>> Live from San Diego, California It's the queue covering Sisqo live US 2019 Tio by Cisco and its ecosystem barters >> Hey, welcome back to the cubes. Coverage of Sisqo Live from San Diego. Sunny San Diego. I'm Lisa Martin with Stew Minutemen today and stew and I are very pleased to welcome to the Cube for the first time. Peter Sprigg gotta distinguished engineer from red Hat. Peter, Welcome. >> Thank you. I'm really excited to be here. >> We're excited to have you here today. I'd like to say Welcome to the sun. Its pretty toasty for in this very cool sales pavilion, which is Ah, very nice. A bright. So we got a lot of bright, but we do have some heat. So you've been with Cisco Cisco? No, actually. >> Was what? Siskel Ugo? >> Two degrees of Kevin Bacon Way where? In this room. Right. You've been with Red Hat since the answerable acquisition. One of the things that was funny that Chuck Robbins mention this morning was this the 30th anniversary of Cisco event with customers and partners. He also mentioned 30 years ago Seinfeld started. So I'm gonna do a Jerry Seinfeld on go digital transformation. What's the deal with that. >> You know, I think that, you know, one of the things that's really exciting and being part of Ansel and actually coming from the network's base. You know, we've had the opportunity to really be out in front of this whole digital transfer station. We've been doing it for you very long time on it's been just It's really been all about a journey on DH. That's really what I think. Earmarks. Really? What answer was all about >> Peter? So another thing. We've been on a journey a long time. That whole automation thing. Yes, we've been talking about that my entire career in the network. So bring us forward. You know, maybe, you know, did not 30 years. But you know what's going on in the last couple of years, That's different about automation, you know, 30 2019. Then we would have talked about, you know, when you first joined. And >> yeah, you know, I think that when I first joined, you know, everything was we were just trying to convince people that this is something you should think about doing you. Now you look around, you see what's going on here, alive and at definite and it's become a whole world unto itself. It's really starting to define its own space and networking, which is really exciting to see because I've been part of this journey really since the get go. And it's just it's really exciting to watch this homeworld start to come together. And people really taken interest in changing really the way that we approached, cooperating in >> person, and I'm glad actually mentioned the definite zone that we're in here. So there's lots of workshops happening right next to us. Hear developers really helping to drive that transformation software a big piece of your world. I'm assuming >> it is. It really is, you know, And I always love to tell the story of, you know, I've got a software development background, but I also have a network operations background watching these two worlds come together. It's so exciting and being out at the forefront, really pushing the envelope off. What we can do from an automation perspective is really been exciting >> so as to mention we're in the definite zone. This definite communities mass it is John Fourier and I had the opportunity to cover definite create back in Mountain View about six or eight weeks ago. I think that number this is Yoo, he mentioned, is 585,000 members, strong looking at Red hat and the spirit of this open source community. Talk to us about sort of the alignment of these communities and how this is helping to drive, not just technology forward, but be able to get that feedback from customers in any industry to drive these emerging technologies into mainstream. >> You know, I think you touched on the key there. It really is all about the customer and the customer's experience. You know, the wonderful thing about open source community is the fact that we can all come together. Vendor supply our customer, you know, consulting team, whoever you are, we all can come together, and it really does become right. We're all better together, and we're all pushing forward and trying. Teo really change the way that we approach how we build design and operate now destruction. >> Peter Peter Wonder if you've got a you know, a customer example. I know sometimes you need to anonymous things are what kind of things are customers Went, went when they're going through this. The outcomes and results that change how their business works, >> you know? So one of the things that and I got one particular customer mind. I can't say who they are, but one particular customer that that we worked a lot of time with him. What >> they were >> able to do is they were actually able. We gave them back the gift of time. That's what we talked about with automation. And what we mean by that is they were able to take a job that used to take them literally weeks to get done, that we could now automate and get it done once a night twice, you know, do