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

>>Okay, we're back. I'm Dave Valante with The Cube and you're watching Evolving Influx DB into the smart data platform made possible by influx data. Anna East Otis 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 realtime analytics. Anna 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 IO X 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 compression efficiency, it's gonna give you faster query speeds, it gonna use store files and object storages. 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 IOCs 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 lift 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 a lot there. Now we talked to Brian about how you're using Rust and and which is not a new programming language and of course we had some drama around Russ during the pandemic with the Mozilla layoffs, but the formation of the Russ 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, 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 Rust was chosen because of his exceptional performance and rebi reliability. So while rust is synt tactically similar to c 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 card for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ, 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 fixed race conditions to protect against buffering overflows and to ensure thread safe ay caching 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 learned about the the new engine and the, 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're really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. 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 IOCs comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of illustrate why calmer 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 neighbor 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 min 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 times stamp and do that for every single row. So you're scanning across a ton more data and that's why row oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, calmer data fit framework. So that's where a lot of the advantages come >>From. Okay. So you've basically described like a traditional database, a row approach, but I've seen like a lot of traditional databases say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native it, is it not as effective as the, is the form 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 to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as its in memory format. So the way that it helps influx DB IOx 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 PDA's data frames as well and all of the machine learning tools associated with pandas. >>Okay. You're also leveraging par K in the platform course. 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 Par K and why is it important? >>Sure. So Par K is the calm oriented durable file format. So it's important because it'll enable bulk import and bulk export. It has compatibility with Python and pandas so it supports a broader ecosystem. Parque 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 these, 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 it 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 IOCs 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 I 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 D DB underscore 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 IOCs, 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 TB 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 you guys super responsive, so really appreciate that. All right, thank you so much and East 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 Yokum. He's the director of engineering for Influx Data and we're gonna talk about how you update a SaaS engine while the plane is flying at 30,000 feet. You don't wanna miss this.

>>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.

>>Okay, welcome back everyone to super cloud 22 here in our live studio performance. You're on stage in Palo Alto. I'm Sean fur. You're host with the queue with Dave ante. My co it's got a great industry ecosystem panel to discuss whether it's realer hype, David MC Janet CEO of Hashi Corp, hugely successful company as will LA forest field CTO, Colu and Victoria over yourgo from VMware guys. Thanks for coming on the queue. Appreciate it. Thanks for having us. So realer, hype, super cloud David. >>Well, I think it depends on the definition. >>Okay. How do you define super cloud start there? So I think we have a, >>I think we have a, like an inherently pragmatic view of super cloud of the idea of super cloud as you talk about it, which is, you know, for those of us that have been in the infrastructure world for a long time, we know there are really only six or seven categories of infrastructure. There's sort of the infrastructure security, networking databases, middleware, and, and, and, and really the message queuing aspects. And I think our view is that if the steady state of the world is multi-cloud, what you've seen is sort of some modicum of standardization across those different elements, you know, take, you know, take confluent. You know, I, I worked in the middleware world years ago, MQ series, and typical multicast was how you did message queuing. Well, you don't do that anymore. All the different cloud providers have their own message, queuing tech, there's, Google pub sub, and the equivalents across the different, different clouds. Kafka has provided a consistent way to do that. And they're not trying to project that. You can run everything connected. They're saying, Hey, you should standardize on Kafka for message cuing is that way you can have operational consistency. So I think to me, that's more how we think about it is sort of, there is sort of layer by layer of sort of de facto standardization for the lingo Franco. >>So a streaming super cloud is how you would think of it, or no, I just, or a component of >>Cloud that could be a super cloud. >>I just, I just think that there are like, if I'm gonna build an application message, queuing is gonna be a necessary element of it. I'm gonna use Kafka, not, you know, a native pub sub engine on one of the clouds, because operationally that's just the only way I can do it. So I think that's more, our view's much more pragmatic rather than trying to create like a single platform that you can run everywhere and deal with the networking realities of like network, you know, hops missing across those different worlds and have that be our responsibility. It's much more around, Hey, let's standardize each layer, operational >>Standardized layer that you can use to build a super cloud if that's in your, your intent or, yeah. Okay. >>And it reminds me of the web services days. You kind of go throwback there. I mean, we're kind of living the next gen of web services, the dream of that next level, because DevOps dev SecOps now is now gone mainstream. That's the big challenge we're hearing devs are doing great. Yep. But the ops teams and screen, they gotta go faster. This seems to be a core, I won't say blocker, but more of a drag to the innovation. >>Well, I I'll just get off, I'll hand it off to, to you guys. But I think the idea that like, you know, if I'm gonna have an app that's running on Amazon that needs to connect to a database that's running on, on the private data center, that's essentially the SOA notion, you know, w large that we're all trying to solve 20 years ago, but is much more complicated because you're brokering different identity models, different networking models. They're just much more complex. So that's where the ops bit is the constraint, you know, for me to build that app, not that complicated for the ops person to let it see traffic is another thing altogether. I think that's, that's the break point for so much of what looks easier to a developer is the operational reality of how you do that. And the good news is those are actually really well solved problems. They're just not broadly understood. >>Well, what's your take, you talk to customers all the time, field CTO, confluent, really doing well, streaming data. I mean, everyone's doing it now. They have to, yeah. These are new things that pop up that need solutions. You guys step up and doing more. What's your take on super cloud? >>Well, I mean, the way we address it honestly is we don't, it's gonna be honest. We don't think about super cloud much less is the fact that SAS is really being pushed down. Like if we rely on seven years ago and you took a look at SAS, like it was obvious if you were gonna build a product for an end consumer or business user, you'd do SAS. You'd be crazy not to. Right. But seven years ago, if you look at your average software company producing something for a developer that people building those apps, chances are you had an open source model. Yeah. Or, you know, self-managed, I think with the success of a lot of the companies that are here today, you know, snowflake data, bricks, Colu, it's, it's obvious that SaaS is the way to deliver software to the developers as well. And as such, because our product is provided that way to the developers across the clouds. That's, that's how they have a unifying data layer, right. They don't necessarily, you know, developers like many people don't necessarily wanna deal with the infrastructure. They just wanna consume cloud data services. Right. So that's how we help our customers span cloud. >>So we evenly that SAS was gonna be either built on a single cloud or in the case of service. Now they built their own cloud. Right. So increasingly we're seeing opportunities to build a Salesforce as well across clouds tap different, different, different services. So, so how does that evolve? Do you, some clouds have, you know, better capabilities in other clouds. So how does that all get sort of adjudicated, do you, do you devolve to the lowest common denominator? Or can you take the best of all of each? >>The whole point to that I think is that when you move from the business user and the personal consumer to the developer, you, you can no longer be on a cloud, right. There has to be locality to where applications are being developed. So we can't just deploy on a single cloud and have people send their data to that cloud. We have to be where the developer is. And our job is to make the most of each, an individual cloud to provide the same experience to them. Right. So yes, we're using the capabilities of each cloud, but we're hiding that to the developer. They don't shouldn't need to know or care. Right. >>Okay. And you're hiding that with the abstraction layer. We talked about this before Victoria, and that, that layer has what, some intelligence that has metadata knowledge that can adjudicate what, what, the best, where the best, you know, service is, or function of latency or data sovereignty. How do you see that? >>Well, I think as the, you need to instrument these applications so that you, you, you can get that data and then make the intelligent decision of where, where, where this, the deploy application. I think what Dave said is, is right. You know, the level of super cloud that they talking about is the standardization across messaging. And, and are you what's happening within the application, right? So you don't, you are not too dependent on the underlying, but then the application say that it takes the form of a, of a microservice, right. And you deploy that. There has to be a way for operator to say, okay, I see all these microservices running across clouds, and I can factor out how they're performing, how I, I, life lifecycle managed and all that. And so I think there is, there is, to me, there's the next level of the super cloud is how you factor this out. So an operator can actually keep up with the developers and make sense of all that and manage it. Like >>You guys that's time. Like its also like that's what Datadog does. So Datadog basically in allows you to instrument all those services, on-prem private data center, you know, all the different clouds to have a consistent view. I think that that's not a good example of a vendor that's created a, a sort of a level of standardization across a layer. And I think that's, that's more how we think about it. I think the notion of like a developer building an application, they can deploy and not have to worry where it exists. Yeah. Is more of a PAs kind of construct, you know, things like cloud Foundry have done a great job of, of doing that. But underneath that there's still infrastructure. There's still security. There's still networking underneath it. And I think that's where, you know, things like confluent and perhaps at the infrastructure layer have standardized, but >>You have off the shelf PAs, if I can call it that. Yeah. Kind of plain. And then, and then you have PAs and I think about, you mentioned snowflake, snowflake is with snow park, seems to be developing a PAs layer that's purpose built for their specific purpose of sharing data and governing data across multiple clouds call super paths. Is, is that a prerequisite of a super cloud you're building blocks. I'm hearing yeah. For super cloud. Is that a prerequisite for super cloud? That's different than PAs of 10 years ago. No, but I, >>But I think this is, there's just different layers. So it's like, I don't know how that the, the snowflake offering is built built, but I would guess it's probably built on Terraform and vault and cons underneath it. Cuz those are the ingredients with respect to how you would build a composite application that runs across multiple. And >>That's how Oracle that town that's how Oracle with the Microsoft announcement. They just, they just made if you saw that that was built on Terraform. Right. But, but they would claim that they, they did some special things within their past that were purpose built for, for sure. Low latency, for example, they're not gonna build that on, you know, open shift as an, as an example, they're gonna, you know, do their own little, you know, >>For sure, for sure. So I think what you're, you're pointing at and what Victoria was talking about is, Hey, can a vendor provided consistent experience across the application layer across these multiple clouds? And I would say, sure, just like, you know, you might build a mobile banking application that has a front end on Amazon in the back end running on vSphere on your private data center. Sure. But the ingredients you use to do that have to be, they can't be the cloud native aspects for how you do that. How do you think about, you know, the connectivity of, of like networking between that thing to this thing? Is it different on Amazon? Is it different on Azure? Is it different on, on Google? And so the, the, the, the companies that we all serve, that's what they're building, they're building composited applications. Snowflake is just an example of a company that we serve this building >>Composite. And, but, but, but don't those don't, you have to hide the complexity of that, those, those cloud native primitives that's your job, right. Is to actually it creates simplicity across clouds. Is it not? >>Why? Go ahead. You. >>Yeah, absolutely. I mean that in fact is what we're doing for developers that need to do event streaming, right. That need to process this data in real time. Now we're, we're doing the sort of things that Victoria was just talking about, like underneath the covers, of course, you know, we're using Kubernetes and we're managing the differences between the clouds, but we're hiding the, that, and we've become sort of a defacto standard across the cloud. So if I'm developing an app in any of those cloud, and I think we all know, and you were mentioning earlier every significant company's multi-cloud now all the large enterprises, I just got back from Brazil and like every single one of 'em have multiple clouds and on-prem right. So they need something that can span those. >>What's the challenge there. If you talk to those customers, because we're seeing the same thing, they have multiple clouds. Yeah. But it was kind of by default or they had some use case, either.net developers there with Azure, they'll do whatever cloud. And it kind of seems specialty relative to the cloud native that they're on what problems do they have because the complexity to run infrastructure risk code across clouds is hard. Right? So the trade up between native cloud and have better integration to complexity of multiple clouds seems to be a topic around super cloud. What are you seeing for, for issues that they might have or concerns? >>Yeah. I mean, honestly it is, it is hard to actually, so here's the thing that I think is kind of interesting though, by the way, is that I, I think we tend to, you know, if you're, if you're from a technical background, you tend to think of multicloud as a problem for the it organization. Like how do we solve this? How do we save money? But actually it's a business problem now, too, because every single one of these companies that have multiple clouds, they want to integrate their data, their products across these, and it it's inhibiting their innovation. It's hard to do, but that's where something like, you know, Hatchie Corp comes in right. Is to help solve that. So you can instrument it. It has to happen at each of these layers. And I suppose if it does happen at every single layer, then voila, we organically have something that amounts to Supercloud. Right. >>I love how you guys are representing each other's firms. And, but, but, and they also correct me if I'm a very similar, your customers want to, it is very similar, but your customers want to monetize, right. They want bring their tools, their software, their particular IP and their data and create, you know, every, every company's a software company, as you know, Andreesen says every company's becoming a cloud company to, to monetize in, in the future. Is that, is that a reasonable premise of super cloud? >>Yeah. I think, think everyone's trying to build composite applications to, to generate revenue. Like that's, that's why they're building applications. So yeah. One, 100%. I'm just gonna make it point cuz we see it as well. Like it's actually quite different by geography weirdly. So if you go to like different geographies, you see actually different cloud providers, more represented than others. So like in north America, Amazon's pretty dominant Japan. Amazon's pretty dominant. You go to Southeast Asia actually. It's not necessarily that way. Like it might be Google for, for whatever reason more hourly Bob. So this notion of multi's just the reality of one's everybody's dealing with. But yeah, I think everyone, everyone goes through the same process. What we've observed, they kind of go, there's like there's cloud V one and there's cloud V two. Yeah. Cloud V one is sort of the very tactical let's go build something on cloud cloud V two is like, whoa, whoa, whoa, whoa. And I have some stuff on Amazon, some stuff on Azure, some stuff on, on vSphere and I need some operational consistency. How do I think about zero trust across that way in a consistent way. And that's where this conversation comes into being. It's sort of, it's not like the first version of cloud it's actually when people step back and say, Hey, Hey, I wanna build composite applications to monetize. How am I gonna do that in an industrialized way? And that's the problem that you were for. It's >>Not, it's not as, it's not a no brainer like it was with cloud, go to the cloud, write an app. You're good here. It's architectural systems thinking, you gotta think about regions. What's the latency, you know, >>It's step back and go. Like, how are we gonna do this, this exactly. Like it's wanted to do one app, but how we do this at scale >>Zero trust is a great example. I mean, Amazon kind of had, was forced to get into the zero trust, you know, discussion that, that wasn't, you know, even a term that they used and now sort of, they're starting to talk about it, but within their domain. And so how do you do zero trust trust across cost to your point? >>I, I wonder if we're limiting our conversation too much to the, the very technical set of developers, cuz I'm thinking back at again, my example of C plus plus libraries C plus plus libraries makes it easier. And then visual BA visual basic. Right. And right now we don't have enough developers to build the software that we want to build. And so I want, and we are like now debating, oh, can we, do we hide that AI API from Google versus that SQL server API from, from Microsoft. I wonder at some point who cares? Right. You know, we, I think if we want to get really economy scale, we need to get to a level of abstraction for developers that really allows them to say, I don't need, for most of most of the procedural application that I need to build as a developer, as a, as a procedural developer, I don't care about this. Some, some propeller had, has done that for me. I just like plug it in my ID and, and I use it. And so I don't, I don't know how far we are from that, but if we don't get to that level, it fits me that we never gonna get really the, the economy or the cost of building application to the level. >>I was gonna ask you in the previous segment about low code, no code expanding the number of developers out there and you talking about propel heads. That's, that's what you guys all do. Yeah. You're the technical geniuses, right. To solve that problem so that, so you can have low code development is that I >>Don't think we have the right here. Cause I, we, we are still, you know, trying to solve that problem at that level. But, but >>That problem has to be solved first, right before we can address what you're talking about. >>Yeah. I, I worked very closely with one of my biggest mentors was Adam Bosworth that built, you know, all the APIs for visual basics and, and the SQL API to visual basic and all that stuff. And he always was on that front. In fact that his last job was at my, at AWS building that no code environment. So I'm a little detached from that. It just hit me as we were discussing this. It's like, maybe we're just like >>Creating, but I would, I would argue that you kind of gotta separate the two layers. So you think about the application platform layer that a developer interfaces to, you know, Victoria and I worked together years ago and one of the products we created was cloud Foundry, right? So this is the idea of like just, you know, CF push, just push this app artifact and I don't care. That's how you get the developer community written large to adopt something complicated by hiding all the complexity. And I think that that is one model. Yeah. Turns out Kubernetes is actually become a peer to that and perhaps become more popular. And that's what folks like Tanza are trying to do. But there's another layer underneath that, which is the infrastructure that supports it. Right? Yeah. Cause that's only needs to run on something. And I think that's, that's the separation we have to do. Yes. We're talking a little bit about the plumbing, but you know, we just easily be talking about the app layer. You need, both of them. Our point of view is you need to standardize at this layer just like you need standardize at this layer. >>Well, this is, this is infrastructure. This is DevOps V two >>Dev >>Ops. Yeah. And this is where I think the ops piece with open source, I would argue that open source is blooming more than ever. So I think there's plenty of developers coming. The automation question becomes interesting because I think what we're seeing is shift left is proving that there's app developers out there that wanna stay in their pipelining. They don't want to get in under the hood. They just want infrastructure as code, but then you got supply chain software issues there. We talked about the Docker on big time. So developers at the top, I think are gonna be fine. The question is what's the blocker. What's holding them back. And I don't see the devs piece Victoria as much. What do you guys think? Is it, is the, is the blocker ops or is it the developer experience? That's the blocker. >>It's both. There are enough people truthfully. >>That's true. Yeah. I mean, I think I sort of view the developer as sort of the engine of the digital innovation. So, you know, if you talk about creative destruction, that's, that was the economic equivalent of softwares, eating the world. The developers are the ones that are doing that innovation. It's absolutely