>> from San Matteo. It's the Cube covering scaler. Innovation Day. Brought to You by Scaler >> Ron Jon Furry with the Cube. We're here for an innovation day at Scale ER's headquarters in San Mateo, California Profile in the hot startups, technology leaders and also value problems. Our next guest is Casey Clark, whose chief customer officer for scale of great to See You See >> you as well. >> Thanks for having us. >> Thanks for coming in. >> So what does it talk about the customer value proposition? Let's get right to it. Who are your customers? Who you guys targeting give some examples of what they're what they're doing with >> you. We sell primarily to engineering driven companies. So you know, the top dog is that the CTO you know, their pride born in the cloud or moving heavily towards the cloud they're using, you know, things like micro services communities may be starting to look at that server list. So really kind of forward thinking, engineering driven businesses or where we start with, you know, some of the companies that we work with, you know, CareerBuilder, scripts, networks, Discovery networks, a lot of kind of modern e commerce media B to B B to C types of sass businesses as well. >> I want it. I want to drill down that little bit later. But, you know, basically born the cloud that seems to be That's a big cloud. Native. Absolutely. All right, So you guys are startup. Siri's a funded, which is, you know, Silicon Valley terms. You guys were right out of the gate. Talk about the status of the product. Evolution of the value proposition stages. You guys are in market selling two customers actively. What's the status of the products? Where Where is it from a customer's standpoint? >> Sure, Yeah, we've got, you know, over 300 customers and so fairly mature in terms of, you know, product market status. We were very fortunate to land some very large customers that pushed us when we were, you know, seven. So on employees, maybe three or four years ago, and so that that four system mature very quickly. Large enterprises that had anyway, this one customers alando in Germany. They're one of the largest commerce businesses in Europe and they have 23 1,000 engineers. He's in the product on the way basis, and we landed them when it was seven employees, you know, three or four years ago. And so that four system insurance it was very easy for us to go to other enterprises and say, Yeah, we can work with you And here's the proof points on how we've helped >> this business >> mature, how they've improved kind of their their speed to truth there. Time to answer whenever they have issues. >> And so the so. The kind of back up the playbook was early on, when had seven folks and growing beta status was that kind of commercially available? When did it? When was the tipping point for commercially available wanted that >> that probably tipped. When I joined about a little under four years ago, I had to convince Steve that he was ready to sell this product, right, as you'd expect with a kind of technical founder. He never thought the product was ready to go, but already had maybe a dozen or so kind of friends and family customers on DH. So I kind of came in and went on my network and started trying to figure out who are the right fit for this. Andi, we immediately found Eun attraction, the product just stood up and we started pushing. And so >> and you guys were tracking some good talent. Just looking. Valley Tech leaders are joining you guys, which is great sign when you got talent coming in on the customer side. Lots changed in four years. I'll see the edge of the network on digital transformation has been a punchline been kind of a cliche, but now I think it's more real. As people see the power of scale to cloud on premises. Seeing hybrid multi cloud is being validated. What is the current customer profile when you look at pure cloud versus on premise, You guys seeing different traction points? Can you share a little bit of color on that? >> Yeah, So I talked a little bit about our ideal customer profile being, you know, if he's kind of four categories e commerce, media BTB, sas B to see sass. You know, most of these companies are running. Some production were close in the cloud and probably majority or in the cloud. When we started this thing and it was only eight of us and Jesus has your were never talked about. We're seeing significant traction with azure and then specific regions. Southeast Asia G C. P. Is very hot. Sourcing a high demand there and then with the proliferation of micro services communities has absolutely taken off. I mean, I'll raise my hand and say I wasn't sure if it was going to communities and bases two years ago. I was say, I think Mason's going to want to bet the company on. Thank God we didn't do that. We want with communities on DH, you know? So we're seeing a lot more of kind of these distributed workloads. Distributed team development. >> Yeah, that's got a lot of head room now. The Cube Khan was just last week, so it's interesting kind of growth of that whole. Yet service measures right around the corner. Yeah, Micro Service is going to >> be a >> serviceman or data. >> Yeah, for sure it's been, and that's one of the big problems that we run in