>> Announcer: From around the globe it's theCUBE. Presenting the Convergence of File and Object brought to you by Pure Storage. >> Okay now we're going to get the customer perspective on object and we'll talk about the convergence of file and object, but really focusing on the object pieces this is a content program that's being made possible by Pure Storage and it's co-created with theCUBE. Christopher CB Bohn is here. He's a lead architect for MicroFocus the enterprise data warehouse and principal data engineer at MicroFocus. CB welcome good to see you. >> Thanks Dave good to be here. >> So tell us more about your role at Microfocus it's a pan Microfocus role because we know the company is a multi-national software firm it acquired the software assets of HP of course including Vertica tell us where you fit. >> Yeah so Microfocus is you know, it's like I can says it's wide, worldwide company that it sells a lot of software products all over the place to governments and so forth. And it also grows often by acquiring other companies. So there is there the problem of integrating new companies and their data. And so what's happened over the years is that they've had a number of different discreet data systems so you've got this data spread all over the place and they've never been able to get a full complete introspection on the entire business because of that. So my role was come in, design a central data repository and an enterprise data warehouse, that all reporting could be generated against. And so that's what we're doing and we selected Vertica as the EDW system and Pure Storage FlashBlade as the communal repository. >> Okay so you obviously had experience with with Vertica in your previous role, so it's not like you were starting from scratch, but paint a picture of what life was like before you embarked on this sort of consolidated approach to your data warehouse. Was it just dispared data all over the place? A lot of M and A going on, where did the data live? >> CB: So >> Right so again the data is all over the place including under people's desks and just dedicated you know their own private SQL servers, It, a lot of data in a Microfocus is one on SQL server, which has pros and cons. Cause that's a great transactional database but it's not really good for analytics in my opinion. So but a lot of stuff was running on that, they had one Vertica instance that was doing some select reporting. Wasn't a very powerful system and it was what they call Vertica enterprise mode where it had dedicated nodes which had the compute and storage in the same locus on each server okay. So Vertica Eon mode is a whole new world because it separates compute from storage. Okay and at first was implemented in AWS so that you could spin up you know different numbers of compute nodes and they all share the same communal storage. But there has been a demand for that kind of capability, but in an on-prem situation. Okay so Pure storage was the first vendor to come along and have an S3 emulation that was actually workable. And so Vertica worked with Pure Storage to make that all happen and that's what we're using. >> Yeah I know back when back from where we used to do face-to-face, we would be at you know Pure Accelerate, Vertica was always there it stopped by the booth, see what they're doing so tight integration there. And you mentioned Eon mode and the ability to scale, storage and compute independently. And so and I think Vertica is the only one I know they were the first, I'm not sure anybody else does that both for cloud and on-prem, but so how are you using Eon mode, are you both in AWS and on-prem are you exclusively cloud? Maybe you could describe that a little bit. >> Right so there's a number of internal rules at Microfocus that you know there's, it's not AWS is not approved for their business processes. At least not all of them, they really wanted to be on-prem and all the transactional systems are on-prem. And so we wanted to have the analytics OLAP stuff close to the OLTP stuff right? So that's why they called there, co-located very close to each other. And so we could, what's nice about this situation is that these S3 objects, it's an S3 object store on the Pure Flash Blade. We could copy those over if we needed it to AWS and we could spin up a version of Vertica there, and keep going. It's like a tertiary GR strategy cause we actually have a, we're setting up a second, Flash Blade Vertica system geo located elsewhere for backup and we can get into it if you want to talk about how the latest version of the Pure software for the Flash Blade allows synchronization across network boundaries of those Flash Blade which is really nice because if, you know there's a giant sinkhole opens up under our Koll of facility and we lose that thing then we just have to switch to DNS. And we were back in business of the DR. And then the third one was to go, we could copy those objects over to AWS and be up and running there. So we're feeling pretty confident about being able to weather whatever comes along. >> Yeah I'm actually very interested in that conversation but before we go there. you mentioned you want, you're going to have the old lab close to the OLTP, was that for latency reasons, data movement reasons, security, all of the above. >> Yeah it's really all of the above because you know we are operating under the same sub-net. So to gain access to that data, you know you'd have to be within that VPN environment. We didn't want to going out over the public internet. Okay so and just for latency reasons also, you know we have a lot of data and we're continually doing ETL processes into Vertica from our production data, transactional databases. >> Right so they got to be approximate. So I'm interested in so you're using the Pure Flash Blade as an object store, most people think, oh object simple but slow. Not the case for you is that right? >> Not the case at all >> Why is that. >> This thing had hoop It's ripping, well you have to understand about Vertica and the way it stores data. It stores data in what they call storage containers. And those are immutable, okay on disc whether it's on AWS or if you had a enterprise mode Vertica, if you do an update or delete it actually has to go and retrieve that object container from disc and it destroys it and rebuilds it, okay which is why you don't, you want to avoid updates and deletes with vertica because the way it gets its speed is by sorting and ordering and encoding the data on disk. So it can read it really fast. But if you do an operation where you're deleting or updating a record in the middle of that, then you've got to rebuild that entire thing. So that actually matches up really well with S3 object storage because it's kind of the same way, it gets destroyed and rebuilt too okay. So that matches up very well with Vertica and we were able to design the system so that it's a panda only. Now we have some reports that we're running in SQL server. Okay which we're taking seven days. So we moved that to Vertica from SQL server and we rewrote the queries, which were had, which had been written in TC SQL with a bunch of loops and so forth and we were to get, this is amazing it went from seven days to two seconds, to generate this report. Which has tremendous value to the company because it would have to have this long cycle of seven days to get a new introspection in what they call the knowledge base. And now all of a sudden it's almost on demand two seconds to generate it. That's great and that's because of the way the data is stored. And the S3 you asked about, oh you know it, it's slow, well not in that context. Because what happens really with Vertica Eon mode is that it can, they have, when you set up your compute nodes, they have local storage also which is called the depot. It's kind of a cache okay. So the data will be drawn from the Flash Blade and cached locally. And that was, it was thought when they designed that, oh you know it's that'll cut down on the latency. Okay but it turns out that if you have your compute nodes close meaning minimal hops to the Flash Blade that you can actually tell Vertica, you know don't even bother caching that stuff just read it directly on the fly from the from the Flash Blade and the performance is still really good. It depends on your situation. But I know for example a major telecom company that uses the same topologies we're talking about here they did the same thing. They just dropped the cache cause the Flash Blade was able to deliver the data fast enough. >> So that's, you're talking about that's speed of light issues and just the overhead of switching infrastructure is that, it's eliminated and so as a result you can go directly to the storage array? >> That's correct yeah, it's like, it's fast enough that it's almost as if it's local to the compute node. But every situation is different depending on your needs. If you've got like a few tables that are heavily used, then yeah put them in the cache because that'll be probably a little bit faster. But if you're have a lot of ad hoc queries that are going on, you know you may exceed the storage of the local cache and then you're better off having it just read directly from the, from the Flash Blade. >> Got it so it's >> Okay. >> It's an append only approach. So you're not >> Right >> Overwriting on a record, so but then what you have automatically re index and that's the intelligence of the system. how does that work? >> Oh this is where we did a little bit of magic. There's not really anything like magic but I'll tell you what it is I mean. ( Dave laughing) Vertica does not have indexes. They don't exist. Instead I told you earlier that it gets a speed by sorting and encoding the data on disk and ordering it right. So when you've got an append-only situation, the natural question is well if I have a unique record, with let's say ID one, two, three, what happens if I append a new version of that, what happens? Well the way Vertica operates is that there's a thing called a projection which is actually like a materialized columnar data store. And you can have a, what they call a top-K projection, which says only put in this projection the records that meet a certain condition. So there's a field that we like to call a discriminator field which is like okay usually it's the latest update timestamp. So let's say we have record one, two, three and it had yesterday's date and that's the latest version. Now a new version comes in. When the data at load time vertical looks at that and then it looks in the projection and says does this exist already? If it doesn't then it adds it. If it does then that one now goes into that projection okay. And so what you end up having is a projection that is the latest snapshot of the data, which would be like, oh that's the reality of what the table is today okay. But inherent in that is that you now have a table that has all the change history of those records, which is awesome. >> Yeah. >> Because, you often want to go back and revisit, you know what it will happen to you. >> But that materialized view is the most current and the system knows that at least can (murmuring). >> Right so we then create views that draw off from that projection so that our users don't have to worry about any of that. They just get oh and say select from this view and they're getting the latest greatest snapshot of what the reality of the data is right now. But if they want to go back and say, well how did this data look two days ago? That's an easy query for them to do also. So they get the best of both worlds. >> So could you just plug any flash array into your system and achieve the same results or is there anything really unique about Pure? >> Yeah well they're the only ones that have got I think really dialed in the S3 object form because I don't think AWS actually publishes every last detail of that S3 spec. Okay so it had, there's a certain amount of reverse engineering they had to do I think. But they got it right. When we've, a couple maybe a year and a half ago or so there they were like at 99%, but now they worked with Vertica people to make sure that that object format was true to what it should be. So that it works just as if Vertica doesn't care, if it is on AWS or if it's on Pure Flash Blade because Pure did a really good job of dialing in that format and so Vertica doesn't care. It just knows S3, doesn't know what it doesn't care where it's going it just works. >> So the essentially vendor R and D abstracted that complexity so you didn't have to rewrite the application is that right? >> Right, so you know when Vertica ships it's software, you don't get a specific version for Pure or AWS, it's all in one package, and then when you configure it, it knows oh okay well, I'm just pointed at the, you know this port, on the Pure storage Flash Blade, and it just works. >> CB what's your data team look like? How is it evolving? You know a lot of customers I talked to they complain that they struggled to get value out of the data and they don't have the expertise, what does your team look like? How is it, is it changing or did the pandemic change things at all? I wonder if you could bring us up to date on that? >> Yeah but in some ways Microfocus has an advantage in that it's such a widely dispersed across the world company you know it's headquartered in the UK, but I deal with people I'm in the Bay Area, we have people in Mexico, Romania, India. >> Okay enough >> All over the place yeah all over the place. So when this started, it was actually a bigger project it got scaled back, it was almost to the point where it was going to be cut. Okay, but then we said, well let's try to do almost a skunkworks type of thing with reduced staff. And so we're just like a hand. You could count the number of key people on this on one hand. But we got it all together, and it's been a traumatic transformation for the company. Now there's, it's one approval and admiration from the highest echelons of this company that, hey this is really providing value. And the company is starting to get views into their business that they didn't have before. >> That's awesome, I mean, I've watched Microfocus for years. So to me they've always had a, their part of their DNA is private equity I mean they're sharp investors, they do great M and A >> CB: Yeah >> They know how to drive value and they're doing modern M and A, you know, we've seen what they what wait, what they did with SUSE, obviously driving value out of Vertica, they've got a really, some sharp financial people there. So that's they must have loved the the Skunkworks, fast ROI you know, small denominator, big numerator. (laughing) >> Well I think that in this case, smaller is better when you're doing development. You know it's a two-minute cooks type of thing and if you've got people who know what they're doing, you know I've got a lot of experience with Vertica, I've been on the advisory board for Vertica for a long time. >> Right And you know I was able to learn from people who had already, we're like the second or third company to do a Pure Flash Blade Vertica installation, but some of the best companies after they've already done it we are members of the advisory board also. So I learned from the best, and we were able to get this thing up and running quickly and we've got you know, a lot of other, you know handful of other key people who know how to write SQL and so forth to get this up and running quickly. >> Yeah so I mean, look it Pure is a fit I mean I sound like a fan boy, but Pure is all about simplicity, so is object. So that means you don't have to ra, you know worry about wrangling storage and worrying about LANs and all that other nonsense and file names but >> I have burned by hardware in the past you know, where oh okay they built into a price and so they cheap out on stuff like fans or other things in these components fail and the whole thing goes down, but this hardware is super good quality. And so I'm happy with the quality of that we're getting. >> So CB last question. What's next for you? Where do you want to take this initiative? >> Well we are in the process now of, we're when, so I designed a system to combine the best of the Kimball approach to data warehousing and the inland approach okay. And what we do is we bring over all the data we've got and we put it into a pristine staging layer. Okay like I said it's a, because it's append-only, it's essentially a log of all the transactions that are happening in this company, just as they appear okay. And then from the Kimball side of things we're designing the data marts now. So that's what the end users actually interact with. So we're taking the, we're examining the transactional systems to say, how are these business objects created? What's the logic there and we're recreating those logical models in Vertica. So we've done a handful of them so far, and it's working out really well. So going forward we've got a lot of work to do, to create just about every object that the company needs. >> CB you're an awesome guest really always a pleasure talking to you and >> Thank you. >> congratulations and good luck going forward stay safe. >> Thank you, you too Dave. >> All right thank you. And thank you for watching the Convergence of File and Object. This is Dave Vellante for theCUBE. (soft music)