essential that you make it super easy for them to consume. Right. So I think, you know, they're nerds, they want to deal with infrastructure to some degree, but I think they understand the value of getting a bag of Legos that they can construct something new around. And I think that's the key because honestly, I mean, no code may help for some things. Maybe I'm just old >>School, >>But I, I went through this before with like Delphy and there were some other ones and, and I hated it. Like I just wanted a code. Yeah. Right. So I think making them more efficient is, is absolutely good. >>But I think what, where you're going with that question is that the, the developers, they tend to stay ahead. They, they just, they're just gear, you know, wired that way. Right. So I think right now where there is a big bottleneck in developers, I think the operation team needs to catch up. Cuz I, I talk to these, these, these people like our customers all the time and I see them still stuck in the old world. Right. Gimme a bunch of VMs and I'll, I know how to manage well that world, you know, although as lag is gonna be there forever, so managing mainframe. But so if they, the world is all about microservices and containers and if the operation team doesn't get on top of it and the security team that then that they're gonna be a bottleneck. >>Okay. I want to ask you guys if the, if the companies can get through that knothole of having their ops teams and the dev teams work well together, what's the benefits of a Supercloud. How do you see the, the outcome if you kind of architect it, right? You think the big picture you zoom as saying what's the end game look like for Supercloud? Is that >>What I would >>Say? Or what's the Nirvana >>To me Nirvana is that you don't care. You just don't don't care. You know, you just think when you running building application, let's go back to the on-prem days. You don't care if it runs on HP or Dell or, you know, I'm gonna make some enemies here with my old, old family, but you know, you don't really care, right. What you want is the application is up and running and people can use it. Right. And so I think that Nirvana is that, you know, there is some, some computing power out there, some pass layer that allows me to deploy, build application. And I just like build code and I deploy it and I get value at a reasonable cost. I think one of the things that the super cloud for as far as we're concerned is cost. How do you manage monitor the cost across all this cloud? >>Make sure that you don't, the economics don't get outta whack. Right? How many companies we know that have gone to the cloud only to realize that holy crap, now I, I got the bill and, and you know, I, as a vendor, when I was in my previous company, you know, we had a whole team figuring out how to lower our cost on the one hyperscaler that we were using. So these are, you know, the, once you have in the super cloud, you don't care just you, you, you go with the path of least the best economics is. >>So what about the open versus closed debate will you were mentioning that we had snowflake here and data bricks is both ends of the spectrum. Yeah. You guys are building open standards across clouds. Clearly even the CLO, the walled gardens are using O open standards, but historically de facto standards have emerged and solved these problems. So the super cloud as a defacto standard, versus what data bricks is trying to do super cloud kind of as an, as an open platform, what are you, what are your thoughts on that? Can you actually have an, an open set of standards that can be a super cloud for a specific purpose, or will it just be built on open source technologies? >>Well, I mean, I, I think open source continues to be an important part of innovation, but I will say from a business model perspective, like the days, like when we started off, we were an open source company. I think that's really done in my opinion, because if you wanna be successful nowadays, you need to provide a cloud native SAS oriented product. It doesn't matter. What's running underneath the covers could be commercial closed source, open source. They just wanna service and they want to use it quite frankly. Now it's nice to have open source cuz the developers can download it and run on their laptop. But I, I can imagine in 10 years time actually, and you see most companies that are in the cloud providing SAS, you know, free $500 credit, they may not even be doing that. They'll just, you know, go whatever cloud provider that their company is telling them to use. They'll spin up their SAS product, they'll start playing with it. And that's how adoption will grow. Right? >>Yeah. I, I think, I mean my personal view is that it's, that it's infrastructure is pervasive enough. It exists at the bottom of everything that the standards emerge out of open source in my view. And you think about how something like Terraform is built, just, just pick one of the layers there's Terraform core. And then there's a plugin for everything you integrate with all of those are open source. There are over 2000 of these. We don't build them. Right. That's and it's the same way that drove Linux standardization years ago, like someone had to build the drivers for every piece of hardware in the world. The market does not do that twice. The market does that once. And so I, I I'm deeply convicted that opensource is the only way that this works at the infrastructure layer, because everybody relies on it at the application layer, you may have different kinds of databases. You may have different kind of runtime environments. And that's just the nature of it. You can't to have two different ways of doing network, >>Right? Because the stakes are so high, basically. >>Yeah. Cuz there's, there's an infinite number of the surface areas are so large. So I actually worked in product development years ago for middleware. And the biggest challenge was how do you keep the adapter ecosystem up to date to integrate with everything in the world? And the only way to do it in our view is through open source. And I think that's a fundamental philosophical view that it we're just, you know, grounded in. I think when people are making infrastructure decisions that span 20 years at the customer base, this is what they think about. They go which standard it will emerge based on the model of the vendor. And I don't think my personal view is, is it's not possible to do in a, in >>A, do you think that's a defacto standard kind of psychological perspective or is there actual material work being done or both in >>There it's, it's, it's a network effect thing. Right? So, so, you know, before Google releases a new service service on Google cloud, as part of the release checklist is does it support Terraform? They do that work, not us. Why? Because every one of their customers uses Terraform to interface with them and that's how it works. So see, so the philosophical view of, of the customers, okay, what am I making a standardize on for this layer for the next 30 years? It's kind of a no brainer. Philosophically. >>I tend, >>I think the standards are organically created based upon adoption. I mean, for instance, Terraform, we have a provider we're again, we're at the data layer that we created for you. So like, I don't think there's a board out there. I mean there are that creating standards. I think those days are kind of done to be honest, >>The, the Terraform provider for vSphere has been downloaded five and a half million times this year. Yeah. Right. Like, so, I >>Mean, these are unifying moments. This are like the de facto standards are really important process in these structural changes. I think that's something that we're looking at here at Supercloud is what's next? What has to unify look what Kubernetes has done? I mean, that's essentially the easy thing to orchestra, but people get behind it. So I see this is a big part of this next, the two. Totally. What do you guys see that's needed? What's the rallying unification point? Is it the past layer? Is it more infrastructure? I guess that's the question we're trying to, >>I think every layer will need that open source or a major traction from one of the proprietary vendor. But I, I agree with David, it's gonna be open source for the most part, but you know, going back to the original question of the whole panel, if I may, if this is reality of hype, look at the roster of companies that are presenting or participating today, these are all companies that have some sort of multi-cloud cross cloud, super cloud play. They're either public have real revenue or about to go public. So the answer to the question. Yeah, it's real. Yeah. >>And so, and there's more too, we had couldn't fit him in, but we, >>We chose super cloud on purpose cuz it kind of fun, John and I kind came up with it and, and but, but do you think it's, it hurts the industry to have this, try to put forth this new term or is it helpful to actually try to push the industry to define this new term? Or should it just be multi-cloud 2.0, >>I mean, conceptually it's different than multi-cloud right. I mean, in my opinion, right? So in that, in that respect, it has value, right? Because it's talking about something greater than just multi-cloud everyone's got multi-cloud well, >>To me multi-cloud is the, the problem I should say the opportunity. Yeah. Super cloud or we call it cross cloud is the solution to that channel. Let's >>Not call again. And we're debating that we're debating that in our cloud already panel where we're talking about is multi-cloud a problem yet that needs to get solved or is it not yet ready for a market to your point? Is it, are we, are we in the front end of coming into the true problem set, >>Give you definitely answer to that. The answer is yes. If you look at the customers that are there, they won, they have gone through the euphoria phase. They're all like, holy something, what, what are we gonna do about this? Right. >>And, but they don't know what to do. >>Yeah. And the more advanced ones as the vendor look at the end of the day, markets are created by vendors that build ed that customers wanna buy. Yeah. Because they get value >>And it's nuance. David, we were sort talking about before, but Goldman Sachs has announced they're analysis software vendor, right? Capital one is a software vendor. I've been really interested Liberty what Cerner does with what Oracle does with Cerner and in terms of them becoming super cloud vendors and monetizing that to me is that is their digital transformation. Do you guys, do you guys see that in the customer base? Am I way too far out of my, of my skis there or >>I think it's two different things. I think, I think basically it's the idea of building applications. If they monetize yeah. There and Cerner's gonna build those. And you know, I think about like, you know, IOT companies that sell that sell or, or you think people that sell like, you know, thermostats, they sell an application that monetizes those thermostats. Some of that runs on Amazon. Some of that runs a private data center. So they're basically in composite applications and monetize monetizing them for the particular vertical. I think that's what we ation every day. That's what, >>Yeah. You can, you can argue. That's not, not anything new, but what's new is they're doing that on the cloud and taking across multiple clouds. Multiple. Exactly. That's what makes >>Edge. And I think what we all participate in is, Hey, in order to do that, you need to drive standardization of how you do provisioning, how you do networking, how you do security to underpin those applications. I think that's what we're all >>Talking about, guys. It's great stuff. And I really appreciate you taking the time outta your day to help us continue the conversation to put out in the open. We wanna keep it out in the open. So in the last minute we have left, let's go down the line from a hash core perspective, confluent and VMware. What's your position on super cloud? What's the outcome that you would like to see from your standpoint, going out five years, what's it look like they will start with you? >>I just think people like sort under understanding that there is a layer by layer of view of how to interact across cloud, to provide operational consistency and decomposing it that way. Thinking about that way is the best way to enable people to build and run apps. >>We wanna help our customers work with their data in real time, regardless of where they're on primer in the cloud and super cloud can enable them to build applications that do that more effectively. That's that's great for us >>For tour you. >>I, my Niana for us is customers don't care, just that's computing out there. And it's a, it's a, it's a tool that allows me to grow my business and we make it all, all the differences and all the, the challenges, you know, >>Disappear, dial up, compute utility infrastructure, ISN >>Code. I open up the thought there's this water coming out? Yeah, I don't care. I got how I got here. I don't wanna care. Well, >>Thank you guys so much and congratulations on all your success in the marketplace, both of you guys and VMware and your new journey, and it's gonna be great to watch. Thanks for participating. Really appreciate it. Thank you, sir. Okay. This is super cloud 22, our events, a pilot. We're gonna get it out there in the open. We're gonna get the data we're gonna share with everyone out in the open on Silicon angle.com in the cube.net. We'll be back with more live coverage here in Palo Alto. After this short break.

>>welcome back everyone. We're back to intermission. I'm hama in case you forgot and hear them with Brett and Peter. So what a great morning afternoon. We've had like we're now in the home stretch and you know, I really want to give a shout out to all of you who are sticking with us, especially if you're in different time zone than pacific. So I then jumped into the community rooms. The spanish won, the Brazilian won the french one. Everybody is just going strong. So again, so much so gratitude for that. Thank you for being so involved and really participating the chat rooms in the community. The chat windows in the community rooms are just going nuts. So it's, it's really good to see that. And as usual, Peter and brat had some great, very interactive panels and that was very exciting to watch. But you know, since they were on the panels, I decided to go and see some other things and I actually attended the last mile of container ization. That was, that was actually a very good session. We had a lot of good interactivity there. Yeah. And then while also talked about the container security in the cloud native world. So that was, I think that was your panel peter. That was, that was very exciting. And um, I want to share with everybody the numbers that we've been seeing for dr khan live. So as, as of, I'm sorry, said we need a drumroll. We do need a drum roll. Can you do a drum roll for me? No, no, no. >>Just a >>symbol. Okay, good. Go. Uh, we're at over 22,000 attendees um, today. So that's amazing. That's great. I love the sound effect. That's a great sound effect. The community rooms continue to be really engaged. We're still seeing hundreds of people in those rooms. So again shout out to everyone who is participating. And I felt again like a kid in a candy store didn't know which sessions to attend. They were all very interesting and you know, we're getting some good feedback on twitter. I want to read out some more tweets that we got and one in particular, I don't know whether to feel happy for this person or sad for this person, but it's uh well the initials are P. W. And he said that he was up at two am to watch the keynotes. So again, I'll let you decide whether you're it's a good thing or not, but we're happy to have you PW is awesome. Um as well. There was someone who said that these features are so needed. The things that dr announced this morning in the keynotes and that doctor has reacted to our pains and I think they mean has addressed their pain. So that was really gratifying to read. Yeah, really wonderful. That's some other countries that I didn't shout out before this just tells you what the breadth and scope of our community is. Indonesia, la paz Bolivia, Greece, Munich, Ukraine, oxford UK Australia Philippines. And there's just more and I'm going to do a special shadow to Montreal because that's where I'm from. So yes, applause for that. It was really great. And so I just want to thank all of you. Um, I want to encourage you when we talked about the power of community. Remember we're doing a fundraiser. So to combat Covid for Covid relief or actually all that money is going to go to UNICEF. Docker is contributing 10,000 and we're doing a go fund me. And the link is there on the screen. So please donate. You know, just $1. 1 person each of you donates $1. We would have raised over $22,000. So please please find it within you to contribute because again, our communities that are, that are the most effective are India and brazil, which are are very active doctor affinity. So please give back. I really appreciate that >>highlighted by the brazil. Yeah. >>You're going to brazil room and get them all to donate. Exactly. Um, also want to encourage, you know, if you're interested in participating in our, in our road map. Our public road map is on GIT hub. So it's get home dot com slash docker slash roadmap. And that's something that you can participate in and vote up features that you want to see. We love to get the community involved and participating in our, in our road map. So make sure to check that out. And I also want to note on that >>Hello can real quick. I'm sorry. Yeah, I talk about our road map all the time, but honestly folks out there are PMS are in their our ceo is in there that we do watch that. That is our roadmap is extremely, extremely important to us. So any features complaints, right, joining the conversation. That's a great way to get uh to interact with Docker in our products. Right. We we really highly valued the road map. Okay, back to your mama, sorry. >>Oh absolutely. And if you want to see us be even more responsive to what you need to participate in that road map discussion. That's really great. Um a couple of things coming up, just want to put the spotlight on. We have at 3 15 what's new with with desktop from our own ue cow. So I highly recommend that you attend that session and of course there's the Woman in tech live panel. So this is very exciting, moderated by yours truly and it has putting a spotlight on our women captains and our women developers. So that's very exciting. But I also hear that we're doing there's a session with jay frog coming up so peter, why don't you talk about that a little bit? >>Yeah, we have a session coming up from our partners from jay frog around devops patterns and anti patterns for continuous software updates. And another one that I'm extremely excited about is uh RM one talk from our very own Tony's from Docker. So if you have an M one and you're interested in multi arc architecture builds, check that out. It's gonna be a great, great talk. Um and then we have melissa McKay also from jay frog, talking about Docker and the container ecosystem and last but definitely not least. We'll check them all out there. Going to be great. But Brett is going to be doing I think the best panel that I'm gonna go watch and he made up a new word, it's called say this. I'm all about the trending new words today about this is gonna be awesome. Yeah. Yeah >>I'm going to have the battle bottle of the panels. >>Yeah. Yeah well mine's before years so we're not competing. So yeah we have we have two excellent panels in a row to finish off the day and just seven list is basically how to run, how can we run containers without managing servers? So it doesn't mean you don't actually have infrastructure just let's not manage service. Um Yeah and we we uh need to wrap it up and >>Uh before we do that I just want to um tell everyone that we actually have a promotion going on. So we um for those that sign up for a pro or team subscription, we're offering a 20% off so there's the U. R. L.. You can check out what the promotion is and this is for a new and returning users so you can use the promo code dr khan 21 all the information is on the website are really encourage you to check that out promotion for 20% off, join us for our panels. And we're doing a wrap up at five p.m. Where we'll have our own Ceo and that wrap up portion. Look forward to seeing there. All right, >>thank you too. All right everyone we'll see you on the next go around coming up next me and some other people awesome and Yeah. Mhm. Mhm. Yeah. >>Yeah. Yeah. Mhm. Is >>a really varied community. There's a lot of people with completely different backgrounds, completely different experience levels and completely different goals about how they want to use Docker. And I think that's really interesting. It's always easy to talk about the technology that I've used for so many years. I really love Doctor and I can find so many ways that it's useful and it's great to use in your day to day work clothes. I've >>used doctor for anything from um tracking airplanes with my son, which was a kind of cool project to more professional projects where we actually Built one of the first database as his services using docker even before it was 10 and I was released and we took it further and we start composing monitoring tools. We really start taking it to the next level. And we got to the point where I was trying to make everything in a container, I love to use >>doctor to make disposable project so I can download the project and it's been that up using Docker compose or something like that in a way that every developer that works in the project doesn't even need to know the underlying technology doesn't just need to run Docker compose up and the whole project is going to be up and running even if >>you're not using doctor and production, there are a lot of other ways that you can use doctor to make your life so much easier. As a developer, you can run your projects on your machine locally. Um as a tester you can actually launch test containers and be able to run um dependencies that your project requires. You can run real life versions so that um you're as close to production as possible. >>I was able to migrate most of the workloads from our on from uh to the cloud. Running complete IEDs inside a docker or running it or using it basically to replace their build scripts or using it to run not web applications but maybe compile c plus plus code or compile um projects that really just require some sort of consistency across their team, >>whether it be a web app or a database, I can control these all the same. That was really the power I saw within Doctors standardization and the portability >>doctor isn't the one that created containers uh and uh but it's the one that made it uh democratically possible, so everyone use it. And this effort has made the technology environment so much better for everyone that uses it, both for developers and for end users. So this >>past year has been quite interesting and I think we're all in the same boat here, so no one has, no one is an exception to this, but what we all learn from it is, you know, the community is very important and to lean on other people for help for assistance. >>Yeah, it's been really challenging of course, but I think the biggest and most obvious thing that I've learned both on a personal and a business perspective is just to be ready to adapt to change and don't be afraid of it either. I think it's worth noting that you should never really take it for granted that the paradigms of, you know, the world or technology or something like that aren't going to shift drastically and very, very quickly. >>I'm looking forward to what is coming down the pipe with doctor. What more are they going to throw our way in order to make our lives easier? >>It's very interesting to see the company grow and adapt the way it has. I mean it as well as the community, it's been very interesting to see, you know, how, you know, the return to develop our focus is now the main focus and I find that's very interesting because, you know, developers are the ones that really boost the doctor to where it is today. And if we keep on encouraging these developer innovation, we'll just see more tools being developed on top of Doctor in the future, and that's what I'm really excited to see with Doctor and the technology in the future.

