>>from the Cube Studios in >>Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Hi. I'm stupid, man. And welcome to a cube conversation. I'm coming to you from our Boston area studio. Happy to welcome to the program. First time guest on the program. Francis Mattis. He is the vice president of engineering at Pensando. Francis. Thanks so much for joining us. >>Thank you. Good to be here. All >>right. So, Frances, you and I actually overlapped. Ah, you know, some of the companies who work with, you know, if anybody familiar with Pensando, you have worked with some of the mpls team over the years through some of those spin ins, but for our audience, give us a little bit about your background. You know, what brought you to help and be part of the team that you started pensando? >>Sure. Yeah. Yeah. So I started my career with Advanced Micro Devices in the mid nineties, got out of school, really wanted to build micro processors. And so, Andy, being in Austin, Texas, and be going to ls you for undergrad was perfect sort of alignment. And so I got to say M. D and Austin built K five worked on that team or kind of team with K seven. And, uh, when I came out to California to help with K, and that brought me to California. And then we got into the dot com era and and being a A and B fighting intel, so to speak, seemed like a hard battle. And so, with the dot com era coming, I just saw this perfect opportunity to jump into the Internet. And so that's how we got into building Internet and data communications equipment, went to the show on systems. We talked a little bit about that earlier, and that got me into storage. From there, I got into a company called on GMO, which was building fibre channel sand equipment. So built chips there, and I got to know the Mpls team there. I always say they hired me off the street. And from that point on, while we've been together since Jews 1001 So 19 years, yeah. Yeah, and I've been building silicon with them and systems for almost 20 years now. So we had quite a journey. Yeah, it's been fun. Great >>stuff. Yeah, you know it's going back, you know, niche on talking about ice scuzzy. You know, in the networking world, you know, it's a little bit of a dark arts in general for most people, you know, understanding the networking protocols and all the various pieces and three and four letter acronyms aren't something that most people are familiar with. Pensando, I'm curious. You know what? You know, networking In general, you're like, I work on Internet stuff and we're the tubes that, you know, Things go around. So when when you describe pensando, you know how to explain that to the people that maybe aren't deep into East, west, south, over on under underlay protocols? >>Yeah, absolutely. So for me, pensando was kind of the sort of the culmination of all the things I've done in my career processing, you know, being able to build compute engines that have programmable, starting with microprocessors, being able to do storage and storage networking with Andy on no, we build a computer with druva and the virtualization layers around the Ethernet interfaces in the adapter with what was really our first smart nick, Um, in 6 4007 timeframe and then with STN in CNI, all of these elements kind of came together. These multiple different layers in the infrastructure stack, if you will, and so pensando for me. What was interesting was the explosion of scale in both space and time with the advent of, let's say, 25 gig 50 gig 100 gig to the server, the notion of very dense computing on in each rack and the need for very high scale After doing all of these technologies and seeing where silicon kind of started to fall in place, I was 16 centimeter. It seemed that bringing this kind of technology to the edge very low power with sort of an end to end security architecture and to end policy engine architecture, distributed services as we're doing all seem to naturally fit into place. And the cloud was already proving this morning when I say the cloud, I mean, the hyper scaler is like Amazon and Microsoft. We are already building these platforms. And so yeah, it dawned on me that, uh I didn't think this was possible unless you built the entire platform. We built the entire system. If you build any one piece, the market transition would take a lot longer. And I think this is true. In technology, history tends to repeat itself, starting with mainframes. When IBM built an entire computer and that built the entire computer, HP built these people. So these kinds of things, um, are important if you want to really push a market transition. And so pensando became this opportunity to take all of these things that I've done in my past life and bring them together in a way that would give a complete stack for the purposes of what I call the new computer, which is basically the data center. And so, um, you know, when my mom asks me, you know, what is it that you're doing? I said, Well, it's just imagine the computer you have right now and multiplying by thousands and thousands stacking in Iraq, and anyone can use it at any one time. And we provide the infrastructure and the mechanisms to be able to Teoh, orchestrate and control that very, very high speed layers. So I don't know if that was a long answer. >>No, no, no. It's fascinating stuff, and you know, when I look at the industry, you know cloud. Of course. Is that just make a wave? That changed the way a lot of people look at this. The way we architect things, there was this belief for a number of years. Well, you know, I'm going to go from this complicated mess that I had in my own data centers and cloud was going to be, you know, inexpensive and easy. And I don't think anybody thinks about inexpensive and easy when they look at cloud computing these days, then add edge into these environments. So I guess what I'm asking is, you know, today's environment, you know, we know I t always is additive. So I have various pieces that I need to put together. You talked about building platforms, and how can it be a complete stack? So companies like Oracle, you know, for many years said we can do everything from the silicon all the way up through your application. Amazon in many ways does the same thing they can. You can build everything on Amazon, but they built out their ecosystem. So how does Pensando fit into this? You know, multi cloud, multi dimensional multi vendor. >>So yeah, so that's a good question. so So one of the things we wanted to do is to be able to bring a systematic management layer two header Genius, beauty. And what I mean by that is in any enterprise data center, modern data center, you're gonna have multiple types of computing. You're gonna have virtual machines, you're gonna have their metal, and you're gonna have containers, or at least in the last, say, three or four years. Chances are you'll have some containers and moving there. And so what we wanted to do was be able to Brighton Infrastructure a management mechanism where all of these head Virginia's types of computing could be managed the same way with respect to policy. What I mean by policy is sort of this declarative or intent based model of I have declared what I'd like to see, whether that the network policy or and and security with data in motion and be able to plot apply it in a distributed manner. Across these different types of hetero genius elements, the cloud has the advantage that it's homogenous for the most part. I mean, they own the entire infrastructure and they can control everything on their now our systems will obviously manage the marginal systems as well, and in many ways that's easier. But bringing together these this notion of heterogeneity these types of computing with one management plane one type of interface for the operator, specifically the networking services operator, was fundamental. That and then the second thing is being able to bring the scale and speed to the edge. So a top of rack switch or something in the in the middle of the network is obviously very dense in terms of this Iot capability. So the silicon area that you spend building a high speed switch is really spent for the most part on the Iot, unless typically, 30 to 40% of the area will be Iot and the rest will be very much hardwired control protocols. We know that as we go to STN services and we want, uh, let's say software defined mechanisms in terms of what the policy looks like, what the protocols look like. The ability to change over time in the lifespan of the computer, which is 3 to 5 years, are you want that to be programmable, very difficult to apply a very dense scale in the core of the network. And so it was an obvious move to bring that to the edge where we could plug it into the server effectively, just like we did. Really? In the UCS system. Uh, no system. >>Yeah, some some really tough engineering challenges. You know, for the longest time, it was very predictable in the networking world, You know, you go from one gig to 10 gig. You know, there was a little discussion how we went the next step, whether, you know, 25 50 40 and 100 gig now. But you talk about containerized architectures. You talk about distributed systems with edge. Things change at a much smaller granular level and change much more frequently. So what are some of the design principles and challenges that you make sure that you're ready for what's happening today but also knowing that, you know, technology changes there always coming, and you need to be able to handle, You know, that next thing. Yeah, >>that's right. Yes. So, uh, I think part of the biggest challenges we have are around power with respect to design power. And then what is the usefulness of each transistor? So, um, when you you have sort of a scale of flexibility. See, views are the most flexible, obviously, but have probably the least performance in them. PG A's are pretty useful in terms of its flexibility, but not very dense in terms of its logic capability. And then you have hardwired a six, which are extremely dense, very much purpose built logic, but completely inflexible. And so the design challenge it was put in front of us is