>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

>>Hello everyone. Welcome to the Super Cloud Event preview. I'm John Forry, host of the Cube, and with Dave Valante, host of the popular Super cloud events. This is Super Cloud two preview. I'm joined by industry leader and Cube alumni, Victoria Vigo, vice president of klos Cross Cloud Services at VMware. Vittorio. Great to see you. We're here for the preview of Super Cloud two on January 17th, virtual event, live stage performance, but streamed out to the audience virtually. We're gonna do a preview. Thanks for coming in. >>My pleasure. Always glad to be here. >>It's holiday time. We had the first super cloud on in August prior to VMware, explore North America prior to VMware, explore Europe prior to reinvent. We've been through that, but right now, super Cloud has got momentum. Super Cloud two has got some success. Before we dig into it, let's take a step back and set the table. What is Super Cloud and why is important? Why are people buzzing about it? Why is it a thing? >>Look, we have been in the cloud now for like 10, 15 years and the cloud is going strong and I, I would say that going cloud first was deliberate and strategic in most cases. In some cases the, the developer was going for the path of risk resistance, but in any sizable company, this caused the companies to end up in a multi-cloud world where 85% of the companies out there use two or multiple clouds. And with that comes what we call cloud chaos, because each cloud brings their own management tools, development tools, security. And so that increase the complexity and cost. And so we believe that it's time to usher a new era in cloud computing, which we, you call the super cloud. We call it cross cloud services, which allows our customers to have a single way to build, manage, secure, and access any application across any cloud. Lowering the cost and simplifying the environment. Since >>Dave Ante and I introduced and rift on the concept of Supercloud, as we talked about at reinvent last year, a lot has happened. Supercloud one, it was in August, but prior to that, great momentum in the industry. Great conversation. People are loving it, they're hating it, which means it's got some traction. Berkeley has come on board as with a position paper. They're kind of endorsing it. They call it something different. You call it cross cloud services, whatever it is. It's kind of the same theme we're seeing. And so the industry has recognized something is happening that's different than what Cloud one was or the first generation of cloud. Now we have something different. This Super Cloud two in January. This event has traction with practitioners, customers, big name brands, Sachs, fifth Avenue, Warner, media Financial, mercury Financial, other big names are here. They're leaning in. They're excited. Why the traction in the customer's industry converts over to, to the customer traction. Why is it happening? You, you get a lot of data. >>Well, in, in Super Cloud one, it was a vendor fest, right? But these vendors are smart people that get their vision from where, from the customers. This, this stuff doesn't happen in a vacuum. We all talk to customers and we tend to lean on the early adopters and the early adopters of the cloud are the ones that are telling us, we now are in a place where the complexity is too much. The cost is ballooning. We're going towards slow down potentially in the economy. We need to get better economics out of, of our cloud. And so every single customers I talked to today, or any sizable company as this problem, the developers have gone off, built all these applications, and now the business is coming to the operators and asking, where are my applications? Are they performing? What is the security posture? And how do we do compliance? And so now they're realizing we need to do something about this or it is gonna be unmanageable. >>I wanna go to a clip I pulled out from the, our video data lake and the cube. If we can go to that clip, it's Chuck Whitten Dell at a keynote. He was talking about what he calls multi-cloud by default, not by design. This is a state of the, of the industry. If we're gonna roll that clip, and I wanna get your reaction to that. >>Well, look, customers have woken up with multiple clouds, you know, multiple public clouds. On-premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was it can be, and it should be, it is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they wanna maintain an on-premise cloud. On-premise clouds are not going away. I mentioned edge Cloud, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud, by default we mean that's the state of the world. Today, our goal is to bring multi-cloud by design, as you heard. Yeah, I >>Mean, I, okay, Vittorio, that's, that's the head of Dell Technologies president. He obvious he runs it. Michael Dell's still around, but you know, he's the leader. This is a interesting observation. You know, he's not a customer. We have some customer equips we'll go to as well, but by default it kind of happened not by design. So we're now kind of in a zoom out issue where, okay, I got this environment just landed on me. What, what is the, what's your reaction to that clip of how multi-cloud has become present in, in everyone's on everyone's plate right now to deal with? Yeah, >>I it is, it is multi-cloud by default, I would call it by accident. We, we really got there by accident. I think now it's time to make it a strategic asset because look, we're using multiple cloud for a reason, because all these hyperscaler bring tremendous innovation that we want to leverage. But I strongly believe that in it, especially history repeat itself, right? And so if you look at the history of it, as was always when a new level of obstruction that simplify things, that we got the next level of innovation at the lower cost, you know, from going from c plus plus to Visual basic, going from integrating application at the bits of by layer to SOA and then web services. It's, it's only when we simplify the environment that we can go faster and lower cost. And the multi-cloud is ready for that level of obstruction today. >>You know, you've made some good points. You know, developers went crazy building great apps. Now they got, they gotta roll it out and operationalize it globally. A lot of compliance issues going on. The costs are going up. We got an economic challenge, but also agility with the cloud. So using cloud and or hybrid, you can get better agility. And also moving to the cloud, it's kind of still slow. Okay, so I get that at reinvent this year and at VMware explorer we were observing and we reported that you're seeing a transition to a new kind of ecosystem partner. Ones that aren't just ISVs anymore. You have ISVs, independent software vendors, but you got the emergence of bigger players that just, they got platforms, they have their own ecosystems. So you're seeing ecosystems on top of ecosystems where, you know, MongoDB CEO and the Databricks CEO both told me, we're not an isv, we're a platform built on a cloud. So this new kind of super cloudlike thing is going on. Why should someone pay attention to the super cloud movement? We're on two, we're gonna continue to do these out in the open. Anyone can participate. Why should people pay attention to this? Why should they come to the event? Why is this important? Is this truly an inflection point? And if they do pay attention, what should they pay attention to? >>I would pay attention to two things. If you are customers that are now starting to realize that you have a multi-cloud problem and the costs are getting outta control, look at what the leading vendors are saying, connect the dots with the early adopters and some of the customers that we are gonna have at Super Cloud two, and use those learning to not fall into the same trap. So I, I'll give you an example. I was talking to a Fortune 50 in Europe in my latest trip, and this is an a CIO that is telling me >>We build all these applications and now for compliance reason, the business is coming to me, I don't even know where they are, right? And so what I was telling him, so look, there are other customers that are already there. What did they do? They built a platform engineering team. What is the platform? Engineering team is a, is an operation team that understands how developers build modern applications and lays down the foundation across multiple clouds. So the developers can be developers and do their thing, which is writing code. But now you as a cio, as a, as a, as a governing body, as a security team can have the guardrail. So do you know that these applications are performing at a lower cost and are secure and compliant? >>Patura, you know, it's really encouraging and, and love to get your thoughts on this is one is the general consensus of industry leaders. I talked to like yourself in the round is the old way was soft complexity with more complexity. The cloud demand simplicity, you mentioned abstraction layer. This is our next inflection point. It's gotta be simpler and it's gotta be easy and it's gotta be performant. That's the table stakes of the cloud. What's your thoughts on this next wave of simplicity versus complexity? Because again, abstraction layers take away complexity, they should make it simpler. What's your thoughts? >>Yeah, so I'll give you few examples. One, on the development side and runtime. You, you one would think that Kubernetes will solve all the problems you have Kubernetes everywhere, just look at, but every cloud has a different distribution of Kubernetes, right? So for example, at VMware with tansu, we create a single place that allows you to deploy that any Kubernetes environment. But now you have one place to set your policies. We take care of the differences between this, this system. The second area is management, right? So once you have all everything deployed, how do you get a single object model that tells you where your stuff is and how it's performing, and then apply policies to it as well. So these are two areas and security and so on and so forth. So the idea is that figure out what you can abstract and make common across cloud. Make that simple and put it in one place while always allowing the developers to go underneath and use the differentiated features for innovation. >>Yeah, one of the areas I'm excited, I want to get your thoughts of too is, we haven't talked about this in the past, but it, I'll throw it out there. I think the, the new AI coming out chat, G P T and other things like lens, you see it and see new kinds of AI coming that's gonna be right in the heavy lifting opportunity to make things easier with AI and automation. I think AI will be a big factor in super cloud and, and cross cloud. What's your thoughts? >>Well, the one way to look at AI is, is one of the main, main services that you would want out of a multi-cloud, right? You want eventually, right now Google seems to have an edge, but you know, the competition creates, you know, innovation. So later on you wanna use something from Azure or from or from Oracle or something that, so you want at some point that is gonna be prone every single service in in the cloud is gonna be prone to obstruction and simplification. And I, I'm just excited about to see >>What book, I can't wait for the chat services to write code automatically for us. Well, >>They >>Do, they do. They're doing it now. They do. >>Oh, the other day, somebody, you know that I do this song par this for. So for fun sometimes. And somebody the other day said, ask the AI to write a parody song for multi-cloud. And so I have the lyrics stay >>Tuned. I should do that from my blog post. Hey, write a blog post on this January 17th, Victoria, thanks for coming in, sharing the preview bottom line. Why should people come? Why is it important? What's your final kind of takeaway? Billboard message >>History is repeat itself. It goes to three major inflection points, right? We had the inflection point with the cloud and the people that got left behind, they were not as competitive as the people that got on top o of this wave. The new wave is the super cloud, what we call cross cloud services. So if you are a customer that is experiencing this problem today, tune in to to hear from other customers in, in your same space. If you are behind, tune in to avoid the, the, the, the mistakes and the, the shortfalls of this new wave. And so that you can use multi-cloud to accelerate your business and kick butt in the future. >>All right. Kicking kick your names and kicking butt. Okay, we're here on J January 17th. Super Cloud two. Momentum continues. We'll be super cloud three. There'll be super cloud floor. More and more open conversations. Join the community, join the conversation. It's open. We want more voices. We want more, more industry. We want more customers. It's happening. A lot of momentum. Victoria, thank you for your time. Thank you. Okay. I'm John Farer, host of the Cube. Thanks for watching.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>Everyone, welcome to the cube here in Palo Alto, California for a special presentation on Cloud native at scale, enabling super cloud modern applications with Platform nine. I'm John Furry, your host of The Cube. We've got a great lineup of three interviews we're streaming today. Mattor Makki, who's the co-founder and VP of Product of Platform nine. She's gonna go into detail around Arlon, the open source products, and also the value of what this means for infrastructure as code and for cloud native at scale. Bickley the chief architect of Platform nine Cube alumni. Going back to the OpenStack days. He's gonna go into why Arlon, why this infrastructure as code implication, what it means for customers and the implications in the open source community and where that value is. Really great wide ranging conversation there. And of course, Vascar, Gort, the CEO of Platform nine, is gonna talk with me about his views on Super Cloud and why Platform nine has a scalable solutions to bring cloud native at scale. So enjoy the program, see you soon. Hello and welcome to the cube here in Palo Alto, California for a special program on cloud native at scale, enabling next generation cloud or super cloud for modern application cloud native developers. I'm John Forry, host of the Cube. Pleasure to have here me Makowski, co-founder and VP of product at Platform nine. Thanks for coming in today for this Cloudnative at scale conversation. >>Thank you for having >>Me. So Cloudnative at scale, something that we're talking about because we're seeing the, the next level of mainstream success of containers Kubernetes and cloud native develop, basically DevOps in the C I C D pipeline. It's changing the landscape of infrastructure as code, it's accelerating the value proposition and the super cloud as we call it, has been getting a lot of traction because this next generation cloud is looking a lot different, but kind of the same as the first generation. What's your view on Super cloud as it fits to cloud native as scales up? >>Yeah, you know, I think what's interesting, and I think the reason why Super Cloud is a really good and a really fit term for this, and I think, I know my CEO was chatting with you as well, and he was mentioning this as well, but I think there needs to be a different term than just multi-cloud or cloud. And the reason is because as cloud native and cloud deployments have scaled, I think we've reached a point now where instead of having the traditional data center style model, where you have a few large distributors of infrastructure and workload at a few locations, I think the model is kind of flipped around, right? Where you have a large number of micro sites. These micro sites could be your public cloud deployment, your private on-prem infrastructure deployments, or it could be your edge environment, right? And every single enterprise, every single industry is moving in that direction. And so you gotta rougher that with a terminology that, that, that indicates the scale and complexity of it. And so I think super cloud is a, is an appropriate term for >>That. So you brought a couple things I want to dig into. You mentioned Edge Notes. We're seeing not only edge nodes being the next kind of area of innovation, mainly because it's just popping up everywhere. And that's just the beginning. Wouldn't even know what's around the corner. You got buildings, you got iot, o ot, and it kind of coming together, but you also got this idea of regions, global infrastructures, big part of it. I just saw some news around cloud flare shutting down a site here, there's policies being made at scale. These new challenges there. Can you share because you can have edge. So hybrid cloud is a winning formula. Everybody knows that it's a steady state. Yeah. But across multiple clouds brings in this new un engineered area, yet it hasn't been done yet. Spanning clouds. People say they're doing it, but you start to see the toe in the water, it's happening, it's gonna happen. It's only gonna get accelerated with the edge and beyond globally. So I have to ask you, what is the technical challenges in doing this? Because it's something business consequences as well, but there are technical challenge. Can you share your view on what the technical challenges are for the super cloud across multiple edges and >>Regions? Yeah, absolutely. So I think, you know, in in the context of this, the, this, this term of super cloud, I think it's sometimes easier to visualize things in terms of two access, right? I think on one end you can think of the scale in terms of just pure number of nodes that you have, deploy number of clusters in the Kubernetes space. And then on the other access you would have your distribution factor, right? Which is, do you have these tens of thousands of nodes in one site or do you have them distributed across tens of thousands of sites with one node at each site? Right? And if you have just one flavor of this, there is enough complexity, but potentially manageable. But when you are expanding on both these access, you really get to a point where that skill really needs some well thought out, well-structured solutions to address it, right? A combination of homegrown tooling along with your, you know, favorite distribution of Kubernetes is not a strategy that can help you in this environment. It may help you when you have one of this or when you, when you scale, is not at the level. >>Can you scope the complexity? Because I mean, I hear a lot of moving parts going on there, the technology's also getting better. We we're seeing cloud native become successful. There's a lot to configure, there's a lot to install. Can you scope the scale of the problem? Because we're talking about at scale Yep. Challenges here. >>Yeah, absolutely. And I think, you know, I I like to call it, you know, the, the, the problem that the scale creates, you know, there's various problems, but I think one, one problem, one way to think about it is, is, you know, it works on my cluster problem, right? So, you know, I come from engineering background and there's a, you know, there's a famous saying between engineers and QA and the support folks, right? Which is, it works on my laptop, which is I tested this change, everything was fantastic, it worked flawlessly on my machine, on production, It's not working. The exact same problem now happens and these distributed environments, but at massive scale, right? Which is that, you know, developers test their applications, et cetera within the sanctity of their sandbox environments. But once you expose that change in the wild world of your production deployment, right? >>And the production deployment could be going at the radio cell tower at the edge location where a cluster is running there, or it could be sending, you know, these applications and having them run at my customer's site where they might not have configured that cluster exactly the same way as I configured it, or they configured the cluster, right? But maybe they didn't deploy the security policies or they didn't deploy the other infrastructure plugins that my app relies on all of these various factors at their own layer of complexity. And there really isn't a simple way to solve that today. And that is just, you know, one example of an issue that happens. I think another, you know, whole new ball game of issues come in the context of security, right? Because when you are deploying applications at scale in a distributed manner, you gotta make sure someone's job is on the line to ensure that the right security policies are enforced regardless of that scale factor. So I think that's another example of problems that occur. >>Okay. So I have to ask about scale because there are a lot of multiple steps involved when you see the success cloud native, you know, you see some, you know, some experimentation. They set up a cluster, say it's containers and Kubernetes, and then you say, Okay, we got this, we can configure it. And then they do it again and again, they call it day two. Some people call it day one, day two operation, whatever you call it. Once you get past the first initial thing, then you gotta scale it. Then you're seeing security breaches, you're seeing configuration errors. This seems to be where the hotpot is. And when companies transition from, I got this to, Oh no, it's harder than I thought at scale. Can you share your reaction to that and how you see this playing out? >>Yeah, so, you know, I think it's interesting. There's multiple problems that occur when, you know, the, the two factors of scale is we talked about start expanding. I think one of them is what I like to call the, you know, it, it works fine on my cluster problem, which is back in, when I was a developer, we used to call this, it works on my laptop problem, which is, you know, you have your perfectly written code that is operating just fine on your machine, your sandbox environment. But the moment it runs production, it comes back with p zeros and POS from support teams, et cetera. And those issues can be really difficult to try us, right? And so in the Kubernetes environment, this problem kind of multi folds, it goes, you know, escalates to a higher degree because yeah, you have your sandbox developer environments, they have their clusters and things work perfectly fine in those clusters because these clusters are typically handcrafted or a combination of some scripting and handcrafting. >>And so as you give that change to then run at your production edge location, like say you radio sell tower site, or you hand it over to a customer to run it on their cluster, they might not have not have configured that cluster exactly how you did it, or they might not have configured some of the infrastructure plugins. And so the things don't work. And when things don't work, triaging them becomes like ishly hard, right? It's just one of the examples of the problem. Another whole bucket of issues is security, which is, is you have these distributed clusters at scale, you gotta ensure someone's job is on the line to make sure that these security policies are configured properly. >>So this is a huge problem. I love that comment. That's not not happening on my system. It's the classic, you know, debugging mentality. Yeah. But at scale it's hard to do that with error prone. I can see that being a problem. And you guys have a solution you're launching, Can you share what our lawn is, this new product, What is it all about? Talk about this new introduction. >>Yeah, absolutely. I'm very, very excited. You know, it's one of the projects that we've been working on for some time now because we are very passionate about this problem and just solving problems at scale in on-prem or at in the cloud or at edge environments. And what arwan is, it's an open source project and it is a tool, it's a Kubernetes native tool for complete end to end management of not just your clusters, but your clusters. All of the infrastructure that goes within and along the sites of those clusters, security policies, your middleware plugins, and finally your applications. So what alarm lets you do in a nutshell is in a declarative way, it lets you handle the configuration and management of all of these components in at scale. >>So what's the elevator pitch simply put for what this solves in, in terms of the chaos you guys are reigning in. What's the, what's the bumper sticker? Yeah, >>What would it do? There's a perfect analogy that I love to reference in this context, which is think of your assembly line, you know, in a traditional, let's say, you know, an auto manufacturing factory or et cetera, and the level of efficiency at scale that that assembly line brings, right online. And if you look at the logo we've designed, it's this funny little robot. And it's because when we think of online, we, we think of these enterprise large scale environments, you know, sprawling at scale creating chaos because there isn't necessarily a well thought through, well structured solution that's similar to an assembly line, which is taking each components, you know, addressing them, manufacturing, processing them in a standardized way, then handing to the next stage. But again, it gets, you know, processed in a standardized way. And that's what Arlon really does. That's like the I pitch. If you have problems of scale of managing your infrastructure, you know, that is distributed. Arlon brings the assembly line level of efficiency and consistency >>For those. So keeping it smooth, the assembly on things are flowing. C C I CD pipelining. Exactly. So that's what you're trying to simplify that ops piece for the developer. I mean, it's not really ops, it's their ops, it's coding. >>Yeah. Not just developer, the ops, the operations folks as well, right? Because developers, you know, there is, the developers are responsible for one picture of that layer, which is my apps, and then maybe that middleware of application that they interface with, but then they hand it over to someone else who's then responsible to ensure that these apps are secure properly, that they are logging, logs are being collected properly, monitoring and observability integrated. And so it solves problems for both those >>Teams. Yeah. It's DevOps. So the DevOps is the cloud native developer. The OP teams have to kind of set policies. Is that where the declarative piece comes in? Is that why that's important? >>Absolutely. Yeah. And, and, and, and you know, Kubernetes really in introduced or elevated this declarative management, right? Because, you know, c communities clusters are Yeah. Or your, yeah, you know, specifications of components that go in Kubernetes are defined in a declarative way. And Kubernetes always keeps that state consistent with your defined state. But when you go outside of that world of a single cluster, and when you actually talk about defining the clusters or defining everything that's around it, there really isn't a solution that does that today. And so online addresses that problem at the heart of it, and it does that using existing open source well known solutions. >>Ed, do I wanna get into the benefits? What's in it for me as the customer developer? But I want to finish this out real quick and get your thoughts. You mentioned open source. Why open source? What's the, what's the current state of the product? You run the product group over at platform nine, is it open source? And you guys have a product that's commercial? Can you explain the open source dynamic? And first of all, why open source? Yeah. And what is the consumption? I mean, open source is great, People want open source, they can download it, look up the code, but maybe wanna buy the commercial. So I'm assuming you have that thought through, can you share open source and commercial relationship? >>Yeah, I think, you know, starting with why open source? I think it's, you know, we as a company, we have, you know, one of the things that's absolutely critical to us is that we take mainstream open source technologies components and then we, you know, make them available to our customers at scale through either a SaaS model on from model, right? But, so as we are a company or startup or a company that benefits, you know, in a massive way by this open source economy, it's only right, I think in my mind that we do our part of the duty, right? And contribute back to the community that feeds us. And so, you know, we have always held that strongly as one of our principles. And we have, you know, created and built independent products starting all the way with fi, which was a serverless product, you know, that we had built to various other, you know, examples that I can give. But that's one of the main reasons why opensource and also opensource because we want the community to really firsthand engage with us on this problem, which is very difficult to achieve if your product is behind a wall, you know, behind, behind a block box. >>Well, and that's, that's what the developers want too. I mean, what we're seeing in reporting with Super Cloud is the new model of consumption is I wanna look at the code and see what's in there. That's right. And then also, if I want to use it, I, I'll do it. Great. That's open source, that's the value. But then at the end of the day, if I wanna move fast, that's when people buy in. So it's a new kind of freemium, I guess, business model. I guess that's the way that, Well, but that's, that's the benefit. Open source. This is why standards and open source is growing so fast. You have that confluence of, you know, a way for helpers to try before they buy, but also actually kind of date the application, if you will. We, you know, Adrian Karo uses the dating me metaphor, you know, Hey, you know, I wanna check it out first before I get married. Right? And that's what open source, So this is the new, this is how people are selling. This is not just open source, this is how companies are selling. >>Absolutely. Yeah. Yeah. You know, I think, and you know, two things. I think one is just, you know, this, this, this cloud native space is so vast that if you, if you're building a close flow solution, sometimes there's also a risk that it may not apply to every single enterprises use cases. And so having it open source gives them an opportunity to extend it, expand it, to make it proper to their use case if they choose to do so, right? But at the same time, what's also critical to us is we are able to provide a supported version of it with an SLA that we, you know, that's backed by us, a SAS hosted version of it as well, for those customers who choose to go that route, you know, once they have used the open source version and loved it and want to take it at scale and in production and need, need, need a partner to collaborate with, who can, you know, support them for that production >>Environment. I have to ask you now, let's get into what's in it for the customer. I'm a customer, why should I be enthused about Arlo? What's in it for me? You know? Cause if I'm not enthused about it, I'm not gonna be confident and it's gonna be hard for me to get behind this. Can you share your enthusiastic view of, you know, why I should be enthused about Arlo customer? >>Yeah, absolutely. And so, and there's multiple, you know, enterprises that we talk to, many of them, you know, our customers, where this is a very kind of typical story that you hear, which is we have, you know, a Kubernetes distribution. It could be on premise, it could be public clouds, native es, and then we have our C I CD pipelines that are automating the deployment of applications, et cetera. And then there's this gray zone. And the gray zone is well before you can you, your CS CD pipelines can deploy the apps. Somebody needs to do all of their groundwork of, you know, defining those clusters and yeah. You know, properly configuring them. And as these things, these things start by being done hand grown. And then as the, as you scale, what typically enterprises would do today is they will have their home homegrown DIY solutions for this. >>I mean, the number of folks that I talk to that have built Terra from automation, and then, you know, some of those key developers leave. So it's a typical open source or typical, you know, DIY challenge. And the reason that they're writing it themselves is not because they want to. I mean, of course technology is always interesting to everybody, but it's because they can't find a solution that's out there that perfectly fits the problem. And so that's that pitch. I think Spico would be delighted. The folks that we've talked, you know, spoken with, have been absolutely excited and have, you know, shared that this is a major challenge we have today because we have, you know, few hundreds of clusters on s Amazon and we wanna scale them to few thousands, but we don't think we are ready to do that. And this will give us >>Stability. Yeah, I think people are scared, not sc I won't say scare, that's a bad word. Maybe I should say that they feel nervous because, you know, at scale small mistakes can become large mistakes. This is something that is concerning to enterprises. And, and I think this is gonna come up at co con this year where enterprises are gonna say, Okay, I need to see SLAs. I wanna see track record, I wanna see other companies that have used it. Yeah. How would you answer that question to, or, or challenge, you know, Hey, I love this, but is there any guarantees? Is there any, what's the SLAs? I'm an enterprise, I got tight, you know, I love the open source trying to free fast and loose, but I need hardened code. >>Yeah, absolutely. So, so two parts to that, right? One is Arlan leverages existing open source components, products that are extremely popular. Two specifically. One is Lon uses Argo cd, which is probably one of the highest rated and used CD open source tools that's out there, right? It's created by folks that are as part of Intuit team now, you know, really brilliant team. And it's used at scale across enterprises. That's one. Second is arlon also makes use of cluster api capi, which is a ES sub-component, right? For lifecycle management of clusters. So there is enough of, you know, community users, et cetera, around these two products, right? Or, or, or open source projects that will find Arlan to be right up in their alley because they're already comfortable, familiar with algo cd. Now Arlan just extends the scope of what Algo CD can do. And so that's one. And then the second part is going back to a point of the comfort. And that's where, you know, Platform nine has a role to play, which is when you are ready to deploy Alon at scale, because you've been, you know, playing with it in your DEF test environments, you're happy with what you get with it, then Platform nine will stand behind it and provide that sla. >>And what's been the reaction from customers you've talked to Platform nine customers with, with, that are familiar with, with Argo and then Arlo? What's been some of the feedback? >>Yeah, I, I, I think the feedback's been fantastic. I mean, I can give you examples of customers where, you know, initially, you know, when you are, when you're telling them about your entire portfolio of solutions, it might not strike a card right away. But then we start talking about Arlan and, and we talk about the fact that it uses Argo CD and they start opening up, they say, We have standardized on Argo and we have built these components, homegrown, we would be very interested. Can we co-develop? Does it support these use cases? So we've had that kind of validation. We've had validation all the way at the beginning of our line before we even wrote a single line of code saying this is something we plan on doing. And the customer said, If you had it today, I would've purchased it. So it's been really great validation. >>All right. So next question is, what is the solution to the customer? If I asked you, Look it, I have, I'm so busy, my team's overworked. I got a skills gap. I don't need another project that's, I'm so tied up right now and I'm just chasing my tail. How does Platform nine help me? >>Yeah, absolutely. So I think, you know, one of the core tenets of Platform nine has always been that we try to bring that public cloud like simplicity by hosting, you know, this in a lot of such similar tools in a