>> Good day, everyone. Thank you for joining me. I'm Cindy Maike, joined by Rick Taylor of Cloudera. We're here to talk about predictive maintenance for the public sector and how to increase asset service reliability. On today's agenda, we'll talk specifically around how to optimize your equipment maintenance, how to reduce costs, asset failure with data and analytics. We'll go into a little more depth on what type of data, the analytical methods that we're typically seeing used, the associated- Brooke will go over a case study as well as a reference architecture. So by basic definition, predictive maintenance is about determining when an asset should be maintained and what specific maintenance activities need to be performed either based upon an assets actual condition or state. It's also about predicting and preventing failures and performing maintenance on your time on your schedule to avoid costly unplanned downtime. McKenzie has looked at analyzing predictive maintenance costs across multiple industries and has identified that there's the opportunity to reduce overall predictive maintenance costs by roughly 50% with different types of analytical methods. So let's look at those three types of models. First, we've got our traditional type of method for maintenance, and that's really about uncorrective maintenance, and that's when we're performing maintenance on an asset after the equipment fails. The challenges with that is we end up with unplanned downtime. We end up with disruptions in our schedules, as well as reduce quality around the performance of the asset. And then we started looking at preventive maintenance and preventative maintenance is really when we're performing maintenance on a set schedule. The challenges with that is we're typically doing it regardless of the actual condition of the asset, which has resulted in unnecessary downtime and expense. And specifically we're really now focused on condition-based maintenance, which is looking at leveraging predictive maintenance techniques based upon actual conditions and real time events and processes. Within that, we've seen organizations and again, source from McKenzie, have a 50% reduction in downtime, as well as overall 40% reduction in maintenance costs. Again, this is really looking at things across multiple industries, but let's look at it in the context of the public sector and based upon some activity by the department of energy several years ago, they really looked at what does predictive maintenance mean to the public sector? What is the benefit of looking at increasing return on investment of assets, reducing, you know, reduction in downtime as well as overall maintenance costs. So corrective or reactive based maintenance is really about performing once there's been a failure and then the movement towards preventative, which is based upon a set schedule. We're looking at predictive where we're monitoring real-time conditions. And most importantly is now actually leveraging IOT and data and analytics to further reduce those overall down times. And there's a research report by the department of energy that goes into more specifics on the opportunity within the public sector. So Rick, let's talk a little bit about what are some of the challenges regarding data, regarding predictive maintenance? >> Some of the challenges include having data silos, historically our government organizations and organizations in the commercial space as well, have multiple data silos. They've spun up over time. There are multiple business units and note, there's no single view of assets. And oftentimes there's redundant information stored in these silos of information. Couple that with huge increases in data volume, data growing exponentially, along with new types of data that we can ingest there's social media, there's semi and unstructured data sources and the real time data that we can now collect from the internet of things. And so the challenge is to collect all these assets together and begin to extract intelligence from them and additional insights and and that in turn, then fuels machine learning and what we call artificial intelligence, which enables predictive maintenance. Next slide. >> Cindy: So let's look specifically at, you know, the types of use cases and I'm going to- Rick and I are going to focus on those use cases, where do we see predictive maintenance coming in to the procurement facility, supply chain, operations and logistics? We've got various level of maturity. So, you know, we're talking about predictive maintenance. We're also talking about using information, whether it be on a connected asset or a vehicle doing monitoring to also leveraging predictive maintenance on how do we bring about looking at data from connected warehouses facilities and buildings? I'll bring an opportunity to both increase the quality and effectiveness of the missions within the agencies to also looking at looking at cost efficiency, as well as looking at risk and safety. And the types of data, you know, that Rick mentioned around, you know, the new types of information. Some of those data elements that we typically have seen is looking at failure history. So when has an asset or a machine or a component within a machine failed in the past? We've also looking at bringing together a maintenance history, looking at a specific machine. Are we getting error codes off of a machine or assets looking at when we've replaced certain components to looking at how are we actually leveraging the assets? What were the operating conditions? Pulling up data from a sensor on that asset? Also looking at the features of an asset, whether it's, you know, engine size it's make and model, where's the asset located? To also looking at who's operated the asset, you know, whether it be their certifications, what's their experience, how are they leveraging the assets? And then also bringing in together some of the pattern analysis that we've seen. So what are the operating limits? Are we getting service reliability? Are we getting a product recall information from the actual manufacturer? So Rick, I know the data landscape has really changed. Let's, let's go over looking at some of those components. >> Rick: Sure. So this slide depicts sort of the, some of the inputs that inform a predictive maintenance program. So we've talked a little bit about the silos of information, the ERP system of record, perhaps the spares and the service history. So we want, what we want to do is combine that information with sensor data, whether it's a facility and equipment sensors, or temperature and humidity, for example. All this stuff is then combined together and then used to develop machine learning models that better inform predictive maintenance, because we do need to take into account the environmental factors that may cause additional wear and tear on the asset that we're monitoring. So here are some examples of private sector maintenance use cases that also have broad applicability across the government. For example, one of the busiest airports in Europe is running Cloudera on Azure to capture secure and correlate sensor data collected from equipment within the airport. The people moving equipment more specifically, the escalators, the elevators, and the baggage carousels. The objective here is to prevent breakdowns and improve airport efficiency and passenger safety. Another example is a container shipping port. In this case, we use IOT data and machine learning to help customers recognize how their cargo handling equipment is performing in different weather conditions to understand how usage relates to failure rates and to detect anomalies in transport systems. These all improve port efficiency. Another example is Navistar. Navistar is a leading manufacturer of commercial trucks, buses, and military vehicles. Typically vehicle maintenance, as Cindy mentioned, is based on miles traveled or based on a schedule or a time since the last service. But these are only two of the thousands of data points that can signal the need for maintenance. And as it turns out, unscheduled maintenance and vehicle breakdowns account for a large share of the total cost for vehicle owners. So to help fleet owners move from a reactive approach to a more predictive