>> Voiceover: Live from Washington, D.C., it's The Cube covering .NEXT Conference. Brought to you by Nutanix. >> Welcome back to Nutanix .NEXT Con, this is The Cube, the leader in live tech coverage, and we're here just outside of Washington, D.C. John Walton is here, he's the CIO of San Mateo County, Cube alum, good to see you again, thanks for coming back on. >> Great to be here, thanks for having me. You're very welcome, so it was good show. You been to .NEXT Con before, or-- >> This is my second one. >> Second one, okay. It's good little meet-ups, still kind of intimate, but they're growing, good buzz, what's your sense so far? >> I think it's good, I like to see the partners here. I've been wandering around, talking to some of my fellow CIOs on the floor here. It seems like people are really starting to understand better where Nutanix is going. I think there's a little bit of, you know, concern in the CIO community when they went public what that would mean, would they going to get bought out? And I think people are just happy to see status quo, heading in the right direction, being stable. You know, we all feel like our money's well invested and they are going to be around for the long run. >> I wonder if we could talk, sort of about the CIO role specifically at the county. And, you know, CIO, a lot of jokes, career is over, and keeping the lights on, that's all you do, and kind of thankless jobs, etc. But times are changing, everybody's talking about digital disruption, everybody's talking about being data driven. The whole big data thing is actually starting to feel real. And a lot of CIOs tell us that, in fact the last guest was saying we were sort of able to shift our attention from doing just nitty gritty infrastructure management to doing fun stuff. Is that what you're seeing in your environment? What are some of the drivers, and what's the environment like for you? >> Yeah, it is that way in San Mateo County. I mean, San Mateo County is interesting 'cause we are kind of the forgotten county between San Jose and San Francisco, right? Everybody commutes through San Mateo to one place or the other. But it's an exciting county to work in because you have so many of the thought leaders who actually live in the county. They run the companies and things. So, you have a community that's a very embracing of technology, and as the CIO for the county, I have the opportunity to play a multiple number of roles. I think what you alluded to, sort of the traditional view of the CIO role was keep the lights on, make sure everybody's got a new PC, don't let anything go down. And in our county certainly there was an aspect of that when I first joined them. And that's how we met Nutanix, was really refreshing our infrastructure, getting our uptime up, getting compute up. But that's all invisible now. That is a thing that technologies like Nutanix have afforded a CIO like myself is after you go through that initial big lift of getting up into the 21st century, and getting your infrastructure modernized in government, then you're able to be that chief innovation officer, chief disruption person, and really say, "What can I do for the community?", "What can I do on a regional scale?", "What can I do through partnerships?", so... You know, I really feel like infrastructure really has to become invisible. Nobody cares what switch is transmitting their data, nobody care what WAP they're connecting to. I mean, the end users don't really care what hyper-convert solution we use to provide the solution. I care, 'cause I'm a geek, and I care about the budget, and I care that my staff are happy, but really at the end of the day, the people who I'm most worried about are, you know, that departments that provide services to the public I'm trying to show relevance to, the elected officials who want to see us heading in the right direction and really adding value as government to the public that pays a lot of taxes, frankly. They want to see benefits. So, I'm really excited about the coming, we just got out of our budget cycle, and sort of really setting that vision for what do we want to do in the coming years. Nutanix powers that, but I don't have to worry about it anymore. >> John, what are some of those drivers that are helping you to innovate or provide more services, what are some of the big things you can share? >> Well I think you have to look at it from a, when you have infrastructure that's robust and it's up and it's cost-affordable, then you don't spend 80% of your time worrying about that. That's not what's keeping you awake at night. I get asked that a lot, "What's keeping you awake at night?" It's no longer that hard work on a crash or fail, or become the thing that delays all of my projects. So now the value-adds we look for is connectivity. You know, we talk about SMC Connect, San Mateo County Connect. It's now that we've created the infrastructure, put all of the services online, how do we get people better to connect to those? Do we need to market them more, or do we need to help understand the value they add to the community more? Do we need more wireless connectivity, do we need fiber connectivity? It's more connecting the public to the backend solution, whether they live in my data center or the cloud, what I care about are, are the applications and data relevant to the public, are they making their lives better, and do they have the tools to connect to those? 'Cause, kind of like San Mateo, it's very diverse. You know, you have sort of a high-tech corridor down the 101 corridor, where you have a lot of high tech area, and then you have a very rural area out towards the coast, and very different population you have to serve. >> Sounds like you're a service provider. >> Yes >> Yeah, yeah. >> Talk about this notion of invisibility. How has it changed the way in which your team works? >> Well, I think, you know, everyone wants to feel valued. I think if you're a network engineer or a server engineer, you want to feel like you add value. The one thing I think we do well in San Mateo County is, you know, we have performance metrics that we publish, that we're trying to achieve. Whether it's uptime or customer satisfaction, those trickle down to every group. So, invisibility means you don't have to worry about it anymore. But we do try to keep some visibility on how every staff person contributes to the ultimate outcome we're trying to achieve. So if people can see how6 they're individual efforts add value to the end result, I think they feel valued and they feel important. Invisibility's important because when I go to board meeting now, I'm not talking about, "Oh I need millions of dollars for this server," "Oh, we need to do this big network refresh." That's too visible. That's making the infrastructure the cornerstone of all your conversations, and it takes about two seconds before the board member's or elected official's eyes glaze over. They don't want to hear it. They want to hear about what are the visible aspects, how are we helping youth and community centers better connect to educational opportunities or job or internships. So, I think there'll always going to be a spend on technology to make things better. But I think as CIOs, when we get trapped in talking about specific technologies or how important infrastructure is, that makes it too visible. That makes it seem like that's all we care about. And I think the biggest compliment I ever got, in a budget meeting, was somebody saying, "What I appreciate is we spent 30 minutes talking about IT, and you never used one technical term." You know, and I think that's the invisibility piece of it is. I think as a CIO, you know you've done your job when you never have to to talk about the technology, right? The people that we serve in the community and the elected officials, they need to assume we're making a good technical decision to make those solutions happen. So I think, in a sense, the technology should be invisible, it should be affordable it should be simple. It should enable the end results, but the nuances of the technology we use, should probably in large be invisible to the public 'cause that's not really their concern. >> So you've suppressed a lot of the mundane, complex infrastructure, kind of low value add discussion, it sounds like, with the board. I imagine one area that you still talk about a lot is security. Is that a topic that is a regular topic at board meetings? >> Absolutely, and I think all the ransomware and virus attacks and hacker attacks, you've seen recently. And, I tweet about those a lot, and we talk about those a lot because we've have real impacts on our organization about things like that, phishing attacks. And this again is back to the value add, I think the message I try to bring to the board is our weakest point in security isn't always necessarily the technology, it's the complexity of the technology, right? So, the more complex we make our systems, the more complex and difficult to manage our infrastructure is, the more opportunities for weakness there are. So, we've gone from taking about security in an ivory tower aspect to, I think the two areas where we can focus on is more simplifying our infrastructure so it's easier to manage and easier to secure from our staff's standpoint, and that really adds value. So, we're really able to rapidly react to and address security issues as they come up because we have simplified our infrastructure. The board doesn't really need to worry about how we've done that, but the staff feel more confident that they're able to react to and manage those things, and then we can do value add things like train the users to be more aware of how phishing attacks happen when there's threats. Communicate better. We spent most of our time in the back room hashing servers, now with the Nutanix infrastructure, it's the easy button upgrade to patch servers and to get things addressed, and we can spend more of our time communicating with the end users about threats that are out there, how they should react, how they should respond to it. >> So John, you're kind of an early adopter of this whole concept of convergence. When we first met at VM Worlds a couple years ago, I think we were talking about traditional converged infrastructure, if I can use that term. Are you still using that type of infrastructure, how does is compare with so-called hyper-converged infrastructure, do you see differences? Is HCI a buzzword, or is it substantive in your view? >> I think it's substantive, you know I was doubtful at first too. You know, I came from, like you said, a few years ago, I think every CIO faces this. Especially in the public sector. It's what I call project ware. You know, you do a project, you do an RFP, you got three or four racks of equipment in of the lowest bidder, and that becomes a little island. And then you do the next RFP and you kind of grow your data center like that. We had tried early on when some of the new, sort of converged infrastructures were coming out, and I spend a lot of time going to EBCs, and talking about reference architectures, and one throat to choke when it came to when there's a problem, is it a compute problem or is it a storage problem? I think the industry has recognized for a while now since we first had these conversations about, again, simplifying and collapsing the complexity of those infrastructures is important. You know, I was doubtful when we first did the pilot with Nutanix. We first did the pilot around just VDI. We just saw Nutanix three years ago as a point solution, sort of the project where this was going to be our VDI platform. We would still maintain these other infrastructures for really important projects that needed the more traditional architectures. And, you know, it's really credited to my staff and engineers, it only took about six months before we had failing infrastructure, they would say, "Hey, we can use Nutanix. Let's hyper-converge, and chime in for other things, for compute. And now we're 100% virtualized. You know, we have over 1,200 servers now, all running on the Nutanix. There hasn't been a time in two years where my staff came to me and said, "The hyper-converged infrastructure we've selected isn't going to work for this, we have to buy something else." And so, to me that's when it goes from the theoretical, it might work, it might just be a... to a reality. If I'm going to go all in, and my staff are going to go all in on something, they have to be pretty confident that that's going to work for 'em. >> Are you Acropolis Hypervisor? >> We are in some things, you know, we don't use it for everything. But I think, you know, it goes back. We still have a very good relationship with VMware, we still think in some cases that VMware tools are still slightly more mature than the Acropolis tools. We think Acropolis has been catching up, we've actually been pushing really hard on Nutanix, to make it mature. And that's one of the reasons we've went with this platform, is we like to see that competition. We'd like to think that the Acropolis product will continue to mature, and challenge Vmware to either continue to evolve ahead of it, or bring their prices down to compete with it. >> You know, John, what's still on your to do list for Nutanix and it's ecosystem in your mind? >> You know, we're really looking at, really now around our disaster-recovery strategy, we're doing local replication between two data centers that are about six miles apart, which from a local building failure standpoint's useful. But my county's on the San Andreas fault, so the likelihood that a large earthquake is going to take both local data center is pretty high. So, we're really looking with Commvault and Nutanix and Amazon Web Services now, sort of about, you know, we have over 200 applications we support, for both public safety, healthcare, really mission-critical things that we can have zero downtime on, and in a disaster situation, healthcare and public safety applications are probably going to be the most needed applications out there. So, we're really pushing to try to see what that future looks like in the next 12 months around the Nutanix infrastructure. I don't say we have everything solved locally, but we're very confident in what we've implemented locally for our local compute, but really that next thing, what is the right balance between cloud compute and local compute? And how does that fit into the DR conversation's important. And back to your question about security, we still have real concerns about how secure is the public cloud. You know, it's not is it going to get hacked, but can the public cloud infrastructure be compromised to the point where in a disaster, if that's not available, how are we still going to get the data and applications up and running we need? So, we really see that there needs to be a balance between the two things. >> It's a response issue for you, and in that case-- >> It is, and we don't believe it's less secure, but we believe there's a RTO we need to meet in a disaster, and having lived through the Japan earthquake when I was in Tokyo when they had the 9.0, response time was critical. You can't say, "Well, we'll have the internet connection back up by then," and be reliant on your partners to do that, you need access to that data right now. So you've got a synchronous connection today, between your two data center, is that right? >> We have two data centers, but not to an out-of-area data center yet. That's what we need to accomplish next. >> Okay, yeah, good. Alright, listen. John, thanks very much for coming to The Cube. >> It's my pleasure. >> Let me give you the last word here on Nutanix, your future with them, or other things that you'd want to share? >> Well, we're excited about it and I'd recommend to any CIO who's watching this or thinking about it, really consider it, and see how it fits into your ecosystem. >> Great, always good having you on. Thanks very much for coming. >> It's my pleasure, thank you gentlemen. >> You're welcome. Alright, keep right there, buddy. Stew and I back with our next guest. This is The Cube, we're live from Nutanix .NEXT Conf. Be right back. >> Voiceover: Robert Herjavec

(gentle upbeat music) >> Everyone welcome to the AWS Startup Showcase presented by theCUBE. I'm John Furrier, host of theCUBE. We are here talking about the next big thing in cloud featuring Explorium. For the AI track, we've got AI cybersecurity and life sciences. Obviously AI is hot, machine learning powering that. Today we're joined by Zach Booth, director of global partnerships and channels like Explorium. Zach, thank you for joining me today remotely. Soon we'll be in person, but thanks for coming on. We're going to talk about rethinking external data. Thanks for coming on theCUBE. >> Absolutely, thanks so much for having us, John. >> So you guys are a hot startup. Congratulations, we just wrote about on SiliconANGLE, you're a new $75 million of fresh funding. So you're part of the Amazon partner network and growing like crazy. You guys have a unique value proposition looking at external data and that having a platform for advanced analytics and machine learning. Can you take a minute to explain what you guys do? What is this platform? What's the value proposition and why do you exist? >> Bottom line, we're bringing context to decision-making. The premise of Explorium and kind of this is consistent with the framework of advanced analytics is we're helping customers to reach better, more relevant, external data to feed into their predictive and analytical models. It's quite a challenge to actually integrate and effectively leverage data that's coming from beyond your organization's walls. It's manual, it's tedious, it's extremely time consuming and that's a problem. It's really a problem that Explorium was built to solve. And our philosophy is it shouldn't take so long. It shouldn't be such an arduous process, but it is. So we built a company, a technology that's capable for any given analytical process of connecting a customer to relevant sources that are kind of beyond their organization's walls. And this really impacts decision-making by bringing variety and context into their analytical processes. >> You know, one of the things I see a lot in my interviews with theCUBE and talking to people in the industry is that everyone talks a big game about having some machine learning and AI, they're like, "Okay, I got all this cool stuff". But at the end of the day, people are still using spreadsheets. They're wrangling data. And a lot of it's dominated by these still fenced-off data warehousing and you start to see the emergence of really companies built on the cloud. I saw the snowflake IPO, you're seeing a whole new shift of new brands emerging that are doing things differently, right? And because there's such a need for just move out of the archaic spreadsheet and data presentation layers, it's a slower antiquated, outdated. How do you guys solve that problem? You guys are on the other side of that equation, you're on the new wave of analytics. What are you guys solving? How do you make that work? How do you get on that way? >> So basically the way Explorium sees the world, and I think that most analytical practitioners these days see it in a similar way, but the key to any analytical problem is having the right data. And the challenge that we've talked about and that we're really focused on is helping companies reach that right data. Our focus is on the data part of data science. The science part is the algorithmic side. It's interesting. It was kind of the first frontier of machine learning as practitioners and experts were focused on it and cloud and compute really enabled that. The challenge today isn't so much "What's the right model for my problem?" But it's "What's the right data?" And that's the premise of what we do. Your model's only as strong as the data that it trains on. And going back to that concept of just bringing context to decision-making. Within that framework that we talked about, the key is bringing comprehensive, accurate and highly varied data into my model. But if my model is only being informed with internal data which is wonderful data, but only internal, then it's missing context. And we're helping companies to reach that external variety through a pretty elegant platform that can connect the right data for my analytical process. And this really has implications across several different industries and a multitude of use cases. We're working with companies across consumer packaged goods, insurance, financial services, retail, e-commerce, even software as a service. And the use cases can range between fraud and risk to marketing and lifetime value. Now, why is this such a challenge today with maybe some antiquated or analog means? With a spreadsheet or with a rule-based approach where we're pretty limited, it was an effective means of decision-making to generate and create actions, but it's highly limited in its ability to change, to be dynamic, to be flexible. And with modeling and using data, it's really a huge arsenal that we have at our fingertips. The trick is extracting value from within it. There's obviously latent value from within our org but every day there's more and more data that's being created outside of our org. And that is a challenge to go out and get to effectively filter and navigate and connect to. So we've basically built that tech to help us navigate and query for any given analytical question. Find me the right data rather than starting with what's the problem I'm looking for, now let me think about the right data. Which is kind of akin to going into a library and searching for a specific book. You know which book you're looking for. Instead of saying, there's a world, a universe of data outside there. I want to access it. I want to tap into what's right. Can I use a tool that can effectively query all that data, find what's relevant for me, connect it and match it with my own and distill signals or features from that data to provide more variety into my modeling efforts yielding a robust decision as an output. >> I love that paradigm of just having that searchable kind of paradigm. I got to ask you one of the big things that I've heard people talk about. I want to get your thoughts on this, is that how do I know if I even have the right data? Is the data addressable? Can I find it? Is it even, can I even be queried? How do you solve that problem for customers when they say, "I really want the best analytics but do I even have the data or is it the right data?" How do you guys look at that? >> So the way our technology was built is that it's quite relevant for a few different profile types of customers. Some of these customers, really the genesis of the company started with those cloud-based, model-driven since day one organizations, and they're working with machine learning and they have models in production. They're quite mature in fact. And the problem that they've been facing is, again, our models are only as strong as the data that they're training on. The only data that they're training on is internal data. And we're seeing diminishing returns from those decisions. So now suddenly we're looking for outside data and we're finding that to effectively use outside data, we have to spend a lot of time. 60% of our time spent thinking of data, going out and getting it, cleaning it, validating it, and only then can we actually train a model and assess if there's an ROI. That takes months. And if it doesn't push the needle from an ROI standpoint, then it's an enormous opportunity cost, which is very, very painful, which goes back to their decision-making. Is it even worth it if it doesn't push the needle? That's why there had to be a better way. And what we built is relevant for that audience as well as companies that are in the midst of their digital transformation. We're data rich, but data science poor. We have lots of data. A latent value to extract from within our own data and at the same time tons of valuable data outside of our org. Instead of waiting 18, 36 months to transform ourselves, get our infrastructure in place, our data collection in place, and really start having models in production based on our own data. You can now do this in tandem. And that's what we're seeing with a lot of our enterprise customers. By using their analysts, their data engineers, some of them in their innovation or kind of center of excellences have a data science group as well. And they're using the platform to inform a lot of their different models across lines of businesses. >> I love that expression, "data-rich". A lot of people becoming full of data too. They have a data problem. They have a lot of it. I think I want to get your thoughts but I think that connects to my next question which is as people look at the cloud, for instance, and again, all these old methods were internal, internal to the company, but now that you have this idea of cloud, more integration's happening. More people are connecting with APIs. There's more access to potentially more signals, more data. How does a company go to that next level to connect in and acquire the data and make it faster? Because I can almost imagine that the signals that come from that context of merging external data and that's the topic of this theme, re-imagining external data is extremely valuable signaling capability. And so it sounds like you guys make it go faster. So how does it work? Is it the cloud? Take us through that value proposition. >> Well, it's a real, it's amazing how fast the rate of change organizations have been moving onto the cloud over the past year during COVID and the fact that alternative or external data, depending on how you refer to it, has really, really blown up. And it's really exciting. This is coming in the form of data providers and data marketplaces, and everybody is kind of, more and more organizations are moving from rule-based decision-making to predictive decision making, and that's exciting. Now what's interesting about this company, Explorium, we're working with a lot of different types of customers but our long game has a real high upside. There's more and more companies that are starting to use data and are transformed or already are in the midst of their transformation. So they need outside data. And that challenge that I described is exists for all of them. So how does it really work? Today, if I don't have data outside, I have to think. It's based on hypothesis and it all starts with that hypothesis which is already prone to error from the get-go. You and I might be domain experts for a given use case. Let's say we're focusing on fraud. We might think about a dozen different types of data sources, but going out and getting it like I said, it takes a lot of time harmonizing it, cleaning it, and being able to use it takes even more time. And that's just for each one. So if we have to do that across dozens of data sources it's going to take far too much time and the juice isn't worth the squeeze. And so I'm going to forego using that. And a metaphor that I like