SaaS hosted manner for our customers, right? So our goal behind doing that is taking away or trying to take away all of that complexity from customer's hands and offloading it to our hands, right? And giving them that full white glove treatment as we call it. And so from a customer's perspective, one, something like arlon will integrate with what they have so they don't have to rip and replace anything. In fact, it will, even in the next versions, it may even discover your clusters that you have today and, you know, give you an inventory and that, >>So customers have clusters that are growing, that's a sign correct call you guys. >>Absolutely. Either they're, they have massive large clusters, right? That they wanna split into smaller clusters, but they're not comfortable doing that today, or they've done that already on say, public cloud or otherwise. And now they have management challenges. So >>Especially operationalizing the clusters, whether they want to kind of reset everything and remove things around and reconfigure Yeah. And or scale out. >>That's right. Exactly. >>And you provide that layer of policy. >>Absolutely. >>Yes. That's the key value >>Here. That's right. >>So policy based configuration for cluster scale up >>Profile and policy based declarative configuration and life cycle management for clusters. >>If I asked you how this enables Super club, what would you say to that? >>I think this is one of the key ingredients to super cloud, right? If you think about a super cloud environment, there's at least few key ingredients that that come to my mind that are really critical. Like they are, you know, life saving ingredients at that scale. One is having a really good strategy for managing that scale, you know, in a, going back to assembly line in a very consistent, predictable way so that our lot solves then you, you need to compliment that with the right kind of observability and monitoring tools at scale, right? Because ultimately issues are gonna happen and you're gonna have to figure out, you know, how to solve them fast. And alon by the way, also helps in that direction, but you also need observability tools. And then especially if you're running it on the public cloud, you need some cost management tools. In my mind, these three things are like the most necessary ingredients to make Super Cloud successful. And, you know, alarm flows >>In one. Okay, so now the next level is, Okay, that makes sense. There's under the covers kind of speak under the hood. Yeah. How does that impact the app developers and the cloud native modern application workflows? Because the impact to me, seems the apps are gonna be impacted. Are they gonna be faster, stronger? I mean, what's the impact if you do all those things, as you mentioned, what's the impact of the apps? >>Yeah, the impact is that your apps are more likely to operate in production the way you expect them to, because the right checks and balances have gone through, and any discrepancies have been identified prior to those apps, prior to your customer running into them, right? Because developers run into this challenge to their, where there's a split responsibility, right? I'm responsible for my code, I'm responsible for some of these other plugins, but I don't own the stack end to end. I have to rely on my ops counterpart to do their part, right? And so this really gives them, you know, the right tooling for >>That. So this is actually a great kind of relevant point, you know, as cloud becomes more scalable, you're starting to see this fragmentation gone of the days of the full stack developer to the more specialized role. But this is a key point, and I have to ask you because if this Arlo solution takes place, as you say, and the apps are gonna be stupid, there's designed to do, the question is, what did, does the current pain look like of the apps breaking? What does the signals to the customer Yeah. That they should be calling you guys up into implementing Arlo, Argo, and, and, and on all the other goodness to automate, What are some of the signals? Is it downtime? Is it, is it failed apps, Is it latency? What are some of the things that Yeah, absolutely would be in indications of things are effed up a little bit. >>Yeah. More frequent down times, down times that are, that take longer to triage. And so you are, you know, the, you know, your mean times on resolution, et cetera, are escalating or growing larger, right? Like we have environments of customers where they, they have a number of folks on in the field that have to take these apps and run them at customer sites. And that's one of our partners. And they're extremely interested in this because the, the rate of failures they're encountering for this, you know, the field when they're running these apps on site, because the field is automating their clusters that are running on sites using their own script. So these are the kinds of challenges, and those are the pain points, which is, you know, if you're looking to reduce your, your meantime to resolution, if you're looking to reduce the number of failures that occur on your production site, that's one. And second, if you are looking to manage these at scale environments with a relatively small, focused, nimble ops team, which has an immediate impact on your, So those are, those are the >>Signals. This is the cloud native at scale situation, the innovation going on. Final thought is your reaction to the idea that if the world goes digital, which it is, and the confluence of physical and digital coming together, and cloud continues to do its thing, the company becomes the application, not where it used to be supporting the business, you know, the back office and the IIA terminals and some PCs and handhelds. Now if technology's running, the business is the business. Yeah. The company's the application. Yeah. So it can't be down. So there's a lot of pressure on, on CSOs and CIOs now and see, and boards is saying, how is technology driving the top line revenue? That's the number one conversation. Yeah. Do you see that same thing? >>Yeah. It's interesting. I think there's multiple pressures at the CXO CIO level, right? One is that there needs to be that visibility and clarity and guarantee almost that, you know, that the, the technology that's, you know, that's gonna drive your top line is gonna drive that in a consistent, reliable, predictable manner. And then second, there is the constant pressure to do that while always lowering your costs of doing it, right? Especially when you're talking about, let's say retailers or those kinds of large scale vendors, they many times make money by lowering the amount that they spend on, you know, providing those goods to their end customers. So I think those, both those factors kind of come into play and the solution to all of them is usually in a very structured strategy around automation. >>Final question. What does cloudnative at scale look like to you? If all the things happen the way we want 'em to happen, The magic wand, the magic dust, what does it look like? >>What that looks like to me is a CIO sipping at his desk on coffee production is running absolutely smooth. And his, he's running that at a nimble, nimble team size of at the most, a handful of folks that are just looking after things with things. So just >>Taking care of, and the CIO doesn't exist. There's no CSO there at the beach. >>Yeah. >>Thank you for coming on, sharing the cloud native at scale here on the cube. Thank you for your time. >>Fantastic. Thanks for having >>Me. Okay. I'm John Fur here for special program presentation, special programming cloud native at scale, enabling super cloud modern applications with Platform nine. Thanks for watching. Welcome back everyone to the special presentation of cloud native at scale, the cube and platform nine special presentation going in and digging into the next generation super cloud infrastructure as code and the future of application development. We're here at Bickley, who's the chief architect and co-founder of Platform nine b. Great to see you Cube alumni. We, we met at an OpenStack event in about eight years ago, or well later, earlier when opens Stack was going. Great to see you and great to see congratulations on the success of platform nine. >>Thank you very much. >>Yeah. You guys have been at this for a while and this is really the, the, the year we're seeing the, the crossover of Kubernetes because of what happens with containers. Everyone now was realized, and you've seen what Docker's doing with the new docker, the open source Docker now just a success Exactly. Of containerization, right? And now the Kubernetes layer that we've been working on for years is coming, bearing fruit. This is huge. >>Exactly. Yes. >>And so as infrastructure's code comes in, we talked to Bacar talking about Super Cloud, I met her about, you know, the new Arlon, our R lawn you guys just launched, the infrastructure's code is going to another level. And then it's always been DevOps infrastructure is code. That's been the ethos that's been like from day one, developers just code. Then you saw the rise of serverless and you see now multi-cloud or on the horizon, connect the dots for us. What is the state of infrastructures code today? >>So I think, I think I'm, I'm glad you mentioned it, everybody or most people know about infrastructures code. But with Kubernetes, I think that project has evolved at the concept even further. And these dates, it's infrastructure as configuration, right? So, which is an evolution of infrastructure as code. So instead of telling the system, here's how I want my infrastructure by telling it, you know, do step A, B, C, and D instead with Kubernetes, you can describe your desired state declaratively using things called manifest resources. And then the system kind of magically figures it out and tries to converge the state towards the one that you specify. So I think it's, it's a even better version of infrastructures code. >>Yeah, yeah. And, and that really means it's developer just accessing resources. Okay. Not declaring, Okay, give me some compute, stand me up some, turn the lights on, turn 'em off, turn 'em on. That's kind of where we see this going. And I like the configuration piece. Some people say composability, I mean now with open source, so popular, you don't have to have to write a lot of code. It's code being developed. And so it's into integration, it's configuration. These are areas that we're starting to see computer science principles around automation, machine learning, assisting open source. Cuz you got a lot of code that's right in hearing software, supply chain issues. So infrastructure as code has to factor in these new, new dynamics. Can you share your opinion on these new dynamics of, as open source grows, the glue layers, the configurations, the integration, what are the core issues? >>I think one of the major core issues is with all that power comes complexity, right? So, you know, despite its expressive power systems like Kubernetes and declarative APIs let you express a lot of complicated and complex stacks, right? But you're dealing with hundreds if not thousands of these yamo files or resources. And so I think, you know, the emergence of systems and layers to help you manage that complexity is becoming a key challenge and opportunity in, in this space that, >>That's, I wrote a LinkedIn post today was comments about, you know, hey, enterprise is the new breed, the trend of SaaS companies moving our consumer comp consumer-like thinking into the enterprise has been happening for a long time, but now more than ever, you're seeing it the old way used to be solve complexity with more complexity and then lock the customer in. Now with open source, it's speed, simplification and integration, right? These are the new dynamic power dynamics for developers. Yeah. So as companies are starting to now deploy and look at Kubernetes, what are the things that need to be in place? Because you have some, I won't say technical debt, but maybe some shortcuts, some scripts here that make it look like infrastructure is code. People have done some things to simulate or or make infrastructure as code happen. Yes. But to do it at scale Yes. Is harder. What's your take on this? What's your >>View? It's hard because there's a per proliferation of methods, tools, technologies. So for example, today it's very common for DevOps and platform engineering tools, I mean, sorry, teams to have to deploy a large number of Kubernetes clusters, but then apply the applications and configurations on top of those clusters. And they're using a wide range of tools to do this, right? For example, maybe Ansible or Terraform or bash scripts to bring up the infrastructure and then the clusters. And then they may use a different set of tools such as Argo CD or other tools to apply configurations and applications on top of the clusters. So you have this sprawl of tools. You, you also have this sprawl of configurations and files because the more objects you're dealing with, the more resources you have to manage. And there's a risk of drift that people call that where, you know, you think you have things under control, but some people from various teams will make changes here and there and then before the end of the day systems break and you have no idea of tracking them. So I think there's real need to kind of unify, simplify, and try to solve these problems using a smaller, more unified set of tools and methodologies. And that's something that we try to do with this new project. Arlon. >>Yeah. So, so we're gonna get into Arlan in a second. I wanna get into the why Arlon. You guys announced that at our GoCon, which was put on here in Silicon Valley at the, at the by intu. They had their own little day over there at their headquarters. But before we get there, Vascar, your CEO came on and he talked about Super Cloud at our inaugural event. What's your definition of super cloud? If you had to kind of explain that to someone at a cocktail party or someone in the industry technical, how would you look at the super cloud trend that's emerging? It's become a thing. What's your, what would be your contribution to that definition or the narrative? >>Well, it's, it's, it's funny because I've actually heard of the term for the first time today, speaking to you earlier today. But I think based on what you said, I I already get kind of some of the, the gist and the, the main concepts. It seems like super cloud, the way I interpret that is, you know, clouds and infrastructure, programmable infrastructure, all of those things are becoming commodity in a way. And everyone's got their own flavor, but there's a real opportunity for people to solve real business problems by perhaps trying to abstract away, you know, all of those various implementations and then building better abstractions that are perhaps business or application specific to help companies and businesses solve real business problems. >>Yeah, I remember that's a great, great definition. I remember, not to date myself, but back in the old days, you know, IBM had a proprietary network operating system, so to deck for the mini computer vendors, deck net and SNA respectively. But T C P I P came out of the osi, the open systems interconnect and remember, ethernet beat token ring out. So not to get all nerdy for all the young kids out there, look, just look up token ring, you'll see, you've probably never heard of it. It's IBM's, you know, connection for the internet at the, the layer too is Amazon, the ethernet, right? So if T C P I P could be the Kubernetes and the container abstraction that made the industry completely change at that point in history. So at every major inflection point where there's been serious industry change and wealth creation and business value, there's been an abstraction Yes. Somewhere. Yes. What's your reaction to that? >>I think this is, I think a saying that's been heard many times in this industry and, and I forgot who originated it, but I think the saying goes like, there's no problem that can't be solved with another layer of indirection, right? And we've seen this over and over and over again where Amazon and its peers have inserted this layer that has simplified, you know, computing and, and infrastructure management. And I believe this trend is going to continue, right? The next set of problems are going to be solved with these insertions of additional abstraction layers. I think that that's really a, yeah, it's gonna continue. >>It's interesting. I just really wrote another post today on LinkedIn called the Silicon Wars AMD Stock is down arm has been on rise, we've remember pointing for many years now, that arm's gonna be hugely, it has become true. If you look at the success of the infrastructure as a service layer across the clouds, Azure, aws, Amazon's clearly way ahead of everybody. The stuff that they're doing with the silicon and the physics and the, the atoms, the pro, you know, this is where the innovation, they're going so deep and so strong at ISAs, the more that they get that gets come on, they have more performance. So if you're an app developer, wouldn't you want the best performance and you'd wanna have the best abstraction layer that gives you the most ability to do infrastructures, code or infrastructure for configuration, for provisioning, for managing services. And you're seeing that today with service MeSHs, a lot of action going on in the service mesh area in, in this community of co con, which will be a covering. So that brings up the whole what's next? You guys just announced our lawn at ar GoCon, which came out of Intuit. We've had Maria Teel at our super cloud event, She's a cto, you know, they're all in the cloud. So they contributed that project. Where did Arlon come from? What was the origination? What's the purpose? Why our lawn, why this announcement? Yeah, >>So the, the inception of the project, this was the result of us realizing that problem that we spoke about earlier, which is complexity, right? With all of this, these clouds, these infrastructure, all the variations around and you know, compute storage networks and the proliferation of tools we talked about the Ansibles and Terraforms and Kubernetes itself, you can think of that as another tool, right? We saw a need to solve that complexity problem, and especially for people and users who use Kubernetes at scale. So when you have, you know, hundreds of clusters, thousands of applications, thousands of users spread out over many, many locations, there, there needs to be a system that helps simplify that management, right? So that means fewer tools, more expressive ways of describing the state that you want and more consistency. And, and that's why, you know, we built AR lawn and we built it recognizing that many of these problems or sub problems have already been solved. So Arlon doesn't try to reinvent the wheel, it instead rests on the shoulders of several giants, right? So for example, Kubernetes is one building block, GI ops, and Argo CD is another one, which provides a very structured way of applying configuration. And then we have projects like cluster API and cross plane, which provide APIs for describing infrastructure. So arlon takes all of those building blocks and builds a thin layer, which gives users a very expressive way of defining configuration and desired state. So that's, that's kind of the inception of, And >>What's the benefit of that? What does that give the, what does that give the developer, the user, in this case, >>The developers, the, the platform engineer, team members, the DevOps engineers, they get a a ways to provision not just infrastructure and clusters, but also applications and configurations. They get a way, a system for provisioning, configuring, deploying, and doing life cycle management in a, in a much simpler way. Okay. Especially as I said, if you're dealing with a large number of applications. >>So it's like an operating fabric, if you will. Yes. For them. Okay, so let's get into what that means for up above and below the, the, this abstraction or thin layer below the infrastructure. We talked a lot about what's going on below that. Yeah. Above our workloads at the end of the day, and I talk to CXOs and IT folks that, that are now DevOps engineers. They care about the workloads and they want the infrastructure's code to work. They wanna spend their time getting in the weeds, figuring out what happened when someone made a push that that happened or something happened. They need observability and they need to, to know that it's working. That's right. And here's my workloads running effectively. So how do you guys look at the workload side of it? Cuz now you have multiple workloads on these fabric, right? >>So workloads, so Kubernetes has defined kind of a standard way to describe workloads and you can, you know, tell Kubernetes, I want to run this container this particular way, or you can use other projects that are in the Kubernetes cloud native ecosystem, like K native, where you can express your application in more at a higher level, right? But what's also happening is in addition to the workloads, DevOps and platform engineering teams, they need to very often deploy the applications with the clusters themselves. Clusters are becoming this commodity. It's, it's becoming this host for the application and it kind of comes bundled with it. In many cases it is like an appliance, right? So DevOps teams have to provision clusters at a really incredible rate and they need to tear them down. Clusters are becoming more, >>It's coming like an EC two instance, spin up a cluster. We've heard people used words like that. That's >>Right. And before arlon you kind of had to do all of that using a different set of tools as, as I explained. So with AR loan you can kind of express everything together. You can say I want a cluster with a health monitoring stack and a logging stack and this ingress controller and I want these applications and these security policies. You can describe all of that using something we call the profile. And then you can stamp out your app, your applications and your clusters and manage them in a very, So >>It's essentially standard, like creates a mechanism. Exactly. Standardized, declarative kind of configurations. And it's like a playbook, just deploy it. Now what there is between say a script like I'm, I have scripts, I can just automate scripts >>Or yes, this is where that declarative API and infrastructure as configuration comes in, right? Because scripts, yes you can automate scripts, but the order in which they run matters, right? They can break, things can break in the middle and, and sometimes you need to debug them. Whereas the declarative way is much more expressive and powerful. You just tell the system what you want and then the system kind of figures it out. And there are these things are controllers which will in the background reconcile all the state to converge towards your desire. It's a much more powerful, expressive and reliable way of getting things done. >>So infrastructure as configuration is built kind of on, it's a super set of infrastructures code because it's >>An evolution. >>You need edge's code, but then you can configure the code by just saying do it. You basically declaring saying Go, go do that. That's right. Okay, so, alright, so cloud native at scale, take me through your vision of what that means. Someone says, Hey, what does cloud native at scale mean? What's success look like? How does it roll out in the future as you, not future next couple years. I mean people are now starting to figure out, okay, it's not as easy as it sounds. Kubernetes has value. We're gonna hear this year at CubeCon a lot of this, what does cloud native at scale >>Mean? Yeah, there are different interpretations, but if you ask me, when people think of scale, they think of a large number of deployments, right? Geographies, many, you know, supporting thousands or tens or millions of, of users there, there's that aspect to scale. There's also an equally important a aspect of scale, which is also something that we try to address with Arran. And that is just complexity for the people operating this or configuring this, right? So in order to describe that desired state, and in order to perform things like maybe upgrades or updates on a very large scale, you want the humans behind that to be able to express and direct the system to do that in, in relatively simple terms, right? And so we want the tools and the abstractions and the mechanisms available to the user to be as powerful but as simple as possible. So there's, I think there's gonna be a number and there have been a number of CNCF and cloud native projects that are trying to attack that complexity problem as well. And Arlon kind of falls in in that >>Category. Okay, so I'll put you on the spot rogue, that CubeCon coming up and now this'll be shipping this segment series out before. What do you expect to see at this year? It's the big story this year. What's the, what's the most important thing happening? Is it in the open source community and also within a lot of the, the people jockeying for leadership. I know there's a lot of projects and still there's some white space in the overall systems map about the different areas get run time and there's ability in all these different areas. What's the, where's the action? Where, where's the smoke? Where's the fire? Where's the piece? Where's the tension? >>Yeah, so I think one thing that has been happening over the past couple of coupon and I expect to continue and, and that is the, the word on the street is Kubernetes is getting boring, right? Which is good, right? >>Boring means simple. >>Well, well >>Maybe, >>Yeah, >>Invisible, >>No drama, right? So, so the, the rate of change of the Kubernetes features and, and all that has slowed but in, in a, in a positive way. But there's still a general sentiment and feeling that there's just too much stuff. If you look at a stack necessary for hosting applications based on Kubernetes, there are just still too many moving parts, too many components, right? Too much complexity. I go, I keep going back to the complexity problem. So I expect Cube Con and all the vendors and the players and the startups and the people there to continue to focus on that complexity problem and introduce further simplifications to, to the stack. >>Yeah. Vic, you've had an storied career VMware over decades with them within 12 years with 14 years or something like that. Big number co-founder here a platform. I you's been around for a while at this game, man. We talked about OpenStack, that project we interviewed at one of their events. So OpenStack was the beginning of that, this new revolution. I remember the early days it was, it wasn't supposed to be an alternative to Amazon, but it was a way to do more cloud cloud native. I think we had a Cloud Aati team at that time. We would joke we, you know, about, about the dream. It's happening now, now at Platform nine. You guys have been doing this for a while. What's the, what are you most excited about as the chief architect? What did you guys double down on? What did you guys pivot from or two, did you do any pivots? Did you extend out certain areas? Cuz you guys are in a good position right now, a lot of DNA in Cloud native. What are you most excited about and what does Platform Nine bring to the table for customers and for people in the industry watching this? >>Yeah, so I think our mission really hasn't changed over the years, right? It's been always about taking complex open source software because open source software, it's powerful. It solves new problems, you know, every year and you have new things coming out all the time, right? Opens Stack was an example and then Kubernetes took the world by storm. But there's always that complexity of, you know, just configuring it, deploying it, running it, operating it. And our mission has always been that we will take all that complexity and just make it, you know, easy for users to consume regardless of the technology, right? So the successor to Kubernetes, you know, I don't have a crystal ball, but you know, you have some indications that people are coming up of new and simpler ways of running applications. There are many projects around there who knows what's coming next year or the year after that. But platform will a, platform nine will be there and we will, you know, take the innovations from the the community. We will contribute our own innovations and make all of those things very consumable to customers. >>Simpler, faster, cheaper. Exactly. Always a good business model technically to make that happen. Yes. Yeah, I think the, the reigning in the chaos is key, you know, Now we have now visibility into the scale. Final question before we depart this segment. What is at scale, how many clusters do you see that would be a watermark for an at scale conversation around an enterprise? Is it workloads we're looking at or, or clusters? How would you, Yeah, how would you describe that? When people try to squint through and evaluate what's a scale, what's the at scale kind of threshold? >>Yeah. And, and the number of clusters doesn't tell the whole story because clusters can be small in terms of the number of nodes or they can be large. But roughly speaking when we say, you know, large scale cluster deployments, we're talking about maybe hundreds, two thousands. >>Yeah. And final final question, what's the role of the hyperscalers? You got AWS continuing to do well, but they got their core ias, they got a PAs, they're not too too much putting a SaaS out there. They have some SaaS apps, but mostly it's the ecosystem. They have marketplaces doing, doing over $2 billion billions of transactions a year and, and it's just like, just sitting there. It hasn't really, they're now innovating on it, but that's gonna change ecosystems. What's the role the cloud play in the cloud need of its scale? >>The, the hyper squares? >>Yeah, yeah. A's Azure Google, >>You mean from a business perspective, they're, they have their own interests that, you know, that they're, they will keep catering to, they, they will continue to find ways to lock their users into their ecosystem of services and, and APIs. So I don't think that's gonna change, right? They're just gonna keep well, >>They got great performance. I mean, from a, from a hardware standpoint, yes. That's gonna be key, >>Right? Yes. I think the, the move from X 86 being the dominant way and platform to run workloads is changing, right? That, that, that, that, and I think the, the hyper skaters really want to be in the game in terms of, you know, the, the new risk and arm ecosystems, the platforms. >>Yeah. Not joking aside, Paul Morritz, when he was the CEO of VMware, when he took over once said, I remember our first year doing the cube. Oh the cloud is one big distributed computer. It's, it's hardware and you got software and you got middleware and he kinda over, well he's kind of tongue in cheek, but really you're talking about large compute and sets of services that is essentially a distributed computer. Yes, >>Exactly. >>It's, we're back in the same game. Thank you for coming on the segment. Appreciate your time. This is cloud native at scale special presentation with Platform nine. Really unpacking super cloud Arlon open source and how to run large scale applications on the cloud, cloud native develop for developers. And John Furrier with the cube. Thanks for Washington. We'll stay tuned for another great segment coming right up. Hey, welcome back everyone to Super Cloud 22. I'm John Fur, host of the Cuba here all day talking about the future of cloud. Where's it all going? Making it super multi-cloud is around the corner and public cloud is winning. Got the private cloud on premise and Edge. Got a great guest here, Vascar Gorde, CEO of Platform nine, just on the panel on Kubernetes. An enabler blocker. Welcome back. Great to have you on. >>Good to see you >>Again. So Kubernetes is a blocker enabler by, with a question mark I put on on there. Panel was really to discuss the role of Kubernetes. Now great conversation operations is impacted. What's just thing about what you guys are doing at Platform nine? Is your role there as CEO and the company's position, kind of like the world spun into the direction of Platform nine while you're at the helm, right? >>Absolutely. In fact, things are moving very well and since they came to us, it was an insight to call ourselves the platform company eight years ago, right? So absolutely whether you are doing it in public clouds or private clouds, you know, the application world is moving very fast in trying to become digital and cloud native. There are many options for you to run the infrastructure. The biggest blocking factor now is having a unified platform. And that's what where we come into >>Patrick, we were talking before we came on stage here about your background and we were kind of talking about the glory days in 2000, 2001 when the first ASPs application service providers came out. Kind of a SaaS vibe, but that was kind of all kind of cloud-like >>It wasn't, >>And web services started then too. So you saw that whole growth. Now, fast forward 20 years later, 22 years later, where we are now, when you look back then to here and all the different cycles, >>In fact, you know, as we were talking offline, I was in one of those ASPs in the year 2000 where it was a novel concept of saying we are providing a software and a capability as a service, right? You sign up and start using it. I think a lot has changed since then. The tooling, the tools, the technology has really skyrocketed. The app development environment has really taken off exceptionally well. There are many, many choices of infrastructure now, right? So I think things are in a way the same but also extremely different. But more importantly now for any company, regardless of size, to be a digital native, to become a digital company is extremely mission critical. It's no longer a nice to have everybody's in the journey somewhere. >>Everyone is going digital transformation here. Even on a so-called downturn recession that's upcoming inflations sea year. It's interesting. This is the first downturn, the history of the world where the hyperscale clouds have been pumping on all cylinders as an economic input. And if you look at the tech trends, GDPs down, but not tech. Nope. Cause pandemic showed everyone digital transformation is here and more spend and more growth is coming even in, in tech. So this is a unique factor which proves that that digital transformation's happening and company, every company will need a super cloud. >>Everyone, every company, regardless of size, regardless of location, has to become modernize their infrastructure. And modernizing infrastructure is not just some, you know, new servers and new application tools. It's your approach, how you're serving your customers, how you're bringing agility in your organization. I think that is becoming a necessity for every enterprise to survive. >>I wanna get your thoughts on Super Cloud because one of the things Dave Alon and I want to do with Super Cloud and calling it that was we, I, I personally, and I know Dave as well, he can, I'll speak from, he can speak for himself. We didn't like multi-cloud. I mean not because Amazon said don't call things multi-cloud, it just didn't feel right. I mean everyone has multiple clouds by default. If you're running productivity software, you have Azure and Office 365. But it wasn't truly distributed. It wasn't truly decentralized, it wasn't truly cloud enabled. It didn't, it felt like they're not ready for a market yet. Yet public clouds booming on premise. Private cloud and Edge is much more on, you know, more, More dynamic, more unreal. >>Yeah. I think the reason why we think Super cloud is a better term than multi-cloud. Multi-cloud are more than one cloud, but they're disconnected. Okay, you have a productivity cloud, you have a Salesforce cloud, you may have, everyone has an internal cloud, right? So, but they're not connected. So you can say, okay, it's more than one cloud. So it's, you know, multi-cloud. But super cloud is where you are actually trying to look at this holistically. Whether it is on-prem, whether it is public, whether it's at the edge, it's a store at