>>from our studios in the heart of Silicon Valley. Palo ALTO, California It is a cute conversation. >>Hi, and welcome to the Cube studios for another cube conversation where we go in depth with thought leaders driving innovation across the technology industry. I'm your host, Peter Boris. Digital businesses affecting every enterprise of every size, small and large, and the types of solutions that required the types of outcomes that are being pursued are extremely complex and require an enormous amount of work from some of the best and brightest people on the business side as well as the technology side. And that means not just from a large company. It means from an entire ecosystem of potential sources of genius and insight and good hard work. So the consequence for every enterprises, how do they cobble together that collection of experts and capabilities that are gonna help them transform their business more successfully, Maur completely and more certainly than they would otherwise? And that's we're gonna talk about today. Today we're here with Kelly Ireland, who's the founder and C E o. C. B Technologies. Kelly. Welcome to the >>Cube. Thank you, Peter. Happy to be here, >>so let's start by finding a little bit about CV Technologies to also about what you do. >>Um, I have a IittIe background, so I have been in it for 40 years. In 2001 I decided I had a better idea of how to both support clients as well as my employees. So I opened CB Technologies were value added reseller, um, and then say about five years ago, I decided to do some transforming of the company itself. I saw what was going on in the industry, and I thought this was the time for us to get going. Turned out we were a little early, but we wanted to transform from what you would call it the value added reseller two systems integrator. Because that was the only words what they had for. You know what that end result would be? Now I've heard it's the, um, domain expert integrator, which we like a lot better. And what we've done is gone from this value add, which we've all seen over the last couple of decades, into actually engineering solutions, and mostly with consortiums, which will talk about of the O. T. I t. Convergence and what's going to be needed for that to make our customers successful. >>Well, you just described. In many respects, the vision that businesses have had and how it's changed over years were first. The asset was the hardware. Hence the var. Today, the asset really is the date of the application and how you're going to apply that to change the way your business operates the customer experiences, you provide the profitability that you're able to return back to shareholders. So let's dig into this because that notion of data that notion of digital transformation is especially important in a number of different names, perhaps no more important than in the whole industrial and end of things domain. That intersection of I t know Tia's, you said, Tell us a little bit about what you're experiencing with your customers as they try to think about new ways of applying technology technology rich data to their business challenges. >>We'll use the perfect word you said dig, because this is all about layers. It's all about it was technology and software. Now it's about technology, software and integration. In fact, the conversations were having with our clients. Right now we don't even talk about a no Yim's name. Where before you would. But we haven't our head. What? We know what would be best. What we look at now is the first thing you do is go in and sit down with the client. And not only with the client, the you know, the executives or the C I or the C T. O's et cetera, but the employees themselves. Because what we've seen with I I I o t o t i t Convergence, it's You have to take into account what the worker needs and the people that are addressing it that way. Um, this project that we started with Hewlett Packard Enterprise, they started up what we call the refinery of the future. It could be acts of the future. It doesn't really matter. But it was getting at least up to five use cases with a consortium of partner companies that could go address five different things within the refinery. And the reason that I think it's been so successful is that the owner, the CEO Doug Smith and the VP of ops Linda Salinas, immediately wrap their arms around bringing employees. They're a small company there, maybe 50. They brought half of them to HPD Lab to show them what a smart pump laws for their chemical plant text. More chemical in Galina Park in Texas. Starting from that, it was like they put him on a party bus, took them down, put them in the lab, told them, showed them what a smart pump was and all of a sudden the lights turned on for the workers. These are people that have been, you know, manual valves and turning knobs and, you know, looking at computer screens they'd never seen what a smart, censored pump waas all of it sudden on the drive back to the company, ideas started turning. And then HP took it from there, brought in partners, sat everybody in the room, and we started feathering out. Okay, what's needed. But let's start with what the client needs. What do those different business users within the chemical plant need, and then build use cases from that? So we ended up building five use cases. >>Well, so what? Get another five years cases in a second? But you just described something very interesting, and I think it's something that partners have historically been able to do somewhat uniquely on that is that the customer journey is not taken by just an individual within the business. What really happens is someone has an idea. They find someone, often a partner, that can help them develop that idea. And then they go off and they recruit others within their business and a local partner that has good domain expertise at the time. And energy and customer commitment could be an absolutely essential feature of building the consensus within the organization to really accelerate that customer journey. If I got that right? >>Absolutely, absolutely. And what we saw with Refinery of the Future was getting those partnerships HP East started. It created the project kind of through information out to many of their ecosystem partners trying to gain interest because the thing was is this was kind of our bet was a very educated bet, but it's our bet to say, Yeah, we think this makes sense. So, you know, like I said, I think there's about 14 partners that all joined in both on the I t om side the ot oh am side and then both Deloitte and CB Technologies for the S. I and like expert domain expert integration where you really get into How do you tie OT and I t together? >>All right, so we've got this situation where this is not As you said, It's not just in the refining process, manufacturing businesses. It's in a lot of business. But in this particular one, you guys have actually fashioned what you call the refinery of of the future has got five clear use cases. Just give us an example of what those look like and how you've been RCB technology has been participated in the process of putting those together. >>Um, the 1st 1 was pretty wrapped around Predictive Analytics, and that was led by Deloitte and has a whole host of OT and I t integration on it >>again, not limited to process manufacturing at all >>at all, but and a good group, you know, you have national instruments, Intel flow. Serve. Oh, it's ice off Snyder Electric, PTC riel, where they're such a host >>of the >>consortium and I I think what was most important to start this whole thing was H P E. Came in and said, Here's an MOU. Here's a contract. You all will be contract ID to the overall resorts results. Not just your use case. Not just one or two use cases you're in, but all five because they all can integrate in some sense so >>that all can help. Each of you can help the others think. Problems. Truce. That's the 1st 1 about the 2nd 1 >>The 2nd 1 is video is a sensor that was Intel CB Technologies. I think we have as you're in there as well, doing some of the analytics, some P T. C. And what that was all about was taking video. And, you know, taking a use case from Linda and saying, Where where do you need some sort of video analytics Taking that processing it and what we ended up doing with that one was being able to identify, you know, animals or aggressive animals within the train yard. A downed worker transients that shouldn't be there because we can't decipher between you know, someone that's in text marks p p ease versus somebody that's in street clothes. So taking all that analyzing the information, the pictures, training it to understand when it needs to throw and alert >>lot of data required for that. And that's one of the major major drivers of some of the new storage technologies out there. New fabrics that are out there. How did that play? A role? >>As you can imagine, H p E is the under underlying infrastructure across the entire refinery. The future from compute with the, uh, EJ data center into the Reuben network into nimble storage for storing on site. Um, what we're finding, no matter who we talked to in the industry, it is. Most of them still want to keep it on Prem. In some sense, security. They're still all extremely cautious. So they want to keep it on Prem. So having the nimble storage right in the date, having the edge data center having everything in the middle of this chemical plant was absolutely a necessity. And having all of that set up having my team, which was the C B Tech team that actually did all the integration of setting up the wireless network, because guess what? When you're in a different kind of environment, not inside a building, you're out where there's metal pumps. There's restrictions because ah, flash could cause an explosion so intrinsically safe we had to set up all that and determined how? How could we get the best coverage? Especially? We want that video signal to move quite fast over the WiFi. How do we get all that set up? So it takes the most advantage of, you know, the facility and the capabilities of the Aruban network. >>So that's 12345 quickly were >>three worker safety, which hasn't started yet. We're still waiting for one of the manufacturers to get the certification they need. Um, four we have is connected worker, which is on fire, having a work >>of connected worker on fire and worker >>safety. >>Yeah, they don't sound, but just think of all the data and having the worker have it right at his fingertips. And, oh, by the way, hands free. So they're being ableto to take in all this data and transmit data, whether it's by voice or on screen back >>from a worker central perspective, from one that sustains the context of where the worker is, what stress there under what else? They've got to do it said. >>And and what are they trying to complete and how quickly? And that's where right now we have r A y that's in the 90% which is off the chart. But it's and and what's great about being at Text Mark is we actually can prove this. I can have somebody walk with me, a client that wants to look at it. They can go walk the process with me, and they will immediately see that we reduce the time by 90%. >>So I've given your four. What's the 5th 1? >>Acid intelligence, which is all about three D Point Cloud three D visualization. Actually being able to pull up a smart pump. You know it really? Any pump, you scan the facility you converted into three D and then in the program that we're using, you can actually pull up a pump. You can rotate it 360 degrees. It's got a database behind it that has every single bit of asset information connected videos, cad cams, P and I. D s. For the oil and gas industry. Everything's in their e mails could be attached to it, and then you can also put compliance reports. So there you might need to look a corrosion. One of those tests that they do on a you know, annual or every five year basis. That's point and click. You pull it up and it tells you where it sits, and then it also shows you green, yellow, red. Anything in red is immediate, attest that tension yellow is you need to address it greens. Everything's 100% running. >>So the complexity that we're talking about, the kind of specificity of these solutions, even though they can be generalized. And you know, you talked about analytics all the way out to asset optimization Intel intelligence. There are We can generalize and structure, but there's always going to be, it seems to us there's going to be a degree of specificity that's required, and that means we're not gonna talk about package software that does this kind of stuff. We're talking about sitting down with a customer with a team of experts from a lot of different places and working together and applying that to achieve customer outcome. So I got that right >>absolutely, and what we did with the consortium looking at everything. How they first addressed it was right along that line, and if you look at software development, agile following agile process, it's exactly what we're doing in four I I o T o R O T I t Convergence, because if you don't include all of those people, it's never going to be successful. I heard it a conference the other day that said, POC is goto I ot to die, and it's because a lot of people aren't addressing it the right way. We do something called Innovation Delivery as a service, which is basically a four day, 3 to 4 day boot camp. You get all the right people in, in in the room. You pull in everything from them. You boot out the executive team partway through, and you really get in depth with workers and you have them say what they wouldn't say in front of their bosses that this happened with Doug and Linda and Linda said it was mind blowing. She goes. I didn't realize we had so many problems because she came back in the room and there was a 1,000,000 stickies. And then she said, the more she read it and the more you know, we refined it down, she said it was absolutely delivered, you know, the use case that she would have eventually ended up with, but loved having all the insights from, >>well, work. Too often, tech companies failed to recognize that there's a difference between inventing something and innovation. Inventing is that engineering act of taking what you know about physics or social circumstance Secreting hardware software innovation is a set of social acts that get the customer to adopt it, get a marketplace to adopt it, change their behaviors. And partners historically have been absolutely essential to driving that innovation, to getting customers to actually change the way to do things and embed solutions in their operations. And increasingly, because of that deep knowledge with customers are trying to doing, they're participating. Maurine, the actual invention process, especially on the softer side of you said, >>Yeah, yeah, I think what's really interesting in this, especially with Coyote. When I look back a few years, I look at cloud and you know everything was cloud and everybody ran to it and everybody jumped in with both feet, and then they got burned. And what we're seeing with this whole thing with I o t you would think we're showing these are lies, return on. Investments were showing all this greatness that can come out of it and and they're very slow at sticking their toe in. But what we've found is no one arrives should say the majority of corporations anymore don't want to jump in and say, Let's do it two or five or $10 million project. We see your power point. No, let's let's depart Owen with with what we're doing, it's, you know, a really small amount of money to go in and really direct our attention at exactly what their problem is. It's not off the shelf. It's but it's off the shelf with customization. It's like we've already delivered on connected worker for oil and gas. But now we're are so starting to deliver multiple other industries because they actually walk through text mark. We could do tours, that text mark. That was kind of the trade off. All these partners brought technology and, you know, brought their intelligence and spent. We were now on two years of proving all this out. Well, they said, Fine, open the kimono will let your customers walk through and see it >>makes text mark look like a better suppliers. >>Well, it's enhanced their business greatly. I can tell you they're just starting a new process in another week. And it was all based on people going through, you know, a client that went through and went. Wait >>a minute. I >>really like this. There are also being able to recruit technologists within the use in industry, which you would think text marks 50 employees. It's a small little plant. It's very specialized. It's very small. They pulled one of the top. Uh, sorry. Lost not. I'm trying to think of what the name >>they're. They're a small number of employees, but the process manufacturing typically has huge assets. And any way you look at it, we're talking about major investments, major monies that require deep expertise. And my guess is the text Mark is able to use that to bring an even smarter and better >>people smarter and better. People that are looking at it going they're ahead of the curve, for they're so far ahead of the curve that they want to be on board were that they're bringing in millennials on they're connected. Worker Carlos is there trainload lead. And he dropped an intrinsically safe camera and it broke and he tried to glue it together, tried to super glue it together. And then he ran back to Linda and he said I broke the case and this case is like £10. They call it the Brick. They gotta lug it up. They got to climb up the train car, leg it up, take a picture that they have sealed the valves on all the cars before they leave. Well, he had used the real where had, you know, device. And he went into Linda and he said, I know there's a camera in there. There's camera capabilities. Can I use that until we get another case? And she's like, Yeah, go ahead. Well, he went through, started using that toe like lean over, say, Take photo. We engineered that it could go directly back to the audit file so that everybody knew the minute that picture was taken, it went back into the audio file. This is where we found the process was reduced by 90% of time. But he turned around and trained his entire team. He wasn't asked to, but he thought, this is the greatest thing. He went in trainable. And now, about every two weeks, Carlos walks in to my team that sits a text mark and comes up with another use case for connected worker. It's amazing. It's amazing what you know were developed right out of the customer by using their workers and then, you know, proactively coming to us going. Hey, I got another idea. Let's add this where I think at version 7.0, for connected worker. Because of that feedback because of that live feed back in production. >>Great story, Kelly. So, once again, Callie Ireland is a co founder and CEO of CB Technologies. Thanks for being on the tube. >>Thank you for having me >>on once again. I wanna thank all of you for joining us for another cute conversation. I'm Peter burgers. See you next time.