>> Welcome back to Cube On Cloud. My name is Paul Gillin, enterprise editor at SiliconANGLE, and I'm pleased to now have as a guest on the show. Amit Zephyr, excuse me, general manager, vice president of business application platform at Google cloud. Amit is a formerly EVP and corporate officer for product development at Oracle cloud, 24 years at Oracle, and by my account a veteran of seven previous appearances on theCube. Amit welcome, thanks for joining us. >> Thanks for having me Paul, it's always good to be back on theCube. >> Now you are... one of your big focus areas right now is on low-code and no-code. Of course this is a market that seems to be growing explosively. We often hear low code/no code used in the same breath as if they're the same thing. In fact, how are they different? >> I think it's a huge difference, now. I think industry started as low-code mode for many, many years. I mean, there were technologies, or tools provided for kind of helping developers be more productive that's what low-code was doing. It was not really meant, even though it was positioned for citizen developers it was very hard for a non technologist to really build application using low code. No-code is really meant as the word stands, no code. So there's really no coding, there's no understanding required about the underlying technology stack, or knowing how constructs works or how the data is laid out. All that stuff is kind of hidden and abstracted out from you. You are really focused as a citizen developer or a line of business user, in kind of delivering what your business application requirements are, and the business flows are, without having to know anything about writing any code. So you can build applications, you can build your interfaces and not have to learn anything about a single line of code. So that's really no-code and I think they getting to a phase now where the platforms have gotten much stronger and better where you can do very good productive applications without having to write a single line of code. So that's really the goal with no-code, and that's really the future in terms of how we will get more and more line of business users, or citizen developers to build applications they need for their day-to-day work. >> So when would you use one or the other? >> I think since low-code you would probably any developer has been around for eight, 10 years, if not longer where you extract out some of this stuff you can do some of the things in terms of not having to write some code where you have a lot of modules pre-built for you, and then when you want to mix a lot of changes, you go and drop into an ID and write some code or make some changes to a code. So you still get into that, and those are really focused towards semi-professional developers or IT in many cases or even developers who want to reduce the time required to start from, write and building an application. so it makes you much more productive. So if you are a really some semi-professional or you are a developer, you can either use use low-code to improve your productivity and not start from scratch. No-code is really used for folks who are really not interested in learning about coding, don't have any experience in it, and still want to be productive and build applications. And that's really when I would start with.. I would not give a low code to a citizen developer or a line of business user who has no experience with any coding. And that's not really.. It will only productive, They'll get frustrated and not deliver what you need, and not get anything out of it and many cases. >> Well, I've been around this industry long enough to remember fourth-generation languages and visual basic >> Yeah and the predecessors that never really caught on in a big way. I mean, they certainly had big audiences but, right now we're seeing 40, 50% annual market growth. Why is this market suddenly so hot? >> Yeah it's not a difference. I think that as you said, the 4G deal and I think a lot of those tools, even if you look at forms, and PLC and we kind of extracted out the technology and made it easier, but it was not very clear who they were targeting with that. They're still targeting the same developer audience. So the they never expanded the universe of users. It was same user base, just making it simpler for them. So, with those low-code tools, it never landed them getting more and more user base out of that. With no-code platforms, you are now expanding the user community. You are giving this capabilities to more and more users than a low-code tools could provide. That's why I think the growth is much faster. So if you find the right no-code platform, you will see a lot more adoption because you're solving a real problem, you are giving them a lot more capabilities and making the user productive without having to depend on IT in many cases, or having to wait for a lot of those big applications to be built for them even though they need it immediately. So I think that's why I think you're solving a real business problem and giving a lot more capabilities to users and no doubt the users love it and they start expanding the usage. It's very viral adoption in many cases after that. >> Historically the rap on these tools has been that, because they're typically interpreted, the performance is never going to be up to that of application written in C plus plus or something. Is that still the case? Is that a sort of structural weakness of no-code tools or is that changing? >> I think the early days probably not any more. I think if you look at what we are doing at Google Cloud for example, it's not interpreted, I mean, it does do a lot of heavy lifting underneath the covers, but, and you don't have to go into the coding part of it but it brings the whole Cloud platform with it, right? So the scalability, the security the performance, availability all that stuff is built into the platform. So it's not a tool, it's a platform. I think that's thing, the big difference. Most of the early days you will see a lot of these things as a tool, which you can use it, and there's nothing underneath the covers the run kinds are very weak, there's really not the full Cloud platform provided with it, but I think the way we seeing it now and over the last many years, what we have done and what we continue to do, is to bring the power of the Cloud platform with it. So you're not missing out on the scalability, the performance, security, even the compliance and governance is built in. So IT is part of the process even though they might not build an application themselves. And that's where I think the barriers have been lifted. And again, it's not a solution for everything also. I'm not saying that this would go in, if you want to build a full end to end e-commerce site for example, I would not use a no-code platform for it, because you're going to do a lot more heavy lifting, you might want to integrate with a lot of custom stuff, you might build a custom experience. All that kind of stuff might not be that doable, but there are a lot of use cases now, which you can deliver with a platform like what we've been building at Google cloud. >> So, talk about what you're doing at Google cloud. Do you have a play in both the low-code and the no-code market? Do you favor one over the other? >> Yeah no I think we've employed technologies and services across the gamut of different requirements, right? I mean, our goal is not that we will only address one market needs and we'll ignore the rest of the things required for our developer community. So as you know, Google cloud has been very focused for many years delivering capabilities for developer community. With technology we deliver the Kubernetes and containers tend to flow for AI, compute storage all that kind of stuff is really developer centric. We have a lot of developers build applications on it writing code. They have abstracted some of this stuff and provide a lot of low-code technologies like Firebase for building mobile apps, the millions of apps mobile apps built by developers using Firebase today that it does abstract out the technology. And then you don't have to do a lot of heavy lifting yourself. So we do provide a lot of low-code tooling as well. And now, as we see the need for no-code especially kind of empowering the line of business user and citizen developers, we acquired a company called AppSheet, early 2020, and integrated that as part of our Google Cloud Platform as well as the workspace. So the G suite, the Gmail, all the technology all the services we provide for productivity and collaboration. And allowed users to now extend that collaboration capabilities by adding a workflow, and adding another app experience as needed for a particular business user needs. So that's how we looking at it like making sure that we can deliver a platform for spectrum of different use cases. And get that flexibility for the end user in terms of whatever they need to do, we should be able to provide as part of a Google Cloud Platform now. >> So as far as Google Cloud's positioning, I mean you're number three in the market you're growing but not really changing the distance between you and Microsoft for what public information we've been able to see in AWS. In Microsoft you have a company that has a long history with developers and of development tools and really as is that as a core strength do you see your low-code/no-code strategy as being a way to make up ground on them? >> Yeah, I think that the way to look at the market, and again I know the industry analyst and the market loves to do rankings in this world but, I think the Cloud business is probably big enough for a lot of vendors. I mean, this is growing as the amazing pace as you know. And it is becoming, it's a large investment. It takes time for a lot of the vendors to deliver everything they need to. But today, if you look at a lot of the net new growth and lot of net new customers, we seeing a huge percentage of share coming to Google Cloud, right? And we continue to announce some of the public things and the results will come out again every quarter. And we tried to break out the Cloud segment in the Google results more regularly so that people get an idea of how well they're doing in the Cloud business. So we are very comfortable where we are in terms of our growth in terms of our adoption, as well as in terms of how we delivering all the value our customers require, right? So, note out one of the parts we want to do is make sure that we have a end to end offering for all of the different use cases customers require and no-code is one of the parts we want to deliver for our customers as well. We've done very good capabilities and our data analytics. We do a lot of work around AIML, industry solutions. You look at the adoption we've had around a lot of those platform and Hybrid and MultiCloud. It's been growing very, very fast. And this one more additional things we are going to do, so that we can deliver what our customers are asking for. We're not too worried about the rankings we are worried about really making sure we're delivering the value to our customers. And we're seeing that it doesn't end very well. And if you look at the numbers now, I mean the growth rate is higher than any other Cloud vendor as well as be seeing a huge amount of demand been on Google Cloud as well. >> Well, not to belabor the point, but naturally your growth rate is going to be higher if you're a third of the market, I mean, how important is it to you to break into, to surpass the number two? How important are rankings within the Google Cloud team, or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about... we are not focused on ranking, or any of that stuff typically, I think we were worried about making sure customers are satisfied, and the adding more and more customers. So if you look at the volume of customers we're signing up, a lot of the large deals they didn't... do we need to look at the announcement we'd made over the last year, has been tremendous momentum around that. Lot of large banks, lot of large telecommunication companies large enterprises, name them. I think all of them are starting to kind of pick up Google Cloud. So if you follow that, I think that's really what is satisfying for us. And the results are starting to show that growth and the momentum. So we can't cover the gap we had in the previous... Because Google Cloud started late in this market. So if Cloud business grows by accumulating revenue over many years. So I cant look at the history, I'm looking at the future really. And if you look at the growth for the new business and the percentage of the net new business, we're doing better than pretty much any other vendor out there. >> And you said you were stepping up your reference to disclose those numbers. Was that what I heard you say? >> I think every quarter you're seeing that, I think we started announcing our revenue and growth numbers, and we started to do a lot of reporting about our Cloud business and that you will start, you see more and more and more of that regularly from Google now. >> Let's get back just briefly to the low-code/no-code discussion. A lot of companies looking at how to roll this out right now. You've got some big governance issues involved here. If you have a lot of citizen developers you also have the potential for chaos. What advice are you giving customers using your tools for how they should organize around citizen development? >> Yeah, no, I think no doubt. If this needs to be adopted by enterprise you can't make it a completely rogue or a completely shadow based development capabilities. So part of our no-code platform, one thing you want to make sure that this is enterprise ready, it has many aspects required for that. One is compliance making sure you have all the regulatory things delivered for data, privacy, security. Second is governance. A lot of the IT departments want to make sure who's using this platform? How are they accessing it? Are they getting the right security privileges associated with that? Are we giving them the right permissions? So in our a no-code platform we adding all this compliance, and governance regulatory stuff as part of our underlying platform, even though the end user might not have to worry about it the person who's building applications shouldn't have to think about it, but we do want to give controls to IT as needed by the large enterprises. So that is a big part of how we deliver this. We're not thinking about this as like go and build it, and then we write it once you have to do things for your enterprise, and then get it to do it again and again. Because then it just a waste of time and you're not getting the benefit of the platform at all. So we bringing those things together where we have a very easy to use, very powerful no-code platform with the enterprise compliance as well as governance built into that platform as well. And that is really resonating. If you look at a lot of the customers we're working with they do require that and they get excited about it as well as the democratizing of all of their line of business users. They're very happy that they're getting that kind of a platform, which they can scale from and deliver the productivity required. >> Certainly going to make businesses look very different in the future. And speaking of futures, It is January it's time to do predictions. What are your predictions (laughs) for the Cloud for this year? >> No I think that I mean no doubt cloud has become the center for pretty much every company now, I think the digital transformation especially with COVID, has greatly accelerated. We have seen many customers now who are thinking of pieces of their platform, pieces of their workflow or business to be digitized. Now that's trying to do it for all of it. So the one part which we see for this year is the need for more and more of efficiency in the industry are verticalized business workflows. It's not just about providing a plain vanilla Cloud Platform but also providing a lot more content and business details and business workflows by industry segments. So we've been doing a lot of work and we expect a huge amount of that to be becoming more and more core part of our offering as well as what customers are asking for. Where you might need things around say know your customer kind of workflow for financial services, Telehealth for healthcare. I mean, every industry has specific things like demand management and demand forecasting for retail but making that as part of a Cloud service not just saying, hey, I have compute storage network. I have some kind of a platform go add it and go and build what you want for your industry needs, We want to provide them that all those kinds of business processes and content for those industries as well. So we identified six, seven, industries. We see that as a kind of the driving factor for our Cloud growth, as well as helping our customers be much more productive as well as seeing the value of Cloud being much more realistic for them versus just a replacement for the data center. I think that's really the big shift in 21 I think. And I think that will make a big difference for all the companies who are really trying to digitize and be in forefront of the needs as their customers require in the future. >> Of course all of this accelerated by the pandemic and all of the specialized needs that have emerged from that. >> And I think the bond, which is important as well, I think as you know, I mean, everybody talks about AIML as like a big thing. No doubt AIML is an important element of it, but if you make that usable and powerful through this kind of workflows and business processes, as well as particular business applications, I think you see a lot more interest in using it than just a plain manila framework or just technology for the technology sake. So we try to bring the power of AI and ML into this business and industry applications, where we have a lot of good technologists at Google who knows how to use all these things. You wanted to bring that into those applications and platforms >> Exciting times ahead. Amit Zavery thank you so much for joining us. You look just as comfortable as I would expect someone to be who is doing his eighth Cube interview. Thanks for joining us. >> (laughing) Thanks for having me, Paul. >> That's it for this segment of Cube On Cloud, I'm Paul Gillin, stay tuned. (soft music)

>> Welcome back to Cube On Cloud. My name is Paul Gillin, enterprise editor at SiliconANGLE, and I'm pleased to now have as a guest on the show. Amit Zephyr, excuse me, general manager, vice president of business application platform at Google cloud. Amit is a formerly EVP and corporate officer for product development at Oracle cloud, 24 years at Oracle, and by my account a veteran of seven previous appearances on theCube. Amit welcome, thanks for joining us. >> Thanks for having me Paul, it's always good to be back on theCube. >> Now you are... one of your big focus areas right now is on low-code and no-code. Of course this is a market that seems to be growing explosively. We often hear low code/no code used in the same breath as if they're the same thing. In fact, how are they different? >> I think it's a huge difference, now. I think industry started as local mode for many, many years. I mean, there were technologies, or tools provided for kind of helping developers be more productive that's what low-code was doing. It was not really meant, even though it was positioned for citizen developers it was very hard for a non technologist to really build application using low code. No-code is really meant as the word stands, no code. So there's really no coding, there's no understanding required about the underlying technology stack, or knowing how constructs works or how the data is laid out. All that stuff is kind of hidden and abstracted out from you. You are really focused as a citizen developer or a line of business user, in kind of delivering what your business application requirements are, and the business flows are, without having to know anything about writing any code. So you can build applications, you can build your interfaces and not have to learn anything about a single line of code. So that's really no-code and I think they getting to a phase now where the platforms have gotten much stronger and better where you can do very good productive applications without having to write a single line of code. So that's really the goal with no-code, and that's really the future in terms of how we will get more and more line of business users, or citizen developers to build applications they need for their day-to-day work. >> So when would you use one or the other? >> I think since low-code you would probably any developer has been around for eight, 10 years, if not longer where you extract out some of this stuff you can do some of the things in terms of not having to write some code where you have a lot of modules pre-built for you, and then when you want to mix a lot of changes, you go and drop into an ID and write some code or make some changes to a code. So you still get into that, and those are really focused towards semi-professional developers or IT in many cases or even developers who want to reduce the time required to start from, write and building an application. so it makes you much more productive. So if you are a really some semi-professional or you are a developer, you can either use use low-code to improve your productivity and not start from scratch. No-code is really used for folks who are really not interested in learning about coding, don't have any experience in it, and still want to be productive and build applications. And that's really when I would start with.. I would not give a low code to a citizen developer or a line of business user who has no experience with any coding. And that's not really.. It will only productive, They'll get frustrated and not deliver what you need, and not get anything out of it and many cases. >> Well, I've been around this industry long enough to remember fourth-generation languages and visual basic >> Yeah and the predecessors that never really caught on in a big way. I mean, they certainly had big audiences but, right now we're seeing 40, 50% annual market growth. Why is this market suddenly so hot? >> Yeah it's not a difference. I think that as you said, the 4G deal and I think a lot of those tools, even if you look at forms, and PLC and we kind of extracted out the technology and made it easier, but it was not very clear who they were targeting with that. They're still targeting the same developer audience. So the they never expanded the universe of users. It was same user base, just making it simpler for them. So, with those low-code tools, it never landed them getting more and more user base out of that. With no-code platforms, you are now expanding the user community. You are giving this capabilities to more and more users than a low-code tools could provide. That's why I think the growth is much faster. So if you find the right no-code platform, you will see a lot more adoption because you're solving a real problem, you are giving them a lot more capabilities and making the user productive without having to depend on IT in many cases, or having to wait for a lot of those big applications to be built for them even though they need it immediately. So I think that's why I think you're solving a real business problem and giving a lot more capabilities to users