>> Hi, I'm Kyle Rourke, VP of Platform Strategy here at Snowflake. And I could not be more excited to be here today with Margaret Sherman, who is the Head of Data Strategy for Sonos. Now, all throughout the day-to-day we've been hearing from lots of customers from all over the world and hearing about their journeys and how they've transformed their business by embracing the data cloud. And of course, this next story is one I am personally very excited about because I'm a huge Sonos customer. And I'm sure many of you are as well. >> Thanks Kyle. As you mentioned, I'm the Head of Data Strategy at Sonos. And what that means is that I set the data priorities for the company, as well as guide the company on where to invest now and in the future, to get the most out of our data resources. I've spent about four years at Sonos. Previously I was the head of product data and worked a lot with setting up Snowflake and the IoT data. And I've been in technology for 20 years and the last 10 years I've spent in data analytics and machine learning. >> That's very cool. Now, how does your company view data in general? How do you guys... How does your team fit in, in the overall strategy of how Sonos leverages data? >> Yeah. So Sonos is a sound experience company and we really pioneered multi-room wireless audio. And we made that experience amazing and truly changed how people listen. And our mission is to inspire the world to listen better. And everything that we do is in service to that. And data is a part of that. So we really believe that data is fundamental to helping us achieve our mission. Data helps us build a better business, build better products and ultimately we think it helps us make our customers happier. >> Now, before Snowflake, maybe let's go back in time. You've been at Sonos for the last four years. Maybe go back in time a little bit for the time pre Snowflake. What were some of your challenges that you guys faced before you started with us? >> Yeah, it was really challenging. So I was actually leading the product data team at the time and we use Snowflake primarily for our IoT data. And so we're collecting tons of data, but we're really struggling to leverage it. Essentially what would happen is, if we wanted to answer a single question about what was happening with the customer experience, we would have to have data engineers go and write some code, spin up clusters. This could take weeks just to extract the data and get it into a shape where analysts and scientists could go and work with it. And so we really went from being in a place where it was taking weeks just to answer a single question, to now we can do things in hours. So it really changed things for us. >> Wow. And so the benefits of obviously being able to go act on information very, very quickly. What are some of the other benefits that's driven for you guys? >> I mean, our data people love it, obviously, because if you think about the process of data science, it's very iterative. So you're going to ask a question, you're going to go and investigate your data. You're going to do some data processing, you're going to do some visualizations and then you're going to come up with more questions. You're going to want to dig in. You're going to want to pull more data and you're going to want to join it together, do different cuts and pivots. And before that was just off the table, because as you can imagine, if every time you have to go weeks to go do that, it's just impossible. Whereas now, our data scientists can churn through these problems very quickly. And the data engineers love it because they're not sitting around waiting for jobs to finish for forever. They are able to get through their code faster. And as one of my engineers likes to say, she was telling me, she said, "Snowflake, she's a beast." (Kyle and Margaret laughing) It's like crunching through the data. >> You mentioned IoT. And I think that's obviously a very challenging space for a lot of customers and there's a lot of interest in it. Maybe give me some more thoughts on how much data are you guys are bringing in? Is it small, large? Has the volume been something that you could handle with Snowflake? >> Yeah, I mean, that was why we chose Snowflake. So prior to having Snowflake, we were really struggling with the vol... I mean, we've very large volumes of data, as you can imagine from an IoT device because we're collecting from over 10 million homes across the world. So it's quite a bit of data. And all of that doesn't fit in a traditional sort of data warehouse. We were trying to push some of it into SQL and we were essentially taking just a handful of our telemetry events. And we were boiling them down to daily and weekly aggregates. And even trying to push that into SQL was just too much for it to handle. And with Snowflake, we had processing jobs that were timing out in SQL server after running for hours and hours, and then Snowflake could just crunch through it in a few minutes. So-- >> Kyle: Wow. >> It was, yeah. I mean, I literally almost fell off my chair. (Margaret laughs) They showed me the comparison numbers. >> Well, so now... So it's been a good experience for you, but let's talk about your customers. And I think, I can say as a customer myself, I've always had a great experience with Sonos, it's probably why I keep buying more and more and more of them. But talk to me about how has data really helped you guys drive that customer experience in some very tangible ways? >> As a Sonos customer, we really hope that you enjoy the great sound and freedom of choice and ease of use that the product brings. But obviously behind all of that, behind that really easy experience, it's very complex. You've got a lot going on, when you're interacting with your Sonos system. So, it's not just a single piece of hardware, you've got a mobile device potentially that you're using to control it, you have third-party voice services, you have music service providers, wifi, network traffic, all of these things are going on. So for us to really make sure that we're creating an amazing experience cause we're super customer obsessed. >> So Margaret, one of the things that I've always experienced as a customer of Sonos, is that, frankly for me it just always works. And that's one of the best parts of the customer experience. Whether I'm pulling up Spotify on my phone to go listen to it, or whether I'm plugging in a soundbar into the TV, everything seems to just work. So maybe just walk me through why it's been such a good experience maybe for me. >> Yeah. So some of the kind of at a high level, an example of what we do is we use Snowflake to... And because of the power of Snowflake, we're able to bring together different kinds of telemetry about what's happening in our products and our services. And then we can try to pinpoint reliability issues and determine what's happening, like what's causing them. That was kind of the first thing that we attacked with Snowflake, was really to go in and dig in and join some different events together and start slicing and dicing and looking for what are the main problems that we're seeing and what are the fixes to them. And the product team was able to find some reliability issues that they were able to fix. And they shipped the fixes and we were able to significantly reduce the rate at which some of these errors were occurring. >> Just by having all the data in one place and being able to go actually act on it quickly and of course in a cost effective manner, it really did let you guys really pinpoint any issue when it did occur and go and go support it, fixed quickly. >> Yeah. I mean, we were able to find things that we didn't even know were happening because we could really drill into the data. >> So it wasn't just having one place, it was also just being able to go dig into a different level of fidelity that you didn't have before? >> I mean, you should see some of the tableau dashboards that we've put on top of Snowflake. So, it's pretty impressive. (laughs) >> That's awesome. That's awesome. So what really set it apart? I mean you've been in the business, you're an expert in this business and there's a lot of options out there. What really set apart Snowflake from everything else at the time, and even now in your opinion? >> Just like you said about Sonos, it just works. (Kyle and Margaret laughing) We were able to stand it up really easily. We were able to load data into it really easily. It's pretty flexible in terms of what you can do. So for example, we use the variant column quite a bit, and that allows us to take kind of this semi-structured data and throw it in there and have an index. And then we can work with it as we want to. We don't have to have like a real complex data pipeline upstream before we throw things into Snowflake. If we don't want to, or we can, we really like, obviously I mentioned the speed. I keep mentioning that cause it's really powerful and it holds a ton of data. But even better is the cost. So we're pushing tons of data into Snowflake and we're not having to pay that much for that cost. We're basically paying S3 costs. But then you pay for what you use. So you're just paying for your processing costs. And even that is pretty easy to optimize. And you guys provide tools for that and support. I mean, you've helped me save thousands dollars a month this year (laughs) and it's really great. >> What's the plan for the future? Where do you see Sonos and Snowflake and the data cloud? Where do you see all that intersecting in the future? >> Yeah, I mean we really want Snowflake to be kind of the center of our data platform and bring all of our data together. We want to live the data dream. (Kyle and Margaret laughing) (indistinct) our data together and do all sorts of cool analysis. So we have a bunch of different projects that we want to be able to do. For example, one thing that we're looking at doing is bringing together our product telemetry data and our customer support data so that we can try to find patterns in terms of the types of errors or sequence of events that happen before somebody calls us, so that we can potentially intervene and fix the problem before somebody even has to reach out to our care team. And then another place that we're looking at using Snowflake is connecting it to salesforce. So for people who are interested in hearing from us, we could do things like when you set up a new product, we can send you information about how to use that product, or if there's new features available for the product that you have, we could send you information about that. And with Snowflake as our backend, it really helps us be able to tailor the customer experience. >> So Margaret, you've talked a lot about Snowflake and using Snowflake and it just works. I mean, you guys are running a very, very large implementation of Snowflake like using IoT data coming from, as you mentioned, millions and millions of devices. What's been the overall lift to the organization just to go manage the whole thing and keep it going and keep it up and running? >> It almost runs itself. (Margaret laughing) I'm almost surprised because we obviously use a lot of different third party tools and most of them require quite a bit of intervention on a regular basis. I would say Snowflake, it stays up and running and you have great tools for managing the whole system. And it's easy to see what's happening, it's easy to see when different clusters are spinning up and spinning down, we have tableau dashboards that we use to monitor all of our usage. And you guys provide all the data for that to make that really easy. So that's really great. Can't say it enough, without Snowflake. (laughs) >> So Margaret, thank you so much. It was great hearing your story about Sonos and how you leverage data cloud. So again, thank you for being a customer. And thank you again for being here today. >> Thanks for having me. It was a pleasure.

