>> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities including data exploration, business intelligence natural language processing, and machine learning directly within the fabric, makes it faster and easier for organizations to gain new insights and power intelligence, predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi. Yeah, thank you so much for having me. And so for this demo we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software or adverse reaction warnings from a clinical risk grouping application and so much more. So I'm really going to be assimilating a patient logging on in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here and I'm going to be looking for information where the last name of this patient is Simmons and their medical record number their patient identifier in the system is 32345. And so as you can see I have this single JSON payload that showed up here of just relevant clinical information for my patient whose last name is Simmons all within a single response. So fantastic, right? Typically though when we see responses that look like this there is an assumption that this service is interacting with a single backend system and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here we have our data fabric coordinator which is going to be in charge of this refinement and analysis those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end we do also support full lifecycle API management within this platform. When you're dealing with APIs I always like to make a little shout out on this that you really want to make sure you have enough like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what contact. >> Can I just interrupt you for a second? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you can have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry and API securities are really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of security. >> And that's been designed in, >> Absolutely, yes. it's not a bolt-on as they like to say. Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like FIRE, interactions with a homegrown enterprise data warehouse for instance may use SQL for a cloud-based solutions managed by a vendor. They may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and and applications. And I'm about to log out so I'm going to keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST or SOAP or SQL or FTP regardless of that protocol there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as, in healthcare we have H7, we have FIRE we have CCDs across the industry. JSON is, you know, really hitting a market strong now and XML, payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR I'm leveraging a standard healthcare messaging format known as FIRE, which is a rest based protocol. And then when I'm working with my health record management system I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So let's, why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out of the box or black box approach to be able to develop things that are specific to their data fabric or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you cannot, you not only get an opportunity to view how we're establishing these connections or how we're building out these processes but you have the opportunity to inject your own kind of processing your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out of the box code that is provided in this data fabric platform from IRIS combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out of the box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. >> It's a lot here. You know, actually, if I could >> I can pause. >> If I just want to sort of play that back. So we went through the connect and the collect phase. >> And the collect, yes, we're going into refine. So it's a good place to stop. >> Yeah, so before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know the ability to bring in different dev tools. We heard about FIRE, which of course big in healthcare. >> Absolutely. >> And that's the standard and then SQL for traditional kind of structured data and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely, and I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refine. >> We're going into refinement, exciting. So how do we actually do refinement? Where does refinement happen and how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator or stands for smart data fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information it's aggregating it and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like and as you can see it follows a flow chart like structure. So there's a start, there is an end and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL Logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce or we make this data fabric a bit smarter and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging 'cause you need to be able to know, you know, if there was an issue, where did that issue happen, in which connected process and how did it affect the other processes that are related to it. In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric to when data was sent back out from that smart data fabric. So I didn't record the time but I bet if you recorded the time it was this time that we sent that request in. And you can see my patient's name and their medical record number here and you can see that that instigated four different calls to four different systems and they're represented by these arrows going out. So we sent something to chart script to our health record management system, to our clinical