>>Hi, everyone. Welcome to the cubes presentation of the AWS startup showcase the theme. This episode is data as code, and this is season two, episode two of the ongoing series covering exciting startups from the ecosystem in cloud and the future of data analytics. I'm your host, John furry. You're getting great featured panel here with AWS heroes, Lynn blankets, the CEO of Lindbergh Lega consulting, Peter Hanson's, founder of cloud Cedar and Alex debris, principal of debris advisory. Great to see all of you here and, uh, remotely and look forward to see you in person at the next re-invent or other event. >>Thanks for having us. >>So Lynn, you're doing a lot of work in healthcare, Peter you're in the middle of all the action as data as code Alex. You're in deep on the databases. We've got a good round up of, of topics here ranging from healthcare to getting under the hood on databases. So as we'll start with you, what are you working on right now? What trends do you see in the database space? >>Yeah, sure. So I do, uh, I do a lot of consulting work working with different people and, you know, often with, with dynamo DB or, or just general serverless technology type stuff. Um, if you want to talk about trends that I'm seeing right now, I would say trends you're seeing as a lot, just more serverless native databases or cloud native databases where you're seeing these cool databases come out that really take advantage of, uh, this new cloud environment, right? Where you have scalability, you have plasticity of the clouds. So you're not having, you know, instant space environments anymore. You're paying for capacity, you're paying for throughput. You're able to scale up and down. You're not managing individual instances. So a lot of cool stuff that we're seeing, you know, um, with this new generation of, of infrastructure and in particular database is taking advantage of this, this new cloud world >>And really lot deep into the database side in terms of like cloud native impact, diversity of database types, when to use certain databases that also a big deal. >>Yeah, absolutely. I like, I totally agree. I love seeing the different types of databases and, you know, AWS has this whole, uh, purpose-built database strategy. And I think that, that makes a lot of sense. Um, you know, I want to go too far with it. I would, I would more think about purpose-built categories and things like that, you know, specialize in an OLTB database within your, within your organization, whether that's dynamo DB or document DB or relational database Aurora or something like that. But then also choose some sort of analytics database, you know, if it's drew it or Redshift or Athena, and then, you know, if you have some specialized needs, you want to show some real time stuff to your users, check out rock site. If you want to, uh, you know, do some graph analytics, fraud detection, checkout tiger graph, a lot of cool stuff that we're seeing from the startup showcase here. >>Looking forward to unpacking that Lynn you've been in love now, a healthcare action with cloud ops, the pandemic pushes hard core on everybody. What are you working on? >>Yeah, it's all COVID data all the time. Uh, before the pandemic, I was supporting research groups for cancer genomics, which I still do, but, um, what's, uh, impactful is the explosive data volumes. You know, when you there's big data and there's genomic data, you know, I've worked with clients that have broken data centers, broken public cloud provider data centers because of the daily volume they're putting in. So there's this volume aspect. And then there's a collaboration, particularly around COVID research because of pandemic. And so you have this explosive volume, you have this, um, need for, uh, computational complexity. And that means cloud the challenge is it, you know, put the pedal to the metal. So you've got all these bioinformatics researchers that are used to single machine. Suddenly they have to deal with distributed compute. So it's a wild time to be in this space. >>What was the big change that you've seen with the, uh, the pandemic and in genomic cloud genomic specifically what's the big change has happened. >>The amount of data that is being put into the public cloud, um, previously people would have their data on their local, uh, capacity, and then they would publish their paper and the data may or may not become available for, uh, reproducing the research, uh, to accelerate for drug discovery and even variant identification. The data sets are being pushed to public cloud repositories, which is a whole new set of concerns. You have not only dealing with the volume and cost, but security, you know, there's federated security is non-trivial and not well understood by this domain. So there's so much work available here. >>Awesome. Peter, you're doing a lot with the data as a platform kind of view and platform engineering data as code is, is something that's being kicked around. What are you working on and how does platform engineering change as data becomes so much more prevalent in its value proposition? >>Yeah. So I'm the founder of cloud Cedar and, um, we sort of built this company out, this consultancy all around the challenges that a lot of companies have got with getting their data sorted, getting it organized, getting it ready for other use cases, such as analytics and machine learning, um, AI workloads and the like. So typically a platform engineering team will look after the organization of a company infrastructure, making sure that it's coherent across the company and a data platform, engineering teams doing something similar in that sense where they're, they're looking at making sure that, uh, data teams have a solid foundation to build upon, uh, that everything's quite predictable and what that enables is a faster velocity and the ability to use data as code as a way of specifying and onboarding data, building that, translating it, transforming it out into its specific domains and then on to data products. >>I have to ask you while you're here. Um, there's a big trend around data meshes right now. You're hearing, we've had a lot of stuff on the cube. Um, what are practical that people are using data mesh, first of all, is it relevant and how are people looking at this data mesh conversation? >>I think it becomes more and more relevant, uh, the bigger the organization that you're dealing with. So, you know, often times in the enterprise, you've got, uh, projects with timelines of five to 10 years often outlasting technology life cycles. The technology that you're building on is probably irrelevant by the time that you complete it. And what we're seeing is that data engineering teams and data teams more broadly, this organizational bottleneck and data mesh is all about, uh, breaking down that, um, bottleneck and decentralizing the work, shifting that work back onto, uh, development teams who oftentimes have got more of the context and a centralized data engineering team. And we're seeing a lot of, uh, Philocity increases as a result of that. >>It's interesting. There's so many different aspects of how data is changing the world. Lynn talks about the volume with the cloud and genomics. We're hearing data engineering at a platform level. You're talking about slicing and dicing and real-time information. You mentioned rock set, Alex. So I'd like to ask each of you to answer this next question, which is how has the team dynamics changed with data engineering because every single company's impacted. So if you're researchers, Lynn, you're pumping more data into the cloud, that's got a little bit of data engineering to it. Do they even understand that is that impacting them? So how has data changed the responsibilities or roles in this new emerging area of data engineering or whatever you want to call it? Lynn, we'll start with you. What do you, what do you see this impact? >>Well, you know, I mean, dev ops becomes data ops and ML ops and, uh, you know, this is a whole emergent area of work and it starts with an understanding of container technologies, which, you know, in different verticals like FinTech, that's a given, right, but in bioinformatics building an appropriately optimized Docker container is something I'm still working with customers now on because they have the concept of a Docker container is just a virtual machine, which obviously it isn't, or shouldn't be. So, um, you have, again, as I mentioned previously, this humongous skill gap, um, concepts like D, which are prevalent in ad tech FinTech, that's not available yet for most of my customers. So those are the things that I'm building. So the whole ops space is, um, this a wide open area. And really it's a question of practicality. Um, you know, I have, uh, a lot of experience with data lakes and, you know, containerizing and using the data lake platform. But a lot of my customers are going to move to like an interim pass based solutions. If they're using spark, for example, they might use to use a managed spark solution as an interim, um, step up to the cloud before they build their own containers. Because the amount of knowledge to do that effectively is non-trivial >>Peter, you mentioned data, you mentioned data lakes, onboarding data into lake house architectures, for instance, something that you're familiar with. Um, this is not obvious to some verticals obvious to others. What do you see this data engineering impact from a personnel standpoint? And then ultimately how things get built, >>You know, are you directing that to me, >>Peter? >>Yeah. So I think, um, first and foremost, you know, the workload that data engineering teams are dealing with is ever increasing. Usually there's a 10 X ratio of, um, software engineers to data engineers within a business and usually double the amount of analysts to data engineers again. And so they're, they're fighting it ever increasing backload. And, uh, so they're fighting an ever increasing backlog of, of, uh, tasks to do and tickets to, to, to churn through. And so what we're seeing is that data engineering teams are becoming data platform engineering teams where they're building capability instead of constantly hamster wheels spinning if you will. And so with that in mind, with onboarding data into, uh, a Lakehouse architecture or a data lake where data engineering teams, uh, uh, getting wins is developing a very good baseline of structure where they're getting the categorization, the data tagging, whether this data is of a particular domain, does it contain some, um, PII data, for instance, uh, and, and, and, and then the security aspects, and also, you