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante snowflake they love the stock at 400 and hated at 165 that's the nature of the business i guess especially in this crazy cycle over the last two years of lockdowns free money exploding demand and now rising inflation and rates but with the fed providing some clarity on its actions the time has come to really dig into the fundamentals of companies and there's no tech company that's more fun to analyze than snowflake hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we look at the action of snowflake stock since its ipo why it's behaved the way it has how some sharp traders are looking at the stock and most importantly what customer demand looks like the stock has really provided some great theater since its ipo i know people who got in at 120 before the open and i know lots of people who kind of held their noses and bought the stock on day one at over 300 a day when it closed at around 240 that first day of trading snowflake hit 164 this week it's all-time low as a public company as my college roommate chip simonton a long time trader told me when great companies trade at all times time lows because of panic it's worth taking a shot he did now of course the stock could go lower there's geopolitical risk and the stock with a 64 billion market cap is expensive for a company that's forecast to do around 2 billion in product revenue this year and remember i don't recommend stocks you shouldn't take my advice and my comments you got to do your own research but i have lots of data and i have opinions and i'm willing to share that with you stocks like snowflake crowdstrike z-scaler octa and companies like this are highly volatile when markets are moving up they're going to move up faster than the mean when they're declining they're going to drop more severely and that's clearly what's happened to snowflake so with a company like this you when you see panic selling you'll also see panic buying sometimes like we we've seen with this name it went from 220 to 320 in a very short period earlier snowflake put in a short-term bottom this week and many traders feel the issue was oversold so they bought okay but not everyone felt this way and you can see this in the headlines snowflake hits low but cloud stocks rise and we're going to come back to that is it a buy don't buy the dip buy the dip and what snowflake investors can learn from microsoft and from the street.com snow stock is sliding on the back of ill-conceived guidance and to that i would say that conservative guidance these days is anything but ill-conceived now let's unpack all this a bit and to do so i reached out to ivana delevska who has been on this program before she's with spear invest a female-led etf that goes deep into understanding supply chains she came on breaking analysis and laid out her thesis to buy the dip on snowflake this is a while ago she told me currently spear still likes snowflake and has doubled its position let me share her analysis she called out two drivers for the downside interest rates you know rising of course in snowflakes guidance which my own publication called weak in that previous chart that i just showed you so let's dig into that a bit snowflake guided for product revenues of 67 year on year which was below buy side expectations but i believe within sell side consensus regardless the guide was nuanced and driven by snowflake's decision to pass along price efficiencies to customers from optimizing processor price performance predominantly from aws's graviton too this is going to hit snowflakes revenue a net of about a hundred million dollars this year but the timing's not precise because it's going to hit 165 million but they're going to make up 65 million in increased demand frank slootman on the earnings call made this very clear he said quote this is not philanthropy this stimulates demand classic slootman the point is spear and other bulls believe that this will result in a gain for snowflake over the medium term and we would agree price goes down roi gets better you throw more projects at snowflakes customers going to buy more snowflake and when that happens and it gives the company an advantage as they continue to build their moat it's a longer term bet on cloud and data which are good bets now some of this could also be competitive pressures there have been you know studies that are out there from competitors attacking snowflakes pricing and price performance and they make comparisons oracle's been pretty aggressive as have others but so far the company's customers continue to consume now at a very fast rate now on on this front what can we learn from microsoft that applies to snowflake that's the headline here from benzinga so the article quoted a wealth manager named josh brown talking about what happened to microsoft after the dot-com bubble burst and how they quadrupled earnings over the next decade and the stock went sideways suggesting the same thing could happen to snowflake now i'd like to make a couple of comments here first at the time microsoft was a 23 billion dollar company and it had a monopoly and was already highly profitable steve ballmer became the ceo of microsoft right after the dot-com bubble burst and he hugged onto windows for dear life and lived off of microsoft's pc software monopoly microsoft became an extremely profitable and remarkably uninteresting caretaker of a pc in on-prem software estate during balmer's tenure so i just don't see the comparison as relevant snowflake you know they're going to make struggle for other reasons but that one didn't really resonate with me what's interesting is this chart it poses the question do cloud and data markets behave differently it's a chart that shows aws growth rates over time and superimposes the revenue in the red in q1 2018 aws generated 5.4 billion dollars in revenue and that was growing at the time at nearly a 50 rate now that rate as you can see decelerated quite significantly as aws grew to a 50 billion dollar run rate company that down below where you see it bottoms now it makes sense right law of large numbers you can't keep growing that fast when you get that big well oops look what happened in 2021 aws's growth rate bottoms in the high 20s and then rockets back up to 40 this past quarter as aws surpasses a 70 billion dollar run rate so you have to ask is cloud different is data different is cloud data different or data cloud different let's put it in the snowflake parlance can cloud because of its consumption model and the speed of innovation and ecosystem depth and breadth enable snowflake to exhibit lots of variability in its growth rates versus a say progressive and somewhat linear decline as the company grows revenue which is what you would expect historically and part of the answer relates to its market size here's a chart we've shared before with some additions it's our version of snowflake's total available market they're tam which snowflake's version that that blue data cloud thing superimposed on the right it shows the various layers of market opportunity that we came up with that that snowflake and others we think have in front of them emerging from the disruption of legacy data lakes and data warehouses to what snowflake refers to as its data cloud we think about the data mesh concept and decentralized data architectures with domain ownership and data product and service builders as consistent with snowflake's data cloud vision where snowflake data stores are nodes they're just simply discoverable nodes on the mesh you could have you know data bricks data lakes you know s3 buckets on that mesh it doesn't matter they can be discovered they can be shared and of course they're governed in a federated model now in snowflake's model it's all inside the snowflake data cloud that's fine then you'll go to the out years it gets a little fuzzy you know from edge locations and ai inference it becomes massive and decision making occurs in real time where machines and machine data take over the world instead of you know clicks and keystrokes sounds out there but it's real and how exactly snowflake plays there at this point is unclear but one thing's for sure there'll be a lot of data and it's going to find its way into snowflake you know snowflake's not a real-time engine it's an analytical system it's moving into the realm of data science and you know we've talked about the need for you know semantic layer between those those two worlds of analytics and data science but expanding the scope further out we think that snowflake is a big role to play in this future and the future is massive okay check you got the big tam now as someone that looks at companies through a fundamentals prism you've got to look obviously at the markets in the tan which we just did but you also want to understand customers and it's not hard to find snowflake customers capital one disney micron alliance sainsbury sonos and hundreds of other companies i've talked to snowflake customers who have also been customers of oracle teradata ibm neteza vertica serious database practitioners and they tell me it's consistent soulflake is different they say it's simpler it's more agile it's less complicated to secure and it's disruptive to their traditional ways of doing data management now of course there are naysayers i've spoken to a number of analysts that feel snowflake is deficient in areas like workload management and course complex joins and it's too specialized in a world where we're seeing the convergence of analytics and transactional workloads our own david floyer believes that what oracle is doing with mysql heatwave is radically disruptive to many of the database architectures and blows away anything out there and he believes that snowflake and the likes of aws are going to have to respond now this the other criticism here is that snowflake is not architected for real-time inference where a lot of that edge activity is is going to happen it's a multi-hundred billion dollar market and so look snowflake has a ton of competition that's the other thing all the major cloud players have very capable and competitive database platforms even though they all partner with snowflake except oracle of course but companies like databricks and have garnered tons of vc other vc funded companies have raised billions of dollars to do this kind of elastic consumption based separate compute from storage stuff so you have to always keep an open mind and be aware of potential blind spots for these companies but to the criticisms i would say look snowflake they got there first and watch their ecosystem it's a real key to its continued success snowflake's not going to go it alone and it's going to use its ecosystem partners to expand its reach and accelerate the network effects and fill those gaps and it will acquire its stock is valuable so it should be doing that just as it did with streamlit a zero revenue company that it bought for 800 million dollars in stock and cash just recently streamlit is an open source python library that gets snowflake further deeper into that data science space that data brick space and look watch what snowflake is doing with snowpark it's an api library for processing data and building data intensive applications we've talked about snowflake essentially being becoming the super cloud and building this sort of path-like layer across clouds rather than trying to do it all themselves it seems snowflake is really staring at the api economy and building its ecosystem to plug those holes so let's come back to the customers here's a chart that shows snowflakes customer spending momentum or net score on the the top line that's the vertical axis and pervasiveness in the data or market share and that bottom brown line snowflake has unprecedented net scores and held them up for many many quarters as you can see here going back you know a couple years all leading to its expanded market penetration and measured as pervasiveness of so-called market share within the etr survey it's not like idc market share it's pervasiveness in the data set now i'll say this i don't see how this is sustainable i've been waiting for this to moderate i wouldn't be surprised to see snowflake come back to earth a little bit i think they'll clearly still be highly elevated based on the data that i've seen but but i could see in in one or more of the etr surveys this year this starting to moderate as they get they get big it's just it has to happen um but i would again expect them to have a high spending velocity score but i think we're going to see snowflake you know maybe porpoise a bit here meaning you know it moderates it comes back up it's just really hard to sustain this piece of momentum and higher train retain and scale without absorbing some some friction and some head woods that's going to slow you down but back to the aws growth example it's entirely possible that we could see a similar dynamic with snowflake that you saw with aws and you kind of see it with salesforce and servicenow very successful large entrenched entrenched companies and it's very possible that snowflake could pull back moderate and then accelerate that growth even though people are concerned about the moderated guidance of 80 percent growth yeah that's that's the new definition of tepid i guess i look i like to look at other some other metrics the one that really called you know my my my attention was the remaining performance obligations this last quarter rpo snowflakes is up to something like 2.6 billion and that is a forward-looking indicator of of future revenues so i want to i'd like to see that growing and it's growing at a fast pace so you're going to see some ups and downs with snowflake i have no doubt but i think things are still looking pretty solid for the company growth companies like snowflake and octa and z scalar those other ones that i mentioned earlier have probably been repriced and refactored by investors while there's always going to be market and of course geopolitical risk especially in these times fundamentals matter you've got huge market well capitalized you got a leadership position great products and strong customer adoption you also have a great team team is something else that we look for we haven't touched on that but i'll leave you with this thought everyone knows about frank slootman mike scarpelli and what they've accomplished in their years of working together that's why the stock you know in ipo was was so overvalued they had seen these guys do it before slootman just documented in all this in his book amp it up which gives great insight into the history of of that though you know that pair and and the teams that they've built the companies that they've built how he thinks about building companies and markets and and how you know total available markets super important but the whole philosophy and culture that that he's building in his management style but you got to wonder right how long is this guy going to keep going what keeps him motivated you know i asked him that one time here's what he said why i mean are you in this for the sport what's the story here uh actually that that's not a bad way of characterizing it i think i am in it uh you know for the sport uh you know the only way to become the best version of yourself is to be uh to be under the gun and uh you know every single day and that's that's certainly uh what we are it sort of has its own rewards building great products building great companies uh you know regardless of you know uh what the spoils may be uh it has its own rewards and i i it's hard for people like us to get off the field and uh you know hang it up so here we are so there you have it he's in it for the sport how great is that he loves building companies and that my opinion that's how frank slootman thinks about success it's not about money money's the byproduct of success as earl nightingale would say success is the progressive realization of a worthy ideal i love that quote building great companies building products that change the world changing people's lives with data and insights creating jobs creating life-altering wealth opportunities not for himself but for thousands of employees and partners i'd say that's a pretty worthy ideal and i hope frank slootman sticks with it for a while okay that's it for today thanks to stephanie chan for the background research she does for breaking analysis alex meyerson on production kristen martin and cheryl knight on social with rob hoff on siliconangle and thanks to ivana delevska of spear invest and my friend chip symington for the angles from the money side of things remember all these episodes are available as podcasts just search breaking analysis podcast i publish weekly on wikibon.com and siliconangle.com and don't forget to check out etr.plus for all the survey data you can reach me at devolante or david.velante siliconangle.com and this is dave vellante for cube insights powered by etrbsafe stay well and we'll see you next time [Music] you