how do we find that sweet spot of extremely programmable, extremely flexible, but still having a cost profile that didn't look like an F PGA And God knows the benefits of the CPU. And and that's where this sort of this notion of domain specific processing came in, which is okay, well, if we're going to solve a few problems, we're going to solve them well. And those few problems are going to be we're gonna bring PC services. We're going to bring networking services. We're going to bring stories, services. We're gonna bring security services around the edge of the computer so that we can offload or let's say, partition correctly the computing problem in a data center. And to do that, we knew a core of sea views wasn't going to do a job that's basically borrowing from this guy to pay this other guy. Right? So what we wanted to do was bring this notion of domain specific processing, and that's where our design challenges came in, which is okay, So now we build around this language called P four, What is the most optimal way to pack? The most amount of threads are processing elements into the silicon while managing the memory bandwidth, which is obviously, you know, packet processing is it has been said to be embarrassingly parallel, which is true. However, the memory bandwidth is insane. And so how do we build a system that insurance that memory is not the bottleneck? Obviously, we're producing a lot of data or, uh, computing a lot of data. And so So these were some of our design challenges. All of that within a power envelope where this part of this device could sit at the edge inside of a computer within a typical power profiling by PC, a attached card in a modern computer. So that was a huge design challenge for us. >>Yeah, I'd love to hear, you know, it was a multi year journey toe solution. And I think of the old World. It was very much a hardware centric 18 to 24 months for design and all the tape out you need to do on this. Sounds like obviously there is still hardware, but it is more software driven. Then it would have been, you know, 10 years ago. So give us some of the ups and downs in that journey. Love to hear any. Any stories that you can share their Well, yeah, I >>think you know, good question. It's always there's always ups and downs in anything you do, especially in the start up. And I think one of the biggest challenges we we've faced is, uh, the exact hardware software boundary. So what is it that you want in hardware? What is it that you want in software And, uh, you know, one of the greatest assets and our company depends on who are the people. We have amazing software and hardware architects who work extremely well together because most of us have been together for so long. So, um, so that always helps when you start to partition the problem. We spent the first year of Pensando, which was basically 2017. The company was founded really thinking through this problem, would it for for all the problems, we wanted to solve the goals that were given to us and and security. Okay, so I want to be able to terminate TCP and initiate TLS connections. What's the right architecture for that? I want to be able to do storage off load and be able to provide encryption of data at rest data in motion. I want to be able to do compression these kinds of things. What's the right part of our software boundary for that? What do we what do we hardwire in silicon versus what we make it programmable and silicon, obviously, but still through a computing engine. And so we spent the first year of the company really thinking through those different partitioning problems, and that was definitely a challenge. And we spent a lot of time and and, uh, you helped me conference rooms and white boards figuring that out. And then 2018. The challenge there was now taking this architecture, this sort of technology substrate, if you will that we built and then executing on it, making sure that it was actually going to yield what we hope that would that we would be able to provide the services. When we talk about El four firewall at line rate, that's completely programmable. Uh, we achieved that. Can we do load balancing? And we do all of it with this before processing engine and the innovations we brought before satisfy all of these requirements we put for us. And so 2018 was really about execution. And there you always have. The challenge is in execution. In terms of, you know, things are going to go wrong. It's not. It's not. If it's when and then how do you deal with it? And so again, um, I would say the biggest challenge and execution is, uh, containing the changes. You know, it's so easy for things to change, especially when you're trying to really build a software platform right, because it's always easy to sort of kick the can and say we'll deal with that later and software. But we know that given what we're trying to do, which is build a system that is highly performance, um, you can't get that. Can you have to deal with it when it comes in. So we spend a lot of time doing performance analysis, making sure that all these applications we were building we're going t yield the right performance. And so that was quite a challenge. And then 2019 was kind of the year of shaping the product. Really lots of product design. Okay, now that we have this technology and it does these, he says that we wanted to do these pieces meaning services. What are all the different ways we can shake this product after talking to customers for, you know, months and months and months. You know, Sony is very much custom, customer driven customer centric. So we we were fortunate enough that we got to spend a lot of time with customers and then that brings us out of challenges, right? Because every customer has a unique problems and so I don't know how to reform this product around a solution that solves quite a bit of problems that really brings value. And so that was the those are the challenges in 2019 which we overcame. Now, obviously we have several releases that we've come out with already. We've got a six and the chips and the It's all there now. So now, 2020. Unfortunately, covitz here, But this is this is a year of growth. This is the year that we really bring it out into the world with our partners and our customers and show how this technology has been developed and benefit will benefit customers over over the next years. Two years. >>Frances really appreciate the insight there. Yeah, that that discussion of the hardware versus software brings back memories for May. Lots of heated debates. A CIO What? One of lines you know we've used on the Cube many times is you know, you know, software will eventually work. Hardware will eventually break. So those trade rto >>taught me something over time ago. He said that uh huh, hardware is hard to change. Software is hard to stop changing. So >>that that's a great one to All right, So you gave us through the last three years journey. Give us a little bit. Look, you know, on the next three years and where you expect pensando to be going >>Sure. Where I see pensando in the next three years as we go through this market transition is uh, both a market leader in a thought leader in terms of the next wave of data center edge computing, whether the, uh in the service provider space, whether it be in the enterprise space or whether it be in the cloud space, the hyper hyper scale of space. As I was mentioning in the beginning, we had when we were talking about, uh, the journey. Market transitions of this major really require understanding the entire stack. If you provide a piece and someone else provides a piece, you will eventually get there. But it's a matter of when, and by the time you get there, there's probably something new. So, you know, uh, time in and of itself is an innovation in this area, especially when you're dealing with the market transition like this. And so we've been fortunate enough that we're building the entire system when we go from the transistors to the rest of the FBI's way, have the entire staff. And so where I see us in three years is not only being a market leader in this space, but also being a thought leader in terms of what does domain specific processing look like at the edge. Um, you know, what are the tools? What are the techniques for? Really a z save? Democratizing the cloud bringing, bringing this technology to everyone. >>Excellent. Well, hey, Frances, That has been a pleasure to talk with you. Thank you so much. Congratulations on the journey so far and I can't wait to see you. How? Thanks for going >>forward. Yeah, we're excited, and I appreciate it. Thank you for your time to. All >>right, check out the cube dot net. We've got lots of back catalogue with pensando. Also, I'm stew minimum. And thank you for watching the Q. Yeah, yeah, yeah.