the branch. You are looking at this as one unit. And that's where we see the term super cloud is more applicable because what are the qualities that you require if you're in a super cloud, right? You need choice of infrastructure, you need, but at the same time you need a single pan or a single platform for you to build your innovations on, regardless of which cloud you're doing it on, right? So I think Super Cloud is actually a more tightly integrated orchestrated management philosophy we think. >>So let's get into some of the super cloud type trends that we've been reporting on. Again, the purpose of this event is as a pilot to get the conversations flowing with, with the influencers like yourselves who are running companies and building products and the builders, Amazon and Azure are doing extremely well. Google's coming up in third Cloudworks in public cloud. We see the use cases on premises use cases. Kubernetes has been an interesting phenomenon because it's become from the developer side a little bit, but a lot of ops people love Kubernetes. It's really more of an ops thing. You mentioned OpenStack earlier. Kubernetes kind of came out of that open stack. We need an orchestration. And then containers had a good shot with, with Docker. They re pivoted the company. Now they're all in an open source. So you got containers booming and Kubernetes as a new layer there. >>What's, >>What's the take on that? What does that really mean? Is that a new defacto enabler? It >>Is here. It's for here for sure. Every enterprise somewhere in the journey is going on. And you know, most companies are, 70 plus percent of them have 1, 2, 3 container based, Kubernetes based applications now being rolled out. So it's very much here. It is in production at scale by many customers. And it, the beauty of it is yes, open source, but the biggest gating factor is the skill set. And that's where we have a phenomenal engineering team, right? So it's, it's one thing to buy a tool and >>Just be clear, you're a managed service for Kubernetes. >>We provide, provide a software platform for cloud acceleration as a service and it can run anywhere. It can run in public private. We have customers who do it in truly multi-cloud environments. It runs on the edge, it runs at this in stores about thousands of stores in a retailer. So we provide that and also for specific segments where data sovereignty and data residency are key regulatory reasons. We also un on-prem as an air gap version. Can >>You give an example on how you guys are deploying your platform to enable a super cloud experience for your customer? Right. >>So I'll give you two different examples. One is a very large networking company, public networking company. They have hundreds of products, hundreds of r and d teams that are building different, different products. And if you look at few years back, each one was doing it on a different platforms, but they really needed to bring the agility. And they worked with us now over three years where we are their build test dev pro platform where all their products are built on, right? And it has dramatically increased their agility to release new products. Number two, it actually is a light out operation. In fact, the customer says like, like the Maytag service person, cuz we provide it as a service and it barely takes one or two people to maintain it for them. >>So it's kinda like an SRE vibe. One person managing a >>Large 4,000 engineers building infrastructure >>On their tools, >>Whatever they want on their tools. They're using whatever app development tools they use, but they use our platform. What >>Benefits are they seeing? Are they seeing speed? >>Speed, definitely. Okay. Definitely they're speeding. Speed uniformity because now they're building able to build, so their customers who are using product A and product B are seeing a similar set of tools that are being used. >>So a big problem that's coming outta this super cloud event that we're, we're seeing and we heard it all here, ops and security teams. Cause they're kind of part of one thing, but option security specifically need to catch up speed wise. Are you delivering that value to ops and security? Right? >>So we, we work with ops and security teams and infrastructure teams and we layer on top of that. We have like a platform team. If you think about it, depending on where you have data centers, where you have infrastructure, you have multiple teams, okay, but you need a unified platform. Who's your buyer? Our buyer is usually, you know, the product divisions of companies that are looking at or the CTO would be a buyer for us functionally cio definitely. So it it's, it's somewhere in the DevOps to infrastructure. But the ideal one we are beginning to see now many large corporations are really looking at it as a platform and saying we have a platform group on which any app can be developed and it is run on any infrastructure. So the platform engineering teams. So >>You working two sides to that coin. You've got the dev side and then >>And then infrastructure >>Side. >>Okay. Another customer that I give an example, which I would say is kind of the edge of the store. So they have thousands of stores. Retail, retail, you know food retailer, right? They have thousands of stores that are on the globe, 50,000, 60,000. And they really want to enhance the customer experience that happens when you either order the product or go into the store and pick up your product or buy or browse or sit there. They have applications that were written in the nineties and then they have very modern AIML applications today. They want something that will not have to send an IT person to install a rack in the store or they can't move everything to the cloud because the store operations has to be local. The menu changes based on it's classic edge. It's classic edge, yeah. Right? They can't send it people to go install rack access servers then they can't sell software people to go install the software and any change you wanna put through that, you know, truck roll. So they've been working with us where all they do is they ship, depending on the size of the store, one or two or three little servers with instructions that >>You, you say little servers like how big one like a box, like a small little box, >>Right? And all the person in the store has to do like what you and I do at home and we get a, you know, a router is connect the power, connect the internet and turn the switch on. And from there we pick it up. >>Yep. >>We provide the operating system, everything and then the applications are put on it. And so that dramatically brings the velocity for them. They manage thousands of >>Them. True plug and play >>Two, plug and play thousands of stores. They manage it centrally. We do it for them, right? So, so that's another example where on the edge then we have some customers who have both a large private presence and one of the public clouds. Okay. But they want to have the same platform layer of orchestration and management that they can use regardless of the locations. >>So you guys got some success. Congratulations. Got some traction there. It's awesome. The question I want to ask you is that's come up is what is truly cloud native? Cuz there's lift and shift of the cloud >>That's not cloud native. >>Then there's cloud native. Cloud native seems to be the driver for the super cloud. How do you talk to customers? How do you explain when someone says what's cloud native, what isn't cloud native? >>Right. Look, I think first of all, the best place to look at what is the definition and what are the attributes and characteristics of what is truly a cloud native, is CNC foundation. And I think it's very well documented, very well. >>Tucan, of course Detroit's >>Coming so, so it's already there, right? So we follow that very closely, right? I think just lifting and shifting your 20 year old application onto a data center somewhere is not cloud native. Okay? You can't put to cloud, not you have to rewrite and redevelop your application in business logic using modern tools. Hopefully more open source and, and I think that's what Cloudnative is and we are seeing a lot of our customers in that journey. Now everybody wants to be cloudnative, but it's not that easy, okay? Because it's, I think it's first of all, skill set is very important. Uniformity of tools that there's so many tools there. Thousands and thousands of tools you could spend your time figuring out which tool to use. Okay? So I think the complexity is there, but the business benefits of agility and uniformity and customer experience are truly being done. >>And I'll give you an example, I don't know how clear native they are, right? And they're not a customer of ours, but you order pizzas, you do, right? If you just watch the pizza industry, how dominoes actually increase their share and mind share and wallet share was not because they were making better pizzas or not, I don't know anything about that, but the whole experience of how you order, how you watch what's happening, how it's delivered. There were a pioneer in it. To me, those are the kinds of customer experiences that cloud native can provide. >>Being agility and having that flow to the application changes what the expectations >>Are >>For the customer. Customer, >>The customer's expectations change, right? Once you get used to a better customer experience, you learn. >>That's to wrap it up. I wanna just get your perspective again. One of the benefits of chatting with you here and having you part of the Super Cloud 22 is you've seen many cycles, you have a lot of insights. I want to ask you, given your career where you've been and what you've done and now let's CEO platform nine, how would you compare what's happening now with other inflection points in the industry? And you've been, again, you've been an entrepreneur, you sold your company to Oracle, you've been seeing the big companies, you've seen the different waves. What's going on right now put into context this moment in time around Super Cloud. >>Sure. I think as you said, a lot of battles. CARSs being been in an asb, being in a real time software company, being in large enterprise software houses and a transformation. I've been on the app side, I did the infrastructure right and then tried to build our own platforms. I've gone through all of this myself with lot of lessons learned in there. I think this is an event which is happening now for companies to go through to become cloud native and digitalize. If I were to look back and look at some parallels of the tsunami that's going on is a couple of paddles come to me. One is, think of it, which was forced to honors like y2k. Everybody around the world had to have a plan, a strategy, and an execution for y2k. I would say the next big thing was e-commerce. I think e-commerce has been pervasive right across all industries. >>And disruptive. >>And disruptive, extremely disruptive. If you did not adapt and adapt and accelerate your e-commerce initiative, you were, it was an existence question. Yeah. I think we are at that pivotal moment now in companies trying to become digital and cloudnative. You know, that is what I see >>Happening there. I think that that e-commerce is interesting and I think just to riff with you on that is that it's disrupting and refactoring the business models. I think that is something that's coming out of this is that it's not just completely changing the gain, it's just changing how you operate, >>How you think and how you operate. See, if you think about the early days of e-commerce, just putting up a shopping cart that made you an e-commerce or e retailer or an e e e customer, right? Or so. I think it's the same thing now is I think this is a fundamental shift on how you're thinking about your business. How are you gonna operate? How are you gonna service your customers? I think it requires that just lift and shift is not gonna work. >>Nascar, thank you for coming on, spending the time to come in and share with our community and being part of Super Cloud 22. We really appreciate, we're gonna keep this open. We're gonna keep this conversation going even after the event, to open up and look at the structural changes happening now and continue to look at it in the open in the community. And we're gonna keep this going for, for a long, long time as we get answers to the problems that customers are looking for with cloud cloud computing. I'm Sean Fur with Super Cloud 22 in the Cube. Thanks for watching. >>Thank you. Thank you. >>Hello and welcome back. This is the end of our program, our special presentation with Platform nine on cloud native at scale, enabling the super cloud. We're continuing the theme here. You heard the interviews Super Cloud and its challenges, new opportunities around solutions around like Platform nine and others with Arlon. This is really about the edge situations on the internet and managing the edge multiple regions, avoiding vendor lock in. This is what this new super cloud is all about. The business consequences we heard and and the wide ranging conversations around what it means for open source and the complexity problem all being solved. I hope you enjoyed this program. There's a lot of moving pieces and things to configure with cloud native install, all making it easier for you here with Super Cloud and of course Platform nine contributing to that. Thank you for watching.

>>Hello, welcome to the Cube here in Palo Alto, California for a special presentation on Cloud native at scale, enabling super cloud modern applications with Platform nine. I'm John Furr, your host of The Cube. We had a great lineup of three interviews we're streaming today. Meor Ma Makowski, who's the co-founder and VP of Product of Platform nine. She's gonna go into detail around Arlon, the open source products, and also the value of what this means for infrastructure as code and for cloud native at scale. Bickley the chief architect of Platform nine Cube alumni. Going back to the OpenStack days. He's gonna go into why Arlon, why this infrastructure as code implication, what it means for customers and the implications in the open source community and where that value is. Really great wide ranging conversation there. And of course, Vascar, Gort, the CEO of Platform nine, is gonna talk with me about his views on Super Cloud and why Platform nine has a scalable solutions to bring cloudnative at scale. So enjoy the program. See you soon. Hello everyone. Welcome to the cube here in Palo Alto, California for special program on cloud native at scale, enabling next generation cloud or super cloud for modern application cloud native developers. I'm John Furry, host of the Cube. A pleasure to have here, me Makoski, co-founder and VP of product at Platform nine. Thanks for coming in today for this Cloudnative at scale conversation. Thank >>You for having me. >>So Cloudnative at scale, something that we're talking about because we're seeing the, the next level of mainstream success of containers Kubernetes and cloud native develop, basically DevOps in the C I C D pipeline. It's changing the landscape of infrastructure as code, it's accelerating the value proposition and the super cloud as we call it, has been getting a lot of traction because this next generation cloud is looking a lot different, but kind of the same as the first generation. What's your view on super cloud as it fits to cloud native as scales up? >>Yeah, you know, I think what's interesting, and I think the reason why Super Cloud is a really good, in a really fit term for this, and I think, I know my CEO was chatting with you as well, and he was mentioning this as well, but I think there needs to be a different term than just multi-cloud or cloud. And the reason is because as cloud native and cloud deployments have scaled, I think we've reached a point now where instead of having the traditional data center style model where you have a few large distributions of infrastructure and workload at a few locations, I think the model is kind of flipped around, right? Where you have a large number of microsites, these microsites could be your public cloud deployment, your private on-prem infrastructure deployments, or it could be your edge environment, right? And every single enterprise, every single industry is moving in that direction. And so you gotta rougher that with a terminology that, that, that indicates the scale and complexity of it. And so I think supercloud is a, is an appropriate term for that. >>So you brought a couple of things I want to dig into. You mentioned edge nodes. We're seeing not only edge nodes being the next kind of area of innovation, mainly because it's just popping up everywhere. And that's just the beginning. Wouldn't even know what's around the corner. You got buildings, you got iot, ot, and IT kind of coming together, but you also got this idea of regions, global infras infrastructures, big part of it. I just saw some news around CloudFlare shutting down a site here. There's policies being made at scale, These new challenges there. Can you share because you can have edge. So hybrid cloud is a winning formula. Everybody knows that it's a steady state. Yeah. But across multiple clouds brings in this new un engineered area, yet it hasn't been done yet. Spanning clouds. People say they're doing it, but you start to see the toe in the water, it's happening, it's gonna happen. It's only gonna get accelerated with the edge and beyond globally. So I have to ask you, what is the technical challenges in doing this? Because there's something business consequences as well, but there are technical challenges. Can you share your view on what the technical challenges are for the super cloud or across multiple edges and regions? >>Yeah, absolutely. So I think, you know, in in the context of this, the, this, this term of super cloud, I think it's sometimes easier to visualize things in terms of two access, right? I think on one end you can think of the scale in terms of just pure number of nodes that you have deploy a number of clusters in the Kubernetes space. And then on the other axis you would have your distribution factor, right? Which is, do you have these tens of thousands of nodes in one site or do you have them distributed across tens of thousands of sites with one node at each site? Right? And if you have just one flavor of this, there is enough complexity, but potentially manageable. But when you are expanding on both these access, you really get to a point where that scale really needs some well thought out, well structured solutions to address it, right? A combination of homegrown tooling along with your, you know, favorite distribution of Kubernetes is not a strategy that can help you in this environment. It may help you when you have one of this or when you, when you scale, is not at the level. >>Can you scope the complexity? Because I mean, I hear a lot of moving parts going on there, the technology's also getting better. We we're seeing cloud native become successful. There's a lot to configure, there's a lot to install. Can you scope the scale of the problem? Because we're talking about at scale Yep. Challenges here. Yeah, >>Absolutely. And I think, you know, I I like to call it, you know, the, the, the problem that the scale creates, you know, there's various problems, but I think one, one problem, one way to think about it is, is, you know, it works on my cluster problem, right? So I, you know, I come from engineering background and there's a, you know, there's a famous saying between engineers and QA and the support folks, right? Which is, it works on my laptop, which is I tested this chain, everything was fantastic, it worked flawlessly on my machine, on production, It's not working. The exact same problem now happens and these distributed environments, but at massive scale, right? Which is that, you know, developers test their applications, et cetera within the sanctity of their sandbox environments. But once you expose that change in the wild world of your production deployment, right? >>And the production deployment could be going at the radio cell tower at the edge location where a cluster is running there, or it could be sending, you know, these applications and having them run at my customer site where they might not have configured that cluster exactly the same way as I configured it, or they configured the cluster, right? But maybe they didn't deploy the security policies, or they didn't deploy the other infrastructure plugins that my app relies on. All of these various factors are their own layer of complexity. And there really isn't a simple way to solve that today. And that is just, you know, one example of an issue that happens. I think another, you know, whole new ball game of issues come in the context of security, right? Because when you are deploying applications at scale in a distributed manner, you gotta make sure someone's job is on the line to ensure that the right security policies are enforced regardless of that scale factor. So I think that's another example of problems that occur. >>Okay. So I have to ask about scale, because there are a lot of multiple steps involved when you see the success of cloud native. You know, you see some, you know, some experimentation. They set up a cluster, say it's containers and Kubernetes, and then you say, Okay, we got this, we can figure it. And then they do it again and again, they call it day two. Some people call it day one, day two operation, whatever you call it. Once you get past the first initial thing, then you gotta scale it. Then you're seeing security breaches, you're seeing configuration errors. This seems to be where the hotspot is in when companies transition from, I got this to, Oh no, it's harder than I thought at scale. Can you share your reaction to that and how you see this playing out? >>Yeah, so, you know, I think it's interesting. There's multiple problems that occur when, you know, the two factors of scale, as we talked about, start expanding. I think one of them is what I like to call the, you know, it, it works fine on my cluster problem, which is back in, when I was a developer, we used to call this, it works on my laptop problem, which is, you know, you have your perfectly written code that is operating just fine on your machine, your sandbox environment. But the moment it runs production, it comes back with p zeros and pos from support teams, et cetera. And those issues can be really difficult to triage us, right? And so in the Kubernetes environment, this problem kind of multi folds, it goes, you know, escalates to a higher degree because you have your sandbox developer environments, they have their clusters and things work perfectly fine in those clusters because these clusters are typically handcrafted or a combination of some scripting and handcrafting. >>And so as you give that change to then run at your production edge location, like say your radio cell tower site, or you hand it over to a customer to run it on their cluster, they might not have not have configured that cluster exactly how you did, or they might not have configured some of the infrastructure plugins. And so the things don't work. And when things don't work, triaging them becomes nightmarishly hard, right? It's just one of the examples of the problem, another whole bucket of issues is security, which is, is you have these distributed clusters at scale, you gotta ensure someone's job is on the line to make sure that these security policies are configured properly. >>So this is a huge problem. I love that comment. That's not not happening on my system. It's the classic, you know, debugging mentality. Yeah. But at scale it's hard to do that with error prone. I can see that being a problem. And you guys have a solution you're launching. Can you share what Arlon is this new product? What is it all about? Talk about this new introduction. >>Yeah, absolutely. Very, very excited. You know, it's one of the projects that we've been working on for some time now because we are very passionate about this problem and just solving problems at scale in on-prem or at in the cloud or at edge environments. And what arlon is, it's an open source project, and it is a tool, it's a Kubernetes native tool for complete end to end management of not just your clusters, but your clusters. All of the infrastructure that goes within and along the site of those clusters, security policies, your middleware, plug-ins, and finally your applications. So what our LA you do in a nutshell is in a declarative way, it lets you handle the configuration and management of all of these components in at scale. >>So what's the elevator pitch simply put for what dissolves in, in terms of the chaos you guys are reigning in, what's the, what's the bumper sticker? Yeah, what >>Would it do? There's a perfect analogy that I love to reference in this context, which is think of your assembly line, you know, in a traditional, let's say, you know, an auto manufacturing factory or et cetera, and the level of efficiency at scale that that assembly line brings, right? Our line, and if you look at the logo we've designed, it's this funny little robot. And it's because when we think of online, we think of these enterprise large scale environments, you know, sprawling at scale, creating chaos because there isn't necessarily a well thought through, well structured solution that's similar to an assembly line, which is taking each component, you know, addressing them, manufacturing, processing them in a standardized way, then handing to the next stage. But again, it gets, you know, processed in a standardized way. And that's what arlon really does. That's like the deliver pitch. If you have problems of scale of managing your infrastructure, you know, that is distributed. Arlon brings the assembly line level of efficiency and consistency for >>Those. So keeping it smooth, the assembly on things are flowing. See c i CD pipe pipelining. Exactly. So that's what you're trying to simplify that ops piece for the developer. I mean, it's not really ops, it's their ops, it's coding. >>Yeah. Not just developer, the ops, the operations folks as well, right? Because developers, you know, there is, developers are responsible for one picture of that layer, which is my apps, and then maybe that middleware of applications that they interface with, but then they hand it over to someone else who's then responsible to ensure that these apps are secure properly, that they are logging, logs are being collected properly, monitoring and observability integrated. And so it solves problems for both >>Those teams. Yeah. It's DevOps. So the DevOps is the cloud needed developer's. That's right. The option teams have to kind of set policies. Is that where the declarative piece comes in? Is that why that's important? >>Absolutely. Yeah. And, and, and, and you know, ES really in introduced or elevated this declarative management, right? Because, you know, s clusters are Yeah. Or your, yeah, you know, specifications of components that go in Kubernetes are defined a declarative way, and Kubernetes always keeps that state consistent with your defined state. But when you go outside of that world of a single cluster, and when you actually talk about defining the clusters or defining everything that's around it, there really isn't a solution that does that today. And so Arlon addresses that problem at the heart of it, and it does that using existing open source well known solutions. >>And do I want to get into the benefits? What's in it for me as the customer developer? But I want to finish this out real quick and get your thoughts. You mentioned open source. Why open source? What's the, what's the current state of the product? You run the product group over at Platform nine, is it open source? And you guys have a product that's commercial? Can you explain the open source dynamic? And first of all, why open source? Yeah. And what is the consumption? I mean, open source is great, People want open source, they can download it, look up the code, but maybe wanna buy the commercial. So I'm assuming you have that thought through, can you share open source and commercial relationship? >>Yeah, I think, you know, starting with why open source? I think it's, you know, we as a company, we have, you know, one of the things that's absolutely critical to us is that we take mainstream open source technologies components and then we, you know, make them available to our customers at scale through either a SaaS model or on-prem model, right? But, so as we are a company or startup or a company that benefits, you know, in a massive way by this open source economy, it's only right, I think in my mind that we do our part of the duty, right? And contribute back to the community that feeds us. And so, you know, we have always held that strongly as one of our principles. And we have, you know, created and built independent products starting all the way with fision, which was a serverless product, you know, that we had built to various other, you know, examples that I can give. But that's one of the main reasons why opensource and also open source, because we want the community to really firsthand engage with us on this problem, which is very difficult to achieve if your product is behind a wall, you know, behind, behind a block box. >>Well, and that's, that's what the developers want too. And what we're seeing in reporting with Super Cloud is the new model of consumption is I wanna look at the code and see what's in there. That's right. And then also, if I want to use it, I'll do it. Great. That's open source, that's the value. But then at the end of the day, if I wanna move fast, that's when people buy in. So it's a new kind of freemium, I guess, business model. I guess that's the way that long. But that's, that's the benefit. Open source. This is why standards and open source is growing so fast. You have that confluence of, you know, a way for developers to try before they buy, but also actually kind of date the application, if you will. We, you know, Adrian Karo uses the dating met metaphor, you know, Hey, you know, I wanna check it out first before I get married. Right? And that's what open source, So this is the new, this is how people are selling. This is not just open source, this is how companies are selling. >>Absolutely. Yeah. Yeah. You know, I think, and you know, two things. I think one is just, you know, this, this, this cloud native space is so vast that if you, if you're building a close flow solution, sometimes there's also a risk that it may not apply to every single enterprises use cases. And so having it open source gives them an opportunity to extend it, expand it, to make it proper to their use case if they choose to do so, right? But at the same time, what's also critical to us is we are able to provide a supported version of it with an SLA that we, you know, that's backed by us, a SAS hosted version of it as well, for those customers who choose to go that route, you know, once they have used the open source version and loved it and want to take it at scale and in production and need, need, need a partner to collaborate with, who can, you know, support them for that production >>Environment. I have to ask you now, let's get into what's in it for the customer. I'm a customer. Yep. Why should I be enthused about Arla? What's in it for me? You know? Cause if I'm not enthused about it, I'm not gonna be confident and it's gonna be hard for me to get behind this. Can you share your enthusiastic view of, you know, why I should be enthused about Arlo? I'm a >>Customer. Yeah, absolutely. And so, and there's multiple, you know, enterprises that we talk to, many of them, you know, our customers, where this is a very kind of typical story that you hear, which is we have, you know, a Kubernetes distribution. It could be on premise, it could be public clouds, native Kubernetes, and then we have our C I C D pipelines that are automating the deployment of applications, et cetera. And then there's this gray zone. And the gray zone is well before you can you, your CS c D pipelines can deploy the apps. Somebody needs to do all of that groundwork of, you know, defining those clusters and yeah. You know, properly configuring them. And as these things, these things start by being done hand grown. And then as the, as you scale, what typically enterprises would do today is they will have their home homegrown DIY solutions for this. >>I mean, the number of folks that I talk to that have built Terra from automation, and then, you know, some of those key developers leave. So it's a typical open source or typical, you know, DIY challenge. And the reason that they're writing it themselves is not because they want to. I mean, of course technology is always interesting to everybody, but it's because they can't find a solution that's out there that perfectly fits the problem. And so that's that pitch. I think Ops FICO would be delighted. The folks that we've talk, you know, spoken with, have been absolutely excited and have, you know, shared that this is a major challenge we have today because we have, you know, few hundreds of clusters on ecos Amazon, and we wanna scale them to few thousands, but we don't think we are ready to do that. And this will give us the >>Ability to, Yeah, I think people are scared. Not sc I won't say scare, that's a bad word. Maybe I should say that they feel nervous because, you know, at scale small mistakes can become large mistakes. This is something that is concerning to enterprises. And, and I think this is gonna come up at co con this year where enterprises are gonna say, Okay, I need to see SLAs. I wanna see track record, I wanna see other companies that have used it. Yeah. How would you answer that question to, or, or challenge, you know, Hey, I love this, but is there any guarantees? Is there any, what's the SLAs? I'm an enterprise, I got tight, you know, I love the open source trying to free fast and loose, but I need hardened code. >>Yeah, absolutely. So, so two parts to that, right? One is Arlan leverages existing open source components, products that are extremely popular. Two specifically. One is Arlan uses Argo cd, which is probably one of the highest and used CD open source tools that's out there. Right's created by folks that are as part of into team now, you know, really brilliant team. And it's used at scale across enterprises. That's one. Second is Alon also makes use of Cluster api cappi, which is a Kubernetes sub-component, right? For lifecycle management of clusters. So there is enough of, you know, community users, et cetera, around these two products, right? Or, or, or open source projects that will find Arlan to be right up in their alley because they're already comfortable, familiar with Argo cd. Now Arlan just extends the scope of what City can do. And so that's one. And then the second part is going back to a point of the comfort. And that's where, you know, platform line has a role to play, which is when you are ready to deploy online at scale, because you've been, you know, playing with it in your DEF test environments, you're happy with what you get with it, then Platform nine will stand behind it and provide that >>Sla. And what's been the reaction from customers you've talked to Platform nine customers with, with that are familiar with, with Argo and then rlo? What's been some of the feedback? >>Yeah, I, I think the feedback's been fantastic. I mean, I can give you examples of customers where, you know, initially, you know, when you are, when you're telling them about your entire portfolio of solutions, it might not strike a card right away. But then we start talking about Arlan and, and we talk about the fact that it uses Argo adn, they start opening up, they say, We have standardized on Argo and we have built these components, homegrown, we would be very interested. Can we co-develop? Does it support these use cases? So we've had that kind of validation. We've had validation all the way at the beginning of our land before we even wrote a single line of code saying this is something we plan on doing. And the customer said, If you had it today, I would've purchased it. So it's been really great validation. >>All right. So next question is, what is the solution to the customer? If I asked you, Look it, I have, I'm so busy, my team's