>>Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, or is modernized now. Yes, let's talk about that. And with me is Ravi, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >>Thank you so much. I'm so glad to be here with you. >>I asked you what the new requirements are around modern applications. I've seen some, you know, some of your comments, you gotta be flexible, distributed, multimodal, mobile edge. It, that those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >>Yeah, I think what has basically happened is that, uh, so far, uh, it's been a transition of sorts. And now we are come to a point where, uh, the tipping point and the tipping point has been, uh, uh, more because of COVID and there COVID has pushed us to a world where we are living, uh, in a sort of, uh, occasionally connected manner where our digital, uh, interactions, precede our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than, uh, in a digital manner, as opposed to sort of making a more specific human contact that has really been the, uh, sort of accelerant to this modernized. Now, as a team in this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. >>They're all sitting behind. Uh, they used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, uh, but they are all centralized still. Uh, but where our engagement happens with the data is, uh, at the edge, uh, at your point of convenience at your point of consumption, not where the data is actually sitting. So this has led to, uh, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? Uh, but it just basically comes down to the fact that the data needs to be where you are engaging with it. And that means if you are doing it on your mobile phone, or if you are sitting, uh, doing something in your body or traveling, or whether you are in a subway, whether you're in a plane or a ship, wherever the data needs to come to you, uh, and be available as opposed to every time you going to the data, which is centrally sitting in some place. >>And that is the fundamental shift in terms of how the modern architecture needs to think, uh, when they, when it comes to digital transformation and, uh, transitioning their old applications to, uh, the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Uh, otherwise people are basically waiting for that circle of death that we all know, uh, and blaming the networks and other pieces. The problem is actually, the data is not where you are engaging with. It has got to be fetched, you know, seven seas away. Um, and that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >>I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this, because date data by its very nature is distributed. It's always been distributed, but w w but distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, uh, of, uh, of a super rock solid database that can handle, you know, distributed data? Yes. >>So there are two issues that you're a little too over there with Forrest is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is, uh, like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data in one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, uh, when you have the data, you can first look at it to perform. >>Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five minutes. Again, this is a, there's a class of problem that we solve that same data. Now, eventually, without you ever having to, uh, sort of do a casting it to a different database, you can now do a solid, uh, acquire. These are classic sequel queries, which is our next magic. We are a no SQL database, but we have a full functional sequel. The sequel has been the language that has talked to data for 40 odd years successfully. Every other database has come and try to implement their own QL query language, but they've all failed only sequel as which stood the test of time of 40 odd years. >>Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is, uh, basically, uh, look at the data and any common tutorial, uh, any, uh, any which way you look at the data. All it will come, uh, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries, select star from where Canada stuff, because it's at an English level, it becomes easy to, so the same data, you didn't have to go move it to another database, do your, uh, sort of transformation of the data and all this stuff. Same day that you do this. >>Now, that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, but Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the ability to query the operational data in a different way. I'll talk budding. What was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. >>So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and find different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, uh, the database management system. And that's where the distributed, uh, platform that we have built enables us to get it to where you need the data to be, you know, in a classic way, we call it CDN in the data as in like content delivery networks. So far do static, uh, uh, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >>The first part of the, the answer to my question, are you saying you could do this without skiing with a no schema on, right? And then you can apply those techniques. >>Uh, fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read things. So because there is no schema, it is just a on document that is sitting inside. And Jason is the lingua franca of the web, as you very well know by now. So it just Jason that we manage, you can do key lookups of the Jason. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and the other sophisticated pieces of technology behind it. >>You can do searching on it, using the, um, the full textual analysis pipeline. You can do ad hoc wedding on the analytic side, and you can write your own custom logic on it using our eventing capabilities. So that's, that's what it allows because we keep the data in the native form of Jason. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing, uh, in the last 40 years because we developed various, uh, database systems and data processing systems of various points. In time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. >>We had queuing systems, all the systems, if you want to use any one of them, our answer has always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this is not one to fly instead, bring the logic to the data. So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this, >>As you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >>Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data casting because it required you to have it in seven schema in one sense at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably flooded, but it not really, uh, how do you say, um, keep to the promise that it actually meant to be? So that's why it was a swamp I need, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it, and you create different types of indexes to manage it. You distribute the index, you distribute the data you have, um, like we were discussing, you have acid semantics on top of, and when you, when you put all these things together, uh, it's, it's, it's a tough proposition, but they have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >>So you predicted the trend around multimodal and converged, uh, databases. Um, you kind of led Couchbase through that. I want to, I always ask this question because it's clearly a trend in the industry and it, it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, we have a little teeny scissors and a knife. That's not that sharp. How do you respond to that? Uh, >>A great one. Um, my answer is always, I use another analogy to tackle that, but is that, have you ever accused a smartphone of being a Swiss army knife? No. No. Nobody does that because it's actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Like as in a moment, it could be a Tom, Tom telling you all the directions, the next one, it's your PDA. >>Third one, it's a fantastic phone. Uh, four, it's a beautiful camera, which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment is a video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just taught that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, they missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app is the economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get the alert saying that today you got to leave home at eight 15 for your nine o'clock meeting. >>And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's gone there's notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place without that, you couldn't even do this simple function, uh, in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build, because half the time you're running sideline to sideline, just, you know, um, integrating data from one system to the other. >>So I love the analogy with the smartphone. I w I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? And so, so, but, but is there, is that a fair, where, in other words, those specialized databases, they say there still is a place for them, but they're getting >>Absolutely, absolutely great analogy and a great extension to the question. That's, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of the music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they haven't, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. >>Yes, it's 90% there or 80% there. It depends on your audio file mess of your, uh, I mean, you don't experience the super specialized ones do not go away. You know, there are, there are places where, uh, the specialized use cases will demand a separate system to exist, but even there that has got to be very closed. Um, how do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that, oh, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car and walk into my living room, that's same songs should continue and play in my living room speakers. Then it's a world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >>I love, I love that example too. When I was a kid, we used to go to Twitter, et cetera. And we'd to play around with, we take off the big four foot speakers. Those stores are out of business too. Absolutely. Um, now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi. >>I believe so. Uh, because I think, uh, what had happened was the relational systems. Uh, I've been where the norm, they rule the roost, if you will, for the last 40 odd years, and then gain this no sequel movement, which was almost as though a rebellion from the relational world, we all inhibited, uh, uh, because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee, they required your DBA and your data architect. And you have to call them just to add one column and stuff like that. And the world had moved on. This was the world of blogs and tweets and, uh, you know, um, mashups and, um, uh, uh, a different generation of digital behavior, digital, native people now, um, who are operating in these and the, the applications, the, the consumer facing applications. >>We are living in this world. And yet the enterprise ones were still living in the, um, in the other, the other side of the divide. So all came this solution to say that we don't need SQL. Actually, the problem was never sequel. No sequel was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations, and the inability for these, the system to scale, the relational systems were built like, uh, airplanes, which is that if, uh, San Francisco Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set in from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the alarm to somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. >>These are called vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world, uh, is make the system how it is only scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guests. I'll add one more coach to it, one more car to it. And the better part of the way we have done this year is that, and we have super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have ID only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. >>You can attach the kind of coaches we call this multi-dimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it quite. So that's the beauty of this architecture. Now, why is that important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because you would say that I cannot run this analytical Barre because then my operational workload will suffer. Then my friend, then we'll slow down millions of customers that impacted that problem. We will solve the same data in which you can do analytical buddy, an operational query because they're separated by these cars, right? As in like we, we fence the, the, the resources, so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or equity. >>And then yet you can run this analytical body, which will take a couple of minutes to run one, not impeding the other. So that's in one sense, sort of the, part of the, um, uh, problems that we have solved here is that relational versus, uh, uh, the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same quality language on top. Y it's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, uh, the internal combustion engine, uh, I think gas, uh, you says, these are the issues we really wanted to solve. Um, so solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, or that are for your shifters. >>Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So, uh, even when you feed people the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blue harder to go fast and lean back for, for it to, you know, uh, to apply a break that's, that's how we seem to define, uh, design software. Instead, we should be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, uh, and the gas bottle and the, um, and the gear shifter is by putting cul back on underneath the surface, we have completely solved, uh, the relational, uh, uh, limitations of schema, as well as scalability. >>So in, in, in that way, and by bringing back the classic acid capabilities, which is what relational systems, uh, we accounted on and being able to do that with the sequel programming language, we call it like multi-state SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up the salt in the modern times, but rather than get, um, sort of pedantic about whether it's, we have no SQL or sequel or new sequel, or, uh, you know, any of that sort of, uh, jargon, oriented debate, uh, this, these are the debates of computer science that they are actually, uh, and they were the solve and they have solved them with, uh, the latest release of $7, which we released a few months ago. >>Right, right. Last July, Ravi, we got to leave it there. I, I love the examples and the analogies. I can't wait to be face to face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >>Fantastic. Thanks for the time. And the Aboriginal Dan was, I mean, very insightful questions really appreciate it. Thank you. >>Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

