, we are enriching it. We are putting it in S3 in real time. Now, this is where Presto comes in. In our research, Presto came out as the best search engine to sit on top of S3. The engine is supported by companies like Facebook and Uber, and it is open source. So open source, like you asked the question. So for companies like us, we cannot depend on a very small technology company to offer mission critical capabilities because what if that company gets acquired, et cetera. In the case of open source, we are able to adopt it. We know there is a community behind it and it will be kind of available for us to use and we will be able to contribute in it for the longterm. Number two, from an open source perspective, we have a strong belief that customers own their own data. Traditionally, like Steven used the word locked in, it's a key term, customers have been locked in into proprietary formats in the past and those days are over. You should be, you own the data and you should be able to use it with us and with other systems of choice. So now you get into a data search engine like Presto, which scales independently of the storage. And then when we start looking at Presto, we came across Ahana. So for every open source system, you definitely need a sort of a for-profit company that invests in the community and then that takes the community forward. Because without a company like this, the community will die. So we are very excited about the partnership with Presto and Ahana. And Ahana provides us the ability to take Presto and cloudify it, or make the cloud operations work plus be our conduit to the Ahana community. Help us speed up certain items on the roadmap, help our team contribute to the community as well. And then you have to take a solution like Presto, you have to put it in the cloud, you have to make it scale, you have to put it on Kubernetes. Standard thing that you need to do in today's world to offer it as sort of a micro service into our architecture. So in all of those areas, that's where our partnership is with Ahana and Presto and S3 and we think, this is the search solution for the future. And with something like this, very soon, we will be able to offer our customers 12 months of data, searchable at extremely fast speeds at very reasonable price points and you will own your own data. So it has very significant business benefits for our customers with the technology partnership that we have set up here. So very excited about this. >> Sachin, it's very inspiring, a couple things there. One, decentralize on your own data, having a democratized, that piece is killer. Open source, great point. >> Absolutely. >> Company goes out of business, you don't want to lose the source code or get acquired or whatever. That's a key enabler. And then three, a fast managed service that has a commercial backing behind it. So, a great, and by the way, Snowflake wasn't around a couple of years ago. So like, so this is what we're talking about. This is the cloud scale. Steven, take us home with this point because this is what innovation looks like. Could you share why it's working? What's some of the things that people could walk away with and learn from as the new architecture for the new NextGen cloud is here, so this is a big part of and share how this works? >> That's right. As you heard from Sachin, every company is becoming data-driven and analytics are central to their business. There's more data and it needs to be analyzed at lower cost without the locked in and people want that flexibility. And so a slide three talks about what Ahana cloud for Presto does. It's the best Presto out of the box. It gives you very easy to use for your operations team. So it can be one or two people just managing this and they can get up to speed very quickly in 30 minutes, be up and running. And that jump starts their movement into an Open Data Lake analytics architecture. That architecture is going to be, it is the one that is at Facebook, Uber, Twitter, other large web scale, internet scale companies. And with the amount of data that's occurring, that's now becoming the standard architecture for everyone else in the future. And so just to wrap, we're really excited about making that easy, giving an open source solution because the open source data stack based off of data lake analytics is really happening. >> I got to ask you, you've seen many waves on the industry. Certainly, you've been through the big data waves, Steven. Sachin, you're on the cutting edge and just the cutting edge billions of signals from one client alone is pretty amazing scale and refactoring that value proposition is super important. What's different from 10 years ago when the Hadoop, you mentioned Hadoop earlier, which is RIP, obviously the cloud killed it. We all know that. Everyone kind of knows that. But like, what's different now? I mean, skeptics might say, I don't believe you, but it's just crazy. There's no way it works. S3 costs way too much. Why is this now so much more of an attractive proposition? What do you say the naysayers out there? With Steve, we'll start with you and then Sachin, I want you to like weigh in too. >> Yeah. Well, if you think about the Hadoop era and if you look at slide three, it was a very complicated system that was done mainly on-prem. And you'd have to go and set up a big data team and a rack and stack a bunch of servers and then try to put all this stuff together and candidly, the results and the outcomes of that were very hard to get unless you had the best possible teams and invested a lot of money in this. What you saw in this slide was that, that right hand side which shows the stack. Now you have a separate compute, which is based off of Intel based instances in the cloud. We run the best in that and they're part of the Presto foundation. And that's now data lakes. Now the distributed compute engines are the ones that have become very much easier. So the big difference in what I see is no longer called big data. It's just called data analytics because it's now become commodified as being easy and the bar is much, much lower, so everyone can get the benefit of this across industries, across organizations. I mean, that's good for the world, reduces the security threats, the ransomware, in the case of Securonix and Sachin here. But every company can benefit from this. >> Sachin, this is really as an example in my mind and you can comment too on if you'd believe or not, but replatform with the cloud, that's a no brainer. People do that. They did it. But the value is refactoring in the cloud. It's thinking differently with the assets you have and making sure you're using the right pieces. I mean, there's no brainer, you know it's good. If it costs more money to stand up something than to like get value out of something that's operating at scale, much easier equation. What's your thoughts on this? Go back 10 years and where we are now, what's different? I mean, replatforming, refactoring, all kinds of happening. What's your take on all this? >> Agreed, John. So we have been in business now for about 10 to 11 years. And when we started my hair was all black. Okay. >> John: You're so silly. >> Okay. So this, everything has happened here is the transition from Hadoop to cloud. Okay. This is what the result has been. So people can see it for themselves. So when we started off with deep partnerships with the Hadoop providers and again, Hadoop is the foundation, which has now become EMR and everything else that AWS and other companies have picked up. But when you start with some basic premise, first, the racking and stacking of hardware, companies having to project their entire data volume upfront, bringing the servers and have 50, 100, 500 servers sitting in their data centers. And then when there are spikes in data, or like I said, as you move to the cloud, your data volume will increase between five to 20x and projecting for that. And then think about the agility that it will take you three to six months to bring in new servers and then bring them into the architecture. So big issue. Number two big issue is that the backend of that was built for HDFS. So Hadoop in my mind was built to ingest large amounts of data in batches and then perform some spark jobs on it, some analytics. But we are talking in security about real time, high velocity, high variety data, which has to be available in real time. It wasn't built for that, to be honest. So what was happening is, again, even if you look at the Hadoop companies today as they have kind of figured, kind of define their next generation, they have moved from HDFS to now kind of a cloud based platform capability and have discarded the traditional HDFS architecture because it just wasn't scaling, wasn't searching fast enough, wasn't searching fast enough for hundreds of analysts at the same time. And then obviously, the servers, et cetera wasn't working. Then when we worked with the Hadoop companies, they were always two to three versions behind for the individual services that they had brought together. And again, when you're talking about this kind of a volume, you need to be on the cutting edge always of the technologies underneath that. So even while we were working with them, we had to support our own versions of Kafka, Solr, Zookeeper, et cetera to really bring it together and provide our customers this capability. So now when we have moved to the cloud with solutions like EMR behind us, AWS has invested in in solutions like EMR to make them scalable, to have scale and then scale out, which traditional Hadoop did not provide because they missed the cloud wave. And then on top of that, again, rather than throwing data in that traditional older HDFS format, we are now taking the same format, the parquet format that it supports, putting it in S3 and now making it available and using all the capabilities like you said, the refactoring of that is critical. That rather than on-prem having servers and redundancies with S3, we get built in redundancy. We get built in life cycle management, high degree of confidence data reliability. And then we get all this innovation from companies like, from groups like Presto, companies like Ahana sitting on double that S3. And the last item I would say is in the cloud we are now able to offer multiple, have multiple resilient options on our side. So for example, with us, we still have some premium searching going on with solutions like Solr and Elasticsearch, then you have Presto and Ahana providing majority of our searching, but we still have Athena as a backup in case something goes down in the architecture. Our queries will spin back up to Athena, AWS service on Presto and customers will still get served. So all of these options, but what it doesn't cost us anything, Athena, if we don't use it, but all of these options are not available on-prem. So in my mind, I mean, it's a whole new world we are living in. It is a world where now we have made it possible for companies to even enterprises to even think about having true security data lakes, which are useful and having real-time analytics. From my perspective, I don't even sign up today for a large enterprise that wants to build a data lake on-prem because I know that is not, that is going to be a very difficult project to make it successful. So we've come a long way and there are several details around this that we've kind of endured through the process, but very excited where we are today. >> Well, we certainly follow up with theCUBE on all your your endeavors. Quickly on Ahana, why them, why their solution? In your words, what would be the advice you'd give me if I'm like, okay, I'm looking at this, why do I want to use it, and what's your experience? >> Right. So the standard SQL query engine for data lake analytics, more and more people have more data, want to have something that's based on open source, based on open formats, gives you that flexibility, pay as you go. You only pay for what you use. And so it proved to be the best option for Securonix to create a self-service system that has all the speed and performance and scalability that they need, which is based off of the innovation from the large companies like Facebook, Uber, Twitter. They've all invested heavily. We contribute to the open source project. It's a vibrant community. We encourage people to join the community and even Securonix, we'll be having engineers that are contributing to the project as well. I think, is that right Sachin? Maybe you could share a little bit about your thoughts on being part of the community. >> Yeah. So also why we chose Ahana, like John said. The first reason is you see Steven is always smiling. Okay. >> That's for sure. >> That is very important. I mean, jokes apart, you need a great partner. You need a great partner. You need a partner with a great attitude because this is not a sprint, this is a marathon. So the Ahana founders, Steven, the whole team, they're world-class, they're world-class. The depth that the CTO has, his experience, the depth that Dipti has, who's running the cloud solution. These guys are world-class. They are very involved in the community. We evaluated them from a community perspective. They are very involved. They have the depth of really commercializing an open source solution without making it too commercial. The right balance, where the founding companies like Facebook and Uber, and hopefully Securonix in the future as we contribute more and more will have our say and they act like the right stewards in this journey and then contribute as well. So and then they have chosen the right niche rather than taking portions of the product and making it proprietary. They have put in the effort towards the cloud infrastructure of making that product available easily on the cloud. So I think it's sort of a no-brainer from our side. Once we chose Presto, Ahana was the no-brainer and just the partnership so far has been very exciting and I'm looking forward to great things together. >> Likewise Sachin, thanks so much for that. And we've only found your team, you're world-class as well, and working together and we look forward to working in the community also in the Presto foundation. So thanks for that. >> Guys, great partnership. Great insight and really, this is a great example of cloud scale, cloud value proposition as it unlocks new benefits. Open source, managed services, refactoring the opportunities to create more value. Stephen, Sachin, thank you so much for sharing your story here on open data lakes. Can open always wins in my mind. This is theCUBE we're always open and we're showcasing all the hot startups coming out of the AWS ecosystem for the AWS Startup Showcase. I'm John Furrier, your host. Thanks for watching. (bright music)

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Hi everyone. Welcome back to the cubes. Live coverage cube live program for re:Invent 2020. This is our Q virtual. We're not in person like we normally are. Today is the AWS public sector. Worldwide celebration day. A lot of content coming from Teresa Carlson and her team and highlighting everything. Of course, the cube channel on the re:Invent events site. Well, the content we streaming there, if you go to the description, you can click on the link and check out all the on-demand interviews. We've done hundreds of videos live before the event pre recorded as well as here live today for public sector day, I'm showing Lisa Martin co-hosts of the cube. Who's been involved in a lot of those interviews. Uh, Lisa, great to see you before we good to see you. Thanks for coming on. >>Likewise. Good to see you too, John. Glad that you're staying safe. >>Well, a lot of good action. And before we get started, I do want to put a plug out for, um, some Salesforce, big party virtual event. Uh, Salesforce is having a big party at re:Invent 2020 a virtual house party with chance the rapper performing an exclusive set with surprise celebrities and DJ in residence December 10th that's tomorrow at 5:00 PM Pacific, go to salesforce.com/big party to check out chance the rapper. Uh, I'm a big fan. Of course my kids are more fans than, uh, check out the sales report. Okay. Back to cube virtual Lisa. Great to see you. >>Likewise John. So public sector day, a lot of transformation mean re:Invent being reinvented, being virtual 500,000 registered. And so, so much has changed, but a lot also that Teresa Carlson spoke about in her keynote and this morning about the transformation across the public sector, that's really been driven by necessity with COVID. It was really impressive to hear and see all of the good things that AWS is facilitating across healthcare, government, education, state, and local. You name it. >>Yeah. The thing I love about Theresa is she's always been ever since I've known her now she's been on the cube every year, since 2013, since we've been covering re:Invent, she's always had a big, bold vision, and she's always kind of stayed on that track. And this year that was really clear out of the box on her, her leadership session. You got to think big and you got to look at the value of the data. That was the key message from her, her and her group public sector, by the way, has been highly active with the COVID pandemic. A lot of public services have been leveraging Amazon cloud to serve, uh, their, their, their people, whether it's getting them the checks for entitlements or getting