and no doubt the users love it and they start expanding the usage. It's very viral adoption in many cases after that. >> Historically the rap on these tools has been that, because they're typically interpreted, the performance is never going to be up to that of application written in C plus plus or something. Is that still the case? Is that a sort of structural weakness of no-code tools or is that changing? >> I think the early days probably not any more. I think if you look at what we are doing at Google Cloud for example, it's not interpreted, I mean, it does do a lot of heavy lifting underneath the covers, but, and you don't have to go into the coding part of it but it brings the whole Cloud platform with it, right? So the scalability, the security the performance, availability all that stuff is built into the platform. So it's not a tool, it's a platform. I think that's thing, the big difference. Most of the early days you will see a lot of these things as a tool, which you can use it, and there's nothing underneath the covers the run kinds are very weak, there's really not the full Cloud platform provided with it, but I think the way we seeing it now and over the last many years, what we have done and what we continue to do, is to bring the power of the Cloud platform with it. So you're not missing out on the scalability, the performance, security, even the compliance and governance is built in. So IT is part of the process even though they might not build an application themselves. And that's where I think the barriers have been lifted. And again, it's not a solution for everything also. I'm not saying that this would go in, if you want to build a full end to end e-commerce site for example, I would not use a no-code platform for it, because you're going to do a lot more heavy lifting, you might want to integrate with a lot of custom stuff, you might build a custom experience. All that kind of stuff might not be that doable, but there are a lot of use cases now, which you can deliver with a platform like what we've been building at Google cloud. >> So, talk about what you're doing at Google cloud. Do you have a play in both the low-code and the no-code market? Do you favor one over the other? >> Yeah no I think we've employed technologies and services across the gamut of different requirements, right? I mean, our goal is not that we will only address one market needs and we'll ignore the rest of the things required for our developer community. So as you know, Google cloud has been very focused for many years delivering capabilities for developer community. With technology we deliver the Kubernetes and containers tend to flow for AI, compute storage all that kind of stuff is really developer centric. We have a lot of developers build applications on it writing code. They have abstracted some of this stuff and provide a lot of low-code technologies like Firebase for building mobile apps, the millions of apps mobile apps built by developers using Firebase today that it does abstract out the technology. And then you don't have to do a lot of heavy lifting yourself. So we do provide a lot of low-code tooling as well. And now, as we see the need for no-code especially kind of empowering the line of business user and citizen developers, we acquired a company called AppSheet, early 2020, and integrated that as part of our Google Cloud Platform as well as the workspace. So the G suite, the Gmail, all the technology all the services we provide for productivity and collaboration. And allowed users to now extend that collaboration capabilities by adding a workflow, and adding another app experience as needed for a particular business user needs. So that's how we looking at it like making sure that we can deliver a platform for spectrum of different use cases. And get that flexibility for the end user in terms of whatever they need to do, we should be able to provide as part of a Google Cloud Platform now. >> So as far as Google Cloud's positioning, I mean you're number three in the market you're growing but not really changing the distance between you and Microsoft for what public information we've been able to see in AWS. In Microsoft you have a company that has a long history with developers and of development tools and really as is that as a core strength do you see your low-code/no-code strategy as being a way to make up ground on them? >> Yeah, I think that the way to look at the market, and again I know the industry analyst and the market loves to do rankings in this world but, I think the Cloud business is probably big enough for a lot of vendors. I mean, this is growing as the amazing pace as you know. And it is becoming, it's a large investment. It takes time for a lot of the vendors to deliver everything they need to. But today, if you look at a lot of the net new growth and lot of net new customers, we seeing a huge percentage of share coming to Google Cloud, right? And we continue to announce some of the public things and the results will come out again every quarter. And we tried to break out the Cloud segment in the Google results more regularly so that people get an idea of how well they're doing in the Cloud business. So we are very comfortable where we are in terms of our growth in terms of our adoption, as well as in terms of how we delivering all the value our customers require, right? So, note out one of the parts we want to do is make sure that we have a end to end offering for all of the different use cases customers require and no-code is one of the parts we want to deliver for our customers as well. We've done very good capabilities and our data analytics. We do a lot of work around AIML, industry solutions. You look at the adoption we've had around a lot of those platform and Hybrid and MultiCloud. It's been growing very, very fast. And this one more additional things we are going to do, so that we can deliver what our customers are asking for. We're not too worried about the rankings we are worried about really making sure we're delivering the value to our customers. And we're seeing that it doesn't end very well. And if you look at the numbers now, I mean the growth rate is higher than any other Cloud vendor as well as be seeing a huge amount of demand been on Google Cloud as well. >> Well, not to belabor the point, but naturally your growth rate is going to be higher if you're a third of the market, I mean, how important is it to you to break into, to surpass the number two? How important are rankings within the Google Cloud team, or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about... we are not focused on ranking, or any of that stuff typically, I think we were worried about making sure customers are satisfied, and the adding more and more customers. So if you look at the volume of customers we're signing up, a lot of the large deals they didn't... do we need to look at the announcement we'd made over the last year, has been tremendous momentum around that. Lot of large banks, lot of large telecommunication companies large enterprises, name them. I think all of them are starting to kind of pick up Google Cloud. So if you follow that, I think that's really what is satisfying for us. And the results are starting to show that growth and the momentum. So we can't cover the gap we had in the previous... Because Google Cloud started late in this market. So if Cloud business grows by accumulating revenue over many years. So I cant look at the history, I'm looking at the future really. And if you look at the growth for the new business and the percentage of the net new business, we're doing better than pretty much any other vendor out there. >> And you said you were stepping up your reference to disclose those numbers. Was that what I heard you say? >> I think every quarter you're seeing that, I think we started announcing our revenue and growth numbers, and we started to do a lot of reporting about our Cloud business and that you will start, you see more and more and more of that regularly from Google now. >> Let's get back just briefly to the low-code/no-code discussion. A lot of companies looking at how to roll this out right now. You've got some big governance issues involved here. If you have a lot of citizen developers you also have the potential for chaos. What advice are you giving customers using your tools for how they should organize around citizen development? >> Yeah, no, I think no doubt. If this needs to be adopted by enterprise you can't make it a completely rogue or a completely shadow based development capabilities. So part of our no-code platform, one thing you want to make sure that this is enterprise ready, it has many aspects required for that. One is compliance making sure you have all the regulatory things delivered for data, privacy, security. Second is governance. A lot of the IT departments want to make sure who's using this platform? How are they accessing it? Are they getting the right security privileges associated with that? Are we giving them the right permissions? So in our a no-code platform we adding all this compliance, and governance regulatory stuff as part of our underlying platform, even though the end user might not have to worry about it the person who's building applications shouldn't have to think about it, but we do want to give controls to IT as needed by the large enterprises. So that is a big part of how we deliver this. We're not thinking about this as like go and build it, and then we write it once you have to do things for your enterprise, and then get it to do it again and again. Because then it just a waste of time and you're not getting the benefit of the platform at all. So we bringing those things together where we have a very easy to use, very powerful no-code platform with the enterprise compliance as well as governance built into that platform as well. And that is really resonating. If you look at a lot of the customers we're working with they do require that and they get excited about it as well as the democratizing of all of their line of business users. They're very happy that they're getting that kind of a platform, which they can scale from and deliver the productivity required. >> Certainly going to make businesses look very different in the future. And speaking of futures, It is January it's time to do predictions. What are your predictions (laughs) for the Cloud for this year? >> No I think that I mean no doubt cloud has become the center for pretty much every company now, I think the digital transformation especially with COVID, has greatly accelerated. We have seen many customers now who are thinking of pieces of their platform, pieces of their workflow or business to be digitized. Now that's trying to do it for all of it. So the one part which we see for this year is the need for more and more of efficiency in the industry are verticalized business workflows. It's not just about providing a plain vanilla Cloud Platform but also providing a lot more content and business details and business workflows by industry segments. So we've been doing a lot of work and we expect a huge amount of that to be becoming more and more core part of our offering as well as what customers are asking for. Where you might need things around say know your customer kind of workflow for financial services, Telehealth for healthcare. I mean, every industry has specific things like demand management and demand forecasting for retail but making that as part of a Cloud service not just saying, hey, I have compute storage network. I have some kind of a platform go add it and go and build what you want for your industry needs, We want to provide them that all those kinds of business processes and content for those industries as well. So we identified six, seven, industries. We see that as a kind of the driving factor for our Cloud growth, as well as helping our customers be much more productive as well as seeing the value of Cloud being much more realistic for them versus just a replacement for the data center. I think that's really the big shift in 21 I think. And I think that will make a big difference for all the companies who are really trying to digitize and be in forefront of the needs as their customers require in the future. >> Of course all of this accelerated by the pandemic and all of the specialized needs that have emerged from that. >> And I think the bond, which is important as well, I think as you know, I mean, everybody talks about AIML as like a big thing. No doubt AIML is an important element of it, but if you make that usable and powerful through this kind of workflows and business processes, as well as particular business applications, I think you see a lot more interest in using it than just a plain manila framework or just technology for the technology sake. So we try to bring the power of AI and ML into this business and industry applications, where we have a lot of good technologists at Google who knows how to use all these things. You wanted to bring that into those applications and platforms >> Exciting times ahead. Amit Zavery thank you so much for joining us. You look just as comfortable as I would expect someone to be who is doing his eighth Cube interview. Thanks for joining us. >> (laughing) Thanks for having me, Paul. >> That's it for this segment of Cube On Cloud, I'm Paul Gillin, stay tuned. (soft music)

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. >>Welcome to the Cubes Coverage of Dell Technologies World 2020. The Digital Experience. I'm Lisa Martin, and I'm pleased to welcome back a Cube alumni and a new Cube member to the program today. China. My Mondal is back with US Director of Solutions Marketing for Dell Technologies China. But it's great to see you at Dell Technologies world, even though we're very specially death. >>Happy to be back. Thank you, Lisa. >>And Thomas Henson is joining us for the first time. Global business development manager for a I and analytics. Thomas, Welcome to the Cube. >>I am excited to be here. It's my first virtual cube. >>Yeah, well, you better make it a good one. All right. I said we're talking about a I so so much has changed John to me. The last time I saw you were probably were sitting a lot closer together. So much has changed in the last 67 months, but a lot has changed with the adoption of Ai Thomas. Kick us off. What are some of the big things feeling ai adoption right now? >>Yeah, I >>would have to >>say the two biggest things right now or as we look at accelerated compute and by accelerated compute we're not just talking about the continuation of Moore's law, but how In Data Analytics, we're actually doing more processing now with GP use, which give us faster insights. And so now we have the ability to get quicker insights in jobs that may have taken, you know, taking weeks to months a song as we were measuring. And then the second portion is when we start to talk about the innovation going on in the software and framework world, right? So no longer do you have toe know C plus plus or a lower level language. You can actually do it in Python and even pull it off of Get Hub. And it's all part of that open source community. So we're seeing Mawr more folks in the field of data science and deep learning that can actually implement some code. And then we've got faster compute to be able to process that. >>Tell me, what are your thoughts? >>Think I want to add? Is the explosive growth off data on that's actually are fulfilling the AI adoption. Think off. Like all the devices we have, the i o t. On age devices are doing data are pumping data into the pipeline. Our high resolution satellite imagery, all social media generating data. No. All of this data are actually helping the adoption off a I because now we have very granular data tow our friend the AI model Make the AI models are much better. Besides, so the combination off both in, uh, data the power off Like GPU, power surfers are coupled with the inefficient in the eye after and tools helping off. Well, the AI growth that we're seeing today >>trying to make one of the things that we've known for a while now is that it's for a I to be valuable. It's about extracting value from that. Did it? You talked about the massive explosion and data, but yet we know for a long time we've been talking about AI for decades. Initiatives can fail. What can Dell Technologies do now to help companies have successfully I project? >>Yeah, eso As you were saying, Lisa, what we're seeing is the companies are trying to add up AI Technologies toe Dr Value and extract value from their data set. Now the way it needs to be framed is there is a business challenge that customers air trying to solve. The business challenge gets transformed into a data science problem. That data scientist is going toe work with the high technology, trained them on it. That data science problem gets to the data science solution on. Then it needs to be mapped to production deployment as a business solution. What happens? Ah, lot off. The time is the companies do not plan for output transition from all scale proof of concept that it a scientists are playing with, like a smaller set of data two, when it goes toe the large production deployment dealing with terabytes toe terabyte self data. Now that's where we come in. At their technologies, we have into end solutions for the, uh for the ai for pollution in the customers journeys starting from proof of concept to production. And it is all a seamless consular and very scalable. >>So if some of the challenges there are just starting with iterations. Thomas question for you as business development manager, those folks that John um I talked about the data scientists, the business. How are you helping them come together from the beginning so that when the POC is initiated, it actually can go on the right trajectory to be successful? >>No, that's a great point. And just to kind of build off of what Shonda my was talking about, You know, we call it that last mile, right? Like, Hey, I've got a great POC. How do I get into production? Well, if you have executive sponsorship and it's like, Hey, everybody was on board, but it's gonna take six months to a year. It's like, Whoa, you're gonna lose some momentum. So where we help our customers is, you know, by partnering with them to show them how to build, you know, from an i t. And infrastructure perspective what that ai architectural looks like, right? So we have multiple solutions around that, and at the end of the day, it's about just like Sean. Um, I was saying, You know, we may start off with a project that maybe it's only half a terabyte. Maybe it's 10 terabytes, but once you go into production, if it turns out to be three petabytes four petabytes. Nobody really, you know, has the infrastructure built unless they built on those solid practices. And that's where our solutions come in. So we can go from small scale laboratory all the way large scale production without having to move any of that data. Right? So, you know, at the heart of that is power scale and giving you that ability to scale your data and no more data migration so that you can handle one PC or multiple PCs as those models continue to improve as you start to move into production >>and I'm sticking with you 1st. 2nd 0, sorry. Trying to go ahead. >>So I was going to add that, uh, just like posthumous said right. So if you were a data scientist, you are working with this data science workstations, but getting the data from, uh, L M c our scales thes scale out platform and, uh, as it is growing from, you see two large kills production data can stay in place with the power scale platform. You can add notes, and it can grow to petabytes. And you can add in not just the workstations, but also our They'll power it, solve our switches building out our enter A I ready solutions are already solution for your production. Giving are very seamless experience from the data scientist with the i t. >>So China may will stick with you then. I'm curious to know in the last 6 to 7 months since 2020 has gone in a very different direction thing we all would have predicted our last Dell Technologies world together. What are you seeing? China. My in terms of acceleration or maybe different industries. What our customers needs, how they changed. I guess I should say in the in 2020. >>So in 2020 we're seeing the adoption off a I even more rapidly. Uh, if you think about customers ranging from like say, uh, media and entertainment industry toe, uh, the customer services off any organization to, uh the healthcare and life sciences with lots off genome analysts is going on in all of these places where we're dealing with large are datasets. We're seeing ah, lot off adoption foster processing off A. I R. Technologies, uh, giving with, say, the all the research that the's Biosciences organizations are happening. Uh, Thomas, I know like you are working with, like, a customer. So, uh, can you give us a little bit more example in there? >>Yes, one of the areas. You know, we're talking about 2021 of the things that we're seeing Mawr and Mawr is just the expansion of Just look at the need for customer support, right arm or folks working remotely their arm or folks that are learning remote. I know my child is going through virtual schools, So think about your I t organization and how Maney calls you're having now to expand. And so this is a great area where we're starting to see innovation within a I and model building to be ableto have you know, let's call it, you know, the next generation of chatbots rights. You can actually build these models off the data toe, augment those soup sports systems >>because you >>have two choices, right? You can either. You know, you you can either expand out your call center right for for we're not sure how long or you can use AI and analytics to help augment to help maybe answer some of those first baseline questions. The great thing about customers who are choosing power scale and Dell Technologies. Their partner is they already have. The resource is to be able to hold on to that data That's gonna help them train those models to help. >>So, Thomas, whenever we're talking about data, the explosions it brings to mind compliance. Protection, security. We've seen ransom where really skyrocket in 2020. Just you know, the other week there was the VA was hit. Um, I think there was also a social media Facebook instagram ticktock, 235 million users because there was an unsecured cloud database. So that vector is expanding. How can you help customers? Customers accelerate their AI projects? Well, ensuring compliance and protection and security of that data. >>Really? That's the sweet spot for power scale. We're talking with customers, right? You know, built on one FS with all the security features in mind. And I, too, came from the analytics world. So I remember in the early days of Hadoop, where, you know, as a software developer, we didn't need security, right? We you know, we were doing researching stuff, but then when we took it to the customer and and we're pushing to production, But what about all the security features. We needed >>the same thing >>for artificial intelligence, right? We want toe. We want to make sure that we're putting those security features and compliance is in. And that's where you know, from from an AI architecture perspective, by starting with one FS is at the heart of that solution. You can know that you're protecting for you know, all the enterprise features that you need, whether it be from compliance, thio, data