(upbeat music) >> Welcome to theCUBE's coverage of VMworld 2021. I'm Lisa Martin. I've got a CUBE alum with me next. Ajay Patel is here, the SVP and GM of Modern Apps and Management at VMware. Ajay, welcome back to the program, it's great to see you. >> Well thank you for having me. It's always great to be here. >> Glad that you're doing well. I want to dig into your role as SVP and GM with Modern Apps and Management. Talk to me about some of the dynamics of your role and then we'll get into the vision and the strategy that VMware has. >> Makes sense. VMware has created a business group called Modern Apps and Management, with the single mission of helping our customers accelerate their digital transformation through software. And we're finding them leveraging both the edge and the multiple clouds they deploy on. So our mission here is helping, them be the cloud diagnostic manager for application development and management through our portfolio of Tazu and VRealize solutions allowing customers to both build and operate applications at speed across these edge data center and cloud deployments And the big thing we hear is all the day two challenges, right of managing costs, risks, security, performance. That's really the essence of what the business group is about. How do we speed idea to production and allow you to operate at scale. >> When we think of speed, we can't help, but think of the acceleration that we've seen in the last 18 months, businesses transforming digitally to first survive the dynamics of the market. But talk to me about how the, the pandemic has influenced catalyzed VMware's vision here. >> You can see in every industry, this need for speed has really accelerated. What used to be weeks and months of planning and execution has materialized into getting something out in production in days. One of great example I can remember is one of my financial services customer that was responsible for getting all the COVID payments out to the small businesses and being able to get that application from idea to production matter of 10 days, it was just truly impressive to see the teams come together, to come up with the idea, put the software together and getting production so that we could start delivering the financial funds the companies needed, to keep them viable. So great social impact and great results in matter of days. >> And again, that acceleration that we've seen there, there's been a lot of silver linings, I think, but I want to get in next to some of the industry trends that are influencing app modernization. What are you seeing in the customer environment? What are some of those key trends that are driving adoption? >> I mean, this move to cloud is here to stay and most of customers have a cloud first strategy, and we rebranded this from VMware the cloud smart strategy, but it's not just about one particular flavor of cloud. We're putting the best workload on the best cloud. But the reality is when I speak to many of the customers is they're way behind on the bar of digital plats. And it's, that's because the simple idea of, you know, lift and shift or completely rewrite. So there's no one fits all and they're struggling with hardware capability, their the development teams, their IT assets, the applications are modernized across these three things. So we see modernization kind of fall in three categories, infrastructure modernization, the practice of development or devops modernization, and the application transform itself. And we are starting to find out that customers are struggling with all three. Well, they want to leverage the best of cloud. They just don't have the skills or the expertise to do that effectively. >> And how does VMware help address that skills gap. >> Yeah, so the way we've looked at it is we put a lot of effort around education. So on the everyone knows containers and Kubernetes is the future. They're looking to build these modern microservices, architectures and applications. A lot of investment in just kind of putting the effort to help customers learn these new tools, techniques, and create best practices. So theCUBE academy and the effort and the investment putting in just enabling the ecosystem now with the skills and capabilities is one big effort that VMware is putting. But more importantly, on the product side, we're delivering solutions that help customers both build design, deliver and operate these applications on Kubernetes across the cloud of choice. I'm most excited about our announcement around this product. We're just launching called Tanzu application platform. It is what we call an application aware platform. It's about making it easy for developers to take the ideas and get into production. It kind of bridging that gap that exists between development and operations. We hear a lot about dev ops, as you know, how do you bring that to life? How do you make that real? That's what Tanzu application platform is about. >> I'm curious of your customer conversations, how they've changed in the last year or so in terms of, app modernization, things like security being board level conversations, are you noticing that that is rising up the chain that app modernization is now a business critical initiative for our businesses? >> So it's what I'm finding is it's the means. It's not that if you think about the board level conversations about digital transformation you know, I'm a financial services company. I need to provide mobile FinTech. I'm competing with this new age application and you're delivering the same service that they offered digitally now, right. Like from a retail bank. I can't go to the store, the retail branch anymore, right. I need to provide the same capability for payments processing all online through my mobile phone. So it's really the digitalization of the traditional processes that we're finding most exciting. In order to do that, we're finding that no applications are in cloud right. They had to take the existing financial applications and put a mobile frontend to it, or put some new business logic or drive some transformation there. So it's really a transformation around existing application to deliver a business outcome. And we're focusing it through our Tanzu lab services, our capabilities of Tanzu application platform, all the way to the operations and management of getting these products in production or these applications in production. So it's the full life cycle from idea to production is what customers are looking for. They're looking to compress the cycle time as you and I spoke about, through this agility they're looking for. >> Right, definitely a compressed cycle time. Talk to me about some of the other announcements that are being made at VMworld with respect to Tanzu and helping customers on the app modernization front, and that aligned to the vision and mission that you talked about. >> Wonderful, I would say they're kind of, I put them in three buckets. One is what are we doing to help developers get access to the new technology. Back to the skills learning part of it, most excited about Tanzu of community edition and Tanzu mission control starter pack. This is really about getting Kubernetes stood up in your favorite deployment of choice and get started building your application very quickly. We're also announcing Tanzu application platform that I spoke about, we're going to beta 2 for that platform, which makes it really easy for developers to get access to Kubernetes capability. It makes development easy. We're also announcing marketplace enhancements, allowing us to take the best of breed IC solutions and making them available to help you build applications faster. So one set of announcements around building applications, delivering value, getting them down to market very quickly. On the management side, we're really excited about the broad portfolio management we've assembled. We're probably in the customer's a way to build a cloud operating model. And in the cloud operating model, it's about how do I do VMs and containers? How do I provide a consistent management control plane so I can deliver applications on the cloud of my choice? How do I provide intrinsic observability, intrinsic security so I can operate at scale. So this combination of development tooling, platform operations, and day two operations, along with enhancements in our cost management solution with CloudHealth or being able to take our universal capabilities for consumption, driving insight and observity that really makes it a powerful story for customers, either on the build or develop or deploy side of the equation. >> You mentioned a couple of things are interesting. Consistency being key from a management perspective, especially given this accelerated time in which we're living, but also you mentioned security. We've seen so much movement on the security front in the last year and a half with the massive rise in ransomware attacks, ransomware now becoming a household word. Talk to me about the security factor and how you're helping customers from a risk mitigation perspective, because now it's not, if we get attacked, it's when. >> And I think it's really starts with, we have this notion of a secure software supply chain. We think of software as a production factory from idea to production. And if you don't start with known good hard attacks to start with, trying to wire in security after attack is just too difficult. So we started with secure content, curated images content catalogs that customers are setting up as best practices. We started with application accelerators. These are best practice that codifies with the right guard rails in place. And then we automate that supply chain so that you have checks in every process, every step of the way, whether it's in the build process and the deploy process or in runtime production. And you had to do this at the application layer because there is no kind of firewall or edge you can protect the application is highly distributed. So things like application security and API security, another area we announced a new offering at VM world around API security, but everything starts with an API endpoint when you have a security. So security is kind of woven in into the design build, deploy and in the runtime operation. And we're kind of wire this in intrinsically to the platform with best of breed security partners now extending in evolving their solution on top of us. >> What's been some of the customer feedback from some of the new technologies that you announced. I'm curious, I imagine knowing how VMware is very customer centric, customers were essential in the development and iteration of the technologies, but just give me some of the idea on customer feedback of this direction that you're going. >> Yeah, there's a great, exciting example where we're working with the army to create a software factory. you would've never imagined right, The US army being a software digital enterprise, we're partnering with what we call the US army futures command in a joint effort to help them build the first ever software development factory where army personnel are actually becoming true cloud native developers, where you're putting the soldiers to do cloud native development, everything in the terms of practice of building software, but also using the Tanzu portfolio in delivering best-in-class capability. This is going to rival some of the top tech companies in Silicon valley. This is a five-year prototype project in which we're picking cohorts of soldiers, making them software developers and helping them build great capability through both combination of classroom based training, but also strong technical foundation and expertise provided by our lab. So this is an example where, you know, the industry is working with the customer to co-innovate, how we build software, but also driving the expertise of these personnel hierarchs. As a soldier, you know, what you need, what if you could start delivering solutions for rest of your members in a productive way. So very exciting, It's an example where we've leapfrogging and delivering the kind of the Silicon valley type innovation to our standard practice. It's traditionally been a procurement driven model. We're trying to speed that and drive it into a more agile delivery factory concept as well. So one of the most exciting projects that I've run into the last six months. >> The army software factory, I love that my dad was an army medic and combat medic in Vietnam. And I'm sure probably wouldn't have been apt to become a software developer. But tell me a little bit about, it's a very cool project and so essential. Talk to me a little bit about the impetus of the army software factory. How did that come about? >> You know, this came back with strong sponsorship from the top. I had an opportunity to be at the opening of the campus in partnership with the local Austin college. And as General Milley and team spoke about it, they just said the next battleground is going to be a digital backup power hub. It's something we're going to have to put our troops in place and have modernized, not just the army, but modernize the way we deliver it through software. It's it speaks so much to the digital transformation we're talking about right. At the very heart of it is about using software to enable whether it's medics, whether it's supplies, either in a real time intelligence on the battlefield to know what's happening. And we're starting to see user technology is going to drive dramatically hopefully the next war, we don't have to fight it more of a defensive mode, but that capability alone is going to be significant. So it's really exciting to see how technology has become pervasive in all aspects, in every format including the US army. And this partnership is a great example of thought leadership from the army command to deliver software as the innovation factory, for the army itself. >> Right, and for the army to rival Silicon valley tech companies, that's pretty impressive. >> Pretty ambitious right. In partnership with one of the local colleges. So that's also starting to show in terms of how to bring new talent out, that shortage of skills we talked about. It's a critical way to kind of invest in the future in our people, right? As we, as we build out this capability. >> That's excellent that investment in the future and helping fill those skills gaps across industries is so needed. Talk to me about some of the things that you're excited about this year's VMworld is again virtual, but what are some of the things that you think are really fantastic for customers and prospects to learn? >> I think as Raghu said, we're in the third act of VM-ware, but more interestingly, but the third act of where the cloud is, the cloud has matured cloud 2.0 was really about shifting and using a public cloud for the IS capabilities. Cloud 3.0 is about to use the cloud of choice for the best application. We are going to increasingly see this distributed nature of application. I asked most customers, where does your application run? It's hard to answer that, right? It's on your mobile device, it's in your storefront, it's in your data center, it's in a particular cloud. And so an application is a collection of services. So what I'm most excited about is all business capables being published as an API, had an opportunity to be part of a company called Sonos and then Apogee. And we talked about API management years ago. I see increasingly this need for being able to expose a business capability as an API, being able to compose these new applications rapidly, being able to secure them, being able to observe what's going on in production and then adjust and automate, you can scale up scale down or deploy the application where it's most needed in minutes. That's a dynamic future that we see, and we're excited that VM was right at the heart of it. Where that in our cloud agnostic software player, that can help you, whether it's your development challenges, your deployment challenges, or your management challenges, in the future of multi-cloud, that's what I'm most excited about, we're set up to help our customers on this cloud journey, regardless of where they're going and what solution they're looking to build. >> Ajay, what are some of the key business outcomes that the cloud is going to deliver across industries as things progress forward? >> I think we're finding the consistent message I hear from our customers is leverage the power of cloud to transform my business. So it's about business outcomes. It's less about technology. It's what outcomes we're driving. Second it's about speed and agility. How do I respond, adjust kind of dynamic contiuness. How do I innovate continuously? How do I adjust to what the business needs? And third thing we're seeing more and more is I need to be able to management costs and I get some predictability and able to optimize how I run my business. what they're finding with the cloud is the costs are running out of control, they need a way, a better way of knowing the value that they're getting and using the best cloud for the right technology. Whether may be a private cloud in some cases, a public cloud or an edge cloud. So they want to able to going to select and move and have that portability. Being able to make those choices optimization is something they're demanding from us. And so we're most excited about this need to have a flexible infrastructure and a cloud agnostic infrastructure that helps them deliver these kinds of business outcomes. >> You mentioned a couple of customer examples and financial services. You mentioned the army software factory. In terms of looking at where we are in 2021. Are there any industries in particular, maybe essential services that you think are really prime targets for the technologies, the