them, you know, pharmacy drugs and whatnot, and helping them with the pandemic. But clearly Amazon has stepped up and helped education with, with, uh, remotes. So Theresa's team has been pretty busy. So I think that they had more time to prepare for the virtual keynote. I should've gotten chock full of more announcements. >>Yeah. And also some great examples. As you mentioned, we heard from UK biobank, some of the interviews also that have already happened on the kid that you've done showed some amazing work that AWS has helped to facilitate for school districts in Los Angeles, for example, the government of Rhode Island. And those are some of the great things cabbage, what they were able to enable Kevin's to do, to deliver small business loans of so quickly. A lot of that, I thought, I wish we're hearing more about how technology is facilitating so much. Goodness, in COVID on the news. Of course, we're hearing a lot of the challenges with online learning, but there's a lot of amazing things that AWS has been able to facilitate incredibly quickly. >>You know, one of the concerns I have with Theresa and her team years and years ago was this idea of national parks, right? You know, we have spaces where we can go visit and why isn't there a cyber version of that. And so you S you saw that progression and she'd been doing a lot of deals where they're using the cloud and donating their technology for the betterment of society. And one of the things that was, um, news today was an advancement of their open data registry, which has been kind of this open commons of, you know, health data and whatnot. And now they have all the sequencing data that's searchable, readable, uh, from the national Institute of health for DNA sequencing. So this is going to be, again, more commons, like approach is starting to see that I think this is going to be a real big trend lease. >>I think you're going to start to see the big companies have to really contribute to society in a way that we've never seen before, because they have the large scale. You can donate large compute to say research projects. So you starting to see, uh, from Teresa's team, the bubbling up of these new shared experiences around technology for the betterment of society. I think that sequencing was one, the renewable energy project. Another one, again, they're investing in women owned businesses and underrepresented minorities, and at small, medium size businesses to fund them, we saw a guy launching stuff in space that can create, you know, synthetic satellites. So you can look through clouds. This is new. I mean, this is interesting. >>It is interesting. And it actually, to your point is impactful at every level across the globe, going from when they talked about we farm creating this network of small scale of farmers, connectivity was their biggest problem. And now there's over a million. I'm sure that number it's probably even bigger. I've connected farmers due to AWS. You talked about also it's the cord 19 search, which is the expansion of their open research dataset. COVID open research data set that is only possible because of cloud computing and AWS hundreds of thousands of assets in there. Um, 200 plus open data sets for genomic research. She talked about how that's been at the of some of the things that we've seen go on so quickly with operation work speed, uh, with respect to the vaccine. So a lot of acceleration when we know public sector kind of traditionally not necessarily fast movers, but of course, as we've all said, a number of times recently necessity is the mother of invention and the speed element and the connectivity element were things that really spoke loudly to me with what Teresa said today, about the importance of extracting value from data. >>You know, when I talked to Andy Jassy and he talked about this in his keynote, the digital transformation is on full display. And the necessity being the mother of invention is a great phrase, the system and sticking because you can't hide. I mean, you have to deliver these services in the public sector, or, you know, people's lives are going to be impacted in certainly this there's death involved, right? So you have that and then you've got education. I mean, people want to see that changed quicker. There's always been conscious, Oh, education has got to be re-imagined well, guess what? There's no school open. So we got to re-imagine it now. So you get a lot of pressure, unprecedented demand. She said, Theresa said, three's a crosswind actually set onstage for education change. Um, so that's huge. Right? And then the other thing that she mentioned, I think that's going to be a big focus. >>It's not as, um, you know, headline news oriented is this whole jobs training piece. Um, that's a huge deal because the, the tsunami that hits so fast on this digital transformation, because the COVID, we're going to have a post COVID era of rapid acceleration of new skills. So people gotta get trained. So this ain't going to be the boring training programs, the guy who get kind of get better. So I think you're going to see some innovation Lisa, around how people think about delivering and constructing training programs to be much more real world thinking outside the box, you're going to start to see new things. Otherwise it's just going to be too slowly, the training right now. It's just, you know, sign up for the courseware and get a certification. Yeah, you got to do those things, but how can you get sort of cases done faster? How do you get people with the skills in their hands and virtual hands, if you will, to stand up more cloud, more AI, the pressure's there. So we can, that's going to be a huge thing to watch. >>Okay. The pressure is there. You're right. And a need is there. She talked about a lot of the demand that their customers are driving for some of the services and the education services as well that they're offering. But I'd like to point about upskilling focusing on the people, not just the people, but also the diversity inclusion. And we all know how impactful thought diversity is. So their, their dedication, their in their focus there, and also her recommendation to be bold. And I think in the education, respect was really critical. There is no time like now to move digital transformation. If education systems aren't there, then you know, it's a huge challenge and it impacts every person, every element of every family. So what they're able to do there, by focusing on the people and enabling folks to get trained faster, more resources online can only be a good, you know, Theresa >>Has always, um, has her own flare to style to her. She's incredible business woman and have such respect for her. She's been so successful. Um, but she always sends her presentations with the, kind of the, the kind of her to dues. Um, and you kind of pointed that out. So just review them with you. And I want to get your reaction. Number one, she said, you got to re-imagine and enable a digital, a digitally enabled business. Number two, identify data has an realized value and then increase your diversity. And she pointed to avis.training. Um, and that's kind of her kind of get out there and do those things so digitally enabled business, get that unrealized day to get it into work and increase your diversity. And then she had had a big party every year just said, instead of a party go out and do a random act of kindness act. So, yeah, typical, three's a flare, you know, she kind of ended it with a random act of kindness, but, but her bold vision, those are practical, uh, mandates. What's your reaction to, to that? >>I bold vision. I absolutely 100% I think right now is the time that no business can afford to be hiding under the covers. We have to be, they have to be very thoughtful and very prescriptive, but be bold. There's so much opportunity right now. We're seeing a ton of invention and innovation, John, that we've seen over the last nine months. There's a lot of COVID catalysts that we've been talking about on the cube that are really fantastic. So I think that recommendation to set a bold vision is absolutely imperative, not easy to achieve, but I think right now more than ever, it could really be what sets apart, the winners and losers of tomorrow. >>Yeah. I love it. I just say that on this final note, um, cloud and AI is really in play cloud-scale machine learning, which essentially feeds AI is all about data compute going down to the chip level, AI and software and data is critical for cloud. So really awesome keynote again, leadership session by Teresa Carlson, and there's a whole site of content available. Checkout the cube page, click down on the main page. You'll see that description. You'll see a link to the re:Invent page and check on public sector. A lot of great content. Lisa final question for us to kind of close out this keynote leadership session analysis here on all sector day. I want to get your take on, um, the interviews you've done with the Amazon folks and partners and customers. What are the themes that have been boiling out of those? What have you have been hearing? What's your take and observation of the common pattern? >>You know, given the fact that we haven't all been able to be together at my last cube event in person was reinvent 2019. And we're so used to having, you know, three, four days of wall-to-wall coverage, two sides, being able to have those close personal conversations with our guests this year really did a phenomenal job of recreating that same experience, digitally there's tremendous amount of innovation happening. I think that was the one thing that really jumped out at me, the speed with which it's happening, how so many different types businesses have pivoted, not once, but again and again, and again, as times are changing and how even I yesterday I interviewed Boone, supersonic CEO, some of the things that they're facilitating to get commercial supersonic flight back that fully cloud and AI machine learning can do that. There was no stoppage of innovation this year. In fact, that actually got faster. And I think that was a resounding theme and a lot of positivity from the guests. >>You know, the cue, his business was to go to events and extract the signal from the noise. Guess what? There's no physical events. We have the cube virtual. We have pivoted. We are now in our eighth, ninth month of cube virtual. It's been a new model. We've gotten more interviews, more people can just click into the cube virtual. We have more virtual sets, the Cuban virtualized Lisa. Although I miss them in real life as a whole new ballgame for us, >>It is a whole new ball game. And it also provides a lot of opportunities for businesses to get their messaging out and connect and engage with their audience, which is important. >>Well, I miss real life. I miss everybody out there. I wish we could be there in person. Uh, the world will stay hybrid. I think with virtual, I think this has been a great format. There's been some great benefits, but we want to be in person. I want you on the desk with us. So, and all the folks out there I wish we could see. And then we'll see you next year. Thanks everyone for watching the key. This is our keynote analysis and leadership analysis of the worldwide public sector. Teresa Carlson, Kenya. I'm John from Lisa Martin. Thanks for watching.

>> Announcer: From around the globe, it's theCUBE with coverage of KubeCon + CloudNativeCon Europe 2020 Virtual brought to you by Red Hat, the Cloud Native Computing Foundation and Ecosystem partners. >> Welcome back I'm Stu Miniman, this is theCUBE's coverage of KubeCon + CloudNativeCon Europe 2020, the virtual event. Of course, when we talk about Cloud Native we talk about Kubernetes there's a lot that's happening to modernize the infrastructure but a very important thing that we're going to talk about today is also what's happening up the stack, what sits on top of it and some of the new use cases and applications that are enabled by all of this modern environment and for that we're going to talk about artificial intelligence and machine learning or AI and ML as we tend to talk in the industry, so happy to welcome to the program. We have two first time guests joining us from Red Hat. First of all, we have Abhinav Joshi and Tushar Katarki they are both senior managers, part of the OpenShift group. Abhinav is in the product marketing and Tushar is in product management. Abhinav and Tushar thank you so much for joining us. >> Thanks a lot, Stu, we're glad to be here. >> Thanks Stu and glad to be here at KubeCon. >> All right, so Abhinav I mentioned in the intro here, modernization of the infrastructure is awesome but really it's an enabler. We know... I'm an infrastructure person the whole reason we have infrastructure is to be able to drive those applications, interact with my data and the like and of course, AI and ML are exciting a lot going on there but can also be challenging. So, Abhinav if I could start with you bring us inside your customers that you're talking to, what are the challenges, the opportunities? What are they seeing in this space? Maybe what's been holding them back from really unlocking the value that is expected? >> Yup, that's a very good question to kick off the conversation. So what we are seeing as an organization they typically face a lot of challenges when they're trying to build an AI/ML environment, right? And the first one is like a talent shortage. There is a limited amount of the AI, ML expertise in the market and especially the data scientists that are responsible for building out the machine learning and the deep learning models. So yeah, it's hard to find them and to be able to retain them and also other talents like a data engineer or app DevOps folks as well and the lack of talent can actually stall the project. And the second key challenge that we see is the lack of the readily usable data. So the businesses collect a lot of data but they must find the right data and make it ready for the data scientists to be able to build out, to be able to test and train the machine learning models. If you don't have the right kind of data to the predictions that your model is going to do in the real world is only going to be so good. So that becomes a challenge as well, to be able to find and be able to wrangle the right kind of data. And the third key challenge that we see is the lack of the rapid availability of the compute infrastructure, the data and machine learning, and the app dev tools for the various personas like a data scientist or data engineer, the software developers and so on that can also slow down the project, right? Because if all your teams are waiting on the infrastructure and the tooling of their choice to be provisioned on a recurring basis and they don't get it in a timely manner, it can stall the projects. And then the next one is the lack of collaboration. So you have all these kinds of teams that are involved in the AI project, and they have to collaborate with each other because the work one of the team does has a dependency on a different team like say for example, the data scientists are responsible for building the machine learning models and then what they have to do is they have to work with the app dev teams to make sure the models get integrated as part of the app dev processes and ultimately rolled out into the production. So if all these teams are operating in say silos and there is lack of collaboration between the teams, so this can stall the projects as well. And finally, what we see is the data scientists they typically start the machine learning modeling on their individual PCs or laptops and they don't focus on the operational aspects of the solution. So what this means is when the IT teams have to roll all this out into a production kind of deployment, so they get challenged to take all the work that has been done by the individuals and then be able to make sense out of it, be able to make sure that it can be seamlessly brought up in a production environment in a consistent way, be it on-premises, be it in the cloud or be it say at the edge. So these are some of the key challenges that we see that the organizations are facing, as they say try to take the AI projects from pilot to production. >> Well, some of those things seem like repetition of what we've had in the past. Obviously silos have been the bane of IT moving forward and of course, for many years we've been talking about that gap between developers and what's happening in the operation side. So Tushar, help us connect the dots, containers, Kubernetes, the whole DevOps movement. How is this setting us up to actually be successful for solutions like AI and ML? >> Sure Stu I mean, in fact you said it right like in the world of software, in the world of microservices, in the world of app modernization, in the world of DevOps in the past 10, 15 years, but we have seen this evolution revolution happen with containers and Kubernetes driving more DevOps behavior, driving more agile behavior so this in fact is what we are trying to say here can ease up the cable to EIML also. So the various containers, Kubernetes, DevOps and OpenShift for software development is directly applicable for AI projects to make them move agile, to get them into production, to make them more valuable to organization so that they can realize the full potential of AI. We already touched upon a few personas so it's