to use when I try to describe what Explorium does to my mom. I basically use this connection to buying your first home. It's a very, very important financial decision. You would, when you're buying this home, you're thinking about all the different inputs in your decision-making. It's not just about the blueprint of the house and how many rooms and the criteria you're looking for. You're also thinking external variables. You're thinking about the school zone, the construction, the property value, alternative or similar neighborhoods. That's probably your most important financial decision or one of the largest at least. A machine learning model in production is an extremely important and expensive investment for an organization. Now, the problem is as a consumer buying a home, we have all this data at our fingertips to find out all of those external-based inputs. Organizations don't, which is kind of crazy when I first kind of got into this world. And so, they're making decisions with their first party data only. First party data's wonderful data. It's the best, it's representative, it's high quality, it's high value for their specific decision-making and use cases but it lacks context. And there's so much context in the form of location-based data and business information that can inform decision-making that isn't being used. It translates to sub-optimal decision-making, let's say. >> Yeah, and I think one of the insights around looking at signal data in context is if by merging it with the first party, it creates a huge value window, it gives you observational data, maybe potentially insights into customer behavior. So totally agree, I think that's a huge observation. You guys are definitely on the right side of history here. I want to get into how it plays out for the customer. You mentioned the different industries, obviously data's in every vertical. And vertical specialization with the data it has to be, is very metadata driven. I mean, metadata and oil and gas is different than fintech. I mean, some overlap, but for the most part you got to have that context, acute context, each one. How are you guys working? Take us through an example of someone getting it right, getting that right set up, taking us through the use case of how someone on boards Explorium, how they put it to use, and what are some of the benefits? >> So let's break it down into kind of a three-step phase. And let's use that example of fraud earlier. An organization would have basically past historical data of how many customers were actually fraudulent in the end of the day. So this use case, and it's a core business problem, is with an intention to reduce that fraud. So they would basically provide, going with your description earlier, something similar to an Excel file. This can be pulled from any database out there, we're working with loads of them, and they would provide this what's called training data. This training data is their historical data and would have as an output, the outcome, the conclusion, was this business fraudulent or not? Yes or no. Binary. The platform would understand that data itself to train a model with external context in the form of enrichments. These data enrichments at the end of the day are important, they're relevant, but their purpose is to generate signals. So to your point, signals is the bottom line what everyone's trying to achieve and identify and discover, and even engineer by using data that they have and data that they yet to integrate with. So the platform would connect to your data, infer and understand the meaning of that data. And based on this matching of internal plus external context, the platform automates the process of distilling signals. Or in machine learning this is called, referred to as features. And these features are really the bread and butter of your modeling efforts. If you can leverage features that are coming from data that's outside of your org, and they're quantifiably valuable which the platform measures, then you're putting yourself in a position to generate an edge in your modeling efforts. Meaning now, you might reduce your fraud rate. So your customers get a much better, more compelling offer or service or price point. It impacts your business in a lot of ways. What Explorium is bringing to the table in terms of value is a single access point to a huge universe of external data. It expedites your time to value. So rather than data analysts, data engineers, data scientists, spending a significant amount of time on data preparation, they can now spend most of their time on feature or signal engineering. That's the more fun and interesting part, less so the boring part. But they can scale their modeling efforts. So time to value, access to a huge universe of external context, and scale. >> So I see two things here. Just make sure I get this right 'cause it sounds awesome. So one, the core assets of the engineering side of it, whether it's the platform engineer or data engineering, they're more optimized for getting more signaling which is more impactful for the context acquisition, looking at contexts that might have a business outcome, versus wrangling and doing mundane, heavy lifting. >> Yeah so with it, sorry, go ahead. >> And the second one is you create a democratization for analysts or business people who just are used to dealing with spreadsheets who just want to kind of play and play with data and get a feel for it, or experiment, do querying, try to match planning with policy - >> Yeah, so the way I like to kind of communicate this is Explorium's this one, two punch. It's got this technology layer that provides entity resolution, so matching with external data, which otherwise is a manual endeavor. Explorium's automated that piece. The second is a huge universe of outside data. So this circumvents procurement. You don't have to go out and spend all of these one-off efforts on time finding data, organizing it, cleaning it, etc. You can use Explorium as your single access point to and gateway to external data and match it, so this will accelerate your time to value and ultimately the amount of valuable signals that you can discover and leverage through the platform and feed this into your own pipelines or whatever system or analytical need you have. >> Zach, great stuff. I love talking with you and I love the hot startup action here. Cause you're again, you're on the net new wave here. Like anything new, I was just talking to a colleague here. (indistinct) When you have something new, it's like driving a car for the first time. You need someone to give you some driving lessons or figure out how to operationalize it or take advantage of the one, two, punch as you pointed out. How do you guys get someone up and running? 'Cause let's just say, I'm like, okay, I'm bought into this. So no brainer, you got my attention. I still don't understand. Do you provide a marketplace of data? Do I need to get my own data? Do I bring my own data to the party? Do you guys provide relationships with other data providers? How do I get going? How do I drive this car? How do you answer that? >> So first, explorium.ai is a free trial and we're a product-focused company. So a practitioner, maybe a data analyst, a data engineer, or data scientist would use this platform to enrich their analytical, so BI decision-making or any models that they're working on either in production or being trained. Now oftentimes models that are being trained don't actually make it to production because they don't meet a minimum threshold. Meaning they're not going to have a positive business outcome if they're deployed. With Explorium you can now bring variety into that and increase your chances that your model that's being trained will actually be deployed because it's being fed with the right data. The data that you need that's not just the data that you have. So how a business would start working with us would typically be with a use case that has a high business value. Maybe this could be a fraud use case or a risk use case and B2B, or even B2SMB context. This might be a marketing use case. We're talking about LTV modeling, lookalike modeling, lead acquisition and generation for our CPGs and field sales optimization. Explore and understand your data. It would enrich that data automatically, it would generate and discover new signals from external data plus from your own and feed this into either a model that you have in-house or end to end in the platform itself. We provide customer success to generate, kind of help you build out your first model perhaps, and hold your hands through that process. But typically most of our customers are after a few months time having run in building models, multiple models in production on their own. And that's really exciting because we're helping organizations move from a more kind of rule-based decision making and being their bridge to data science. >> Awesome. I noticed that in your title you handle global partnerships and channels which I'm assuming is you guys have a network and ecosystem you're working with. What are some of the partnerships and channel relationships that you have that you bring to bear in the marketplace? >> So data and analytics, this space is very much an ecosystem. Our customers are working across different clouds, working with all sorts of vendors, technologies. Basically they have a pretty big stack. We're a part of that stack and we want to symbiotically play within our customer stack so that we can contribute value whether they sit here, there, or in another place. Our partners range from consulting and system integration firms, those that perhaps are building out the blueprint for a digital transformation or actually implementing that digital transformation. And we contribute value in both of these cases as a technology innovation layer in our product. And a customer would then consume Explorium afterwards, after that transformation is complete as a part of their stack. We're also working with a lot of the different cloud vendors. Our customers are all cloud-based and data enrichment is becoming more and more relevant with some wonderful machine-learning tools. Be they AutoML, or even some data marketplaces are popping up and very exciting. What we're bringing to the table as an edge is accelerating the connection between the data that I think I want as a company and how to actually extract value from that data. Being part of this ecosystem means that we can be working with and should be working with a lot of different partners to contribute incremental value to our end customers. >> Final question I want to ask you is if I'm in a conference room with my team and someone says, "Hey, we should be rethinking our external data." What would I say? How would I pound my fist on the table or raise my hand in saying, "Hey, I have an idea, we should be thinking this way." What would be my argument to the team, to re-imagine how we deal with external data? >> So it might be a scenario that rather than banging your hands on the table, you might be banging your heads on the table because it's such a challenging endeavor today. Companies have to think about, What's the right data for my specific use cases? I need to validate that data. Is it relevant? Is it real? Is it representative? Does it have good coverage, good depth and good quality? Then I need to procure that data. And this is about getting a license from it. I need to integrate that data with my own. That means I need to have some in-house expertise to do so. And then of course, I need to monitor and maintain that data on an ongoing basis. All of this is a pretty big thing to undertake and undergo and having a partner to facilitate that external data integration and ongoing refresh and monitoring, and being able to trust that this is all harmonized, high quality, and I can find the valuable ones without having to manually pick and choose and try to discover it myself is a huge value add, particularly the larger the organization or partner. Because there's so much data out there. And there's a lot of noise out there too. And so if I can through a single partner or access point, tap into that data and quantify what's relevant for my specific problem, then I'm putting myself in a really good position and optimizing the allocation of my very expensive and valuable data analysts and engineering resources. >> Yeah, I think one of the things you mentioned earlier I thought was a huge point was good call out was it goes beyond the first party data because and even just first party if you just in an internal view, some of the best, most successful innovators that we've been covering with cloud scale is they're extending their first party data to external providers. So they're in the value chains of solutions that share their first party data with other suppliers. And so that's just, again, more of an extension of the first party data. You're kind of taking it to a whole 'nother level of there's another external, external set of data beyond it that's even more important. I think this is a fascinating growth area and I think you guys are onto it. Great stuff. >> Thank you so much, John. >> Well, I really appreciate you coming on Zach. Final word, give a quick plug for the company. What are you up to, and what's going on? >> What's going on with Explorium? We are growing very fast. We're a very exciting company. I've been here since the very early days and I can tell you that we have a stellar working environment, a very, very, strong down to earth, high work ethic culture. We're growing in the sense of our office in San Mateo, New York, and Tel Aviv are growing rapidly. As you mentioned earlier, we raised our series C so that totals Explorium to raising I think 127 million over the past two years and some change. And whether you want to partner with Explorium, work with us as a customer, or join us as an employee, we welcome that. And I encourage everybody to go to explorium.ai. Check us out, read some of the interesting content there around data science, around the processes, around the business outcomes that a lot of our customers are seeing, as well as joining a free trial. So you can check out the platform and everything that has to offer from machine learning engine to a signal studio, as well as what type of information might be relevant for your specific use case. >> All right Zach, thanks for coming on. Zach Booth, director of global partnerships and channels that explorium.ai. The next big thing in cloud featuring Explorium and a part of our AI track, I'm John Furrier, host of theCUBE. Thanks for watching.

>> Narrator: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Hey, welcome to this CUBE Conversation. I'm John Furrier, Host of theCUBE here in Palo Alto, California, for a special conversation with an industry analyst who's been, who travels a lot, does a lot of events, covers the industry, up and down, economically and also some of the big trends, to talk about how the at scale problem that the COVID-19 is causing. Whether it's a lot of people are working at home for the first time, to at scale network problems, the pressure points that this is exposing for what I would call the mainstream world is a great topic. Zeus Kerravala, Founder and Principal Analyst at ZK Research, friend of theCUBE. Zeus, welcome back to theCUBE. Good to see you remotely. We're, as you know, working in place here. I came to the studio for, with our quarantine crew here, to get these stories out, 'cause they're super important. Thanks for spending the time. >> Hi, yeah, thanks, it's certainly been an interesting last couple months and we're probably, maybe half way through this, I'm guessing. >> Yeah, and no matter what happens the new reality of this current situation or mess or whatever you want to call it is the fact that it has awakened what us industry insiders have been seeing for a long time, big data, new networks, cloud native, micro-services, kind of at scale, scale out infrastructure, kind of the stuff that we've been kind of covering is now exposed for the whole world to see on a Petri dish that is called COVID-19, going, "Wow, this world has changed." This is highlighting the problems. Can you share your view of what are some of those things that people are experiencing for the first time and what's the reaction, what's your reaction to it all? >> Yeah, it's been kind of an interesting last couple of months when I talk to CIO's about how they're adapting to this. You know, when, before I was an analyst, John, I was actually in corporate IT. I was part of a business continuity plans group for companies and the whole definition of business continuity's changed. When I was in corporate IT, we thought of business continuity as being able to run the company with a minimal set of services for a week or a month or something like that. So, for instance, I was in charge of corporate technology and financial services firm and we thought, "Well, if we have 50 traders, can we get by with 10", right? Business continuity today is I need to run the entire organization with my full staff for an indefinite period of time, right? And that is substantially different mandate than thinking of how I run a minimal set of services to just maintain the bare minimum business operations and I think that's exposed a lot of things for a lot of companies. You know, for instance, I've talked to so many companies today where the majority of their employees have never worked remote. For you or I, we're mobile professionals. We do this all the time. We travel around. We go to conferences. We do this stuff all, it's second nature. But for a lot of employees, you think of contact center agents, in store people, things like that, they've never worked from home before. And so, all of a sudden, the new reality is they've got to set up a computer in the kitchen or their bedroom or something like that and start working from home. Also for companies, they've never had to think about a world where everybody worked remotely, right? So the VP in Infrastructure would have, the cloud apps they have, the remote access technology they have was set up for a subset of users, maybe 10%, maybe 15%, but certainly not everybody. And so now we're seeing corporate networks get crushed. All the cloud providers are getting crushed. I know some of the conferencing companies, the video companies are having to double, triple capacity. And so I think to your point when you started this, we would have seen this eventually with all the data coming in and all the new devices being connected. I think what COVID did was just accelerate it just to the point where it's exposed to everything at once. >> Yeah, and you know, I have a lot of, being an entrepreneur and done a lot of corporate legal contracts. The word force majeure is always a phrase that's a legal jargon, which means act of God or so to speak, something you can't control. I think what's interesting to your point is that the playbook in IT, even some of the most cutting edge IT, is forecasting some disruption, but never like this. And also disaster recovery and business continuity, as you mentioned, have been practices, but state of the art has been percentages of overall. But disaster recovery was a hurricane, or a power outage, so generators, fail over sites or regions of your cloud, not a change in a new vector. So the disruption is not disruption. It's an amplification of a new work stream. That's the disruption. That's what you're saying. >> Yeah, you know, that's correct. Business continuity used to be very data center-focused. It was, how do I get my power? How do I create some, replicate my office and have 50 desks in here, instead of 500? But now it's everybody working remotely, so I got to have ways for them to collaborate. I have to have ways for them to talk to customers. I have to have ways for them to deliver services. I have to enable people to do what they did in the office, but not in the office, right? And so that's been the big challenge and I think it's been an interesting test for CIO's that have been going through digital transformation plans. I think it's shifted a lot of budgets around and made companies look at the way they do things. There's also the social aspect of a job. People like to go to the office. They like to interact with co-workers. And I've talked to some companies where they're bringing in medical doctors, they're bringing in psychologists to talk to their employees, because if you're never worked from home before, it's quite a big difference. The other aspect of this that's underappreciated, I think, is the fact that now our kids are home, right? >> John: Yeah. (laughter) >> So we've got to contend with that. And I know that the first day that the shelter in place order got put in place for the San Francisco area, a new call, I believe a new version of Call of Duty had just come out. You know, we had some new shows pop up in Netflix, some series continuances. So now these kids who are at home are bored. They're downloading content. They're playing games. At the same time, we're trying to work and we're trying to do video calls and we're trying to bring in multiple video streams or even if they're in classrooms, they're doing Zoom-based calls, that type of thing, or using WebEx or an application like that, and it's played havoc on corporate networks, not just company networks, and so... >> Also Comcast and the providers, AT&T. You've got the fiber seems to be doing well, but Comcast is throttling. I mean, this is the crisis. It's a new vector of disruption. But how do you develop... >> Yeah, YouTube said that they're going to throttle down. Well, I think what this is is it makes you look at how you handle your traffic. And I think there's plenty of bandwidth out there. And even the most basic home routers are capable of prioritizing traffic and I think there's a number of IT leaders I've talked who have actually gone through the steps of helping their employees understand how you use your home networking technology to be able to prioritize video and corporate voice traffic over top. There are corporate ways to do that. You know, for instance, Aruba and Extreme Networks both offer these remote access points where you just plug 'em in and you're connected through a corporate network and you pick up all the policies. But even without that, there's ways to do with home. So I think it's made us rethink networking. Instead of the network being a home network, a WiFi network, a data center network, right, the Internet, we need to think about this grand network as one network and then how we control the quality of a cloud app when the person's home to the cloud, all the way back to the company, because that's what drives user experience. >> I think you're highlighting something really important. And I just want to illustrate and have you double down on more commentary on this, because I think, you know, the one network where we're all part of one network concept shows that the perimeter's dead. That's what we've been saying about the cloud, but also if you think about just the crimes of opportunity that are happening. You've got the hacker and hacking situation. You have all kinds of things that are impacted. There's crimes of opportunity, and there's disruption that's happening because of the opportunity. Can you just share more and unpack that concept of this one network? What are some of the things that business are thinking about now? You've got the VPN. You've got collaboration tools that sometimes are half-baked. I mean, I love Zoom and all, but Zoom is crashing too. I mean, WebEx is more corporate-oriented, but not really as strong as what Zoom is for the consumer. But still they have an opportunity, but they have a challenge as well. So all these work tools are kind of half-baked too. (laughing) >> Well, the thing is they were never designed... I remember seeing in an interview that Chuck Robbins had on CNBC where he said, "We didn't design WebEx to support everybody working from home". It just, that wasn't even a thought. Nowhere did he ever go to his team and say, build this for the whole world to connect, right? And so, every one of the video providers and the cloud collaboration providers have problems, and I don't really blame them, because this is a dynamic we were never expecting to see. I think you brought up a good point on the security side. We, a lot has been written about how more and more companies are moving to these online tools, like Zoom and WebEx and applications like that to let us communicate, but what does that mean from a security perspective? Now`all of sudden I have people working from home. They're using these Web-based applications. I remember a conversation I had about six months ago with one of the world's most famous hackers who does nothing but penetration tests now. He said that the cloud-based applications are his number one entry point into companies and to penetrate them, because people's passwords and things like that are fairly weak. So, now we're moving everything to the cloud. We're moving everything to these SaaS apps, right? And so now it's creating more exposure points. We've got fishers out there that are using the term COVID or Corona as a way to get people to click on links they shouldn't. And so now our whole security paradigm has blown up, right? So we used to have this hard shell we could drop around our company. We can't do that anymore. And we have to start worrying about things on an app-by-app basis. And it's caused companies to rethink security, to look at multi-factor authentication tools. I think those are a lot better. We have to look at Casb tools, the cloud access tools, kind of monitor what apps people are using, what they're not using. Trying to cut down on the use of consumer tools, right? So it's a lot for the security practice to take ahold of too. And you have to understand, even from a company standpoint, your security operations center was built on the concept they pull all their data into one location. SOC engineers aren't used to working remotely as well, so that's a big change as well. How do I get my data analyzed and to my SOC engineers when they're working from home? >> You know, we have coined the term Black Friday for the day after, you know, Thanksgiving. >> Thanksgiving, yeah. >> You know, the big surge, but that's a term to describe that first experience of, holy shit, everyone's going to the websites and they all crashed. So we're kind of having that same moment now, to your point earlier. So I want to read a statement that was on Nima Baidey's LinkedIn. He's at Google now, former Pivotal guy. You probably know him. He had a little graphic that says, "Who led the digital transformation of your company?" It's got a poll with a question mark. "A) Your CEO, B) your CTO, or C) COVID-19"? And it circles COVID-19 and that's the image and that's the meme that's going around. But the reality is it is highlighting it and I want to get your thoughts on this next track of thinking around how people may shift their focus and their spend, because, hey, hybrid cloud's