new announcements that you're making at VM world. >> You know, what we are trying to see is this is a broad change that's happening. If you're in retail, you know, you're kind of running a hybrid world of digital and physical. So we're seeing this blending of physical and digital reality coming together. You know, FedEx is a great customer of ours and you see them as spoken as example of it, you know, they're continue to both drive operational change in terms of being delivering the packages to you on time at a lower cost, but on the other side, they're also competing with their primary partners and retailers and in some cases, right, from a distribution perspective for Amazon, with Amazon prime. So in every industry, you're starting to see the lines are blurring between traditional partners and competitors. And in doing so, they're looking for a way to innovate, innovate at speed and leverage technology. So I don't think there is a specific industry that's not being disrupted whether it's FinTech, whether it's retail, whether it's transportation logistics, or healthcare telemedicine, right? The way you do pharmaceutical, how you deliver medicine, it's all changing. It's all being driven by data. And so we see a broad application of our technology, but financial services, healthcare, telco, government tend to be a kind of traditional industries that are with us but I think the reaches are pretty broad. >> Yeah, it is all changing. Everything is becoming more and more data-driven and many businesses are becoming data companies or if they're not, they need to otherwise their competition, as you mentioned, is going to be right in the rear view mirror, ready to take their place. But that's something that we see that isn't being talked about. I don't think enough, as some of the great innovations coming as a result of the situation that we're in. We're seeing big transformations in industries where we're all benefiting. I think we need to get that, that word out there a little bit more so we can start showing more of those silver linings. >> Sure. And I think what's happening here is it's about connecting the people to the services at the end of the day, these applications are means for delivering value. And so how do we connect us as consumers or us employees or us as partners to the business to the operator with both digitally and in a physical way. And we bring that in a seamless experience. So we're seeing more and more experience matters, you know, service quality and delivery matter. It's less about the technologies back again to the outcomes. And so very much focused in building that the platform that our customers can use to leverage the best of the cloud, the best of their people, the best of the innovation they have within the organization. >> You're right. It's all about outcomes. Ajay, thank you for joining me today, talking about some of the new things that the mission of your organization, the vision, some of the new products and technologies that are being announced at VM world, we appreciate your time and hopefully next year we'll see you in person. >> Thank you again and look forward to the next VMWorld in person. >> Likewise for Ajay Patel. You're very welcome for Ajay Patel. I'm Lisa Martin, and you're watching theCUBEs coverage of VMWorld of 2021. (soft music)

>> Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, is modernize now. Yes, let's talk about that. And with me is Ravi mayor him, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >> Thank you so much. I'm so glad to be here with you. >> I want to ask you what the new requirements are around modern applications. I've seen some of your comments, you got to be flexible, distributed, multimodal, mobile, edge. Those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >> Yeah, I think what has basically happened is that so far it's been a transition of sorts. And now we are come to a point where that tipping point and that tipping point has been more because of COVID and there are COVID has pushed us to a world where we are living in a in a sort of occasionally connected manner where our digital interactions precede, our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than in a digital manner, as opposed to sort of making a more specific human contact. That does really been the sort of accelerant to this modernize Now, as a team. In this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. They're all sitting behind. They used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, but they are all centralized still, but where our engagement happens with the data is at the edge at your point of convenience, at your point of consumption, not where the data is actually sitting. So this has led to, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? But it just basically comes down to the fact that the data needs to be there, if you are engaging with it. And that means if you are doing it on your mobile phone, or if you're sitting, but doing something in your while you're traveling, or whether you're in a subway, whether you're in a plane or a ship, wherever the data needs to come to you and be available, as opposed to every time you going to the data, which is centrally sitting in some place. And that is the fundamental shift in terms of how the modern architecture needs to think when they, when it comes to digital transformation and, transitioning their old applications to the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Otherwise, people are basically waiting for that circle of death that we all know, and blaming the networks and other pieces. The problem was actually, the data is not where you are engaging with it. It's got to be fetched, you know, seven sea's away. And that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >> I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this because date data by its very nature is distributed. It's always been distributed, but with the distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, of, of a super rock solid database that can handle, you know, distributed data? >> Yes. So there are two issues that you alluded little too over there. The first is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data. In one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, when you have the data, you can first look at it to perform. Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five milliseconds, this is, this is a class of problem that we solve that same data. Now, eventually, without you ever having to sort of do a casting it to a different database, you can now do solid queries. Our classic SQL queries, which is our next magic. We are a no SQL database, but we have a full functional SQL. The SQL has been the language that has talked to data for 40 odd years successfully. Every other database has come and tried to implement their own QL query language, but they've all failed only SQL has stood the test of time of 40 odd years. Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is basically a look at the data and any common editorial, any, any which way you look at the data, all it will come, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries select star from where, kind of stuff, because it's at an English level becomes easy to so the same day that you didn't have to go move it to another database, do your sort of transformation of the data and all the stuff, same day that you do this. Now that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, what Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the, your ability to query the operational data in a different way. And talk querying, what was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and apply different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, the database management system. And that's where the distributed platform that we have built enables us to get it to where you need the data to be, you know, in the classic way we call it CDN'ing the data as in like content delivery networks. So far do static, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >> And on the first part of, of the, the, the answer to my question, are you saying you could do this without scheme with a no schema on, right? And then you can apply those techniques. >> Fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read thing. So, because there is no schema, it is just a Json document that is sitting inside. And Json is the lingua franca of the web, as you very well know by now. So it just Json that we manage, you can do key value look ups of the Json. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and other sophisticated pieces of technology behind it. You can do searching on it, using the, the full textual analysis pipeline. You can do ad hoc webbing on the analytics side, and you can write your own custom logic on it using or inventing capabilities. So that's, that's what it allows because we keep the data in the native form of Json. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, bring, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing in the last 40 years, because we developed various database systems and data processing systems at various points in time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. We had queuing systems, all these systems, if you want to use any one of them are answered. It always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this, it's not going to fly instead, bring the logic to the data, right? So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this. >> But as you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >> Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data recasting because it required you to have it in seven schema in one sense at, at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably related, but it not really, how do you say keep to the promise that it actually meant to be? So that's why it was a swamp I mean, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it. And you create different types of indexes to manage it. You distribute the index, you distribute the data you have, like we were discussing, you have ACID semantics on top of, and when you, when you put all these things together, it's, it's, it's a tough proposition, but we have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >> So you predicted the trend around multimodal and converged databases. You kind of led Couchbase through that. I, I want, I always ask this question because it's clearly a trend in the industry and it, and it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, where you have have a little teeny scissors and a knife, that's not that sharp. How, how do you respond to that? >> A great one. My answer is always, I use another analogy to tackle that, and is that, have you ever accused a smartphone of being a Swiss army knife? - No. No. >> Nobody does. That because it actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Right? As in a moment, it could be a TomTom, telling you all the directions, the next one, it's your PDA. Third one. It's a fantastic phone. Four. It's a beautiful camera which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment, it's the video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just thought that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, he missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app based economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get an alert saying that today you got to leave home at >> 8: 15 for your nine o'clock meeting. And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's got this notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place. Without that, you couldn't even do this simple function in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build. Because half the time you're running sideline to sideline, just, you know, integrating data from one system to the other. >> So I love the analogy with the smartphone. I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? So, so, but, but is there, is that a fair and where, in other words, those specialized databases, they say there still is a place for them, but they're getting. >> Absolutely, absolutely great analogy and a great extension to the question. That's like, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of my music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they, I mean, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. Yes, it's 90% there or 80% there. It depends on your audio file-ness of your, I mean, your experience super specialized ones do not go away. You know, there are, there are places where the specialized use cases will demand a separate system to exist. But even there that has got to be very closed. How do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that all, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car, walk into my living room, that same songs should continue and play in my living room speakers. Then it's a connected world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >> I love, I love that example too. When I was a kid, we used to go to Tweeter, et cetera. And we used to play around with three, take home, big four foot speakers. Those stores are out of business too. Absolutely. And now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi? >> I believe so, because I think what had happened was relational systems. I've mean where the norm, they rule the roost, if you will, for the last 40 odd years and then gain this no SQL movement, which was almost as though a rebellion from the relational world, we all inhabited because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee. They required your DBA and your data architect. And you had to call them just to add one column and stuff like that. And the world had moved on. This was a world of blogs and tweets and, you know, mashups and a different generation of digital behavior, There are digital, native people now who are operating in these and the, the applications, the, the consumer facing applications. We are living in this world. And yet the enterprise ones were still living in the, in the other, the other side of the divide. So out came this solution to say that we don't need SQL. Actually the problem was never SQL. No SQL was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations and the inability for these, the system to scale, the relational systems were built like airplanes, which is that if a San Francisco, Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the allowance that you'll somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. These are all vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world is make the system horizontally scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guest. I'll add one more coach to it, one more car to it. And the better part of the way we have done this here is that, and we are super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have, I need only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. You can attach the kind of coaches we call this multidimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it, right? So that's the beauty of this architecture. Now, why is that architecture important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because, you would say that I cannot run this analytical query because then my operational workload will suffer. Then my front end, then we'll slow down millions of customers that impacted that problem. They'll solve the same data once again, do analytical query, an operational query because they're separated by these cars, right? As in like we, we, we fence the, the, the resources so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or a query. And then yet you can run this analytical query, which will take a couple of minutes to them. One, not impeding the other. So that's in one sense, sort of the part of the problems that we have solved it here is that relational versus the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same query language on top. Why? It's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, the internal combustion engine the gas, you says, these were the issues we really wanted to solve. So solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, over there your gear shifters. Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So even when you feed people, the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blow harder to go fast. And they lean back for, for it to, you know, to apply a break that's, that's how we seem to define design software. Instead, we shouldn't be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, and the gas pedal and the, and the gear shifters by putting SQL back on underneath the surface, we have completely solved the relational limitations of schema, as well as scalability. So in, in, in that way, and by bringing back the classic ACID capabilities, which is what relational systems we accounted on, and being able to do that with the SQL programming language, we call it like multi-statement SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up to solve in the modern times, rather than get sort of pedantic about whether it's we have no SQL or SQL or new SQL, or, you know, any of that sort of jargon oriented debate. This is, these are the debates of computer science that they are actually, and they were the solve, and they have solved them with the latest release of 7.0, which we released a few months ago. >> Right, right. Last July, Ravi, we got got to leave it there. I love the examples and the analogies. I can't wait to be face-to-face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >> Fantastic. Thanks for the time. And the opportunity I was, I mean, very insightful questions really appreciate it. - Thank you. >> Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