know, the mechanisms on which to do the data transformations, >>Alex, on the database side, those are known personas in an enterprise, a them, the database team, but now the scale is so big. Um, and there's so much going on in databases. How does the data engineering impact organizations from your standpoint? >>Yeah, absolutely. I think definitely, you know, gone are the days where you have a single relational database that is serving operational queries for your users, and you can also serve analytics queries, you know, for your internal teams. It's, it's now split up into those purpose-built databases, like we've said. Uh, but now you've got two different teams managing it and they're, they're designing their data model for different things. You know? So L LLTP might have a more de-normalized model, something that works for very fast operations and it's optimized for that, but now you need to suck that data out and get it elsewhere so that your, your PM or your business analyst, or whoever can crunch through some of that. And, you know, now it needs to be in a more normalized format. How do you sort of bridge that gap? That's a tough one. I think you need to, you know, build empathy on each side of, of what each side is doing and, and build the tools to say, Hey, this is going to help you, uh, you know, LLTP team, if we know what, what users are actually doing, and, and if you can get us into the right format there, so that then I can, you know, we can analyze it, um, on the backend. >>So I think, I think building empathy across those teams is helpful. >>When I left to come back to, you mentioned a health and informatics is coming back. Um, but it's interesting, you know, I look at a database world and you look at the solutions that are out there. A lot of companies that build data solutions don't have a data problem. They've never, they're not swimming in a lot of data, but then you look at like the field that you're working in right now with the genomics and health and, and quantum, they're always, they're dealing with data all the time. So you have people who deal with a lot of data all the time are breaking through New Zealand. People who are don't have that experience are now becoming data full, right? So people are now either it's a first time problem, or they've always been swimming in a ton of data. So it's more of what's the new playbook. And then, wow, I've never had to deal with a lot of data before. What's your take? >>It's interesting. Cause they know, uh, bioinformatics hires, um, uh, grad students. So grad students, you know, use their, our scripts with their file on their laptop. And so, um, to get those folks to understand distributed container-based computing is like I said, a not non-trivial problem. What's been really interesting with the money pouring in to COVID research is when I first started, some of the workflows would take, you know, literally 500 hours and that was just okay. And coming out of FinTech, I was, uh, I could, I was blown away like FinTech is like, could that please take a millisecond rather than a second? Right. And so what has now happened, which makes it, you know, like I said, even more fun to work in this domain is, uh, the research dollars have really gone up because of the pandemic. And so there are, there are, there's this blending of people like me with more of a big data background coming into bioinformatics and working side by side. >>So it's this interesting sort of translation because you have the whole taxonomy of bioinformatics with genomics and sequencers and all the weird file types that you get. And then you have the whole taxonomy of dev ops data ops, you know, containers and Kubernetes and all that. And trying to get that into pipelines that can actually, you know, be efficient, given the constraints. Of course, we, on the tech side, we always want to make it super optimized. I had a customer that we got it down from 500 hours to minutes, but they wanted to stay with the past solution because it was easier for them to go from 500 hours to five hours was good enough, but you know, the techies want to get it down to five minutes. >>This is, this is, we've seen this movie before dev ops, um, edge and op operations, you know, IOT, world scenes, the convergence of cultures. Now you have data and then old, old school operations kind of coming up. So this kind of supports the thesis. That data as code is the next infrastructure as code. What do you guys, what's the reaction there for you guys? What do you think about that? What does data's code mean? If infrastructure's code was cloud and dev ops, what is data as code? What does that mean? >>I could take it if you like. I think, um, data teams, organizations, um, have been long been this bottleneck within the organization and there's like this dark matter of untapped energy and potential waiting to be unleashed a data with the advent of open source projects like DBT, um, have been slowly sort of embracing software development, lifecycle practices. And this is really sort of seeing a, a big steep increase in, um, in their velocity. And, and this is only going to increase and improve as we're seeing data teams, um, embrace starter as code. I think it's, uh, the future is bright for data. So I'm very excited. >>Lynn Peter reaction. I mean, agility data is code is developer concept CICB pipeline. You mentioned it new operational workflows coming into traditional operations reaction. >>Yeah. I mean, I think Peter's right on there. I'd say, you know, some of those tools we're seeing come in from, from software, like, like DBT, basically giving you that infrastructure as code, but applied to that data realm. Also there have been a few, like get for data type things, pack a derm, I believe is one and a few other ones where you bring that in and you also see a lot of immutability concepts flowing into the data realm. So I think just seeing some of those software engineering concepts come over to the data world has, has been pretty interesting >>What we'll literally just versioning datasets and the identification of what's in a data set. What's not in a data set. Some of this is around ethical AI as well, um, is a whole, uh, area that has come out of research groups. Um, mostly AI research groups, but is being applied to medical data and needs to be obviously, um, so this, this, this, um, metadata and versioning around data sets is really, I think, a very of the moment area. >>Yeah, I think we, we, you guys are bringing up a really good kind of direction that's happening in data. And that is something that you're seeing on the software side, open source and now dev ops. And now going to data is that the supply chain challenges of we've been talking about it here on the cube and this, this, um, this episode is, you know, we've seen Ukraine war, but some open source, you know, malware hitting datasets is data secure. What is that going to look like? So you starting to get into this what's the supply chain, is it verified data sets if data sets have to be managed a whole nother level of data supply chain comes up, what do you guys think about that? >>I'll jump in. Oh, sorry. I'll jump in again. I think that, you know, there's, there's, um, some, some of the compliance requirements, um, around financial data are going to be applied to other types of data, probably health data. So immutability reproducibility, um, that is, uh, legally required. Um, also some of the privacy requirements that originated in Europe with GDPR are going to be replicated as more and more, um, types of data. And again, I'm always going to speak for health, but there's other types as well coming out of personal devices and that kind of stuff. So I think, you know, this idea of data as code is it's, it goes down to versioning and controlling and, um, that's, uh, that's sort of a real succinct way to say it that we didn't used to think about that. We just put it in our, you know, relational database and we were good to go, but, um, versioning and controlling in the global ecosystem is kind of, uh, where I'm focusing my efforts. >>It brings up a good question. If databases, if data is going to be part of the development process has to be addressable, which means horizontally scalable. That means it has to be accessible and open. How do you make that work and not foreclose it with a lot of restrictions? >>I think the use of data catalogs and appropriate tagging and categorization, you know, I think, you know, everyone's heard of the term data swamp, and I think that just came about because that everyone saw like, oh, wow, S3, you know, infinite storage. We just, you know, throw whatever in there for as long as we want. And I think at times, you know, the proliferation of S3 buckets, um, and the like, you know, we've just seen, uh, perhaps security, not maintained as well as it could have been. And I think that's kind of where data platform engineering teams have really sort of, uh, come into the, for, you know, creating a governance set of buckets like formation on top. But I think that's kind of where we need to see a lot more work with appropriate tags and also the automatic publishing of metadata into data catalogs so that, um, folks can easily search and address particular data sets and also control the access. You know, for instance, you've got some PII data, perhaps really only your marketing folks should be looking at email addresses and the like not perhaps your finance folks. So I think, you know, there's, there's a lot to be leveraged there in formation and other solutions, >>Alex, let's back up and talk about what's in it for the customer, right. Let's zoom back and saying reality is I just got to get my data to make sure it's secure always on and not going to be hackable. And I just got to get my data available on river performance. So then, then I got to start thinking about, okay, how do I intersect it? So what should teams be thinking about right now as I look up all their data options or databases across their enterprise? >>Yeah, it's, it's a, it's a good question. I just, you know, I think Peter made some good points there and you can think of history as sort of ebbing and flowing between centralization and decentralization a lot of times. And you know, when storage was expensive, data was going to be sort of centralized and Maine maintained, sort of a, you know, by the, uh, the people that are in charge of it. But then when, when S3 comes along, it really decreases storage. Now we can do a lot more experiments on it. We can store a lot more of our data, keep it around and do different things on it. You