(soft upbeat music) >> Okay, we're kicking things off with Brian Gilmore. He's the director of IoT, an emerging technology at InfluxData. Brian, welcome to the program. Thanks for coming on. >> Thanks, Dave, great to be here. I appreciate the time. >> Hey, explain why InfluxDB, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >> No, no, not at all. I mean, I think, for us it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like SQL query support, things like that, we have to figure out a way to execute those for them in a way that will scale long term. And then we also want to make sure we're innovating, we're sort of staying ahead of the market as well, and sort of anticipating those future needs. So, you know, this is really a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine. But, you know, initially, the customers who are using us are going to see just great improvements in performance, you know, especially those that are working at the top end of the workload scale, you know, the massive data volumes and things like that. >> Yeah, and we're going to get into that today and the architecture and the like. But what was the catalyst for the enhancements? I mean, when and how did this all come about? >> Well, I mean, like three years ago, we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product. And sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was a long journey. (chuckles) I guess, you know, phase one was, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to optimize for like multi-tenant, multi-cloud, be able to host it in a truly like SAS manner where we could use, you know, some type of customer activity or consumption as the pricing vector. And that was sort of the birth of the real first InfluxDB cloud, you know, which has been really successful. We've seen, I think, like 60,000 people sign up. And we've got tons and tons of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a daily basis. And having that sort of big pool of very diverse and varied customers to chat with as they're using the product, as they're giving us feedback, et cetera, has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that, and then also making these big leaps as we're doing with this new engine. >> All right, so you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really want to understand how much of a pivot this is, and what does it take to make that shift from, you know, time series specialist to real time analytics and being able to support both? >> Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. Time series data is always going to be fundamental in sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. The time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics. If we're being honest though, I think our user base is well aware that the way we were architected was much more towards those sort of like backwards-looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a time to response on the queries, and can we get that to the point where the result sets are coming back so quickly from the time of query that we can like, limit that window down to minutes and then seconds? And now with this new engine, we're really starting to talk about a query window that could be like returning results in, you know, milliseconds of time since it hit the ingest queue. And that's really getting to the point where, as your data is available, you can use it and you can query it, you can visualize it, you can do all those sort of magical things with it. And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the real time queries, the multiple language query support. But, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a limited number of customers, strategic customers and strategic availabilities zones to start, but, you know, everybody over time. >> So you're basically going from what happened to, and you can still do that, obviously, but to what's happening now in the moment? >> Yeah. Yeah. I mean, if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the underlying data collection, the architecture, the infrastructure, the devices, and you know, the sort of highly distributed nature of all of this. So, yeah, I mean, getting a customer or a user to be able to use the data as soon as it is available, is what we're after here. I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >> Yeah, I mean, it is operationally, or operational real time is different. And that's one of the things that really triggered us to know that we were heading in the right direction is just how many sort of operational customers we have, you know, everything from like aerospace and defense. We've got companies monitoring satellites. We've got tons of industrial users using us as a process historian on the plant floor. And if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're going to do here is we're going to start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their historians and databases. >> Is this available, these innovations to InfluxDB cloud customers, only who can access this capability? >> Yeah, I mean, commercially and today, yes. I think we want to emphasize that for now our goal is to get our latest and greatest and our best to everybody over time of course. You know, one of the things we had to do here was like we doubled down on sort of our commitment to open source and availability. So, like, anybody today can take a look at the libraries on our GitHub and can inspect it and even can try to implement or execute some of it themselves in their own infrastructure. We are committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. And so just, you know, being careful, maybe a little cautious in terms of how big we go with this right away. Just sort of both limits, you know, the risk of any issues that can come with new software roll outs, we haven't seen anything so far. But also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products. But once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's going to be exciting time for the whole ecosystem. >> Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are going to help deliver on this vision. What should we know there? >> Well, I mean, I think, foundationally, we built the new core on Rust. This is a new very sort of popular systems language. It's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well, and if it does find error conditions. I mean, we've loved working with Go, and a lot of our libraries will continue to be sort of implemented in Go, but when it came to this particular new engine, that power performance and stability of Rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parquet for persistence. I think, for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our time series merge trees, this is a big break from that. You know, Arrow on the sort of in mem side and then Parquet in the on disk side. It allows us to present, you know, a unified set of APIs for those really fast real time queries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that Parquet format, which is also cool because there's an entire ecosystem sort of popping up around Parquet in terms of the machine learning community. And getting that all to work, we had to glue it together with Arrow Flight. That's sort of what we're using as our RPC component. It handles the orchestration and the transportation of the columnar data now, we're moving to like a true columnar database model for this version of the engine. You know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like, blurring that line between real time and historical data, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >> Yeah, again, I mean, it's funny. You mentioned Rust. It's been around for a long time but it's popularity is, you know, really starting to hit that steep part of the S-curve. And we're going to dig into more of that, but give us, is there anything else that we should know about, Brian? Give us the last word. >> Well, I mean, I think first, I'd like everybody sort of watching, just to like, take a look at what we're offering in terms of early access in beta programs. I mean, if you want to participate or if you want to work sort of in terms of early access with the new engine, please reach out to the team. I'm sure, you know, there's a lot of communications going out and it'll be highly featured on our website. But reach out to the team. Believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to, because we can flip a lot of stuff on, especially in cloud through feature flags. But if there's something new that you want to try out, we'd just love to hear from you. And then, you know, our goal would be, that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to sort of build the next versions of your business. Because, you know, the whole database, the ecosystem as it expands out into this vertically-oriented stack of cloud services, and enterprise databases, and edge databases, you know, it's going to be what we all make it together, not just those of us who are employed by InfluxDB. And then finally, I would just say, please, like, watch and Anais' and Tim's sessions. Like, these are two of our best and brightest. They're totally brilliant, completely pragmatic, and they are most of all customer-obsessed, which is amazing. And there's no better takes, like honestly, on the sort of technical details of this than theirs, especially when it comes to the value that these investments will bring to our customers and our communities. So, encourage you to, you know, pay more attention to them than you did to me, for sure. >> Brian Gilmore, great stuff. Really appreciate your time. Thank you. >> Yeah, thanks David, it was awesome. Looking forward to it. >> Yeah, me too. I'm looking forward to see how the community actually applies these new innovations and goes beyond just the historical into the real time. Really hot area. As Brian said, in a moment, I'll be right back with Anais Dotis-Georgiou to dig into the critical aspects of key open source components of the InfluxDB engine, including Rust, Arrow, Parquet, Data Fusion. Keep it right there. You don't want to miss this. (soft upbeat music)

>>Individuals create developers, translate ideas to code, to create great applications and great applications. Touch everyone. A Docker. We know that collaboration is key to your innovation sharing ideas, working together. Launching the most secure applications. Docker is with you wherever your team innovates, whether it be robots or autonomous cars, we're doing research to save lives during a pandemic, revolutionizing, how to buy and sell goods online, or even going into the unknown frontiers of space. Docker is launching innovation everywhere. Join us on the journey to build, share, run the future. >>Hello and welcome to Docker con 2021. We're incredibly excited to have more than 80,000 of you join us today from all over the world. As it was last year, this year at DockerCon is 100% virtual and 100% free. So as to enable as many community members as possible to join us now, 100%. Virtual is also an acknowledgement of the continuing global pandemic in particular, the ongoing tragedies in India and Brazil, the Docker community is a global one. And on behalf of all Dr. Khan attendees, we are donating $10,000 to UNICEF support efforts to fight the virus in those countries. Now, even in those regions of the world where the pandemic is being brought under control, virtual first is the new normal. It's been a challenging transition. This includes our team here at Docker. And we know from talking with many of you that you and your developer teams are challenged by this as well. So to help application development teams better collaborate and ship faster, we've been working on some powerful new features and we thought it would be fun to start off with a demo of those. How about it? Want to have a look? All right. Then no further delay. I'd like to introduce Youi Cal and Ben, gosh, over to you and Ben >>Morning, Ben, thanks for jumping on real quick. >>Have you seen the email from Scott? The one about updates and the docs landing page Smith, the doc combat and more prominence. >>Yeah. I've got something working on my local machine. I haven't committed anything yet. I was thinking we could try, um, that new Docker dev environments feature. >>Yeah, that's cool. So if you hit the share button, what I should do is it will take all of your code and the dependencies and the image you're basing it on and wrap that up as one image for me. And I can then just monitor all my machines that have been one click, like, and then have it side by side, along with the changes I've been looking at as well, because I was also having a bit of a look and then I can really see how it differs to what I'm doing. Maybe I can combine it to do the best of both worlds. >>Sounds good. Uh, let me get that over to you, >>Wilson. Yeah. If you pay with the image name, I'll get that started up. >>All right. Sen send it over >>Cheesy. Okay, great. Let's have a quick look at what you he was doing then. So I've been messing around similar to do with the batter. I've got movie at the top here and I think it looks pretty cool. Let's just grab that image from you. Pick out that started on a dev environment. What this is doing. It's just going to grab the image down, which you can take all of the code, the dependencies only get brunches working on and I'll get that opened up in my idea. Ready to use. It's a here close. We can see our environment as my Molly image, just coming down there and I've got my new idea. >>We'll load this up and it'll just connect to my dev environment. There we go. It's connected to the container. So we're working all in the container here and now give it a moment. What we'll do is we'll see what changes you've been making as well on the code. So it's like she's been working on a landing page as well, and it looks like she's been changing the banner as well. So let's get this running. Let's see what she's actually doing and how it looks. We'll set up our checklist and then we'll see how that works. >>Great. So that's now rolling. So let's just have a look at what you use doing what changes she had made. Compare those to mine just jumped back into my dev container UI, see that I've got both of those running side by side with my changes and news changes. Okay. So she's put Molly up there rather than mobi or somebody had the same idea. So I think in a way I can make us both happy. So if we just jumped back into what we'll do, just add Molly and Moby and here I'll save that. And what we can see is, cause I'm just working within the container rather than having to do sort of rebuild of everything or serve, or just reload my content. No, that's straight the page. So what I can then do is I can come up with my browser here. Once that's all refreshed, refresh the page once hopefully, maybe twice, we should then be able to see your refresh it or should be able to see that we get Malia mobi come up. So there we go, got Molly mobi. So what we'll do now is we'll describe that state. It sends us our image and then we'll just create one of those to share with URI or share. And we'll get a link for that. I guess we'll send that back over to you. >>So I've had a look at what you were doing and I'm actually going to change. I think that might work for both of us. I wondered if you could take a look at it. If I send it over. >>Sounds good. Let me grab the link. >>Yeah, it's a dev environment link again. So if you just open that back in the doc dashboard, it should be able to open up the code that I've changed and then just run it in the same way you normally do. And that shouldn't interrupt what you're already working on because there'll be able to run side by side with your other brunch. You already got, >>Got it. Got it. Loading here. Well, that's great. It's Molly and movie together. I love it. I think we should ship it. >>Awesome. I guess it's chip it and get on with the rest of.com. Wasn't that cool. Thank you Joey. Thanks Ben. Everyone we'll have more of this later in the keynote. So stay tuned. Let's say earlier, we've all been challenged by this past year, whether the COVID pandemic, the complete evaporation of customer demand in many industries, unemployment or business bankruptcies, we all been touched in some way. And yet, even to miss these tragedies last year, we saw multiple sources of hope and inspiration. For example, in response to COVID we saw global communities, including the tech community rapidly innovate solutions for analyzing the spread of the virus, sequencing its genes and visualizing infection rates. In fact, if all in teams collaborating on solutions for COVID have created more than 1,400 publicly shareable images on Docker hub. As another example, we all witnessed the historic landing and exploration of Mars by the perseverance Rover and its ingenuity drone. >>Now what's common in these examples, these innovative and ambitious accomplishments were made possible not by any single individual, but by teams of individuals collaborating together. The power of teams is why we've made development teams central to Docker's mission to build tools and content development teams love to help them get their ideas from code to cloud as quickly as possible. One of the frictions we've seen that can slow down to them in teams is that the path from code to cloud can be a confusing one, riddle with multiple point products, tools, and images that need to be integrated and maintained an automated pipeline in order for teams to be productive. That's why a year and a half ago we refocused Docker on helping development teams make sense of all this specifically, our goal is to provide development teams with the trusted content, the sharing capabilities and the pipeline integrations with best of breed third-party tools to help teams ship faster in short, to provide a collaborative application development platform. >>Everything a team needs to build. Sharon run create applications. Now, as I noted earlier, it's been a challenging year for everyone on our planet and has been similar for us here at Docker. Our team had to adapt to working from home local lockdowns caused by the pandemic and other challenges. And despite all this together with our community and ecosystem partners, we accomplished many exciting milestones. For example, in open source together with the community and our partners, we open sourced or made major contributions to many projects, including OCI distribution and the composed plugins building on these open source projects. We had powerful new capabilities to the Docker product, both free and subscription. For example, support for WSL two and apple, Silicon and Docker, desktop and vulnerability scanning audit logs and image management and Docker hub. >>And finally delivering an easy to use well-integrated development experience with best of breed tools and content is only possible through close collaboration with our ecosystem partners. For example, this last year we had over 100 commercialized fees, join our Docker verified publisher program and over 200 open source projects, join our Docker sponsored open source program. As a result of these efforts, we've seen some exciting growth in the Docker community in the 12 months since last year's Docker con for example, the number of registered developers grew 80% to over 8 million. These developers created many new images increasing the total by 56% to almost 11 million. And the images in all these repositories were pulled by more than 13 million monthly active IP addresses totaling 13 billion pulls a month. Now while the growth is exciting by Docker, we're even more excited about the stories we hear from you and your development teams about how you're using Docker and its impact on your businesses. For example, cancer researchers and their bioinformatics development team at the Washington university school of medicine needed a way to quickly analyze their clinical trial results and then share the models, the data and the analysis with other researchers they use Docker because it gives them the ease of use choice of pipeline tools and speed of sharing so critical to their research. And most importantly to the lives of their patients stay tuned for another powerful customer story later in the keynote from Matt fall, VP of engineering at Oracle insights. >>So with this last year behind us, what's next for Docker, but challenge you this last year of force changes in how development teams work, but we felt for years to come. And what we've learned in our discussions with you will have long lasting impact on our product roadmap. One of the biggest takeaways from those discussions that you and your development team want to be quicker to adapt, to changes in your environment so you can ship faster. So what is DACA doing to help with this first trusted content to own the teams that can focus their energies on what is unique to their businesses and spend as little time as possible on undifferentiated work are able to adapt more quickly and ship faster in order to do so. They need to be able to trust other components that make up their app together with our partners. >>Docker is doubling down and providing development teams with trusted content and the tools they need to use it in their applications. Second, remote collaboration on a development team, asking a coworker to take a look at your code used to be as easy as swiveling their chair around, but given what's happened in the last year, that's no longer the case. So as you even been hinted in the demo at the beginning, you'll see us deliver more capabilities for remote collaboration within a development team. And we're enabling development team to quickly adapt to any team configuration all on prem hybrid, all work from home, helping them remain productive and focused on shipping third ecosystem integrations, those development teams that can quickly take advantage of innovations throughout the ecosystem. Instead of getting locked into a single monolithic pipeline, there'll be the ones able to deliver amps, which impact their businesses faster. >>So together with our ecosystem partners, we are investing in more integrations with best of breed tools, right? Integrated automated app pipelines. Furthermore, we'll be writing more public API APIs and SDKs to enable ecosystem partners and development teams to roll their own integrations. We'll be sharing more details about remote collaboration and ecosystem integrations. Later in the keynote, I'd like to take a moment to share with Docker and our partners are doing for trusted content, providing development teams, access to content. They can trust, allows them to focus their coding efforts on what's unique and differentiated to that end Docker and our partners are bringing more and more trusted content to Docker hub Docker official images are 160 images of popular upstream open source projects that serve as foundational building blocks for any application. These include operating systems, programming, languages, databases, and more. Furthermore, these are updated patch scan and certified frequently. So I said, no image is older than 30 days. >>Docker verified publisher images are published by more than 100 commercialized feeds. The image Rebos are explicitly designated verify. So the developers searching for components for their app know that the ISV is actively maintaining the image. Docker sponsored open source projects announced late last year features images for more than 200 open source communities. Docker sponsors these communities through providing free storage and networking resources and offering their community members unrestricted access repos for businesses allow businesses to update and share their apps privately within their organizations using role-based access control and user authentication. No, and finally, public repos for communities enable community projects to be freely shared with anonymous and authenticated users alike. >>And for all these different types of content, we provide services for both development teams and ISP, for example, vulnerability scanning and digital signing for enhanced security search and filtering for discoverability packaging and updating services and analytics about how these products are being used. All this trusted content, we make available to develop teams for them directly to discover poll and integrate into their applications. Our goal is to meet development teams where they live. So for those organizations that prefer to manage their internal distribution of trusted content, we've collaborated with leading container registry partners. We announced our partnership with J frog late last year. And today we're very pleased to announce our partnerships with Amazon and Miranda's for providing an integrated seamless experience for joint for our joint customers. Lastly, the container images themselves and this end to end flow are built on open industry standards, which provided all the teams with flexibility and choice trusted content enables development teams to rapidly build. >>As I let them focus on their unique differentiated features and use trusted building blocks for the rest. We'll be talking more about trusted content as well as remote collaboration and ecosystem integrations later in the keynote. Now ecosystem partners are not only integral to the Docker experience for development teams. They're also integral to a great DockerCon experience, but please join me in thanking our Dr. Kent on sponsors and checking out their talks throughout the day. I also want to thank some others first up Docker team. Like all of you this last year has been extremely challenging for us, but the Docker team rose to the challenge and worked together to continue shipping great product, the Docker community of captains, community leaders, and contributors with your welcoming newcomers, enthusiasm for Docker and open exchanges of best practices and ideas talker, wouldn't be Docker without you. And finally, our development team customers. >>You trust us to help you build apps. Your businesses rely on. We don't take that trust for granted. Thank you. In closing, we often hear about the tenant's developer capable of great individual feeds that can transform project. But I wonder if we, as an industry have perhaps gotten this wrong by putting so much emphasis on weight, on the individual as discussed at the beginning, great accomplishments like innovative responses to COVID-19 like landing on Mars are more often the results of individuals collaborating together as a team, which is why our mission here at Docker is delivered tools and content developers love to help their team succeed and become 10 X teams. Thanks again for joining us, we look forward to having a great DockerCon with you today, as well as a great year ahead of us. Thanks and be well. >>Hi, I'm Dana Lawson, VP of engineering here at get hub. And my job is to enable this rich interconnected community of builders and makers to build even more and hopefully have a great time doing it in order to enable the best platform for developers, which I know is something we are all passionate about. We need to partner across the ecosystem to ensure that developers can have a great experience across get hub and all the tools that they want to use. No matter what they are. My team works to build the tools and relationships to make that possible. I am so excited to join Scott on this virtual stage to talk about increasing developer velocity. So let's dive in now, I know this may be hard for some of you to believe, but as a former CIS admin, some 21 years ago, working on sense spark workstations, we've come such a long way for random scripts and desperate systems that we've stitched together to this whole inclusive developer workflow experience being a CIS admin. >>Then you were just one piece of the siloed experience, but I didn't want to just push code to production. So I created scripts that did it for me. I taught myself how to code. I was the model lazy CIS admin that got dangerous and having pushed a little too far. I realized that working in production and building features is really a team sport that we had the opportunity, all of us to be customer obsessed today. As developers, we can go beyond the traditional dev ops mindset. We can really focus on adding value to the customer experience by ensuring that we have work that contributes to increasing uptime via and SLS all while being agile and productive. We get there. When we move from a pass the Baton system to now having an interconnected developer workflow that increases velocity in every part of the cycle, we get to work better and smarter. >>And honestly, in a way that is so much more enjoyable because we automate away all the mundane and manual and boring tasks. So we get to focus on what really matters shipping, the things that humans get to use and love. Docker has been a big part of enabling this transformation. 10, 20 years ago, we had Tomcat containers, which are not Docker containers. And for y'all hearing this the first time go Google it. But that was the way we built our applications. We had to segment them on the server and give them resources. Today. We have Docker containers, these little mini Oasys and Docker images. You can do it multiple times in an orchestrated manner with the power of actions enabled and Docker. It's just so incredible what you can do. And by the way, I'm showing you actions in Docker, which I hope you use because both are great and free for open source. >>But the key takeaway is really the workflow and the automation, which you certainly can do with other tools. Okay, I'm going to show you just how easy this is, because believe me, if this is something I can learn and do anybody out there can, and in this demo, I'll show you about the basic components needed to create and use a package, Docker container actions. And like I said, you won't believe how awesome the combination of Docker and actions is because you can enable your workflow to do no matter what you're trying to do in this super baby example. We're so small. You could take like 10 seconds. Like I am here creating an action due to a simple task, like pushing a message to your logs. And the cool thing is you can use it on any the bit on this one. Like I said, we're going to use push. >>You can do, uh, even to order a pizza every time you roll into production, if you wanted, but at get hub, that'd be a lot of pizzas. And the funny thing is somebody out there is actually tried this and written that action. If you haven't used Docker and actions together, check out the docs on either get hub or Docker to get you started. And a huge shout out to all those doc writers out there. I built this demo today using those instructions. And if I can do it, I know you can too, but enough yapping let's get started to save some time. And since a lot of us are Docker and get hub nerds, I've already created a repo with a Docker file. So we're going to skip that step. Next. I'm going to create an action's Yammel file. And if you don't Yammer, you know, actions, the metadata defines my important log stuff to capture and the input and my time out per parameter to pass and puts to the Docker container, get up a build image from your Docker file and run the commands in a new container. >>Using the Sigma image. The cool thing is, is you can use any Docker image in any language for your actions. It doesn't matter if it's go or whatever in today's I'm going to use a shell script and an input variable to print my important log stuff to file. And like I said, you know me, I love me some. So let's see this action in a workflow. When an action is in a private repo, like the one I demonstrating today, the action can only be used in workflows in the same repository, but public actions can be used by workflows in any repository. So unfortunately you won't get access to the super awesome action, but don't worry in the Guild marketplace, there are over 8,000 actions available, especially the most important one, that pizza action. So go try it out. Now you can do this in a couple of ways, whether you're doing it in your preferred ID or for today's demo, I'm just going to use the gooey. I'm going to navigate to my actions tab as I've done here. And I'm going to in my workflow, select new work, hello, probably load some workflows to Claire to get you started, but I'm using the one I've copied. Like I said, the lazy developer I am in. I'm going to replace it with my action. >>That's it. So now we're going to go and we're going to start our commitment new file. Now, if we go over to our actions tab, we can see the workflow in progress in my repository. I just click the actions tab. And because they wrote the actions on push, we can watch the visualization under jobs and click the job to see the important stuff we're logging in the input stamp in the printed log. And we'll just wait for this to run. Hello, Mona and boom. Just like that. It runs automatically within our action. We told it to go run as soon as the files updated because we're doing it on push merge. That's right. Folks in just a few minutes, I built an action that writes an entry to a log file every time I push. So I don't have to do it manually. In essence, with automation, you can be kind to your future self and save time and effort to focus on what really matters. >>Imagine what I could do with even a little more time, probably order all y'all pieces. That is the power of the interconnected workflow. And it's amazing. And I hope you all go try it out, but why do we care about all of that? Just like in the demo, I took a manual task with both tape, which both takes time and it's easy to forget and automated it. So I don't have to think about it. And it's executed every time consistently. That means less time for me to worry about my human errors and mistakes, and more time to focus on actually building the cool stuff that people want. Obviously, automation, developer productivity, but what is even more important to me is the developer happiness tools like BS, code actions, Docker, Heroku, and many others reduce manual work, which allows us to focus on building things that are awesome. >>And to get into that wonderful state that we call flow. According to research by UC Irvine in Humboldt university in Germany, it takes an average of 23 minutes to enter optimal creative state. What we call the flow or to reenter it after distraction like your dog on your office store. So staying in flow is so critical to developer productivity and as a developer, it just feels good to be cranking away at something with deep focus. I certainly know that I love that feeling intuitive collaboration and automation features we built in to get hub help developer, Sam flow, allowing you and your team to do so much more, to bring the benefits of automation into perspective in our annual October's report by Dr. Nicole, Forsgren. One of my buddies here at get hub, took a look at the developer productivity in the stork year. You know what we found? >>We found that public GitHub repositories that use the Automational pull requests, merge those pull requests. 1.2 times faster. And the number of pooled merged pull requests increased by 1.3 times, that is 34% more poor requests merged. And other words, automation can con can dramatically increase, but the speed and quantity of work completed in any role, just like an open source development, you'll work more efficiently with greater impact when you invest the bulk of your time in the work that adds the most value and eliminate or outsource the rest because you don't need to do it, make the machines by elaborate by leveraging automation in their workflows teams, minimize manual work and reclaim that time for innovation and maintain that state of flow with development and collaboration. More importantly, their work is more enjoyable because they're not wasting the time doing the things that the machines or robots can do for them. >>And I remember what I said at the beginning. Many of us want to be efficient, heck even lazy. So why would I spend my time doing something I can automate? Now you can read more about this research behind the art behind this at October set, get hub.com, which also includes a lot of other cool info about the open source ecosystem and how it's evolving. Speaking of the open source ecosystem we at get hub are so honored to be the home of more than 65 million developers who build software together for everywhere across the globe. Today, we're seeing software development taking shape as the world's largest team sport, where development teams collaborate, build and ship products. It's no longer a solo effort like it was for me. You don't have to take my word for it. Check out this globe. This globe shows real data. Every speck of light you see here represents a contribution to an open source project, somewhere on earth. >>These arts reach across continents, cultures, and other divides. It's distributed collaboration at its finest. 