>>Welcome back to the big apple, everybody. The Cube's continuous coverage here of MongoDB world 2022. We're at the new Javet center. It's it's quite nice. It was built during the pandemic. I believe on top of a former bus terminal. I'm told by our next guest Tony bear, who's the principal at DB insight of data and database expert, longtime analyst, Tony. Good to see you. Thanks for coming >>On. Thanks >>For having us. You face to face >>And welcome to New York. >>Yeah. Right. >>New York is open for business. >>So, yeah. And actually, you know, it's interesting. We've been doing a lot of these events lately and, and especially the ones in Vegas, it's the first time everybody's been out, you know, face to face, not so much here, you know, people have been out and about a lot of masks >>In, >>In New York city, but, but it's good. And, and this new venue is fantastic >>Much nicer than the old Javits. >>Yeah. And I would say maybe 3000 people here. >>Yeah. Probably, but I think like most conferences right now are kind of, they're going through like a slow ramp up. And like for instance, you know, sapphires had maybe about one third, their normal turnout. So I think that you're saying like one third to one half seems to be the norm right now are still figuring out how we're, how and where we're gonna get back together. Yeah. >>I think that's about right. And, and I, but I do think that that in most of the cases that we've seen, it's exceeded people's expectations at tenants, but anyway sure. Let's talk about Mongo, very interesting company. You know, we've been kind of been watching their progression from just sort of document database and all the features and functions they're adding, you just published a piece this morning in venture beat is time for Mongo to get into analytics. Yes. You know? Yes. One of your favorite topics. Well, can they expand analytics? They seem to be doing that. Let's dig into it. Well, >>They're taking, they've been taking slow. They've been taking baby steps and there's good reason for that because first thing is an operational database. The last thing you wanna do is slow it down with very complex analytics. On the other hand, there's huge value to be had if you would, if you could, you know, turn, let's say a smart, if you can turn, let's say an operational database or a transaction database into a smart transaction database. In other words, for instance, you know, let's say if you're, you're, you're doing, you know, an eCommerce site and a customer has made an order, that's basically been out of the norm. Whether it be like, you know, good or bad, it would be nice. Basically, if at that point you could then have a next best action, which is where analytics comes in. But it's a very lightweight form of analytics. It's not gonna, it's actually, I think probably the best metaphor for this is real time credit scoring. It's not that they're doing your scoring you in real time. It's that the model has been computed offline so that when you come on in real time, it can make a smart decision. >>Got it. Okay. So, and I think it was your article where I, I wrote down some examples. Sure. Operational, you know, use cases, patient data. There's certainly retail. We had Forbes on earlier, right? Obviously, so very wide range of, of use cases for operational will, will Mongo, essentially, in your view, is it positioned to replace traditional R D BMS? >>Well, okay. That's a long that's, that's much, it's >>Sort of a loaded question, but >>That's, that's a very loaded question. I think that for certain cases, I think it will replace R D BMS, but I still, I mean, where I, where I depart from Mongo is I do not believe that they're going to replace all R D D BMSs. I think, for instance, like when you're doing financial transactions, you know, the world has been used to table, you know, you know, columns and rows and tables. That's, it's a natural form for something that's very structured like that. On the other hand, when you take a look, let's say OT data, or you're taking a look at home listings that tends to more naturally represent itself as documents. And so there's a, so it's kind of like documents are the way that let's say you normally see the world. Relational is the way that you would structure the world. >>Okay. Well, I like that. So, but I mean, in the early days, obviously, and even to this day, it's like the target for Mongo has been Oracle. Yeah. Right, right. And so, and then, you know, you talk to a lot of Oracle customers as do I sure. And they are running the most mission, critical applications in the world, and it's like banking and financial and so many. And, and, and, you know, they've kind of carved out that space, but are we, should we be rethinking the definition of, of mission critical? Is that changing? >>Well, number one, I think what we've traditionally associated mission critical systems with is our financial transaction systems and to a less, and also let's say systems that schedule operations. But the fact is there are many forms of operations where for instance, let's say you're in a social network, do you need to have that very latest update? Or, you know, basically, can you go off, let's say like, you know, a server that's eventually consistent. In other words, the, do you absolutely have, you know, it's just like when you go on Twitter, do you naturally see all the latest tweets? It's not the system's not gonna crash for that reason. Whereas let's say if you're doing it, you know, let's say an ATM banking ATM system, that system better be current. So I think there's a delineation. The fact is, is that in a social network, arguably that operational system is mission critical, but it's mission critical in a different way from a, you know, from, let's say a banking system. >>So coming back to this idea of, of this hybrid, I think, you know, I think Gartner calls it H tab hybrid, transactional analytics >>Is changed by >>The minute, right. I mean, you mentioned that in, in your article, but basically it's bringing analytics to transactions bringing those, those roles together. Right. Right. And you're saying with Mongo, it's, it's lightweight now take, you use two other examples in your article, my SQL heat wave. Right. I think you had a Google example as well, DB, those are, you're saying much, much heavier analytics, is that correct? Or >>I we'll put it this way. I think they're because they're coming from a relational background. And because they also are coming from companies that already have, you know, analytic database or data warehouses, if you will, that their analytic, you know, capabilities are gonna be much more fully rounded than what Mongo has at this point. It's not a criticism of a Mongo MongoDB per >>Per, is that by design though? Or ne not necessarily. Is that a function of maturity? >>I think it's function of maturity. Oh, okay. I mean, look, to a certain extent, it's also a function of design in terms of that the document model is a little, it's not impossible to basically model it for analytics, but it takes more, you know, transformation to, to decide which, you know, let's say field in that document is gonna be a column. >>Now, the big thing about some of these other, these hybrid systems is, is eliminating the need for two databases, right? Eliminating the need for, you know, complex ETL. Is, is that a value proposition that will emerge with, with Mongo in your view? >>You know, I, I mean, put it this way. I think that if you take a look at how they've, how Mongo is basically has added more function to its operations, someone talking about analytics here, for instance, adding streaming, you know, adding, adding, search, adding time series, that's a matter of like where they've eliminated the need to do, you know, transformation ETL, but that's not for analytics per se for analytics. I think through, you know, I mean through replication, there's still gonna be some transformation in terms of turning, let's say data, that's, that's formed in a document into something that's represented by columns. There is a form of transformation, you know, so that said, and Mongo is already, you know, it has some NA you know, nascent capability there, but it's all, but this is still like at a rev 1.0 level, you know, I expect a lot more >>Of so refin you, how Amazon says in the fullness of time, all workloads will be in the cloud. And we could certainly debate that. What do we mean by cloud? So, but there's a sort of analog for Mongo that I'll ask you in the fullness of time, will Mongo be in a position to replace data warehouses or data lakes? No. Or, or, or, and we know the answer is no. So that's of course, yeah. But are these two worlds on a quasi collision course? I think they >>More on a convergence course or the collision course, because number one is I said, the first principle and operational database is the last thing you wanna do is slow it down. And to do all this complex modeling that let's say that you would do in a data bricks, or very complex analytics that you would do in a snowflake that is going to get, you know, you know, no matter how much you partition the load, you know, in Atlas, and yes, you can have separate nodes. The fact is you really do not wanna burden the operational database with that. And that's not what it's meant for, but what it is meant for is, you know, can I make a smart decision on the spot? In other words, kinda like close the loop on that. And so therefore there's a, a form of lightweight analytic that you can perform in there. And actually that's also the same principle, you know, on which let's say for instance, you know, my SQL heat wave and Allo DBR based on, they're not, they're predicated on, they're not meant to replace, you know, whether it be exit data or big query, the idea there is to do more of the lightweight stuff, you know, and keep the database, you know, keep the operations, you know, >>Operating. And, but from a practitioner's standpoint, I, I, I can and should isolate you're saying that node, right. That's what they'll do. Sure. How does that affect cuz my understanding is that that the Mon Mongo specifically, but I think document databases generally will have a primary node. Right? And then you can set up secondary nodes, which then you have to think about availability, but, but would that analytic node be sort of fenced off? Is that part of the >>Well, that's actually what they're, they've already, I mean, they already laid the groundwork for it last year, by saying that you can set up separate nodes and dedicate them to analytics and what they've >>As, as a primary, >>Right? Yes, yes. For analytics and what they've added, what they're a, what they are adding this year is the fact to say like that separate node does not have to be the same instance class, you know, as, as, as, as the, >>What, what does that mean? Explain >>That in other words, it's a, you know, you could have BA you know, for instance, you could have a node for operations, that's basically very eye ops intensive, whereas you could have a node let's say for analytics that might be more compute intensive or, or more he, or, or more heavily, you know, configured with, with memory per se. And so the idea here is you can tailor in a node to the workload. So that's, you know what they're saying with, you know, and I forget what they're calling it, but the idea that you can have a different type, you can specify a different type of node, a different type of instance for the analytic node, I think is, you know, is a major step forward >>And that, and that that's enabled by the cloud and architecture. >>Of course. Yes. I mean, we're separating, compute from data is, is, is the starter. And so yeah. Then at that point you can then start to, you know, you know, to go less vanilla. I think, you know, the re you know, the, you know, the, I guess the fruition of this is going to be when they say, okay, you can run your, let's say your operational nodes, you know, dedicated, but we'll let you run your analytic nodes serverless. Can't do it yet, but I've gotta believe that's on the roadmap. >>Yeah. So seq brings a lot of overhead. So you get MQL, but now square this circle for me, cuz now you got Mago talking sequel. >>They had to start doing that some time. I mean, and I it's been a court take I've had from them from the, from the get go, which I said, I understand that you're looking at this as an alternative to SQL and that's perfectly valid, but don't deny the validity of SQL or the reason why we, you know, we need it. The fact is that you have, okay, the number, you know, according to Ty index, JavaScript is the seventh, most popular language. Most SQL follows closely behind at the ninth, most popular language you don't want to cl. And the fact is those people exist in the enterprise and they're, and they're disproportionately concentrated in analytics. I mean, you know, it's getting a little less, so now we're seeing like, you know, basically, you know, Python, the programmatic, but still, you know, a lot of sequel expertise there. It does not make, it makes no sense for Mongo to, to, to ignore or to overlook that audience. I think now they're, you know, you know, they're taking baby steps to start, you know, reaching out to them. >>It's interesting. You see it going both ways. See Oracle announces a Mongo, DB, Mongo. I mean, it's just convergence. You called it not, I love collisions, you know, >>I know it's like, because you thrive on drama and I thrive on can't. We all love each other, but you know, act. But the thing is actually, I've been, I wrote about this. I forget when I think it was like 2014 or 2016. It's when we, I was noticed I was noting basically the, you know, the rise of all these specialized databases and probably Amazon, you know, AWS is probably the best exemplar of that. I've got 15 or 16 or however, number of databases and they're all dedicated purpose. Right. But I also was, you know, basically saw that inevitably there was gonna be some overlap. It's not that all databases were gonna become one and the same we're gonna be, we're gonna become back into like the, you know, into a pan G continent or something like that. But that you're gonna have a relational database that can do JSON and, and a, and a document database that can do relational. I mean, you know, it's, to me, that's a no brainer. >>So I asked Andy Ja one time, I'd love to get your take on this, about those, you know, multiple data stores at the time. They probably had a thousand. I think they're probably up to 15 now, right? Different APIs, different S et cetera. And his response. I said, why don't you make it easier for, for customers and maybe build an abstraction or converge these? And he said, well, it's by design. What if you buy this? And, and what your thoughts are, cuz I, you know, he's a pretty straight shooter. Yeah. It's by design because it allows us as the market moves, we can move with it. And if we, if we give developers access to those low level primitives and APIs, then they can move with, with at market speed. Right. And so that again, by design, now we heard certainly Mongo poo pooing that today they didn't mention, they didn't call out Amazon. Yeah. Oracle has no compunction about specifically calling out Amazon. They do it all the time. What do you make of that? Can't Amazon have its cake and eat it too. In other words, extend some of the functionality of those specific databases without going to the Swiss army. >>I I'll put it this way. You, you kind of tapped in you're, you're sort of like, you know, killing me softly with your song there, which is that, you know, I was actually kind of went on a rant about this, actually know in, you know, come, you know, you know, my year ahead sort of out predictions. And I said, look, cloud folks, it's great that you're making individual SAS, you know, products easy to use. But now that I have to mix and match SAS products, you know, the burden of integration is on my shoulders. Start making my life easier. I think a good, you know, a good example of this would be, you know, for instance, you could take something like, you know, let's say like a Google big query. There's no reason why I can't have a piece of that that might, you know, might be paired, say, you know, say with span or something like that. >>The idea being is that if we're all working off a common, you know, common storage, we, you know, it's in cloud native, we can separate the computer engines. It means that we can use the right engine for the right part of the task. And the thing is that maybe, you know, myself as a consumer, I should not have to be choosing between big query and span. But the thing is, I should be able to say, look, I want to, you know, globally distribute database, but I also wanna do some analytics and therefore behind the scenes, you know, new microservices, it could connect the two wouldn't >>Microsoft synapse be an example of doing that. >>It should be an example. I wish I, I would love to hear more from Microsoft about this. They've been radio silent for about the past two or three years in data. You hardly hear about it, but synapse is actually those actually one of the ideas I had in mind now keep in mind that with synapse, you're not talking about, let's say, you know, I mean, it's, it's obviously a sequel data warehouse. It's not pure spark. It's basically their, it was their curated version of spark, but that's fine. But again, I would love to hear Microsoft talk more about that. They've been very quiet. >>Yeah. You, you, the intent is there to >>Simplify >>It exactly. And create an abstraction. Exactly. Yeah. They have been quiet about it. Yeah. Yeah. You would expect that, that maybe they're still trying to figure it out. So what's your prognosis from Mongo? I mean, since this company IP, you know, usually I, I tell and I tell everybody this, especially my kids, like don't buy a stock at IPO. You'll always get a better chance at a cheaper price to buy it. Yeah. And even though that was true with Mongo, you didn't have a big window. No. Like you did, for instance, with, with Facebook, certainly that's been the case with snowflake and sure. Alibaba, I mean, I name a zillion style was almost universal. Yeah. But, but since that, that, that first, you know, few months, period, this, this company has been on a roll. Right. And it, it obviously has been some volatility, but the execution has been outstanding. >>No question about that. I mean, the thing is, look what I, what I, and I'm just gonna talk on the product side on the sales side. Yeah. But on the product side, from the get go, they made a product that was easy for developers. Whereas let's say someone's giving an example, for instance, Cosmo CB, where to do certain operations. They had to go through multiple services in, you know, including Azure portal with Atlas, it's all within Atlas. So they've really, it's been kinda like design thinking from the start initially with, with the core Mongo DB, you know, you, the on premise, both this predates Atlas, I mean, part of it was that they were coming with a language that developers knew was just Javas script. The construct that they knew, which was JS on. So they started with that home core advantage, but they weren't the only ones doing that. But they did it with tooling that was very intuitive to developers that met developers, where they lived and what I give them, you know, then additional credit for is that when they went to the cloud and it wasn't an immediate thing, Atlas was not an overnight success, but they employed that same design thinking to Atlas, they made Atlas a good cloud experience. They didn't just do a lift and shift the cloud. And so that's why today basically like five or six years later, Atlas's most of their business. >>Yeah. It's what, 60% of the business now. Yeah. And then Dave, on the, on the earning scholar, maybe it wasn't Dave and somebody else in response to question said, yeah, ultimately this is the future will be be 90% of the business. I'm not gonna predict when. So my, my question is, okay, so let's call that the midterm midterm ATLA is gonna be 90% of the business with some exceptions that people just won't move to the cloud. What's next is the edge. A new opportunity is Mongo architecturally suited for the, I mean, it's certainly suited for the right, the home Depot store. Sure. You know, at the edge. Yeah. If you, if you consider that edge, which I guess it is form of edge, but how about the far edge EVs cell towers, you know, far side, real time, AI inferencing, what's the requirement there, can Mongo fit there? Any thoughts >>On that? I think the AI and the inferencing stuff is interesting. It's something which really Mongo has not tackled yet. I think we take the same principle, which is the lightweight stuff. In other words, you'll say, do let's say a classification or a prediction or some sort of prescriptive action in other words, where you're not doing some convolution, neural networking and trying to do like, you know, text, text to voice or, or, or vice versa. Well, you're not trying to do all that really fancy stuff. I think that's, you know, if you're keeping it SIM you know, kinda like the kiss principle, I think that's very much within Mongo's future. I think with the realm they have, they basically have the infrastructure to go out to the edge. I think with the fact that they've embraced GraphQL has also made them a lot more extensible. So I think they certainly do have, you know, I, I do see the edge as being, you know, you know, in, in, you know, in their, in their pathway. I do see basically lightweight analytics and lightweight, let's say machine learning definitely in their >>Future. And, but, and they would, would you agree that they're in a better position to tap that opportunity than say a snowflake or an Oracle now maybe M and a can change that. R D can maybe change that, but fundamentally from an architectural standpoint yeah. Are they in a better position? >>Good question. I think that that Mongo snowflake by virtual fact, I mean that they've been all, you know, all cloud start off with, I think makes it more difficult, not impossible to move out to the edge, but it means that, and I, and know, and I, and I said, they're really starting to making some tentative moves in that direction. I'm looking forward to next week to, you know, seeing what, you know, hearing what we're gonna, what they're gonna be saying about that. But I do think, right. You know, you know, to answer your question directly, I'd say like right now, I'd say Mongo probably has a, you know, has a head start there. >>I'm losing track of time. I could go forever with you. Tony bear DB insight with tons of insights. Thanks so much for coming back with. >>It's only one insight insight, Dave. Good to see you again. All >>Right. Good to see you. Thank you. Okay. Keep it right there. Right back at the Java center, Mongo DB world 2022, you're watching the cube.

>>Welcome to the convergence of file and object, a special program made possible by pure storage and co-created with the cube we're running. What I would call a little mini series and we're exploring the conversions of file and object storage. What are the key trends? Why would you want to converge file and object? What are the use cases and architectural considerations and importantly, what are the business drivers of U F F O so-called unified fast file and object in this program, you'll hear from Matt Burr, who was the GM of pure flash blade business. And then we'll bring in the perspectives of a solutions architect, Garrett who's from CDW, and then the analyst angle with Scott St. Claire of the enterprise strategy group ESG. And then we'll wrap with a really interesting technical conversation with Chris and bond CB bond, who is a lead data architect at Microfocus. And he's got a really cool use case to share with us. So sit back and enjoy the pros.