model, Navistar built an IOT enabled remote diagnostics platform called On Command. The platform brings in over 70 sensor data feeds for more than 375,000 connected vehicles. These include engine performance, trucks speed, acceleration, coolant temperature and break ware. This data is then correlated with other Navistar and third-party data sources, including weather, geolocation, vehicle usage, traffic, warranty, and parts inventory information. So the platform then uses machine learning and advanced analytics to automatically detect problems early and predict maintenance requirements. So how does the fleet operator use this information? They can monitor truck health and performance from smartphones or tablets and prioritize needed repairs. Also, they can identify that the nearest service location that has the relevant parts, the train technicians and the available service space. So sort of wrapping up the benefits. Navistar's helped fleet owners reduce maintenance costs by more than 30%. This same platform has also used to help school buses run safely and on time. For example, one school district with 110 buses that travel over a million miles annually reduce the number of tows needed year over year, thanks to predictive insights, delivered by this platform. So I'd like to take a moment and walk through the data life cycle as depicted in this diagram. So data ingest from the edge may include feeds from the factory floor or things like connected vehicles, whether they're trucks, aircraft, heavy equipment, cargo vessels, et cetera. Next, the data lands on a secure and governed data platform where it is combined with data from existing systems of record to provide additional insights. And this platform supports multiple analytic functions working together on the same data while maintaining strict security, governance and control measures. Once processed the data is used to train machine learning models, which are then deployed into production, monitored and retrained as needed to maintain accuracy. The process data is also typically placed in a data warehouse and use to support business intelligence analytics and dashboards. And in fact, this data life cycle is representative of one of our government customers doing condition-based maintenance across a variety of aircraft. And the benefits they've discovered include; less unscheduled maintenance and a reduction in mean man hours to repair, increased maintenance efficiencies, improved aircraft availability, and the ability to avoid cascading component failures, which typically costs more in repair cost and downtime. Also, they're able to better forecast the requirements for replacement parts and consumables and last, and certainly very importantly, this leads to enhanced safety. This chart overlays the secure open source Cloudera platform used in support of the data life cycle we've been discussing. Cloudera data flow, provides the data ingest, data movement and real time streaming data query capabilities. So data flow gives us the capability to bring data in from the asset of interest, from the internet of things. While the data platform provides a secure governed data lake and visibility across the full machine learning life cycle eliminates silos and streamlines workflows across teams. The platform includes a integrated suite of secure analytic applications. And two that we're specifically calling out here are Cloudera machine learning, which supports the collaborative data science and machine learning environment, which facilitates machine learning and AI and the Cloudera data warehouse, which supports the analytics and business intelligence, including those dashboards for leadership Cindy, over to you. >> Cindy: Rick, Thank you. And I hope that Rick and I provided you some insights on how predictive maintenance condition-based maintenance is being used and can be used within your respective agency, bringing together data sources that maybe you're having challenges with today, bringing that more real-time information in from a streaming perspective, blending that industrial IOT, as well as historical information together to help actually optimize maintenance and produce costs within each of your agencies. To learn a little bit more about Cloudera and our, what we're doing from a predictive maintenance, please visit us at Cloudera.com/Solutions/PublicSector And we look forward to scheduling a meeting with you. And on that, we appreciate your time today and thank you very much.

(upbeat music) >> We're back with Charley Dublin. He's the Vice President of Product Management at Acquia. Great to see you, Charley welcome to theCUBE. >> Nice to meet you Dave. >> Acquia, tell us about the company. >> Sure, so Acquia is the largest and best provider of Drupal hosting capabilities. We rank number two in the digital experience platform space, just behind Adobe. Very strong business growing well and innovating every day. >> Drupal open source, super deep high quality content management system. And more experience, you call it an experience platform. >> An experience platform, open, flexible. We want our customers to have choice the ability to solve their problems how they want leveraging the power of the open source community. >> What were the big challenges? Just describe your, kind of the business drivers. We're going to talk about StormForge but the things that you were facing some of the challenges that's kind of led you to StormForge. >> Sure, so our objective first is to provide the best experience with Drupal. So that entails lots of capabilities around ease of use for Drupal itself. But that has to run on a world class platform. It has to be the most performance. It has to be the most secure. It needs to be flexible to enable customers to run Drupal however they want to run Drupal. And so that involves the ability to support thousands of different kinds of modules that come out of the community. We want our customers to have choice with Drupal and to be able to support those choices on our platform. >> So optionality is key. Sometimes that creates other challenges. Like you've got one of everything. How do you deal with that challenge? >> That's a great question. Every strength is a form of weakness. And so our objective is really first to provide that choice but to do it in a cost efficient way. So we try to provide reference architectures for customers, opinionation for our customers to standardize take out some of the complexity that they might have if everything were a snowflake. But our objective is really to support their needs and err on the side of that flexibility. >> So you guys had to go through a major replatforming effort around containers and Kubernetes can you talk about that and what role StormForge played? >> Sure, so tied to the last point, our objective is to provide customers the highest performance, most secure platform. The entire industry of course is moving to Kubernetes and leveraging containers. We are a large consumer of AWS Services and are undergoing a major replatforming away from Legacy AWS towards Kubernetes and containers. And so that major replatforming effort is intending to enable customers to run applications how they want to and the power of Kubernetes and containers is to support that. And so we looked at StormForge as a way for us to right size resource capacity to support our customer's applications. >> I love it, AWS is now Legacy. But Andy Jassy one time said that if they had to redo Amazon they'd it in Lambda using serverless and so, it's been around a long time now. Okay so what were the outcomes that you were seeking? Was it, better management, cost reduction and how'd that go? >> Our customers run a wide range of applications. We support customers leveraging Drupal in every industry. Globally we do business in 30 different countries. And so what you have is a very wide range of applications and consumer and consumption models. And so we felt that leveraging StormForge would put us in a position where we'd be able to right size resource to those different kinds of applications. Essentially let the platform align to how customers wanted to operate their applications. And so StormForge's capability in conjunction with Kubernetes and containers really puts us in a position where