with logs that people just say that they're too voluminous. It's either too hard to search through it. It's too expensive. We don't know what to deal with it. And so they're trying to find other ways to kind of get observe ability and so you see, kind of a growth of some of the metrics companies like data dog infrastructure monitoring, phenomenal infrastructure, modern company. You've got lots of tracing companies come out and and really, they're coming out because there's just so many logs that's either too expensive, too hard, too slow to search through all that data. That's where your answers live on DH there, just extracting, summarizing value to try to kind of minimize the amount of search. You have to >> talk about the competition because you mentioned a few of them splunk ce out there as well, and there public a couple years ago and this different price point they get that. But what's why can't they scale to the level of you guys have because and how do you compare to them? Because, I mean, I know that is getting larger, but what's different about you guys visited the competition? >> Absolutely. This is one of the reasons why I joined the company. What excites me the most is I got to go talk to engineers and I could just talk shop. I don't really talk about the business value quite as much. We get there at some point, obviously, but we made some very key decisions early on in the company's history. I mean, really, before the company started to kind of main back and architectural decisions. One we don't use elastics search losing any sort of Cuban indexing, which is what you know. Almost every single logging tool use is on the back end. Keyword indexes. Elastic search are great for human legible words. Relatively stale lists where you're not looking through, you know, infinite numbers of high carnality kind of machine data. So we made an optimized decision to use no sequel databases Proprietary column in our database. So that's one aspect of things. How we process in store. The data is highly efficient. The other pieces is worse, asked business, But we're true. SAS were true multi tenant. And so when you put a query into the scaler, every CP corn every server is executing on just that quarry is very similar way. Google Search works. So not only do we get better performance, we get better costume better scalability across all of our customers, >> and you guys do sail to engineering led buyer, and you mentioned that a lot of sass companies that are a lot of time trying to come in and sell that market bump into people who want to build their own. Yeah, I don't need your help. I think I might get fired or it might make me look good. That seems to be a go to market dynamic or and or consumption peace. What's your response to that? How does that does that fared for you guys? >> Engineers want to engineer whether it's the right thing or not, right? And so that is always hard. And I can't come in and tell your baby's ugly right because your baby is beautiful in your eyes and so that is a hard conversation have. But that's why I kind of go back to what I was saying. If we just talk shop, we talk about, you know, the the engineering decisions around, you know, is that the right database? Is this the right architecture? And they think that they started nodding and nodding, nodding, And then we say, And the values are going to be X y and Z cost performance scale ability on dso when you kind of get them to understand that like Elastics, which is great for a lot of things. Product search Web search. Phenomenal, but log management, high card. Now that machine did. It's not what it's designed for. Okay. Okay, okay. And then we start to get them to come around and say, Not only can you reallocate I mean, we talked about how getting talent is. It's hard. Well, let's put them back on mission critical business, You know, ensuring objectives. And we get, you know, service that this is all we do. Like you gonna have a couple people in there part time managing a long service. This is all we do. And so you get things like like tracing that were rolling out this quarter, you know, better cost optimization, better scalability. Things you would never get with an >> open. So the initial reaction might be to go in and sell on hey, cheaper solution. And is an economic buyer. Not really for these kinds of products, because you're dealing with engineers. Yeah. They want to talk shop first. That seems to be the playbook. >> Are artists is getting that first meeting and the 1st 1 is hard because that, you know, they're busy. Everybody's busy, They just wave you off. They ignore the email, the calls in and we get that. But once we get in, we have kind of this consultation, you know, conversation around. Why, why we made these technology decisions. They get it. >> Let's do a first meeting right now. People watching this video, What's the architectural advantages? Let's talk shop. Yeah, why, you guys? >> Yeah, absolutely so kind of too technical differentiators. And then three sort of benefits that come from those two technical choices. One is what I mentioned this proprietary, you know, columnar. No sequel database specifically designed for kind of high card in ality machine, right? There is no