we're running what i would call a little mini series and we're exploring the convergence of file and object storage what are the key trends why would you want to converge file an object what are the use cases and architectural considerations and importantly what are the business drivers of uffo so-called unified fast file and object in this program you'll hear from matt burr who is the gm of pure's flashblade business and then we'll bring in the perspectives of a solutions architect garrett belsner who's from cdw and then the analyst angle with scott sinclair of the enterprise strategy group esg he'll share some cool data on our power panel and then we'll wrap with a really interesting technical conversation with chris bond cb bond who is a lead data architect at microfocus and he's got a really cool use case to share with us so sit back and enjoy the program from around the globe it's thecube presenting the convergence of file and object brought to you by pure storage we're back with the convergence of file and object a special program made possible by pure storage and co-created with the cube so in this series we're exploring that convergence between file and object storage we're digging into the trends the architectures and some of the use cases for unified fast file and object storage uffo with me is matt burr who's the vice president and general manager of flashblade at pure storage hello matt how you doing i'm doing great morning dave how are you good thank you hey let's start with a little 101 you know kind of the basics what is unified fast file and object yeah so look i mean i think you got to start with first principles talking about the rise of unstructured data so um when we think about unstructured data you sort of think about the projections 80 of data by 2025 is going to be unstructured data whether that's machine generated data or um you know ai and ml type workloads uh you start to sort of see this um i don't want to say it's a boom uh but it's sort of a renaissance for unstructured data if you will we move away from you know what we've traditionally thought of as general purpose nas and and file shares to you know really things that focus on uh fast object taking advantage of s3 cloud native applications that need to integrate with applications on site um you know ai workloads ml workloads tend to look to share data across you know multiple data sets and you really need to have a platform that can deliver both highly performant and scalable fast file and object from one system so talk a little bit more about some of the drivers that you know bring forth that need to unify file an object yeah i mean look you know there's a there's there's a real challenge um in managing you know bespoke uh bespoke infrastructure or architectures around general purpose nas and daz etc so um if you think about how a an architect sort of looks at an application they might say well okay i need to have um you know fast daz storage proximal to the application um but that's going to require a tremendous amount of dams which is a tremendous amount of drives right hard drives are you know historically pretty pretty pretty unwieldy to manage because you're replacing them relatively consistently at multi-petabyte scale um so you start to look at things like the complexity of daz you start to look at the complexity of general purpose nas and you start to just look at quite frankly something that a lot of people don't really want to talk about anymore but actual data center space right like consolidation matters the ability to take you know something that's the size of a microwave like a modern flash blade or a modern um you know uffo device uh replaces something that might be you know the size of three or four or five refrigerators so matt what why is is now the right time for this i mean for years nobody really paid much attention to object s3 already obviously changed you know that course most of the world's data is still stored in file formats and you get there with nfs or smb why is now the time to think about unifying object and file well because we're moving to things like a contactless society um you know the the things that we're going to do are going to just require a tremendous amount more compute power network um and quite frankly storage throughput and you know i can give you two sort of real primary examples here right you know warehouses are being you know taken over by robots if you will um it's not a war it's a it's a it's sort of a friendly advancement in you know how do i how do i store a box in a warehouse and you know we have we have a customer who focuses on large sort of big box distribution warehousing and you know a box that carried a an object two weeks ago might have a different box size two weeks later well that robot needs to know where the space is in the data center in order to put it but also needs to be able to process hey i don't want to put the thing that i'm going to access the most in the back of the warehouse i'm going to put that thing in the front of the warehouse all of those types of data you know sort of real time you can think of the robot as almost an edge device is processing in real time unstructured data in its object right so it's sort of the emergence of these new types of workloads and i give you the opposite example the other end of the spectrum is ransomware right you know today you know we'll talk to customers and they'll say quite commonly hey if you know anybody can sell me a backup device i need something that can restore quickly um if you had the ability to restore something in 270 terabytes an hour or 250 terabytes an hour uh that's much faster when you're dealing with a ransomware attack you want to get your data back quickly you know so i want to add i was going to ask you about that later but since you brought it up what is the right i guess call it architecture for for for ransomware i mean how and explain like how unified object and file which appointment i get the fast recovery but how how would you recommend a customer uh go about architecting a ransomware proof you know system yeah well you know with with flashblade and and with flasharray there's an actual feature called called safe mode and that safe mode actually protects uh the snapshots and and the data from uh sort of being a part of the of the ransomware event and so if you're in a type of ransomware situation like this you're able to leverage safe mode and you say okay what happens in a ransomware attack is you can't get access to your data and so you know the bad guy the perpetrator is basically saying hey i'm not going to give you access to your data until you pay me you know x in bitcoin or whatever it might be right um with with safe mode those snapshots are actually protected outside of the ransomware blast zone and you can bring back those snapshots because what's your alternative if you're not doing something like that your alternative is either to pay and unlock your data or you have to start retouring restoring excuse me from tape or slow disk that could take you days or weeks to get your data back so leveraging safe mode um you know in either the flash for the flash blade product uh is a great way to go about architecting against ransomware i got to put my my i'm thinking like a customer now so safe mode so that's an immutable mode right can't change the data um is it can can an administrator go in and change that mode can you turn it off do i still need an air gap for example what would you recommend there yeah so there there are still um uh you know sort of our back or roll back role-based access control policies uh around who can access that safe mode and who can right okay so uh anyway subject for a different day i want to i want to actually bring up uh if you don't object a topic that i think used to be really front and center and it now be is becoming front and center again i mean wikibon just produced a research note forecasting the future of flash and hard drives and those of you who follow us know we've done this for quite some time and you can if you could bring up the chart here you you could and we see this happening again it was originally we forecast the the the death of of quote-unquote high spin speed disc drives which is kind of an oxymoron but you can see on here on this chart this hard disk had a magnificent journey but they peaked in volume in manufacturing volume in 2010 and the reason why that is is so important is that volumes now are steadily dropping you can see that and we use wright's law to explain why this is a problem and wright's law essentially says that as you your cumulative manufacturing volume doubles your cost to manufacture decline by a constant percentage now i won't go too much detail on that but suffice it to say that flash volumes are growing very rapidly hdd volumes aren't and so flash because of consumer volumes can take advantage of wright's law and that constant reduction and that's what's really important for the next generation which is always more expensive to build uh and so this kind of marks the beginning of the end matt what do you think what what's the future hold for spinning disc in your view uh well i can give you the answer on two levels on a personal level uh it's why i come to work every day uh you know the the eradication or or extinction of an inefficient thing um you know i like to say that uh inefficiency is the bane of my existence uh and i think hard drives are largely inefficient and i'm willing to accept the sort of long-standing argument that um you know we've seen this transition in block right and we're starting to see it repeat itself in in unstructured data and i'm going to accept the argument that cost is a vector here and it most certainly is right hdds have been considerably cheaper uh than than than flash storage um you know even to this day uh you know up up to this point right but we're starting to approach the point where you sort of reach a a 3x sort of um you know differentiator between the cost of an hdd and an std and you know that really is that point in time when uh you begin to pick up a lot of volume and velocity and so you know that tends to map directly to you know what you're seeing here which is you know a a slow decline uh which i think is going to become even more rapid kind of probably starting around next year um where you start to see sds excuse me ssds uh you know really replacing hdds uh at a much more rapid clip particularly on the unstructured data side and it's largely around cost the the workloads that we talked about robots and warehouses or you know other types of advanced machine learning and artificial intelligence type applications and workflows you know they require a degree of performance that a hard drive just can't deliver we are we are seeing sort of the um creative innovative uh disruption of an entire industry right before our eyes it's a fun thing to live through yeah and and we would agree i mean it doesn't the premise there is that it doesn't have to be less expensive we think it will be by you know the second half or early second half of this decade but even if it's a we think around a 3x delta the value of of ssd relative to spinning disk is going to overwhelm just like with your laptop you know it got to the point where you said why would i ever have a spinning disc in my laptop we see the same thing happening here um and and so and we're talking about you know raw capacity you know put in compression and d-dupe and everything else that you really can't do with spinning discs because of the performance issues you can do with flash okay let's come back to uffo can we dig into the challenges specifically that that this solves for customers give me give us some examples yeah so you know i mean if we if we think about the examples um you know the the robotic one um i think is is is the one that i think is the marker for you know kind of of of the the modern side of of of what we see here um but what we're you know what we're what we're seeing from a trend perspective which you know not everybody's deploying robots right um you know there's there's many companies that are you know that aren't going to be in either the robotic business uh or or even thinking about you know sort of future type oriented type things but what they are doing is green field applications are being built on object um generally not on not on file and and not on block and so you know the rise of of object as sort of the the sort of let's call it the the next great protocol for um you know for uh for for modern workloads right this is this is that that modern application coming to the forefront and that could be anything from you know financial institutions you know right down through um you we've even see it and seen it in oil and gas uh we're also seeing it across across healthcare uh so you know as as as companies take the opportunity as industries to take this opportunity to modernize you know they're modernizing not on things that are are leveraging you know um you know sort of archaic disk technology they're they're they're really focusing on on object but they still have file workflows that they need to that they need to be able to support and so having the ability to be able to deliver those things from one device in a capacity orientation or a performance orientation uh while at the same time dramatically simplifying uh the overall administration of your environment both physically and non-physically is a key driver so the great thing about object is it's simple it's a kind of a get put metaphor um it's it scales out you know because it's got metadata associated with the data uh and and it's cheap uh the drawback is you don't necessarily associate it with high performance and and and as well most applications don't you know speak in that language they speak in the language of file you know or as you mentioned block so i i see real opportunities here if i have some some data that's not necessarily frequently accessed you know every day but yet i want to then whether end of quarter or whatever it is i want to i want to or machine learning i want to apply some ai to that data i want to bring it in and then apply a file format uh because for performance reasons is that right maybe you could unpack that a little bit yeah so um you know we see i mean i think you described it well right um but i don't think object necessarily has to be slow um and nor does it have to be um you know because when you think about you brought up a good point with metadata right being able to scale to a billions of objects being able to scale to billions of objects excuse me is of value right um and i think people do traditionally associate object with slow but it's not necessarily slow anymore right we we did a sort of unofficial survey of of of our of our customers and our employee base and when people described object they thought of it as like law firms and storing a word doc if you will um and that that's just you know i think that there's a lack of understanding or a misnomer around what modern what modern object has become and perform an object particularly at scale when we're talking about billions of objects you know that's the next frontier right um is it at pace performance wise with you know the other protocols no uh but it's making leaps and grounds so you talked a little bit more about some of the verticals that you see i mean i think when i think of financial services i think transaction processing but of course they have a lot of tons of unstructured data are there any patterns you're seeing by by vertical market um we're you know we're not that's the interesting thing um and you know um as a as a as a as a company with a with a block heritage or a block dna those patterns were pretty easy to spot right there were a certain number of databases that you really needed to support oracle sql some postgres work et cetera then kind of the modern databases around cassandra and things like that you knew that there were going to be vmware environments you know you could you could sort of see the trends and where things were going unstructured data is such a broader horizontal thing right so you know inside of oil and gas for example you have you know um you have specific applications and bespoke infrastructures for those applications um you know inside of media entertainment you know the same thing the the trend that we're seeing the commonality that we're seeing is the modernization of you know object as a starting point for all the all the net new workloads within within those industry verticals right that's the most common request we see is what's your object roadmap what's your you know what's your what's your object strategy you know where do you think where do you think object is going so um there isn't any um you know sort of uh there's no there's no path uh it's really just kind of a wide open field in front of us with common requests across all industries so the amazing thing about pure just as a kind of a little you know quasi you know armchair historian the industry is pure was really the only company in many many years to be able to achieve escape velocity break through a billion dollars i mean three part couldn't do it isilon couldn't do it compellent couldn't do it i could go on but pure was able to achieve that as an independent company and so you become a leader you look at the gartner magic quadrant you're a leader in there i mean if you've made it this far you've got to have some chops and so of course it's very competitive there are a number of other storage suppliers that have announced products that unify object and file so i'm interested in how pure differentiates why pure um it's a great question um and it's one that uh you know having been a long time puritan uh you know i take pride in answering um and it's actually a really simple answer um it's it's business model innovation and technology right the the technology that goes behind how we do what we do right and i don't mean the product right innovation is product but having a better support model for example um or having on the business model side you know evergreen storage right where we sort of look at your relationship to us as a subscription right um you know we're going to sort of take the thing that that you've had and we're going to modernize that thing in place over time such that you're not rebuying that same you know terabyte or you know petabyte of storage that you've that you that you've paid for over time so um you know sort of three legs of the stool uh that that have made you know pure clearly differentiated i think the market has has recognized that um you're right it's it's hard to break through to a billion dollars um but i look forward to the day that you know we we have two billion dollar products and i think with uh you know that rise in in unstructured data growing to 80 by 2025 and you know the massive transition that you know you guys have noted in in in your hdd slide i think it's a huge opportunity for us on you know the other unstructured data side of the house you know the other thing i'd add matt i've talked to cause about this is is it's simplicity first i've asked them why don't you do this why don't you do it and the answer is always the same is that adds complexity and we we put simplicity for the customer ahead of everything else and i think that served you very very well what about the economics of of unified file an object i mean if you bring in additional value presumably there's a there there's a cost to that but there's got to be also a business case behind it what kind of impact have you seen uh with customers yeah i mean look i'll i'll i'll go back to something i mentioned earlier which is just the reclamation of floor space and power and cooling right um you know there's a you know there's people people people want to search for kind of the the sexier element if you will when it comes to looking at how we how you derive value from something but the reality is if you're reducing your power consumption by you know by by a material percentage power bills matter in big in big data centers um you know customers typically are are facing you know a paradigm of well i i want to go to the cloud but you know the clouds are not being more expensive than i thought it was going to be or you know i figured out what i can use in the cloud i thought it was going to be everything but it's not going to be everything so hybrid's where we're landing but i want to be out of the data center business and i don't want to have a team of 20 storage people to match you know to administer my storage um you know so there's sort of this this very tangible value around you know hey if i could manage um you know multiple petabytes with one full-time engineer uh because the system uh to yoran kaz's point was radically simpler to administer didn't require someone to be running around swapping drives all the time would that be a value the answer is yes 100 of the time right and then you start to look at okay all right well on the uffo side from a product perspective hey if i have to manage a you know bespoke environment for this application if i have to manage a bespoke environment for this application and a bespoke environment for this application and this book environment for this application i'm managing four different things and can i actually share data across those four different things there's ways to share data but most customers it just gets too complex how do you even know what your what your gold.master copy is of data if you have it in four different places or you try to have it in four different places and it's four different siloed infrastructures so when you get to the sort of the side of you know how do we how do you measure value in uffo it's actually being able to have all of that data concentrated in one place so that you can share it from application to application got it i'm interested we use a couple minutes left i'm interested in the the update on flashblade you know generally but also i have a specific question i mean look getting file right is hard enough uh you just announced smb support for flashblade i'm interested in you know how that fits in i think it's kind of obvious with file and object converging but give us the update on on flashblade and maybe you could address that specific question yeah so um look i mean we're we're um you know tremendously excited about the growth of flashblade uh you know we we we found workloads we never expected to find um you know the rapid restore workload was one that was actually brought to us from from from a customer actually and has become you know one of our one of our top two three four you know workloads so um you know we're really happy with the trend we've seen in it um and you know mapping back to you know thinking about hdds and ssds you know we're well on a path to building a billion dollar business here so you know we're very excited about that um but to your point you know you don't just snap your fingers and get there right um you know we've learned that doing file and object uh is is harder than block um because there's more things that you have to go do for one you're basically focused on three protocols s b nfs and s3 not necessarily in that order um but to your point about smb uh you know we we are uh on the path through to releasing um you know smb uh full full native smb support in in the system that will allow us to uh service customers we have a limitation with some customers today where they'll have an s b portion of their nfs workflow um and we do great on the nfs side um but you know we didn't we didn't have the ability to plug into the s p component of their workflow so that's going to open up a lot of opportunity for us um on on that front um and you know we continue to you know invest significantly across the board in in areas like security which is you know become more than just a hot button you know today security's always been there but it feels like it's blazing hot today um and so you know going through the next couple years we'll be looking at uh you know developing some some um you know pretty material security elements of the product as well so uh well on a path to a billion dollars is the net on that and uh you know we're we're fortunate to have have smb here and we're looking forward to introducing that to to those customers that have you know nfs workloads today with an s p component yeah nice tailwind good tam expansion strategy matt thanks so much really appreciate you coming on the program we appreciate you having us and uh thanks much dave good to see you [Music] okay we're back with the convergence of file and object in a power panel this is a special content program made possible by pure storage and co-created with the cube now in this series what we're doing is we're exploring the coming together of file and object storage trying to understand the