customers are able to get the performance that they want, and when they need it on demand. A lot of the auto scaling capabilities that you get from Kubernetes and containers supports that. And so it really enables customers to run their applications how they want to functionally, as well as from a performance perspective. >> So this move toward containers and microservices sort of modern application development coincides with a modern platform like StormForge. And so there are, I'm sure there are alternatives out there, why StormForge? Maybe you could explain a little bit more about why, from your perspective what it does and why you chose them. >> So we leverage AWS in many respects in terms of the underlying platform, but we are a very strong DIY for how that platform supports Drupal applications. We view our expertise as being the best of Drupal. And so we felt like for us to true really maximize Kubernetes and containers and the power of those underlying technologies. On the one hand allows us to automate more and do more for customers. On the other side of it, it puts a tremendous burden on the level of expertise in order to do that well for every customer every day at scale. And so that at scale part of that was the challenge. And so we leverage StormForge to enable us to rightsize applications for performance, provide us cost benefits, allocate what you need when you need it for our customers. And that at scale piece is a critical part. We could do elements of it internally. We tried to do elements of that internally, but as you start getting to scale from, a few apps to hundreds of apps to certainly across our fleet of tens of thousands of applications, you really need something that leverages machine learning. You really need a technology that's integrated well within AWS and StormForge provided that solution. >> Make sure I got this right. So it sounds like you sort of from a skill standpoint transitioned or applied your skills from turning knobs if you will, to automation and scale. >> Correct. >> And what was that like? Was the team leaning into that, loving it? Was it a, a challenging thing for you guys to get there? >> That's a good question. The benefit in the way that StormForge applies it. So they leverage machine learning to enable us to make better decisions. So we still have the control elements, but we have much greater insight into what that would mean ahead of time before customers would be affected. So we still have the knobs we need, but we're able to do it at scale. And then from the automation point, it allows us to focus our deep expertise on making Drupal and the core hosting platform capabilities awesome. Sort of the stuff and resource allocation resource consumption. That's an enabler we can outsource that to StormForge >> This is not batch it's, you're basically doing this in sort of near realtime Optimize Live, is the capability, maybe you can describe what it is. >> So Optimize Live is new, we're in testing with that. We've done extensive testing with StormForge on the core call it decision making logic that allows for the right sizing of consumption and resources for our customer application. So that has already been tested. So the core engine's been tested. Optimize Live allows us to do that in real time to make policy decisions across our fleet on what's the right trade off between performance cost, other parameters. Again, it informs our decision making and our management of our platform. That would be very, very difficult otherwise. Without StormForge we'd have to do massive data aggregation. We'd have to have machine learning and additional infrastructure to manage to derive this information, and, and, and. And that is not our core business. We don't want to be doing that. We want insights to manage our platform to enable customers and StormForge for provides that. >> So it's kind of human in the loop thing. Hey, here's what like our recommendation or here's some options that you might want to, here's a path that you want to go down, but it's not taking that action for you necessarily. You don't want that. You want to make sure that the experts are have a hand in it still, is that correct? >> Correct, you still want the experts to have a hand in it but you don't want them to have a hand in it on each individual app. You need that, that machine learning capability that insight that allows you to do that at scale. >> So if you had to step back and think about your relationship with StormForge what was the business impact of bringing them in? >> First, from a time to market perspective we're able to get to market with a higher performing more cost effective solution earlier. So there's that benefit. Second benefit to the earlier point is that we're able to make resource allocation decisions focused on where our core competency is, not into the guts of Kubernetes containers and the like. Third is that the machine learning talent that StormForge brings to the table is world class. I've run machine learning teams, data science teams and would put them in the top 1% of any team that I've worked with in terms of their expertise. The logic and decision making and insights is outstanding. So we can get to the best decision, the optimal decision much more quickly. And then when you accompany that with the newer product in Optimize Live with that automation component you mentioned, all the better. So we're able to make decisions quicker, get it implemented in our platform and realize the benefits. What customers get from that is much better performance of their applications. More real time, higher, able to scale more dynamically. What we get is resource efficiency and our network and platform efficiency. We're not over allocating a capacity that costs us more money than we should. We're under allocating capacity that could have a lower performance solution for our customers. >> So that puts money in your pocket and your customers are happier. So there are higher renewal rates, less churn, high air prices over time as you add more capabilities. >> That's correct. >> What's it like, new application approach, Kubernetes containers, fine. Okay I need a modern platform but it's a relatively new company StormForge. What's it like working with them? >> Their talent level is world class. I wasn't familiar with them when I joined Acquia came to know them and been very impressed. There's many other providers in the market that will speak to some similar capabilities and will make many claims. But from our assessment our view is that they're the right partner for us, they're the right size, they're flexible, excellent team. They've evolved their technology roadmap very quickly. They deliver on their promises and commits a very good team to work with. So I've been very impressed for such an early stage company to deliver and to support our business so rapidly. So I think that's a strength. And then I think again the quality that people that's been manifested in the product itself, it's a high quality product. I think it's unique to the market. >> So Napoleon Hill famous writer, thinker, he wrote "Think and Grow Rich." If you haven't read it, check it out. One of his concepts is this a lever, small lever can move a big rock. It can be very powerful. Do you see StormForge as having that kind of effect on your business that change on your business? >> I do. Like I said, I think the engagement with them has proven, and this isn't, debatable based on the results that we've had with them. We ran that team through the ringer to validate the technology. Again, we'd heard lots of promises from other companies. Ran that team through the ringer with extensive testing across many customers, large and small, many use cases, to really stress test their capabilities. And they came out well ahead of any metric we put forth even well ahead of claims that they had coming into the engagement. They exceeded that. And so that's why I'm here. Why I'm an advocate. Why I think they're an outstanding company with a tremendous amount of potential. >> Thinking about, what can you tell us about where you want to take the company and the partnership with StormForge. >> I think the main next step is for us to engage with StormForge to drive automation drive decisioning, as we expand and move more and more customers over to our new platform. We're going to uncover use cases, different challenges as we go. So I think the, it's a learning process for both both sides, but I think the it's been successful so far and has a lot potential. >> Sounds like you had a great business and a great new partnership. So thanks so much for coming on theCUBE, appreciate it. >> Thank you very much, appreciate your time. >> My pleasure. And thank you for watching theCUBE, you're global leader in enterprise tech coverage. (upbeat music)