useful to think about who the users are, who the personas are. Abhinav I talked about data scientists these are the people who obviously do the machine learning itself, do the modeling. Then there are data engineers who do the plumbing who provide the essential data. Data is so essential to machine learning and deep learning and so there are data engineers that are app developers who in some ways will then use the output of what the data scientists have produced in terms of models and then incorporate them into services and of course, none of these things are purely cast in stone there's a lot of overlap you could find that data scientists are app developers as well, you'll see some of app developers being data scientist later data engineer. So it's a continuum rather than strict boundaries, but regardless what all of these personas groups of people need or experts need is self service to that preferred tools and compute and storage resources to be productive and then let's not forget the IT, engineering and operations teams that need to make all this happen in an easy, reliable, available manner and something that is really safe and secure. So containers help you, they help you quickly and easily deploy a broad set of machine learning tools, data tools across the cloud, the hybrid cloud from data center to public cloud to the edge in a very consistent way. Teams can therefore alternatively modify, change a shared container images, machine learning models with (indistinct) and track changes. And this could be applicable to both containers as well as to the data by the way and be transparent and transparency helps in collaboration but also it could help with the regulatory reasons later on in the process. And then with containers because of the inherent processes solution, resource control and protection from threat they can also be very secure. Now, Kubernetes takes it to the next level first of all, it forms a cluster of all your compute and data resources, and it helps you to run your containerized tools and whatever you develop on them in a consistent way with access to these shared compute and centralized compute and storage and networking resources from the data center, the edge or the public cloud. They provide things like resource management, workload scheduling, multi-tendency controls so that you can be a proper neighbors if you will, and quota enforcement right? Now that's Kubernetes now if you want to up level it further if you want to enhance what Kubernetes offers then you go into how do you write applications? How do you actually make those models into services? And that's where... and how do you lifecycle them? And that's sort of the power of Helm and for the more Kubernetes operators really comes into the picture and while Helm helps in installing some of this for a complete life cycle experience. A kubernetes operator is the way to go and they simplify the acceleration and deployment and life cycle management from end-to-end of your entire AI, ML tool chain. So all in all organizations therefore you'll see that they need to dial up and define models rapidly just like applications that's how they get ready out of it quickly. There is a lack of collaboration across teams as Abhinav pointed out earlier, as you noticed that has happened still in the world of software also. So we're talking about how do you bring those best practices here to AI, ML. DevOps approaches for machine learning operations or many analysts and others have started calling as MLOps. So how do you kind of bring DevOps to machine learning, and fosters better collaboration between teams, application developers and IT operations and create this feedback loop so that the time to production and the ability to take more machine learning into production and ML-powered applications into production increase is significant. So that's kind of the, where I wanted shine the light on what you were referring to earlier, Stu. >> All right, Abhinav of course one of the good things about OpenShift is you have quite a lot of customers that have deployed the solution over the years, bring us inside some of your customers what are they doing for AI, ML and help us understand really what differentiates OpenShift in the marketplace for this solution set. >> Yeah, absolutely that's a very good question as well and we're seeing a lot of traction in terms of all kinds of industries, right? Be it the financial services like healthcare, automotive, insurance, oil and gas, manufacturing and so on. For a wide variety of use cases and what we are seeing is at the end of the day like all these deployments are focused on helping improve the customer experience, be able to automate the business processes and then be able to help them increase the revenue, serve their customers better, and also be able to save costs. If you go to openshift.com/ai-ml it's got like a lot of customer stories in there but today I will not touch on three of the customers we have in terms of the different industries. The first one is like Royal Bank of Canada. So they are a top global financial institution based out of Canada and they have more than 17 million clients globally. So they recently announced that they build out an AI-powered private cloud platform that was based on OpenShift as well as the NVIDIA DGX AI compute system and this whole solution is actually helping them to transform the customer banking experience by being able to deliver an AI-powered intelligent apps and also at the same time being able to improve the operational efficiency of their organization. And now with this kind of a solution, what they're able to do is they're able to run thousands of simulations and be able to analyze millions of data points in a fraction of time as compared to the solution that they had before. Yeah, so like a lot of great work going on there but now the next one is the ETCA healthcare. So like ETCA is one of the leading healthcare providers in the country and they're based out of the Nashville, Tennessee. And they have more than 184 hospitals as well as more than 2,000 sites of care in the U.S. as well as in the UK. So what they did was they developed a very innovative machine learning power data platform on top of our OpenShift to help save lives. The first use case was to help with the early detection of sepsis like it's a life-threatening condition and then more recently they've been able to use OpenShift in the same kind of stack to be able to roll out the new applications that are powered by machine learning and deep learning let say to help them fight COVID-19. And recently they did a webinar as well that had all the details on the challenges they had like how did they go about it? Like the people, process and technology and then what the outcomes are. And we are proud to be a partner in the solution to help with such a noble cause. And the third example I want to share here is the BMW group and our partner DXC Technology what they've done is they've actually developed a very high performing data-driven data platform, a development platform based on OpenShift to be able to analyze the massive amount of data from the test fleet, the data and the speed of the say to help speed up the autonomous driving initiatives. And what they've also done is they've redesigned the connected drive capability that they have on top of OpenShift that's actually helping them provide various use cases to help improve the customer experience. With the customers and all of the customers are able to leverage a lot of different value-add services directly from within the car, their own cars. And then like last year at the Red Hat Summit they had a keynote as well and then this year at Summit, they were one of the Innovation Award winners. And we have a lot more stories but these are the three that I thought are actually compelling that I should talk about here on theCUBE. >> Yeah Abhinav just a quick follow up for you. One of the things of course we're looking at in 2020 is how has the COVID-19 pandemic, people working from home how has that impacted projects? I have to think that AI and ML are one of those projects that take a little bit longer to deploy, is it something that you see are they accelerating it? Are they putting on pause or are new project kicking off? Anything you can share from customers you're hearing right now as to the impact that they're seeing this year? >> Yeah what we are seeing is that the customers are now even more keen to be able to roll out the digital (indistinct) but we see a lot of customers are now on the accelerated timeline to be able to say complete the AI, ML project. So yeah, it's picking up a lot of momentum and we talk to a lot of analyst as well and they are reporting the same thing as well. But there is the interest that is actually like ramping up on the AI, ML projects like across their customer base. So yeah it's the right time to be looking at the innovation services that it can help improve the customer experience in the new virtual world that we live in now about COVID-19. >> All right, Tushar you mentioned that there's a few projects involved and of course we know at this conference there's a very large ecosystem. Red Hat is a strong contributor to many, many open source projects. Give us a little bit of a view as to in the AI, ML space who's involved, which pieces are important and how Red Hat looks at this entire ecosystem? >> Thank you, Stu so as you know technology partnerships and the power of open is really what is driving the technology world these days in any ways and particularly in the AI ecosystem. And that is mainly because one of the machine learning is in a bootstrap in the past 10 years or so and a lot of that emerging technology to take advantage of the emerging data as well as compute power has been built on the kind of the Linux ecosystem with openness and languages like popular languages like Python, et cetera. And so what you... and of course tons of technology based in Java but the point really here is that the ecosystem plays a big role and open plays a big role and that's kind of Red Hat's best cup of tea, if you will. And that really has plays a leadership role in the open ecosystem so if we take your question and kind of put it into two parts, what is the... what we are doing in the community and then what we are doing in terms of partnerships themselves, commercial partnerships, technology partnerships we'll take it one step at a time. In terms of the community itself, if you step back to the three years, we worked with other vendors and users, including Google and NVIDIA and H2O and other Seldon, et cetera, and both startups and big companies to develop this Kubeflow ecosystem. The Kubeflow is upstream community that is focused on developing MLOps as we talked about earlier end-to-end machine learning on top of Kubernetes. So Kubeflow right now is in 1.0 it happened a few months ago now it's actually at 1.1 you'll see that coupon here and then so that's the Kubeflow community in addition to that we are augmenting that with the Open Data Hub community which is something that extends the capabilities of the Kubeflow community to also add some of the data pipelining stuff and some of the data stuff that I talked about and forms a reference architecture on how to run some of this on top of OpenShift. So the Open Data Hub community also has a great way of including partners from a technology partnership perspective and then tie that with something that I mentioned earlier, which is the idea of Kubernetes operators. Now, if you take a step back as I mentioned earlier, Kubernetes operators help manage the life cycle of the entire application or containerized application including not only the configuration on day one but also day two activities like update and backups, restore et cetera whatever the application needs. Afford proper functioning that a "operator" needs for it to make sure so anyways, the Kubernetes operators ecosystem is also flourishing and we haven't faced that with the OperatorHub.io which is a community marketplace if you will, I don't call it marketplace a community hub because it's just comprised of community operators. So the Open Data Hub actually can take community operators and can show you how to run that on top of OpenShift and manage the life cycle. Now that's the reference architecture. Now, the other aspect of it really is as I mentioned earlier is the commercial aspect of it. It is from a customer point of view, how do I get certified, supported software? And to that extent, what we have is at the top of the... from a user experience point of view, we have certified operators and certified applications from the AI, ML, ISV community in the Red Hat marketplace. And from the Red Hat marketplace is where it becomes easy for end users to easily deploy these ISVs and manage the complete life cycle as I said. Some of the examples of these kinds of ISVs include startups like H2O although H2O is kind of well known in certain sectors PerceptiLabs, Cnvrg, Seldon, Starburst et cetera and then on the other side, we do have other big giants also in this which includes partnerships with NVIDIA, Cloudera et cetera that we have announced, including our also SaaS I got to mention. So anyways these provide... create that rich ecosystem for data scientists to take advantage of. A TEDx Summit back in April, we along with Cloudera, SaaS Anaconda showcased a live demo that shows all these things to working together on top of OpenShift with this operator kind of idea that I talked about. So I welcome people to go and take a look the openshift.com/ai-ml that Abhinav already referenced should have a link to that it take a simple Google search might download if you need some of that, but anyways and the other part of it is really our work with the hardware OEMs right? And so obviously NVIDIA GPUs is obviously hardware, and that accelerations is really important in this world but we are also working with other OEM partners like HP and Dell to produce this accelerated AI platform that turnkey solutions to run your data-- to create this open AI platform for "private cloud" or the data center. The other thing obviously is IBM, IBM Cloud Pak for Data is based on OpenShift that has been around for some time and is seeing very good traction, if you think about a very turnkey solution, IBM Cloud Pak is definitely kind of well ahead in that and then finally Red Hat is about driving innovation in the open-source community. So, as I said earlier, we are doing the Open Data Hub which that reference architecture that showcases a combination of upstream open source projects and all these ISV ecosystems coming together. So I welcome you to take a look at that at opendatahub.io So I think that would be kind of the some total of how we are not only doing open and community building but also doing certifications and providing to our customers that assurance that they can run these tools in production with the help of a rich certified ecosystem. >> And customer is always key to us so that's the other thing that the goal here is to provide our customers with a choice, right? They can go with open source or they can go with a commercial solution as well. So you want to make sure that they get the best in cloud experience on top of our OpenShift and our broader portfolio as well. >> All right great, great note to end on, Abhinav thank you so much and Tushar great to see the maturation in this space, such an important use case. Really appreciate you sharing this with theCUBE and Kubecon community. >> Thank you, Stu. >> Thank you, Stu. >> Okay thank you and thanks a lot and have a great rest of the show. Thanks everyone, stay safe. >> Thanks you and stay with us for a lot more coverage from KubeCon + CloudNativeCon Europe 2020, the virtual edition I'm Stu Miniman and thank you as always for watching theCUBE. (soft upbeat music plays)