great and multicloud's the next big wave, but screw multicloud. If I can't actually fix my current situation, maybe I'll push off some of the multicloud stuff or maybe I won't. So, how do you see the give and get of project prioritization, because I think this is going to wake everyone up. You mentioned security, clearly. >> Yeah, well, I think it has woken everybody up and I think companies now are really rethinking how they operate. I don't believe we're going to stop traveling. I think once this is over, people are going to hop back on planes. I also don't believe that we'll never go back into the office. I think the big shift here though, John, is we will see more acceptance to hire people out of region. I think that it's proved that you don't have to be in the office, right, which will drive these collaboration tools. And I also think we'll see less use of desktop phones and more use of video means. So now that people are getting used to using these types of tools, I think they're starting to like the experience. And so voice calls get replaced by video calls and that is going to crush our networks in buildings. So we've got WiFi 6 coming. We've got 5G coming, right. We've got lots of security tools out there. And I think you'll see a lot of prioritization to the network and that's kind of an interesting thing, because historically, the network didn't get a lot of C level time, right? It was those people in the basement. We didn't really know what they did. I'm a former network engineer. I was treated that way. (laughing) But most digital organizations now have to come to the realization that they're network-centric, and then so the network is the business and that's not something that anybody's ever put a lot of focus on. But if you look at the building blocks of digital IoT, mobility, cloud, the writing's been on the wall for a while, and I've written this several times. But you need to pay more attention to the network. And I think we're finally going to see that transition, some prioritization of dollars there. >> Yeah, I will attest you have been very vocal and right on point on that, so props to that. I do want to also double amplify your point. The network drives everything, that's clear. I think the other thing that's interesting and used to be kind of a cliche in a pejorative way is the user is the product. I think that's a term that's been coined to Facebook. You know, you're data. You're the product. If you're the product, that's a problem, you know. To describe Facebook as the app that monetizes you, the user. I think this situation has really pointed out that yes, it's good to be the product. The user value and the network are two now end points of the spectrum. The network's got to be kick ass from the ground up, but the user is the product now, and it should be, in a good way, not exploiting. So I think if you're thinking about user-centric value, how my kid can play Call of Duty, how my family can watch the new episode on Netflix, how I can do a kick ass Zoom call, that's my experience. The network does its job. The application service takes advantage of making me happy. So I think this is interesting, right. So we're getting a new thing here. How real do you think that is? Where are we on the spectrum of that nirvana? >> I think we're rapidly approaching that. I think it's been well documented that 2020 was the year that customer experience become the number one brand differentiate, right. In fact, I think it was actually 2018 that that happened, but Walker and Gartner and a few other companies would be 2020. And what that means is that if you're a business, you need to provide exemplary customer service in order to gain share. I think one of the things that was lost in there is that employee experience has to be best in class as well. And so I think a lot of businesses over-rotated the spin away from employee experience to customer experience, and rightfully so, but now they got to rotate back to make sure their workers have the right tools, have the right services, have the right data, to do their jobs better, because when they do, they can turn around and provide customers better experience. So this isn't just about training your people to service customers well. It's about making sure people have the right data, the right information to do their jobs, to collaborate better, right. And there's really a tight coupling now between the consumer and the employee, or the customer and the employee. And, you know, Corona kind of exposed to that, 'cause it shows that we're all connected, in a way. And the connection of people, whether they're the customers or employees or something, that businesses have to focus on. So I think we'll see some dollars sign back to internal, not just customer facing. >> Yeah, well, great insight. And, first of all, we all connect to your great CUBE alumni. But you're also right up the street in California. We're in Palo Alto. You're in San Mateo. You literally could have driven here, but we're sheltering in place. >> We're sheltered in place. >> Great insight and, you know, thanks for sharing that and I think it's good content for people, you know, be aware of this. Obviously they're living in it right now, but I think the world is going to be back to business soon, but it's never going to be the same. I think it's digital... >> No, it'll never be the same. I think this is a real watershed point for the way we work and the way we treat our employees and our customers. I think you'll see a lot of companies make a lot of change. And that's good for the whole industry, 'cause it'll drive innovation. And I think we'll have some innovation come out of this that we never saw before. >> Quick final word for the folks that are on this big wave that's happening. It's reality. It's the current situation now. What's your advice for them as they get on their surfboard, so to speak, and ride this wave? What's your advice to them? >> Yeah, I think use this opportunity to find those weak points in your networks and find out where the bottlenecks are, because I think having everybody work remotely exposes a lot of problems in processes and where a lot of the hiccups happen. But I do think my final word is invest in the network. I think a lot of the networks out there have been badly under-invested in, which I think is why people get frustrated when they're in stadiums or hotels or casinos. I think the world is shifting. Applications and people are becoming network-centric. And if those don't work, nothing works. And I think that's really been proven over the last couple months. If our networks can't handle the traffic and our networks can't handle what we're doing, nothing works. >> You know, you and I could do a podcast show called "No Latency"... >> (mumbles) so it'll be good. >> Zeus, thanks for coming on. I appreciate taking the time. >> No problem, John. >> Stay safe. And I want to follow up with you and get a check in further down the road, in a couple days or maybe next week, if you can. >> Yeah, looking forward to it. >> Thanks a lot. Okay, I'm John Furrier here in Palo Alto Studios doing the remote interviews, getting the quick stories that matter, help you out, and (mumbles) great guest there. Check out ZK Research, a great friend of theCUBE, cutting edge, knows the networking. This is an important area. The network, the users' experience is critical. Thanks for coming and watching today. I'm John Furrier. Thanks for watching. (lighthearted music)

>> From Cambridge, Massachusetts it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by, SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts, everybody. You're watching theCUBE, the leader in live tech coverage. I'm Dave Vellante with my co-host, Paul Gillan. Chris Lynch, good friend is here CEO, newly minted CEO and AtScale and legend. Good to see you. >> In my own mind. >> In mine too. >> It's great to be here. >> It's awesome, thank you for taking time. I know how busy you are, you're running around like crazy your next big thing. I was excited to hear that you got back into it. I predicted it a while ago you were a very successful venture capitalists but at heart, you're startup guy, aren't ya? >> Yeah 100%, 100%. I couldn't be more thrilled, I feel invigorated. I think I've told you many times, when you've interviewed me and asked me about the transition from being an entrepreneur to being a VC and since it's a PG show, I've got a different analog than the one I usually give you. I used to be a movie star and now I'm an executive producer of movies. Now am back to being a movie star, hopefully. >> yeah well, so you told me when you first became a VC you said, I look for startups that have a 10X impact either 10X value, 10X cost reduction. What was it that attracted you to AtScale? What's the 10X? >> AtScale, addresses $150 billion market problem which is basically bringing traditional BI to the cloud. >> That's the other thing you told me, big markets. >> Yeah, so that's the first thing massive market opportunity. The second is, the innovation component and where the 10X comes we're uniquely qualified to virtualize data into the pipeline and out. So I like to say that we're the bridge between BI and AI and back. We make every BI user, a citizen data scientist and that's a game changer. And that's sort of the new futuristic component of what we do. So one part is steeped in, that $150 billion BI marketplace in a traditional analytics platforms and then the second piece is into you delivering the data, into these BI excuse me, these AI machine learning platforms. >> Do you see that ultimately getting integrated into some kind of larger, data pipeline framework. I mean, maybe it lives in the cloud or maybe on prem, how do you see that evolving over time? >> So I believe that, with AtScale as one single pane of glass, we basically are providing an API, to the data and to the user, one single API. The reason that today we haven't seen the delivery of the promise of big data is because we don't have big data. Fortunate 2000 companies don't have big data. They have lots of data but to me big data means you can have one logical view of that data and get the best data pumped into these models in these tools, and today that's not the case. They're constricted by location they're constricted by vendor they're constricted by whether it's in the cloud or on prem. We eliminate those restrictions. >> The single API, I think is important actually. Because when you look at some of these guys what they're doing with their data pipeline they might have 10 or 15 unique API's that they're trying to manage. So there's a simplification aspect to, I suppose. >> One of the knocks on traditional BI has always been the need for extract databases and all the ETL that goes that's involved in that. Do you guys avoid that stage? You go to the production data directly or what's the-- >> It's a great question. The way I put it is, we bring Moses to the mountain the mountain being the data, Moses being the user. Traditionally, what people have been trying to do is bring the mountain to Moses, doesn't scale. At AtScale, we provide an abstraction a logical distraction between the data and the BI user. >> You don't touch, you don't move the data. >> We don't move the data. Which is what's unique and that's what's delivering I think, way more than a 10X delivery in value. >> Because you leave the data in place you bring that value to wherever the data is. Which is the original concept of Hadoop, by the way. That was what was profound about Hadoop everybody craps on it now, but that was the game changer and if you could take advantage of that that's how you tap your 10X. >> To the difference is, we're not, to your point we're not moving the data. Hadoop, in my humble opinion why it plateaued is because to get the value, you had to ask the user to bring and put data in yet another platform. And the reason that we're not delivering on big data as an industry, I believe is because we've too many data sources, too many platforms too many consumers of data and too many producers. As we build all these islands of data, with no connectivity. The idea is, we'll create this big data lake and we're going to physically put everything in there. Guess what? Someday turned out to be never. Because people aren't going to deal with the business disruption. We move thousands of users from a platform like Teradata to a platform like Snowflake or Google BigQuery, we don't care. We're a multi-cloud and we're a hybrid cloud. But we do it without any disruption. You're using Excel, you just continue and use it. You just see the results are faster. You use Tableau, same difference. >> So we had all the vertical rock stars in here. So we had Colin in yesterday, we had Stonebraker around earlier. Andy Palmer just came on and Chris here with the CEO who ultimately sold the company to HP. That really didn't do anything with it and then spun it off and now it's back. Aaron was, he had a spring in his step yesterday. So when you think about, Vertica. The technology behind Vertica go back 10 years and where we come now give us a little journey of, your data journey. >> So I think it plays into the, the original assertion is that, vertical is a best-in-class platform for analytics but it was yet another platform. The analog I give now, is now we have Snowflake and six months, 12 months from now we're going to have another one. And that creates a set of problems if you have to live in the physical world. Because you've all these islands of data and I believe, it's about the data not about the models, it's about the data. You can't get optimal results if you don't have an optimal access to the pertinent data. I believe that having that Universal API is going to make the next platform that more valuable. You're not going to be making the trade-off is, okay we have this platform that has some neat capability but the trade-off is from an enterprise architecture perspective we're never going to be able to connect all this stuff. That's how all of these things proliferated. My view is, in a world where you have that single pane of glass, that abstraction layer between the user and the data. Then innovation can be spawned quicker and you can use these tools effectively 'cause you're not compromising being able to get a logical view of the data and get access to it as a user. >> What's your issue with Snowflake you mentioned them, Mugli's company-- >> No issue, they're a great partner of ours. We eliminate the friction between the user going from an on-prem solution to the cloud. >> Slootman just took over there. So you know where that's going. >> Yep (laughing) >> Frank's got the magic touch. Okay good, you say they're a partner yours how are you guys partnering? >> They refer us into customers that, if you want to buy Snowflake now the next issue is, how do i migrate? You don't. You put our virtualization layer in and then we allow you access to Snowflake in a non-disruptive way, versus having to move data into their system or into a particular cloud which creates sales friction. >> Moving data is just, you want to avoid it at all cost. >> I do want to ask you because I met with your predecessors, Dave Mariani last year and I know he was kind of a reluctant CEO he didn't really want to be CEO but wanted to be CTO, which is what he is now. How did that come about, that they found you that you connected with them and decided this was the right opportunity. >> That's a great question. I actually looked at the company at the seed stage when I was in venture, but I had this thing as you know that, I wanted to move companies to Boston and they're about my vintage age-wise and he's married with four kids so that wasn't in the cards. I said look, it doesn't make sense for me to seed this company 'cause I can't give you the time you're out in California everything I'm instrumenting is around Boston. We parted friends. And I was skeptical whether he could build this 'cause people have been talking about building a heterogeneous universal semantic layer, for years and it's never come to fruition. And then he read in Fortune or Forbes that I was leaving Accomplice and that I was looking for one more company to operate. He reached out and he told me what they were doing that hey, we really built it but we need help and I don't want to run this. It's not right for the company and the opportunity So he said, "I'll come and I'll consult to you." I put together a plan and I had my Vertica and data robot. NekTony guys do the technical diligence to make sure that the architecture wasn't wedded to the dupe, like all the other ones were and when I saw it wasn't then I knew the market opportunity was to take that, rifle and point it at that legacy $150 billion BI market not at the billion dollar market of Hadoop. And when we did that, we've been growing at 162% quarter-over-quarter. We've built development centers in Bulgaria. We've moved all operations, non-technical to Boston here down in our South Station. We've been on fire and we are the partner of choice of every cloud manner, because we eliminate the sales friction, for customers being able to take advantage of movement to the cloud and we're able through our intelligent pipeline and capability. We're able to reduce the cost significantly of queries because we understand and we were able to intelligently cash those queries. >> Sales ops is here, all-- >> Sales marketing, customer support, customer success and we're building a machine learning team here at Dev team here. >> Where are you in that sort of Boston build-out? >> We have an office on 711 Atlantic that we opened in the fall. We're actually moving from 4,000 square feet to 10,000 this month. In less than six months and we'll house by the first year, 100 employees in Boston 100 in Bulgaria and about that same hundred in San Mateo. >> Are you going after net new business mainly? Or there's a lot of legacy BI out there are you more displacing those products? >> A couple of things. What we find is that, customers want to evolve into the cloud, they don't want a revolution they want a evolution. So we allow them, because we support hybrid cloud to keep some data behind the firewall and then experiment with moving other data to the cloud platform of choice but we're still providing that one logical view. I would say most of our customers are looking to reap platform, off of Teradata or something onto a, another platform like Snowflake. And then we have a set of customers that see that as part of the solution but not the whole solution. They're more true hybrids but I would say that 80% of our customers are traditional BI customers that are trying to contemporize their environments and be able to take advantage of tabular support and multidimensional, the things that we do in addition to the cube world. >> They can keep whatever they're using. >> Correct, that's the key. >> Did you do the series D, you did, right? >> Yes, Morgan Stanely led. >> So you're not actively but you're good for now, It was like $50 million >> Yeah we raised $50 million. >> You're good for a bit. Who's in the Chris Lynch target? (laughs) Who's the enemy? Vertica, I could say it was the traditional database guys. Who's the? >> We're in a unique position, we're almost Switzerland so we could be friend to foe, of anybody in that ecosystem because we can, non-disruptively re-platform customers between legacy platforms or from legacy platforms to the cloud. We're an interesting position. >> So similar to the file sharing. File virtualization company >> The Copier. >> Copier yeah. >> It puts us in an interesting position. They need to be friends with us and at the same time I'm sure that they're concerned about the capabilities we have but we have a number of retail customers for instance that have asked us to move down from Amazon to Google BigQuery, which we accommodate and because we can do that non-disruptively. The cost and the ability to move is eliminated. It gives customers true freedom of choice. >> How worried are you, that AWS tries to replicate what you guys do. You're in their sights. >> I think there are technical, legal and structural barriers to them doing that. The technical is, this team has been at it for six and a half years. So to do what we do, they'll have to do what we've done. Structurally from a business perspective if they could, I'm not sure they want to. The way to think about Amazon is, they're no different than Teradata, except for they want the same vendor lock-in except they want it to be the Amazon Cloud when Teradata wanted it to be, their data warehouse. >> They don't promote multi-cloud versus-- >> Yeah, they don't want multi-cloud they don't want >> On Prem >> Customers to have a freedom of choice. Would they really enable a heterogeneous abstraction layer, I don't think they would nor do I think any of the big guys would. They all claim to have this capability for their system. It's like the old IBM adage I'm in prison but the food's going to get three squares a day, I get cable TV but I'm in prison. (laughing) >> Awesome, all right, parting thoughts. >> Parting thoughts, oh geez you got to give me a question I'm not that creative. >> What's next, for you guys? What should we be paying attention to? >> I think you're going to see some significant announcements in September regarding the company and relationships that I think will validate the impact we're having in the market. >> Give you some leverage >> Yeah, will give us, better channel leverage. We have a major technical announcement that I think will be significant to the marketplace and what will be highly disruptive to some of the people you just mentioned. In terms of really raising the bar for customers to be able to have the freedom of choice without any sort of vendor lock-in. And I think that that will create some counter strike which we'll be ready for. (laughing) >> If you've never heard of AtScale before trust me you're going to in the next 18 months. Chris Lynch, thanks so much for coming on theCUBE. >> It's my pleasure. >> Great to see you. All right, keep it right there everybody we're back with our next guest, right after this short break you're watching theCUBE from MIT, right back. (upbeat music)

>> from San Matteo. It's the Cube covering Scaler. Innovation Day. Brought to You by Scaler >> The Run Welcome to this special on the Ground Innovation Day. I'm John for a host of The Cube. We're here at scale. His headquarters in San Mateo, California Hardest Silicon Valley. But here the cofounder and CEO Steve, It's Irwin Ski and Jeff Low product marketing director. Thanks for having us. Thanks for having us. Thank you. But a great day so far talked Teo, the other co founders and team here. Great product opportunity. You guys been around for a couple of years, Got a lot of customers, Uh, just newly minted funded syriza and standard startup terms. That seems early, but you guys are far along, you guys, A unique architecture. What's so unique about the architecture? >> Well, thinks there's really three elements of the architecture's designed that I would highlight that differentiates us from our competitors. Three things that really set us apart. I think the biggest the 1st 1 is our use of a common our database. This is what allows us to provide a really superior search experience even though we're not using keyword indexing. Its purpose built for this problem domain and just provides us with great performance in scale. The second thing I would highlight would be the use of well, essentially were a cloud native solution. We have been architected in such a way that we can leverage the great advantage of cloud the scale, ability that cloud gives you the theological city. That cloud gives you andare. Architecture was built from the ground up to leverage that, uh and finally I would point out the way that we do our data. Um, the way that we don't silo data by data type, essentially any type of observe ability, data, whether it's logs or tracing or metrics. All that data comes into this great platform that we were in that provides a really great superior query performance over, >> and we talked earlier about Discover ability. I want to just quickly ask you about the keyword indexing and the cloud native. To me, that seems to be a two big pieces because a lot of the older all current standards people who are state of the art few years ago, 10 years ago, keyword index thing was a big part of it, and cloud native was still emerging except for those folks that were born the clouds. So >> this is a dynamic. How important is that? Oh, it's It's just critical. I mean, here, when we go to the white board, I love to talk about this in a little more detail in particular. So let's let's talk about keyword indexing, right? Because you're right. This is a lot of the technology that people leverage right now. It's what all of our competitors do in keyword indexing. Let's let's look at this from the point of view of a log ingestion pipeline. So in your first stage, you have your input, right? You've got your raw logs coming in. The first thing you do after that typically is parse. You're goingto parse out whatever fields you want from your logs. Now, all of our competitors, after they do that, they do in indexing step. Okay, this has a lot of expense to it. In fact, I'm going to dig into that after the log content is index. It's finally available for search. Where will be returned as a search result. Okay, this one little box, this little index box actually has a lot of costs associated with it. It contributes to the bloat of storage. It contributes to the cost of the overall product. In fact, that's why I love our competitors. Charge you based on how much you're indexing now, even how much you're ingesting. When you look at the cost for indexing, I think you can break it down into a few different categories. First of all, building the index. There's certain costs with just taking this data, building the index and storing it. Computational storage, memory, everything okay, But you build the index in order to get superior query performance, Right? So that kind of tells you that you're going to have another cost. You're going tohave an optimization cost. Where the index is that you're building are dependent on the queries that your users want to conduct, right, because you're trying to make sure you get as good of query performance as possible. So you have to take a look at the career. Is that your user performing the types of logs that you're coming in and you have to decide what indexing that you want to do? Okay. And that cost is shouldered by the burden of the customers. Um, okay, but nothing static in this world. So at some point your logs are going to change. The type of logs here in Justin is going to change. Maybe your query is goingto change. And so you have another category of costs, which is maintenance, right? You're going to have to react to changes in your infrastructure. It's used the type of logs you're ingesting, and basically, this is just creates a whole big loop where you have to keep an eye on your