(bright upbeat music) >> The adoption of container orchestration platforms is accelerating at a rate as fast or faster than any category in enterprise IT. Survey data from Enterprise Technology Research shows Kubernetes specifically leads the pack into both spending velocity and market share. Now like virtualization in its early days, containers bring many new performance and tuning challenges in particular insuring consistent and predictable application performance is tricky especially because containers, they're so flexible and they enable portability. Things are constantly changing. DevOps pros have to way through a sea of observability data and tuning the environment becomes a continuous exercise of trial and error. This endless cycle taxes resources and kills operational efficiency. So teams often just capitulate and simply dial up and throw unnecessary resources at the problem. StormForge is a company founded mid last decade that is attacking these issues with a combination of machine learning and data analysis. And with me to talk about a new offering that directly addresses these concerns is Matt Provo, founder and CEO of StormForge. Matt, welcome to theCUBE. Good to see you. >> Good to see you. Thanks for having me. >> Yeah, so we saw you guys at a KubeCon sort of first introduce you to our community but add a little color to my intro there if you will. >> Yeah, well, Semi stole my thunder but I'm okay with that. Absolutely agree with everything you said in the intro. You know, the problem that we have set out to solve which is tailor made for the use of real machine learning not machine learning kind of as a marketing tag is connected to how workloads on Kubernetes are really managed from a resource efficiency standpoint. And so a number of years ago, we built the core machine learning engine and have now turned that into a platform around how Kubernetes resources are managed at scale. And so organizations today as they're moving more workloads over, sort of drink the Kool-Aid of the flexibility that comes with Kubernetes and how many knobs you can turn. And developers in many ways love it. Once they start to operationalize the use of Kubernetes and move workloads from pre-production into production, they run into a pretty significant complexity wall. And this is where StormForge comes in to try to help them manage those resources more effectively in ensuring and implementing the right kind of automation that empowers developers into the process ultimately does not automate them out of it. >> So you've got news. You had launch coming to further address these problems. Tell us about that. >> Yeah, so historically, you know, like any machine learning engine, we think about data inputs and what kind of data is going to feed our system to be able to draw the appropriate insights out for the user. And so historically we've kind of been single threaded on load and performance tests in a pre-production environment. And there's been a lot of adoption of that, a lot of excitement around it and frankly amazing results. My vision has been for us to be able to close the loop, however, between data coming out of pre-production and the associated optimizations and data coming out of production environment and our ability to optimize that. A lot of our users along the way have said these results in pre-production are fantastic. How do I know they reflect reality of what my application is going to experience in a production environment? And so we're super excited to announce kind of the a second core module for our platform called Optimize Live. The data input for that is observability and telemetry data coming out of APM platforms and other data sources. >> So this is like Nirvana. So I wonder if we could talk a little bit more about the challenges that this addresses. I mean, I've been around a while and it really have observed... And I used to ask, you know, technology companies all the time. Okay, so you're telling me beforehand what the optimal configuration should be and resource allocation. What happens if something changes? >> Yeah. >> And then it's always, always a pause. >> Yeah. >> And Kubernetes is more of a rapidly changing environment than anything we've ever seen. So specifically the problem you're addressing. Maybe talk about that a little bit. >> Yeah, so we view what happens in pre-production as sort of the experimentation phase. And our machine learning is allowing the user to experiment in scenario plan. What we're doing with Optimize Live and adding the the production piece is what we kind of also call kind of our observation phase. And so you need to be able to run the appropriate checks and balances between those two environments to ensure that what you're actually deploying and monitoring from an application performance, from a cost standpoint is with your SLOs and your SLAs as well as your business objectives. And so that's the entire point of this edition is to allow our users to experience hopefully the Nirvana associated with that because it's an exciting opportunity for them and really something that no else is doing from the standpoint of closing that loop. >> So you said front machine learning not as a marketing tag. So I want you to sort of double click on that. What's different than how other companies approach this problem? >> Yeah, I mean, part of it is a bias for me and a frustration as a founder of the reason I started the company in the first place. I think machine learning or AI gets tagged to a lot of stuff. It's very buzzwordy. It looks good. I'm fortunate to have found a number of folks from the outset of the company with, you know, PhDs in Applied Mathematics and a focus on actually building real AI at the core that is connected to solving the right kind of actual business problems. And so, you know, for the first three or four years of the company's history, we really operated as a lab. And that was our focus. We then decided, we're trying to connect a fantastic team with differentiated technology to the right market timing. And when we saw all these pain points around how fast the adoption of containers and Kubernetes have taken place but the pain that the developers are running into, we actually found for ourselves that this was the perfect use case. >> So how specifically does Optimize Live work? Can you add a little detail on that? >> Yes, so when you... Many organizations today have an existing monitoring APM observability suite really in place. They've also got a metric source. So this could be something like Datadog or Prometheus. And once that data starts flowing, there's an out of the box or kind of a piece of Kubernetes that ships with it called the VPA or the Vertical Pod Autoscaler. And less than, really than 1% of Kubernetes users take advantage of the VPA mostly because it's really challenging to configure and it's not super compatible with the the tool set or, you know, the ecosystem of tools in a Kubernetes environment. And so our biggest competitor is the VPA. And what's happening in this environment or in this world for developers is they're having to make decisions on a number of different metrics or resource elements typically things like memory and CPU. And they have to decide what are the requests I'm going to allow application and what are the limits? So what are those thresholds that I'm going to be okay with so that I can, again, try to hit my business objectives and keep in line with my SLAs? And to your earlier point in the intro, it's often guesswork. You know, they either have to rely on out of the box recommendations that ship with the databases and other services that they are using or it's a super manual process to go through and try to configure and tune