know, now we've got regulations again, we were, we gotta, we gotta be more realistic about, about keeping that data secure and make sure we're, we're doing the right things with it. So it's, we're gonna probably go through a period of, of centralization as we work out some of this tooling around, you know, tagging and, and ethical AI that, that both Peter. And when we're talking about here and maybe get us into that, that next wearable world of de-centralization again. But I, I think that ebb and flow is going to be natural in response to, you know, the problems of the, the other extreme, >>Where are we in the market right now from progress standpoint, because data lakes don't want to be data swamps. You seeing lake formation as a data architecture, as an example, where are we with customers? What are they doing right now? Where would you put them in the progress bar of, of evolution towards the Nirvana of having this data sovereignty? And this data is code environment. Are they just now in the data lake store, everything real-time and historical? >>Well, I can jump in there. Um, SQL on files is the, is the driver. And so we know when Amazon got Athena, um, that really drove a lot of the customers to really realistically look at data lake technologies, but data warehouses are not going away. And the integration between the two is not seamless. No, we, we are partners with AWS, but we don't work for them. So we can tell you the truth here. Um, there's, there's work to it, but it really, for my customers, it really upped the ante around data lake, uh, because Athena and technologies like that, the serverless, um, SQL queries or the familiar quarry, um, uh, libraries really drove a movement away from either OLTB or OLAP, more expensive, more cumbersome structures, >>But they still need that. Oh, LTP, like if they have high latency issues, they want to be low latency. Can they have the best of both worlds? That's the question. >>I mean, I w I would say we're getting, you know, we're getting closer. We're always going to be, uh, you know, that technology is going to be moving forward, and then we'll just move the goalpost again, in terms of, of what we're asking from it. But I think, you know, the technology that's getting out there, you can get, get really well. And then, you know, just what I work in the dynamo DB world. So you can get really great low latency. So, you know, single digit millisecond LLTP response times on that. I think some of the analytics stuff has been a problem with that. And there, there are different solutions out there to where you can export dynamo to S3, and then you can be doing SQL on your FA your files with Athena Lakeland's talking about, or now you see, you know, rock set of partner here that that'll just ingest your dynamo, DB data, you know, make all those changes. So if you're doing a lot of, uh, changes to your data and dynamo is going to reflect in Roxanna, and then you can do analytics queries, you can do complex filters, different things like that. So, you know, I, I think we continue to push the envelope and then we moved the goalpost again. But, um, you know, I think we're in a, a lot better place than we were a few years ago, for sure. >>Where do you guys see this going relative to the next level? If data as code becomes that next agile, um, software defined environment with open source? Well, all of these new tools with serverless things happening with data lakes are built in with nice architectures with data warehouses, where does it go next? What happens next? If this becomes an agile environment, what's the impact? >>Well, I don't want to be so dominant, but I have, I feel strongly, so I'm going to jump in here. So, so I, um, I feel like, you know, now for my, my, my most computationally intensive workloads, I'm using GPS, I'm bursting to GPU for TensorFlow neural networks. So I've been doing quite a bit of exploration around Amazon bracket for QPS and it's early. Um, and it's specialty. It's not, you know, for everybody. And the learning curve again is pretty daunting, but, um, there are some use cases out there. I mean, I got ahold of a paper where some people did some, um, it was a Q CNN, um, quantum convolutional neural network for lung cancer images, um, from COVID patients and the, the, uh, the QP Hugh, um, algorithm pipeline performed more accurately and faster. So I think, um, bursting to quantum is something to pay attention to. >>Awesome. Peter, what's your take on what's next? >>Well, I think there's still, um, that, that was absolutely fascinating from Lynn, but I think also there's, there's, uh, you know, some more sort of low-level, uh, low-hanging fruit available in, in the data stack. I think there's a lot of, there's still a lot of challenges around the transformation there, getting our data from sort of raw landed data into business domains, and that sort of talks to a lot of what data mesh is all about. I think if we can somehow make that a little more frictionless, because that that's really where the like labor intensive work is. That's, that's kinda dominating, uh, data engineering teams and where we're sort of trying to push that, that workload back onto, um, you know, software engineering teams. >>Alice will give you the final word. What's the impact. What's the next step? What's it look like in the future? >>Yeah, for sure. I mean, I've never had the, uh, breaking a data center problem that wind's had, or the bursting the quantum problem, for sure. But, you know, if you're in that, you know, the pool I swim and of terabytes of data and below and things like that, I think it's a good time. It just like we saw, you know, like we were talking about dev ops and, and pushing, uh, you know, allowing software engineers to handle more of, of the operation stuff. I think the same thing with data can happen where, you know, software engineering teams can handle not just their code, not just, you know, deploying and operating it, but also thinking about their data around the code. And that doesn't mean you won't have people assist you within your organization. You won't have some specialists in there, but I think pushing more stuff, even onto the individual development teams where they have ownership of that. And they're thinking about it through all this different life cycle. I mean, I'm pretty bullish on that. And I think that's an exciting development >>Was that shift, what left with left is security. What does that mean to >>Shipped so much stuff left, but now, you know, the things that were at the end are back at the end again, but, uh, you know, at least we think we can think about that stuff early in the process, which is good, >>Great conversation, very provocative, very realistic and great impact on the future data as code is real, the developers I do believe will have a great operational role and the data stack concept and impacting things like quantum, it's all kind of lining up nicely. Um, and it's a great opportunity to be in this field from a science and policy standpoint. Um, data engineering is legit. It's going to continue to grow and thanks for unpacking that here on the queue. Appreciate it. Okay. Great panel D AWS heroes. They work with AWS and the ecosystem independently out there. They're in the trenches doing the front lines, cracking the code here with data as code season two, episode two of the ongoing series of the 80, but startups I'm John for your host. Thanks for watching.

>>From the cube studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is a cute conversation. Welcome to this cube conversation. I'm Lisa Martin pleased to be joined once again by the co CEO of mem sequel, Raj Verma, Raj, welcome back to the program. >>Thank you very much, Lisa. Great to see you as always. >>It's great to see you as well. I always enjoy our conversations. So why don't you start off because something that's been in the news the last couple of months besides COVID is one of your competitors, snowflake confidentially filed IPO documents with the sec a couple months ago. Just wanted to get your perspective on from a market standpoint. What does that signify? >>Yeah. Firstly, congratulations to the snowflake team. Uh, you know, I've, I have a bunch of friends there, you know, John McMahon, my explosives on the board. And I remember having a conversation with him about seven years ago and it was just starting off and I'm just so glad for him and Bob Mobileye. And, and as I said, a bunch of my friends who are there, um, they're executed brilliantly and, uh, I'm thrilled for that. So, um, we are hearing as to what the outcomes are likely to be. And, uh, it just seems like, uh, you know, it's going to be a great help. Um, and I think what it signifies is firstly, if you have a bit technology and if you execute well, good things happen and there's enough room for innovation here. So that is one, the second aspect is I think, and I think more importantly, what it signifies is a change of thought in the database market. >>If you really see, um, and know if my memory serves me right in the last two decades or probably two and a half buckets, we just had one company go public in the database space and that was Mongo. And, um, and that was in, I think October, 2017 and then, uh, two and a half years. So three years we've seen on other ones and uh, from the industry that we know, um, you know, there are going to be a couple that are going to go out in the next 18 months, 24 months as well. So the fact is that we had a, the iron grip on the database market for almost, you know, more than two decades. It was Oracle, IBM that a bit of Sybase and SAP HANA. And now there are a bunch of companies which are helping solve the problems of tomorrow with the technology of the month. >>And, uh, and that is, um, that is snowflake is a primary example of that. Um, so that's a, that's good change. God is good. I do think the incumbents are gonna find it harder and harder going forward. And also if you really see the evolution of the database market, the first sort of workloads that moved to the cloud with the developer workloads and the big benefactor that that was the no secret movement and one company that executed in my opinion, the best was Mongol. And they were the big benefactor of that, that sort of movement to the cloud. The second was the very large, but Moisey database data warehouse market, and a big benefactor of that has been snowflake big queries, the other one as well. However, the biggest set of tsunami of data that's we are seeing move to the cloud is the operational data, which is the marriage of historical data with real time data to