it in a single night as opposed to them taking ways to get that job done. That frees them up to doing the more high value work. That networking here's really wanted you and not saddle them with more Monday and stuff. >> So just to follow up on that because, you know, traditionally that's been one of the pieces right is how do you know make my employees mohr efficient? Howto I give them more environment, something that they talked about. The keynote this morning is some of the scale and some of the you know you're dealing with EJ applications and all these environments is even if I had the resource, I probably couldn't keep up with the pace of change. Correct. They're doing so when you start throwing in things like a I and ML on top of those. But there's time to find their way intersect with what you're doing. >> Absolutely, they really are. And it's areas that we're starting to look into a swell. You know, Ansel's been doing this for a long time, but we're starting to see how do we bring some of these other two separate pieces and bring them together underneath this automation umbrella? And really again, we want to drive out that that everyday task out of of the operations Hansel. They can focus on the high value things of evaluating technology and moving things forward for their organizations. >> You say you were able to give that particular customer back the gift of time. I've got everybody breathing on the planet today, wants back the gift of time. But I would love to follow that story down the road because the gift of time has so much potential. Posit did impact all the way up to the C suite. Teo, you know, being able to move resources around to identify new revenue streams, new business nodules, new products, new services expanded into new markets. So that gift of time is transformative. >> Absolutely. Without, without a doubt, it is. And you know what we're seeing and what we're getting feedback from our customers on is that because of that gift of time, they're able to now focus on pushing their businesses forward. Right? And they're starting to solve challenges that have always been on that traditional, ever going task list. Right? That never you never get Teo. And they're really starting to be able to focus on those tasks such that they can start to become more innovative. They become more agile and they focus on their business, not on the active managing technology. >> All right, So, Peter, another another big theme of the show here is multi cloud, something we heard. A lot of red has something. Also, it's this skill set that one of the biggest challenges for customers working behind between those various environment. How sensible helping customers bridge some of those worlds today. >> Well, so you know, obviously, Ansel's not just a network to write. We automate anything and everything. And we like to talk about Ansel as the language of automation and really what it does for organizations. Whether you're looking at at infrastructure, whether you're looking at hybrid Cloud, what we do is we bring a language to the operations team where you get these two separate teams talking in a dialect that they can understand each other. And that's really what Ancel starts to bring your two. Those organizations. >> That internal collaboration. Absolutely. Maybe bridging business folks and folks who not wouldn't normally necessarily be driving towards the same types of solution. Correct? Correct. And it really >> kind of starts. And this is actually how we see Answer will kind of unfolding most organizations, right? It starts in these pockets, and small teams will start to use answerable. And then it just kind of grows and grows and grows. And what we find is all of a sudden, you've got, you know, a cloud Administrator's going out talk to a network engineer, and they can talk through this language of automation instead of trying to figure out how to communicate. They actually become productive immediately. >> OKay, Peter, Some of the big waves coming down the line that we're talking the keynote this morning, You know, five g y 56 You know, just incremental changes, you know, in your world. Or, you know, what will some of these new architectures that they're talking about, you know, have some dramatic impacts? >> Well, they're gonna have huge. In fact, you know, I think you know one of the things That's very interesting. You look at some of these technologies coming down, the coming down the ways now is everything is getting faster. I mean, that's nothing that we've been. You know, anyone who's been a knight for any period of time knows it's always faster, faster, faster. But what it's doing is is it's really motivating us to look at ants one and rethink how we do certain things so that we can keep up with the demand and allow organizations to, you know, meet the demands of their customers in accelerating their time to market. >> Maybe dig into that a little bit more in terms of the customer feedback. How are you guys? How is answerable being able to work with your customers