risk grouping application into my EMR through their FIRE server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems and we bundle them together. And for my FIRE lovers, here's our FIRE bundle that we got back from our FIRE server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping or errors that were thrown alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Etcher, who did what, when, where what did the machine do? What went wrong and where did that go wrong? >> Exactly. >> Right in your fingertips. >> Right, and I'm a visual person so a bunch of log files to me is not the most helpful. Well, being able to see this happened at this time in this location gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information, it's transforming that data, in a format that your consumer's not going to understand it's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? This all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well we can keep going. 'Cause right now, I mean we can, oh, we're at 18 minutes. God help us. You can cut some of this. (laughs) I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this but essentially if we go back to our coordinator here we can see here's that original that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at and we're running it through a machine learning model that exists within the smart data fabric pipeline and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL like syntax to be able to construct and execute these predictions. So it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge. >> Yes. >> Right, because it directly affects the cost of for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment or you know, as an outpatient perhaps to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely, absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day which is what makes this so exciting. >> Awesome demo. >> Thank you. >> Fantastic people, are you cool? If people want to get in touch with you? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy and we'd love to hear from you. I always love talking about this topic, so would be happy to engage on that. >> Great stuff, thank you Jess, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment we're going to dig into the use cases where data fabric is driving business value. Stay right there.

(lively classical music) >> Hello everyone, welcome to this special Cube conversation, we are here at the Power Panel Conversation. I'm John Furrier, in Palo Alto, California, theCUBE studies we have remote on the line here, talk about the cloud technology's impact on entrepreneurship and startups and overall ecosystem is Jim Long, who's the CEO of Didja, which is a startup around disrupting digital TV, also has been an investor and a serial entrepreneur, Sarbjeet Johal, who's the in-cloud influencer of strategy and investor out of Berkeley, California, The Batchery, and also Joseph Jacks, CUBE alumni, actually you guys are all CUBE alumni, so great to have you on. Joseph Jacks is the founder and general partner of OSS Capital, Open Source Software Capital, a new fund that's been raised specifically to commercialize and fund startups around open source software. Guys, we got a great panel here of experts, thanks for joining us, appreciate it. >> Go Bears! >> Nice to be here. >> So we have a distinguished panel, it's the Power Panel, we're on cloud technos, first I'd like to get you guys' reaction you know, you're to seeing a lot of negative news around what Facebook has become, essentially their own hyper-scale cloud with their application. They were called the digital, you know, renegades, or digital gangsters in the UK by the Parliament, which was built on open source software. Amazon's continuing to win, Azure's doing their thing, bundling Office 365, making it look like they've got more revenue with their catching up, Google, and then you got IBM and Oracle, and then you got an ecosystem that's impacted by this large scale, so I want to get your thoughts on first point here. Is there room for more clouds? There's a big buzzword around multiple clouds. Are we going to see specialty clouds? 'Causes Salesforce is a cloud, so is there room for more cloud? Jim, why don't you start? >> Well, I sure hope so. You know, the internet has unfortunately become sort of the internet of monopolies, and that doesn't do anyone any good. In fact, you bring up an interesting point, it'd be kind of interesting to see if Facebook created a social cloud for certain types of applications to use. I've no idea whether that makes any sense, but Amazon's clearly been the big gorilla now, and done an amazing job, we love using them, but we also love seeing, trying out different services that they have and then figuring out whether we want to develop them ourselves or use a specialty service, and I think that's going to be interesting, particularly in the AI area, stuff like that. So I sure hope more clouds are around for all of us to take advantage of. >> Joseph, I want you to weigh in here, 'cause you were close to the Kubernetes trend, in fact we were at a OpenStack event when you started Kismatic, which is the movement that became KubeCon Cloud Native, many many years ago, now you're investing in open source. The world's built on open source, there's got to be room for more clouds. Your thoughts on the opportunities? >> Yeah, thanks for having me on, John. I think we need a new kind of open collaborative cloud, and to date, we haven't really seen any of the existing major sort of large critical mass cloud providers participate in that type of model. Arguably, Google