give you real time insights as, or what we call the now are now. >>And that's going to be much, much bigger than, uh, than both the, you know, sequel or the developer data movement and the data warehouse. And we hope to be a benefactor of that. And then the shake up that happens in the database market and the change that's happening there, isn't a vendor take on market anymore, and that's good because you don't then have the stranglehold that Oracle had and you know, some of the ways that are treated as customers and help them to run some, et cetera, um, yeah. And giving customers choice so that they can choose what's best for the business is going to be, it's going to be great. And me are going to see seven to 10 really good database companies in large, in the next decade. And we surely hope them secret as one of them of, we definitely have the, have the potential to be one of them. >>You have the market, we have the product, we have the customers. So, you know, as I tell my team, it's up to us as to what we make of it. And, um, you know, we don't worry that much about competition. You did mention snowflake being advantage station. We, yeah, sure. You know, we do compete on certain opportunities. However, their value proposition is a little more single-threaded than ours. So they are more than the Datavail house space are. Our vision of the board is that, uh, you know, you should have a single store for data, whether it's database house, whether it's developer data or whether it's operational data or DP data. And, uh, you know, watch this space from orders. We make somebody exciting announcements. >>So dig into that a little bit more because some of the news and the commentary Raj in the last, maybe six weeks since the snowflake, um, IPO confidential information was released was, is the enterprise data warehouse dead. And you just had a couple of interesting things we're talking about now, we're seeing this momentum, huge second database to go public in two and a half bigots. That's huge, but that's also signifying to a point you made earlier. There's, there's a shift. So memes SQL isn't, we're not talking about an EDW. We're talking about operational real time. How do you see that if you're not looking in the rear view mirror, those competitors, how do you see that market and the opportunities? >>Yeah, I, I don't think the data warehouse market is dead at thought. I think the very fact that, you know, smoke makers going out at whatever valuation they go out, which is, you know, tens of billions of dollars is, um, is a testimony to the fact that, you know, it's a fancy ad master. This is what it is. I mean, data warehouses have existed for decades and, uh, there is a better way of doing it. So it's a fancy of mousetrap and, and that's great. I mean, that's way to money and it's clearly been demonstrated. Now what we are saying is that I think that is a better way to manage the organization's data rather than having them categorized in buckets of, you know, data warehouse, data developer, data DP, or transactional data, you know, uh, analytical data. Is there a way to imagine the future where there is one single database that you can quit eat, or data warehouse workloads for operational workloads, for OLTB work acknowledge and gain insights. And that's not a fancier mousetrap that is a data strategy reimagine. And, uh, and that's our mission. That's our purpose in life right now and are very excited about it's going to be hard. It's not, it's not a given it's a hard problem to solve. Otherwise, if you can solve it before we have the, uh, we have the goods to deliver and the talent, the deliberate, and, um, we are, we are trying it out with some very, very marquee customers. So we've been very excited about, >>Well, changing of the guard, as you mentioned, is hard. The opposite is easy, the opposite, you know, ignoring and not wanting to get out of that comfort zone. That's taken the easy route in my opinion. So it seems like we've got in the market, this, this significant changing of the guard, not just in, you know, what some of your competition is doing, but also from a customer's perspective, how do you help customers, especially institutions that have been around for decades and decades and decades pivot quickly so that the changing of the guard doesn't wipe them out. >>Yeah. Um, I actually think slightly differently. I think changing of the guard, um, wiping out a customer is if they stick or are resistant to the fact that there is a change of God, you know, and if they, if they hold on to, as we said in our previous conversation, if you stick onto the decisions of yesterday, you will not see the Sundays of tomorrow. So I do think that, uh, you know, change, you have a, God is a, is a symbolism, not even a symbolism as a statement to our customers to say, there is a better way of doing, uh, what you are doing to solve tomorrow's problem. And then doesn't have to be the Oracles and the BB tools and the psychosis of the world. So that's, that's one aspect of it. The second thing is, as I've always said, you're not really that obsessed about, uh, competition. >>The competition will do what they do. Uh, we are really very focused on having an impact in the shortest period of time on our customers and, uh, hopefully a positive impact. And if you can't do it, then, you know, I've had conversations with a few of them saying, maybe be not the company for you. Uh, it's not as if I have to sort of, software's a good one. I supply to the successful customers in the bag to do the unsuccessful with customers. The fact is that, you know, in certain, certain places there isn't an organizational alignment and you don't succeed. However, we do have young, we have in the last 14 months or so made tremendous investments into really ease of use of flexibility of architecture, which is hybrid and tactile, and that shrinking the total time to value for our customers. Because if I, if I believe you, if you do these three things, you will have an impact, a positive impact on the customer, in the sharpest, uh, amount of time and your Lindy or yourself. And I think that is more important than worrying needlessly about competition. And then the competition will do what they do. But if you keep your customers happy by having a positive impact, um, successes, only amount of time, >>Customers and employees are essential to that. But I like that you talked about customer obsession because you see it all over the place. Many people use it as descriptors of themselves and their LinkedIn profiles, for example, but for it actually to be meaningful, you talked about the whole objective is to make an impact for your customers. How do you define that? So that it's not just, I don't want to say marketing term, but something that everyone says they're customer obsessed showing it right within the pudding. >>It's easy to say we are customer obsessed. I mean, this organization is going to say we don't care about our customer. So, you know, of course we all want our customers to be successful. How do you, that's easy, you know, having a cultural value that we put our customers first is, was easy, but we didn't choose to do that. What we said is how do you have an impact on your customer in the shortest amount of time, right? That is, that is what you have. I'm sequel and Lee have now designed every process in mem sequel to align with that word. If, if that is a decision that we have to make a B essentially lenses through the fact of what is in the best interest of our customer and what will get us to have an impact, a positive impact on the customer in the shortest amount of time, that is a decision, which is a buy decision for us to make. >>A lot of times it's more expensive. It's a, a lot duffel. It stresses the, um, the, the, the organization, um, and the people in it. But that's, uh, that's what you have to do if you are. Um, if you are, you know, as, as they say, customer obsessed, um, it is, it's just a term which is easy to use, but very difficult to put here too. And we want to be a tactic. It right to be, we are going to continue to learn. It's a, it's not a destination, it's a journey. And we continue to take decisions and refine our processes do, as I said, huh, impact on our customers in the shortest amount of time. Now, obsessiveness, a lot of times is seen as a negative in the current society that we live in. And there's a reason for that because the, they view view obsession, but I view obsession and aggression is that is a punishing expression, which is really akin to just being cruel, you know, leading by fear and all the rest of it, which is as no place in any organization. >>And I actually think that in society at large, nothing, I believe that doesn't have any place in society. And then there's something which I dumb as instrumentalists, which is, this is where we were. This is where we are. This is where we are going and how do we track our progress on a daily, weekly, monthly basis? And if we, aren't sort of getting to that level that we believe we should get to, if our customers, aren't seeing the value of dramas in the shortest amount of time, what is it that we need to do better? Um, is that obsession, our instrumental aggression is, is, is what we are all about. And that brings with it a level of intensity, which is not what everyone, but then when you are, you know, challenging the institutions which have, uh, you know, the also has to speak for naked, it's gonna take a Herculean effort to ask them. And, uh, you know, the, the basically believed that instrumental aggression in terms of the, uh, you know, having an impact on customer in the shop to smile at time is gonna get us there. And a, and B are glad to have people who actually believe in that. And, uh, and that's why we've made tremendous progress over the course of last, uh, two years. >>So instrumental aggression. Interesting. How you talked about that, it's a provocative statement, but the way that you talk about it almost seems it's a prescriptive, very strategic, well thought out type of moving the business forward, busting through the old guard. Cause let's face it, you know, the big guys, the Oracles they're there, they're not easy for customers to rip and replace, but instrumental aggression seems to kind of go hand in hand with the changing of the guard. You've got to embrace one to be able to deliver the other, right. >>Yeah. So ducks, I think even a fever inventing something new. Um, I mean, yeah, it just requires instrumental aggression, I believe is a, uh, uh, anchor core to most successful organizations, whether in IP or anywhere