across any industry, get their feedback to really accelerate what you guys are able to then deliver back to the market. What's that feedback loop? Well, I think >> you know, when you think about automation, automation is certainly it's a technology, but it's also very much about how organizations work, right? I like to talk about automation is really more a state of mind, Not so necessarily a state of action. And so therefore, you know, we spend a lot of time with our customers to understand how do they run their business and how Khun Automation become a way that they think about running the organization and really help them move forward. So we spent a lot of time understanding our customers business before we ever get into the bits and bytes of what automation really is. >> Yeah, you mentioned some of those organizational pieces, like the cloud guy in the network guy. What are some of the biggest challenges that you're seeing customers these days, and, you know, how are they helping to, you know, mature the organization to this new modern, multi cloud developer centric? You know, software defined, you know, Buzz, word of the day. >> You know, I think that you know, the biggest challenge that we see every single day with our car? Does Moses. You know, just where to get started, how you get started with. There's so much of it out there. Now it's it's they're looking at, and how do you get started with this? And how do you let this thing take on a life of its own? And so we spent a lot of time just getting them. You 123 steps down the road, get going in the open source and then let it expand from there. And we bring a whole suite of capabilities, then to the customer, whether it's through red at consulting, whether it's you're working through our open source communities to really help them on that journey. >> Wondering customer meetings. Where is this conversation now with respect to automation? Is he talked about giving the gift back of time. That would go all the way up to the C suite. So much potential there. Are you still having the conversation with more? The technical folks are where the lines of business or maybe even the executive sweet in terms of being a part of this decision in understanding the massive impact that automation will deliver. >> Yeah, it was just starting to see that that trend transition. Now, you know, we just came off of Redhead Summit, and we spent a lot of time talking with senior directors. See sweet individuals about kind of that transition in how automation is. As I mentioned before, it's no longer just a technical tool in the tool back. It really is becoming a business tool and how you could leverage it to really drive the business. So that's those conversations air starting now. We're just starting to see that, and it's really it's really exciting is really an exciting time to be part of this. >> All right, Peter, what will tell us a little bit about what red hats got going out of the show? I happen to show this to stop down the show floor, I saw the like command line video game, which I see that Red House seems that's making the go around there. I know your team's having a lot of fun team who can get the high score. What else at the show should people be looking at for red hat? >> Well, so you know, In addition, to answer. Well, of course, we also spent a lot of time talking about open shift, which is the other big red hat, you know, flagship product and really, what we're doing in terms of being able to deliver and the multi G hybrid cloud infrastructure and be able to run workloads in any cloud infrastructure, no matter where that may be. And then, of course, they'd always always comes back. Tio the operating system Red hat. Lennox, you know, they go hand in hand, way are always gonna be about the operating system, and everything kind of bubbles up from there. >> So here we are, halfway through calendar year 2019 which is scary. What are some of the things that you're looking forward to as the rest of the year progresses? Some, you know, exciting things going with Red had a big blue, for example. >> Well, there there is there. Certainly that although you could probably tell me more about how that's going that I get to know even anymore. But you know, I think really, What? What's exciting about the second half of this year and you're going to hear more about it? Actually, a definite this is a good time for me to mention this is that you know, we're doing a lot with Cisco right now. One of the things that course you know, Cisco's making a huge investment in definite and Red Hat is really becoming a very key partner with Cisco in that. So you're going to see a lot of open source community work around red Hand Cisco collaborating together to enhance what Ansel's doing and try and bring even more traditional and nontraditional people into these communities. >> More collaboration, I presume, over some of their cognitive collaborations, >> like absolutely, absolutely. >> That does work on linen because I've been using blue jeans most the time. >> It does. I You know, I I I pushed them really hard because yes, at first I had troubles with it, But yes, now it worked fantastic on Lenny. I couldn't be happier. >> You heard it. Here, Peter, Thank you so much for joining stew and me on the Cube this afternoon. We appreciate your time. I >> appreciate it. Thank you so much for >> having all right. It was fun for stupid aman. I am Lisa Martin. You're watching the Cube live from Cisco live in sunny San Diego. Thanks for watching