performance. You have to be constantly optimizing, maintaining and just going around in the circle. Right? And for us, we just thought that was ridiculous because all this costs is being born by the customer. And so when we designed the system, we just wanted to get rid of that. >> That's the classic shark fin. You see a fin on anything great whites going to eat you up or iceberg. You see that tip you don't see what's underneath? This seems to be the key problem, because the trend is more data. New data micro services gonna throw off new data type so that types is going up a CZ. Well, that's what that does that consistent with what you got just >> that's consistent. I mean, what we hear from our customers is they want flexibility, right? These are customers that are building service oriented, highly scalable applications on top of new infrastructure. They're reacting to changes everywhere, so they want to be able to not have to, you know, optimize their careers. They're not goingto want to maintain things. They just want to search product that works. That works over everything that they're ingesting. >> So, good plan. You eliminate that fly wheel of cost right for the index. But you guys, you were proprietary columnist, Or that's the key on >> your That's a Chiana and flexibility on data types. Yes, it does. And here, let me draw a little something to kind of highlight that because, you know, of course, it's a it begs the question. Okay, we're not doing keyword indexing. What do you do? What we do actually is leverage decades of research and distribute systems on commoner databases, and I'll use an example on or two >> People know that the data is, well, that's super fast, like a It's like a Ferrari. >> Yes, it's a fryer because you're able to do much more targeted essentially analysis on the data that you want to be searching over, right? And one way to look at this is, uh, no, Let's take a look at ah, Web access lock. Okay. And when we think about this and tables, we think that each line in the table represents, ah, particular entry from the access log. Right. And your columns represent what fields you've extracted. So for example, one the fields you might extract is thie HP status code. You know, Was it, um, a success or not? Right. Or you might have the your eye, or you might have the user agent of the incoming web request. Okay. Now, if you're not using a commoner database approach to execute a quarry where you're trying to count the number of non two hundreds that you've your Web server has responded with, you'd have to load in all the data for this >> table, right? >> And that's just its overkill in a commoner database. Essentially, what you do is you organize your data such that each column essentially has saved as a separate file. So if I'm doing a search where I just want to count the number of non two hundreds. I just have to read in these bites. And when your main bottleneck, it's sloshing bites in and out of Main Ram. This just gives you orders of magnitude better performance. And we've just built this optimize engine that does essentially this at its core and doesn't really well, really fast leveraging commoner database technology. >> So it lowers the overhead. You have to love the whole table in. That's going to take time. Clearing the table is going to take time. That seems to be the update. That's exactly right. Awesome, right? Okay. All right, Jeff. So you're the director of product marketing. So you got a genius pool of co founders here? Scaler. Been there, done that ball have successful track records as tech entrepreneurs, Not their first rodeo, making it all work. Getting it packaged for customers is the challenge that you guys have you been successful at it? What does it all mean? >> Yeah, it essentially means helping them explore and discover their data a lot more effectively than they happen before, you know, With applications and infrastructure becoming much more complex, much more distributed, our engineering customers are finding it increasingly difficult to find answers And so all of this technology that we've built is specifically designed to help him do that at much greater speed, Much greater ease, much more affordably and at scale. We always like to say we're fast, easy, affordable, at scale. >> You know, I noticed in getting to know you guys and interviewing people around around company. The tagline built by engineers for engineers is interesting. One. You guys are all super nerdy and geeky, so you get attacked and you take pride in the tech in the code. But also, your buyers are also engineers because they're dealing with cloud Native Wholenother Dev ops, level of scale where they love scale people in that market love infrastructures code. This is kind of the ethos of that market, but speed scale is what they live for, and that's their competitive advantage in most cases. How do you hit that point there? What's the alignment with the customers on scale and speed? >> Yeah, you know, with the couple of things that Stephen had mentioned, you know, the columnar database on DH, he mentioned cloud native. We like to refer to that as massively parallel or true multi tendency in the cloud those 11 two things give us really to key advantages when it comes to speed. So speed on in just that goes back to what Steven was talking about with the column. In our database, we're not having a weight to build the index so weakening unjust orders of magnitude faster than traditional solutions. So whereas a conventional solution might taking minutes even up to hours to ingest large sets of data, we can literally do it in seconds. It's the data's available immediately for used in research. One of our customers, in fact, that I'm thinking of down Australia actually uses our live tail because it actually works and as they push code out to production that can actually monitor what happens and see if the changes are impacting anything positively or negatively >> and speed two truths, a tagline the marking people came up with, which is cool. I love that kind of our fallouts. We have to get the content out there and get that let the people decide. But in your business, ingestion is critical. Getting the ingestion to value time frame nailed down is table stakes. People engineers want to test stuff. It doesn't work out of the box we ingest and they don't see value. They're not gonna kind of be within next levels. Kind of a psychology of the customer. >> Yeah, You know, when you're pushing code, you know, on an hourly basis, sometimes even minutes now, the last thing you want to do is wait for your data to analyse it, especially when a problem occurs. When a problem occurs and it's impacting a customer or impacting your overall business. You immediately go into firefighting mode, and you just can't wait to have that data become available so that speed to ingest becomes critical. You don't want to wait. The other aspect on the speed topic is B to search. So we talked about the types of searches that are calling. Our database affords us a couple that, within massively parallel and true multi tendency approach, basically means that you could do very, very ad hoc searches extremely quickly. You don't have to bill the keyword index. You don't have to have two, even build a query or learn how to build queries on DH, then run and then wait for it. And maybe in the meantime, wait to get a coffee or something like that. >> I mean, we grew up in Google search. Everyone who's exactly the Web knows what searches and discoveries kind the industry word in discovering navigation. But one of the things about searches about that made Google say Greg was relevance. You guys seem to have that same ethos around data discover, ability, speed and relevance. Talk about the relevance piece, because I think that, to me is what is everyone's trying to figure out as more data comes in? You mentioned some of the advantages Steven around, you know, complexity around data types. You know, Maur data types are coming on, so Relevance sees, is what everyone's chasing. >> So one of >> the things that I think we are very good at is helping people discover what is relevant. There are solutions out there. In fact, there's a lot of solutions out there that will focus on summarizing data, letting you easily monitor with a set of metrics, or even trace a single transaction from point A to point B through a set of services. Those are great for telling you that there is a problem or that problem exist. Maybe in this one service, this one server. But where we really shine is understanding why something has happened. Why a problem has occurred. And the ability to explore and discover through your data is what helps us get to that relevancy. >> Ameren meeting Larry and Sergey back into 1998. And you know, from day one it's fine. What you looking for him? And they did their thing. So I want to just quickly have you guys explain it. I think one thing that also has come up love to get your take on it, guys, is multi tendency urine in the clouds to get a lot of scale. We're out of resource talk about the debt. Why multi tendency is an important piece and what does that specifically mean? But the customer visa be potentially competitive solutions. And what do you guys bring for the tables? That seems to be an important discussion Point >> sure know. And it is one of the key piece of our architecture. I mean, when we talk about being designed for the cloud, this is a central part of that right? When you look at our competitors, for the most part, a lot of them have taken existing open the source off the shelf technologies and kind of taking that and shoved it into this, you know, square hole of, you know, let's run in the cloud, right? And so they're building. These SAS services were essentially they pretend like everyone's got access to a lot. Resource is but under the covers there, sitting there, spinning up thes open source solutions. Instances for each of the customers each of these instances are on ly provisioned with enough ramsi pew for that customer's needs, right? And so heaven forbid you try to issue more crews than you normally do or try to use Mohr you know, storage than you normally do, because your instance will just be capped out, right? Um, and also it's kind of inefficient in that when your users aren't issue inquiries, those CPU and RAM researchers are just sitting there idle instead, what we've done is we've built a system where we essentially have a big pool of resource is we have a big pool of CPU, a big pool of ram, a big pool of disc. Everyone comes in, get access to that, so it doesn't matter what customer you are. Your queries get full access to all these si pues that we have run around right? And that's that's the core of multi tendency is that we're able to not provision for just one look for each individual customer. But we have a big pool of resource is that everyone gets the >> land that's gonna hit the availability question on. And it's also have a side effect for all those app developers who want to build a I and stuff used data and build these micro services systems. >> They're going to get >> the benefit because you have that closed loop. Are you fly? Will, if you will. >> Yeah, yeah, the fight could just add the multi tendency really gives us a lot of economies of scale, both from, you know, the over provisioning and the ability to really effectively use resources. We also have the ability to pass those savings on to our customers. So there's that affordability piece that I think is extremely important. Find answers, this architectural force that >> Stephen I want to ask you because, you know, I know the devil's work pretty well. People are they're hard core, you know. They build their own stuff. They don't want us, have a vendor. Kuo. I can do this myself. There's always comes up there. But this use cases here. You guys seem to be doing well in that environment again. Engineering led solution, which I think gives you guys a great advantage. But what's the How do you handle the objection when you hear someone say, Well, I could do it. Just go do it myself. >> What I always like to point at is, yes, you can up to a decree, right? We often hear people that use open source technologies like elk. They can get that running and they can run it up to a certain scale like a you know, tens of gigabytes per day of logs. They're fine, right? But with those technologies, once it goes above a certain scale, it just becomes a lot more difficult to run. It's one those classic things you know, getting 50% of the way. There is easy getting 80% of the way. There is a lot harder. Getting 100% is almost impossible, right? And you, as whatever company that that that you're doing whatever product you're building, do you really want to spend your engineer? Resource is pushing through that curve, getting 80%. 100% of kind of good, a great solution. No, what we always pitches like Look, we've always solve these problems. These hard problems for this problem, too may come and leverage our technology. You don't have to spend your engineering capital on that. >> And then the people who are doing that scale that you guys provide, they want, they need those engineering resource is somewhere else. So I have to ask, you just basically followed question. Which is how does the customer know whether they have a non scaleable for scaleable solution? Because some of these SAS services air masquerading as scaleable solutions. >> No, they are. I mean, we we actually encourage our customers when they're in the pre sale stage to benchmark against us. We have ah customer right now that sending us terabytes of data per day as a trial just to show that we can meet the scale that they need. We encourage those same customers to go off and ask the other competitors to do that. And, you know, the proof is in the pudding. >> And how's the results look good? Yeah. So bring on the ingest Yes, that's that's That's the sales pitch. Yes, guys, thanks so much for sharing the inside. Even. Appreciate it, Jeff. Thanks for sharing. Appreciate it. I'm John for the Cube. Here for a special innovation Days scales >> headquarters in the heart of >> Silicon Valley's sent Matteo California. Thanks for watching.

from San Mateo its the cube covering scaler innovation day brought to you by scaler Livan welcome to the special innovation day with the cube here in San Mateo California heart of Silicon Valley John for the cube our next guest as Steve Newman the co-founder scaler congratulations thanks for having us you guys got a great company here Thanks yeah go ahead glad to have you here so tell the story what's the backstory you guys found it interesting pedigree of founders all tech entrepreneurs tech tech savvy tech athletes as we say tell the backstory how'd it all start and had it all come together so I also traced the story back to I was part of the team that built the original Google Docs and a lot of the early people here at scaler either were part of that Google Docs team or you know they're people we met while we were at Google and really scaler is an outgrowth of the it's a solution to problems we were having trying to run that system at Google you know Google Docs of course became part of a whole ecosystem with Google Drive and Google sheets and there's that you know all these applications working together it's a very complicated system and keeping that humming behind the scenes became a very complicated problem well congratulate ago Google Docs is used by a lot of people so been great success scale is different though you guys are taking a different approach than the competition what's unique about it can you share kind of like the history of where it's going and where it came from and where it's going yeah so you know maybe it'd be helpful like just to kind of set the context a little bit to the blackboard yeah so you know I you know I talked about it's kind of probably put a little flesh on what I was saying about you know there's a very complicated system that we're trying to run in the whole Google Drive ecosystem too there are all these trends in the industry nowadays you know the move to the cloud and micro services and kubernetes and serverless and can use deployment is all everything like these are all great innovations makes you know people are building more complex applications they're evolving faster but it's making things a lot more complicated and to make that concrete imagine that you're running an e-commerce site back in the calm web 1.0 era so you're gonna have a web server maybe a patchy you've got a MySQL database behind that with your inventory and your shopping carts you may be an email gateway and some kind of payment gateway and that's about it that's your that's your system each one of these pieces involved you know going to Fry's buying a computer driving it over the data center slotting it into a rack you know a lot of sweat went into every one of those boxes but there's only about four boxes it's your whole system if you wanted to go faster you threw more hardware at it more ram exactly and like and you know not literally through but literally carried you literally brought in more hardware and so you know took a lot of work just to do the you know that simple system fast forward a couple of decades if you're running uh running an e-commerce site today well you know you're certainly not seeing the inside of a data center you know stripe will run the payments for you you know somebody's on will run the database server and say you know like this is much much you know you know one guy can get this going in an afternoon literally but nobody's running this today this is not a competitive operation today if you're an e-commerce today you also have personalization and advertising based on the surf service history or purchase history and you know there's a separate flow for gifts and you know then printing the you know interfacing to your delivery service and and you know you've got 150 blocks on this diagram and maybe your engineering team doesn't have to be so much larger because each one of those box is so much easier to run but it's still a complicated system and trying to actually understand what's working what's not working why isn't it working and and tracking that down and fixing it this is the challenge day and this and this is where we come in and that's the main focus for today is that you can figure it out but the complexity of the moving parts is the problem exactly so you know and so you see oh you know 10% of the time that somebody comes in to open their shopping cart it fails well you know the problem pops out here but the the root cause turns out to be a problem with your database system back here and and figuring that out you know that's that's the challenge okay so with cloud technology economics has changed how is cloud changing the game so it's interesting you know changes changes the game for our customers and it changes the game for us so for a customer you know kind of we touched on this a little bit like things are a lot easier people run stuff for you you know you're not running your own hardware you're not you know you're often you're not even running your own software you're just consuming a service it's a lot easier to scale up and down so you can do much more ambitious things and you can move a lot faster but you have these complexity problems for us what it presents an an economy of scale opportunity so to you know we step in to help you on the telemetry side what's happening in my system why is it happening when did it start happening what's causing it to happen that all takes a lot of data log data other kinds of data so every one of those components is generating data and by the way for our customers know that they're running a hundred and 50 services instead of four they are generating a lot more data and so traditionally if you're trying to manage that yourself running your own log management cluster or whatever solution you know it's a real challenge to you as you scale up as your system gets more complex you've got so much data to manage we've taken an approach where we're able to service all of our customers out of a single centralized cluster meaning we get an economy of scale each one of our customers gets to work with a basically log management engine that's to scale to our scale rather than the individual customers scale so the older versions of log management had the same kind of complexity challenges you just drew a lot ecommerce as the data types increase so does their complexity is that so the complexity increases and but you also get into just a data scale problem you know suddenly you're generating terabytes of data but you don't you know the you only want to devote a certain budget to the computing resources that are gonna process that data because we can share our processing across all of our customers we we fundamentally changed economics it's a little bit like when you go and run a search and Google thousands literally thousands of servers in that tenth of a second that Google is processing the query 3,000 servers on the Google site may have been involved those aren't your 3,000 servers you know you're sharing those with you know 50 million other people in your data center region but but for a millisecond there those 3,000 servers are all for you and that's that's a big part of how Google is able to give such amazing results so quickly but in still economically yeah economically for them and that's basically on a smaller scale that's what we're doing is you know taking the same hardware and making it all of it available to all of the customers people talk about metrics as the solution to scaling problems is that correct so this is a really interesting question so you know metrics are great you know basically the you know if you look up the definition of a metric it's basically just a measurement on number and you know and it's a great way to boil down you know so I've had 83 million people visit my website today and they did 163 million things in this add mirror and that's you can't make sense of that you can boil it down to you know this is the amount of traffic on the site this was the error rate this was the average response time so these you know these are great it's a great summarization to give you an overall flavor of what's going on the challenge with metrics is that they tend to measure they can be a great way to measure your problems your symptoms sites up it's down it's fast its slow when you want to get to then to the cause of that problem all right exactly why is the site now and I know something's wrong with the database but what's the error message and what you know what's the exact detail here and a metric isn't going to give that to you and in particular when people talk about metrics they tend to have in mind a specific approach to metrics where this flood of events and data very early is distilled down let's count the number of requests measure the average time and then throw away the data and keep the metric that's efficient you know throwing away data means you don't have to pay to manage the data and it gives you this summary but then as soon as you want to drill down you don't have any more data so if you want to look at a different metric one that you didn't set up in advance you can't do it and if you need to go into the the details you can't do an interesting story about that you know when you were at Google you mentioned you the problem statements came from Google but one of things I love about Google is they really kind of nailed the sre model and they clearly decoupled roles you know developers and site reliability engineers who are essentially one-to-many relationship with all the massive hardware and that's a nice operating model it's had a lot of efficiencies was tied together but you guys are kind of saying in a way that does developers use the cloud they become their own sres in a way because this cloud can give them that kind of Google like scale and in smaller ways not like Google size but but that's similar dynamic where there's a lot of compute and a lot of things happening on behalf of the application or the engineers developer as developers become the operator through their role what challenges do they have and what do you see that happening because that's interesting trim because as applications become larger cloud can service them at scale they then become their own sres what yeah well how does that roll out most how do you see that yes I mean and so this is something we see happening at more and more of our customers and one of the implications of that is you have all these people these developers who are now responsible for operations but but they're not special you know they're not that specialist SRE team they're specialists in developing code not in operations they're you know they they minor in operations and and they don't think of it as their real job you know that's the distraction something goes wrong all right they're they're called upon to help fix it they want to get it done as quickly as possible so they can get back to their real job so they're not gonna make the same mental investment in becoming an expert at operations and an expert at the operations tools and the telemetry tools you know they're not gonna be a log management expert on metrics expert um and so they need they need tools that have a gentle learning Kurt have a gentle learning curve and are gonna make it easy for them to get Ian's not really know what they're doing on this side of things but find an answer solve the problem and get back out and that's kind of a concept you guys have of speed to truth exactly so and we mean a couple of things by that sort of most literally we our tool is it's a high performance solution you you hand us your terabytes of log data you ask some question you know what's the trend on this error in this service over the last day and we you know we give you a quick answer Big Data scan through a give you a quick answer but really it's you know that's just part of the overall chain of events which goes from the you know the developer with a problem until they have a solution so they they have to figure out even how to approach the problem what question to ask us you know they have to pose the query and in our interface and so we've done a lot of work to to simplify that learning curve where instead of a complicated query language you can click a button get a graph and then start breaking down that just visually break that down which okay here's the error rate but how does that break down by server or user or whatever dimension and be able to drill down and explore in a you know very kind of straightforward way how would you describe the culture at scaler I mean you guys been around for a while you still growing fast growing startup you haven't done the B round yet got any you guys self-funded it got customers early they pushed you again now 300 plus customers what's the culture like here so you know it's been this has been a fun company to build in part because you know we're into you know the the heart of this company is the