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Hi, I'm stupid, man. And welcome to a cube conversation. I'm coming to you from our Boston area studio, and we're gonna be talking about the networking giant. So, uh, joining me is the first time on the program some of the members been on and the cover launch of Pensando so vivid Jane, his CTO and co founder of Pensando Bipin thanks so much for joining us. >>Thank you. It was very nice talking to you. >>All right, so in a big theme we've been talking about for a number of years now is multi cloud. And, you know, I go back and think about you know, that the concept of cloud and even, you know, I've been around long enough You think about the and one of the challenges you look at is well, security is always a challenge. The other things network bandwidth is not infinite. The speed of light has not been solved, though you know, help us understand is you know the first I guess give our audience a little bit of your background. As I said, anybody in the networking world knows less team, though. Tell us, you know, have you been on the journey with them for all of that? Or And you know what brought you and Sandy? >>Yes. Yes. Um, I mean, I've been in the journey with the team since 2000 and six, so it's pretty long, I would say 14 years now, and it's been tremendous. Um uh, at heart, I'm an engineer who takes, you know, Brian brilliant things and taking upon challenges. And I've got multiple startups before this been in a new era, The more startups before that. And of course, you know, they were not experience more independent startups. And, you know, all through the course, I have gained appreciation for, like, you know, starting all the way from silicon to build a distributed systems and a u io all the way up to the fully consumable, you know, system. So I I totally understand the the angle I need to look at this time in a holistic manner. Having contributed to Cisco, UCS of and Nexus products on. Before joining pensando, I was, um I was contributing with my own open source container networking project, which is quite exciting to see How do you evangelize, You know, my own my own core, and that was fun. And that's where I come from, But, uh, but I I'm I'm a software engineer. To start off it started contributing to a six, then started going into the application world with containers trying to pull a container networking with, Ah, we did a server product with Cisco UCS and on and pretty much all over the stack with respect, participation. So that's my background. Um, but it's being exciting to consider what's next for me. And I was largely trying to see >>so, so definitive, actually, if I If I could jump in there, right, you know, I think back the UCS it was, You know, some of those ways I gather virtualization had been around for quite a number of years at that point. But, you know, how do you optimize it you're in. How do you transform infrastructure toe live for those environments, though? You know, UCS, You know, remember, people get back saying, you know, Cisco getting into services like Well, they are. They are because they're changing that compute model really caught that. You know, Cisco led that way. If the urge instructor, so many things you talked about that we'll get to later in the interview open for station. When I look out today, you know infrastructure's paint a lot and cloud obviously, is a huge impact, but also the application. So help us understand kind of the the waves that were writing together And, you know, what was it that you know in Santo decided to build in order to meet what you know, the customers of a require >>Um So I think, you know, going back to the UCS common that you had We started off thinking, for example, what are what were the challenges with respect is scaling out the deployment of servers and we quickly realized that manageability is number one challenge on. And of course, you know when we speak about manageability, it comes down to the underpinnings of what you're building. Are you Are you able to see the entire infrastructure together, or are you still seeing those big pieces? And that's when I think UCS was born to say that Look, we need to bring everything together that could be consumed in a holistic manner. And for that you have to have all those components there are There are somewhat independent to be consumed as a unified thing. And which is why I think it was a unified computing system. UCS. Um and then I think, you know, and Sanders a journey that takes it to probably not just that concept, but in general, the the challenges and the disruptions that we're seeing to the next level. So, I mean, just to summarize, I would say we started off looking at all the disruptions that are happening in the industry. And there are many of those I'm happy to talk about, which means we looked at, uh and then we looked at What are the consumption models that people are largely, you know, finding it very appealing these days because the days in which you're going to write a spirit to do something is still pretty old you want to be able to consume and most this after consumable way, How can we build, you know, how can you build systems that are programmable in the field? Those kind of things? The consumption model reliability software is the friendly factor there, and highly appealing to you guys and all their last one. You know, at least we also we also wanted to be really heard in the game, competitiveness wise. So those were like the the overarching set of things there that we started to think about, like, what descriptions are we going to solve, um, and how the consumption model needs to be for or ah, for the future of infrastructure. And how can we get that key, which is which is far ahead and better than anything that exists out there? So that's where we started to look at. Let's bring something which is bigger B sphere and and something. Even if we have the possibility of feeling it. Let's go ahead and they're doing their anything. >>Yeah, and absolutely. There's been so much discussion over the last decade or so about about software's eating the world and what's going on there yet you know, your your team mates. It's a lot of times it's been the chip set. There have been some huge ripples in the industry, you know, major acquisitions by some of the big, disruptive companies out there. Apple made a silicon acquisition, you know, everybody paid and that will have. You can't talk about disruption today without talking about Amazon. And, of course, when Amazon bought Annapurna Labs, you know, those of us looking at the Enterprise and the clouds base was like, Keep an eye on this. And absolutely, it's been something over the last year or so now, where we've seen Amazon roll things out and, of course, a critical component of what Amazon's doing from outposts. So with that as the stage there, you talked about wanting to be interesting leading, you know Amazon, you know, is really sick, and it's setting the bar that everyone is measured against. And when I look at the solution pensando, the kind of best comparable analogy that we've seen is, you know, look at what Nitro chip can do. This is an alternative for all of the other 1000 for customers that might not want to get them from Amazon. Is that a fair comparison? And how would you line up what founder is doing compared to what Amazon has done there? >>Um, so you know, what you've seen in the Amazon announcement really is possible. Amazon is a great benchmark to beat eso No make mistakes. We are very happy to say that, you know, we are We are doing by comfortably so But then, you know, Amazon is more than more than just the just the chips that are that they are building. I mean, what you consume is what they're building and underneath the engines are really part up by by the nicety off all these things that they're very, um, having said that, you know, And Sandra was consisting off both the you know, it's recognized us as a team which has been in traditionally building chips. But yet I think you know, the the Iot or the the previous venture from Mpls Team was somewhat of an eye opening as to how bringing things together is much more value in op, ex and and simplifying things is a huge, huge value compared to just putting performance and those things. So why this is important? That is another aspect which is important in trying to simplify things and make it consumable like software. And Sandra itself has probably, you know, I would say, Ah, good chunk, like about 60% of people in software team and not the, you know, basic harbor t This is not to say that, you know, we, you know, we are under emphasizing one versus the other. Software is a bigger beast when you start trying to build all those programs on a programmable and doing that here and start to roll out those applications on. So that's why I think the emphasis on software is there. Having said that, you know, it's the software that runs the data path pipeline. There's also a layer of software that we're building that can help manage all you know, all the product in a more cohesive manner and unified. >>Okay, that's Ah, thank you for laying that out. You mentioned you've got some background and open for definitely an area where, for a number of years, you know, Amazon has not exactly, uh, open source. Not exactly been a strength for AWS. They have put a lot of effort. They've done some president IRS over the last couple of years. >>And >>how do you see open source fitting into the space? What is I kind of the philosophy of pensando when it comes to open source. And where do you see it playing in the You know, this network piece of the multi cloud. >>Yeah, no, I think it's It's ah, it's a squared, relevant in a way that you know of the cloud native movement on how applications with very Onda normalization of AP eyes across multiple clouds. Israel, We are all seeing the benefits offered. And I think that that trend will continue and which is all driven through open source Ah, you know, community that exist in, you know, in the heart of the word. So personally for me, I think I learned a whole lot of things in the open source community. You know, the importance off evangelizing whatever you're working on, the reason to have convinced other people about contributing into what you're working on on. Frankly, I also learned how difficult it is to make revenues in an open source based part of that strategy. So I think you know that those were the things that I got away from it when I was doing my own open source project of container networking. Um, but at the same time, pensando, uh, you know, we have to make sure that we are 100% aligned with anything that's happening in open source. Never replicated, Um, anything that might be that might be happening in open source instead tried to make people use those things in the best possible way and in the most efficient way and the easiest possible way to use those. So our strategy largely is that, you know, embrace open source which exists are there from an infrastructure point of view, we are collaborating and communicating with less of the users are Hello. I think we're going to standardize most of things we're looking in before community. So our stands largely is that, you know, if we are building a programmable platform than the community is what is gonna driver and we are very much working towards a step by step, of course, trying to get through, you know, a stable