>> Announcer: Live from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017. Brought to you by Hortonworks. >> Hey, welcome back to theCUBE. Day two of the DataWorks Summit, I'm Lisa Martin with my cohost, George Gilbert. We've had a great day and a half so far, learning a ton in this hyper-growth, big data world meets IoT, machine learning, data science. George and I are excited to welcome our next guests. We have Josh Klahr, the VP of Product Management from AtScale. Welcome George, welcome back. >> Thank you. >> And we have Prashanthi Paty, the Head of Data Engineering for GoDaddy. Welcome to theCUBE. >> Thank you. >> Great to have you guys here. So, wanted to kind of talk to you guys about, one, how you guys are working together, but two, also some of the trends that you guys are seeing. So as we talked about, in the tech industry, it's two degrees of Kevin Bacon, right. You guys worked together back in the day at Yahoo. Talk to us about what you both visualized and experienced in terms of the Hadoop adoption maturity cycle. >> Sure. >> You want to start, Josh? >> Yeah, I'll start, and you can chime in and correct me. But yeah, as you mentioned, Prashanthi and I worked together at Yahoo. It feels like a long time ago. In our central data group. And we had two main jobs. First job was, collect all of the data from our ad systems, our audience systems, and stick that data into a Hadoop cluster. At the time, we were kind of doing it while Hadoop was kind of being developed. And the other thing that we did was, we had to support a bunch of BI consumers. So we built cubes, we built data marts, we used MicroStrategy, Tableau, and I would say the experience there was a great experience with Hadoop in terms of the ability to have low-cost storage, scale out data processing of all of, what were really, billions and billions, tens of billions of events a day. But when it came to BI, it felt like we were doing stuff the old way. And we were moving data off cluster, and making it small. In fact, you did a lot of that. >> Well, yeah, at the end of the day, we were using Hadoop as a staging layer. So we would process a whole bunch of data there, and then we would scale it back, and move it into, again, relational stores or cubes, because basically we couldn't afford to give any accessibility to BI tools or to our end users directly on Hadoop. So while we surely did a large-scale data processing in Hadoop layer, we failed to turn on the insights right there. >> Lisa: Okay. >> Maybe there's a lesson in there for folks who are getting slightly more mature versions of Hadoop now, but can learn from also some of the experiences you've had. Were there issues in terms of, having cleaned and curated data, were there issues for BI with performance and the lack of proper file formats like Parquet? What was it that where you hit the wall? >> It was both, you have to remember this, we were probably one of the first teams to put a data warehouse on Hadoop. So we were dealing with Pig versions of like, 0.5, 0.6, so we were putting a lot of demand on the tooling and the infrastructure. Hadoop was still in a very nascent stage at that time. That was one. And I think a lot of the focus was on, hey, now we have the ability to do clickstream analytics at scale, right. So we did a lot of the backend stuff. But the presentation is where I think we struggled. >> So would that mean that you did do, the idea is that you could do full resolution without sampling on the backend, and then you would extract and presumably sort of denormalize so that you could, essentially run data match for subject matter interests. >> Yeah, and that's exactly what we did is, we took all of this big data, but to make it work for BI, which were two things, one was performance. It was really, can you get an interactive query and response time. And the other thing was the interface. Can a Tableau user connect and understand what they're looking at. You had to make the data small again. And that was actually the genesis of AtScale, which is where I am today, was, we were frustrated with this, big data platform and having to then make the data small again in order to support BI. >> That's a great transition, Josh. Let's actually talk about AtScale. You guys saw BI on Hadoop as this big white space. How have you succeeded there, and then let's talk about what GoDaddy is doing with AtScale and big data. >> Yeah, I think that we definitely learned, we took the learnings from our experience at Yahoo, and we really thought about, if we were to start from