indexes that need to be backed up or tuned. You know, it's it's It's a massively parallel grab t its simplest form. So one pieces that database. The other piece is that architecture where we get, you know, one performance benefits of throwing every CP corn every several unjust trickery. Very someone way. Google Search works If I go say, How do I make a pizza and Google? It's not like it goes like Casey server in a data center in Alaska and runs for a bit. They're throwing a tonic and pure power every query. So there's the performance piece. There is the scale, ability piece. We have one huge massive pool of shared compute resource is And so you're logged, William. Khun, Spike. But relative to the capacity we have, it means nothing. Right? But all these other services, they're single tenant, you know, hosted services. You know, there's a capacity limit. And you a single customer. If you're going, you know, doubles. Well, it wasn't designed to handle that log falling, doubling. And then, you know, the last piece is the cost. There is a huge economies of scale shared services. We we run the system at a significantly lower cost than what anybody else can. And so you get, you know, cost, benefits, performance by defense and scale, ability >> and the life of the engineer. The buyer here. What if some of the day in the life use case pain in the butt so they have a mean its challenges. There's a dead Bob's is basically usually the people who do Dev ups are pretty hard core, and they they love it and they tend to love the engineering side of it. But what of the hassles with them? >> Yeah, Yeah, >> but you saw >> So you know, kind of going back to what we're all about were all about speed to truth, right? In kind of a modern environment where you're deploying everyday multiple times per day. Ah, lot of times there's no que es your point directly to the production, right? And you're kind of but is on the line. When that code goes live, you need to be able to kind of get speed to truth as quickly as possible, right? You need to be able to identify one of problem went wrong when something went wrong immediately, and they needed to be able to come up with a resolution. Right? There's always two things that we always talk about. Meantime, to restore it meantime, to resolution right there is. You know, maybe the saris are responsible for me. Time to restore. So they're in scaler. They get alert there, immediately diving through the logs to regret. Okay, it's this service. Either we need to restart it. Or how do we kind of just put a Band Aid on top? It's to make sure customers don't see it right. And then it gets kicked over to developer who wrote the code and say, Okay, now. Meantime, the resolution, How long until we figure out what went wrong and how do we fix it to make sure it doesn't happen again? And that's where we help. >> You know, It's interesting case he mentioned the resolution piece. A lot of engineers that become operationalized prove your service, not operations. People just being called Deb ops is that they have to actually do this as an SL a basis when they do a lot of AP AP and only gets more complicated with service meshes right now with these micro services framework, because now you have service is being stood up and torn down and literally, without it, human intervention. So this notion of having a path of validation working with other services could be a pain in the butt time. >> Yeah, I mean, it's very difficult. We've, you know, with some of the large organizations we work with you worked with. They've tried to build their own service, mashes and they, you know, got into a massive conference room and try to write out a letter from services that are out there in the realities they can't figure out. There's no good way for them to map out like, who talks toe what? When and know each little service knows, like Okay, well, here's the downstream effects, and they kind of know what's next to them. They know their Jason sees, but they don't really know much further than that on the nice thing about, you know, logs and all kind of the voluminous data that is in there, which makes it very difficult to manage. But the answers are are in there, right? And so we provide a lot of value by giving you one place to look through all of >> that cube con. This has been a big topic because a lot of times just to be more hard core is that there could be downtime on the services They don't even know about >> it. Yeah. Yeah, That's exactly >> what discovering and visualizing that are surfacing is huge. Okay, what's the one thing that people should know about scaler that haven't talked you guys or know about? You guys should know about you guys Consider. >> Yeah. I mean, I think the reality is everybody's trying to move as quickly as possible. And there is a better way, you know, observe, ability, telemetry, monitoring, whatever you call your team Is court of the business right? Its core to moving faster, its core to providing a better user experience. And we have, you know, spent a significant amount of time building. You need technology to support your business is growth. Andi, I think you know you can look at the benefits I've talked about them cost performance, scalability. Right? But these airline well, with whatever you're looking at it, it's PML. If it's, you know, service up time. That's exactly what we provide. Is is a tool to help you give a better experience to your own customers. >> Casey. Thanks for spend the time. Is sharing that insight? Of course. We'd love speed the truth. It's our model to Cuba. Go to the events and try to get the data out there. We're here. The innovation dates scales Headquarters. I'm John for you. Thanks for watching