state where we could we could not just empower people who are who are taking up the open source efforts which are going on. But at the same time, we can also contribute our program are programs into the open source community and defining the right abstractions into into the community. Um, because we came out of stealth pretty recently, you'll start seeing that and helping those activity as Well, >>excellent. Well, you know the launch of Pensando you had a phenomenal lineup. Not only you know, John Chambers obviously has the relationship with your theme, but you know, oh, am partners of Hewlett Packard, Enterprise and IBM, as well as the Marquis of Goldman Sachs. Things look a little bit different in the first half of 2020 and then they didn't end of 29 teams. So, you know, curious, You know that the global pandemic, the rippling financial implications, you know, what does that mean? The pensando. How has that impacted conversations that you're having with your >>Well, one thing I know at a broader level, let me cover, um, where things are heading. And in that sense, you know, I see that network and the infrastructure in general cloud infrastructure networking it's going to become. And we have realized it's this during during during recent early 20 twenties that that is going to be very important to have the have a new underpinning infrastructure that is not just working efficiently, securely, but is, you know, highly cost effective and very high performance, you know, ranging from people who are trying to connect from home to people who are trying to use videoconferencing and people who are going to be more and more use cloud based services even to order simple of the data being, you know, going to source for so network will become essential, you know, essential element for four things as we go forward. And we do see that being embraced by our customers and and things where we were trying to communicate that, you know, look, you will need performance and cost benefits are becoming more and more real Now. It's like, oh, things that we were having things in the pipeline for us. We need to work on that now. And the reason is because the things that we anticipated the demand increase, which is gonna happen over the fear of years, is happening literally in a few months. And so that is what we see. We are definitely, you know, very well poised to take advantage of their of their demand for sure. But also the fact that you know it needs to be done super efficiently. And so I think we are. You know, we are right. Well, I would say, you know, situated to be able to take advantage of start. >>Yeah, absolutely. You know, one thing you can't control as a company is you know what the global situation is when you come out of stealth and, you know, move through some of those early phases, you know, you've been part of You said a number of startups you've been part of been in give us a little bit of the inside baseball of, you know, being part of Rondo. You know, any stories on a little some of the ups and downs on the multi year journey to get where you are today? >>Definitely. I think. You know, um, minutes aren't good. They are largely an execution play. Relatively independent startup is is going to be about you know, how we cracked the overall market market fit and, ah, on execution, Of course, on deal with maybe in a competition in a different way, of course, like maybe big companies are our great partners. At the same time, you have to navigate that. So the overall the overall landscape in Spain and forces forces not is it's quite different. We can be much more border than we are independent company In trying to disrupt almost anything because we don't have any point of view to define per se. We do exactly, You know, what could be the most disruptive way, too, to potentially benefit the users on day? That's a big, you know, big change. I would say, um, we are being but paranoid as well at the same time, impractical to look at. You know how how we could navigate this situation in a very practical may. And the journey off, often independent startup is, you know, personally, for me, this is this is my fourth in different and start and best off. Off off, all independent. Once, I would stay largely because the kind of tradition that we're getting being an independent company is so huge. I'm just concerned about those things. But what We're really trying to trying to ensure that, you know, we can't get our stuff, but I want you and we started. >>Excellent. Guess what? One of the other things about being a startup is You know what you know adjustments You need to make along the way. So I'm curious. As you know, you've gone to some of your early customers. Any feedback or adjustments in some of the use cases or, you know, things that you've learned along the way that you can share. >>Um, fundamentally, at a base level, we haven't shifted from what we started off. We look at disruptions on on how consumption models are going to be changing, how speeds and feeds are gonna become important because, you know, because most law is going to be almost operating, how we how we deliver things into and containers are going to be a primary, you know, vehicle to deliver and build applications. So we recognize those disruptions, and we haven't changed, But normally from those disruptions that we wanted people after her. Uh, but at the same time, I think, you know, as we went and socialize our ideas and on architecture and designs with customers, we realized that that they are giving us lots more feedback on work all we could do and ah, and starting to become like we could take on different segments of market and not just one. So why stick ourselves to the data center power? Why not work on something on edge, blur wine or wine are real solutions for five G where latency and and performance is super crucial. Why don't take up on, you know, branch that use cases. So there are many things that are opening up. Um, and largely the you know, the shift. Or I would say the the inclination of what we should change versus not is happening with respect to where our customers are driving us. And and it is very important to make sure that you know the users of our lives Articulating all of the shift happens as opposed to, uh, you know, as opposed to anything else. We listen to them like super, super carefully, uh, and at the same time trying to make sure that we not only meet their means for you there their demands. So, um, definitely, you know, from the from the overall landscape of things, we are starting to get a lot more than what we are capture, which is good news For the same time, we're trying to also, uh, take on one part. I'm you know, >>all right, Vivienne, I can't let you off the hook as the cto without talking a little bit about that. You know, I think earlier in my career there was the old discussion and said you know, we should have started it, you know, a year or two ago. But, you know, we didn't. So we should start it today with changing pace of technology. You know, I've always said, you know, if I could I'd rather wait a year because I could take the next generation. I can take advantage of all these other things, but I can't wait, because then I'd never ship any things that I need to start now, Give us a little bit, you know, Look out in the future. How is your architecture designed to be able to take advantage of all the wonders coming with five G and everything there, Um, and anything that we should be looking at, You know, through the next kind of 12 18 months on the roadmap that you could share >>your Ah, yes. So, um, I would first of all say that we didn't build a part of, actually, what we build was a platform on which we can build multiple products. And we started we started off going there because we thought that, you know, the the platform that we're building is capable of capable of doing a lot more things than than one use case that we start off with. And so, to that point, I would say that yes. I mean, he started focusing on one product initially on the possibilities off. Trying to take it to multiple segments is is normally very much there. But we are already, you know, having those conversations to see what is the core set of use cases that we could we could get into for different segments. Besides the data center, you know, public Private Data center, you're looking at edge. We're looking by the looking at, Yeah, you mentioned this is as well as the, you know, storage and conversion infrastructure. So I would say that the food of all those things that we're starting to engage is going to start showing up in next 18 months. I could actually I think we are very well boys to take advantage of what we have. The hardware that we're shipping is going to be 100% compatible with four programs, but I don't those. So that is that is lot more possibilities are interesting. More use cases as people. The software's architecture that we have built is very extensible as well. Eh so we believe that. You know, uh, we believe that we can normally satisfy those use cases, but we're starting to you get into those things now, which will start to show up in and actually useful products of unusable for us with customer testimonials and then maybe 12 to 18 months from now. All >>right, well, thank you so much. It's great to catch up with. You really appreciate you coming on. >>Thank you to Because they're talking to you. And, you know, I appreciate your time. >>All right, I'm stew minimum. And be sure to check out the cube dot net for all the coverage. Go see the launch that we did. So in the second half of 2019. Thank you for watching you. Yeah, Yeah, yeah, yeah.