scratch, and solve the problem the way we wanted it to be solved, what would that system look like. And it was a few things. One was an interface that worked for BI. I don't want to date myself, but my experience in the software space started with OLAP. And I can tell you OLAP isn't dead. When you go and talk to an enterprise, a fortune 1000 enterprise and you talk about OLAP, that's how they think. They think in terms of measures and dimensions and hierarchies. So one important thing for us was to project an OLAP interface on top of data that's Hadoop native. It's Hive tables, Parquet, ORC, you kind of talk about all of the mess that may sit underneath the covers. So one thing was projecting that interface, the other thing was delivering performance. So we've invested a lot in using the Hadoop cluster natively to deliver performing queries. We do this by creating aggregate tables and summary tables and being smart about how we route queries. But we've done it in a way that makes a Hadoop admin very happy. You don't have to buy a bunch of AtScale servers in addition to your Hadoop cluster. We scale the way the Hadoop cluster scales. So we don't require separate technology. So we fit really nicely into that Hadoop ecosystem. >> So how do you make, making the Hadoop admin happy is a good thing. How do you make the business user happy, who needs now, as we were here yesterday, to kind of merge more with the data science folks to be able to understand or even have the chance to articulate, "These are the business outcomes "we want to look for and we want to see." How do you guys, maybe, under the hood, if you will, AtScale, make the business guys and gals happy? >> I'll share my opinion and then Prashanthi can comment on her experience but, as I've mentioned before, the business users want an interface that's simple to use. And so that's one thing we do, is, we give them the ability to just look at measures and dimensions. If I'm a business, I grew up using Excel to do my analysis. The thing I like most as an analyst is a big fat wide table. And so that's what, we make an underlying Hadoop cluster and what could be tens or hundreds of tables look like a single big fat wide table for a data analyst. You talk to a data scientist, you talk to a business analyst, that's the way they want to view the world. So that's one thing we do. And then, we give them response times that are fast. We give them interactivity, so that you could really quickly start to get a sense of the shape of the data. >> And allowing them to get that time to value. >> Yes. >> I can imagine. >> Just a follow-up on that. When you have to prepare the aggregates, essentially like the cubes, instead of the old BI tools running on a data mart, what is the additional latency that's required from data coming fresh into the data lake and then transforming it into something that's consumption ready for the business user? >> Yeah, I think I can take that. So again, if you look at the last 10 years, in the initial period, certainly at Yahoo, we just threw engineering resources at that problem, right. So we had teams dedicated to building these aggregates. But the whole premise of Hadoop was the ability to do unstructured optimizations. And by having a team find out the new data coming in and then integrating that into your pipeline, so we were adding a lot of latency. And so we needed to figure out how we can do this in a more seamless way, in a more real-time way. And get the, you know, the real premise of Hadoop. Get it at the hands of our business users. I mean, I think that's where AtScale is doing a lot of the good work in terms of dynamically being able to create aggregates based on the design that you put in the cube. So we are starting to work with them on our implementation. We're looking forward to the results. >> Tell us a little bit more about what you're looking to achieve. So GoDaddy is a customer of AtScale. Tell us a little bit more about that. What are you looking to build together, and kind of, where are you in your journey right now? >> Yeah, so the main goal for us is to move beyond predefined models, dashboards, and reports. So we want to be more agile with our schema changes. Time to market is one. And performance, right. Ability to put BI tools directly on top of Hadoop, is one. And also to push as much of the semantics as possible down into the Hadoop layer. So those are the things that we're looking to do. >> So that sounds like a classic business intelligence component, but sort of rethought for a big data era. >> I love that quote, and I feel it. >> Prashanthi: Yes. >> Josh: Yes. (laughing) >> That's exactly what we're trying to do. >> But that's also, some of the things you mentioned are non-trivial. You want to have this, time goes in to the pre-processing of data so that it's consumable, but you also wanted it to be dynamic, which is sort of a trade-off, which means, you know, that takes time. So is that a sort of a set of requirements, a wishlist for AtScale, or is that something that you're