>>you welcome to the cubes, continuing coverage of Splunk dot com. 21 I'm lisa martin of a couple guests here with me. Next talking about Splunk H P E N. Deloitte, please welcome Casey Clark, Managing Director and chief architect at Deloitte and Elias Alanya Master Technologists Office of the North American C T O at H P E. Guys welcome to the program. Great to have you. >>Thank you lisa. It's great to be here. >>Thanks lisa >>Here we still are in this virtual world the last 18 months, so many challenges, some opportunities, some silver linings but some of the big challenges that organizations are facing this rapid shift to remote work. The rapid acceleration In digital transformation ran somewhere up nearly 11 x in the first half of this year alone. Solar winds talk to me about some of the challenges that organizations are facing and how you're helping them deal with that Casey >>we'll start with you So most of our clients as we move to virtual um have accelerated their adoption of multiple cloud platforms. You know, moving into a W S into Azure into google. And one of the biggest challenges is in this distributed environment, they still have significant workloads on prem Part of the workloads are in office 3 65. Part of them are in salesforce part of them they're moving into AWS or big data workloads into google. How do you make this all manageable from both. A security point of view and accelerating threats. Uh make that much worse but also from an operational point of view, you know, how do I do application performance management when I have workloads in the cloud calling. Api is back on prem into the mainframe. How do I make an operationally when I have tons of containers and virtual machines operating out there? So the importance of Splunk and good log management observe ability along with all the security management and the security logs and being able to monitor for your environment in this complex distributed environment is absolutely critical and it's just going to get more complex as we get more distributed. >>How can companies given the complexity? How can companies with these complicated I. T. Landscapes get ahead of some of these issues? >>One of the things that we really focused on making sure that you're getting ahead of those and you know we work with organizations like Splunk and Deloitte is how do we how do we collect all of the data? Not just a little bit of it, you know Splunk, help and Deloitte are helping us look across all of those places. We want to make sure that we can can really ingest everything that's out there and then let the tools like Splunk then use all of that data. We found a lot of organizations really struggle with that and with the retention of that data it's been a challenge. So those are things that we really worked hard on figuring out with organizations out there um how to how to ingest retain and then modernize how they do those things at the same time. >>I was reading the Splunk state of Security report which they surveyed over 500 security leaders I think it was over nine um global economies and they said 78% of security and I. T. Leaders worry 78% that they're going to be hit by something like solar winds. Um That style of attack Splunk saying security is a data problem but also looking at all this talk about being on the defensive and preventing attacks the threat landscape escaping companies also have to plan for growth. They have to plan for agility. How do you both help them accomplished? Both at the same time Casey will start with you. >>Well fundamentally on the security front you start with security by design. You're designing the logging the monitoring the defenses into the systems as they are being designed up front as opposed to adding them when you get to Um you know you 80 or production environment. So security by design much like devops and Fc cops is pushing that attitude towards security back earlier in the process so that each of the systems as we're developing them um have the defenses that are needed and have the logging that are embedded in them and the standards for logging so that you don't just get a lot of different kinds of data you get the data you actually need coming into the system and then setting up the correlation of that data so you can identify those threats early through a i through predictive analytics, you get to identify things more quickly. You know, it's all about reducing cycle times and getting better information by designing it in from the beginning, >>standing in from the beginning that shifting left Elias. What are your thoughts about this, enabling that defense, designing an upfront and also enabling organizations to have the agility to grow and expand? >>Yes, sort of reminded of something our friends