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. Everybody. This is the Cube. The leader in live coverage. My name is David wanted. I'm here with my co host, Paul Gill, in this day to coverage of the M I t cdo I Q conference. A lot of acronym stands for M I. T. Of course, the great institution. But Chief Data officer information quality event is his 13th annual event. Lars to Maria's here is the managing partner of Brass Rat Capital. Cool name Lars. Welcome to the Cube. Great. Very much. Glad I start with a name brass around Capitol was That's >> rat is reference to the M I t school. Okay, Beaver? Well, he is, but the students call it a brass rat, and I'm third generation M i t. So it's just seen absolutely appropriate. That is a brass rods and capital is not a reference to money, but is actually referenced to the intellectual capital. They if you have five or six brass rats in the same company, you know, we Sometimes engineers arrive and they could do some things. >> And it Boy, if you put in some data data capital in there, you really explosions. We cause a few problems. So we're gonna talk about some new regulations that are coming down. New legislation that's coming down that you exposed me to yesterday, which is gonna have downstream implications. You get ahead of this stuff and understand it. You can really first of all, prepare, make sure you're in compliance, but then potentially take advantage for your business. So explain to us this notion of open government act. >> Um, in the last five years, six years or so, there's been an effort going on to increase the transparency across all levels of government. Okay, State, local and federal government. The first of federal government laws was called the the Open Data Act of 2014 and that was an act. They was acted unanimously by Congress and signed by Obama. They was taking the departments of the various agencies of the United States government and trying to roll up all the expenses into one kind of expense. This is where we spent our money and who got the money and doing that. That's what they were trying to do. >> Big picture type of thing. >> Yeah, big picture type thing. But unfortunately, it didn't work, okay? Because they forgot to include this odd word called mentalities. So the same departments meant the same thing. Data problem. They have a really big data problem. They still have it. So they're to G et o reports out criticizing how was done, and the government's gonna try and correct it. Then in earlier this year, there was another open government date act which said in it was signed by Trump. Now, this time you had, like, maybe 25 negative votes, but essentially otherwise passed Congress completely. I was called the Open as all capital O >> P E >> n Government Data act. Okay, and that's not been implemented yet. But there's live talking around this conference today in various Chief date officers are talking about this requirement that every single non intelligence defense, you know, vital protection of the people type stuff all the like, um, interior, treasury, transportation, those type of systems. If you produce a report these days, which is machine, I mean human readable. You must now in two years or three years. I forget the exact invitation date. Have it also be machine readable. Now, some people think machine riddle mil means like pdf formats, but no, >> In fact, what the government did is it >> said it must be machine readable. So you must be able to get into the reports, and you have to be able to extract out the information and attach it to the tree of knowledge. Okay, so we're all of sudden having context like they're currently machine readable, Quote unquote, easy reports. But you can get into those SEC reports. You pull out the net net income information and says its net income, but you don't know what it attaches to on the tree of knowledge. So, um, we are helping the government in some sense able, machine readable type reporting that weaken, do machine to machine without people being involved. >> Would you say the tree of knowledge You're talking about the constant >> man tick semantic tree of knowledge so that, you know, we all come from one concept like the human is example of a living thing living beast, a living Beeston example Living thing. So it also goes back, and they're serving as you get farther and farther out the tree, there's more distance or semantic distance, but you can attach it back to concept so you can attach context to the various data. Is this essentially metadata? That's what people call it. But if I would go over see sale here at M I t, they would turn around. They call it the Tree of Knowledge or semantic data. Okay, it's referred to his semantic dated, So you are passing not only the data itself, but the context that >> goes along with the data. Okay, how does this relate to the financial transparency? >> Well, Financial Transparency Act was introduced by representative Issa, who's a Republican out of California. He's run the government Affairs Committee in the House. He retired from Congress this past November, but in 2017 he introduced what's got referred to his H R 15 30 Um, and the 15 30 is going to dramatically change the way, um, financial regulators work in the United States. Um, it is about it was about to be introduced two weeks ago when the labor of digital currency stuff came up. So it's been delayed a little bit because they're trying to add some of the digital currency legislation to that law. >> A front run that Well, >> I don't know exactly what the remember soul coming out of Maxine Waters Committee. So the staff is working on a bunch of different things at once. But, um, we own g was asked to consult with them on looking at the 15 30 act and saying, How would we improve quote unquote, given our technical, you know, not doing policy. We just don't have the technical aspects of the act. How would we want to see it improved? So one of the things we have advised is that for the first time in the United States codes history, they're gonna include interesting term called ontology. You know what intelligence? Well, everyone gets scared by the word. And when I read run into people, they say, Are you a doctor? I said, no, no, no. I'm just a date. A guy. Um, but an intolerant tea is like a taxonomy, but it had order has important, and an ontology allows you to do it is ah, kinda, you know, giving some context of linking something to something else. And so you're able Thio give Maur information with an intolerant that you're able to you with a tax on it. >> Okay, so it's a taxonomy on steroids? >> Yes, exactly what? More flexible, >> Yes, but it's critically important for artificial intelligence machine warning because if I can give them until ology of sort of how it goes up and down the semantics, I can turn around, do a I and machine learning problems on the >> order of 100 >> 1000 even 10,000 times faster. And it has context. It has contacts in just having a little bit of context speeds up these problems so dramatically so and it is that what enables the machine to machine? New notion? No, the machine to machine is coming in with son called SP R M just standard business report model. It's a OMG sophistication of way of allowing the computers or machines, as we call them these days to get into a standard business report. Okay, so let's say you're ah drug company. You have thio certify you >> drugged you manufactured in India, get United States safely. Okay, you have various >> reporting requirements on the way. You've got to give extra easy the FDA et cetera that will always be a standard format. The SEC has a different format. FERC has a different format. Okay, so what s p r m does it allows it to describe in an intolerant he what's in the report? And then it also allows one to attach an ontology to the cells in the report. So if you like at a sec 10 Q 10 k report, you can attach a US gap taxonomy or ontology to it and say, OK, net income annual. That's part of the income statement. You should never see that in a balance sheet type item. You know his example? Okay. Or you can for the first time by having that context you can say are solid problem, which suggested that you can file these machine readable reports that air wrong. So they believe or not, There were about 50 cases in the last 10 years where SEC reports have been filed where the assets don't equal total liabilities, plus cheryl equity, you know, just they didn't add >> up. So this to, >> you know, to entry accounting doesn't work. >> Okay, so so you could have the machines go and check scale. Hey, we got a problem We've >> got a problem here, and you don't have to get humans evolved. So we're gonna, um uh, Holland in Australia or two leaders ahead of the United States. In this area, they seem dramatic pickups. I mean, Holland's reporting something on the order of 90%. Pick up Australia's reporting 60% pickup. >> We say pick up. You're talking about pickup of errors. No efficiency, productivity, productivity. Okay, >> you're taking people out of the whole cycle. It's dramatic. >> Okay, now what's the OMG is rolling on the hoof. Explain the OMG >> Object Management Group. I'm not speaking on behalf of them. It's a membership run organization. You remember? I am a >> member of cold. >> I'm a khalid of it. But I don't represent omg. It's the membership has to collectively vote that this is what we think. Okay, so I can't speak on them, right? I have a pretty significant role with them. I run on behalf of OMG something called the Federated Enterprise Risk Management Group. That's the group which is focusing on risk management for large entities like the federal government's Veterans Affairs or Department offense upstairs. I think talking right now is the Chief date Officer for transportation. OK, that's a large organization, which they, they're instructed by own be at the, um, chief financial officer level. The one number one thing to do for the government is to get an effective enterprise worst management model going in the government agencies. And so they come to own G let just like NIST or just like DARPA does from the defense or intelligence side, saying we need to have standards in this area. So not only can we talk thio you effectively, but we can talk with our industry partners effectively on space. Programs are on retail, on medical programs, on finance programs, and so they're at OMG. There are two significant financial programs, or Sanders, that exist once called figgy financial instrument global identifier, which is a way of identifying a swap. Its way of identifying a security does not have to be used for a que ce it, but a worldwide. You can identify that you know, IBM stock did trade in Tokyo, so it's a different identifier has different, you know, the liberals against the one trading New York. Okay, so those air called figgy identifiers them. There are attributes associated with that security or that beast the being identified, which is generally comes out of 50 which is the financial industry business ontology. So you know, it says for a corporate bond, it has coupon maturity, semi annual payment, bullets. You know, it is an example. So that gives you all the information that you would need to go through to the calculation, assuming you could have a calculation routine to do it, then you need thio. Then turn around and set up your well. Call your environment. You know where Ford Yield Curves are with mortgage backed securities or any portable call. Will bond sort of probabilistic lee run their numbers many times and come up with effective duration? Um, And then you do your Vader's analytics. No aggregating the portfolio and looking at Shortfalls versus your funding. Or however you're doing risk management and then finally do reporting, which is where the standardized business reporting model comes in. So that kind of the five parts of doing a full enterprise risk model and Alex So what >> does >> this mean for first? Well, who does his impact on? What does it mean for organizations? >> Well, it's gonna change the world for basically everyone because it's like doing a clue ends of a software upgrade. Conversion one's version two point. Oh, and you know how software upgrades Everyone hates and it hurts because everyone's gonna have to now start using the same standard ontology. And, of course, that Sarah Ontology No one completely agrees with the regulators have agreed to it. The and the ultimate controlling authority in this thing is going to be F sock, which is the Dodd frank mandated response to not ever having another chart. So the secretary of Treasury heads it. It's Ah, I forget it's the, uh, federal systemic oversight committee or something like that. All eight regulators report into it. And, oh, if our stands is being the adviser Teff sock for all the analytics, what these laws were doing, you're getting over farm or more power to turn around and look at how we're going to find data across the three so we can come up consistent analytics and we can therefore hopefully take one day. Like Goldman, Sachs is pre payment model on mortgages. Apply it to Citibank Portfolio so we can look at consistency of analytics as well. It is only apply to regulated businesses. It's gonna apply to regulated financial businesses. Okay, so it's gonna capture all your mutual funds, is gonna capture all your investment adviser is gonna catch her. Most of your insurance companies through the medical air side, it's gonna capture all your commercial banks is gonna capture most of you community banks. Okay, Not all of them, because some of they're so small, they're not regularly on a federal basis. The one regulator which is being skipped at this point, is the National Association Insurance Commissioners. But they're apparently coming along as well. Independent federal legislation. Remember, they're regulated on the state level, not regularly on the federal level. But they've kind of realized where the ball's going and, >> well, let's make life better or simply more complex. >> It's going to make life horrible at first, but we're gonna take out incredible efficiency gains, probably after the first time you get it done. Okay, is gonna be the problem of getting it done to everyone agreeing. We use the same definitions >> of the same data. Who gets the efficiency gains? The regulators, The companies are both >> all everyone. Can you imagine that? You know Ah, Goldman Sachs earnings report comes out. You're an analyst. Looking at How do I know what Goldman? Good or bad? You have your own equity model. You just give the model to the semantic worksheet and all turn around. Say, Oh, those numbers are all good. This is what expected. Did it? Did it? Didn't you? Haven't. You could do that. There are examples of companies here in the United States where they used to have, um, competitive analysis. Okay. They would be taking somewhere on the order of 600 to 7. How 100 man hours to do the competitive analysis by having an available electronically, they cut those 600 hours down to five to do a competitive analysis. Okay, that's an example of the type of productivity you're gonna see both on the investment side when you're doing analysis, but also on the regulatory site. Can you now imagine you get a regulatory reports say, Oh, there's they're out of their way out of whack. I can tell you this fraud going on here because their numbers are too much in X y z. You know, you had to fudge numbers today, >> and so the securities analyst can spend Mme. Or his or her time looking forward, doing forecasts exactly analysis than having a look back and reconcile all this >> right? And you know, you hear it through this conference, for instance, something like 80 to 85% of the time of analysts to spend getting the data ready. >> You hear the same thing with data scientists, >> right? And so it's extent that we can helped define the data. We're going thio speed things up dramatically. But then what's really instinct to me, being an M I t engineer is that we have great possibilities. An A I I mean, really great possibilities. Right now, most of the A miles or pattern matching like you know, this idea using face shield technology that's just really doing patterns. You can do wonderful predictive analytics of a I and but we just need to give ah lot of the a m a. I am a I models the contact so they can run more quickly. OK, so we're going to see a world which is gonna found funny, But we're going to see a world. We talk about semantic analytics. Okay. Semantic analytics means I'm getting all the inputs for the analysis with context to each one of the variables. And when I and what comes out of it will be a variable results. But you also have semantics with it. So one in the future not too distant future. Where are we? We're in some of the national labs. Where are you doing it? You're doing pipelines of one model goes to next model goes the next mile. On it goes Next model. So you're gonna software pipelines, Believe or not, you get them running out of an Excel spreadsheet. You know, our modern Enhanced Excel spreadsheet, and that's where the future is gonna be. So you really? If you're gonna be really good in this business, you're gonna have to be able to use your brain. You have to understand what data means You're going to figure out what your modeling really means. What happens if we were, You know, normally for a lot of the stuff we do bell curves. Okay, well, that doesn't have to be the only distribution you could do fat tail. So if you did fat tail descriptions that a bell curve gets you much different results. Now, which one's better? I don't know, but, you know, and just using example >> to another cut in the data. So our view now talk about more about the tech behind this. He's mentioned a I What about math? Machine learning? Deep learning. Yeah, that's a color to that. >> Well, the tech behind it is, believe or not, some relatively old tech. There is a technology called rd F, which is kind of turned around for a long time. It's a science kind of, ah, machine learning, not machine wearing. I'm sorry. Machine code type. Fairly simplistic definitions. Lots of angle brackets and all this stuff there is a higher level. That was your distracted, I think put into standard in, like, 2000 for 2005. Called out. Well, two point. Oh, and it does a lot at a higher level. The same stuff that already f does. Okay, you could also create, um, believer, not your own special ways of a communicating and ontology just using XML. Okay, So, uh, x b r l is an enhanced version of XML, okay? And so some of these older technologies, quote unquote old 20 years old, are essentially gonna be driving a lot of this stuff. So you know you know Corbett, right? Corba? Is that what a maid omg you know, on the communication and press thing, do you realize that basically every single device in the world has a corpus standard at okay? Yeah, omg Standard isn't all your smartphones and all your computers. And and that's how they communicate. It turns out that a lot of this old stuff quote unquote, is so rigidly well defined. Well done that you can build modern stuff that takes us to the Mars based on these old standards. >> All right, we got to go. But I gotta give you the award for the most acronyms >> HR 15 30 fi G o m g s b r >> m fsoc tarp. Oh, fr already halfway. We knew that Owl XML ex brl corba, Which of course >> I do. But that's well done. Like thanks so much for coming. Everyone tried to have you. All right, keep it right there, everybody, We'll be back with our next guest from M i t cdo I Q right after this short, brief short message. Thank you