has probably participated and contributed the most in the open source ecosystem, contributing TensorFlow and Kubernetes and Go, lots of different open source projects, but they're ultimately focused on gravitating huge amounts of compute and storage cycles to their cloud platform. So I think one of the big missing links in the industry is, as we continue to see the rise of these large vertically integrated proprietary control planes for computing and storage and applications and services, I think as the open source community and the open source ecosystem continues to grow and explode, we'll need a third sort of provider, one that isn't based on monopoly or based on a traditional proprietary software business like Microsoft kind of transitioning their enterprise customers to services, sort of Amazon in the first camp vertically integrated many a buffet of all these different compute, storage, networking services, application, middleware. Microsoft focused on sort of building managed services of their software portfolio. I think we need a third model where we have sort of an open set of interfaces and an open standards based cloud provider that might be a pure software company, it might be a company that builds on the rails and the infrastructure that Amazon has laid down, spending tens of billions in cap ex, or it could be something based on a project like Kubernetes or built from the community ecosystem. So I think we need something like that just to sort of provide, speed the innovation, and disaggregate the services away from a monolithic kind of closed vendor like Amazon or Azure. >> I want to come back to that whole startup opportunity, but I want to get Sarbjeet in here, because we've been in the B2B area with just last week at IBM Think 2019. Obviously they're trying to get back into the cloud game, but this digital transformation that has been the cliche for almost a couple of years now, if not five or plus. Business has got to move to the cloud, so there's a whole new ball game of complete cultural shift. They need stability. So I want to talk more about this open cloud, which I love that conversation, but give me the blocking and tackling capabilities first, 'cause I got to get out of that old cap ex model, move to an operating model, transform my business, whether it's multi clouds. So Sarbjeet, what's your take on the cloud market for say, the enterprise? >> Yeah, I think for the enterprise... you're just sitting in that data center and moving those to cloud, it's a cumbersome task. For that to work, they actually don't need all the bells and whistles which Amazon has in the periphery, if you will. They need just core things like compute, network, and storage, and some other sort of services, maybe database, maybe data share and stuff like that, but they just want to move those applications as is to start with, with some replatforming and with some changes. Like, they won't make changes to first when they start moving those applications, but our minds are polluted by this thinking. When we see a Facebook being formed by a couple of people, or a company of six people sold for a billion dollars, it just messes up with our mind on the enterprise side, hey we can do that too, we can move that fast and so forth, but it's sort of tragic that we think that way. Well, having said that, and I think we have talked about this in the past. If you are doing anything in the way of systems innovation, if your building those at, even at the enterprise, I think cloud is the way to go. To your original question, if there's room for newer cloud players, I think there is, provided that we can detach the platforms from the environments they are sitting on. So the proprietariness has to kinda, it has to be lowered, the degree of proprietariness has to be lower. It can be through open source I think mainly, it can be from open technologies, they don't have to be open source, but portable. >> JJ was mentioning that, I think that's a big point. Jim Long, you're an entrepreneur, you've been a VC, you know all the VCs, been around for a while, you're also, you're an entrepreneur, you're a serial entrepreneur, starting out at Cal Berkeley back in the day. You know, small ideas can move fast, and you're building on Amazon, and you've got a media kind of thing going on, there's a cloud opportunity for you, 'cause you are cloud native, 'cause you're built in the cloud. How do you see it playing out? 'Cause you're scaling with Amazon. >> Well, so we obviously, as a new startup, don't have the issues the enterprise folks have, and I could really see the enterprise customers, what we used to call the Fortune 500, for example, getting together and insisting on at least a base set of APIs that Amazon and Microsoft et cetera adopt, and for a startup, it's really about moving fast with your own solution that solves a problem. So you don't necessarily care too much that you're tied into Amazon completely because you know that if you need to, you can make a change some day. But they do such a good job for us, and their costs, while they can certainly be lower, and we certainly would like more volume discounts, they're pretty darn amazing across the network, across the internet, we do try to price out other folks just for the heck of it, been doing that recently with CDNs, for example. But for us, we're actually creating a hybrid cloud, if you will, a purpose-built cloud to support local television stations, and we do think that's going to be, along with using Amazon, a unique cloud with our own APIs that we will hopefully have lots of different TV apps use our hybrid