this. And so with Optimize Live, we're making that one click. And so we're continuously and consistently observing and watching the data that's flowing through these tools and we're serving back recommendations for the user. They can choose to let those recommendations automatically patch and deploy or they can retain some semblance of control over are the recommendations and manually deploy them into their environment themselves. And we, again, really believe that the user knows their application. They know the goals that they have and we don't. But we have a system that's smart enough to align with the business objectives and ultimately provide the relevant recommendations at that point. >> So the business objectives are an input from the application team? >> Yep. >> And then your system is smart enough to adapt and address those. >> Application over application, right? And so the thresholds in any given organization across their different ecosystem of apps or environment could be different. The business objectives could be different. And so we don't want to predefine that for people. We want to give them the opportunity to build those thresholds in and then allow the machine learning to learn and to send recommendations within those bounds. >> And we're going to hear later from a customer who's hosting a Drupal, one of the largest Drupal hosts. So it's all do it yourself across thousands of customers so it's, you know, very unpredictable. I want to make something clear though as to where you fit in the ecosystem. You're not an observability platform, you leverage observability platforms, right? So talk about that and where you fit into the ecosystem. >> Yeah, so it's a great point. We're also, you know, a series B startup and growing. We've the choice to be very intentionally focused on the problems that we've solve. And we've chosen to partner or integrate otherwise. And so we do get put into the APM category from time to time. We are really an intelligence platform. And that intelligence and insights that we're able to draw is because of the core machine learning we've built over the years. And we also don't want organizations or users to have to switch from tools and investments that they've already made. And so we were never going to catch up to to Datadog or Dynatrace or Splunk or AppDynamics or some of the other. And we're totally fine with that. They've got great market share and penetration. They do solve real problems. Instead, we felt like users would want a seamless integration into the tools they're already using. And so we view ourselves as kind of the Intel inside for that kind of a scenario. And it takes observability and APM data and insights that were somewhat reactive. They're visualized and somewhat reactive. And we add that proactive nature onto it, the insights and ultimately the appropriate level of automation. >> So when I think, Matt, about cloud native and I go back to the sort of origins of CNCF who's a, you know, handful of companies. And now you look at the participants it'll, you know, make your eyes bleed. How do you address dealing with all those companies and what is the partnership strategy? >> Yeah, it's so interesting because it's just that even that CNCF landscape has exploded. It was not too long ago where it was as small or smaller than the FinOps landscape today which by the way, the FinOps piece is also on a a neck breaking, you know, growth curve. We, I do see, although there are a lot of companies and a lot of tools, we're starting to see a significant amount of consistency or hardening of the tool chain, you know, with our customers and users. And so we've made strategic and intentional decisions on deep partnerships in some cases like OEM uses of our technology and certainly, you know, intelligent and seamless integrations into a few. So, you know, we'll be announcing a really exciting partnership with AWS and that specifically what they're doing with EKS, their Kubernetes distribution and services. We've got a deep partnership and integration with Datadog and then with Prometheus and specifically a few other cloud providers that are operating, manage Prometheus environments. >> Okay, so where do you want to take this thing? You're not taking the observability guys head on, smart move. So many of those even entering the market now. But what is the vision? >> Yeah, so we've had this debate a lot as well 'cause it's super difficult to create a category. You know, on one hand, you know, I have a lot of respect for founders and companies that do that. On the other hand from a market timing standpoint, you know we fit into AIOps, that's really where we fit. You know, we've made a bet on the future of Kubernetes and what that's going to look like. And so from a containers and Kubernetes standpoint, that's our bet. But we're an AIOps platform. You know, we'll continue getting better at the problems we solve with machine learning and we'll continue adding data inputs. So we'll go, you know, we'll go beyond the application layer which is really where we play now. We'll add, you know, kind of whole cluster optimization capabilities across the full stack. And the way we will get there is by continuing to add different data inputs that make sense across the different layers of the stack. And it's exciting. We can stay vertically oriented on the problems that we're really good at solving but we can become more applicable and compatible over time. >> So that's your next concentric circle. As the observability vendors expand their observation space, you can just play right into that. >> Yeah. >> The more data you get because your purpose built to solving these types of problems. >> Yeah, so you can imagine a world right now out of observability, we're taking things like telemetry data pretty quickly. You can imagine a world where we take traces and logs and other data inputs as that ecosystem continues to grow, it just feeds our own, you know, we are reliant on data. >> Excellent, Matt, thank you so much. >> Thanks for having me. >> Appreciate for coming on. Okay, keep it right there in a moment. We're going to hear from a customer with a highly diverse and constantly changing environment that I mentioned earlier. They went through a major replatforming with Kubernetes on AWS. You're watching theCUBE, you are leader in enterprise tech coverage. (bright upbeat music)

(upbeat music) >> We're back with Charley Dublin. He's the Vice President of Product Management at Acquia. Great to see you, Charley welcome to theCUBE. >> Nice to meet you Dave. >> Acquia, tell us about the company. >> Sure, so Acquia is the largest and best provider of Drupal hosting capabilities. We rank number two in the digital experience platform space, just behind Adobe. Very strong business growing well and innovating every day. >> Drupal open source, super deep high quality content management system. And more experience, you call it an experience platform. >> An experience platform, open, flexible. We want our customers to have choice the ability to solve their problems how they want leveraging the power of the open source community. >> What were the big challenges? Just describe your, kind of the business drivers. We're going to talk about StormForge but the things that you were facing some of the challenges that's kind of led you to StormForge. >> Sure, so our objective first is to provide the best experience with Drupal. So that entails lots of capabilities around ease of use for Drupal itself. But that has to run on a world class platform. It has to be the most performance. It has to be the most secure. It needs to be flexible to enable customers to run Drupal however they want to run Drupal. And so that involves the ability to support thousands of different kinds of modules that come out of the community. We want our customers to have choice with Drupal and to be able to support those choices on our platform. >> So optionality is key. Sometimes that creates other challenges. Like you've got one of everything. How do you deal with that challenge? >> That's a great question. Every strength is a form of weakness. And so our objective is really first to provide that choice but to do it in a cost efficient way. So we try to provide reference architectures for