>> live from Boston, Massachusetts. It's the you covering your red have some twenty nineteen rots. You buy bread hat. >> Good to have you back here on the Cube as we continue our coverage. Live at the Red Had Summit twenty nineteen, Day three of our coverage with you since Tuesday. And now it's just fresh off the keynote stage, joining stew, Minutemen and myself. Chris. Right? VP and chief technology officer at Red Hat. Good job there, Chris. Thanks for being with us this morning. Yeah. >> Thank you. Glad to be here. >> Great. Right? Among your central things, you talked about this, this new cycle of innovation and those components and how they're integrating to create all these great opportunities. So if you would just share for those with those at home who didn't have an opportunity to see the keynote this morning, it's what you were talking about. I don't think they play together. And where that lies with red hat. Yeah, you bet. >> So, I think an important first kind of concept is a lot of what we're doing. Is lane a foundation or a platform? Mean red hats focuses in the platform space. So we think of it as building this platform upon which you build an innovate. And so what we're seeing is a critical part of the future is data. So we're calling it a Kino data centric. It's the data centric economy. Along with that is machine learning. So all the intelligence that comes, what do you dividing? The insights you're grabbing from that data. It introduces some interesting challenges data and privacy and what we do with that data, I mean, we're all personally aware of this. You see the Cambridge Analytica stuff, and you know, we all have concerns about our own data when you combine all of us together techniques for how we can create insights from data without compromising privacy. We're really pushing the envelope into full distributed systems, EJ deployments, data coming from everywhere and the insights that go along with that. So it's really exciting time built on a consistent platform like lycopene shift. >> So, Chris, I always loved getting to dig in with you because that big trend of distributed systems is something that you know we've been working on for quite a long time. But, you know, we fully agree. You said data at the center of everything and that roll of even more distributed system. You know, the multi cloud world. You know, customers have their stuff everywhere and getting their arms around that, managing it, being about leverage and take advantage. That data is super challenging. So you know where where, you know, help us understand some of the areas that red hat in the communities are looking to solve those problems, you know, where are we and what's going well and what's still left to work on. >> Well, there's a couple of different aspect. So number one we're building these big, complex systems. Distributed systems are challenging distribute systems, engineers, air really solving harder problems. And we have to make that accessible to everybody operations teams. And it's one of the things that I think the cloud taught us when you sort of outsource your operations is somebody else. You get this encapsulated operational excellence. We need to bring that to wherever your work clothes are running. And so we talked a lot about a I ops, how you harness the value of data that's coming out of this complex infrastructure, feed it through models and gain insights, and then predict and really Ultimately, we're looking at autonomic computing how we can create autonomous clouds, things that really are operating themselves as much as possible with minimal human intervention. So we get massive scale. I think that's one of the key pieces. The other one really talking about a different audience. The developers. So developers air trying to incorporate similar types of intelligence into their applications were making recommendations. You're tryingto personalize applications for end users. They need easy access to that data. They need easy access to train models. So how do we do that? How do we make that challenging data scientist centric workflow accessible to developers? >> Yeah, just some of the challenges out there. I think about, you know, ten, fifteen years ago, you talk to people, it was like, Well, I had my central source of truth and it was a database. And you talk to most companies now and it's like, Well, I've got a least a dozen different database and you know, my all my different flavors of them and whether in the cloud or whether I have them in my environment, you know, things like a ops trying to help people get involved with them. You talked a little bit in your keynote about some of the partners that you're working on. So how do you, you know, bring these together and simplify them when they're getting, you know, even more and more fragmented? >> Well, it's part of the >> challenge of innovation. I mean, I think there's a there's a natural cycle. Creativity spawns new ideas. New ideas are encapsulated in projects, so there's a wave of expansion in any kind of new technology time frame. And then there's ultimately, you see some contraction as we get really clear winners and the best ideas and in the container orchestration space communities is a great example of that. We had a lot of proliferation of different ways of doing it. Today we're consolidating as an industry around Cooper Netease. So what we're doing is building a platform, building a rich ecosystem around that platform and bringing our partners in who have specific solutions. They look at whether it's the top side of the house, talking to the operations teams or whether it's giving developers easy access to data and training models through some partners that we had today, like perceptive labs and each to a A I this partnership. Bringing it to a common platform, I think, is a critical part of helping the industry move forward and ultimately will see where these best of breed tools come into play. >> Here, uh, you know, maybe help a little bit with with in terms of practical application, you got, you know, open source where you've got this community development going on and then people customized based on their individual needs all well, great, right? How does the inverse happen? Where somebody who does some custom ization and comes up with a revelation of some kind and that applies back to the general community. And we can think of a time where maybe something I'm thinking like Boston children, their imaging, that hospital we saw actually related to another industry somehow and gave them an ah ha moment that maybe they weren't expecting an open source. Roy was the driver that >> Yeah, I think what we showed today were some examples of what If you distill it down to the core, there's some common patterns. There's data, they're streaming data. There's the data processing, and there's a connection of that processed data or train model to an application. So we've been building an open source project called Open Data Hub, where we can bring people together to collaborate on what are the tools that we need to be in this stack of this kind of framework or stack And and then, as we do, that we're talking to banks. They're looking at any money laundering and fraud detection. We're talking to these hospitals that were looking at completely different use cases like HC Healthcare, which is taking data to reduce the amount of time nurses need to spend, gathering information from patients and clearly identify Septus sepsis concerns totally different applications, similar framework. And so getting that industry level collaboration, I think is the key, and that having common platforms and common tools and a place to rally around these bigger problems is exactly how we do that through open source. >> So Lynn exits and an interesting place in the stack is you talked about the one commonality and everything like that. But we're actually at a time where the proliferation of what's happen to get the hardware level is something that you know of an infrastructure and harbor guy by background, and it was like, Oh, I thought We're going to homogenize everything, standardize everything, and it's like, Oh, you're showing off Colin video stuff. And when we're doing all these pieces there, there's all these. You know, new things, Every been things you know you work from the mainframe through the latest armed processors. Give us a little insight as to how your team's geeking out, making sure that they provide that commonality yet can take advantage of some of the cool, awesome stuff that's out there that's enabling that next wave of innovation. >> Yeah, so I share that infrastructure geek nous with you. So I'm so stoked the word that we're in this cycle of harbor innovation, I'll say something that maybe you sounds controversial if we go back in time just five years or a little, a little more. The focus was around cloud computing and bringing massive number of APS to the cloud, and the cloud had kind of a T shirt size, small, medium, large view of the world of computer. It created this notion that Khun computers homogenous. It's a lie. If you go today to a cloud provider and count the number of different machine types they have or instance types it's It's not just three, it's a big number. And those air all specialized. It's for Io throughput. It's for storage acceleration. It's big memory, you know. It's all these different use cases that are required for the full set of applications. Maybe you get the eighty percent in a common core, but there's a whole bunch of specific use cases that require performance optimization that are unique. And what we're seeing, I think, is Moore's law. The laws of physics are kind of colliding a little bit, and the way to get increased acceleration is through specialized hardware. So we see things like TP use from Google. We see until doing deal boost. We've got GPS and even F p G A s and the operating system is there TIO give a consistent application run time while enabling all those hardware components and bringing it all together so the applications can leverage the performance acceleration without having to be tied directly to it. >> Yeah, you actually think you wrote about that right now, one of your a block post that came about how hardware plays this hugely important role. You also talked about innovation and change happening incrementally and And that's not how we kind of think about like big Banks, right? Yeah. Wow, this is But you pointed out in the open source, it really is step by step by step. Which way? Think about disruption is being very dramatic. And there's nothing sexy about step by step. Yeah, that's how we get to Yeah, disruption. I kind of >> hate this innovation, disruption and their buzz words. On the one hand, that's what captures attention. It's not necessarily clear what they mean. I like the idea that, you know, in open source, we do every day, incremental improvements. And it's the culmination of all these improvements over time that unlock new opportunities. And people ask me all the time, where is the future? What do we do and what's going on? You know, we're kind of doing the same thing we've been doing for a long time. You think about micro services as a way to encapsulate functionality, share and reuse with other developers. Well, object oriented programming decades ago was really tryingto tryingto established that same capability for developers. So, you know, the technologies change we're building on our history were always incrementally improving. You bring it all together. And yes, occasionally you can apply that in a business case that totally disrupts an industry and changes the game. But I really wanted encourage people to think about what are the incremental changes you can make to create something fundamentally new. >> All right, I need to poke it that a little bit, Chris, because there's one thing you know, I looked back in my career and look back a decade or two decades. We used to talk about things like intelligence and automation. Those have been around my entire career. Yeah, you look it today, though, you talk about intelligence and talk about automation, it's not what we were doing, you know, just the amount of degrees, what we're having there. It is like if we'd looked at it before, it was like, Oh, my gosh, science fiction's here so, you know, way sometimes lose when we're doing step by step, that something's there making step function, improvements. And now the massive compact, massive changes. So love your opinions there. >> Yeah, well, I think it's a combination, so I talk about the perpetual pursuit of excellence. So you pick up, pick a field, you know, we're talking about management. We got data and how you apply that data. We've been working towards autonomic computing for decades. Concepts and research are old, the details and the technologies and the tools that we have today are quite different. But I'm not. You know, I'm not sure that that's always a major step function. I think part of that is this incremental change. And you look at the number for the amount of kind of processing power and in the GPU today No, this is a problem that that industry has been working on for quite a long time. At some point, we realize, Hey, the vector processing capabilities in the GPU really, really suit the machine learning matrix multiplication world real world news case. So that was a fundamental shift which unlocked a whole bunch of opportunity in terms of how we harness data and turn it into knowledge. >> Yes. So are there any areas that you look at? Now that we've been working at that, you feel we're kind of getting to those tipping points or the thie waves of technology or coming together to really enable Cem Cem massive change? >> I do think our ability to move data around, like generate data. For one thing, move data around efficiently, have access to it from a processing capability. And turning that into ah, >> model >> has so fundamentally changed in the past couple of decades that we are tapping into the next generation of what's possible and things like having this. This holy grail of a self healing, self optimizing, self driving cluster is not as science fiction as it felt twenty years ago. It's >> kind of exciting. You talk about you've been there in the past, the president, but there is very much a place in the future, right? And how would that future looks like just from from again? That aye aye perspective. It's a little scary, sometimes through to some people. So how are you going about, I guess, working with your partners to bring them along and accept certain notions that maybe five six years ago I've been a little tough to swallow or Teo feel comfortable with? >> Yeah, well, there's a couple of different dimensions there. One is, uh, finding tasks that air computers are great at that augment tasks that humans were great at and the example we had today. I love the example, which was, Let's have computers, crunch numbers and nurses do what they do best, which is provide care and empathy for the patients. So it's not taking the nurse's job away. In fact, is taking the part that is drudgery ITT's computation >> and you forget what was the >> call it machine enhanced human intelligence right on a couple of different ways of looking at that, with the idea that we're not necessarily trying to eliminate humans out of the loop. We're trying to get humans to do what they do best and take away the drudgery that computers air awesome at repetitive tasks. Big number crunching. I think that's one piece. The other pieces really, from that developer point of view, how do you make it easily accessible? And then the one step that needs to come after that is understanding the black box. What happens inside the machine learning model? How is it creating the insights that it's creating and there's definitely work to be done there? There's work that's already underway. Tto help understand? Uh, the that's really what's behind the inside so that we don't just trust, which can create some problems when we're introducing data that itself might already be biased. Then we assumed because we gave data to a computer which is seemingly unbiased, it's going to give us an unbiased result, right? Garbage in garbage out. >> So we got really thoughtful >> about what the models are and what the data is that we're feeding >> It makes perfect sense it. Thanks for the time. Good job on the keynote stage again this morning. I know you've got a busy afternoon scheduled as well, so yeah, I will let you. We'Ll cut you loose. But thank you again. Always good to see you. >> Yeah. I always enjoy being here >> right at that's right. Joining us from red hat back with Wharton Red Hat Summit forty nineteen. You're watching live here on the Cube?