else. That is a, that is a site to that obsession. And not, I'm not talking about instrumental aggression here, but I'm really talking about the obsession to succeed, uh, which, uh, you know, gave rise to what I think someone called us brilliant jerks and all the rest of it, because that is the sort of negative side of off obsession. And I think the challenge of leadership in our times is how do you foster the positivity of obsession, which needs to change a garden? And that's the instrumental aggression as a, as a tool to, to go there. And how do you prevent the negative side of it, which says that the end justifies the means and, and that's just not true. >>Uh, there is, there is something that's right, and there's something that's wrong. And, uh, and if that is made very clear that the end does not justify the meanings, it creates a lot of trust between, um, Austin, our customers, also not employees. And when their inherent trust, um, happens, then you foster, as I said, the positive side of obsession and, um, get away from the negative side of obsession that you've seen in certain very, very large companies. Now, the one thing that instrumental aggression and obsession brings to a company is that, uh, it makes a lot of people uncomfortable, and this is what I continue to tell. Um, our, our employees and my audience is, um, you know, be comfortable being uncomfortable because what you're trying to do is odd. And it's going to take a, as I say, a Herculean effort. So let's, uh, let's be comfortable being uncomfortable, uh, and have fun doing it. If there's, uh, how many people get a chance to change, uh, industry, which was dominated by a few bears and have such a positive impact, not only on our estimates, but society at large. And, uh, I think it's a privilege. Pressure is a privilege. And, uh, I'm grateful for the opportunity that's been afforded to me and to my colleagues. And, uh, >>It's a great way. Sorry. That's a great way of looking at it. Pressure is a privilege. If you think about, I love what you said, I always say, get, you know, get comfortably uncomfortable. It is a heart in any aspect, whether it's your workouts or your discipline, you know, working from home, it's a hard thing to do to your point. There's a lot of positivity that can come from it. If we think of what's happening this week alone and the U S political climate changing of the old guard, we've got Kamala Harris as our first female VP nominee and how many years, but also from a diversity angle, from a women leadership perspective, blowing the door wide open. >>It's great to see that, um, you know, we have someone that my daughter's going to look up to and say that, uh, you know, yes, there is, there is a place for us in society and we can have a meaningful contribution to society. So I actually think that San Antonio versus nomination is, um, you know, it's a simple ism of change of God, for sure. Um, I have no political agendas, um, at all. Then you can see how it pans out in November, but the one thing is for sure, but it's going to make a lot of people uncomfortable, a change of God, or this makes a lot of people. And, and, uh, and you know, I was reflecting back on something else and in everything that I've actually achieved, which is, is something I'm proud of. I had to go through a zone, but I was extremely uncomfortable. >>Uh, Gould only happens when you have uncomfortable, um, girl to happens in your conference room. And, um, whether it's, um, you know, running them sequel, uh, or are having a society change, uh, if you stick to your comfort zone, you stick to your prejudices and viruses because it's just comfortable there, there's a, uh, wanting to be awkward. And, uh, and, and I think that that's that essential change of God. As I said, at the cost of repeating myself will make a lot of people uncomfortable, but I honestly believe will move the society forward. And, uh, yeah, I, um, I couldn't be more proud of, uh, having a California San Diego would be nominated and it's a, she brings diversity multicultural. And what I loved about it was, you know, we talk about culture and all the rest of it. And she, she was talking about how our parents who were both, uh, uh, at the Berkeley when she was growing up, we were picking up from and she be, you know, in our, in our prime going to protests and Valley. >>And so it was just, uh, it was ingrained in her to be able to challenge the status school and move the society forward. And, uh, you know, she was comfortable being uncomfortable when she was in that, you know, added that. And that's good. Maybe not. I think we sort of, uh, yeah, I, yeah, let's see, let's see what November brings to us, but, um, I think just a nomination has, uh, exchanged a lot of things and, uh, if it's not this time, it can be the next time, but at the time off the bat, but you're going to have a woman by woman president in my lifetime. Um, that's um, I minced about them, uh, and that's just great. >>Well, I should hope so too. And there's so many, I know we've got to wrap here, but so many different data points that show that that technology company actually, companies, excuse me, with women in leadership position are significantly 10, 20% more profitable. So the changing of the guard is hard as you said, but it's time to get uncomfortable. And this is a great example of that as well as the culture that you have at mem sequel Raja. It's always a pleasure and a philosophical time talking with you. I thank you for joining me on the cube today. >>Thank you me since I'm just stay safe, though. >>You as well for my guest, Raj Burma, I'm Lisa Martin. Thank you for watching this cube conversation.

>> Announcer: Live from the Bill Graham Auditorium in San Francisco, it's The Cube, covering Pure Storage Accelerate 2018. Brought to you by Pure Storage. (upbeat techno music) >> Welcome back to The Cube, we are live at Pure Accelerate 2018 at the Bill Graham Civic Auditorium in San Francisco. I'm Lisa Martin, moonlighting as Prince today, joined by Dave Vellante, moonlighting as The Who. Should we call you Roger? >> Yeah, Roger. Keith. (all chuckling) I have a moon bat. (laughing) >> It's a very cool concert venue, in case you don't know that. We are joined by a couple of guests, Cube alumnae, welcoming them back to The Cube. Rajiev Rajavasireddy, the VP of Product Management and Solutions at Pure Storage and Siva Sivakumar, the Senior Director of Data Center Solutions at Cisco. Gentlemen, welcome back. >> Thank you. >> Thank you. >> Rajiev: Happy to be here. >> So talk to us about, you know, lots of announcements this morning, Cisco and Pure have been partners for a long time. What's the current status of the Cisco-Pure partnership? What are some of the things that excite you about where you are in this partnership today? >> You want to take that, Siva, or you want me to take it? >> Sure, sure. I think if you look back at what brought us together, obviously both of us are looking at the market transitions and some of the ways that customers were adopting technologies from our site. The converged infrastructure is truly how the partnership started. We literally saw that the customers wanted simplification, wanted much more of a cloud-like experience. They wanted to see infrastructure come together in a much more easier fashion. That we bring the IT, make it easier for them, and we started, and of course, the best of breed technology on both sides, being a Flash leader from their side, networking and computer leader on our side, we truly felt the partnership brought the best value out of both of us. So it's a journey that started that way and we look back now and we say that this is absolutely going great and the best is yet to come. >> So from my side, basically Pure had started what we now call FlashStack, a converged infrastructure offering, roughly about four years ago. And about two and a half years ago, Cisco started investing a lot in this partnership. We're very thankful to them, because they kind of believed in us. We were growing, obviously. But we were not quite as big as we are right now. But they saw the potential early. So about roughly two-and-a-half years ago, I talked about them investing in us. I'm not sure how many people know about what a Cisco validated design is. It's a pretty exhaustive document. It takes a lot of work on Cisco's site to come up with one of those. And usually, a single CVD takes about two or three of their TMEs, highly technical resources and about roughly three to six months to build those. >> Per CVD? >> Per CVD. >> Wow. >> Like I said, it's very exhaustive, I mean you get your building materials, your versions, your interoperability, your, you can actually, your commands that you actually use to stand up that infrastructure and the applications, so on and so forth. So in a nine-month span, they kind of did seven CVDs for us. That was phenomenal. We were very, very thankful that they did that. And over time, that investment paid off. There was a lot of good market investment that Cisco and Pure jointly made, all those investments paid off really well in terms of the customer adoption, the acquisition. And essentially we are at a really good point right now. When we came out with our FlashArray X70 last April, Cisco was about the same time, they were coming out with the M5 servers. And so they invested again, and gave us five more CVDs. And just recently they've added FlashBlade to that portfolio. As you know, FlashBlade is a new product offering. Well not so new, but relatively new, product offering from PR, so we have a new CV that just got released that includes FlashArray and Flash Blade for Oracle. So FlashArray does the online transaction processing, FlashBlade does data warehousing, obviously Cisco networking and Cisco servers do everything OLTB and data warehouse, it's an end to an architecture. So that was what Matt Burr had talked about on stage today. We are also excited to announce that we had that we had introduced AIRI AI-ready infrastructure along with Nvidia at their expo recently. We are excited to say that Cisco is now part of that AIRI infrastructure that Matt Burr had talked about on stage as well. So as you can tell, in a two and half year period we've come a really long way. We have a lot of customer adoption every quarter. We keep adding a ton of customers and we are mutually benefiting from this partnership. >> So I want to ask you about, follow up on the Oracle solution. Oracle would obviously say, "Okay, you buy our database, "buy our SAS, buy the Red Stack, "single throat to choke, "You're going to run better, "take advantage