customers, opinionation for our customers to standardize take out some of the complexity that they might have if everything were a snowflake. But our objective is really to support their needs and err on the side of that flexibility. >> So you guys had to go through a major replatforming effort around containers and Kubernetes can you talk about that and what role StormForge played? >> Sure, so tied to the last point, our objective is to provide customers the highest performance, most secure platform. The entire industry of course is moving to Kubernetes and leveraging containers. We are a large consumer of AWS Services and are undergoing a major replatforming away from Legacy AWS towards Kubernetes and containers. And so that major replatforming effort is intending to enable customers to run applications how they want to and the power of Kubernetes and containers is to support that. And so we looked at StormForge as a way for us to right size resource capacity to support our customer's applications. >> I love it, AWS is now Legacy. But Andy Jassy one time said that if they had to redo Amazon they'd it in Lambda using serverless and so, it's been around a long time now. Okay so what were the outcomes that you were seeking? Was it, better management, cost reduction and how'd that go? >> Our customers run a wide range of applications. We support customers leveraging Drupal in every industry. Globally we do business in 30 different countries. And so what you have is a very wide range of applications and consumer and consumption models. And so we felt that leveraging StormForge would put us in a position where we'd be able to right size resource to those different kinds of applications. Essentially let the platform align to how customers wanted to operate their applications. And so StormForge's capability in conjunction with Kubernetes and containers really puts us in a position where customers are able to get the performance that they want, and when they need it on demand. A lot of the auto scaling capabilities that you get from Kubernetes and containers supports that. And so it really enables customers to run their applications how they want to functionally, as well as from a performance perspective. >> So this move toward containers and microservices sort of modern application development coincides with a modern platform like StormForge. And so there are, I'm sure there are alternatives out there, why StormForge? Maybe you could explain a little bit more about why, from your perspective what it does and why you chose them. >> So we leverage AWS in many respects in terms of the underlying platform, but we are a very strong DIY for how that platform supports Drupal applications. We view our expertise as being the best of Drupal. And so we felt like for us to true really maximize Kubernetes and containers and the power of those underlying technologies. On the one hand allows us to automate more and do more for customers. On the other side of it, it puts a tremendous burden on the level of expertise in order to do that well for every customer every day at scale. And so that at scale part of that was the challenge. And so we leverage StormForge to enable us to rightsize applications for performance, provide us cost benefits, allocate what you need when you need it for our customers. And that at scale piece is a critical part. We could do elements of it internally. We tried to do elements of that internally, but as you start getting to scale from, a few apps to hundreds of apps to certainly across our fleet of tens of thousands of applications, you really need something that leverages machine learning. You really need a technology that's integrated well within AWS and StormForge provided that solution. >> Make sure I got this right. So it sounds like you sort of from a skill standpoint transitioned or applied your skills from turning knobs if you will, to automation and scale. >> Correct. >> And what was that like? Was the team leaning into that, loving it? Was it a, a challenging thing for you guys to get there? >> That's a good question. The benefit in the way that StormForge applies it. So they leverage machine learning to enable us to make better decisions. So we still have the control elements, but we have much greater insight into what that would mean ahead of time before customers would be affected. So we still have the knobs we need, but we're able to do it at scale. And then from the automation point, it allows us to focus our deep expertise on making Drupal and the core hosting platform capabilities awesome. Sort of the stuff and resource allocation resource consumption. That's an enabler we can outsource that to StormForge >> This is not batch it's, you're basically doing this in sort of near realtime Optimize Live, is the capability, maybe you can describe what it is. >> So Optimize Live is new, we're in testing with that. We've done extensive testing with StormForge on the core call it decision making logic that allows for the right sizing of consumption and resources for our customer application. So that has already been tested. So the core engine's been tested. Optimize Live allows us to do that in real time to make policy decisions across our fleet on what's the right trade off between performance cost, other parameters. Again, it informs our decision making and our management of our platform. That would be very, very difficult otherwise. Without StormForge we'd have to do massive data aggregation. We'd have to have machine learning and additional infrastructure to manage to derive this information, and, and, and. And that is not our core business. We don't want to be doing that. We want insights to manage our platform to enable customers and StormForge for provides that. >> So it's kind of human in the loop thing. Hey, here's what like our recommendation or here's some options that you might want to, here's a path that you want to go down, but it's not taking that action for you necessarily. You don't want that. You want to make sure that the experts are have a hand in it still, is that correct? >> Correct, you still want the experts to have a hand in it but you don't want them to have a hand in it on each individual app. You need that, that machine learning capability that insight that allows you to do that at scale. >> So if you had to step back and think about your relationship with StormForge what was the business impact of bringing them in? >> First, from a time to market perspective we're able to get to market with a higher performing more cost effective solution earlier. So there's that benefit. Second benefit to the earlier point is that we're able to make resource allocation decisions focused on where our core competency is, not into the guts of Kubernetes containers and the like. Third is that the machine learning talent that StormForge brings to the table is world class. I've run machine learning teams, data science teams and would put them in the top 1% of any team that I've worked with in terms of their expertise. The logic and decision making and insights is outstanding. So we can get to the best decision, the optimal decision much more quickly. And then when you accompany that with the newer product in Optimize Live with that automation component you mentioned, all the better. So we're able to make decisions quicker, get it implemented in our platform and realize the benefits. What customers get from that is much better performance of their applications. More real time, higher, able to scale more dynamically. What we get is resource efficiency and our network and platform efficiency. We're not over allocating a capacity that costs us more money than we should. We're under allocating capacity that could have a lower performance solution for our customers. >> So that puts money in your pocket and your customers are happier. So there are higher renewal rates, less churn, high air prices over time as you add more capabilities. >> That's correct. >> What's it like, new application approach, Kubernetes containers, fine. Okay I need a modern platform but it's a relatively new company StormForge. What's it like working with them? >> Their talent level is world class. I wasn't familiar with them when I joined Acquia came to know them and been very