with the Blue oval used to say in manufacturing quality isn't inspected, it's built in right and and two cases point you have to build it in. We've we've definitely worked with delight to do that and we've set up systems so that they have true agility. We've done things like container ice block with kubernetes uh you know, work with object storage. A lot of the new modern technologies that maybe organizations aren't quite accustomed to yet are still getting on board with. And so we wrap those up in our HP Green Lake managed services so that we can provide those things to organizations that aren't maybe aren't ready for them yet. But the threat landscape is such that you have to be able to do those things if you're not orchestrating these thousands and thousands of containers with something like kubernetes, it's just it becomes such a manual labor intensive process. And so that that labor intensive, non automated process. That's the thing that we're trying to remove. >>Well that's an inhibitor to growth, right number one there, let's go ahead and dig into the HP. Deloitte Splunk solution case. I'm going to go back over to, you talk to me about kind of the catalyst for developing the solution and then we'll dig into it in terms of what it's delivering. >>So Deloitte has had long term partnerships with both H B E and Splunk and we're very excited about working together with them on this solution. Um the HP Green Light, which is hardware by subscription, the flexibility of that platform, you know, the cost effectiveness of the platform. Be able to run workloads like Splunk on it that are constantly changing. You have peaks and valleys depending on, you know, how much work you're doing, how many logs are coming in and so being able to expand that environment quickly through containerized architecture, Oz Funk, which is what we worked on, um you know, with the HP Green Light team uh and and also with spunk so that we can Federated the workloads and everything that's going on on prem with workloads that are in the cloud and doing it very flexibly with the HP on prim platform as well as, you know, Splunk on google and Azure and Splunk cloud um and then having one pane of glass that goes across all of it has been very exciting. You know, we were getting lots of interest in the demo of what we've done on the Green light platform and the partnership has been going great, uh >>that single pane of glass is so critical. We talked about cloud complexity a few minutes ago, customers are dealing with so many different applications there now in this hybrid multi cloud world, it's probably only going to proliferate, Let's talk to me about H P. S perspective and how you're going to help reduce the cloud complexity that customers in every industry are facing. >>Yeah, so within the HP Green Lake umbrella of portfolio, we have set up our uh admiral container platform, for example, are Green Lake management services. We bring all these things together in a way that that really can accelerate applications uh that can make the magic that Deloitte does work underneath. And so when, when our friends at Deloitte go and build something, someone has to, has to bring that to life, has to run it for for our customers. And so that's what Hb Green Lake does, then we do that in a way that fundamentally aligns to the business cycles that go on. And so, uh you know, we think of cloud as an operating model, not necessarily just a physical destination. And so we work on prem Coehlo public hybrid Green Lake spans across all of those and can bring together in a way that really helps customers. We've seen so many times, they have these silos and islands of data. Um you know, you've got uh data being generated in the cloud. Well, you need Splunk in the cloud, you've got the energy generated in uh, Amelia, Well you've got spunk into me and so so Deloitte's really done some great things to help us put that together and then we, we underpin that with the, with the green like uh management services with our software and our infrastructure to make it all >>work. Yeah, Elias, one of the areas that you just mentioned is is one of the hottest trends that we've noticed out there. A lot of clients, you know, with the competition for skilled resources out there on the engineering side and operations are looking at managed services as an option to building, you know, their own technology, you know, hiring their own team, running it themselves and the work that we do with both on the security side as well as operations to provide managed services for our clients in collaboration with companies like HP E and running of the Green Lake platform platforms as well as one cloud, those combined services together and delivered as a managed service uh to our clients is an exciting trend out there that um, is increasingly seen as very cost effective for our clients >>saving cost is key case. I want to get your perspective on what you think differentiates this, this solution, the technology alliance, what are the differentiators in this from Deloitte's lens. >>So bringing the expertise of a company like HP and the flexibility and expand ability of the Green lake platform and the container ization that they've done with Israel, you