overworked. I got a skills gap. I don't need another project that's, I'm so tied up right now and I'm just chasing my tail. How does Platform nine help me? >>Yeah, absolutely. So I think, you know, one of the core tenets of Platform nine has always been been that we try to bring that public cloud like simplicity by hosting, you know, this in a lot of such similar tools in a SaaS hosted manner for our customers, right? So our goal behind doing that is taking away or trying to take away all of that complexity from customers' hands and offloading it to our hands, right? And giving them that full white glove treatment, as we call it. And so from a customer's perspective, one, something like arlon will integrate with what they have so they don't have to rip and replace anything. In fact, it will, even in the next versions, it may even discover your clusters that you have today and you know, give you an inventory. And that will, >>So if customers have clusters that are growing, that's a sign correct call you guys. >>Absolutely. Either they're, they have massive large clusters, right? That they wanna split into smaller clusters, but they're not comfortable doing that today, or they've done that already on say, public cloud or otherwise. And now they have management challenges. So >>Especially operationalizing the clusters, whether they want to kind of reset everything and remove things around and reconfigure Yep. And or scale out. >>That's right. Exactly. And >>You provide that layer of policy. >>Absolutely. >>Yes. That's the key value here. >>That's right. >>So policy based configuration for cluster scale up, >>Well profile and policy based declarative configuration and lifecycle management for clusters. >>If I asked you how this enables supercloud, what would you say to that? >>I think this is one of the key ingredients to super cloud, right? If you think about a super cloud environment, there's at least few key ingredients that that come to my mind that are really critical. Like they are, you know, life saving ingredients at that scale. One is having a really good strategy for managing that scale, you know, in a, going back to assembly line in a very consistent, predictable way so that our lot solves then you, you need to compliment that with the right kind of observability and monitoring tools at scale, right? Because ultimately issues are gonna happen and you're gonna have to figure out, you know, how to solve them fast. And arlon by the way, also helps in that direction, but you also need observability tools. And then especially if you're running it on the public cloud, you need some cost management tools. In my mind, these three things are like the most necessary ingredients to make Super Cloud successful. And you know, our alarm fills in >>One. Okay. So now the next level is, Okay, that makes sense. Is under the covers kind of speak under the hood. Yeah. How does that impact the app developers and the cloud native modern application workflows? Because the impact to me, seems the apps are gonna be impacted. Are they gonna be faster, stronger? I mean, what's the impact if you do all those things, as you mentioned, what's the impact of the apps? >>Yeah, the impact is that your apps are more likely to operate in production the way you expect them to, because the right checks and balances have gone through, and any discrepancies have been identified prior to those apps, prior to your customer running into them, right? Because developers run into this challenge to their, where there's a split responsibility, right? I'm responsible for my code, I'm responsible for some of these other plugins, but I don't own the stack end to end. I have to rely on my ops counterpart to do their part, right? And so this really gives them, you know, the right tooling for that. >>So this is actually a great kind of relevant point, you know, as cloud becomes more scalable, you're starting to see this fragmentation gone of the days of the full stack developer to the more specialized role. But this is a key point, and I have to ask you because if this RLO solution takes place, as you say, and the apps are gonna be stupid, they're designed to do, the question is, what did does the current pain look like of the apps breaking? What does the signals to the customer Yeah. That they should be calling you guys up into implementing Arlo, Argo and, and all the other goodness to automate? What are some of the signals? Is it downtime? Is it, is it failed apps, Is it latency? What are some of the things that Yeah, absolutely would be indications of things are effed up a little bit. Yeah. >>More frequent down times, down times that are, that take longer to triage. And so you are, you know, the, you know, your mean times on resolution, et cetera, are escalating or growing larger, right? Like we have environments of customers where they're, they have a number of folks on in the field that have to take these apps and run them at customer sites. And that's one of our partners. And they're extremely interested in this because they're the, the rate of failures they're encountering for this, you know, the field when they're running these apps on site, because the field is automating their clusters that are running on sites using their own script. So these are the kinds of challenges, and those are the pain points, which is, you know, if you're looking to reduce your meantime to resolution, if you're looking to reduce the number of failures that occur on your production site, that's one. And second, if you are looking to manage these at scale environments with a relatively small, focused, nimble ops team, which has an immediate impact on your budget. So those are, those are the signals. >>This is the cloud native at scale situation, the innovation going on. Final thought is your reaction to the idea that if the world goes digital, which it is, and the confluence of physical and digital coming together, and cloud continues to do its thing, the company becomes the application, not where it used to be supporting the business, you know, the back office and the maybe terminals and some PCs and handhelds. Now if technology's running, the business is the business. Yeah. Company's the application. Yeah. So it can't be down. So there's a lot of pressure on, on CSOs and CIOs now and boards is saying, How is technology driving the top line revenue? That's the number one conversation. Yep. Do you see that same thing? >>Yeah. It's interesting. I think there's multiple pressures at the CXO CIO level, right? One is that there needs to be that visibility and clarity and guarantee almost that, you know, that the, the technology that's, you know, that's gonna drive your top line is gonna drive that in a consistent, reliable, predictable manner. And then second, there is the constant pressure to do that while always lowering your costs of doing it, right? Especially when you're talking about, let's say retailers or those kinds of large scale vendors, they many times make money by lowering the amount that they spend on, you know, providing those goods to their end customers. So I think those, both those factors kind of come into play and the solution to all of them is usually in a very structured strategy around automation. >>Final question. What does cloudnative at scale look like to you? If all the things happen the way we want 'em to happen, The magic wand, the magic dust, what does it look like? >>What that looks like to me is a CIO sipping at his desk on coffee production is running absolutely smooth. And his, he's running that at a nimble, nimble team size of at the most, a handful of folks that are just looking after things, but things are >>Just taking care of the CIO doesn't exist. There's no ciso, they're at the beach. >>Yep. >>Thank you for coming on, sharing the cloud native at scale here on the cube. Thank you for your time. >>Fantastic. Thanks for >>Having me. Okay. I'm John Fur here for special program presentation, special programming cloud native at scale, enabling super cloud modern applications with Platform nine. Thanks for watching. Welcome back everyone to the special presentation of cloud native at scale, the cube and platform nine special presentation going in and digging into the next generation super cloud infrastructure as code and the future of application development. We're here with Bickley, who's the chief architect and co-founder of Platform nine Pick. Great to see you Cube alumni. We, we met at an OpenStack event in about eight years ago, or later, earlier when OpenStack was going. Great to see you and great to see congratulations on the success of platform nine. >>Thank you very much. >>Yeah. You guys have been at this for a while and this is really the, the, the year we're seeing the, the crossover of Kubernetes because of what happens with containers. Everyone now has realized, and you've seen what Docker's doing with the new docker, the open source Docker now just the success Exactly. Of containerization, right? And now the Kubernetes layer that we've been working on for years is coming, bearing fruit. This is huge. >>Exactly. Yes. >>And so as infrastructures code comes in, we talked to Bacar talking about Super Cloud, I met her about, you know, the new Arlon, our, our lawn, and you guys just launched the infrastructures code is going to another level, and then it's always been DevOps infrastructures code. That's been the ethos that's been like from day one, developers just code. Then you saw the rise of serverless and you see now multi-cloud or on the horizon, connect the dots for us. What is the state of infrastructure as code today? >>So I think, I think I'm, I'm glad you mentioned it, everybody or most people know about infrastructures code. But with Kubernetes, I think that project has evolved at the concept even further. And these dates, it's infrastructure is configuration, right? So, which is an evolution of infrastructure as code. So instead of telling the system, here's how I want my infrastructure by telling it, you know, do step A, B, C, and D instead with Kubernetes, you can describe your desired state declaratively using things called manifest resources. And then the system kind of magically figures it out and tries to converge the state towards the one that you specified. So I think it's, it's a even better version of infrastructures code. >>Yeah. And that really means it's developer just accessing resources. Okay. That declare, Okay, give me some compute, stand me up some, turn the lights on, turn 'em off, turn 'em on. That's kind of where we see this going. And I like the configuration piece. Some people say composability, I mean now with open source so popular, you don't have to have to write a lot of code, this code being developed. And so it's into integration, it's configuration. These are areas that we're starting to see computer science principles around automation, machine learning, assisting open source. Cuz you got a lot of code that's right in hearing software, supply chain issues. So infrastructure as code has to factor in these new dynamics. Can you share your opinion on these new dynamics of, as open source grows, the glue layers, the configurations, the integration, what are the core issues? >>I think one of the major core issues is with all that power comes complexity, right? So, you know, despite its expressive power systems like Kubernetes and declarative APIs let you express a lot of complicated and complex stacks, right? But you're dealing with hundreds if not thousands of these yamo files or resources. And so I think, you know, the emergence of systems and layers to help you manage that complexity is becoming a key challenge and opportunity in, in this space. >>That's, I wrote a LinkedIn post today was comments about, you know, hey, enterprise is a new breed. The trend of SaaS companies moving our consumer comp consumer-like thinking into the enterprise has been happening for a long time, but now more than ever, you're seeing it the old way used to be solve complexity with more complexity and then lock the customer in. Now with open source, it's speed, simplification and integration, right? These are the new dynamic power dynamics for developers. Yeah. So as companies are starting to now deploy and look at Kubernetes, what are the things that need to be in place? Because you have some, I won't say technical debt, but maybe some shortcuts, some scripts here that make it look like infrastructure is code. People have done some things to simulate or or make infrastructure as code happen. Yes. But to do it at scale Yes. Is harder. What's your take on this? What's your view? >>It's hard because there's a per proliferation of methods, tools, technologies. So for example, today it's very common for DevOps and platform engineering tools, I mean, sorry, teams to have to deploy a large number of Kubernetes clusters, but then apply the applications and configurations on top of those clusters. And they're using a wide range of tools to do this, right? For example, maybe Ansible or Terraform or bash scripts to bring up the infrastructure and then the clusters. And then they may use a different set of tools such as Argo CD or other tools to apply configurations and applications on top of the clusters. So you have this sprawl of tools. You, you also have this sprawl of configurations and files because the more objects you're dealing with, the more resources you have to manage. And there's a risk of drift that people call that where, you know, you think you have things under control, but some people from various teams will make changes here and there and then before the end of the day systems break and you have no idea of tracking them. So I think there's real need to kind of unify, simplify, and try to solve these problems using a smaller, more unified set of tools and methodologies. And that's something that we try to do with this new project. Arlon. >>Yeah. So, so we're gonna get into Arlan in a second. I wanna get into the why Arlon. You guys announced that at AR GoCon, which was put on here in Silicon Valley at the, at the community meeting by in two, they had their own little day over there at their headquarters. But before we get there, vascar, your CEO came on and he talked about Super Cloud at our in AAL event. What's your definition of super cloud? If you had to kind of explain that to someone at a cocktail party or someone in the industry technical, how would you look at the super cloud trend that's emerging? It's become a thing. What's your, what would be your contribution to that definition or the narrative? >>Well, it's, it's, it's funny because I've actually heard of the term for the first time today, speaking to you earlier today. But I think based on what you said, I I already get kind of some of the, the gist and the, the main concepts. It seems like super cloud, the way I interpret that is, you know, clouds and infrastructure, programmable infrastructure, all of those things are becoming commodity in a way. And everyone's got their own flavor, but there's a real opportunity for people to solve real business problems by perhaps trying to abstract away, you know, all of those various implementations and then building better abstractions that are perhaps business or applications specific to help companies and businesses solve real business problems. >>Yeah, I remember that's a great, great definition. I remember, not to date myself, but back in the old days, you know, IBM had a proprietary network operating system, so of deck for the mini computer vendors, deck net and SNA respectively. But T C P I P came out of the osi, the open systems interconnect and remember, ethernet beat token ring out. So not to get all nerdy for all the young kids out there, look, just look up token ring, you'll see, you've probably never heard of it. It's IBM's, you know, connection for the internet at the, the layer two is Amazon, the ethernet, right? So if T C P I P could be the Kubernetes and the container abstraction that made the industry completely change at that point in history. So at every major inflection point where there's been serious industry change and wealth creation and business value, there's been an abstraction Yes. Somewhere. Yes. What's your reaction to that? >>I think this is, I think a saying that's been heard many times in this industry and, and I forgot who originated it, but I think that the saying goes like, there's no problem that can't be solved with another layer of indirection, right? And we've seen this over and over and over again where Amazon and its peers have inserted this layer that has simplified, you know, computing and, and infrastructure management. And I believe this trend is going to continue, right? The next set of problems are going to be solved with these insertions of additional abstraction layers. I think that that's really a, yeah, it's gonna >>Continue. It's interesting. I just, when I wrote another post today on LinkedIn called the Silicon Wars AMD stock is down arm has been on a rise. We remember pointing for many years now that arm's gonna be hugely, it has become true. If you look at the success of the infrastructure as a service layer across the clouds, Azure, aws, Amazon's clearly way ahead of everybody. The stuff that they're doing with the silicon and the physics and the, the atoms, the pro, you know, this is where the innovation, they're going so deep and so strong at ISAs, the more that they get that gets come on, they have more performance. So if you're an app developer, wouldn't you want the best performance and you'd wanna have the best abstraction layer that gives you the most ability to do infrastructures, code or infrastructure for configuration, for provisioning, for managing services. And you're seeing that today with service MeSHs, a lot of action going on in the service mesh area in in this community of, of co con, which will be a covering. So that brings up the whole what's next? You guys just announced our lawn at Argo Con, which came out of Intuit. We've had Mariana Tessel at our super cloud event. She's the cto, you know, they're all in the cloud. So they contributed that project. Where did Arlon come from? What was the origination? What's the purpose? Why our lawn, why this announcement? >>Yeah, so the, the inception of the project, this was the result of us realizing that problem that we spoke about earlier, which is complexity, right? With all of this, these clouds, these infrastructure, all the variations around and, you know, compute storage networks and the proliferation of tools we talked about the Ansibles and Terraforms and Kubernetes itself. You can, you can think of that as another tool, right? We saw a need to solve that complexity problem, and especially for people and users who use Kubernetes at scale. So when you have, you know, hundreds of clusters, thousands of applications, thousands of users spread out over many, many locations, there, there needs to be a system that helps simplify that management, right? So that means fewer tools, more expressive ways of describing the state that you want and more consistency. And, and that's why, you know, we built our lawn and we built it recognizing that many of these problems or sub problems have already been solved. So Arlon doesn't try to reinvent the wheel, it instead rests on the shoulders of several giants, right? So for example, Kubernetes is one building block, GI ops, and Argo CD is another one, which provides a very structured way of applying configuration. And then we have projects like cluster API and cross plane, which provide APIs for describing infrastructure. So arlon takes all of those building blocks and builds a thin layer, which gives users a very expressive way of defining configuration and desired state. So that's, that's kind of the inception of, And >>What's the benefit of that? What does that give the, what does that give the developer, the user, in this case, >>The developers, the, the platform engineer, team members, the DevOps engineers, they get a a ways to provision not just infrastructure and clusters, but also applications and configurations. They get a way, a system for provisioning, configuring, deploying, and doing life cycle management in a, in a much simpler way. Okay. Especially as I said, if you're dealing with a large number of applications. >>So it's like an operating fabric, if you will. Yes. For them. Okay, so let's get into what that means for up above and below the the, this abstraction or thin layer below as the infrastructure. We talked a lot about what's going on below that. Yeah. Above our workloads. At the end of the day, you know, I talk to CXOs and IT folks that are now DevOps engineers. They care about the workloads and they want the infrastructures code to work. They wanna spend their time getting in the weeds, figuring out what happened when someone made a push that that happened or something happened. They need observability and they need to, to know that it's working. That's right. And is my workloads running effectively? So how do you guys look at the workload side of it? Cuz now you have multiple workloads on these fabric, >>Right? So workloads, so Kubernetes has defined kind of a standard way to describe workloads and you can, you know, tell Kubernetes, I want to run this container this particular way, or you can use other projects that are in the Kubernetes cloud native ecosystem like K native, where you can express your application in more at a higher level, right? But what's also happening is in addition to the workloads, DevOps and platform engineering teams, they need to very often deploy the applications with the clusters themselves. Clusters are becoming this commodity. It's, it's becoming this host for the application and it kind of comes bundled with it. In many cases it is like an appliance, right? So DevOps teams have to provision clusters at a really incredible rate and they need to tear them down. Clusters are becoming more, >>It's kinda like an EC two instance, spin up a cluster. We very, people used words like that. That's >>Right. And before arlon you kind of had to do all of that using a different set of tools as, as I explained. So with Armon you can kind of express everything together. You can say I want a cluster with a health monitoring stack and a logging stack and this ingress controller and I want these applications and these security policies. You can describe all of that using something we call a profile. And then you can stamp out your app, your applications and your clusters and manage them in a very, so >>Essentially standard creates a mechanism. Exactly. Standardized, declarative kind of configurations. And it's like a playbook. You deploy it. Now what's there is between say a script like I'm, I have scripts, I could just automate scripts >>Or yes, this is where that declarative API and infrastructures configuration comes in, right? Because scripts, yes you can automate scripts, but the order in which they run matters, right? They can break, things can break in the middle and, and sometimes you need to debug them. Whereas the declarative way is much more expressive and powerful. You just tell the system what you want and then the system kind of figures it out. And there are these things about controllers which will in the background reconcile all the state to converge towards your desire. It's a much more powerful, expressive and reliable way of getting things done. >>So infrastructure has configuration is built kind of on, it's as super set of infrastructures code because it's >>An evolution. >>You need edge's code, but then you can configure the code by just saying do it. You basically declaring and saying Go, go do that. That's right. Okay, so, alright, so cloud native at scale, take me through your vision of what that means. Someone says, Hey, what does cloud native at scale mean? What's success look like? How does it roll out in the future as you, not future next couple years? I mean people are now starting to figure out, okay, it's not as easy as it sounds. Could be nice, it has value. We're gonna hear this year coan a lot of this. What does cloud native at scale >>Mean? Yeah, there are different interpretations, but if you ask me, when people think of scale, they think of a large number of deployments, right? Geographies, many, you know, supporting thousands or tens or millions of, of users there, there's that aspect to scale. There's also an equally important a aspect of scale, which is also something that we try to address with Arran. And that is just complexity for the people operating this or configuring this, right? So in order to describe that desired state and in order to perform things like maybe upgrades or updates on a very large scale, you want the humans behind that to be able to express and direct the system to do that in, in relatively simple terms, right? And so we want the tools and the abstractions and the mechanisms available to the user to be as powerful but as simple as possible. So there's, I think there's gonna be a number and there have been a number of CNCF and cloud native projects that are trying to attack that complexity problem as well. And Arlon kind of falls in in that >>Category. Okay, so I'll put you on the spot road that CubeCon coming up and obviously this will be shipping this segment series out before. What do you expect to see at Coan this year? What's the big story this year? What's the, what's the most important thing happening? Is it in the open source community and also within a lot of the, the people jogging for leadership. I know there's a lot of projects and still there's some white space in the overall systems map about the different areas get run time and there's ability in all these different areas. What's the, where's the action? Where, where's the smoke? Where's the fire? Where's the piece? Where's the tension? >>Yeah, so I think one thing that has been happening over the past couple of cons and I expect to continue and, and that is the, the word on the street is Kubernetes is getting boring, right? Which is good, right? >>Boring means simple. >>Well, well >>Maybe, >>Yeah, >>Invisible, >>No drama, right? So, so the, the rate of change of the Kubernetes features and, and all that has slowed but in, in a, in a positive way. But there's still a general sentiment and feeling that there's just too much stuff. If you look at a stack necessary for hosting applications based on Kubernetes, there are just still too many moving parts, too many components, right? Too much complexity. I go, I keep going back to the complexity problem. So I expect Cube Con and all the vendors and the players and the startups and the people there to continue to focus on that complexity problem and introduce further simplifications to, to the stack. >>Yeah. Vic, you've had an storied career, VMware over decades with them obviously in 12 years with 14 years or something like that. Big number co-founder here at Platform. Now you guys have been around for a while at this game. We, man, we talked about OpenStack, that project you, we interviewed at one of their events. So OpenStack was the beginning of that, this new revolution. And I remember the early days it was, it wasn't supposed to be an alternative to Amazon, but it was a way to do more cloud cloud native. I think we had a cloud ERO team at that time. We would to joke we, you know, about, about the dream. It's happening now, now at Platform nine. You guys have been doing this for a while. What's the, what are you most excited about as the chief architect? What did you guys double down on? What did you guys tr pivot from or two, did you do any pivots? Did you extend out certain areas? Cuz you guys are in a good position right now, a lot of DNA in Cloud native. What are you most excited about and what does Platform nine bring to the table for customers and for people in the industry watching this? >>Yeah, so I think our mission really hasn't changed over the years, right? It's been always about taking complex open source software because open source software, it's powerful. It solves new problems, you know, every year and you have new things coming out all the time, right? OpenStack was an example when the Kubernetes took the world by storm. But there's always that complexity of, you know, just configuring it, deploying it, running it, operating it. And our mission has always been that we will take all that complexity and just make it, you know, easy for users to consume regardless of the technology, right? So the successor to Kubernetes, you know, I don't have a crystal ball, but you know, you have some indications that people are coming up of new and simpler ways of running applications. There are many projects around there who knows what's coming next year or the year after that. But platform will a, platform nine will be there and we will, you know, take the innovations from the the community. We will contribute our own innovations and make all of those things very consumable to customers. >>Simpler, faster, cheaper. Exactly. Always a good business model technically to make that happen. Yes. Yeah, I think the, the reigning in the chaos is key, you know, Now we have now visibility into the scale. Final question before we depart this segment. What is at scale, how many clusters do you see that would be a watermark for an at scale conversation around an enterprise? Is it workloads we're looking at or, or clusters? How would you, Yeah, how would you describe that? When people try to squint through and evaluate what's a scale, what's the at scale kind of threshold? >>Yeah. And, and the number of clusters doesn't tell the whole story because clusters can be small in terms of the number of nodes or they can be large. But roughly speaking when we say, you know, large scale cluster deployments, we're talking about maybe hundreds, two thousands. >>Yeah. And final final question, what's the role of the hyperscalers? You got AWS continuing to do well, but they got their core ias, they got a PAs, they're not too too much putting a SaaS out there. They have some SaaS apps, but mostly it's the ecosystem. They have marketplaces doing over $2 billion billions of transactions a year and, and it's just like, just sitting there. It hasn't really, they're now innovating on it, but that's gonna change ecosystems. What's the role the cloud play in the cloud native of its scale? >>The, the hyperscalers, >>Yeahs Azure, Google. >>You mean from a business perspective? Yeah, they're, they have their own interests that, you know, that they're, they will keep catering to, they, they will continue to find ways to lock their users into their ecosystem of services and, and APIs. So I don't think that's gonna change, right? They're just gonna keep, >>Well they got great I performance, I mean from a, from a hardware standpoint, yes, that's gonna be key, right? >>Yes. I think the, the move from X 86 being the dominant way and platform to run workloads is changing, right? That, that, that, that, and I think the, the hyperscalers really want to be in the game in terms of, you know, the the new risk and arm ecosystems and the platforms. >>Yeah, not joking aside, Paul Morritz, when he was the CEO of VMware, when he took over once said, I remember our first year doing the cube. Oh the cloud is one big distributed computer, it's, it's hardware and he got software and you got middleware and he kind over, well he's kind of tongue in cheek, but really you're talking about large compute and sets of services that is essentially a distributed computer. >>Yes, >>Exactly. It's, we're back on the same game. Vic, thank you for coming on the segment. Appreciate your time. This is cloud native at scale special presentation with Platform nine. Really unpacking super cloud Arlon open source and how to run large scale applications on the cloud Cloud Native Phil for developers and John Furrier with the cube. Thanks for Washington. We'll stay tuned for another great segment coming right up. Hey, welcome back everyone to Super Cloud 22. I'm John Fur, host of the Cuba here all day talking about the future of cloud. Where's it all going? Making it super multi-cloud clouds around the corner and public cloud is winning. Got the private cloud on premise and edge. Got a great guest here, Vascar Gorde, CEO of Platform nine, just on the panel on Kubernetes. An enabler blocker. Welcome back. Great to have you on. >>Good to see you >>Again. So Kubernetes is a blocker enabler by, with a question mark. I put on on that panel was really to discuss the role of Kubernetes. Now great conversation operations is impacted. What's interest thing about what you guys are doing at Platform nine? Is your role there as CEO and the company's position, kind of like the world spun into the direction of Platform nine while you're at the helm? Yeah, right. >>Absolutely. In fact, things are moving very well and since they came to us, it was an insight to call ourselves the platform company eight years ago, right? So absolutely whether you are doing it in public clouds or private clouds, you know, the application world is moving very fast in trying to become digital and cloud native. There are many options for you do on the infrastructure. The biggest blocking factor now is having a unified platform. And that's what we, we come into, >>Patrick, we were talking before we came on stage here about your background and we were gonna talk about the glory days in 2000, 2001, when the first as piece application service providers came out, kind of a SaaS vibe, but that was kind of all kind of cloudlike. >>It wasn't, >>And and web services started then too. So you saw that whole growth. Now, fast forward 20 years later, 22 years later, where we are now, when you look back then to here and all the different cycles, >>I, in fact you, you know, as we were talking offline, I was in one of those ASPs in the year 2000 where it was a novel concept of saying we are providing a software and a capability as a service, right? You sign up and start using it. I think a lot has changed since then. The tooling, the tools, the technology has really skyrocketed. The app development environment has really taken off exceptionally well. There are many, many choices of infrastructure now, right? So I think things are in a way the same but also extremely different. But more importantly now for any company, regardless of size, to be a digital native, to become a digital company is extremely mission critical. It's no longer a nice to have everybody's in the journey somewhere. >>Everyone is going digital transformation here. Even on a so-called downturn recession that's upcoming inflation's here. It's interesting. This is the first downturn in the history of the world where the hyperscale clouds have been pumping on all cylinders as an economic input. And if you look at the tech trends, GDPs down, but not tech. >>Nope. >>Cuz the pandemic showed everyone digital transformation is here and more spend and more growth is coming even in, in tech. So this is a unique factor which proves that that digital transformation's happening and company, every company will need a super cloud. >>Everyone, every company, regardless of size, regardless of location, has to become modernize their infrastructure. And modernizing Infras infrastructure is not just some new servers and new application tools, It's your approach, how you're serving your customers, how you're bringing agility in your organization. I think that is becoming a necessity for every enterprise to survive. >>I wanna get your thoughts on Super Cloud because one of the things Dave Ante and I want to do with Super Cloud and calling it that was we, I, I personally, and I know Dave as well, he can, I'll speak from, he can speak for himself. We didn't like multi-cloud. I mean not because Amazon said don't call things multi-cloud, it just didn't feel right. I mean everyone has multiple clouds by default. If you're running productivity software, you have Azure and Office 365. But it wasn't truly distributed. It wasn't truly decentralized, it wasn't truly cloud enabled. It didn't, it felt like they're not ready for a market yet. Yet public clouds booming on premise. Private cloud and Edge is much more on, you know, more, more dynamic, more real. >>Yeah. I think the reason why we think super cloud is a better term than multi-cloud. Multi-cloud are more