20 years ago, we had no concept of dev ops, SecOps and lots, or the new ops that are going to be happening. But today's development and ops teams are connected like ever before. This is only going to continue to evolve at a rapid pace, especially as we continue to empower the next hundred million developers, automation helps us focus on what's important and to greatly accelerate innovation. Just this past year, we saw some of the most groundbreaking technological advancements and achievements I'll say ever, including critical COVID-19 vaccine trials, as well as the first power flight on Mars. This past month, these breakthroughs were only possible because of the interconnected collaborative open source communities on get hub and the amazing tools and workflows that empower us all to create and innovate. Let's continue building, integrating, and automating. So we collectively can give developers the experience. They deserve all of the automation and beautiful eye UIs that we can muster so they can continue to build the things that truly do change the world. Thank you again for having me today, Dr. Khan, it has been a pleasure to be here with all you nerds. >>Hello. I'm Justin. Komack lovely to see you here. Talking to developers, their world is getting much more complex. Developers are being asked to do everything security ops on goal data analysis, all being put on the rockers. Software's eating the world. Of course, and this all make sense in that view, but they need help. One team. I told you it's shifted all our.net apps to run on Linux from windows, but their developers found the complexity of Docker files based on the Linux shell scripts really difficult has helped make these things easier for your teams. Your ones collaborate more in a virtual world, but you've asked us to make this simpler and more lightweight. You, the developers have asked for a paved road experience. You want things to just work with a simple options to be there, but it's not just the paved road. You also want to be able to go off-road and do interesting and different things. >>Use different components, experiments, innovate as well. We'll always offer you both those choices at different times. Different developers want different things. It may shift for ones the other paved road or off road. Sometimes you want reliability, dependability in the zone for day to day work, but sometimes you have to do something new, incorporate new things in your pipeline, build applications for new places. Then you knew those off-road abilities too. So you can really get under the hood and go and build something weird and wonderful and amazing. That gives you new options. Talk as an independent choice. We don't own the roads. We're not pushing you into any technology choices because we own them. We're really supporting and driving open standards, such as ISEI working opensource with the CNCF. We want to help you get your applications from your laptops, the clouds, and beyond, even into space. >>Let's talk about the key focus areas, that frame, what DACA is doing going forward. These are simplicity, sharing, flexibility, trusted content and care supply chain compared to building where the underlying kernel primitives like namespaces and Seagraves the original Docker CLI was just amazing Docker engine. It's a magical experience for everyone. It really brought those innovations and put them in a world where anyone would use that, but that's not enough. We need to continue to innovate. And it was trying to get more done faster all the time. And there's a lot more we can do. We're here to take complexity away from deeply complicated underlying things and give developers tools that are just amazing and magical. One of the area we haven't done enough and make things magical enough that we're really planning around now is that, you know, Docker images, uh, they're the key parts of your application, but you know, how do I do something with an image? How do I, where do I attach volumes with this image? What's the API. Whereas the SDK for this image, how do I find an example or docs in an API driven world? Every bit of software should have an API and an API description. And our vision is that every container should have this API description and the ability for you to understand how to use it. And it's all a seamless thing from, you know, from your code to the cloud local and remote, you can, you can use containers in this amazing and exciting way. >>One thing I really noticed in the last year is that companies that started off remote fast have constant collaboration. They have zoom calls, apron all day terminals, shattering that always working together. Other teams are really trying to learn how to do this style because they didn't start like that. We used to walk around to other people's desks or share services on the local office network. And it's very difficult to do that anymore. You want sharing to be really simple, lightweight, and informal. Let me try your container or just maybe let's collaborate on this together. Um, you know, fast collaboration on the analysts, fast iteration, fast working together, and he wants to share more. You want to share how to develop environments, not just an image. And we all work by seeing something someone else in our team is doing saying, how can I do that too? I can, I want to make that sharing really, really easy. Ben's going to talk about this more in the interest of one minute. >>We know how you're excited by apple. Silicon and gravis are not excited because there's a new architecture, but excited because it's faster, cooler, cheaper, better, and offers new possibilities. The M one support was the most asked for thing on our public roadmap, EFA, and we listened and share that we see really exciting possibilities, usership arm applications, all the way from desktop to production. We know that you all use different clouds and different bases have deployed to, um, you know, we work with AWS and Azure and Google and more, um, and we want to help you ship on prime as well. And we know that you use huge number of languages and the containers help build applications that use different languages for different parts of the application or for different applications, right? You can choose the best tool. You have JavaScript hat or everywhere go. And re-ask Python for data and ML, perhaps getting excited about WebAssembly after hearing about a cube con, you know, there's all sorts of things. >>So we need to make that as easier. We've been running the whole month of Python on the blog, and we're doing a month of JavaScript because we had one specific support about how do I best put this language into production of that language into production. That detail is important for you. GPS have been difficult to use. We've added GPS suppose in desktop for windows, but we know there's a lot more to do to make the, how multi architecture, multi hardware, multi accelerator world work better and also securely. Um, so there's a lot more work to do to support you in all these things you want to do. >>How do we start building a tenor has applications, but it turns out we're using existing images as components. I couldn't assist survey earlier this year, almost half of container image usage was public images rather than private images. And this is growing rapidly. Almost all software has open source components and maybe 85% of the average application is open source code. And what you're doing is taking whole container images as modules in your application. And this was always the model with Docker compose. And it's a model that you're already et cetera, writing you trust Docker, official images. We know that they might go to 25% of poles on Docker hub and Docker hub provides you the widest choice and the best support that trusted content. We're talking to people about how to make this more helpful. We know, for example, that winter 69 four is just showing us as support, but the image doesn't yet tell you that we're working with canonical to improve messaging from specific images about left lifecycle and support. >>We know that you need more images, regularly updated free of vulnerabilities, easy to use and discover, and Donnie and Marie neuro, going to talk about that more this last year, the solar winds attack has been in the, in the news. A lot, the software you're using and trusting could be compromised and might be all over your organization. We need to reduce the risk of using vital open-source components. We're seeing more software supply chain attacks being targeted as the supply chain, because it's often an easier place to attack and production software. We need to be able to use this external code safely. We need to, everyone needs to start from trusted sources like photography images. They need to scan for known vulnerabilities using Docker scan that we built in partnership with sneak and lost DockerCon last year, we need just keep updating base images and dependencies, and we'll, we're going to help you have the control and understanding about your images that you need to do this. >>And there's more, we're also working on the nursery V2 project in the CNCF to revamp container signings, or you can tell way or software comes from we're working on tooling to make updates easier, and to help you understand and manage all the principals carrier you're using security is a growing concern for all of us. It's really important. And we're going to help you work with security. We can't achieve all our dreams, whether that's space travel or amazing developer products ever see without deep partnerships with our community to cloud is RA and the cloud providers aware most of you ship your occasion production and simple routes that take your work and deploy it easily. Reliably and securely are really important. Just get into production simply and easily and securely. And we've done a bunch of work on that. And, um, but we know there's more to do. >>The CNCF on the open source cloud native community are an amazing ecosystem of creators and lovely people creating an amazing strong community and supporting a huge amount of innovation has its roots in the container ecosystem and his dreams beyond that much of the innovation is focused around operate experience so far, but developer experience is really a growing concern in that community as well. And we're really excited to work on that. We also uses appraiser tool. Then we know you do, and we know that you want it to be easier to use in your environment. We just shifted Docker hub to work on, um, Kubernetes fully. And, um, we're also using many of the other projects are Argo from atheists. We're spending a lot of time working with Microsoft, Amazon right now on getting natural UV to ready to ship in the next few. That's a really detailed piece of collaboration we've been working on for a long term. Long time is really important for our community as the scarcity of the container containers and, um, getting content for you, working together makes us stronger. Our community is made up of all of you have. Um, it's always amazing to be reminded of that as a huge open source community that we already proud to work with. It's an amazing amount of innovation that you're all creating and where perhaps it, what with you and share with you as well. Thank you very much. And thank you for being here. >>Really excited to talk to you today and share more about what Docker is doing to help make you faster, make your team faster and turn your application delivery into something that makes you a 10 X team. What we're hearing from you, the developers using Docker everyday fits across three common themes that we hear consistently over and over. We hear that your time is super important. It's critical, and you want to move faster. You want your tools to get out of your way, and instead to enable you to accelerate and focus on the things you want to be doing. And part of that is that finding great content, great application components that you can incorporate into your apps to move faster is really hard. It's hard to discover. It's hard to find high quality content that you can trust that, you know, passes your test and your configuration needs. >>And it's hard to create good content as well. And you're looking for more safety, more guardrails to help guide you along that way so that you can focus on creating value for your company. Secondly, you're telling us that it's a really far to collaborate effectively with your team and you want to do more, to work more effectively together to help your tools become more and more seamless to help you stay in sync, both with yourself across all of your development environments, as well as with your teammates so that you can more effectively collaborate together. Review each other's work, maintain things and keep them in sync. And finally, you want your applications to run consistently in every single environment, whether that's your local development environment, a cloud-based development environment, your CGI pipeline, or the cloud for production, and you want that micro service to provide that consistent experience everywhere you go so that you have similar tools, similar environments, and you don't need to worry about things getting in your way, but instead things make it easy for you to focus on what you wanna do and what Docker is doing to help solve all of these problems for you and your colleagues is creating a collaborative app dev platform. >>And this collaborative application development platform consists of multiple different pieces. I'm not going to walk through all of them today, but the overall view is that we're providing all the tooling you need from the development environment, to the container images, to the collaboration services, to the pipelines and integrations that enable you to focus on making your applications amazing and changing the world. If we start zooming on a one of those aspects, collaboration we hear from developers regularly is that they're challenged in synchronizing their own setups across environments. They want to be able to duplicate the setup of their teammates. Look, then they can easily get up and running with the same applications, the same tooling, the same version of the same libraries, the same frameworks. And they want to know if their applications are good before they're ready to share them in an official space. >>They want to collaborate on things before they're done, rather than feeling like they have to officially published something before they can effectively share it with others to work on it, to solve this. We're thrilled today to announce Docker, dev environments, Docker, dev environments, transform how your team collaborates. They make creating, sharing standardized development environments. As simple as a Docker poll, they make it easy to review your colleagues work without affecting your own work. And they increase the reproducibility of your own work and decreased production issues in doing so because you've got consistent environments all the way through. Now, I'm going to pass it off to our principal product manager, Ben Gotch to walk you through more detail on Docker dev environments. >>Hi, I'm Ben. I work as a principal program manager at DACA. One of the areas that doc has been looking at to see what's hard today for developers is sharing changes that you make from the inner loop where the inner loop is a better development, where you write code, test it, build it, run it, and ultimately get feedback on those changes before you merge them and try and actually ship them out to production. Most amount of us build this flow and get there still leaves a lot of challenges. People need to jump between branches to look at each other's work. Independence. Dependencies can be different when you're doing that and doing this in this new hybrid wall of work. Isn't any easier either the ability to just save someone, Hey, come and check this out. It's become much harder. People can't come and sit down at your desk or take your laptop away for 10 minutes to just grab and look at what you're doing. >>A lot of the reason that development is hard when you're remote, is that looking at changes and what's going on requires more than just code requires all the dependencies and everything you've got set up and that complete context of your development environment, to understand what you're doing and solving this in a remote first world is hard. We wanted to look at how we could make this better. Let's do that in a way that let you keep working the way you do today. Didn't want you to have to use a browser. We didn't want you to have to use a new idea. And we wanted to do this in a way that was application centric. We wanted to let you work with all the rest of the application already using C for all the services and all those dependencies you need as part of that. And with that, we're excited to talk more about docket developer environments, dev environments are new part of the Docker experience that makes it easier you to get started with your whole inner leap, working inside a container, then able to share and collaborate more than just the code. >>We want it to enable you to share your whole modern development environment, your whole setup from DACA, with your team on any operating system, we'll be launching a limited beta of dev environments in the coming month. And a GA dev environments will be ID agnostic and supporting composts. This means you'll be able to use an extend your existing composed files to create your own development environment in whatever idea, working in dev environments designed to be local. First, they work with Docker desktop and say your existing ID, and let you share that whole inner loop, that whole development context, all of your teammates in just one collect. This means if you want to get feedback on the working progress change or the PR it's as simple as opening another idea instance, and looking at what your team is working on because we're using compose. You can just extend your existing oppose file when you're already working with, to actually create this whole application and have it all working in the context of the rest of the services. >>So it's actually the whole environment you're working with module one service that doesn't really understand what it's doing alone. And with that, let's jump into a quick demo. So you can see here, two dev environments up and running. First one here is the same container dev environment. So if I want to go into that, let's see what's going on in the various code button here. If that one open, I can get straight into my application to start making changes inside that dev container. And I've got all my dependencies in here, so I can just run that straight in that second application I have here is one that's opened up in compose, and I can see that I've also got my backend, my front end and my database. So I've got all my services running here. So if I want, I can open one or more of these in a dev environment, meaning that that container has the context that dev environment has the context of the whole application. >>So I can get back into and connect to all the other services that I need to test this application properly, all of them, one unit. And then when I've made my changes and I'm ready to share, I can hit my share button type in the refund them on to share that too. And then give that image to someone to get going, pick that up and just start working with that code and all my dependencies, simple as putting an image, looking ahead, we're going to be expanding development environments, more of your dependencies for the whole developer worst space. We want to look at backing up and letting you share your volumes to make data science and database setups more repeatable and going. I'm still all of this under a single workspace for your team containing images, your dev environments, your volumes, and more we've really want to allow you to create a fully portable Linux development environment. >>So everyone you're working with on any operating system, as I said, our MVP we're coming next month. And that was for vs code using their dev container primitive and more support for other ideas. We'll follow to find out more about what's happening and what's coming up next in the future of this. And to actually get a bit of a deeper dive in the experience. Can we check out the talk I'm doing with Georgie and girl later on today? Thank you, Ben, amazing story about how Docker is helping to make developer teams more collaborative. Now I'd like to talk more about applications while the dev environment is like the workbench around what you're building. The application itself has all the different components, libraries, and frameworks, and other code that make up the application itself. And we hear developers saying all the time things like, how do they know if their images are good? >>How do they know if they're secure? How do they know if they're minimal? How do they make great images and great Docker files and how do they keep their images secure? And up-to-date on every one of those ties into how do I create more trust? How do I know that I'm building high quality applications to enable you to do this even more effectively than today? We are pleased to announce the DACA verified polisher program. This broadens trusted content by extending beyond Docker official images, to give you more and more trusted building blocks that you can incorporate into your applications. It gives you confidence that you're getting what you expect because Docker verifies every single one of these publishers to make sure they are who they say they are. This improves our secure supply chain story. And finally it simplifies your discovery of the best building blocks by making it easy for you to find things that you know, you can trust so that you can incorporate them into your applications and move on and on the right. You can see some examples of the publishers that are involved in Docker, official images and our Docker verified publisher program. Now I'm pleased to introduce you to marina. Kubicki our senior product manager who will walk you through more about what we're doing to create a better experience for you around trust. >>Thank you, Dani, >>Mario Andretti, who is a famous Italian sports car driver. One said that if everything feels under control, you're just not driving. You're not driving fast enough. Maya Andretti is not a software developer and a software developers. We know that no matter how fast we need to go in order to drive the innovation that we're working on, we can never allow our applications to spin out of control and a Docker. As we continue talking to our, to the developers, what we're realizing is that in order to reach that speed, the developers are the, the, the development community is looking for the building blocks and the tools that will, they will enable them to drive at the speed that they need to go and have the trust in those building blocks. And in those tools that they will be able to maintain control over their applications. So as we think about some of the things that we can do to, to address those concerns, uh, we're realizing that we can pursue them in a number of different venues, including creating reliable content, including creating partnerships that expands the options for the reliable content. >>Um, in order to, in a we're looking at creating integrations, no link security tools, talk about the reliable content. The first thing that comes to mind are the Docker official images, which is a program that we launched several years ago. And this is a set of curated, actively maintained, open source images that, uh, include, uh, operating systems and databases and programming languages. And it would become immensely popular for, for, for creating the base layers of, of the images of, of the different images, images, and applications. And would we realizing that, uh, many developers are, instead of creating something from scratch, basically start with one of the official images for their basis, and then build on top of that. And this program has become so popular that it now makes up a quarter of all of the, uh, Docker poles, which essentially ends up being several billion pulse every single month. >>As we look beyond what we can do for the open source. Uh, we're very ability on the open source, uh, spectrum. We are very excited to announce that we're launching the Docker verified publishers program, which is continuing providing the trust around the content, but now working with, uh, some of the industry leaders, uh, in multiple, in multiple verticals across the entire technology technical spec, it costs entire, uh, high tech in order to provide you with more options of the images that you can use for building your applications. And it still comes back to trust that when you are searching for content in Docker hub, and you see the verified publisher badge, you know, that this is, this is the content that, that is part of the, that comes from one of our partners. And you're not running the risk of pulling the malicious image from an employee master source. >>As we look beyond what we can do for, for providing the reliable content, we're also looking at some of the tools and the infrastructure that we can do, uh, to create a security around the content that you're creating. So last year at the last ad, the last year's DockerCon, we announced partnership with sneak. And later on last year, we launched our DACA, desktop and Docker hub vulnerability scans that allow you the options of writing scans in them along multiple points in your dev cycle. And in addition to providing you with information on the vulnerability on, on the vulnerabilities, in, in your code, uh, it also provides you with a guidance on how to re remediate those vulnerabilities. But as we look beyond the vulnerability scans, we're also looking at some of the other things that we can do, you know, to, to, to, uh, further ensure that the integrity and the security around your images, your images, and with that, uh, later on this year, we're looking to, uh, launch the scope, personal access tokens, and instead of talking about them, I will simply show you what they look like. >>So if you can see here, this is my page in Docker hub, where I've created a four, uh, tokens, uh, read-write delete, read, write, read only in public read in public creeper read only. So, uh, earlier today I went in and I, I logged in, uh, with my read only token. And when you see, when I'm going to pull an image, it's going to allow me to pull an image, not a problem success. And then when I do the next step, I'm going to ask to push an image into the same repo. Uh, would you see is that it's going to give me an error message saying that they access is denied, uh, because there is an additional authentication required. So these are the things that we're looking to add to our roadmap. As we continue thinking about the things that we can do to provide, um, to provide additional building blocks, content, building blocks, uh, and, and, and tools to build the trust so that our DACA developer and skinned code faster than Mario Andretti could ever imagine. Uh, thank you to >>Thank you, marina. It's amazing what you can do to improve the trusted content so that you can accelerate your development more and move more quickly, move more collaboratively and build upon the great work of others. Finally, we hear over and over as that developers are working on their applications that they're looking for, environments that are consistent, that are the same as production, and that they want their applications to really run anywhere, any environment, any architecture, any cloud one great example is the recent announcement of apple Silicon. We heard from developers on uproar that they needed Docker to be available for that architecture before they could add those to it and be successful. And we listened. And based on that, we are pleased to share with you Docker, desktop on apple Silicon. This enables you to run your apps consistently anywhere, whether that's developing on your team's latest dev hardware, deploying an ARM-based cloud environments and having a consistent architecture across your development and production or using multi-year architecture support, which enables your whole team to collaborate on its application, using private repositories on Docker hub, and thrilled to introduce you to Hughie cower, senior director for product management, who will walk you through more of what we're doing to create a great developer experience. >>Senior director of product management at Docker. And I'd like to jump straight into a demo. This is the Mac mini with the apple Silicon processor. And I want to show you how you can now do an end-to-end arm workflow from my M one Mac mini to raspberry PI. As you can see, we have vs code and Docker desktop installed on a, my, the Mac mini. I have a small example here, and I have a raspberry PI three with an led strip, and I want to turn those LEDs into a moving rainbow. This Dockerfile here, builds the application. We build the image with the Docker, build X command to make the image compatible for all raspberry pies with the arm. 64. Part of this build is built with the native power of the M one chip. I also add the push option to easily share the image with my team so they can give it a try to now Dr. >>Creates the local image with the application and uploads it to Docker hub after we've built and pushed the image. We can go to Docker hub and see the new image on Docker hub. You can also explore a variety of images that are compatible with arm processors. Now let's go to the raspberry PI. I have Docker already installed and it's running Ubuntu 64 bit with the Docker run command. I can run the application and let's see what will happen from there. You can see Docker is downloading the image automatically from Docker hub and when it's running, if it's works right, there are some nice colors. And with that, if we have an end-to-end workflow for arm, where continuing to invest into providing you a great developer experience, that's easy to install. Easy to get started with. As you saw in the demo, if you're interested in the new Mac, mini are interested in developing for our platforms in general, we've got you covered with the same experience you've come to expect from Docker with over 95,000 arm images on hub, including many Docker official images. >>We think you'll find what you're looking for. Thank you again to the community that helped us to test the tech previews. We're so delighted to hear when folks say that the new Docker desktop for apple Silicon, it just works for them, but that's not all we've been working on. As Dani mentioned, consistency of developer experience across environments is so important. We're introducing composed V2 that makes compose a first-class citizen in the Docker CLI you no longer need to install a separate composed biter in order to use composed, deploying to production is simpler than ever with the new compose integration that enables you to deploy directly to Amazon ECS or Azure ACI with the same methods you use to run your application locally. If you're interested in running slightly different services, when you're debugging versus testing or, um, just general development, you can manage that all in one place with the new composed service to hear more about what's new and Docker desktop, please join me in the three 15 breakout session this afternoon. >>And now I'd love to tell you a bit more about bill decks and convince you to try it. If you haven't already it's our next gen build command, and it's no longer experimental as shown in the demo with built X, you'll be able to do multi architecture builds, share those builds with your team and the community on Docker hub. With build X, you can speed up your build processes with remote caches or build all the targets in your composed file in parallel with build X bake. And there's so much more if you're using Docker, desktop or Docker, CE you can use build X checkout tonus is talk this afternoon at three 45 to learn more about build X. And with that, I hope everyone has a great Dr. Khan and back over to you, Donnie. >>Thank you UA. It's amazing to hear about what we're doing to create a better developer experience and make sure that Docker works everywhere you need to work. Finally, I'd like to wrap up by showing you everything that we've announced today and everything that we've done recently to make your lives better and give you more and more for the single price of your Docker subscription. We've announced the Docker verified publisher program we've announced scoped personal access tokens to make it easier for you to have a secure CCI pipeline. We've announced Docker dev environments to improve your collaboration with your team. Uh, we shared with you Docker, desktop and apple Silicon, to make sure that, you know, Docker runs everywhere. You need it to run. And we've announced Docker compose version two, finally making it a first-class citizen amongst all the other great Docker tools. And we've done so much more recently as well from audit logs to advanced image management, to compose service profiles, to improve where you can run Docker more easily. >>Finally, as we look forward, where we're headed in the upcoming year is continuing to invest in these themes of helping you build, share, and run modern apps more effectively. We're going to be doing more to help you create a secure supply chain with which only grows more and more important as time goes on. We're going to be optimizing your update experience to make sure that you can easily understand the current state of your application, all its components and keep them all current without worrying about breaking everything as you're doing. So we're going to make it easier for you to synchronize your work. Using cloud sync features. We're going to improve collaboration through dev environments and beyond, and we're going to do make it easy for you to run your microservice in your environments without worrying about things like architecture or differences between those environments. Thank you so much. I'm thrilled about what we're able to do to help make your lives better. And now you're going to be hearing from one of our customers about what they're doing to launch their business with Docker >>I'm Matt Falk, I'm the head of engineering and orbital insight. And today I want to talk to you a little bit about data from space. So who am I like many of you, I'm a software developer and a software developer about seven companies so far, and now I'm a head of engineering. So I spend most of my time doing meetings, but occasionally I'll still spend time doing design discussions, doing code reviews. And in my free time, I still like to dabble on things like project oiler. So who's Oberlin site. What do we do? Portal insight is a large data supplier and analytics provider where we take data geospatial data anywhere on the planet, any overhead sensor, and translate that into insights for the end customer. So specifically we have a suite of high performance, artificial intelligence and machine learning analytics that run on this geospatial data. >>And we build them to specifically determine natural and human service level activity anywhere on the planet. What that really means is we take any type of data associated with a latitude and longitude and we identify patterns so that we can, so we can detect anomalies. And that's everything that we do is all about identifying those patterns to detect anomalies. So more specifically, what type of problems do we solve? So supply chain intelligence, this is one of the use cases that we we'd like to talk about a lot. It's one of our main primary verticals that we go after right now. And as Scott mentioned earlier, this had a huge impact last year when COVID hit. So specifically supply chain intelligence is all about identifying movement patterns to and from operating facilities to identify changes in those supply chains. How do we do this? So for us, we can do things where we track the movement of trucks. >>So identifying trucks, moving from one location to another in aggregate, same thing we can do with foot traffic. We can do the same thing for looking at aggregate groups of people moving from one location to another and analyzing their patterns of life. We can look at two different locations to determine how people are moving from one location to another, or going back and forth. All of this is extremely valuable for detecting how a supply chain operates and then identifying the changes to that supply chain. As I said last year with COVID, everything changed in particular supply chains changed incredibly, and it was hugely important for customers to know where their goods or their products are coming from and where they were going, where there were disruptions in their supply chain and how that's affecting their overall supply and demand. So to use our platform, our suite of tools, you can start to gain a much better picture of where your suppliers or your distributors are going from coming from or going to. >>So what's our team look like? So my team is currently about 50 engineers. Um, we're spread into four different teams and the teams are structured like this. So the first team that we have is infrastructure engineering and this team largely deals with deploying our Dockers using Kubernetes. So this team is all about taking Dockers, built by other teams, sometimes building the Dockers themselves and putting them into our production system, our platform engineering team, they produce these microservices. So they produce microservice, Docker images. They develop and test with them locally. Their entire environments are dockerized. They produce these doctors, hand them over to him for infrastructure engineering to be deployed. Similarly, our product engineering team does the same thing. They develop and test with Dr. Locally. They also produce a suite of Docker images that the infrastructure team can then deploy. And lastly, we have our R and D team, and this team specifically produces machine learning algorithms using Nvidia Docker collectively, we've actually built 381 Docker repositories and 14 million. >>We've had 14 million Docker pools over the lifetime of the company, just a few stats about us. Um, but what I'm really getting to here is you can see actually doctors becoming almost a form of communication between these teams. So one of the paradigms in software engineering that you're probably familiar with encapsulation, it's really helpful for a lot of software engineering problems to break the problem down, isolate the different pieces of it and start building interfaces between the code. This allows you to scale different pieces of the platform or different pieces of your code in different ways that allows you to scale up certain pieces and keep others at a smaller level so that you can meet customer demands. And for us, one of the things that we can largely do now is use Dockers as that interface. So instead of having an entire platform where all teams are talking to each other, and everything's kind of, mishmashed in a monolithic application, we can now say this team is only able to talk to this team by passing over a particular Docker image that defines the interface of what needs to be built before it passes to the team and really allows us to scalp our development and be much more efficient. >>Also, I'd like to say we are hiring. Um, so we have a number of open roles. We have about 30 open roles in our engineering team that we're looking to fill by the end of this year. So if any of this sounds really interesting to you, please reach out after the presentation. >>So what does our platform do? Really? Our platform allows you to answer any geospatial question, and we do this at three different inputs. So first off, where do you want to look? So we did this as what we call an AOI or an area of interest larger. You can think of this as a polygon drawn on the map. So we have a curated data set of almost 4 million AOIs, which you can go and you can search and use for your analysis, but you're also free to build your own. Second question is what you want to look for. We do this with the more interesting part of our platform of our machine learning and AI capabilities. So we have a suite of algorithms that automatically allow you to identify trucks, buildings, hundreds of different types of aircraft, different types of land use, how many people are moving from one location to another different locations that people in a particular area are moving to or coming from all of these different analyses or all these different analytics are available at the click of a button, and then determine what you want to look for. >>Lastly, you determine when you want to find what you're looking for. So that's just, uh, you know, do you want to look for the next three hours? Do you want to look for the last week? Do you want to look every month for the past two, whatever the time cadence is, you decide that you hit go and out pops a time series, and that time series tells you specifically where you want it to look what you want it to look for and how many, or what percentage of the thing you're looking for appears in that area. Again, we do all of this to work towards patterns. So we use all this data to produce a time series from there. We can look at it, determine the patterns, and then specifically identify the anomalies. As I mentioned with supply chain, this is extremely valuable to identify where things change. So we can answer these questions, looking at a particular operating facility, looking at particular, what is happening with the level of activity is at that operating facility where people are coming from, where they're going to, after visiting that particular facility and identify when and where that changes here, you can just see it's a picture of our platform. It's actually showing all the devices in Manhattan, um, over a period of time. And it's more of a heat map view. So you can actually see the hotspots in the area. >>So really the, and this is the heart of the talk, but what happened in 2020? So for men, you know, like many of you, 2020 was a difficult year COVID hit. And that changed a lot of what we're doing, not from an engineering perspective, but also from an entire company perspective for us, the motivation really became to make sure that we were lowering our costs and increasing innovation simultaneously. Now those two things often compete with each other. A lot of times you want to increase innovation, that's going to increase your costs, but the challenge last year was how to do both simultaneously. So here's a few stats for you from our team. In Q1 of last year, we were spending almost $600,000 per month on compute costs prior to COVID happening. That wasn't hugely a concern for us. It was a lot of money, but it wasn't as critical as it was last year when we really needed to be much more efficient. >>Second one is flexibility for us. We were deployed on a single cloud environment while we were cloud thought ready, and that was great. We want it to be more flexible. We want it to be on more cloud environments so that we could reach more customers. And also eventually get onto class side networks, extending the base of our customers as well from a custom analytics perspective. This is where we get into our traction. So last year, over the entire year, we computed 54,000 custom analytics for different users. We wanted to make sure that this number was steadily increasing despite us trying to lower our costs. So we didn't want the lowering cost to come as the sacrifice of our user base. Lastly, of particular percentage here that I'll say definitely needs to be improved is 75% of our projects never fail. So this is where we start to get into a bit of stability of our platform. >>Now I'm not saying that 25% of our projects fail the way we measure this is if you have a particular project or computation that runs every day and any one of those runs sale account, that is a failure because from an end-user perspective, that's an issue. So this is something that we know we needed to improve on and we needed to grow and make our platform more stable. I'm going to something that we really focused on last year. So where are we now? So now coming out of the COVID valley, we are starting to soar again. Um, we had, uh, back in April of last year, we had the entire engineering team. We actually paused all development for about four weeks. You had everyone focused on reducing our compute costs in the cloud. We got it down to 200 K over the period of a few months. >>And for the next 12 months, we hit that number every month. This is huge for us. This is extremely important. Like I said, in the COVID time period where costs and operating efficiency was everything. So for us to do that, that was a huge accomplishment last year and something we'll keep going forward. One thing I would actually like to really highlight here, two is what allowed us to do that. So first off, being in the cloud, being able to migrate things like that, that was one thing. And we were able to use there's different cloud services in a more particular, in a more efficient way. We had a very detailed tracking of how we were spending things. We increased our data retention policies. We optimized our processing. However, one additional piece was switching to new technologies on, in particular, we migrated to get lab CICB. >>Um, and this is something that the costs we use Docker was extremely, extremely easy. We didn't have to go build new new code containers or repositories or change our code in order to do this. We were simply able to migrate the containers over and start using a new CIC so much. In fact, that we were able to do that migration with three engineers in just two weeks from a cloud environment and flexibility standpoint, we're now operating in two different clouds. We were able to last night, I've over the last nine months to operate in the second cloud environment. And again, this is something that Docker helped with incredibly. Um, we didn't have to go and build all new interfaces to all new, different services or all different tools in the next cloud provider. All we had to do was build a base cloud infrastructure that ups agnostic the way, all the different details of the cloud provider. >>And then our doctors just worked. We can move them to another environment up and running, and our platform was ready to go from a traction perspective. We're about a third of the way through the year. At this point, we've already exceeded the amount of customer analytics we produce last year. And this is thanks to a ton more albums, that whole suite of new analytics that we've been able to build over the past 12 months and we'll continue to build going forward. So this is really, really great outcome for us because we were able to show that our costs are staying down, but our analytics and our customer traction, honestly, from a stability perspective, we improved from 75% to 86%, not quite yet 99 or three nines or four nines, but we are getting there. Um, and this is actually thanks to really containerizing and modularizing different pieces of our platform so that we could scale up in different areas. This allowed us to increase that stability. This piece of the code works over here, toxin an interface to the rest of the system. We can scale this piece up separately from the rest of the system, and that allows us much more easily identify issues in the system, fix those and then correct the system overall. So basically this is a summary of where we were last year, where we are now and how much more successful we are now because of the issues that we went through last year and largely brought on by COVID. >>But that this is just a screenshot of the, our, our solution actually working on supply chain. So this is in particular, it is showing traceability of a distribution warehouse in salt lake city. It's right in the center of the screen here. You can see the nice kind of orange red center. That's a distribution warehouse and all the lines outside of that, all the dots outside of that are showing where people are, where trucks are moving from that location. So this is really helpful for supply chain companies because they can start to identify where their suppliers are, are coming from or where their distributors are going to. So with that, I want to say, thanks again for following along and enjoy the rest of DockerCon.