trends that are driving this convergence the architectural considerations that users should be aware of and which use cases make the most sense for so-called unified fast file in object storage and with me are three great guests to unpack these issues garrett belsner is the data center solutions architect he's with cdw scott sinclair is a senior analyst at enterprise strategy group he's got deep experience on enterprise storage and brings that independent analyst perspective and matt burr is back with us gentlemen welcome to the program thank you hey scott let me let me start with you uh and get your perspective on what's going on the market with with object the cloud a huge amount of unstructured data out there that lives in files give us your independent view of the trends that you're seeing out there well dave you know where to start i mean surprise surprise date is growing um but one of the big things that we've seen is we've been talking about data growth for what decades now but what's really fascinating is or changed is because of the digital economy digital business digital transformation whatever you call it now people are not just storing data they actually have to use it and so we see this in trends like analytics and artificial intelligence and what that does is it's just increasing the demand for not only consolidation of massive amounts of storage that we've seen for a while but also the demand for incredibly low latency access to that storage and i think that's one of the things that we're seeing that's driving this need for convergence as you put it of having multiple protocols consolidated onto one platform but also the need for high performance access to that data thank you for that a great setup i got like i wrote down three topics that we're going to unpack as a result of that so garrett let me let me go to you maybe you can give us the perspective of what you see with customers is is this is this like a push where customers are saying hey listen i need to converge my file and object or is it more a story where they're saying garrett i have this problem and then you see unified file and object as a solution yeah i think i think for us it's you know taking that consultative approach with our customers and really kind of hearing pain around some of the pipelines the way that they're going to market with data today and kind of what are the problems that they're seeing we're also seeing a lot of the change driven by the software vendors as well so really being able to support a disaggregated design where you're not having to upgrade and maintain everything as a single block has really been a place where we've seen a lot of customers pivot to where they have more flexibility as they need to maintain larger volumes of data and higher performance data having the ability to do that separate from compute and cache and those other layers are is really critical so matt i wonder if if you could you know follow up on that so so gary was talking about this disaggregated design so i like it you know distributed cloud etc but then we're talking about bringing things together in in one place right so square that circle how does this fit in with this hyper-distributed cloud edge that's getting built out yeah you know i mean i i could give you the easy answer on that but i could also pass it back to garrett in the sense that you know garrett maybe it's important to talk about um elastic and splunk and some of the things that you're seeing in in that world and and how that i think the answer to dave's question i think you can give you can give a pretty qualified answer relative what your customers are seeing oh that'd be great please yeah absolutely no no problem at all so you know i think with um splunk kind of moving from its traditional design and classic design whatever you want you want to call it up into smart store um that was kind of one of the first that we saw kind of make that move towards kind of separating object out and i think you know a lot of that comes from their own move to the cloud and updating their code to basically take advantage of object object in the cloud uh but we're starting to see you know with like vertica eon for example um elastic other folks taking that same type of approach where in the past we were building out many 2u servers we were jamming them full of uh you know ssds and nvme drives that was great but it doesn't really scale and it kind of gets into that same problem that we see with you know hyper convergence a little bit where it's you know you're all you're always adding something maybe that you didn't want to add um so i think it you know again being driven by software is really kind of where we're seeing the world open up there but that whole idea of just having that as a hub and a central place where you can then leverage that out to other applications whether that's out to the edge for machine learning or ai applications to take advantage of it i think that's where that convergence really comes back in but i think like scott mentioned earlier it's really folks are now doing things with the data where before i think they were really storing it trying to figure out what are we going to actually do with it when we need to do something with it so this is making it possible yeah and dave if i could just sort of tack on to the end of garrett's answer there you know in particular vertica with neon mode the ability to leverage sharded subclusters give you um you know sort of an advantage in terms of being able to isolate performance hot spots you an advantage to that is being able to do that on a flashblade for example so um sharded subclusters allow you to sort of say i'm you know i'm going to give prioritization to you know this particular element of my application and my data set but i can still share those share that data across those across those subclusters so um you know as you see you know vertica advance with eon mode or you see splunk advance with with smart store you know these are all sort of advancements that are you know it's a chicken in the egg thing um they need faster storage they need you know sort of a consolidated data storage data set um and and that's what sort of allows these things to drive forward yeah so vertica eon mode for those who don't know it's the ability to separate compute and storage and scale independently i think i think vertica if they're if they're not the only one they're one of the only ones i think they might even be the only one that does that in the cloud and on-prem and that sort of plays into this distributed you know nature of this hyper-distributed cloud i sometimes call it and and i'm interested in the in the data pipeline and i wonder scott if we could talk a little bit about that maybe we're unified object and file i mean i'm envisioning this this distributed mesh and then you know uffo is sort of a node on that that i i can tap when i need it but but scott what are you seeing as the state of infrastructure as it relates to the data pipeline and the trends there yeah absolutely dave so when i think data pipeline i immediately gravitate to analytics or or machine learning initiatives right and so one of the big things we see and this is it's an interesting trend it seems you know we continue to see increased investment in ai increased interest and people think and as companies get started they think okay well what does that mean well i got to go hire a data scientist okay well that data scientist probably needs some infrastructure and what they end what often happens in these environments is where it ends up being a bespoke environment or a one-off environment and then over time organizations run into challenges and one of the big challenges is the data science team or people whose jobs are outside of it spend way too much time trying to get the infrastructure to to keep up with their demands and predominantly around data performance so one of the one of the ways organizations that especially have artificial intelligence workloads in production and we found this in our research have started mitigating that is by deploying flash all across the data pipeline we have we have data on this sorry interrupt but yeah if you could bring up that that chart that would be great um so take us through this uh uh scott and share with us what we're looking at here yeah absolutely so so dave i'm glad you brought this up so we did this study um i want to say late last year uh one of the things we looked at was across artificial intelligence environments now one thing that you're not seeing on this slide is we went through and we asked all around the data pipeline and we saw flash everywhere but i thought this was really telling because this is around data lakes and when when or many people think about the idea of a data lake they think about it as a repository it's a place where you keep maybe cold data and what we see here is especially within production environments a pervasive use of flash storage so i think that 69 of organizations are saying their data lake is mostly flash or all flash and i think we have zero percent that don't have any flash in that environment so organizations are finding out that they that flash is an essential technology to allow them to harness the value of their data so garrett and then matt i wonder if you could chime in as well we talk about digital transformation and i sometimes call it you know the coveted forced march to digital transformation and and i'm curious as to your perspective on things like machine learning and the adoption and scott you may have a perspective on this as well you know we had to pivot we had to get laptops we had to secure the end points you know and vdi those became super high priorities what happened to you know injecting ai into my applications and and machine learning did that go in the back burner was that accelerated along with the need to digitally transform garrett i wonder if you could share with us what you saw with with customers last year yeah i mean i think we definitely saw an acceleration um i think folks are in in my market are still kind of figuring out how they inject that into more of a widely distributed business use case but again this data hub and allowing folks to now take advantage of this data that they've had in these data lakes for a long time i agree with scott i mean many of the data lakes that we have were somewhat flash accelerated but they were typically really made up of you know large capacity slower spinning near-line drive accelerated with some flash but i'm really starting to see folks now look at some of those older hadoop implementations and really leveraging new ways to look at how they consume data and many of those redesigned customers are coming to us wanting to look at all flash solutions so we're definitely seeing it we're seeing an acceleration towards folks trying to figure out how to actually use it in more of a business sense now or before i feel it goes a little bit more skunk works kind of people dealing with uh you know in a much smaller situation maybe in the executive offices trying to do some testing and things scott you're nodding away anything you can add in here yeah so first off it's great to get that confirmation that the stuff we're seeing in our research garrett's seeing you know out in the field and in the real world um but you know as it relates to really the past year it's been really fascinating so one of the things we study at esg is i.t buying intentions what are things what are initiatives that companies plan to invest in and at the beginning of 2020 we saw a heavy interest in machine learning initiatives then you transition to the middle of 2020 in the midst of covid some organizations continued on that path but a lot of them had the pivot right how do we get laptops to everyone how do we continue business in this new world well now as we enter into 2021 and hopefully we're coming out of this uh you know the pandemic era um we're getting into a world where organizations are pivoting back towards these strategic investments around how do i maximize the usage of data and actually accelerating those because they've seen the importance of of digital business initiatives over the past year yeah matt i mean when we exited 2019 we saw a narrowing of experimentation and our premise was you know that that organizations are going to start now operationalizing all their digital transformation experiments and and then we had a you know 10 month petri dish on on digital so what do you what are you seeing in this regard a 10 month petri dish is an interesting way to interesting way to describe it um you know we saw another there's another there's another candidate for pivot in there around ransomware as well right um you know security entered into the mix which took people's attention away from some of this as well i mean look i'd like to bring this up just a level or two um because what we're actually talking about here is progress right and and progress isn't is an inevitability um you know whether it's whether whether you believe that it's by 2025 or you or you think it's 2035 or 2050 it doesn't matter we're on a forced march to the eradication of disk and that is happening in many ways uh you know in many ways um due to some of the things that garrett was referring to and what scott was referring to in terms of what are customers demands for how they're going to actually leverage the data that they have and that brings me to kind of my final point on this which is we see customers in three phases there's the first phase where they say hey i have this large data store and i know there's value in there i don't know how to get to it or i have this large data store and i've started a project to get value out of it and we failed those could be customers that um you know marched down the hadoop path early on and they they got some value out of it um but they realized that you know hdfs wasn't going to be a modern protocol going forward for any number of reasons you know the first being hey if i have gold.master how do i know that i have gold.4 is consistent with my gold.master so data consistency matters and then you have the sort of third group that says i have these large data sets i know how to extract value from them and i'm already on to the verticas the elastics you know the splunks etc um i think those folks are the folks that that ladder group are the folks that kept their their their projects going because they were already extracting value from them the first two groups we we're seeing sort of saying the second half of this year is when we're going to begin really being picking up on these on these types of initiatives again well thank you matt by the way for for hitting the escape key because i think value from data really is what this is all about and there are some real blockers there that i kind of want to talk about you mentioned hdfs i mean we were very excited of course in the early days of hadoop many of the concepts were profound but at the end of the day it was too complicated we've got these hyper-specialized roles that are that are you know serving the business but it still takes too long it's it's too hard to get value from data and one of the blockers is infrastructure that the complexity of that infrastructure really needs to be abstracted taking up a level we're starting to see this in in cloud where you're seeing some of those abstraction layers being built from some of the cloud vendors but more importantly a lot of the vendors like pew are saying hey we can do that heavy lifting for you uh and we you know we have expertise in engineering to do cloud native so i'm wondering what you guys see uh maybe garrett you could start us off and other students as some of the blockers uh to getting value from data and and how we're going to address those in the coming decade yeah i mean i i think part of it we're solving here obviously with with pure bringing uh you know flash to a market that traditionally was utilizing uh much slower media um you know the other thing that i that i see that's very nice with flashblade for example is the ability to kind of do things you know once you get it set up a blade at a time i mean a lot of the things that we see from just kind of more of a you know simplistic approach to this like a lot of these teams don't have big budgets and being able to kind of break them down into almost a blade type chunk i think has really kind of allowed folks to get more projects and and things off the ground because they don't have to buy a full expensive system to run these projects so that's helped a lot i think the wider use cases have helped a lot so matt mentioned ransomware you know using safe mode as a place to help with ransomware has been a really big growth spot for us we've got a lot of customers very interested and excited about that and the other thing that i would say is bringing devops into data is another thing that we're seeing so kind of that push towards data ops and really kind of using automation and infrastructure as code as a way to now kind of drive things through the system the way that we've seen with automation through devops is really an area we're seeing a ton of growth with from a services perspective guys any other thoughts on that i mean we're i'll tee it up there we are seeing some bleeding edge which is somewhat counterintuitive especially from a cost standpoint organizational changes at some some companies uh think of some of the the the internet companies that do uh music uh for instance and adding podcasts etc and those are different data products we're seeing them actually reorganize their data architectures to make them more distributed uh and actually put the domain heads the business heads in charge of the the data and the data pipeline and that is maybe less efficient but but it's again some of these bleeding edge what else are you guys seeing out there that might be yes some harbingers of the next decade uh i'll go first um you know i think specific to um the the construct that you threw out dave one of the things that we're seeing is um you know the the application owner maybe it's the devops person but it's you know maybe it's it's it's the application owner through the devops person they're they're becoming more technical in their understanding of how infrastructure um interfaces with their with their application i think um you know what what we're seeing on the flashblade side is we're having a lot more conversations with application people than um just i.t people it doesn't mean that the it people aren't there the it people are still there for sure they have to deliver the service etc um but you know the days of of i.t you know building up a catalog of services and a business owner subscribing to one of those services you know picking you know whatever sort of fits their need um i don't think that constru i think that's the construct that changes going forward the application owner is becoming much more prescriptive about what they want the infrastructure to fit how they want the infrastructure to fit into their application and that's a big change and and for for um you know certainly folks like like garrett and cdw um you know they do a good job with this being able to sort of get to the application owner and bring those two sides together there's a tremendous amount of value there for us it's been a little bit of a retooling we've traditionally sold to the i.t side of the house and um you know we've had to teach ourselves how to go talk the language of of applications so um you know i think you pointed out a good a good a good construct there and and you know that that application owner taking playing a much bigger role in what they're expecting uh from the performance of it infrastructure i think is is is a key is a key change interesting i mean that definitely is a trend that's put you guys closer to the business where the the infrastructure team is is serving the business as opposed to sometimes i talk to data experts and they're frustrated uh especially data owners or or data product builders who are frustrated that they feel like they have to beg beg the the data pipeline team to get you know new data sources or get data out how about the edge um you know maybe scott you can kick us off i mean we're seeing you know the emergence of edge use cases ai inferencing at the edge a lot of data at the edge what are you seeing there and and how does this unified object i'll bring us back to that and file fit wow dave how much time do we have um two minutes first of all scott why don't you why don't you just tell everybody what the edge is yeah you got it figured out all right how much time do you have matt at the end of the day and that that's that's a great question right is if you take a step back and i think it comes back today of something you mentioned it's about extracting value from data and what that means is when you extract value from data what it does is as matt pointed out the the influencers or the users of data the application owners they have more power because they're driving revenue now and so what that means is from an i.t standpoint it's not just hey here are the services you get use them or lose them or you know don't throw a fit it is no i have to i have to adapt i have to follow what my application owners mean now when you bring that back to the edge what it means is is that data is not localized to the data center i mean we just went through a nearly 12-month period where the entire workforce for most of the companies in this country had went distributed and business continued so if business is distributed data is distributed and that means that means in the data center that means at the edge that means that the cloud that means in all other places in tons of places and what it also means is you have to be able to extract and utilize data anywhere it may be and i think that's something that we're going to continue to and continue to see and i think it comes back to you know if you think about key characteristics we've talked about things like performance and scale for years but we need to start rethinking it because on one hand we need to get performance everywhere but also in terms of scale and this ties back to some of the other initiatives and getting value from data it's something i call that the massive success problem one of the things we see especially with with workloads like machine learning is businesses find success with them and as soon as they do they say well i need about 20 of these projects now all of a sudden that overburdens it organizations especially across across core and edge and cloud environments and so when you look at environments ability to meet performance and scale demands wherever it needs to be is something that's really important you know so dave i'd like to um just sort of tie together sort of two things that um i think that i heard from scott and garrett that i think are important and it's around this concept of scale um you know some of us are old enough to remember the day when kind of a 10 terabyte blast radius was too big of a blast radius for people to take on or a terabyte of storage was considered to be um you know an exemplary budget environment right um now we sort of think as terabytes kind of like we used to think of as gigabytes in some ways um petabyte like you don't have to explain anybody what a petabyte is anymore um and you know what's on the horizon and it's not far are our exabyte type data set workloads um and you start to think about what could be in that exabyte of data we've talked about how you extract that value we've talked about sort of um how you start but if the scale is big not everybody's going to start at a petabyte or an exabyte to garrett's point the ability to start small and grow into these products or excuse me these projects i think a is a really um fundamental concept here because you're not going to just go by i'm going to kick off a five petabyte project whether you do that on disk or flash it's going to be expensive right but if you could start at a couple hundred terabytes not just as a proof of concept but as something that you know you could get predictable value out of that then you could say hey this either scales linearly or non-linearly in a way that i can then go map my investments to how i can go dig deeper into this that's how all of these things are gonna that's how these successful projects are going to start because the people that are starting with these very large you know sort of um expansive you know greenfield projects at multi-petabyte scale it's gonna be hard to realize near-term value excellent we gotta wrap but but garrett i wonder if you could close when you look forward you talk to customers do you see this unification of of file and object is it is this an evolutionary trend is it something that is that that is that is that is going to be a lever