of all the hooks we have." You've heard it before. And it's an industry discussion. >> Rajiev: Of course. >> Customer have it, Oracle comes in hard. So what's the advantage of working with you guys, versus going with an all-Red Stack? Let's talk about that a little bit. >> Sure. Do you want to do it? >> I think if you look at the Oracle databases being deployed, this is a, this really powers many companies. This is really the IT platform. And one of the things that customers, or major customers standardize on this. Again, if they have a standardization from an Oracle perspective, they have a standardization from an infrastructure perspective. Just a database alone is not necessarily easy to put on a different infrastructure, manage them, operate them, go through lifecycle. So they look for a architecture. They look for something that's a overall platform for IT. "I want to do some virtualization. "I want to run desktop virtualization. "I want to do Oracle. "I want to do SAP." So the typical IT operates as more of "I want to manage my infrastructure as a whole. "I want to manage my database and data as its own. "I want its own way of looking." So while there are way to make very appliancey behaviors, that actually operates one better, the approach we took is truly delivering a architecture for data center. The fact that the network as well as the computer is so programmable it makes it easy to expand. Really brings a value from a complete perspective. But if you look at Pure again, their FlashArrays truly have world-class performance. So the customer also looks at, "Well I can get everything from one vendor. "Am I getting the best of breed? "Am I getting the world-class technology from "every one of those aspects and perspectives?" So we certainly think there are a good class of customers who value what we bring to the table and who certainly choose us for what we are. >> And to add to what Siva has just said, right? So if you looked at pre-Flash, you're mostly right in the sense that, hey, if you built an application, especially if it was mission-vertical application, you wanted it siloed, you didn't want another application jumping in and kind of messing up the performance and response times and all that good stuff, right? So in those kind of cases, yeah, appliances made sense. But now, when you have all Flash, and then you have servers and networking that can actually elaborates the performance of Flash, you don't really have to worry about mixing different applications and messing up performance for one at the expense of the other. That's basically, it's a win-win for the customers to have much more of a consolidated platform for multiple applications as opposed to silos. 'Cause silos are always hard to manage, right? >> Siva, I want to ask you, you know, Pure has been very bullish, really, for many years now. Obviously Cisco works with a lot of other vendors. What was it a couple years ago? 'Cause you talked about the significant resource investment that Cisco has been making for a couple of years now in Pure Storage. What is it that makes this so, maybe this Flash tech, I'm kind of thinking of the three-legged stool that Charlie talked about this morning. But what were some of the things that you guys saw a few years ago, even before Pure was a public company, that really drove Cisco to make such a big investment in this? >> I think they, when you look at how Cisco has evolved our data center portfolio, I mean, we are a very significant part of the enterprise today powered by Cisco, Cisco networking, and then we grew into the computer business. But when you looked at the way we walked into this computer business, the traditional storage as we know today is something we actually led through a variety of partnerships in the industry. And our approach to the partnership is, first of all, technology. Technology choice was very very critical, that we bring the best of breed for the customers. But also, again, the customer themself, speaking to us, and then our channel partners, who are very critical for our enablement of the business, is very very critical. So the way we, and when Pure really launched and forayed into all Flash, and they created this whole notion that storage means Flash and that was never the patterning before. That was a game-changing, sort of a model of offering storage, not just capacity but also Flash as my capacity as well as the performance point. We really realized that was going to be a good set of customers will absorb that. Some select workloads will absorb that. But as Flash in itself evolved to be much more mainstream, every day's data storage can be in a Flash medium. They realize, customers realized, this technology, this partner, has something very unique. They've thought about a future that was coming, which we realized was very critical for us. When we evolved network from 10-gig fabric to 40-gig to 100-gig, the workloads that are the slowest part of any system is the data movement. So when Flash became faster and easier for data to be moved, the fabric became a very critical element for the eventual success of our customer. We realized a partnership with Pure, with all Flash and the faster network, and faster compute, we realized there is something unique that we can bring to bear for the customer. So our partnership minds had really said, "This is the next big one that we are going to "invest time and energy." And so we clearly did that and we continue to do that. I mean we continue to see huge success in the customer base with the joint solutions. >> This issue of "best of breed" versus a kind of integrated stacks, it's been around forever, it's not going to go away. I mean obviously Cisco, in the early days of converged infrastructure, put a lot of emphasis on integrating, and obviously partnerships. Since that time, I dunno what it was, 2009 or whatever it was, things have changed a lot. Y'know, cloud was barely a thought back then. And the cloud has pushed this sort of API economy. Pure talks about platforms and integrating through APIs. How has that changed your ability to integrate "best of breed" more seamlessly? >> Actually, you know, I've been working with UCS since it started, right? And it's perhaps, it was a first server system that was built on an API-first philosophy. So everything in the Cisco UCS system can be basically, anything you can do to it GUI or the command line, you can do it their XML API, right? It's an open API that they provide. And they kind of emphasized the openness of it. When they built the initial converged infrastructure stacks, right, the challenge was the legacy storage arrays didn't really have the same API-first programmability mentality, right? If you had to do an operation, you had a bunch of, a ton of CLI commands that you had to go through to get to one operation, right? So Pure, having the advantage of being built from scratch, when APIs are what people want to work with, does everything through rest APIs. All function features, right? So the huge advantage we have is with both Pure, Pure actually unlocks the potential that UCS always had. To actually be a programmable infrastructure. That was somewhat held back, I don't know if Siva agrees or not, but I will say it. That kind of was held back by legacy hardware that didn't have rest space APIs or XML or whatever. So for example, they have Python, and PowerShell-based toolkits, based on their XML APIs that they built around that. We have Python PowerShell toolkits that we built around our own rest APIs. We have puppet integration installed, and all the other stuff that you saw on the stage today. And they have the same things. So if you're a customer, and you've standardized, you've built your automation around any of these things, right, If you have the Intuit infrastructure that is completely programmable, that cloud paradigms that you're talking about is mainly because of programmability, right, that people like that stuff. So we offer something very similar, the joint-value proposition. >> You're being that dev-ops kind of infrastructure-as-code mentality to systems design and architecture. >> Rajiev: Yeah. >> And it does allow you to bring the cloud operating model to your business. >> An aspect of the cloud operating model, right. There's multiple different things that people, >> Yeah maybe not every single feature, >> Rajiev: Right. >> But the ones that are necessary to be cloud-like. >> Yeah, absolutely. >> Dave: That's kind of what the goal is. >> Let's talk about some customer examples. I think Domino's was on stage last year. >> Right. >> And they were mentioned again this morning about how they're leveraging AI. Are they a customer of Flash tech? Is that maybe something you can kind of dig into? Let's see how the companies that are using this are really benefiting at the business level with this technology. >> I think, absolutely, Domino's is one of our top examples of a Flash tech customer. They obviously took a journey to actually modernize, consolidate many applications. In fact, interestingly, if you look at many of the customer journeys, the place where we find it much much more valuable in this space is the customer has got a variety of workloads and he's also looking to say, "I need to be cloud ready. "I need to have a cloud-like concept, "that I have a hybrid cloud strategy today "or it'll be tomorrow. "I need to be ready to catch him and put him on cloud." And the customer also has the mindset that "While I certainly will keep my traditional applications, "such as Oracle and others, "I also have a very strong interest in the new "and modern workloads." Whether it is analytics, or whether it is even things like containers micro-services, things like that which brings agility. So while they think, "I need to have a variety "of things going." Then they start asking the question, "How can I standardize on a platform, "on an architecture, on something that I can "reuse, repeat, and simplify IT." That's, by far, it may sound like, you know, you got everything kind of thing, but that is by far the single biggest strength of the architecture. That we are versatile, we are multi-workload, and when you really build and deploy and manage, everything from an architecture, from a platform perspective looks the same. So they only worry about the applications they are bringing onboard and worry about managing the lifecycle of the apps. And so a variety of customers, so what has happened because of that is, we started with commercial or mid-size customers, to larger commercial. But now we are much more in enterprise. Large, many large IT shops are