than one cloud, but they're disconnected. Okay, you have a productivity cloud, you have a Salesforce cloud, you may have, everyone has an internal cloud, right? So, but they're not connected. So you can say okay, it's more than one cloud. So it's you know, multi-cloud. But super cloud is where you are actually trying to look at this holistically. Whether it is on-prem, whether it is public, whether it's at the edge, it's a store at the branch. You are looking at this as one unit. And that's where we see the term super cloud is more applicable because what are the qualities that you require if you're in a super cloud, right? You need choice of infrastructure, you need, but at the same time you need a single pain, a single platform for you to build your innovations on regardless of which cloud you're doing it on, right? So I think Super Cloud is actually a more tightly integrated orchestrated management philosophy we think. >>So let's get into some of the super cloud type trends that we've been reporting on. Again, the purpose of this event is to, as a pilots, to get the conversations flowing with with the influencers like yourselves who are running companies and building products and the builders, Amazon and Azure are doing extremely well. Google's coming up in third cloudworks in public cloud. We see the use cases on premises use cases. Kubernetes has been an interesting phenomenon because it's become from the developer side a little bit, but a lot of ops people love Kubernetes. It's really more of an ops thing. You mentioned OpenStack earlier. Kubernetes kind of came out of that open stack. We need an orchestration and then containers had a good shot with, with Docker. They re pivoted the company. Now they're all in an open source. So you got containers booming and Kubernetes as a new layer there. What's the, what's the take on that? What does that really mean? Is that a new defacto enabler? It >>Is here. It's for here for sure. Every enterprise somewhere else in the journey is going on. And you know, most companies are, 70 plus percent of them have won two, three container based, Kubernetes based applications now being rolled out. So it's very much here, it is in production at scale by many customers. And the beauty of it is, yes, open source, but the biggest gating factor is the skill set. And that's where we have a phenomenal engineering team, right? So it's, it's one thing to buy a tool >>And just be clear, you're a managed service for Kubernetes. >>We provide, provide a software platform for cloud acceleration as a service and it can run anywhere. It can run in public private. We have customers who do it in truly multi-cloud environments. It runs on the edge, it runs at this in stores are thousands of stores in a retailer. So we provide that and also for specific segments where data sovereignty and data residency are key regulatory reasons. We also un OnPrem as an air gap version. >>Can you give an example on how you guys are deploying your platform to enable a super cloud experience for your >>Customer? Right. So I'll give you two different examples. One is a very large networking company, public networking company. They have, I dunno, hundreds of products, hundreds of r and d teams that are building different, different products. And if you look at few years back, each one was doing it on a different platforms but they really needed to bring the agility and they worked with us now over three years where we are their build test dev pro platform where all their products are built on, right? And it has dramatically increased their agility to release new products. Number two, it actually is a light out operation. In fact the customer says like, like the Maytag service person cuz we provide it as a service and it barely takes one or two people to maintain it for them. >>So it's kinda like an SRE vibe. One person managing a >>Large 4,000 engineers building infrastructure >>On their tools, >>Whatever they want on their tools. They're using whatever app development tools they use, but they use our platform. >>What benefits are they seeing? Are they seeing speed? >>Speed, definitely. Okay. Definitely they're speeding. Speed uniformity because now they're building able to build, so their customers who are using product A and product B are seeing a similar set of tools that are being used. >>So a big problem that's coming outta this super cloud event that we're, we're seeing and we've heard it all here, ops and security teams cuz they're kind of too part of one theme, but ops and security specifically need to catch up speed wise. Are you delivering that value to ops and security? Right. >>So we, we work with ops and security teams and infrastructure teams and we layer on top of that. We have like a platform team. If you think about it, depending on where you have data centers, where you have infrastructure, you have multiple teams, okay, but you need a unified platform. Who's your buyer? Our buyer is usually, you know, the product divisions of companies that are looking at or the CTO would be a buyer for us functionally cio definitely. So it it's, it's somewhere in the DevOps to infrastructure. But the ideal one we are beginning to see now many large corporations are really looking at it as a platform and saying we have a platform group on which any app can be developed and it is run on any infrastructure. So the platform engineering teams, >>You working two sides of that coin. You've got the dev side and then >>And then infrastructure >>Side side, okay. >>Another customer like give you an example, which I would say is kind of the edge of the store. So they have thousands of stores. Retail, retail, you know food retailer, right? They have thousands of stores that are on the globe, 50,000, 60,000. And they really want to enhance the customer experience that happens when you either order the product or go into the store and pick up your product or buy or browse or sit there. They have applications that were written in the nineties and then they have very modern AIML applications today. They want something that will not have to send an IT person to install a rack in the store or they can't move everything to the cloud because the store operations has to be local. The menu changes based on, It's a classic edge. It's classic edge. Yeah. Right. They can't send it people to go install rack access servers then they can't sell software people to go install the software and any change you wanna put through that, you know, truck roll. So they've been working with us where all they do is they ship, depending on the size of the store, one or two or three little servers with instructions that >>You, you say little servers like how big one like a net box box, like a small little >>Box and all the person in the store has to do like what you and I do at home and we get a, you know, a router is connect the power, connect the internet and turn the switch on. And from there we pick it up. >>Yep. >>We provide the operating system, everything and then the applications are put on it. And so that dramatically brings the velocity for them. They manage >>Thousands of them. True plug and play >>Two, plug and play thousands of stores. They manage it centrally. We do it for them, right? So, so that's another example where on the edge then we have some customers who have both a large private presence and one of the public clouds. Okay. But they want to have the same platform layer of orchestration and management that they can use regardless of the location. So >>You guys got some success. Congratulations. Got some traction there. It's awesome. The question I want to ask you is that's come up is what is truly cloud native? Cuz there's lift and shift of the cloud >>That's not cloud native. >>Then there's cloud native. Cloud native seems to be the driver for the super cloud. How do you talk to customers? How do you explain when someone says what's cloud native, what isn't cloud native? >>Right. Look, I think first of all, the best place to look at what is the definition and what are the attributes and characteristics of what is truly a cloud native, is CNC foundation. And I think it's very well documented where you, well >>Con of course Detroit's >>Coming here, so, so it's already there, right? So, so we follow that very closely, right? I think just lifting and shifting your 20 year old application onto a data center somewhere is not cloud native. Okay? You can't put to cloud native, you have to rewrite and redevelop your application and business logic using modern tools. Hopefully more open source and, and I think that's what Cloudnative is and we are seeing a lot of our customers in that journey. Now everybody wants to be cloudnative, but it's not that easy, okay? Because it's, I think it's first of all, skill set is very important. Uniformity of tools that there's so many tools there. Thousands and thousands of tools you could spend your time figuring out which tool to use. Okay? So I think the complexities there, but the business benefits of agility and uniformity and customer experience are truly them. >>And I'll give you an example. I don't know how clear native they are, right? And they're not a customer of ours, but you order pizzas, you do, right? If you just watch the pizza industry, how dominoes actually increase their share and mind share and wallet share was not because they were making better pizzas or not, I don't know anything about that, but the whole experience of how you order, how you watch what's happening, how it's delivered. There were a pioneer in it. To me, those are the kinds of customer experiences that cloud native can provide. >>Being agility and having that flow to the application changes what the expectations of the, for the customer. >>Customer, the customer's expectations change, right? Once you get used to a better customer experience, you learn >>Best car. To wrap it up, I wanna just get your perspective again. One of the benefits of chatting with you here and having you part of the Super Cloud 22 is you've seen many cycles, you have a lot of insights. I want to ask you, given your career where you've been and what you've done and now the CEO platform nine, how would you compare what's happening now with other inflection points in the industry? And you've been, again, you've been an entrepreneur, you sold your company to Oracle, you've been seeing the big companies, you've seen the different waves. What's going on right now put into context this moment in time around Super >>Cloud. Sure. I think as you said, a lot of battles. Cars being been, been in an asp, been in a realtime software company, being in large enterprise software houses and a transformation. I've been on the app side, I did the infrastructure right and then tried to build our own platforms. I've gone through all of this myself with a lot of lessons learned in there. I think this is an event which is happening now for companies to go through to become cloud native and digitalize. If I were to look back and look at some parallels of the tsunami that's going on is a couple of paddles come to me. One is, think of it, which was forced to honors like y2k. Everybody around the world had to have a plan, a strategy, and an execution for y2k. I would say the next big thing was e-commerce. I think e-commerce has been pervasive right across all industries. >>And disruptive. >>And disruptive, extremely disruptive. If you did not adapt and adapt and accelerate your e-commerce initiative, you were, it was an existence question. Yeah. I think we are at that pivotal moment now in companies trying to become digital and cloudnative that know that is what I see >>Happening there. I think that that e-commerce was interesting and I think just to riff with you on that is that it's disrupting and refactoring the business models. I think that is something that's coming out of this is that it's not just completely changing the game, it's just changing how you operate, >>How you think, and how you operate. See, if you think about the early days of eCommerce, just putting up a shopping cart didn't made you an eCommerce or an E retailer or an e e customer, right? Or so. I think it's the same thing now is I think this is a fundamental shift on how you're thinking about your business. How are you gonna operate? How are you gonna service your customers? I think it requires that just lift and shift is not gonna work. >>Mascar, thank you for coming on, spending the time to come in and share with our community and being part of Super Cloud 22. We really appreciate, we're gonna keep this open. We're gonna keep this conversation going even after the event, to open up and look at the structural changes happening now and continue to look at it in the open in the community. And we're gonna keep this going for, for a long, long time as we get answers to the problems that customers are looking for with cloud cloud computing. I'm Sean Feer with Super Cloud 22 in the Cube. Thanks for watching. >>Thank you. Thank you, John. >>Hello. Welcome back. This is the end of our program, our special presentation with Platform nine on cloud native at scale, enabling the super cloud. We're continuing the theme here. You heard the interviews Super Cloud and its challenges, new opportunities around the solutions around like Platform nine and others with Arlon. This is really about the edge situations on the internet and managing the edge multiple regions, avoiding vendor lock in. This is what this new super cloud is all about. The business consequences we heard and and the wide ranging conversations around what it means for open source and the complexity problem all being solved. I hope you enjoyed this program. There's a lot of moving pieces and things to configure with cloud native install, all making it easier for you here with Super Cloud and of course Platform nine contributing to that. Thank you for watching.

>>Welcome back everyone to the cubes coverage here in Seattle, Washington, AWS's marketplace seller conference. It's the big news within the Amazon partner network, combining with marketplaces, forming the Amazon partner organization, part of a big reorg as they grow the next level NextGen cloud mid-game on the chessboard. Cube's got cover. I'm John fur, host of Cub, a great guests here from data bricks, both cube alumnis, Jack Anderson, GM of the and VP of the data bricks partnership team. For ADOS, you handle that relationship and Joel Minick vice president of product and partner marketing. You guys are the, have the keys to the kingdom with data, bricks, and AWS. Thanks for joining. Thanks for good to see you again. Thanks for >>Having us back. Yeah, John, great to be here. >>So I feel like we're at reinvent 2013 small event, no stage, but there's a real shift happening with procurement. Obviously it makes it's a no brainer on the micro, you know, people should be buying online self-service cloud scale, but Amazon's got billions being sold to their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website marketplace merge our partner to have more synergy and friction, less experiences so everyone can make more money and customer's gonna be happier. >>Yeah, that's right. >>I mean, you're run relationship. You're in the middle of it. >>Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do is to work with Amazon on these really, you know, unique use cases. >>You know, I interviewed Ali many times over the years. I remember many years ago, I think six, maybe six, seven years ago, we were talking. He's like, we're all in ons. Obviously. Now the success of data bricks, you've got multiple clouds. See that customers have choice, but I remember the strategy early on. It was like, we're gonna be deep. So this is speaks volumes to the, the relationship you have years. Jack take us through the relationship that data bricks has with AWS from a, from a partner perspective, Joel, and from a product perspective, because it's not like you got to Johnny come lately new to the new, to the scene, right? We've been there almost president creation of this wave. What's the relationship and has it relate to what's going on today? >>So, so most people may not know that data bricks was born on AWS. We actually did our first 100 million of revenue on Amazon. And today we're obviously available on multiple clouds, but we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon and marketplace broadens our reach. And so we think of marketplace in three different aspects. We've got the marketplace, private offer business, which we've been doing for a number of years. Matter of fact, we we're driving well over a hundred percent year over year growth in private offers and we have a nine figure business. So it's a very significant business. And when a customer uses a private offer that private offer counts against their private pricing agreement with AWS. So they get pricing power against their, their private pricing. >>So it's really important. It goes on their Amazon bill in may. We launched our pay as you go on demand offering. And in five short months, we have well over a thousand subscribers. And what this does is it really reduces the barriers to entry it's low friction. So anybody in an enterprise or startup or public sector company can start to use data bricks on AWS and pay consumption based model and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation pilots, POCs, they're, they're really learning the value of that first use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offers C P O super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with data bricks on behalf of customers. >>So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data coming in with, with the buyers you go. And obviously the integration piece all fitting in there. Exactly. Exactly. Okay. So that's that those are the offers that's current and what's in marketplace today. Is that the products, what are, what are people buying? I mean, I guess what's the Joel, what are, what are people buying in the marketplace and what does it mean for >>Them? So fundamentally what they're buying is the ability to take silos out of their organization. And that's, that is the problem that data bricks is out there to solve, which is when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data, and your teams are trying to use all of this data to solve really complicated problems. And as data bricks as the lake house company, what we're helping customers do is how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find through the marketplace, those rapid adoption use cases where they can get rid of these data, warehousing data lake silos they've had in the past, get their unstructured and structured data onto one data platform and open data platform that is no longer adherent to any proprietary formats and standards and something. >>They can very much, very easily integrate into the rest of their data environment, apply one common data governance layer on top of that. So that from the time they ingest that data to the time they use that data to the time they share that data inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform with that common governance solution, they'd being able to bring all of those use cases together across their real time, streaming their data engineering, their BI, their AI, all of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges. They just couldn't solve before a good example of this, you know, one of the world's now largest data streaming platforms runs on data bricks with AWS. >>And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses, that they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data from what the customers are doing with their platform and the recommendations they wanna push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex, but by building it on data bricks, they were able to release it in record time and have grown at, at record pace >>To not be that's product platform that's impacting product development. Absolutely. I mean, this is like the difference between lagging months of product development to like days. Yes. Pretty much what you're getting at. Yeah. So total agility. I got that. Okay. Now I'm a customer I wanna buy in the marketplace, but I also, you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from a Davis's leadership, Chris was up there speaking and, and, and moment I was, Hey, he's a CRO conference, chief revenue officer conversation, which means someone's getting compensated. So if I'm the sales rep at data bricks, what's my motion to the customer. Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Is there or an audio lift? >>Well, I I'd add what Joel just talked about with, with, you know, what the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, the entire data bricks offering and >>The flagship, all the, the top, >>Everything, the flagship, the complete offering. So it's not, it's not segmented. It's not a sub segment. It's it's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we, we, we view this two, two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend data bricks for the right situation. Same thing with data bricks. Our Salesforce wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get data bricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars, you have $5 million of spend, you put that spend through the flywheel with AWS marketplace. And then you can use that in your negotiations with AWS to get better pricing overall. So that's how we, >>We do it. So customers are driving. This sounds like, correct. For sure. So they're looking at this as saying, Hey, I'm gonna just get purchasing power with all my relationships because it's a solution architectural market, right? >>Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider, if they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing >>Power, okay, Jill, we're gonna date ourselves. At least I will. So back in the old days, it used to be, do a Barney deal with someone, Hey, let's go to market together. You gotta get paper, you do a biz dev deal. And then you gotta say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for data bricks or any company is to go find those partners and do deals versus now Amazon is the center point for the customer so that you can still do those joint deals. But this seems to be flipping the script a little bit. >>Well, it is, but we still have VAs and consulting partners that are doing implementation work very valuable work advisory work that can actually work with marketplace through the C PPO offering. So the marketplace allows multiple ways to procure your >>Solution. So it doesn't change your business structure. It just makes it more efficient. That's >>Correct. >>That's a great way to say it. Yeah, >>That's great. So that's so that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >>Yes, >>Absolutely. Economically. Yeah. >>E economically it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon, especially when it comes back to co-selling right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution and our teams are working backwards from those use cases, you know, to collaborate, land them. >>Yeah. I want, I wanna get that out there. Go ahead, Joel. >>So one of the other things I might add to that too, you know, and why this is advantageous for, for companies like data bricks to, to work through the marketplace, is it makes it so much easier for customers to deploy a solution. It's, it's very, literally one click through the marketplace to get data bricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. You >>Know, it's interesting. I wanna bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself EDI back in the old days, you know, all that craziness. Now this is all the, all the internet, basically through the console, I get the infrastructure side, you know, spin up and provision. Some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage little nuance, most enterprises aren't yet there of integrating tech on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrator's dilemma, not an innovator's dilemma. So like, I want to integrate, so now I have integration points with data bricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you gotta build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or, or am I off because no, one's gonna be buying software. Like they used to, they buy software to integrate it. >>Yeah, >>No, I, cause everything's integrated. >>I think AWS has done a great job at creating a partner ecosystem, right. To give customers the right tools for the right jobs. And those might be with third parties, data bricks is doing the same thing with our partner connect program. Right. We've got customer, customer partners like five tra and D V T that, you know, augment and enhance our platform. And so you, you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >>Yeah. It's almost like, you know, bundling and unbundling. I was talking about this with, with Dave ante about Supercloud, which is why wouldn't a customer want the best solution in their architecture period. And it's class. If someone's got API security or an API gateway. Well, you know, I don't wanna be forced to buy something because it's part of a suite and that's where you see things get suboptimized where someone dominates a category and they have, oh, you gotta buy my version of this. Yeah. >>Joel, Joel. And that's Joel and I were talking, we're actually saying what what's really important about Databricks is that customers control the data. Right? You wanna comment on that? >>Yeah. I was say the, you know what you're pushing on there we think is extraordinarily, you know, the way the market is gonna go is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically I think really strong places, data, bricks, and AWS have lined up is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the lake house, one thing we've always been extremely committed to as a company is building the data platform on an open foundation. And we do that primarily through Delta lake and making sure that to Jack's point with data bricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed data bricks to have the breadth of integrations that it has with all the other data tools out there, because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >>When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what's the, what am I foreclosing? If I go with something else that's not as open what what's the customer's downside as you think about what's around the corner in the industry. Cuz if you believe it's gonna be open, open source, which I think opens our software is the software industry and integration is a big deal, cuz software's gonna be plentiful. Let's face it. It's a good time to be in software business, but cloud's booming. So what's the downside from your data bricks perspective, you see a buyer clicking on data bricks versus that alternative what's potentially is should they be a nervous about down the road if they go with a more proprietary or locked in approach? Well, >>I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also then beholden to the pace at which that provider is able to innovate. And I think we've seen lots of times over history where, you know, a proprietary format may run ahead for a while on a lot of innovation. But as that market control begins to solidify that desire to innovate begins to, to degrade, whereas in the open format. So >>Extract rents versus innovation. Exactly. >>Yeah, exactly. >>But >>I'll say it in the open world, you know, you have to continue to innovate. Yeah. And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you were always going to be at the forefront of what is the >>Latest, you know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked for proprietary. Yeah. You know, SNA at IBM deck net was digital, you know, the rest is, and then TCP, I P was part of the open systems, interconnect, revolutionary Oly, a big part of that as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. Yeah. But that helped interoperate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid game. Then you got the end game and we're not there. The end game yet cloud the cloud. >>There's, there's always some form of lock in, right. Andy jazzy will, will address it, you know, when making a decision. But if you're gonna make a decision you want to reduce as you don't wanna be limited. Right. So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now is an AI driven business. Right? And so it has to do with, can you get that data outta silos? Can you, can you organize it and secure it? And then can you work with data scientists to feed those models? Yeah. In a, in a very consistent manner. And so the tools of tomorrow will to Joel's point will be open and we want interoperability with those >>Tools and, and choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds is that they have to compete directly too. Redshift competes directly with a lot of other stuff, but they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're gonna be they're onto it. This is >>The Amazon's credit by having these, these solutions that may compete with native services in marketplace, they are providing customers with choice, low >>Price and access to the S and access to the core value. Exactly. Which the >>Hardware, which is their platform. Okay. So I wanna get you guys thought on something else. I, I see emerging, this is again kind of cube rumination moment. So on stage Chris unpacked, a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing compensation, workflows services behind the curtain. And one of the things he mentioned was they talk about resellers or channel partners, depending upon what you talk about. We believe Dave and I believe on the cube that the entire indirect sales channel of the industry is gonna be disrupted radically because those players were selling hardware in the old days and software, that game is gonna change. You know, you mentioned you guys have a program, want to get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in which means that the old reseller channels are gonna be rewritten. They're gonna be refactored with this new kinds of access. Cuz you've got scale, you've got money and you've got product and you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, value added reseller or V or business, >>You've gotta evolve. >>You gotta, you gotta be here. Yes. How are you guys working with those partners? Cuz you say you have a part in your marketplace there. How do I make money? If I'm a reseller with data bricks with eight Amazon, take me through that use case. >>Well I'll let Joel comment, but I think it's, it's, it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations, they need expertise from consulting and SI partners. If those consulting SI partners happen to resell the solution as well. Well, that's another aspect of their business, but I really think it is the expertise that the partners bring to help customers get outcomes. >>Joel, channel big opportunity for re re Amazon to reimagine this. >>For sure. Yeah. And I think, you know, to your comment about how to resellers take advantage of that, I think what Jack was pushing on is spot on, which is it's becoming more about more and more about the expertise you bring to the table and not just transacting the software, but now actually helping customers make the right choices. And we're seeing, you know, both SI begin to be able to resell solutions and finding a lot of opportunity in that. Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's gonna be the evolution that >>This gets at the end of the day. It's about services for sure, for sure. You've got a great service. You're gonna have high gross profits. And >>I think that the managed service provider business is alive and well, right? Because there are a number of customers that want that, that type of a service. >>I think that's gonna be a really hot, hot button for you guys. I think being the way you guys are open this channel partner services model coming in to the fold really kind of makes for kind of that super cloudlike experience where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on within data bricks for sure. On top of this ecosystem, how does that work? This is kinda like hasn't been written up in business school and case studies yet this is new. What is this? >>I think, you know, what it comes down to is you're seeing ecosystems begin to evolve around the data platforms and that's gonna be one of the big kind of new horizons for us as we think about what drives ecosystems it's going to be around. Well, what is the, what's the data platform that I'm using and then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around data bricks in its Lakehouse platform, as well as customers are looking at well, if I'm standing these Lakehouse up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >>I mean you think about ecosystem theory, we're living a whole nother dream and I'm, and I'm not kidding. It hasn't yet been written up and for business school case studies is that we're now in a whole nother connective tissue ecology thing happening where you have dependencies and value proposition economics connectedness. So you have relationships in these ecosystems. >>And I think one of the great things about relationships with these ecosystems is that there's a high degree of overlap. Yeah. So you're seeing that, you know, the way that the cloud business is evolving, the, the ecosystem partners of data bricks are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for >>You. Joel, Jack, I love it because you know what it means the best ecosystem will win. If you keep it open. Sure. You can see everything. If you're gonna do it in the dark, you know, you don't know the outcome. I mean, this is really kind we're talking about. >>And John, can I just add that when I was in Amazon, we had a, a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? Yeah. And so there's a platform decision being made and that ecosystem gonna evolve around the >>Platform. Yeah. And I totally agree. And, and, and the word innovation get kicks around. That's why, you know, when we had our super cloud panel was called the innovators dilemma with a slash through it called the integrated dilemma, innovation is the digital transformation. So absolutely like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating if APIs are the connective tissue, what's automation, what's the service message look like. I mean, a whole nother set of kind of thinking goes on and these new ecosystems and these new products >>And that, and that thinking is, has been born in Delta sharing. Right? So the idea that you can have a multi-cloud implementation of data bricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud >>Solution. Well, data bricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great grow ecosystem. And again, I think in a shining example of what every enterprise is going to do, build on top of something operating model, get that operating model, driving revenue. >>Yeah. >>Well we, whether whether you're Goldman Sachs or capital one or XYZ corporation >>S and P global NASDAQ, right. We've got, you know, these, the biggest verticals in the world are solving tough problems with data breaks. I think we'd be remiss cuz if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions, whether it's the relationship we have with our engineering and service teams. Yeah. Our marketing teams, you know, product development and we're gonna be at reinvent the big presence of reinvent. We're looking forward to seeing you there again. >>Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening this next gen cloud is here. We're seeing this evolve kind of new economics, new value propositions kind of scaling up, producing more so you guys are doing a great job. Thanks for coming on the Cuban, taking time. Chill. Great to see you at the check. Thanks for having us. Thanks. Going. Okay. Cube coverage here. The world's changing as APN comes to give the marketplace for a new partner organization at Amazon web services, the Cube's got a covered. This should be a very big growing ecosystem as this continues, billions of being sold through the marketplace. Of course the buyers are happy as well. So we've got it all covered. I'm John furry, your host of the cube. Thanks for watching.