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Hey, welcome back to the cubes. Coverage of AWS 19 from Las Vegas. This is Day two of our coverage of three days. Two sets, lots of cute content. Lisa Martin here with Justin Warren, founder and chief analyst. A pivot nine. Justin and I are joined by a couple of guests New to the Cube. We've got David Meister next to meet Global head of sales for Io Tahoe. Welcome. Eddie Edwards with a cool name. Global Data Service is director from Direct Energy. Welcome, Eddie. Thank you. Okay, So, David, I know we had somebody from Io Tahoe on yesterday, but I'd love for you to give her audience an overview of Io Tahoe, and then you gotta tell us what the name means. >>Okay. Well, day pie stir. Io Tahoe thinks it's wonderful event here in AWS and excited to be here. Uh, I, oh, Tahoe were located in downtown on Wall Street, New York on and I Oh, Tahoe. Well, there's a lot of different meanings, but mainly Tahoe for Data Lake Input output into the lake is how it was originally meant So But ah, little background on Io Tahoe way are 2014. We came out way started in stealth came out of stealth in 2017 with two signature clients. When you're going to hear from in a moment direct energy, the other one g e and we'll speak to those in just a moment I owe Tahoe takes a unique approach way have nine machine learning machine learning algorithms 14 future sets that interrogates the data. At the data level, we go past metadata, so solving that really difficult data challenge and I'm gonna let Eddie describe some of the use cases that were around data migration, P II discovery, and so over to you >>a little bit about direct energy. What, you where you're located, What you guys do and how data is absolutely critical to your business. Yeah, >>sure. So direct energy. Well, it's the largest residential energy supplier in the er us around 5000 employees. Loss of this is coming from acquisitions. So as you can imagine, we have a vast amount of data that we need some money. Currently, I've got just under 1700 applications in my portfolio. Onda a lot. The challenges We guys are around the cost, driving down costs to serve so we can pass that back onto our consumers on the challenge that with hard is how best to gain that understanding. Where I alter whole came into play, it was vainly around off ability to use the products quickly for being able to connect to our existing sources to discover the data. What, then, that Thio catalog that information to start applying the rules around whether it be legislation like GDP, are or that way gets a lot of cases where these difference between the states on the standings and definitions so the product gives us the ability to bring a common approach So that information a good success story, would be about three months ago, we took the 30 and applications for our North America home business. We were able to running through the product within a week on that gave us the information to them, consolidate the estate downwards, working with bar business colleagues Thio, identify all the data we don't see the archival retention reels on, bring you no more meaning to the data on actually improve ourselves opportunities by highlights in that rich information that was not known >>previously. Yes, you mentioned that you growing through acquisition. One thing that people tend to underestimate around I t. Is that it's not a heterogeneous. It's not a homogeneous environments hatred genius. Like as soon as you buy another company, you've got another. You got another silent. You got another day to say. You got something else. So walk us through how iota who actually deals with that very disparity set of data that you've night out inherited from just acquiring all of these different companies? >>Yeah, so exactly right. You know, every time we a private organization, they would have various different applications that were running in the estate. Where would be an old article? I say, Hey, sequel tap environment. What we're able to do is use the products to plug in a name profile to understand what's inside knowledge they have around their customer base and how we can number in. That's in to build up a single view and offer additional products value adding products or rewards for customers, whether that be, uh on our hay truck side our heat in a ventilation and air con unit, which again we have 4600 engineers in that space. So it's opening up new opportunities and territories to us. >>Go ahead, >>say additionally to that, we're across multiple sectors, but the problem death by Excel was in the financial service is we're located on Wall Street. As I mentioned on this problem of legacy to spirit, data, sources and understanding, and knowing your data was a common problem, banks were just throwing people at the problem. So his use case with 1700 applications, a lot of them legacy is fits right into what we d'oh and cataloging is he mentioned. We catalogue with that discover in search engine that we have. We enable search cross enterprise. But Discovery we auto tag and auto classify the sensitive data into the catalog automatically, and that's a key part of what we do. And it >>was that Dave is something in thinking of differentiation, wanting to know what is unique about Iota. What was the opportunity that you guys saw? But is the cataloging and the sensitive information one of the key things that makes it a difference >>Way enabled data governance. So it's not just sensitive information way catalog, entire data set multiple data sets. And what makes us what differentiates us is that the machine learning way Interrogate in brute force The data So every single so metadata beyond so 1,000,000,000 rose. 100,000 columns. Large, complex data sets way. Interrogate every field value. And we tell you what this looks like A phone number. This looks like an address. This looks like a first name. This looks like the last name and we tagged at to the catalog. And then anything that sensitive in nature will color coded red green, highly sensitive, sensitive. So that's our big differentiator. >>So is that like 100% visibility into the granularity of what is in this data? >>Yes, that's that's one of the issues is who were here ahead of us. We're finding a lot of folks are wanting to go to the cloud, but they can't get access to the data. They don't know their data. They don't understand it. On DSO where that bridge were a key strategic partner for aws Andi we're excited about the opportunity that's come about in the last six months with AWS because we're gonna be that key geese for migration to the cloud >>so that the data like I love the name iota, How But in your opinion, you know, you could hear so many different things about Data Lake Data's turning into data Swamp is there's still a lot of value and data lakes that customers just like you're saying before, you just don't know what they have. >>Well, what's interesting in this transition to one of other clients? But on I just want to make a note that way actually started in the relational world. So we're already a mess. We're across header genius environment so but Tahoe does have more to do with Lake. But at a time a few years back, everybody was just dumping data into the lake. They didn't understand what what was in there, and it's created in this era of privacy, a big issue, and Comcast had this problem. The large Terry Tate instance just dumping into the lake, not understanding data flows, how they're data's flowing, not understanding what's in the lake, sensitivity wise, and they want to start, you know they want enable b I. They want they want to start doing analytics, but you gotta understand and know the data, right? So for Comcast, we enable data ops for them automatically with our machine learning. So that was one of the use cases. And then they put the information and we integrated with Apache Atlas, and they have a large JW aws instance, and they're able to then better govern their data on S O N G. Digital. One other customer very complex use case around their data. 36 e. R. P s being migrated toe one virtually r p in the lake. And think about finance data How difficult that is to manage and understand. So we were a key piece in helping that migration happen in weeks rather than months. >>David, you mentioned cloud. Clearly weird. We're at a cloud show, but you mentioned knowing your data. One of the aspect of that cloud is that it moves fast, and it's a much bigger scale than what we've been used to. So I'm interested. Maybe, Eddie, you can. You can fill us in here as well about the use of a tool to help you know your data when we're not creating any less stated. There's just more and more data. So at this speed and this scale, how important is it that you actually have tooling to provide to the to the humans who have to go on that operate on all of this data >>building on what David was saying around the speed in the agility side, you know, now all our information I would know for North America home business is in AWS Hold on ns free bucket. We are already starting work with AWS connect on the call center side. Being able to stream that information through so we're getting to the point now is an organization where we're able to profile the data riel. Time on. Take that information Bolts predict what the customers going going to do is part that machine learning side. So we're starting to trial where we will interject into a call to say, Well, you know, a customer might be on your digital site trying to do a journey. You can see the challenges around data, and you could Then they go in with a chop using, say, the new AWS trap that's just coming through at the moment. So >>one of the things that opportunities I'm here. Sorry, Eddie is the opportunity to leverage the insights into the data to deliver more. You mentioned like customer words, are more personalized experience or a call center agent. Knowing this is the problem of this customer is experiencing this way. Have tried X, y and Z to resolve, or this customer is loyal to pay their bills on time. They should be eligible for some sort of reward program. I think consumers that I think amazon dot com has created us this demanding consumer that way expect you to know us. I expect you to serve us up things that you think we want. Talk to me about the opportunity that I owe Ty was is giving your business to be able to delight customers in ways that you probably couldn't even have predicted? >>Well, they touched on the tagging earlier, you know, survive on the stunned in the data that's coming through. Being