>>Lai from Orlando, Florida. It's the cube covering Microsoft ignite brought to you by Cohesity. >>Good afternoon, cube viewers. We are here at Microsoft ignite at the orange County convention center. I'm your host, Rebecca Knight. Along with my cohost Stu Miniman. We're joined by Edward Thompson. He is the product manager at get hub. Thank you so much for coming on the queue. So the get hub acquisition closed this time last year, uh, for our viewers who are maybe unfamiliar with get hub, explain what get hub is and then also tell us a little bit how it's going since the, >>yeah, I'd be happy to. So yeah, get hub is like the home for software development. If you're a, if you're a software developer, uh, you know, get hub rehost, you know, most of the open source repositories in the world. Um, you know, just to give you some stats. So at this time, last year, about the time the acquisition happened, um, we announced ad get hub universe, which is our annual developer conference, uh, that we had 30 million developers on GitHub and a hundred million repositories. So that's, that's a huge number of developers. I haven't seen the latest numbers. We'll announce the newest, uh, at get hub university this year, which is coming up next week. Uh, but the last number I saw was 40 million developers. So that's a growth of, you know, 10 million developers in just a year. Unbelievable. And that, that also means the 25% of our developers on get hub have joined within the last year. So that's just absolutely incredible. Um, and so yeah, I get hope. Is, is, is that, is that place for development? >>Yeah, it's really interesting when I look at some acquisitions that Microsoft has made back in 2016, they spent $26 billion for LinkedIn, which is most people's resume. And if you look last year it was seven and a half billion dollars for my friends in the software world. Get hub is their resume. That's right. Oh, when you talk about how you do things online, so you've got an interesting perspective on this because you've worked for Microsoft and get hub a couple of times. So give us a little bit about, you know, the relationship when you joined Microsoft 10 years ago, you know, open source developers, developers, developers weren't exactly on everyone's lips. So it gives a little bit of viewpoint through the various incarnations. >>So as you said, I joined Microsoft about 10 years ago. I came in through a little acquisition. Uh, we were just a very small software company, but we were building enterprise cross platform developer tools and we were about five engineers. And when you're building for, you know, Mac OS, Linux, Sonos, all these different platforms you use with so many people with so few rather, so few developers, you really need to take as much off the shelf as possible. You can't build all that yourself, you know. So if, if you needed a logging library, we would just go and use some open source products. We're not going to spend our time working on that when we could be building customer value in step. So when Microsoft acquired that company, they looked at, you know, they did their due diligence, they looked at the source code and they saw all this open source and they, I mean it was almost a deal breaker. >>They really lost their mind. Um, they were not geared up to deal with open source, to use open source, certainly not to contribute to open source. Uh, and so that's the Microsoft that I first saw. And, and to get from there to here is, is incredible. You know, over time. Um, we worked closely with some open source tools. We worked closely with get hub at Microsoft and that was really one of the early sort of unions between Microsoft and get hub was starting to work together on, on some open source software. And so we kind of started to know each other. We started to understand each other's companies and each other's cultures. And we started to, I don't know, I dare say like each other. Like I still count some of those early get hub employees that I met, uh, as some of my closest friends. Uh, and so at some point, uh, they became such close friends that I went to go work with them and get hub and then of course the Microsoft acquisition and so on. But I really think that that, you know, the, the transformation in Microsoft between, uh, the 10 years ago, Microsoft that really didn't get open source and today is, is just incredible. >>Well, let me just sit in that, in that culture and maybe culture clash a little bit the first time around because Microsoft developers have their own culture and their own uniform and their own way of interacting with each other. The, the, the hours that they work, which is very different from Microsoft, which is a pretty middle-aged Volvo driving kind of organization. So how, how does that work and, and what is, what has it been like the second time around the Microsoft as a middle aged Volvo driver? I think you can, you can >>wear a hoodie in and drive a Volvo. Um, no, I think it's been, I think it's been really great. The interesting thing about Microsoft is that it's not, you know, with so many people, it's not just like a homogenous big company. Um, we do have, you know, the developer tools division is a little bit different than offices, a little bit different than windows. And so they all have their own sort of unique cultures and, and now get hub slots in as its own unique culture. And we can, you know, we can talk to each other and we can understand each other, but we don't necessarily have to be all the same, you know, we can get hub team does kind of work some, some of us do work kind of weird hours. And, and I think that somehow that that works, especially with, you know, new tools coming to, um, coming to the marketplace, uh, you know, chat applications, we can be a lot less synchronous. We can be a lot more online and leave a message for each other. You know, we get out, we use get hub issues and pull requests to collaborate on almost everything, whether it's legal, uh, or, you know, our, our PR department. And it's not just developers. So we're trying to take these, these tools and, and sort of apply them to allow us to have the culture that we want at get hub. And I think Microsoft's doing the same thing as well. >>So speaking of new tools and you're, you're speaking here at ignite, you're about to announce the new repository with lots of new capabilities, enabling users to deploy at to any cloud. So tell us a little bit about, about the, this new tool. >>Yeah, so, uh, we announced, we call it get hub actions. We announced it last year at, at get hub universe. Our, uh, again, our, our annual developer conference. And our goal with GitHub actions was to allow people to take, you know, we've got 100 million repositories on get hub. We wanted our users to, to take those repositories and automate common tasks. Let me, let me give you a concrete example. Um, a lot of times somebody will open an issue on a, on a good hub repository, you know, uh, Hey, this doesn't work. I've got a bug report, you know, and they'll fill out an issue. And often either they didn't understand things or, um, the issue resolved itself, you know, who knows. We call that, uh, an issue that goes stale. And so you can build a workflow around that repository that will look for these stale issues and it will, uh, you know, just close them automatically. >>That gets rid of the mental tacks for somebody who, for a, for a developer who owns this repository to allow this, you know, this workload to just do it automatically. And so that's an example of a, a get hub actions workflow. Um, some people, uh, don't like swearing in their repository and you know, so if somebody were to open a bug report, you know, they might be angry. And so you could actually have a get hub workflow that looks for certain words and then replies and says, Hey, that's, here's our code of conduct. That's not the way we roll here. And actually a lot of people find that that feedback coming from a robot, uh, is a lot easier to take than a feedback coming from a human cause. They might want to meet with a person, can't argue with a robot. Well, not successfully. >>I think I have argued with the chat bot in my day. But anyway, >>yeah. So that's what we did a year ago and we opened it up into the beta program and we really quickly got feedback that, that people liked it and people were doing some really innovative things. But the one thing that people really wanted to automate was their bills. They wanted it to be able to build their code and deploy it. And we were just not set up for that. We, we, we didn't build, get hub actions as that platform in 2018 so we kind of had to pause our beta program. You know, I, they, they, they say that no, uh, no good plan survives first contact with the customer. So we had to, we had to hit pause on that. Uh, and we retooled. Um, we, we just sort of, I don't know, iterated on it, I guess. Uh, and we basically built a new platform that supported all of that repository automation capability that we had planned for in the first place. But also allowed for continuous integration build and deployments. So, um, we brought Macko S we brought Linux and windows runners, uh, that we host, uh, in our cloud, um, that people can use to build their software and then deploy it. And again, yeah, we want to be absolutely a tool agnostic. So any, any operating system, any, uh, language and cloud agnostic, we want to let anybody deploy anywhere, whether it's to a public cloud or on premises. >>Yeah. Uh, so, and with this, the second year we've done our program at this show and we really feel it's gone through a transformation. You know, this is a multi decade in a windows office. Uh, the business applications, uh, you know, cloud seeped in, developers are all over the place here. The day two keynote was all about app dev. Um, I'd love to get a little compare and contrast as to, you know, what you see here at Microsoft ignite versus, and I guess what I would call a pure dev show next week. Get hub universe happening in San Francisco. >>It's true. Get up universe is pretty much a pure dev show. Um, we, we have fewer booths, we have smaller booths. Uh, but, uh, and, and honestly, we have fewer sort of, um, I don't know, enterprise sorta. It, it pro crowd is what we used to call them. Um, but we do of course have a lot of dev ops. So, you know, we get up university has a lot of developers, but, uh, we're seeing a lot of dev ops, so there's a lot of meeting in the middle because, you know, I started out my career as assistant man actually. So I remember just, you know, doing everything manually. Um, but that's not the way we do things anymore. We automate all of our, uh, automate, uh, deployments. We automate all of our builds. You know, I don't want to sit there and type something into a console cause I'm going to get it wrong. Um, you know, I've accidentally deleted config files on production servers and that's, that's no good. So I think that they're, uh, get up universe is very different. A to ignite, it's much smaller, it's more intimate, but at the same time, there's a lot of, uh, overlap, especially around dev ops. >>Yeah. Uh, Satya Nadella yesterday in the keynote talked about the citizen developer as a big push for Microsoft. He said 61% of job openings for developers are outside the tech sector. Um, w what do you see in that space? Uh, the different developer roles these days? >>Uh, I think it's, it's absolutely fascinating. When I, uh, when I started my career, you know, you were, you were a developer and you, and you wrote code probably at a development company. Um, but now like everybody is automating tools, everybody's adopting machine learning. Um, when I look around at some of my friends in finance, uh, it's not about, it's not about anything but tech anymore. That's th they're, they're putting technology into absolutely everything that they do to succeed. Uh, and I think that, I think that it's amazing. Um, uh, like I said earlier, uh, 25% of developers on get hub have joined within the last year. So it's clear that it's just exploding. Um, everybody is doing, uh, software now. Yeah. >>There's something for the citizen developer on get hub though. Or is it too high level? I think >>I don't think it's too high level. I think that, uh, I think that that's a great challenge that we need to really step up to. Yeah. So Edward, >>the other big themes we heard here is talking about trust. So, you know, we talked about how Microsoft is different today than it wasn't in past, but I'm curious what good hub seen because you know, in social media when the acquisition first happened, it was, wait, I love GoodHub hub, I love all those people, but Hey, get lab. Hey, some of these other things I'm, you know, I'm fleeing for the woods. And every time I've seen an open source company get bought by a public company, there's always that online backlash. What are you seeing? How has the community reacted over the last year? >>I understand that skepticism. Uh, you know, I would be skeptical of any, uh, sort of change really. I, you know, the, the whole notion of who moved my cheese. But I think that the only way that we can, we can counter that is just to prove ourselves. And I think that we have, I think that Microsoft has allowed get hub to operate independently. And I think that, you know, I think a lot of people expect it to all of a sudden everything to change. And I don't think everything did change. I think that, uh, get hub now has more resources than it used to to be able to tackle bigger and more challenging problems. I think that get hub, uh, now can hire more and, and, and deploy to more places. And so I really just think that we're just going to keep doing exactly what we've been doing just better. So I think it's great. >>So universe happening next week teed up a little bit for us. What are some of the most exciting things that you're looking forward to? What kinds of conversations that will you be having? Presentations? >>So the big one for me is, is actions. I've, I, I've been just completely heads down working on, on get hub actions. So I'm really excited to be able to put that out there and, and you know, finally give it to everybody. Cause you know, we've been in beta now. Uh, like I said, we've been in beta for a year, which sounds like a ridiculous amount of time. Uh, but you know, it, it did involve a lot of retooling and rethinking and, and iteration with our, our beta testers. Um, and so the biggest thing for me is, is talking to people about actions and showing what they can do with actions. I'm super excited about that, but we've got a lot of other interesting stuff. You know, we've done a lot in the last year since our last universe. We've done a lot in the security space. Um, we've done, uh, we've both built tools and we've acquired some. Um, and so we'll be talking about those, uh, get hood package registry, which goes along really well with get hub actions. Uh, I'm super excited about that too. But yeah, I mean my, my calendar is, is, is just booked. Um, it's great. So many people like want to want to sit down and talk that I'm, I'm super excited about it. >>Excellent. Well great note to end on Edgar Thompson. Thank you so much for coming on the queue. We appreciate it. Thank you. I'm Rebecca Knight. First two minutes, stay tuned for more of the cubes live coverage from Microsoft ignite.