starting to standardize on Flash tech, and many of our customers are really measured by the number of repeat purchases they will come back and buy. Because once they like and they bought, they really love it and they come back and buy a lot more. And this is the place where it gets very exciting for all of us that these customers come back and tell us what they want. Whether we build automation or build management architecture, our customer speaks to us and says, "You guys better get together and do this." That's where we want to see our partners come to us and say, "We love this architecture but we want these features in there." So our feedback and our evolution really continues to be a journey driven by the demand and the market. Driven by the customers who we have. And that's hugely successful. When you are building and launching something into the marketplace, your best reward is when customer treats you like that. >> So to basically dovetail into what Siva was talking about, in terms of customers, so he brought up a very valid point. So what customers are really looking for is an entire stack, an infrastructure, that is near invisible. It's programmable, right? And it's, you can kind of cookie-cutter that as you scale. So we have an example of that. I'm not going to use the name of the customer, 'cause I'm sure they're going to be okay with it, but I just don't want to do it without asking their permission. It's a healthcare service provider that has basically, literally dozens of these Flash techs that they've standardized on. Basically, they have vertical applications but they also offer VM as a service. So they have cookie-cuttered this with full automation, integration, they roll these out in a very standard way because of a lot of automation that they've done. And they love the Flash tech just because of the programmability and everything else that Siva was talking about. >> With new workloads coming on, do you see any, you know, architectural limitations? When I say new workloads, data-driven, machine intelligence, AI workloads, do we see any architectural limitations to scale, and how do you see that being addressed in the near future? >> Rajiev: Yeah, that's actually a really good question. So basically, let's start with the, so if you look at Bare Metal VMs and containers, that is one factor. In that factor, we're good because, you know, we support Bare Metal and so does the entire stack, and when I say we, I'm talking about the entire Flash tech servers and storage and network, right. VMs and then also containers. Because you know, most of the containers in the early days were ephemeral, right? >> Yeah. >> Rajiev: Then persistent storage started happening. And a lot of the containers would deploy in the public cloud. Now we are getting to a point where customers are kind of, basically experimenting with large enterprises with containers on prem. And so, the persistent storage that connects to containers is kind of nascent but it's picking up. So there's Kubernetes and Docker are the primary components in there, right? And Docker, we already have Docker native volume plug-ins and Cisco has done a lot of work with Docker for the networking and server pieces. And Kubernetes has flex volumes and we have Kubernetes flex volume integration and Cisco works really well with Kubernetes. So there are no issues in that factor. Now if you're talking about machine learning and Artificial Intelligence, right? So it depends. So for example, Cisco's servers today are primarily driven by Intel-based CPUs, right? And if you look at the Nvidia DGXs, these are mostly GPUs. Cisco has a great relationship with Nvidia. And I will let Siva speak to the machine learning and artificial intelligence pieces of it, but the networking piece for sure, we've already announced today that we are working with Cisco in our AIRI stack, right? >> Dave: Right. >> Yeah, no, I think that the next generation workloads, or any newer workloads, always comes with a different set of, some are just software-level workloads. See typically, software-type of innovation, given the platform architecture is more built with programmability and flexibility, adopting our platforms to a newer software paradigm, such as container micro-services, we certainly can extend the architecture to be able to do that and we have done that several times. So that's a good area that covers. But when there are new hardware innovations that comes with, that is interconnect technologies, or that is new types of Flash models, or machine-learning GPU-style models, what we look at from a platform perspective is what can we bring from an integrated perspective. That, of course, allows IT to take advantage of the new technology, but maintain the operational and IT costs of doing business to be the same. That's where our biggest strength is. Of course Nvidia innovates on the GPU factor, but IT doesn't just do GPUs. They have to integrate into a data center, flow the data into the GPU, run compute along that, and applications to really get most out of this information. And then, of course, processing for any kind of real-time, or any decision making for that matter, now you're really talking about bringing it in-house and integrating into the data center. >> Dave: Right. >> Any time you start in that conversation, that's really where we are. I mean, that's our, we welcome more innovation, but we know when you get into that space, we certainly shine quite well. >> Yeah, it's secured, it's protected, it's move it, it's all kind of things. >> So we love these innovations but then our charter and what we are doing is all in making this experience of whatever the new be, as seamless as possible for IT to take advantage of that. >> Wow, guys, you shared a wealth of information with us. We thank you so much for talking about these Cisco-Pure partnership, what you guys have done with FlashStack, you're helping customers from pizza delivery with Domino's to healthcare services to really modernize their infrastructures. Thanks for you time. >> Thank you. >> Thank you very much. >> For Dave Vellante and Lisa Martin, you're watching the Cube live from Pure Accelerate 2018. Stick around, we'll be right back.

>>Okay. We're back live in Silicon valley and San Jose, California for the continuous coverage of siliconangle.tv and have duke world 2012. This is ground zero for the alpha geeks in big data. Uh, just the tech elite. We call them tech athletes and, uh, we're excited to cover it on the ground. Extract the signal from the noise here. This is the cube, our flagship telecast. I'm joining my co-host Jeff Kelly from Wiki bond.org, the best analyst in the business. Jeff, welcome back for another segment. End of the day, day one loving every minute. Okay. We're here with our guest. Jack Norris is a cm of map bar Jack. Welcome back to the cube. You've been on a few times. Um, so you guys have some news. Yes. So let's get right to the news. So you guys are a player in the business, so share with your news, the folks. Excellent jump right in. >>So, uh, two big announcements today, we announced that Amazon is integrating map bar as part of their Lastic MapReduce service and both edition or, or free edition. M three is available as well as M five directly with Amazon, Amazon in the cloud. >>So what's the value proposition. Why would a customer say, all right, I want to do this in the cloud manpower, an Amazon cloud rather than doing it on premise. >>Okay. So let's start with, I mean, there's a lot of value propositions, all balled up into one here. Uh, first of all, in the cloud, it allows them to spin up very quickly. Within a couple minutes, you can get, uh, you know, hundreds of nodes available. Um, and, uh, and depending on where you're processing the data, if you've got a lot of data in the cloud already makes a lot of sense to do the Hadoop processing directly there. So that's, that's one area. A second is you might have an on-premise cloud deployment and need to have a disaster recovery. So map R provides point in time, snapshots, uh, as well as, as a white area replication. So you can use mirroring having Amazon available as a target is a huge advantage. And then there's also a third application area where you can do processing of the data in the cloud and then synchronize those results to an on-premise. So basically process where the data is combined the results into a cluster on premise. So you >>Don't have to move the raw data. Uh, >>On-premise actually, it's all about let's do the processing on the data. Well, you know, the whole, >>The value proposition and big data in general is let's not move, move data as little as possible. Yep. Uh, you know, so you bring the computation to the data, if you can. Uh, so what are your take on this event? I mean, we've got, uh, this is a, you know, the 4th of June summit, uh, you know, Hortonworks is now fully taken over the show and talk about what you see out here in terms of, uh, the other vendors that play. And, uh, just to kind of the attendees, the vibe you're seeing, >>Uh, it's a lot of excitement. I think a big difference between last year, which seemed to be very developer focused. We're seeing a lot of, a lot of presentations by customers. A lot of information was shared by our customers today. It was fun to see that, uh, comScore's shared, uh, shared their success. Boeing gap map is, uh, it was great for us. >>Fantastic. We look at Amazon, Amazon, first of all, is the gold standard for public cloud. Right? They've knocked it out of the park. Everyone knows Amazon. Um, but they've been criticized on the big data front because of the cycle times involve on. Um, and some developers and mean for web service spending up and down. No problem. Um, and we're seeing businesses like Netflix run on Amazon. So Amazon is not a stranger to running scale for cloud, but Hadoop has kind of been a klugey thing for Amazon. So I think, you know, talk about why Amazon and you guys is a good fit out to the market. The market reach is great. So you guys know and have a huge addressable market. Are you guys helping solve some of that complexity with the, uh, with the MapReduce side? What's, >>What's the core, I guess the first comment first response would be, I think every customer should have that type of Kluge. Uh, uh, they could have the success that Amazon has in Hadoop. They have a huge number of, of, uh, of Hadoop deployments have been very, very successful. I think, >>I mean, you know what I mean by it's natural, it's, cloogy everywhere right now. That's the problem. But Amazon has huge scale, um, and had not a natural fit. There >>Is