building on your own? >> I think there's a lot happening in that space. They are one of the first people to come out with their product, which is solving a real problem that we tried to solve for a long time. And I think as we start using them more and more, we'll surely be pushing them to bring in more features. I think the algorithm that they have to dynamically generate aggregates is something that we're giving quite a lot of feedback to them on. >> Our last guest from Pentaho was talking about, there was, in her keynote today, the quote from I think McKinsey report that said, "40% of machine learning data is either not fully "exploited or not used at all." So, tell us, kind of, where is big daddy regarding machine learning? What are you seeing? What are you seeing at AtScale and how are you guys going to work together to maybe venture into that frontier? >> Yeah, I mean, I think one of the key requirements we're placing on our data scientists is, not only do you have to be very good at your data science job, you have to be a very good programmer too to make use of the big data technologies. And we're seeing some interesting developments like very workload-specific engines coming into the market now for search, for graph, for machine learning, as well. Which is supposed to give the tools right into the hands of data scientists. I personally haven't worked with them to be able to comment. But I do think that the next realm on big data is this workload-specific engines, and coming on top of Hadoop, and realizing more of the insights for the end users. >> Curious, can you elaborate a little more on those workload-specific engines, that sounds rather intriguing. >> Well, I think interactive, interacting with Hadoop on a real-time basis, we see search-based engines like Elasticsearch, Solr, and there is also Druid. At Yahoo, we were quite a bit shop of Druid actually. And we were using it as an interactive query layer directly with our applications, BI applications. This is our JavaScript-based BI applications, and Hadoop. So I think there are quite a few means to realize insights from Hadoop now. And that's the space where I see workload-specific engines coming in. >> And you mentioned earlier before we started that you were using Mahout, presumably for machine learning. And I guess I thought the center of gravity for that type of analytics has moved to Spark, and you haven't mentioned Spark yet. We are not using Mahout though. I mentioned it as something that's in that space. But yeah, I mean, Spark is pretty interesting. Spark SQL, doing ETL with Spark, as well as using Spark SQL for queries is something that looks very, very promising lately. >> Quick question for you, from a business perspective, so you're the Head of Engineering at GoDaddy. How do you interact with your business users? The C-suite, for example, where data science, machine learning, they understand, we have to have, they're embracing Hadoop more and more. They need to really, embracing big data and leveraging Hadoop as an enabler. What's the conversation like, or maybe even the influence of the GoDaddy business C-suite on engineering? How do you guys work collaboratively? >> So we do have very regular stakeholder meeting. And these are business stakeholders. So we have representatives from our marketing teams, finance, product teams, and data science team. We consider data science as one of our customers. We take requirements from them. We give them peek into the work we're doing. We also let them be part of our agile team so that when we have something released, they're the first ones looking at it and testing it. So they're very much part of the process. I don't think we can afford to just sit back and work on this monolithic data warehouse and at the end of the day say, "Hey, here is what we have" and ask them to go get the insights from it. So it's a very agile process, and they're very much part of it. >> One last question for you, sorry George, is, you guys mentioned you are sort of early in your partnership, unless I misunderstood. What has AtScale help GoDaddy achieve so far and what are your expectations, say the next six months? >> We want the world. (laughing) >> Lisa: Just that. >> Yeah, but the premise is, I mean, so Josh and I, we were part of the same team at Yahoo, where we faced problems that AtScale is trying to solve. So the premise of being able to solve those problems, which is, like their name, basically delivering data at scale, that's the premise that I'm very much looking forward to from them. >> Well, excellent. Well, we want to thank you both for joining us on theCUBE. We wish you the best of luck in attaining the world. (all laughing) >> Josh: There we go, thank you. >> Excellent, guys. Josh Klahr, thank you so much. >> My pleasure. Prashanthi, thank you for being on theCUBE for the first time. >> No problem. >> You've been watching theCUBE live at the day two of the DataWorks Summit. For my cohost George Gilbert, I am Lisa Martin. Stick around guys, we'll be right back. (jingle)