(upbeat music) >> Announcer: From downtown San Francisco, it's theCUBE covering RSA North America 2018. Hey welcome back everybody, Jeff Frick here with theCUBE. We're at RSA's North American Conference 2018 at downtown San Francisco. 40,000 plus people talking about security. Security continues to be an important topic, an increasingly important topic, and a lot more complex with the, having a public cloud, hybrid cloud, all these API's and connected data sources. So, it's really an interesting topic, it continues to get complex. There is no right answer, but there's a lot of little answers to help you get kind of closer to nirvana. And we're excited to have Misha Govshteyn. He's the co-founder and SVP of Alert Logic, CUBE alumni, it's been a couple years since we've seen you, Misha, great to see you again. >> That's right, I'm glad to be back, thank you. >> Yeah, so since we've seen you last, nothing has happened more than the dominance of public cloud and they continue to eat up-- >> I think I predicted it on my past visits. >> Did you predict it? Wow that's good. >> But I think it happened. >> But it's certainly happening, right. Amazon's AWS' run rate is 20 billion last reported. Google's making moves. >> Their conference is bigger than ours right now. >> Is it? >> That's 45,000 people. >> Yeah, it's 45,000, re:Invent, it's nuts, it's crazy. and then obviously Microsoft's making big moves, as is Google cloud. So, what do you see from the client's perspective as the dominance of public cloud continues to grow, yet they still have stuff they have to keep inside? We have our GDPR regs are going to hit in about a month. >> Well one thing's for sure is, it's not getting any easier, right? Because I think cloud is turning things upside down and it's making things disruptive, right, so there's a lot of people that are sitting there and looking at their security programs, and asking themselves, "Does this stuff still work? "When more and more of my workloads "are going to cloud environments? "Does security have to change?" And the answer is obviously, it does but it always has to change because the adversaries are getting better as well, right. >> Right. >> There's no shortage of things for people to worry about. You know when I talk to security practitioners, the big thing I always hear is, "I'm having a good year if I don't get fired." >> Well it almost feels like it's inevitable, right? It's almost like you're going to, it seems like you're going to get hit. At some way, shape, or form you're going to get hit. So it's almost, you know how fast can you catch it? How do you react? >> That's a huge change from five years ago, right? Five years ago we were still kind of living in denial thinking that we can stop this stuff. Now it's all about detection and response and how does your answer to the response process works? That's the reason why, you know last year, I think we saw a whole bunch of noise about, you know machine learning and anomaly detection, and AI everywhere and a whole lot of next-generation antivirus products. This year, it seems like a lot of it is, a lot of the conversation is, "What do I do with all this stuff? "How do I make use of it?" >> Well then how do you leverage the massive investment that the public cloud people are making? So, you know, love James Hamilton's Tuesday night show and he talks about just the massive investments Amazon is making in networking, in security, and you know, he's got so many resources that he can bring to bear, to the benefit of people on that cloud. So where does the line? How do I take advantage of that as a customer? And then where are the holes that I need to augment with other types of solutions? >> You know here's the way I think about it. We had to go through this process at Alert Logic internally as well. Because we obviously are a fairly large IT organization, so we have 20 petabytes of data that we manage. So at some point we had to sit down and say, "Are we're going to keep managing things the way we have been "or are we going to overhaul the whole thing?" So, I think what I would do is I would watch where my infrastructure goes, right. If my infrastructure is still on-prem, keep investing in what you've been doing before, get it better, right? But if you're seeing more and more of your infrastructure move to the cloud, I think it's a good time to think about blowing it up and starting over again, right? Because when you rebuild it, you can build it right, and you can build it using some of the native platform offerings that AWS and Azure and GCP offer. You can work with somebody like Alert Logic. There's others as well right, to harness those abilities. I'll go out on a limb and say I can build a more secure environment now in a cloud than I ever could on-prem, right. But that requires rethinking a bunch of stuff, right. >> And then the other really important thing is you said the top, the conversation has changed. It's not necessarily about being 100% you know locked down. It's really incident response, and really, it's a business risk trade-off decision. Ultimately it's an investment, and it's kind of like insurance. You can't invest infinite resources in security, and you don't want to just stay at home and not go outside. Now that's not going to get it done. So ultimately, it's trade-offs. It's making very significant trade-off decisions as to where's the investment? How much investment? When is the investment then hit a plateau where the ROI is not there anymore? So how do people think through that? Because, the end of the day there's one person saying, "God, we need more, more, more." You know, anything is bad. At the other hand, you just can't use every nickel you have on security. >> So I'll give you two ends of the spectrum right, and on one end are those companies that are moving a lot of their infrastructure to the cloud and they're rethinking how they're going to do security. For them, the real answer becomes it's not just the investment in technology, and investing into better getting information from my cloud providers, getting a better security layer in place. Some of it is architecture right, and some of the basics right, there's thousands of applications running in most enterprises. Each one of those applications on the cloud, could be in its own virtual private cloud, right. So if it gets broken into, only one domino falls down. You don't have this scenario where the entire network falls down, because you can easily move laterally. If you're doing things right in the cloud, you're solving that problem architecturally, right. Now, aside from the cloud, I think the biggest shift we're seeing now, is towards kind of focusing on outcomes, right. You have your technology stack, but really it's all about people, analytics, data. What do you, how do you make sense of all this stuff? And this is classic I think, with the Target breach and some of the classic breaches we've seen, all the technology in the world, right? They had all the tools they needed. The real thing that broke down is analytics and people. >> Right, and people. And we hear time and time again where people had, like you said, had the architecture in place, had the systems in the place, and somebody mis-configured a switch. Or I interviewed a gal who did a live social hack at Black Hat, just using some Instagram pictures and some information on your browser. No technology, just went in through the front door, said, you know, hey, "I'm trying to get the company picnic "site up, can you please test this URL?" She's got a 100% hit rate! But I think it's really important, because as you said, you guys offer not only software solutions, but also services to help people actually be successful in implementing security. >> And the big question is, if somebody does that to you, can you really block it? And the answer a lot of times is, you can't. So the next battlefront is all about can you identify that kind of breach happening, right? Can you identify abnormal activity that starts to happen? You know, going back to the Equifax breach, right, one of the abnormal things that happened that they should've seen and for some reason didn't, you know, 30 web shells were stood up. Which is the telltale sign of, maybe you don't know how you got broken into, but because there's a web shell in your environment you know somebody's controlling your servers remotely, that should be one of those indicators that, I don't know how it happened, I don't know maybe I missed it and I didn't see the initial attack, but there's definitely somebody on a network poking around. There's still time, right? There's, you know for most companies, it takes about a hundred days on average, to steal the data. I think the latest research is if you can find the breach in less than a day, you eliminate 96% of the impact. That's a pretty big number right? That means that if you, the faster you respond, the better off you are. And most people, I think when you ask 'em, and you ask 'em, "Honestly assess your ability to quickly detect, respond, eradicate the threat." A lot of them will say, "It depends" But really the answer is "Not really." >> Right, 'cause the other, the sad stat that's similar to that one, is usually it takes many, many days, months, weeks, to even know that you've been breached, to figure out the pattern, that you can even start, you know, the investigation and the fixing. >> Somewhat not surprising, right? I don't think there's that many Security Operation Centers out there, right? There's not, you know, not every company has a SOC right? Not every company can afford a SOC. I think the latest number is, for enterprises, right, this is Fortune 2000, right, 15% of them have a SOC. What are the other 85% doing? You know, are they buying a slice of a SOC somewhere else? That's the service that we offer, but I think, suffice to say, there's not enough security people watching all this data to make sense of it right. That's the biggest battle I think going forward. We can't make enough people doing that, that requires a lot of analytics, right. >> Which really then begs, for the standalone single enterprise, that they really need help, right? They're not going to be able to hire the best of the best for their individual company. They're not going to be able to leverage you know best-in-breed, Which I think is kind of an interesting part of the whole open-source ethos, knowing that the smartest brains aren't necessarily in your four walls. That you need to leverage people outside those four walls. So, as it continues to morph, what do you see changing now? What are you looking forward to here at RSA 2018? >> So I made some big predictions five years ago, so I'll say you know, five years from now, I think we're going to see a lot more companies outsource major parts of their security right, and that's just because you can't do it all in-house right. There's got to be a lot more specialization. There's still people today buying AI products right, and having machine learning models they invest in to, there's no company I'm aware of, unless they're, you know, maybe the top five financial firms out there, that should have a, you know, security focused data scientist on staff, right? And if you have somebody like that in your environment, you're probably not spending money the right way, right. So, I think security is going to get outsourced in a pretty big way. We're going to focus on outcomes more and more. I think the question is not going to be, "What algorithm are you using to identify this breach?" The question is going to be, "How good are your identifying breaches?" Period. And some of the companies that offer those outcomes are going to grow very rapidly. And some of the companies that offer just, you know, picks and shovels, are going to probably not do nearly as well. >> Right. >> So five years from now, I'll come back and we'll talk about it then. >> Well, the other big thing, that's going to be happening in a big way five years from now, is IoT and IIoT and 5G. So, the size of the attacked surface, the opportunities to breach-- >> The data volume. >> The data volume, and the impact. You know it's not necessarily stealing credit cards, it's taking control of somebody's vehicle, moving down the freeway. So, you know, the implications are only going to get higher. >> We collect a lot of logs from our customers. Usually, the log footprint, grows at three times the rate of our revenue and customers, right. So, you know, thank god-- >> The log, the log-- >> The log volume grows-- >> volume that you're tracking for a customer, grows at three times your revenue for that customer? >> That's right. I mean, they're not growing at three times that rate, annually right, but annually, you know, we've clocked anywhere between 200% to 300% growth in data that we collect from them, IoT makes that absolutely explode, right. You know, if every device out there, if you actually are watching it, and if you have any chance of stopping the breaches on IoT networks, you got to collect a lot of that data, that's the fuel for a lot of the machine learning models, because you can't put human eyes on small RTUs and you know, in factories. That means even more data. >> Right, well and you know the model that we've seen in financial services and ad-tech, in terms of, you know, an increasing amount of the transactions are going to happen automatically, with no human intervention, right, it's hardwired stuff. >> So I think it's that balance between data size and data volume, analytics, but most important, what do you feed the humans that are sitting on top of it? Can you feed them just the right signal to know what's a breach and what's just noise? That's the hardest part. >> Right, and can you get enough good ones? >> That's right. >> Underneath your own, underneath your own shell, which is probably, "No", well, hopefully. >> I think building this from scratch for every company is madness, right. There's a handful of companies out there that can pull it off, but I think ultimately everybody will realize, you know, I'm a big audio nerd so I Looked it up, right, you used to build all of your own speakers, right. You'd buy a cabinet and you'd buy some tools, and you would build all the stuff. Now you go to the store and you buy an audio system, right? >> Right, yeah, well at least audio, you had, speakers are interesting 'cause there's a lot of mechanical interpretations about how to take that signal and to make sound, but if you're making CDs you know you got to go, with the standard right? You buy Sonos now, and Sonos is a fully integrated system. What is Sonos for security, right? It doesn't exist yet. And that's, I think that's where Security as a Service is going. Security as a Service should be something you subscribe to that gives you a set of outcomes for your business, and I think that's the only way to consume this stuff. It's too complex for somebody to integrate from best-of-breed products and assemble it just the right way. I think the parallels are going to be exactly the same. I'm not building my car either, right? I'm going to buy one. Alright Misha, well, thanks for the update, and hopefully we'll see you before five years, maybe in a couple and get an update. >> We'll do some checkpoints along the way. >> Alright. Alright, he's Misha, I'm Jeff. You're watching theCUBE from RSA North America 2018 in downtown, San Francisco. Thanks for watching. (techno music)