that customers use how do you see it evolving over the next two three years and beyond yeah i mean i think from our perspective i mean just from what we're seeing from the numbers within the market the amount of growth that's happening with unstructured data is really just starting to finally really kind of hit this data deluge or whatever you want to call it that we've been talking about for so many years it really does seem to now be becoming true as we start to see things scale out and really folks settle into okay i'm going to use the cloud to to start and maybe train my models but now i'm going to get it back on prem because of latency or security or whatever the the um decision points are there this is something that is not going to slow down and i think you know folks like pure having the ability to have the tools that they give us um to use and bring to market with our customers are really key and critical for us so i see it as a huge growth area and a big focus for us moving forward guys great job unpacking a topic that you know it's covered a little bit but i think we we covered some ground that is uh that is new and so thank you so much for those insights and that data really appreciate your time thanks steve thanks yeah thanks dave okay and thank you for watching the convergence of file and object keep it right there right back after this short break innovation impact influence welcome to the cube disruptors developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe enjoy the best this community has to offer on the cube your global leader in high-tech digital coverage [Music] okay now we're going to get the customer perspective on object and we'll talk about the convergence of file and object but really focusing on the object piece this is a content program that's being made possible by pure storage and it's co-created with the cube christopher cb bond is here he's a lead architect for microfocus the enterprise data warehouse and principal data engineer at microfocus cb welcome good to see you thanks dave good to be here so tell us more about your role at microfocus it's a pan microfocus role of course we know the company is a multinational software firm and acquired the software assets of hp of course including vertica tell us where you fit yeah so microfocus is uh you know it's like i said wide worldwide uh company that uh sells a lot of software products all over the place to governments and so forth and um it also grows often by acquiring other companies so there is the problem of of integrating new companies and their data and so what's happened over the years is that they've had a a number of different discrete data systems so you've got this data spread all over the place and they've never been able to get a full complete introspection on the entire business because of that so my role was come in design a central data repository an enterprise data warehouse that all reporting could be generated against and so that's what we're doing and we selected vertica as the edw system and pure storage flashblade as the communal repository okay so you obviously had experience with with vertica in your in your previous role so it's not like you were starting from scratch but but paint a picture of what life was like before you embarked on this sort of consolidated a approach to your your data warehouse what was it just disparate data all over the place a lot of m a going on where did the data live right so again the data was all over the place including under people's desks in just dedicated you know their their own private uh sql servers it a lot of data in in um microfocus is run on sql server which has pros and cons because that's a great uh transactional database but it's not really good for analytics in my opinion so uh but a lot of stuff was running on that they had one vertica instance that was doing some select uh reporting wasn't a very uh powerful system and it was what they call vertica enterprise mode where had dedicated nodes which um had the compute and storage um in the same locus on each uh server okay so vertica eon mode is a whole new world because it separates compute from storage you mentioned eon mode uh and the ability to to to scale storage and compute independently we wanted to have the uh analytics olap stuff close to the oltp stuff right so that's why they're co-located very close to each other and so uh we could what's nice about this situation is that these s3 objects it's an s3 object store on the pure flash plate we could copy those over if we needed to uh aws and we could spin up um a version of vertica there and keep going it's it's like a tertiary dr strategy because we actually have a we're setting up a second flashblade vertica system geo-located elsewhere for backup and we can get into it if you want to talk about how the latest version of the pure software for the flashblade allows synchronization across network boundaries of those flash plays which is really nice because if uh you know there's a giant sinkhole opens up under our colo facility and we lose that thing then we just have to switch the dns and we were back in business off the dr and then if that one was to go we could copy those objects over to aws and be up and running there so we're feeling pretty confident about being able to weather whatever comes along so you're using the the pure flash blade as an object store um most people think oh object simple but slow uh not the case for you is that right not the case at all it's ripping um well you have to understand about vertica and the way it stores data it stores data in what they call storage containers and those are immutable okay on disk whether it's on aws or if you had a enterprise mode vertica if you do an update or delete it actually has to go and retrieve that object container from disk and it destroys it and rebuilds it okay which is why you don't you want to avoid updates and deletes with vertica because the way it gets its speed is by sorting and ordering and encoding the data on disk so it can read it really fast but if you do an operation where you're deleting or updating a record in the middle of that then you've got to rebuild that entire thing so that actually matches up really well with s3 object storage because it's kind of the same way uh it gets destroyed and rebuilt too okay so that matches up very well with vertica and we were able to design this system so that it's append only now we had some reports that were running in sql server okay uh which were taking seven days so we moved that to uh to vertica from sql server and uh we rewrote the queries which were which had been written in t sql with a bunch of loops and so forth and we were to get this is amazing it went from seven days to two seconds to generate this report which has tremendous value uh to the company because it would have to have this long cycle of seven days to get a new introspection in what they call their knowledge base and now all of a sudden it's almost on demand two seconds to generate it that's great and that's because of the way the data is stored and uh the s3 you asked about oh you know is it slow well not in that context because what happens really with vertica eon mode is that it can they have um when you set up your compute nodes they have local storage also which is called the depot it's kind of a cache okay so the data will be drawn from the flash and cached locally uh and that was it was thought when they designed that oh you know it's that'll cut down on the latency okay but it turns out that if you have your compute nodes close meaning minimal hops to the flashblade that you can actually uh tell vertica you know don't even bother caching that stuff just read it directly on the fly from the from the flashblade and the performance is still really good it depends on your situation but i know for example a major telecom company that uh uses the same topology as we're talking about here they did the same thing they just they just dropped the cache because the flash player was able to to deliver the the data fast enough so that's you're talking about that that's speed of light issues and just the overhead of of of switching infrastructure is that that gets eliminated and so as a result you can go directly to the storage array that's correct yeah it's it's like it's fast enough that it's it's almost as if it's local to the compute node uh but every situation is different depending on your uh your knees if you've got like a few tables that are heavily used uh then yeah put them um put them in the cash because that'll be probably a little bit faster but if you have a lot of ad hoc queries that are going on you know you may exceed the storage of the local cache and then you're better off having it uh just read directly from the uh from the flash blade got it look it pure's a fit i mean i sound like a fanboy but pure is all about simplicity so is object so that means you don't have to you know worry about wrangling storage and worrying about luns and all that other you know nonsense and and file i've been burned by hardware in the past you know where oh okay they're building to a price and so they cheap out on stuff like fans or other things and these these components fail and the whole thing goes down but this hardware is super super good quality and uh so i'm i'm happy with the quality that we're getting so cb last question what's next for you where do you want to take this uh this this initiative well we are in the process now of we um when so i i designed this system to combine the best of the kimball approach to data warehousing and the inland approach okay and what we do is we bring over all the data we've got and we put it into a pristine staging layer okay like i said it's uh because it's append only it's essentially a log of all the transactions that are happening in this company just they appear okay and then from the the kimball side of things we're designing the data marts now so that that's what the end users actually interact with and so we're we're taking uh the we're examining the transactional systems to say how are these business objects created what's what's the logic there and we're recreating those logical models in uh in vertica so we've done a handful of them so far and it's working out really well so going forward we've got a lot of work to do to uh create just about every object that that the company needs cb you're an awesome guest to really always a pleasure talking to you and uh thank you congratulations and and good luck going forward stay safe thank you [Music] okay let's summarize the convergence of file and object first i want to thank our guests matt burr scott sinclair garrett belsener and c.b bohn i'm your host dave vellante and please allow me to briefly share some of the key takeaways from today's program so first as scott sinclair of esg stated surprise surprise data's growing and matt burr he helped us understand the growth of unstructured data i mean estimates indicate that the vast majority of data will be considered unstructured by mid-decade 80 or so and obviously unstructured data is growing very very rapidly now of course your definition of unstructured data and that may vary across across a wide spectrum i mean there's video there's audio there's documents there's spreadsheets there's chat i mean these are generally considered unstructured data but of course they all have some type of structure to them you know perhaps it's not as strict as a relational database but there's certainly metadata and certain structure to these types of use cases that i just mentioned now the key to what pure is promoting is this idea of unified fast file and object uffo look object is great it's inexpensive it's simple but historically it's been less performant so good for archiving or cheap and deep types of examples organizations often use file for higher performance workloads and let's face it most of the world's data lives in file formats what pure is doing is bringing together file and object by for example supporting multiple protocols ie nfs smb and s3 s3 of course has really given new life to object over the past decade now the key here is to essentially enable customers to have the best of both worlds not having to trade off performance for object simplicity and a key discussion point that we've had on the program has been the impact of flash on the long slow death of spinning disk look hard disk drives they had a great run but hdd volumes they peaked in 2010 and flash as you well know has seen tremendous volume growth thanks to the consumption of flash in mobile devices and then of course its application into the enterprise and that's volume is just going to keep growing and growing and growing the price declines of flash are coming down faster than those of hdd so it's the writing's on the wall it's just a matter of time so flash is riding down that cost curve very very aggressively and hdd has essentially become you know a managed decline business now by bringing flash to object as part of the flashblade portfolio and allowing for multiple protocols pure hopes to eliminate the dissonance between file and object and simplify the choice in other words let the workload decide if you have data in a file format no problem pure can still bring the benefits of simplicity of object at scale to the table so again let the workload inform what the right strategy is not the technical infrastructure now pure course is not alone there are others supporting this multi-protocol strategy and so we asked matt burr why pure or what's so special about you and not surprisingly in addition to the product innovation he went right to pure's business model advantages i mean for example with its evergreen support model which was very disruptive in the marketplace you know frankly pure's entire business disrupted the traditional disk array model which was fundamentally was flawed pure forced the industry to respond and when it achieved escape velocity velocity and pure went public the entire industry had to react and a big part of the pure value prop in addition to this business model innovation that we just discussed is simplicity pure's keep its simple approach coincided perfectly with the ascendancy of cloud where technology organizations needed cloud-like simplicity for certain workloads that were never going to move into the cloud they're going to stay on-prem now i'm going to come back to this but allow me to bring in another concept that garrett and cb really highlighted and that is the complexity of the data pipeline and what do you mean what do i mean by that and why is this important so scott sinclair articulated he implied that the big challenge is organizations their data full but insights are scarce scarce a lot of data not as much insights it takes time too much time to get to those insights so we heard from our guests that the complexity of the data pipeline was a barrier to getting to faster insights now cb bonds shared how he streamlined his data architecture using vertica's eon mode which allowed him to scale compute independently of storage so that brought critical flexibility and improved economics at scale and flashblade of course was the back-end storage for his data warehouse efforts now the reason i think this is so important is that organizations are struggling to get insights from data and the complexity associated with the data pipeline and data life cycles let's face it it's overwhelming organizations and there the answer to this problem is a much longer and different discussion than unifying object and file that's you know i can spend all day talking about that but let's focus narrowly on the part of the issue that is related to file and object so the situation here is that technology has not been serving the business the way it should rather the formula is twisted in the world of data and big data and data architectures the data team is mired in complex technical issues that impact the time to insights now part of the answer is to abstract the underlying infrastructure complexity and create a layer with which the business can interact that accelerates instead of impedes innovation and unifying file and object is a simple example of this where the business team is not blocked by infrastructure nuance like does this data reside in a file or object format can i get to it quickly and inexpensively in a logical way or is the infrastructure in a stovepipe and blocking me so if you think about the prevailing sentiment of how the cloud is evolving to incorporate on premises workloads that are hybrid and configurations that are working across clouds and now out to the edge this idea of an abstraction layer that essentially hides the underlying infrastructure is a trend we're going to see evolve this decade now is uffo the be all end-all answer to solving all of our data pipeline challenges no no of course not but by bringing the simplicity and economics of object together with the ubiquity and performance of file uffo makes it a lot easier it simplifies life organizations that are evolving into digital businesses which by the way is every business so we see this as an evolutionary trend that further simplifies the underlying technology infrastructure and does a better job supporting the data flows for organizations so they don't have to spend so much time worrying about the technology details that add a little value to the business okay so thanks for watching the convergence of file and object and thanks to pure storage for making this program possible this is dave vellante for the cube we'll see you next time [Music] you

>> Thank you, sir. All right, you ready to roll? >> Ready. >> All right, we'll go ahead and go in five, four, three, two. >> Okay, let's summarize the convergence of file and object. First, I want to thank our guests, Matt Burr, Scott Sinclair, Garrett Belsner, and CB Bonne. I'm your host, Dave Vellante, and please allow me to briefly share some of the key takeaways from today's program. So first, as Scott Sinclair of ESG stated surprise, surprise, data's growing. And Matt Burr, he helped us understand the growth of unstructured data. I mean, estimates indicate that the vast majority of data will be considered unstructured by mid decade, 80% or so. And obviously, unstructured data is growing very, very rapidly. Now, of course, your definition of unstructured data, now that may vary across a wide spectrum. I mean, there's video, there's audio, there's documents, there's spreadsheets, there's chat. I mean, these are generally considered unstructured data but of course they all have some type of structure to them. You know, perhaps it's not as strict as a relational database, but there's certainly metadata and certain structure to these types of use cases that I just mentioned. Now, the key to what Pure is promoting is this idea of unified fast file and object, U-F-F-O. Look, object is great, it's inexpensive, it's simple, but historically, it's been less performant, so good for archiving, or cheap and deep types of examples. Organizations often use file for higher performance workloads and let's face it, most of the world's data lives in file formats. What Pure is doing is bringing together file and object by, for example, supporting multiple protocols, ie, NFS, SMB, and S3. S3, of course, has really given a new life to object over the past decade. Now, the key here is to essentially enable customers to have the best of both worlds, not having to trade off performance for object simplicity. And a key discussion point that we've had in the program has been the impact of Flash on the long, slow, death of spinning disk. Look, hard disk drives, they had a great run, but HDD volumes, they peaked in 2010, and Flash, as you well know, has seen tremendous volume growth thanks to the consumption of Flash in mobile devices and then of course, its application into the enterprise. And as volume is just going to keep growing and growing, and growing. the price declines of Flash are coming down faster than those of HDD. So it's, the writing's on the wall. It's just a matter of time. So Flash is riding down that cost curve very, very aggressively and HDD has essentially become a managed decline business. Now, by bringing Flash to object as part of the FlashBlade portfolio and allowing for multiple protocols, Pure hopes to eliminate the dissonance between file and object and simplify the choice. In other words, let the workload decide. If you have data in a file format, no problem. Pure can still bring the benefits of simplicity of object at scale to the table. So again, let the workload inform what the right strategy is not the technical infrastructure. Now Pure, of course, is not alone. There are others supporting this multi-protocol strategy. And so we asked Matt Burr why Pure, what's so special about you? And not surprisingly, in addition to the product innovation, he went right to Pure's business model advantages. I mean, for example, with its Evergreen support model which was very disruptive in the marketplace. You know, frankly, Pure's entire business disrupted the traditional disk array model which was, fundamentally, it was flawed. Pure forced the industry to respond. And when it achieved escape velocity and Pure went public, the entire industry had to react. And a big part of the Pure value prop in addition to this business model innovation that we just discussed is simplicity. Pure's keep it simple approach coincided perfectly with the ascendancy of cloud where technology organizations needed cloud-like simplicity for certain workloads that were never going to move into the cloud. They were going to stay on-prem. Now I'm going to come back to this but allow me to bring in another concept that Garrett and CB really highlighted, and that is the complexity of the data pipeline. And what do I mean, what do I mean by that, and why is this important? So Scott Sinclair articulated or he implied that the big challenge is organizations, they're data full, but insights are scarce; a lot of data, not as much insights, and it takes time, too much time to get to those insights. So we heard from our guests that the complexity of the data pipeline was a barrier to getting to faster insights. Now, CB Bonne shared how he streamlined his data architecture using Vertica's Eon Mode which allowed him to scale, compute, independently of storage, so that brought critical flexibility and improved economics at scale. And FlashBlade, of course, was the backend storage for his data warehouse efforts. Now, the reason I think this is so important is that organizations are struggling to get insights from data and the complexity associated with the data pipeline and data lifecycles, let's face it, it's overwhelming organizations. And there, the answer to this problem is a much longer and different discussion than unifying object and file. That's, you know, I could spend all day talking about that, but let's focus narrowly on the part of the issue that is related to file and object. So the situation here is the technology has not been serving the business the way it should. Rather, the formula is twisted in the world of data and big data, and data architectures. The data team is mired in complex technical issues that impact the time to insights. Now, part of the answer is to abstract the underlying infrastructure complexity and create a layer with which the business can interact that accelerates instead of impedes innovation. And unifying file and object is a simple example of this where the business team is not blocked by infrastructure nuance, like does this data reside in the file or object format? Can I get to it quickly and inexpensively in a logical way or is the infrastructure in a stovepipe and blocking me? So if you think about the prevailing sentiment of how the cloud is evolving to incorporate on premises, workloads that are hybrid, and configurations that are working across clouds, and now out to the edge, this idea of an abstraction layer that essentially hides the underlying infrastructure is a trend we're going to see evolve this decade. Now, is UFFO the be-all end-all answer to solving all of our data pipeline challenges? No, no, of course not. But by bringing the simplicity and economics of object together with the ubiquity and performance of file, UFFO makes it a lot easier. It simplifies a life organizations that are evolving into digital businesses, which by the way, is every business. So, we see this as an evolutionary trend that further simplifies the underlying technology infrastructure and does a better job supporting the data flows for organizations so they didn't have to spend so much time worrying about the technology details that add little value to the business. Okay, so thanks for watching the convergence of file and object and thanks to Pure Storage for making this program possible. This is Dave Vellante for theCUBE. We'll see you next time.