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. >>Welcome to the Cubes Coverage of Dell Technologies World 2020. The digital coverage Find Lisa Martin And then we started to be talking with one of Dell Technologies customers. Earth Camp. Joining Me is built sharp, the senior VP of product development and strategy from Earth Camp Phil, Welcome to the Cube. >>Thank you so much. >>So talk to me a little bit. About what Earth Cam does this very interesting Web can technology? You guys have tens of thousands of cameras and sensors all over the globe give her audience and understanding of what you guys are all about. >>Sure thing. The world's leading provider of Webcam technologies and mentioned content services were leaders and live streaming time lapse imaging primary focus in the vertical construction. So a lot of these, the most ambitious, largest construction projects around the world, you see, these amazing time lapse movies were capturing all of that imagery. You know, basically, around the clock of these cameras are are sending all of that image content to us when we're generating these time lapse movies from it. >>You guys, you're headquartered in New Jersey and I was commenting before we went live about your great background. So you're actually getting to be on site today? >>Yes, Yes, that's where lives from our headquarters in Upper Saddle River, New Jersey. >>Excellent. So in terms of the types of information that you're capturing. So I was looking at the website and see from a construction perspective or some of the big projects you guys have done the Hudson Yards, the Panama Canal expansion, the 9 11 Museum. But you talked about one of the biggest focus is that you have is in the construction industry in terms of what type of data you're capturing from all of these thousands of edge devices give us a little bit of insight into how much data you're capturing high per day, how it gets from the edge, presumably back to your court data center for editing. >>Sure, and it's not just construction were also in travel, hospitality, tourism, security, architectural engineering, basically, any any industry that that need high resolution visualization of their their projects or their their performance or of their, you know, product flow. So it's it's high resolution documentation is basically our business. There are billions of files in the isil on system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that air up to 30 giga pixel, sometimes typically around 1 to 2 giga pixel. But that composite imagery Eyes represents millions of images per per month coming into the storage system and then being, uh, stitched together to those those composites >>the millions of images coming in every month. You mentioned Isil on talk to me a little bit about before you were working with Delhi, EMC and Power Scale. How are you managing this massive volume of data? >>Sure we had. We've used a number of other enterprise storage systems. It was really nothing was as easy to manage Azazel on really is there was there was a lot of a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, you to manage that, uh, and and it's interesting with the amount of data that we handle. This is being billions of relatively small files there there, you know, half a megabyte to a couple of megabytes each. It's an interesting data profile, which, which isil on really is well suited for. >>So if we think about some of the massive changes that we've all been through the last in 2020 what are some of the changes that that Earth Kemp has seen with respect to the needs for organizations? Or you mentioned other industries, like travel hospitality? Since none of us could get to these great travel destinations, Have you seen a big drive up in the demand and the need to process data more data faster? >>Yeah, that's an injury interesting point with with the Pandemic. Obviously we had to pivot and move a lot of people toe working from home, which we were able to do pretty quickly. But there's also an interesting opportunity that arose from this, where so many of our customers and other people also have to do the same. And there is an increased demand for our our technology so people can remotely collaborate. They can. They can work at a distance. They can stay at home and see what's going on in these projects sites. So we really so kind of an uptick in the in the need for our products and services. And we've also created Cem basically virtual travel applications. We have an application on the Amazon Fire TV, which is the number one app in the travel platform of people can kind of virtually travel when they can't really get out there. So it's, uh, we've been doing kind of giving back Thio to people that are having having some issues with being able to travel around. We've done the fireworks of the Washington Mall around the Statue of Liberty for the July 4th, and this year will be Webcasting and New Year's in Times Square for our 25th year, actually. So again, helping people travel virtually and be, uh, maintain can be collectivity with with each other and with their projects, >>which is so essential during these times, where for the last 67 months everyone is trying to get a sense of community, and most of us just have the Internet. So I also heard you guys were available on Apple TV, someone to fire that up later and maybe virtually travel. Um, but tell me a little bit about how working in conjunction with Delta Technologies and Power Cell How is that enabled you to manage this massive volume change you've experienced this year? Because, as you said, it's also about facilitating collaboration, which is largely online these days. >>Yeah, I mean, the the great things they're working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we worked with in the past we've always found ourselves kind of second guessing. Obviously, resolutions are increasing. The camera performance is increasing. Streaming video is everything is is constantly getting bigger and better, faster. Maurits And we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at the second guess we're at with it With with this, this did L infrastructure. That's been it's been fantastic. We don't really have to think about that as much. We just continue innovating everything scales as we needed to dio. It's it's much easier to work with, >>so you've got power scale at your core data center in New Jersey. Tell me a little bit about how data gets from thes tens of thousands of devices at the edge, back to your editors for editing and how power scale facilitates faster editing, for example. >>Basically, you imagine every one of these cameras on It's not just camera. We have mobile applications. We have fixed position of robotic cameras. There's all these different data acquisition systems were integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the Internet, so these are all endpoints in our network. Eso that's that's constantly being ingested into our network and say WTO. I salon the big the big thing that's really been a timesaver Working with the video editors is, instead of having to take that content, move it into an editing environment where we have we have a whole team of award winning video editors. Creating these time lapse is we don't need to keep moving that around. We're working natively on Iselin clusters. They're doing their editing, their subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all it happened right there on that live environment on the retention. Is there if we have to go back later on all of our customers, data is really kept within that 11 area. It's consolidated, its secure. >>I was looking at the Del Tech website. There's a case study that you guys did earth campaign with Deltek saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I could imagine what the volumes changing so much now but on Li not only is huge for your business, but to the demands that your customers have as well, depending on where there's demands are coming from >>absolutely and and just being able to do that a lot faster and be more nimble allows us to scale. We've added actually against speaking on this pandemic, we've actually added person who we've been hiring people. A lot of those people are working remotely, as as we've stated before on it's just with the increase in business. We have to continue to keep building on that on this storage environments been been great. >>Tell me about what you guys really kind of think about with respect to power scale in terms of data management, not storage management and what that difference means to your business. >>Well, again, I mean number number one was was really eliminating the amount of resource is amount of time we have to spend managing it. We've almost eliminated any downtime of any of any kind. We have greater storage density, were able to have better visualization on how our data is being used, how it's being access so as thes as thes things, a revolving. We really have good visibility on how the how the storage system is being used in both our production and our and also in our backup environments. It's really, really easy for us Thio to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >>And you mentioned hiring folks during the pandemic, which is fantastic but also being able to do things much in a much more streamlined way with respect to managing all of this data. But I am curious in terms of of innovation and new product development. What have you been able to achieve because you've got more resource is presumably to focus on being more innovative rather than managing storage >>well again? It's were always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 Giga pixel. You know, people are talking about megapixel images were stitching hundreds of these together. We've we're just really changing the way imagery is used, uh, both in the time lapse and also just in archival process. Ah, lot of these things we've done with the interior. You know, we have this virtual reality product where you can you can walk through and see in the 3 60 bubble. We're taking that imagery, and we're combining it with with these been models who are actually taking the three D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress and different things that are happening on the site. Look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people, time and money on these construction sites. We've also introduced a I machine learning applications into directly into the workflow in this in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure, it really is seamless and working with YSL on now. >>Imagine, by being able to infuse AI and machine learning, you're able to get insight faster to be ableto either respond faster to those construction customers, for example, or alert them. If perhaps something isn't going according to plan. >>A lot of it's about schedule. It's about saving money about saving time and again, with not as many people traveling to the sites, they really just have have constant visualization of what's going on. Day to day, we're detecting things like different types of construction equipment and things that are happening on the side. We're partnering with people that are doing safety analytics and things of that nature. So these these are all things that are very important to construction sites. >>What are some of the things as we are rounding out the calendar year 2020? What are some of the things that you're excited about going forward in 2021? That Earth cam is going to be able to get into and to deliver >>it, just MAWR and more people really, finally seeing the value. I mean, I've been doing this for 20 years, and it's just it's it's It's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with things they can do with this imagery. That's what we're all about that's really exciting to us in a very challenging environment right now is that people are are recognizing the need for this technology and really starting to put it on a lot more projects. >>Well, it's You can kind of consider an essential service, whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget on schedule, as you said, Or maybe even just the essential nous of helping folks from any country in the world connect with a favorite favorite travel location or sending the right to help. From an emotional perspective, I think the essential nous of what you guys are delivering is probably even more impactful now, don't you think? >>Absolutely and again about connecting people and when they're at home. And recently we we webcast the president's speech from the Flight 93 9 11 observation from the memorial. There was something where the only the immediate families were allowed to travel there. We webcast that so people could see that around the world we have documented again some of the biggest construction projects out there. The new rate years greater stadium was one of the recent ones, uh, is delivering this kind of flagship content. Wall Street Journal is to use some of our content recently to really show the things that have happened during the pandemic in Times Square's. We have these cameras around the world. So again, it's really bringing awareness of letting people virtually travel and share and really remain connected during this this challenging time on and again, we're seeing a really increase demand in the traffic in those areas as well. >>I can imagine some of these things that you're doing that you're achieving now are going to become permanent, not necessarily artifacts of Cove in 19 as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value off this type of video to be able to reach consumers that they probably could never reach before. >>Yeah, I think the whole nature of business and communication and travel on everything is really going to be changed from this point forward. It's really people are looking at things very, very differently and again, seeing the technology really can help with so many different areas that, uh, that it's just it's gonna be a different kind of landscape out there we feel on that's really, you know, continuing to be seen on the uptick in our business and how many people are adopting this technology. We're developing a lot more. Partnerships with other companies were expanding into new industries on again. You know, we're confident that the current platform is going to keep up with us and help us, you know, really scale and evolved as thes needs air growing. >>It sounds to me like you have the foundation with Dell Technologies with power scale to be able to facilitate the massive growth that you're saying and the skill in the future like you've got that foundation. You're ready to go? >>Yeah, we've been We've been We've been using the system for five years already. We've already added capacity. We can add capacity on the fly, Really haven't hit any limits. And what we can do, It's It's almost infinitely scalable, highly redundant. Gives everyone a real sense of security on our side. And, you know, we could just keep innovating, which is what we do without hitting any any technological limits with with our partnership. >>Excellent. Well, Bill, I'm gonna let you get back to innovating for Earth camp. It's been a pleasure talking to you. Thank you so much for your time today. >>Thank you so much. It's been a pleasure >>for Bill Sharp and Lisa Martin. You're watching the cubes. Digital coverage of Dell Technologies World 2020. Thanks for watching. Yeah,

>> Announcer: From around the globe, it's theCUBE! With digital coverage of Dell Technologies World, digital experience. Brought to you by Dell Technologies. >> Welcome to theCUBE's coverage of Dell Technologies World 2020, the digital coverage. I'm Lisa Martin, and I'm excited to be talking with one of Dell Technologies' customers EarthCam. Joining me is Bill Sharp, the senior VP of product development and strategy from EarthCam. Bill, welcome to theCUBE. >> Thank you so much. >> So talk to me a little bit about what EarthCam does. This is very interesting webcam technology. You guys have tens of thousands of cameras and sensors all over the globe. Give our audience an understanding of what you guys are all about. >> Sure thing. The world's leading provider of webcam technologies, you mentioned content and services, we're leaders in live streaming, time-lapse imaging, primary focus in the vertical construction. So with a lot of these, the most ambitious, largest construction projects around the world that you see these amazing time-lapse movies, we're capturing all of that imagery basically around the clock, these cameras are sending all of that image content to us and we're generating these time-lapse movies from it. >> You guys are headquartered in New Jersey. I was commenting before we went live about your great background. So you're actually getting to be onsite today? >> Yes, yes. We're live from our headquarters in upper Saddle River, New Jersey. >> Excellent, so in terms of the types of information that you're capturing, so I was looking at the website, and see from a construction perspective, some of the big projects you guys have done, the Hudson Yards, the Panama Canal expansion, the 9/11 museum. But you talked about one of the biggest focuses that you have is in the construction industry. In terms of what type of data you're capturing from all of these thousands of edge devices, give us a little bit of an insight into how much data you're capturing per day, how it gets from the edge, presumably, back to your core data center for editing. >> Sure, and it's not just construction. We're also in travel, hospitality, tourism, security, architecture, engineering, basically any industry that need high resolution visualization of their projects or their performance or their product flow. So it's high resolution documentation is basically our business. There are billions of files in the Isilon system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that are up to 30 gigapixel sometimes. Typically around one to two gigapixel but that composite imagery represents millions of images per month coming into the storage system and then being stitched together to those composites. >> So millions of images coming in every month, you mentioned Isilon. Talk to me a little bit about before you were working with Dell EMC and PowerScale, how were you managing this massive volume of data? >> Sure, we've used a number of other enterprise storage systems. It was really nothing was as easy to manage as Isilon really is. There was a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, to manage that. And it's interesting with the amount of data that we handle, being billions of relatively small files. They're, you know, a half a megabyte to a couple of megabytes each. It's an interesting data profile which Isilon really is well suited for. >> So if we think about some of the massive changes that we've all been through in the last, in 2020, what are some of the changes that EarthCam hasn't seen with respect to the needs for organizations, or you mentioned other industries like travel, hospitality, since none of us can get to these great travel destinations, have you seen a big drive up in the demand and the need to process more data faster? >> Yeah, that's an interesting point with the pandemic. I mean, obviously we had to pivot and move a lot of people to working from home, which we were able to do pretty quickly, but there's also an interesting opportunity that arose from this where so many of our customers and other people also have to do the same. And there is an increased demand for our technology. So people can remotely collaborate. They can work at a distance, they can stay at home and see what's going on in these project sites. So we really saw kind of an uptick in the need for our products and services. And we've also created some basically virtual travel applications. We have an application on the Amazon Fire TV which is the number one app in the travel platform, and people can kind of virtually travel when they can't really get out there. So it's, we've been doing kind of giving back to people that are having some issues with being able to travel around. We've done the fireworks at the Washington Mall around the Statue of Liberty for July 4th. And this year we'll be webcasting New Years in Times Square for our 25th year, actually. So again, helping people travel virtually and maintain connectivity with each other, and with their projects. >> Which is so essential during these times where for the last six, seven months, everyone is trying to get a sense of community and most of us just have the internet. So I also heard you guys were available on the Apple TV, someone should fire that up later and maybe virtually travel. But tell me a little bit about how working in conjunction with Dell Technologies and PowerScale. How has that enabled you to manage this massive volume change that you've experienced this year? Because as you said, it's also about facilitating collaboration which is largely online these days. >> Yeah, and I mean, the great things of working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we've worked with in the past we've always found ourselves kind of second guessing. We're constantly innovating. Obviously resolutions are increasing. The camera performance is increasing, streaming video is, everything is constantly getting bigger and better, faster, more, and we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at them, second guess where we're at with it. With the Dell infrastructure it's been fantastic. We don't really have to think about that as much. We just continue innovating, everything scales as we need it to do. It's much easier to work with. >> So you've got PowerScale at your core data center in New Jersey. Tell me a little bit about how data gets from these tens of thousands of devices at the edge, back to your editors for editing, and how PowerScale facilitates faster editing, for example. >> Well, basically you can imagine every one of these cameras, and it's not just cameras. It's also, you know, we have 360 virtual reality kind of bubble cameras. We have mobile applications, we have fixed position and robotic cameras. There's all these different data acquisition systems we're integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the internet. So these are all endpoints in our network. So that's constantly being ingested into our network and saved to Isilon. The big thing that's really been a time saver working with the video editors is instead of having to take that content, move it into an editing environment where we have a whole team of award-winning video editors creating these time lapses. We don't need to keep moving that around. We're working natively on Isilon clusters. They're doing their editing there, and subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all, can happen right there on that live environment. And the retention is there. If we have to go back later on, all of our customers' data is really kept within that one area, it's consolidated and it's secure. >> I was looking at the Dell Tech website, and there's a case study that you guys did, EarthCam did with Dell Tech saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I can imagine with the volumes changing so much now, not only is huge to your business but to the demands that your customers have as well, depending on where those demands are coming from. >> Absolutely. And just being able to do that a lot faster and be more nimble allows us to scale. We've added actually, again, speaking of during this pandemic, we've actually added personnel, we've been hiring people. A lot of those people are working remotely as we've stated before. And it's just with the increase in business, we have to continue to keep building on that, and this storage environment's been great. >> Tell me about what you guys really kind of think about with respect to PowerScale in terms of data management, not storage management, and what that difference means to your business. >> Well, again, I mean, number one was really eliminating the amount of resources. The amount of time we have to spend managing it. We've almost eliminated any downtime of any kind. We have greater storage density, we're able to have better visualization on how our data is being used, how it's being accessed. So as these things are evolving, we really have good visibility on how the storage system is being used in both our production and also in our backup environments. It's really, really easy for us to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >> And you mentioned hiring folks during the pandemic, which is fantastic, but also being able to do things in a much more streamlined way with respect to managing all of this data. But I am curious in terms of innovation and new product development, what have you been able to achieve? Because you've got more resources presumably to focus on being more innovative rather than managing storage. >> Well, again, it's, we're always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 gigapixels, people are talking about megapixel images, we're stitching hundreds of these together. We're just really changing the way imagery is used both in the time lapse and also just in archival process. A lot of these things we've done with the interior, we have this virtual reality product where you can walk through and see in a 360 bubble, we're taking that imagery and we're combining it with these BIM models. So we're actually taking the 3D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress, and different things that are happening on the site, look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people time and money on these construction sites. We've also introduced AI and machine learning applications directly into the workflow in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure. It really is seamless and working with Isilon now. >> I imagine by being able to infuse AI and machine learning, you're able to get insights faster, to be able to either respond faster to those construction customers, for example, or alert them if perhaps something isn't going according to plan. >> Yeah, a lot of it's about schedule, it's about saving money, about saving time. And again, with not as many people traveling to these sites, they really just have to have constant visualization of what's going on day to day. We're detecting things like different types of construction equipment and things that are happening on the site. We're partnering with people that are doing safety analytics and things of that nature. So these are all things that are very important to construction sites. >> What are some of the things as we are rounding out the calendar year 2020, what are some of the things that you're excited about going forward in 2021, that EarthCam is going to be able to get into and to deliver? >> Just more and more people really finally seeing the value. I mean I've been doing this for 20 years and it's just, it's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with the things they can do with this imagery. That's what we're all about. And that's really exciting to us in a very challenging environment right now is that people are recognizing the need for this technology and really starting to put it on a lot more projects. >> Well, you can kind of consider it an essential service whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget, on schedule, as you said, or maybe even just the essentialness of helping folks from any country in the world connect with a favorite travel location, or (indistinct) to help from an emotional perspective. I think the essentialness of what you guys are delivering is probably even more impactful now, don't you think? >> Absolutely. And again about connecting people when they're at home, and recently we webcast the president's speech from the Flight 93 9/11 observation from the memorial, there was something where only the immediate families were allowed to travel there. We webcast that so people could see that around the world. We've documented, again, some of the biggest construction projects out there, the new Raiders stadium was one of the recent ones, just delivering this kind of flagship content. Wall Street Journal has used some of our content recently to really show the things that have happened during the pandemic in Times Square. We have these cameras around the world. So again, it's really bringing awareness. So letting people virtually travel and share and really remain connected during this challenging time. And again, we're seeing a real increased demand in the traffic in those areas as well. >> I can imagine some of these things that you're doing that you're achieving now are going to become permanent not necessarily artifacts of COVID-19, as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value of this type of video to be able to reach consumers that they probably could never reach before. >> Yeah, I think the whole nature of business and communication and travel and everything is really going to be changed from this point forward. It's really, people are looking at things very, very differently. And again, seeing that the technology really can help with so many different areas that it's just, it's going to be a different kind of landscape out there we feel. And that's really continuing to be seen as on the uptick in our business and how many people are adopting this technology. We're developing a lot more partnerships with other companies, we're expanding into new industries. And again, you know, we're confident that the current platform is going to keep up with us and help us really scale and evolve as these needs are growing. >> It sounds to me like you have the foundation with Dell Technologies, with PowerScale, to be able to facilitate the massive growth that you were saying and the scale in the future, you've got that foundation, you're ready to go. >> Yeah, we've been using the system for five years already. We've already added capacity. We can add capacity on the fly, really haven't hit any limits in what we can do. It's almost infinitely scalable, highly redundant. It gives everyone a real sense of security on our side. And you know, we can just keep innovating, which is what we do, without hitting any technological limits with our partnership. >> Excellent, well, Bill, I'm going to let you get back to innovating for EarthCam. It's been a pleasure talking to you. Thank you so much for your time today. >> Thank you so much. It's been a pleasure. >> For Bill Sharp, I'm Lisa Martin, you're watching theCUBE's digital coverage of Dell Technologies World 2020. Thanks for watching. (calm music)

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hey, welcome back, everybody. Jeff Frick with theCUBE. We're in our Palo Alto Studios on this kind of continuing leadership series that we've put together. Reaching out to the community for tips and tricks on kind of getting through what is, this kind of ongoing COVID crisis and situation as it continues to go weeks and weeks and weeks. And I'm really excited to have one of my favorite members of our community, is Nick Mehta, the CEO of Gainsight. Had the real pleasure of interviewing him a couple times and had to get him on. So Nick, thanks for taking some time out of your very busy day to join us. >> Jeff, honored to be here, thank you. >> Pleasure, so let's just jump into it. One of the reasons I wanted to get you on, is that Gainsight has been a distributed company from the beginning, and so I think the COVID, suddenly everyone got this work from home order, there was no prep, there was no planning, it's like this light switch digital transformation moment. So love to hear from someone who's been doing it for awhile. What are some of the lessons? How should people think about running a distributed company? >> Yeah, it's really interesting, Jeff, 'cause we are just by happenstance, from the beginning, distributed where we have, our first two offices were St. Louis and Hyderabad, India. So two places you cannot get there through one flight. So, you have to figure out how to collaborate asynchronously and then over time, we have offices in the Bay Area. We have tons of people that work from home. And so we try to tell people we don't have a headquarters. The headquarters is wherever you are, wherever you live and wherever you want to work. And so we've always been super flexible about come in to the office if you want, don't come in, et cetera. So different than some companies in that respect. And because of that, pre-COVID, we always a very heavy video culture, lots of video conferencing. Even if some people were in an office, there's always somebody else dialing in. One benefit we got from that is you never had to miss your kids' stuff or your family things. I would go to my daughter's performance in the middle of the day and know I can just dial into a call on the way there. And so we always had that. But what's amazing is now we're all on a level playing field, there's nobody in our office. And I got to say, this is, in some ways, even better 'cause I feel like when you're the person dialed in, and a lot of people are in a room, you probably had that experience, and it feels like you're kind of not on the same playing field, right? Hard to hear the jokes or the comments and you might not feel like you're totally in crowd, so to speak, right? But now everyone's just at their computer, sitting there in a chair all day doing these Zooms and it does feel like it's equalizing a little bit. And what it's caused us to do is say, hey, what are ways we can all recreate that community from home? So as an example, every 7:45 a.m. every day, we have a Zoom call that's just pure joy and fun. Trivia, pets, kids. The employees' kids announce people's birthdays and the weather. And so these ways we've been able to integrate our home and our work that we never could before, it's really powerful. It's a tough situation overall, and we feel for all the people affected. But even in tough situations, there are silver linings, and we're finding 'em. >> Yeah, it's funny, we just had Darren Murph on the other day. I don't know if you know Darren. He is the head of Remote Work at GitLab, and he-- >> Oh, yeah. >> And he talked about kind of the social norms. And one of the instances that he brought up was, back in the day when you had some people in the office, some people joining via remote, that it is this kind of disharmony because they're very different situations. So one of his suggestions was have everybody join via their laptop, even if they're sitting at their desk, right? So, as you said, you get kind of this level playing field. And the other thing which dovetails off what you just said is he always wanted executives to have a forcing function to work from home for an extended period of time, so they got to understand what it's all about. And it's not only looking through a little laptop or this or that, but it's also the distractions of the kids and the dogs and whatever else is happening around the house. So it is wild how this forcing function has really driven it. And his kind of takeaway is, as we, like say, move from can we get it into cloud to cloud first? And does it work on mobile to mobile first? >> Now it's really-- >> Yeah. >> It's really remote first. And if you-- >> Remote first. >> A remote first attitude about it and kind of turn it on it's head, it's why shouldn't it be remote versus can it be remote? It really changes the conversation and the dynamic of the whole situation. >> I love that. And just, GitLab, by the way, has been a true inspiration 'cause they are the most remote, remote company. And they share so much, I love what you said. As just two examples of reacting to what you said, pre-COVID, we always wanted to keep a level playing field. So we actually moved our all-hands meetings to be instead of being broadcast from one room, and you're kind of seeing this small screen with all these people, we all just were at computers presenting. And so everyone's on a level playing field. So I thought what GitLab said is great. And then the other point, I think post-COVID we have learned is the kids and the dogs aren't distractions, they're part of our life. And so embracing those and saying, hey, I see that kid in the background, bring them onto the screen. Even during work meetings, even customer meetings, you know? And I'm seeing, I'm on a customer meeting and the customer's bringing their kids onto the screen and it's kind of breaking this artificial wall between who we are at home and who we are at work 'cause we're human beings all throughout. At Gainsight, we talk about a human first approach to business and we've never been more human as a world than we are right now. >> Love it, love it. So another, get your thoughts on, is this whole idea of measurement and productivity at home. And it's really, I have to say, disturbing to see some of the new product announcements that are coming out in terms of people basically snoopin' on people. Whether it's trackin' how many hours of Zoom calls they're on, or how often are they in the VPN, or having their camera flip on every so many minutes or something. We had Marten Mickos on, who's now the CEO of HackerOne. He was CEO at MySQL years ago before it went to Sun and he had the great line, he said, it's so easy to fake it at the office, but when you're at home and you're only output is your deliverable, it makes it a lot easier. So I wonder if you can share some of your thoughts in terms of kind of managing output, setting expectations, to get people to get their work done. And then, as you see some of these new tools for people that are just entering this thing, it's just not right (chuckles). >> Yeah, I agree with you and Marten. I'm a huge fan of Marten, as well, I totally agree with both of it. And I think there's an older approach to work, which is more like a factory. It's like you got to see how many widgets you're processing and you got to micromanage and you got to monitoring and inspecting. Look, I don't run a factory, so maybe there are places where that model makes sense. So I'm not going to speak for every leader, but I could say if you're in a world where your job is information, services, software, where the value is the people and their knowledge, managing them that way is a losing battle. I go back to, some folks probably know, this famous TED Talk by Dan Pink on basically what motivates people. And in these knowledge worker jobs, it's autonomy, mastery and purpose. So autonomy, we have the freedom to do what we want. Mastery, we feel like we're getting better at jobs. And purpose, which is I have a why behind what I do. And I think, take that time you spend on your micromanagement and your Zoom, analyzing the Zoom sessions, and spend it on inspiring your team, on the purpose. Spend it on enabling your team in terms of mastery. Spend it on taking away barriers so they have more autonomy. I think you'll get way more out of your team. >> Yeah, I agree. I think it's, as Darren said, again, he's like, well, would you trust your people if you're on the fourth floor and they're on the sixth? So just-- >> Yeah, exactly. >> If you don't trust your people, you got to bigger issue than worrying about how many hours they're on Zoom, which is not the most productive use of time. >> People waste so much time in the office, and getting to the office. And by the way, I'm not saying that it's wrong, it's fine too. But it's not like the office is just unfettered productivity all the time, that's a total myth. >> Yes, so let's shift gears a little bit and talk about events. So, obviously, the CUBE's in the event business. We've had to flip completely 'cause all the events are, well, they're all going digital for sure, and/or postponing it or canceling. So we've had to flip and do all dial-ins and there's a whole lot of stuff about asynchronous. But for you, I think it's interesting because as a distributed company, you had Gainsight Pulse as that moment to bring people together physically. You're in the same boat as everybody else, physical is not an option this year. So how are you approaching Gainsight Pulse, both because it's a switch from what you've done in the past, but you at least had the benefit of being in a distributed world? So you probably have a lot of advantages over people that have never done this before. >> Yeah, that's a really interesting, insightful observation. So just for a context, Pulse is an event we do every year to bring together the customer success community. 