(soft electronic music) >> Okay, we're back with Tim Yocum who is the Director of Engineering at InfluxData. Tim, welcome, good to see you. >> Good to see you, thanks for having me. >> You're really welcome. Listen, we've been covering opensource software on theCUBE for more than a decade and we've kind of watched the innovation from the big data ecosystem, the cloud is being built out on opensource, mobile, social platforms, key databases, and of course, InfluxDB. And InfluxData has been a big consumer and crontributor of opensource software. So my question to you is where have you seen the biggest bang for the buck from opensource software? >> So yeah, you know, Influx really, we thrive at the intersection of commercial services and opensource 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, templating engines. 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 InfluxDB. >> But I got to ask you, Tim, because one of the challenge that we've seen, in particular, you saw this in the heyday of Hadoop, the innovations come so fast and furious, and as a software company, you got to place bets, you got to commit people, and sometimes those bets can be risky and not pay off. So how have you managed this challenge? >> Oh, it moves fast, yeah. That's a benefit, though, because the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we tend to do 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 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 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 is kind of, it's been off the charts and seen the most significant adoption and velocity particularly along with cloud, but really, Kubernetes is just, you know, still up and to the right consistently, even with the macro headwinds and all of the other stuff that we're sick of talking about. So what do you do with Kubernetes in the platform? >> Yeah, it's really central to our ability to run the product. When we first started out, we were just on AWS and the way we were running 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 a followup on that, is it now, so I presume it sounds like there's a PaaS layer there to allow you guys to have a consistent experience across clouds and out to the edge, wherever. Is that correct? >> Yeah, so we've basically built more or less platform engineering is this the new, hot phrase. 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 just gets all of the underlying infrastructure out of the way and lets them focus on delivering Influx cloud. >> And I know I'm taking a little bit of a tangent, but is that, I'll call it a PaaS layer, if I can use that term, are there specific attributes to InfluxDB or is it kind of just generally off-the-shelf PaaS? Is there any purpose built capability there that is value-add or is it pretty much generic? >> So we really build, we look at things with a build versus buy, through a build versus buy lens. Some things we want to leverage, cloud provider services, for instance, POSTGRES databases for metadata, perhaps. Get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can deliver on, that has consistency, that is all generated from code. that we can, 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 in no time. >> So sometimes you build, sometimes you buy it. How do you make those decisions and what does that mean for the platform and for customers? >> Yeah, so what we're doing is, it's like everybody else will do. We're looking for trade-offs that make sense. We really want to protect our customers' data, so we look for services that support our own software with the most up-time 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 our customers; you don't even see that. But we don't want to try to reinvent the wheel, like I had mentioned with SQL datasource 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 help our developers then focus on the InfluxData software, the Influx cloud software. >> So take it to the customer level. What does it mean for them, what's the value that they're going to get out of all these innovations that we've been talking about today, and what can they expect in the future? >> So first of all, people who use the OSS product are really going to 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 four billion series keys that people have stored, so there's a proven ability to scale. Now in terms of the opensource software and how we've developed the platform, you're getting highly available, high cardinality time-series platform. We manage it and really, as I had mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in realtime. We deploy to our platform every day, repeatedly, all the time. And it's that continuous deployment that allow 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 earllier, Kubernetes, I mean, that allows us to get that done. We couldn't do it without having that platform as a base layer for us to then put our software on. So we iterate quickly. When you're on the Influx cloud platform, you really are able to take advantage of new features immediately. We roll things out every day and as those things go into production, you have the ability to use them. And so in the then, we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let us do that for you. >> That makes sense. Are the innovations that we're talking about in the evolution of InfluxDB, do you see that as sort of a natural evolution for existing customers? 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's a little bit of both. Any engineer will say, "Well it depends." So cloud-native technologies 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 want to manage infrastructure. What we've started to see are people that use the cloud service as their datastore backbone and then they use edge computing with our OSS product to ingest data from say, multiple production lines, and down-sample that data, send the rest of that data off to 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 business of trying to manage that big data, have us take care of that. And, of course, as we change the platform, endusers benefit from that immediately. >> And so obviously you've taken away a lot of the heavy lifting for the infrastructure. Would you say the same things about security, especially as you go out to IoT at the edge? How should we be thinking about the value that you bring from a security perspective? >> We take security super seriously. It's built into our DNA. We do a lot of work to ensure that our platform is secure, that the data that we store is kept private. It's, of course, always a concern, you see in the news all the time, companies being compromised. 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 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's just part of our jobs to make sure that the platform that we're running has fully vetted software. And you know, with opensource especially, that's a lot of work, and so it's definitely new territory. Supply chain attacks are definitely happening at a higher clip that they used to but that is really just part of a day in the life for folks like us that are building platforms. >> And that's key, especially when you start getting into the, you know, that we talk about IoT and the operations technologies, the engineers running that infrastrucutre. You know, historically, as you know, Tim, they would air gap everything; that's how they kept it safe. But that's not feasible anymore. Everything's-- >> Can't do that. >> connected now, right? And so you've got to have a partner that is, again, take away that heavy lifting to R&D so you can focus on some of the other activities. All right, give us the last word and the key takeaways from your perspective. >> Well, you know, from my perspective, I see it as a two-lane approach, with Influx, with any time-series data. You've got a lot of stuff that you're going to run on-prem. What you had mentioned, air gapping? Sure, there's plenty of need for that. But at the end of the day, people that don't want to run big datacenters, people that want to entrust their data to a company 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 a more hybrid approach 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 theCUBE. (soft electronic music)

>>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.

(upbeat music) >> Okay, we're back with Tim Yoakum, 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 on the Cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem, the cloud is 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 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, templating engines. 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 got to ask you, Tim, because one of the challenge that we've seen, in particular, you saw this in the heyday of Hadoop. The innovations come so fast and furious, and as a software company, you got to place bets, you got to, you know, commit people, and sometimes those bets can be risky and not pay off. How have you managed this challenge? >> Oh, it moves fast, yeah. That's a benefit though, because the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we tend to do 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 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 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 really Kubernetes is just, you know, still up and to the right consistently, even with, you know the macro headwinds and all of the other stuff that we're sick of talking about. So what are you doing with Kubernetes in the platform? >> Yeah, it's really central to our ability to run the product. When we first started out, we were just on AWS, and the way we were running 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 a follow up on that, is it, now, so I presume it sounds like there's a PaaS layer there to allow you guys to have a consistent experience across clouds and up 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, 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 just gets all of the underlying infrastructure out of the way and lets them focus on delivering Influx Cloud. >> Yeah, and I know I'm taking a little bit of a tangent, but is that, I'll call it a PaaS layer if I can use that term, are there specific attributes to Influx DB, or is it kind of just generally off the shelf PaaS? You know, is there any purpose built capability there that is value add, or is it pretty much generic? >> So we really build, we look at things with a build versus buy, through a build versus buy lens. Some things we want to leverage, cloud provider services for instance, Postgres databases for metadata perhaps, get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can deliver on, that has consistency, that is all generated from code that we can, 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 in no time. >> So how, so sometimes you build, sometimes you buy it. How do you make those decisions, and what does that mean for the platform and for customers? >> Yeah, so what we're doing is, it's like everybody else will do. 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 had mentioned with SQL data storage for metadata perhaps. Let's build on top of what 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 going to 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 going to 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 software, and how we've developed the platform, you're getting highly available, high cardinality time series platform. We manage it, and really 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 we had mentioned earlier Kubernetes, I mean that allows us to get that done. We couldn't do it without having that platform as a base layer for us to then put our software on. So we iterate quickly. When you're on the Influx Cloud platform, you really are able to take advantage of new features immediately. We roll things out every day. And as those things go into production, you have the ability to use them. And so in the end, we want you to focus on getting actionable insights from your data instead of running infrastructure. You know, let us do that for you. >> And that makes sense, but so is the, are the innovations that we're talking about in the evolution of Influx DB, do you see that as sort of a natural evolution for existing customers? 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's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies 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 want to manage infrastructure. What we've started to see are people that use the cloud service as their data store backbone, and then they use edge computing with our OSS product to ingest data from say multiple production lines and down-sample that data, send the rest of that data off to 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 business of 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 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 security super seriously. 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 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 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's just part of our jobs, to make sure that the platform that we're running has fully vetted software. And with open source especially, that's a lot of work. And so it's definitely new territory. Supply chain attacks are definitely happening at a higher clip than they used to. But that is really just part of a day in the life for folks like us that are building platforms. >> Yeah, and that's key. I mean, especially when you start getting into the, you know, we talk about IoT and the operations technologies, the engineers running that infrastructure. You know, historically, as you know, Tim, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >> Can't do that. >> connected now, right? And so you've got to 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. All right. Give us the last word and the key takeaways from your perspective. >> Well, you know, from my perspective, I see it as a a two lane approach. With Influx, with any any time series data, you know, you've got a lot of stuff that you're going to run on-prem. What you 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 to entrust their data to a company 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 a more hybrid approach 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. (gentle music)

[Music] the cyber security landscape has changed dramatically over the past 24 to 36 months rapid cloud migration has created a new layer of security defense sure but that doesn't mean csos can relax in many respects it further complicates or at least changes the cso's scope of responsibilities in particular the threat surface has expanded and that creates more seams and csos have to make sure their teams pick up where the hyperscaler clouds leave off application developers have become a critical execution point for cyber assurance shift left is the kind of new buzz phrase for devs but organizations still have to shield right meaning the operational teams must continue to partner with secops to make sure infrastructure is resilient so it's no wonder that an etr's latest survey of nearly 1500 cios and it buyers that business technology executives cite security as their number one priority well ahead of other critical technology initiatives including collaboration software cloud computing and analytics rounding out the top four but budgets are under pressure and csos have to prioritize it's not like they have an open checkbook they have to contend with other key initiatives like those just mentioned to secure the funding and what about zero trust can you go out and buy zero trust or is it a framework a mindset in a series of best practices applied to create a security consciousness throughout the organization can you implement zero trust in other words if a machine or human is not explicitly allowed access then access is denied can you implement that policy without constricting organizational agility the question is what's the most practical way to apply that premise and what role does infrastructure play as the enforcer how does automation play in the equation the fact is that today's approach to cyber resilient type resilience can't be an either or it has to be an and conversation meaning you have to ensure data protection while at the same time advancing the mission of the organization with as little friction as possible and don't even talk to me about the edge that's really going to keep you up at night hello and welcome to the special cube presentation a blueprint for trusted infrastructure made possible by dell technologies in this program we explore the critical role that trusted infrastructure plays in cyber security strategies how organizations should think about the infrastructure side of the cyber security equation and how dell specifically approaches securing infrastructure for your business we'll dig into what it means to transform and evolve toward a modern security infrastructure that's both trusted and agile first up are pete gear and steve kenniston they're both senior cyber security consultants at dell technologies and they're going to talk about the company's philosophy and approach to trusted infrastructure and then we're going to speak to paris our godaddy who's a senior consultant for storage at dell technologies to understand where and how storage plays in this trusted infrastructure world and then finally rob emsley who heads product marketing for data protection and cyber security he's going to take a deeper dive with rob into data protection and explain how it has become a critical component of a comprehensive cyber security strategy okay let's get started pete gear steve kenniston welcome to the cube thanks for coming into the marlboro studios today great to be here dave thanks dave good to see you great to see you guys pete start by talking about the security landscape you heard my little rap up front what are you seeing i thought you wrapped it up really well and you touched on all the key points right technology is ubiquitous today it's everywhere it's no longer confined to a monolithic data center it lives at the edge it lives in front of us it lives in our pockets and smartphones along with that is data and as you said organizations are managing sometimes 10 to 20 times the amount of data that they were just five years ago and along with that cyber crime has become a very profitable uh enterprise in fact it's been more than 10 years since uh the nsa chief actually called cybercrime the biggest transfer of wealth in history that was 10 years ago and we've seen nothing but accelerating cybercrime and really sophistication of how those attacks are are perpetrated and so the new security landscape is really more of an evolution we're finally seeing security catch up with all of the technology adoption all the build out the work from home and work from anywhere that we've seen over the last couple of years we're finally seeing organizations and really it goes beyond the i.t directors it's a board level discussion today security's become a board level discussion so yeah i think that's true as well it's like it used to be the security was okay the sec ops team you're responsible for security now you've got the developers are involved the business lines are involved it's part of onboarding for most companies you know steve this concept of zero trust it was kind of a buzzword before the pandemic and i feel like i've often said it's now become a a mandate but it's it's it's still fuzzy to a lot of people how do you guys think about zero trust what does it mean to you how does it fit yeah i thought again i thought your opening was fantastic in this whole lead into to what is zero trust it had been a buzzword for a long time and now ever since the federal government came out with their implementation or or desire to drive zero trust a lot more people are taking a lot more seriously because i don't think they've seen the government do this but ultimately let's see ultimately it's just like you said right if you don't have trust to those particular devices applications or data you can't get at it the question is and and you phrase it perfectly can you implement that as well as allow the business to be as agile as it needs to be in order to be competitive because we're seeing with your whole notion around devops and the ability to kind of build make deploy build make deploy right they still need that functionality but it also needs to be trusted it needs to be secure and things can't get away from you yeah so it's interesting we attended every uh reinforce since 2019 and the narrative there is hey everything in this in the cloud is great you know and this narrative around oh security is a big problem is you know doesn't help the industry the fact is that the big hyperscalers they're not strapped for talent but csos are they don't have the the capabilities to really apply all these best practices they're they're playing whack-a-mole so they look to companies like yours to take their your r d and bake it into security products and solutions so what are the critical aspects of the so-called dell trusted infrastructure that we should be thinking about yeah well dell trusted infrastructure for us is a way for us to describe uh the the work that we do through design development and even delivery of our it system so dell trusted infrastructure includes our storage it includes our servers our networking our data protection our hyper-converged everything that infrastructure always has been it's just that today customers consume that infrastructure at the edge as a service in a multi-cloud environment i mean i view the cloud as really a way for organizations to become more agile and to become more flexible and also to control costs i don't think organizations move to the cloud or move to a multi-cloud environment to enhance security so i don't see cloud computing as a panacea for security i see it as another attack surface and another uh aspect in front that organizations and and security organizations and departments have to manage it's part of their infrastructure today whether it's in their data center in a cloud or at the edge i mean i think it's a huge point because a lot of people think oh the data's in the cloud i'm good it's like steve we've talked about oh why do i have to back up my data it's in the cloud well you might have to recover it someday so i don't know if you have anything to add to that or any additional thoughts on it no i mean i think i think like what pete was saying when it comes to when it comes to all these new vectors for attack surfaces you know people did choose the cloud in order to be more agile more flexible and all that did was open up to the csos who need to pay attention to now okay where can i possibly be attacked i need to be thinking about is that secure and part of the part of that is dell now also understands and thinks about as we're building solutions is it is it a trusted development life cycle so we have our own trusted development life cycle how many times in the past did you used to hear about vendors saying you got to patch your software because of this we think about what changes to our software and what implementations and what enhancements we deliver can actually cause from a security perspective and make sure we don't give up or or have security become a whole just in order to implement a feature we got to think about those things yeah and as pete alluded to our secure supply chain so all the way through knowing what you're going to get when you actually receive it is going to be secure and not be tampered with becomes vitally important and pete and i were talking earlier when you have tens of thousands of devices that need to be delivered whether it be storage or laptops or pcs or or whatever it is you want to be tr you want to know that that that those devices are can be trusted okay guys maybe pete you could talk about the how dell thinks about it's its framework and its philosophy of cyber security and then specifically what dell's advantages are relative to the competition yeah definitely dave thank you so i we've talked a lot about dell as a technology provider but one thing dell also is is a partner in this larger ecosystem we realize that security whether it's a zero trust paradigm or any other kind of security environment is an ecosystem with a lot of different vendors so we look at three areas uh one is protecting data in systems we know that it starts with and ends with data that helps organizations combat threats across their entire infrastructure and what it means is dell's embedding security features consistently across our portfolios of storage servers networking the second is enhancing cyber resiliency over the last decade a lot of the funding and spending has been in protecting or trying to prevent cyber threats not necessarily in responding to and recovering from threats right we call that resiliency organizations need to build resiliency across their organization so not only can they withstand a threat but they can respond recover and continue with their operations and the third is overcoming security complexity security is hard it's more difficult because of the the things we've talked about about distributed data distributed technology and and attack surfaces everywhere and so we're enabling organizations to scale confidently to continue their business but know that all all the i.t decisions that they're making um have these intrinsic security features and are built and delivered in a consistent security so those are kind of the three pillars maybe we could end on what you guys see as the key differentiators uh that people should know about that that dell brings to the table maybe each of you could take take a shot at that yeah i i think first of all from from a holistic portfolio perspective right the secure supply chain and the secure development life cycle permeate through everything dell does when building things so we build things with security in mind all the way from as pete mentioned from from creation to delivery we want to make sure you have that that secure device or or asset that permeates everything from servers networking storage data protection through hyper converge through everything that to me is really a key asset because that means you can you understand when you receive something it's a trusted piece of your infrastructure i think the other core component to think about and pete mentioned as dell being a partner for um making sure you can deliver these things is that even though those are that's part of our framework these pillars are our framework of how we want to deliver security it's also important to understand that we are partners and that you don't need to rip and replace but as you start to put in new components you can be you can be assured that the components that you're replacing as you're evolving as you're growing as you're moving to the cloud as you're moving to more on-prem type services or whatever that your environment is secure i think those are two key things got it okay pete bring us home yeah i think one of one of the big advantages of dell uh is our scope and our scale right we're a large technology vendor that's been around for decades and we develop and sell almost every piece of technology we also know that organizations are might make different decisions and so we have a large services organization with a lot of experienced services people that can help customers along their security journey depending on uh whatever type of infrastructure or solutions that they're looking at the other thing we do is make it very easy to consume our technology whether that's traditional on-premise in a multi-cloud environment uh or as a service and so the best of breed technology can be consumed in any variety of fashion and know that you're getting that consistent secure infrastructure that dell provides well and dell's forgot the probably top supply chain not only in the tech business but probably any business and so you can actually take take your dog food and then and allow other your champagne sorry allow other people to you know share share best practices with your with your customers all right guys thanks so much for coming thank you appreciate it okay keep it right there after this short break we'll be back to drill into the storage domain you're watching a blueprint for trusted infrastructure on the cube the leader in enterprise and emerging tech coverage be right back you

the cyber security landscape has changed dramatically over the past 24 to 36 months rapid cloud migration has created a new layer of security defense sure but that doesn't mean csos can relax in many respects it further complicates or at least changes the ciso's scope of responsibilities in particular the threat surface has expanded and that creates more seams and cisos have to make sure their teams pick up where the hyperscaler clouds leave off application developers have become a critical execution point for cyber assurance shift left is the kind of new buzz phrase for devs but organizations still have to shield right meaning the operational teams must continue to partner with secops to make sure infrastructure is resilient so it's no wonder that in etr's latest survey of nearly 1500 cios and it buyers that business technology executives cite security as their number one priority well ahead of other critical technology initiatives including collaboration software cloud computing and analytics rounding out the top four but budgets are under pressure and csos have to prioritize it's not like they have an open checkbook they have to contend with other key initiatives like those just mentioned to secure the funding and what about zero trust can you go out and buy xero trust or is it a framework a mindset in a series of best practices applied to create a security consciousness throughout the organization can you implement zero trust in other words if a machine or human is not explicitly allowed access then access is denied can you implement that policy without constricting organizational agility the question is what's the most practical way to apply that premise and what role does infrastructure play as the enforcer how does automation play in the equation the fact is that today's approach to cyber resilient type resilience can't be an either or it has to be an and conversation meaning you have to ensure data protection while at the same time advancing the mission of the organization with as little friction as possible and don't even talk to me about the edge that's really going to keep you up at night hello and welcome to the special cube presentation a blueprint for trusted infrastructure made possible by dell technologies in this program we explore the critical role that trusted infrastructure plays in cyber security strategies how organizations should think about the infrastructure side of the cyber security equation and how dell specifically approaches securing infrastructure for your business we'll dig into what it means to transform and evolve toward a modern security infrastructure that's both trusted and agile first up are pete gear and steve kenniston they're both senior cyber security consultants at dell technologies and they're going to talk about the company's philosophy and approach to trusted infrastructure and then we're going to speak to paris arcadi who's a senior consultant for storage at dell technologies to understand where and how storage plays in this trusted infrastructure world and then finally rob emsley who heads product marketing for data protection and cyber security he's going to take a deeper dive with rob into data protection and explain how it has become a critical component of a comprehensive cyber security strategy okay let's get started pete gear steve kenniston welcome to the cube thanks for coming into the marlboro studios today great to be here dave thanks dave good to see you great to see you guys pete start by talking about the security landscape you heard my little rap up front what are you seeing i thought you wrapped it up really well and you touched on all the key points right technology is ubiquitous today it's everywhere it's no longer confined to a monolithic data center it lives at the edge it lives in front of us it lives in our pockets and smartphones along with that is data and as you said organizations are managing sometimes 10 to 20 times the amount of data that they were just five years ago and along with that cyber crime has become a very profitable enterprise in fact it's been more than 10 years since uh the nsa chief actually called cyber crime the biggest transfer of wealth in history that was 10 years ago and we've seen nothing but accelerating cyber crime and really sophistication of how those attacks are perpetrated and so the new security landscape is really more of an evolution we're finally seeing security catch up with all of the technology adoption all the build out the work from home and work from anywhere that we've seen over the last couple of years we're finally seeing organizations and really it goes beyond the i t directors it's a board level discussion today security's become a board level discussion yeah i think that's true as well it's like it used to be the security was okay the secops team you're responsible for security now you've got the developers are involved the business lines are involved it's part of onboarding for most companies you know steve this concept of zero trust it was kind of a buzzword before the pandemic and i feel like i've often said it's now become a mandate but it's it's it's still fuzzy to a lot of people how do you guys think about zero trust what does it mean to you how does it fit yeah i thought again i thought your opening was fantastic in in this whole lead into to what is zero trust it had been a buzzword for a long time and now ever since the federal government came out with their implementation or or desire to drive zero trust a lot more people are taking a lot more seriously because i don't think they've seen the government do this but ultimately let's see ultimately it's just like you said right if if you don't have trust to those particular devices uh applications or data you can't get at it the question is and and you phrase it perfectly can you implement that as well as allow the business to be as agile as it needs to be in order to be competitive because we're seeing with your whole notion around devops and the ability to kind of build make deploy build make deploy right they still need that functionality but it also needs to be trusted it needs to be secure and things can't get away from you yeah so it's interesting we attended every uh reinforce since 2019 and the narrative there is hey everything in this in the cloud is great you know and this narrative around oh security is a big problem is you know doesn't help the industry the fact is that the big hyperscalers they're not strapped for talent but csos are they don't have the the capabilities to really apply all these best practices they're they're playing whack-a-mole so they look to companies like yours to take their r your r d and bake it into security products and solutions so what are the critical aspects of the so-called dell trusted infrastructure that we should be thinking about yeah well dell trusted infrastructure for us is a way for us to describe uh the the work that we do through design development and even delivery of our it system so dell trusted infrastructure includes our storage it includes our servers our networking our data protection our hyper converged everything that infrastructure always has been it's just that today customers consume that infrastructure at the edge as a service in a multi-cloud environment i mean i view the cloud as really a way for organizations to become more agile and to become more flexible and also to control costs i don't think organizations move to the cloud or move to a multi-cloud environment to enhance security so i don't see cloud computing as a panacea for security i see it as another attack surface and another uh aspect in front that organizations and and security organizations and departments have to manage it's part of their infrastructure today whether it's in their data center in a cloud or at the edge i mean i think it's a huge point because a lot of people think oh data's in the cloud i'm good it's like steve we've talked about oh why do i have to back up my data it's in the cloud well you might have to recover it someday so i don't know if you have anything to add to that or any additional thoughts on it no i mean i think i think like what pete was saying when it comes to when it comes to all these new vectors for attack surfaces you know people did choose the