impressed. There's many other providers in the market that will speak to some similar capabilities and will make many claims. But from our assessment our view is that they're the right partner for us, they're the right size, they're flexible, excellent team. They've evolved their technology roadmap very quickly. They deliver on their promises and commits a very good team to work with. So I've been very impressed for such an early stage company to deliver and to support our business so rapidly. So I think that's a strength. And then I think again the quality that people that's been manifested in the product itself, it's a high quality product. I think it's unique to the market. >> So Napoleon Hill famous writer, thinker, he wrote "Think and Grow Rich." If you haven't read it, check it out. One of his concepts is this a lever, small lever can move a big rock. It can be very powerful. Do you see StormForge as having that kind of effect on your business that change on your business? >> I do. Like I said, I think the engagement with them has proven, and this isn't, debatable based on the results that we've had with them. We ran that team through the ringer to validate the technology. Again, we'd heard lots of promises from other companies. Ran that team through the ringer with extensive testing across many customers, large and small, many use cases, to really stress test their capabilities. And they came out well ahead of any metric we put forth even well ahead of claims that they had coming into the engagement. They exceeded that. And so that's why I'm here. Why I'm an advocate. Why I think they're an outstanding company with a tremendous amount of potential. >> Thinking about, what can you tell us about where you want to take the company and the partnership with StormForge. >> I think the main next step is for us to engage with StormForge to drive automation drive decisioning, as we expand and move more and more customers over to our new platform. We're going to uncover use cases, different challenges as we go. So I think the, it's a learning process for both both sides, but I think the it's been successful so far and has a lot potential. >> Sounds like you had a great business and a great new partnership. So thanks so much for coming on theCUBE, appreciate it. >> Thank you very much, appreciate your time. >> My pleasure. And thank you for watching theCUBE, you're global leader in enterprise tech coverage. (upbeat music)

(bright upbeat music) >> The adoption of container orchestration platforms is accelerating at a rate as fast or faster than any category in enterprise IT. Survey data from Enterprise Technology Research shows Kubernetes specifically leads the pack in both spending velocity and market share. Now like virtualization in its early days, containers bring many new performance and tuning challenges. In particular, ensuring consistent and predictable application performance is tricky especially because containers they're so flexible and the enabled portability things are constantly changing. DevOps pros have to wade through a sea of observability data and tuning the environment becomes a continuous exercise of trial and error. This endless cycle taxes, resources, and kills operational efficiencies so teams often just capitulate and simply dial up and throw unnecessary resources at the problem. StormForge is a company founded in mid last decade that is attacking these issues with a combination of machine learning and data analysis. And with me to talk about a new offering that directly addresses these concerns, is Matt Provo, founder and CEO of StormForge. Matt, welcome to thecube. Good to see you. >> Good to see you, thanks for having me. >> Yeah. So we saw you guys at CubeCon, sort of first introduce you to our community but add a little color to my intro if you will. >> Yeah, well you semi stole my thunder but I'm okay with that. Absolutely agree with everything you said in the intro. You know, the problem that we have set out to solve which is tailor made for the use of real machine learning not machine learning kind of as a marketing tag is connected to how workloads on Kubernetes are really managed from a resource efficiency standpoint. And so a number of years ago we built the core machine learning engine and have now turned that into a platform around how Kubernetes resources are managed at scale. And so organizations today as they're moving more workloads over sort of drink the Kool-Aid of the flexibility that comes with Kubernetes and how many knobs you can turn and developers in many ways love it. Once they start to operationalize the use of Kubernetes and move workloads from pre-production into production, they run into a pretty significant complexity wall. And this is where StormForge comes in to try to help them manage those resources more effectively in ensuring and implementing the right kind of automation that empowers developers into the process ultimately does not automate them out of it. >> So you've got news, your hard launch coming in to further address these problems. Tell us about that. >> Yeah so historically, you know, like any machine learning engine, we think about data inputs and what kind of data is going to feed our system to be able to draw the appropriate insights out for the user. And so historically we are, we've kind of been single-threaded on load and performance tests in a pre-production environment. And there's been a lot of adoption of that, a lot of excitement around it and frankly, amazing results. My vision has been for us to be able to close the loop however between data coming out of pre-production and the associated optimizations and data coming out of production, a production environment, and our ability to optimize that. A lot of our users along the way have said these results in pre-production are fantastic. How do I know they reflect reality of what my application is going to experience in a production environment? And so we're super excited to announce kind of the second core module for our platform called Optimize Live. The data input for that is observability and telemetry data coming out of APM platforms and other data sources. >> So this is like Nirvana. So I wonder if we could talk a little bit more about the challenges that this addresses. I mean, I've been around a while and it really have observed and I used to ask technology companies all the time, okay, so you're telling me beforehand what the optimal configuration should be in resource allocation, what happens if something changes? And then it's always a pause. And Kubernetes is more of a rapidly changing environment than anything we've ever seen. So this is specifically the problem you're addressing. Maybe talk about that a little bit. >> Yeah so we view what happens in pre-production as sort of the experimentation phase and our machine learning is allowing the user to experiment and scenario plan. What we're doing with Optimize Live and adding the production piece is what we kind of also call kind of our observation phase. And so you need to be able to run the appropriate checks and balances between those two environments to ensure that what you're actually deploying and monitoring from an application performance, from a cost standpoint, is aligning with your SLOs and your SLAs as well as your business objectives. And so that's the entire point of this addition is to allow our users to experience hopefully the Nirvana associated with that because it's an exciting opportunity for them and really something that nobody else is doing from the standpoint of closing that loop. >> So you said upfront machine learning not as a marketing tag. So I want you to sort of double click on that. What's different than how other companies approach this problem? >> Yeah I mean, part of it is a bias for me and a frustration as a founder of the reason I started the company in the first place. I think machine learning our AI gets tagged to a lot of stuff. It's very buzzwordy, it looks good. I'm fortunate to have found a number of folks from the outset of the company with, you know, PhDs in Applied Mathematics and a focus on actually building real AI at the core that is connected to solving the right kind of actual business problems. And so, you know, for the first three or