strategy, toe backup and recovery as well. >>So when we're talking about big data volumes Chanda, mind we have to talk about the hyper scale er's talk to us about, you know, they each offer azure A W s Google cloud hundreds of AI services. So how does DEL help customers use the public cloud the data that's created outside of it and use all of those use that the right AI services to extract that value? >>Yeah. Now, as you mentioned, all of these hyper scholars are they differentiate with our office is like a i m l r Deep Learning Technologies, right? And as our customer, you want toe leverage based off all the, uh, all the cloud has to offer and not stuck with one particular cloud provider. However, we're talking about terabytes off data, right? So if you are happy with what doing service A from cloud provider say Google what you want to move to take advantage off another surface off from Asia? It comes with a very high English p a migration risk on time it will take to move the data itself. Now that's not good, right? As the customer, we should be able to live for it. Best off breed our cloud services for AI and for that matter, for anything across the board. Now, how we help customers is you can have all of your data say, in a managed, uh, managed cloud service provider running on power scale. But then you can connect from this managed cloud service provider directly toe any off the hyper scholars. You can connect toe aws, azure, Google Cloud and even, like even, uh, the in place analytics that power scale offers you can run. Uh, those, uh I mean, run those clouds AI services directly on that data simultaneously from these three, and I'll add like one more thing, right? Thes keep learning. Technologies need GPU power solvers, right? and cloud even within like one cloud is not homogeneous environment. Like sometimes you'll find a US East has or gp part solvers. But like you are in the West and the same for other providers. No, with our still our technologies cloud power scale for multi cloud our scale is sitting outside off those hyper scholars connected directly to our any off this on. Then you can burst into different clouds, take advantage off our spot. Instances on are like leverage. All the GP is not from one particular service provider part. All of those be our hyper scholars. So those are some examples off the work we're doing in the multi cloud world for a I >>So that's day. You're talking about data there. So powers failed for multi cloud for data that's created outside the public club. But Thomas, what about for data that's created inside the cloud? How does Del help with that? >>Yes. So, this year, we actually released a solution, uh, in conjunction with G C. P. So within Google Cloud, you can have power scale for one fs, right? And so that's that native native feature. So, you know, goes through all the compliance and all the features within being a part of that G c p natively eso counts towards your credits and your GP Google building as well. But it's still all the features that you have. And so we've been running some, actually, some benchmarks. So we've got a couple of white papers out there, that kind of detail. You know what we can do from an artificial intelligence perspective back to Sean Demise Example. We were just talking about, you know, being able to use more and more GPU. So we we've done that to run some of our AI benchmarks against that and then also, you know, jumped into the Hadoop space. But because you know, that's 11 area from a power scale, prospective customers were really interested. Um, and they have been for years. And then, really, the the awesome portion about this is for customers that are looking for a hybrid solution. Or maybe it's their first kickoff to it. So back Lisa to those compliance features that we were talking about those air still inherent within that native Google G C P one fs version, but then also for customers that have it on prim. You can use those same features to burst your data into, um, your isil on cluster using all the same native tools that you've been using for years within your enterprise. >>God, it's so starting out for power. Skill for Google Cloud Trying to get back to you Kind of wrapping things up here. What are some of the things that we're going to see next from Dell from an AI Solutions perspective? >>Yes. So we are working on many different interesting projects ranging from, uh, the latest, uh, in video Salford's that they have announced d d x a 100. And in fact, two weeks ago at GTC, uh, Syria announced take too far parts with, uh, it takes a 100 solvers. We're part off that ecosystem. And we are working with, uh, the leading, uh uh, solutions toe benchmark, our ai, uh, environments, uh, for all the storage, uh, ensuring, like we are providing, like, all the throughput and scalability that we have to offer >>Thomas finishing with you from the customer perspective. As we talked about so many changes this year alone as we approach calendar year 2021 what are some of the things that Dell is doing with its customers with its partners, the hyper scale er's and video, for example, Do you think customers are really going to be able to truly accelerate successful AI projects? >>Yeah. So the first thing I'd like to talk about is what we're doing with the D. G. S A 100. So this month that GTC you saw our solution for a reference architecture for the G s, a 100 plus power scale. So you talk about speed and how we can move customers insights. I mean, some of the numbers that we're seeing off of that are really a really amazing right. And so this is gives the customers the ability to still, you know, take all the features and use use I salon and one f s, um, like they have in the past, but now combined with the speed of the A 100 still be ableto speed up. How fast they're using those building out those deep learning models and then secondly, with that that gives them the ability to scale to. So there's some features inherent within this reference architecture that allow for you to make more use, right? So bring mawr data scientists and more modelers GP use because that's one thing you don't see Data scientist turning away right there always like, Hey, you know, I mean, this this project here needs needs a GPU. And so, you know, from a power scale one fs perspective, we want to be able to make sure that we're supporting that. So that as that data continues to grow, which, you know we're seeing is one of the large factors. Whenever we're talking about artificial intelligence is the scale for the data. We wanna them to be able to continue to build out that data consolidation area for all these multiple different workloads. That air coming in. >>Excellent, Thomas. Thanks for sharing that. Hopefully next time we get to see you guys in person and we can talk about a customer who has done something very successful with you guys. Kind of me. Always great to talk to you. Thank you for joining us. >>Thank you. Thank you >>for China. May Mandel and Thomas Henson. I'm Lisa Martin. You're watching the cubes Coverage of Dell Technologies, World 2020

>>from around the globe. It's the Cube with coverage of Coop con and cloud, native con Europe 2020 Virtual brought to you by Red Hat, The Cloud Native Computing Foundation and its ecosystem partners >>Welcome back. This is the Cube coverage of Cube Con Cloud, native con, the European show for 2020. I'm your host to Minuteman. And when we talk about the container world, we talk about what's happening in cloud. Native storage has been one of those sticking points. One of those things that you know has been challenging, that we've been looking to mature and really happy to welcome back to the program two of our cube alumni to give us the update on the state of storage for the container world. Both of them are oh, founders and CEOs. First of all, we have Xiang Yang from Rancher Labs, of course, was recently acquired by Sue Save it and the intention to acquire on and also joining us from early the relay. Who is with port works? Shang Amerli. Thanks so much for joining us. Thank you. Thank you. Alright. So early. I actually I'm going to start with you just cause you know we've seen, you know, a couple of waves of companies working on storage. In this environment, we know storage is difficult. Um, And when we change how we're building things, there's architectural things that can happen. Eso maybe if you could just give us a snapshot, you know, Port works, you know, was created to help unpack this. You know, straight on here in 2020 you know, where you see things in the overall kind of computer storage landscape? >>Absolutely. Still, before I kind of jump into port works. I just want to take a minute to publicly congratulate the the whole rancher team, and and Shang and Shannon And will China have known those folks for a while there? They're kind of true entrepreneurs. They represent the serial entrepreneur spirit that that so many folks know in the valley, and so, you know, great outcome for them. We're very happy for them and ah, big congrats and shout out to the whole team. What works is is a little over five years old, and we've been kind of right from the inception of the company recognized that to put containers in production, you're gonna have to solve, not just the orchestration problem. But the issue of storage and data orchestration and so in a natural kubernetes orchestrates containers and what works orchestrates storage and data. And more specifically, by doing that, what we enable is enterprises to be able to take APS that are containerized into production at scale and and have high availability. Disaster recovery, backup all of the things that for decades I t has had to do and has done to support application, reliability and availability. But essentially we're doing it for purpose with the purpose build solution for containerized workloads. >>Alright, shaming. Of course, storage is a piece of the overall puzzle that that ranchers trying to help with. Maybe if you could just refresh our audience on Longhorn, which your organization has its open source. It's now being managed by the CN. CF is my understanding. So help us bring Longhorn into the discussion >>thanks to. So I'm really glad to be here. We've I think rancher and port work started about the same time, and we started with a slightly different focus. More is exactly right to get containers going, you really need both so that the computer angle orchestrating containers as well as orchestrating the storage and the data. So rancher started with, ah, it's slightly stronger focus on orchestrating containers themselves, but pretty quickly, we realized, as adoption of containers grow, we really need it to be able to handle ah, storage feather. And like any new technology, you know, uh, Kubernetes and containers created some interesting new requirements and opportunities, and at the time, really, they weren't. Ah, a lot of good technologies available, you know, technologies like rook and SEF at the time was very, very premature, I think, Ah, the You know, we actually early on try to incorporate ah, the cluster technology. And it was just it was just not easy. And And at the time I think port Works was, ah, very busy developing. Ah, what turned out to be there flagship product, which we end up, end up, uh, partnering very, very closely. But but early on, we really had no choice but to start developing our own storage technology. So Long horn. As a piece of container storage technology, it's actually almost as oh, there's rancher itself. When about funding engineers, we hired he he ended up, you know, working on it and Then over the years, you know the focus shift that I think the original version was written in C plus plus, and over the years it's now being completely re written in Golan. It was originally written more for Docker workload. Now, of course, everything is kubernetes centric. And last year we you know, we we decided to donate the Longhorn Open Source project to CN CF. And now it's a CN CF sandbox project, and the adoption is just growing really quickly. And just earlier this year, we we finally ah decided to we're ready to offer a commercial support for it. So So that's that's where rancher is. And with longhorn and container storage technology. >>Yeah, it has been really interesting to watch in this ecosystem. A couple of years ago, one of the Q con shows I was talking to people coming out of the Believe It was the Sigs, the special interest group for storage, and it was just like, Wow, it was heated. Words were, you know, back and forth. There's not a lot of agreement there. Anybody that knows the storage industry knows that you know standards in various ways of doing things often are contentious and there's there's differences of opinion. Look at the storage industry. You know, there's a reason why there's so many different solutions out there. So maybe it love to hear from early. From your standpoint, things are coming to get a little bit more. There are still a number of options out there. So you know, why is this kind of coop petition? I actually good for the industry? >>Yeah, I think this is a classic example of Coop petition. Right? Let's let's start with the cooperation part right? The first part of time the you know, the early days of CN, CF, and even sort of the Google Communities team, I think, was really very focused on compute and and subsequent years. In the last 34 years, there's been a greater attention to making the whole stack works, because that's what it's going to take to take a the enterprise class production and put it in, you know, enterprise class application and put it in production. So extensions like C and I for networking and CS I container storage interface. We're kind of put together by a working group and and ah ah you know both both in the CN CF, but also within the kubernetes Google community. That's you talked about six storage as an example. And, you know, as always happens, right? Like it It looks a little bit in the early days. Like like a polo game, right where folks are really? Ah, you know, seemingly, uh, you know, working with each other on on top of the pool. But underneath they're kicking each other furiously. But that was a long time back, and we've graduated from then into really cooperating. And I think it's something we should all be proud of. Where now the CS I interface is really a A really very, very strong and complete solution tow, allowing communities to orchestrate storage and data. So it's really strengthened both communities and the kubernetes ecosystem. Now the competition part. Let's kind of spend. I want to spend a couple of minutes on that too, right? Um, you know, one of the classic things that people sometimes confuse is the difference between an overlay and an interface. CSC is wonderful because it defines how the two layers off essentially kind of old style storage. You know, whether it's a san or ah cloud, elastic storage bucket or all of those interact with community. So the the definition of that interface kind of lay down some rules and parameters for how that interaction should happen. However, you still always need an overlay like Port Works that that actually drives that interface and enables Kubernetes to actually manage that storage. And that's where the competition is. And, you know, she mentioned stuff and bluster and rook and kind of derivatives of those. And I think those have been around really venerable and and really excellent products for born in a different era for a different time open stack, object storage and all of that not really meant for kind of primary workloads. And they've been they've been trying to be adapted for, for for us, for this kind of workload. Port Works is really a built from right from the inception to be designed for communities and for kubernetes workloads at enterprise scale. And so I think, you know, as I as I look at the landscape, we welcome the fact that there are so many more people acknowledging that there is a vital need for data orchestration on kubernetes right, that that's why everybody and their brother now has a CS I interface. However, I think there's a big difference between having an interface. This is actually having the software that provides the functionality for H. A, D R. And and for backup, as as the kind of life cycle matures and doing it not just at scale, but in a way that allows kind of really significant removal or reduction off the storage admin role and replaces it with self service that is fully automated within communities. Yeah, if I >>can, you know, add something that that I completely agree. I mean, over the Longhorns been around for a long time. Like I said, I'm really happy that over the years it hasn't really impacted our wonderful collaborative partnership with what works. I mean, Poll works has always been one of our premier partners. We have a lot of, ah, common customers in this fight. I know these guys rave about what works. I don't think they'll ever get out for works. Ah, home or not? Uh huh. Exactly. Like Morissette, you know, in the in the storage space, there's interface, which a lot of different implementations can plugging, and that's kind of how rancher works. So we always tell people Rancher works with three types of storage implementations. One is let we call legacy storage. You know, your netapp, your DMC, your pure storage and those are really solid. But they were not suddenly not designed to work with containers to start with, but it doesn't matter. They've all written CS I interfaces that would enable containers to take advantage of. The second type is some of the cloud a block storage or file storage services like EBS, GFS, Google Cloud storage and support for these storage back and the CS I drivers practically come with kubernetes itself, so those are very well supported. But there's still a huge amount of opportunities for the third type of you know, we call container Native Storage. So that is where Port Works and the Longhorn and other solutions like open EBS storage OS. All these guys fitting is a very vibrant ecosystem of innovation going on there. So those solutions are able to create basically reliable storage from scratch. You know, when you from from just local disks and they're actually also able to add a lot of value on top of whatever traditional or cloud based, persistent storage you already have. So so the whole system, the whole ecosystem, is developing very quickly. A lot of these solutions work with each other, and I think to me it's really less of a competition or even Coop petition. It's really more off raising the bar for for the capabilities so that we can accelerate the amount of workload that's been moved onto this wonderful kubernetes platform in the end of the benefit. Everyone, >>Well, I appreciate you both laying out some of the options, you know, showing just a quick follow up on that. I think back if you want. 15 years ago was often okay. I'm using my GMC for my block. I'm using my netapp for the file. I'm wondering in the cloud native space, if we expect that you might have multiple different data engine types in there you mentioned you know, I might want port works for my high performance. You said open EBS, very popular in the last CN CF survey might be another one there. So is do we think some of it is just kind of repeating itself that storage is not monolithic and in a micro service architecture. You know, different environments need different storage requirements. >>Yeah, I mean quick. I love to hear more is view as well, especially about you know, about how the ecosystem is developing. But from my perspective, just just the range of capabilities that's now we expect out of storage vendors or data management vendors is just increased tremendously. You know, in the old days, if you can store blocks to object store file, that's it. Right. So now it's this is just table stakes. Then then what comes after that? There will be 345 additional layers of requirements come all the way from backup, restore the our search indexing analytics. So I really think all of this potentially off or in the in the bucket of the storage ecosystem, and I just can't wait to see how this stuff will play out. I think we're still very, very early stages, and and there, you know what? What, what what containers did is they made fundamentally the workload portable, but the data itself still holds a lot of gravity. And then just so much work to do to leverage the fundamental work load portability. Marry that with some form of universal data management or data portability. I think that would really, uh, at least the industry to the next level. Marie? >>Yeah. Shanghai Bean couldn't. Couldn't have said it better. Right? Let me let me let me kind of give you Ah, sample. Right. We're at about 160 plus customers now, you know, adding several by the month. Um, just with just with rancher alone, right, we are. We have common customers in all common video expedient Roche March X, Western Asset Management. You know, charter communications. So we're in production with a number off rancher customers. What are these customers want? And why are they kind of looking at a a a Port works class of solution to use, You know, Xiang's example of the multiple types, right? Many times, people can get started with something in the early days, which has a CS I interface with maybe say, $10 or 8 to 10 nodes with a solution that allows them to at least kind of verify that they can run the stack up and down with, say, you know, a a rancher type orchestrator, workloads that are containerized on and a network plug in and a storage plugging. But really, once they start to get beyond 20 notes or so, then there are problems that are very, very unique to containers and kubernetes that pop up that you don't see in a in a non containerized environment, right? Some. What are some of these things, right? Simple examples are how can you actually run 10 to hundreds of containers on a server, with each one of those containers belonging to a different application and having different requirements? How do you actually scale? Not to 16 nodes, which is sort of make typically, maybe Max of what a San might go to. But hundreds and thousands of notes, like many of our customers, are doing like T Mobile Comcast. They're running this thing at 600 thousands of notes or scale is one issue. Here is a critical critical difference that that something that's designed for Kubernetes does right. We are providing all off the storage functions that Shang just described at container granted, granularity versus machine granularity. One way to think about this is the old Data center was in machine based construct. Construct everything you know. VM Ware is the leader, sort of in that all of the way. You think of storage as villains. You think of compute and CPUs, everything. Sub sub nets, right? All off. Traditional infrastructure is very, very machine centric. What kubernetes and containers do is move it into becoming an app defined control plane, right? One of the things were super excited about is the fact that Kubernetes is really not just a container orchestrator, but actually a orchestrator for infrastructure in an app defined way. And by doing that, they have turned, uh, you know, control off the infrastructure via communities over to a kubernetes segment. The same person who uses rancher uses port works at NVIDIA, for example to manage storage as they use it, to manage the compute and to manage containers. And and that's marvellous, because now