cloud in order to be more agile more flexible and all that did was open up to the csos who need to pay attention to now okay where can i possibly be attacked i need to be thinking about is that secure and part of the part of that is dell now also understands and thinks about as we're building solutions is it is it a trusted development life cycle so we have our own trusted development life cycle how many times in the past did you used to hear about vendors saying you got to patch your software because of this we think about what changes to our software and what implementations and what enhancements we deliver can actually cause from a security perspective and make sure we don't give up or or have security become a whole just in order to implement a feature we got to think about those things yeah and as pete alluded to our secure supply chain so all the way through knowing what you're going to get when you actually receive it is going to be secure and not be tampered with becomes vitally important and pete and i were talking earlier when you have tens of thousands of devices that need to be delivered whether it be storage or laptops or pcs or or whatever it is you want to be you want to know that that that those devices are can be trusted okay guys maybe pete you could talk about the how dell thinks about it's its framework and its philosophy of cyber security and then specifically what dell's advantages are relative to the competition yeah definitely dave thank you so we've talked a lot about dell as a technology provider but one thing dell also is is a partner in this larger ecosystem we realize that security whether it's a zero trust paradigm or any other kind of security environment is an ecosystem uh with a lot of different vendors so we look at three areas one is protecting data in systems we know that it starts with and ends with data that helps organizations combat threats across their entire infrastructure and what it means is dell's embedding security features consistently across our portfolios of storage servers networking the second is enhancing cyber resiliency over the last decade a lot of the funding and spending has been in protecting or trying to prevent cyber threats not necessarily in responding to and recovering from threats right we call that resiliency organizations need to build resiliency across their organization so not only can they withstand a threat but they can respond recover and continue with their operations and the third is overcoming security complexity security is hard it's more difficult because of the things we've talked about about distributed data distributed technology and and attack surfaces everywhere and so we're enabling organizations to scale confidently to continue their business but know that all all the i.t decisions that they're making um have these intrinsic security features and are built and delivered in a consistent security so those are kind of the three pillars maybe we could end on what you guys see as the key differentiators that people should know about that that dell brings to the table maybe each of you could take take a shot at that yeah i think first of all from from a holistic portfolio perspective right the uh secure supply chain and the secure development life cycle permeate through everything dell does when building things so we build things with security in mind all the way from as pete mentioned from from creation to delivery we want to make sure you have that that secure device or or asset that permeates everything from servers networking storage data protection through hyper converge through everything that to me is really a key asset because that means you can you understand when you receive something it's a trusted piece of your infrastructure i think the other core component to think about and pete mentioned as dell being a partner for making sure you can deliver these things is that even though those are that's part of our framework these pillars are our framework of how we want to deliver security it's also important to understand that we are partners and that you don't need to rip and replace but as you start to put in new components you can be you can be assured that the components that you're replacing as you're evolving as you're growing as you're moving to the cloud as you're moving to a more on-prem type services or whatever that your environment is secure i think those are two key things got it okay pete bring us home yeah i think one of one of the big advantages of dell is our scope and our scale right we're a large technology vendor that's been around for decades and we develop and sell almost every piece of technology we also know that organizations are might make different decisions and so we have a large services organization with a lot of experienced services people that can help customers along their security journey depending on whatever type of infrastructure or solutions that they're looking at the other thing we do is make it very easy to consume our technology whether that's traditional on-premise in a multi-cloud environment uh or as a service and so the best of breed technology can be consumed in any variety of fashion and know that you're getting that consistent secure infrastructure that dell provides well and dell's forgot the probably top supply chain not only in the tech business but probably any business and so you can actually take take your dog food and then and allow other billionaire champagne sorry allow other people to you know share share best practices with your with your customers all right guys thanks so much for coming thank you appreciate it okay keep it right there after this short break we'll be back to drill into the storage domain you're watching a blueprint for trusted infrastructure on the cube the leader in enterprise and emerging tech coverage be right back concern over cyber attacks is now the norm for organizations of all sizes the impact of these attacks can be operationally crippling expensive and have long-term ramifications organizations have accepted the reality of not if but when from boardrooms to i.t departments and are now moving to increase their cyber security preparedness they know that security transformation is foundational to digital transformation and while no one can do it alone dell technologies can help you fortify with modern security modern security is built on three pillars protect your data and systems by modernizing your security approach with intrinsic features and hardware and processes from a provider with a holistic presence across the entire it ecosystem enhance your cyber resiliency by understanding your current level of resiliency for defending your data and preparing for business continuity and availability in the face of attacks overcome security complexity by simplifying and automating your security operations to enable scale insights and extend resources through service partnerships from advanced capabilities that intelligently scale a holistic presence throughout it and decades as a leading global technology provider we'll stop at nothing to help keep you secure okay we're back digging into trusted infrastructure with paris sarcadi he's a senior consultant for product marketing and storage at dell technologies parasaur welcome to the cube good to see you great to be with you dave yeah coming from hyderabad awesome so i really appreciate you uh coming on the program let's start with talking about your point of view on what cyber security resilience means to to dell generally but storage specifically yeah so for something like storage you know we are talking about the data layer name and if you look at cyber security it's all about securing your data applications and infrastructure it has been a very mature field at the network and application layers and there are a lot of great technologies right from you know enabling zero trust advanced authentications uh identity management systems and so on and and in fact you know with the advent of you know the the use of artificial intelligence and machine learning really these detection tools for cyber securities have really evolved in the network and the application spaces so for storage what it means is how can you bring them to the data layer right how can you bring you know the principles of zero trust to the data layer uh how can you leverage artificial intelligence and machine learning to look at you know access patterns and make intelligent decisions about maybe an indicator of a compromise and identify them ahead of time just like you know how it's happening and other ways of applications and when it comes to cyber resilience it's it's basically a strategy which assumes that a threat is imminent and it's a good assumption with the severity of the frequency of the attacks that are happening and the question is how do we fortify the infrastructure in the switch infrastructure to withstand those attacks and have a plan a response plan where we can recover the data and make sure the business continuity is not affected so that's uh really cyber security and cyber resiliency and storage layer and of course there are technologies like you know network isolation immutability and all these principles need to be applied at the storage level as well let me have a follow up on that if i may the intelligence that you talked about that ai and machine learning is that do you do you build that into the infrastructure or is that sort of a separate software module that that points at various you know infrastructure components how does that work both dave right at the data storage level um we have come with various data characteristics depending on the nature of data we developed a lot of signals to see what could be a good indicator of a compromise um and there are also additional applications like cloud iq is the best example which is like an infrastructure wide health monitoring system for dell infrastructure and now we have elevated that to include cyber security as well so these signals are being gathered at cloud iq level and other applications as well so that we can make those decisions about compromise and we can either cascade that intelligence and alert stream upstream for uh security teams um so that they can take actions in platforms like sign systems xtr systems and so on but when it comes to which layer the intelligence is it has to be at every layer where it makes sense where we have the information to make a decision and being closest to the data we have we are basically monitoring you know the various parallels data access who is accessing um are they crossing across any geo fencing uh is there any mass deletion that is happening or a mass encryption that is happening and we are able to uh detect uh those uh patterns and flag them as indicators of compromise and in allowing automated response manual control and so on for it teams yeah thank you for that explanation so at dell technologies world we were there in may it was one of the first you know live shows that that we did in the spring certainly one of the largest and i interviewed shannon champion and a huge takeaway from the storage side was the degree to which you guys emphasized security uh within the operating systems i mean really i mean powermax more than half i think of the features were security related but also the rest of the portfolio so can you talk about the the security aspects of the dell storage portfolio specifically yeah yeah so when it comes to data security and broadly data availability right in the context of cyber resiliency dell storage this you know these elements have been at the core of our um a core strength for the portfolio and the source of differentiation for the storage portfolio you know with almost decades of collective experience of building highly resilient architectures for mission critical data something like power max system which is the most secure storage platform for high-end enterprises and now with the increased focus on cyber security we are extending those core technologies of high availability and adding modern detection systems modern data isolation techniques to offer a comprehensive solution to the customer so that they don't have to piece together multiple things to ensure data security or data resiliency but a well-designed and well-architected solution by design is delivered to them to ensure cyber protection at the data layer got it um you know we were talking earlier to steve kenniston and pete gear about this notion of dell trusted infrastructure how does storage fit into that as a component of that sort of overall you know theme yeah and you know and let me say this if you could adjust because a lot of people might be skeptical that i can actually have security and at the same time not constrict my organizational agility that's old you know not an ore it's an end how do you actually do that if you could address both of those that would be great definitely so for dell trusted infrastructure cyber resiliency is a key component of that and just as i mentioned you know uh air gap isolation it really started with you know power protect cyber recovery you know that was the solution more than three years ago we launched and that was first in the industry which paved way to you know kind of data isolation being a core element of data management and uh for data infrastructure and since then we have implemented these technologies within different storage platforms as well so that customers have the flexibility depending on their data landscape they can approach they can do the right data isolation architecture right either natively from the storage platform or consolidate things into the backup platform and isolate from there and and the other key thing we focus in trusted infrastructure dell infra dell trusted infrastructure is you know the goal of simplifying security for the customers so one good example here is uh you know being able to respond to these cyber threats or indicators of compromise is one thing but an i.t security team may not be looking at the dashboard of the storage systems constantly right storage administration admins may be looking at it so how can we build this intelligence and provide this upstream platforms so that they have a single pane of glass to understand security landscape across applications across networks firewalls as well as storage infrastructure and in compute infrastructure so that's one of the key ways where how we are helping simplify the um kind of the ability to uh respond ability to detect and respond these threads uh in real time for security teams and you mentioned you know about zero trust and how it's a balance of you know not uh kind of restricting users or put heavy burden on you know multi-factor authentication and so on and this really starts with you know what we're doing is provide all the tools you know when it comes to advanced authentication uh supporting external identity management systems multi-factor authentication encryption all these things are intrinsically built into these platforms now the question is the customers are actually one of the key steps is to identify uh what are the most critical parts of their business or what are the applications uh that the most critical business operations depend on and similarly identify uh mission critical data where part of your response plan where it cannot be compromised where you need to have a way to recover once you do this identification then the level of security can be really determined uh by uh by the security teams by the infrastructure teams and you know another you know intelligence that gives a lot of flexibility uh for for even developers to do this is today we have apis um that so you can not only track these alerts at the data infrastructure level but you can use our apis to take concrete actions like blocking a certain user or increasing the level of authentication based on the threat level that has been perceived at the application layer or at the network layer so there is a lot of flexibility that is built into this by design so that depending on the criticality of the data criticality of the application number of users affected these decisions have to be made from time to time and it's as you mentioned it's it's a balance right and sometimes you know if if an organization had a recent attack you know the level of awareness is very high against cyber attacks so for a time you know these these settings may be a bit difficult to deal with but then it's a decision that has to be made by security teams as well got it so you're surfacing what may be hidden kpis that are being buried inside for instance the storage system through apis upstream into a dashboard so that somebody could you know dig into the storage tunnel extract that data and then somehow you know populate that dashboard you're saying you're automating that that that workflow that's a great example and you may have others but is that the correct understanding absolutely and it's a two-way integration let's say a detector an attack has been detected at a completely different layer right in the application layer or at a firewall we can respond to those as well so it's a two-way integration we can cascade things up as well as respond to threats that have been detected elsewhere um uh through the api that's great all right hey api for power skill is the best example for that uh excellent so thank you appreciate that give us the last word put a bow on this and and bring this segment home please absolutely so a dell storage portfolio um using advanced data isolation um with air gap having machine learning based algorithms to detect uh indicators of compromise and having rigor mechanisms with granular snapshots being able to recover data and restore applications to maintain business continuity is what we deliver to customers uh and these are areas where a lot of innovation is happening a lot of product focus as well as you know if you look at the professional services all the way from engineering to professional services the way we build these systems the way we we configure and architect these systems um cyber security and protection is a key focus uh for all these activities and dell.com securities is where you can learn a lot about these initiatives that's great thank you you know at the recent uh reinforce uh event in in boston we heard a lot uh from aws about you know detent and response and devops and machine learning and some really cool stuff we heard a little bit about ransomware but i'm glad you brought up air gaps because we heard virtually nothing in the keynotes about air gaps that's an example of where you know this the cso has to pick up from where the cloud leaves off but that was in front and so number one and number two we didn't hear a ton about how the cloud is making the life of the cso simpler and that's really my takeaway is is in part anyway your job and companies like dell so paris i really appreciate the insights thank you for coming on thecube thank you very much dave it's always great to be in these uh conversations all right keep it right there we'll be right back with rob emsley to talk about data protection strategies and what's in the dell portfolio you're watching thecube data is the currency of the global economy it has value to your organization and cyber criminals in the age of ransomware attacks companies need secure and resilient it infrastructure to safeguard their data from aggressive cyber attacks [Music] as part of the dell technologies infrastructure portfolio powerstor and powermax combine storage innovation with advanced security that adheres to stringent government regulations and corporate compliance requirements security starts with multi-factor authentication enabling only authorized admins to access your system using assigned roles tamper-proof audit logs track system usage and changes so it admins can identify suspicious activity and act with snapshot policies you can quickly automate the protection and recovery process for your data powermax secure snapshots cannot be deleted by any user prior to the retention time expiration dell technologies also make sure your data at rest stays safe with power store and powermax data encryption protects your flash drive media from unauthorized access if it's removed from the data center while adhering to stringent fips 140-2 security requirements cloud iq brings together predictive analytics anomaly detection and machine learning with proactive policy-based security assessments monitoring and alerting the result intelligent insights that help you maintain the security health status of your storage environment and if a security breach does occur power protect cyber recovery isolates critical data identifies suspicious activity and accelerates data recovery using the automated data copy feature unchangeable data is duplicated in a secure digital vault then an operational air gap isolates the vault from the production and backup environments [Music] architected with security in mind dell emc power store and powermax provides storage innovation so your data is always available and always secure wherever and whenever you need it [Music] welcome back to a blueprint for trusted infrastructure we're here with rob emsley who's the director of product marketing for data protection and cyber security rob good to see a new role yeah good to be back dave good to see you yeah it's been a while since we chatted last and you know one of the changes in in my world is that i've expanded my responsibilities beyond data protection marketing to also focus on uh cyber security marketing specifically for our infrastructure solutions group so certainly that's you know something that really has driven us to you know to come and have this conversation with you today so data protection obviously has become an increasingly important component of the cyber security space i i don't think necessarily of you know traditional backup and recovery as security it's to me it's an adjacency i know some companies have said oh yeah now we're a security company they're kind of chasing the valuation for sure bubble um dell's interesting because you you have you know data protection in the form of backup and recovery and data management but you also have security you know direct security capability so you're sort of bringing those two worlds together and it sounds like your responsibility is to to connect those those dots is that right absolutely yeah i mean i think that uh the reality is is that security is a a multi-layer discipline um i think the the days of thinking that it's one uh or another um technology that you can use or process that you can use to make your organization secure uh are long gone i mean certainly um you actually correct if you think about the backup and recovery space i mean people have been doing that for years you know certainly backup and recovery is all about the recovery it's all about getting yourself back up and running when bad things happen and one of the realities unfortunately today is that one of the worst things that can happen is cyber attacks you know ransomware malware are all things that are top of mind for all organizations today and that's why you see a lot of technology and a lot of innovation going into the backup and recovery space because if you have a copy a good copy of your data then that is really the the first place you go to recover from a cyber attack and that's why it's so important the reality is is that unfortunately the cyber criminals keep on getting smarter i don't know how it happens but one of the things that is happening is that the days of them just going after your production data are no longer the only challenge that you have they go after your your backup data as well so over the last half a decade dell technologies with its backup and recovery portfolio has introduced the concept of isolated cyber recovery vaults and that is really the you know we've had many conversations about that over the years um and that's really a big tenant of what we do in the data protection portfolio so this idea of of cyber security resilience that definition is evolving what does it mean to you yeah i think the the analyst team over at gartner they wrote a very insightful paper called you will be hacked embrace the breach and the whole basis of this analysis is so much money has been spent on prevention is that what's out of balance is the amount of budget that companies have spent on cyber resilience and cyber resilience is based upon the premise that you will be hacked you have to embrace that fact and be ready and prepared to bring yourself back into business you know and that's really where cyber resiliency is very very different than cyber security and prevention you know and i think that balance of get your security disciplines well-funded get your defenses as good as you can get them but make sure that if the inevitable happens and you find yourself compromised that you have a great recovery plan and certainly a great recovery plan is really the basis of any good solid data protection backup and recovery uh philosophy so if i had to do a swot analysis we don't have to do the wot but let's focus on the s um what would you say are dell's strengths in this you know cyber security space as it relates to data protection um one is we've been doing it a long time you know we talk a lot about dell's data protection being proven and modern you know certainly the experience that we've had over literally three decades of providing enterprise scale data protection solutions to our customers has really allowed us to have a lot of insight into what works and what doesn't as i mentioned to you one of the unique differentiators of our solution is the cyber recovery vaulting solution that we introduced a little over five years ago five six years parapatek cyber recovery is something which has become a unique capability for customers to adopt uh on top of their investment in dell technologies data protection you know the the unique elements of our solution already threefold and it's we call them the three eyes it's isolation it's immutability and it's intelligence and the the isolation part is really so important because you need to reduce the attack surface of your good known copies of data you know you need to put it in a location that the bad actors can't get to it and that really is the the the the essence of a cyber recovery vault interestingly enough you're starting to see the market throw out that word um you know from many other places but really it comes down to having a real discipline that you don't allow the security of your cyber recovery vault to be compromised insofar as allowing it to be controlled from outside of the vault you know allowing it to be controlled by your backup application our cyber recovery vaulting technology is independent of the backup infrastructure it uses it but it controls its own security and that is so so important it's like having a vault that the only way to open it is from the inside you know and think about that if you think about you know volts in banks or volts in your home normally you have a keypad on the outside think of our cyber recovery vault as having its security controlled from inside of the vault so nobody can get in nothing can get in unless it's already in and if it's already in then it's trusted exactly yeah exactly yeah so isolation is the key and then you mentioned immutability is the second piece yeah so immutability is is also something which has been around for a long time people talk about uh backup immunoability or immutable backup copies so immutability is just the the the additional um technology that allows the data that's inside of the vault to be unchangeable you know but again that immutability you know your mileage varies you know when you look across the uh the different offers that are out there in the market especially in the backup industry you make a very valid point earlier that the backup vendors in the market seems to be security washing their marketing messages i mean everybody is leaning into the ever-present danger of cyber security not a bad thing but the reality is is that you have to have the technology to back it up you know quite literally yeah no pun intended and then actually pun intended now what about the intelligence piece of it uh that's that's ai ml where does that fit for sure so the intelligence piece is delivered by um a solution called cybersense and cybersense for us is what really gives you the confidence that what you have in your cyber recovery vault is a good clean copy of data so it's looking at the backup copies that get driven into the cyber vault and it's looking for anomalies so it's not looking for signatures of malware you know that's what your antivirus software does that's what your endpoint protection software does that's on the prevention side of the equation but what we're looking for is we're looking to ensure that the data that you need when all hell breaks loose is good and that when you get a request to restore and recover your business you go right let's go and do it and you don't have any concern that what you have in the vault has been compromised so cyber sense is really a unique analytic solution in the market based upon the fact that it isn't looking at cursory indicators of of um of of of malware infection or or ransomware introduction it's doing full content analytics you know looking at you know has the data um in any way changed has it suddenly become encrypted has it suddenly become different to how it was in the previous scan so that anomaly detection is very very different it's looking for um you know like different characteristics that really are an indicator that something is going on and of course if it sees it you immediately get flagged but the good news is is that you always have in the vault the previous copy of good known data which now becomes your restore point so we're talking to rob emsley about how data protection fits into what dell calls dti dell trusted infrastructure and and i want to come back rob to this notion of and not or because i think a lot of people are skeptical like how can i have great security and not introduce friction into my organization is that an automation play how does dell tackle that problem i mean i think a lot of it is across our infrastructure is is security has to be built in i mean intrinsic security within our servers within our storage devices uh within our elements of our backup infrastructure i mean security multi-factor authentication you know elements that make the overall infrastructure secure you know we have capabilities that you know allow us to identify whether or not configurations have changed you know we'll probably be talking about that a little bit more to you later in the segment but the the essence is is um security is not a bolt-on it has to be part of the overall infrastructure and that's so true um certainly in the data protection space give us the the bottom line on on how you see dell's key differentiators maybe you could talk about dell of course always talks about its portfolio but but why should customers you know lead in to dell in in this whole cyber resilience space um you know staying on the data protection space as i mentioned the the the work we've been doing um to introduce this cyber resiliency solution for data protection is in our opinion as good as it gets you know the you know you've spoken to a number of our of our best customers whether it be bob bender from founders federal or more recently at delton allergies world you spoke to tony bryson from the town of gilbert and these are customers that we've had for many years that have implemented cyber recovery vaults and at the end of the day they can now sleep at night you know that's really the the peace of mind that they have is that the insurance that a data protection from dell cyber recovery vault a parapatex cyber recovery solution gives them you know really allows them to you know just have the assurance that they don't have to pay a ransom if they have a an insider threat issue and you know all the way down to data deletion is they know that what's in the cyber recovery vault is good and ready for them to recover from great well rob congratulations on the new scope of responsibility i like how you know your organization is expanding as the threat surface is expanding as we said data protection becoming an adjacency to security not security in and of itself a key component of a comprehensive security strategy rob emsley thank you for coming back in the cube good to see you again you too dave thanks all right in a moment i'll be back to wrap up a blueprint for trusted infrastructure you're watching the cube every day it seems there's a new headline about the devastating financial impacts or trust that's lost due to ransomware or other sophisticated cyber attacks but with our help dell technologies customers are taking action by becoming more cyber resilient and deterring attacks so they can greet students daily with a smile they're ensuring that a range of essential government services remain available 24 7 to citizens wherever they're needed from swiftly dispatching public safety personnel or sending an inspector to sign off on a homeowner's dream to protecting restoring and sustaining our precious natural resources for future generations with ever-changing cyber attacks targeting organizations in every industry our cyber resiliency solutions are right on the money providing the security and controls you need we help customers protect and isolate critical data from ransomware and other cyber threats delivering the highest data integrity to keep your doors open and ensuring that hospitals and healthcare providers have access to the data they need so patients get life-saving treatment without fail if a cyber incident does occur our intelligence analytics and responsive team are in a class by themselves helping you reliably recover your data and applications so you can quickly get your organization back up and running with dell technologies behind you you can stay ahead of cybercrime safeguarding your business and your customers vital information learn more about how dell technology's cyber resiliency solutions can provide true peace of mind for you the adversary is highly capable motivated and well equipped and is not standing still your job is to partner with technology vendors and increase the cost of the bad guys getting to your data so that their roi is reduced and they go elsewhere the growing issues around cyber security will continue to drive forward thinking in cyber resilience we heard today that it is actually possible to achieve infrastructure security while at the same time minimizing friction to enable organizations to move quickly in their digital transformations a xero trust framework must include vendor r d and innovation that builds security designs it into infrastructure products and services from the start not as a bolt-on but as a fundamental ingredient of the cloud hybrid cloud private cloud to edge operational model the bottom line is if you can't trust your infrastructure your security posture is weakened remember this program is available on demand in its entirety at thecube.net and the individual interviews are also available and you can go to dell security solutions landing page for for more information go to dell.com security solutions that's dell.com security solutions this is dave vellante thecube thanks for watching a blueprint for trusted infrastructure made possible by dell we'll see you next time