cloud for part of their application to service local TV. So it's kind of a interesting play for us, the B2B part of it, we're hoping to be pretty successful as well, and we hope to maybe have multiple cloud vendors in our mix, you know. Not that our users will know who's behind us, maybe Amazon, for something, Limelight for another, or whatever, for example. >> Well you got to be concerned about lock-in as you become in the cloud, that's something that everybody's worried about. JJ, I want to get back to you on the investment thesis, because you have a cutting edge business model around investing in open source software, and there's two schools of thought in the open source community, you know, free contribution's great, and let tha.t be organic, and then there's now commercialization. There's real value being created in open source. You had put together a chart with your team about the billions of dollars in exits from open source companies. So what are you investing in, what do you see as opportunities for entrepreneurs like Jim and others that are out there looking at scaling their business? How do you look at success, what's your advice, what do you see as leading indicators? >> I think I'll broadly answer your question with a model that we've been thinking a lot about. We're going to start writing publicly about it and probably eventually maybe publish a book or two on it, and it's around the sort of fundamental perspective of creating value and capturing value. So if you model a famous investor and entrepreneur in Silicon Valley who has commonly modeled these things using two different letter variables, X and Y, but I'll give you the sort of perspective of modeling value creation and value capture around open source, as compared to closed source or proprietary software. So if you look at value creation modeled as X, and value capture modeled as Y, where X and Y are two independent variables with a fully proprietary software company based approach, whether you're building a cloud service or a proprietary software product or whatever, just a software company, your value creation exponent is typically bounded by two things. Capital and fundraising into the entity creating the software, and the centralization of research and development, meaning engineering output for producing the software. And so those two things are tightly coupled to and bounded to the company. With commercial open source software, the exact opposite is true. So value creation is decoupled and independent from funding, and value creation is also decentralized in terms of the research and development aspect. So you have a sort of decentralized, community-based, crowd-sourced, or sort of internet, global phenomena of contributing to a code base that isn't necessarily owned or fully controlled by a single entity, and those two properties are sort of decoupled from funding and decentralized R and D, are fundamentally changing the value creation kind of exponent. Now let's look at the value capture variable. With proprietary software company, or proprietary technology company, you're primarily looking at two constituents capturing value, people who pay for accessing the service or the software, and people who create the software. And so those two constituents capture all the value, they capture, you know, the vendor selling the software captures maybe 10 or 20% of the value, and the rest of the value, I would would express it say as the customer is capturing the rest of the value. Most economists don't express value capture as capturable by an end user or a customer. I think that's a mistake. >> Jim, you're-- >> So now... >> Okay, Jim, your reaction to that, because there's an article went around this weekend from Motherboard. "The internet was built on free labor "of open source developers. "Is that sustainable?" So Jim, what's your reaction to JJ's comments about the interactions and the dynamic between value creation, value capture, free versus sustainable funding? >> Well if you can sort of mix both together, that's what I would like, I haven't really ever figured out how to make open source work in our business model, but I haven't really tried that hard. It's an intriguing concept for sure, particularly if we come up with APIs that are specific to say, local television or something like that, and maybe some special processes that do things that are of interest to the wider community. So it's something I do plan to look at because I do agree that if you, I mean we use open source, we use this thing called FFmpeg, and several other things, and we're really happy that there's people out there adding value to them, et cetera, and we have our own versions, et cetera, so we'd like to contribute to the community if we could figure out how. >> Sarbjeet, your reactions to JJ's thesis there? >> I think two things. I will comment on two different aspects. One is the lack of standards, and then open source becoming the standard, right. I think open source kind of projects take birth and life in its own, because we have lack of standard, 'cause these different vendors can't agree on standards. So remember we used to have service-oriented architecture, we have Microsoft pushing some standards from one side and IBM pushing from other, SOAP versus xCBL and XML, different sort of paradigms, right, but then REST API became the de facto standard, right, it just took over, I think what REST has done for software in last about 10 years or so, nothing has done that for us. >> well Kubernetes is right now looking pretty good. So if you look at JJ, Kubernetes, the movement you were really