not a natural fit >>For the data for the data component. And, uh, uh, the HBase for example, >>Component. So where were Amazons, you know, made it very frictionless is the ability to spin up Hadoop to do the analysis. The gap that was missing is some of the, the ha capabilities. The data protection features the disaster recovery, and, you know, we're map are now it gives options to those customers. You know, if they want those kinds of enterprise enterprise grade features, now they have an option within EMR. It can select a M five and, and get moving if they want a performance. And in NFS, they've got the M three options. >>Well, congratulations. I think it's a great deal for you guys and for Amazon customers. My question for you is, as you guys explore the enterprise ready equation, which has been a big topic this week, um, what does that mean to you guys? Cause it means different things to different people depends on where, how high up to OLTB do you go? Right? I mean, we're how far from batch to real time transactional, um, levels you go, I mean, low bash, no problem. But as you start to get more near real time, it's going to be a little bit different gray in this house used security HDFS. Yeah. >>Yeah. So, so duke represents the strategic platform, right? Deploying that in an organization, um, you know, moving from kind of an experimental kind of lab based to production environment creates a different set of feature requirements. How available is it? How easy is it to integrate, right? How do I kind of protect that information and how do I share it? So when we say enterprise grade, we mean you can have SLA, she can put the data there and, and be confident that the data will remain there, that you can have a point in time recovery for an application error or user mistake. Uh, you can have a disaster recovery features in place. And then the integration is about not recreating the wheel to get access to the information. So Hadoop is very powerful, but it requires interacting through an HDFS API. If you can leverage it like through map bar with NFS standard file based access standard ODBC access, open it up. >>So I can use a standard file browser applications to see and manipulate the data really opens up the use cases. And then finally, what we announced in two dot oh, was multitenancy features. So as you share that information, all of a sudden the SLA is of different groups and well, these guys need it immediately. And if you've got some low grade batch jobs are going to impact that. So you want the ability to protect, to isolate, to secure information, and basically have virtual clusters within a cluster. And those features are important to cloud, but they're also important to on-premise >>So great for the hybrid cloud environments out there. I mean, the multitenancy cracking the code on that. Exactly huge. I mean, that is basically, I mean, right now most enterprises are like private cloud because it's like, they're basically extension of their data center and you're seeing a lot more activity in the hybrid cloud as a gateway to the public cloud. So, >>And, and, you know, frankly, people are kind of struggling with in an experimental with Apache Hadoop and the other distributions, the policies are either at the individual file level or the whole cluster. And it all almost forced the creation of separate physical clusters, which kind of goes against the whole Hadoop concept. So the ability to manage it, a logical layer have separate volumes where you can apply policies to apply that applies to all the content underneath really kind of makes it much, much easier for administrators to kind of deal with these multiple use cases. >>Amazon, Amazon has always been one of those cases for the enterprise where it's been one of those and they've, this has been talked about for years, put the credit card down, go play on Amazon, but then bring it back into the it group for certification. And so I think this is a nice product for you guys to bring that comfort. You know, we're very >>Excited the enterprise saying, Hey, >>Come play in Amazon. It's Bulletproof enterprise. Ready? So congratulations. >>I wonder, can we talk, uh, talk use cases. So what are you seeing in terms of, uh, evolving use cases as, as, uh, duke continues to become more enterprise grade, uh, depending on your definition, uh, but how is that impacting what you're seeing in terms of, even if it's just, uh, you know, the, the, um, the mindset even people think now, okay, now it's enterprise grade, well, maybe, you know, in, in, depending on who you talk to, it's been that way for a bit, but what kind of, uh, use cases are you seeing develop now that it's kind of starting to gain acceptance? It's like, okay, we can trust our data is going to be there, et cetera. >>So th there's a huge range of use cases that, uh, different by industry, different by kind of dataset that's being used against everything from really a deep store where you can do analytics on it. So you're selecting the content to something that's very, very analytic machine learning intensive, where you're doing sophisticated clustering algorithms, uh, et cetera, um, where we've seen kind of an expansion of use cases are around real-time streaming and you get streaming data sets that are kind of entering into the cloud. And, um, some of the more mission, critical data moving beyond just maybe click stream data or things that if you happen to drop a few, you know, not a big deal, right. Versus the kind of trust the business type of content. >>Talk a little bit about the streaming, uh, aspects, uh, because of course, you know, we think of duke, we think of a batch system in terms of streaming data into Hadoop. You know, that's, that's a different, uh, that's something we don't, we haven't heard a lot about. So how do you guys approach that? >>So, uh, one of the artifacts of, of HDFS, which is a, is a distributed file system that scores in the underlying Linux file system, it's append only. So as an administrator, you decide, how frequently do I close the file item? I going to do that an hourly basis on it every eight hours, because you have to close the file for other applications to see the data that's been written. Right? So one of the innovations that, uh, that we pursued was to rewrite that create this dynamic read-write layer. So you can continue to write data in any application is seeing the latest data that's written. So you can Mount the cluster as if it's storage and just continue to write data. There really opens up what's, uh, what's possible companies like Informatica, they're all from a messaging product integrates directly in with, with Matt BARR and provides. >>So what kind of advantage does that provide to the end user? What w w translate that into real business value? Why, why is that important? >>Well, so one example is comScore, comScore handles 30 billion, uh, objects a day, uh, as they go out and try to measure the use of, of the web and being able to continually write and stream that information and scale and handle that in a real time and do analytics and turn around data faster, has tremendous business value to them. If they're stuck in a batch environment where the load times lengthen to the point where all of a sudden they can't keep up and they're actually reporting on, you know, old news. And I think the analogy is forecasting rain a day after it's wet. Isn't exactly valuable. >>Yeah. So you guys, obviously a great deal of the enterprise ready for Amazon, big story, big coup for the company. What's next for you. I want to ask that and make sure you get that out there on your agenda for the next year, but then I want you to take a step back a year, maybe a year and a half ago. Look back at how much has changed in this landscape. Um, share your perspective because the market has gone through an evolution where there's been a market opportunity, and then everyone goes, oh my God, it's bigger than we actually thought. I mean, Jeff, Kelly's a groundbreaking report about the $50 billion market is now being talked about as too low. So big data has absolutely opened up to a huge, and it's changed some of the tactics around strategies. So your strategy, Hortonworks strategy, even cloud era. So, and it's still evolving. So what's changed for the folks out there from a year and a half ago, a year ago to today, and then look out for the next 12 months. What's on your agenda. >>Well, if, if you look back, I think we've been fairly consistent. Um, uh, I'm, I'm not going to take credit for the vision of our CEO and CTO. Uh, but they recognized early on that Hadoop was, uh, was a strategic platform and to be a strategic platform that applied to the broadest number of use cases and organizations required some, some areas, uh, of innovation and particularly the how it, how it scaled, how it was managed, how you stored and protected the information needed a rearchitecture. And I think that, you know, architecture matters when you're going through a paradigm shift, having the right one in place creates this, this ability, you know, to speed innovation. And I think that's, if there's anything that's changed, I think it's the speed of innovation has even increased in the Hadoop community. I think it's, it's created a focus on these enterprise grade features on how do we store this valuable information and, and continue to explore. >>And I think one of the observations I'll make is that on that note is that it really focuses everyone to be just mind your own business and get the products out. You know what I'm saying? We've seen everyone, the product focus be the number one conversation. >>What we've seen is customers, you know, start and they expand rapidly. Some of that student data growth, but a lot of it is student more and more applications are being delivered and, and, uh, and, and the values kind of extracted from the hoop platform and success breeds success. Well, >>Congratulations for all your success, great win with Amazon web services and make that a little bit more easier, more robust, and more, more features for them and you, uh, more revenue for part of our, um, and I want to personally thank you for your support to the cube. Uh, we've expanded with a new studio B software for extra extra interviews, um, and wanna expand the conversation, thanks to your generous support. You can bring the independent coverage out to the market and, um, great community, thanks for helping us out. And we appreciate it. So thank you. Okay. Jack Dorsey with Matt bar, we'll be right back to wrap up day one with that. Jeff and I will give our analysis right at the short break.