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube at ETR. This is "Breaking Analysis" with Dave Vellante. >> The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409A valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider, is hard to find. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR. In this "Breaking Analysis", we unlock some of the secrets that non-public, emerging tech companies may or may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years, it's called the Emerging Technologies Survey, and it is packed with sentiment data and performance data based on surveys of more than a thousand CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Erik Bradley of ETR to help explain the survey and the data that we're going to cover today. Erik, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome. Good to see you again. >> Great to see you too, Dave, and I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. >> Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? >> Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we track that are not yet public and move 'em over to the ETS. And there isn't a lot of information out there. If you're not in Silicon (indistinct), you're not going to get this stuff. So PitchBook and Tech Crunch are two out there that gives some data on these guys. But what we really wanted to do was go out to our community. We have 6,000, ITDMs in our community. We wanted to ask them, "Are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them," and really just kind of figure out what we can do. So this particular survey, as you can see, 1000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing 'em, then we can also figure out your sales conversion or churn. So this is interesting, not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up. But it's also really interesting for the tech vendors themselves to see how they're performing. >> And you can see 2/3 of the respondents are director level of above. You got 28% is C-suite. There is of course a North America bias, 70, 75% is North America. But these smaller companies, you know, that's when they start doing business. So, okay. We're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies. And then we're going to do the same for the smaller private companies, the ones that don't have as much mindshare. And then I'm going to put those two groups together and we're going to look at two dimensions, actually three dimensions, which companies are being evaluated the most. Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is a silent killer of companies. And then finally, we're going to look at the sentiment and mindshare for two key areas that we like to cover often here on "Breaking Analysis", security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies like TensorFlow or Kubernetes, for example. And we'll call that out during this discussion. The reason this is done is for context, because everyone is using open source. It is the heart of innovation and many business models are super glued to an open source offering, like take MariaDB, for example. There's the foundation and then there's with the open source code and then there, of course, the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mindshare. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mindshare on the horizontal axis and note the open source tool, see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Erik, please explain what we're looking at here, how it's derived and what the data tells us. >> Certainly, so there is a lot here, so we're going to break it down first of all by explaining just what mindshare and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one so that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub sectors. Mindshare is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they're clearly be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, is open source. This de facto standard for all container orchestration, and it should be that far up into the right, because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis, 'cause it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust . They're repeating as top net sentiment performers here. And then also the design vendors. People don't spend a lot of time on 'em, but Miro and Figma. This is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming after them. But Databricks, we all know probably would've been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. >> And I'll just add, so you see automation anywhere on there, the big UiPath competitor company that was not able to get to the public markets. They've been trying. Snyk, Peter McKay's company, they've raised a bunch of money, big security player. They're doing some really interesting things in developer security, helping developers secure the data flow, H2O.ai, Dataiku AI company. We saw them at the Snowflake Summit. Redis Labs, Netskope and security. So a lot of names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mindshare, Erik. Take us through this one. >> On the previous slide too real quickly, I wanted to pull that security scorecard and we'll get back into it. But this is a newcomer, that I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mindshare, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio is in the smaller breakout. But still when you talk about net sentiment, it's about the leader, it's the highest one there is. So really interesting to point out. Then we see other names like Collibra in the data side really performing well. And again, as always security, very well represented here. We have Aqua, Wiz, Armis, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have AnyScale, which is doing a second best in this and the best name in the survey Hugging Face, which is a machine learning AI tool. Also doing really well on a net sentiment, but they're not as far along on that access of mindshare just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. >> Hugging Face sounds like something you do with your two year old. Like you said, you see high performers, AnyScale do machine learning and you mentioned them. They came out of Berkeley. Collibra Governance, InfluxData is on there. InfluxDB's a time series database. And yeah, of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which Sisense does vis, Yellowbrick Data is a NPP database. How should we interpret the red dots, Erik? I mean, is it necessarily a bad thing? Could it be misinterpreted? What's your take on that? >> Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, "Hey, this is where the lump of average comes in. This is where everyone normally stands." So you either have to be an outperformer or an underperformer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. And the VCs and the PEs out there that are backing these companies, they're the ones who mostly are interested in this data. >> Yeah. Oh, that's great explanation. Thank you for that. No, nice benchmarking there and yeah, you don't want to be in the red. All right, let's get into the next segment here. Here going to look at evaluation rates, adoption and the all important churn. First new evaluations. Let's bring up that slide. And Erik, take us through this. >> So essentially I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of 'em and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see, awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates. H2O, we mentioned. SecurityScorecard jumped up again. Chargebee, Snyk, Salt Security, Armis. A lot of security names are up here, Aqua, Netskope, which God has been around forever. I still can't believe it's in an Emerging Technology Survey But so many of these names fall in data and security again, which is why we decided to pick those out Dave. And on the lower side, Vena, Acton, those unfortunately took the dubious award of the lowest evaluations in our survey, but I prefer to focus on the positive. So SecurityScorecard, again, real standout in this one, they're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of a real interest right now amongst our ITDM community. >> Yeah, I mean, I think those, and then Arctic Wolf is up there too. They're doing managed services. You had mentioned Netskope. Yeah, okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and are above that standard deviation in the green. Take us through this, Erik. >> Sure, yet again, what we're looking at is, okay, we went from awareness, we went to evaluation. Now it's about utilization, which means a survey respondent's going to state "Yes, we evaluated and we plan to utilize it" or "It's already in our enterprise and we're actually allocating further resources to it." Not surprising, again, a lot of open source, the reason why, it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non open source, we're going to see Databricks, Automation Anywhere, Rubrik all have the highest mindshare. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. These are the names that have the highest mindshare. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software and, again, they're going after an Autodesk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again is something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Collibra again, we see Hugging Face again. And these are names that are obviously in the data governance, ML, AI side. So we're seeing a ton of data, a ton of security and Rubrik was interesting in this one, too, high utilization and high mindshare. We know how pervasive they are in the enterprise already. >> Erik, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik. Cohesity's not on there. They're sort of the big one. We're going to talk about them in a moment. Puppet is interesting to me because you remember the early days of that sort of space, you had Puppet and Chef and then you had Ansible. Red Hat bought Ansible and then Ansible really took off. So it's interesting to see Puppet on there as well. Okay. So now let's look at the churn because this one is where you don't want to be. It's, of course, all red 'cause churn is bad. Take us through this, Erik. >> Yeah, definitely don't want to be here and I don't love to dwell on the negative. So we won't spend as much time. But to your point, there's one thing I want to point out that think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can look at this data and say, "Hm." Same thing with Puppet. You noticed that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving Puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Tanium. It's kind of jumbled in there. It's hard to see in the middle, but Tanium, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it, like it. It really kind of spreads into not only vulnerability management, but also that endpoint detection and response side. So I was surprised by that one, mostly to see Tanium in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. >> So you're saying if you're in both... Alex, bring that back up if you would. So if you're in both like MariaDB is for example, I think, yeah, they're in both. They're both green in the previous one and red here, that's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonen. So this could be a go to market issue, right? I mean, 'cause Cohesity has got a great product and they got really happy customers. So they're just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonen, I guarantee, is going to have that company cranking. I mean they had been doing very well on the surveys and had fallen off of a bit. The other interesting things wondering the previous survey I saw Cvent, which is an event platform. My only reason I pay attention to that is 'cause we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hopin on here. Hopin raised a billion dollars during the pandemic. And we were like, "Wow, that's going to blow up." And so you see Hopin on the churn and you didn't see 'em in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't. You know, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data. Where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but also gives an indicator of how much money the firm has raised, which is the size of that bubble. And to tell us if a company is punching above its weight and efficiently using its venture capital. Erik, take us through this slide. Explain the dots, the size of the dots. Set this up please. >> Yeah. So again, the axis is still the same, net sentiment and mindshare, but what we've done this time is we've taken publicly available information on how much capital company is raised and that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying relative to the amount of money they've raised, how are they doing in our data? So when you see a Netskope, which has been around forever, raised a lot of money, that's why you're going to see them more leading towards red, 'cause it's just been around forever and kind of would expect it. Versus a name like SecurityScorecard, which is only raised a little bit of money and it's actually performing just as well, if not better than a name, like a Netskope. OneTrust doing absolutely incredible right now. BeyondTrust. We've seen the issues with Okta, right. So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about right? So raised a ton of money. It's doing well on net sentiment, but the mindshare isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. And hasn't raised as much money, but is really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away. But as we'll get to later in the program, Dave, I'm not sure in this current market environment, if people are as willing to do POCs and switch away from their security provider, right. There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight cooling in overall evaluations on the security side versus historical levels a year ago. >> Now let's stay on here for a second. So a couple things I want to point out. So it's interesting. Now Snyk has raised over, I think $800 million but you can see them, they're high on the vertical and the horizontal, but now compare that to Lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. It's a Mike Speiser company. He was the founding investor in Snowflake. So people watch that very closely, but that's an example of where they're not punching above their weight. They recently had a layoff and they got to fine tune things, but I'm still confident they they're going to do well. 'Cause they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netskope. Yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is where Lacework is, right. They've got some work to do to really take advantage of the money that they raised last November and prior to that. >> Yeah, if you're seeing that more neutral color, like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be. It's when you're seeing that red on a Lacework where we all know, wow, you raised a ton of money and your mindshare isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great standouts, like Salt Security and SecurityScorecard and Abnormal. You know they haven't raised that much money yet, but their net sentiment's higher and their mindshare's doing well. So those basically in a nutshell, if you're a PE or a VC and you see a small green circle, then you're doing well, then it means you made a good investment. >> Some of these guys, I don't know, but you see these small green circles. Those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and ML AI. First, we're going to look at the database sector. So Alex, thank you for bringing that up. Alright, take us through this, Erik. Actually, let me just say Postgres SQL. I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres the open source database, which is a transaction system and kind of an open source Oracle. You see MariaDB is a database, but open source database. But the companies they've raised over $200 million and they filed an S-4. So Erik looks like this might be a little bit of mashup of companies and open source products. Help us understand this. >> Yeah, it's tough when you start dealing with the open source side and I'll be honest with you, there is a little bit of a mashup here. There are certain names here that are a hundred percent for profit companies. And then there are others that are obviously open source based like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're a hundred percent accurate on this slide. I think one of the things here that's important to note though, is just how important open source is to data. If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side actually really, really high. When you think about it's the third overall net sentiment for a niche database play. It's not as big on the mindshare 'cause it's use cases aren't as often, but third biggest play on net sentiment. I found really interesting on this slide. >> And again, so MariaDB, as I said, they filed an S-4 I think $50 million in revenue, that might even be ARR. So they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed the day that Oracle bought Sun in which they got MySQL and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL HeatWave, which was kind of the Oracle version of MySQL. So there's some interesting battles going on there. If you think about the LAMP stack, the M in the LAMP stack was MySQL. And so now it's all MariaDB replacing that MySQL for a large part. And then you see again, the red, you know, you got to have some concerns about there. Aerospike's been around for a long time. SingleStore changed their name a couple years ago, last year. Yellowbrick Data, Fire Bolt was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone. So they got some work to do. >> And Dave, real quick for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. So we can cross over this with that because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB with a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. >> All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Erik. >> Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went to blank on that. So gimme one second to catch up. >> So I could set it up while you're doing that. You got Grafana up and to the right. I mean, this is huge right. >> Got it thank you. I lost my screen there for a second. Yep. Again, open source name Grafana, absolutely up and to the right. But as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing than I want to call out are names like ThoughtSpot. It's been around forever. Their mindshare of course is second best here but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves. That's very high net sentiment for that company. It's, you know, what, third, fourth position overall in this entire area, Another name like Fivetran, Matillion is doing well. Fivetran, even though it's got a high net sentiment, again, it's raised so much money that we would've expected a little bit more at this point. I know you know this space extremely well, but basically what we're looking at here and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. InfluxData, however, second highest net sentiment. And it's really pretty early on in this stage and the feedback we're getting on this name is the use cases are great, the efficacy's great. And I think it's one to watch out for. >> InfluxData, time series database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green. Those are the ones we were saying before, look for those guys. They might be some of the interesting companies out there and then observe Jeremy Burton's company. They do observability on top of Snowflake, not green, but kind of in that gray. So that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay. So I can spend all day on this stuff, Erik, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart it's similar in its dimensions, of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot. We wanted to cover that here, Erik, explain this please. Why TensorFlow is highlighted and walk us through this chart. >> Yeah, it's funny yet again, right? Another open source name, TensorFlow being up there. And I just want to explain, we do break out machine learning, AI is its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is Databricks. We started to cover Databricks in machine learning, AI. That company has grown into much, much more than that. So I do want to state to you Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the MA/AI into other sectors. So we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down into Fortune 500 and Fortune 1000, both Databricks and Tensorflow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down, the data by vertical, these two names still are the outstanding players. >> I just also want to call it H2O.ai. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more. Anaconda, another one. Dataiku consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there. The Cube guy, Cube alum, Chris Lynch stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money raising deal. So that's pissed a lot of people off. And so they're now going through some kind of uncomfortable things, which is unfortunate because DataRobot, I noticed, we haven't covered them that much in "Breaking Analysis", but I've noticed them oftentimes, Erik, in the surveys doing really well. So you would think that company has a lot of potential. But yeah, it's an important space that we're going to continue to watch. Let me ask you Erik, can you contextualize this from a time series standpoint? I mean, how is this changed over time? >> Yeah, again, not show here, but in the data. I'm sorry, go ahead. >> No, I'm sorry. What I meant, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers 'cause they're more risky. What have you seen? >> Yeah, and it was interesting before we went live, you and I were having this conversation about "Is the downturn stopping people from evaluating these private companies or not," right. In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners? The people with the budget, the people with the decision making. And so what I did is, we have historical data as you know, I went back to the Emerging Technology Survey we did in November of 21, right at the crest right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so, we're seeing less evaluations than we were in November 21. So I broke it down. On cloud security, net sentiment went from 21% to 16% from November '21. That's a pretty big drop. And again, that sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of the name. Again in database, we saw it drop a little bit from 19% to 13%. However, in analytics we actually saw it stay steady. So it's pretty interesting that yes, cloud security and security in general is always going to be important. But right now we're seeing less overall net sentiment in that space. But within analytics, we're seeing steady with growing mindshare. And also to your point earlier in machine learning, AI, we're seeing steady net sentiment and mindshare has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady, actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. >> You know it's interesting, we were on a round table, Erik does these round tables with CISOs and CIOs, and I remember one time you had asked the question, "How do you think about some of these emerging tech companies?" And one of the executives said, "I always include somebody in the bottom left of the Gartner Magic Quadrant in my RFPs. I think he said, "That's how I found," I don't know, it was Zscaler or something like that years before anybody ever knew of them "Because they're going to help me get to the next level." So it's interesting to see Erik in these sectors, how they're holding up in many cases. >> Yeah. It's a very important part for the actual IT practitioners themselves. There's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. Like everyone's a software company. Now everyone's a tech company, no matter what you're doing. So these guys have to stay in on top of it. And that's what this ETS can do. You can go in here and look and say, "All right, I'm going to evaluate their technology," and it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be I'm using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. >> Erik, we got to leave it there. I could spend all day. I'm going to definitely dig into this on my own time. Thank you for introducing this, really appreciate your time today. >> I always enjoy it, Dave and I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer. And, you know, I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. >> You got it. I also want to thank the team at ETR, not only Erik, but Darren Bramen who's a data scientist, really helped prepare this data, the entire team over at ETR. I cannot tell you how much additional data there is. We are just scratching the surface in this "Breaking Analysis". So great job guys. I want to thank Alex Myerson. Who's on production and he manages the podcast. Ken Shifman as well, who's just coming back from VMware Explore. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some great editing for us. Thank you. All of you guys. Remember these episodes, they're all available as podcast, wherever you listen. All you got to do is just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch david.vellante@siliconangle.com. You can DM me at dvellante or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Erik Bradley and The Cube Insights powered by ETR. Thanks for watching. Be well. And we'll see you next time on "Breaking Analysis". (upbeat music)

(upbeat music) >> The cybersecurity landscape has changed dramatically over the past 24 to 36 months. Rapid cloud migration has created a new layer of security defense, sure, but that doesn't mean CISOs can relax. In many respects, it further complicates, or at least changes, the CISO's scope of responsibilities. In particular, the threat surface has expanded. And that creates more seams, and CISOs have to make sure their teams pick up where the hyperscaler clouds leave off. Application developers have become a critical execution point for cyber assurance. "Shift left" is the kind of new buzz phrase for devs, but organizations still have to "shield right," meaning the operational teams must continue to partner with SecOps to make sure infrastructure is resilient. So it's no wonder that in ETR's latest survey of nearly 1500 CIOs and IT buyers, that business technology executives cite security as their number one priority, well ahead of other critical technology initiatives including collaboration software, cloud computing, and analytics rounding out the top four. But budgets are under pressure and CISOs have to prioritize. It's not like they have an open checkbook. They have to contend with other key initiatives like those just mentioned, to secure the funding. And what about zero trust? Can you go out and buy zero trust or is it a framework, a mindset in a series of best practices applied to create a security consciousness throughout the organization? Can you implement zero trust? In other words, if a machine or human is not explicitly allowed access, then access is denied. Can you implement that policy without constricting organizational agility? The question is, what's the most practical way to apply that premise? And what role does infrastructure play as the enforcer? How does automation play in the equation? The fact is, that today's approach to cyber resilience can't be an "either/or," it has to be an "and" conversation. Meaning, you have to ensure data protection while at the same time advancing the mission of the organization with as little friction as possible. And don't even talk to me about the edge. That's really going to keep you up at night. Hello and welcome to this special CUBE presentation, "A Blueprint for Trusted Infrastructure," made possible by Dell Technologies. In this program, we explore the critical role that trusted infrastructure plays in cybersecurity strategies, how organizations should think about the infrastructure side of the cybersecurity equation, and how Dell specifically approaches securing infrastructure for your business. We'll dig into what it means to transform and evolve toward a modern security infrastructure that's both trusted and agile. First up are Pete Gerr and Steve Kenniston, they're both senior cyber security consultants at Dell Technologies. And they're going to talk about the company's philosophy and approach to trusted infrastructure. And then we're going to speak to Parasar Kodati, who's a senior consultant for storage at Dell Technologies to understand where and how storage plays in this trusted infrastructure world. And then finally, Rob Emsley who heads product marketing for data protection and cyber security. We're going to going to take a deeper dive with Rob into data protection and explain how it has become a critical component of a comprehensive cyber security strategy. Okay, let's get started. Pete Gerr, Steve Kenniston, welcome to theCUBE. Thanks for coming into the Marlborough studios today. >> Great to be here, Dave. Thanks. >> Thanks, Dave. Good to see you. >> Great to see you guys. Pete, start by talking about the security landscape. You heard my little wrap up front. What are you seeing? >> I thought you wrapped it up really well. And you touched on all the key points, right? Technology is ubiquitous today. It's everywhere. It's no longer confined to a monolithic data center. It lives at the edge. It lives in front of us. It lives in our pockets and smartphones. Along with that is data. And as you said, organizations are managing sometimes 10 to 20 times the amount of data that they were just five years ago. And along with that, cyber crime has become a very profitable enterprise. In fact, it's been more than 10 years since the NSA chief actually called cyber crime the biggest transfer of wealth in history. That was 10 years ago. And we've seen nothing but accelerating cyber crime and really sophistication of how those attacks are perpetrated. And so the new security landscape is really more of an evolution. We're finally seeing security catch up with all of the technology adoption, all the build out, the work from home and work from anywhere that we've seen over the last couple of years. We're finally seeing organizations, and really it goes beyond the IT directors, it's a board level discussion today. Security's become a board level discussion. >> Yeah, I think that's true as well. It's like it used to be that security was, "Okay, the SecOps team. You're responsible for security." Now you've got, the developers are involved, the business lines are involved, it's part of onboarding for most companies. You know, Steve, this concept of zero trust. It was kind of a buzzword before the pandemic. And I feel like I've often said it's now become a mandate. But it's still fuzzy to a lot of people. How do you guys think about zero trust? What does it mean to you? How does it fit? >> Yeah. Again, I thought your opening was fantastic. And this whole lead in to, what is zero trust? It had been a buzzword for a long time. And now, ever since the federal government came out with their implementation or desire to drive zero trust, a lot more people are taking it a lot more seriously, 'cause I don't think they've seen the government do this. But ultimately, it's just like you said, right? If you don't have trust to those particular devices, applications, or data, you can't get at it. The question is, and you phrase it perfectly, can you implement that as well as allow the business to be as agile as it needs to be in order to be competitive? 'Cause we're seeing, with your whole notion around DevOps and the ability to kind of build, make, deploy, build, make, deploy, right? They still need that functionality but it also needs to be trusted. It needs to be secure and things can't get away from you. >> Yeah. So it's interesting. I've attended every Reinforce since 2019, and the narrative there is, "Hey, everything in the cloud is great. And this narrative around, 'Oh, security is a big problem.' doesn't help the industry." The fact is that the big hyperscalers, they're not strapped for talent, but CISOs are. They don't have the capabilities to really apply all these best practices. They're playing Whac-A-Mole. So they look to companies like yours, to take your R&D and bake it into security products and solutions. So what are the critical aspects of the so-called Dell Trusted Infrastructure that we should be thinking about? >> Yeah, well, Dell Trusted Infrastructure, for us, is a way for us to describe the the work that we do through design, development, and even delivery of our IT system. So Dell Trusted Infrastructure includes our storage, it includes our servers, our networking, our data protection, our hyper-converged, everything that infrastructure always has been. It's just that today customers consume that infrastructure at the edge, as a service, in a multi-cloud environment. I mean, I view the cloud as really a way for organizations to become more agile and to become more flexible, and also to control costs. I don't think organizations move to the cloud, or move to a multi-cloud environment, to enhance security. So I don't see cloud computing as a panacea for security, I see it as another attack surface. And another aspect in front that organizations and security organizations and departments have to manage. It's part of their infrastructure today, whether it's in their data center, in a cloud, or at the edge. >> I mean, I think that's a huge point. Because a lot of people think, "Oh, my data's in the cloud. I'm good." It's like Steve, we've talked about, "Oh, why do I have to back up my data? It's in the cloud?" Well, you might have to recover it someday. So I don't know if you have anything to add to that or any additional thoughts on it? >> No, I mean, I think like what Pete was saying, when it comes to all these new vectors for attack surfaces, you know, people did choose the cloud in order to be more agile, more flexible. And all that did was open up to the CISOs who need to pay attention to now, okay, "Where can I possibly be attacked? I need to be thinking about is that secure?" And part of that is Dell now also understands and thinks about, as we're building solutions, is it a trusted development life cycle? So we have our own trusted development life cycle. How many times in the past did you used to hear about vendors saying you got to patch your software because of this? We think about what changes to our software and what implementations and what enhancements we deliver can actually cause from a security perspective, and make sure we don't give up or have security become a hole just in order to implement a feature. We got to think about those things. And as Pete alluded to, our secure supply chain. So all the way through, knowing what you're going to get when you actually receive it is going to be secure and not be tampered with, becomes vitally important. And then Pete and I were talking earlier, when you have tens of thousands of devices that need to be delivered, whether it be storage or laptops or PCs, or whatever it is, you want to be know that those devices can be trusted. >> Okay, guys, maybe Pete, you could talk about how Dell thinks about its framework and its philosophy of cyber security, and then specifically what Dell's advantages are relative to the competition. >> Yeah, definitely, Dave. Thank you. So we've talked a lot about Dell as a technology provider. But one thing Dell also is is a partner in this larger ecosystem. We realize that security, whether it's a zero trust paradigm or any other kind of security environment, is an ecosystem with a lot of different vendors. So we look at three areas. One is protecting data in systems. We know that it starts with and ends with data. That helps organizations combat threats across their entire infrastructure. And what it means is Dell's embedding security features consistently across our portfolios of storage, servers, networking. The second is enhancing cyber resiliency. Over the last decade, a lot of the funding and spending has been in protecting or trying to prevent cyber threats, not necessarily in responding to and recovering from threats. We call that resiliency. Organizations need to build resiliency across their organization, so not only can they withstand a threat, but they can respond, recover, and continue with their operations. And the third is overcoming security complexity. Security is hard. It's more difficult because of the things we've talked about, about distributed data, distributed technology, and attack surfaces everywhere. And so we're enabling organizations to scale confidently, to continue their business, but know that all the IT decisions that they're making have these intrinsic security features and are built and delivered in a consistent, secure way. >> So those are kind of the three pillars. Maybe we could end on what you guys see as the key differentiators that people should know about that Dell brings to the table. Maybe each of you could take a shot at that. >> Yeah, I think, first of all, from a holistic portfolio perspective, right? The secure supply chain and the secure development life cycle permeate through everything Dell does when building things. So we build things with security in mind, all the way from, as Pete mentioned, from creation to delivery, we want to make sure you have that secure device or asset. That permeates everything from servers, networking, storage, data protection, through hyperconverged, through everything. That to me is really a key asset. Because that means you understand when you receive something it's a trusted piece of your infrastructure. I think the other core component to think about, and Pete mentioned, as Dell being a partner for making sure you can deliver these things, is that even though that's part of our framework, these pillars are our framework of how we want to deliver security, it's also important to understand that we are partners and that you don't need to rip and replace. But as you start to put in new components, you can be assured that the components that you're replacing as you're evolving, as you're growing, as you're moving to the cloud, as you're moving to more on-prem type services or whatever, that your environment is secure. I think those are two key things. >> Got it. Okay. Pete, bring us home. >> Yeah, I think one of the big advantages of Dell is our scope and our scale, right? We're a large technology vendor that's been around for decades, and we develop and sell almost every piece of technology. We also know that organizations might make different decisions. And so we have a large services organization with a lot of experienced services people that can help customers along their security journey, depending on whatever type of infrastructure or solutions that they're looking at. The other thing we do is make it very easy to consume our technology, whether that's traditional on premise, in a multi-cloud environment, or as a service. And so the best-of-breed technology can be consumed in any variety of fashion, and know that you're getting that consistent, secure infrastructure that Dell provides. >> Well, and Dell's got probably the top supply chain, not only in the tech business, but probably any business. And so you can actually take your dog food, or your champagne, sorry, (laughter) allow other people to share best practices with your customers. All right, guys, thanks so much for coming up. I appreciate it. >> Great. Thank you. >> Thanks, Dave. >> Okay, keep it right there. After this short break, we'll be back to drill into the storage domain. You're watching "A Blueprint for Trusted Infrastructure" on theCUBE, the leader in enterprise and emerging tech coverage. Be right back. (upbeat music)