'Cause, as you observed, there is value in coming together. And so this is not just for our employees, this is for all the customer success people, and actually increasingly product management people out there, coming together around this common goal of driving success for your customers. And it started in 2013 with 300 people, and last year, we had 5,000 people at our event in San Francisco. We had similar events in London and Sydney. And so it's a big deal. And there's a lot of value to coming together physically. But obviously, that's not possible now, nor is it advisable. And we said, okay, how do we convert this and not lose what's special about Pulse? And leverage, like you said, Jeff, the fact that we're good at distributed stuff in general. And so we created what we call Pulse Everywhere. We didn't want to call it Pulse Virtual or something like that, Pulse Webinar, because we didn't want to set the bar as just like, oh, my virtual event, my webinar. This is something different. And we called it Everywhere, 'cause it's Pulse wherever you are. And we joke, it's in your house, it's in your backyard, it's on the peloton, it's walking the dog. You could be wherever you are and join Pulse this year, May 13th and 14th. And what's amazing is last year we had 5,000 people in person, this year we already have 13,000 people registered as of the end of April. And so we'll probably have more than three times the number of people at Pulse Everywhere. And we're really bringing that physical event concept into the virtual, literally with, instead of a puppy pit, where you're in a physical event, you'll bring puppies often, we have a puppy cam where you can see the puppies. We're not giving up on all of our silly music videos and jokes and we actually ship cameras and high-end equipment to all the speakers' houses. So they're going to have a very nice digital experience, our attendees are. It's not going to be like watching a video conference call. It's going to be like watching a TV show, one much like what you try to do here, right? And so we have this amazing experience for all of our presenters and then for the audience. And we're really trying to say how do we make it so it feels like you're in this really connected community? You just happen to not be able to shake people's hands. So it's coming up in a few weeks. It's a big experiment, but we're excited about it. >> There's so many conversations, and we jumped in right away, when this was all going down, what defines a digital event? And like you, I don't like the word virtual. There's nothing fake or virtual. To me, virtual's second to life. And kind of-- >> Yeah. >> Video game world. And like you, we did, it can't be a webinar, right? And so, if you really kind of get into the attributes of what is a webinar? It's generally a one-way communication for a significant portion of the allocated time and you kind of get your questions in and hopefully they take 'em, right? It's not a truly kind of engaged process. That said, as you said, to have the opportunity to separate creation, distribution and consumption of the content, now opens up all types of opportunity. And that's before you get into the benefits of the democratization, as you said, we're seeing that with a lot of the clients we work with. Their registration numbers are giant. >> Totally. >> Because-- >> You're not traveling to spend money, yeah. >> It'll be curious to see what the conversion is and I don't know we have a lot of data there. But, such a democratizing opportunity. And then, you have people that are trying to force, as Ben Nelson said on, you know Ben from Minerva, right? A car is not a mechanical horse, they're trying to force this new thing into this old paradigm and have people sit for, I saw one today, 24 hours, in front of their laptop. It's like a challenge. And it's like, no, no, no. Have your rally moment, have your fun stuff, have your kind of your one-to-many, but really there's so much opportunity for many-to-many. >> Many-to-many. >> Make all the content out there, yeah. >> We've created this concept in this Pulse Everywhere event called Tribes. And the idea is that when you go to an event, the goal is actually partially content, but a lot of times it's connection. And so in any given big event, there's lots of little communities out there and you want to meet people "like you". Might be people in a similar phase of their career, a similar type of company, in our case, it could be companies in certain industry. And so these Tribes in our kind of Pulse Everywhere experience, let people break out into their own tribes, and then kind of basically chat with each other throughout the event. And so it's not the exact same thing as having a drink with people, but at least a little bit more of that serendipitous conversation. >> Right, no, it's different and I think that's really the message, right? It's different, it's not the same. But there's a lot of stuff you can do that you can't do in the physical way, so quit focusing on what you can't do and embrace what you can. So that's great. And good luck on the event. Again, give the plug for it. >> Yeah, it's May 13th and 14th. If you go to gainsightpulse.com you can sign up, and it's basically anything related to driving better success for your customers, better retention, less churn, and better product experience. It's a great event to learn. >> Awesome, so I want to shift gears one more time and really talk about leadership. That's really kind of the focus of this series that we've been doing. And tough times call for great leadership. And it's really an opportunity for great leaders to show their stuff and let the rest of us learn. You have a really fantastic style. You know I'm a huge fan, we're social media buddies. But you're very personable and you're very, kind of human, I guess, is really the best word, in your communications. You've got ton of frequency, ton of variety. But really, most of it has kind of this human thread. I wonder if you can share kind of your philosophy behind social, 'cause I think a lot of leaders are afraid of it. I think they're afraid that there is reward for saying something stupid is not worth the benefit of saying okay things. And I think also a lot of leaders are afraid of showing some frailty, showing some emotion. Maybe you're a little bit scared, maybe we don't have all the answers. And yet you've really, you're not afraid at all. And I think it's really shines in the leadership activities and behaviors and things you do day in and day out. So how do you think about it? What's your strategy? >> Yeah, it's really interesting you ask, Jeff, because I'm in a group of CEOs that get together on a regular basis, and I'm going to be leading a session on social media for CEOs. And honestly, when I was putting it together, I was like, it's 2020, does that still need to exist? But somehow, there is this barrier. And I'll talk more about it, but I think the barrier isn't just about social media, it's just about how a CEO wants to present herself or himself into the world. And I think, to me, the three things to ask yourself are, first of all, why? Why do you want to be on social media? Why do you want to communicate to the outside? You should have a why. Hopefully you enjoy it, but also you're connecting from a business perspective with your customers. And for us, it's been a huge benefit to really be able to connect with our customers. And then, who are you targeting? So, I actually think an important thing to think about is it's okay to have a micro-audience. I don't have millions of Twitter followers like Lady Gaga, but within the world of SaaS and customer success and retention, I probably have a decent number. And that means I can really connect with my own specific audience. And then, what. So, the what is really interesting 'cause I think there's a lot of non-obvious things about, it's not just about your business. So I can tweet about customer success or retention and I do, but also the, what, about you as an individual, what's happening in your family? What's happening in the broader industry, in my case of SaaS? What's happening in the world of leading through COVID-19? All the questions you've asked, Jeff, are in this lens. And then that gets you to the final which is the, how. And I think the, how, is the most important. It's basically whether you can embrace the idea of being vulnerable. There's a famous TED Talk by Brene Brown. She talks about vulnerability is the greatest superpower for leaders. I think the reason a lot of people have a hard time on social media, is they have a hard time really being vulnerable. And just saying, look, I'm just a human being just like all of you. I'm a privileged human being. I have a lot of things that luckily kind of came my way, but I'm just a human being. I get scared, I get anxious, I get lonely, all those things. Just like all of you, you know. And really being able to take off your armor of, oh, I'm a CEO. And then when you do that, you are more human. And it's like, this goes back to this concept of human first business. There's no work persona and home persona, there's just you. And I think it's surprising when you start doing it, and I started maybe seven, eight, nine years ago, it's like, wow, the world wants more human leaders. They want you to just be yourself, to talk about your challenges. I had the kids, when we got to 13,000 registrations for Pulse Everywhere, they pied me in the face. And the world wants to see CEOs being pied in the face. Probably that one, for sure, that's a guaranteed crowd pleaser. CEOs being pied in the face. But they want to see what you're into outside of work and the pop culture you're into. And they want to see the silly things that you're doing. They want you to be human. And so I think if you're willing to be vulnerable, which takes some bravery, it can really, really pay off for your business, but I think also for you as a person. >> Yeah, yeah. I think it's so insightful. And I think people are afraid of it for the wrong reasons, 'cause it is actually going to help people, it's going to help your own employees, as well, get to know you better. >> Totally, they love it. >> And you touched on another concept that I think is so important that I think a lot of people miss as we go from kind of the old broadcast world to more narrow casting, which is touching your audience and developing your relationship with your audience. So we have a concept here at theCUBE that one is greater than 1% of 100. Why go with the old broadcast model and just spray and you hope you have these really ridiculously low conversion rates to get to that person that you're trying to get to, versus just identifying that person and reaching out directly to those people, and having a direct engagement and a relative conversation within the people that care. And it's not everybody, but, as you said, within the population that cares about it it's meaningful and they get some value out of it. So it's a really kind of different strategy. So-- >> I love that. >> You're always get a lot of stuff out, but you are super prolific. So you got a bunch of projects that are just hitting today. So as we're getting ready to sit down, I see you just have a book came out. So tell us a little bit about the book that just came out. >> Sure, yeah, it's funny. I need to get my physical copy too at my home. I've got so a few, just for context. Five years ago, we released this first book on "Customer Success" which you can kind of see here. It's surprising really, really popular in this world of SaaS and customer success and it ties, Jeff, to what you just said which is, you don't need to be the book that everyone in the world reads, you need to be the book that everyone in your world reads. And so this book turned out to be that. Thousands of company management teams and CEOs in software and SaaS read it. And so, originally when this came out, it was just kind of an introduction to what we call customer success. Basically, how do you retain your customers for the long-term? How do you get them more value? And how do you get them to use more of what they've bought and eventually spend more money with you? And that's a mega-trend that's happening. We decided that we needed an update. So this second book is called "Customer Success Economy." It just came out, literally today. And it's available on Amazon. And it's about the idea that customer success started in tech companies, but it's now gone into many, many industries, like healthcare, manufacturing, services. And it started with a specific team called the customer success management team. But now it's affecting how companies build products, how they sell, how they market. So it's sort of this book is kind of a handbook for management teams on how to apply customer success to your whole business and we call it "Customer Success Economy" 'cause we do think the future of the economy isn't about marketing and selling transactional products, but it's about making sure what your customers are buying is actually delivering value for them, right? That's better for the world, but it's also just necessary 'cause your customers have the power now. You and I have the power to decide how to transport ourselves, whether it's buying a car or rideshare, in the old world when we could leave our house. And we have the power to decide how we're going to stay in a city, whether it's a hotel or Airbnb or whatever. And so customers have the power now, and if you're not driving success, you're not going to be able to keep those customers. And so "Customer Success Economy" is all about that. >> Yeah, and for people that aren't familiar with Gainsight, obviously, there's lots of resources that they can go. They should go to the show in a couple weeks, but also, I think, the interview that we did at PagerDuty, I think you really laid out kind of a great definition of what customer success is. And it's not CRM, it has nothing to do with CRM. CRM is tracking leads and tracking ops. It's not customer success. So, people can also check that. But I want to shift gears again a little bit because one, you also have your blog, MehtaPhysical, that came out. And you just came out again recently with a new post. I don't know when you, you must have a army of helper writers, but you talk about something that is really top of mind right now. And everyone that we get on theCUBE, especially big companies that have the benefit of a balance sheet with a few bucks in it, say we want to help our customers, we want to help our people be safe, obviously, that's first. But we also want to help our customers. But nobody ever really says what exactly does that mean? And it's pretty interesting. You lay out a bunch of things that are happening in the SaaS world, but I jumped on, I think it's number 10 of your list, which is how to think about helping your customers. And you give some real specific kind of guidance and guidelines and definitions, if you will, of how do you help our customers through these tough times. >> Yeah, so I'll summarize for the folks listening. One of the things we observed is, in this terrible tough times right now, your customers are in very different situations. And for simplicity, we thought about three categories. So the companies that we call category one, which are unfortunately, adversely affected by this terrible crisis, but also by the shutdown itself, and that's hotels, restaurants, airlines, and you can put other folks in that example. What do those customers need? Well, they probably need some financial relief. And you have to figure out what you're going to do there and that's a hard decision. And they also just need empathy. It's not easy and the stress level they have is massive. Then you've got, on the other extremes, a small number of your customers might be doing great despite this crisis or maybe even because of it, because they make video conferencing technology or remote work technology, or they make stuff for virtual or telemedicine. And those folks actually are likely to be super busy because they're just trying to keep up with the demand. So what they need from you is time and help. And then you got the people in between. Most companies, right, where there may be a mix of some things going well, some don't. And so what we recommended is think about your strategy, not just inside out, what you want, but outside in, what those clients need. And so as an example, you might think about in that first category, financial relief. The second category, the companies in the middle, they may need, they may not be willing to spend more money, but they may want to do more stuff. So maybe you unlock your product, make it available, so they can use everything in your suite for a while. And maybe in that third category, they're wiling to spend money, but they're just really busy. So maybe you offer services for them or things to help them as they scale. >> Yeah, so before I let you go, I just want to get your reaction to one more great leader. And as you can tell, I love great leaders and studying great leaders. Back when I was in business school we had Dave Pottruck, who at that time was the CEO of Schwab, come and speak and he's a phenomenal speaker and if you ever get a chance to see him speak. And at that point in time, Schwab had to reinvent their business with online trading and basically kill their call-in brokerage for online brokerage, and I think that they had a fixed price of 19.99, whatever it was. This was back in the late 90s. But he was a phenomenal speaker. And we finished and he had a small dinner with a group of people, and we just said, David, you are a phenomenal speaker, why, how, why're you so good? And he goes, you know, it's really pretty simple. As a CEO, I have one job. It's to communicate. And I have three constituencies. I kind of have the street and the market, I have my internal people, and then I have my customers and my ecosystem. And so he said, I, and he's a wrestler, he said, you know I treated it like wrestling. I hired a coach, I practiced my moves, I did it over and over, and I embraced it as a skill and it just showed so brightly. And it's such a contrast to people that get wrapped around the axle with their ego, or whatever. And I think you're such a shiny example of someone who over communicates, arguably, in terms of getting the message out, getting people on board, and letting people know what you're all about, what the priorities are, and where you're going. And it's such a sheer, or such a bright contrast to the people that don't do that that I think is so refreshing. And you do it in a fun and novel and in your own personal way. >> That's awesome to hear that story. He's a inspirational leader, and I've studied him, for sure. But I hadn't heard this specific story, and I totally agree with you. Communication is not something you're born with. Honestly, you might know this, Jeff, or not, as a kid, I was super lonely. I didn't really have any friends and I was one of those kids who just didn't fit in. So I was not the one they would pick to be on stage in front of thousands of people or anything else. But you just do it over and over again and you try to get better and you find, I think a big thing is finding your own voice, your own style. I'm not a super formal style, I try to be very human and authentic. And so finding your style that works for you, I agree, it's completely learnable. >> Yeah, well, Nick, thank you. Thanks for taking a few minutes. I'm sure you're super, super busy getting ready for the show in a couple weeks. But it's always great to catch up and really appreciate you taking some time to share your thoughts and insights with us. >> Thank you, Jeff, it's an honor. >> All right, he's Nick Mehta, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (soft music)

>> Live from Las Vegas, it's theCUBE, covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone, to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight, along with my cohost, John Furrier. We have two guests for this segment. We have Charlie Emer. He is the senior director data management and governance strategy at Honeywell. Thanks for joining us. >> Thank you. >> And Randy Mickey, senior vice president global professional services at Informatica. Thanks for coming on theCUBE. >> Thank you. >> Charlie, I want to start with you. Honeywell is a household name, but tell us a little bit about the business now and about your role at Honeywell. >> Think about it this way. When I joined Honeywell, even before I knew Honeywell, all I thought was thermostats. That's what people would think about Honeywell. >> That's what I thought. >> But Honeywell's much bigger than that. Look, if you go back to the Industrial Revolution, back in, I think, '20s, we talked about new things. Honeywell was involved from the beginning making things. But we think this year and moving forward in this age, Honeywell is looking at it as the new Industrial Revolution. What is that? Because Honeywell makes things. We make aircraft engines, we make aircraft parts. We make everything, household goods, sensors, all types of sensors. We make things. So when we say the new Industrial Revolution is about the Internet of Things, who best to participate because we make those things. So what we are doing now is what we call IIOT, Industrial Internet of Things. Now, that is what Honeywell is about, and that's the direction we are heading, connecting those things that we make and making them more advancing, sort of making life easier for people, including people's quality of life by making those things that we make more usable for them and durable. >> Now, you're a broad platform customer of Informatica. I'd love to hear a little bit from both of you about the relationship and how it's evolved over the years. >> Look, we look at Informatica as supporting our fundamentals, our data fundamentals. For us to be successful in what we do, we need to have good quality data, well governed, well managed, and secure. Not only that, and also accessible. And we using Informatica almost end to end. We are using Informatica for our data movement ETL platform. We're using Informatica for our data quality. We're using Informatica for our master data management. And we have Informatica beginning now to explore and to use Informatica big data management capabilities. And more to that, we also utilize Informatica professional services to help us realize those values from the platforms that we are deploying. IIoT, Industrial IoT has really been a hot trend. Industrial implies factories building big things, planes, wind farms, we've heard that before. But what's interesting is these are pre-existing physical things, these plants and all this manufacturing. When you add digital connectivity to it and power, it's going to change what they were used to be doing to new things. So how do you see Industrial IoT changing or creating a builder culture of new things? Because this connect first, got to have power and connectivity. 5G's coming around, Wi-Fi 6 is around the corner. This is going to light up all these devices that might have had battery power or older databases. What's the modernization of these industrial environments going to look like in your view? First of all, let me give you an example of the value that is coming with this connectivity. Think of it, if you are an aircraft engineer. Back in the day, a plane landed in Las Vegas. You went and inspected it, physically, and checked in your manual when to replace a part. But now Honeywell is telling you, we're connecting directly to the mechanic who is going to inspect the plane, and there will be sort of in their palms they can see and say wait a minute. This part, one more flight and I should replace this part. Now, we are advising you now, doing some predictive analytics, and telling you when this part could even fail. We're telling you when to replace it. So we're saying okay, the plane is going to fly from here to California. Prepare the mechanics in California when it lands with the part so they can replace it. That's already safety 101. So guaranteeing safety, sort of improving the equity or the viability of the products that we produce. When we're moving away from continue to build things because people still need those things built, safety products, but we're just making them more. We've heard supply chain's a real low-hanging fruit on this, managing the efficiency so there's no waste. Having someone ready at the plane is efficient. That's kind of low-hanging fruit. Any ideas on some of the creativity of new applications that's going to come from the data? Because now you start getting historical data from the connections, that's where I think the thing can get interesting here. Maybe new jobs, new types of planes, new passenger types. >> We are not only using the data to improve on the products and help us improve customer needs, design new products, create new products, but we also monitorizing that data, allowing our partners to also get some insights from that data to develop their own products. So creating sort of an environment where there is a partnership between those who use our products. And guess what, most of the people who use our products, our products actually input into their products. So we are a lot more business-to-business company than a B2C. So I see a lot of value in us being able to share that intelligence, that insight, in our data at a level of scientific discovery for our partners. >> Randy, I want to bring you into the conversation a little bit here (laughs). >> Thanks. >> So you lead Informatica's professional services. I'm interested to hear your work with Honeywell, and then how it translates to the other companies that you engage with. Honeywell is such a unique company, 130 years of innovation, inventor of so many important things that we use in our everyday lives. That's not your average company, but talk a little bit about their journey and how it translates to other clients. >> Sure, well, you could tell, listening to Charlie, how strategic data is, as well as our relationship. And it's not just about evolution from their perspective, but also you mentioned the historicals and taking advantage of where you've been and where you need to go. So Charlie's made it very clear that we need to be more than just a partner with products. We need to be a partner with outcomes for their business. So hence, a professional services relationship with Honeywell and Charlie and the organization started off more straightforward. You mentioned ETL, and we started off 2000, I believe, so 19 years ago. So it's been a journey already, and a lot more to go. But over the years you can kind of tell, using data in different ways within the organization, delivering business outcomes has been at the forefront, and we're viewed strategically, not just with the products, but professional services as well, to make sure that we can continue to be there, both in an advisory capacity, but also in driving the right outcomes. And something that Charlie even said this morning was that we were kind of in the fabric. We have a couple of team members that are just like Honeywell team members. We're in the fabric of the organization. I think that's really critically important for us to really derive the outcomes that Charlie and the business need. >> And data is so critical to their business. You have to be, not only from professional services, but as a platform. Yes. This is kind of where the value comes from. Now, I can't help but just conjure up images of space because I watch my kids that watch, space is now hot. People love space. You see SpaceX landing their rocket boosters to the finest precision. You got Blue Origin out there with Amazon. And they are Honeywell sensors either. Honeywell's in every manned NASA mission. You have a renaissance of activity going on in a modern way. This is exciting, this is critical. Without data, you can't do it. >> Absolutely, I mean, also sometimes we take a break. I'm a fundamentalist. I tell everybody that excitement is great, but let's take a break. Let's make sure the fundamentals are in place. And we actually know what is it, what are those critical data that we need to be tracking and managing? Because you don't just have to manage a whole world of data. There's so much of it, and believe me, there's not all value in everything. You have to be critical about it and strategic about it. What are the critical data that we need to manage, govern, and actually, because it's expensive to manage the critical data. So we look at a value tree as well, and say, okay, if we, as Honeywell, want to be able to be also an efficient business enabler, we have to be efficient inside. So there's looking out, and there's also looking inside to make sure that we are in the right place, we are understanding our data, our people understand data. Talking about our relationship with IPS, Informatica Professional Services, one of the things that we're looking at is getting the right people, the engineers, the people to actually realize that okay, we have the platform, we've heard of Clare, We heard of all those stuff. But where are the people to actually go and do the real stuff, like actually programming, writing the code, connecting things and making it work? It's not easy because the technology's going faster than the capabilities in terms of people, skills. So the partnership we're building with Informatica professional services, and we're beginning to nurture, inside that, we want to be in a position were Honeywell doesn't have to worry so much about the churn in terms of getting people and retraining and retraining and retraining. We want to have a reliable partner who is also moving with the certain development and the progress around the products that we bought so we can have that success. So the partnership with IPS is for the-- >> The skill gaps we've been talking about, I know she's going to ask next, but I'll just jump in because I know there's two threads here. One is there's a new generation coming into the workforce, okay, and they're all data-full. They've been experiencing the digital lifestyle, the engineering programs. To data, it's all changing. What are some of the new expertise that really stand out when evaluating candidates, both from the Informatica side and also Honeywell? What's the ideal candidate look like, because there's no real four-year degree anymore? Well, Berkeley just had their first class of data analytics. That's new two-generation. But what are some of those skills? There's no degree out there. You can't really get a degree in data yet. >> Do you want to talk about that? >> Sure, I can just kick off with what we're looking at and how we're evolving. First of all, the new graduates are extremely innovative and exciting to bring on. We've been in business for 26 years, so we have a lot of folks that have done some great work. Our retention is through the roof, so it's fun to meld the folks that have been doing things for over 10, 15 years, to see what the folks have new ideas about how to leverage data. The thing I can underscore is it's business and technology, and I think the new grads get that really, really well in terms of data. To them, data's not something that's stored somewhere in the cloud or in a box. It's something that's practically applied for business outcomes, and I think they get that right out of school, and I think they're getting that message loud and clear. Lot of hybrid programs. We do hire direct from college, but we also hire experienced hires. And we look for people that have had degrees that are balanced. So the traditional just CS-only degrees, still very relevant, but we're seeing a lot of people do hybrids because they know they want to understand supply chain along with CS and data. And there are programs around just data, how organizations can really capitalize on that. >> And also we're hearing, too, that having domain expertise is actually just as important as having the coding skills because you got to know what an outcome looks like before you collect the data. You got to know what checkmate is if you're going to play chess. That's the old expression, right? >> I think people with the domain, both the hybrid experience or expertise, are more valuable to the company because maybe from the product perspective, from building products, you could be just a scientist, code the code. But when you come to Honeywell, for example, we want you to be able to understand, think about materials. Want you to be able to understand what are the products, what are the materials that we use. What are the inputs that we have to put into these products? Now a simple thing like a data scientist deciding what the right correct value of what an attribute should be, that's not something that because you know code you can determine. You have to understand the domain, the domain you're dealing with. You have to understand the context. So that comes, the question of context management, understanding the context and bringing it together. That is a big challenge, and I can tell you that's a big gap there. >> Big gap indeed, and understand the business and the data too. >> Yes. >> Charles, Randy, thank you both so much for coming on theCUBE. It's been a great conversation. >> Thank you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (funky techno music)

(upbeat music) >> Hey welcome back, Jeff Rick here with theCUBE. We're in San Francisco International, actually at gate 54B if you're trying to to track us down. It's the Alaska Airlines improved flight experience launch event. A lot of vendors here, they're rebranding their planes, they've rebranded all the Virgin Airbus planes, and they've taken that opportunity to add a lot of new innovations. So we're excited to be here, to talk to some of the people participating, and our first guest. It's Rene Donkworth, he is the general manager of Aircraft Seating America's for Recaro. Rene, great to see you. >> Thank you, great to be here. >> So I've seen a lot of people are familiar with the Recaro seats, we think of them as racing seats or, you know, upgrading our cars when we were kids, everybody wanted a Recaro seat. I had no idea you guys played such a major role in aviation. >> Absolutely. And we are since the early 70s already in the aircraft seating business, and really a major player, a global player in this business and you know it's a very long term experience and people are often flying and they're sitting on an aircraft and to be comfortable in traveling is very important and it's our mission. >> Right, it's funny because people probably usually don't think of the seat specifically until they're uncomfortable or, you know, they're in it. But you've got a lot of technology and a lot of innovation in the past but also some of these new seats that you're showing here today. >> Right. So we are showing the seat for first class here that we have displayed for Alaska Airlines, and we developed together in a very intensive process, a lot of thing on the seat here. We have a memory foam cushion with netting, a six way head rest which overall comes to a very comfortable seating experience for the passenger, and that's really one step ahead of other products, and we went through a very intensive process with Alaska and we are proud to present it and to see the roll out now because it's exciting. If you've worked all the time on such a project to see it's flying now. >> So there's a couple components to this seat, right? There's obviously the safety, its' got to stay bolted on, but you've got kind of this limited ergonomic space in terms of what the pitch is from one seat to the other. What are some of the unique challenges there and what are some of the things you guys have done to operate, you know, in kind of a restrained space? >> Of course it's always to optimize everything with the given conditions that you have. But really looking into the small details. Reduced pressure points on the body, we are using kind of pressure mapping methods to develop that together with the customer, looking into a cushy experience for the passenger, optimizing it so that you have really kinds of luxury feeling on the seat. But in addition it's also important to look into solutions like content. How is content provided and what kind of tablet integration is there, so we have very smart solutions there that we are showing today with the right viewing angles there's the right power, the high power USB which support the power, so the overall package needs to be optimized, and that's what we are working with. >> And that's where I was going to go next, is when you're sitting there for 2 hours, 5 hours, 10 hours now we're talking about 20 hour flights, right, some of these crazy ones, people are doing things in their seat. They're not just sitting, as you said. They want power, they want connectivity, they want to watch their movie on their laptop or their tablet or their phone. So you guys have really incorporated kind of that next gen entertainment experience into this new seat. >> Right. So as I explained, there is a lot about tablet integration, not only for the first class as well also for the economy class that you can see today that you can experience. But there's also a lot about stowage in total. You know, stowage is always a big topic. Where do you stow your belongings? And there you will also see here smart solutions, lots of stowage options. For example also on the coach class seat you can use the tablet, you have the right viewing angle. In addition you can fold or unfold the table, you can use the stowages, so everything is really optimized in the details. >> And this is a huge kind of change in thought process when you think of the entertainment world, right, where it used to be you have a projector TV and then they put individual seat screens, but the airlines woke up and figured out everyone's already packing their screen of choice so how do we support that experience versus putting our own screen on that seat. >> Yeah, that's where we are going, and if you look into today's passengers almost everybody has his own tablet or iPhone or whatever with him, so it's important to be able to stow everything, to connect every kind of device, to have the power. But I think then the content is really important to be provided. The integrated solutions are not so important anymore. >> Right, well Rene congratulations and enjoy the flight and seeing all your hard work up in the air. >> Thank you very much. >> Alright, he's Rene, I'm Jeff, we're at the San Fransisco International Gate 54B at the Alaska Airlines Elevated Flight Experience. Thanks for watching.