able to use the data flow technology on dhe categorizing were able than telling kidding with wider estate. So David mentioned Comcast around 36 e. R. P. You know, we've just gone through the same in other parts of our organization. We're driving the additional level of value, turning away from being a manually labor intensive task. So I used to have 20 architects that daily goal through trying to build an understanding the relationship. I do not need that now. I just have a couple of people that are able to take the outputs and then be able to validate the information using the products. >>And I like that. There's just so much you mentioned customer 360. Example at a call centre. There's so much data ops that has to happen to make that happen on. That's the most difficult challenge to solve. And that's where we come in. And after you catalogue the data, I just want to touch on this. We enable search for the enterprise so you're now connected to 50 115 100 sources with our software. Now you've catalogued it. You profiled it. Now you can search Karen Kim Kim Smith, So your your your engineers, your architect, your data stewards influences your business analysts. This is folks can now search anything they want and find anything sensitive. Find that person find an invoice, and that helps enable. But you mentioned the customer >>360. But I can Also. What I'm hearing is, as it has the potential to enable a better relationship between I t in the business. >>Absolutely. It brings those both together because they're so siloed. In this day and age, your data siloed and your business is siloed in a different business unit. So this helps exactly collaborate crowdsource, bring it all together. One platform >>and how many you so 1700 applications. How many you mentioned the 36 or so air peace. What percentage? If you can guess who have you been able to reduce duplicate triplicate at center applications? And what are some of the overarching business benefits that direct energy is achieving? >>So incentive the direct senator, decide that we're just at the beginning about journey. We're about four months in what? We've already decommissioned 12. The applications I was starting to move out to the wider side in terms of benefits are oh, I probably around 300% of the moment >>in a 300% r A y in just a few months. >>Just now, you know you've got some of the basic savings around the story side. We're also getting large savings from some of the existing that support agreements that we have in place. David touched on data Rob's. I've been able to reduce the amount of people that are required to support the team. There is now a more common on the standing within the organization and have money to turn it more into a self care opportunity with the business operations by pushing the line from being a technical problem to a business challenge. And at the end of the day, they're the experts. They understand the data better than any IittIe fault that sat in a corner, right? So I'm >>gonna ask you one more question. What gave you the confidence that I Oh, Tahoe was the right solution for you >>purely down Thio three Open Soul site. So we come from a you know I've been using. I'll tell whole probably for about two years in parts of the organization. We were very early. Adopters are over technologies in the open source market, and it was just the ability thio on the proof of concept to be able to turn it around iTunes, where you'll go to a traditional vendor, which would take a few months large business cases. They need any of that. We were able to show results within 24 48 hours on now buys the confidence. And I'm sure David would take the challenge of being able to plug in some day. It says on to show you the day. >>Cool stuff, guys. Well, thank you for sharing with us what you guys are doing. And I have a Iot Tahoe keeping up data Lake Blue and the successes that you're cheating in such a short time, but direct energy. I appreciate your time, guys. Thank you. Excellent. Our pleasure. >>No, you'll day. >>Exactly know your data. My guests and my co host, Justin Warren. I'm Lisa Martin. I'm gonna go often. Learn my data. Now you've been watching the Cube and AWS reinvent 19. Thanks for watching

>> Announcer: Live, from Orlando, Florida, it's TheCUBE, covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to sunny Orlando everybody. This is TheCUBE, the leader in live tech coverage. My name is Dave Vellante and this is PentahoWorld, #PWorld17. Don DeLoach here, he's the co-chair of the midwest IoT council. Thanks so much for coming on TheCUBE. >> Good to be here. >> So you've just written a new book. I got it right in my hot off the presses in my hands. The Future of IoT, leveraging the shift to a data-centric world. Can you see that okay? Alright, great, how's that, you got that? Well congratulations on getting the book done. >> Thanks. >> It's like, the closest a male can come to having a baby, I guess. But, so, it's fantastic. Let's start with sort of the premise of the book. What, why'd you write it? >> Sure, I'll give you the short version, 'cause that in and of itself could go on forever. I'm a data guy by background. And for the last five or six years, I've really been passionate about IoT. And the two converged with a focus on data, but it was kind of ahead of where most people in IoT were, because they were mostly focused on sensor technology and communications, and to a limited extent, the workflow. So I kind of developed this thesis around where I thought the market was going to go. And I would have this conversation over and over and over, but it wasn't really sticking and so I decided maybe I should write a book to talk about it and it took me forever to write the book 'cause fundamentally I didn't know what I was doing. Fortunately, I was able to eventually bring on a couple of co-authors and collectively we were able to get the book written and we published it in May of this year. >> And give us the premise, how would you summarize? >> So the central thesis of the book is that the market is going to shift from a focus on IoT enabled products like a smart refrigerator or a low-fat fryer or a turbine in a factory or a power plant or whatever. It's going to shift from the IoT enabled products to the IoT enabled enterprise. If you look at the Harvard Business Review article that Jim Heppelmann and Michael Porter did in 2014, they talked about the progression from products to smart products to smart, connected products, to product systems, to system of systems. We've largely been focused on smart, connected products, or as I would call IoT enabled products. And most of the technology vendors have focused their efforts on helping the lighting vendor or the refrigerator vendor or whatever IoT enable their product. But when that moves to mass adoption of IoT, if you're the CIO or the CEO of SeaLand or Disney or Walmart or whatever, you're not going to want to be a company that has 100,000 IoT enabled products. You're going to want to be an IoT enabled company. And the difference is really all around data primacy and how that data is treated. So, right now, most of the data goes from the IoT enabled product to the product provider. And they tell you what data you can get. But that, if you look at the progression, it's almost mathematically impossible that that is sustainable because company, organizations are going to want to take my, like let's just say we're talking about a fast food restaurant. They're going to want to take the data from the low-fat fryer and the data from the refrigerator or the shake machine or the lighting system or whatever, and they're going to want to look at it in the context of the other data. And they're going to also want to combine it with their point-of-sale or crew scheduling, or inventory and then if they're smart, they'll start to even pull in external data, like pedestrian traffic or street traffic or microweather or whatever, and they'll create a much richer signature. And then, it comes down to governance, where I want to create this enriched data set, and then propagate it to the right constituent in the right time in the right way. So you still give the product provider back the data that they want, and there's nothing that precludes you from doing that. And you give the low-fat fryer provider the data that they want, but you give your regional and corporate offices a different view of the same data, and you give the FDA or your supply chain partner, it's still the same atomic data, but what you're doing is you're separating the creation of the data from the consumption of the data, and that's where you gain maximum leverage, and that's really the thesis of the book. >> It's data, great summary by the way, so it's data in context, and the context of the low-fat fryer is going to be different than the workflow within that retail operation. >> Yeah, that's right and again, this is where, the product providers have initially kind of pushed back because they feel like they have stickiness and loyalty that's bred out of that link. But, first of all, that's going to change. So if you're Walmart or a major concern and you say, "I'm going to do a lighting RFP," and there's 10 vendors that say, "Hey, we want to compete for this," and six of 'em will allow Walmart to control the data, and four say, "No, we have to control the data," their list just went to six. They're just not going to put up with that. >> Dave: Period, the end, absolutely. >> That's right. So if the product providers are smart, they're going to get ahead of this and say, "Look, I get where the market's going. "We're going to need to give you control of the data, "but I'm going to ask for a contract that says "I'm going to get the data I'm already getting, "'cause I need to get that, and you want me to get that. "But number two, I'm going to recognize that "they can give, Walmart can give me my data back, "but enrich it and contextualize it "so I get better data back." So everybody can win, but it's all about the right architecture. >> Well and the product guys going to have the Trojan horse strategy of getting in