(clicking) >> I am Lisa Martin with theCUBE on the ground at Google for the sixth annual Top Women in Cloud Awards event with CloudNOW. Very excited to be joined by next guest, Ajit George, the Managing Director of the Shanti Bhavan Children's Project. Welcome to the cube. >> Hi Lisa, it's great to be here. >> So, I was so excited to have a chat with you. The Shanti Bhavan Children's Project is incredible. Tell us about it, 20 years now, tell us about what that is, how your family is involved, and what it's helping to do for these young children in Bangelore, India? >> Sure, Shanti Bhavan was founded by my father, Dr. Abraham George, 20 years ago, and its goal is to educate children, but also to eliminate poverty and change entire systems of communities and governments. It, the way we achieve this goal is by taking children from the poorest communities in India, giving them a high-quality, boarding school education, from the age of four until they graduate from 12th grade, and we cover everything during that period. So, their healthcare, their clothing, their boarding, food, all of that is taken care of, as well as training in soft skills. So, debate, interpersonal and interview skills, leadership skills, and the whole nine yards. While we educate them in the highest curriculum, the toughest standards in India, and then we pay for their entire college degree afterwards. So, that is 17 years of a high-quality intervention per child from the very first day they start school to the very first day of work. >> That's incredible and you have a very high college graduation rate, isn't that correct? Yeah, that is correct. If they pass out of high school, their high school graduation rate is about 77%, University graduation rate is 98% and so- >> Wow, 98%. >> It's been pretty exciting and they go on from those, from college to multinational companies, like Mercedes-Benz or Amazon, or Goldman Sachs. So, our kids who come from urban slums or rural villages with huts with no running water or electricity are making more in their first five years, than their parents make in a lifetime. So, it's a quantum leap, it is a genuine breaking the cycle of poverty, and the ability to become both, either the primary or the sole breadwinner for their entire family. So, four or five other people are dependent on them at the age of 21. >> And that's incredible, I was watching, there is a Daughters of Destiny, Netflix Original Docuseries. I saw the trailer of it today, incredibly profound. One of the things that, a couple things that really stuck out to me was, this is taking children from poverty to possibility. And also, one of the young girls that was in that trailer had said, "I've got a lot to lose, it's now or never for me." These children seem to really understand the gravity of their situation, and genuinely recognize the opportunity that they've been given. >> Yeah, sure, every single Shanti Bhavan child understands, it's almost like they've won the lottery, they've had an opportunity that no one in their families have ever had, but no one from their communities have had either. They're the first person in their family for generations to get any kind of education, and so that's a powerful opportunity, but it's also an important obligation or duty to give back to the family and to make an impact for the community because they are given this golden ticket, and they want to do something important with it. If they don't succeed, nobody gives them a second chance. Kids from that kind of community, and from that kind of circumstance, don't really have a second chance if they aren't able to make the most of it. So when you hear those stories they're talking about, "hey, I really need to seize this moment." "I need to seize this opportunity," maybe, "my mother's back at home and she needs my help," maybe, "my father's bedridden." A lot of these kids have generational debt, so they owe money to, like a money lender, which is an illegal lender and that's a couple generations back. Maybe their grandparents have taken out this debt, so they have all these debts piled up on them, and they have healthcare bills piled up on them, and they've got housing and all of these other problems. Then they have to educate their younger brothers and sisters and pay for dowries for their family members. It's the enormous responsibilities on one child is huge, but they're able to step up because they're given this powerful education, this great opportunity, so there's a lot of pressure, but there's also this great knowledge that they have a horizon out there that no one in their family has ever had before. >> That's incredible and so in the last couple minutes here, CloudNOW, where we are at the awards event tonight, they've teamed up with Intel, Apcera, and CB Technologies, to launch the Daughters of Destiny STEM scholarship. So exciting, what's that going to mean for current students, at Shanti Bhavan or the future students? >> Right, I think I'm really, really thankful, first of all to CB Technologies, Intel, and Apcera, as well as the CloudNOW. This scholarship is the first of its kind within our program and it allows these three young ladies, who are the first recipients of the scholarship, and hopefully there'll be many more recipients, but these young ladies to get a high-quality college education in the STEM fields, which is their passion. So, it opens doors for them for their education, potentially for internships and maybe job opportunities after college. So, I think this is a gateway to something bright and beautiful. >> Oh, I love that and how you described it for these children as a quantum leap, is as profound as what's been shown in the Netflix series. So, Ajit, thank you so much for joining. I wish we had more time, this is such an incredible project that you're working on, but we thank you for stopping by theCUBE and sharing it with us. >> Thank you so much, Lisa, it's great to be here. >> We want to thank you for watching theCUBE. I'm Lisa Martin on the ground at Google for the CloudNOW, Top Women in Technology Awards. Bye for now. (closing music)

(digital clicking noise) >> Hi. Lisa Martin with the CUBE. On the ground at Google for the 6th annual CloudNOW Top Women in Cloud Awards event. We're very excited to be here. And now to be joined by the founder of CloudNOW, Jocelyn Degance Graham. Welcome back to the CUBE. >> Lisa, we are so happy to have you and the CUBE back for the second year. So our 6th annual event and the second year that you've been broadcasting. We're just really delighted to have your team be able to shine a spotlight on the incredible accomplishments of these women in tech. >> It's always so inspiring, Jocelyn, I was telling you before we went live, that I love reading about the people that you're honoring. But you yourself have been awarded a number of times. So you're quite the women in technology as well. >> (laughs) >> I wanted to talk a little bit about CloudNOW and what you've guys have done. Two really big announcements this year. Tell us about that. >> So the big things we've really been working on for 2017 are the scholarships, Lisa. I have to say of all the professional things this year, I really am the most heartened by the work in the scholarships. It is what is most important to me. As so we start by identifying two exceptional academic partners. We had looked at a number of ... We had read the research, we've been looking at how do you most make impact. And have more women join tech, join technical ranks, right? And so there's been a lot of debate and a lot of research about that. And what we have found is that it's very important for women to have a role model in an organization. It does not necessarily even have to be a mentor. It needs to be a role model. The other piece of the equation is the ambition gap. So it's not just about getting tons of women in the pipeline It's also about getting women that really want to take it the whole way. So this kind of combination factor of that next generation of leader that's really going to be able to get to that upper echelon of office. So the academic partners that we selected, we feel like they've really have done a great job of identifying those future leaders. For us to be able to place our investments with them. To gather corporate partnerships that are willing to be able to fund that next generation of leaders. So we have exceptional partners. We have exceptional academic institutions. If I can, I'd love to tell you just a little bit about the academic partners that we've selected. >> Yes, absolutely, please do. >> Yeah, so the first one is Holberton School. And Holberton is in San Francisco. They have a really unique model. They don't charge students any kind of tuition up front. What they do is once the student has gotten their first full-time job, then they start paying back what they would have paid in tuition. And so, it's a remarkably equitable kind of format for education. >> Lisa: It is. >> It's very different than what most people are seeing for colleges and universities. The problem is in how expensive it is to live in San Francisco. >> Lisa: Right. >> So the scholarships are actually a living wage stipend. Because the school is too intensive for the students to actually be able to work. It's a very compact program. Instead of four year, the students are done in two. So that's our first academic partner. The students are getting jobs at fantastic companies like LinkedIn, and NASA. And they are actually out-competing MIT and Stanford grads for those jobs. >> That's phenomenal. >> It is phenomenal. So we are more than happy to suggest to our corporate funders that they put their money on those bets. >> Lisa: Excellent. >> So we've got Google and we've got Accenture that are funding those Holberton scholarships. And then the second academic partner is in Bangalore, India. And it's Shanti Bhavan. You might have seen this with the Netflix documentary, "Daughters of Destiny." >> Lisa: It was incredible. >> Absolutely incredible and absolutely moving. The Shanti Bhavan school, for your viewers that are unfamiliar with it, they take children from the poorest of the poor background, in rural India. They commit to educating these children from the age of four all the way through the university level. The scholarships we put together with the help of Intel and Apcera and CB Technologies are to fund girls studying STEM at the university level in Bangalore. And this is just the beginning, Lisa. We really hope that in 2018 we can increase the number of scholarships and we really hope that we'll be able to increase the number of corporate partnerships as well. Because these students are doing phenomenal things and we really believe that they're going to be taking their place along side any of what the Ivy League graduates would be doing. >> I love that. And in our last minute, talk to us about Google and Google's involvement with you. Because that's pretty remarkable what you've been able to achieve for CloudNOW with Google. >> Thank you. The Google involvement has definitely been an involving partnership. And the funding for Google actually happened ... It was a happy circumstance that I ran into Vint Serf at a party and got introduced to him. I gave him a quick 30 second overview of what CloudNOW had been doing and he handed me his business card and said, "It sounds really interesting, send me an email." >> Wow, from one of the fathers of the internet. That's pretty amazing. >> I couldn't believe how accessible or easy-going he was. But I went ahead and I emailed him. I said, "What I'm looking for is some money for a scholarship fund. I'm not asking you for it, I just know if you were to endorse this, the money would very easily be found." So I went to sleep. Woke up, the very next morning there was a response from Vint and he had sent me the money. >> Oh my goodness. >> And we were done. The fund was closed, we were on our way. >> Wow. >> And what he said in response, it was so beautiful, Lisa. He said, "One does what one can to be of service." That message, I've been really holding it with me for the last several months. "One does what one can to be of service" Because I think it's just a very inspiring message, especially as we all go into 2018 and think about what we're grateful for. I hope there are people in your audience that feel like they can do what they can and will join us in this very heart-felt mission. >> Wow. You are so inspiring Jocelyn. With what you and your partners have created with CloudNOW. We thank you so much for asking us to be here. Our second year with the CUBE. It's a great event to cover. But be proud of what you've accomplished. >> Thank you, Lisa. >> Because it's incredible. >> Thank you for all of your support, it really means a lot to me. >> Excellent. We want to thank you for watching the CUBE, I'm Lisa Martin on the ground at Google for the 6th annual CloudNOW Top Women in Cloud event. Thanks for watching. (digital beat music)

>> Las Vegas. Extensive signal from the noise. It's the Q covering interconnect 2016. Brought to you by IBM. Now your host, John Hurry and Dave Ilan. >> Okay, Welcome back, everyone. We are here live in Las Vegas for exclusive coverage of IBM interconnect 2016. This is Silicon Angles. The Q. That's our flagship program. We go out to the events and extract the signal from the noise. I'm John Ferrier with my Coast Day Volante. Our next guest, Steve Robinson News. The GM of client technical engagement before that, in the cloud doing all the blue mix now has the army of technical soldiers out there doing all the action because it's so much robust. So much demand for horizontally scale. The sluices with vertically targeted, prepackaged application development. That's horrible. First you name it big data. Welcome back. Good to see you, John. Thanks. Good to be with you again. Always, like great to have you on because you got a great perspective. You understand the executive viewpoint. A 20 mile stare in the industry. But also you got the in the nuts and bolts in under the hood. >> That's right. A >> lot of action happening under the hood. So let's get that right away. Blue, Mrs Hot Night. Now it's about the developers. What's going on under the hood right now that customers are caring about? >> I always love the Cube. You guys were like one of the first guys talking to us two years ago when we just launched a blue makes on stage. We walked off, got in front of cameras here, and it was great. Over the past year, it's been it's been outstanding. We we're writing about 20,000 folks toe blue mix right now on public, we came out with dedicated and then what people had really been warning was local blue mix as well. So we finally have full hybrid chain that goes from behind the firewall to a single client dedicated cloud all the way up to the public as well. So we've been building that out with service is as well, so have over 106 service is on top of it. You'll see things like Watson, which is unique, our Dash CB analytics, which is unique Internet of things coming in as well. So it's been a great year old building it out and getting more clients on top of it, >> it's like really trying to change the airplane engine in 30,000 feet. Or, in your case, you guys were taken off and from the runway. How has that been? It's been growing pains, of course. Unlearning What? What's going on? What have you learned? Give us the update on >> changing the engine while the plane is flying, and we've used that analogy quite a bit in the labs and way have to show relevance in this market. You know, this market is probably the fastest face technical market I think I've ever been in, and it's moving at such a rapid pace. We had to ship a lot of technology out last year is well, we have every new middleware group in IBM. Putting service is on top of blue mix, so let's get it out there. Let's get it out fast. Now, of course, this year we're gonna harden it up a little bit as well. So more architectures, more points of view. Better look on how this stuff works together hardening up our container strategy, pulling it all the way back to the virtual machine. So both continue to expand it out but let's make it enterprise grade at the same time. >> And also, some differentiation with Watts has been a big play around Catnip. Yeah, really is different because right now with the quote, um, market the way it is court monetization is on number one's mind. Start from startups to enterprises. If you're in business, you want you're top line if you're starting to get monetization. So there's a little bit of IBM in here for people to take in. Well, >> you know, if you look at Watson, you know, when we first started with it, you know, it was this very large big chunk of software that she had to buy. And and we work with Mike Rodents Team toe. Can we chop it up into a set of service is Let's really make this a set of AP eyes, and we started noticing, you know, you saw in Main stage the other day out from Otis. You know, this was a pure startup. He's started picking up the social semantics. Let's pick up the you know, some of the works to text etcetera, conversions, and all of a sudden they're starting to add it in. They said they would have never had access to this technology before way Have that a P I said. Not growing up to 28 we announced a couple cool things this morning. We even showed how would improve your dating life. Probably need some of that with my wife is well to translate between the sexes there, but what people are doing with it now, it's kind of like blowing people. His mind is far beyond what the initial exception waas. >> So your team of your niche is when they get right. It's a large team. It's, but it's a new initiative. New Justice unit, New role for you Talk about that >> way. Kinda had >> a couple pockets of this, but way clearly found that getting clients to the cloud is both a technology challenge as well as a cultural challenge as well. So he brought together some technical experts to kind of help through that entire life chain help up front. You know, many clients are trying to figure out what their overall cloud strategy is, where they truly today and where do they want to get to be? And how can we help him with a road map? That kind of helps them through the transition. Many accounts are very comfortable with the only wanting to be private and only glimpsing forward Thio