were pioneering on, it's having similar dynamic, I mean Kubernetes is becoming a forcing function for solidarity in the community of cloud native, as well as an actual interoperable orchestration layer for multiple clouds and other services. So JJ, your thoughts on how open source continues as some of these new technologies, like Kubernetes, continue to hit the scene. Is there any trajectory change in open source that you see, that you could share, I'd love to get your insights on what's next behind, you know, the rise of Kubernetes is happening, what's next? >> I think more abstractly from Kubernetes, we believe that if you just look at the rate of innovation as a primary factor for progress and forward change in the world, open source software has the highest rate of innovation of any technology creation phenomena, and as a consequence, we're seeing more standards emerge from the open source ecosystem, we're seeing more disruption happen from the open source ecosystem, we're seeing more new technology companies and new paradigms and shifts happen from the open source ecosystem, and kind of all progress across the largest, most difficult sort of compound, sensitive problems, influenced and kind of sourced from the open source ecosystem and the open source world overall. Whether it's chip design, machine learning or computing innovations or new types of architectures, or new types of developer paradigms, you know, biological breakthroughs, there's kind of things up and down the technology spectrum that have a lot to sort of thank open source for. We think that the future of technology and the future of software is really that open source is at the core, as opposed to the periphery or the edges, and so today, every software technology company, and cloud providers included, have closed proprietary cores, meaning that where the core is, the data path, the runtime, the core business logic of the company, today that core is proprietary software or closed source software, and yet what is also true, is at the edges, the wrappers, the sort of crust, the periphery of every technology company, we have lots of open source, we have client libraries and bindings and languages and integrations, configuration, UIs and so on, but the cores are proprietary. We think the following will happen over the next few decades. We think the future will gradually shift from closed proprietary cores to open cores, where instead of a proprietary core, an open core is where you have core open source software project, as the fundamental building block for the company. So for example, Hadoop caused the creation of MapR and Cloudera and Hortonworks, Spark caused the creation of Databricks, Kafka caused the creation of Confluent, Git caused the creation of GitHub and GitLab, and this type of commercial open source software model, where there's a core open source project as the kernel building block for the company, and then an extension of intellectual property or wrappers around that open source project, where you can derive value capture and charge for licensed product with the company, and impress customer, we think that model is where the future is headed, and this includes cloud providers, basically selling proprietary services that could be based on a mixture of open source projects, but perhaps not fundamentally on a core open source project. Now we think generally, like abstractly, with maybe somewhat of a reductionist explanation there, but that open core future is very likely, fundamentally because of the rate of innovation being the highest with the open source model in general. >> All right, that's great stuff. Jim, you're a historian of tech, you've lived it. Your thoughts on some of the emerging trends around cloud, because you're disrupting linear TV with Didja, in a new way using cloud technology. How do you see cloud evolving? >> Well, I think the long lines we discussed, certainly I think that's a really interesting model, and having the open source be the center of the universe, then figure out how to have maybe some proprietary stuff, if I can use that word, around it, that other people can take advantage of, but maybe you get the value capture and build a business on that, that makes a lot of sense, and could certainly fit in the TV industry if you will from where I sit... Bring services to businesses and consumers, so it's not like there's some reason it wouldn't work, you know, it's bound to, it's bound to figure out a way, and if you can get a whole mass of people around the world working on the core technology and if it is sort of unique to what mission of, or at least the marketplace you're going after, that could be pretty interesting, and that would be great to see a lot of different new mini-clouds, if you will, develop around that stuff would be pretty cool. >> Sarbjeet, I want you to talk about scale, because you also have experience working with Rackspace. Rackspace was early on, they were trying to build the cloud, and OpenStack came out of that, and guess what, the world was moving so fast, Amazon was a bullet train just flying down the tracks, and it just felt like Rackspace and their cloud, you know OpenStack, just couldn't keep up. So is scale an issue, and how do people compete against scale in your mind? >> I think scale is an issue, and software chops is an issue, so there's some patterns, right? So one pattern is that we tend to see that open source is now not very good at the application side. You will hardly see any applications being built as open source. And also on the extreme side, open source is pretty sort of lame if you will, at