(bright upbeat music) >> Hello, everyone, and welcome to this #CUBEConversation here in Palo Alto, California. I'm John Furrier, host of "theCUBE." We have the co-founder and CTO of Armis here, Nadir Izrael. Thanks for coming on. Appreciate it. Armis is hot company, RSA, we just happened. Last week, a lot of action going on. Thanks for coming on. >> Thank you for having me. Sure. >> I love CTOs and co-founders. One, you have the entrepreneurial DNA, also technical in a space with cyber security, that is the hottest most important area. It's always been important, but now more than ever, as the service areas are everywhere, tons of attacks, global threats. You got national security at every level, and you got personal liberties for privacy, and other things going on for average citizens. So, important topic. Talk about Armis? Why did you guys start this company? What was the motivation? Give a quick commercial what you guys do, and then we'll get into some of the questions around, who you guys are targeting. >> Sure, so yeah, I couldn't agree more about the importance of cybersecurity, especially I think in these days. And given some of the geopolitical changes happening right now, more than ever, I would say that if we go back 6.5 years or so, when Armis was founded, we at the time talked to dozens of different CIOs, CSOs, it managers. And every single one of them told us the same thing. And this was at least to me surprising at the time. We have no idea what we have. We have no idea what the assets that are connected to our network, or our environment are. At the time, when we started Armis, we thought this was simply, let's call it the other devices. IOT, OT, all kinds of different buzzwords that were kind of flying around at the time, and really that's, what we should focus on. But with time, what we understood, it's actually a problem of scale. Organizations are growing massively. The diversity of different assets they have to deal with is incredible. And if 6.5 or 7 years ago, it was all about just growth of actual physical devices, these days it's virtual, it's containerized, it's cloud-based. It's actually quite insane. And organizations find themselves really quickly dealing with billions of assets within their environment, but no real way to see, account for them, and be able to manage them. That's what Armis is here to solve. It's here to bring back visibility and order into the mix. It's here to bring a complete map of everything within the organization, and the ability to manage different security processes on top of that. And it couldn't have come, I think at a better time for organizations, because the ability to manage these days, the attack surface of an organization, understand where are different weak spots, what way to invest in? They start and end with a complete asset map, and that's really what we're here to solve. >> As I look at your story and understand what you guys are doing, certainly, a lot of great momentum at RSA. But also digging under the hood, you guys really crack the code with on the scale side as well. And also it's lockstep with the environment. If you look at the trends that we've been covering on "theCUBE," system on chip, you're seeing a lot of Silicon action going on, on all the hyperscalers. You're starting to see, again, you mentioned IOT devices and OT, IP enabled processors. I mean, that's basically you can run multi-threaded applications on a light bulb, basically. So, you have these new things going on that are just popping in into the environment. Just people are hanging them on the network. So, anything on the network is risk and that's happening massively, so I see that. But also you guys have this contextualization capability, scope the problem statement for us? How hard is it to do this? Because you got tons of challenges. What's the scale of the problem that you guys have been solving? 'Cause it's not easy. I mean, it's not network management, not just doing auto discovery, there's a lot of secret sauce there, scope the problem? >> Okay, so first of all, just to get a measure of how difficult this is, organizations have been trying to solve this for the better part of the last two decades. I think even when the problem was way smaller, they've still been struggling with being able to do this. It's an age old problem, that for the most part, I got to say that when I describe the problem the way that I did, usually, what the reaction from clients are, "Yes, I'd love for you to solve that." "I just heard this pitch from like five other vendors and I've yet to solve this problem. So, how do you do it?" So, as I kind of scope this, it's also a measure of just basically, how do you go about solving a complex situation where, to kind of list out some of the bold claims here in what I said. Number one, it's the ability to just fingerprint and be able to understand what your assets are. Secondly, being able to do it with very dirty data, if you will. I would say, in many cases, solutions that exist today, basically tell clients, or tell the users, were as good as the data that you provide us. And because the data isn't very good, the results aren't very good. Armis aspires to do something more than that. It aspires to create a logically perfect map of your assets despite being hindered by incomplete and basically wrong data, many times. And third, the ability to infer things about the environment where no source data even exists. So, to all of that, really Armis' approach is pretty straightforward, and it relies on something that we call our collective intelligence. We basically use the power and scale of these masses to our advantage, and not just as a shortcoming. What I mean by that, is Armis today tracks overall, over 2 billion assets worldwide. That's an astounding number. And it thanks to the size of some of the organization that we work with. Armis proudly serves today, for instance, over 35 of Fortune 100. Some of those environments, let me tell you, are huge. So, what Armis basically does, is really simple. It uses thousands, tens of thousands, hundreds of thousands sometimes, of instances of the same device and same assets to basically figure out what it is. Figure out how to fingerprint it best. Figure out how to marry conflicting data sources about it and figure out what's the right host name? What's the right IP address? What are all the different details that you should know about it? And be able to basically find the most minimalist fingerprints for different attributes of an asset in a changing environment. It's something that works really, really well. It's something that we honestly, may have applied to this problem, but it's not something that we fully invented. It's been used effectively to solve other problems as well. For instance, if you think about any kind of mapping software. And I use that analogy a lot. But if you think about mapping software, I happened to work for Google in the past, and specifically on Google Map. So, I know quite a bit about how to solve similar problems. But I can tell you that you think about something like a mapping software, it takes very dirty, incomplete data from lots of different sources, and creates not a pixel perfect map, but a logically perfect map for the use cases you need it to be. And that's exactly what Armis strives to do. Build the Google Maps, if you will, of your organization, or the kind of real time map of everything, and be able to supply that or project that for different business processes. >> Yeah, I love the approach, and I love that search analogy. Discover is a big part of mapping as you know, and reasoning in there with the metadata you have and the dirty data is critical. And by the way, we love bold statements on "theCUBE," because as long as you can back 'em up, then we'll dig into that. But let's back up some of those bold claims. Okay, you have a lot of devices, you've got the collective intelligence. How do you manage the real time nature of devices changing in real time? 'Cause if you do fingerprint on it, and you got some characteristics of the assets in the map, what happens in real time? How fast are you guys managing that? What's the process for that? >> So, very quickly, I think another quick analogy I like to use, because I think it orients people around kind of how Armis operates, is imagine that Armis is kind of like a Shazam for assets. We take different attributes coming from your environment, and we match it up, that collective intelligence to figure out what that asset is. So, we recognize an asset based off of its behavioral fingerprint, or based off of different attributes, figure out what it is. Now, if you take something that recognizes tunes on the radio or anything like that, it's built pretty similarly. Once you have access to different sources. Once we see real environments that introduce new devices or new assets, Armis is immediately learning. It's immediately taking those different queues, those different attributes and learning from them. And to your point, even if something changes its behavioral fingerprint. For instance, it gets updated, a new patch rolls out, something that changes a meaningful aspect of how that asset operates, Armis sees so many environments, and so much these days that it reacts in almost real time to the introduction of these new things. A patch rolls out, it starts changing multiple devices and multiple different environments around the world, Armis is already learning and adapting this model for the new type of asset and device out there. It works very quickly, and it's part of the effectiveness of being able to operate at the scale that we do. >> Well, Nadir, you guys got a great opportunity there at Armis. And as co-founder, you must be pretty pumped, actually working hard, stay up to date, and got a great, great opportunity there. How was RSA this year? And what's your take on the landscape? Because you're kind of in this, I call the new category of lockstep with an environment. Obviously, there's no perimeter, everyone knows that. Service area is the whole internet, basically, distributed computing paradigms and understanding things like discovery and mapping data that you guys are doing. And it's a data problem as well. It's a lot of problems that you guys are solving. But the industry's got some old beggars, as I still hear endpoint protection, zero trust. I hear trust, if you're talking about supply chain, software supply chain, S bombs, you mentioned in a previous interview. You got software supply chain issues with open source, 'cause everything's open source now on infrastructure, so that's happening. How do you manage all that? I mean, is it zero trust or is it trust? 'Cause as you hear, I hear you talking about Armis, it's like, you got to have trusted components in there and you got to trust the data. So, that's not zero trust, that's trust. So, where zero trust and trust solve? What's your take on that? How do you resolve? What's your reaction to that? >> Usually, I wait for someone else to bring up the zero trust buzzword before I touch on that. So, because to your point, it's such an overused buzzword. But let me try and tackle that for a second. First of all, I think that Armis treats assets in a way as, let's call it the vessels of everything. And what I mean by that, is that at a very atomic aspect, assets are the atoms of the environment. They're the vessels of everything. They're the vessels of vulnerabilities. There's the vessels of actual attacks. Like something, some asset needs to exist for something to happen. And every aspect of trust or zero trust, or anything like that applies to basically assets. Now, to your point, Armis, ironically, or like a lot of security tools, I think it assists greatly or even manages a zero trust policy within the environment. It provides the asset intelligence into the mix of how to manage an effective zero trust policy. But in essence, you need to trust Armis, right? I mean, Armis is a critical function now within your environment. And there has to be a degree of trust, but I would say, trust but verified. And that's something that I think the security industry as a whole is evolving into quite a bit, especially post events like solar, winds, or other things that happened in recent years. Armis is a SaaS platform. And in being a SaaS platform, there is an inherent aspect of trust and risk that you take on as a security organization. I think anyone who says differently, is either lying or mistaken. I mean, there are no foolproof, a 100% systems out there. But to mitigate some of that risk, we adhere to a very strict risk in security policy on our end. What that means, is we're incredibly transparent about every aspect of our own environment. We publish to our clients our latest penetration test reports. We publish our security controls and policies. We're very transparent about the different aspects we're involve in our own environment. We give our clients access to our own internal security organization, our own CSO, to be able to provide them with all the security controls they need. And we take a very least privileged approach in how we deploy Armis within an environment. No need for extra permissions. Everything read-only unless there is an explicit reason to do else... I think differently within the environment. And something that we take very seriously, is also anything that we deploy within the environment, should be walled off, except for whatever lease privilege that we need. On top of that, I'd add one more thing that adds, I think a lot of peace of mind to our clients. We are FeRAMP ready, and soon to be certified, We work with DOD clients within the U.S kind of DOD apparatus. And I think that this gives a lot of peace of mind to our clients, even commercial clients, because they know that we need to adhere to hundreds of different security controls that are monitored and government by U.S federal agencies. And that I think gives a lot of extra security measures, a lot of knowledge that this risk is being mitigated and controlled, and governed by different agencies. >> Good stuff there. Also at RSA, you kind of saw people come back together face-to-face, which is great. A lot of kind of similar, everyone kind of knows each other in the security business, but it's getting bigger. What was the big takeaways from you for the folks watching here that didn't get to go to RSA this year? What was the most important stories that came out of RSA this year? Just generally across the industry, from your perspective that people should pay attention to? >> First of all, I think that people were just really happy to get back together. I think it was a really fun RSA. I think that people had a lot of energy and excitement, and they love just walking around. I am obviously, somewhat biased here, but I will say, I've heard from other people too, that our event there, and the formal party that was there was by far the kind of the the talk of the show. And we were fortunate to do that with Sentinel One. with Torque who are both great partners of ours, and, of course, Insight partners. I think a lot of the themes that have come up during RSA, are really around some of the things that we already talked about, visibility as a driver for business processes. The understanding of where do assets and tax surfaces, and things like that play in. But also, I think that everything was, in light of macroeconomics and geopolitics that are kind of happening in the background, that no one can really avoid that. On the one hand, if we look at macroeconomics, obviously, markets are going through quite a shake up right now. And especially, when you talk about tech, the one thing that was really, really evident though, is it's cybersecurity is, I think market-wise just faring way better than others because the demand is absolutely there. I think that no one has slowed down one bit on buying and arming themselves, I'd say, with defensive solutions for cybersecurity. And the reason, is that the threats are there. I mean, we're all very, very much aware of that. And even in situations where companies are spending less on other things, they're definitely spending on cybersecurity, because the toll on the industry is going up significantly year by year, which really ties into also the geopolitics. One of the themes that I've heard significantly, is all the buzz around different initiatives coming from both U.S federal agencies, as well as different governing bodies around anything, from things like shields up in critical infrastructure, all the way to different governance aspects of the TSA. Or even the SCC on different companies with regards to what are they doing on cyber? If some of the initiatives coming from the SCC on public companies come out the way that they are right now, cyber security companies will elevate... Well, sorry, companies in general, would actually elevate cyber security to board level discussions on a regular basis. And everyone wants to be ready to answer effectively, different questions there. And then on top of all of that, I think we're all very aware of, I think, and not to be too doom and gloom here, but the geopolitical aspect of things. It's very clear that we could be facing a very significant and very different cyber warfare aspect than anything that we've seen before in the coming months and years. I think that one of the things you could hear a lot of companies and clients talk about, is the fact that it used to be that you could say, "Look, if a nation state is out to get me, then a nation state is out to get me, and they're going to get me. And I am out to protect myself from common criminals, or cybersecurity criminals, or things like that." But it's no longer the case. I mean, you very well might be attacked by a nation state, and it's no longer something that you can afford to just say, "Yeah, we'll just deal with that if that happens." I think some of the attacks on critical infrastructure in particular have proven to us all, that this is a very, very important topic to deal with. And companies are paying a lot of attention to what can give them visibility and control over their extended attack surface, and anything in between. >> Well, we've been certainly ringing the bell for years. I've been a hawk on this for many, many years, saying we're at cyber war, well below everyone else. So, we've been pounding our fist on the table saying, it's not just a national security issue. Finally, they're waking up and kind of figuring out countermeasures. But private companies don't have their own, they should have their own militia basically. So, what's the role of government and all this? So, all this is about competency and actually understanding what's going on. So, the whole red line, lowering that red line, the adversaries have been operating onside our infrastructure for years. So, the industrial IOT side has been aware of this for years, now it's being streamed, right? So, what do we do? Is the government going to come in and help, and bring some cyber militia to companies to protect their business? I mean, if troops dropped on our shores, I'm sure the government would react, right? So, where is that red line, Nadir? Where do you see the gap being filled? Certainly, people will defend their companies, they have assets obviously. And then, you critical infrastructure on the industrial side is super important, that's the national security issue. What do we do? What's the action here? >> That is such a difficult question. Such a good question I think to tackle, I think, there are similarities and there are differences, right? On the one hand, we do and should expect the government to do more. I think it should do more in policy making. I mean, really, really work to streamline and work much faster on that. And it would do good to all of us because I think that ultimately, policy can mean that the third party vendors that we use are more secure, and in turn, our own organizations are more secure in how they operate. But also, they hold our organizations accountable. And in doing so, consumers who use different services feel safer as well because basically, companies are mandated to protect data, to protect themselves, and do everything else. On the other hand, I'd say that government's support on this is difficult. I think the better way to look at this, is imagine for a second, no troops landing on our kind of shores, if you will. But imagine instead, a situation where Americans are spread all over the world and expect the government to protect them in any country, or in any situation they're at. I think that depicts maybe a little better, how infrastructure looks like today. If you look at multinational companies, they have offices everywhere. They have assets spread out everywhere. They have people working from everywhere around the world. It's become an attack surface, that I think you said this earlier, or in a different interview as well. There's no more perimeter to speak of. There are no more borders to this virtual country, if you will. And so, on the one hand, we do expect our government to do a lot. But on the other hand, we also need to take responsibility as companies, and as vendors, and as suppliers of services, we need to take accountability and take responsibility for the assets that we deploy and put in place. And we should have a very security conscious mind in doing this. >> Yeah. >> So, I think tricky government policy aspect to tackle. I think the government should be doing more, but on the other hand, we should absolutely be pointing internally at where can we do better as companies? >> And the asset understanding the context of what's critical asset too, can impact how you protect it, defend it, and ensure it, or manage it. I mean, this is what people want. It's a data problem in flight, at rest, and in action. So, Armis, you guys are doing a great job there. Congratulations, Nadir on the venture, on your success. I love the product, love the approach. I think it scales nicely with the industry where it's going. So, especially with the intelligent edge booming, and it's just so much happening, you guys are in the middle of it. Thanks for coming on "theCUBE." Appreciate it. >> Thank you so much. As I like to say, it takes a village, and there's so many people in the company who make this happen. I'm just the one who gets to take credit for it. So, I appreciate the time today and the conversation. And thank you for having me. >> Well, we'll check in with you. You guys are right there with us, and we'll be in covering you guys pretty deeply. Thanks for coming on. Appreciate it. Okay, it's #CUBEConversation here in Palo Alto. I'm John Furrier. Thanks for watching. Clear. (bright upbeat music)

(upbeat music) >> Welcome to this CUBE Power Panel where we're going to talk about application modernization, also success templates, and take a look at some new survey data to see how CIOs are thinking about digital transformation, as we get deeper into the post isolation economy. And with me are three familiar VIP guests to CUBE audiences. Tony Bear, the principal at DB InSight, Doug Henschen, VP and principal analyst at Constellation Research and Sanjeev Mohan principal at SanjMo. Guys, good to see you again, welcome back. >> Thank you. >> Glad to be here. >> Thanks for having us. >> Glad to be here. >> All right, Doug. Let's get started with you. You know, this recent survey, which was commissioned by Couchbase, 650 CIOs and CTOs, and IT practitioners. So obviously very IT heavy. They responded to the following question, "In response to the pandemic, my organization accelerated our application modernization strategy and of course, an overwhelming majority, 94% agreed or strongly agreed." So I'm sure, Doug, that you're not shocked by that, but in the same survey, modernizing existing technologies was second only behind cyber security is the top investment priority this year. Doug, bring us into your world and tell us the trends that you're seeing with the clients and customers you work with in their modernization initiatives. >> Well, the survey, of course, is spot on. You know, any Constellation Research analyst, any systems integrator will tell you that we saw more transformation work in the last two years than in the prior six to eight years. A lot of it was forced, you know, a lot of movement to the cloud, a lot of process improvement, a lot of automation work, but transformational is aspirational and not every company can be a leader. You know, at Constellation, we focus our research on those market leaders and that's only, you know, the top 5% of companies that are really innovating, that are really disrupting their markets and we try to share that with companies that want to be fast followers, that these are the next 20 to 25% of companies that don't want to get left behind, but don't want to hit some of the same roadblocks and you know, pioneering pitfalls that the real leaders are encountering when they're harnessing new technologies. So the rest of the companies, you know, the cautious adopters, the laggards, many of them fall by the wayside, that's certainly what we saw during the pandemic. Who are these leaders? You know, the old saw examples that people saw at the Amazons, the Teslas, the Airbnbs, the Ubers and Lyfts, but new examples are emerging every year. And as a consumer, you immediately recognize these transformed experiences. One of my favorite examples from the pandemic is Rocket Mortgage. No disclaimer required, I don't own stock and you're not client, but when I wanted to take advantage of those record low mortgage interest rates, I called my current bank and some, you know, stall word, very established conventional banks, I'm talking to you Bank of America, City Bank, and they were taking days and weeks to get back to me. Rocket Mortgage had the locked in commitment that day, a very proactive, consistent communications across web, mobile, email, all customer touchpoints. I closed in a matter of weeks an entirely digital seamless process. This is back in the gloves and masks days and the loan officer came parked in our driveway, wiped down an iPad, handed us that iPad, we signed all those documents digitally, completely electronic workflow. The only wet signatures required were those demanded by the state. So it's easy to spot these transformed experiences. You know, Rocket had most of that in place before the pandemic, and that's why they captured 8% of the national mortgage market by 2020 and they're on track to hit 10% here in 2022. >> Yeah, those are great examples. I mean, I'm not a shareholder either, but I am a customer. I even went through the same thing in the pandemic. It was all done in digital it was a piece of cake and I happened to have to do another one with a different firm and stuck with that firm for a variety of reasons and it was night and day. So to your point, it was a forced merge to digital. If you were there beforehand, you had real advantage, it could accelerate your lead during the pandemic. Okay, now Tony bear. Mr. Bear, I understand you're skeptical about all this buzz around digital transformation. So in that same survey, the data shows that the majority of respondents said that their digital initiatives were largely reactive to outside forces, the pandemic compliance changes, et cetera. But at the same time, they indicated that the results while somewhat mixed were generally positive. So why are you skeptical? >> The reason being, and by the way, I have nothing against application modernization. The problem... I think the problem I ever said, it often gets conflated with digital transformation and digital transformation itself has become such a buzzword and so overused that it's really hard, if not impossible to pin down (coughs) what digital transformation actually means. And very often what you'll hear from, let's say a C level, you know, (mumbles) we want to run like Google regardless of whether or not that goal is realistic you know, for that organization (coughs). The thing is that we've been using, you know, businesses have been using digital data since the days of the mainframe, since the... Sorry that data has been digital. What really has changed though, is just the degree of how businesses interact with their customers, their partners, with the whole rest of the ecosystem and how their business... And how in many cases you take look at the auto industry that the nature of the business, you know, is changing. So there is real change of foot, the question is I think we need to get more specific in our goals. And when you look at it, if we can boil it down to a couple, maybe, you know, boil it down like really over simplistically, it's really all about connectedness. No, I'm not saying connectivity 'cause that's more of a physical thing, but connectedness. Being connected to your customer, being connected to your supplier, being connected to the, you know, to the whole landscape, that you operate in. And of course today we have many more channels with which we operate, you know, with customers. And in fact also if you take a look at what's happening in the automotive industry, for instance, I was just reading an interview with Bill Ford, you know, their... Ford is now rapidly ramping up their electric, you know, their electric vehicle strategy. And what they realize is it's not just a change of technology, you know, it is a change in their business, it's a change in terms of the relationship they have with their customer. Their customers have traditionally been automotive dealers who... And the automotive dealers have, you know, traditionally and in many cases by state law now have been the ones who own the relationship with the end customer. But when you go to an electric vehicle, the product becomes a lot more of a software product. And in turn, that means that Ford would have much more direct interaction with its end customers. So that's really what it's all about. It's about, you know, connectedness, it's also about the ability to act, you know, we can say agility, it's about ability not just to react, but to anticipate and act. And so... And of course with all the proliferation, you know, the explosion of data sources and connectivity out there and the cloud, which allows much more, you know, access to compute, it changes the whole nature of the ball game. The fact is that we have to avoid being overwhelmed by this and make our goals more, I guess, tangible, more strictly defined. >> Yeah, now... You know, great points there. And I want to just bring in some survey data, again, two thirds of the respondents said their digital strategies were set by IT and only 26% by the C-suite, 8% by the line of business. Now, this was largely a survey of CIOs and CTOs, but, wow, doesn't seem like the right mix. It's a Doug's point about, you know, leaders in lagers. My guess is that Rocket Mortgage, their digital strategy was led by the chief digital officer potentially. But at the same time, you would think, Tony, that application modernization is a prerequisite for digital transformation. But I want to go to Sanjeev in this war in the survey. And respondents said that on average, they want 58% of their IT spend to be in the public cloud three years down the road. Now, again, this is CIOs and CTOs, but (mumbles), but that's a big number. And there was no ambiguity because the question wasn't worded as cloud, it was worded as public cloud. So Sanjeev, what do you make of that? What's your feeling on cloud as flexible architecture? What does this all mean to you? >> Dave, 58% of IT spend in the cloud is a huge change from today. Today, most estimates, peg cloud IT spend to be somewhere around five to 15%. So what this number tells us is that the cloud journey is still in its early days, so we should buckle up. We ain't seen nothing yet, but let me add some color to this. CIOs and CTOs maybe ramping up their cloud deployment, but they still have a lot of problems to solve. I can tell you from my previous experience, for example, when I was in Gartner, I used to talk to a lot of customers who were in a rush to move into the cloud. So if we were to plot, let's say a maturity model, typically a maturity model in any discipline in IT would have something like crawl, walk, run. So what I was noticing was that these organizations were jumping straight to run because in the pandemic, they were under the gun to quickly deploy into the cloud. So now they're kind of coming back down to, you know, to crawl, walk, run. So basically they did what they had to do under the circumstances, but now they're starting to resolve some of the very, very important issues. For example, security, data privacy, governance, observability, these are all very big ticket items. Another huge problem that nav we are noticing more than we've ever seen, other rising costs. Cloud makes it so easy to onboard new use cases, but it leads to all kinds of unexpected increase in spikes in your operating expenses. So what we are seeing is that organizations are now getting smarter about where the workloads should be deployed. And sometimes it may be in more than one cloud. Multi-cloud is no longer an aspirational thing. So that is a huge trend that we are seeing and that's why you see there's so much increased planning to spend money in public cloud. We do have some issues that we still need to resolve. For example, multi-cloud sounds great, but we still need some sort of single pane of glass, control plane so we can have some fungibility and move workloads around. And some of this may also not be in public cloud, some workloads may actually be done in a more hybrid environment. >> Yeah, definitely. I call it Supercloud. People win sometimes-- >> Supercloud. >> At that term, but it's above multi-cloud, it floats, you know, on topic. But so you clearly identified some potholes. So I want to talk about the evolution of the application experience 'cause there's some potholes there too. 81% of their respondents in that survey said, "Our development teams are embracing the cloud and other technologies faster than the rest of the organization can adopt and manage them." And that was an interesting finding to me because you'd think that infrastructure is code and designing insecurity and containers and Kubernetes would be a great thing for organizations, and it is I'm sure in terms of developer productivity, but what do you make of this? Does the modernization path also have some potholes, Sanjeev? What are those? >> So, first of all, Dave, you mentioned in your previous question, there's no ambiguity, it's a public cloud. This one, I feel it has quite a bit of ambiguity because it talks about cloud and other technologies, that sort of opens up the kimono, it's like that's everything. Also, it says that the rest of the organization is not able to adopt and manage. Adoption is a business function, management is an IT function. So I feed this question is a bit loaded. We know that app modernization is here to stay, developing in the cloud removes a lot of traditional barriers or procuring instantiating