(music) >> Live from Orlado, Florida, it's theCUBE. Covering Microsoft Ignite. Brought to you by Cohesity. and theCube's ecosystem partners. >> Welcome back, everyone, to theCube's live coverage of Microsoft Ignite. Happy hour has started. The crowd is roaring. I'm your host Rebecca Knight, along with my cohost, Stu Miniman. We are joined by Thomas LaRock. >> He is the Head Geek at SolarWinds. Thanks so much for coming on the show. >> Thanks for having me. >> Great title: Head Geek >> Yes. >> So, tell our viewers a little bit about what - tell us about SolarWinds and also about what you do. >> SolarWinds is a company that offers about forty different products to help with your enterprise infrastructure monitoring. Really unify management of your systems. Been in the business for about twenty years and I've been with them for about eight now. Head Geek is really, uh, you can equate it to being a technical evangelist. >> Okay. So you're out there trying to win the hearts and minds, trying to tell everyone what you do. >> Yes, I need you all to love me. (laughing) and love my products. >> So, Thomas, and for those who don't already follow you on Twitter, you're a SQL rockstar. >> Yes, yes [Stu] - I need to say, "thank you," because you helped connect me with a lot of the community here, especially on the data side of the house. You and I have known each other for a bunch of years. You're a Microsoft MVP. So maybe give us a little bit of community aspect: what it means to be a Microsoft MVP for those who don't know. You're an evangelist in this space and you've been on this show many times. >> I usually don't talk about myself a lot, but sure. (Rebecca laughing) Let's go for it. I've been a Microsoft data platform MVP for about 10 year now. And it was intresting when you reached out, looking to get connected. I was kind of stunned by how many people I actually knew or knew how to get in touch with for you. I help you line up, I guess, a handful of people to be on the show because you were telling me you hadn't been here at Microsoft Ignite and I just thought, "well I know people," and they should know Stu, and we should get them connected so that you guys can have some good conversations. But, yeah, it's been a wild ride for me those ten years where Microsoft awards people MVP designation. It's kind of being an evangelist for Microsoft and some of the great stuff that they've been doing over the past ten years. >> It's a phenomenal program. Most people in the technology industry know the Microsoft MVP program. I was a Vmware expert for a number of years. Many of the things were patterned off of that. John Troyer is a friend of mine. He said that was one the things he looked at. Sytrics has programs like this. Many of the vendors here have evangelists or paragons showing that technology out here. Alight. So talk a little bit about community. Talk about database space. Data and databases have been going through such, you know, explosion of what's going on out there, right? SQL's still around. It's not all cosmos and, you know, microservices-based, cloud, native architecture. >> So the SQL Server box product is still around, but what I think is more amazing to me has been the evalution of...Let's take for example, one of the announcements today, the big data cluster. So, it's essentially a container that's going to run SQL servers, Spark and Hadoop, all in one. Basically, a pod that will get deployed by kubernetes. When you wrap all that together, what you start to realize is that the pattern that Microsoft has been doing for the past few years, which is, essentially, going to where the people are. What I mean is: you have in the open-source world, you have people and developers that have embraced things like DevOps much faster than what the Windows developers have been doing. So instead of taking your time trying to drag all these people where you want them to be, they've just start building all the cool stuff where all the cool kids already are, and everybody's just going to gravitate. Data has gravity, right? So, you're building these things, and people are going to follow it. Now, it's not that they're expecting to sell a billion dollars woth of licenses. No. They just need to be a part of the conversation. So if you're a company that's using those technologies, now all of a sudden, it's like, this is an option. Are you interested in it? Microsoft is the company that's best poised to bring enterprises to the cloud. Amazon has a huge share. We all know that, but Microsoft's already that platform of choice for these enterprises. Microsoft is going to be the one to help them get to the cloud. [Stu]- Thomas, Explain what you mean by that because the strength I look at Microsoft is look, they've got your application. Business productivity: that's where they are. Apologize for cutting you off there. Is that what you mean? The applications are changing and you trusted Microsoft and the application and therefore, that's a vendor of choice. >> Absolutely. If it's already your vendor of choice then, I don't want to say, "Lock in," but if it's already your preference and if they can help get to the cloud, or in the hybrid situation or just lift and shift and just get there, then that's the one you going to want to do it. Everything they're building and all the services they're providing... At the end of the day, they and Amazon, they're the new electric company. They want data. That's the electricity. They don't care how you get it, but between... even Vmware. Between Amazon, Vmware and Microsoft, they're going to be the ones to help... They're going to be your infrastructure companies. Microsoft-managed desktop now. We'll manage your laptop for you. >> Everything that they're doing essentially like, don't even need my own IT department. Microsoft's going to be the largest MSP in history, right? That's where they're headed. They're going to manage everything for you. The data part of it, of course for me, I just love talking about data. But the data part of it...Data is essential to everything we do. It's all about the data. They're doing their best to manage it and secure it. Security is a huge thing. There were some security announcements today as well, which were awesome. The advanced threat detection, the protection that they have. I'm always amazed when I walk through the offering they have for SQL injection protection. I try and ask people, "Who's right now monitoring for SQL injection?" And they're like, "We're not doing that." For fifteen dollars a month, you could do this for your servers. They're like, "that's amazing what they're offerening." Why wouldn't you want that as a service? Why wouldn't you sign-up tomorrow for this stuff? So, I get excited about it. I think all this stuff they're building is great. The announcements today were great. I think they have more coming out over the next couple days. Or at least in the sessions, we'll start seeing a lot of hands-on stuff. I'm excited for it. >> So when you were talking about Microsoft being the automatic vendor of choice. Why wouldn't you? You treated it as a no brainer. What does Microsoft need to do to make sure customers feel that way too? >> I think Microsoft is going to do that... How I would do that. A couple ways. One, at the end of the day, Microsoft wants what we all want, what I want, is they want happy customers. So they're going to do whatever it takes so their customers are happy. So one way you do that is you get a lot of valuable feedback from customers. So, one thing Microsoft has done in the past is they've increased the amount telemetry they're collecting from their products. So they know the usage. They know what the customers want. They know what the customers need. But they also collect simple voice to the customer. You're simply asking the customer, "What do you want?" And you're doing everything you can to keep them happy. And you're finding out where the struggles are. You're helping them solve those problems. How do you not earn trust as a result of all that, right? I think that's the avenue they've been doing for, at least, ten years. Well, let's say, eight years. That's the avenue and the approach they've been doing. I'd say it's been somewhat successful. >> Thomas, as our team was preparing for this show, we understand that Microsoft has a lot of strengths, but if I look at the AI space, Microsoft is not the clear leader today. Um, we think that some of the connections that Microsoft has, everything that you said, down to the desktop. Heck, even in the consumer space, they're down to the Xbox. There's a lot of reasons why Microsoft... You can say, "Here's a path of how Microsoft could become. You know number one, number two in the AI space over time. But, we're listening to things, like the Open Data Initiative that they announced today, which, obviously, Microsoft's working with a lot of partners out there, but it's a big ecosystem. Data plays everywhere. I mean, Google obviously has strong play in data. We've talked plenty about Amazon. What does Microsoft need to do to take the strength that they have in data move forward in AI and become even stronger player in the marketplace? >> So, AI, itself, is kind of that broad term. I mean, AI is a simple if-then statement. It doesn't really have to do anything, right? So let's talk about machine learning, predictive analytics, or even deep learning. That's really the are that we're talking about. What does Microsoft have to do? Well, they have to offer the services. But they don't have offer, say, new things. They just have to offer things that already exist. For example, the idea of, um, incorperating Jupiter notebooks into the Azure Data Studio. So if that could be achieved, you know, now you're bringing the workspaces people are using into the Microsoft platform a little bit, making it a little bit easier. So instead of these people in these enterprises... They already trust Microsoft. They already have the tools. But I got to go use these other things. Well, eventually, those other things come into the Microsoft tools, and now you don't have to use that other stuff either. I would talk about the ability to publish these models as a service. I've done the Academy program. I've earned a few certifications on some of this stuff. I was amazed at how easy it was with a few clicks, you know, published as a service as an API. It's sitting there. I sent in my data and I get back a result, a prediction. I was like, that was really easy. So I know they're not the leaders, but they're making it easy, especially for somebody like me who can start at zero and get to where I need to be. They made it incredibly easy and in some cases, it was intuative. I'm like, oh, I know what to do next with this widgit I'm building. I think it will take time for them to kind of get all that stuff in place. I don't know how long. But does Microsoft have to be the leader in AI? They have the Cognitive Toolkit. They have all that stuff with Cortana. They have the data. I think the customers are coming along. I think they get there just by attrition. I'm not sure there's something they're going to build where everybody just says, "There it is." Except there's the Quantum stuff. And last year's announcement of Quantum, I thought was one of the most stunning things. It just hit me. I had no idea working on it. So, who knows? A year from now there could be something similar to that type of announcement, where we're like, now I get it, now I got to go have this thing. I don't think we all need, you know, a hotdog not hotdog app, which seems to be the bulk of the examples out there. Some of the image classification stuff that you have out there is fabulous. There are a lot of use cases for it. Um, I'm not sure how they get there. But, I do think eventually over time, the platform that they offer, they do get just through attrition. >> One of the things you brought up earlier in this conversation was the Open Source Initiative and Stu, we had expressed a bit of skepticism that it's still going to take three to five years, for, really, customers to see the value of this. But once...The announcement was made today, so now we're going to go forward with this Initiative. What do you see as the future? >> Yeah, I was trying to, even, figure it out. So it sounds like the three companies are sharing data with each other. They pledged to be open. So if you buy one of their products, that data can seamlessly go into that other product is what it sounded like. And they were open, if I heard it right, they were open to partnering with other companies as well. >> Correct. >> Yes. Yes. >> Other vendors or customers, even that could tie in into these APIs, doing everything that they're doing. Open data models. >> Speaking as a data guy, that means if I trust one, I have to trust them all. (Stu Laughing) >> Right? So I don't know. I have trus&t issues. (Rebecca laughing) >> Clearly. >> I'm a DBA, by heart, so I have trust issues. I need to know a little more about it, but on the surface, just the words, "open data," sound great. I just don't know the practical, uh, practicality of it. It sounds like it's a way for people, or these companies, to partner with each other to get more of your data into their platform and their infrastructure. >> Yeah. I think next time we have Thomas on, we're going to spend some time talking about the dark side of data. >> Yes, indeed. >> We can talk dark data. Oh, sure. (Rebecca laughing) >> Well, Thomas, it was so much fun having you on this show and I should just plug your book. You are the author of "DBA Survivor." >> I am. Yes. It was a little book. So being a DBA, uh, I had some challenges in my role and I decided, as my friend Kevin Kline put it to me, he goes, "You should write the book you wish had written for you and handed to you on day zero of being a DBA." And I said, "Oh." It took m&e, I think, like, three weeks. It was just so easyto write all of that. >> It just flowed (laughing.) >> It was just stuff I had to say. But, yeah, thank you. >> Excellent. I'm Rebecca Knight for Stu Miniman. We will have more from theCUBE's live coverage of Microsoft Ignite coming up in just a little bit. (music playing)

(upbeat music) >> Live from Orlando, Florida, it's theCUBE covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome everyone to day one of theCUBE's live coverage of Microsoft Ignite here at the Orange County Civic Center. I'm here -- I'm Rebecca Knight -- my cohost, Stu Miniman. This is the first CUBE show ever at Microsoft. It's unbelievable! >> Yeah, Rebecca, it's a little surprising. You know, we started back in 2010 doing these events, we've done hundreds of shows, we've done thousands of interviews, we've had lots of Microsoft people, but the first time at a Microsoft show, there's plenty of people I've bumped into that don't know theCUBE. 30,000 people in attendance here, so really excited to dig into this community and ecosystem and show 'em what it's all about. >> We're making history. So today, we had Satya Nadella up there on the main stage. What is your big takeaway from his keynote, Stu? >> Yeah, so Rebecca, Satya Nadella, obviously has really helped turn around Microsoft's -- really, the way people think about Microsoft. 'Cause it's interesting, when I look at the people we're going to be talking this week, lots of them have been with Microsoft ten years, twenty years, or more, so. Microsoft is one of those stalwarts in technology, they are obviously critical in a lot of environments. Everything from the latest Windows 2019 got announced today, there's excitement there, but they're playing in the cloud, they're playing all over the environment but Satya has brought new energy, some change to the culture I know you're going to want to talk about, and really came out talking about the vision for the future and what was interesting to me compared to some other big tech shows that I go to, it wasn't product focused, it wasn't on the new widget. They touched on things like Azure and, of course, AI, and some future things but it was really business productivity at its core is what I think about. If you think about Microsoft, I mean, we've all used the Office Suite and watched that go from Microsoft getting into the apps to being the main apps to pushing people to Office 365, so. I hear things about like business productivity and when they put in the Intelligent Cloud and the Intelligent Edge, it wasn't product categories they went into, but really speaking to broader terms to the business, so. It was interesting and a little bit different from what I would hear at say the companies you compare them to. The Amazons of the world, the VMwares of the world. So, a slightly different messaging. >> I couldn't agree with you more, and just talking about the different kind of energy that Nadella brings to this company. Microsoft, as you said, a lot of the people here are veterans; they've been here ten or twenty years. Microsoft is pushing on forty-five years old. This is a company that's entering middle age in an industry that is all about the new, the fresh, the buzzy. And so, he really does bring that kind of fresh outlook to it. His catchword of the day is "tech intensity" and this is what he talked about how we not only need to be adopting the latest and greatest technology, we also need to be building it. Seems like he was really doubling down on this idea that industry leaders need to be pushing boundaries in whatever industry they may be in. >> And I did like that, 'cause it's interesting. The easy compare, and I hope I don't do it too much, but you look at Amazon: Amazon talks to those builders. That's like the core, what you say when you go to the airports that have their branding, it's all about the builders, so. To the cloud native piece, I want the developer, developer, developer - and Microsoft knows a thing about developers too - but they bridge that gap. When we first talked about the world hybrid cloud, Microsoft's one of the first companies that comes to mind when I think about because they have such a base in the legacy world, they're modernizing that world, and they are helping to build that next generation space. Microsoft isn't one to necessarily chase the new shiny. They've done lots of big acquisitions, I