what has happened is this thing is now fully automated at scale and and actually can run without the intervention off a storage admin. No more trouble tickets, right? No more requests to say, Hey, give me another 20 terabytes. All of that happens automatically with the solution like port works. And in fact, if you think about it in the world of real time services that we're all headed towards right Services like uber now are expected in enterprises machine learning. Ai all of these things analytics that that change talk about are things that you expect to run in a fully automated way across vast amounts of data that are distributed sometimes in the edge. And you can't do that unless you're fully automated and and not really the storage admin intervention. And that's kind of the solution that we provide. >>Alright, well, we're just about out of time. If I could just last piece is, you know, early and saying to talk about where we are with long for and what we should expect to see through the rest of this year and get some early for you to you know, what differentiates port works from Just, you know, the open source version. So And maybe if we start with just kind of long or in general and then really from from your standpoint, >>yeah, so it's so so the go along one is really to lower the bar for folks to run state for workloads on on kubernetes we want you know, the the Longhorn is 100% open source and it's owned by CN cf now. So we in terms of features and functionalities is obviously a small subset of what a true enterprise grade solution like Port Works or, um, CEO on that that could provide. So there's just, you know, the storage role. Ah, future settle. The roadmap is very rich. I don't think it's not really Ranchers go Oh, our Longhorns goal to, you know, to try to turn itself into a into a plug in replacement for these enterprise, great storage or data management solutions. But But they're you know, there's some critical critical feature gaps that we need address. And that's what the team is gonna be focusing on, perhaps for the rest of the year. >>Yeah, uh, still, I would I would kind of, you know, echo what Chang said, right? I think folks make it started with solutions, like longer or even a plug in connector plug in with one of their existing storage vendors, whether it's pure netapp or or EMC from our viewpoint, that's wonderful, because that allows them to kind of graduate to where they're considering storage and data as part of the stack. They really should that's the way they're going to succeed by by looking at it as a whole and really with, You know, it's a great way to get started on a proof of concept architecture where your focus initially is very much on the orchestration and the container ization part. But But, as Xiang pointed out, you know what what rancher did, what I entered it for Kubernetes was build a simple, elegant, robust solution that kind of democratized communities. We're doing the same thing for communities storage right? What Port works does is have a solution that is simple, elegant, fully automated, scalable and robust. But more importantly, it's a complete data platform, right? We we go where all these solutions start, but don't kind of venture forward. We are a full, complete lifecycle management for data across that whole life cycle. So there's many many customers now are buying port works and then adding deal right up front, and then a few months later they might come back and I'd backup from ports. So two shanks point right because of the uniqueness of the kubernetes workload, because it is an app defined control plane, not machine to find what is happening is it's disrupting, Just like just like virtualization day. VM exist today because because they focused on a VM version off. You know, the their backup solution. So the same thing is happening. Kubernetes workloads are district causing disruption of the D r and backup and storage market with solutions like sports. >>Wonderful. Merlin Chang. Thank you so much for the updates. Absolutely. The promise of containers A Z you were saying? Really, is that that Atomic unit getting closer to the application really requires storage to be a full and useful solution. So great to see the progress that's being made. Thank you so much for joining us. >>Welcome, Shannon. We look forward to ah, working with you as you reach for the stars. Congratulations again. We look >>forward to the containing partnership morally and thank you. Still for the opportunity here. >>Absolutely great talking to both of you And stay tuned. Lots more coverage of the Cube Cube Con cloud, native con 2020 Europe. I'm stew minimum. And thank you for watching the Cube. Yeah, yeah, yeah, yeah, yeah, yeah

>>Live from Orlando, Florida. It's the cube covering Microsoft ignite brought to you by Cohesity. >>Welcome back everyone to the cubes live coverage of Microsoft ignite. I'm your host, Rebecca Knight, along with my co host to minimun. We are doing joined by Donna Sarkar. She is the advocate lead Microsoft power platform at Microsoft. Thank you so much for coming on the show. Thank you very much for having me. Tai cube land. So tell us a little bit about power platform. It's something we're hearing some buzz about, but we still need the overview. What is it all about? All right, so for years, decades we in the tech industry, you have been on this mission where we say everyone in the world can benefit from learning to code, right? Uh, whether you're a farmer and accountant, a teacher, a lawyer, a doctor, some sort of code will help you do your job better and you'll be able to automate away boring tasks and make apps and websites to solve your business problems. >>Right? We've been saying this forever and soon we started to realize like, why are we asking everyone to learn to code when the end goal is to solve those business problems, right? So instead of learning to code, why not create a suite of low code or no code tools? So all of these people who we call citizen developers who may not be professional developers as in they didn't go to computer science school, they didn't do a coding boot camp. They don't live in visual studio all day. How can they use these low code tools to solve their specific business problems? So that's like the vision of power platform and they're, I would say six independent pillars of it. Um, the first one, the one that most people know is power BI, which is a dashboard to visualize data and you know, um, traction in your business and all of that. >>So that's the one that most of the fortune 500 are like quite familiar with. The second one that I think a lot of people have used, used to be called Microsoft flow. So this is a automation tool where you'd say, if I get an email, send me a text, you know, a kind of a, if this happens, then that happens. It's just a logical tool that connects lots and lots of services in our life together that has been renamed to power automate to focus more on the automation that many businesses have that we actually have not thought about for decades. How do we automate some of these processes that people have to do all the time? Third thing if I could. So of course one of the new announcements this week, power automate is the RPA piece. Yes. Come out there. So I guess it's a suite and this is a new offering as RPA. >>The robotic process automation is how we can, um, do UI automation, which is a huge pain in the neck. Like it's terrible because you say, Oh click box, wait three seconds, wait for this thing to happen. Sleep 10 sec. It is terrible. I've done UI automation, I hate it. UI automation. So much. So RPA, what it does for you is you perform the act actions and the code is generated and it replays. So that is this powerful tool for anyone who has to do any sort of repetitive scan form, scan, form, scan form, you know, sort of thing. So power automate. The third pillar is PowerApps, which I think everyone hears a lot about today, which is um, apps that are generated from whatever data source that you've got. Say you've got an Excel spreadsheet having, and I saw all of your guests are it all tracked in an Excel spreadsheet, right? >>Donna's coming now, Christina is coming next and there's Christina now and imagine you can see them in an app instead. And all of you have this app on your phone, you can say, Oh, what's on the docket for today, right? Donna's showing up at 11 Christina's at 1130 what are the questions we want to ask Donna? Click on the Donna tab, you get all the questions you want to talk to her about, et cetera. So PowerApps is a way to quickly generate an app from a data source without code. We have a whole bunch of templates depending on what you're trying to do. So maybe you're trying to make a gallery of photos or you're trying to make like an expense tool or like a gas mileage tool or whatever you're trying to do that every single business in the world has the same tools, slightly different. >>So the fourth thing is, um, a new announcement called power virtual assist, which is, um, think about it as simplified chatbots, right? Chatbots are everywhere. Uh, the way people think about making them is, Oh, I have to go get Azure cognitive services and learn it deeply and become a AI expert and learn to like speak natural language processing stuff. But in fact, you can build a chat bot in five minutes using power virtual assist, which was fantastic and really cool. And running through all of this is my favorite that I learned a lot about this week, which is called the AI builder. And AI builder is a tool really that brings intelligence to all of these things and makes you feel it kind of a badass. I'm like, Oh, I trained an AI model and deployed it and tested it on stage. That's crazy and cool. And I learned to do that in five minutes and believe you me, I'm not a data scientist. >>So it was a really, really cool set of tools that I personally, even as a pro developer, I'm very excited about. Well, I want to dig into the tools more than what they can do. But I first want to ask you a personal question because you're new to the role. You've been there two weeks. What made you, what was exciting to you about working with power platforms? So I've been at Microsoft for 14 years and I've always been in the windows division and I've always worked in a software engineering function. So always dealing with like C plus plus code comm code, how do we, what product code do we changes, do we make to windows the. And recently I've been realizing that my personal mission that anyone in the world should have my opportunities. It's, that's really important to me. Right? I grew up underserved society in Detroit, Michigan, right? >>I don't, I often feel like I don't deserve this life that I have and I fell into it because of luck and circumstance and I want other people to have these opportunities and not feel that same kind of impostor thing. So I always believe that tech is this, you know, this sword, this weapon that you can wield and it will as you make your way through the world and it creates so many opportunities, right? It, the opera and anyone in the world wants to hire a software engineer. Every company, right? Every company wants to hire devs. It doesn't matter if you're like government or like oil rigs, you want software developers. And I thought, what an amazing economic power and I want lots of people to have that. And lo and behold, I was offered the opportunity to head up a brand new advocacy team for the power platform, um, as part of the Azure advocates organization. >>And I said, Oh, that's amazing to be able to line up my personal passion with a mission in the company that doesn't come along very often. So I love my job. So it's interesting thought. I would love your viewpoint as someone that's been with Microsoft for 14 years, cause I know a lot of the advocacy people and many of them are ones that if you ask them if they would have joined Microsoft five years ago, I'm not sure. Sure. So you know, moving from windows to there. Tell us a little bit about culturally what's different about Microsoft today and you know, much more obviously than just windows. Yeah. Um, I would say that there's three things that are dramatically different. There's a lot of like things that people notice, but three things I think that are just, you can't even argue about it. One, we are definitely a learn it all mindset rather than a nodal where it's actually much better now to say, I do not know. >>Let's go find out, let's go do an experiment and then we'll have an answer. And that's much better than with great confidence saying something wrong. Right. Oh I know this will work for sure. I guarantee you. And then it not working because you're being a know it all rather than the learn it all. So that tolerance is off the charts. It's, it's expected. If you come in with a strong opinion with no sort of experimental data to back it up, that's no longer a good thing right now. People almost are suspicious. Like, really? Why do you, why do you think that? Have you checked it? Have you done the experiment? The second thing is, um, this co-creating with customers before, like you're asking about windows. I've worked on windows five versions and it always went a little like this, right? We as the developers would go and hide in Redmond, Washington for three and a half years and one day we would show up and say, here is your operating system. >>We'll see you in three years, have fun using it by, and then we go off and make another operating system. Right? We didn't stick around to figure out, is this operating system working for you? Are you being successful? What's you're trying to do? Are your customer successful? We just went ahead and made what we thought was next, right? Because we were convinced we knew better. But with windows 10 and every other product at Microsoft, now we actually cocreate with our customers, right? That feedback loop is part of the product cycle where we don't ship a product without having a feedback loop. So we shipped something. How are we getting feedback? What is the time baked in to actually take that feedback and make changes? So that's one thing. It's dramatically different. Um, it used to all be timed to code, product, time to fix bugs. >>That's it. Now it's code product, listen to customer feedback, fixed bugs from customers. That's it. So it dramatically shorten the amount of time it took to build an operating system because we don't need to make a three year long product. Instead we make like a six month long product. And when I ran the windows insider program, we were testing windows every week, right? Twice a week we're rolling out versions of windows to millions of people and getting their feedback in real time. And the third thing I'd say that's been a dramatic transformation is this inclusivity of not just different kinds of, you know, race in the city, but work styles, the kinds of businesses we do work with. Like we're a, we do Linux now, right? We do eggs. Um, our platform itself pulls from all sorts of data sources. We don't just say we only pull from Microsoft tech. >>Like if you have Excel, if you have access, if you have Azure, if you've sequel, we support you and everyone else go the heck away. No, we're, we're saying whatever data source you've got, we don't care. We'll build you a power app based on your data source. Bring your whole self to work, right? It's that bring your whole self to a work mindset that I think has permeated just across the company and a chosen our products. So you were talking about this feedback loop and I'm interested because these, these, the power platform was rolled out into 2018 we haven't seen any major revenue yet, but Microsoft sees a ton of promise here. So what was the customer feedback you were given in terms of these updates that you've just announced here at ignite and what were customers demanding, wanting, needing from these platforms, these, these, these tools? >>Well, there's been a few things. One, um, the uptake in power platform, especially power apps is the fastest growth of any business app in Microsoft history. Um, in the last like just two years we've reached 84% of the fortune 500 are running power. Now. That's kind of wild, right? When you think these are normally traditional companies who can be quite conservative, but they've got people, whether it's an it, it's a citizen dove or a PRODA, they're actually building power apps to supplement their business needs, right? So it's been just astronomical growth, which is fantastic. Um, and the feedback from this group is actually what dictates all of the changes we've been making. So one of the key things a lot of people said was we just adopted teams like last year, right? Our company adopted teams, we're all in on teams. All of our communication like realtime has done on teams, but power platform is not with teams. >>What's, what's the deal with that? Right? So the par platform dev team engineering team actually went and figured out how can you have a teams channel, how can you build a power plant, a power app, and then share that power app within your teams specifically. So say the three of us are working on a teams channel and I make a Oh, track your attendees app, the one we're talking about, I can share it within the teams itself and we can just see it from within the team's window. So it'll run within the teams window. Um, we can just deploy it to our phones as well. And with the same team's credentials as we're working, that applies to the app as well. So that's something that just rolled out this week as direct feedback from people who say we're, we want an on the latest and greatest. And that means teams. That's one means SharePoint online. That means our platform. That means all the things now. >>Yeah. So Donna, one of the things I love that you talked about is it doesn't take months to get started on this. So many announcements that you talked through all the six pillars and everything. For those people out there seeing what's new, give them some final tips as to how they should get started with, with the power platform family. >>I would say that um, one of the best things you can do is just get your hands on it, right? Stop reading about it. Stop looking at the announcements. Just get your hands on it. Because I was at first reading all these blog posts trying to understand CDs, power platform, AI builder, all this stuff. Stop. Just don't do it. The best thing to do is to go get on Microsoft learn. There's a start, a starter tutorial called canvas apps for power platform. Um, and go do the tutorial. All it does is it deploys an Excel spreadsheet to your personal machine or your personal one drive, whatever it is and using that, it's just carpet, right? It's like black carpet, white carpet and shows pictures of carpet and then you generate a power app. And it shows it in a gallery view on an app that you just see on your computer and then you deploy it to your phone. >>All it does is show you the power of an Excel spreadsheet converted into an app. So I've created a short URL for it just to make life easier for everyone. So it's AKA dot. Ms power up, super straight forward, super simple. And I talk about this tutorial all the time, not because I think it's the best tutorial that's ever existed, but for someone who has absolutely no idea and they're feeling intimidated to start, this is exactly the right thing to do because this tutorial, I am not kidding you both of you can do it in five minutes. Like on the next break. Once you're finished with me and Christina, I challenge you to do the tutorial. All right? Okay. Challenge accepted. One, one final thing. So you are known for this Ted talk that you gave Unimpossible syndrome earlier in this, in our conversation you said you fell into this like, Oh absolutely, you've gotten lucky, but yet you're a smart woman. >>Talk about imposter syndrome. And then and then give your best advice for the young people out there and an old people to frankly who are suffering. Imposter syndrome is a killer because it is a disease that is a global epidemic. It's not. Some people think it's a woman's problem, it's a people of color problem. No, it's not. It's an everyone problem. Every time I give this talk, the Ted audience was thousands of people. I would say about 70% men and when I asked how many of you feel these symptoms? Hands are up. 70% of people, and this was men too, who feel like I got here. You know the thoughts are usually I got here by accident. It was dumb luck. There's a mistake in the process. I slipped in under the radar any minute. Now someone's going to show up here and say, you don't belong here. >>Get out or someone's going to check my credentials or ask me like, how do you think you're as good as the people around you? Or why are you qualified to speak on this topic? Right? People are convinced this is going to happen. Like, almost everyone is convinced and it's wild. And I've realized the reason that happens is because we are not used to doing that thing yet. That's it. We don't imposter about the things we do every day. You don't imposter about being in camera on front of the camera in front of everyone because you do it all the time and you've gotten good reviews and obviously people come to talk to you. But if tomorrow I was to be like you and I are going to write office abs, you may say, ah, I don't think I'm qualified to do that. I don't know if you are or not. >>I'm just making stuff up at this point. Um, and you may say, I am not qualified to do that. And the reason you say that is because you've never done it before. Why would you be qualified to do that? It's like me trying to be qualified to ride a unicycle, right? Which I can't. So my advice to people who feels this, well I don't feel like I belong here, is break it down right into steps, debug this process and say, all right, there are parts of this process that I feel qualified to do and there's parts I do not feel qualified to do. What are they? So from my own example, I absolutely do not feel qualified to lead an advocacy team for power platform. Right. I said, I joined this team two weeks ago. I just learned about this product last year. How am I qualified to lead advocacy for this? >>So I had to break it down and I said, what? What am I feeling and posturing about? Is it leading advocacy? No, I did it for windows. I did it for hollow lens. I do know how to do that. Is it speaking in front of lots of people? Not really. I do that all the time. Is it writing content so others can learn? Not really. I do that all the time. Is it the product? Yes, it's the product. It's the, I don't feel like I know the ins and outs of the product that well. So if you were to ask me where exactly is the connector for, you know, Azure sequeled or PowerApps, I would just freeze. Like I do not know. I think it's in the Azure portal somewhere, somewhere. So I would feel that sense of imposter and like, Oh, I don't know. >>So I don't belong here. It's no, I just don't know the product that well. That's okay. I know advocacy well, so what I need to do now is identify things. I'm good at advocacy things. I'm not good at product, learn the product. That's it. It just becomes a really easy to do list or to learn list. Right. Learn it all mindset, not know it all. Mindset. I love it. Thank you so much. Thank you is a really terrific conversation. Wonderful. Thanks for having me. I'm Rebecca Knight for Stu Miniman. Stay tuned for more of the cubes live coverage of Microsoft ignite.