infrastructure. In addition, developers today have so many more advanced tools. So they're able to develop the application faster because they have like low-code/no-code options, they have notebooks to write the machine learning code, they have the entire DevOps CI/CD tool chain that makes it easy to version control and push changes. But there are potholes. For example, are developers really interested in fixing data quality problems, all data, privacy, data, access, data governance? How about monitoring? I doubt developers want to get encumbered with all of these operationalization management pieces. Developers are very keen to deliver new functionality. So what we are now seeing is that it is left to the data team to figure out all of these operationalization productionization things that the developers have... You know, are not truly interested in that. So which actually takes me to this topic that, Dave, you've been quite actively covering and we've been talking about, see, the whole data mesh. >> Yeah, I was going to say, it's going to solve all those data quality problems, Sanjeev. You know, I'm a sucker for data mesh. (laughing) >> Yeah, I know, but see, what's going to happen with data mesh is that developers are now going to have more domain resident power to develop these applications. What happens to all of the data curation governance quality that, you know, a central team used to do. So there's a lot of open ended questions that still need to be answered. >> Yeah, That gets automated, Tony, right? With computational governance. So-- >> Of course. >> It's not trivial, it's not trivial, but I'm still an optimist by the end of the decade we'll start to get there. Doug, I want to go to you again and talk about the business case. We all remember, you know, the business case for modernization that is... We remember the Y2K, there was a big it spending binge and this was before the (mumbles) of the enterprise, right? CIOs, they'd be asked to develop new applications and the business maybe helps pay for it or offset the cost with the initial work and deployment then IT got stuck managing the sprawling portfolio for years. And a lot of the apps had limited adoption or only served a few users, so there were big pushes toward rationalizing the portfolio at that time, you know? So do I modernize, they had to make a decision, consolidate, do I sunset? You know, it was all based on value. So what's happening today and how are businesses making the case to modernize, are they going through a similar rationalization exercise, Doug? >> Well, the Y2K era experience that you talked about was back in the days of, you know, throw the requirements over the wall and then we had waterfall development that lasted months in some cases years. We see today's most successful companies building cross functional teams. You know, the C-suite the line of business, the operations, the data and analytics teams, the IT, everybody has a seat at the table to lead innovation and modernization initiatives and they don't start, the most successful companies don't start by talking about technology, they start by envisioning a business outcome by envisioning a transformed customer experience. You hear the example of Amazon writing the press release for the product or service it wants to deliver and then it works backwards to create it. You got to work backwards to determine the tech that will get you there. What's very clear though, is that you can't transform or modernize by lifting and shifting the legacy mess into the cloud. That doesn't give you the seamless processes, that doesn't give you data driven personalization, it doesn't give you a connected and consistent customer experience, whether it's online or mobile, you know, bots, chat, phone, everything that we have today that requires a modern, scalable cloud negative approach and agile deliver iterative experience where you're collaborating with this cross-functional team and course correct, again, making sure you're on track to what's needed. >> Yeah. Now, Tony, both Doug and Sanjeev have been, you know, talking about what I'm going to call this IT and business schism, and we've all done surveys. One of the things I'd love to see Couchbase do in future surveys is not only survey the it heavy, but also survey the business heavy and see what they say about who's leading the digital transformation and who's in charge of the customer experience. Do you have any thoughts on that, Tony? >> Well, there's no question... I mean, it's kind like, you know, the more things change. I mean, we've been talking about that IT and the business has to get together, we talked about this back during, and Doug, you probably remember this, back during the Y2K ERP days, is that you need these cross functional teams, we've been seeing this. I think what's happening today though, is that, you know, back in the Y2K era, we were basically going into like our bedrock systems and having to totally re-engineer them. And today what we're looking at is that, okay, those bedrock systems, the ones that basically are keeping the lights on, okay, those are there, we're not going to mess with that, but on top of that, that's where we're going to innovate. And that gives us a chance to be more, you know, more directed and therefore we can bring these related domains together. I mean, that's why just kind of, you know, talk... Where Sanjeev brought up the term of data mesh, I've been a bit of a cynic about data mesh, but I do think that work and work is where we bring a bunch of these connected teams together, teams that have some sort of shared context, though it's everybody that's... Every team that's working, let's say around the customer, for instance, which could be, you know, in marketing, it could be in sales, order processing in some cases, you know, in logistics and delivery. So I think that's where I think we... You know, there's some hope and the fact is that with all the advanced, you know, basically the low-code/no-code tools, they are ways to bring some of these other players, you know, into the process who previously had to... Were sort of, you know, more at the end of like a, you know, kind of a... Sort of like they throw it over the wall type process. So I do believe, but despite all my cynicism, I do believe there's some hope. >> Thank you. Okay, last question. And maybe all of you could answer this. Maybe, Sanjeev, you can start it off and then Doug and Tony can chime in. In the survey, about a half, nearly half of the 650 respondents said they could tangibly show their organizations improve customer experiences that were realized from digital projects in the last 12 months. Now, again, not surprising, but we've been talking about digital experiences, but there's a long way to go judging from our pandemic customer experiences. And we, again, you know, some were great, some were terrible. And so, you know, and some actually got worse, right? Will that improve? When and how will it improve? Where's 5G and things like that fit in in terms of improving customer outcomes? Maybe, Sanjeev, you could start us off here. And by the way, plug any research that you're working on in this sort of area, please do. >> Thank you, Dave. As a resident optimist on this call, I'll get us started and then I'm sure Doug and Tony will have interesting counterpoints. So I'm a technology fan boy, I have to admit, I am in all of all these new companies and how they have been able to rise up and handle extreme scale. In this time that we are speaking on this show, these food delivery companies would have probably handled tens of thousands of orders in minutes. So these concurrent orders, delivery, customer support, geospatial location intelligence, all of this has really become commonplace now. It used to be that, you know, large companies like Apple would be able to handle all of these supply chain issues, disruptions that we've been facing. But now in my opinion, I think we are seeing this in, Doug mentioned Rocket Mortgage. So we've seen it in FinTech and shopping apps. So we've seen the same scale and it's more than 5G. It includes things like... Even in the public cloud, we have much more efficient, better hardware, which can do like deep learning networks much more efficiently. So machine learning, a lot of natural language programming, being able to handle unstructured data. So in my opinion, it's quite phenomenal to see how technology has actually come to rescue and as, you know, billions of us have gone online over the last two years. >> Yeah, so, Doug, so Sanjeev's point, he's saying, basically, you ain't seen nothing yet. What are your thoughts here, your final thoughts. >> Well, yeah, I mean, there's some incredible technologies coming including 5G, but you know, it's only going to pave the cow path if the underlying app, if the underlying process is clunky. You have to modernize, take advantage of, you know, serverless scalability, autonomous optimization, advanced data science. There's lots of cutting edge capabilities out there today, but you know, lifting and shifting you got to get your hands dirty and actually modernize on that data front. I mentioned my research this year, I'm doing a lot of in depth looks at some of the analytical data platforms. You know, these lake houses we've had some conversations about that and helping companies to harness their data, to have a more personalized and predictive and proactive experience. So, you know, we're talking about the Snowflakes and Databricks and Googles and Teradata and Vertica and Yellowbrick and that's the research I'm focusing on this year. >> Yeah, your point about paving the cow path is right on, especially over the pandemic, a lot of the processes were unknown. But you saw this with RPA, paving the cow path only got you so far. And so, you know, great points there. Tony, you get the last word, bring us home. >> Well, I'll put it this way. I think there's a lot of hope in terms of that the new generation of developers that are coming in are a lot more savvy about things like data. And I think also the new generation of people in the business are realizing that we need to have data as a core competence. So I do have optimism there that the fact is, I think there is a much greater consciousness within both the business side and the technical. In the technology side, the organization of the importance of data and how to approach that. And so I'd like to just end on that note. >> Yeah, excellent. And I think you're right. Putting data at the core is critical data mesh I think very well describes the problem and (mumbles) credit lays out a solution, just the technology's not there yet, nor are the standards. Anyway, I want to thank the panelists here. Amazing. You guys are always so much fun to work with and love to have you back in the future. And thank you for joining today's broadcast brought to you by Couchbase. By the way, check out Couchbase on the road this summer at their application modernization summits, they're making up for two years of shut in and coming to you. So you got to go to couchbase.com/roadshow to find a city near you where you can meet face to face. In a moment. Ravi Mayuram, the chief technology officer of Couchbase will join me. You're watching theCUBE, the leader in high tech enterprise coverage. (bright music)

>> "theCUBE," presents Dell Technologies World, brought to you by Dell. >> Hey, welcome back to "theCUBE's" live coverage of Dell Technologies World 2022. Live from the Venetian in Las Vegas, Lisa Martin, Dave Vellante joins me. Dan O'Brien joins us next. The senior vice president of technology solutions at Presidio. Dan, welcome to "theCUBE." >> It's great to be here. Great to be in Vegas too. >> Is it great to be back live in person, three dimensional? >> You have no idea. >> Oh, I know. >> Yeah. >> Just the seeing people again and the vibe here day-one is already fantastic. >> Yeah. >> Talk to us about Presidio and Dell's relationship? What's going on with Presidio? >> Yeah, so I'll tell you just Presidio as as a whole, and part of why I joined about a year ago and I'm still just excited as I was on day one. We're a digital services and solutions provider with deep engineering expertise in networking, cloud, security, collaboration, and modern technologies. And we'll help our customers acquire, deploy, and then operate and manage the solutions that we have. So, we're a Dell titanium black partner. We just got that, we're a super excited about it. And they're a critical part of how we deliver solutions to our customers. >> So, you joined during an interesting time during the pandemic. What are some of the challenges your customers are facing now? Aging infrastructure, labor shortages, supply chain. What do you, what are you seeing from the customers lens? >> Yeah, you know, all of the above. I think when the pandemic first hit, every customer that we spoke with basically said, Cash is king. We want to preserve it, we don't know what the future holds. So, all of the spend that happened was on the things that drove their business forward. So, I got a distributed workforce. How do I go invest in technology to make them productive? A lot of them had to take a digital agenda that was five years long and do it in three months to survive, so they spent it and that generally meant cloud. But what they didn't spend money on, was infrastructure inside the data center. And now what they're finding, is things are old, maintenance bills are going up, the cost to get it is going up. And sometimes supply chain is over 12 months long to be able to actually do something about it. >> You know, when "theCUBE" first started in 2010, it was EFC World 2010 now, 'cause Dell is really our legacy here. So, we said that companies that sell, it's kind of a pejorative, but sell boxes are going to be in trouble because of the cloud. Interesting, right? So, it was partly true because the cloud just intermediated a lot of that sort of box selling business. We said they have to become more value added players, identify. And so, when I watched Presidio, the transformation that you guys went through, and you're relatively new. Cloud has actually become an opportunity. And you're doing stuff around digital, a lot of stuff around security. It's cyber, probably automation, life cycle management. >> True. >> Talk about that transformation? And I'm interested in why you joined Presidio? >> So, I'll tell you why I joined Presidio, is I was talking to a lot of customers every day in my old role, I love doing that part. And the conversation started with, "Dan, I can't spend money on my data center right now because we're in a pandemic. I've got to innovate faster and the answer is to cloud. I don't know how to actually make my workforce productive because they're all over the place now. And we didn't invest in technology. And now I've got a threat surface with people working everywhere in workloads in different places. I don't know how to approach that." And I looked at what Presidio had built, I'm like, that's exactly what we did. But what's been fun for me, has been the answer to most of our customers is this the end? It's not the public cloud, it's not the private cloud. It's, you need to do both of them really well and have the skills and expertise to leverage 'em for the right application, or workload, or use case. And that's why I'm super excited to be here, 'cause we're really helping our customers in both areas. >> You mentioned security. We've seen a number of announcements today from Dell Technologies with respect to cybersecurity. We know the stats are what they are. It's no longer a matter of, if we're going to get hit by a cyber attack, it's when? Most organizations are going to get hit by 2025. Where is security in the conversation now? How high up in the priority is it? >> I would say it's, we don't have a single customer meeting without having that conversation. And what we're finding, is you look at the stats that say, you know, 30% of companies that have a cyber attack, don't come back from it. 20% pay the ransom, and then they don't even get their data back. So, while we want to stop the attacks, I think you're right on that the answer is, it's not a matter of if, it's a matter of when? But what's great about Dell Technologies, is we have a complete portfolio that can meet any SLA of our customers. It's in backup technology, it's in primary storage, they can do a mutable backups and recoveries everywhere. But what happened this week, where they announced partnerships with the cloud, that's huge because the same resource constraints that customers have in their data centers today, are the same ones you have to deploy infrastructure to be able to make this work and be able to accelerate recovery. So, the partnership and the integration with the public cloud, really gives a great integration point for a lot of our customers. >> At the analyst of the event today, we had a meeting with Jen Felch, the CIO of Dell. And I said to her, you know, our survey data from ETR shows that security now, number one priority, it kind of always was, but it's distance itself from the number two, which is cloud migration. And I asked her, I said, "Obviously, cloud migration is not your number two, 'cause number security number one was number two?" And she said, "Let me help you interpret that data. Because for us, we have the scale, we can do our own cloud essentially." What her interpretation, was what those customers are really saying is modernization. Now, you must see that. Now, of course, you're leaning into cloud. Dell is not defensive really more about cloud, like, hey, we could take advantage of it as well. So, what are you seeing in terms of the changing priorities of IT kind of pre-post pandemic? Is it like a rubber band that goes and then comes back to where it was, or is it kind of permanent? >> I think that the both worlds together are absolutely permanent. And there's no way we're going to go back from one or the other. And then we're always going to have a world where you might lean more into one. To innovate, you might lean more into one for disaster recovery. But I truly think the world and the answer for us and our perspective, has to be both. But you said something to interesting earlier, is the key I think to what customers are doing is you can't just pick up a workload and move it to the cloud, it doesn't solve a problem. You use that term modernize. And we've invested, acquisitions and continued engineering resources that were hiring around modernization because the economics and the true benefit of actually running a workload and running right at the right SLAs and meeting your customer's objectives, aren't going to work right if you're just picking an application up and moving it over there. So, we're really focused there. >> So, Couch Base, just ran a survey. We did a power panel on it with a bunch of database analysts. And it was a survey of 650 CIOs and CTOs. And it was really interesting 'cause it's an IT bias. But they said like 2/3 of the survey base said that IT is responsible for setting the digital transformation strategy of the company. And I went, "Well, I wonder what the business guys say to that. It was sort of a red flag to me. But I wonder what you're seeing 'cause there's obviously you get a difference when you talk to different worlds. So, I guess what is modernization, was kind of one of the big questions that came out of it? And who's driving the agenda? >> So, it really depends upon the customer, right? But the key to what you said, and there was an article that came out. I won't say where it was from, but it really kind of opened my eyes. But the article was titled that, "It's Time To Get Rid of the IT Department." And for someone like me and a lot of customers, that kind of scares people. But the whole underpin of it, was they were studying customers that took IT and actually disparaged, like broke 'em apart and put them into business units. So, it said, it's your turn to wake up every day and figure out what that business unit needs to be successful. Because the answer is, David, it's both, right? You need both parties on board, right? Where, you've got a business stakeholder that clearly knows want to do, understands technology's the answer but you need IT to be able to go make it work and be a true partner, and help go actually make it work. >> It reminds me of when Nicholas Carr wrote that article if you're, you guys are probably too young to remember, "But Does IT Matter?" It was kind of post Y2K, right? And then everybody went crazy. All the CIO was when nuts. And in fact, IT matters more than ever, but it's a different context, as you're saying. A question on things like skill shortages, supply chain, I mean, obviously, top of mind. >> Yeah. >> Are you helping people with that? And if so, how so? >> Yeah, so two ways I would look at this, is when you look at the supply chain, I mean, Intel I think spent a $100 million on standing up new Silicon plants. We won't see a benefit from that from 2025. So, it's real. So, a lot of what we're doing on a supply chain is how can we help a customer reach in and have certain targeted ways to leverage the cloud? Because we can't physically solve for the physics issue. The other part of it, the people shortage. I mean, it's real. I mean, everyone's sitting at home they're pondering whether or not, you know, what they're doing is fulfilling their dreams. Now, geography doesn't matter, you can do a job from anywhere. And technology is the heart of everything. So, the people shortage is real. So, we're finding that our focus on managed services we're essentially allowing our customers to run and deploy things across every technology aspect, is something that we used to have to drive to our customers. And now, we can't get out of a conversation without them asking for it 'cause they just don't have the people- >> Yeah, they're calling you into that need. >> Yeah. >> Can you share that customer example that you think really articulates the value of the Dell Technologies that Presidio is delivering? It's really been able to truly modernize in the last couple of years? >> Yeah, so looking specifically to Dell, I mean, for us, one of the taking technical data out of the data center and modernizing, their HCI portfolio together with VMware, is a complete home run. It takes multiple products, brings it into a single common solution, uses a common tool set for all the operators that are there so you don't need the number of people to run it. But if you do it right, it solves for the portability issue in some of the public cloud options, especially with things like VMC where you can have an on and off-prem and an automation between 'em, so you can pick and choose dynamically. That for us has been a home run in driving modernization strategies. >> From a multi-cloud perspective, it's going to be a big focus of this event the next couple of days. What are you seeing from customers' perspective? They're probably in multi-cloud environments for a variety of reasons, that's going to be persisting. The hyperscalers are all growing. What's going on there? How are you helping customers to manage the multi-cloud environment with just much more simplicity? >> Yeah, so I think there's a couple parts to that, right? I mean, obviously, Dell together with VMware has a great set of technologies to be able to manage the deployment of that. But what we're trying to do, is number one, help a customer determine which workload should be running in which place, right? Understand application dependencies. But as we work through a migration strategy with a lot of our customers, the key part that a lot of people don't realize, is we all think security but the networking is probably the hardest part if you want to have portability in a well running cloud. So, having years and years in network heritage, it's been a great synergy on us kind of moving in that direction to help our cloud customers make sure that the right SLA, the right connectivity, and the right availability to make that world work. >> Yeah, so multicloud, obviously, a big topic of of discussion this morning with Chuck Whitten. And that's another one of those, well, what do you mean by that? I have a sort of a premise I want to test on you, Dan. I've always said, it just comes from talking to customers, multi-cloud is kind of multi-vendor. I got to run some workloads in AWS, I run some On Prem. I run some in Google, some in Azure, and many of them, a handful like the big banks, for instance, they say, "Well we're building our own abstraction layer so we can control the policies, the security." And it seems like that's a direction that the industry generally in Dell specifically is headed. Do you buy that? And what's driving that need? >> Yeah, so I would buy it based on the size of the customer. So, when you take a big bank, a lot of what drives them to go to one cloud or the other, is that the big cloud providers they're innovating constantly. Every day there's a new tool or capability that exists there. And certain ones of them are going to match, a use case that, that large customer has- >> You can't resist? >> So, they're going to end up with multiple clouds, so it makes perfect sense. When you get into smaller customer, they really have to want to be successful. They got to pick one, right? They can't afford the people, and the scale, and the process. So, I think that's... The answer would depend based on the customer. The larger ones, I think they're going to build a full orchestration stack and small customers are going to look for one and someone maybe with managed services to help them augment the skills and staffing to make it work. >> For a while, I haven't heard it much lately, but you'd hear about repatriation, people come to me like, "Dave, you got to look into this repatriation thing." And I did, and I was like, "Eh, I really see, it a little bit, little pockets." But I do see hybrid. I mean, that's very clear. And I do see a lot of people went into the cloud, they didn't have a great experience. And okay, so there's some of that going on. I guess you could call that repatriation. But what are you seeing in terms of both of those? Is repatriation a trend or is it really an hybrid? >> So, I've interesting perspective coming from Dell, right? Where we're a very infrastructure focused in there. I see a little bit of repatriation in like a workload, like virtual desktops where you picked it up and you threw it in the cloud and make your workforce productive. But generally speaking, what we're seeing is not repatriation, which is, "Hey I move things. My cost is out of control, I don't know how to manage it. Can you help me get better controls on cost? Can you help me automate a lot of the things that are running here so I've got better control of cost and we're where things are running in my security posture?" So, it's much more about optimization that we're finding than it is. Let's bring it back. >> So, it's fine tuning the knobs? >> There you go. >> Right? And that seems to be the trend over the next couple of years? >> 110%. Yeah. >> Excellent. >> Have you seen any industries, in particular the last year that you've been with Presidio really leading edge in terms of modernization? >> Yeah. I mean, it's so interesting enough. I mean, I could give you a few examples, right? When we look in our public sector business, a lot of the educational institutions had to invest in new platforms they interact and engage with students. Our financial institutions, believe it or not, continue to innovate. I mean, what people don't realize, is the mainframe still has the transaction where your money lives in the ledger, but all the supporting ecosystem is digitalized and is completely modernized to interact with you. And, of course, retail for us. I mean, retail, they had to change their business model in many cases overnight, not even to survive, but to serve the communities they were working in. >> Yeah, I think one of the things that we've all learned in the last couple of years, is just the access, the e-commerce, the access online. We expect that now in the brick and mortar stores to be able to deliver that connected store, make sure that they have the inventory that I'm looking for with a frictionless experience. >> Yeah, and I tell you my favorite one, is you look at the healthcare industry, and while obviously with loans, and healthcare, and billing, all had to change. But that was really exciting for us, I mean, as consumers, right? Is the fact that we can interact with doctors online at the click of a button now. I mean, that part for us has been super exciting. >> Everything's at the click of the button now. >> Yeah. >> Oh, my gosh. Well, Dan, thank you so much for joining Dave and me on the program today, sharing what's new with Presidio, what you guys are doing together with Dell, and how you're helping companies in every industry to modernize. >> Perfect. I appreciate it. >> Great to have you. >> Likewise. >> Thank you. >> With Dave Vellante, I'm Lisa Martin, and you're watching "theCUBE's" coverage of Dell Technologies World live from the Venetian in Las Vegas. Stick around, and Dave and I will be right back with our next guest. (bright upbeat music)

(upbeat music) >> The ascendancy of cloud and SAS has shown new light on how organizations think about, pay for, and value hardware. Once sought after skills for practitioners with expertise in hardware troubleshooting, configuring ports, tuning storage arrays, and maximizing server utilization has been superseded by demand for cloud architects, DevOps pros, developers with expertise in microservices, container, application development, and like. Even a company like Dell, the largest hardware company in enterprise tech touts that it has more software engineers than those working in hardware. Begs the question, is hardware going the way of Coball? Well, not likely. Software has to run on something, but the labor needed to deploy, and troubleshoot, and manage hardware infrastructure is shifting. At the same time, we've seen the value flow also shifting in hardware. Once a world dominated by X86 processors value is flowing to alternatives like Nvidia and arm based designs. Moreover, other componentry like NICs, accelerators, and storage controllers are becoming more advanced, integrated, and increasingly important. The question is, does it matter? And if so, why does it matter and to whom? What does it mean to customers, workloads, OEMs, and the broader society? Hello and welcome to this week's Wikibon theCUBE Insights powered by ETR. In this breaking analysis, we've organized a special power panel of industry analysts and experts to address the question, does hardware still matter? Allow me to introduce the panel. Bob O'Donnell is president and chief analyst at TECHnalysis Research. Zeus Kerravala is the founder and principal analyst at ZK Research. David Nicholson is a CTO and tech expert. Keith Townson is CEO and founder of CTO Advisor. And Marc Staimer is the chief dragon slayer at Dragon Slayer Consulting and oftentimes a Wikibon contributor. Guys, welcome to theCUBE. Thanks so much for spending some time here. >> Good to be here. >> Thanks. >> Thanks for having us. >> Okay before we get into it, I just want to bring up some data from ETR. This is a survey that ETR does every quarter. It's a survey of about 1200 to 1500 CIOs and IT buyers and I'm showing a subset of the taxonomy here. This XY axis and the vertical axis is something called net score. That's a measure of spending momentum. It's essentially the percentage of customers that are spending more on a particular area than those spending less. You subtract the lesses from the mores and you get a net score. Anything the horizontal axis is pervasion in the data set. Sometimes they call it market share. It's not like IDC market share. It's just the percentage of activity in the data set as a percentage of the total. That red 40% line, anything over that is considered highly elevated. And for the past, I don't know, eight to 12 quarters, the big four have been AI and machine learning, containers, RPA and cloud and cloud of course is very impressive because not only is it elevated in the vertical access, but you know it's very highly pervasive on the horizontal. So what I've done is highlighted in red that historical hardware sector. The server, the storage, the networking, and even PCs despite the work from home are depressed in relative terms. And of course, data center collocation services. Okay so you're seeing obviously hardware is not... People don't have the spending momentum today that they used to. They've got other priorities, et cetera, but I want to start and go kind of around the horn with each of you, what is the number one trend that each of you sees in hardware and why does it matter? Bob O'Donnell, can you please start us off? >> Sure Dave, so look, I mean, hardware is incredibly important and one comment first I'll make on that slide is let's not forget that hardware, even though it may not be growing, the amount of money spent on hardware continues to be very, very high. It's just a little bit more stable. It's not as subject to big jumps as we see certainly in other software areas. But look, the important thing that's happening in hardware is the diversification of the types of chip architectures we're seeing and how and where they're being deployed, right? You refer to this in your opening. We've moved from a world of x86 CPUs from Intel and AMD to things like obviously GPUs, DPUs. We've got VPU for, you know, computer vision processing. We've got AI-dedicated accelerators, we've got all kinds of other network acceleration tools and AI-powered tools. There's an incredible diversification of these chip architectures and that's been happening for a while but now we're seeing them more widely deployed and it's being done that way because workloads are evolving. The kinds of workloads that we're seeing in some of these software areas require different types of compute engines than traditionally we've had. The other thing is (coughs), excuse me, the power requirements based on where geographically that compute happens is also evolving. This whole notion of the edge, which I'm sure we'll get into a little bit more detail later is driven by the fact that where the compute actually sits closer to in theory the edge and where edge devices are, depending on your definition, changes the power requirements. It changes the kind of connectivity that connects the applications to those edge devices and those applications. So all of those things are being impacted by this growing diversity in chip architectures. And that's a very long-term trend that I think we're going to continue to see play out through this decade and well into the 2030s as well. >> Excellent, great, great points. Thank you, Bob. Zeus up next, please. >> Yeah, and I think the other thing when you look at this chart to remember too is, you know, through the pandemic and the work from home period a lot of companies did put their office modernization projects on hold and you heard that echoed, you know, from really all the network manufacturers anyways. They always had projects underway to upgrade networks. They put 'em on hold. Now that people are starting to come back to the office, they're looking at that now. So we might see some change there, but Bob's right. The size of those market are quite a bit different. I think the other big trend here is the hardware companies, at least in the areas that I look at networking are understanding now that it's a combination of hardware and software and silicon that works together that creates that optimum type of performance and experience, right? So some things are best done in silicon. Some like data forwarding and things like that. Historically when you look at the way network devices were built, you did everything in hardware. You configured in hardware, they did all the data for you, and did all the management. And that's been decoupled now. So more and more of the control element has been placed in software. A lot of the high-performance things, encryption, and as I mentioned, data forwarding, packet analysis, stuff like that is still done in hardware, but not everything is done in hardware. And so it's a combination of the two. I think, for the people that work with the equipment as well, there's been more shift to understanding how to work with software. And this is a mistake I think the industry made for a while is we had everybody convinced they had to become a programmer. It's really more a software power user. Can you pull things out of software? Can you through API calls and things like that. But I think the big frame here is, David, it's a combination of hardware, software