>> We'll come back here ready. Geoffrey here with the Cube were at San Francisco International Airport, Gate fifty four d. If you want to stop by for getting ready to go on a little Alaska flight because it's an exciting day, they took advantage of the opportunity after the Virgin merger to kind of rebrand everything. We look at the technology of everything from the seats to the WiFi, everything in between. We're excited at the guy who's responsible for everything. He's Brett Catlin, the managing director >> of alliances and product. Bret, great to see you. >> Thanks for having my job. I really appreciate it. >> So first off, congratulations. You're a whole lot of work. Went into this day absolutely the >> team effort over the past few years, and we're just thrilled to see it all come together to deliver a better experience for our guests. >> So it's pretty interesting because I think you know, you guys are obviously thinking about this. I don't know if people are is aware that when you think of the total experience, the engagement that I have, when I'm taking a flight from San Francisco to Seattle, it's a lot more than just the air miles with my butt in a seat and moving down down the road. You guys really think that >> whole experience absolutely. Look at the entire journey from when you arrive at the airport to your lounge experience. When you walk on board, what's the Jet Jeffords feel like? The lighting, the music. When you enter the aircraft, the configuration, the seats, comfort and then ultimately, a big thing crosses food and beverage. So making sure that it's healthy local speaks to the West Coast values that we're so proud of. >> And how do you how do you kind of get input from the customers >> is toe, You know, these are things that you guys spend a lot of time on, and there are a lot of little things that add up to a total experience. How where customers are, kind of are they get in, Or do they suddenly like, Wow, you know, I feel a little bit more arrested because of a particular type of sound or a particular type of configuration on the seat. >> How do you get feedback >> on all these different things? >> Absolutely great questions on the front end. We obviously quite a bit of guest research, both kind of online quantitative studies, but then also in person with focus groups. Now that we have a lot of product and market, our focus is kind of elevating and improving. What we have and how we get that feedback is every guest receives a survey after every flight. And so we look. >> Every guest receives a survey after every flight. >> Exactly. And so we have hundreds of thousands of response as every year, which allows us to make small tweaks around the margin, but also more material changes. >> That's pretty wild. So I'm just curious some of the more crazy things that have come come through that either good things that you could actually execute on that maybe never thought about or just just funny things to make put a smile on your face and tell you it really is a mixture >> of to tell you the truth, and a lot of things are items that we want action. So certain health restrictions where maybe we didn't realize a certain kind of food wasn't hitting the mark with a wide section of our guests. We could make tweets there, but also, when you think about maybe our in flight entertainment. Do we have the right content? Are the movies that people watch resonating? So we look at all that data to say, Well, look, this kind of movie. It does really well in flight. So people love thrillers when you think about movies and flight, for whatever reason. So we try and put more thrillers onboard. >> I thought they go, Mort. The romantic comedies in the airplane. I don't know that. What a swell. But the suspense people love, right? Right. And it really goes to this bigger question of this total experience. An engagement with the airline. So I wonder you can speak to about technology in the role of technology and how you guys are using that across all these various product. Absolutely. So being >> a West Coast airline technologies critically important for us, one of the things we're focused on is offering high spider highspeed WiFi and offer a mainline aircraft. We have about a dozen done right now, by the end of twenty nineteen will have one hundred twenty five. And so the key there is you'll be all the stream entertainment on board our aircraft. Your outlook for your core, Primo will be zippy, The real basics. When you're flying coast to coast or to Hawaii, You're super excited about that. Then we look at a couple other things as well. Mobile order and one great example. So before you board your flight, you can reserve your meal in first class with the main cabin to make sure you get exactly what you want. So there's some basics like that. Then we're also looking longer term. How do we improve the technology experience in our lounge is to maybe being ableto order a barista beverage while you're still approaching the AARP point. >> Pretty thing. And a lot of that's got to be through your mobile app, right? Absolutely. Has this very significant point of contact between you and your customers? >> That's exactly right. >> Excellent. Well, thanks for taking a few minutes of your time. Way. Looked forward to drop it on the plane and get to experience some of this. And again, congratulations on the Integrative X when it's my pleasure. Thank you, Jeffrey. Really appreciate it. All right. >> He's Brad. I'm Jeff. You're watching the Cube. Where at San Francisco International Gave fifty four b. Thanks for watching. We'll catch you next time.

(uplifting orchestral music) >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at the Palo Alto studios, having theCUBE Conversation, a little break in the action of the conference season before things heat up, before we kind of come to the close of 2018. It's been quite a year. But it's nice to be back in the studio. Things are a little bit less crazy, and we're excited to talk about one of the really hot topics right now, which is edge computing, fog computing, cloud computing. What do all these things mean, how do they all intersect, and we've got with us today David King. He's the CEO of FogHorn Systems. David, first off, welcome. >> Thank you, Jeff. >> So, FogHorn Systems, I guess by the fog, you guys are all about the fog, and for those that don't know, fog is kind of this intersection between cloud, and on prem, and... So first off, give us a little bit of the background of the company and then let's jump into what this fog thing is all about. >> Sure, actually, it all dovetails together. So yeah, you're right, FogHorn, the name itself, came from Cisco's invented term, called fog computing, from almost a decade ago, and it connoted this idea of computing at the edge, but didn't really have a lot of definition early on. And so, FogHorn was started actually by a Palo Alto Incubator, just nearby here, that had the idea that hey, we got to put some real meaning and some real meat on the bones here, with fog computing. And what we think FogHorn has become over the last three and a half years, since we took it out of the incubator, since I joined, was to put some real purpose, meaning, and value in that term. And so, it's more than just edge computing. Edge computing is a related term. In the industrial world, people would say, hey, I've had edge computing for three, 40, 50 years with my production line control and also my distributed control systems. I've got hard wired compute. I run, they call them, industrial PCs in the factory. That's edge compute. The IT roles come along and said, no, no, no, fog compute is a more advanced form of it. Well, the real purpose of fog computing and edge computing, in our view, in the modern world, is to apply what has traditionally been thought of as cloud computing functions, big, big data, but running in an industrial environment, or running on a machine. And so, we call it as really big data operating in the world's smallest footprint, okay, and the real point of this for industrial customers, which is our primary focus, industrial IoT, is to deliver as much analytic machine learning, deep learning AI capability on live-streaming sensor data, okay, and what that means is rather than persisting a lot of data either on prem, and then sending it to the cloud, or trying to stream all this to the cloud to make sense of terabytes or petabytes a day, per machine sometimes, right, think about a jet engine, a petabyte every flight. You want to do the compute as close to the source as possible, and if possible, on the live streaming data, not after you've persisted it on a big storage system. So that's the idea. >> So you touch on all kinds of stuff there. So we'll break it down. >> Unpack it, yeah. >> Unpack it. So first off, just kind of the OT/IT thing, and I think that's really important, and we talked before turning the cameras on about Dr. Tom from HP, he loves to make a big symbolic handshake of the operations technology, >> One of our partners. >> Right, and IT, and the marriage of these two things, where before, as you said, the OT guys, the guys that have been running factories, you know, they've been doing this for a long time, and now suddenly, the IT folks are butting in and want to get access to that data to provide more control. So, you know, as you see the marriage of those two things coming together, what are the biggest points of friction, and really, what's the biggest opportunity? >> Great set of questions. So, quite right, the OT folks are inherently suspicious of IT, right? I mean, if you don't know the history, 40 plus years ago, there was a fork in the road, where in factory operations, were they going to embrace things like ethernet, the internet, connected systems? In fact, they purposely air gapped an island of those systems 'cause they was all about machine control, real-time, for safety, productivity, and uptime of the machine. They don't want any, you can't use kind of standard ethernet, it has to be industrial ethernet, right? It has to have time bound and deterministic. It can't be a retry kind of a system, right? So different MAC layer for a reason, for example. What did the physical wiring look like? It's also different cabling, because you can't have cuts, jumps in the cable, right? So it's a different environment entirely that OT grew up in, and so, FogHorn is trying to really bring the value of what people are delivering for AI, essentially, into that environment in a way that's non-threatening to, it's supplemental to, and adds value in the OT world. So Dr. Tom is right, this idea of bringing IT and OT together is inherently challenging, because these were kind of fork in the road, island-ed in the networks, if you will, different systems, different nomenclature, different protocols, and so, there's a real education curve that IT companies are going through, and the idea of taking all this OT data that's already been produced in tremendous volumes already before you add new kinds of sensing, and sending it across a LAN which it's never talked to before, then across a WAN to go to a cloud, to get some insight doesn't make any sense, right? So you want to leverage the cloud, you want to leverage data centers, you want to leverage the LAN, you want to leverage 5G, you want to leverage all the new IT technologies, but you have to do it in a way that makes sense for it and adds value in the OT context. >> I'm just curious, you talked about the air gapping, the two systems, which means they are not connected, right? >> No, they're connected with a duct, they're connected to themselves, in the industrial-- >> Right, right, but before, the OT system was air gapped from the IT system, so thinking about security and those types of threats, now, if those things are connected, that security measure has gone away, so what is the excitement, adoption scare when now, suddenly, these things that were separate, especially in the age of breaches that we know happen all the time as you bring those things together? >> Well, in fact, there have been cyber breaches in the OT context. Think about Stuxnet, think about things that have happened, think about the utilities back keys that were found to have malwares implanted in them. And so, this idea of industrial IoT is very exciting, the ability to get real-time kind of game changing insights about your production. A huge amount of economic activity in the world could be dramatically improved. You can talk about trillions of dollars of value which the McKenzie, and BCG, and Bain talk about, right, by bringing kind of AI, ML into the plant environment. But the inherent problem is that by connecting the systems, you introduce security problems. You're talking about a huge amount of cost to move this data around, persist it then add value, and it's not real-time, right? So, it's not that cloud is not relevant, it's not that it's not used, it's that you want to do the compute where it makes sense, and for industrial, the more industrialized the environment, the more high frequency, high volume data, the closer to the system that you can do the compute, the better, and again, it's multi-layer of compute. You probably have something on the machine, something in the plant, and something in the cloud, right? But rather than send raw OT data to the cloud, you're going to send processed intelligent metadata insights that have already been derived at the edge, update what they call the fleet-wide digital twin, right? The digital twin for that whole fleet of assets should sit in the cloud, but the digital twin of the specific asset should probably be on the asset. >> So let's break that down a little bit. There's so much good stuff here. So, we talked about OT/IT and that marriage. Next, I just want to touch on cloud, 'cause a lot of people know cloud, it's very hot right now, and the ultimate promise of cloud, right, is you have infinite capacity >> Right, infinite compute. >> Available on demand, and you have infinite compute, and hopefully you have some big fat pipes to get your stuff in and out. But the OT challenge is, and as you said, the device challenge is very, very different. They've got proprietary operating systems, they've been running for a very, very long time. As you said, they put off boatloads, and boatloads, and boatloads of data that was never really designed to feed necessarily a machine learning algorithm, or an artificial intelligence algorithm when these things were designed. It wasn't really part of the equation. And we talk all the time about you know, do you move the compute to the data, you move the data to the compute, and really, what you're talking about in this fog computing world is kind of a hybrid, if you will, of trying to figure out which data you want to process locally, and then which data you have time, relevance, and other factors that just go ahead and pump it upstream. >> Right, that's a great way to describe it. Actually, we're trying to move as much of the compute as possible to the data. That's really the point of, that's why we say fog computing is a nebulous term about edge compute. It doesn't have any value until you actually decide what you're trying to do with it, and what we're trying to do is to take as much of the harder compute challenges, like analytics, machine learning, deep learning, AI, and bring it down to the source, as close to the source as you can, because you can essentially streamline or make more efficient every layer of the stack. Your models will get much better, right? You might have built them in the cloud initially, think about a deep learning model, but it may only be 60, 70% accurate. How do you do the improvement of the model to get it closer to perfect? I can't go send all the data up to keep trying to improve it. Well, typically, what happens is I down sample the data, I average it and I send it up, and I don't see any changes in the average data. Guess what? We should do is inference all the time and all the data, run it in our stack, and then send the metadata up, and then have the cloud look across all the assets of a similar type, and say, oh, the global fleet-wide model needs to be updated, and then to push it down. So, with Google just about a month ago, in Barcelona, at the IoT show, what we demonstrated was the world's first instance of AI for industrial, which is closed loop machine learning. We were taking a model, a TensorFlow model, trained in the cloud in the data center, brought into our stack and referring 100% inference-ing in all the live data, pushing the insights back up into Google Cloud, and then automatically updating the model without a human or data scientist having to look at it. Because essentially, it's ML on ML. And that to us, ML on ML is the foundation of AI for industrial. >> I just love that something comes up all the time, right? We used to make decisions based on the sampling of historical data after the fact. >> That's right, that's how we've all been doing it. >> Now, right, right now, the promise of streaming is you can make it based on all the data, >> All the time. >> All the time in real time. >> Permanently. >> This is a very different thing. So, but as you talked about, you know, running some complex models, and running ML, and retraining these things. You know, when you think of edge, you think of some little hockey puck that's out on the edge of a field, with limited power, limited connectivity, so you know, what's the reality of, how much power do you have at some of these more remote edges, or we always talk about the field of turbines, oil platforms, and how much power do you need, and how much compute that it actually starts to be meaningful in terms of the platform for the software? >> Right, there's definitely use cases, like you think about the smart meters, right, in the home. The older generation of those meters may have had very limited compute, right, like you know, talking about single megabyte of memory maybe, or less, right, kilobytes of memory. Very hard to run a stack on that kind of footprint. The latest generation of smart meters have about 250 megabytes of memory. A Raspberry Pi today is anywhere from a half a gig to a gig of memory, and we're fundamentally memory-bound, and obviously, CPU if it's trying to really fast compute, like vibration analysis, or acoustic, or video. But if you're just trying to take digital sensing data, like temperature, pressure, velocity, torque, we can take humidity, we can take all of that, believe it or not, run literally dozens and dozens of models, even train the models in something as small as a Raspberry Pi, or a low end x86. So our stack can run in any hardware, we're completely OS independent. It's a full up software layer. But the whole stack is about 100 megabytes of memory, with all the components, including Docker containerization, right, which compares to about 10 gigs of running a stream processing stack like Spark in the Cloud. So it's that order of magnitude of footprint reduction and speed of execution improvement. So as I said, world's smallest fastest compute engine. You need to do that if you're going to talk about, like a wind turbine, it's generating data, right, every millisecond, right. So you have high frequency data, like turbine pitch, and you have other conceptual data you're trying to bring in, like wind conditions, reference information about how the turbine is supposed to operate. You're bringing in a torrential amount of data to do this computation on the fly. And so, the challenge for a lot of the companies that have really started to move into the space, the cloud companies, like our partners, Google, and Amazon, and Microsoft, is they have great cloud capabilities for AI, ML. They're trying to move down to the edge by just transporting the whole stack to there. So in a plant environment, okay, that might work if you have massive data centers that can run it. Now I still got to stream all my assets, all the data from all of my assets to that central point. What we're trying to do is come out the opposite way, which is by having the world's smallest, fastest engine, we can run it in a small compute, very limited compute on the asset, or near the asset, or you can run this in a big compute and we can take on lots and lots of use cases for models simultaneously. >> I'm just curious on the small compute case, and again, you want all the data-- >> You want to inference another thing, right? >> Does it eventually go back, or is there a lot of cases where you can get the information you need off the stream and you don't necessarily have to save or send that upstream? >> So fundamentally today, in the OT world, the data usually gets, if the PLC, the production line controller, that has simple KPIs, if temperature goes to X or pressure goes to Y, do this. Those simple KPIs, if nothing is executed, it gets dumped into a local protocol server, and then about every 30, 60, 90 days, it gets written over. Nobody ever looks at it, right? That's why I say, 99% of the brown field data in OT has never really been-- >> Almost like a security-- >> Has never been mined for insight. Right, it just gets-- >> It runs, and runs, and runs, and every so often-- >> Exactly, and so, if you're doing inference-ing, and doing real time decision making, real time actual with our stack, what you would then persist is metadata insights, right? Here is an event, or here is an outcome, and oh, by the way, if you're doing deep learning or machine learning, and you're seeing deviation or drift from the model's prediction, you probably want to keep that and some of the raw data packets from that moment in time, and send that to the cloud or data center to say, oh, our fleet-wide model may not be accurate, or may be drifting, right? And so, what you want to do, again, different horses for different courses. Use our stack to do the lion's share of the heavy duty real time compute, produce metadata that you can send to either a data center or a cloud environment for further learning. >> Right, so your piece is really the gathering and the ML, and then if it needs to go back out for more heavy lifting, you'll send it back up, or do you have the cloud application as well that connects if you need? >> Yeah, so we build connectors to you know, Google Cloud Platform, Google IoT Core, to AWS S3, to Microsoft Azure, virtually any, Kafka, Hadoop. We can send the data wherever you want, either on plant, right back into the existing control systems, we can send it to OSIsoft PI, which is a great time series database that a lot of process industries use. You could of course send it to any public cloud or a Hadoop data lake private cloud. You can send the data wherever you want. Now, we also have, one of our components is a time series database. You can also persist it in memory in our stack, just for buffering, or if you have high value data that you want to take a measurement, a value from a previous calculation and bring it into another calculation during later, right, so, it's a very flexible system. >> Yeah, we were at OSIsoft PI World earlier this year. Some fascinating stories that came out of-- >> 30 year company. >> The building maintenance, and all kinds of stuff. So I'm just curious, some of the easy to understand applications that you've seen in the field, and maybe some of the ones that were a surprise on the OT side. I mean, obviously, preventative maintenance is always towards the top of the list. >> Yeah, I call it the layer cake, right? Especially when you get to remote assets that are either not monitored or lightly monitored. They call it drive-by monitoring. Somebody shows up and listens or looks at a valve or gauge and leaves. Condition-based monitoring, right? That is actually a big breakthrough for some, you know, think about fracking sites, or remote oil fields, or mining sites. The second layer is predictive maintenance, which the next generation is kind of predictive, prescriptive, even preventive maintenance, right? You're making predictions or you're helping to avoid downtime. The third layer, which is really where our stack is sort of unique today in delivering is asset performance optimization. How do I increase throughput, how do I reduce scrap, how do I improve worker safety, how do I get better processing of the data that my PLC can't give me, so I can actually improve the performance of the machine? Now, ultimately, what we're finding is a couple of things. One is, you can look at individual asset optimization, process optimization, but there's another layer. So often, we're deployed to two layers on premise. There's also the plant-wide optimization. We talked about wind farm before, off camera. So you've got the wind turbine. You can do a lot of things about turbine health, the blade pitch and condition of the blade, you can do things on the battery, all the systems on the turbine, but you also need a stack running, like ours, at that concentration point where there's 200 plus turbines that come together, 'cause the optimization of the whole farm, every turbine affects the other turbine, so a single turbine can't tell you speed, rotation, things that need to change, if you want to adjust the speed of one turbine, versus the one next to it. So there's also kind of a plant-wide optimization. Talking about time that's driving, there's going to be five layers of compute, right? You're going to have the, almost what I call the ECU level, the individual sub-system in the car that, the engine, how it's performing. You're going to have the gateway in the car to talk about things that are happening across systems in the car. You're going to have the peer to peer connection over 5G to talk about optimization right between vehicles. You're going to have the base station algorithms looking at a micro soil or macro soil within a geographic area, and of course, you'll have the ultimate cloud, 'cause you want to have the data on all the assets, right, but you don't want to send all that data to the cloud, you want to send the right metadata to the cloud. >> That's why there are big trucks full of compute now. >> By the way, you mentioned one thing that I should really touch on, which is, we've talked a lot about what I call traditional brown field automation and control type analytics and machine learning, and that's kind of where we started in discrete manufacturing a few years ago. What we found is that in that domain, and in oil and gas, and in mining, and in agriculture, transportation, in all those places, the most exciting new development this year is the movement towards video, 3D imaging and audio sensing, 'cause those sensors are now becoming very economical, and people have never thought about, well, if I put a camera and apply it to a certain application, what can I learn, what can I do that I never did before? And often, they even have cameras today, they haven't made use of any of the data. So there's a very large customer of ours who has literally video inspection data every product they produce everyday around the world, and this is in hundreds of plants. And that data never gets looked at, right, other than training operators like, hey, you missed the defects this day. The system, as you said, they just write over that data after 30 days. Well, guess what, you can apply deep learning tensor flow algorithms to build a convolutional neural network model and essentially do the human visioning, rather than an operator staring at a camera, or trying to look at training tapes. 30 days later, I'm doing inference-ing of the video image on the fly. >> So, do your systems close loop back to the control systems now, or is it more of a tuning mechanism for someone to go back and do it later? >> Great question, I just got asked that this morning by a large oil and gas super major that Intel just introduced us to. The short answer is, our stack can absolutely go right back into the control loop. In fact, one of our investors and partners, I should mention, our investors for series A was GE, Bosch, Yokogawa, Dell EMC, and our series debuted a year ago was Intel, Saudi Aramco, and Honeywell. So we have one foot in tech, one foot in industrial, and really, what we're really trying to bring is, you said, IT, OT together. The short answer is, you can do that, but typically in the industrial environment, there's a conservatism about, hey, I don't want to touch, you know, affect the machine until I've proven it out. So initially, people tend to start with alerting, so we send an automatic alert back into the control system to say, hey, the machine needs to be re-tuned. Very quickly, though, certainly for things that are not so time-sensitive, they will just have us, now, Yokogawa, one of our investors, I pointed out our investors, actually is putting us in PLCs. So rather than sending the data off the PLC to another gateway running our stack, like an x86 or ARM gateway, we're actually, those PLCs now have Raspberry Pi plus capabilities. A lot of them are-- >> To what types of mechanism? >> Well, right now, they're doing the IO and the control of the machine, but they have enough compute now that you can run us in a separate module, like the little brain sitting right next to the control room, and then do the AI on the fly, and there, you actually don't even need to send the data off the PLC. We just re-program the actuator. So that's where it's heading. It's eventually, and it could take years before people get comfortable doing this automatically, but what you'll see is that what AI represents in industrial is the self-healing machine, the self-improving process, and this is where it starts. >> Well, the other thing I think is so interesting is what are you optimizing for, and there is no right answer, right? It could be you're optimizing for, like you said, a machine. You could be optimizing for the field. You could be optimizing for maintenance, but if there is a spike in pricing, you may say, eh, we're not optimizing now for maintenance, we're actually optimizing for output, because we have this temporary condition and it's worth the trade-off. So I mean, there's so many ways that you can skin the cat when you have a lot more information and a lot more data. >> No, that's right, and I think what we typically like to do is start out with what's the business value, right? We don't want to go do a science project. Oh, I can make that machine work 50% better, but if it doesn't make any difference to your business operations, so what? So we always start the investigation with what is a high value business problem where you have sufficient data where applying this kind of AI and the edge concept will actually make a difference? And that's the kind of proof of concept we like to start with. >> So again, just to come full circle, what's the craziest thing an OT guy said, oh my goodness, you IT guys actually brought some value here that I didn't know. >> Well, I touched on video, right, so without going into the whole details of the story, one of our big investors, a very large oil and gas company, we said, look, you guys have done some great work with I call it software defined SCADA, which is a term, SCADA is the network environment for OT, right, and so, SCADA is what the PLCs and DCSes connect over these SCADA networks. That's the control automation role. And this investor said, look, you can come in, you've already shown us, that's why they invested, that you've gone into brown field SCADA environments, done deep mining of the existing data and shown value by reducing scrap and improving output, improving worker safety, all the great business outcomes for industrial. If you come into our operation, our plant people are going to say, no, you're not touching my PLC. You're not touching my SCADA network. So come in and do something that's non-invasive to that world, and so that's where we actually got started with video about 18 months ago. They said, hey, we've got all these video cameras, and we're not doing anything. We just have human operators writing down, oh, I had a bad event. It's a totally non-automated system. So we went in and did a video use case around, we call it, flare monitoring. You know, hundreds of stacks of burning of oil and gas in a production plant. 24 by seven team of operators just staring at it, writing down, oh, I think I had a bad flare. I mean, it's a very interesting old world process. So by automating that and giving them an AI dashboard essentially. Oh, I've got a permanent record of exactly how high the flare was, how smoky was it, what was the angle, and then you can then fuse that data back into plant data, what caused that, and also OSIsoft data, what was the gas composition? Was it in fact a safety violation? Was it in fact an environmental violation? So, by starting with video, and doing that use case, we've now got dozens of use cases all around video. Oh, I could put a camera on this. I could put a camera on a rig. I could've put a camera down the hole. I could put the camera on the pipeline, on a drone. There's just a million places that video can show up, or audio sensing, right, acoustic. So, video is great if you can see the event, like I'm flying over the pipe, I can see corrosion, right, but sometimes, like you know, a burner or an oven, I can't look inside the oven with a camera. There's no camera that could survive 600 degrees. So what do you do? Well, that's probably, you can do something like either vibration or acoustic. Like, inside the pipe, you got to go with sound. Outside the pipe, you go video. But these are the kind of things that people, traditionally, how did they inspect pipe? Drive by. >> Yes, fascinating story. Even again, I think at the end of the day, it's again, you can make real decisions based on all the data in real time, versus some of the data after the fact. All right, well, great conversation, and look forward to watching the continued success of FogHorn. >> Thank you very much. >> All right. >> Appreciate it. >> He's David King, I'm Jeff Frick, you're watching theCUBE. We're having a CUBE conversation at our Palo Alto studio. Thanks for watching, we'll see you next time. (uplifting symphonic music)