four years of the company's history, we really operated as a lab and that was our focus. We then decided we're trying to connect a fantastic team with differentiated technology to the right market timing. And when we saw all of these pain points around how fast the adoption of containers and Kubernetes have taken place but the pain that the developers are running into, we found it, we actually found for ourselves that this was the perfect use case. >> So how specifically does Optimize Live work? Can you add a little detail on that? >> Yeah so when you, many organizations today have an existing monitoring APM observability suite really in place. They've also got, they've also got a metric source, so this could be something like Datadog or Prometheus. And once that data starts flowing, there's an out of the box or kind of a piece of Kubernetes that ships with it called the VPA or the Vertical Pod Autoscaler. And less than really less than 1% of Kubernetes users take advantage of the VPA mostly because it's really challenging to configure and it's not super compatible with the tool set or the, you know, the ecosystem of tools in a Kubernetes environment. And so our biggest competitor is the VPA. And what's happening in this environment or in this world for developers is they're having to make decisions on a number of different metrics or resource elements typically things like memory and CPU. And they have to decide what are the, what are the requests I'm going to allow for this application and what are the limits? So what are those thresholds that I'm going to be okay with? So that I can again try to hit my business objectives and keep in line with my SLAs. And to your earlier point in the intro, it's often guesswork. You know, they either have to rely on out of the box recommendations that ship with the databases and other services that they are using or it's a super manual process to go through and try to configure and tune this. And so with Optimize Live, we're making that one-click. And so we're continuously and consistently observing and watching the data that's flowing through these tools and we're serving back recommendations for the user. They can choose to let those recommendations automatically patch and deploy or they can retain some semblance of control over the recommendations and manually deploy them into their environment themselves. And we again, really believe that the user knows their application, they know the goals that they have, we don't. But we have a system that's smart enough to align with the business objectives and ultimately provide the relevant recommendations at that point. >> So the business objectives are an input from the application team and then your system is smart enough to adapt and adjust those. >> Application over application, right? And so the thresholds in any given organization across their different ecosystem of apps or environment could be different. The business objectives could be different. And so we don't want to predefine that for people. We want to give them the opportunity to build those thresholds in and then allow the machine learning to learn and to send recommendations within those bounds. >> And we're going to hear later from a customer who is hosting a Drupal, one of the largest Drupal host, is it? So it's all do it yourself across thousands of customers so it's very unpredictable. I want to make something clear though, as to where you fit in the ecosystem. You're not an observability platform, you leverage observability platforms, right? So talk about that and where you fit in into the ecosystem. >> Yeah so it's a great point. We, we're also you know, a series B startup and growing. We've made the choice to be very intentionally focused on the problems that we've solve and we've chosen to partner or integrate otherwise. And so we do get put into the APM category from time to time. We're really an intelligence platform. And that intelligence and insights that we're able to draw is because we, because of the core machine learning we've built over the years. And we also don't want organizations or users to have to switch from tools and investments that they've already made. And so we were never going to catch up to Datadog or Dynatrace or Splunk or AppDynamics or some of the other, and we're totally fine with that. They've got great market share and penetration and they do solve real problems. Instead, we felt like users would want a seamless integration into the tools they're already using. And so we view ourselves as kind of the Intel inside for that kind of a scenario. And it takes observability and APM data and insights that were somewhat reactive, they're visualized and somewhat reactive and we make those, we add that proactive nature onto it, the insights and ultimately the appropriate level of automation. >> So when I think Matt about cloud native and I go back to the sort of origins of CNCF, it was a, you know, handful of companies, and now you look at the participants, you know, make your eyes bleed. How do you address dealing with all those companies and what's the partnership strategy? >> Yeah it's so interesting because it's just that even at CNCF landscape has exploded. It was not too long ago where it was as smaller than the finOps Landscape today which by the way the FinOps pieces is also on a neck breaking, you know, growth curve. We, I do see although there are a lot of companies and a lot of tools, we're starting to see a significant amount of consistency or hardening of the tool chain with our customers and users. And so we've made strategic and intentional decisions on deep partnerships in some cases like OEM users of our technology and certainly, you know, intelligent and seamless integrations into a few. So, you know, we'll be announcing a really exciting partnership with AWS and specifically what they're doing with EKS, their Kubernetes distribution and services. We've got a deep partnership and integration with Datadog and then with Prometheus and specifically cloud provider, a few other cloud providers that are operating manage Prometheus environments. >> Okay so where do you want to take this thing? If it's not, you're not taking the observability guys head on, smart move, so many of those even entering the market now, but what is the vision? >> Yeah so we've had this debate a lot as well because it's super difficult to create a category. You know, on one hand, I have a lot of respect for founders and companies that do that, on the other hand from a market timing standpoint, you know, we fit into AIOps. That's really where we fit. You know we are, we've made a bet on the future of Kubernetes and what that's going to look like. And so from a containers and Kubernetes standpoint that's our bet. But we're an AIOps platform, we'll continue getting better at what, at the problems we solve with machine learning and we'll continue adding data inputs so we'll go beyond the application layer which is really where we play now. We'll add kind of whole cluster optimization capabilities across the full stack. And the way we'll get there is by continuing to add different data inputs that make sense across the different layers of the stack and it's exciting. We can stay vertically oriented on the problems that we're really good at solving but we become more applicable and compatible over time. >> So that's your next concentric circle. As the observability vendors expand their observation space you can just play right into that. The more data you get could be because you're purpose built to solving these types of problems. >> Yeah so you can imagine a world right now out of observability, we're taking things like telemetry data pretty quickly. You can imagine a world where we take traces and logs and other data inputs as that ecosystem continues to grow, it just feeds our own, you know, we are reliant on data. So. >> Excellent. Matt, thank you so much. Thanks for hoping on. >> Yeah, appreciate it. >> Okay. Keep it right there. In a moment, We're going to hear from a customer with a highly diverse and constantly changing environment that I mentioned earlier, they went through a major re-platforming with Kubernetes on AWS. You're watching theCube, your a leader in enterprise tech coverage. (bright music)