>> Announcer: Live from San Jose, it's theCUBE, presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partner. (upbeat electronic music) >> Welcome back to The Cube. I'm Lisa Martin with George Gilbert. We are live at our event, Big Data SV, in downtown San Jose down the street from the Strata Data Conference. We're joined by a new guest to theCUBE, Sastry Malladi, the CTO Of FogHorn. Sastry, welcome to theCUBE. >> Thank you, thank you, Lisa. >> So FogHorn, cool name, what do you guys do, who are you? Tell us all that good stuff. >> Sure. We are a startup based in Silicon Valley right here in Mountain View. We started about three years ago, three plus years ago. We provide edge computing intelligence software for edge computing or fog computing. That's how our company name got started is FogHorn. For our particularly, for our IoT industrial sector. All of the industrial guys, whether it's transportation, manufacturing, oil and gas, smart cities, smart buildings, any of those different sectors, they use our software to predict failure conditions in real time, or do condition monitoring, or predictive maintenance, any of those use cases and successfully save a lot of money. Obviously in the process, you know, we get paid for what we do. >> So Sastry... GE populized this concept of IIoT and the analytics and, sort of the new business outcomes you could build on it, like Power by the Hour instead of selling a jet engine. >> Sastry: That's right. But there's... Actually we keep on, and David Floor did some pioneering research on how we're going to have to do a lot of analytics on the edge for latency and bandwidth. What's the FogHorn secret sauce that others would have difficulty with on the edge analytics? >> Okay, that's a great question. Before I directly answer the question, if you don't mind, I'll actually even describe why that's even important to do that, right? So a lot of these industrial customers, if you look at, because we work with a lot of them, the amount of data that's produced from all of these different machines is terabytes to petabytes of data, it's real. And it's not just the traditional digital sensors but there are video, audio, acoustic sensors out there. The amount of data is humongous, right? It's not even practical to send all of that to a Cloud environment and do data processing, for many reasons. One is obviously the connectivity, bandwidth issues, and all of that. But the two most important things are cyber security. None of these customers actually want to connect these highly expensive machines to the internet. That's one. The second is the lack of real-time decision making. What they want to know, when there is a problem, they want to know before it's too late. We want to notify them it is a problem that is occurring so that have a chance to go fix it and optimize their asset that is in question. Now, existing solutions do not work in this constrained environment. That's why FogHorn had to invent that solution. >> And tell us, actually, just to be specific, how constrained an environment you can operate in. >> We can run in about less than 100 to 150 megabytes of memory, single-core to dual-core of CPU, whether it's an ARM processor, an x86 Intel-based processor, almost literally no storage because we're a real-time processing engine. Optionally, you could have some storage if you wanted to store some of the results locally there but that's the kind of environment we're talking about. Now, when I say 100 megabytes of memory, it's like a quarter of Raspberry Pi, right? And even in that environment we have customers that run dozens of machinery models, right? And we're not talking -- >> George: Like an ensemble. >> Like an anomaly detection, a regression, a random forest, or a clustering, or a gamut, some of those. Now, if we get into more deep learning models, like image processing and neural net and all of that, you obviously need a little bit more memory. But what we have shown, we could still run, one of our largest smart city buildings customer, elevator company, runs in a raspberry Pi on millions of elevators, right? Dozens of machinery algorithms on top of that, right? So that's the kind of size we're talking about. >> Let me just follow up with one question on the other thing you said, with, besides we have to do the low-latency locally. You said a lot of customers don't want to connect these brown field, I guess, operations technology machines to the internet, and physically, I mean there was physical separation for security. So it's like security, Bill Joy used to say "Security by obscurity." Here it's security by -- >> Physical separation, absolutely. Tell me about it. I was actually coming from, if you don't mind, last week I was in Saudi Arabia. One of the oil and gas plants where we deployed our software, you have to go to five levels of security even to get to there, It's a multibillion dollar plant and refining the gas and all of that. Completely offline, no connectivity to the internet, and we installed, in their existing small box, our software, connected to their live video cameras that are actually measuring the stuff, doing the processing and detecting the specific conditions that we're looking for. >> That's my question, which was if they want to be monitoring. So there's like one low level, really low hardware low level, the sensor feeds. But you could actually have a richer feed, which is video and audio, but how much of that, then, are you doing the, sort of, inferencing locally? Or even retraining, and I assume that since it's not the OT device, and it's something that's looking at it, you might be more able to send it back up the Cloud if you needed to do retraining? >> That's exactly right. So the way the model works is particularly for image processing because you need, it's a more complex process to train than create a model. You could create a model offline, like in a GPU box, an FPGA box and whatnot. Import and bring the model back into this small little device that's running in the plant, and now the live video data is coming in, the model is inferencing the specific thing. Now there are two ways to update and revise the model: incremental revision of the model, you could do that if you want, or you can send the results to a central location. Not internet, they do have local, in this example for example a PIDB, an OSS PIDB, or some other local service out there, where you have an opportunity to gather the results from each of these different locations and then consolidate and retrain the model, put the model back again. >> Okay, the one part that I didn't follow completely is... If the model is running ultimately on the device, again and perhaps not even on a CPU, but a programmable logic controller. >> It could, even though a programmable controller also typically have some shape of CPU there as well. These days, most of the PLCs, programmable controllers, have either an RM-based processor or an x86-based processor. We can run either one of those too. >> So, okay, assume you've got the model deployed down there, for the, you know, local inferencing. Now, some retraining is going to go on in the Cloud, where you have, you're pulling in the richer perspective from many different devices. How does that model get back out to the device if it doesn't have the connectivity between the device and the Cloud? >> Right, so if there's strictly no connectivity, so what happens is once the model is regenerated or retrained, they put a model in a USB stick, it's a low attack. USB stick, bring it to the PLC device and upload the model. >> George: Oh, so this is sort of how we destroyed the Iranian centrifuges. >> That's exactly right, exactly right. But you know, some other environments, even though it's not connectivity to the Cloud environment, per se, but the devices have the ability to connect to the Cloud. Optionally, they say, "Look, I'm the device "that's coming up, do you have an upgraded model for me?" Then it can pull the model. So in some of the environments it's super strict where there are absolutely no way to connect this device, you put it in a USB stick and bring the model back here. Other environments, device can query the Cloud but Cloud cannot connect to the device. This is a very popular model these days because, in other words imagine this, an elevator sitting in a building, somebody from the Cloud cannot reach the elevator, but an elevator can reach the Cloud when it wants to. >> George: Sort of like a jet engine, you don't want the Cloud to reach the jet engine. >> That's exactly right. The jet engine can reach the Cloud it if wants to, when it wants to, but the Cloud cannot reach the jet engine. That's how we can pull the model. >> So Sastry, as a CTO you meet with customers often. You mentioned you were in Saudi Arabia last week. I'd love to understand how you're leveraging and gaging with customers to really help drive the development of FogHorn, in terms of being differentiated in the market. What are those, kind of bi-directional, symbiotic customer relationships like? And how are they helping FogHorn? >> Right, that's actually a great question. We learn a lot from customers because we started a long time ago. We did an initial version of the product. As we begin to talk to the customers, particularly that's part of my job, where I go talk to many of these customers, they give us feedback. Well, my problem is really that I can't even do, I can't even give you connectivity to the Cloud, to upgrade the model. I can't even give you sample data. How do you do that modeling, right? And sometimes they say, "You know what, "We are not technical people, help us express the problem, "the outcome, give me tools "that help me express that outcome." So we created a bunch of what we call OT tools, operational technology tools. How we distinguish ourselves in this process, from the traditional Cloud-based vendor, the traditional data science and data analytics companies, is that they think in terms of computer scientists, computer programmers, and expressions. We think in terms of industrial operators, what can they express, what do they know? They don't really necessarily care about, when you tell them, "I've got an anomaly detection "data science machine algorithm", they're going to look at you like, "What are you talking about? "I don't understand what you're talking about", right? You need to tell them, "Look, this machine is failing." What are the conditions in which the machine is failing? How do you express that? And then we translate that requirement, or that into the underlying models, underlying Vel expressions, Vel or CPU expression language. So we learned a ton from user interface, capabilities, latency issues, connectivity issues, different protocols, a number of things that we learn from customers. >> So I'm curious with... More of the big data vendors are recognizing data in motion and data coming from devices. And some, like Hortonworks DataFlow NiFi has a MiNiFi component written in C plus plus, really low resource footprint. But I assume that that's really just a transport. It's almost like a collector and that it doesn't have the analytics built in -- >> That's exactly right, NiFi has the transport, it has the real-time transport capability for sure. What it does not have is this notion of that CEP concept. How do you combine all of the streams, everything is a time series data for us, right, from the devices. Whether it's coming from a device or whether it's coming from another static source out there. How do you express a pattern, a recognition pattern definition, across these streams? That's where our CPU comes in the picture. A lot of these seemingly similar software capabilities that people talk about, don't quite exactly have, either the streaming capability, or the CPU capability, or the real-time, or the low footprint. What we have is a combination of all of that. >> And you talked about how everything's time series to you. Is there a need to have, sort of an equivalent time series database up in some central location? So that when you subset, when you determine what relevant subset of data to move up to the Cloud, or you know, on-prem central location, does it need to be the same database? >> No, it doesn't need to be the same database. It's optional. In fact, we do ship a local time series database at the edge itself. If you have a little bit of a local storage, you can down sample, take the results, and store it locally, and many customers actually do that. Some others, because they have their existing environment, they have some Cloud storage, whether it's Microsoft, it doesn't matter what they use, we have connectors from our software to send these results into their existing environments. >> So, you had also said something interesting about your, sort of, tool set, as being optimized for operations technology. So this is really important because back when we had the Net-Heads and the Bell-Heads, you know it was a cultural clash and they had different technologies. >> Sastry: They sure did, yeah. >> Tell us more about how selling to operations, not just selling, but supporting operations technology is different from IT technology and where does that boundary live? >> Right, so typical IT environment, right, you start with the boss who is the decision maker, you work with them and they approve the project and you go and execute that. In an industrial, in an OT environment, it doesn't quite work like that. Even if the boss says, "Go ahead and go do this project", if the operator on the floor doesn't understand what you're talking about, because that person is in charge of operating that machine, it doesn't quite work like that. So you need to work bottom up as well, to convincing them that you are indeed actually solving their pain point. So the way we start, where rather than trying to tell them what capabilities we have as a product, or what we're trying to do, the first thing we ask is what is their pain point? "What's your problem? What is the problem "you're trying to solve?" Some customers say, "Well I've got yield, a lot of scrap. "Help me reduce my scrap. "Help me to operate my equipment better. "Help me predict these failure conditions "before it's too late." That's how the problem starts. Then we start inquiring them, "Okay, what kind of data "do you have, what kind of sensors do you have? "Typically, do you have information about under what circumstances you have seen failures "versus not seeing failures out there?" So in the process of inauguration we begin to understand how they might actually use our software and then we tell them, "Well, here, use your software, "our software, to predict that." And, sorry, I want 30 more seconds on that. The other thing is that, typically in an IT environment, because I came from that too, I've been in this position for 30 plus years, IT, UT and all of that, where we don't right away talk about CEP, or expressions, or analytics, and we don't talk about that. We talk about, look, you have these bunch of sensors, we have OT tools here, drag and drop your sensors, express the outcome that you're trying to look for, what is the outcome you're trying to look for, and then we drive behind the scenes what it means. Is it analytics, is it machine learning, is it something else, and what is it? So that's kind of how we approach the problem. Of course, if, sometimes you do surprisingly occasionally run into very technical people. From those people we can right away talk about, "Hey, you need these analytics, you need to use machinery, "you need to use expressions" and all of that. That's kind of how we operate. >> One thing, you know, that's becoming clearer is I think this widespread recognition that's data intensive and low latency work to be done near the edge. But what goes on in the Cloud is actually closer to simulation and high-performance compute, if you want to optimize a model. So not just train it, but maybe have something that's prescriptive that says, you know, here's the actionable information. As more of your data is video and audio, how do you turn that into something where you can simulate a model, that tells you the optimal answer? >> Right, so this is actually a good question. From our experience, there are models that require a lot of data, for example, video and audio. There are some other models that do not require a lot of data for training. I'll give you an example of what customer use cases that we have. There's one customer in a manufacturing domain, where they've been seeing a lot of finished goods failures, there's a lot of scrap and the problem then was, "Hey, predict the failures, "reduce my scrap, save the money", right? Because they've been seeing a lot of failures every single day, we did not need a lot of data to train and create a model to that. So, in fact, we just needed one hour's worth of data. We created a model, put the thing, we have reduced, completely eliminated their scrap. There are other kinds of models, other kinds of models of video, where we can't do that in the edge, so we're required for example, some video files or simulated audio files, take it to an offline model, create the model, and see whether it's accurately predicting based on the real-time video coming in or not. So it's a mix of what we're seeing between those two. >> Well Sastry, thank you so much for stopping by theCUBE and sharing what it is that you guys at FogHorn are doing, what you're hearing from customers, how you're working together with them to solve some of these pretty significant challenges. >> Absolutely, it's been a pleasure. Hopefully this was helpful, and yeah. >> Definitely, very educational. We want to thank you for watching theCUBE, I'm Lisa Martin with George Gilbert. We are live at our event, Big Data SV in downtown San Jose. Come stop by Forager Tasting Room, hang out with us, learn as much as we are about all the layers of big data digital transformation and the opportunities. Stick around, we will be back after a short break. (upbeat electronic music)

>>Hi, welcome to the cube. I'm your host, Lisa Martin. And we are on the ground at Google with cloud now, which is a nonprofit organization for and with leading women in cloud technologies and converging technologies. We're here tonight with cloud now to celebrate their fifth annual top women in cloud innovations award. In fact, this year they had so many submissions for outstanding females. So it's actually expanded the winners circle, which is fantastic. And we're very excited to be joined by one of those winners, Madeira Makoski. You are the co-founder of VP of product at platform nine systems. Welcome back to the cube. You've been on the cube before. That's right. Thank you. Congratulations on your award. >>Thank you. Talk >>To us a little bit about the project that you're leading as VP of product at platform nine. What are some of the, the cloud innovations that your team is helping to deliver? >>Yeah, definitely. So I'm a co-founder and re VP of product at platform nine systems. And what platform nine does simply put is we take best of breed, open source frameworks, such as OpenStack and Kubernetes, and we deliver them as a SA service. So what we did is we pioneered this really unique deployment model for these really complex, but popular and powerful open source frameworks where they're delivered as a SA service. So you can zoom them just like you can zoom Gmail or you can zoom Salesforce. Okay. And so that delivers a very differentiated experience to the end users where there's very little complexity in consuming these frameworks and going through the process of updates or upgrades through the frameworks, et >>Cetera. Excellent. How long, how old is platform nine? >>So platform nine was founded in 2013, so we just became three years old, about few months ago. Okay. >>Congratulations. Happy birthday. Tell us a little bit about the founding of that. What was it from a career perspective that was, was a driver or some of the drivers that led you to with your, co-founders say, let's do this. >>Yeah. So I remember reaching a point in my career. I think it was around maybe 20 10, 20 11 or so where I felt that I have completely stagnated. Right. And, and it was an interesting point for me because prior to that, I had never thought that I'm gonna start a company. In fact, my, my father is an entrepreneur. My brother is an entrepreneur and I had seen them go through the ups and downs of entrepreneurship. And so I had realized for myself early on, or I thought I'd realized that it's not for me, but when I reached that point in my career where none of the other options really seemed interesting enough, right. I, I tried interviewing, I tried going for large companies or small companies, different roles, but nothing sounded challenging enough. And then I was fortunate enough to realize that my, my current co-founders who were then my coworkers at VMware, they were independently going through very similar journey. Right. They were, they were trying to figure out what is it that they wanna do next. And that's really where a lot of our brainstorming over lunch sessions started. And that's kind of where platform line also got started. >>Wow, fantastic. So let's take a little bit of a look at your, your career path, how you got to be where you are. Were you always like naturally inclined towards engineering, computer science from the time you were small? Or was it something that you discovered a little bit later? >>Yeah, so I remember when I picked computer science for my bachelor's major, right. I, I pretty much picked it because it was the most popular stream or specialization to choose. And most of majority of students were doing that, or majority of top students were doing that. I didn't quite pick it because I, I had a particular inclination towards it. I didn't even have a computer in my house at that time. Wow. And so it really started for me, it started because after starting my bachelor's program, I started taking these, these off school C plus plus classes. And those classes were taught by this X professor who had in stopped teaching, but he, he would run this little workshop in, in his house garage at nighttime, remember nine 30 or 10. My mom would almost, she almost didn't want me to go out at that time, but right. We went out anyways and went to these classes and the, just the way he encouraged us to be almost little competitive in terms of edging each other a little bit in understanding really the core principles of C plus plus I just absolutely loved his teaching style. And, and I realized I'm, this is something I'm really good at. So that's where my, my interest in programming really, I think, was awakened for me. And then that's where my kind of my journey in computer science started. Wow, >>Fantastic. So I love that the, the old garage inspiration, you know, I think as a women in tech myself, we get inspiration from a, a lot of different sources, whether it's people that we know or not. And gender really doesn't matter in that. But talk to us a little bit more. You said that that sort of the, the catalyst for you and your co-founders getting together to start platform nine was you were at a, a position or a point in your career where you felt kind of stagnant. What were you doing then? And what was it that sort of gave you that boost to go? We're gonna do this. >>Yeah. So we were, I was a senior engineer at VMware. At that time. I was part of the tech lead or the architect team as part of various products in VMware's management portfolio, suite of products. During that time specifically, we were working on this project within VMware called we cloud vCloud director. And what that project really gave us was the opportunity to interface with a lot of VMware's mid to large size enterprise customers. So we got to observe a lot of their pain points, and we could clearly see that the traditional model of building infrastructure software, which is the shrink wrap way of building software, where someone deploys it, downloads it and then babysits it, maintains it over the life cycle of that software. Right. We realize that that model really cannot stand compared to the very high bar that public cloud was setting. And it's, it was really from that experience that we realized that there is an opportunity, there's a pain point demand >>Is there. >>Yes. And we, we realized it was big enough that we could form a company out of it. >>So in terms of your company, you're, you're relatively new from a, and you are obviously a senior female leader. Is that part of the corporate culture at platform nine? How important is helping other women to get into technology to you as personally and to your company? >>Yeah. I mean, platform nine is 100% supportive of talent regardless of gender. Right? So we are, I would say we are a very, what I think a very typical next gen tech startup in the bay area in that sense where my experience just in the tech industry in the bay area has been that the community is extremely encouraging and opening, open and welcoming. Right. I have myself personally never experienced any kind of bias and I've not seen my other coworkers, et cetera, experiencing that neither a platform line nor at VMware as well. So I am a big believer that the tech community in the bay area does a really fantastic job of, of not introducing a gender bias. >>Fantastic. Well, Madeira, thank you so much for joining us again on the cube. Congratulations again, on your award and being a very inspiring female tech leader. If you know, other female tech leaders that you think should be featured on our show, please tweet us at the cube, hashtag women in tech. Thanks again for watching and we'll see you next time. Thanks.