>> Announcer: Live, from Washington, DC, it's theCUBE. Covering .cof2017. Brought to you by Splunk. (upbeat techno music) >> Welcome back to the nation's capital, theCUBE coming to you here from the Walter Washington Convention Center at .conf2017, Splunk's annual get-together, along with Dave Vellante, I'm John Walls. Good to have you with us here on theCUBE and how's your flying experience these days here in the States? Baggage, security, you happy? Well we're going to make you a little less... (laughs) Dubai Airports has just an exceptional network of operations that are going on right now, from soup to nuts and Michael Ibbitson is the VP of Technology and Infrastructure at Dubai. He joins us now here on theCUBE, and Michael, first off, glad to have you here in the States. >> Thank you. Good to be here. >> Good to see you, sir. You ran through on the key note stage a litany of checks that we all go through here in the States of, yes we'd love... better security, better baggage, even the golden bathroom, which I can't wait to hear about. But tell me about your focus with technology and Dubai, and what you're bringing to the job and how you're trying to revolutionize the travel experience. >> Yeah so, in Dubai we're really, really pushing the envelope in terms of volume, numbers of people going through the airport, but also we want to make it the best passenger experience we possibly can. We're already the biggest international airport in the world, going to be doing nearly 90 million passengers this year, growing to 100 million by the end of the decade, but we want to drive experience as well. And the airport is constrained, so we've got a limited site, so we now have to figure out how to do it to greater efficiency, automation, making the passenger experience better. You can get much better throughput in an airport if you take out all the queues. So that's a better experience. So you get both at the same time. >> Well, tell us about the security lines then, because Dave and I both relate to this here in the States. Sometimes they can be tedious to work through. So how are you addressing that through technology. >> Well, through lots of different ways. I mean, we put sensors all over the airport for lots of different things, and one of the key areas we've done it is in security. So we have some sensors that measure the queue length for us, which is really important. It allows us to understand what's happening now, in real time, deploy additional staff to support that, but also predicts what's happening over the next few hours, so we can be ready for whatever's coming next. On top of that, we then take data out of the lane itself, in real time, so we can see how many people are passing through, how many alarms they're setting off, and then we can use that data over time to understand the behavior of passengers. Certain destinations drive more security alarms, so we can now understand that and then try and pre-inform those passengers about what to do so everybody gets through faster. >> So kind of, like a way, is in reverse for the security line. >> Yeah, exactly. >> Love it, that's great. >> So, you mentioned the golden bathroom, I got to ask you-- (laughter) We saw some data that only 10 percent of the people admit that they don't wash their hands when they leave the bathroom, but your data suggests its 25 percent of the people do not. I wonder if part of that reason is the reason that I often get frustrated is, when you put your hands underneath, nothing comes out. (laughter) >> John: You're waving underneath, right? >> In modern Dubai, airport bathrooms must actually give me water when I ask for it, is that right? >> Yeah, well, we like to think we've got pretty efficiently working bathrooms, that's for sure, but I think the challenge with the bathroom one, we wanted to understand how to make bathrooms cleaner and a nicer environment for everybody, and when you're doing 90 million passengers a year, that's a lot of people going to the bathroom. We put sensors all over the bathrooms, not CCTV, want to make sure that's clear. It's all like presence sensors, door lock sensors, when the faucets are on or off, people stood at basins, and that's just given us so much insight into how people actually use the bathroom. So we know that at peak hours, the number are quite low in terms of people who wash their hands after using the bathroom, but off-peak when it's quiet, the number goes right up to 100 percent. So, we think we've got some work to do on capacity, and understanding how people use the bathroom, and also maybe on the cleanliness. Maybe people are leaving because it's the lesser of two evils. Do I wash my hands, which doesn't look like a nice environment to wash my hands, or do I just walk out? >> So, some of the stats. 90 million passengers a year go through your airport and that'll be 100 million, over 100 million by 2020, is that right? >> Michael: On the current growth, yeah. >> And then 150 million bags, you handle, each year. >> Michael: That's correct. >> So there's a lot of data that you're collecting. So hence we're here at .conf. How do you use Splunk to sort of manage all this data? >> So we have two Splunk instances. We have one that does all of our IT stuff, and then we have one that's focused on all the business services, operations, if you like. And it's the business one that is kind of the most interesting because it drives the most debate and discussions about the future, and how we should plan the airport, and how we should drive performance. We have about four and a half billion data points in our Splunk, in our business Splunk instance, and it grows by somewhere around 12 to 14 million data points a day. Just baggage alone, every bag generates about 200 data points. Now, people don't probably think that from the outside, when you put the bag in, you drop the bag at the check-in desk and then you don't see it again til you to the other end, but there's so many check points that it passes, security screening that it goes through. It gets transferred in terms of jurisdiction between airline, airport, ground handler, and then it gets loaded onto the aircraft. All of these things, we create data points for all of those. So we can track it through your whole journey. I think these are fantastic opportunities for us to start thinking about how we might share that data the consumer in the future. We'd like to get to a point where your bag journey is just as well-informed as your own journey. >> Yeah, so, a little bit more on that then, I mean, just in terms of what your real life experience, what you hope it will be, in terms of your baggage, You were talking about taking down baggage arrival to a matter of seconds? >> Yeah so, you as a passenger, you arrive at the airport. You've got a process to go through before you're going to get reunited with your baggage, and that might be 10 minutes or 30 minutes, depending on the size and the nature of the airport that you arrive at. But as we know now, based on the data we have in Splunk, and we've been analyzing this data over the last four or five months, we know exactly how long it takes to get a bag from any aircraft stand to any point where you pick it up. And we can average that over a serious period of time. So if we can do that historically, we can start to predict that into the future. Based on the current conditions of the airport, we should be able to give you and exact time that your bag's going to arrive on that carousel. Maybe it will be down to a few seconds, maybe it'll be in the next 30 seconds your bag will arrive, type of message, but we want to give you that message to your phone. >> Think how nice that would be, Dave, if you're waiting at the baggage carousel, with another 150 of your best friends, and everybody's crowding around, watching for their bag to come out, but you know your bags about to come out in 20 seconds. >> Well, I always say it's one of my pet peeves everybody crowds around, and you can't see. Take three steps back and we'll all be better off. I wanted to ask you Michael, though, as a consumer of airline products and services, there seems to be a difference between the airport and the airline in terms of their data. You have a lot of data, the airlines obviously have a lot of data. Of course, they're competitive with each other. What kind of collaboration do you have with the airline, what kind of data do you share? >> So, I mean it really depends on the nature of your airport. Are you a hub for a big carrier, or do you have lots of small airlines all operating there, to how you might go about doing that. In both the airports that I've worked at recently, we've run projects to integrate the airline data into our systems. Cause we're just so much more well informed about what's happening and what's going to happen in the future when we do that. We spent the last couple of years working with Emirates, who's our biggest airline, to integrate their data, but we also have FlyDubai, who've got a huge flying program with us as well, and integrate their data so that we can start to combine the two data sets. And we do that within Splunk, so we know what's going on. The baggage data that I talked about, the 200 data points, I mean that comes from three different entities in reality. It's the airline at check-in, and the passengers data about their booking and everything else, the baggage system itself, and the security process it goes through, which is our data, and then the ground handler, which again is another set of data, because that bag then onto the aircraft, and inform the airline of where it is. And then that all gets combined back again at the point where you board the aircraft to make sure that that passenger and the bag are all on the same flight. So we've been pulling all that data into our systems and then sharing that back across the teams, to provide people with a lot more insight. So the airline wants to know the bags are going through successfully, the ground handler wants to know how many more they've got to come. So by sharing that data through a platform like Splunk, we're hopefully making a lot of breakthroughs. >> I think that's huge, because the mobile app is a game changer for an airline passenger. But the diversity of mobile apps, and the quality of the mobile apps is the function of the data model that each airline and their back-end processes, and you can tell some of the airlines that have sort of antiquated back-end processes, and those that don't have as much baggage, right? No pun intended. And so, my question is, with tools like Splunk and some innovation on your end, are you able to sort of unify those disparities? >> Yeah, and you've also got to remember something about the passenger, right? No passenger comes to an airport for an airport tour. They're coming because their going to fly somewhere, right? (laughs) And this is important. So they book a ticket to an airline, we might be able to integrate that data from all these different organizations at the airport, but who are you as the passenger really going to get that information from at the last moment? Probably from the airline because you're going to use their app, because you bought your ticket through it, and you're going to check in through it, and you maybe have a car service booked through it. So we would rather... we could be the combiner of that data, but then pass it back to the airline to display to you as the passenger, cause that makes more sense. But what's important for the passenger is that data is consistent at every point in the journey, whether you find it out from the airport, or whether you find it out from the airline, you want it to be the same. You don't want conflicting information. So that's what we can do by deciding to join these things together, but make sure that the consumer interface is the right one for the right time. Now that wouldn't work for us with Emirates because they're so huge and they have so many passengers for us, but for some of the smaller airlines, like British Airways, Virgin Atlantic, you know they have two, three flights a day with us, it might make more sense for their passengers to use our app in that situation. So it really depends who you are and what you're flying for, but we see that there's opportunity across that space, but what would be important is that every app tells the same story and has the same data >> John: It's like uniformity, right? >> Yeah, because that gives you so much confidence as a customer if that flight screen changes at the same time that your app pushes a notification to you, and it's exactly the same data, that's a huge amount of confidence that this is all really accurate and timely, and then you get to make decisions off that. >> I was struck by the comment that you guys are out of space and I think the way you phrase it is the city grew up around the airport. You'd think Dubai, I have not been, but you'd think Dubai, planning ahead, has lots of resources, but they're subject to the whims of metropolitan growth. Your challenge then is to use efficiency to squeeze more out of that fixed space. What are you doing in that regard? I mean that's a major CIO challenge. How do you deal with that? >> Yeah, I have to admit, that was the challenge that attracted me to the role, like how do you take this airport... when I joined it was about 78 million, couple of years back, and now pushing 90, pushing 100 by the end of the decade. That was the challenge for me, and that was the focus of the CEO, he said the only way we're going to grow this business is to figure out how to do more people, or more planes, through the same space. And that's really exciting, and the only way to do that is looking cutting out the waste wherever you can. Redefining the processes in areas, and removing all of the queues and all of the bottlenecks in the airport, whether that be in the airspace, on the airfield, in the terminal buildings for the passengers, in the baggage area for the bags. You've got to remove all those bottlenecks and I think, as a passenger, queuing up just wastes time and space. If we can make sure nobody ever queues, then everybody will get through the airport faster, which means we can do more people. We can take more people through the airport. So that's really the focus, and we have an internal project that we call queue-busting and it's literally just about busting the queues, busting the lines, as you call them here, and getting rid of them, because they're the thing that creates the capacity constraint-- >> Yeah, you talk about all these sensors you have around the airport, you talk about all the data that you're gathering, billions and billions of data points, so what don't you know that wish you did, or that you hope you can, relatively soon? >> I mean one of the things, so we know, like, the queuing time, all the major touch points, and that's been fantastic and we've, in our transfer security areas, in the last two years, we've lowered transfer security queuing from over eight minutes to an average of four minutes and 47 seconds, so we're really precise on this stuff now, it's great. But what we don't know is, the people's entire journey. So, we know that you queued in a certain place for four minutes, and you might queued up at check-in for maybe 10 minutes as well, but what we don't know is how long it took you to get between those points, which route you took, what's the most efficient, how to get you to spend more money in the airport because we... that's our business model, right? So that is where we need to learn a lot more, and I think there's a lot of work going on in that space, and we're doing some trials on some cool technology to figure out how to help you find your journey, make the most efficient overall journey through the airport, not just at the key check points. And obviously give you more time to enjoy the experience, we have shops and restaurants, we've got spas and swimming pools and hotels inside our airport, which we'd love for people to use more of, and I think we can do that if we can help them plan their journey better, so, I think there's still a lot of data out there. >> Well and, when you look at your strategic planning road map, how much runway do you have? I mean, you're using efficiency to utilize your space better, drive more revenue, customer satisfaction, avoiding the huge cutbacks of building another airport, which is not going to be as convenient. How much, again no pun intended, how much runway do you have in terms of that strategic plan? >> Well based on our current expectations, predictions that we have, we're looking at this site being able to do about 120 million, maybe we can squeeze a bit more out of it, >> Decade, plus? >> Yeah, I mean there's lots of exciting things we might have to do with the airfield to try and land more planes. We do about 65 flights an hour, off our two runways. We don't have the luxury of really wide-space runways, so we may have to come with some new ideas on that front. But about 120 million we think, which would be easily the biggest airport in the world. It's helped by the enormous fleet of A380s that Emirates uses. Of course we get a lot more passengers for every flight. But that's probably about as far as we can go. But the airport was designed for 90 to 95 million, so we're already going to bust that by about 30 million. So yeah, hopefully we can extract that, and then you never know what we might be able to do. >> Great, great story. >> Hopefully go further. >> Well it's fascinating, it really was. Great job on the key note stage today and certainly wish you continued success down the road here, I think we've run out of puns. (laughter) So, I'll leave it at that, but safe travels, if you will, home, and thanks for being with us here on theCUBE. >> Michael: Thanks very much. >> Michael Ibbitson from Dubai airports. Back with more from theCUBE here in just a bit. Washington, DC coming to you live. Back with more in a bit. (upbeat techno music)

>> Narrator: Live from Las Vegas, it's the Cube, covering VMWorld 2017. Brought to you by VMWare and its ecosystem partners. (bright music) >> Hi, I'm Stu Miniman with the Cube, here with my guest host, Justin Warren. Happy to have a returning Cube alum, but in a different role then we had. It's been a few years. Tal Klein, who is the author of The Punch Escrow. >> Au-tor, please. No, I'm just kidding. (laughing) Tal, thanks so much for joining us. It's great for you to be able to find time to hang out with the tech geeks rather than all the Hollywood people that you've been with recently. (laughing) >> You guys are more interesting. (laughing) >> Well thank you for saying that. So last time we interviewed you, you were working for a sizable tech company. You were talking about things like, you know, virtualization, everything like that. Your Twitter handle's VirtualTal. So how does a guy like that become not only an author but an author that's been optioned for a movie, which those of us that, you know, are geeks and everything are looking at, as a matter of fact, Pac Elsiger this morning said, "we are seeing science fiction become science fact." >> That's right. >> Stu: So tell us a little of the journey. >> Yeah, cool, I hope you read the book. (laughing) I don't know, the journey is really about marketing, right? Cause a lot of times when we talk about virtual, like, in fact last time I was on the Cube, we were talking about the idea that desktops could be virtual. Cause back then it was still this, you know, almost hypothetical notion, like could desktops be virtual, and so today, you know, so much of our life is virtual. So much of the things that we do are not actually direct. I was watching this great video by Apple's new augmented reality product, where you sit in the restaurant and you look at it with your iPad, and it's your plate, and you can just shift the menu items, and you see the menu items on your plate in the context of the restaurant and your seat and the person you're sitting across from. So I think the future is now. >> Yeah, it reminds of, you know, the movie Wall-E, the animated one. We're all going to be sitting in chairs with our devices or Ready Player One, you know, very popular sci-fi book that's being done by Speilberg, I believe. >> Yes, yeah, very exciting. >> Tell us a little bit about your book, you know, we talked, when I was younger and used to read a lot of sci-fi, it was like, what stuff had they done 50 years ago that now's reality, and what stuff had they predicted, like, you know, we're going to go away from currency and go digital currency, and it's like we're almost there. But we still don't have flying cars. >> Yeah, we're, I mean, the main problem with flying cars is that we need pilots. And I think actually we're very close to flying cars, cause once we have self-driving vehicles and we no longer need to worry about it being a person behind the joystick, then we're in really good shape. That's really the issue, you know, the problem with flying cars is that we are so incompetent at driving and or flying. That's not our core competency, so let's just put things that do understand how to make those things happen and eliminate us from the equation. >> Everything is a people problem. >> Yeah, so when I wrote the book, Punch Escrow, Punch Escrow, (laughing) when I wrote the book, I really thought about all the things that I read growing up in science fiction, you know, things like teleportation, things like nanotechnology, things like digital currency, you know, how do we make those, how do we present those in a viable way that doesn't seem too science fictiony. Like one of the things I really get when people read the book is it feels really near-future, even though it's set like 100 plus years in the future, all the concepts in it feel very pragmatic or within reach, you know? >> Yeah, absolutely. It's interesting, we look at, you know, what things happen in a couple of years and what things take a long time. So artificial intelligence, machine learning, it's not like these are new concepts, you know? I read a great book by, you know, it was Isaacson, The Innovators. You go back to like Aida Lovelace, and the idea of what a machine or computer would be able to do. So 100 years from now, what's real, what's not real? We still all have jobs or something? >> We have jobs but different. Remember, I don't know if you're a historian, but back in the industrial age, there was a whole bunch of people screaming doom and gloom. In fact, if we go way back to the age of the Luddites, who just hated machines of any kind. I think that in general, we don't like, you know, we're scared of change. So I do think a lot of the jobs that exist today are going to be done by machines or code. That doesn't mean the jobs are going away. It means jobs are changing. A lot of the jobs that people have today didn't exist in the industrial age. So I think that we have to accept that we are going to be pragmatic enough to accept the fact that humans will continue to evolve as the infrastructure powering our world evolves, you know? We talk about living in the age of the quantified self, right? There's a whole bunch that we don't understand how to do yet. For example, I can think of a whole industry that tethers my FitBit to my nutrition. You know, like there's so much opportunity that for us to say, oh that's going to be the end of jobs, or the end of innovation or the end of capitalism, is insane. I think this just ushers in a whole new age of opportunity. And that's me, I'm just an optimist that way, you know. >> So the Luddites did famously try to destroy the machines. But the thing is, the Luddites weren't wrong. They did lose their jobs. So what about the people whose jobs are replaced, as you say net new, there's a net new number of jobs. But specific individuals, like people who manufacture cars for example, lose their jobs because a robot can do that job safer and better and faster than a human can do it. So what do we do with those humans? Because how do we get people to have new jobs and retrain themselves? >> I address some of these notions in the book. For example, one of the weird things that we're suffering from is the lack of welders in society today, cause welding has become this weird thing that we don't think we need people for, so people don't really get trained up in it because, you know, machines do a lot of welding but there's actually specialty welding that machines can't do. So I think the people who are really good at the things that they do will continue to have careers. I think their careers will become more niche. Therefore they'll be able to create, to demand a higher wage for it because almost like a carpenter, you know, a specialist carpenter will be able to earn a much higher wage today by having fewer customers who want really custom carpentry versus things that can be carved up by a machine. So I think what we end up seeing is that it's not that those jobs go away. It's they become more specialized. People still want Rolls Royces. People still want McLarens. Those are not done by machines. Those are hand-made, you know? >> That's an interesting point, so the value of something being hand-made becomes, instead of it being a worse product, it's actually- >> Tal: That's a big concept in the book. >> Oh okay, right. >> A big concept in the book is that we place a lot of value on the uniqueness of an object. And that parlays in multiple ways. So one of the examples that I use in the book is the value of a Big Mac actually coming from McDonald's. Like, you can make a Big Mac. We know the recipe for a Big Mac. But there is a weird sort of nacent value to getting a Big Mac from McDonald's. It's something in our brain that clicks that tethers it to an originality. Diamonds, another really good example. Or you know, we know there's synthetic diamonds. We still want the ones that get mined in the cave. Why? We don't know. Right, they're just special. >> Because De Beers still has really good marketing. (laughing) >> So I think there's- >> That's interesting, so the concept of uniqueness, which again comes to scarcity and so on. As an author, someone who is no doubt, signed a lot of his book, that means that that book is unique because it's signed by the author, unlike something which is mass produced and there is hopefully thousands and thousands of copies that you sell. >> Going into this, I actually thought about that a lot. And that's why I've created like multiple editions of the book. So like the first 500 people who pre-ordered it, they get like a special edition of the book that's like stamped and all this kind of stuff. I even used different pens. (laughs) I appreciate that because I'm also a collector. I collect music, I collect books. And you know, so I see those aspects in myself. So I know what I value about them, you know? >> And the crossover between music and books is interesting. So as someone who has a musical background, I know that there's a lot of musicians who'll come out with special editions, and you know, because this is an age where we can download it. You can download the book. Do you think there is something, is there something that is intrinsic to having a physical object in a virtual world? >> I think to our generation, yes. I'm not so sure about millennials, when they grow up. But there are, for example, I'm going to see U2 next week, I'm very lucky to see that. But part of the U2 buying experience, to get access to the presale, you need to be part of their fan club. To be a part of their fan club, you need to get, you get like a whole bunch of limited edition posters, limited edition vinyl, and all this kind of stuff. So there's an experience. It's no longer just about going to see U2 at a concert. There's like the entire package of you being a special U2 fan. And they surround it with uniqueness. It's not necessarily limited, but there's an enhanced experience that can't just be, it's not just about you having a ticket to a single concert. >> Justin: Yeah, okay. >> I'm curious, the genre, if you'd call it, is hard science fiction. >> Yes. >> The challenge with that is, you know, what is an extension of what we're doing, and what is fiction? And people probably poke at that. Have you had any interesting experience, things like that? I mean, I've listened to a lot of stuff like Andy Weir, like let the community give feedback before he created the final The Martian. (laughing) But so yeah, what's it like, cause we can, the geeks can be really harsh. >> Yes, I've learned from my Reddit experience that, so what's really funny about it is the first draft of this novel was hard as nails. It was crazy. And my publisher read it, and it would have made all the hard science fiction guys super happy. My publisher read it, he was like, you've written a really great hard science fiction book, and all five people who read it are going to love it. (laughing) You know, but like, I came here with my buddy Danny. He couldn't even get through the first three pages of it. He's like, he wanted to read it. So part of working through the editorial process is saying, look, I care a lot about the science because one of my deep goals is to write a STEM-oriented book that gets people excited about technology and present the future as not a dystopian place. And so I wanted the science to be there and have a sort of gravity to the narrative. But yeah, it's tough. I worked with a physicist, a biologist, a geneticist, an anthropologist, and a lawyer. (laughs) Just to try to figure out, how do we carve out, you know, what does the future look like, what does the evolution of each individual sciences, we talked about the mosquitoes, right? You know, we're already doing a lot of crazy stuff with mosquitoes. We're modifying them so that the males mate with females that carry the Zika virus, you know, give birth to offspring that never reach maturity. I mean, this is just crazy, it's science fiction. And now that they're working on modifying female mosquitoes into vaccine carriers instead of disease carriers. I mean, this is science fiction, right? Like who believes this stuff? It's crazy. >> Christopher is amazing. >> Yeah, I've loved, there's been a bunch of movies recently that have kind of helped to educate on STEM some, you know, Martian got a lot of people excited, you know, Hidden Figures, the one that I could being my kids that are teenagers now into it and they get excited, oh, science is great. So the movie, how much will you be involved? You know, what can you share about that experience, too, so far? >> It's been, it's very surreal. That's the word is use to describe it, the honest, god's honest truth, I mean. I've been very lucky in that my representation in Hollywood is this rock-solid guy called Howie Sanders. And he's this bigger-than-life Hollywood agent guy. He's hooked me up, we've made a lot of business decisions that we're focused less on the money and more on the team, which is nice to be, like when you're in your 40s and you're more financially settled, you're not in the kind of situation where you might be in your 20s and just going to sign the first deal that people give you. So we really focused on hooking up with like the director, James Bovin is, you know, he's the guy who co-created Flight of the Concords. He did the Muppets movie, you know, Alice Through the Looking Glass. Really professional guy but also really understands the tone of the book, which is like humorous, you know, kind of sarcastic. It's not just about the technology. It's also about the characters. Same thing with the production team. The two producers, Mandeville Productions, I was just talking to Todd Lieberman, and we're talking about just what is augmented reality, like how does it look like on the screen? So I'm not- >> It's not going to look like Blade Runner is what I'm hearing. >> (laughs) I don't know. It's going to look real. I imagine, I don't know, they're going to make whatever movie they're going to make, but their perspective, one of the things we talked about is keeping the movie very grounded. Like you know, one of the big questions they ask first going into it is before we even had any sort of movie discussions is like is this more of like a Looper, Gattica, or District Nine, or is it more like The Fifth Element, you know, I mean, is it like, do you want it to be this sort of grounded movie that feels authentic and real and near future or do you want this to be like completely alien and weird and out of it. And the story is more grounded. So I think a lot, hopefully what we display on the screen will not feel that far away from reality. >> Okay, yeah. >> You do marketing in your day job. >> I do. >> I'm curious as you look at this, kind of the balance of educating, reaching a broad audience, you have passion for STEM, what's your thoughts around that? Is it, I worry there's so much general, like television or things like that, when I see the science stuff, it like makes me groan. Because you know, it's like I don't understand that. >> I am the worst, because I got a security background too, so that's the one I get scrambled on. The war, I mean, like. >> Wait, thank goodness I updated my firewall settings because I saved the world from terrorists. >> Hang on, we're breaking through the first firewall. Now we're through the second firewall. (laughing) Now we're going through the third firewall, like 15 firewalls. And let me upload the virus, like all that stuff. It's difficult for me. I think that, you know, hopefully, there's also a group in Hollywood called the Hollywood Science and Entertainment Exchange. And they're a group of scientists who work with film makers on, you know, reigning things in. And film makers don't usually take all their advice, i.e. Interstellar, (laughing) but you know, I think (laughing) in many cases there's some really good ideas that come to play into it that hopefully bring up, like I think Jarvis for example, in Iron Man or the Avengers is a really cool implementation of what the future of AI systems might be like. And I know they used the Hollywood Science Exchange to figure out how is that going to work? And I think the marketing aspect is, you know, the reason I came up with the idea for this book is because my CEO of a company I used to work for, he had this whole conversation about teleportation, like teleportation was impossible. And he's like, it's not because the science, yes, the science is a problem right now, but we'll get over it. The main issue is that nobody would ever step foot into a device that vaporizes them and then printed them out somewhere else. And I said, well that's great, cause that's a marketing problem. (laughing) >> Yeah, you're dead every time you do it. But it's the same you, I can't tell the difference. >> Well, you say you're dead, I'm saying you're just moving. (laughing) >> Artificial intelligence, you know, kind of a big gap between the hype to where we need to go. What's your thoughts on that space in general? >> I think that we have, it's a great question because I feel like that's a term that gets thrown around a lot, and I think as a result it's becoming watered down. So you've this sort of artificial intelligence that comes with like, you know, Google building an app that can beat the world's best Go player, which is a really, really difficult puzzle. The problem is, that app can do one thing, and that's play Go. You put in it a chess game, and it's like I don't know what's going on. >> It's a very specialized kind of intelligence, yeah. >> Now with Open AI, you know, they just had some pretty interesting implementations where they actually played video games with a real live competition and won. Again, you know, but without the smack talk, which really I think would add a lot. Now you got to get an AI to smack talk. So I think the problem is we haven't figured out a really good way of creating a general purpose AI. And there's a lot of parallels to the evolution of computing in general because if you look at how computers were before we had general purpose operating systems like Unix, every computer was built to do a very, very specific function, and that's kind of what AI is right now. So we're still waiting to have a sort of general purpose AI that can do a lot of specialized activities. >> Even most robots are still very single-purpose today. >> That's the fundamental problem. But you're seeing the Cambridge guys are working on sort of the bipedal robot that can do lots of things. And Siri's getting better, Cortana's getting better, Watson's getting better, but we're not there. We still need to find a really good way of integrating deep knowledge with general purpose conversational AI. Cause that's really what you need to like, Stu, what do you need? Here, let me give it to you, you know? >> Do you draw a distinction between AI that's able to simply sort of react as a fairly complex machine or something that can create new things and add something? >> That's in the book as well. So the fundamental thing that I don't think we get around even in the future is giving computers the ability to actually come up with new ideas. There's actually a career, the main job of the protagonist in the book, his job is a salter. And his job is to salt AI algorithms to introduce entropy so they can come up with new ideas. >> Okay, interesting. >> So based off the sort of chaos theory. >> Like chaos monkey, right? >> Yeah. And that's really what you're trying to do is like, okay, react to things that are happening because you can't just come up with them on their own. There's a whole, I don't want to bore you, but there's a whole bunch of stuff in the book about how that works. >> It's like hand-carving ideas that are then mass produced by machines. >> Yeah, I don't know if you guys are going to have Simon Crosby on here, he's kind of like an expert on that. He was the Dean of Kings College, which is where Turing came from. So he really knows a lot about that. He's got a lot of strong ideas about it. But I learned a lot from him in that regard. There's a lot of like, the snarky spirit of Simon Crosby lives on in my book somewhere. But he's just funny cause he's, coming from that field, he immediately sees a lot of BS right off the bat, whenever anybody's presenting. He's got like the ability to just cut through it. Because he understands what it would actually take to make that happen, you know? So I tried to preserve some of that in the book. >> That is refreshing in the tech industry. >> So Tal, I need to let you, you know, wrap this up. Give us a plug for the book, tell us, when are we going to be able to see this on the big screen? >> I don't know about the big screen, but the Punch Escrow is now available. You can get it on Amazon, Barnes and Noble, anywhere books are sold. It's been optioned by Lionsgate. The director attached to it is James Bovin, production team is Mandeville Productions. I'm very excited about it. Go check it out. It's a pretty quick read, reads like a technothriller. It's not too hard. And it's fun for the whole family. I think one of the coolest things about it is that the feedback I've been getting has been that it really is appealing to everybody. I've got mother-in-laws reading it, you know, it's pretty cool. Initially I sold it, my initial audience is like us, but it's kind of cool, like, Stu will finish the book, he'll give it to, you know, wife, daughter, anything, and they're really digging it. So it's kind of fun. >> Justin: Thanks a lot. >> Tal Klein, really appreciate you coming. Congratulations on the book, we look forward to the movie. Maybe, you know, we'll get the Cube involved down the road. (laughing) >> And we're giving away 75 copies of it here at Lakeside booth, if you guys want to come. >> Tal Klein, author of The Punch Escrow, also CMO of Lakeside, who is here in the thing. But yeah, (laughing) a lot of stuff. Justin and I will be back with more coverage here from VMWorld 2017. You're watching the Cube. (bright music)