working together that really make a difference. And you know how much you invest in hardware versus software kind of depends on the performance requirements you have. And I'll talk about that later but that's really the big shift that's happened here. It's the vendors that figured out how to optimize performance by leveraging the best of all of those. >> Excellent. You guys both brought up some really good themes that we can tap into Dave Nicholson, please. >> Yeah, so just kind of picking up where Bob started off. Not only are we seeing the rise of a variety of CPU designs, but I think increasingly the connectivity that's involved from a hardware perspective, from a kind of a server or service design perspective has become increasingly important. I think we'll get a chance to look at this in more depth a little bit later but when you look at what happens on the motherboard, you know we're not in so much a CPU-centric world anymore. Various application environments have various demands and you can meet them by using a variety of components. And it's extremely significant when you start looking down at the component level. It's really important that you optimize around those components. So I guess my summary would be, I think we are moving out of the CPU-centric hardware model into more of a connectivity-centric model. We can talk more about that later. >> Yeah, great. And thank you, David, and Keith Townsend I really interested in your perspectives on this. I mean, for years you worked in a data center surrounded by hardware. Now that we have the software defined data center, please chime in here. >> Well, you know, I'm going to dig deeper into that software-defined data center nature of what's happening with hardware. Hardware is meeting software infrastructure as code is a thing. What does that code look like? We're still trying to figure out but servicing up these capabilities that the previous analysts have brought up, how do I ensure that I can get the level of services needed for the applications that I need? Whether they're legacy, traditional data center, workloads, AI ML, workloads, workloads at the edge. How do I codify that and consume that as a service? And hardware vendors are figuring this out. HPE, the big push into GreenLake as a service. Dale now with Apex taking what we need, these bare bone components, moving it forward with DDR five, six CXL, et cetera, and surfacing that as cold or as services. This is a very tough problem. As we transition from consuming a hardware-based configuration to this infrastructure as cold paradigm shift. >> Yeah, programmable infrastructure, really attacking that sort of labor discussion that we were having earlier, okay. Last but not least Marc Staimer, please. >> Thanks, Dave. My peers raised really good points. I agree with most of them, but I'm going to disagree with the title of this session, which is, does hardware matter? It absolutely matters. You can't run software on the air. You can't run it in an ephemeral cloud, although there's the technical cloud and that's a different issue. The cloud is kind of changed everything. And from a market perspective in the 40 plus years I've been in this business, I've seen this perception that hardware has to go down in price every year. And part of that was driven by Moore's law. And we're coming to, let's say a lag or an end, depending on who you talk to Moore's law. So we're not doubling our transistors every 18 to 24 months in a chip and as a result of that, there's been a higher emphasis on software. From a market perception, there's no penalty. They don't put the same pressure on software from the market to reduce the cost every year that they do on hardware, which kind of bass ackwards when you think about it. Hardware costs are fixed. Software costs tend to be very low. It's kind of a weird thing that we do in the market. And what's changing is we're now starting to treat hardware like software from an OPEX versus CapEx perspective. So yes, hardware matters. And we'll talk about that more in length. >> You know, I want to follow up on that. And I wonder if you guys have a thought on this, Bob O'Donnell, you and I have talked about this a little bit. Marc, you just pointed out that Moore's laws could have waning. Pat Gelsinger recently at their investor meeting said that he promised that Moore's law is alive and well. And the point I made in breaking analysis was okay, great. You know, Pat said, doubling transistors every 18 to 24 months, let's say that Intel can do that. Even though we know it's waning somewhat. Look at the M1 Ultra from Apple (chuckles). In about 15 months increased transistor density on their package by 6X. So to your earlier point, Bob, we have this sort of these alternative processors that are really changing things. And to Dave Nicholson's point, there's a whole lot of supporting components as well. Do you have a comment on that, Bob? >> Yeah, I mean, it's a great point, Dave. And one thing to bear in mind as well, not only are we seeing a diversity of these different chip architectures and different types of components as a number of us have raised the other big point and I think it was Keith that mentioned it. CXL and interconnect on the chip itself is dramatically changing it. And a lot of the more interesting advances that are going to continue to drive Moore's law forward in terms of the way we think about performance, if perhaps not number of transistors per se, is the interconnects that become available. You're seeing the development of chiplets or tiles, people use different names, but the idea is you can have different components being put together eventually in sort of a Lego block style. And what that's also going to allow, not only is that going to give interesting performance possibilities 'cause of the faster interconnect. So you can share, have shared memory between things which for big workloads like AI, huge data sets can make a huge difference in terms of how you talk to memory over a network connection, for example, but not only that you're going to see more diversity in the types of solutions that can be built. So we're going to see even more choices in hardware from a silicon perspective because you'll be able to piece together different elements. And oh, by the way, the other benefit of that is we've reached a point in chip architectures where not everything benefits from being smaller. We've been so focused and so obsessed when it comes to Moore's law, to the size of each individual transistor and yes, for certain architecture types, CPUs and GPUs in particular, that's absolutely true, but we've already hit the point where things like RF for 5g and wifi and other wireless technologies and a whole bunch of other things actually don't get any better with a smaller transistor size. They actually get worse. So the beauty of these chiplet architectures is you could actually combine different chip manufacturing sizes. You know you hear about four nanometer and five nanometer along with 14 nanometer on a single chip, each one optimized for its specific application yet together, they can give you the best of all worlds. And so we're just at the very beginning of that era, which I think is going to drive a ton of innovation. Again, gets back to my comment about different types of devices located geographically different places at the edge, in the data center, you know, in a private cloud versus a public cloud. All of those things are going to be impacted and there'll be a lot more options because of this silicon diversity and this interconnect diversity that we're just starting to see. >> Yeah, David. David Nicholson's got a graphic on that. They're going to show later. Before we do that, I want to introduce some data. I actually want to ask Keith to comment on this before we, you know, go on. This next slide is some data from ETR that shows the percent of customers that cited difficulty procuring hardware. And you can see the red is they had significant issues and it's most pronounced in laptops and networking hardware on the far right-hand side, but virtually all categories, firewalls, peripheral servers, storage are having moderately difficult procurement issues. That's the sort of pinkish or significant challenges. So Keith, I mean, what are you seeing with your customers in the hardware supply chains and bottlenecks? And you know we're seeing it with automobiles and appliances but so it goes beyond IT. The semiconductor, you know, challenges. What's been the impact on the buyer community and society and do you have any sense as to when it will subside? >> You know, I was just asked this question yesterday and I'm feeling the pain. People question, kind of a side project within the CTO advisor, we built a hybrid infrastructure, traditional IT data center that we're walking with the traditional customer and modernizing that data center. So it was, you know, kind of a snapshot of time in 2016, 2017, 10 gigabit, ARISTA switches, some older Dell's 730 XD switches, you know, speeds and feeds. And we said we would modern that with the latest Intel stack and connected to the public cloud and then the pandemic hit and we are experiencing a lot of the same challenges. I thought we'd easily migrate from 10 gig networking to 25 gig networking path that customers are going on. The 10 gig network switches that I bought used are now double the price because you can't get legacy 10 gig network switches because all of the manufacturers are focusing on the more profitable 25 gig for capacity, even the 25 gig switches. And we're focused on networking right now. It's hard to procure. We're talking about nine to 12 months or more lead time. So we're seeing customers adjust by adopting cloud. But if you remember early on in the pandemic, Microsoft Azure kind of gated customers that didn't have a capacity agreement. So customers are keeping an eye on that. There's a desire to abstract away from the underlying vendor to be able to control or provision your IT services in a way that we do with VMware VP or some other virtualization technology where it doesn't matter who can get me the hardware, they can just get me the hardware because it's critically impacting projects and timelines. >> So that's a great setup Zeus for you with Keith mentioned the earlier the software-defined data center with software-defined networking and cloud. Do you see a day where networking hardware is monetized and it's all about the software, or are we there already? >> No, we're not there already. And I don't see that really happening any time in the near future. I do think it's changed though. And just to be clear, I mean, when you look at that data, this is saying customers have had problems procuring the equipment, right? And there's not a network vendor out there. I've talked to Norman Rice at Extreme, and I've talked to the folks at Cisco and ARISTA about this. They all said they could have had blowout quarters had they had the inventory to ship. So it's not like customers aren't buying this anymore. Right? I do think though, when it comes to networking network has certainly changed some because there's a lot more controls as I mentioned before that you can do in software. And I think the customers need to start thinking about the types of hardware they buy and you know, where they're going to use it and, you know, what its purpose is. Because I've talked to customers that have tried to run software and commodity hardware and where the performance requirements are very high and it's bogged down, right? It just doesn't have the horsepower to run it. And, you know, even when you do that, you have to start thinking of the components you use. The NICs you buy. And I've talked to customers that have simply just gone through the process replacing a NIC card and a commodity box and had some performance problems and, you know, things like that. So if agility is more important than performance, then by all means try running software on commodity hardware. I think that works in some cases. If performance though is more important, that's when you need that kind of turnkey hardware system. And I've actually seen more and more customers reverting back to that model. In fact, when you talk to even some startups I think today about when they come to market, they're delivering things more on appliances because that's what customers want. And so there's this kind of app pivot this pendulum of agility and performance. And if performance absolutely matters, that's when you do need to buy these kind of turnkey, prebuilt hardware systems. If agility matters more, that's when you can go more to software, but the underlying hardware still does matter. So I think, you know, will we ever have a day where you can just run it on whatever hardware? Maybe but I'll long be retired by that point. So I don't care. >> Well, you bring up a good point Zeus. And I remember the early days of cloud, the narrative was, oh, the cloud vendors. They don't use EMC storage, they just run on commodity storage. And then of course, low and behold, you know, they've trot out James Hamilton to talk about all the custom hardware that they were building. And you saw Google and Microsoft follow suit. >> Well, (indistinct) been falling for this forever. Right? And I mean, all the way back to the turn of the century, we were calling for the commodity of hardware. And it's never really happened because you can still drive. As long as you can drive innovation into it, customers will always lean towards the innovation cycles 'cause they get more features faster and things. And so the vendors have done a good job of keeping that cycle up but it'll be a long time before. >> Yeah, and that's why you see companies like Pure Storage. A storage company has 69% gross margins. All right. I want to go jump ahead. We're going to bring up the slide four. I want to go back to something that Bob O'Donnell was talking about, the sort of supporting act. The diversity of silicon and we've marched to the cadence of Moore's law for decades. You know, we asked, you know, is Moore's law dead? We say it's moderating. Dave Nicholson. You want to talk about those supporting components. And you shared with us a slide that shift. You call it a shift from a processor-centric world to a connect-centric world. What do you mean by that? And let's bring up slide four and you can talk to that. >> Yeah, yeah. So first, I want to echo this sentiment that the question does hardware matter is sort of the answer is of course it matters. Maybe the real question should be, should you care about it? And the answer to that is it depends who you are. If you're an end user using an application on your mobile device, maybe you don't care how the architecture is put together. You just care that the service is delivered but as you back away from that and you get closer and closer to the source, someone needs to care about the hardware and it should matter. Why? Because essentially what hardware is doing is it's consuming electricity and dollars and the more efficiently you can configure hardware, the more bang you're going to get for your buck. So it's not only a quantitative question in terms of how much can you deliver? But it also ends up being a qualitative change as capabilities allow for things we couldn't do before, because we just didn't have the aggregate horsepower to do it. So this chart actually comes out of some performance tests that were done. So it happens to be Dell servers with Broadcom components. And the point here was to peel back, you know, peel off the top of the server and look at what's in that server, starting with, you know, the PCI interconnect. So PCIE gen three, gen four, moving forward. What are the effects on from an interconnect versus on performance application performance, translating into new orders per minute, processed per dollar, et cetera, et cetera? If you look at the advances in CPU architecture mapped against the advances in interconnect and storage subsystem performance, you can see that CPU architecture is sort of lagging behind in a way. And Bob mentioned this idea of tiling and all of the different ways to get around that. When we do performance testing, we can actually peg CPUs, just running the performance tests without any actual database environments working. So right now we're at this sort of imbalance point where you have to make sure you design things properly to get the most bang per kilowatt hour of power per dollar input. So the key thing here what this is highlighting is just as a very specific example, you take a card that's designed as a gen three PCIE device, and you plug it into a gen four slot. Now the card is the bottleneck. You plug a gen four card into a gen four slot. Now the gen four slot is the bottleneck. So we're constantly chasing these bottlenecks. Someone has to be focused on that from an architectural perspective, it's critically important. So there's no question that it matters. But of course, various people in this food chain won't care where it comes from. I guess a good analogy might be, where does our food come from? If I get a steak, it's a pink thing wrapped in plastic, right? Well, there are a lot of inputs that a lot of people have to care about to get that to me. Do I care about all of those things? No. Are they important? They're critically important. >> So, okay. So all I want to get to the, okay. So what does this all mean to customers? And so what I'm hearing from you is to balance a system it's becoming, you know, more complicated. And I kind of been waiting for this day for a long time, because as we all know the bottleneck was always the spinning disc, the last mechanical. So people who wrote software knew that when they were doing it right, the disc had to go and do stuff. And so they were doing other things in the software. And now with all these new interconnects and flash and things like you could do atomic rights. And so that opens up new software possibilities and combine that with alternative processes. But what's the so what on this to the customer and the application impact? Can anybody address that? >> Yeah, let me address that for a moment. I want to leverage some of the things that Bob said, Keith said, Zeus said, and David said, yeah. So I'm a bit of a contrarian in some of this. For example, on the chip side. As the chips get smaller, 14 nanometer, 10 nanometer, five nanometer, soon three nanometer, we talk about more cores, but the biggest problem on the chip is the interconnect from the chip 'cause the wires get smaller. People don't realize in 2004 the latency on those wires in the chips was 80 picoseconds. Today it's 1300 picoseconds. That's on the chip. This is why they're not getting faster. So we maybe getting a little bit slowing down in Moore's law. But even as we kind of conquer that you still have the interconnect problem and the interconnect problem goes beyond the chip. It goes within the system, composable architectures. It goes to the point where Keith made, ultimately you need a hybrid because what we're seeing, what I'm seeing and I'm talking to customers, the biggest issue they have is moving data. Whether it be in a chip, in a system, in a data center, between data centers, moving data is now the biggest gating item in performance. So if you want to move it from, let's say your transactional database to your machine learning, it's the bottleneck, it's moving the data. And so when you look at it from a distributed environment, now you've got to move the compute to the data. The only way to get around these bottlenecks today is to spend less time in trying to move the data and more time in taking the compute, the software, running on hardware closer to the data. Go ahead. >> So is this what you mean when Nicholson was talking about a shift from a processor centric world to a connectivity centric world? You're talking about moving the bits across all the different components, not having the processor you're saying is essentially becoming the bottleneck or the memory, I guess. >> Well, that's one of them and there's a lot of different bottlenecks, but it's the data movement itself. It's moving away from, wait, why do we need to move the data? Can we move the compute, the processing closer to the data? Because if we keep them separate and this has been a trend now where people are moving processing away from it. It's like the edge. I think it was Zeus or David. You were talking about the edge earlier. As you look at the edge, who defines the edge, right? Is the edge a closet or is it a sensor? If it's a sensor, how do you do AI at the edge? When you don't have enough power, you don't have enough computable. People were inventing chips to do that. To do all that at the edge, to do AI within the sensor, instead of moving the data to a data center or a cloud to do the processing. Because the lag in latency is always limited by speed of light. How fast can you move the electrons? And all this interconnecting, all the processing, and all the improvement we're seeing in the PCIE bus from three, to four, to five, to CXL, to a higher bandwidth on the network. And that's all great but none of that deals with the speed of light latency. And that's an-- Go ahead. >> You know Marc, no, I just want to just because what you're referring to could be looked at at a macro level, which I think is what you're describing. You can also look at it at a more micro level from a systems design perspective, right? I'm going to be the resident knuckle dragging hardware guy on the panel today. But it's exactly right. You moving compute closer to data includes concepts like peripheral cards that have built in intelligence, right? So again, in some of this testing that I'm referring to, we saw dramatic improvements when you basically took the horsepower instead of using the CPU horsepower for the like IO. Now you have essentially offload engines in the form of storage controllers, rate controllers, of course, for ethernet NICs, smart NICs. And so when you can have these sort of offload engines and we've gone through these waves over time. People think, well, wait a minute, raid controller and NVMe? You know, flash storage devices. Does that make sense? It turns out it does. Why? Because you're actually at a micro level doing exactly what you're referring to. You're bringing compute closer to the data. Now, closer to the data meaning closer to the data storage subsystem. It doesn't solve the macro issue that you're referring to but it is important. Again, going back to this idea of system design optimization, always chasing the bottleneck, plugging the holes. Someone needs to do that in this value chain in order to get the best value for every kilowatt hour of power and every dollar. >> Yeah. >> Well this whole drive performance has created some really interesting architectural designs, right? Like Nickelson, the rise of the DPU right? Brings more processing power into systems that already had a lot of processing power. There's also been some really interesting, you know, kind of innovation in the area of systems architecture too. If you look at the way Nvidia goes to market, their drive kit is a prebuilt piece of hardware, you know, optimized for self-driving cars, right? They partnered with Pure Storage and ARISTA to build that AI-ready infrastructure. I remember when I talked to Charlie Giancarlo, the CEO of Pure about when the three companies rolled that out. He said, "Look, if you're going to do AI, "you need good store. "You need fast storage, fast processor and fast network." And so for customers to be able to put that together themselves was very, very difficult. There's a lot of software that needs tuning as well. So the three companies partner together to create a fully integrated turnkey hardware system with a bunch of optimized software that runs on it. And so in that case, in some ways the hardware was leading the software innovation. And so, the variety of different architectures we have today around hardware has really exploded. And I think it, part of the what Bob brought up at the beginning about the different chip design. >> Yeah, Bob talked about that earlier. Bob, I mean, most AI today is modeling, you know, and a lot of that's done in the cloud and it looks from my standpoint anyway that the future is going to be a lot of AI inferencing at the edge. And that's a radically different architecture, Bob, isn't it? >> It is, it's a completely different architecture. And just to follow up on a couple points, excellent conversation guys. Dave talked about system architecture and really this that's what this boils down to, right? But it's looking at architecture at every level. I was talking about the individual different components the new interconnect methods. There's this new thing called UCIE universal connection. I forget what it stands answer for, but it's a mechanism for doing chiplet architectures, but then again, you have to take it up to the system level, 'cause it's all fine and good. If you have this SOC that's tuned and optimized, but it has to talk to the rest of the system. And that's where you see other issues. And you've seen things like CXL and other interconnect standards, you know, and nobody likes to talk about interconnect 'cause it's really wonky and really technical and not that sexy, but at the end of the day it's incredibly important exactly. To the other points that were being raised like mark raised, for example, about getting that compute closer to where the data is and that's where again, a diversity of chip architectures help and exactly to your last comment there Dave, putting that ability in an edge device is really at the cutting edge of what we're seeing on a semiconductor design and the ability to, for example, maybe it's an FPGA, maybe it's a dedicated AI chip. It's another kind of chip architecture that's being created to do that inferencing on the edge. Because again, it's that the cost and the challenges of moving lots of data, whether it be from say a smartphone to a cloud-based application or whether it be from a private network to a cloud or any other kinds of permutations we can think of really matters. And the other thing is we're tackling bigger problems. So architecturally, not even just architecturally within a system, but when we think about DPUs and the sort of the east west data center movement conversation that we hear Nvidia and others talk about, it's about combining multiple sets of these systems to function together more efficiently again with even bigger sets of data. So really is about tackling where the processing is needed, having the interconnect and the ability to get where the data you need to the right place at the right time. And because those needs are diversifying, we're just going to continue to see an explosion of different choices and options, which is going to make hardware even more essential I would argue than it is today. And so I think what we're going to see not only does hardware matter, it's going to matter even more in the future than it does now. >> Great, yeah. Great discussion, guys. I want to bring Keith back into the conversation here. Keith, if your main expertise in tech is provisioning LUNs, you probably you want to look for another job. So maybe clearly hardware matters, but with software defined everything, do people with hardware expertise matter outside of for instance, component manufacturers or cloud companies? I mean, VMware certainly changed the dynamic in servers. Dell just spun off its most profitable asset and VMware. So it obviously thinks hardware can stand alone. How does an enterprise architect view the shift to software defined hyperscale cloud and how do you see the shifting demand for skills in enterprise IT? >> So I love the question and I'll take a different view of it. If you're a data analyst and your primary value add is that you do ETL transformation, talk to a CDO, a chief data officer over midsize bank a little bit ago. He said 80% of his data scientists' time is done on ETL. Super not value ad. He wants his data scientists to do data science work. Chances are if your only value is that you do LUN provisioning, then you probably don't have a job now. The technologies have gotten much more intelligent. As infrastructure pros, we want to give infrastructure pros the opportunities to shine and I think the software defined nature and the automation that we're seeing vendors undertake, whether it's Dell, HP, Lenovo take your pick that Pure Storage, NetApp that are doing the automation and the ML needed so that these practitioners don't spend 80% of their time doing LUN provisioning and focusing on their true expertise, which is ensuring that data is stored. Data is retrievable, data's protected, et cetera. I think the shift is to focus on that part of the job that you're ensuring no matter where the data's at, because as my data is spread across the enterprise hybrid different types, you know, Dave, you talk about the super cloud a lot. If my data is in the super cloud, protecting that data and securing that data becomes much more complicated when than when it was me just procuring or provisioning LUNs. So when you say, where should the shift be, or look be, you know, focusing on the real value, which is making sure that customers can access data, can recover data, can get data at performance levels that they need within the price point. They need to get at those datasets and where they need it. We talked a lot about where they need out. One last point about this interconnecting. I have this vision and I think we all do of composable infrastructure. This idea that scaled out does not solve every problem. The cloud can give me infinite scale out. Sometimes I just need a single OS with 64 terabytes of RAM and 204 GPUs or GPU instances that single OS does not exist today. And the opportunity is to create composable infrastructure so that we solve a lot of these problems that just simply don't scale out. >> You know, wow. So many interesting points there. I had just interviewed Zhamak Dehghani, who's the founder of Data Mesh last week. And she made a really interesting point. She said, "Think about, we have separate stacks. "We have an application stack and we have "a data pipeline stack and the transaction systems, "the transaction database, we extract data from that," to your point, "We ETL it in, you know, it takes forever. "And then we have this separate sort of data stack." If we're going to inject more intelligence and data and AI into applications, those two stacks, her contention is they have to come together. And when you think about, you know, super cloud bringing compute to data, that was what Haduck was supposed to be. It ended up all sort of going into a central location, but it's almost a rhetorical question. I mean, it seems that that necessitates new thinking around hardware architectures as it kind of everything's the edge. And the other point is to your point, Keith, it's really hard to secure that. So when you can think about offloads, right, you've heard the stats, you know, Nvidia talks about it. Broadcom talks about it that, you know, that 30%, 25 to 30% of the CPU cycles are wasted on doing things like storage offloads, or networking or security. It seems like maybe Zeus you have a comment on this. It seems like new architectures need to come other to support, you know, all of that stuff that Keith and I just dispute. >> Yeah, and by the way, I do want to Keith, the question you just asked. Keith, it's the point I made at the beginning too about engineers do need to be more software-centric, right? They do need to have better software skills. In fact, I remember talking to Cisco about this last year when they surveyed their engineer base, only about a third of 'em had ever made an API call, which you know that that kind of shows this big skillset change, you know, that has to come. But on the point of architectures, I think the big change here is edge because it brings in distributed compute models. Historically, when you think about compute, even with multi-cloud, we never really had multi-cloud. We'd use multiple centralized clouds, but compute was always centralized, right? It was in a branch office, in a data center, in a cloud. With edge what we creates is the rise of distributed computing where we'll have an application that actually accesses different resources and at different edge locations. And I think Marc, you were talking about this, like the edge could be in your IoT device. It could be your campus edge. It could be cellular edge, it could be your car, right? And so we need to start thinkin' about how our applications interact with all those different parts of that edge ecosystem, you know, to create a single experience. The consumer apps, a lot of consumer apps largely works that way. If you think of like app like Uber, right? It pulls in information from all kinds of different edge application, edge services. And, you know, it creates pretty cool experience. We're just starting to get to that point in the business world now. There's a lot of security implications and things like that, but I do think it drives more architectural decisions to be made about how I deploy what data where and where I do my processing, where I do my AI and things like that. It actually makes the world more complicated. In some ways we can do so much more with it, but I think it does drive us more towards turnkey systems, at least initially in order to, you know, ensure performance and security. >> Right. Marc, I wanted to go to you. You had indicated to me that you wanted to chat about this a little bit. You've written quite a bit about the integration of hardware and software. You know, we've watched Oracle's move from, you know, buying Sun and then basically using that in a highly differentiated approach. Engineered systems. What's your take on all that? I know you also have some thoughts on the shift from CapEx to OPEX chime in on that. >> Sure. When you look at it, there are advantages to having one vendor who has the software and hardware. They can synergistically make them work together that you can't do in a commodity basis. If you own the software and somebody else has the hardware, I'll give you an example would be Oracle. As you talked about with their exit data platform, they literally are leveraging microcode in the Intel chips. And now in AMD chips and all the way down to Optane, they make basically AMD database servers work with Optane memory PMM in their storage systems, not MVME, SSD PMM. I'm talking about the cards itself. So there are advantages you can take advantage of if you own the stack, as you were putting out earlier, Dave, of both the software and the hardware. Okay, that's great. But on the other side of that, that tends to give you better performance, but it tends to cost a little more. On the commodity side it costs less but you get less performance. What Zeus had said earlier, it depends where you're running your application. How much performance do you need? What kind of performance do you need? One of the things about moving to the edge and I'll get to the OPEX CapEx in a second. One of the issues about moving to the edge is what kind of processing do you need? If you're running in a CCTV camera on top of a traffic light, how much power do you have? How much cooling do you have that you can run this? And more importantly, do you have to take the data you're getting and move it somewhere else and get processed and the information is sent back? I mean, there are companies out there like Brain Chip that have developed AI chips that can run on the sensor without a CPU. Without any additional memory. So, I mean, there's innovation going on to deal with this question of data movement. There's companies out there like Tachyon that are combining GPUs, CPUs, and DPUs in a single chip. Think of it as super composable architecture. They're looking at being able to do more in less. On the OPEX and CapEx issue. >> Hold that thought, hold that thought on the OPEX CapEx, 'cause we're running out of time and maybe you can wrap on that. I just wanted to pick up on something you said about the integrated hardware software. I mean, other than the fact that, you know, Michael Dell unlocked whatever $40 billion for himself and Silverlake, I was always a fan of a spin in with VMware basically become the Oracle of hardware. Now I know it would've been a nightmare for the ecosystem and culturally, they probably would've had a VMware brain drain, but what does anybody have any thoughts on that as a sort of a thought exercise? I was always a fan of that on paper. >> I got to eat a little crow. I did not like the Dale VMware acquisition for the industry in general. And I think it hurt the industry in general, HPE, Cisco walked away a little bit from