(upbeat music) >> Hey everyone. And welcome to this CUBE Conversation which is part of the AWS startup showcase. Season two, episode four of our ongoing series. The theme of this episode is cybersecurity, detect and protect against threats. I'm your host, Lisa Martin, and I'm pleased to be joined by the founder and CEO of Hunters.AI, Uri May. Uri, welcome to theCUBE. It's great to have you here. >> Thank you, Lisa. It's great to be here. >> Tell me a little bit about your background and the founders story. This company was only founded in 2018, so you're quite young. But gimme that backstory about what you saw in the market that really determined, this is needed. >> Yeah, absolutely. So, I mean, I think the biggest thing for us was the understanding that significant things have happened in the cybersecurity landscape for customers and technology stayed the same. I mean, we tried on solving the same... We tried on solving a big problem with the same old tools when we actually noticed that the problem has changed significantly. And we saw that change happening in two different dimensions. The first is the types of attacks that we're defending against. A decade ago, we were mostly focused on these highly sophisticated nation state efforts that included unknown techniques and tactics and highly sophisticated kind of methods. Nowadays, we're talking a lot about cyber crime gangs, whoops of people that are financially motivated or using off the shelf tools, of the shelf malware, coordinating in the dark web, attacking for money and ransom basically, versus sophisticated intelligence kind of objectives. And in the same time of that happening, we also saw what we like to refer to as explosion of the securities stack. So some of our customers are using more than 60 or 70 different security tools that are generating sometimes tens of terabytes a day of flows. That explosion of data, together with a very persistent and consistent threat that is continuously affecting customers, create a very different environment, where you need to analyze a big variety of data and you need to constantly defend yourself against stuff that are happening all the time. And that was kind of like our wake moment when we understand that the tools that are out there now might have been the right tools a decade ago, they are probably not the right tools to solve the problem now. So yeah, I think that that was kind of what led us to Hunters. And in the same time, and I think that that's my personal kind of story behind it. We used to talk a lot about the fact that we want to solve a fundamental problem. And we, as part of the ideation around Hunters and us zooming in on exactly the areas that we want to focus on in security, we talked with a lot of CSOs, we talked with a lot of industry experts, everyone directed us to the security operation center. I mean the notion that there's a lot of tools and there's always going to be a lot of tools, but eventually decisions are being made by people that are running security operation center, that are actually acting as the first line of defense. And that's where you feel that the processes are woke. That's where you feel that that technology doesn't really meet the rabel, and the rabel doesn't really meet the hold. And for us, it was a very clear sign that this is where we need to focus on. And that set us on a journey to explore red hunting and then understand that we can solve something bigger than that. And then eventually get to where we are today, which is go to market around. So holistic a platform that can help SOC analysts doing the day to day job defending the organizations. >> So you saw back in 2018, probably even before that that the SIEM market was prime and right for disruption. And only in a four year time period, there's been some pretty significant milestones and accomplishment that the team at Hunters has made in that short timeframe. Talk to me about some of those big milestones that the company has reached in just four years. >> Yeah, I think that the biggest thing and I know that it's going to sound like a cliche, but we're actually believing that I think it's the team. I mean, we're able to go to an organization of around 150 employees. All over the world, the course, I think I mean the last time that I checked, like 15 countries. That's the most amazing feeling that you can have. That ability to attract people to a single mission from all over the world and to get them collaborate and do amazing things and achieve unbelievable accomplishment. I think that's the biggest thing. The other thing for us was customers. I mean, think about it like, SIEM it's such a central and critical system. So for us as a young startup from Tel Aviv to go out to Enterprise America and convince the biggest enterprise around the world to rip and replace the the existing solutions that are being built by the biggest software brands out there and install Hunters instead, that's a huge leap of trust, that we are very grateful for, and we're trying to handle with a lot of care and a lot of responsibility. And obviously, I think that other than that, is all of the investors that we were able to attract that basically enabled all of that customer acquisition and team building and product development. And we're very fortunate to work with the biggest names out there, both from a strategic perspective and also from tier one VCs from mainly from the U.S., but from all over the world, actually that are backing us. >> Great customers, solid foundation. Hunters is built for the clouds, is powered by Snowflake. This is AWS built. Talk to me about what's in it for me from an AWS customer perspective. What's that value in it for them? >> Yeah, so I think that the most important thing, in my opinion, at least, is the security value that you're getting from it. Other than the fact that Hunters is a multi-tenant SaaS application running in AWS, it's also a system that is highly tuned and specifically built to be very effective against detecting threats inside AWS environments. So we invested a lot of time in research, in analyzing the way attackers are operating inside cloud environments, specifically in AWS. And then we model these techniques and tactics and procedures into the system. We're leveraging data sets like AWS CloudRail and CloudWatch and VPC Flow Logs, obviously AWS GuardDuty which is an amazing detection system that AWS offer to its customer, and we're able to leverage it, correlate it with other signals. And at the same time, there's also the commercial aspect and the business aspect. I mean, we're allowing AWS customers to leverage the AWS credits to the marketplace to fund same projects like Hunters that comes with a lot of efficiencies also. And with a lot of additional capabilities like I mentioned earlier. >> So let's crack open Hunters.AI. What makes this approach different? You talked about the challenges that you guys saw in the market that were gaps there, and why technology needed to come in from a disruption standpoint. But describe the differentiators. When you're talking to perspective customers, what are those key differentiators that Hunters brings to the table? >> Yeah, absolutely. So we like to divide it into three main pillars. The first pillar is everything that we do with data, that is very different from our competitors. We believe that data should be completely liberated from the analytical layer. And that's why we're storing data in a dedicated data warehouse. Snowflake, as you mentioned earlier, is one of our go to data warehouses. And that give customers the ability to own their own data. So you as a customer can opt in into using Hunters on top of your Snowflake. It's not the only way. You can also get Snowflake bundled as part of that, your Hunter subscription, but for some customers that ability to reduce vendor lock risk on data on your own and also level security data for other kind of workflows is something that is really huge. So that's the first thing that is very different. The second thing is what we like to call security engineering as a service. So when you buy Hunters, you don't just buy a data platform. You actually buy a system, a SOC platform that is already populated with use cases. So what we are saying is that in today's world the threats that we're handling as a SOC, as security operations center professionals are actually shared by 80% of the customers out there. So 80% of the customers share around 80% of the threat. And what we're basically saying is let us as a vendor, solve the detection response around that 80%. So you as a customer could focus on the 20% that is unique to your environment. Then in a lot of cases generate 80% of the impact. So that means that you are getting a lot of rebuilt tools and detections, data modeling to your integrations, automatic investigations, scoring correlations. All of these things are being continuously deployed and delivered by us because we're multi tenant SaaS. And also allowing you again to get this effortless tail key kind of solution that is very different from your experience with your current SIEM tools that usually involves a lot of tuning, professional services, configuration, et cetera. And the last aspect of it, is everything that we're doing around automation. We're leveraging very unique graph technology and what we call automatic investigation enrichments that allows us to take all of these signals that we're extracting from all over the attacks, of say AWS included, but also the endpoint and the email and the network and IOT environments and whatever automatically investigate them, load them into a graph and then automatically correlate them to what we call stones, which are basically representation of incidents that are happening across your tax office. And that's a very unique capability that we bring into the table that demonstrates our focus on the analytical lens. So it's not just log aggregation, and querying and dashboarding kind of system. It's actually a security analytic system that is able to drive real insights on top of the data that you're plugging into it. >> So talk to me, Uri, when you're in customer conversations these days the market is there's so many dynamics and flux that customers are dealing with. Obviously, the threat landscape continues to expand and really become quite amorphous as that perimeter blends. What are some of the specific challenges that security operation center or SOC teams come to you saying, help us eliminate this. We have so many tools, we've probably got limited resources. What are those challenges and how does Hunters really wipe those off the plate? >> Yeah, so I think the first and foremost has to do with the second pillar that I mentioned earlier and that's security engineering. So for most security operations centers and most organizations around the world, the feeling is that they're kind of like stuck on this third wheel. They keep on buying tools and then implementing these tools and then writing rules and then generating noise and then fine tuning the rules. And then testing the rules and understanding that the fine tuning actually generated misdetections. And they're kind of like stuck on this vicious side. And no one can really help because a lot of the stuff that they're building, they're building it in their environment. And what we're saying is that, let us do it for you. Well, that 80% that we've mentioned earlier and allows you to really focus on the stuff that you're doing and even offset your talent. So, we're not talking about really a talent reduction. Because everyone needs more talent in cybersecurity nowadays but we're talking a lot about offset. I mean, if we had a team of five people investing efforts in building walls, building automation, and now three or four of these people can go and do advanced investigations, instant response, threat hunting interval, that's meaningful. For a lot of SOCs, in a lot of cases that means either identifying and analyzing a threat in time or missing it. So, I mean, I think that that's the biggest thing. And the other thing has to do with the first thing that I mentioned earlier, and these are the data challenges. Data challenges in terms of cost, performance, the ability to absorb data sets that today's tools can't really support. I mean, for example, one of the biggest data sets that we're loading that is tremendously helpful is raw data for EDR products. Raw data for EDR products in large enterprises can get to 10, 15, 20 terabytes a day. In today's SIEMs and SOC platforms that the customers are using, this thing is just as prohibited from SOC. They can't really analyze it because it's so costly. So what we're saying is a lot of what we're seeing is a lot of customers, either not analyzing it at all, or saving it for a very little amount of time, account of days. Because they can't support the retention around it. So the ability to store huge data sets for longer period of time makes it something that a lot of big enterprises need. And to be honest, I think that in the next couple of years they would also be forced to have these kind of capabilities, even from a compliance perspective. >> So in terms of outcomes, I'm hearing reduction in costs really helping security teams utilize their resources, the ability to analyze growing volumes of data. That's only going to continue to increase as we know. Is there a customer story, Uri that you have that really, where the value proposition of Hunters really shines through? >> Yeah, I think that one thing comes to mind from those hospitality vertical and actually it's a reference customer. I mean, we can share the name. His name is booking.com. It's also publicly shown on our website. And they think the coolest thing that we were able to do with booking is give them that capability to stay up to date with the threats that they're facing. So it's not just that we saved a lot of efforts from them because we came with a lot of out of the box capabilities that they can use. We also kept them up to date with everything that they were facing. And there was a couple of cases, where we were able to detect threats that were very recently from threat perspective. Based on our ability to invest research time and efforts in everything that is going on in the ecosystem and the feedback that we got from the customer, and it's not a single of feedback. Like we're getting it a lot, is that, without you guys we wouldn't be able to do the effective research and then the implementation of this and the threat modeling and the implementation of these things in time. And walking with you kind of like made the difference between analyzing it and reacting in time and potentially blocking like a very serious bridge versus maybe finding out when it's too late. >> Huge impact there. And I'm kind of thinking, Hunters aim, might be one of the reasons that booking.com's tagline it's booking.com, booking.yeah. Yeah, we're secure. We know if we can demonstrate that to everyone that uses our service. I noticed kind of wrapping things up here, Uri. I noticed that back in I think it was January of 2022, Hunters raised about 60 million in series C. You talked about kind of being in the GTM phase, where are some of those strategic investments? What have you been doing, focusing on this year and what's to come as we round out 22? >> Yeah, absolutely. So, I mean, there's a lot of building going on. Yeah. Still, right. I mean, we're getting into that scale mode and scale phase but we're very much also building our capabilities, building our infrastructure, building our teams, building our business processes. So there's a lot of efforts going into that, but in the same time, I mean, we've being able to vary, to depending our relationship with DataBlitz which is a very important partner of us. And we got some big news coming up on that. And they were a strategic investor that participated in our series C. And in the same time we're walking in the air market which is a very interesting market for us. And we get a lot of support from one other strategic investor that joined the series C, Deutsche Telekom. And they are a huge provider in IT and security in email, other than doing a lot of other things and including T-systems and T-Mobile and everything that has to do with that. So we're getting a lot of support from them. And regardless, I think, and that ties back to what we've mentioned earlier, the ability for us to come to really big customers with the quality of investors that we have is a very important external validation. It's basically saying like this company is here to stay. We're aiming at disrupting the market. We're building something big. You can count on us by replacing this critical system that we're talking about. And sometimes it makes a difference, like sometimes for some of the customers, it means that this is something that I can rely on. Like it's not a startup that is going to be sold two months after I'm deploying it. And it's not a founder that is going to disappear on me. And for a lot of customers, these things happen, especially in an ecosystem like cybersecurity, that is so big with such a huge variety of different systems. So, yeah, I think that we're getting ready for that scale mode and hopefully it'll happen sooner than what we think. >> A lot of growth already as we mentioned in the beginning of the program. Since just 2018 it sounds like from a foundation perspective, you guys are strong, you're rocking away and ready to really take things into 2023 with such force. Uri, thank you so much for joining me on the program, talking about what Hunters.AI is up to and how you're different and why you're disrupting the SIEM market. We appreciate your insights and your time. >> Absolutely. Lisa, the pleasure was all mine. Thank you for having me. >> Likewise. For Uri May, I'm Lisa Martin. Thank you for watching our CUBE Conversation as part of the AWS startup showcase. Keep it right here for more actions on theCUBE, your leader in tech coverage. (upbeat music)

>>Welcoming into the cubes presentation of AWS startup showcase open cloud innovations. This is season two episode one of the ongoing series covering exciting hot startups from the AWS ecosystem. Today's episode. One of season two theme is open source community and the open cloud innovations. I'm your host, John farrier of the cube. And today we're excited to be joined by Loris Dajani who is the C T O chief technology officer and founder of cystic found that in his backyard with some wine and beer. Great to see you. We're here to talk about Falco finding cloud threats in real time. Thank you for joining us, Laura. Thanks. Good to see you >>Love that your company was founded in your backyard. Classic startup story. You have been growing very, very fast. And the key point of the showcase is to talk about the startups that are making a difference and, and that are winning and doing well. You guys have done extremely well with your business. Congratulations, but thank you. The big theme is security and as organizations have moved their business critical applications to the cloud, the attackers have followed. This is Billy important in the industry. You guys are in the middle of this. What's your view on this? What's your take? What's your reaction? >>Yeah. As we, as a end ecosystem are moving to the cloud as more and more, we are developing cloud native applications. We relying on CACD. We are relying on orchestrations in containers. Security is becoming more and more important. And I would say more and more complex. I mean, we're reading every day in the news about attacks about data leaks and so on. There's rarely a day when there's nothing major happening and that we can see the press from this point of view. And definitely things are evolving. Things are changing in the cloud. In for example, Cisco just released a cloud native security and usage report a few days ago. And the mundane things that we found among our user base, for example, 60, 66% of containers are running as rude. So still many organizations adopting a relatively relaxed way to deploy their applications. Not because they like doing it, but because it tends to be, you know, easier and a little bit with a little bit less ration. >>We also found that that 27% of users unnecessary route access in the 73% of the cloud accounts, public has three buckets. This is all stuff that is all good, but can generate consequences when you make a mistake, like typically, you know, your data leaks, no, because of super sophisticated attacks, but because somebody in your organization forgets maybe some data on it on a public history bucket, or because some credentials that are not restrictive enough, maybe are leaked to another team member or, or, or a Gita, you know, repository or something like that. So is infrastructures and the software becomes a let's a more sophisticated and more automated. There's also at the same time, more risks and opportunities for misconfigurations that then tend to be, you know, very often the sewers of, of issues in the cloud. >>Yeah, those self-inflicted wounds definitely come up. We've seen people leaving S3 buckets open, you know, it's user error, but, you know, w w those are small little things that get taken care of pretty quickly. That's just hygiene. It's just discipline. You know, most of the sophisticated enterprises are moving way past that, but now they're adopting more cloud native, right. And as they get into the critical apps, securing them has been challenging. We've talked to many CEOs and CSOs, and they say that to us. Yeah. It's very challenging, but we're on it. I have to ask you, what should people worry about when secure in the cloud, because they know is challenging, then they'll have the opportunity on the other side, what are they worried about? What do you see people scared of or addressing, or what should I be worried about when securing the cloud? >>Yeah, definitely. Sometimes when I'm talking about the security, I like to compare, you know, the old data center in that the old monolithic applications to a castle, you know, in middle aged castle. So what, what did you do to protect your castle? You used to build very thick walls around it, and then a small entrance and be very careful about the entrance, you know, protect the entrance very well. So what we used to doing that, that data center was protect everything, you know, the, the whole perimeter in a very aggressive way with firewalls and making sure that there was only a very narrow entrance to our data center. And, you know, as much as possible, like active security there, like firewalls or this kind of stuff. Now we're in the cloud. Now, it's everything. Everything is much more diffused, right? Our users, our customers are coming from all over the planet, every country, every geography, every time, but also our internal team is coming from everywhere because they're all accessing a cloud environment. >>You know, they often from home for different offices, again, from every different geography, every different country. So in this configuration, the metaphor data that they like to use is an amusement park, right? You have a big area with many important things inside in the users and operators that are coming from different dangerous is that you cannot really block, you know, you need to let everything come in and in operate together in these kinds of environment, the traditional protection is not really effective. It's overwhelming. And it doesn't really serve the purpose that we need. We cannot build a giant water under our amusement park. We need people to come in. So what we're finding is that understanding, getting visibility and doing, if you Rheodyne is much more important. So it's more like we need to replace the big walls with a granular network of security cameras that allow us to see what's happening in the, in the different areas of our amusement park. And we need to be able to do that in a way that is real time and allows us to react in a smart way as things happen because in the modern world of cloud five minutes of delay in understanding that something is wrong, mean that you're ready being, you know, attacked and your data's already being >>Well. I also love the analogy of the amusement park. And of course, certain rides, you need to be a certain height to ride the rollercoaster that I guess, that's it credentials or security credentials, as we say, but in all seriousness, the perimeter is dead. We all know that also moats were relied upon as well in the old days, you know, you secure the firewall, nothing comes in, goes out, and then once you're in, you don't know what's going on. Now that's flipped. There's no walls, there's no moats everyone's in. And so you're saying this kind of security camera kind of model is key. So again, this topic here is securing real time. Yeah. How do you do that? Because it's happening so fast. It's moving. There's a lot of movement. It's not at rest there's data moving around fast. What's the secret sauce to making real identifying real-time threats in an enterprise. >>Yeah. And in, in our opinion, there are some key ingredients. One is a granularity, right? You cannot really understand the threats in your amusement park. If you're just watching these from, from a satellite picture. So you need to be there. You need to be granular. You need to be located in the, in the areas where stuff happens. This means, for example, in, in security for the clowning in runtime, security is important to whoever your sensors that are distributed, that are able to observe every single end point. Not only that, but you also need to look at the infrastructure, right? From this point of view, cloud providers like Amazon, for example, offer nice facilities. Like for example, there's CloudTrail in AWS that collects in a nice opinionated consistent way, the data that is coming from multiple cloud services. So it's important from one point of view, to go deep into, into the endpoint, into the processes, into what's executing, but also collect his information like the cultural information and being able to correlate it to there's no full security without covering all of the basics. >>So a security is a matter of both granularity and being able to go deep and understanding what every single item does, but also being able to go abroad and collect the right data, the right data sources and correlated. And then the real time is really critical. So decisions need to be taken as the data comes in. So the streaming nature of security engines is becoming more and more important. So the step one of course, security, especially cost security, posture management was very much let's ball. Once in a while, let's, let's involve the API and see what's happening. This is still important. Of course, you know, you need to have the basics covered, but more and more, the paradigm needs to change to, okay, the data is coming in second by second, instead of asking for the data manually, once in a while, second by second, there's the moment it arrives. You need to be able to detect, correlate, take decisions. And so, you know, machine learning is very important. Automation is very important. The rules that are coming from the community on a daily basis are, are very important. >>Let me ask you a question, cause I love this topic because it's a data problem at the same time. There's some network action going on. I love this idea of no perimeter. You're going to be monitoring anything, but there's been trade offs in the past, overhead involved, whether you're monitoring or putting probes in the network or the different, there's all kinds of different approaches. How does the new technology with cloud and machine learning change the dynamics of the kinds of approaches? Because it's kind of not old tech, but you the same similar concepts to network management, other things, what what's going on now that's different and what makes this possible today? >>Yeah, I think from the friction point of view, which is one very important topic here. So this needs to be deployed efficiently and easily in this transparency, transparent as possible, everywhere, everywhere to avoid blind spots and making sure that everything is scheduled in front. His point of view, it's very important to integrate with the orchestration is very important to make use of all of the facilities that Amazon provides in the it's very important to have a system that is deployed automatically and not manually. That is in particular, the only to avoid blind spots because it's manual deployment is employed. Somebody would forget, you know, to deploy where somewhere where it's important. And then from the performance point of view, very much, for example, with Falco, you know, our open source front-end security engine, we really took key design decisions at the beginning to make sure that the engine would be able to support in Paris, millions of events per second, with minimal overhead. >>You know, they're barely measure measurable overhead. When you want to design something like that, you know, that you need to accept some kind of trade-offs. You need to know that you need to maybe limit a little bit this expressiveness, you know, or what can be done, but ease of deployment and performance were more important goals here. And you know, it's not uncommon for us is Dave to have users of Farco or commercial customers that they have tens of thousands, hundreds of thousands of machines. You know, I said two machines and sometimes millions of containers. And in these environments, lightweight is key. You want death, but you want overhead to be really meaningful and >>Okay, so a amusement park, a lot of diverse applications. So integration, I get that orchestration brings back the Kubernetes angle a little bit and Falco and per overhead and performance cloud scale. So all these things are working in favor. If I get that right, is that, am I getting that right? You get the cloud scale, you get the integration and open. >>Yeah, exactly. Any like ingredients over SEP, you know, and that, and with these ingredients, it's possible to bake a, a recipe to, to have a plate better, can be more usable, more effective and more efficient. That may be the place that we're doing in the previous direction. >>Oh, so I've got to ask you about Falco because it's come up a lot. We talked about it on our cube conversations already on the internet. Check that out. And a great conversation there. You guys have close to 40 million plus million downloads of, of this. You have also 80 was far gate integration, so six, some significant traction. What does this mean? I mean, what is it telling us? Why is this successful? What are people doing with Falco? I see this as a leading indicator, and I know you guys were sponsoring the project, so congratulations and propelled your business, but there's something going on here. What does this as a leading indicator of? >>Yeah. And for, for the audience, Falco is the runtime security tool of the cloud native generation such. And so when we, the Falco, we were inspired by previous generation, for example, network intrusion detection, system tools, and a post protection tools and so on. But we created essentially a unique tool that would really be designed for the modern paradigm of containers, cloud CIC, and salt and Falco essentially is able to collect a bunch of brainer information from your applications that are running in the cloud and is a religion that is based on policies that are driven by the community, essentially that allow you to detect misconfigurations attacks and normals conditions in your cloud, in your cloud applications. Recently, we announced that the extension of Falco to support a cloud infrastructure and time security by parsing cloud logs, like cloud trail and so on. So now Falba can be used at the same time to protect the workloads that are running in virtual machines or containers. >>And also the cloud infrastructure to give the audience a couple of examples, focused, able to detect if somebody is running a shelf in a radius container, or if somebody is downloading a sensitive by, from an S3 bucket, all of these in real time with Falco, we decided to go really with CR study. This is Degas was one of the team members that started it, but we decided to go to the community right away, because this is one other ingredient. We are talking about the ingredients before, and there's not a successful modern security tool without being able to leverage the community and empower the community to contribute to it, to use it, to validate and so on. And that's also why we contributed Falco to the cloud native computing foundation. So that Falco is a CNCF tool and is blessed by many organizations. We are also partnering with many companies, including Amazon. Last year, we released that far gate support for Falco. And that was done is a project that was done in cooperation with Amazon, so that we could have strong runtime security for the containers that are running in. >>Well, I've got to say, first of all, congratulations. And I think that's a bold move to donate or not donate contribute to the open source community because you're enabling a lot of people to do great things. And some people might be scared. They think they might be foreclosing and beneficial in the future, but in the reality, that is the new business model open source. So I think that's worth calling out and congratulations. This is the new commercial open source paradigm. And it kind of leads into my last question, which is why is security well-positioned to benefit from open source besides the fact that the new model of getting people enabled and getting scale and getting standards like you're doing, makes everybody win. And again, that's a community model. That's not a proprietary approach. So again, source again, big part of this. Why was security benefit from opensource? >>I am a strong believer. I mean, we are in a better, we could say we are in a war, right? The good guys versus the bad guys. The internet is full of bad guys. And these bad guys are coordinated, are motivated, are sometimes we'll find it. And we'll equip. We win only if we fight this war as a community. So the old paradigm of vendors building their own Eva towers, you know, their own self-contained ecosystems and that the us as users as, as, as customers, every many different, you know, environments that don't communicate with each other, just doesn't take advantage of our capabilities. Our strength is as a community. So we are much stronger against the big guys and we have a much better chance doing when this war, if we adopt a paradigm that allows us to work together. Think only about for example, I don't know, companies any to train, you know, the workforce on the security best practices on the security tools. >>It's much better to standardize on something, build the stack that is accepted by everybody and tell it can focus on learning the stack and becoming a master of the steak rounded rather than every single organization naming the different tool. And, and then B it's very hard to attract talent and to have the right, you know, people that can help you with, with your issues in, in, in, in, in, with your goals. So the future of security is going to be open source. I'm a strong believer in that, and we'll see more and more examples like Falco of initiatives that really start with, with the community and for the community. >>Like we always say an open, open winds, always turn the lights on, put the code out there. And I think, I think the community model is winning. Congratulations, Loris Dajani CTO and founder of SIS dig congratulatory success. And thank you for coming on the cube for the ADB startup showcase open cloud innovations. Thanks for coming on. Okay. Is the cube stay with us all day long every day with the cube, check us out the cube.net. I'm John furrier. Thanks for watching.