>> Narrator: Live, from Munich, Germany, it's the CUBE. Covering IBM, Fast Track Your Data. Brought to you by IBM. >> Welcome to Munich everybody. This is a special presentation of the CUBE, Fast Track Your Data, brought to you by IBM. My name is Dave Vellante. And I'm here with my cohost, Jim Kobielus. Jim, good to see you. Really good to see you in Munich. >> Jim: I'm glad I made it. >> Thanks for being here. So last year Jim and I hosted a panel at New York City on the CUBE. And it was quite an experience. We had, I think it was nine or 10 data scientists and we felt like that was a lot of people to organize and talk about data science. Well today, we're going to do a repeat of that. With a little bit of twist on topics. And we've got five data scientists. We're here live, in Munich. And we're going to kick off the Fast Track Your Data event with this data science panel. So I'm going to now introduce some of the panelists, or all of the panelists. Then we'll get into the discussions. I'm going to start with Lillian Pierson. Lillian thanks very much for being on the panel. You are in data science. You focus on training executives, students, and you're really a coach but with a lot of data science expertise based in Thailand, so welcome. >> Thank you, thank you so much for having me. >> Dave: You're very welcome. And so, I want to start with sort of when you focus on training people, data science, where do you start? >> Well it depends on the course that I'm teaching. But I try and start at the beginning so for my Big Data course, I actually start back at the fundamental concepts and definitions they would even need to understand in order to understand the basics of what Big Data is, data engineering. So, terms like data governance. Going into the vocabulary that makes up the very introduction of the course, so that later on the students can really grasp the concepts I present to them. You know I'm teaching a deep learning course as well, so in that case I start at a lot more advanced concepts. So it just really depends on the level of the course. >> Great, and we're going to come back to this topic of women in tech. But you know, we looked at some CUBE data the other day. About 17% of the technology industry comprises women. And so we're a little bit over that on our data science panel, we're about 20% today. So we'll come back to that topic. But I don't know if there's anything you would add? >> I'm really passionate about women in tech and women who code, in particular. And I'm connected with a lot of female programmers through Instagram. And we're supporting each other. So I'd love to take any questions you have on what we're doing in that space. At least as far as what's happening across the Instagram platform. >> Great, we'll circle back to that. All right, let me introduce Chris Penn. Chris, Boston based, all right, SMI. Chris is a marketing expert. Really trying to help people understand how to get, turn data into value from a marketing perspective. It's a very important topic. Not only because we get people to buy stuff but also understanding some of the risks associated with things like GDPR, which is coming up. So Chris, tell us a little bit about your background and your practice. >> So I actually started in IT and worked at a start up. And that's where I made the transition to marketing. Because marketing has much better parties. But what's really interesting about the way data science is infiltrating marketing is the technology came in first. You know, everything went digital. And now we're at a point where there's so much data. And most marketers, they kind of got into marketing as sort of the arts and crafts field. And are realizing now, they need a very strong, mathematical, statistical background. So one of the things, Adam, the reason why we're here and IBM is helping out tremendously is, making a lot of the data more accessible to people who do not have a data science background and probably never will. >> Great, okay thank you. I'm going to introduce Ronald Van Loon. Ronald, your practice is really all about helping people extract value out of data, driving competitive advantage, business advantage, or organizational excellence. Tell us a little bit about yourself, your background, and your practice. >> Basically, I've three different backgrounds. On one hand, I'm a director at a data consultancy firm called Adversitement. Where we help companies to become data driven. Mainly large companies. I'm an advisory board member at Simply Learn, which is an e-learning platform, especially also for big data analytics. And on the other hand I'm a blogger and I host a series of webinars. >> Okay, great, now Dez, Dez Blanchfield, I met you on Twitter, you know, probably a couple of years ago. We first really started to collaborate last year. We've spend a fair amount of time together. You are a data scientist, but you're also a jack of all trades. You've got a technology background. You sit on a number of boards. You work very active with public policy. So tell us a little bit more about what you're doing these days, a little bit more about your background. >> Sure, I think my primary challenge these days is communication. Trying to join the dots between my technical background and deeply technical pedigree, to just plain English, every day language, and business speak. So bridging that technical world with what's happening in the boardroom. Toe to toe with the geeks to plain English to execs in boards. And just hand hold them and steward them through the journey of the challenges they're facing. Whether it's the enormous rapid of change and the pace of change, that's just almost exhaustive and causing them to sprint. But not just sprint in one race but in multiple lanes at the same time. As well as some of the really big things that are coming up, that we've seen like GDPR. So it's that communication challenge and just hand holding people through that journey and that mix of technical and commercial experience. >> Great, thank you, and finally Joe Caserta. Founder and president of Caserta Concepts. Joe you're a practitioner. You're in the front lines, helping organizations, similar to Ronald. Extracting value from data. Translate that into competitive advantage. Tell us a little bit about what you're doing these days in Caserta Concepts. >> Thanks Dave, thanks for having me. Yeah, so Caserta's been around. I've been doing this for 30 years now. And natural progressions have been just getting more from application development, to data warehousing, to big data analytics, to data science. Very, very organically, that's just because it's where businesses need the help the most, over the years. And right now, the big focus is governance. At least in my world. Trying to govern when you have a bunch of disparate data coming from a bunch of systems that you have no control over, right? Like social media, and third party data systems. Bringing it in and how to you organize it? How do you ingest it? How do you govern it? How do you keep it safe? And also help to define ownership of the data within an organization within an enterprise? That's also a very hot topic. Which ties back into GDPR. >> Great, okay, so we're going to be unpacking a lot of topics associated with the expertise that these individuals have. I'm going to bring in Jim Kobielus, to the conversation. Jim, the newest Wikibon analyst. And newest member of the SiliconANGLE Media Team. Jim, get us started off. >> Yeah, so we're at an event, at an IBM event where machine learning and data science are at the heart of it. There are really three core themes here. Machine learning and data science, on the one hand. Unified governance on the other. And hybrid data management. I want to circle back or focus on machine learning. Machine learning is the coin of the realm, right now in all things data. Machine learning is the heart of AI. Machine learning, everybody is going, hiring, data scientists to do machine learning. I want to get a sense from our panel, who are experts in this area, what are the chief innovations and trends right now on machine learning. Not deep learning, the core of machine learning. What's super hot? What's in terms of new techniques, new technologies, new ways of organizing teams to build and to train machine learning models? I'd like to open it up. Let's just start with Lillian. What are your thoughts about trends in machine learning? What's really hot? >> It's funny that you excluded deep learning from the response for this, because I think the hottest space in machine learning is deep learning. And deep learning is machine learning. I see a lot of collaborative platforms coming out, where people, data scientists are able to work together with other sorts of data professionals to reduce redundancies in workflows. And create more efficient data science systems. >> Is there much uptake of these crowd sourcing environments for training machine learning wells. Like CrowdFlower, or Amazon Mechanical Turk, or Mighty AI? Is that a huge trend in terms of the workflow of data science or machine learning, a lot of that? >> I don't see that crowdsourcing is like, okay maybe I've been out of the crowdsourcing space for a while. But I was working with Standby Task Force back in 2013. And we were doing a lot of crowdsourcing. And I haven't seen the industry has been increasing, but I could be wrong. I mean, because there's no, if you're building automation models, most of the, a lot of the work that's being crowdsourced could actually be automated if someone took the time to just build the scripts and build the models. And so I don't imagine that, that's going to be a trend that's increasing. >> Well, automation machine learning pipeline is fairly hot, in terms of I'm seeing more and more research. Google's doing a fair amount of automated machine learning. The panel, what do you think about automation, in terms of the core modeling tasks involved in machine learning. Is that coming along? Are data scientists in danger of automating themselves out of a job? >> I don't think there's a risk of data scientist's being put out of a job. Let's just put that on the thing. I do think we need to get a bit clearer about this meme of the mythical unicorn. But to your call point about machine learning, I think what you'll see, we saw the cloud become baked into products, just as a given. I think machine learning is already crossed this threshold. We just haven't necessarily noticed or caught up. And if we look at, we're at an IBM event, so let's just do a call out for them. The data science experience platform, for example. Machine learning's built into a whole range of things around algorithm and data classification. And there's an assisted, guided model for how you get to certain steps, where you don't actually have to understand how machine learning works. You don't have to understand how the algorithms work. It shows you the different options you've got and you can choose them. So you might choose regression. And it'll give you different options on how to do that. So I think we've already crossed this threshold of baking in machine learning and baking in the data science tools. And we've seen that with Cloud and other technologies where, you know, the Office 365 is not, you can't get a non Cloud Office 365 account, right? I think that's already happened in machine learning. What we're seeing though, is organizations even as large as the Googles still in catch up mode, in my view, on some of the shift that's taken place. So we've seen them write little games and apps where people do doodles and then it runs through the ML library and says, "Well that's a cow, or a unicorn, or a duck." And you get awards, and gold coins, and whatnot. But you know, as far as 12 years ago I was working on a project, where we had full size airplanes acting as drones. And we mapped with two and 3-D imagery. With 2-D high res imagery and LiDAR for 3-D point Clouds. We were finding poles and wires for utility companies, using ML before it even became a trend. And baking it right into the tools. And used to store on our web page and clicked and pointed on. >> To counter Lillian's point, it's not crowdsourcing but crowd sharing that's really powering a lot of the rapid leaps forward. If you look at, you know, DSX from IBM. Or you look at Node-RED, huge number of free workflows that someone has probably already done the thing that you are trying to do. Go out and find in the libraries, through Jupyter and R Notebooks, there's an ability-- >> Chris can you define before you go-- >> Chris: Sure. >> This is great, crowdsourcing versus crowd sharing. What's the distinction? >> Well, so crowdsourcing, kind of, where in the context of the question you ask is like I'm looking for stuff that other people, getting people to do stuff that, for me. It's like asking people to mine classifieds. Whereas crowd sharing, someone has done the thing already, it already exists. You're not purpose built, saying, "Jim, help me build this thing." It's like, "Oh Jim, you already "built this thing, cool. "So can I fork it and make my own from it?" >> Okay, I see what you mean, keep going. >> And then, again, going back to earlier. In terms of the advancements. Really deep learning, it probably is a good idea to just sort of define these things. Machine learning is how machines do things without being explicitly programmed to do them. Deep learning's like if you can imagine a stack of pancakes, right? Each pancake is a type of machine learning algorithm. And your data is the syrup. You pour the data on it. It goes from layer, to layer, to layer, to layer, and what you end up with at the end is breakfast. That's the easiest analogy for what deep learning is. Now imagine a stack of pancakes, 500 or 1,000 high, that's where deep learning's going now. >> Sure, multi layered machine learning models, essentially, that have the ability to do higher levels of abstraction. Like image analysis, Lillian? >> I had a comment to add about automation and data science. Because there are a lot of tools that are able to, or applications that are able to use data science algorithms and output results. But the reason that data scientists aren't in risk of losing their jobs, is because just because you can get the result, you also have to be able to interpret it. Which means you have to understand it. And that involves deep math and statistical understanding. Plus domain expertise. So, okay, great, you took out the coding element but that doesn't mean you can codify a person's ability to understand and apply that insight. >> Dave: Joe, you have something to add? >> I could just add that I see the trend. Really, the reason we're talking about it today is machine learning is not necessarily, it's not new, like Dez was saying. But what's different is the accessibility of it now. It's just so easily accessible. All of the tools that are coming out, for data, have machine learning built into it. So the machine learning algorithms, which used to be a black art, you know, years ago, now is just very easily accessible. That you can get, it's part of everyone's toolbox. And the other reason that we're talking about it more, is that data science is starting to become a core curriculum in higher education. Which is something that's new, right? That didn't exist 10 years ago? But over the past five years, I'd say, you know, it's becoming more and more easily accessible for education. So now, people understand it. And now we have it accessible in our tool sets. So now we can apply it. And I think that's, those two things coming together is really making it becoming part of the standard of doing analytics. And I guess the last part is, once we can train the machines to start doing the analytics, right? And get smarter as it ingests more data. And then we can actually take that and embed it in our applications. That's the part that you still need data scientists to create that. But once we can have standalone appliances that are intelligent, that's when we're going to start seeing, really, machine learning and artificial intelligence really start to take off even more. >> Dave: So I'd like to switch gears a little bit and bring Ronald on. >> Okay, yes. >> Here you go, there. >> Ronald, the bromide in this sort of big data world we live in is, the data is the new oil. You got to be a data driven company and many other cliches. But when you talk to organizations and you start to peel the onion. You find that most companies really don't have a good way to connect data with business impact and business value. What are you seeing with your clients and just generally in the community, with how companies are doing that? How should they do that? I mean, is that something that is a viable approach? You don't see accountants, for example, quantifying the value of data on a balance sheet. There's no standards for doing that. And so it's sort of this fuzzy concept. How are and how should organizations take advantage of data and turn it into value. >> So, I think in general, if you look how companies look at data. They have departments and within the departments they have tools specific for this department. And what you see is that there's no central, let's say, data collection. There's no central management of governance. There's no central management of quality. There's no central management of security. Each department is manages their data on their own. So if you didn't ask, on one hand, "Okay, how should they do it?" It's basically go back to the drawing table and say, "Okay, how should we do it?" We should collect centrally, the data. And we should take care for central governance. We should take care for central data quality. We should take care for centrally managing this data. And look from a company perspective and not from a department perspective what the value of data is. So, look at the perspective from your whole company. And this means that it has to be brought on one end to, whether it's from C level, where most of them still fail to understand what it really means. And what the impact can be for that company. >> It's a hard problem. Because data by its' very nature is now so decentralized. But Chris you have a-- >> The thing I want to add to that is, think about in terms of valuing data. Look at what it would cost you for data breach. Like what is the expensive of having your data compromised. If you don't have governance. If you don't have policy in place. Look at the major breaches of the last couple years. And how many billions of dollars those companies lost in market value, and trust, and all that stuff. That's one way you can value data very easily. "What will it cost us if we mess this up?" >> So a lot of CEOs will hear that and say, "Okay, I get it. "I have to spend to protect myself, "but I'd like to make a little money off of this data thing. "How do I do that?" >> Well, I like to think of it, you know, I think data's definitely an asset within an organization. And is becoming more and more of an asset as the years go by. But data is still a raw material. And that's the way I think about it. In order to actually get the value, just like if you're creating any product, you start with raw materials and then you refine it. And then it becomes a product. For data, data is a raw material. You need to refine it. And then the insight is the product. And that's really where the value is. And the insight is absolutely, you can monetize your insight. >> So data is, abundant insights are scarce. >> Well, you know, actually you could say that intermediate between insights and the data are the models themselves. The statistical, predictive, machine learning models. That are a crystallization of insights that have been gained by people called data scientists. What are your thoughts on that? Are statistical, predictive, machine learning models something, an asset, that companies, organizations, should manage governance of on a centralized basis or not? >> Well the models are essentially the refinery system, right? So as you're refining your data, you need to have process around how you exactly do that. Just like refining anything else. It needs to be controlled and it needs to be governed. And I think that data is no different from that. And I think that it's very undisciplined right now, in the market or in the industry. And I think maturing that discipline around data science, I think is something that's going to be a very high focus in this year and next. >> You were mentioning, "How do you make money from data?" Because there's all this risk associated with security breaches. But at the risk of sounding simplistic, you can generate revenue from system optimization, or from developing products and services. Using data to develop products and services that better meet the demands and requirements of your markets. So that you can sell more. So either you are using data to earn more money. Or you're using data to optimize your system so you have less cost. And that's a simple answer for how you're going to be making money from the data. But yes, there is always the counter to that, which is the security risks. >> Well, and my question really relates to, you know, when you think of talking to C level executives, they kind of think about running the business, growing the business, and transforming the business. And a lot of times they can't fund these transformations. And so I would agree, there's many, many opportunities to monetize data, cut costs, increase revenue. But organizations seem to struggle to either make a business case. And actually implement that transformation. >> Dave, I'd love to have a crack at that. I think this conversation epitomizes the type of things that are happening in board rooms and C suites already. So we've really quickly dived into the detail of data. And the detail of machine learning. And the detail of data science, without actually stopping and taking a breath and saying, "Well, we've "got lots of it, but what have we got? "Where is it? "What's the value of it? "Is there any value in it at all?" And, "How much time and money should we invest in it?" For example, we talk of being about a resource. I look at data as a utility. When I turn the tap on to get a drink of water, it's there as a utility. I counted it being there but I don't always sample the quality of the water and I probably should. It could have Giardia in it, right? But what's interesting is I trust the water at home, in Sydney. Because we have a fairly good experience with good quality water. If I were to go to some other nation. I probably wouldn't trust that water. And I think, when you think about it, what's happening in organizations. It's almost the same as what we're seeing here today. We're having a lot of fun, diving into the detail. But what we've forgotten to do is ask the question, "Well why is data even important? "What's the reasoning to the business? "Why are we in business? "What are we doing as an organization? "And where does data fit into that?" As opposed to becoming so fixated on data because it's a media hyped topic. I think once you can wind that back a bit and say, "Well, we have lot's of data, "but is it good data? "Is it quality data? "Where's it coming from? "Is it ours? "Are we allowed to have it? "What treatment are we allowed to give that data?" As you said, "Are we controlling it? "And where are we controlling it? "Who owns it?" There's so many questions to be asked. But the first question I like to ask people in plain English is, "Well is there any value "in data in the first place? "What decisions are you making that data can help drive? "What things are in your organizations, "KPIs and milestones you're trying to meet "that data might be a support?" So then instead of becoming fixated with data as a thing in itself, it becomes part of your DNA. Does that make sense? >> Think about what money means. The Economists' Rhyme, "Money is a measure for, "a systems for, a medium, a measure, and exchange." So it's a medium of exchange. A measure of value, a way to exchange something. And a way to store value. Data, good clean data, well governed, fits all four of those. So if you're trying to figure out, "How do we make money out of stuff." Figure out how money works. And then figure out how you map data to it. >> So if we approach and we start with a company, we always start with business case, which is quite clear. And defined use case, basically, start with a team on one hand, marketing people, sales people, operational people, and also the whole data science team. So start with this case. It's like, defining, basically a movie. If you want to create the movie, You know where you're going to. You know what you want to achieve to create the customer experience. And this is basically the same with a business case. Where you define, "This is the case. "And this is how we're going to derive value, "start with it and deliver value within a month." And after the month, you check, "Okay, where are we and how can we move forward? "And what's the value that we've brought?" >> Now I as well, start with business case. I've done thousands of business cases in my life, with organizations. And unless that organization was kind of a data broker, the business case rarely has a discreet component around data. Is that changing, in your experience? >> Yes, so we guide companies into be data driven. So initially, indeed, they don't like to use the data. They don't like to use the analysis. So that's why, how we help. And is it changing? Yes, they understand that they need to change. But changing people is not always easy. So, you see, it's hard if you're not involved and you're not guiding it, they fall back in doing the daily tasks. So it's changing, but it's a hard change. >> Well and that's where this common parlance comes in. And Lillian, you, sort of, this is what you do for a living, is helping people understand these things, as you've been sort of evangelizing that common parlance. But do you have anything to add? >> I wanted to add that for organizational implementations, another key component to success is to start small. Start in one small line of business. And then when you've mastered that area and made it successful, then try and deploy it in more areas of the business. And as far as initializing big data implementation, that's generally how to do it successfully. >> There's the whole issue of putting a value on data as a discreet asset. Then there's the issue, how do you put a value on a data lake? Because a data lake, is essentially an asset you build on spec. It's an exploratory archive, essentially, of all kinds of data that might yield some insights, but you have to have a team of data scientists doing exploration and modeling. But it's all on spec. How do you put a value on a data lake? And at what point does the data lake itself become a burden? Because you got to store that data and manage it. At what point do you drain that lake? At what point, do the costs of maintaining that lake outweigh the opportunity costs of not holding onto it? >> So each Hadoop note is approximately $20,000 per year cost for storage. So I think that there needs to be a test and a diagnostic, before even inputting, ingesting the data and storing it. "Is this actually going to be useful? "What value do we plan to create from this?" Because really, you can't store all the data. And it's a lot cheaper to store data in Hadoop then it was in traditional systems but it's definitely not free. So people need to be applying this test before even ingesting the data. Why do we need this? What business value? >> I think the question we need to also ask around this is, "Why are we building data lakes "in the first place? "So what's the function it's going to perform for you?" There's been a huge drive to this idea. "We need a data lake. "We need to put it all somewhere." But invariably they become data swamps. And we only half jokingly say that because I've seen 90 day projects turn from a great idea, to a really bad nightmare. And as Lillian said, it is cheaper in some ways to put it into a HDFS platform, in a technical sense. But when we look at all the fully burdened components, it's actually more expensive to find Hadoop specialists and Spark specialists to maintain that cluster. And invariably I'm finding that big data, quote unquote, is not actually so much lots of data, it's complex data. And as Lillian said, "You don't always "need to store it all." So I think if we go back to the question of, "What's the function of a data lake in the first place? "Why are we building one?" And then start to build some fully burdened cost components around that. We'll quickly find that we don't actually need a data lake, per se. We just need an interim data store. So we might take last years' data and tokenize it, and analyze it, and do some analytics on it, and just keep the meta data. So I think there is this rush, for a whole range of reasons, particularly vendor driven. To build data lakes because we think they're a necessity, when in reality they may just be an interim requirement and we don't need to keep them for a long term. >> I'm going to attempt to, the last few questions, put them all together. And I think, they all belong together because one of the reasons why there's such hesitation about progress within the data world is because there's just so much accumulated tech debt already. Where there's a new idea. We go out and we build it. And six months, three years, it really depends on how big the idea is, millions of dollars is spent. And then by the time things are built the idea is pretty much obsolete, no one really cares anymore. And I think what's exciting now is that the speed to value is just so much faster than it's ever been before. And I think that, you know, what makes that possible is this concept of, I don't think of a data lake as a thing. I think of a data lake as an ecosystem. And that ecosystem has evolved so much more, probably in the last three years than it has in the past 30 years. And it's exciting times, because now once we have this ecosystem in place, if we have a new idea, we can actually do it in minutes not years. And that's really the exciting part. And I think, you know, data lake versus a data swamp, comes back to just traditional data architecture. And if you architect your data lake right, you're going to have something that's substantial, that's you're going to be able to harness and grow. If you don't do it right. If you just throw data. If you buy Hadoop cluster or