mean, you talk developers, they bought GitHub. That's the center, it's like, if you're a developer, "What's your resume?" "Oh, well just check me out on GitHub, see how many stars I have." That kind of stuff. So that's where Microsoft lives and as you said, right, "tech intensity" - that balance between what are you buying and what are you build. I like that commentary from Satya. What I liked about him is saying, "Look, there are things that have been commoditized out there and you probably shouldn't waste your time building." I always tell companies, "Look, there's things that you suck at, or things that other people do way better. Let them do that. Why are you spending your cycles reinventing the wheel?" The thing I didn't love as much is he was like, "Well, you got to be careful who you partner with, you don't want to necessarily partner with somebody that's going to be your competitor." Come on. When I talked to a couple users coming out and I'm like, "What'd you think of that?" And they're like, "Look, here's the thing: love Microsoft, use Microsoft, but we use Amazon, we're going to use both, it's a multi-cloud world." Lots of SAS, multiple public clouds, and I want to hear about how Microsoft lives in that world. They can't not partner with Amazon. Matter of fact, I was reading one of the press releases. Oh, Skype will be available on the new Amazon Echo Show. So, it's the world of co-opetition. You've got, look around this ecosystem: everybody -- you partner where you can, you try to overlook the places where you fight, and you got to help the customers, and I think Microsoft does a good job, but you can't just say, "Let's not talk about Amazon or AWS because oh, that's going to be competitive." You know, really. >> And also, it's sort of, what he says and what he does, which are two different things. Because he also brought up the CEO of Adobe and the CEO of SAP up there to talk about this new Open Data Initiative. He talked - all three CEOS - talked at length about this small data problem that companies have, which is that they have all of this vast amount of digital information that they are creating and storing and manipulating, but it's all kept in silos. And so, they know a lot, but this end isn't talking to this end. So they want to change that, they're setting out to change it. >> You know, three companies that, if you were to tell me, okay, who's helping and doing well with digital transformation, and understands my data? Well, you couldn't do much better than starting with Microsoft, Adobe, and SAP. Absolutely, great suite. Adobe and SAP both made acquisitions in this phase, they understand the data. And I have to give huge kudos to Microsoft on how they're doing in open source. I've got enough years in the industry that I think back to when things like Linux were going to help try to topple Microsoft. And you see, Microsoft embracing almost half of the workloads in Azure or Linux. They had announcements, they were talking up on stage about partnering with Red Hat. And Microsoft, working with developers, working in the cloud, open source is critically important there. Talk about AI, open source has to be a key piece of these. And the Open Data Initiative: I like what I saw. Big names, there were definitely some surprise out of it. It was kind of the biggest news out of Satya Nadella's keynote this morning. The thing I will drop back on and say okay, we've all seen some of these announcements out there. Would've loved to see a customer or an example. Satya Nadella did a good talking about some of the IOT solutions that are going to get to AI, and I think it was a utility that was like, here they have, they're trialing it out and everything. So how do we measure the success of this? It's extensible and they said absolutely, other partners and other customers can tie into this. But -- is this a year, two years, how long before this becomes reality? Hopefully, three years from now, we look back and say we were there with something really important to help customers own and take their data and take it to the next level, but as of right now, it's a good move by some very strong players. And, of course, Microsoft partnership's key to what they're doing. >> They've identified the problem and that's what today was about. Sort of, we know this is a problem, we're going to work on this together. And I think it's also, talking about the open source angle which you brought up, it really is emblematic of this kinder, gentler Microsoft, which is all about inclusivity, all about helping everyone do better at their job and in their lives. >> Rebecca, I love your take. You talk about diversity, you talk about the culture of change, I mean. Satya leading from the top. We covered a few years ago, he put his foot in his mouth at a Grace Hopper event. But very much a lot of women involved, we're going to have a number of women executives on the program here. What do you see from Microsoft in this space? >> So the incident you're referring to is when he was asked about how a woman should ask for a raise and he basically said, "Oh, you really shouldn't ask - just do your best work and the rewards will come to you." Well, any woman in any industry, regardless of technology, knows that's just not the way it works. And I think, particularly now, he can look back and say, "Oh my gosh, that was a gaff." But even then, he recognized it and he apologized immediately and said, "No, things have got to change and I need to be part of the solution." So he does have a lot of initiatives around diversity in tech and helping women reach leadership positions. In terms of the cultural transformation that you reference at the very beginning of the show, his book is called Hit Refresh and it really is all about the growth mindset. Which is the work that Carol Dweck has done, and Angela Duckworth too. So this is really about this constant learning, this constant curiosity, this constant "don't be a know it all, be a learn it all," be so willing to collaborate and hear other perspectives and don't dismiss other people's perspective out of hand. And that's really, that's the way they want to operate as a company and as a culture. And then they also want to push that out into how its products behave in the workplace and how they help teams work together. >> Yeah, and that "be a learn it all, not a know it all," not only resonates with me but it part of the mission of what we do here on theCUBE. Look, my first Microsoft show. Trust me, I've been studying hard on this. I mean, I've known Microsoft since my earliest days working in the tech community and the like, but first time coming in. We always know that people need to learn, they want to learn, and that's one of the things that we hope our three days of coverage is going to help people understand, get a taste for all the things that are going on in the show. There are hundreds if not thousands of sessions that are all recorded. How do I choose what to go dig into, what announcements mean the most, what am I going to want to dig into? So that's one of the things that I was excited to hear and excited to help bring to our community here. >> Right, so we're going to help our viewers do that and we're going to learn a lot from our great lineup of guests. So Stu, it's really exciting to be here. We're going to kick off three days of coverage in just a little bit. I'm Rebecca Knight for Stu Miniman. Stay tuned to theCUBE here at Microsoft Ignite.

>> Narrator: Live from Dublin, Ireland. It's theCUBE, covering Hadoop Summit Europe 2016. Brought to you by Hortonworks. Now your host, John Furrier and Dave Vellante. >> Okay, welcome back here, we are here live in Dublin, Ireland, it's theCUBE SiliconANGLEs flagship program where we go out to the events and extract the signal from the noise, I'm John Furrier, my cohost Dave Vellante, our next guest is Emer Coleman who's with Disruption Limited, Open Data Governance Board in Ireland and Transport API, a growing startup built self-sustainable, growing business, open data, love that keynote here at Hadoop Summit, very compelling discussion around digital goods, digital future. Emer, welcome to theCUBE. >> It's great to be here. >> So what was your keynote? Let's just quickly talk about what you talked about, and then we can get in some awesome conversation. >> Sure. So the topic yesterday was we need to talk about techno ethics. So basically, over the last couple of months, I've been doing quite a lot of research on ethics and technology, and many people have different interpretations of that, but yesterday I said it's basically about three things. It's about people, it's about privacy, and it's about profits. So it's asking questions about how do we look at holistic technology development that moves away from a pure technocratic play and looks at the deep societal impacts that technology has. >> One of the things that we're super excited about and passionate about is this new era of openness going to a whole another level. Obviously, open source tier one software development environment, cloud computing allows for instant access to resources, almost limitless at this point, as you can project it forward with Moore's Law and whatnot. But the notion that digital assets are not just content, it's data, it's people, it's the things you mentioned about, create a whole new operating environment or user experience, user expectations with mobile phones and Internet of Things and Transport API which you have, if it moves, you capture it, and you're providing value there. So a whole new economy is developing around digital capital. Share your thoughts around this, because this is an area that you're passionate about, you've just done work here, what's your thoughts on this new digital economy, digital capital, digital asset opportunity? >> I think there's huge excitement about the digital economy, isn't there? And I think one of the things I'm concerned about is that that excitement will lead us to the same place that we are now, where we're not really thinking through what are the equitable distribution in that economy, because it seems to me that the spoils are going to a very tiny elite at the tops. So if you look at Instagram, 13 employees when it was purchased by Facebook for a billion dollars, but that's all our stuff, so I'm not getting any shares in the billion, those 13 people are. That's fantastic that you can build a business, build it to that stage and sell, but you have to think about two things, really: what are we looking at in terms of sustainable businesses into the future that create ethical products, and also the demands from citizens to get some value for their data back, because we're becoming shadow employees, we're shadow employees of Google, so when we email, we're not just corresponding, we're creating value for that company. >> And Facebook is a great example. >> And Facebook, and the thing is, when we were at the beginning of that digital journey, it was quite naive. So we were very seduced by free, and we thought, "This is great," and so we're happy with the service. And then the next stage of that, we realize what if we're not paying for the service, we're the product? >> John: Yeah. >> But we were too embedded in the platform to extricate ourselves. But now, I think, when we look at the future of work and great uncertainty that people are facing, when their labor's not going to be required to the same degree, are we going to slavishly keep producing capital and value for companies like Google, and ask for nothing more than the service in return? I don't think so. >> And certainly, the future will be impacted, and one of the things we see now in our business of online media and online open data, is that the data's very valuable. We see that, I'll say data is the new capital, new oil, whatever phrases of the day is used, and the brand marketers are the first ones to react to it, 'cause they're very data driven. Who are you, how do I sell stuff to you? And so what we're seeing is, brand marketers are saying, "Hey, I'm going to money to try to reach out to people, "and I'm going to activate that base and connect with, "engage with them on Facebook or other platform. "I'm going to add value to your Facebook or Google platform, "but yet I'm parasitic to your platform for the data. "Why just don't I get it directly?" So again, you're starting to see that thinking where I don't want to be a parasite or parasitic to a network that the value's coming from. The users have not yet gotten there, and you're teasing that out. What's your thoughts there, progression, where we're at, have people realized this? Have you seen any movement in the industry around this topic? >> No, I think there's a silence around... Technology companies want to get all the data they can. They're not going to really declare as much as they should, because it bends their service model a bit. Also, the data is emergent. Zuckerberg didn't start Facebook as something that was going to be a utility for a billion people, he started it as a social network for a university. And what grew out of that, we learned as we went along. So I'm thinking, now that we have that experience, we know that happens, so let's start the thinking now. And also, this notion of just taking data because you can, almost speculatively getting data at the point of source, without even knowing what you want it for but thinking, "I'm going to monetize this in the end." Jaron Lanier in his book Who Owns The Future talks about micro licensing back content. And I think that's what we need to do. We start, at the very beginning, we need to start baking in two things: privacy by design and different business models where it's not a winner takes all. It's a dialog between the user and the service, and that's iterated together. >> This idea that it's not a zero sum game is very important, and I want to go back to your Instagram and Facebook example. At its peak, I think Eastman Kodak had hundreds of thousands of employees, maybe four or five hundred, 450,000 employees, huge. Facebook has many many more photos, but maybe a few thousand employees? Wow, so all the jobs are gone, but at the same time, we don't want to be protecting the past from the future, so how do you square that circle? >> Correct, but I think what we know is that the rise of robotics and software is going to eat jobs, and basically, there's going to be a hollowing out of the middle class. You know, for sure, whether it's medicine, journalism, retail, exactly. >> Dave: It's not future, it's now. (laughs) >> Exactly. So we maybe come into a point where large swaths of people don't have work. Now, what do you do in a world where your labor is no longer required? Think about the public policy implications of that. Do we say you either fit in this economy or you die? Are we going to look at ideas which they are looking at in Europe, which is like a universal wage? And all of these things are a challenge to government, because they're going to have a citizenry who are not included in this brave new world. So some public policy thinking has to go into what happens when our kids can't get jobs. When the jobs that used to be done by people like us are done by machines. I'm not against the movement of technology, what I'm saying is there are deep societal implications that need some thinking, because if we get to a point where we suddenly realize, if all of these people who are unemployed and can't get work, this isn't a future we envisioned where robots would take all the crap jobs and we would go off to do wonderful things, like how are we going to bring the bacon home? >> It seems like in a digital world that the gap is creativity to combine technologies and knowledge. I find that it's scary when you talk about maybe micromanaging wages and things like that, education is the answer, but that's... How do you just transfer that knowledge? That's sort of the discussion that we're having in the United States anyway. >> I think some of the issue is that the technology is so, we're kind of seduced by simplicity. So we don't see the complexity underneath, and that's the ultimate aim of a technology, is to make something so simple, that complexity is masked. That's what the iPhone did wonderfully. But that's actually how society is looking now. So we're seduced by this simplicity, we're not seeing the complexity underneath, and that complexity would be about what do we do in a world where our labor is no longer required? >> And one of the things that's interesting about the hollowing of the middle class is the assumption is there's no replacements, so one of the things that could be counter argued is that, okay, as the digital natives, my daughter, she's a freshman in high school, my youngest son's eighth grade, they're natives now, so they're going to commit. So what is the replacement capital and value for companies that can be sustained in the new economy versus the decay and the darwinism of the old? So the digital darwinism aspect's interesting, that's one dilemma. The other one is business models, and I want to get your thoughts on this 'cause this is something we were teasing out with this whole value extraction and company platform issue. A company like Twitter. Highly valuable company, it's a global network of people tweeting and sharing, but yet is under constant pressure from Wall Street and investors that they basically suck. And they don't, they're good, people love Twitter, so they're being forced to behave differently against their mission because their profit motive doesn't really match maybe something like Facebook, so therefore they're instantly devalued, yet the future of someone connecting on Twitter is significantly high. That being said, I want to get your thoughts on that and your advice to Twitter management, given the fact it is a global network. What should they do? >> It's the same old capitalism, just it's digital, it's a digital company, it's a digital asset. It's the same approach, right? Twitter has been a wonderful thing. I've been a Twitter user for years. How amazing, it's played a role in the Arab Spring, all sorts of things. So they're really good, but I think you need as a company, so for example, in our company, in Transport API, we're not really