when nobody was really looking. >> Don: That's right. >> And okay, so they've got there. Do you envision, Don, a point at which the Walmart might say, "No, that's our data "and you don't get it." >> Um, not really- >> or is there going to be a quid pro quo? >> and here's why. The argument that the product providers have made all along is, almost in a condescending way sometimes, although not intentionally condescending, it's been, look, we're selling you this low-fat fryer for your fast food restaurant. And you say you want the data, but you know, we had a team of people who are experts in this. Leave that to us, we'll analyze the data and we'll give you back what you need. Now, there's some truth to the fact that they should know their products better than anybody, and if I'm the fast food chain, I want them to get that data so that they can continually analyze and help me do my job better. They just don't have to get that data at my expense. There are ways to cooperatively work this, but again, it comes back to just the right architecture. So what we call the first receiver is in essence, setting up an abstraction close to the point of the ingestion of all this data. Upon which it's cleansed, enriched, and then propagated again to the right constituent in the right time in the right way. And by the way, I would add, with the right security considerations, and with the right data privacy considerations, 'cause like, if you look around the market now, things like GEP are in Europe and what we've seen in the US just in the wake of the elections and everything around how data is treated, privacy concerns are going to be huge. So if you don't know how to treat the data in the context of how it needs to be leveraged, you're going to lose that leverage of the data. >> Well, plus the widget guys are going to say "Look, we have to do predictive maintenance "on those devices and you want us to do that." You know, they say follow the money. Let's follow the data. So, what's the data flow look like in your mind? You got these edge devices. >> Yep, physical or virtual. Doesn't have to be a physical edge. Although, in a lot of cases, there are good reasons why you'd want a physical edge, but there's nothing technologically that says you have to have a physical edge. >> Elaborate on that, would you? What do you mean by virtual? >> Sure, so let's say I have a server inside a retail outfit. And it's collecting all of my IoT data and consolidating it and persisting it into a data store and then propagating it to a variety of constituents. That would be creating the first receiver in the physical edge. There's nothing that says that that edge device can't grab that data, but then persist it in a distributed Amazon cloud instance, or a Rackspace instance or whatever. It doesn't actually need to be persisted physically on the edge, but there's no reason it can't either. >> Okay, now I understand that now. So the guys at Wikibon, which is a sort of sister company to TheCUBE, have envisioned this three tiered data model where you've got the devices at the edge where real-time activity's going on, real-time analytics, and then you've got this sort of aggregation point, I guess call it a gateway. And then you've got, and that's as I say, aggregation of all these edge devices. And then you've got the cloud where the heavy modeling is done. It could be your private cloud or your public cloud. So does that three tier model make sense to you? >> Yeah, so what you're describing as the first tier is actually the sensor layer. The gateway layer that you're describing, in the book would be characterized as the first receiver. It's basically an edge tier that is augmented to persist and enrich the data and then apply the proper governance to it. But what I would argue is, in reality, I mean, your reference architecture is spot-on. But if you actually take that one step further, it's actually an n-tier architecture. Because there's no reason why the data doesn't go from the ten franchise stores, to the regional headquarters, to the country headquarters, to the corporate headquarters, and every step along the way, including the edge, you're going to see certain types of analytics and computational work done. I'll put a plug for my friends at Hitachi Lumada in on this, you know, there's like 700 horizontal IoT platforms out there. There aren't going to be 700 winners. There's going to be probably eight to 10, and that's only because the different specific verticals will provide for more winners than it would be if it was just one like a search engine. But, the winners are going to have to have an extensible architecture that is, will ultimately allow enterprises to do the very things I'm talking about doing. And so there are a number out there, but one of the things, and Rob Tiffany, who's the CTO of Lumada, I think has a really good handle on his team on an architecture that is really plausible for accomplishing this as the market migrates into the future. >> And that architecture's got to be very flexible, not just elastic, but sometimes we use the word plastic, plasticity, being able to go in any direction. >> Well, sure, up to and including the use of digital twins and avatars and the logic that goes along with that and the ability to spin something up and spin something down gives you that flexibility that you as an enterprise, especially the larger the enterprise, the more important that becomes, need. >> How much of the data, Don, at that edge do you think will be persisted, two part question? It's not all going to be persisted, is it? Isn't that too expensive? Is it necessary to persist all of that data? >> Well, no. So this is where, you'll hear the notion of data exhaust. What that really means is, let's just say I'm instrumenting every room in this hotel and each room has six different sensors in it and I'm taking a reading once a second. The ratio of inconsequential to consequential data is probably going to be over 99 to one. So it doesn't really make sense to persist that data and it sure as hell doesn't make sense to take that data and push it into a cloud where I spend more to reduce the value of the payload. That's just dumb. But what will happen is that, there are two things, one, I think people will see the value in locally persisting the data that has value, the consequential data, and doing that in a way that's stored at least for some period of time so you can run the type of edge analytics that might benefit from having that persisted store. The other thing that I think will happen, and this is, I don't talk much, I talk a little bit about it in the book, but there's this whole notion where when we get to the volumes of data that we really talk about where IoT will go by like 2025, it's going to push the physical limitations of how we can accommodate that. So people will begin to use techniques like developing statistical metadata models that are a highly accurate metadata representation of the entirety of the data set, but probably in about one percent of the space that's queryable and suitable for machine learning where it's going to enable you to do what you just physically couldn't do before. So that's a little bit into the future, but there are people doing some fabulous work on that right now and that'll creep into the overall lexicon over time. >> Is that a lightweight digital twin that gives you substantially the same insight? >> It could augment the digital twin in ways that allow you to stand up digital twins where you might not be able to before. The thing that, the example that most people would know about are, like in the Apache ecosystem, there are toolsets like SnappyData that are basically doing approximation, but they're doing it via sampling. And that is a step in that direction, but what you're looking for is very high value approximation that doesn't lose the outlier. So like in IoT, one of the things you normally are looking for is where am I going to pick up on anomalous behavior? Well if I'm using a sample set, and I'm only taking 15%, I by definition am going to lose a lot of that anomalous behavior. So it has to be a holistic representation of the data, but what happens is that that data is transformed into statistics that can be queryable as if it was the atomic data set, but what you're getting is a very high value approximation in a fraction of the space and time and resources. >> Ok, but that's not sampling. >> No, it's statistical metadata. There are, there's a, my last company had developed a thing that we called approximate query, and it was based on that exact set of patents around the formation of a statistical metadata model. It just so happens it's absolutely suited for where IoT is going. It's kind of, IoT isn't really there yet. People are still trying to figure out the edge in its most basic forms, but the sheer weight of the data and the progression of the market is going to force people to be innovative in how they look at some of these things. Just like, if you look at things like privacy, right now, people think in terms of anonymization. And that's, basically, I'm going to de-link data contextually where I'm going to effectively lose the linkages to the context in order to conform with data privacy. But there are techniques, like if you look at GDCAR, their techniques, within certain safe harbors, that allow you to pseudonymize the data where you can actually relink it under certain conditions. And there are some smart people out there solving these problems. That's where the market's going to go, it's just going to get there over time. And what I would also add to this equation is, at the end of the day, right now, the concepts that are in the book about the first receiver and the create, the abstraction of the creation of the data from the consumption of the data, look, it's a pretty basic thing, but it's the type of shift that is going to be required for enterprises to truly leverage the data. The things about statistical metadata and pseudonymization, pseudonymization will come before the statistical metadata. But the market forces are going to drive