that VMware relationship. But when I talked to customers, they loved it. You know, I got to be honest. They absolutely loved the integration. The VxRail, VxRack solution exploded. Nutanix became kind of a afterthought when it came to competing. So that spin in, when we talk about the ability to innovate and the ability to create solutions that you just simply can't create because you don't have the full stack. Dell was well positioned to do that with a potential span in of VMware. >> Yeah, we're going to be-- Go ahead please. >> Yeah, in fact, I think you're right, Keith, it was terrible for the industry. Great for Dell. And I remember talking to Chad Sakac when he was running, you know, VCE, which became Rack and Rail, their ability to stay in lockstep with what VMware was doing. What was the number one workload running on hyperconverged forever? It was VMware. So their ability to remain in lockstep with VMware gave them a huge competitive advantage. And Dell came out of nowhere in, you know, the hyper-converged market and just started taking share because of that relationship. So, you know, this sort I guess it's, you know, from a Dell perspective I thought it gave them a pretty big advantage that they didn't really exploit across their other properties, right? Networking and service and things like they could have given the dominance that VMware had. From an industry perspective though, I do think it's better to have them be coupled. So. >> I agree. I mean, they could. I think they could have dominated in super cloud and maybe they would become the next Oracle where everybody hates 'em, but they kick ass. But guys. We got to wrap up here. And so what I'm going to ask you is I'm going to go and reverse the order this time, you know, big takeaways from this conversation today, which guys by the way, I can't thank you enough phenomenal insights, but big takeaways, any final thoughts, any research that you're working on that you want highlight or you know, what you look for in the future? Try to keep it brief. We'll go in reverse order. Maybe Marc, you could start us off please. >> Sure, on the research front, I'm working on a total cost of ownership of an integrated database analytics machine learning versus separate services. On the other aspect that I would wanted to chat about real quickly, OPEX versus CapEx, the cloud changed the market perception of hardware in the sense that you can use hardware or buy hardware like you do software. As you use it, pay for what you use in arrears. The good thing about that is you're only paying for what you use, period. You're not for what you don't use. I mean, it's compute time, everything else. The bad side about that is you have no predictability in your bill. It's elastic, but every user I've talked to says every month it's different. And from a budgeting perspective, it's very hard to set up your budget year to year and it's causing a lot of nightmares. So it's just something to be aware of. From a CapEx perspective, you have no more CapEx if you're using that kind of base system but you lose a certain amount of control as well. So ultimately that's some of the issues. But my biggest point, my biggest takeaway from this is the biggest issue right now that everybody I talk to in some shape or form it comes down to data movement whether it be ETLs that you talked about Keith or other aspects moving it between hybrid locations, moving it within a system, moving it within a chip. All those are key issues. >> Great, thank you. Okay, CTO advisor, give us your final thoughts. >> All right. Really, really great commentary. Again, I'm going to point back to us taking the walk that our customers are taking, which is trying to do this conversion of all primary data center to a hybrid of which I have this hard earned philosophy that enterprise IT is additive. When we add a service, we rarely subtract a service. So the landscape and service area what we support has to grow. So our research focuses on taking that walk. We are taking a monolithic application, decomposing that to containers, and putting that in a public cloud, and connecting that back private data center and telling that story and walking that walk with our customers. This has been a super enlightening panel. >> Yeah, thank you. Real, real different world coming. David Nicholson, please. >> You know, it really hearkens back to the beginning of the conversation. You talked about momentum in the direction of cloud. I'm sort of spending my time under the hood, getting grease under my fingernails, focusing on where still the lions share of spend will be in coming years, which is OnPrem. And then of course, obviously data center infrastructure for cloud but really diving under the covers and helping folks understand the ramifications of movement between generations of CPU architecture. I know we all know Sapphire Rapids pushed into the future. When's the next Intel release coming? Who knows? We think, you know, in 2023. There have been a lot of people standing by from a practitioner's standpoint asking, well, what do I do between now and then? Does it make sense to upgrade bits and pieces of hardware or go from a last generation to a current generation when we know the next generation is coming? And so I've been very, very focused on looking at how these connectivity components like rate controllers and NICs. I know it's not as sexy as talking about cloud but just how these opponents completely change the game and actually can justify movement from say a 14th-generation architecture to a 15th-generation architecture today, even though gen 16 is coming, let's say 12 months from now. So that's where I am. Keep my phone number in the Rolodex. I literally reference Rolodex intentionally because like I said, I'm in there under the hood and it's not as sexy. But yeah, so that's what I'm focused on Dave. >> Well, you know, to paraphrase it, maybe derivative paraphrase of, you know, Larry Ellison's rant on what is cloud? It's operating systems and databases, et cetera. Rate controllers and NICs live inside of clouds. All right. You know, one of the reasons I love working with you guys is 'cause have such a wide observation space and Zeus Kerravala you, of all people, you know you have your fingers in a lot of pies. So give us your final thoughts. >> Yeah, I'm not a propeller heady as my chip counterparts here. (all laugh) So, you know, I look at the world a little differently and a lot of my research I'm doing now is the impact that distributed computing has on customer employee experiences, right? You talk to every business and how the experiences they deliver to their customers is really differentiating how they go to market. And so they're looking at these different ways of feeding up data and analytics and things like that in different places. And I think this is going to have a really profound impact on enterprise IT architecture. We're putting more data, more compute in more places all the way down to like little micro edges and retailers and things like that. And so we need the variety. Historically, if you think back to when I was in IT you know, pre-Y2K, we didn't have a lot of choice in things, right? We had a server that was rack mount or standup, right? And there wasn't a whole lot of, you know, differences in choice. But today we can deploy, you know, these really high-performance compute systems on little blades inside servers or inside, you know, autonomous vehicles and things. I think the world from here gets... You know, just the choice of what we have and the way hardware and software works together is really going to, I think, change the world the way we do things. We're already seeing that, like I said, in the consumer world, right? There's so many things you can do from, you know, smart home perspective, you know, natural language processing, stuff like that. And it's starting to hit businesses now. So just wait and watch the next five years. >> Yeah, totally. The computing power at the edge is just going to be mind blowing. >> It's unbelievable what you can do at the edge. >> Yeah, yeah. Hey Z, I just want to say that we know you're not a propeller head and I for one would like to thank you for having your master's thesis hanging on the wall behind you 'cause we know that you studied basket weaving. >> I was actually a physics math major, so. >> Good man. Another math major. All right, Bob O'Donnell, you're going to bring us home. I mean, we've seen the importance of semiconductors and silicon in our everyday lives, but your last thoughts please. >> Sure and just to clarify, by the way I was a great books major and this was actually for my final paper. And so I was like philosophy and all that kind of stuff and literature but I still somehow got into tech. Look, it's been a great conversation and I want to pick up a little bit on a comment Zeus made, which is this it's the combination of the hardware and the software and coming together and the manner with which that needs to happen, I think is critically important. And the other thing is because of the diversity of the chip architectures and all those different pieces and elements, it's going to be how software tools evolve to adapt to that new world. So I look at things like what Intel's trying to do with oneAPI. You know, what Nvidia has done with CUDA. What other platform companies are trying to create tools that allow them to leverage the hardware, but also embrace the variety of hardware that is there. And so as those software development environments and software development tools evolve to take advantage of these new capabilities, that's going to open up a lot of interesting opportunities that can leverage all these new chip architectures. That can leverage all these new interconnects. That can leverage all these new system architectures and figure out ways to make that all happen, I think is going to be critically important. And then finally, I'll mention the research I'm actually currently working on is on private 5g and how companies are thinking about deploying private 5g and the potential for edge applications for that. So I'm doing a survey of several hundred us companies as we speak and really looking forward to getting that done in the next couple of weeks. >> Yeah, look forward to that. Guys, again, thank you so much. Outstanding conversation. Anybody going to be at Dell tech world in a couple of weeks? Bob's going to be there. Dave Nicholson. Well drinks on me and guys I really can't thank you enough for the insights and your participation today. Really appreciate it. Okay, and thank you for watching this special power panel episode of theCube Insights powered by ETR. Remember we publish each week on Siliconangle.com and wikibon.com. All these episodes they're available as podcasts. DM me or any of these guys. I'm at DVellante. You can email me at David.Vellante@siliconangle.com. Check out etr.ai for all the data. This is Dave Vellante. We'll see you next time. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> New data from ETR's soon to be released April survey, shows a clear deceleration in spending and a more cautious posture from technology buyers. Just this week, we saw sell side downgrades in hardware companies like Dell and HP and revised guidance from high flyer UiPath, citing exposures to Russia, Europe and certain sales execution challenges, but these headlines, we think are a canary in the coal mine. According to ETR analysis and channel checks in theCUBE, the real story is these issues are not isolated. Rather we're seeing signs of caution from buyers across the board in enterprise tech. Hello and welcome to this week's Wikibon CUBE insights powered by ETR. In this Breaking Analysis, we are the bearers of bad news. Don't shoot the messenger. We'll share a first look at fresh data that suggests a tightening in tech spending calling for 6% growth this year which is below our January prediction of 8% for 2022. Now, unfortunately the party may be coming to an end at least for a while. You know, it's really not surprising, right? We've had a two year record run in tech spending and meteoric rises in high flying technology stocks. Hybrid work, equipping and securing remote workers. The forced march to digital that we talk about sometimes. These were all significant tailwinds for tech companies. The NASDAQ peaked late last year and then as you can see in this chart, bottomed in mid-March of 2022, and it made a nice run up through the 29th of last month, but the mini rally appears to be in jeopardy with FED rate hikes, Russia, supply chain challenges. There's a lot of uncertainty so we should expect the C-suite to be saying, hey, wait slow down. Now we don't think the concerns are confined to companies with exposure to Russia and Europe. We think it's more broad based than that and we're seeing caution from technology companies and tech buyers that we think is prudent, given the conditions. You know, looks like the two year party has ended and as my ETR colleague Erik Bradley said, a little hangover shouldn't be a surprise to anybody. So let's get right to the new spending data. I'm limited to what I can share with you today because ETR is in its quiet period and hasn't released full results yet outside of its client base. But, they did put out an alert today and I can share this slide. It shows the expectation on spending growth from more than a thousand CIOs and IT buyers who responded in the most recent survey. It measures their expectations for spending. The key focus areas that I want you to pay attention to in this data are the yellow bars. The most recent survey is the yellow compared to the blue and the gray bars, which are the December and September '21 surveys respectively. And you can see a steep drop from last year in Q1, lowered expectations for Q2 in the far right, a drop from nearly 9% last September to around 6% today. Now you may think a 200 basis point downgrade from our prediction in January of 8% seems somewhat benign, but in a $4 trillion IT market, that's 80 billion coming off the income statements of some tech companies. Now the good news is that 6% growth is still very healthy and higher than pre pandemic spending levels. And the buyers we've talked to this week are saying, look, we're still spending money. We just have to be more circumspect about where and how fast. Now, there were a few other callouts in the ETR data and in my discussions today with Erik Bradley on this. First, it looks like in response to expected supply chain constraints that buyers pulled forward their orders late last year and earlier this year. You remember when we couldn't buy toilet paper, people started the stockpile and it created this rubber banding effect. So we see clear signs of receding momentum in the PC and laptop market. But as we said, this is not isolated to PCs, UiPath's earning guidance confirm this but the story doesn't end there. This isn't isolated to UiPath in our view, rather it's a more based slowdown. The other big sign is spending in outsourced IT which is showing a meaningful deceleration in the last survey, showing a net score drop from 13% in January to 6% today. Net score remember is a measure of the net percentage of customers in the survey that on balance are spending more than last survey. It's derived by subtracting the percent of customers spending less from those spending more. And there's a, that's a 700 basis point drop in three months. This isn't a market where you can't hire enough people. The percent of companies hiring has gone from 10% during the pandemic to 50% today according to recent data from ETR. And we know there's still an acute skills shortage. So you would expect more IT outsourcing, but you don't see that in the data, it's down. And as this quote from Erik Bradley explains, historically, when outsourced IT drops like this, especially in a tight labor market, it's not good news for IT spending. All right, now, the other interesting callout from ETR were some specific company names that appear to be seeing the biggest change in spending momentum. Here's the list of those companies that all have meaningful exposure to Europe. That's really where the focus was. SAP has big exposure to on-premises installations and of course, Europe as well. ServiceNow has European exposure and also broad based exposure in IT in across the globe, especially in the US. Zoom didn't go to the moon, no surprise there given the quasi return to work and Zoom fatigue. McAfee is a bit of a concern because security seemed to be one of those areas, when you look at some of the other data, that is per actually insulated from all the spending caution. Of course we saw the Okta hack and we're going to cover that next week with hopefully some new data from ETR, but generally security's been holding up pretty well. You look at CrowdStrike, you look at Zscaler in particular. Adobe's another company that's had a nice bounce in the last couple of weeks. Accenture, again, speaks to that outsourcing headwinds that we mentioned earlier. And now the Google Cloud platform is a bit of a concern. It's still elevated overall, you know but down and well down in Europe. Under that magic, you know we often show that magic 40% dotted line, that red dotted line of net score anything above that we cite as elevated. Well, some important callouts to hear that you see companies that have Euro exposure. And again, we see this as just not confined to Europe and this is something we're going to pay close attention to and continue to report on in the next several weeks and months. All right, so what should we expect from here? The Ark investment stocks of Cathie Wood fame have been tracking in a downward trend since last November, meaning, you know, these high PE stocks are making lower lows and higher, sorry, lower highs and lower lows since then, right? The trend is not their friend. Investors I talk to are being much more cautious about buying the dip. They're raising cash and being a little bit more patient. You know, traders can trade in this environment but unless you can pay attention to in a minute by minute you're going to get whipsawed. Investors tell me that they're still eyeing big tech even though Apple has been on a recent tear and has some exposure with supply change challenges, they're looking for maybe entry points in, within that chop for Apple, Amazon, Microsoft, and Alphabet. And look, as I've been stressing, 6% spending growth is still very solid. It's a case of resetting the outlook relative to previous expectations. So when you zoom out and look at the growth in data, getting digital right, security investments, automation, cloud, AI containers, all the fundamentals are really strong and they have not changed. They're all powering this new digital economy and we believe it's just prudence versus a shift in the importance of IT. Now, one point of caution is there's a lot of discussion around a shift in global economies. Supply chain uncertainty, persistent semiconductor shortages especially in areas like, you know driver ICs and boring things like parts for displays and analog and micro controllers and power regulators. Stuff that's, you know, just not playing nice these days and wreaking havoc. And this creates uncertainty, which sometimes can pick up momentum in a snowballing effect. And that's something that we're watching closely and we're going to be vigilant reporting to you when we see changes in the data and in our forecast even when we think our forecast are wrong. Okay, that's it for today. Thanks to Alex Merson who does the production and podcasts for Breaking Analysis and Stephanie Chan who provides background research. Kristen Martin and Cheryl Knight, and all theCUBE writers they help get the word out, and thanks to Rob Hof, our EIC over at SiliconANGLE. Remember I publish weekly on wikibon.com and siliconangle.com. These episodes are all available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcasts. etr.ai that's where you can get access to all this survey data and make your own cuts. It's awesome, check that out. Keep in touch with me. You can email me at dave.vellante@siliconangle.com. You can hit me up on LinkedIn. This is Dave Vellante for theCUBE insights powered by ETR. Be safe, stay well, and we'll see you next time. (gentle music)

(bright upbeat music) >> According to the latest survey from Enterprise Technology Research, container orchestration is the number one category as measured by customer spending momentum. It's ahead of AIML, it's ahead of cloud computing, and it's ahead of robotic process automation. All of which also show highly elevated levels of customer spending velocity. Now, we drill deeper into the survey of more than 1200 CIOs and IT buyers, and we find that a whopping 70% of respondents are spending more on Kubernetes initiatives in 2022 as compared to last year. The rise of Kubernetes came about through a series of improbable events that change the way applications are developed, deployed and managed. Very early on Kubernetes committers chose to focus on simplicity in massive adoption rather than deep enterprise functionality. It's why initially virtually all activity around Kubernetes focused on stateless applications. That has changed. As Kubernetes adoption has gone mainstream, the need for stronger enterprise functionality has become much more pressing. You hear this constantly when you attend the various developer conference, and the talk is all around, let's say, shift left to improve security and better cluster management, more complete automation capabilities, support for data-driven workloads and very importantly, vastly better application performance in visibility and management. And that last topic is what we're here to talk about today. Hello, this is Dave Vellante, and welcome to this special CUBE conversation where we invite into our East Coast Studios Matt Provo, who's the founder and CEO of StormForge and Chandler Hoisington, the general manager of EKS Edge in Hybrid at AWS. Gentlemen, welcome, it's good to see you. >> Thanks. >> Thanks for having us. >> So Chandler, you have this convergence, you've got application performance, you've got developer speed and velocity and you've got cloud economics all coming together. What's driving that convergence and why is it important for customers? >> Yeah, yeah, great question. I think it's important to kind of understand how we got here in the first place. I think Kubernetes solves a lot of problems for users, but the complexity of Kubernetes of just standing up a cluster to begin with is not always simple. And that's where services like EKS comes in and where Amazon tried to solve that problem for users saying, "Hey the control plane, it's made up of 10, 15 different components, standing all these up, patching them, you know, handling the CBEs for it et cetera, et cetera, is a very complicated process, let me help you do that." And where EKS has been so successful and with EKS Anywhere which we launched last year, that's what we're helping customers do, a very similar thing in their own data centers. So we're kind of solving this problem of bringing the cluster online and helping customers launch their first application on it. But then what do you do once your application's there? That's the question. And so now you launched your application and does it have enough resources? Did you tune the right CPU? Did you tune the right amount of memory for it? All those questions need to be answered and that's where working with folks like StormForge come in. >> Well, it's interesting Matt because you're all about optimization and trying to maximize the efficiency which might mean people's lower their AWS bill, but that's okay with Amazon, right? You guys have shown the cheaper it is, the more they buy, well. >> Yeah. And it's all about loyalty and developer experience. And so when you can help create or add to the developer experience itself, over time that loyalty's there. And so when we can come alongside EKS and services from Amazon, well, number one StormForge is built on Amazon, on AWS, and so it's a nice fit, but when we don't have to require developers to choose between things like cost and performance, but they can focus on, you know, innovation and connecting the applications that they're managing on Kubernetes as they operationalize them to the actual business objectives that they have, it's a pretty powerful combination. >> So your entry into the market was in pre-production. >> Yeah. >> You can kind of simulate what performance is going to look like and now you've announced optimized live. >> Yep. >> So that should allow you to turn the crank a little bit more. >> Yeah. >> Get a little bit more accurate and respond more quickly. >> Yeah. So we're the only ones that give you both views. And so we want to, you know, we want to provide a view in what we call kind of our experimentation side of our platform, which is pre-production, as well as on ongoing and continuous view which we kind of call our observation, the observation part of our solution, which is in production. And so for us, it's about providing that view, it's also about taking an increased number of data inputs into the platform itself so that our machine learning can learn from that and ultimately be able to automate the right kinds of tasks alongside the developers to meet their objectives. >> So, Chandler, in my intro I was talking about the spending velocity and how Kubernetes was at the top. But when we had other survey questions that ETR did, and this is post pandemic, it was interesting. We asked what's the most important initiative? And the two top ones were security, no surprise, and it popped up really after the pandemic hit in the lockdown even more prominent and cloud migration, >> Right. >> was number two. And so how are you working with StormForge to effect cloud migrations? Talk about that relationship. >> Yeah. I think it's, you know, different enterprises to have different strategies on how they're going to get their workloads to the cloud. Some of 'em want to have modernize in place in their data centers and then take those modernized applications and move them to the cloud, and that's where something like I mentioned earlier, EKS Anywhere comes into play really nicely because we can bring a consistent experience, a Kubernetes experience to your data center, you can modernize your applications and then you can bring those to EKS in the cloud. And as you're moving them back and forth you have a more consistent experience with Kubernetes. And luckily StormForge works on prem as well even in air gapped environments for StormForge. So, you know, that's, you can get your applications tuned correctly for your data center workloads, and then you're going to tune them differently when you move them to the cloud and you can get them tuned correctly there but StormForge can run consistently in both environments. >> Now, can you add some color as to how you optimize EKS? >> Yeah, so I think from a EKS standpoint, when you, again, when the number of parameters that you have to look at for your application inside of EKS and then the associated services that will go alongside that the packages that are coming in from a Kubernetes standpoint itself, and then you start to transition and operationalize where more and more of these are in production, they're, you know, connected to the business, we provide the ability to go beyond what developers typically do which is sort of take the, either the out of the box defaults or recommendations that ship with the services that they put into their application or the any human's ability to kind of keep up with a couple parameters at a time. You know, with two parameters for the typical Kubernetes application, you might have about a 100 different possible combinations that you could choose from. And sometimes humans can keep up with that, at least statically. And so for us, we want to blow that wide open. We want developers to be able to take advantage of the entire footprint or environment itself. And, you know, by using machine learning to help augment what the developers themselves are doing, not replacing them, augmenting them and having them be a part of that process. Now this whole new world of optimization opens up to them, which is pretty fantastic. And so how the actual workloads are configured, you know, on an ongoing basis and predictively based on upcoming business events, or even unknowns many times is a pretty powerful position to be in. >> I mean, you said not to replace development. I mentioned robotic process automation in my intro, and of course in the early days, I was like, oh, it's going to replace my job. What's actually happened is it's replacing all the mundane tasks. >> Yeah. >> So you can actually do your job. >> Yeah. >> Right? We're all working 24/7, 365 these days, so that the extent that you can automate the things that I hate doing, >> Yeah. >> That's a huge win. So Chandler, how do people get started? You mentioned EKS Anywhere, are they starting on prem and then kind of moving into the cloud? If I'm a customer and I'm interested and I'm sort of at the beginning, where do I start? >> Yeah. Yeah. I mean, it really depends on your workload. Any workload that can run in the cloud should run in the cloud. I'm not just saying that because I work at Amazon but I truly think that that is the case. And I think customers think that as well. More and more customers are trying to move workloads to the cloud for that elasticity and all the benefits of using these huge platforms and, you know, hundreds of services that you have advantage of in the cloud but some workloads just can't move to the cloud yet. You have workloads that have latency requirements like some gaming workloads, for example, where we don't have regions close enough to the consumers yet. So, you know, you want to put workloads in Turkey to service Egypt customers or something like this. You also have workloads that are, you know, on cruise ships and they lose connectivity in the middle of the Atlantic, or maybe you have highly secure workloads in air gapped environments or something like this. So there's still a lot of use cases that keep workloads on prem and sometimes customers just have existing investments in hardware that they don't want to eat yet, right? And they want to slowly phase those out as they move to the cloud. And again, that's where EKS Anywhere really plays well for the workloads that you want to keep on prem, but then as you move to the cloud you can take advantage of obviously EKS. >> I'll put you in the spot. >> Sure. >> And don't hate me for doing this, but so Andy Jassy, Adam Selipsky, I've certainly heard Maylan Thompson Bukavek talk about this, and in fullness of time, all workloads will be in the cloud. >> Yeah. >> And I've said the cloud is expanding. We're going to bring the cloud to the edge. Edge is in your title. >> Yeah. >> Is that a correct interpretation and obvious it relates >> Absolutely. >> to Kubernetes. >> And you'll see that in Amazon strategy. I mean, without posts and wavelengths and local zones, like we're, at the end of the day, Amazon tries to satisfy customers. And if customers are saying, "Hey, I need workloads in San, I want to run a workload in San Francisco. And it's really important to me that it's close to those users, the end users that are in that area," we're going to help them do that at Amazon. And there's a variety of options now to do that. EKS Anywhere is actually only one piece of that kind of whole strategy. >> Yeah. I mean, here you have your best people working on the speed of light problem, but until that's solved, sure, sure. >> That's right. >> We'll give you the last word. >> How do you know about that? >> Yeah. Yeah. (all laughing) >> It's a top secret. Sorry. You heard it on the CUBE first. Matt, we'll give you the last word, bring us home. >> I, so I couldn't agree more. The, you know, the cloud is where workloads are going. Whether what I love is the ability to look at, you know, for the same enterprises, a lot of the ones we work with, want a, they want a public and a private view, public cloud, private cloud view. And they want that flexibility to, depending on the nature of the applications to be able to shift between from time to time where, you know, really decide. And I love EKS Anywhere. I think it's a fantastic addition to the, you know, to the ecosystem. And, you know, I think for us, we're about staying focused on the set of problems that we solve. No developer that I've ever met and probably neither of you have met, gets super excited about getting out of bed to manually tune their applications. And so what we find is that, you know, the time spent doing that, literally just is, there's like a one-to-one correlation. It means they're not innovating and they're not doing what they love to be doing. And so when we can come alongside that and automate away the manual task to your point, I think there are a lot of parallels to RPA in that case, it becomes actually a pretty empowering process for our users, so that they feel like they're, again, meeting the business objectives that they have, they get to innovate and yet, you know, they're exploring this whole new world around not having to choose between something like cost and performance for their applications. >> Well, and we're entering an entire new era of scale. >> Yeah. >> We've never seen before and human just are not going to be able to keep up with that. >> Yep. >> And that affect quality and speed and everything else. Guys, hey, thanks so much for coming in a great conversation. And thank you for watching this CUBE conversation. This is Dave Vellante, and we'll see you next time. (upbeat music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante data mesh is a new way of thinking about how to use data to create organizational value leading edge practitioners are beginning to implement data mesh in earnest and importantly data mesh is not a single tool or a rigid reference architecture if you will rather it's an architectural and organizational model that's really designed to address the shortcomings of decades of data challenges and failures many of which we've talked about on the cube as important by the way it's a new way to think about how to leverage data at scale across an organization and across ecosystems data mesh in our view will become the defining paradigm for the next generation of data excellence hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we welcome the founder and creator of data mesh author thought leader technologist jamaak dagani shamak thank you for joining us today good to see you hi dave it's great to be here all right real quick let's talk about what we're going to cover i'll introduce or reintroduce you to jamaac she joined us earlier this year in our cube on cloud program she's the director of emerging tech at dot works north america and a thought leader practitioner software engineer architect and a passionate advocate for decentralized technology solutions and and data architectures and jamaa since we last had you on as a guest which was less than a year ago i think you've written two books in your spare time one on data mesh and another called software architecture the hard parts both published by o'reilly so how are you you've been busy i've been busy yes um good it's been a great year it's been a busy year i'm looking forward to the end of the year and the end of these two books but it's great to be back and um speaking with you well you got to be pleased with the the momentum that data mesh has and let's just jump back to the agenda for a bit and get that out of the way we're going to set the stage by sharing some etr data our partner our data partner on the spending profile and some of the key data sectors and then we're going to review the four key principles of data mesh just it's always worthwhile to sort of set that framework we'll talk a little bit about some of the dependencies and the data flows and we're really going to dig today into principle number three and a bit around the self-service data platforms and to that end we're going to talk about some of the learnings that shamak has captured since she embarked on the datamess journey with her colleagues and her clients and we specifically want to talk about some of the successful models for building the data mesh experience and then we're going to hit on some practical advice and we'll wrap with some thought exercises maybe a little tongue-in-cheek some of the community questions that we get so the first thing i want to do we'll just get this out of the way is introduce the spending climate we use this xy chart to do this we do this all the time it shows the spending profiles and the etr data set for some of the more data related sectors