engineering team our customers our engineers so you know we're kind of the kind of the same group and that keeps the you know it kind of keeps the inside in the outside very close together and I think that's been a part of the culture we've built is you know we all know why we're building this what it's for you know we use scalar extensively internally but you but even you know even if we weren't we're it's the kind of thing we've used in the past and we're gonna use in the future and so you know I think people are really excited here because you know we understand why and you have an opinion of the future on how it should roll out what's the big problem statement you guys are solving as a company what's it how would you boil that down if asked so by a customer and engineer out there what real problem are you solving that's core problem big problem that's gonna be helping me you know at the end of the day it's giving people the confidence to keep you know building these kind of complicated systems and move quickly because because and this is the business pressure everyone is under you know whatever business you're in it has a digital element and your competitors are in the same you know doing the same thing and they are building these sophisticated systems and they're adding functionality and they're moving quickly you need to be able to do the same thing but it's easy then to get tangled up in this complexity so at the end of the day you know we're giving people the ability to understand those systems and and and the functionality and the software's getting stronger and stronger more complicated with service meshes and micro services as applications start to have these the ability to stand up and tear down services on the fly that's so annoying and they'll even wield more data exact you get more data it gets more complicated actually if you don't mind there's a little story I'd like to tell so hold on just will I clear this out this is going back back to Google and again you know kind of part of the inspiration of you know how he came to build scalar and this doesn't be a story of frustration of you know probably get ourselves into that operation and motivation yep so we were we were working on this project it was building a file system that could tie together Google Docs Google sheets Google Drive Google photos and the black diagram looks kind of like the thing I just erased but there was one particular problem we had that took us months and literally months and months and months to track down you know you'd like to solve a problem in a few minutes or a few hours but this one took months and it had to do with the the indexing system so you have all these files in Google Drive you wanna be able to search and so we had modeled out how we were gonna build this or this search engine you'd think you know Google searches a solve problem but actually so Google web search is four things the whole world can see there's also like Gmail search which is four things that only one person can see so it's lots of separate little indexes those are both solve problems at Google Google Drive is for things a few people can see you share it with your coworker or your whoever and it's actually a very different problem and but we looked at the statistics and we found that the average document our average file was shared with about 1.1 people in other words things were mostly private or maybe you share with one or two people so we said we're just gonna make if something's shared to three people we're just gonna make three copies of it and then now we have just the Gmail problem each copy is for one person and we did the math on how how much work is this going to be to build these indexes and in round numbers we were looking at something like at the time this would be so much larger now but at the time we had maybe one billion documents and files in the system each one was shared to about 1.1 people maybe it was a thousand words long on average and maybe it would change be edited once per day on average so we had about a trillion word updates per day if you multiply all that together and so we allocate it we put in a request and purchase machines to handle that much traffic and we started bringing up the system and immediately collapsed it was completely overloaded and we checked our numbers and we check them again yeah 1.1 about a billion whatever and but then work into the system with just way beyond them and we looked at our metrics so you know measuring the number documents measuring each of these things all the metrics looked right to make a month's long story short these metrics and averages were hiding some funny business there turned out there was this type of use case read of occasional documents that were shared to thousands of people and one of there was a specific example it was the signup sheet for the Google company picnic this is a spreadsheet it was shared to about 5,000 people so it wasn't the whole company but you know a big chunk of Mountain View which meant it was I don't know let's say 20 thousand words long because it had you know the name and a couple other things for each person this is one document but shared to 5,000 people and you know during the period people were signing up maybe it was changing a couple thousand times per day so you multiply out just this document and you get 200 billion word updates for that one document in a day where we're estimating a trillion for the whole earth and so there was something like a hundred documents in this kid Google was hamstringing your own thing we were hamstrung our own thing there were about a hundred examples like this so now we're up to 20 trillion and like that was the whole problem these hundred files and we would have never found that until we got way down into the details of the the logs which in this two months just took month so because we didn't have the tools because we didn't have scaler yeah and I think this is the kind of anomaly you might see with Web Services evolving with micro services which someone has an API interface with some other SAS as apps start to rely on each other this is a new dynamic we're seeing as SLA s are also tied together so the question is whose fault is it exactly you have to whose fault is it and also things get so much more varied now you know again web 1.0 e-commerce you buy a thing you buy a thing that's all the same now you're building a social media site or whatever you've got 8 followers you've got 8 million followers this person has three movies rented on Netflix this person has three thousand movies everything's different and so then you get these funny things hiding yeah you're flying blind if you don't get all the data exposed it's like it's like you know blind person trying to read Braille as we heard earlier see if thanks so much for sharing the insight great story I'm John furry you're here for the q4 innovation day at scalers headquarters thanks for watching

(upbeat music) >> From San Mateo, it's theCUBE, covering Scalyr Innovation Day, brought to you by Scalyr. >> Hello and welcome to the special Cube Innovation Day here in Silicon Valley in San Mateo, California at Scalyr's Headquarters. I'm John Furrier, host of theCUBE. John Hart's the Tech Lead Back End Engineering here at Scalyr. Thanks for having us. >> Thanks for having me John. >> So what's the secret sauce at Scalyr? You guys have unique differentiate as we have covered with some of your peers and the founders are all talking about it. But, you guys have a unique secret sauce. Take a minute to explain that. >> I think, yeah, it's a few different things. First of all, you've got just the design level, which is we don't use keyword indexes. So that's a big one right there off the top. On top of that, you've got a couple of different implementation paths. We've got our own custom written data store. So we're able to really control all the way down to the bytes on disk, how we lay things out, optimize for speed. We have a novel kind of scatter-gather approach for fanning out a query, to make sure we can get all of our nodes involved as quickly as possible. Then, finally, and this is just kind of being smart, which is we have a time series database for repetitive queries and that's on demand. You don't have to do anything, but we're going to speed up your queries in the background if we know it's a good idea. >> Talk about the time series. I think that's interesting because that comes to play. We hear about real time a lot. We talk a lot about in cyber security that time series has been beneficial. Where does time series fit for you guys in here? >> That's a good question. I think one of the big differences with Scalyr versus other uses of time series database is with Scalyr you're outputting your logs, there's all kinds of information in your logs. Some of that might be a good thing to put in a time series database, but I think with a lot of other products, you would have to decide that ahead of time. Like, hey, let's get this metric into the database. With Scalyr, the moment you have anything in your logs that you might want to put into a time series you just start querying it. You put in a dashboard (snaps) you've got a time series. So we're going to back propagate that for everything you've already given us. So all of those queries are fast from there on out. >> So it's built in from the beginning. >> Exactly, and you don't have to do anything. It's just on demand. >> So keywords been what other people have been used for years. That's been standard for these log management software packages and indexes. Indexes can slow things down. We've got a tutorial on that. Why is those two areas, haven't been innovated in awhile? When people just haven't figured it out, you guys have first? What's the differentiation for you guys? Why'd you guys get there? >> I think the main reason is that log data is just fundamentally different than most other things that you might use a database for. There's a couple of different reasons for that. So with log data, you're not in control of it. You can't design it. You know, an index is great if you're making a relational database. You've got control of your columns. You know what you're going to join on. You know what you want to index. Nobody designs their logs like they design their database tables. It's just a bunch of stuff. It's from systems you don't control. It's changing all the time. So just the number of distinct fields that you would have to index is really, really high. So if your system depends on indexing for good performance, you're going to have to make a lot of indexes. And indexes, of course, they're right amplifying. If you've got one gigabyte of raw data, then you've got to put five or six hundred indexes on top of it. You're going to have five or ten gigabytes of raw plus index data. That means you got to do a lot more IO, and at the end of the day, how much you have to read from disk, determines how fast your query's going to be. >> So, in essence indexes creates a lot of overhead. You shouldn't even need to do because of the nature of log files. >> Because the nature of log data, it's overhead that doesn't serve log data very well, yeah. >> And what about the log data that's changing? Cause one of the things we're seeing, Internet of Things, more connected devices, imagine the Teslas that are going to be connecting in, with all their data. >> Right >> All this stuff, cameras. You've got a huge amount of new kind of data. Up, down, status. This is going to be a tsunami of new types of log data. >> Yeah, and none of it are you going to have a ton of control over. Right, it's going to be changing a ton. Maybe you've got 20 different versions of devices out there that are all sending you different versions of logs. You've got to be able to handle all of it. So you want a system that is adaptive to your needs as they come up, as opposed to something you have to plan out with indexes ahead of time. >> So if someone asks you, say you guys say you're faster. Why? Is that true? Is the statement you're faster than others, and if so why? >> It is true. (laughs) And that really comes down to the secret sauce. The brute force, the key to brute force, and I think we've talked about this a little bit today, is you got to bring a lot of force, as quickly as you possibly can. And we do that. We've got a lot of custom code. We're not using off-the-shelf components. We're trying to get that time quick as we can. So I think our median performance is still better than 100 milliseconds. That might be for a query that's talking to two or three hundred machines, or maybe even more. All of which, to get, maybe it's going to scan a terabyte of data. All of that is going to come back within 100 milliseconds. It's extremely fast. >> Talk about why log data is different from other data types, for folks that are in these cloud native environments. Their time is precious. They are looking at a lot of different data. How is log data different? >> I think the fact that it's dynamic in terms of what's coming out is something new. It changes so rapidly. The other really big thing too is the way you query it changes from day to day. Most of the time you're going to your logs, you're trying to troubleshoot a problem. Today's problems are different than yesterdays problems. So every time you go in, you're using it in a different way. So it has to be very fast. It has to be exploratory. And that's one of the big things about Scalyr's speed. Is it enables this really exploratory. You can kind of move through the data quickly, as opposed to making a query, getting a cup of coffee, waiting for the query, and then deciding what you're going to do next. I'm kind of dating myself here, but it's like the first time you ever used Google. You're like, "Whoa, how did that happen?" That's what it's like the first time you use Scalyr. >> And you guys have a unique architecture, we talked about that. You guys have certain speeds. But it's not just the query speed. It's the time it takes to do the query. So you factor in a much bigger perspective than if someone has to build a query and then takes 15 minutes. >> Right. >> Game's over. >> Yeah, and instead you're just clicking on things. We're trying to make it very easy for you to move from oh here's an alert. Well here are the log files that caused that alert. Oh, what's the thread stack for that particular lock. Oh, I can go and look at everything else that happened in that thread. That's five or 10 seconds of Scalyr tops. >> You guys have unique engineering culture, that targets engineers, products built by engineers, for engineers. >> Yep. >> Great story. And it's real, and you guys building it everyday. What is the engineer threshold of pain when it comes to locked data? Have you seen any anecdotal, I mean, 'cause engineers that are in this space, they need access to it. There's SLAs now tied to it. People are sharing data. There's all kind of new ways, reasons why you need to have the Scalyr solution. But what's the pain point for most people to tolerate an inferior solution? >> Well for me, I actually have an answer for this. Right, because before I was Scalyr employee, I was a Scalyr customer and before I was a Scalyr customer, I was a Splunk customer. I used Splunk for about five years before I think Scalyr even necessarily existed and I was really happy with it because I needed it. Right? I had my own company. We were generating tons of logs. My support guys needed to use those logs. And, prior to using something like a Splunk, I was SSHing it to servers to check the log files, which is of course, not scalable. So I was really happy with the product as an idea existed, but it just kept gnawing at us. You know, every time we would query, sometimes it would be fast, sometimes it would be really slow. Sometimes the results would be down because an indexing server was down. It was just. >> You mean the Splunk solution? >> Yeah, the Splunk solution. Yeah, it was just extremely painful. So I read, actually, one of the blog posts written by Steve Newman and thought, that's a great idea. That is how you should attack this problem. No indexes. Brute forces. All the flexibility you get from that. I loved it and then I forgot about it for like six months. (laughs) Because I was busy, right. But then six months later I was really frustrated again with Splunk again being really, really slow, and I thought, what was the name of that company again? I looked them up. I installed it. And within, certainly within a day, I was blown away by the performance. Within a week, I had uninstalled Scalyr, excuse me, Splunk, from every single one of my servers and switched to Scalyr instead. >> And you're happy with that? Does it work for you? Came to join the company? >> Yeah, exactly. In kind of conversations with the support team here, I was one of their early customers to use Windows, so I had a lot of questions, they had questions for me, how did I get it working, it wasn't a supported platform. And all of my emails were responded to by two guys named Steve. So I figured that was probably the support team. Pretty funny they've got a support team of two people, both named Steve. And then at one point, in one email, Steve Newman said to me, "You may have realized there's only two of us here." And that's when I kind of went, "Oh wait, so there's two people total." And two guys I assumed in a basement. They weren't in a basement, but I assumed they were in a basement. They had software that was way better for my needs than Splunk, which at the time was worth probably eight, ten billion dollars. It's a public company. Thousands of engineers. So that's when I thought, "Huh. When I get a chance, "Maybe I should go work with these guys." >> You know it's interesting. Maybe create a new category, brute force as a service. >> Yeah. >> This is what they're doing. They're bringing in the right tool at the right time. >> Yep. >> For the right problem, for speed, and to solve the problem, no? >> Yeah. >> They care how it gets done. >> Get as much data as you can and get that answer back as quickly as you can. >> So this is the big challenge. Final question for you is obviously, you know, a lot of people we talked to in the DevOps world they're really fickle. On one hand, they'll try anything. If they like it, they'll stay with it. But if they don't, you'll know about it. Where's the value point for people to start thinking about Scalyr. Is it ingest to value, ingesting is one part, that's kind of a trial. Where's the value immediately come in? Where do you see, what's the first sign of light value, once the ingestion happens. >> So part of it is this, it's a very short period of time from the ingestion to the time you're querying on it is very, very short. So you got a real time view of what's happening on your servers not a five minutes ago view. That by itself can pay for it right there. If you're a DevOps person and you've got some alarm pinging. If that alarm is from 10 minutes ago, that means your customers are already annoyed. If you're going to have to wait another 10 minutes just to even see what's happening, you've got a really big problem, right. So being able to have the alarm, and you know that's triggering on something that happened a second or two ago, and then immediately being able to dive in with no interruption to your work flow, no reason not to dive in, that's a pretty big one right there. >> So pretty immediate impact. >> Yeah. >> So okay, for people that don't know Scalyr, what should they know about Scalyr as a company from a value proposition as a former customer now, key employee in the back end, and engineering. What is the key things they should know about? >> So speed, we keep talking about it, right? We have a really really good cost basis. Because we're not making those indexes, we don't have to store as much data. It's just generally cheaper for it to run. Right, so we actually have a really good cost point. And we get you from the alerts. You don't have to decide stuff ahead of time. You can do it all on the fly, ad hoc, we get you from the alerts, to your answers as quickly as you possibly can. That's pretty good. >> Every culture has its own unique kind of feature. What's Scalyr's culture here? I mean Intel was Moore's law, Cadence was Moore's law. What's the culture here, at Scalyr like? >> That's a good question. I guess I would say I'm just tremendously proud to be working with these engineers. Right? We're all here because we want to get better and we want to work on really, really hard problems writing our own code, not just running and kind of patching together open source systems that already exist. We want to be doing something cutting edge. So that's I would say the biggest one. >> And big problem's behind that, you've got AI right around the corner. Applying AI is going to be a natural extension. >> Yeah, 'cause we got the data. And can deal with the data. >> Ciao, thanks for the insight. Appreciate it. >> Thank you. Good talking to you. >> John Furrier here. Innovation Day with theCUBE here in Silicon Valley in San Mateo, at Scalyr's headquarters. I'm John Furrier. Thanks for watching. (upbeat music)

from San Mateo its the cube covering scalar innovation day brought to you by scaler but I'm John four with the cube we are here in San Mateo California official innovation day at Skylar's headquarters with Steve Neumann the founder of scalar and Jeff Mathis a software engineer guys thanks for joining me today thanks for having us thanks great to have you here so you guys introduced power queries what is all this about yes so the vision for scalar is to become the platform users trust when they want to observe their systems and power queries is a really important step along that journey power queries provide new insights into data with a powerful and expressive query language that's still easy to use so why is this important so we like to scaler we like to think that we're all about speed and a lot of what we're known for is the kind of the raw performance of the query engine that we've built that's sitting underneath this product which is one measure of speed but really we like to think of speed as the time from a question in someone's head to an answer on their screen and so the whole kind of user journey is part of that and you know kind of traditionally in our product we've we provided a set of basic capabilities for searching and counting and graphing that are kind of very easy for people to access and so you can get in quickly pose your question get an answer without even having to learn a query language and and that's been great but there are sometimes the need goes a little bit beyond that the question that some wants to ask is a little bit more complicated or the data needs a little bit of massaging and it just goes beyond the boundaries what you can do in kind of those basic you know sort of basic set of predefined abilities and so that's where we wanted to take a step forward and you know kind of create this more advanced language for for those more advanced cases you know I love the name power query so they want power and it's got to be fast and good so that aside you know queries been around people know search engines search technology discovery finding stuff but as ai/an comes around and more scales and that the system this seems to be a lot more focus on like inference into intuiting what's happening this has been a big trend what do you what's your opinion on that because this has become a big opportunity using data we've seen you know file companies go public we know who they are and they're out there but there's more data coming I mean it's not like it's stopping anytime soon so what's the what's the innovation that that just gonna take power queries to the next level yes so one of the features that I'm really excited about in the future of power queries is our autocomplete feature we've taken a lot of inspiration from just what your navbar does in the browser so the idea is to have a context-sensitive predictive autocomplete feature that's going to take into account a number of individual the syntactic context of where you are in the query what fields you have available to you what fields you've searched recently those kinds of factors Steve what's your take before we get to the customer impact what's the what's the difference it different what's weird whereas power queries gonna shine today and tomorrow so it's some it was a kind of both an interesting and fun challenge for us to design and build this because you're you know we're trying to you know by definition this is for the you know the more advanced use cases the more you know when you need something more powerful and so a big part of the design question for us is how do we how do we let people you know do more sophisticated things with their logs when the when they have that that use case while still making it some you know kind of preserving that that's speed and ease of use that that we like to think we're known for and and in particular you know they've been you know something where you know step one is go you know read this 300 page reference manual and you know learn this complicated query language you know if that was the approach then you know then we would have failed before we started and we had we have the benefit of a lot of hindsight you know there a lot of different sister e of people manipulating data you know working with these sophisticated different and different kinds of systems so there are you know we have users coming to us who are used to working with other other log management tools we have users or more comfortable than SQL we have users who really you know their focus is just a more conventional programming languages especially because you know one of the constituencies we serve our you know it's a trend nowadays that development engineers are responsible also for keeping their code working well in production so they're not experts in this stuff they're not log management experts they're not you know uh telemetry experts and we want them to be able to come in and kind of casual you know coming casually to this tool and get something done but we had all that context of drawn with these different history of languages that people are used to so we came up with about a dozen use cases that we thought kind of covered the spectrum of you know what would people bring bring people into a scenario like this and we actually game to those out well how would you solve this particular question if we were using an SQL like approach or an approach based on this tool or which based on that tool and so we we did this like big exploration and we were able to boil down boil everything down to about ten fairly simple commands that they're pretty much covered the gamut by comparison you know there are there other solutions that have over a hundred commands and it obviously if it's just a lot to learn there at the other end of the spectrum um SQL really does all this with one command select and it's incredibly powerful but you also really have to be a wizard sometimes to kind of