a Cloud platform and just throw your data out there and say, "We have a lake now." yeah, you're going to create a mess. And I think taking the time to really understand, you know, the new paradigm of data architecture and modern data engineering, and actually doing it in a very disciplined way. If you think about it, what we're doing is we're building laboratories. And if you have a shabby, poorly built laboratory, the best scientist in the world isn't going to be able to prove his theories. So if you have a well built laboratory and a clean room, then, you know a scientist can get what he needs done very, very, very efficiently. And that's the goal, I think, of data management today. >> I'd like to just quickly add that I totally agree with the challenge between on premise and Cloud mode. And I think one of the strong themes of today is going to be the hybrid data management challenge. And I think organizations, some organizations, have rushed to adopt Cloud. And thinking it's a really good place to dump the data and someone else has to manage the problem. And then they've ended up with a very expensive death by 1,000 cuts in some senses. And then others have been very reluctant as a result of not gotten access to rapid moving and disruptive technology. So I think there's a really big challenge to get a basic conversation going around what's the value using Cloud technology as in adopting it, versus what are the risks? And when's the right time to move? For example, should we Cloud Burst for workloads? Do we move whole data sets in there? You know, moving half a petabyte of data into a Cloud platform back is a non-trivial exercise. But moving a terabyte isn't actually that big a deal anymore. So, you know, should we keep stuff behind the firewalls? I'd be interested in seeing this week where 80% of the data, supposedly is. And just push out for Cloud tools, machine learning, data science tools, whatever they might be, cognitive analytics, et cetera. And keep the bulk of the data on premise. Or should we just move whole spools into the Cloud? There is no one size fits all. There's no silver bullet. Every organization has it's own quirks and own nuances they need to think through and make a decision themselves. >> Very often, Dez, organizations have zonal architectures so you'll have a data lake that consists of a no sequel platform that might be used for say, mobile applications. A Hadoop platform that might be used for unstructured data refinement, so forth. A streaming platform, so forth and so on. And then you'll have machine learning models that are built and optimized for those different platforms. So, you know, think of it in terms of then, your data lake, is a set of zones that-- >> It gets even more complex just playing on that theme, when you think about what Cisco started, called Folk Computing. I don't really like that term. But edge analytics, or computing at the edge. We've seen with the internet coming along where we couldn't deliver everything with a central data center. So we started creating this concept of content delivery networks, right? I think the same thing, I know the same thing has happened in data analysis and data processing. Where we've been pulling social media out of the Cloud, per se, and bringing it back to a central source. And doing analytics on it. But when you think of something like, say for example, when the Dreamliner 787 from Boeing came out, this airplane created 1/2 a terabyte of data per flight. Now let's just do some quick, back of the envelope math. There's 87,400 fights a day, just in the domestic airspace in the USA alone, per day. Now 87,400 by 1/2 a terabyte, that's 43 point five petabytes a day. You physically can't copy that from quote unquote in the Cloud, if you'll pardon the pun, back to the data center. So now we've got the challenge, a lot of our Enterprise data's behind a firewall, supposedly 80% of it. But what's out at the edge of the network. Where's the value in that data? So there are zonal challenges. Now what do I do with my Enterprise versus the open data, the mobile data, the machine data. >> Yeah, we've seen some recent data from IDC that says, "About 43% of the data "is going to stay at the edge." We think that, that's way understated, just given the examples. We think it's closer to 90% is going to stay at the edge. >> Just on the airplane topic, right? So Airbus wasn't going to be outdone. Boeing put 4,000 sensors or something in their 787 Dreamliner six years ago. Airbus just announced an 83, 81,000 with 10,000 sensors in it. Do the same math. Now the FAA in the US said that all aircraft and all carriers have to be, by early next year, I think it's like March or April next year, have to be at the same level of BIOS. Or the same capability of data collection and so forth. It's kind of like a mini GDPR for airlines. So with the 83, 81,000 with 10,000 sensors, that becomes two point five terabytes per flight. If you do the math, it's 220 petabytes of data just in one day's traffic, domestically in the US. Now, it's just so mind boggling that we're going to have to completely turn our thinking on its' head, on what do we do behind the firewall? What do we do in the Cloud versus what we might have to do in the airplane? I mean, think about edge analytics in the airplane processing data, as you said, Jim, streaming analytics in flight. >> Yeah that's a big topic within Wikibon, so, within the team. Me and David Floyer, and my other colleagues. They're talking about the whole notion of edge architecture. Not only will most of the data be persisted at the edge, most of the deep learning models like TensorFlow will be executed at the edge. To some degree, the training of those models will happen in the Cloud. But much of that will be pushed in a federated fashion to the edge, or at least I'm predicting. We're already seeing some industry moves in that direction, in terms of architectures. Google has a federated training, project or initiative. >> Chris: Look at TensorFlow Lite. >> Which is really fascinating for it's geared to IOT, I'm sorry, go ahead. >> Look at TensorFlow Lite. I mean in the announcement of having every Android device having ML capabilities, is Google's essential acknowledgment, "We can't do it all." So we need to essentially, sort of like a setting at home. Everyone's smartphone top TV box just to help with the processing. >> Now we're talking about this, this sort of leads to this IOT discussion but I want to underscore the operating model. As you were saying, "You can't just "lift and shift to the Cloud." You're not going to, CEOs aren't going to get the billion dollar hit by just doing that. So you got to change the operating model. And that leads to, this discussion of IOT. And an entirely new operating model. >> Well, there are companies that are like Sisense who have worked with Intel. And they've taken this concept. They've taken the business logic and not just putting it in the chip, but actually putting it in memory, in the chip. So as data's going through the chip it's not just actually being processed but it's actually being baked in memory. So level one, two, and three cache. Now this is a game changer. Because as Chris was saying, even if we were to get the data back to a central location, the compute load, I saw a real interesting thing from I think it was Google the other day, one of the guys was doing a talk. And he spoke about what it meant to add cognitive and voice processing into just the Android platform. And they used some number, like that had, double the amount of compute they had, just to add voice for free, to the Android platform. Now even for Google, that's a nontrivial exercise. So as Chris was saying, I think we have to again, flip it on its' head and say, "How much can we put "at the edge of the network?" Because think about these phones. I mean, even your fridge and microwave, right? We put a man on the moon with something that these days, we make for $89 at home, on the Raspberry Pie computer, right? And even that was 1,000 times more powerful. When we start looking at what's going into the chips, we've seen people build new, not even GPUs, but deep learning and stream analytics capable chips. Like Google, for example. That's going to make its' way into consumer products. So that, now the compute capacity in phones, is going to, I think transmogrify in some ways because there is some magic in there. To the point where, as Chris was saying, "We're going to have the smarts in our phone." And a lot of that workload is going to move closer to us. And only the metadata that we need to move is going to go centrally. >> Well here's the thing. The edge isn't the technology. The edge is actually the people. When you look at, for example, the MIT language Scratch. This is kids programming language. It's drag and drop. You know, kids can assemble really fun animations and make little movies. We're training them to build for IOT. Because if you look at a system like Node-RED, it's an IBM interface that is drag and drop. Your workflow is for IOT. And you can push that to a device. Scratch has a converter for doing those. So the edge is what those thousands and millions of kids who are learning how to code, learning how to think architecturally and algorithmically. What they're going to create that is beyond what any of us can possibly imagine. >> I'd like to add one other thing, as well. I think there's a topic we've got to start tabling. And that is what I refer to as the gravity of data. So when you think about how planets are formed, right? Particles of dust accrete. They form into planets. Planets develop gravity. And the reason we're not flying into space right now is that there's gravitational force. Even though it's one of the weakest forces, it keeps us on our feet. Oftentimes in organizations, I ask them to start thinking about, "Where is the center "of your universe with regard to the gravity of data." Because if you can follow the center of your universe and the gravity of your data, you can often, as Chris is saying, find where the business logic needs to be. And it could be that you got to think about a storage problem. You can think about a compute problem. You can think about a streaming analytics problem. But if you can find where the center of your universe and the center of your gravity for your data is, often you can get a really good insight into where you can start focusing on where the workloads are going to be where the smarts are going to be. Whether it's small, medium, or large. >> So this brings up the topic of data governance. One of the themes here at Fast Track Your Data is GDPR. What it means. It's one of the reasons, I think IBM selected Europe, generally, Munich specifically. So let's talk about GDPR. We had a really interesting discussion last night. So let's kind of recreate some of that. I'd like somebody in the panel to start with, what is GDPR? And why does it matter, Ronald? >> Yeah, maybe I can start. Maybe a little bit more in general unified governance. So if i talk to companies and I need to explain to them what's governance, I basically compare it with a crime scene. So in a crime scene if something happens, they start with securing all the evidence. So they start sealing the environment. And take care that all the evidence is collected. And on the other hand, you see that they need to protect this evidence. There are all kinds of policies. There are all kinds of procedures. There are all kinds of rules, that need to be followed. To take care that the whole evidence is secured well. And once you start, basically, investigating. So you have the crime scene investigators. You have the research lab. You have all different kind of people. They need to have consent before they can use all this evidence. And the whole reason why they're doing this is in order to collect the villain, the crook. To catch him and on the other hand, once he's there, to convict him. And we do this to have trust in the materials. Or trust in basically, the analytics. And on the other hand to, the public have trust in everything what's happened with the data. So if you look to a company, where data is basically the evidence, this is the value of your data. It's similar to like the evidence within a crime scene. But most companies don't treat it like this. So if we then look to GDPR, GDPR basically shifts the power and the ownership of the data from the company to the person that created it. Which is often, let's say the consumer. And there's a lot of paradox in this. Because all the companies say, "We need to have this customer data. "Because we need to improve the customer experience." So if you make it concrete and let's say it's 1st of June, so GDPR is active. And it's first of June 2018. And I go to iTunes, so I use iTunes. Let's go to iTunes said, "Okay, Apple please "give me access to my data." I want to see which kind of personal information you have stored for me. On the other end, I want to have the right to rectify all this data. I want to be able to change it and give them a different level of how they can use my data. So I ask this to iTunes. And then I say to them, okay, "I basically don't like you anymore. "I want to go to Spotify. "So please transfer all my personal data to Spotify." So that's possible once it's June 18. Then I go back to iTunes and say, "Okay, I don't like it anymore. "Please reduce my consent. "I withdraw my consent. "And I want you to remove all my "personal data for everything that you use." And I go to Spotify and I give them, let's say, consent for using my data. So this is a shift where you can, as a person be the owner of the data. And this has a lot of consequences, of course, for organizations, how to manage this. So it's quite simple for the consumer. They get the power, it's maturing the whole law system. But it's a big consequence of course for organizations. >> This is going to be a nightmare for marketers. But fill in some of the gaps there. >> Let's go back, so GDPR, the General Data Protection Regulation, was passed by the EU in 2016, in May of 2016. It is, as Ronald was saying, it's four basic things. The right to privacy. The right to be forgotten. Privacy built into systems by default. And the right to data transfer. >> Joe: It takes effect next year. >> It is already in effect. GDPR took effect in May of 2016. The enforcement penalties take place the 25th of May 2018. Now here's where, there's two things on the penalty side that are important for everyone to know. Now number one, GDPR is extra territorial. Which means that an EU citizen, anywhere on the planet has GDPR, goes with them. So say you're a pizza shop in Nebraska. And an EU citizen walks in, orders a pizza. Gives her the credit card and stuff like that. If you for some reason, store that data, GDPR now applies to you, Mr. Pizza shop, whether or not you do business in the EU. Because an EU citizen's data is with you. Two, the penalties are much stiffer then they ever have been. In the old days companies could simply write off penalties as saying, "That's the cost of doing business." With GDPR the penalties are up to 4% of your annual revenue or 20 million Euros, whichever is greater. And there may be criminal sanctions, charges, against key company executives. So there's a lot of questions about how this is going to be implemented. But one of the first impacts you'll see from a marketing perspective is all the advertising we do, targeting people by their age, by their personally identifiable information, by their demographics. Between now and May 25th 2018, a good chunk of that may have to go away because there's no way for you to say, "Well this person's an EU citizen, this person's not." People give false information all the time online. So how do you differentiate it? Every company, regardless of whether they're in the EU or not will have to adapt to it, or deal with the penalties. >> So Lillian, as a consumer this is designed to protect you. But you had a very negative perception of this regulation. >> I've looked over the GDPR and to me it actually looks like a socialist agenda. It looks like (panel laughs) no, it looks like a full assault on free enterprise and capitalism. And on its' face from a legal perspective, its' completely and wholly unenforceable. Because they're assigning jurisdictional rights to the citizen. But what are they going to do? They're going to go to Nebraska and they're going to call in the guy from the pizza shop? And call him into what court? The EU court? It's unenforceable from a legal perspective. And if you write a law that's unenforceable, you know, it's got to be enforceable in every element. It can't be just, "Oh, we're only "going to enforce it for Facebook and for Google. "But it's not enforceable for," it needs to be written so that it's a complete and actionable law. And it's not written in that way. And from a technological perspective it's not implementable. I think you said something like 652 EU regulators or political people voted for this and 10 voted against it. But what do they know about actually implementing it? Is it possible? There's all sorts of regulations out there that aren't possible to implement. I come from an environmental engineering background. And it's absolutely ridiculous because these agencies will pass laws that actually, it's not possible to implement those in practice. The cost would be too great. And it's not even needed. So I don't know, I just saw this and I thought, "You know, if the EU wants to," what they're essentially trying to do is regulate what the rest of the world does on the internet. And if they want to build their own internet like China has and police it the way that they want to. But Ronald here, made an analogy between data, and free enterprise, and a crime scene. Now to me, that's absolutely ridiculous. What does data and someone signing up for an email list have to do with a crime scene? And if EU wants to make it that way they can police their own internet. But they can't go across the world. They can't go to Singapore and tell Singapore, or go to the pizza shop in Nebraska and tell them how to run their business. >> You know, EU overreach in the post Brexit era, of what you're saying has a lot of validity. How far can the tentacles of the EU reach into other sovereign nations. >> What court are they going to call them into? >> Yeah. >> I'd like to weigh in on this. There are lots of unknowns, right? So I'd like us to focus on the things we do know. We've already dealt with similar situations before. In Australia, we introduced a goods and sales tax. Completely foreign concept. Everything you bought had 10% on it. No one knew how to deal with this. It was a completely new practice in accounting. There's a whole bunch of new software that had to be written. MYRB had to have new capability, but we coped. No one actually went to jail yet. It's decades later, for not complying with GST. So what it was, was a framework on how to shift from non sales tax related revenue collection. To sales tax related revenue collection. I agree that there are some egregious things built into this. I don't disagree with that at all. But I think if I put my slightly broader view of the world hat on, we have well and truly gone past the point in my mind, where data was respected, data was treated in a sensible way. I mean I get emails from companies I've never done business with. And when I follow it up, it's because I did business with a credit card company, that gave it to a service provider, that thought that I was going to, when I bought a holiday to come to Europe, that I might want travel insurance. Now some might say there's value in that. And other's say there's not, there's the debate. But let's just focus on what we're talking about. We're talking about a framework for governance of the treatment of data. If we remove all the emotive component, what we are talking about is a series of guidelines, backed by laws, that say, "We would like you to do this," in an ideal world. But I don't think anyone's going to go to jail, on day one. They may go to jail on day 180. If they continue to do nothing about it. So they're asking you to sort of sit up and pay attention. Do something about it. There's a whole bunch of relief around how you approach it. The big thing for me, is there's no get out of jail card, right? There is no get out of jail card for not complying. But there's plenty of support. I mean, we're going to have ambulance chasers everywhere. We're going to have class actions. We're going to have individual suits. The greatest thing to do right now is get into GDPR law. Because you seem to think data scientists are unicorn? >> What kind of life is that if there's ambulance chasers everywhere? You want to live like that? >> Well I think we've seen ad blocking. I use ad blocking as an example, right? A lot of organizations with advertising broke the internet by just throwing too much content on pages, to the point where they're just unusable. And so we had this response with ad blocking. I think in many ways, GDPR is a regional response to a situation where I don't think it's the exact right answer. But it's the next evolutional step. We'll see things evolve over time. >> It's funny you mentioned it because in the United States one of the things that has happened, is that with the change in political administrations, the regulations on what companies can do with your data have actually been laxened, to the point where, for example, your internet service provider can resell your browsing history, with or without your consent. Or your consent's probably buried in there, on page 47. And so, GDPR is kind of a response to saying, "You know what? "You guys over there across the Atlantic "are kind of doing some fairly "irresponsible things with what you allow companies to do." Now, to Lillian's point, no one's probably going to go after the pizza shop in Nebraska because they don't do business in the EU. They don't have an EU presence. And it's unlikely that an EU regulator's going to get on a plane from Brussels and fly to Topeka and say, or Omaha, sorry, "Come on Joe, let's get the pizza shop in order here." But for companies, particularly Cloud companies, that have offices and operations within the EU, they have to sit up and pay attention. So if you have any kind of EU operations, or any kind of fiscal presence in the EU, you need to get on board. >> But to Lillian's point it becomes a boondoggle for lawyers in the EU who want to go after deep pocketed companies like Facebook and Google. >> What's the value in that? It seems like regulators are just trying to create work for themselves. >> What about the things that say advertisers can do, not so much with the data that they have? With the data that they don't have. In other words, they have people called data scientists who build models that can do inferences on sparse data. And do amazing things in terms of personalization. What do you do about all those gray areas? Where you got machine learning models and so forth? >> But it applies-- >> It applies to personally identifiable information. But if you have a talented enough data scientist, you don't need the PII or even the inferred characteristics. If a certain type of behavior happens on your website, for example. And this path of 17 pages almost always leads to a conversion, it doesn't matter who you are or where you're coming from. If you're a good enough data scientist, you can build a model that will track that. >> Like you know, target, infer some young woman was pregnant. And they inferred correctly even though that was never divulged. I mean, there's all those gray areas that, how can you stop that slippery slope? >> Well I'm going to weigh in really quickly. A really interesting experiment for people to do. When people get very emotional about it I say to them, "Go to Google.com, "view source, put it in seven point Courier "font in Word and count how many pages it is." I guess you can't guess how many pages? It's 52 pages of seven point Courier font, HTML to render one logo, and a search field, and a click button. Now why do we need 52 pages of HTML source code and Java script just to take a search query. Think about what's being done in that. It's effectively a mini operating system, to figure out who you are, and what you're doing, and where you been. Now is that a good or bad thing? I don't know, I'm not going to make a judgment call. But what I'm saying is we need to stop and take a deep breath and say, "Does anybody need a 52 page, "home page to take a search query?" Because that's just the tip of the iceberg. >> To that point, I like the results that Google gives me. That's why I use Google and not Bing. Because I get better search results. So, yeah, I don't mind if you mine my personal data and give me, our Facebook ads, those are the only ads, I saw in your article that GDPR is going to take out targeted advertising. The only ads in the entire world, that I like are Facebook ads. Because I actually see products I'm interested in. And I'm happy to learn about that. I think, "Oh I want to research that. "I want to see this new line of products "and what are their competitors?" And I like the targeted advertising. I like the targeted search results because it's giving me more of the information that I'm actually interested in. >> And that's exactly what it's about. You can still decide, yourself, if you want to have this targeted advertising. If not, then you don't give consent. If you like it, you give consent. So if a company gives you value, you give consent back. So it's not that it's restricting everything. It's giving consent. And I think it's similar to what happened and the same type of response, what happened, we had the Mad Cow Disease here in Europe, where you had the whole food chain that needed to be tracked. And everybody said, "No, it's not required." But now it's implemented. Everybody in Europe does it. So it's the same, what probably going to happen over here as well. >> So what does GDPR mean for data scientists? >> I think GDPR is, I think it is needed. I think one of the things that may be slowing data science down is fear. People are afraid to share their data. Because they don't know what's going to be done with it. If there are some guidelines around it that should be enforced and I think, you know, I think it's been said but as long as a company could prove that it's doing due diligence to protect your data, I think no one is going to go to jail. I think when there's, you know, we reference a crime scene, if there's a heinous crime being committed, all right, then it's going to become obvious. And then you do go directly to jail. But I think having guidelines and even laws around privacy and protection of data is not necessarily a bad thing. You can do a lot of data, really meaningful data science, without understanding that it's Joe Caserta. All of the demographics about me. All of the characteristics about me as a human being, I think are still on the table. All that they're saying is that you can't go after Joe, himself, directly. And I think that's okay. You know, there's still a lot of things. We could still cure diseases without knowing that I'm Joe Caserta, right? As long as you know everything else about me. And I think that's really at the core, that's what we're trying to do. We're trying to protect the individual and the individual's data about themselves. But I think as far as how it affects data science, you know, a lot of our clients, they're afraid to implement things because they don't exactly understand what the guideline is. And they don't want to go to jail. So they wind up doing nothing. So now that we have something in writing that, at least, it's something that we can work towards, I think is a good thing. >> In many ways, organizations are suffering from the deer in the headlight problem. They don't understand it. And so they just end up frozen in the headlights. But I just want to go back one step if I could. We could get really excited about what it is and is not. But for me, the most critical thing there is to remember though, data breaches are happening. There are over 1,400 data breaches, on average, per day. And most of them are not trivial. And when we saw 1/2 a billion from Yahoo. And then one point one billion and then one point five billion. I mean, think about what that actually means. There were 47,500 Mongodbs breached in an 18 hour window, after an automated upgrade. And they were airlines, they were banks, they were police stations. They were hospitals. So when I think about frameworks like GDPR, I'm less worried about whether I'm going to see ads and be sold stuff. I'm more worried about, and I'll give you one example. My 12 year old son has an account at a platform called Edmodo. Now I'm not going to pick on that brand for any reason but it's a current issue. Something like, I think it was like 19 million children in the world had their username, password, email address, home address, and all this social interaction on this Facebook for kids platform called Edmodo, breached in one night. Now I got my hands on a copy. And everything about my son is there. Now I have a major issue with that. Because I can't do anything to undo that, nothing. The fact that I was able to get a copy, within hours on a dark website, for free. The fact that his first name, last name, email, mobile phone number, all these personal messages from friends. Nobody has the right to allow that to breach on my son. Or your children, or our children. For me, GDPR, is a framework for us to try and behave better about really big issues. Whether it's a socialist issue. Whether someone's got an issue with advertising. I'm actually not interested in that at all. What I'm interested in is companies need to behave much better about the treatment of data when it's the type of data that's being breached. And I get really emotional when it's my son, or someone else's child. Because I don't care if my bank account gets hacked. Because they hedge that. They underwrite and insure themselves and the money arrives back to my bank. But when it's my wife who donated blood and a blood donor website got breached and her details got lost. Even things like sexual preferences. That they ask questions on, is out there. My 12 year old son is out there. Nobody has the right to allow that to happen. For me, GDPR is the framework for us to focus on that. >> Dave: Lillian, is there a comment you have? >> Yeah, I think that, I think that security concerns are 100% and definitely a serious issue. Security needs to be addressed. And I think a lot of the stuff that's happening is due to, I think we need better security personnel. I think we need better people working in the security area where they're actually looking and securing. Because I don't think you can regulate I was just, I wanted to take the microphone back when you were talking about taking someone to jail. Okay, I have a background in law. And if you look at this, you guys are calling it a framework. But it's not a framework. What they're trying to do is take 4% of your business revenues per infraction. They want to say, "If a person signs up "on your email list and you didn't "like, necessarily give whatever "disclaimer that the EU said you need to give. "Per infraction, we're going to take "4% of your business revenue." That's a law, that they're trying to put into place. And you guys are talking about taking people to jail. What jail are you? EU is not a country. What jurisdiction do they have? Like, you're going to take pizza man Joe and put him in the EU jail? Is there an EU jail? Are you going to take them to a UN jail? I mean, it's just on its' face it doesn't hold up to legal tests. I don't understand how they could enforce this. >> I'd like to just answer the question on-- >> Security is a serious issue. I would be extremely upset if I were you. >> I personally know, people who work for companies who've had data breaches. And I respect them all. They're really smart people. They've got 25 plus years in security. And they are shocked that they've allowed a breach to take place. What they've invariably all agreed on is that a whole range of drivers have caused them to get to a bad practice. So then, for example, the donate blood website. The young person who was assist admin with all the right skills and all the right experience just made a basic mistake. They took a db dump of a mysql database before they upgraded their Wordpress website for the business. And they happened to leave it in a folder that was indexable by Google. And so somebody wrote a radio expression to search in Google to find sql backups. Now this person, I personally respect them. I think they're an amazing practitioner. They just made a mistake. So what does that bring us back to? It brings us back to the point that we need a safety net or a framework or whatever you want to call it. Where organizations have checks and balances no matter what they do. Whether it's an upgrade, a backup, a modification, you know. And they all think they do, but invariably we've seen from the hundreds of thousands of breaches, they don't. Now on the point of law, we could debate that all day. I mean the EU does have a remit. If I was caught speeding in Germany, as an Australian, I would be thrown into a German jail. If I got caught as an organization in France, breaching GDPR, I would be held accountable to the law in that region, by the organization pursuing me. So I think it's a bit of a misnomer saying I can't go to an EU jail. I don't disagree with you, totally, but I think it's regional. If I get a speeding fine and break the law of driving fast in EU, it's in the country, in the region, that I'm caught. And I think GDPR's going to be enforced in that same approach. >> All right folks, unfortunately the 60 minutes flew right by. And it does when you have great guests like yourselves. So thank you very much for joining this panel today. And we have an action packed day here. So we're going to cut over. The CUBE is going to have its' interview format starting in about 1/2 hour. And then we cut over to the main tent. Who's on the main tent? Dez, you're doing a main stage presentation today. Data Science is a Team Sport. Hillary Mason, has a breakout session. We also have a breakout session on GDPR and what it means for you. Are you ready for GDPR? Check out ibmgo.com. It's all free content, it's all open. You do have to sign in to see the Hillary Mason and the GDPR sessions. And we'll be back in about 1/2 hour with the CUBE. We'll be running replays all day on SiliconAngle.tv and also ibmgo.com. So thanks for watching everybody. Keep it right there, we'll be back in about 1/2 hour with the CUBE interviews. We're live from Munich, Germany, at Fast Track Your Data. This is Dave Vellante with Jim Kobielus, we'll see you shortly. (electronic music)