of the ecr etr taxonomy they they dropped their october data last friday so i'm using the july survey here we'll get into the october survey in future weeks but about 1500 respondents i don't see a dramatic change coming in the october survey but the the y-axis is net score or spending momentum the horizontal axis is market share or presence in the data set and that red line that 40 percent anything over that we consider elevated so for the past eight quarters or so we've seen machine learning slash ai rpa containers and cloud is the four areas where cios and technology buyers have shown the highest net scores and as we've said what's so impressive for cloud is it's both pervasive and it shows high velocity from a spending standpoint and we plotted the three other data related areas database edw analytics bi and big data and storage the first two well under the red line are still elevated the storage market continues to kind of plot along and we've we've plotted the outsourced it just to balance it out for context that's an area that's not so hot right now so i just want to point out that these areas ai automation containers and cloud they're all relevant to data and they're fundamental building blocks of data architectures as are the two that are directly related to data database and analytics and of course storage so it just gives you a picture of the spending sector so i wanted to share this slide jamark uh that that we presented in that you presented in your webinar i love this it's a taxonomy put together by matt turk who's a vc and he called this the the mad landscape machine learning and ai and data and jamock the key point here is there's no lack of tooling you've you've made the the data mesh concept sort of tools agnostic it's not like we need more tools to succeed in data mesh right absolutely great i think we have plenty of tools i think what's missing is a meta architecture that defines the landscape in a way that it's in step with organizational growth and then defines that meta architecture in a way that these tools can actually interoperable and to operate and integrate really well like the the clients right now have a lot of challenges in terms of picking the right tool regardless of the technology they go down the path either they have to go in and big you know bite into a big data solution and then try to fit the other integrated solutions around it or as you see go to that menu of large list of applications and spend a lot of time trying to kind of integrate and stitch this tooling together so i'm hoping that data mesh creates that kind of meta architecture for tools to interoperate and plug in and i think our conversation today around self-subjective platform um hopefully eliminate that yeah we'll definitely circle back because that's one of the questions we get all the time from the community okay let's review the four main principles of data mesh for those who might not be familiar with it and those who are it's worth reviewing jamar allow me to introduce them and then we can discuss a bit so a big frustration i hear constantly from practitioners is that the data teams don't have domain context the data team is separated from the lines of business and as a result they have to constantly context switch and as such there's a lack of alignment so principle number one is focused on putting end-to-end data ownership in the hands of the domain or what i would call the business lines the second principle is data as a product which does cause people's brains to hurt sometimes but it's a key component and if you start sort of thinking about it you'll and talking to people who have done it it actually makes a lot of sense and this leads to principle number three which is a self-serve data infrastructure which we're going to drill into quite a bit today and then the question we always get is when we introduce data meshes how to enforce governance in a federated model so let me bring up a more detailed slide jamar with the dependencies and ask you to comment please sure but as you said the the really the root cause we're trying to address is the siloing of the data external to where the action happens where the data gets produced where the data needs to be shared when the data gets used right in the context of the business so it's about the the really the root cause of the centralization gets addressed by distribution of the accountability end to end back to the domains and these domains this distribution of accountability technical accountability to the domains have already happened in the last you know decade or so we saw the transition from you know one general i.t addressing all of the needs of the organization to technology groups within the itu or even outside of the iit aligning themselves to build applications and services that the different business units need so what data mesh does it just extends that model and say okay we're aligning business with the tech and data now right so both application of the data in ml or inside generation in the domains related to the domain's needs as well as sharing the data that the domains are generating with the rest of the organization but the moment you do that then you have to solve other problems that may arise and that you know gives birth to the second principle which is about um data as a product as a way of preventing data siloing happening within the domain so changing the focus of the domains that are now producing data from i'm just going to create that data i collect for myself and that satisfy my needs to in fact the responsibility of domain is to share the data as a product with all of the you know wonderful characteristics that a product has and i think that leads to really interesting architectural and technical implications of what actually constitutes state has a product and we can have a separate conversation but once you do that then that's the point in the conversation that cio says well how do i even manage the cost of operation if i decentralize you know building and sharing data to my technical teams to my application teams do i need to go and hire another hundred data engineers and i think that's the role of a self-serve data platform in a way that it enables and empowers generalist technologies that we already have in the technical domains the the majority population of our developers these days right so the data platform attempts to mobilize the generalist technologies to become data producers to become data consumers and really rethink what tools these people need um and the last last principle so data platform is really to giving autonomy to domain teams and empowering them and reducing the cost of ownership of the data products and finally as you mentioned the question around how do i still assure that these different data products are interoperable are secure you know respecting privacy now in a decentralized fashion right when we are respecting the sovereignty or the domain ownership of um each domain and that leads to uh this idea of both from operational model um you know applying some sort of a federation where the domain owners are accountable for interoperability of their data product they have incentives that are aligned with global harmony of the data mesh as well as from the technology perspective thinking about this data is a product with a new lens with a lens that all of those policies that need to be respected by these data products such as privacy such as confidentiality can we encode these policies as computational executable units and encode them in every data product so that um we get automation we get governance through automation so that's uh those that's the relationship the complex relationship between the four principles yeah thank you for that i mean it's just a couple of points there's so many important points in there but the idea of the silos and the data as a product sort of breaking down those cells because if you have a product and you want to sell more of it you make it discoverable and you know as a p l manager you put it out there you want to share it as opposed to hide it and then you know this idea of managing the cost you know number three where people say well centralize and and you can be more efficient but that but that essentially was the the failure in your other point related point is generalist versus specialist that's kind of one of the failures of hadoop was you had these hyper specialist roles emerge and and so you couldn't scale and so let's talk about the goals of data mesh for a moment you've said that the objective is to extend exchange you call it a new unit of value between data producers and data consumers and that unit of value is a data product and you've stated that a goal is to lower the cognitive load on our brains i love this and simplify the way in which data are presented to both producers and consumers and doing so in a self-serve manner that eliminates the tapping on the shoulders or emails or raising tickets so how you know i'm trying to understand how data should be used etc so please explain why this is so important and how you've seen organizations reduce the friction across the data flows and the interconnectedness of things like data products across the company yeah i mean this is important um as you mentioned you know initially when this whole idea of a data-driven innovation came to exist and we needed all sorts of you know technology stacks we we centralized um creation of the data and usage of the data and that's okay when you first get started with where the expertise and knowledge is not yet diffused and it's only you know the privilege of a very few people in the organization but as we move to a data driven um you know innovation cycle in the organization as we learn how data can unlock new new programs new models of experience new products then it's really really important as you mentioned to get the consumers and producers talk to each other directly without a broker in the middle because even though that having that centralized broker could be a cost-effective model but if you if we include uh the cost of missed opportunity for something that we could have innovated well we missed that opportunity because of months of looking for the right data then that cost parented the cost benefit parameters and formula changes so um so to to have that innovation um really embedded data-driven innovation embedded into every domain every team we need to enable a model where the producer can directly peer-to-peer discover the data uh use it understand it and use it so the litmus test for that would be going from you know a hypothesis that you know as a data scientist i think there is a pattern and there is an insight in um you know in in the customer behavior that if i have access to all of the different informations about the customer all of the different touch points i might be able to discover that pattern and personalize the experience of my customer the liquid stuff is going from that hypothesis to finding all of the different sources be able to understanding and be able to connect them um and then turn them them into you know training of my machine learning and and the rest is i guess known as an intelligent product got it thank you so i i you know a lot of what we do here in breaking it house is we try to curate and then point people to new resources so we will have some additional resources because this this is not superficial uh what you and your colleagues in the community are creating but but so i do want to you know curate some of the other material that you had so if i bring up this next chart the left-hand side is a curated description both sides of your observations of most of the monolithic data platforms they're optimized for control they serve a centralized team that has hyper-specialized roles as we talked about the operational stacks are running running enterprise software they're on kubernetes and the microservices are isolated from let's say the spark clusters you know which are managing the analytical data etc whereas the data mesh proposes much greater autonomy and the management of code and data pipelines and policy as independent entities versus a single unit and you've made this the point that we have to enable generalists to borrow from so many other examples in the in the industry so it's an architecture based on decentralized thinking that can really be applied to any domain really domain agnostic in a way yes and i think if i pick one key point from that diagram is really um or that comparison is the um the the the data platforms or the the platform capabilities need to present a continuous experience from an application developer building an application that generates some data let's say i have an e-commerce application that generates some data to the data product that now presents and shares that data as as temporal immutable facts that can be used for analytics to the data scientist that uses that data to personalize the experience to the deployment of that ml model now back to that e-commerce application so if we really look at this continuous journey um the walls between these separate platforms that we have built needs to come down the platforms underneath that generate you know that support the operational systems versus supported data platforms versus supporting the ml models they need to kind of play really nicely together because as a user i'll probably fall off the cliff every time i go through these stages of this value stream um so then the interoperability of our data solutions and operational solutions need to increase drastically because so far we've got away with running operational systems an application on one end of the organization running and data analytics in another and build a spaghetti pipeline to you know connect them together neither of the ends are happy i hear from data scientists you know data analyst pointing finger at the application developer saying you're not developing your database the right way and application point dipping you're saying my database is for running my application it wasn't designed for sharing analytical data so so we've got to really what data mesh as a mesh tries to do is bring these two world together closer because and then the platform itself has to come closer and turn into a continuous set of you know services and capabilities as opposed to this disjointed big you know isolated stacks very powerful observations there so we want to dig a little bit deeper into the platform uh jamar can have you explain your thinking here because it's everybody always goes to the platform what do i do with the infrastructure what do i do so you've stressed the importance of interfaces the entries to and the exits from the platform and you've said you use a particular parlance to describe it and and this chart kind of shows what you call the planes not layers the planes of the platform it's complicated with a lot of connection points so please explain these planes and how they fit together sure i mean there was a really good point that you started with that um when we think about capabilities or that enables build of application builds of our data products build their analytical solutions usually we jump too quickly to the deep end of the actual implementation of these technologies right do i need to go buy a data catalog or do i need you know some sort of a warehouse storage and what i'm trying to kind of elevate us up and out is to to to force us to think about interfaces and apis the experiences that the platform needs to provide to run this secure safe trustworthy you know performance mesh of data products and if you focus on then the interfaces the implementation underneath can swap out right you can you can swap one for the other over time so that's the purpose of like having those lollipops and focusing and emphasizing okay what is the interface that provides a certain capability like the storage like the data product life cycle management and so on the purpose of the planes the mesh experience playing data product expense utility plan is really giving us a language to classify different set of interfaces and capabilities that play nicely together to provide that cohesive journey of a data product developer data consumer so then the three planes are really around okay at the bottom layer we have a lot of utilities we have that mad mac turks you know kind of mad data tooling chart so we have a lot of utilities right now they they manage workflow management you know they they do um data processing you've got your spark link you've got your storage you've got your lake storage you've got your um time series of storage you've got a lot of tooling at that level but the layer that we kind of need to imagine and build today we don't buy yet as as long as i know is this linger that allows us to uh exchange that um unit of value right to build and manage these data products so so the language and the apis and interface of this product data product experience plan is not oh i need this storage or i need that you know workflow processing is that i have a data product it needs to deliver certain types of data so i need to be able to model my data it needs to as part of this data product i need to write some processing code that keeps this data constantly alive because it's receiving you know upstream let's say user interactions with a website and generating the profile of my user so i need to be able to to write that i need to serve the data i need to keep the data alive and i need to provide a set of slos and guarantees for my data so that good documentation so that some you know someone who comes to data product knows but what's the cadence of refresh what's the retention of the data and a lot of other slos that i need to provide and finally i need to be able to enforce and guarantee certain policies in terms of access control privacy encryption and so on so as a data product developer i just work with this unit a complete autonomous self-contained unit um and the platform should give me ways of provisioning this unit and testing this unit and so on that's why kind of i emphasize on the experience and of course we're not dealing with one or two data product we're dealing with a mesh of data products so at the kind of mesh level experience we need a set of capabilities and interfaces to be able to search the mesh for the right data to be able to explore the knowledge graph that emerges from this interconnection of data products need to be able to observe the mesh for any anomalies did we create one of these giant master data products that all the data goes into and all the data comes out of how we found ourselves the bottlenecks to be able to kind of do those level machine level capabilities we need to have a certain level of apis and interfaces and once we decide and decide what constitutes that to satisfy this mesh experience then we can step back and say okay now what sort of a tool do i need to build or buy to satisfy them and that's that is not what the data community or data part of our organizations used to i think traditionally we're very comfortable with buying a tool and then changing the way we work to serve to serve the tool and this is slightly inverse to that model that we might be comfortable with right and pragmatists will will to tell you people who've implemented data match they'll tell you they spent a lot of time on figuring out data as a product and the definitions there the organizational the getting getting domain experts to actually own the data and and that's and and they will tell you look the technology will come and go and so to your point if you have those lollipops and those interfaces you'll be able to evolve because we know one thing's for sure in this business technology is going to change um so you you had some practical advice um and i wanted to discuss that for those that are thinking about data mesh i scraped this slide from your presentation that you made and and by the way we'll put links in there your colleague emily who i believe is a data scientist had some really great points there as well that that practitioners should dig into but you made a couple of points that i'd like you to summarize and to me that you know the big takeaway was it's not a one and done this is not a 60-day project it's a it's a journey and i know that's kind of cliche but it's so very true here yes um this was a few starting points for um people who are embarking on building or buying the platform that enables the people enables the mesh creation so it was it was a bit of a focus on kind of the platform angle and i think the first one is what we just discussed you know instead of thinking about mechanisms that you're building think about the experiences that you're enabling uh identify who are the people like what are the what is the persona of data scientists i mean data scientist has a wide range of personas or did a product developer the same what is the persona i need to develop today or enable empower today what skill sets do they have and and so think about experience as mechanisms i think we are at this really magical point i mean how many times in our lifetime we come across a complete blanks you know kind of white space to a degree to innovate so so let's take that opportunity and use a bit of a creativity while being pragmatic of course we need solutions today or yesterday but but still think about the experiences not not mechanisms that you need to buy so that was kind of the first step and and the nice thing about that is that there is an evolutionary there is an iterative path to maturity of your data mesh i mean if you start with thinking about okay which are the initial use cases i need to enable what are the data products that those use cases depend on that we need to unlock and what is the persona of my or general skill set of my data product developer what are the interfaces i need to enable you can start with the simplest possible platform for your first two use cases and then think about okay the next set of data you know data developers they have a different set of needs maybe today i just enable the sql-like querying of the data tomorrow i enable the data scientists file based access of the data the day after i enable the streaming aspect so so have this evolutionary kind of path ahead of you and don't think that you have to start with building out everything i mean one of the things we've done is taking this harvesting approach that we work collaboratively with those technical cross-functional domains that are building the data products and see how they are using those utilities and harvesting what they are building as the solutions for themselves back into the back into the platform but at the end of the day we have to think about mobilization of the large you know largest population of technologies we have we'd have to think about diffusing the technology and making it available and accessible by the generous technologies that you know and we've come a long way like we've we've gone through these sort of paradigm shifts in terms of mobile development in terms of functional programming in terms of cloud operation it's not that we are we're struggling with learning something new but we have to learn something that works nicely with the rest of the tooling that we have in our you know toolbox right now so so again put that generalist as the uh as one of your center personas not the only person of course we will have specialists of course we will always have data scientists specialists but any problem that can be solved as a general kind of engineering problem and i think there's a lot of aspects of data michigan that can be just a simple engineering problem um let's just approach it that way and then create the tooling um to empower those journalists great thank you so listen i've i've been around a long time and so as an analyst i've seen many waves and we we often say language matters um and so i mean i've seen it with the mainframe language it was different than the pc language it's different than internet different than cloud different than big data et cetera et cetera and so we have to evolve our language and so i was going to throw a couple things out here i often say data is not the new oil because because data doesn't live by the laws of scarcity we're not running out of data but i get the analogy it's powerful it powered the industrial economy but it's it's it's bigger than that what do you what do you feel what do you think when you hear the data is the new oil yeah i don't respond to those data as the gold or oil or whatever scarce resource because as you said it evokes a very different emotion it doesn't evoke the emotion of i want to use this i want to utilize it feels like i need to kind of hide it and collect it and keep it to myself and not share it with anyone it doesn't evoke that emotion of sharing i really do think that data and i with it with a little asterisk and i think the definition of data changes and that's why i keep using the language of data product or data quantum data becomes the um the most important essential element of existence of uh computation what do i mean by that i mean that you know a lot of applications that we have written so far are based on logic imperative logic if this happens do that and else do the other and we're moving to a world where those applications generating data that we then look at and and the data that's generated becomes the source the patterns that we can exploit to build our applications as in you know um curate the weekly playlist for dave every monday based on what he has listened to and the you know other people has listened to based on his you know profile so so we're moving to the world that is not so much about applications using the data necessarily to run their businesses that data is really truly is the foundational building block for the applications of the future and then i think in that we need to rethink the definition of the data and maybe that's for a different conversation but that's that's i really think we have to converge the the processing that the data together the substance substance and the processing together to have a unit that is uh composable reusable trustworthy and that's that's the idea behind the kind of data product as an atomic unit of um what we build from future solutions got it now something else that that i heard you say or read that really struck me because it's another sort of often stated phrase which is data is you know our most valuable asset and and you push back a little bit on that um when you hear people call data and asset people people said often have said they think data should be or will eventually be listed as an asset on the balance sheet and i i in hearing what you said i thought about that i said well you know maybe data as a product that's an income statement thing that's generating revenue or it's cutting costs it's not necessarily because i don't share my my assets with people i don't make them discoverable add some color to this discussion i think so i think it's it's actually interesting you mentioned that because i read the new policy in china that cfos actually have a line item around the data that they capture we don't have to go to the political conversation around authoritarian of um collecting data and the power that that creates and the society that leads to but that aside that big conversation little conversation aside i think you're right i mean the data as an asset generates a different behavior it's um it creates different performance metrics that we would measure i mean before conversation around data mesh came to you know kind of exist we were measuring the success of our data teams by the terabytes of data they were collecting by the thousands of tables that they had you know stamped as golden data none of that leads to necessarily there's no direct line i can see between that and actually the value that data generated but if we invert that so that's why i think it's rather harmful because it leads to the wrong measures metrics to measure for success so if you invert that to a bit of a product thinking or something that you share to delight the experience of users your measures are very different your measures are the the happiness of the user they decrease lead time for them to actually use and get value out of it they're um you know the growth of the population of the users so it evokes a very different uh kind of behavior and success metrics i do say if if i may that i probably come back and regret the choice of word around product one day because of the monetization aspect of it but maybe there is a better word to use but but that's the best i think we can use at this point in time why do you say that jamar because it's too directly related to monetization that has a negative connotation or it might might not apply in things like healthcare or you know i think because if we want to take your shortcuts and i remember this conversation years back that people think that the reason to you know kind of collect data or have data so that we can sell it you know it's just the monetization of the data and we have this idea of the data market places and so on and i think that is actually the least valuable um you know outcome that we can get from thinking about data as a product that direct cell an exchange of data as a monetary you know exchange of value so so i think that might redirect our attention to something that really matters which is um enabling using data for generating ultimately value for people for the customers for the organizations for the partners as opposed to thinking about it as a unit of exchange for for money i love data as a product i think you were your instinct was was right on and i think i'm glad you brought that up because because i think people misunderstood you know in the last decade data as selling data directly but you really what you're talking about is using data as a you know ingredient to actually build a product that has value and value either generate revenue cut costs or help with a mission like it could be saving lives but in some way for a commercial company it's about the bottom line and that's just the way it is so i i love data as a product i think it's going to stick so one of the other things that struck me in one of your webinars was one of the q a one of the questions was can i finally get rid of my data warehouse so i want to talk about the data warehouse the data lake jpmc used that term the data lake which some people don't like i know john furrier my business partner doesn't like that term but the data hub and one of the things i've learned from sort of observing your work is that whether it's a data lake a data warehouse data hub data whatever it's it should be a discoverable node on the mesh it really doesn't matter the the technology what are your your thoughts on that yeah i think the the really shift is from a centralized data warehouse to data warehouse where it fits so i think if you just cross that centralized piece uh we are all in agreement that data warehousing provides you know interesting and capable interesting capabilities that are still required perhaps as a edge node of the mesh that is optimizing for certain queries let's say financial reporting and we still want to direct a fair bit of data into a node that is just for those financial reportings and it requires the precision and the um you know the speed of um operation that the warehouse technology provides so i think um definitely that technology has a place where it falls apart is when you want to have a warehouse to rule you know all of your data and model canonically model your data because um it you have to put so much energy into you know kind of try to harness this model and create this very complex the complex and fragile snowflake schemas and so on that that's all you do you spend energy against the entropy of your organization to try to get your arms around this model and the model is constantly out of step with what's happening in reality because reality the model the reality of the business is moving faster than our ability to model everything into into uh into one you know canonical representation i think that's the one we need to you know challenge not necessarily application of data warehousing on a node i want to close by coming back to the issues of standards um you've specifically envisioned data mesh to be technology agnostic as i said before and of course everyone myself included we're going to run a vendor's technology platform through a data mesh filter the reality is per the matt turc chart we showed earlier there are lots of technologies that that can be nodes within the data mesh or facilitate data sharing or governance etc but there's clearly a lack of standardization i'm sometimes skeptical that the vendor community will drive this but maybe like you know kubernetes you know google or some other internet giant is going to contribute something to open source that addresses this problem but talk a little bit more about your thoughts on standardization what kinds of standards are needed and where do you think they'll come from sure i mean the you write that the vendors are not today incentivized to create those open standards because majority of the vet not all of them but some vendors operational model is about bring your data to my platform and then bring your computation to me uh and all will be great and and that will be great for a portion of the clients and portion of environments where that complexity we're talking about doesn't exist so so we need yes other players perhaps maybe um some of the cloud providers or people that are more incentivized to open um open their platform in a way for data sharing so as a starting point i think standardization around data sharing so if you look at the spectrum right now we have um a de facto sound it's not even a standard for something like sql i mean everybody's bastardized to call and extended it with so many things that i don't even know what this standard sql is anymore but we have that for some form of a querying but beyond that i know for example folks at databricks to start to create some standards around delta sharing and sharing the data in different models so i think data sharing as a concept the same way that apis were about capability sharing so we need to have the data apis or analytical data apis and data sharing extended to go beyond simply sql or languages like that i think we need standards around computational prior policies so this is again something that is formulating in the operational world we have a few standards around how do you articulate access control how do you identify the agents who are trying to access with different authentication mechanism we need to bring some of those our ad our own you know our data specific um articulation of policies uh some something as simple as uh identity management across different technologies it's non-existent so if you want to secure your data across three different technologies there is no common way of saying who's the agent that is acting uh to act to to access the data can i authenticate and authorize them so so those are some of the very basic building blocks and then the gravy on top would be new standards around enriched kind of semantic modeling of the data so we have a common language to describe the semantic of the data in different nodes and then relationship between them we have prior work with rdf and folks that were focused on i guess linking data across the web with the um kind of the data web i guess work that we had in the past we need to revisit those and see their practicality in the enterprise con context so so data modeling a rich language for data semantic modeling and data connectivity most importantly i think those are some of the items on my wish list that's good well we'll do our part to try to keep the standards you know push that push that uh uh movement jamaica we're going to leave it there i'm so grateful to have you uh come on to the cube really appreciate your time it's just always a pleasure you're such a clear thinker so thanks again thank you dave that's it's wonderful to be here now we're going to post a number of links to some of the great work that jamark and her team and her books and so you check that out because we remember we publish each week on siliconangle.com and wikibon.com and these episodes are all available as podcasts wherever you listen listen to just search breaking analysis podcast don't forget to check out etr.plus for all the survey data do keep in touch i'm at d vallante follow jamac d z h a m a k d or you can email me at david.velante at siliconangle.com comment on the linkedin post this is dave vellante for the cube insights powered by etrbwell and we'll see you next time you