shoehorn that into yeah even though sequels out there people know that but people want it easier ultimately machines are gonna be taking over you get the ten commands you almost couldn't get to the efficiency level simplifying the use cases what's the customer scenario looked like what's that why is design important what's what's in it for the customer yeah absolutely so the user experience was a really important focus for us when designing power queries we knew from the start that if tool took you ten minutes to relearn every time you wanted to use it then the query takes ten minutes to execute it doesn't take seconds to execute so one of the ways we approached this problem was to make sure we're constantly giving the user feedback that starts as soon you load the page you've immediately got access to some of the documentation you need you use the feature if you have type in correct syntax you'll get feedback from the system about how to fix that problem and so really focusing on the user experience was a big part of the yeah people gonna factor in the time it takes to actually do the query write it up if you have to code it up and figure it out that's time lag right there you want be as fast as possible interesting design point radical right absolutely so Steve how does it go fast Jeff how does it go fast what are you guys looking at here what's the magic so let me I'm going to step over to the whiteboard shock board here and we'll so chog in one hand Mike in the other will will evaluate my juggling skills but I wanted to start by showing an example of what one of these queries looks like you know I talked about how we kind of boil everything down to about 10 commands so so let's talk through a simple scenario let's say I'm running a tax site you know people come to our web site and they're you know they're putting their taxes together and they're downloading forms and tax laws are different in every state so I have different code that's running for you know you know people in California versus people in Michigan or whatever and I can you know it's easy to do things like graph the overall performance and error rate for my site but I might have a problem with the code for one specific state and it might not show up in those overall statistics very clearly so I don't know I want to get a sense of how well I'm how I am performing for each of the 50 states so I'm gonna and I'm gonna simplify this a little bit but you know I might have an access log for this system where we'll see entries like you know we're loading the tax form and it's for the state of California and the status code was 200 which means that was successful and then we load the tax form and the state is Texas and again that was a success and then we load the tax form for Michigan and the status was a 502 which is a server error and then you know and millions of these mixing with other kinds of logs from other parts of my system and so I want to pull up a report what percentage of requests are succeeding or failing by state and so let me sketch for it first with the query would look like for that and then I'll talk about how how we execute this at speed so so first of all I have to say what which you know of all my other you know I've drawn just the relevant logs but this is gonna be mixed in with all the other logs for my system I need to say which which logs I care about well maybe as simple as just calling out they all have the this page name in them tax form so that that's the first step of my query I'm searching for tax form and now I want to count these count how many of these there are how many of them succeeded or failed and I want to cluster that by state so I'm gonna clustering is with the group command so I'm gonna say I want to count the total number of requests which is just the count so count is a part of the language total is what I'm choosing to name that and I want to count the errors which is also going to be the count command but now I'm going to give it a condition I want to only count where the status is at least 500 and I rather you can see that but behind the plant is a 500 and I'm gonna group that by state so we're we're counting up how many of these values were above 500 and we're grouping it by this field and what's gonna come out of that is a table that'll say for each state the total number of requests the number of errors oh and sorry I actually left out a couple of steps but so it's but actually let's draw what this would give us so far so it's gonna show me for California maybe I had nine thousand one hundred and fifty two requests thirteen of them were errors for Texas I had and so on but I'm still not really there you know that might show me that California had you know maybe California had thirteen errors and Rodi had 12 errors but only there were only 12 requests for Rhode Island Rhode Island is broke you know I've broken my code for Rhode Island but it's only 12 errors because it's a smaller population so that's you know this analysis is still not quite gonna get me where I need to go so I can now add another command I've done this group now I'm gonna say I'm gonna say let which triggers a calculation let error rate equal errors divided by total and so that's going to give me the fraction and so for California you know that might be 0.01 or whatever but for Rhode Island it's gonna be one 100% of the requests are failing and then I can add another command to sort by the error rate and now my problem states are gonna pop to the top so real easy to use language it's great for the data scientists digging in their practitioners you don't need to be hard core coder to get into this exactly that's the idea you know groups or you know very simple commands that just directly you know kind of match the English description of what you're trying to do so then but you know yeah asked a great question then which is how do we take this whole thing and execute it quickly so I'm gonna erase here you're getting into speed now right so yeah bit like that how you get the speed exactly speed is good so simplicity to use I get that it's now speed becomes the next challenge exactly and the speed feeds into the simplicity also because you know step one for anything any tool like this is learning the tool yeah and that involves a lot of trial and error and if the trial and error involves waiting and then at the end of the wait for a query to run you learn that oh you did the query wrong that's very discouraging to people and so we actually think of speed really then becomes some ease of use but all right so how do we actually do this so you've got you know you'll have your whole mass of log data tax forms other forms internal services database logs that are you got your whole you know maybe terabytes of log data somewhere in there are the the really important stuff the tax form errors as well as all the other tax form logs mixed in with a bigger pile of everything else so step one is to filter from that huge pile of all your logs down to just the tax form logs and for that we were able to leverage our existing query engine and one of the main things that makes that engine there's kind of two things that make that that engine as fast it is as it is it's massively parallel so we we segment the data across hundreds of servers our servers so all this data is already distributed across all these servers and once your databases you guys build your own in-house ok got it exactly so this is on our system so we've already collected we're collecting the logs in real time so by the time the user comes and types in that query we already have the data and it's already spread out across all these service then the you know the first step of that query was just a search for tax form and so that's our existing query engine that's not the new thing we've built for power queries so that existing very highly optimized engine this server scans through these logs this service insula these logs each server does its share and they collectively produce a smaller set of data which is just the tax form logs and that's still distributed by the way so really each server is doing this independently and and is gonna continue locally doing the next step so so we're harnessing the horsepower of all these servers each page I only have to work with a small fraction of the data then the next step was that group command we were counting the requests counting the errors and rolling that up by state so that's the new engine we've built but again it each server can do just its little share so this server is gonna take whichever tax form logs it found and produce a little table of counts in it by state this server is gonna do the same thing so at each produce they're a little grouping table with just their share of the logs and then all of that funnels down to one central server where we do the later steps we do the division divide number of errors by total count and and then sort it but by now you know here we might have you might have trillions of log messages down to millions or billions of messages that are relevant to your query now we here we have 50 records you know just one for each state so suddenly the amount of data is very small and so the you know the later steps may be kind of interesting from a processing perspective but they're easy from a speed perspective so you solve a lot of database challenges by understanding kind of how things flow once you've got everything with the columnar database is there just give up perspective of like what if the alternative would be if we this is like I just drew this to a database and I'm running sequel trillions of log files I mean it's not trivial I mean it's a database problem then it's a user problem kind of combine what's order of magnitude difference if I was gonna do the old way yeah so I mean I mean the truth is there's a hundred old ways know how much pain yes they're healthy you know if you're gonna you know if you try to just throw this all into one you know SQL sir you know MySQL or PostgreSQL bytes of data and and by the way we're glossing over the data has to exist but also has to get into the system so you know in you know when you're checking you know am i letting everyone in Rhode Island down on the night before you know the 15th you need up to the moment information but the date you know your database is not necessarily even if it could hold the data it's not necessarily designed to be pulling that in in real time so you know just sort of a simple approach like let me spin up my SQL and throw all the data in it's it's just not even gonna happen I'm gonna have so now you're sharding the data or you're looking at some you know other database solution or ever in it it's a heavy lift either way it's a lot of extra effort taxing on the developers yeah you guys do the heavy lifting yeah okay what's next where's the scale features come in what do you see this evolving for the customers so you know so Jeff talked about Auto complete which you were really excited about because it's gonna again you know a lot of this is for the casual user you know they're you know they're a power user of you know JavaScript or Java or something you're they're building the code and then they've got to come in and solve the problem and get back to what they think of as their real job and so you know we think autocomplete and the way we're doing it we're we're really leveraging both the context of what you're typing as well as the history of what you and your team have done in queried in the past as well as the content of your data every think of it a little bit like the the browser location bar which somehow you type about two letters and it knows exactly which page you're looking for because it's relying on all those different kinds of cues yeah it seems like that this is foundational heavy-lift you myself minimize all that pain then you get the autocomplete start to get in a much more AI machine learning kicks in more intelligent reasoning you start to get a feel for the data it seems like yeah Steve thanks for sharing that there it is on the whiteboard I'm trying for a year thanks for watching this cube conversation

>> 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

>> from San Matteo. It's the Cube covering scaler. Innovation Day Brought to You by scaler. >> Welcome to this Special Cube Innovation Day. Here in San Mateo, California Scale is headquarters for a coast of the Cube. We're here with two great guests. Claudia Carpenter co founder Andy McAlister, Who's Dev evangelist? Uh, great to have you guys here a chat before we came on. Thanks for having us >> Great to be >> so scaler. It's all about the logs. The answer is in the logs. That's the title of the segment Them. I'll see the log files with a lot of exhaust in their data value extracting that, but it's got more operational impact. What's what's the Why is the answer in the locks? >> Because that's where the real information is. It's one thing to be able to tell that something is going around when your systems, but what is going wrong as engineers, what we tend to do is the old print. If it's like here's everything I can think of in this moment and leave it as breadcrumbs for myself to find later, then I need to go and look at those bread crumbs >> in a challenge. Of course, with this is that logs themselves are proliferating. There's lots of data. There's lots of services inside this logs, so you've gotta be able to find your answers as fast as possible. You can't afford Teo. Wait for something else. T lead you to them. You need to deep dive >> the way you guys have this saying it's the place to start. What does that mean? Why? Why is that the new approach? >> What We're trying to differentiate because there's this trend right now in the Dev Ops world towards metrics because they're much smaller to store it, pre digesting what's going on in your systems. And then you just play a lot of graphs and things like that. We agree with that. You do need to be able to see what's going on. You need to be able to set alerts. Metrics are good, but they only get you so far. A lot of people will go through. Look at metrics, dig through and then they stop, switch over and go to their logs. We like to start with the logs, build our metrics from them, and then we go direct to >> the source. I think a minute explain what you mean by metrics, because that has multiple meanings. Because the current way around metrics and you kind of talked about a new approach. Could you just take a minute? Explain what you meant by metrics and how logs are setting up the measures. The difference there. >> So to me, metrics is just counting things right? So at log files of these long textual representations of what's going on in my system and it's impossible to visually parce that I mean literally 10,000 lines. So you count. I've got five of this one in six of this one, and it's much smaller to store. I've got five of this one and six of this one, but that's also not very much information, so that's really the difference. >> But, you know, we have customers who use their metrics to help them indicate something might be wrong inside of here. The problem is, is that modern environments where we have instant gratification, needs and people you know, we'd be wait five seconds. Basically, it's a law sale online here. You need to know what's went wrong, not just where we went wrong or that something went wrong. So building for the logs to the metrics allows you to also have a perfect time back to that specific entrance ancient entrance that lets you be you out. What was wrong? >> He mention Claudia Death ops. And this is really kind of think of fun market because Dev Ops is now going mainstream and see the enterprise now started to adopt. It's still Jean Kim from Enterprise. Debs estimates only 3% of enterprise really there yet. So the action's on the cloud Native Public Cloud side where it's, you know, full blown, you know, cloud native more services. They're coming to see Cooper Netease things of that nature out there. And these services are being stood up and torn down while the rhythmically like. So with who the hell stores that data? That's the logs. The nature of log files and data is changing radically with Dev ops. I'm certainly this is going to be more complications but developers and figuring out what's what. How do you see that? What's your reaction to that trend? >> Yeah, so Dev Ops is a very exciting thing. At were Google. It was sort of like the new thing is the developers had to do their own operations, and that's where this comes from. Unfortunately, a lot of enterprise will just rename their ops people devil apps, and that's not the same thing. It's literally developers doing operations, Um, and right now that it's never been so exciting as as it is right now in the text axe, because you could get so much that's open source. Pre built glue this all these things together. But since you haven't written the code yourself, you've no way deal which going on. So it's kind of like Braille. You've got to go back and look and feel your way through it to figure out what's going on. And that's where logs come into play. >> The logs essentially, you know, lift up, get people eyesight into visibility of things that they care about. Absolute. So what's this red thing? Somebody read what is written? Rennes. >> One of the approaches. You'll hear things like golden signals. You'll hear youse, and you'll hear a red Corvette stands for rates, a rose and duration. And ready is a concept that says, How do you actually work with some of these complex technologies working with you're talking about and actually determined where your problems are. So if you think about it, rate is kind of how much traffic's going through a signal for this as a metric, it's accumulative number. So to back to Claudia's point, it's just number here. But if you're trapping goes up, you want to know what's going wrong here is self explanatory. Something broke, fix it, and then duration is how long things took. You talked about communities, Communities works hands in hands with this concept of micro services. Micro services are everywhere, and there were Khun B places that have thousands of little services, all serving the bigger need here. If one of them goes slow, you need to know what went slow as fast as possible. So rate duration and air is actually combined to give you the overall health of your system. While at the same point logs elect, you figure out what was causing >> the problem we'll take. I'm intrigued by what Claudia said. They're on this. You know, Braille concept is essentially a lot of people are flying blind date with what's going on, but you mentioned micro services. That's one area that's coming. Got state full data. Stateless data. They were given a P I economy. Certainly a state becomes important for these applications. You know, the developers don't may or may not know what's happening, so they need to have some intelligence. Also, security we've seen in the cloud. When you have a lot of people standing up instances whether it's on Amazon or other clouds, they don't actually have security on some of their things. So they got it. Figure out the trails of what the data looks like they need the log files to have understanding of. Did something happened? What happened? Why? What is the bottom line here? Claudia? What what people do to kind of get visibility So they're not flying blind as developers and organizations. >> Well, you gotta log everything you can within reason. They always have to take into account privacy and security. But logs much as you can and pull logs from every one of the components in your systems. The micro services that day was just talking about are so cool. And as engineers, we can't resist them way. Love, complexity >> and cool things. >> Things especially cool things and new things. >> New >> green things. Sorry, easily distracted. But there they are, harder to support. They can be a really difficult environment. So again it's back to bread crumbs, leaving that that trail and being able to go back and reconstruct what happened. >> Okay, what's the coolest thing about scaler since we thought about cool and relevant? You guys certainly in the relevant side thing. Check the box there. What's cool? What's cool about scaler telling us? >> That's great. Answer What isn't. But you know, honestly, when I came to work here, I no idea I was familiar with Log Management was really with long search and so forth. And the first time I actually saw the product, my jaw dropped. Okay, I now go to a trade show, for instance, and I'm showing people to use this. And I hit my return button to get my results. And you showed band with can be really bad and it stalls for 1/10 of a second, and I complain about it now. No, there is nothing quite as thrilling as getting your results as fast as you can think about them. Almost your thought processes the slow part of determining what's going on, and that is mind boggling. >> So the speed is the killer. >> The speed is like what killed me. But honestly, something that Chloe's been heavily involved in It takes you two minutes to get started. I mean, there's no long learning curve there. You get the product and you are there. You're ready to go >> close about ease of use and simplicity, because developers are fickle, but they're also loyal. Do you have a good product? They loved to get in that love the freebie. You know, the 30 day trial, They'll they'll kick the tires on anything. But the product isn't working. You hear about it when it does work. This mass traffic to people you know pound at the doorstep of the product. What's the compelling value proposition for the developer out there? Because they >> don't want to >> waste time. That's like the killer death to any product for development. Waste their time. They don't want to deal with it. >> So we live in the TL D our world right now. Frankly, if I have to read something, I usually move on on DH. That's the approach we take with scaler as well. Yes, we have some documentation, but I always feel like I have failed with the user interface design. If I require you to go read the documentation. So I try to take that into account with everything that we that we put out there making it really easy and fast it just jumping in, try stuff. >> How do you get to solve the complex complexity problem through attraction software? What's the secret sauce for the simplicity of this system? >> For me, it's a complete lack of patients. It's just like I wouldn't put up with that. I'm not gonna ask you to. Frankly, I view this sounds a little bit trite, but I've you Software's a relationship, and I view whoever is looking at it as a peer of mine, and I would be embarrassed if they couldn't figure it out if it wasn't obvious. But it is. We do have this sort of slope here of people who really know what's going on and people wanna optimize. This is your 80 20 split on people that don't know what just want to come in. I want both of them to be happy, so we need to blend those >> to talk about the value proposition of what you guys have because we've been covering you know log file mentioned Lock Management's Splunk events. We've gone, too. There's been no solution that I think may be going on 10 years old, that were once cutting edge. But the world changes so fast with Amazon Web services with Google Cloud with azure. Then get the international clouds out there as well. It's it's here. I mean, the scale is there, you got compute. You got the edge of the network right around the corner in the data problem's not going away. Log files going be needed. You have all this data exhausted, these value. >> If anything, there's always going to be more data that's out there. You're going to have more sources of that data coming in here. You're talking a little bit about you have the hybrid cloud. Where's part on prom? Part in the cloud. You could have multi clouds where across his boundaries. You're gonna have the wonderful coyote world where you have no idea when or where you're going to get an upload from too. This too and EJ environment. And you've got to worry about those and at the same time time, the logging, everything, the breadcrumbs. You have ephemeral events. They're not always there, and those are the ones that kill you. So the model is really simple and applaud Claudia for conning concept wise. But you're playing with concept of kiss, right? We'll hear its keep it simple and sophisticated at the same time. So I can teach you to do this demo in two minutes flat, and from there you can teach yourself everything else that this product's capable of doing it. That simple >> talk about who? The person out there that you want to use his product and why should they give scale or look what's in it for them. >> So for me, I think the perfect is to have Dev ops use it. It's developers. We really have designed a product less for ops and more for engineers. So one of the things that is different about scaler is you have somebody come in and set it up, parsed logs that ingestion of logs, which is different than splunk and sumo on DH. Then it's ready to use right out of the box. So for me, I think that our sweet spot, his engineers, because a lot of our formulations of things you do are more technical you're thinking about about you know what air the patterns here. I'm not going to say it's calculus, because then that wouldn't be simple. But it's along. Those >> engineers might be can also cloud Native is a really key party. People who were cloud native. We're actually looking at four in the cloud or cloud migration, >> right way C a lot. For instance, in the Croup. In any space from the Cloud Native Compute Foundation, we're seeing a tremendous instrument interest in Prometheus. We're seeing a lot of interest in usto with service mesh. The nice thing is that they are already all admitting logs themselves. And so, from our viewpoint, we bring them in. We put them together. So now you can look at each piece as it relates to the very other piece >> Claudia share with the folks who, watching this just some anecdotal use cases of what you guys have used internally, whether customers that give him a feel for how awesome scaler is and what's the what could they expect? >> Well, put me on the spot here. Um, >> I'll kick off. So we have a customer in Germany there need commerce shop, They have 1,000 engineers for here. When we started the product we replace because it was on a charge basis that was basically per user. They came back and they said, Oh my God, you don't understand our queries Air taking 15 minutes to get back By the time the quarry comes back, the engineer's forgotten why he asked the question for this. And so they loaded up. They rapidly discovered something unique. It's that they can discover things because anyone can use it. We now have 500 engineers that touch the log files every day, I will attest. Having written code myself, nobody reads log files for fun. But Scaler makes it easy to discover new things and new connections. And they actually look at what house >> discoveries of real value, proper >> discovery is a massive value proposition. Uh, where you figure out things that you don't know about back to that events thing that Claudia started about was, you can only measure the events that you can already considered. You can't measure things that didn't happen >> close. It quickly thought what the culture on David could chime in. What's the culture like here scaler? >> It is a unique culture and I know everyone probably says that about their startup, but we keep work life balance as a very important component. We're such nerds and unabashedly nerds. Wait, what we do. It's a joyful atmosphere to work in. Our founder, Steve Newman, is there in his flat, his flannel shirt, his socks cruising around. Um, and we are very much into our quality barcode. We have a lot of the principles of Google sort of combined into a start up. I mean to say it's a very honest environment, >> Sol. Heart problems make it a good environment. >> Yeah, and I value provide real values, are critical >> for me and have fun at the same point in time. The people here work hard, but they share what they're working on. They share information. They're not afraid to answer the what are you working on? Question. But we always managed to have fun. We are a pretty tight group that way. >> Well, thanks for sharing that insight. We have a lot of fun here in Innovation Day with the Q p. I'm John Furia. Thanks for watching