more and more into those areas, but you got to walk before you run. Right now, most people still have silos, which is interesting, because when you think about the whole notion of the internet of things, it infers that it's this exploitation of understanding the state of physical assets in a very broad based environment. And yet, the funny thing is, most IoT devices are silos that emulate M2M, sort of peer to peer networks just using the internet as a communication vehicle. But that'll change. >> Right, and that's really again, back to the premise of the book. We're going from these individual products, where all the data is locked into the product silo, to this digital fabric, that is an enterprise context, not a product context. >> That's right and if you go to the toolsets that Pentaho offers, the analytic toolsets. Let's just say, now that I've got this rich data set, assuming I'm following basic architectural principles so that I can leverage the maximum amount of data, that now gives me the ability to use these type of toolsets to do far better operational analytics to know what's going on, far better forensic analysis and investigative analytics to mine through the date and do root cause analysis, far better predictive analytics and prescriptive analytics to figure out what will go on, and ultimately feed the machine learning algorithms ultimately to get to in essence, the living organism, the adaptive systems that are continuously changing and adapting to circumstances. That's kind of the Holy Grail. >> You mentioned Hitachi Vantara before. I'm curious what your thoughts are on the Hitachi, you know, two years ago, we saw the acquisition, said, okay, now what? And you know, on paper it sounded good, and now it starts to come together, it starts to make more sense. You know, storage is going to the cloud. HDS says, alright, well we got this Hitachi relationship. But what do you make of that? How do you assess it, and where do you see it going? >> First of all, I actually think the moves that they've done are good. And I would not say that if I didn't think it. I'd just find a politically correct way not to say that. But I do think it's good. So they created the Hitachi Insight Group about a year and a half ago, and now that's been folded into Hitachin Vantara, alongside HDS and Pentaho and I think that it's a fairly logical set of elements coming together. I think they're going down the right path. In full disclosure, I worked for Hitachi Data Systems from '91 til '94, so it's not like I'm a recent employee of them, it's 25 years ago, but my experience with Hitachi corporate and the way they approach things has been unlike a lot of really super large companies, who may be super large, but may not be the best engineers, or may not always get everything done so well, Hitachi's a really formidable organization. And I think what they're doing with Pentaho and HDS and the Insight Group and specifically Lumada, is well thought out and I'm optimistic about where they're going. And by the way, they won't be the only winner in the equation. There's going to be eight or nine different key players, but they'll, I would not short them whatsoever. I have high hopes for them. >> The TAM is enormous. Normally, Hitachi eventually gets to where it wants to go. It's a very thoughtful company. I've been watching them for 30 years. But to a lot of people, the Pentaho and the Insight's play make a lot of sense, and then HDS, you used to work for HDS, lot of infrastructure still, lot of hardware, but a relationship with Hitachi Limited, that is quite strong, where do you see that fit, that third piece of the stool? >> So, this is where there's a few companies that have unique advantages, with Hitachi being one of them. Because if you think about IoT, IoT is the intersection of information technology and operational technology. So it's one thing to say, "I know how to build a database." or "I can build machine learning algorithms," or whatever. It's another thing to say, "I know how to build trains "or CAT scans or smart city lighting systems." And the domain expertise married with the technology delivers a set of capabilities that you can't match without that domain expertise. And, I mean, if you even just reduce it down to artificial intelligence and machine learning, you get an expert ML or AI guy, and they're only as good as the limits of their domain expertise. So that's why, and again, that's why I go back to the comparison to search engines, where there's going to be like, there's Google and maybe Yahoo. There's probably going to be more platform winners because the vertical expertise is going to be very, very important, but there's not going to be 700 of 'em. But Hitachi has an advantage that they bring to the table, 'cause they have very deep roots in energy, in medical equipment, in transportation. All of that will manifest itself in what they're doing in a big way, I think. >> Okay, so, but a lot of the things that you described, and help me understand this, are Hitachi Limited. Now of course, Hitachi Data Systems started as, National Advance Systems was a distribution arm for Hitachi IT products. >> Don: Right, good for you, not many people remember. >> I'm old. So, like I said, I had a 30 year history with this company. Do you foresee that that, and by the way, interestingly, was often criticized back when you were working for HDS, it was like, it's still a distribution hub, but in the last decade, HDS has become much more of a contributor to the innovation and the product strategy and so forth. Having said that, it seems to me advantageous if some of those things you discussed, the trains, the medical equipment, can start flowing back through HDS. I'm not sure if that's explicitly the plan. I didn't necessarily hear that, but it sort of has to, right? >> Well, I'm not privy to those discussions, so it would be conjecture on my part. >> Let's opine, but right, doesn't that make sense? >> Don: It makes perfect sense. >> Because, I mean HDS for years was just this storage silo. And then storage became a very uninteresting business, and credit to Hitachi for pivoting. But it seems to me that they could really, and they probably have a, I had Brian Householder on earlier I wish I had explored this more with him. But it just seems, the question for them is, okay, how are you going to tap those really diverse businesses. I mean, it's a business like a GE or a Siemens. I mean, it's very broad based. >> Well, again, conjecture on my part, but one way I would do it would be to start using Lumada in the various operations, the domain-specific operations right now with Hitachi. Whether they plan to do that or not, I'm not sure of. I've heard that they probably will. >> That's a data play, obviously, right? >> Well it's a platform play. And it's enabling technology that should augment what's already going on in the various elements of Hitachi. Again, I'm, this is conjecture on my part. But you asked, let's just go with this. I would say that makes a lot of sense. I'd be surprised if they don't do that. And I think in the process of doing that, you start to crosspollinate that expertise that gives you a unique advantage. It goes back to if you have unique advantages, you can choose to exploit them or not. Very few companies have the set of unique advantages that somebody like Hitachi has in terms of their engineering and massive reach into so many, you know, Hitachi, GE, Siemens, these are companies that have big reach to the extent that they exploit them or not. One of the things about Hitachi that's different than almost anybody though is they have all this domain expertise, but they've been in the technology-specific business for a long time as well, making computers. And so, they actually already have the internal expertise to crosspollinate, but you know, whether they do it or not, time will tell. >> Well, but it's interesting to watch the big whales, the horses in the track, if you will. Certainly GE has made a lot of noise, like, okay, we're a software company. And now you're seeing, wow, that's not so easy, and then again, I'm sanguine about GE. I think eventually they'll get there. And then you see IBM's got their sort of IoT division. They're bringing in people. Another company with a lot of IT expertise. Not a lot of OT expertise. And then you see Hitachi, who's actually got both. Siemens I don't know as well, but presumably, they're more OT than IT and so you would think that if you had to evaluate the companies' positions, that Hitachi's in a unique position. Certainly have a lot of software. We'll see if they can leverage that in the data play, obviously Pentaho is a key piece of that. >> One would assume, yeah for sure. No, I mean, I again, I think, I'm very optimistic about their future. I think very highly of the people I know inside that I think are playing a role here. You know, it's not like there aren't people at GE that I think highly of, but listen, you know, San Ramon was something that was spun up recently. Hitachi's been doing this for years and years and years. You know, so different players have different capabilities, but Hitachi seems to have sort of a holistic set of capabilities that they can bring together and to date, I've been very impressed with how they've been going about it. And especially with the architecture that they're bringing to bear with Lumada. >> Okay, the book is The Future of IoT, leveraging the shift to a data-centric world. Don DeLoach, and you had a co-author here as well. >> I had two co-authors. One is Wael Elrifai from Pentaho, Hitachi Vantara and the other is Emil Berthelsen, a Gartner analyst who was with Machina Research and then Gartner acquired them and Emil has stayed on with them. Both of them great guys and we wouldn't have this book if it weren't for the three of us together. I never would have pulled this off on my own, so it's a collective work. >> Don DeLoach, great having you on TheCUBE. Thanks very much for coming on. Alright, keep it right there buddy. We'll be back. This is PentahoWorld 2017, and this is TheCUBE. Be right back.