>>Okay, We're back at AWS Re. Invent. You're watching the >>cubes. Continuous coverage >>coverage. This is Day four. I think it's the first time it reinvent. We've done four days. This is our ninth year covering Reinvent. Tom Miller is here is the senior vice president of Alliances. And he's joined by Anchor Jane. Who's the global cloud? Practically practise lead at Merkel. Guys, good to see you. Thanks for coming on. Thank you, Tom. Tell us about Merkel. For those who might not be familiar with you. >>So Merkel is a customer experience management company. That is, um, under the Dentsu umbrella. Dense. Who is a global media agency? We represent one of the pillars which is global, our customer experience management. And they also have media and creative. And what Merkel does is provide that technology to help bring that creative and media together. They're a tech company. Yes. >>Okay, so there's some big big tail winds, changes, trends going on in the market. Obviously the pandemic. You know, the force marched to digital. Uh, there's regulation. What are some of the big waves that you guys are seeing that you're trying to ride? >>So what we're seeing is, uh we've got, uh, as a start. We've got a lot of existing databases with clients that are on Prem that we manage today within a sequel environment or so forth. And they need to move that to a cloud environment to be more flexible, more agile, provide them with more data to be able to follow that customer experience that they want with their clients, that they're all realising they need to be in a digital environment. And so that's a big push for us working with AWS and helping move our clients into that cloud environments. >>And you're relatively new to the ws world, right? Maybe you can talk >>about that anchor actually, as a partner. We may be new, but Merkel works with AWS has been working with AWS for over five years as a customer as a customer. So what we did was last year we formalise the relationship with us to be, uh, an advanced partner now. So we were part of the restock programme, basically which is a pool of very select partners. And Merkel comes in with the specialisation of marketing. So as Tom said, you know, we're part of, uh Dentsu umbrella are our core focuses on customer experience, transformation and how we do that Customer experience. Transformation is through digital transformation, data transformation. And that's where we see AWS being a very good partner to us to modernise the solutions that Martin can take to the market. >>So your on Prem databases is probably a lot of diversity on a lot of technical that when the cloud more agility, infinite resources do you have a tech stack? Are you more of an integrator? Right tool for the right job? Maybe you could describe >>your I can take that what time just described. So let me give you some perspective on what these databases are. These databases are essentially Markle, helping big brands 1400 Fortune 500 brands to organise their marketing ecosystem, especially Martek ecosystem. So these databases, they house customer touchpoints customer customer data from disparate sources, and they basically integrate that data in one central place and then bolt on analytics, data science, artificial intelligence, machine learning on top of it, helping them with those email campaigns or direct mail campaigns, social campaigns. So that's what these databases are all about, and and these databases currently set on Prem on Merkel's own data centre. And we have a huge opportunity to kind of take those databases and modernise them. Give all these ai ml type of capabilities advanced analytic capabilities to our customers by using AWS is the platform to kind of migrate. And you do that as a service. We do that as a service. >>Strategically, you're sort of transforming your business to help your customers transform their business right? Take away. It's it's classic. I mean, you really it's happening. This theme of, you know a W started with taking away the undifferentiated heavy lifting for infrastructure. Now you're seeing NASDAQ. Goldman Sachs. You guys in the media world essentially building your own clouds, right? That's the strategy. Yes, super clouds. We call >>them Super Cloud. Yeah, it's about helping our clients understand What is it they're trying to accomplish? And for the most part, they're trying to understand the customer journey where the customer is, how they're driving that experience with them and understanding that experience through the journey and doing that in the cloud makes it tremendously easier and more economical form. >>I was listening to the, uh, snowflake earnings call from last night and they were talking about, you know, a couple of big verticals, one being media and all. I keep talking about direct direct to consumer, right? You're hearing that a lot of media companies want to interact and build community directly. They don't want to necessarily. I mean, you don't want to go through a third party anymore if you don't have to, Technology is enabling that is that kind of the play here? >>Yes, Director Consumer is a huge player. Companies which were traditionally brick and mortar based or relied on a supply chain of dealers and distributors are now basically transforming themselves to be direct to consumer. They want to sell directly to the consumer. Personalisation comes becomes a big theme, especially indeed to see type of environment, because now those customers are expecting brands to know what's there like. What's their dislike? Which products which services are they interested in? So that's that's all kind of advanced analytics machine learning powered solutions. These are big data problems that all these brands are kind of trying to solve. That's where Merkel is partnering with AWS to bring all those technologies and and build those next generation solutions for access. So what kind >>of initiatives are you working >>on? So there are, like, 34 areas that we are working very closely with AWS number one. I would say Think about our marketers friend, you know, and they have a transformation like direct to consumer on the channel e commerce, these types of capabilities in mind. But they don't know where to start. What tools? What technologies will be part of that ecosystem. That's where Merkel provides consulting services to to give them a road map, give them recommendations on how to structure these big, large strategic initiatives. That's number one we are doing in partnership with AWS to reach out to our joint customers and help them transform those ecosystems. Number two as Tom mentioned migrations, helping chief data officers, chief technology officers, chief marketing officers modernise their environment by migrating them to cloud number three. Merkel has a solution called mercury, which is essentially all about customer identity. How do we identify a customer across multiple channels? We are Modernising all that solution of making that available on AWS marketplace for customers to actually easily use that solution. And number four, I would say, is helping them set up data foundation. That's through intelligent marketing Data Lake leveraging AWS technologies like blue, red shift and and actually modernise their data platforms. And number four is more around clean rooms, which is bring on your first party data. Join it with Amazon data to see how those customers are behaving when they are making a purchase on amazon dot com, which gives insights to these brands to reshape their marketing strategy to those customers. So those are like four or five focus areas. So I was >>gonna ask you about the data and the data strategy like, who owns the data? You're kind of alchemists that your clients have first party data and you might recommend bringing in other data sources. And you're sort of creating this new cocktail. Who owns the data? >>Well, ultimately, client also data because that that's their customers' data. Uh, to your point on, we helped them enrich that data by bringing in third party data, which is what we call is. So Merkel has a service called data source, which is essentially a collection of data that we acquire about customers. Their likes, their dislikes, their buying power, their interests so we monetise all that data. And the idea is to take those data assets and make them available on AWS data exchange so that it becomes very easy for brands to use their first party data. Take this third party data from Merkel and then, uh, segment their customers much more intelligently. >>And the CMO is your sort of ideal customer profile. >>Yeah, CMO is our main customer profile and we'll work with the chief data officer Will work with the chief technology officer. We kind of we bridge both sides. We can go technology and marketing and bring them both together. So you have a CMO who's trying to solve for some type of issue. And you have a chief technology officer who wants to improve their infrastructure. And we know how to bring them together into a conversation and help both parties get both get what they want. >>And I suppose the chief digital officer fits in there too. Yeah, he fits in their CDOs. Chief Digital officer CMO. Sometimes they're all they're one and the same. Other times they're mixed. I've seen see IOS and and CDOs together. Sure, you sort of. It's all data. It's all >>day. >>Yeah, some of the roles that come into play, as as Tom mentioned. And you mentioned C I o c T. O s chief information officer, chief technology officer, chief data officer, more from the side. And then we have the CMOS chief digital officers from the marketing side. So the secret sauce that Merkel brings to the table is that we know the language, what I t speaks and what business speaks. So when we talk about the business initiatives like direct to consumer Omni Channel E commerce, those are more business driven initiatives. That's where Merkel comes in to kind of help them with our expertise over the last 30 years on on how to run these strategic initiatives. And then at the same time, how do we translate translate those strategic initiatives into it transformation because it does require a lot of idea transformation to happen underneath. That's where AWS also helps us. So we kind of span across both sides of the horizon. >>So you got data. You've got tools, you've got software. You've got expertise that now you're making that available as a as a service. That's right. How far are you into that? journey of satisfying your business. >>Well, the cloud journey started almost, I would say, 5 to 7 years ago at Merkel, >>where you started, where you began leveraging the cloud. That's right. And then the light bulb went off >>the cloud again. We use clouds in multiple aspects, from general computing perspective, leveraging fully managed services that AWS offers. So that's one aspect, which is to bring in data from disparate sources, house it, analyse it and and derive intelligence. The second piece on the cloud side is, uh, SAS, offering software as a service offerings like Adobe Salesforce and other CDP platforms. So Merkel covers a huge spectrum. When it comes to cloud and you got >>a combination, you have a consulting business and also >>so Merkel has multiple service lines. Consulting business is one of them where we can help them on how to approach these transformational initiatives and give them blueprints and roadmaps and strategy. Then we can also help them understand what the customer strategy should be, so that they can market very intelligently to their end customers. Then we have a technology business, which is all about leveraging cloud and advanced analytics. Then we have data business that data assets that I was talking about, that we monetise. We have promotions and loyalty. We have media, so we recover multiple services portfolio. >>How do you mentioned analytics a couple times? How do you tie that? Back to the to the to the sales function. I would imagine your your clients are increasingly asking for analytics so they can manage their dashboards and and make sure they're above the line. How is that evolving? Yes, >>So that's a very important line because, you know, data is data, right? You bring in the data, but what you do with the data, how you know, how you ask questions and how you derive intelligence from it? Because that's the actionable part. So a few areas I'll give you one or two examples on how those analytics kind of come into picture. Let's imagine a brand which is trying to sell a particular product or a particular service to the to a set of customers Now who those set of customers are, You know where they should target this, who their target customers are, what the demographics are that's all done through and analytics and what I gave you is a very simple example. There are so many advanced examples, you know, that come into artificial intelligence machine learning those type of aspects as well. So analytics definitely play a huge role on how these brands need to sell and personalised the offerings that they're going to offer to. The customers >>used to be really pure art, right? It's really >>not anymore. It's all data driven. Moneyball. Moneyball? >>Yes, exactly. Exactly. Maybe still a little bit of hard in there, right? It doesn't hurt. It doesn't hurt to have a little creative flair still, but you've got to go with the data. >>That's where the expertise comes in, right? That's where the experience comes in and how you take that science and combine it with the art to present it to the end customer. That's exactly you know. It's a combination, >>and we also take the time to educate our clients on how we're doing it. So it's not done in a black box, so they can learn and grow themselves where they may end up developing their own group to handle it, as opposed to outsourcing with Merkel, >>teach them how to fish. Last question. Where do you see this in 2 to 3 years. Where do you want to take it? >>I think future is Cloud AWS being the market leader. I think aws has a huge role to play. Um, we are very excited to be partners with AWS. I think it's a match made in heaven. AWS cells in, uh, majority of the sales happen in our focus is marketing. I think if we can bring both the worlds together, I think that would be a very powerful story for us to be >>good news for AWS. They little your DNA can rub off on them would be good, guys. Thanks so much for coming to the Cube. Thank you. All right. Thank you for watching everybody. This is Dave Volonte for the Cube Day four aws re invent. Were the Cube the global leader in high tech coverage? Right back. Mhm. Mhm. Mhm.

>>mhm mhm. Mhm Welcome back to convert connections 2021. This is the power panel. My name is Dave vellante joined by Reza more like a body was the ceo of calm vault. Isabel geese is the CMO of calm vault and john Gallagher he leads global enterprise infrastructure at sing Creon. And folks welcome to the cube. Thanks for coming on. >>Thank you. >>Thank you. >>So john we heard you this morning. You know, great job you guys are in the industrial logistics business. So supply chains at all a hot topic today. It's got to be challenging. Maybe you could talk about what you're seeing there, but specifically how are you thinking about data management in the context of your overall IT strategy. >>Okay, thank you. So in terms of data management, Synchrotron has 100 sites globally. So if we were to rewind by say 10 years we had data residing a lot out of those remote sites. Uh so over the last few years were basically consolidated a lot of that data and also centralist. So we've brought that into our data centers that we now have, which is a very, very centralist model. So that, that makes it a lot easier to understand where all of that data resides. >>So in the decision pie, as it relates to data, it sounds like cost efficiency is pretty, pretty ranks pretty highly. How does that impact your data management strategy and approach? I mean, is is that like the number one consideration? Is that one of many factors. How should we think about that? >>I would say cost is one of many factors. So obviously cost is key, but you don't want to introduce unnecessary risks. So you've got to keep costs at the forefront. But that's just one of the factors. Obviously data protection is one of the factors ensuring that data is protected and safe. And also understanding, is that exactly where that data resides, making sure that data is encrypted. So I would say that cost is just one of the factors. >>So Isabel good to see you again. I wonder if you could talk about how you're seeing your customers and what they're thinking about, how they're thinking differently about data management today, Are they changing the way they manage data giving given the escalation of ransomware that comprise the called the forced march to digital over the last 18, 19 months, but you've got new threats, new business dynamics, how is that affecting organizations? >>It does, it does affect them a lot. It's um we see a lot more. Actually, I asked a lot of virtual coffee talks with our customers so they can share best practices and a lot of C IOS network ending end with sizzles and they have a readiness plan because they know the question is not if they're going to have an attack, but when and how to recover from it is critical. So all the security team is really looking at the prevention but they know that if they can't stop it all, then they have a plan of end of to the data team for recovery. I see a lot more thoughtfulness because not all data is created equal. So which one is in the cloud and you can recover which one you need fast for minimum business was sorry, minimum business disruption and you keep on prime and which one you cannot lose and you have a go. So we see a lot more planning, a lot more collaboration across all verticals. We have also new services that help customers before the attacks to design and plan and also helping them post attack to recover so very much and to end and as we've seen the king out right now it's all about the people enabling them to do the business while you're risking the business too. So >>All right, thank you for that. So Reza and the fact that your Ceo is C I O. Uh so you must have some interesting conversations there but and you can be a sort of tap Sanjay's brain, how did you handle this kind of thing? And and uh that's a nice collaboration I bet. But what advice can you give to other, see IOS or grappling with cyber threats, data volumes and just the ongoing pressure to do more with less that never changes does it? >>It doesn't. And you're absolutely right. And I obviously as part of my job attracted benchmarks about budgets and everything else that before the pandemic used to track about like 3% growth year over year which is a hard to kind of do a whole lot with them. Um What what I can tell you is not for C I O not two areas the areas of investments are not created equal and from my perspective the biggest areas of investment for somebody like me in my position should be data and protecting the data. So that means that you have to find ways of on the budget side, find ways of shifting money whether you reallocate resources, whether you reform or a really organized differently, automate simplified etcetera. My background is operation so when you talk about people process technology outside of things, I leave the technology to the people that are really good at it and I focus on people and process side and for me that's about again efficiencies and finding ways that you can reorganize, you probably have the people that do the work that you want them to do and you just have to think about reorganizing them differently. And the last thing I said is prioritize prioritize initiatives across the board and it is like partner in crime in these things and we don't always say yes to her and what she wants because we need to be transparent so where we put our money >>so rest, I want to stay with you for me, I want to talk about data sprawl was interesting john during your session this morning I was sort of laying down some of my thoughts because I I feel like data sprawl, it's like social change. You can't fight it. You can maybe, you know for a period of time control it. But data is is out of control. So how do you address data sprawl in an organization? Both from a management perspective there's obviously risk. Somebody said this morning we used to keep, I think it was the C. I. A. In New Jersey. We used to keep everything forever. But that's risky. So how do you deal with that result from an organizational and management perspective? >>Yeah. You again, I'm gonna have to agree with you. As as I said in in a morning session, I like it's a natural phenomenon for a company to go through it. I've seen it in companies that are 150% people and I've seen it in companies that have tens of thousands of people. It's like a foundation onto what what entropy is in thermodynamics. It's the natural order of events. If you don't apply structure, organization data is going to go haywire and everything else. The only way. The best way that I know when the pendulum is here and everybody is doing the wrong thing is to push the pedal on the other side at least for a while to centralize, pick a few of your brightest people that know the data in and out, put them in a team and say you're responsible for making sense out of these things. Identify sources of truth for us and architect them differently. But but start with executive level metrics and board level metrics and push them down. >>So I see. I I agree with that with that. I think the people who have the data context are in the best position to add value as to whether it's data quality and how to get the most out of that data. But the problem is uh john I'd love to pick your brain on this. Especially your urine mia. You got all these different regulations and data silos, which I believe are a byproduct of how we organize. Uh, but but anyway, you have a lot of the considerations to deal with whether it's G. D. P. R. Or or or or data sovereignty etcetera. How do you approach that? >>So one of the first approaches we took when we moved over to con vault with our data protection was to reduce the number of products we used for the data protection. So we had six products through various acquisitions that we, we've done over the last 10-15 years. We've now reduced that six products down to one single product. So it means that all of your data is managed through a sort of single pane, which definitely gives you a much better insight. And also just going back to the costs that you mentioned in the previous question. Obviously going down from six products to one product, we managed to strip around $500,000 out of our costs over three years. We also moved data like I said into the center and allowed us to also concentrate the teams. So also the teams became more efficient because less people were dealing with that data as well. But yes you are right around GDP are there is definitely compliance to be considered and you just have to make sure you're up to date on all of those compliance regulations. >>So it's interesting resident here you talk about you know Isabelle, she's got needs but I would say Isabel that you probably know in your team, you know the marketing data better than anybody but there's got to be Federated governance, you've got to enforce policy in this data sprawl world. So anyway this is sort of a side but Sanjay Isabelle talk today about as a service growing like crazy and given your background I wonder if you can share any insights about how and why you think customers are going to be looking towards SAs I mean the whole world is becoming SAs ified you had some data on that this morning from, from Gardner. What are your thoughts? >>Yeah, no, absolutely, you're right experience this percent coming from cell phones and yes angie mentioned in the keynote by I think 2025 85% of business will be delivered through SAAS apps and that's very simple look at the world today the market dynamics of business changes. You mentioned the supply chain is you were talking you know all the line of business people of the business executives have to change fast. And the fastest way to do that is SAS because it has speed agility and you get the value faster problem being then it becomes very complex or I. T. Because you have workloads in multiple clouds on premise multiple apps and and what convo stands for and what everybody should look at is being able to enable all this innovation but at the same time removing the complexity for I. T. To protect this data to recover it and that's really where you know we're focusing our attention that is unavoidable. It's all about business and gT but it doesn't mean that you should compromise on data management. Yeah. >>Yeah I think you know we gotta we have to wrap here but I think the model, you know again it's about you coming from salesforce, we've contextualized our operational systems. You know, whether it's you know the sales cloud, the logistics, cloud, it's the lines of business actually have a good handle on this. And where I see the role of calm vault is that that notion of Federated governance, you've got to have centralized policy but you've got to programmatically and automate that out to the lines of business and I think that is kind of where the where the future is headed. Uh And I think that's really kind of controlled strategy. I'm hearing a lot on automation cloud like services and pushing that out. Um And so I see a new era in data coming and you guys talked a lot about this but but Isabelle will give you the last word. Put a put a bumper sticker on the on the panel for us. >>Well absolutely. I mean you said it's not left for no workload, sorry, it should be left behind and that's why you know you need a single architecture. I think businesses is changing fast and it's exciting. Uh And as long as you know you got a great I. T. Team with a great plan to have your back as a business leader. Every company should really embrace um all the change and innovation. So thank you day for for giving me the last world >>go. Thank you guys. I really appreciate you coming on the cube has been a fun day. We got more here that convulsed connections, keep it right there. We're gonna come back right after this short break my nose and I are going to wrap up and summarize the day. Yeah

>>mm we are back and we're continuing the coverage on H P E. Aruba's news today around the D S X six S C X 10-K with Pensando. Now we want to get the perspective of a system Integrator because they're in the front lines, they understand how to put the pieces together where you're happy to bring john Galatea of Dasher technologies dashes and in the end I. T solutions provider, they gotta focus a lot of expertise on infrastructure, jOHn welcome. Good to see you. >>Thank you for having me. Good to be here. >>That's our pleasure. So I wonder if you could give us a little bit more color on Dasher where you focus what your core companies competencies are, what industries you focus on etcetera. >>Yeah, absolutely. So a dasher, we assess architect implement and manage I. T. Solutions that digitally transform businesses. Our practice areas include cybersecurity, networking, cloud data center and we also offer pro professional services around those practice areas. We partner with all the major tech companies in the space. Some of the examples are HP, Cisco, Aruba Palo Alto eight Ws and many others that fill out the, you know that practice area. >>Well that's great. So you have a very wide observation space, that's why we like talking to SA as you have an independent mindset and you can kind of tell it like it is. But so what are you seeing with customers? It's exactly, we hear a lot about digital transformation, you mentioned security, you're obviously doing cloud that's it's almost like john these pieces are all coming together to power Digital and digital transformation and we were forced into it over the past 18 months. And now people are stepping back saying hey okay we have all these resources, how do we put them together and really transform our business? What do you see? >>Yeah, seeing similar things. So you know, our customers are telling us that they're looking for more speed, more agility, um you know, limited complexity because they're trying to do more every single day with less staffing and a sophistication of integrating functionality that breaks down I. T. Silos. Um there also evaluating security span versus effectiveness And they're moving towards zero trust. >>Yeah. So I want to, I'm gonna come back and ask you about that. So I've written a lot about this is that you look at how much we spend versus as you say the effectiveness and there's sort of an imbalance there, it's like we can't spend enough, it budgets they're not infinite. And even though security is top of top priority for ceos, they've got other things that they have to fund and then zero trust, you know, before the pandemic john that was a buzzword and now it's become a mandate. Any thoughts on that >>in terms of zero Trust? Absolutely yeah it is a mandate, we've seen more and more of our customers moving toward in this direction and defending themselves against cyber threats and yeah absolutely. It accelerated during the pandemic and is continuing to accelerate today. >>Right? And I think there's some things that were reported now going to be permanent with regard to obviously hybrid and the like, cloud security and so forth. So, okay, let's get into some of the news here. What's the big trend, john can you explain the relevance of the H P E, Aruba and Pensando news? >>Yeah, I mean when I first heard of it, you know, I I looked at it as a whole new category because it's a category that's going to deliver cloud scale distributed services closer to where applications are. It's going to simplify. One of the things we mentioned earlier was limiting complexity. So it simplifies the network um, by putting security provisions and operations in a unified management platform and it helps improve your security posture around moving towards zero trust and limits the appliance and vendor sprawl that you might ordinarily have in a in a existing network today. >>Okay, so that's kind of the business cases, you're consolidating a lot of piece parts and that's, you know, from a system integrator standpoint. You know, it's funny people often say, well, isn't that bad for the s I'm like, no, they don't want to be in the business of plumbing, they want to be in the business of, you know, more strategy if they if they just end up bolting stuff together, they're going to go out of business, They need to extend their value. So as a strategic partner, you got an early preview of this launch? The D S S D S S C X 10,000, what was your initial impression reaction you called it? A new category? What do you mean by that? >>Well, it's a new category of of of a data center switch in the digital infrastructure because it includes or incorporates security. Um And more specifically it includes security around east west traffic, which is it doesn't eliminate your perimeter firewall but it actually incorporates more functionality which leads to better simplicity and easier use of management of a platform. So for us, I'm really excited to go position and talk to our clients about this. >>Yes. So we're seeing the flattening of that network, that's even it's obviously been accelerated by the pandemic, everybody talks about that. But if you think about the traditional headquarter hierarchical network and now all of a sudden everybody's working remotely using more cloud. Using more distributed infrastructure that flattens the network. That creates security challenges because you can't just build a perimeter and say, okay, we're safe. You now have to go to where the adversary is and that's everywhere. So what's your sense as to how customers are going to react to this new category of switch? >>I think really my sense is that I've got a really positive outlook on this product. I mean hardware, firewalls are costly and deploying software agents can be very disruptive and when you're integrating it into the switch layer. So um I think the C X 10,000 provides a great alternative to an embedded accelerated services embedded in accelerate service into the D C fabric. Um, it's great for brownfield migration, um, rack pod and you know, and the standards based leaf, you know, L two, L three um, and it doesn't necessarily replace, as I mentioned earlier the perimeter security, but um, it can cap and grow with DSS and east west firewall traffic. >>Yeah. And I think we've seen when we talked to see so, so like you said, it does, it doesn't replace the traditional perimeter security but you're going to see a shift and spending priorities obviously to a comedy because as I said earlier, there's not infinite budget but john give us the big takeaway, Bring us home. What what, what do you want to leave our audience with? >>Yeah, I think, you know, the number one takeaway is that it's a massive opportunity to reduce complexity, enhanced security and lower costs in the data center by eliminating dedicated devices and embedding services through software capability in the network closer to where workloads are are moving. So that's the big takeaway for me and for, I think for our clients, um, you know, other things are, you know, you're the data center perimeter is no longer confined and open an on prem location but extends out, right. We're seeing customers extend out to the cloud and across uh, you know, disparate locations, co locations. So The traditional architecture isn't going to be well suited for this, and I think the CX- 10,000 and its feature set are going to be really great for addressing the changing market. >>Yeah, that's, that's all. I mean, again, we're seeing the democratization of everything and and networking is, is no exception. The notion of simplify simplification, john really appreciate your time. Thanks for coming on. >>Thank you for having me. >>You're welcome. Okay, keep it right. There were unpacking the changing trends in networking generally, and specifically switch networking with HP, Aruba and Pensando and the cube. Keep it right there.