(royal music) >> Announcer: John Cleese. >> There's a lot of people out there who have no idea what they're doing, but they have absolutely no idea that they have no idea what they're doing. Those are the ones with the confidence and stupidity who finish up in power. That's why the planet doesn't work. >> Announcer: Knowledgeable, insightful, and a true gentleman. >> The guy at the counter recognized me and said... Are you listening? >> John Furrier: Yes, I'm tweeting away. >> No, you're not. >> I tweet, I'm tweeting away. >> He is kind of rude that way. >> You're on your (bleep) keyboard. >> Announcer: John Cleese joins the Cube alumni. Welcome, John. >> John Cleese: Have you got any phone calls you need to answer? >> John Furrier: Hold on, let me check. >> Announcer: Live from San Jose, it's the Cube, presenting Big Data Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. (busy music) >> Hey, welcome back to the Cube's continuing coverage of our event, Big Data SV. I'm Lisa Martin with my co-host, George Gilbert. We are down the street from the Strata Data Conference. This is our second day, and we've been talking all things big data, cloud data science. We're now excited to be joined by the CEO of a company called Data Science, Ian Swanson. Ian, welcome to the Cube. >> Thanks so much for having me. I mean, it's been a awesome two days so far, and it's great to wrap up my trip here on the show. >> Yeah, so, tell us a little bit about your company, Data Science, what do you guys do? What are some of the key opportunities for you guys in the enterprise market? >> Yeah, absolutely. My company's called datascience.com, and what we do is we offer an enterprise data science platform where data scientists get to use all they tools they love in all the languages, all the libraries, leveraging everything that is open source to build models and put models in production. Then we also provide IT the ability to be able to manage this massive stack of tools that data scientists require, and it all boils down to one thing, and that is, companies need to use the data that they've been storing for years. It's about, how do you put that data into action. We give the tools to data scientists to get that data into action. >> Let's drill down on that a bit. For a while, we thought if we just put all our data in this schema-on-read repository, that would be nirvana. But it wasn't all that transparent, and we recognized we have to sort of go in and structure it somewhat, help us take the next couple steps. >> Ian: Yeah, the journey. >> From this partially curated data sets to something that turns into a model that is actionable. >> That's actually been the theme in the show here at the Strata Data Conference. If we went back years ago, it was, how do we store data. Then it was, how do we not just store and manage, but how do we transform it and get it into a shape that we can actually use it. The theme of this year is how do we get it to that next step, the next step of putting it into action. To layer onto that, data scientists need to access data, yes, but then they need to be able to collaborate, work together, apply many different techniques, machine learning, AI, deep learning, these are all techniques of a data scientist to be able to build a model. But then there's that next step, and the next is, hey, I built this model, how do I actually get it in production? How does it actually get used? Here's the shocking thing. I was at an event where there's 500 data scientists in the audience, and I said, "Stand up if you worked on a model for more than nine months "and it never went into production." 90% of the audience stood up. That's the last mile that we're all still working on, and what's exciting is, we can make it possible today. >> Wanting to drill down into the sort of, it sounds like there's a lot of choice in the tools. But typically, to do a pipeline, you either need well established APIs that everyone understands and plugs together with, or you need an end to end sort of single vendor solution that becomes the sort of collaboration backbone. How are you organized, how are you built? >> This might be self-serving, but datascience.com, we have enterprise data science platform, we recommend a unified platform for data science. Now, that unified platform needs to be highly configurable. You need to make it so that that workbench, you can use any tool that you want. Some data scientists might want to use a hammer, others want to be able to use a screwdriver over here. The power is how configurable, how extensible it is, how open source you can adopt everything. The amazing trends that we've seen have been proprietary solutions going back decades, to now, the rise of open source. Every day, dozens if not hundreds of new machine learning libraries are being released every single day. We've got to give those capabilities to data scientists and make them scale. >> OK, so the, and I think it's pretty easy to see how you would have incorporate new machine learning libraries into a pipeline. But then there's also the tools for data preparation, and for like feature extraction and feature engineering, you might even have some tools that help you with figuring out which algorithm to select. What holds all that together? >> Yeah, so orchestrating the enterprise data science stack is the hardest challenge right now. There has to be a company like us that is the glue, that is not just, do these solutions work together, but also, how do they collaborate, what is that workflow? What are those steps in that process? There's one thing that you might have left out, and that is, model deployment, model interpretation, model management. >> George: That's the black art, yeah. >> That's where this whole thing is going next. That was the exciting thing that I heard in terms of all these discussion with business leaders throughout the last two days is model deployment, model management. >> If I can kind of take this to maybe shift the conversation a little bit to the target audience. Talked a lot about data scientists and needing to enable them. I'm curious about, we just talked with, a couple of guests ago, about the chief data officer. How, you work with enterprises, how common is the chief data officer role today? What are some of the challenges they've got that datascience.com can help them to eliminate? >> Yeah, the CIO and the chief data officer, we have CIOs that have been selecting tools for companies to use, and now the chief data officer is sitting down with the CEO and saying, "How do we actually drive business results?" We work very closely with both of those personas. But on the CDO side, it's really helping them educate their teams on the possibilities of what could be realized with the data at hand, and making sure that IT is enabling the data scientists with the right tools. We supply the tools, but we also like to go in there with our customers and help coach, help educate what is possible, and that helps with the CDO's mission. >> A question along that front. We've been talking about sort of empowering the data scientist, and really, from one end of the modeling life cycle all the way to the end or the deployment, which is currently the hardest part and least well supported. But we also have tons of companies that don't have data science trained people, or who are only modestly familiar. Where do, what do we do with them? How do we get those companies into the mainstream in terms of deploying this? >> I think whether you're a small company or a big company, digital transformation is the mandate. Digital transformation is not just, how do I make a taxi company become Uber, or how do I make a speaker company become Sonos, the smart speaker, it's how do I exploit all the sources of my data to get better and improved operational processes, new business models, increased revenue, reduced operation costs. You could start small, and so we work with plenty of smaller companies. They'll hire a couple data scientists, and they're able to do small quick wins. You don't have to go sit in the basement for a year having something that is the thing, the unicorn in the business, it's small quick wins. Now we, my company, we believe in writing code, trained, educated, data scientists. There are solutions out there that you throw data at, you push a button, it gets an output. It's this magic black box. There's risk in that. Model interpretation, what are the features it's scoring on, there's risk, but those companies are seeing some level of success. We firmly believe, though, in hiring a data science team that is trained, you can start small, two or three, and get some very quick wins. >> I was going to say, those quick wins are essential for survivability, like digital transformation is essential, but it's also, I mean, to survival at a minimum, right? >> Ian: Yes. >> Those quick wins are presumably transformative to an enterprise being able to sustain, and then eventually, or ideally, be able to take market share from their competition. >> That is key for the CDO. The CDO is there pitching what is possible, he's pitching, she's pitching the dream. In order to be able to help visualize what that dream and the outcome could be, we always say, start small, quick wins, then from there, you can build. What you don't want to do is go nine months working on something and you don't know if there's going to be outcome. A lot of data science is trial and error. This is science, we're testing hypotheses. There's not always an outcome that's to be there, so small quick wins is something we highly recommend. >> A question, one of the things that we see more and more is the idea that actionable insights are perishable, and that latency matters. In fact, you have a budget for latency, almost, like in that short amount of time, the more sort of features that you can dynamically feed into a model to get a score, are you seeing more of that? How are the use cases that you're seeing, how's that pattern unfolding? >> Yeah, so we're seeing more streaming data use cases. We work with some of the biggest technology companies in the world, so IoT, connected services, streaming real time decisions that are happening. But then, also, there are so many use cases around org that could be marketing, finance, HR related, not just tech related. On the marketing side, imagine if you're customer service, and somebody calls you, and you know instantly the lifetime value of that customer, and it kicks off a totally new talk track, maybe get escalated immediately to a new supervisor, because that supervisor can handle this top tier customer. These are decisions that can happen real time leveraging machine learning models, and these are things that, again, are small quick wins, but massive, massive impact. It's about decision process now. That's digital transformation. >> OK. Are you seeing patterns in terms of how much horsepower customers are budgeting for the training process, creating the model? Because we know it's very compute intensive, like, even Intel, some people call it, like, high performance compute, like a supercomputer type workload. How much should people be budgeting? Because we don't see any guidelines or rules of thumb for this. >> I still think the boundaries are being worked out. There's a lot of great work that Nvidia's doing with GPU, we're able to do things faster on compute power. But even if we just start from the basics, if you go and talk to a data scientist at a massive company where they have a team of over 1,000 data scientists, and you say to do this analysis, how do you spin up your compute power? Well, I go walk over to IT and I knock on the door, and I say, "Set up this machine, set up this cluster." That's ridiculous. A product like ours is able to instantly give them the compute power, scale it elastically with our cloud service partners or work with on-prem solutions to be able to say, get the power that you need to get the results in the time that's needed, quick, fast. In terms of the boundaries of the budget, that's still being defined. But at the end of the day, we are seeing return on investment, and that's what's key. >> Are you seeing a movement towards a greater scope of integration for the data science tool chain? Or is it that at the high end, where you have companies with 1,000 data scientists, they know how to deal with specialized components, whereas, when there's perhaps less of, a smaller pool of expertise, the desire for end to end integration is greater. >> I think there's this kind of thought that is not necessarily right, and that is, if you have a bigger data science team, you're more sophisticated. We actually see the same sophistication level of 1,000 person data science team, in many cases, to a 20 person data science team, and sometimes inverse, I mean, it's kind of crazy. But it's, how do we make sure that we give them the tools so they can drive value. Tools need to include collaboration and workflow, not just hammers and nails, but how do we work together, how do we scale knowledge, how do we get it in the hands of the line of business so they can use the results. It's that that is key. >> That's great, Ian. I also like that you really kind of articulated start small, quick ins can make massive impact. We want to thank you so much for stopping by the Cube and sharing that, and what you guys are doing at Data Science to help enterprises really take advantage of the value that data can really deliver. >> Thanks so much for having datascience.com on, really appreciate it. >> Lisa: Absolutely. George, thank you for being my co-host. >> You're always welcome. >> We want to thank you for watching the Cube. I'm Lisa Martin with George Gilbert, and we are at our event Big Data SV on day two. Stick around, we'll be right back with our next guest after a short break. (busy music)