>> from San Matteo. It's the Cube covering scaler Innovation Day Brought to you by scaler. >> I'm John for the Cube. We are here in San Mateo, California, for special Innovation Day with scaler at their headquarters. Their new headquarters here. I'm here. She here. Lou, Who's Xia Liu? Who's the software engineering team? Good to see you. Thanks for joining. >> Thank you. >> So tell us, what do you do here? What kind of programming? What kind of engineering? >> Sure. Eso i'ma back and suffer engineer at scaler. What I work on from the day to day basis is building our highly scaleable distributed systems and serving our customers fast queries. >> What's the future that you're building? >> Yeah. So one of the project that I'm working on right now is it will help our infrastructure to move towards a more stateless infrastructure s o. The project itself is a meta data storage component and a series of AP ice that Comptel are back and servers where to find a lock file. That might sound really simple, but at the massive scale of ours, it is actually a significant challenge to do it fast and reliably. >> And you're getting date is a big challenge or run knows that data is the new oil date is the goal. Whatever the people saying, the states is super important. You guys have a unique architecture around data ingest What's so unique about it? You mind sharing? >> Of course, s O. We have a lot of things that we do or don't do. Uniquely. I would like to start with the ingestion front of things and what we don't do on that front. So we don't do keywords indexing which most other extinct existing solutions, too. By not doing that, not keeping the index files up to date with every single log message that's incoming. We saved a lot of time and resource, actually, from the moment that our customers applications generate a logline Teo that logline becoming available to for search in scaler. You y that takes just a couple of seconds on DH on other existing solutions. That can take hours. >> So that's the ingests I What about the query side? Because you got in just now. Query. What's that all about? >> Yeah, of course. Actually. Do you mind if we go to black board a little bit? >> Take a look. >> Okay. Grab a chart real quick. Um, so we have a lot of servers around here. We have, uh, Q >> servers. Let's see. >> These are accused servers and, um, a lot of back and servers, Um, just to reiterate on the interest inside a little bit. When locks come in, they will hit one of these Q servers, and you want them Any one of them. And the Q server will kind of batch the log messages together and then pick one of the bag and servers at random and send the batch of locks. Do them any Q can reach any back in servers. And that's how we kind of were able to handle gigs of laughs. How much ever log that you give us way in jazz? Dozens of terabytes of data on a daily basis. Um, and then it is this same farm of back and servers. That's kind of helping us on the query funds crave front. Um, our goal is when a query comes in, we summon all of these back and servers at once. We get all of their computation powers, all of their CPU cores, to serve this one queer Ari, And that is just a massively scalable multi tenant model and in my mind is really economies of scale at its best. >> So scales huge here. So they got the decoupled back in and accused Q system. But yet they're talking to each other. So what's the impact of the customer? What some of the order of magnitude scale we're talking about here? >> Absolutely. So for on the loch side, we talked about seconds response time from logs being generated, too. They see the lock show up and on the query side, um, the median response time of our queries is under 100 milli second. And we defined that response time from the moment the customer hit in the return button on their laptop to they see results show up and more than 90% of our queries return results in under one second. >> So what's the deployment model for the customers? So I'm a customer. Oh, that sounds great. Leighton sees a huge issue one of low late and seek. His legacy is really the lag issue for data. Do I buy it as a service on my deploying boxes? What does this look like here? >> Nope. Absolutely. Adult were 100 plan cloud native. All of this is actually in our cloud infrastructure and us a customer. You just start using us as a sulfur is a service, and when you submit a query, all of our back and servers are at your service. And what's best about this model is that asks Keller's business girls. We will add more back and servers at more computation power and you as a customer's still get all of that, and you don't need to pay us any extra for the increased queries. >> What's the customer news case for this given you, given example of who would benefit from this? >> Absolutely. So imagine your e commerce platform and you're having this huge black Friday sales. Seconds of time might mean millions of revenues to you, And you don't wantto waste any time on the logging front to debug into your system to look at your monitoring and see where the problem is. If you ever have a problem, so we give you a query response time on the magnitude of seconds versus other is existing solutions. Maybe you need to wait for minutes anxiously in front of your computer. >> She What's the unique thing here? This looks like a really good actor, decoupling things that might make sense. But what's the What's the secret sauce? You? What's the big magic here? >> Yeah, absolutely. So anyone can kind of do a huge server farm Route Fours query approach. But the 1st 80% of a brute force algorithm is easy. It's really the last 20%. That's kind of more difficult, challenging and really differentiate. That's from the rest of others. Solutions. So to start with, we make every effort we can teo identify and skip the work that we don't have to do. S O. Maybe we can come back to your seats. >> Cut. >> Okay, so it's so it's exciting. >> Yeah. So we there are a couple things we do here to skip the work that we don't have to do. As we always say, the fastest queries are those we don't even have to run, which is very true. We have this Colin, our database that wee boat in house highly performance for our use case that can lead us only scan the columns that the customer cares about and skipped all the rest. And we also build a data structure called bloom Filters And if a query term does not occur in those boom filters, we can just skip the whole data set that represents >> so that speed helps on the speed performance. >> Absolutely. Absolutely. If we don't even have to look at that data set, >> You know, I love talking to suffer engineers, people on the cutting edge because, you know, you guys were startup. Attracting talent is a big thing, and people love to work on hard problems. What's the hard problem that you guys are solving here? >> Yeah, absolutely. S o we we have this huge server farm at at our disposal. It's, however, as we always say, the key to brute force algorithms is really to recruit as much force as possible as fast as we can. If you have hundreds thousands, of course lying around. But you don't have an effective way to some of them around when you need them. Then there's no help having them around 11 of the most interesting things that my team does is we developed this customised scatter gather algorithm in order to assign the work in a way that faster back and servers will dynamically compensate for slower servers without any prior knowledge. And I just love that >> how fast is going to get? >> Well, I have no doubt that will one day reach light speed. >> Specialist. Physics is a good thing, but it's also a bottleneck. Just what? Your story. How did you get into this? >> Yeah, s o. I joined Scaler about eight months ago as an ap s server, Actually. Sorry. As an FBI engineer, actually eso during my FBI days. I use scaler, the product very heavily. And it just became increasingly fascinated about the speed at which our queria runs. And I was like, I really want to get behind the scene and see what's going on in the back end. That gives us such fast query. So here I am. Two months ago, I switched the back and team. >> Well, congratulations. And thanks for sharing that insight. >> Thank you, John. Thank >> jumper here with Cuban Sites Day and Innovation Day here in San Mateo. Thanks for watching

(music) >> Everyone, welcome to a special CUBE Conversation. We're here in Palo Alto, theCUBE Studios, I'm John Furrier, the host of theCUBE video, we're here with a very special guest and the new CEO of a hot startup, Christine Heckart, CEO Scalyr. Welcome to theCUBE, great to see you. >> Thank you. >> Thanks for coming on. So, you're the new CEO of Scalyr, the CEO transitioned. >> Super great founder, great engineering team. >> Yes, yes. >> Hot startup, lot of finance and a lot of customers. Tell us about Scalyr. >> So, Scalyr was founded by a guy named Steve Newman. He is a serial entrepreneur. Scalyr is his 7th company. His 6th company was called Writely and it got bought by Google and is what we all know and love as Google Docs today. So, when he was inside Google, building out Google Docs he had the same problem that a lot of engineers do right now especially if they're on a modern stack. It's really hard to troubleshoot. It's hard to figure out what's running well and if there's a problem where it's at and fix it quickly. And so he left in 2011 and he founded Scalyr. >> And so, the company has how many employees? Just give us the quick numbers of employees, funding, venture involved, customers... Give us the quick numbers. >> The company has a little over 50 employees. It just took a Series A round about a year, a little under a year-and-a-half ago. Led by Shasta Ventures. There are 300 paying customers. We grew the core customer base last year by 170% revenue. So, it's growing very quickly. We more than doubled the employees in the last year. So, like you say, it's on fire and we're trying to scale up ourselves as we help our customers scale. >> So growth is obviously rocket ship growth is an attractive, enticing opportunity for you. You've been there, done that. So, what else attracted you to the opportunity? What made you make the move to take the leadership helm as the chief of Scalyr? >> The thing that attracted me most to Scalyr is that the world runs on code right now. And for companies for whom the code is the company downtime is money, it's critical. But, in these modern stacks, it's really hard to figure out where the problem is. Everything's been so abstracted. And if you're cloud-based, if you're moving to serverless, if you're on Kubernetes or some kind of container platform trying to do orchestration... Any of that makes it faster and easier to build a service but a lot harder to figure out if and where there's a problem within the service. And Scalyr's designed by engineers for engineers on modern stacks to help them figure out where that problem is and get it solved very quickly. >> So obviously the new wave is the cloud. Cloud natives search for big opportunities converging. What's the market opportunity? What are you guys going after in terms of, if you look at the marketplace, what's the segment you're going after? Lay that out, what segment are you in? Is it just cloud, is it a piece of cloud native, what's the market opportunity? >> We serve customers who have applications built on a new stack a cloud-based stack. And typically the people who use us most and who love us most are the site-reliability engineers, responsible for keeping it up and running. Dev Ops, true developers... One of our largest customers is a company called Zalando. They're an older company that did a digital transition, and so they do online e-commerce now, one of the largest in Europe. And for their engineers, 25% of their engineers use the product daily. 50% use it weekly. So, it's part of the workflow. It helps them do their jobs better. So, it's a utility. And the founder, you said, worked at Google, obviously he saw the scale there. They have a site reliability engineer concept that's obviously run a huge infrastructure. Is that kind-of the market you're going after? Dev Ops, SRE types? >> Yep, so we're an observability tool. There's kind-of two camps of observability. We've started in the logging space. So, what we're really known for is the fast logging tool. And the reason why we're known for being fast is unlike all the other architectures that were optimized for the more traditional stack, we've been written and optimized for the new stack and we're the only architecture that doesn't use keyword index in order to do that search. And that's what makes us fast. But it's also what makes us more affordable. And it contributes to, the architecture contributes to the simplicity of how you can use the tool and how the tool is written. >> So, the core tech is, under the hood would be, what, what's the core tech in that. Because speed obviously means you've got some technology there. What's the core technology that makes that speed work? >> So, we're a true multi-tenancy product, we run on Amazon ourself, it's a multi-tenancy system, it uses massive parallel processing. And basically we can ingest any data, in fact we're designed for machine data, for logs, for things that don't, they're not full documents, it's not like a video or something on the World Wide Web. These are little tiny events that come in and there's lots and lots and lots of them. Scalyr is the name of the company, we scale up and we scale out. And what we do is, when you go to run a query we throw every processor in our system at every query that comes in. And the reason why that becomes important in this multi-tenancy architecture is the more customers we have, the more data that we ingest, the more servers we have to throw at every query for every customer. So as we grow, the service gets better, it gets faster, it gets more affordable for all customers. >> That's the best thing about the cloud, you can bring that compute to bear so you have a little flywheel of acceleration. Talk about the role of data, because this is interesting, one of the core problems we hear a lot in the cloud native world is that so many, now, sets of services being deployed Kubernetes is becoming the de-facto sceme for orchestration around micro-services, containers obviously they're our standard as well. Which means there's more instrumentation, right? So, I could almost see how the founder saw this future because he lived it. >> Exactly. >> He lived the future, and now the real world's going "hey, we have that Google-like problem, we have tons of services playing around but it's not just logging and getting a query back in minutes. These services are talking to applications through each other. This is like mission critical. >> Very mission critical. >> Is this what you guys are doing? >> Right, if you are running in a traditional environment and you're running sort-of traditional applications there are really good logging solutions out there for that. That's what Splunk was founded on, they're amazing at doing that. But, nobody had built an optimized logging system and an observability system for the new stack. And that's what we're designed to do. And you use, you said, in minutes. And minutes is what it takes for most log queries in a traditional environment. 96% of all of our queries happen in less than a second. We're fast. >> So, this is really what the Agile teams need, Dev Ops teams need. >> Yes. When code is money, when it's the company, when every second of downtime, or even a service that's impaired, it might not be hard down but it's not running the way that it should, that impacts the customer experience, it impacts how many customers you can get if you're a real-time business, it impacts revenue. It's important to get that service up and running quickly. >> So, you guys are re-imagining logging, which is more mission critical rather than okay, where the breach is, what's going on in the basic logs, like Splunk used to do. So, talk about the product. Who's the target persona, how is it consumed, you mentioned on the cloud, is it SAS? How does someone get involved, do they just download it, do they get a consult, talk about the product and the target audiences. >> So, it is SAS, it's delivered by SAS. We don't have a non-prime service today or an offering. And, typically it's the site-reliability engineer, the architects, the developers themselves, Dev Ops for sure, Cloud Ops, they're the ones that are using the tool day-to-day. And it's a beautiful dashboard, a lot of it is just point and click. You can go in, if you want to add English-language query, you don't have to learn a special query language to use this, that's why people say it's so fast and easy to learn to use and I think that's why we get the kind of daily usage we have. You don't have to be an expert in the tool, it's very intuitive, you get a dashboard, you can just keep clicking down off of a chart and get all the way to the code. In fact, we can link you from where the problem is straight into the code that underlies that so you can then go and solve the problem. >> So, it's really easy to get into. >> Very. >> So I don't need do any kind of elaborate configurations? >> No. You don't need to do elaborate configurations and, as importantly, you don't need to learn a new specialized query language. Which, again, in the more traditional systems you find that there's only a few people that really know how to use the product because you have to learn the query language. It's kind-of like CLI or something in networking. And so there's a few specialists and they're very good, but if you're an engineer and there's a problem and you want to use the tool, you don't have time to become an expert. You've got to just use it. And so, even though it's designed to search machine language, you can use English, it's pretty easy to figure out how to write that query, and it comes back so quickly, if you didn't get it quite right you can just refine and do the search again and narrow down. >> I can see why the V.C.'s like this, the venture capitalists, because it markets good, big wave, cloud native lot of growth there. Certainly hyper-scalers, enterprisers are coming next, so I can imagine that's more head room. Product is consumable, SAS, in the cloud, technology that's fast, compelling, >> You're good, you can be on the pitch team. >> Final check box is customers. >> Yes. >> So, how many customers do you have? >> We have 300 paying customers. That doubled in the last year, and we have some big names and a lot of small companies. So, some of the fun ones are Giphy, my kids love that, my husband, right? Using them every day. NBC Universal, kind-of on the other side of that. Companies for whom the application is the business. And it can be a traditional company that's trying to launch new digital transformation initiatives, or it can be companies that were born in the cloud. >> And that's only going to get better, again, the markup. There's more companies going to the cloud. Talk about multi-cloud, because you know we had conversations in the past before you came on Scalyr around multi-cloud. That's only going to increase the sets of microservices and the role of data. Not just code, because code is data. Data is code. It's going to be a whole data ops movement coming soon, we see that tsunami coming. How does the multi-cloud fit into all of this in your mind? Is it too early, is that coming later? Or, is it available now? Could your customers have the multi-cloud now? >> For our customers, if they are in a multi-cloud environment today, we're an ideal tool for them 'cause we can run on any of their clouds. Most customers are not yet in multi-cloud, but they're trying to get there. Just like most customers are not yet fully containerized, but you want to pick a tool today that will grow with you and get you to tomorrow. And that's where Scalyr comes in, because we are designed and optimized for that environment. And, there's kind-of no scale too big for us. The company was named very deliberately. We can scale up, we can scale out, and we can continue to be simple and fast as your business scales. >> Christine, you've had a track record, you've had a great career, you've seen a lot of waves of innovation. You've been working for big companies, a dozen start-ups, now you're back at a start-up. So, I got to ask you a personal question, how does it feel? What's it like back into the trenches? And, you've got a hot start-up here. One month on the job, what's going on there? >> I love it. I really love it. You know, there's 50 people in the company every one of them is high-energy they're so committed to the cause. You know, when the world runs on code and you help that code run better, you're making an impact on the world every single day. These people know it, they feel it. They're very committed. And, unlike some of the much bigger companies I've been at, you can innovate so quickly. So, I just finished my first 30 days onboarding, I have talked to our big customers, a couple dozen of our really big customers. And, they all say a couple of things over and over again, there's just some consistent themes. Fast always comes up, it's usually the first word. Simple comes up. Affordable, which is nice. People pay a lot of money for these tools and they don't always feel good about all that money. We can come in and be much more affordable and they appreciate that. But, the thing that kept coming up over and over again was the customer service and the customer support. And nobody, I come from worlds where nobody ever raves about customer service and customer support. So, it was odd and I dug a little bit, and there were two pieces to that. One, because we're 50 people, when somebody has a problem, we're all-in. It gets solved quickly. A lot of times we can sort-of flag that problem for the customer because we're keeping track. But the other thing that was brought up is when they need something that maybe we don't deliver today they ask for it. And a lot of times we can give it to them pretty quickly. There's not some big, huge long roadmap process. We're a small company, we can't always do it quickly, but a lot of times we can turn stuff around and it's great. >> Well, you're hitting the ground running, got your running shoes on, sounds like a great opportunity. You've got a lot of work to do! What are some of the priorities? I'm sure hiring is big. Take a minute to give the plug on for any hirings you have. >> So, we're just moving to brand new facilities in downtown San Mateo a couple blocks from Caltrain. And that is because we doubled the company size last year, and we need to double it again this year. So, we are hiring, if you know of any great people, please send them to us. We announced some new things at Amazon Reinvent, late last year, one of which is new distributed tracing. We're on the very leading edge of this trend, and it's an important one. It's probably a conversation maybe with Steve himself. Yeah, he's very knowledgeable, and it's a fascinating area because the APM systems, again, kind-of the traditional if you can say that for APM, have all been built for the front-end, for the websites. But, once you move into these container environments you need that same kind of capability for the back end. And so you need something called distributed tracing. It turns out that if you're born in the logs like we are doing that distributed tracing which links them together and gives you a picture systemically of what's happening and how you link the events for a fuller picture. We're kind-of uniquely good at that. So, we've got that coming out later this quarter. >> That'll attract some engineers 'cause that's a hard problem. >> It's a hard, a lot of the problems we solve are hard, interesting problems, and they're problems for the new stack, and they're problems at scale. And smart engineers like to work on that. >> You know, state's a big one, stateless applications, state is a huge problem I'm sure you guys are on, this is where the tracing plays in. >> Yes, exactly. >> Final question for you before we end is competition. Certainly people who are in the new world, going cloud native, they get it, they get the complexity, they get the opportunity as well. So, there's a lot of investment there. But, the folks that are looking at Scalyr like "ooh, what's the competitive lens"? How do you answer that? What's your response to differentiate, being different from the competition? So, there's lots and lots of observability tools, and even logging tools in the market. And from that standpoint you could say there's tons of competition. They're all built on keyword indexing, so they're all optimized for looking back, for yesterday's world. We're the only ones that are built on this very new architecture, designed for the future stack, designed for the new stack. And, we're the only ones that don't use keyword indexing. And, what we have is this amazing, multi-tenancy, columnar-based approach that gives you these advantages of fast, simple, and affordable. >> So you're staking the ground in the marketplace of speed, sub-second response, 2 queries, 4 runtime applications that are mission critical to businesses. Is that right? >> Said very well, thank you. >> Well, that's what we do here at theCUBE, we figure it out, we get the data. >> Christine, thanks for coming out. Congratulations on the new role. We'll be following you guys. Love the name, Scalyr. Scaling is table stakes now in the cloud. If you don't compete at scale, or operate at scale, or develop at scale, you're probably going to be in trouble. So, theCUBE's covering it as always. Thanks for watching, I'm John Furrier.