know, it's, it's bringing that cloud like automation and virtual and flexibility to on uh, the on prem and the hybrid cloud solution combined with Splunk who is rapidly expanding not only what they do in the security space where the constantly changing security landscape out there, but also in observe ability application, performance management, um, Ai ops, um, you know, fully automated and integrated response to operational events that are out there. So HP is doing what they do really well and adapting to this new world. Splunk is constantly changing their products to make it easier for us to go after those operational issues. And Deloitte is coming in with both the industry and the technical experience to bring it all together, you know, how do you log the right things, you know, how do you identify, you know, the real signal versus the noise out there? You know, when you're collecting massive amounts of log data, you know, how do you make it actionable? How can you automate those actions? So by bringing together all three of these berms together, uh we can bring a much better, much, much more effective solutions to our clients in much shorter time frames, >>Shorter time frames are key given that one of the things we've learned in the last 18 months, is that real time is really business critical for companies in every industry unless I want to get your perspective from a technology lens, talk to me about the differentiators here, what this solution is three way alliance brings to your customers. >>Yeah, sure thing. We've done a lot of work with Deloitte and with Intel also on performance optimization, which is, is key for any application and that gets to what I mentioned earlier of bringing more data in some of the work that we've done with until we've able been able to accelerate Are the ingest rate of Splunk by about 17 times, which is pretty incredible. Uh, and that allows us to do more or do more with less and that can help reduce the cost. Also done a lot of work on the, on the setup side. So there's a lot of complexities in running a big enterprise application like Splunk. Um, it does a lot of great things but with that comes some complications for sure. And so, uh, a lot of the work that we've done is to help really make this production ready at scale disaster tolerance and bring all of those things together. And that >>requires a fair amount of >>work on the back end to make sure that we can, we can do that at scale and, and to be a, you know, to run, you know, in a way that businesses of significant size can take advantage of these things without having to worry about what happens if I lose a data center or what happens if I lose a region. Um And and to do those things with absolute assurance >>That's critical case you have a question for you. How will this solution help facilitate one of the positives that we've seen during the last 18 months and that is the strengthening of the IT security relationship. What are your thoughts there? >>I think one of the important things here is that the standardization and automation of what we're what we're bringing together you know so that security can monitor all the different things that are being configured because I can go in and look at the automation that it's creating them. So we have a very dynamic environment now with the new cloud based and virtualized environment so going in and manually configuring anything anymore. It's just not possible. Not when you're managing tens of thousands of servers out there. So security working together very closely with operations and collaborating on that automation so that the managed services are are configured right from the beginning as we talked about security about design. Operations by design in the beginning it's that early collaboration and that shift left that is giving us the very close collaboration that results in good telemetry, good visibility you know good reaction times on the other end. >>That collaboration is something that we've also seen is really a key theme that's emerged I think from all of us in every industry in the last 18 months. And I want to punt the last question to you and that's where can customers go to learn more information? How do they get started with this solution? >>A great way to get started is to reach out to our partners like Deloitte, they can help you on that journey. Hp. Es there, of course. Hp dot com. We have a number of white papers, collateral presentations, reference architecture is you name it, it's out there. But really every organization is unique. Every every challenge that we come up with always requires a little bit of hard thinking and and so that's why we have the partnership >>to be able to work with customers and collaborate. I'll say to really identify what their challenges are, how they help them in this very dynamic. No doubt continuing to be dynamic market. Thank you both so much for joining me talking to me about what Deloitte Splunk NHP are doing, how you're helping customers address that cloud complexity from the security lens, the operations lens. We appreciate your time. >>Thanks lisa. Thank you lisa tonight >>For my guests. I'm Lisa Martin, you're watching the cubes coverage of splunk.com 21. Yeah. Mhm