>>Hello everyone Welcome to the cubes coverage of cubic on cloud native come here in person in L A 2021. I'm john ferrier, host of the Cuban Dave Nicholson host cloud host for the cube and of course former host of the cube steve minutemen. Now at red hat stew, we do our normal keynote reviews. We had to have you come back first while hazard and red hat >>john it's phenomenal. Great to see you nice to have Dave be on the program here too. It's been awesome. So yeah, a year and a day since I joined Red hat and uh, I do miss you guys always enjoyed doing the interviews in the cube. But you know, we're still in the community and still interacting lots, >>but we love you too. And Davis, your new replacement and covering the cloud angles. He's gonna bring little stew mo jokes of the interview but still, we've always done the wrap up has always been our favorite interviews to do an analysis of the keynote because let's face it, that's where all the action is. Of course we bring the commentary, but this year it's important because it's the first time we've had an event in two years too. So a lot of people, you know, aren't saying this on camera a lot, but they're kind of nervous. They're worried they're weirded out. We're back in person again. What do I feel? I haven't seen people, I've been working with people online. This is the top story. >>Yeah, john I thought they did a really good job in the keynote this morning. Normally, I mean this community in general is good with inclusion. Part of that inclusion is hey, what are you comfortable with if your remote? We still love you and it's okay. And if you're here in person, you might see there's wrist bands of green, yellow, red as in like, hey, you okay with a handshake. You want to do there or stay the f away from me because I'm not really that comfortable yet being here and it's whatever you're comfortable with. That's okay. >>I think the inclusion and the whole respect for the individual code of conduct, C N C. F and limits Foundation has been on the front end of all those trends. I love how they're taking it to a whole nother level. David, I want to get your take because now with multi cloud, we heard the same message over and over again that hey, open winds, okay. Open winds and still changing fast. What's your take? >>Open absolutely wins. It's uh, it's the present. It's the future. I know in some of the conversations we've had with folks looking back over the last seven years, a lot of things have changed. Um, whenever I think of open source anything, I go back to the foundations of Lennox and I remember a time when you had to reboot a Linux server to re scan a scuzzy bus to add a new storage device and we all sort of put our penguin hats on and kind of ignored that for a while. And uh, and, and as things are developed, we keep coming into these new situations. Multi cluster management was a big, big point of conversation in the keynote today. It's fascinating when you start thinking about something that was once sort of a back room science experiment. Absolutely. It's the center of the enterprise now from a software >>from an open tour standpoint security has been one of those front and center things. One of the day, zero events that got a lot of buzz coming at the beginning of the week was secure supply chain. So with the Solar Wind act going in there, you know, we remember cloud, wait, can I trust it with the security? Open source right now. Open source and security go together. Open source and the security in the cloud all go together. So you know that that wave of open source, obviously one of the things that brought me to red hat, I'd had a couple of decades, you know, working within the enterprise and open source and that that adoption curve which went through a few bumps in the road over time and it took time. But today, I mean open sources have given this show in this ecosystem are such proof >>points of a couple things. I noticed one, I want to do a shout out for the folks who put a nice tribute for dan Kaminsky who has passed away and we miss him. We saw on the Cube 2019, I believe he's on the Cube that year with Adam on big influence, but the inclusiveness do and the community is changing. I think security has changed a lot and I want to get your guys take on this. Security has forced a lot of things happen faster data, open data. Okay. And kubernetes to get hardened faster stew. I know your team's working on it. We know what Azure and amazon is working on it. What do you guys think about how security's been forcing the advances in kubernetes and making that stable? >>Yeah. So john security, you know, is job one, it is everyone's responsibility. We talk about it from a container and kubernetes standpoint. We think we have a relatively good handle on what's happening in the kubernetes space red hat, we made an acquisition earlier this year of stack rocks, which was one of the leading kubernetes native security pieces. But you know, john we know security isn't just a moat anymore in a wall that you put up every single piece. You need to think about it. Um, I've got a person from the stack rocks acquisition actually on my team now and have told him like hey, you need to cross train all of us. We need to understand this more from a marketing standpoint, we need to talk about it from a developer standpoint. We need to have consideration of it. It's no longer, hey, it works okay on my machine. Come on, It needs to go to production. We all know this shift left is something we've been talking about for many years. So yes, security, security, security, we cannot overemphasize how important is um, you know, when it comes to cooper, I think, you know, were relatively mature, we're crossing the chasm, the adoption numbers are there, so it's not an impediment anymore. >>It's totally next level. I don't agree with this too. David, get your thoughts on this whole adoption um, roadmap that put it together, one of the working groups that we interviewed has got that kind of navigate, kinda like trailheads for salesforce, but that speaks to the adoption by mainstream enterprises, not the hard core, >>you know, >>us devops guys, but like it goes into mainstream main main street enterprise had I. T. Department and security groups there, like we got a program faster. How do you see the cloud guys in this ecosystem competing and making that go faster. >>So it's been interesting over the last decade or more often, technology has been ahead of people's comfort level with that technology for obvious reasons, it's not just something went wrong, it's something went wrong. I lost my job. Really, really bad things happened. So we tend to be conservative. Rightfully so in the sometimes there are these seminal moments where a shift happens go back sort of analogous go back to a time when people's main concern with VM ware was how can I get support from Microsoft and all of a sudden it went from that within weeks to how can I deploy this in my enterprise very, very quickly. And I'm fascinated by this concept of locking down the supply chain of code, uh sort of analogous to https, secure, http. It's the idea of making sure that these blocks of code are validated and secure as they get implemented. You mentioned, you mentioned things like cluster and pad's security and infrastructure security. >>Well, David, you brought up a really good point. So get off is the instance creation of that. How can I have my infrastructure as code? How can I make sure that I don't have drift? It's because I could just, it'll live and get hub and therefore it's version controlled. If I try to do something, it will validate that it's there and keep me on version because we know john we talked about it for years on the cube, we've gone beyond human scale if I don't build automation into it, if I don't have the guard rails in place because humans will mess things up so we need to make sure that we have the processes and the automation in place and kubernetes was built for that automation at its core, putting in, we've seen get up the Argosy, D was only went graduated, you know, the one dato was supported as coupon europe. Earlier this year, we already had a number of our customers deploying it using it. Talking publicly >>about it too. I want to get the kid apps angle and that's a good call out there and, and mainly because when we were on the cute, when you work, you post with with us, we were always cheerleading for Cuban. It we love because we've been here every single coupon. We were one saying this is gonna be big trust us and it is, it happens to so, but now we've been kind of, we don't have to sell it anymore. We don't, I mean not that we're selling it, but like we don't have to be a proponent of something we knew was going to happen, it happened. You're now work for a vendor red hat you talk to customers. What is that next level conversation look like now that they know it's real, they have to do it. How is the tops and then modern applications development, changing. What are your observations? Can you share with us from a redhead perspective as someone who's talking to customers, you know, what does real look like? >>Yeah. So get off is a great example of that. So, you know, certain of our government agencies that we work with, you know, obviously very secured about, you know, we want zero trust who do we put in charge of things. So if they can have, you know that that source of truth and know that that is maintained and lockdown and not await some admin is gonna mess something up on us either maliciously or oops, by accident or anything in between. That's why they were pushing that adoption of that kind of technology. So absolutely they, for the most part john they don't want to have to think about the infrastructure piece anymore. What if developers want the old past days was I want to be able to, you know, write once deploy anywhere, live anywhere, containers helps that a little bit. We even have in the container space. Now you can, you can use a service deployment model with Okay. Natives, the big open source project that, you know, VM ware ourselves are working on google's involved in it. So, you know, having us be able to focus on the business and not, you know, running the plumbing anymore. >>That's exactly, that's exactly, that's what we're so psyched for. Okay guys, let's wrap this up and and review the keynote day will start with you. What do you think of the keynote? What were the highlights? What do you take away from the taste keynote? >>So you touched on a couple of things, uh inclusion from all sorts of different angles. Really impressive. This sort of easing back into the world of being face to face. I think they're doing a fantastic job at that. The thing that struck me was something I mentioned earlier. Um moving into multi cluster management in a way that really speaks to enterprise deployments and the complexity of enterprise deployments moving forward? It's not just, it's not just, I'm a developer, I'm using resources in the cloud. I'm doing things this way, the rest of the enterprises doing it a legacy way. It's really an acknowledgement that these things are coming together increasingly. That's what really struck me >>to do. What's your takeaway from the end? >>So there's been a discussion in the industry, you know, what do the next million cloud customers look like we've crossed the chasm on kubernetes. One of the things they announced the keynote is they have a new associate level certification because I tell you before the keynote, I stopped by the breakfast area, saturday table, talk to a couple people. One guy was like, hey, I'm been on amazon for a bunch of years, but I'm a kubernetes newbie, I'm here to learn about that. It's not the same person that five years ago was like, I'm gonna grab all these projects and pull them down from getting, build my stack and you know, have a platform team to manage it from a red hat standpoint, we're delivering our biggest growth areas in cloud services where hey, I've got an SRE team, they can manage all that because can you do it? Sure you got people maybe you'll hire him, but wouldn't you rather have them work on, you know, that security initiative or that new application or some of these pieces, you know, what can you shift to your vendor? What can you offload from your team because we know the only constant is that things are gonna there's gonna be gonna be new pieces and I don't want to have to look at, oh there's another 20 new projects and how does that fit? Can I have a partner or consultant in sc that can help me integrate that into my environment when it makes sense for me because otherwise, oh my God, cloud, So much innovation. How do I grasp what I want? >>Great stuff guys, I would just say my summary is that okay? I'm excited this community has broken through the pandemic and survived and thrived people were working together during the pandemic. It's like a V. I. P. Event here. So that my keynote epiphany was this is like the who's who some big players are here. I saw Bill Vaz from amazon on the on the ground floor on monday night, He's number two at a W. S. I saw some top Vcs here. Microsoft IBM red hat the whole way tracks back. Whole track is back and it's a hybrid event. So I think we're here for the long haul with hybrid events where you can see a lot more in person, V. I. P. Like vibe people are doing deals. It feels alive too and it's all open. So it's all cool. And again, the team at C. N. C. F. They do an exceptional job of inclusion and making people feel safe and cool. So, great job. Thanks for coming on. I appreciate it. Good stuff. Okay. The keynote review from the cube Stupid Man shot for Dave Nicholson. Thanks for watching >>mm mm mm.

>>from the bellagio hotel >>in Las Vegas, it's the cube covering Ui Path forward. Four brought to you >>by Ui Path. Welcome back from the bellagio in Las Vegas. The Cubans live at Ui Path forward for I'm lisa martin here with Dave Volonte. We're gonna be talking about roadblocks to automation and how to navigate around them, joining us next as Pernando Panini expert associate partner at bain and company per night. Welcome to the program. >>Thanks lisa. Happy to be here. >>Talk to us about some of the use cases that bain is working on with you I Path and then we'll dig into some of those roadblocks that you guys have uncovered. >>Yes. Uh I started a few months ago where we're working with Brandon who's the product lead on the Ui part side. We wanted to understand what's the state of citizen development and what are the blockers and how we should Both from the product side. But also on the automation journey side we need to dig deeper and understand where each of the clients and the employees are going through the journey together >>and if you look at it from the citizen developer perspective, what are some of those roadblocks? >>There are a few. So when like if you before we go to the roadblocks there are three main concerns or I would say critical groups that are involved in being successful with automation. The organization or bu leaders, the I. T. And employees. So each of the groups have different perceptions on like misconceptions or perceptions on benefits of automation and how to go up go about it. The blockers that we have seen where like a three sets of blockers. The first is cognitive where employees are unaware of automation on the benefits of automation and the second one is more organizational where organization leaders and how they feel about automation or how the how they think about employees when we introduce automation to them. One part of that is there is a misconception without nation leaders that employees are fearful of job loss when you introduce automation. What we have seen in our research is it's completely the opposite of employees are eager to adopt automation have given an opportunity, they are willing to upscale themselves and they are willing to save the time so that they can spend that on critical value added activities for um for their customers in the process. And a third blocker that we have seen is more on the product side where the some of the employees that we talked to as much as progress has been made by RPF vendors and local local vendors. It's still these tools are not intuitive user friendly for business users. They still feel they need to go through some training programs and have a better user friendly interface is >>what's the entry point she would organization first time I ever heard of Arpaio Years and years and years ago was at a CFO conference. Okay so that's cool. It seems like it forward for there's a lot more C. I. O presence here and that. Is that relatively new or did I just miss it before? >>It is relatively new. So like when we looked at like in the past few years the empty point has been someone in finance or I. T. Has heard about R. P. A. The benefits of head. They went and bought a handful of licenses and then they went and implemented it but it's just a handful of processes. It's not organizational wide. It has been mostly on a smaller sub scale of processes. And projects now that like organizations are realizing employees are asking and we are like slowly growing up with automation ceo es it's now it's intersecting with the C XL level of if it has to intersect with your or if you want to reinvent your business through automation, it has to come from the sea X level and that's where we're seeing more and more. See IOS are being involved in decisions on automation journeys, the technologies they have to buy and adopt for the business processes. >>So I. T. Can be an enabler of course. Also sometimes it can be a blocker. Um and you know, certainly from security standpoint governance etcetera. And so one of the things that we heard today in the keynotes was you don't want to automate the C I. O. He or she owns this application portfolio and everybody wants to do new projects because that's the fun stuff we heard from one CFO. Yeah. You add up all the NPV from the new projects. It's bigger than the valuation of the company. Right. But the C i O is stuck having to manage the infrastructure and all the processes around the existing application portfolio. One of things I heard today was don't automate an application or a process that you're trying to retire because we never get rid of stuff in it. So I wonder should automation like an enterprise wide automation? Should there be kind of an application rationalization exercise or a business process rationalization coincident with that >>initiative? Absolutely. I think that was one of the blockers that we have seen. Like some of the misconceptions and some of the blockers when I looked at it for them, they consider like you're bringing all these tools you're asking business users to like who haven't had haven't been trained in technology or programming, You're asking them to build these automation ins So one they have to manage with the all the applications and the tools for all that happens. And to manage these automation is after business users have either left the company or moved on. So it is essential for them to think through and provide a streamline tools it on on two aspects. one it needs to be as as you started off, it needs to be an enabler to provide them the specific tools that they can, they have already blessed. They've curated it which are ready for business consumption. A second part I can also do is providing collaboration platforms so that business users can learn from each other and from it so that they can one are developing the right processes with the right methodology that is governed by I. T. And no security or data governance issues. Come through. >>One of the things that you mentioned in terms of the three roadblocks ceo uncovered was that you were surprised that the results of the research showed that in fact employees are really wanting to adopt automation. In fact I think the stat is um 86% of employees want automation but only 30% of leaders are giving them the opportunity to use that. That's a big gap. Why do you think that is >>so a few things. Right. I mean as we talked about the three constituents that you have right one is automation leaders. If you consider from them. Their view is their employees are not capable of adopting or building on the automation is using these tools and they need technical skills. But the all the automation vendors have made progress and if you look at the tools today are much more user friendly and business users are willing to adopt. The second part as we talked about is like the fear of job loss from the employee standpoint. Whereas employees are looking at it as an opportunity for them to up skill but also eliminate the pain points that they have today in the day to day activities using the automation tools. And for them it is like this is helping them spend the time with the customers where it matters on critical value added activities versus going through reparative process of the journey. And the third part we talked about earlier with I. T. I. T. Has this notion that they need to build and develop anything technical. Business users will not be able to build or manage and they're also worried about the governance, the security and the third part which you brought up earlier is that tool sprawl, It's like we need to manage like this volume of tools that are coming in which is only adding to their plate of already busy busy workforce. >>I have one of those. It depends questions and it's a good consultant I'm sure you say well it depends but are there patterns best practice or even more than best pressures? Are there sort of play books if you will? And patterns? I'm sure it's situational. But are you seeing patterns emerge, you can say okay this sort of category should approach it this way. Here's another one in a different, maybe it's a department bottoms up top down, can you help us sort of squint through that? >>Yeah. So in terms of approaches like at least up till now the prevalent thing that is happening is like C. O. Es went and buy some licenses they talk about like opportunities that they have. So it's more of a top down driven uh like ceo driven agenda. What we're seeing now especially with citizen automation or democratisation of automation is there's a new approach of including employees into the journey and bringing the bottoms up approach. So there's a happy path where you marry up the top down approach with bottoms up and one you will find opportunities which are organizational wide with the bu leaders and they are ones which are on the long tail of opportunities which employees feel the pain but I. T. Or C. O. He doesn't have the time to come and implement or automate these activities. Um considering like one part we have seen which is increasingly helpful for people who have done this properly is including employees. And one thing we talked yesterday is invest in employees. They consider automation as investment in employees rather than something they're doing to employees. So it's kind of collaborating with employees to make progress which seems to be helping evangelize and also benefit with automation. How >>Have the events of the last 18 months impacted this as well, we've seen so much acceleration and the mandate for automation. What are some of the things that you've seen? >>Sure. So for us like even before the pandemic we've seen in our research so like more than close to 50% of the organizations that they started the automation journey were unable to achieve the savings or targets that they set themselves for whatever the success factors are. Which which hard. A few reasons one they didn't have the organizational support, not they were taking the end to end journey or a customer journey to figure out like what are these big opportunities that they can go through and they haven't included employees and to figure out what are the major pain points to go through the journey. One thing it was clear was with covid, no one expected this kind of disruption in a pan and a pandemic. There are a lot of offshore centres or like pretty much different geography is got disconnected from the work that's being done. You still need to support your customers, there is still a higher demand, what do you do? It's not like you can scale up your employees in a pandemic, that's where like we have seen increasing push towards automation and technology to see that can help and support and scale in a pandemic environment uh and also help your customers in the journey. >>So has in your opinion has automation become a mandate? Uh As a result of the pandemic >>I would say. Yeah I I would consider it's more of like now it's become a I would say uh business won a competitive differentiator to say like one I needed to keep my lights on and resiliency but also the companies have done really well they saw the advantage and they whether the pandemic better with the customers now they use that as a platform to create a competitive differentiation against their peers and push things forward. >>one of the things we heard of today and the keynotes is you got to think about my words, the life cycle, you don't just put in the bot and then just leave it alone. You really have to think through that. And that seems to me to be where you would help customers think through how to get the most return out of their investment. You I passed product company I think it's great. And so you talk about the value layer that you guys bring. >>So for us it's it's like when we talked to mostly be bringing from the business side of the house to understand what are the key drivers that you need to work on. I mean even before we talk about technology, we talk about, let's understand from the customer standpoint what is your customer journey into end and look through that journey lens and let's take the process and to end, let's look at redesigning process and making it more optimal and streamlined and where technology fits in. That's when we talk about like if it is an RPG or if it's a UI Path platform that can support, let's go through that journey versus taking the tool itself as the solution and trying to find every nail that you can hurt, which usually is not sustainable to your point. Like we need to think through the whole life cycle, make sure this is going to last. Or if you are retiring. Like in the ceo panel that was a discussion where that we need to think through when we are going to retire and make sure like we are in that journey versus building all these automation zor bringing all these tools and leaving them alone for I. T. To manage long term. >>No. Again the last 18 months. Again, question about the the um reactions catalyzed facilitated thinking about those three roadblocks. The cognitive roadblocks the organizational roadblocks since particularly what I'm interested in this question and product, what are some of the conversations that you've seen or trends that you seem to help those organizations better understand how to collaborate with each other so that what they're not doing is putting in our P. A point tools but really starting to build the right part of the nomination and and journey into their digital transformation plans. Yeah. >>I mean in a way to again, I'll go back to the three concerns that we talked earlier, right? It's it can only go so far and automate so much because they haven't seen the business lens of like how the processes are what they have to do and to end, which is where you need to involve the business leaders who can give you that view from the business side and employees who are seeing the work day to day and where they can eliminate the pain points. So the organisms that are successful, they are creating a collaborative environment between the three groups to push things forward. You >>have to have that collaboration that's critical. Otherwise, that's probably one of the road blockers as well. >>Yeah, absolutely. >>Where does automation fit? I mean you're obviously heavily into automation, but let's think about the bane portfolio, the boardroom discussions. Where does automation fit? I mean there's security, there's how do we embed ai into our business? How do we sas if I our business um how do we do transform digitally? Where's automation fit in that hole discourse? >>So I think the automation is like at the heart of digital transformation, the part which we have seen where the gap is is not taking the business angle and actually thinking through the process and to end versus picking up a tool and trying to go solve a problem or find a problem to solve. And that's where we think in our discussions with boardrooms, it's more of let's think through how you want to reimagine your company or how you want to be more competitive looking into the future and like walk back from that standpoint and then started part from, I mean, the way we call it the future back like where you are today and now, like let's go forward and to what your end status and where technology broadly a digital tools and where automation fits in the process. >>How do you see what you i path is talking about at this conference? The announcements from yesterday? There's a lot of people here which is fantastic. How do you see what they're announcing? The vision that they set out a couple years ago that they're now delivering on. How is that a facilitator of organizations removing those roadblocks? Because as you said automation is a huge competitive differentiator these days and If we've learned nothing in the last 19 months you gotta you gotta be careful because there's always a competitor in the rear view mirror who might be smaller faster more agile ready to take your place. >>Yeah so like a few things that we've seen in the product roadmap that you talked about is they are providing the collaboration platform or tools where the I. T. Business owners can work through. Like for automation hub is what they talked at length yesterday is that's the platform where business users can provide their ideas. Like you provide process mining tools which can capture the process and the business users understand the process and they are the ones who are putting in an opportunity on the road map. So you have now a platform where all the ideas are being catalogued and once you implement they're being tracked on the automation hub so that that is providing a platform for everyone to collaborate together. The second one which Brandon talked yesterday is the tool itself for Studio X. When we're talking about citizen developers, employees trying to use and make it more user friendly. Is that where the Studio X which is providing that you are interface? Which is easy intuitive for business users to build basic automation is and try to take that long tail of opportunities that we talked about. So all these tools are coming together as one platform play, which you ipod has been talking about all through the conference and that is critical for everyone to collaborate to make a progress versus only thinking it's an easy job to implement the automation opportunities. That >>collaboration is business critical these days. Right. Thank you for joining David me and the program talking about some of the roadblocks that you've uncovered, but also some of the ways that organizations in any industry can navigate around them and really empower those employees who want automation in their jobs. We appreciate your insights. >>Happy to be here. Thanks for having us. You're welcome >>for day Volonte. I'm lisa martin live in las Vegas at UI Path forward for we'll be right back with our next guest. Yeah. >>Yeah. Mm. Mhm