Live from Berlin Germany, it's the queue Covering NetApp insight 2017 brought to you by Neda Welcome back to the cubes live coverage of net app insight here in Berlin Germany I'm your host Rebecca Knight along with my co-host Peter Burris. We are joined by Mark Bregman. He is the CTO of net app Thanks so much for coming on the cube Thanks for taking the time so you have been recently looking into your crystal ball to predict the future and you have some some fun sometimes counterintuitive Predictions about what we're going to be seeing in the next Year and decade to come right so so your first pitch in you said data will become Self-aware right what do you mean by that? Well the title is kind of provocative really the idea is that? Data is going to carry with it much more of its metadata Metadata becomes almost more important than the data in many cases and we can anticipate Sort of architectures in which the data drives the processing whereas today? We always have data is sort of a pile of data over here And then we have a process that we execute against the data that's our been our tradition in the computing world for a long long time as data becomes more self-aware the data as it passes through Will determine what processes get executed on it? So let me give you a simple analogy from a different field from the past in The communications world we used to have circuit switched systems There was some central authority that understood the whole network If you and I wanted to communicate it would figure out the circuit set up the circuit And then we would communicate and that's sort of similar to traditional Processing of data the process knows everything it wants to do it knows where to find the data. It does that it puts It somewhere else But in the communications world we move to packets which data, so now the packet the data Carries with it the information about what should happen to it And I no longer have to know everything about the network nobody has to know everything about the network I pass it to the nearest neighbor who says well I don't know where it's ultimately going, but I know it's going generally in that direction and eventually it gets there now Why is that better? It's very robust it's much more scalable and Particularly in a world where the rules might be changing. I don't have to necessarily redo the program I can change the the markup if you will the tagging of the data You can think of different examples imagine the data That's sitting in a autonomous vehicle and there's an accident now There are many people who want access to that data the insurance company the authorities the manufacturer the data has contained within it the Knowledge of who can do what would that data? So I don't have to now have a separate program that can determine Can I use that data or not the data says sorry you're not allowed to see this. This is private data You can't see this part of it Maybe the identify our data for the obviously the insurance company needs to know who the car owner is But maybe they don't need to know something else like where I came from The authorities might need both well he came from a bar So you can imagine that as an example if you the implications, yes marker are important for example if I Wanted to develop an application. That would be enhanced by having access to data I had to do programming to get to that data because some other application control that data and that data was defined contextually by that application right and so everything was handled by the application by moving the metadata into the data now I can bring that data to my Application more easily less overhead and that's crucial because the value of data accretes It grows as you can combine it in new and interesting ways so by putting the metadata end of the data I can envision a world where it becomes much faster much more Fasil to combine data and new and Exactly it. Also is easier to move the Processing through the data to the data because the processing is no longer a monolithic program It's some large set of micro services and the data organizes which ones to execute So I think we'll see I mean this is not a near-term prediction This is not one for next year because it requires rethinking How we think about data and processing, but I think we'll see it with the emergence of micro services compositional programming Metadata together with the data will see more functional programs little programs well That's your quick rush before we go on to the next one. It's almost like in the early night or the late 1970s It was networks of devices ARPANET the became the Internet and then the web was networks of pages And then we moved into networks of application services Do you foresee a day where it's going to be literally networks of data? Yes, and in fact That's a great example because if you think about what happened in the evolution of the web through what we called web 2.0 That the pages were static data They came alive in the web 2.0, and there was a much less of a distinction between the data and the program In the web layer right so that's what we're saying we see that emerging even further Next prediction was about virtual machines becoming rideshare machines well this is somewhat complementary to the first one they all kind of fit together and Here the idea is you know if we go back in the earlier days of IT it wasn't that long ago that if you needed? Something you ordered the server, and you installed it you owned it and then we got to the model of the public cloud, which is like a rental and by the same analogy if in the past if I