Public Cloud Helping us bridge across that as well. Then we have the lab service's teams and these air the rial ninjas, the Navy seals. They go as low as you can go and what they're helping. A good way. Yeah, that's good. That's good. That's why they're helping with this very specific technical issue. Technical deployments. A lot of our dedicated local environment. These guys, they're they're really helping it wire in a cz Well, and then we have the garages, you know, we're up Thio. Five of those were going. We announced four new Blockchain garages as well. And this is where firms air coming in to kind of explore do the innovative type project as well. So I think all the way from the initial inception through rolling it out into production, having that team to be able to support him across the >> board. And so this capability existed in IBM previously, But it existed in a sort of bespoke fashion that coordinated >> couple pockets here and there. We always have supports. We had various pockets a lap service's. But we won't really wanna have the capability of seeing that client all the way through their journey, bringing it all under me. We now can easily pass the baton, Handoff says. We need to have that consistent skill there with the clients all the way through their >> journey and is the What's the life cycle of these service is? Is it Is it both pre sales in and post there? Just posted >> many times we'll get involved like our cloud advisers would get involved. Presale. They'll say a specific workload wants to go to the cloud. What are the steps we need to take to make that happen? A CZ well, with our Laps Service's teams, you know, we kind of have, you know, anywhere from a 4 to 6 week engagement. Thio do a specific technology. Let's get it in place. Let's get it wired in et cetera, and then in the garage is you know, we could just take a very novel idea and get it up to, ah, minimal viable product in about a six week period. So again, we're not doing dance lessons for life but strategically placing key skills in with accounts toe. Help him get over that next hump of their journey. >> Steve, when you look at the spectrum from from public all the way down to private and everything in between are you, I wonder if you could describe the level of capability that you are able to achieve with the best practice on Prem with regard to cloud ability. It's service is all the wonderful attributes of child that we've come to know and love. Are you able to, you know, somewhat replicate that roughly replicate that largely replicate, exactly. Replicate that. Where are we today? >> Yeah, I think >> it's a great question. I think. You know, I think most of the clients that we're dealing with have been dealing with some virtualized infrastructure, probably more VMC as they as they've been kind of progressing. That story. One of the things we did it IBM is Could we bring a true cloud infrastructure back behind the firewall? Could we bring an open stack? We bring a cloud foundry base past all the way back through because the goal, of course, is if we could have the same infrastructure private, dedicated and public as they continue to grow and got more comfortable with the public cloud that could start taking work clothes that they had built in one location and start to migrate it out with you. That that local cloud the Maur used for EJ cases. So taking that system of record and building a p i's and allowing to do extensions to that allowing you access into data records that you have today dealing with a lot of extension type cases, you know the core application still needs to be federally regulated. It needs to be under compliance domain. It's gotta be under audit. But maybe I wantto connect it in with a Fitbit or connected in with with a lot Soon are connected in with the Internet of things sensor. I gotta go public cloud for that as well. So locally we can bring that same infrastructure in and then they could doom or service. Is that extended out in the hybrid scenario >> code basis? Because this has come up. Oracle claims this is their big claim to fame. That code base is the same on premise hybrid public. Is that an issue with that? Is that just their marketing, or does it matter what's IBM take on this? >> But we've done ah lot of work with the open standard communities to let's get to a true reference implementation. So on open Stack, we've been doing a lot of work with them, and this is one of the reasons we picked up the Blue box acquisition. Could we really provide a standard open stack locally and also replicate that dedicated and, of course, have it match a reference architecture in public as well? We've also done the same thing with clout. Foundry worked with Sam Ram G to be one of the first vendors, have a certified cloud. Foundry instance is the same local dedicated in public. I think that's kind of the Holy Grail. If you could get the same infrastructural base across all, three, magic can happen. >> But management's important and integration piece becomes the new complexity. I mean, I would say it sounds easy, but it's really hard. Okay, developing in the clouds. Easy, easier ways always used to be right, right well, but not for large enterprises. The integration becomes that new kind of like criteria, right? That separates kind of the junior from the senior type players. I mean do you see the same thing and what we believe >> we do? I think there's usually two issues. We start to see that this model looks great. Let's have the same code base across all three environments. What things? We noticed that a lot of folks, when you get into Private Cloud, had tried to roll their own. You know, open Stack is an open source Project clout. Foundry is an open source project. Let's pull it down and let's see units roll it out and manage it ourselves. These air a little bit you they're very dynamic environments, and they're also a bit punishing if you don't stay current with them, both of them update on a very regular basis. And we found a lot of firms once they applied tenor well, folks to it, they just could not keep up with the right pace of change. So when the technologies we invented was a notion called relay on, this allowed us to actually to use the public cloud is our master copy and then we could provide updates to get down to the dedicated environment and down to the local. This takes the headache completely away from the firm's on trying to keep that local version current. It's not manage service, but it's kind of a new way that we can provide manage patches down to that environment. >> So one of the problems we hear in our community is and presume IBM has some visibility on this. I'm thinking about last year, John, we're at the IBM Z announcement in January, rose 1,000,000 company talked a lot about bringing transaction analytic capabilities together. But one of the problems that our community has practitioners in our community course the data for analytics. A lot of it's in the cloud and a lot of transaction data sitting, you know, on the mainframe, something. How do they bring those two together? Do I remove the data into the data center? Do I do I move pieces in how you see >> we're seeing a lot of that. A lot of it was. Bring the technology down to where the data is, and and now you know the three amount of integration you can do with public data sources, private data sources, et cetera. We're seeing a lot more of the compute want to go out to the cloud as well. You know, we've done some things like around the dash, CB Service's et cetera, where I can start to extract some of that transactional data, but maybe only need a few pieces to really make the data set. That is important to me as I move it out, so I can actually, you know, extract that record. I can actually mask it into being something brand new, and then I could minute we mix it with public data tohave. It do brand new things as well, so I think you're gonna see a lot of dynamic capability across that with or cloud computing technologies coming back behind the firewall and then more ability to release that data be intermixed with public data as well. >> What's the number one thing that you're seeing from customers that you guys were executing on? There's always the low hanging fruit for the easy winds from bringing a team of street team, if you will out. Technical service is out to clients where they really putting that gather, not their five year plans, but their one year. Of course, there's a lot of that agile going on right now. New technologies. You can't isolate one thing and break everything. Za new model. What a customer is caring about, right? What's that? What's the common thing? I think >> over there in 2015 I think the discussion changed and went from Are we going to go to the cloud or we're going to the cloud now? How are we going to do it? And the nice thing about I think a lot of enterprise architecture groups kind of took a step back to say, What do we truly have to do? What is a common platform? What is an integration layer? How do we take some of our old applications and decomposed those into a set of AP eyes? How can we then mix that with public AP eyes? So probably taking one or two projects to be proof points so they could say, this thing really has the magic associated with it. We can really build stuff fast. If we do it the right way, it's gonna be in a catalyst to have the I t. Organization now take the tough steps in what's gonna be the commonality? What common service is are we going to use and how do we start breaking up >> around things you know, we have our own data science and our backcourt operation and one of the things that we always looked at with bloom. It's way start our Amazon. But now, with blue mix, you have a couple things kind of coming together in real time. You said it's getting hard, but those hardened areas are important identity. For instance, where's the data is an instruction and structure. I want a little mongo year or something over there, but with blue mix and compose, I oh, really has a nice fit. I want to explain to the folks we talked before he came on about this new dynamic of composed Io and some of the things that are gluing around blue mix. Could you share this >> William Davis King right? And I think people look to the Cloud Data Service is air. Probably it's the most critical, the most visible, and the one we have to harden up the most is well, even though IBM has been well known for D. B two and we've been a >> wire composed right >> that we did Cognos first, and then we followed up with composed by you because recent waded about, we did compose. I know about eight months ago what we liked about it was all of your favorite flavors, you know? So your your progress, your mongo, you're you're ready. But really having it behave like Like what you would want an enterprise database to do. You can back it up. You can have multiple versions of it. We can replicate itself >> is a perfect cloud need of civic >> class. It has all the cloud properties to it and all the enterprise. Great capabilities with it. Yeah, we've got that now in public, and then you're gonna start seeing dedicated, and you want >> to go bare metal, Just go to soft layer. It's not required right on these things where this will work in the cloud, and then you get the bare metal object you want pushed up the bare metal. No problem. Well, I think >> you know it. Almost hybrid is not gonna get a new definition around it. So it's all gonna be around control and automation, more automation. You need to go all the way up to a cloud foundry where it's managing all the health, checking and keeping your apple. I've etcetera. If you want to go all the way down to bare metal so you can tune it audited et cetera. You can do that as well. I think I've got one of the broader spectrum, is there? >> I'm impressed with the composer. I got to say, Go ahead, get hotel Excited by what? I get excited by just about every way. Just love the whole Dev Ops has been just a game changer in extras. Code has been around for a while, but it's actually going totally mainstream. That's right. The benefits are just off the charts. With Mobile, we have the mobile first guys on. Earlier in the Swift, we had 10 made 12 year old kid. I mean, it's just really amazing. Now that the APS themselves aren't the discussion, it's the under the hood. That's right, so you can have an app look and feel like it's targeted for a vertical, say, retail or whatever. But the actions under the hood yeah, yeah, more than ever. Now >> it's, you know it's funny this year, you know, Dick Tino to the Devil Obsession yesterday and you're the amount of proof points we had around it last year. We were scrambling a little bit and this year it's just we always had to thin out. That's how many guys were having great success with this stuff is coming into its own. >> It totally is. And you guys are give you guys Props were running as fast as you can and you're working hard. And it's not just talk. Yeah, it's It's it's legit. I'm gonna ask you a question. What's the big learnings from last year? This year? What's happened? What do you look back and say? Wow, we really learned a lot or something that might have been Magda ified for you in this journey this past year. >> A lot of it goes back to, you know, this changing culture at IBM, you know, the amount of code we put out in two years was just just unbelievable. But I think also the IBM becoming a true cloud company. Some of that we did with our own shop some, but we did through injecting it with acquisitions. You know, like to compose Io the cloud and team, the blue box guys, et cetera. I think we got the chops now to play it play pro ball way worked very hard, Teoh. How many folks, Can we attract the blue mix? We're getting up to 20,000 week. Right now. We're starting. Get some great recognition and the successes are rolling in as well. So a lot of hard work and a lot of busted knuckles. A lot of guys are tired. Definitely, definitely straight in the game now. >> Ready for the crow bait? Taking the pro GameCube madness starts on cute madness. There were, you know, keep matched all the brackets of the Cube alumni and vote on it turns into a hack a phone because everyone stuffed the ballots. Let's talk about pro ball for next year, a CZ. You guys continue? Sure. The theme here obviously is developer. I mean, the show could be dedicated 100%. The blooming LeBlanc up there kind of going fast at the end of this booth on the clock anymore. Time >> right. Like the Star Wars trailer we had >> going up, he needed more time. So it's good props you got for this year. What's going on the road map this year? What if some of the critical goals that you guys see on your group and then just in general for the thing a >> lot of the activities were gonna be doing again is hardening the stack. I've got a brand new team now called a Solution Architecture, where we're looking at it from top to bottom, taking customer scenarios and really testing it out. How do you do? Back up. How do you do? Disaster recovery? How do you do? Multi geography, You know, things like PC I compliance. The rial enterprise problems are now coming to the class global and their global. And with security and compliance, they're changing in a very dynamic fashion. We have to show how you can do those in the cloud. You'd be amazed on how many conversations we have with Si SOS every single week. Is the cloud secure? How do we do enterprise? Great workloads. IBM is bringing that story to the cloud as well. That's the story of >> a potato that content >> Curation is unbelievable, right? That's the hardest part. And it's not that we have it fixed either. But you were doing more of aggregating it together so that we can really pull it all together. I call it the diamond Mine versus the jewelry store. You know, we always have really did you got yet? The great answers out there somewhere. But if you don't start to pull it together into a single place So one of things we did this year was launched the blue mixed garage methodology where we took all of our best practices. We took text test cases, even sample code, and brought it into a single methodology site where people start to go out, pull it down, use it, etcetera. Previously, we had it scattered all over the place, and we're gonna be doing more things like that. Bring in the assets to the programmers, things that we've tried, things we've tested being more open about it, putting in a single location. >> Well, we certainly would like to help promote that. Any kind of those kind of customer reference architectures. Happy to pump on silicon angle with the bond outlook for the vibe. I'm sorry. Five for the show things year. What's the vibe this year? You know, I think I've >> been very impressed with it, and I think, you know, I've been stepping up its game If you go down to the blue. Mixed garages are motives. A motorcycle on stage, you know, kind of getting a little more hip and happening as well. But I think the clients here and this is always about the customer stories and some of the things that we're hearing from the three guys start ups that are doing GPS logistical management 22 to the big accounts, and the big banks that you really see have embraced the cloud and doing great stories on it as well. I think people come to this show so they see what their peers were doing. And they definitely walk away with a sense that the cloud Israel it's happening and 2016. It is really going to driving it home. That has to be part of everybody. Strategy motorcycles I had put on the Harley Man. We'll take it for a spin guarantee. Come on down >> and give my wife. When I got married, it was terms of conditions. That's right. That's right. Last, Watson that Yeah, Thanks, Steve. Thanks. Taking the time and great to see you again. Congratulations. What? They get technical engagement team that you have all the work that you did that blue mix noted certainly by the cube. Congratulations and continued success with Loomis congratulating >> you guys. Well, always a pleasure. >> Okay. Cube Madness, March 15th Cube Gems go to Twitter. And speaking of jewelry, we have Cube gems hashtag Cube gems. That's the highlights of the videos up there. Real time. And, of course, we're gonna get that TV for all. All the action videos are up there right now. I'll be right back with more coverage after this short break here in Las Vegas.