[Music] hi I'm Stu Mittleman and welcome to a cube conversation really excited to have to the program a first-time guest and a user Vinny Chopra is an IT engineer with Medallia Vinny thank you so much for joining us thank you and - Vinny's left we have Krishnan bad Rena Ryan in who's a director of product marketing with Nutanix Chris thanks so much for you here okay so we always love to be able to dig in with the customers understand the challenges they're facing Chris let's set the table first I'm very familiar with Nutanix we go to all the new tannic shows and the like but for customers what is Nutanix to them why do they turn to Nutanix okay absolutely so I think it's a great time to be in IT you see new businesses that are sprouting at all the last 10 years or so starting with uber Airbnb specifically the ones we've really heard of that have disrupted some really really big industries right so technology is making it happen while IT teams are the ones that help make that happen and helps those CEOs disrupt they're not in the best of positions to utilize infrastructure they have today the way it's set up to be able to get more done be more agile and truly serve the needs of the business and help create those competitive differentiation which is why neutronics is here to help our partners within companies such as yourself to be able to be those people to lean in and help CEOs really achieve what they're trying to get that yeah that's great yeah we definitely see it used to be okay IT was a cost center IT you know business would actually ask for something in IT would often be the no or be really slow and do they work with that so Vinnie before we dig into the IDE piece of it tell us a little bit about Medallia the business what's happening what's Sherma Delia's been around for about 15 years now we're located in it we're headquartered in San Mateo we used to be in Palo Alto moved last year we have a brand new building right off 101 a 92 we our analytics company and we and there's a lot of lots of fields in analytics we specialize in an area called CX which stands for customer experience and our goal is to make our customers customers happy which therefore makes our customers happy and we specialize in doing surveys and then especially in designing surveys for different types of companies and then and then we analyze that data you know surveys well Vinny I I find there's very few companies that I talked to whose industries are stagnant or not changing much the analytic space space that we cover heavily you know here here on the cube and with our research it's boy has that changed a lot I mean five years ago we were talking very much about Big Data today you know all the AI ml and and things like that what what give us a little bit about what's it like being in that business you know fast driving your silicon valley-based I have to imagine that the business is going through a lot of changes that put stresses and strains on IT oh definitely so I better the IT industry for many years and IT area different big companies Sun Microsystems Juniper Networks NetApp in the past excite calm which was a search engine way back when before Google days I remember excite you know because Microsoft didn't they buy that or things well there was an early cerulean at home there's a partnership with that on but yeah excited people would confuse as to wait excite calm what kind of site was that it's like no no it's a search engine back before by the way audience for those of you that haven't been around a while it wasn't all just being in Google there were a lot of predecessors that there was four or five big search engines at that time so most of my company had been out we've always been packaging stuff in a box and selling it in this is my first time at an analytics company and it's it's like you said it's a fast-moving field things are being the things there's no development staging production type of stuff things are just continuously being put into production changes are made you know customized you know customer's applications and their interface so it's it's a very fast-moving alright and Vinny you say IT engineers your job what does that encompass what your role how many people in the group what is your sure so we have basically two IT groups we have one that manages our production data centers which are which our customers interface with and we have one that supports our engineers so I'm part of that group and it's kind of a week up art of the IT system and engineering team and that involves traditional IT tasks like backups monitoring application install new server installs managing storage networking basically keeping infrastructure and applications running as efficiently as possible and therefore keeping our engineers happy because they can get their work done and their development done okay sounds like a you know pretty typical from from what I hear from companies is it what do you hear from customers structure-wise challenges they're facing absolutely so it's very much in line with what you were just talking about where there's these multiple needs from the business and customer expectations so how do you really help IT organizations be able to keep up with those needs infrastructure needs to be the big quittez data needs to be Vic witness application services need to be Vic Willis and you need to be able to scale out as your business needs needs to do so to be able to serve all those multiple requirements so whether it's standardizing internal applications that are delivered through virtual desktops or deploying databases are starting up customer websites you need to be able to do that and respond as quickly as possible and if you're spending cycles on acquiring infrastructure deploying it making sure it's well integrated and then once it's up and running figuring out what went wrong and enjoying those multiple nights of pizza right to figure out how to get this thing going back to the way it was it's it just distracts you from what's important so it's only when you make infrastructure invisible and truly scalable very much cloud-like and and make it your own as a process of doing so can you truly be that business partner and you and I hope we've done that with you definitely all right so Bennie let's go inside was there a specific project rollout that you would that led towards Nutanix was there a pain point you were having would give us kind of the before and what was the mature so traditionally an IT you would you want to set up a new application at you in your infrastructure environment you would buy servers and you would buy storage you would buy HBA cards which helps you connect the servers to the storage you've got things like worldwide numbers to worry about getting the right cables getting the right cards and then you put it all together you get all the stuff delivered and then two weeks later you might have things working and but you having some permission issues security issues so it was always a big challenge to get things up and running so it was the fun of ideas let's roll up our sleeves let's turn those geek knobs and you know optimize everything and yeah within six months I'm sure everything's rocking in right everything's rocking rolling but you're still not quite confident that things are running you're worried that a card might go bad you're worried that a world-wide number might change somewhere or somebody might you know mess up your security so you would spend a lot of time just getting things up and running versus spending time on development and you know working with your people you're supporting and trying to try to enhance things versus just keeping things getting things up and running so Nutanix you know with the hyper-converged infrastructure you know what kind of we're not worried about those things anymore it has our storage needs it has our compute needs it has our memory needs so what was it a refresh cycle what was the impetus that led to looking at a new arc sugar as we were growing and entering base was growing an IT was growing and our requests and you know what we need to satisfy was increasing tremendously we before we were working with just individual desks like desktops or blade servers but each one was kind of working individually with its own storage its own applications not the notion things weren't being shared or anything and we were just growing fast so we needed some we need a new infrastructure where we could actually have everything working of most efficiently and be secure and fast and and easy to manage and so we did look at we did some analysis on a few products and Nutanix you know after some a few pocs Nutanix was our product of choice yeah I mean you described something we heard a lot is it used to be every application you would kind of build your own temple for it yeah let me build it let me get the performance I need let me optimize certain things let me forecast how it's gonna grow but I get islands out there as opposed to I want to be able to scale I don't want to worry about you know here's one of the challenges out there most people and across the board forecasting is really hard or impossible I either overestimated a bunch and then I bought stuff I didn't eat her right under missed it estimate it and then oh my gosh I need to look to a new architecture yeah and then things ended up burning like at 10% of you know you utilizing temperature of the resources that you're purchasing yeah I remain poor virtualization it was like you know six seven percent is usually what we were running awesome so challenges before and we had you know silos out there I couldn't share I couldn't do talk about that that role how did you get from that old environment to the new one there's something I said when you you look at this wave of really a distributed architecture in the old world migrations were really really tough yeah and you had to do it with every cycle hopefully moving to an architecture like this this is your last migration it was like you know my wife always said the last time that's the last time I never want to have to move well I T I'm sure those migrations were always painful what was the experience my heading to migrations was is one thing that we went through but also just now it's just setting up new VMs or new applications new servers it's you know within a few minutes versus hours as far as migration we were we were running a hypervisor before but like I said it was on individual servers so the migration was basically picking your VMs or your servers one at a time and just migrating over to Tenex once it was there and you know with the hypervisor tools that are available it's very easy to use it's like things like vmotion or different types of migration tools that Nutanix offers with their hv hypervisor so it was just it was pretty seamless it was just you just pick and choose and identify your destination host ons Nutanix node or Nutanix cluster and all your stories that you want to move it to and just go okay so so Vinnie you went through a bit of a bake-off to figure out the solution tell us when you finish the deployment how are you measuring what does success mean to in deployment of your stand point and give us the after what show does this change for your process your organization sure qualitatively success is when our engineers are smiling and not calling us too much and asking us go to lunch versus telling us about issues they're having so that's qualitatively quantitatively looking at performance CPU memory I ops performance on a storage how our applications responding that that's what we measured it quantitatively yeah did you know like what kind of utilization you're getting on your current infrastructure then with the Nutanix um also currently you meet as far as uh what you said you were lucky to get 10% in the old world do you measure that yeah we met her that week we kind of um you know we have our kind of have our choices of how much storage you want to use how much CPU remember you want to allocate to each VM and we we just monitor it and through the prism interface that Nutanix offers the image you can actually see performance of each VM and you can decide when to throttle things so but as far as you know how much we're utilizing we're you know we have it we have a structured where we have room to grow so yeah absolutely and if we do need to grow later we can easily add nodes or you know chassis wood notes yeah I think back to the early years of you know what we call hyper converge environments and it was like oh well they are monolithic blocks even if they're small and but you don't have flexibility there when I look at you know many of the solutions especially what Nutanix ups there's a lot of flexibility into how I can grow in scale and get the the utilization that I need but get the performance the ops and everything what I think from your customers how is that story play out today yeah I mean ultimately it's all about empowering people right it's about making IT people truly successful broadening their skillset giving them greater control over the full stack if you will right so it's no longer siloed across functions you're no longer found helpless relying on a different team to deliver upon something that was promised based on a certain SLA so how do we do that how do we make evolved functional specialists into IT journalists would then become cloud engineers true cloud engineers right the world is changing technology is adapting businesses are a craving for more and the only way we can keep up is to adapt ourselves and utilize the best of breed technologies that gives us that power so as a result we hear that a lot where we find a lot of a customer's progressing from being either storage admins network specialists but most likely virtualization admins who then become these cloud engineers if you will they reorganize that way they tend to be in a position where they are a lot more infrastructure we're talking about 100x of what they used to do prior in the in the earlier days so the the number of the ratios just grow immensely as well as the quality of service provided the SAS are far reduced as they used to be so all of that goodness that our customers are able to deliver to their state goes in the organization makes us feel good about what we do if any would love we talked about you know this the engineers now they're smiling and going out to further then you know fighting bugs anything complaining about is yeah anything kind of when you look at skill set if they're you know I've talked to some entertainment customer he's like oh you know I had that security project that was sitting on my desk for years I can finally tackle that or there's I can be more responsive to the business so that they don't you know I can engage with them rather than just going off running it and do in stealth IT any anything along those lines that you can share I mean one thing like IT admins we typically want to know everything right so we all know what's happening behind the scenes with Nutanix we don't have to as much but we still like to and so we we take the opportunity to you know do trainings learn what's happening in an interface you support when needed so as far as yeah as far as skills go I think it's you know the skills you keep up with it's just different like Chris mentioned it's different different type of administration like we're managing virtualization or managing cloud you know you're not just managing loans and cables you know I love you sounds like you've got a team that's got that intellectual curiosity wants to understand what's going on how was the how was the on-ramp how was the kind of the cycle to understand the Nutanix piece how did you yeah so we learned a lot of the POC of course that's when you kind of you know you can play around with stuff and break stuff and try to break stuff if you want we use professional we used some freshly served since to help us get set up originally and after that it was just kind of learning day to day and just improving improving our knowledge in different areas like not if we're not used to having everything in one like in you know in one kind of a couple jassi's storage and you know compute so that was a networking as well so that was a little bit not challenged technically but just just you just need to reset the mindset these are the way I used to do things versus the the way now I can't do three and in troubleshooting um you know the great thing is when we have troubleshooting we're not calling three different vendors like a networking company a storage company in a compute company and having them point fingers oh it's networking now we if I ever have an issue or a question I call Nutanix supporting it so if any how long has it been since you the solution was deployed about two and a half years now awesome so it but you first of all I love your viewpoint as to how Nutanix has changed in those two those two years and along those lines too now that you look at things through the lens of 2018 if you could go back to peers of yours what would you tell them now that you wish you had known back when you rolled this out a couple of years ago I would you know how to tell them there's a much easier way to minute you know the deploy and manager infrastructure and you know this is this is one of the new techniques is definitely something you should look at alright Chris what what advice do you give to the IP people of the world that you know I'm sure most of them heard about this but you know what misconceptions might they have what what things do we want to make sure we open the door for sure so as a former developer myself you know several years ago I think it's very easy for us to forget the role we play in our organizations we're not all about the applications we're not all over the speeds and feeds we had a critical core part of how businesses go to market and achieve success right so let us recognize that and use the best approaches that are available out there to be able to deliver that value right if it means going where the good hyper-converged infrastructure solution if it means leaning in and building new disruptive technologies and such that can help your businesses do better the other thing that I want to highlight is just as you are in the the customer service business I believe we are as well we pride ourselves on our support so if you have if ask questions about how hyper-converged infrastructure can add value call us give support a call you would be put in touch with anyone who can speak about all the values we deliver to our customers and begin to get some of those ideas all right Vinnie uh want to ask you you you've got some experience works for some of the you know really well-known companies you not only here in the valley but in tech in general what's exciting you these days what do you look at either in the analytic space or an IT that that's getting you excited for me it's I like to get up without stress and so ease of management ease of deployment in the IT area is very that that's one of things I look forward to like you know being able to do other stuff than just focusing on data you know routine stuff yeah and one of those lines if I could give you you know the one wish to help make that goal even more either from Nutanix or you know the broad ecosystem out there what would what would make your job even easier you know it's it's I don't know I'm trying to think of a good answer but it's typically you know when issues once them all we have application issues it would just be some kind of self-healing type things you know maybe or maybe some automatic adjustments that could be done that maybe something in the future yeah like I just means as far as resources allocated to different types of yeah all right Chris sure I'll let you have the final word there cuz absolutely once we simplify modernize the platform modernizing the application some it's definitely something I've heard from many of your customers as to you know that role of infrastructure really is to serve up and support those applications and that seems to be where it's going that's right that's right the the business partners right partners the business CFO whoever on the other side of the fence they care about applications and services not so much about all the blood sweat and tears we put into the infrastructure so I think it's an opportunity for us to help us elevate beyond the infrastructure and focus on apps and services along with making sure we have some of those self-healing capabilities such that take care of us and not require us to pay heat to all those infrastructure speeds and feeds so it's a great opportunity to do and you know be truly strategic in the company right alright well Chris really appreciate you sharing the updates Vinny really appreciate you sharing your customer story it's our purpose here at the cube to always help bring out the information so make sure to check out the cube net if you actually go to the top there's a search we've got over five or six thousand interviews we've done including many customers including many of Nutanix go in search Nutanix you'll find a plethora of content out there if you ever have any question for us please reach out to us see us at any of the shows or in between so I'm Stu minimun and thanks again for watching the cube thank you