wanted a vehicle I had to buy it and Then the rental car agencies came up, and I said well, you know when I go to Berlin I'm not gonna buy a car for three days I'll rent a car, but I can choose which car I want do I want the BMW, or do I want you know of Volkswagen That's very similar to the way the cloud works today. I pick what instances I want and They they meet my needs And if I make the right choice great and by the way I pay for it while I have it not for the work It's getting done so if I forget to return that instance. I'm still getting charged But the rideshare is kind of like uber and we're starting to see that with things like serverless computing In the model that I say I want to get this work done The infrastructure decides what shows up in the same way that when I call uber I don't get to pick what car shows up they send me the one that's most convenient for them and me and I get charged for the work going from point A to point B. Not for the amount of time There's some differentiation if there is so cool Ah, they come to that and and so that's more like a rideshare But as you point out even in the rideshare world. I have some choices. I can't choose if I want a large SUV I might get a BMW SUV or I might get a Mercedes SUV I can't choose that I can't choose it the silver or black But I get a higher class and what we're seeing with the cloud Or these kind of instances virtual solutions is they are also becoming more specialized I might it might be that for a particular workload I want some instance that has have GPUs in them or some neural chip or something else In much the same way that The rental model would say go choose the exact one you want The rideshare model would say I need to get this work done and the infrastructure might decide this is best serviced by five instances with GPU or Because of availability and cost maybe it's 25 instances of standard processors because you don't care about how long it takes so It's this compromise and it's really very analogous to the rideshare model now coming back to the earlier discussion as The units of work gets smaller and smaller and smaller and become really micro services Now I can imagine the data driving that decision hailing the cab hailing the rideshare and driving What needs to be done? So that's why I see them in somewhat complementary and so what's the upshot though? For the employee and for the company I think there are two things one is you got to make the right decision? You know if I were to use uber to commute to Sunnyvale every day It'd break the bank, and it would be kind of stupid so for that particular task I own my vehicle But if I'm gonna go to Tahoe for the weekend, and I meet an SUV I'm not gonna buy one neither am I going to take an uber I'm in a rent one because that's the right vehicle on the other hand when I'm going from you know where I live to the marina within San Francisco, that's a 15 minute drive I On demand I take an uber and I don't really care now if I have 10 friends I might pick a big one or a small one But again that the distinction is there so I think for companies They need to understand the implications and a lot of times as with many people they make the wrong initial choice And then they have then they learn from it so You know there are people who take uber everywhere And I talked and I said I had a friend who was commuting to HP every day by uber from the city from San Francisco That just didn't make sense he kind of knew that but The next one is data will grow faster than the ability to transport it, but that's ok it doesn't sound ok it Doesn't sound ok and for a long time. We've worried about that. We've done compression, and we've done all kinds of things We've built bigger pipes And we've but we were fundamentally transporting data between data centers or more recently between the data center and the cloud big chunks of data What this really talks about is with the emergence of quality IOT in a broad sense? Telematics IOT digital health many different cases there's going to be more and more and more data both generated and ultimately stored at the edge and That will not be able to be shipped all of that will not be able to be shipped back to the core And it's okay not to do that because there's also Processing at the edge so in an autonomous vehicle where you may be generating 20 megabytes per hour or more You're not gonna ship that all back You're gonna store it you're gonna do some local processing you're gonna send the summary of it the appropriate summary back But you're also gonna keep it there for a while because maybe there's an accident and now I do need all that data I didn't ship it back from every vehicle But that one I care about and now I'm gonna bring it back or I'm gonna do some different processing than I originally Thought I would do so again the ability to Manage this is going to be important, but it's managed in a different way. It means we need to figure out ways to do overall Data lifecycle management all the way from the edge where historically that was a silo we didn't care about it Probably all the way through the archive or through the cloud where we're doing machine learning rules generation and so on but it also suggests that we're going to need to do a better job of Discriminating or demarcating different characteristic yen classes of data, and so that data at the edge Real-world data that has real-world implications right now is different from data that summarizes business events which is different from data that Summarized as things models that might be integrated something somewhere else And