very core of the things, like OpenStack failed for that reason, right? But it's pretty good in the middle as Joseph said, right? So building pipes, building some platforms based on open source, so the hooks, integration, is pretty good there, actually. I think that pattern will continue. Hopefully it will go deeper into the core, which we want to see. The other pattern is I think the software chops, like one vendor has to lead the project for certain amount of time. If that project goes into sort of open, like anybody can grab it, lot of people contribute and sort of jump in very quickly, it tends to fail. That's what happened to, I think, OpenStack, and there were many other reasons behind that, but I think that was the main reason, and because we were smaller, and we didn't have that much software chops, I hate to say that, but then IBM could control like hundred parties a week, at the project >> They did, and look where they are. >> And so does HP, right? >> And look where they are. All right, so I'd love to have a Power Panel on open source, certainly JJ's been in the thick of it as well as other folks in the community. I want to just kind of end on lightweight question for you guys. What have you guys learned? Go down the line, start with Jim, Sarbjeet, and then JJ we'll finish with you. Share something that you've learned over the past three months that moved you or that people should know about in tech or cloud trends that's notable. What's something new that you've learned? >> In my case, it was really just spending some time in the last few months getting to know our end users a little bit better, consumers, and some of the impact that having free internet television has on their lives, and that's really motivating... (distorted speech) Something as simple as you might take for granted, but lower income people don't necessarily have a TV that works or a hotel room that has a TV that works, or heaven forbid they're homeless and all that, so it's really gratifying to me to see people sort of tuning back into their local media through television, just by offering it on their phone and laptops. >> And what are you going to do as a result of that? Take a different action, what's the next step for you, what's the action item? >> Well we're hoping, once our product gets filled out with the major networks, et cetera, that we actually provide a community attachment to it, so that we have over-the-air television channels is the main part of the app, and then a side part of the app could be any IP stream, from city council meetings to high schools, to colleges, to local community groups, local, even religious situations or festivals or whatever, and really try to tie that in. We'd really like to use local television as a way to strengthening all local media and local communities, that's the vision at least. >> It's a great mission you guys have at Didja, thanks for sharing that. Sarbjeet, what have learned over the past quarter, three months that was notable for you and the impact and something that changed you a little bit? >> What actually I have gravitated towards in last three to six months is the blockchain, actually. I was light on that, like what it can do for us, and is there really a thing behind it, and can we leverage it. I've seen more and more actually usage of that, and sort of full SCM, supply chain management and healthcare and some other sort of use cases if you will. I'm intrigued by it, and there's a lot of activity there. I think there's some legs behind it, so I'm excited about that. >> And are doing a blockchain project as a result, or are you still tire-kicking? >> No actually, I will play with it, I'm a practitioner, I play with it, I write code and play with it and see (Jim laughs) what does that level of effort it takes to do that, and as you know, I wrote the Alexa scale couple of weeks back, and play with AI and stuff like that. So I try to do that myself before I-- >> We're hoping blockchain helps even out the TV ad economy and gets rid of middle men and makes more trusting transactions between local businesses and stuff. At least I say that, I don't really know what I'm talking about. >> It sounds good though. You get yourself a new round of funding on that sound byte alone. JJ, what have you learned in the past couple months that's new to you and changed you or made you do something different? >> I've learned over the last few months, OSS Capital is a few months and change old, and so just kind of getting started on that, and it's really, I think potentially more than one decade, probably multi-decade kind of mostly consensus building effort. There's such a huge lack of consensus and agreement in the industry. It's a fascinatingly polarizing area, the sort of general topic of open source technology, economics, value creation, value capture. So my learnings over the past few months have just intensified in terms of the lack of consensus I've seen in the industry. So I'm trying to write a little bit more about observations there and sort of put thoughts out, and that's kind of been the biggest takeaway over the last few months for me. >> I'm sure you learned about all the lawyer conversations, setting up a fund, learnings there probably too right, (Jim laughs) I mean all the detail. All right, JJ, thanks so much, Sarbjeet, Jim, thanks for joining me on this Power Panel, cloud conversation impact, to entrepreneurship, open source. Jim Long, Sarbjeet Johal and Joseph Jacks, JJ, thanks for joining us, theCUBE Conversation here in Palo Alto, I'm John Furrier, thanks for watching. >> Thanks John. (lively classical music)