looking to build to this massive IPO, we're trying to build a sustainable company in a traditional way using digital. So I think if you let yourself be seduced by the idea of phenomenal IPO, you kind of take your eye off the ball. >> Or in case this, in case you got IPOed, now you're under pressure to produce-- >> Emer: Absolutely, yeah. >> Which changes your behavior. But in Twitter's management defense, they see the value of their product. Now, they got there by accident and everyone loves it, but now they're not taking the bait to try to craft a short term solution to essentially what is already a valuable product, but not on the books. >> Yes, and also I think where the danger is, we know that their generation shifts across channel. So teenagers probably look at Facebook, I think one of them said, like an awkward family dinner they can't quite leave. But for next gen, they're just not going to go there, 'cause that's where your grandmother is. So the same is true of Twitter and Snapchat, these platforms come and go. It's an interesting phenomenon then to see Wall Street putting that much money into something which is essentially quite ephemeral. I'm not saying that Twitter won't be around for years, it may be, but that's the thing about digital, isn't it? Something else comes in and it's well, that becomes the platform of choice. >> Well, it's interesting, right? Everybody, us included, we criticize the... Michael Dell calls it the 90 day shock clock. But it's actually worked out pretty well, I mean, economically, for the United States companies. Maybe it doesn't in the future. What are your thoughts on that, particularly from a European perspective? Where you're reporting maybe twice a year, there's not as much pressure, but yet from a technology industry standpoint, companies outside the Silicon Valley in particular seem to be less competitive, why? >> For example, in our company, in Transport API, we've got some pretty heavyweight clients, we have a wonderful angel investor who has given us two rounds of investment. And it isn't that kind of avaricious absolutely built this super price. And that's allowed us to build from starting off with 2, now to a team of 10, and we're just about coming into break even, so it's doable. But I think it's a philosophy. We didn't want necessarily to build something huge, although we want to go global, but it was let's do this in a sustainable way with reasonable wages, and we've all put our own soul and money into it, but it's a different cultural proposition, I think. >> Well, the valuations always drive the markets. It's interesting too, to your point about things come and go channels, kind of reminds me, Dave and I used to joke about social networks like nightclubs, they're hot and then it's just too crowded and nobody goes there, as Yogi Bear would say. And then they shift and they go out of business, some don't open with fanfare, no one goes 'cause it's got different context. You have a contextual challenge in the world now. Technology can change things, so I want to ask you about identity 'cause there was a great article posted by the founder of the company called Secret which is one of these anonymous apps like Yik Yak and whatnot, and he shut it down. And he wrote a post, kind of a postmortem, saying, "These things come and go, they don't work, "they're not sustainable because there's no identity." So the role of identity in a social global virtual world, virtual being not just virtual reality, is interesting. You live in a world, and your company, Transport API, provides data which enables stuff and the role of identity. So anonymous versus identity, thoughts there, and that impact to the future of work? If you know who you're dealing with, and if they're present, these are concepts that are now important, presence, identity, attention. >> And that's the interesting thing, isn't it? Who controls that identity? Mark Zuckerberg said, "You only have one identity," which is what he said when he set up Facebook. You think, really? No, that's what a young person thinks. When we're older, we know. >> He also said that young people are smarter than older people. >> Yeah, right, okay. (John laughs) He could be right there, he could be right there, but we all have different identities in different parts of our lives. Who we are here, the Hadoop summit is different from what we're at home to when we're with friends. So identity is a multifaceted thing. But also, who gets to determine your identity? So I have 16 years of my search life and Google. Now, who am I in that server, compared to who I am? I am the sum total of my searches. But I'm not just the sum total of my searches, am I? Or even that contextualized, so I'll give you an example. A number of years ago I was searching for a large, very large waterproof plastic bag. And I typed it in, and I thought, "Oh my god, that sounds like I'm going to murder my husband "and try to bury him." (John and Dave laugh) It was actually-- >> John: Into the compost. >> Right, right. And I thought, "Oh my god, what does this look like "on the other side?" Now, it was actually for my summer garden furniture. But the point is, if you looked at that in an analytic way, who would I be? And so I think identity is very, you know-- >> John: Mistaken. >> Yeah, and also this idea of what Frank Pasquale calls the black box society. These secret algorithms that are controlling flows of money and information. How do they decide what my identity is? What are the moral decisions that they make around that? What does it say if I search for one thing over another? If I search constantly for expensive shoes, does that make me shallow? What do these things say? If I search for certain things around health. >> And there's a value judgment now associated with that that you're talking about, that you do not control. >> Absolutely, and which is probably linked to other things which will determine things like whether I get credit or not, but these can almost be arbitrary decisions, 'cause I have no oversight of the logic that's creating that decision making algorithm. So I think it's not just about identity, it's about who's deciding what that identity is. >> And it's also the reality that you're in, context, situations. Dark side, bright side of technology in this future where this new digital asset economy, digital capital. There's going to be good and bad, education can be consumed non-linear, new forms of consumptions, metadata, as you're pointing out, with the algorithms. Where do you see some bright spots and where do you see the danger areas? >> I think the great thing is, when you were saying software is the future. It's our present, but it's going to be even more so in our future. Some of the brightest brains in the world are involved in the creation of new technology. I just think they need to be focusing a bit more of that intellectual rigor towards the impact they're having on society and how they could do it better. 'Cause I think it's too much of a technocratic solution. Technologists say, "We can do this." The questions is, should they? So I think what we need to do is to loop them back into the more social and philosophical side of the discussion. And of course it's a wonderful thing, hopefully technology is going to do amazing things around health. We can't even predict how amazing it's going to be. But all I'm saying is that, if we don't ask the hard questions now about the downsides, we're going to be in a difficult societal position. But I'm hoping that we will, and I'm hoping that raising issues like techno ethics will get more of that discussion going. >> Well, transparency and open data make a big difference. >> Emer: Absolutely. >> Well, and public policy, as you said earlier, can play a huge role here. I wonder if you could give us your perspective on... Public policy, we're in the US most of the time, but it's interesting when we talk to customers here. To hear about the emphasis, obviously, on privacy, data location and so forth, so in the digital world, do you see Europe's emphasis and, I think, leading on those types of topics as an advantage in a digital world, or does it create friction from an economic standpoint? >> Yeah, but it's not all about economics. Friction is a good thing. There are some times when friction is a good thing. Most technologists think all friction is bad. >> Sure, and I'm not implying that it's necessarily good or bad, I'm curious though, is it potentially an economic advantage to have thought through and have policy on some of those issues? >> Well, what we're seeing here-- >> Because I feel like the US is a ticking time bomb on a lot of these issues. >> I was talking to VCs, some VC friends of mine here in the UK, and what they said they're seeing more and more, VCs asking what we call SMEs, small to medium enterprises, about their data policies, and SMEs not being able to answer those questions, and VCs getting nervous. So I think over time it's going to be a competitive advantage that we've done that homework, that we're basically not just rushing to get more users, but that we're looking at it across the piece. Because, fundamentally, that's more sustainable in the longer term. People will not be dumb too forever. They will not, and so doing that thinking now, where we work with people as we create our technology products, I think it's more sustainable in the long term. When you look at economics, sustainability is really important. >> I want to ask you about the Transport API business, 'cause in the US, same thing, we've seen some great openness of data and amazing innovations that have come out of nowhere. In some cases, unheard of entrepreneurs and/or organizations that better society for the betterment of people, from delivering healthcare to poor areas and whatnot. What has been the coolest thing, or of things you've seen come out of your enablement of the transport data. Use cases, have you seen any things that surprised you? >> It's quite interesting, because when I worked for the mayor of London as his director of digital projects, my job was to set up the London data store, which was to open all of London's public sector data. So I was kind of there from the beginning as a lobbyist, and when I was asking agencies to open up their data, they'd go, "What's the ROI?" And I'd just say, "I don't know." Because government's one and oh, I'm saying that was a chicken and egg, you got to put it out there. And we had a funny incident where some of the IT staff in transport for London accidentally let out this link, which is to the tracker net feed, and that powers the tube notice boards that says, "Your next tube is in a minute," whatever. And so the developer community went, "Ooh, this is interesting." >> John: Candy! >> Yeah, and of course, we had no documentation with it because it kind of went out under the radar. And one developer called Mathew Somerville made this map which showed the tubes on a map in real time. And it was like surfacing the underground. And people just thought, "Oh my god, that is amazing." >> John: It's illuminating. >> Yeah. It didn't do anything, but it showed the possibility. The newspapers picked it up, it was absolutely brilliant example, and the guy made it in half a day. And that was the first time people saw their transport system kind of differently. So that was amazing, and then we've seen hundreds of different applications that are being built all the time. And what we're also seeing is integration of transport data with other things, so one of our clients in Transport API is called Toothpick, and they're an online dental booking agency. And so you can go online, you can book your dental appointment with your NHS dentist, and then they bake in transport information to tell you how to get there. So we have pubs using them, and screens so people can order their dinner, and then they say, "You've got 10 minutes till the next bus." So all sorts of cross-platform applications. >> That you never could've envisioned. >> Emer: Never. >> And it's just your point earlier about it's not a zero sum game, you're giving so many ways to create value. >> Emer: Right, right. >> Again, I come back to this notion of education and creativity in the United States education system, so unattainable for so many people, and that's a real concern, and you're seeing the middle class get hollowed out. I think the stat is, the average wage in the United States was 55,000 in 1999, it's 50,000 today. The political campaigns are obviously picking at that scab. What's the climate like in Europe from that standpoint? >> In terms of education? >> No, just in terms of, yes, the education, middle class getting hollowed out, the sentiment around that. >> I don't think people are up to speed with that yet, I really don't think that they're aware of the scale. I think when they think robots or automation, they don't really think software. They think robots like there were in the movies, that would come, as I say, and do those jobs nobody wanted. But not like software. So when I say to them, look, E-discovery software, when it's applied retrospectively, what it shows is that human lawyers are only 60% accurate compared to it. Now, that's a no-brainer, right? If software is 100% accurate, I'm going to use the software. And the ratio difference is 1 to 500. Where you needed 500 lawyers before you need 1. So I don't think people are across the scale of change. >> But it's interesting, you're flying to Heathrow, you fly in and out, you're dealing with a kiosk. You drive out, the billboards are all electronic. There aren't guys doing this anymore. So it's tangible. >> And I think, to your point about education, I'm not as familiar with the education system in the US, but I certainly think, in Europe and in the UK, the education system is not capable of dealing even with the latest digital natives. They're still structuring their classrooms in the same way. These kids, you know-- >> John: They have missed the line with the technology. >> Absolutely. >> So reading, writing and arithmetic, fine. And the cost of education is maybe acceptable. But they may be teaching the wrong thing. >> Asynchronous non-linear, is the thing. >> There's a wonderful example of an Indian academic called Sugata Mitra, who has a fabulous project called a Hole in the Wall. And he goes to non-English speaking little Indian villages, and he builds a computer, and he puts a roof over it so only the children can do it. They don't speak English. And he came back, and he leaves a little bit of stuff they have to get around before they can play a game. And he came back six months later, and he said to them, "What did you think?" And one of the children said, "We need a faster CPU and a better mouse." Now, his point is self-learning, once you have access to technology, is amazing, and I think we have to start-- >> Same thing with the non-linear consumption, asynchronous, all this, the API economy enabling new kinds of expectation and opportunities. >> And it was interesting because the example, some UK schools tried to follow his example. And six months later, they rang him up and they said, "It's not working," and he said, "What did you do?" And they said, "Well, we got every kid a laptop." He said, "That's not the point." The point was putting a scarce resource that the children had to collaborate over. So in order to get to the game, they had figure out certain things. >> I think you're right on some of these (mumbles) that no one's talking about. And Dave and I are very passionate on this, and we're actually investing in a whole new e-learning concept. But it's not about doing that laptop thing or putting courseware online. That's old workflow in a new model. Come on, old wine in a new bottle. So that's interesting. I want to get your thoughts, so a personal question to end this segment. What are you passionate about now, what are you working, outside of the venture, which is exciting. You have a lot of background going back to technology entrepreneurship, public policy, and you're in the front lines now, thought leading on this whole new wide open sea of opportunity, confusion, enabling it. What are you passionate about, what are you working on? Share with the folks that are watching. >> So one of the main things we're trying to do. I work as an associate with Ernst & Young in London. And we've been having discussions over the past couple of months around techno ethics, and I've basically said, "Look, let's see if we can get EY "to build to build an EY good governance index." Like, what does good governance look like in this space, a massively complex area, but what I would love is if people would collaborate with us on that. If we could help to draw up an ethical framework that would convene the technology industry around some ethical good governance issues. So that's what I'm going to be working on as hard as I can over the next while, to try and get as much collaboration from the community, because I think we'd be so much more powerful if the technology industry was to say, "Yeah, let's try and do this better "rather than waiting for regulation," which will come, but will be too clunky and not fit for purpose. >> And which new technology that's emerging do you get most excited about? >> Hmm. Drones. (laughter) >> How about anything with bitcoin, block chains? >> Absolutely, absolutely, block chain. Yeah, block chain, you have to say, yeah. I think, 'cause bitcoin, you know, it's worth 20 p today, it's worth 200,000 tomorrow. >> Dave: Yeah, but block chain. >> Right, right. I mean, that is incredible potentiality. >> New terms like federated, that's not a new term, but federation, universal, unification. These are the themes right now. >> Emer: Well, it's like the road's been coated, isn't it? And we don't know where it's going to go. What a time we live in, right? >> Emer Coleman, thank you so much for spending your time and joining us on theCUBE here, we really appreciate the conversation. Thanks for sharing that great insight here on theCUBE, thank you. It's theCUBE, we are live here in Dublin, Ireland. I'm John Furrier with Dave Vellante. We'll we right back with more SiliconANGLEs, theCUBE and extracting the signal from the noise after this short break. (bright music)