we have to do a better job of really understanding the relationships between data It's use its asset characteristics etcetera, would you agree with that absolutely and maybe you see the method in my madness now? Which is that data will have? Associated with it the metadata that describes that so that I don't misuse it you know think about The video data off of a vehicle I might want to have a sample of that every I don't know 30 seconds, but now if there's really a problem and it may be not an accident Maybe it's a performance problem. You skidded I'd like to go back and see why was there a Physical issue with the vehicle that I need to think about as an engineering problem was it Your driving ability was it a cat jumped in front of the car so But I need to be able to as you pointed out in a systematic way distinguish what data I'm looking at and where it belongs and where it came from The final prediction it concerns the evolution from Big Data to huge data so that is Really driven by the Increasing need we have to do machine learning AI Very large amounts of data being analyzed in near real time to meet new needs for business And there's again a little like many of these things There's a little bit of a feedback loop so that drives us to new architectures for example being able to do in memory analytics But in-memory analytics with all that important data. I want to have persistence technologies are coming along like Storage class memories that are allowing us to build persistent storage persistent memory We'll have to re our Kotak the applications, but at the same time that persistent memory data I don't want to lose it so it has to be thought of also as a part of the storage system Historically we've had systems the compute system, and there's a pipe and there's a storage system And they're separate they're kind of coming together, and so you're seeing the storage Impinge on the system the compute system our announcement of Plexus store acquisition is how we're getting there But at the same time you see what might have been thought of is the memory of the computer System really be an extended part of the storage system with all the things related to copy management backup and and And so on so that's really what that's talking about and you know it's being driven by another factor I think which is a higher level factor. We started in the first 50 years of the IT industry was all about automating processes That ran the business they didn't change the business. They made it more efficient accounting systems etc since probably 2000 there's been a little bit of a shift Because of the web and mobile to say oh I can use this to change the relationship with my customer Customer in density I can use mobile and and I can change the banking business Maybe you don't ever come to the bank for cash anymore even to an ATM because they've changed that The wave that's starting now which is driving This is the realization in many organizations, and I truly believe eventually in all organizations that They can have new data-driven businesses That are transforming their fundamental view of their business so an example I would use is imagine a shoe maker a shoe manufacturer well for 50 years. They made better shoes They had better distribution, and they could do better inventory management and get better cost and all of that with IT in the last Seven or ten years, they've started to be able to build a relationship with their client. Maybe they put some Sensors in the shoe, and they're doing you know Fitbit like stuff mostly for them That was about a better client relationship, so they could sell better shoes cuz I wrench eiated now The next step is what happens if they wake up and say wait a minute We could take all this data and sell it to the insurance companies or healthcare companies or the city planners Because we now know where everyone's walking all the time That's a completely different business But that requires new kind of lytx that we can't almost not imagine in the current storage model so it drives these new architectures And there is one more prediction, okay? Which is that and it comes back again? It kind of closed the whole cycle as we see these Intelligence coming to the data and new processing forms and so on we also need a way to change data management to give us really Understanding of data through its whole lifecycle one of the one example would be how can I ensure? That I understand the chain of custody of data the example of an automobile there's an accent well How do I know that data was an alter or? how can I know whose touch this data along the way because I might have an audit trail and So we see the emergence of a new Distributed and mutable management framework if when I say those two words together you probably think Blockchain which is the right thing to think but it's not the blockchain. We know today there may be something It's something like that But it will be a distributed and immutable ledger that will give us new ways to access and understand our data Once you open up the once you open up Trying to get the metaphor once you decide to put the metadata next to the data Then you're going to decide to put a lot more control information in that metadata Exactly, so this is just an extension said it kind of closes the loop exactly Mark well, thanks so much for coming on the show and for talking about the future with us It was really fun to have you on the show we should come back in a year and see if maybe you're right exactly exactly Thank you. I'm Rebecca night. We will have more from NetApp insight. Just after this