>>The cube at big data SV 2014 is brought to you by headline sponsors. When disco we make Hadoop invincible and Aptean accelerating big data, 2.0, >>Okay. We're back here, live in Silicon valley. This is big data. It has to be, this is Silicon England, Wiki bonds, the cube coverage of big data in Silicon valley and all around the world covering the strata conference. All the latest news analysis here in Silicon valley, the cube was our flagship program about the events extract the signal from noise. I'm John furrier, the founders of looking angle. So my co-host and co-founder of Wiki bond.org, Dave Volante, uh, George Matthew CEO, altruist on the cube again, back from big data NYC just a few months ago. Um, our two events, um, welcome back. Great to be here. So, um, what fruit is dropped into the blend or the change, the colors of the big data space this this time. So we were in new Yorkers. We saw what happened there. A lot of talk about financial services, you know, big business, Silicon valley Kool-Aid is more about innovation. Partnerships are being formed, channel expansion. Obviously the market's hot growth is still basing. Valuations are high. What's your take on the current state of the market? >>Yeah. Great question. So John, when we see this market today, I remember even a few years ago when I first visited the cave, particularly when it came to a deep world and strata a few years back, it was amazing that we talked about this early innings of a ballgame, right? We said it was like, man, we're probably in the second or third inning of this ball game. And what has progressed particularly this last few years has been how much the actual productionization, the actual industrialization of this activity, particularly from a big data analytics standpoint has merged. And that's amazing, right? And in a short span, two, three years, we're talking about technologies and capabilities that were kind of considered things that you play with. And now these are things that are keeping the lights on and running, you know, major portions of how better decision-making and analytics are done inside of organizations. So I think that industrialization is a big shift forward. In fact, if you've listened to guys like Narendra Mulani who runs most of analytics at Accenture, he'll actually highlight that as one of the key elements of how not only the transformation is occurring among organizations, but even the people that are servicing a large companies today are going through this big shift. And we're right in the middle of it. >>We saw, you mentioned a censure. We look at CSC, but service mesh and the cloud side, you seeing the consulting firms really seeing build-out mandates, not just POC, like let's go and lock down now for the vendors. That means is people looking for reference accounts right now? So to me, I'm kind of seeing the tea leaves say, okay, who's going to knock down the reference accounts and what is that going to look like? You know, how do you go in and say, I'm going to tune up this database against SAP or this against that incumbent legacy vendor with this new scale-out, all these things are on in play. So we're seeing that, that focus of okay, tire kicking is over real growth, real, real referenceable deployments, not, not like a, you know, POC on steroids, like full on game-changing deployments. Do you see that? And, and if you do, what versions of that do you seeing happening and what ending of that is that like the first pitch of the sixth inning? Uh, w what do you, how would you benchmark that? >>Yeah, so I, I would say we're, we're definitely in the fourth or fifth inning of a non ballgame now. And, and there's innings. What we're seeing is I describe this as a new analytic stack that's emerged, right? And that started years ago when particularly the major Hadoop distro vendors started to rethink how data management was effectively being delivered. And once that data management layer started to be re thought, particularly in terms of, you know, what the schema was on read what the ability to do MPP and scale-out was in terms of how much cheaper it is to bring storage and compute closer to data. What's now coming above that stack is, you know, how do I blend data? How do I be able to give solutions to data analysts who can make better decisions off of what's being stored inside of that petabyte scale infrastructure? So we're seeing this new stack emerge where, you know, Cloudera Hortonworks map are kind of that underpinning underlying infrastructure where now our based analytics that revolution provides Altrix for data blending for analytic work, that's in the hands of data analysts, Tableau for visual analysis and dashboarding. Those are basically the solutions that are moving forward as a capability that are package and product. >>Is that the game-changing feature right now, do you think that integration of the stack, or is that the big, game-changer this sheet, >>That's the hardening that's happening as we speak right now, if you think about the industrialization of big data analytics that, you know, as I think of it as the fourth or fifth inning of the ballgame, that hardening that ability to take solutions that either, you know, the Accentures, the KPMGs, the Deloitte of the world deliver to their clients, but also how people build stuff internally, right? They have much better solutions that work out of the box, as opposed to fumbling with, you know, things that aren't, you know, stitched as well together because of the bailing wire and bubblegum that was involved for the last few years. >>I got it. I got to ask you, uh, one of the big trends you saw in certainly in the tech world, you mentioned stacks, and that's the success of Amazon, the cloud. You're seeing integrated stacks being a key part of the, kind of the, kind of the formation of you said hardening of the stack, but the word horizontally scalable is a term that's used in a lot of these open source environments, where you have commodity hardware, you have open source software. So, you know, everything it's horizontally scalable. Now, that's, that's very easy to envision, but thinking about the implementation in an enterprise or a large organization, horizontally scalable is not a no brainer. What's your take on that. And how does that hyperscale infrastructure mindset of scale-out scalable, which is a big benefit of the current infrastructure? How does that fit into, into the big day? >>Well, I think it fits extremely well, right? Because when you look at the capabilities of the last, as we describe it stack, we almost think of it as vertical hardware and software that's factually built up, but right now, for anyone who's building scale in this world, it's all about scale-out and really being able to build that stack on a horizontal basis. So if you look at examples of this, right, say for instance, what a cloud era recently announced with their enterprise hub. And so when you look at that capability of the enterprise data hub, a lot of it is about taking what yarn has become as a resource manager. What HDFS has been ACOM as a scale-out storage infrastructure, what the new plugin engines have merged beyond MapReduce as a capability for engines to come into a deep. And that is a very horizontal description of how you can do scale out, particularly for data management. >>When we built a lot of the work that was announced at strata a few years ago, particularly around how the analytics architecture for Galerie, uh, emerged at Altryx. Now we have hundreds of, of apps, thousands of users in that infrastructure. And when we built that out was actually scaling out on Amazon where the worker nodes and the capability for us to manage workload was very horizontal built out. If you look at servers today of any layer of that stack, it is really about that horizontal. Scale-out less so about throwing more hardware, more, uh, you know, high-end infrastructure at it, but more about how commodity hardware can be leveraged and use up and down that stack very easily. So Georgia, >>I asked you a question, so why is analytics so hard for so many companies? Um, and you've been in this big data, we've been talking to you since the beginning, um, and when's it going to get easier? And what are you guys specifically doing? You know, >>So facilitate that. Sure. So a few things that we've seen to date is that a lot of the analytics work that many people do internal and external to organizations is very rote, hand driven coding, right? And I think that's been one of the biggest challenges because the two end points in analytics have been either you hard code stuff that you push into a, you know, a C plus plus or a Java function, and you push it into database, or you're doing lightweight analytics in Excel. And really there needs to be a middle ground where someone can do effective scale-out and have repeatability in what's been done and ease of use. And what's been done that you don't have to necessarily be a programmer and Java programmer in C plus plus to push an analytic function and database. And you certainly don't have to deal with the limitations of Excel today. >>And really that middle ground is what Altryx serves. We look at it as an opportunity for analysts to start work with a very repeatable re reasonable workflow of how they would build their initial constructs around an analytic function that they would want to deploy. And then the scale-out happens because all of the infrastructure works on that analyst behalf, whether that be the infrastructure on Hadoop, would that be the infrastructure of the scale out of how we would publish an analytic function? Would that be how the visualizations would occur inside of a product like Tableau? And so that, I think Dave is one of the biggest things that needs to shift over where you don't have the only options in front of you for analytics is either Excel or hard coding, a bunch of code in C plus plus, or Java and pushing it in database. Yeah. >>And you correct me if I'm wrong, but it seems to be building your partnerships and your ecosystem really around driving that solution and, and, and really driving a revolution in the way in which people think about analytics, >>Ease of use. The idea is that ultimately if you can't get data analysts to be able to not only create work, that they can actually self-describe deploy and deliver and deliver success inside of an organization. And scale that out at the petabyte scale information that exists inside of most organizations you fail. And that's the job of folks like ourselves to provide great software. >>Well, you mentioned Tableau, you guys have a strong partnership there, and Christian Chabot, I think has a good vision. And you talked about sort of, you know, the, the, the choices of the spectrum and neither are good. Can you talk a little bit more about that, that, that partnership and the relationship and what you guys are doing together? Yeah. >>Uh, I would say Tableau's our strongest and most strategic partner today. I mean, we were diamond sponsors of their conference. I think I was there at their conference when I was on the cube the time before, and they are diamond sponsors of our conference. So our customers and particular users are one in the same for Tablo. It really becomes a, an experience around how visual analysis and dashboard, and can be very easily delivered by data analysts. And we think of those same users, the same exact people that Tablo works with to be able to do data blending and advanced analytics. And so that's why the two software products, that's why the two companies, that's where our two customer bases are one in the same because of that integrated experience. So, you know, Tableau is basically replacing XL and that's the mission that thereafter. And we feel that anyone who wants to be able to do the first form of data blending, which I would think of as a V lookup in Excel, should look at Altryx as a solution for that one. >>So you mentioned your conference it's inspire, right? It >>Is inspiring was coming up in June, >>June. Yeah. Uh, how many years have you done inspire? >>Inspire is now in its fifth year. And you're gonna bring the >>Cube this year. Yeah. >>That would be great. You guys, yeah, that would be fun. >>You should do it. So talk about the conference a little bit. I don't know much about it, but I mean, I know of it. >>Yeah. It's very centered around business users, particularly data analysts and many organizations that cut across retail, financial services, communications, where companies like Walmart at and T sprint Verizon bring a lot of their underlying data problems, underlying analytic opportunities that they've wrestled with and bring a community together this year. We're expecting somewhere in the neighborhood of 550 600 folks attending. So largely to, uh, figure out how to bring this, this, uh, you know, game forward, really to build out this next rate analytic capability that's emerging for most organizations. And we think that that starts ultimately with data analysts. All right. We think that there are well over two and a half million data analysts that are underserved by the current big data tools that are in this space. And we've just been highly focused on targeting those users. And so far, it's been pretty good at us. >>It's moving, it's obviously moving to the casual user at some levels, but I ended up getting there not soon, but I want to, I want to ask you the role of the cloud and all this, because when you have underneath the hood is a lot of leverage. You mentioned integrates that's when to get your perspective on the data cloud, not data cloud is it's putting data in the cloud, but the role of cloud, the role of dev ops that intersection, but you're seeing dev ops, you know, fueling a lot of that growth, certainly under the hood. Now on the top of the stack, you have the, I guess, this middle layer for lack of a better description, I'm of use old, old metaphor developing. So that's the enablement piece. Ultimately the end game is fully turnkey, data science, personalization, all that's, that's the holy grail. We all know. So how do you see that collision with cloud and the big, the big data? >>Yeah. So cloud is basically become three things for a lot of folks in our space. One is what we talked about, which is scale up and scale out, uh, is something that is much more feasible when you can spin up and spin down infrastructure as needed, particularly on an elastic basis. And so many of us who built our solutions leverage Amazon being one of the most defacto solutions for cloud based deployment, that it just makes it easy to do the scale-out that's necessary. This is the second thing it actually enables us. Uh, and many of our friends and partners to do is to be able to bring a lower cost basis to how infrastructure stood up, right? Because at the end of the day, the challenge for the last generation of analytics and data warehousing that was in this space is your starting conversation is two to $3 million just in infrastructure alone before you even buy software and services. >>And so now if you can rent everything that's involved with the infrastructure and the software is actually working within days, hours of actually starting the effort, as opposed to a 14 month life cycle, it's really compressing the time to success and value that's involved. And so we see almost a similarity to how Salesforce really disrupted the market. 10 years ago, I happened to be at Salesforce when that disruption occurred and the analytics movement that is underway really impacted by cloud. And the ability to scale out in the cloud is really driving an economic basis. That's unheard of with that >>Developer market, that's robust, right? I mean, you have easy kind of turnkey development, right? Tapping >>It is right, because there's a robust, uh, economy that's surrounding the APIs that are now available for cloud services. So it's not even just at the starting point of infrastructure, but there's definite higher level services where all the way to software as industry, >>How much growth. And you'll see in those, in that, as that, that valley of wealth and opportunity that will be created from your costs, not only for the companies involved, but the company's customers, they have top line focus. And then the goal of the movement we've seen with analytics is you seeing the CIO kind of with less of a role, more of the CEO wants to the chief data officer wants most of the top line drivers to be app focused. So you seeing a big shift there. >>Yeah. I mean, one of the, one of the real proponents of the cloud is now the fact that there is an ability for a business analyst business users and the business line to make impacts on how decisions are done faster without the infrastructure underpinnings that were needed inside the four walls in our organization. So the decision maker and the buyer effectively has become to your point, the chief analytics officer, the chief marketing officer, right. Less so that the chief information officer of an organization. And so I think that that is accelerating in a tremendous, uh, pace, right? Because even if you look at the statistics that are out there today, the buying power of the CMO is now outstrip the buying power of the CIO, probably by 1.2 to 1.3 X. Right. And that used to be a whole different calculus that was in front of us before. So I would see that, uh, >>The faster, so yeah, so Natalie just kind of picked this out here real time. So you got it, which we all know, right. I went to the it world for a long time service, little catalog. Self-service, you know, Sarah's already architectures whatever you want to call it, evolve in modern era. That's good. But on the business side, there's still a need for this same kind of cataloguing of tooling platform analytics. So do you agree with that? I mean, do you see that kind of happening that way, where there's still some connection, but it's not a complete dependency. That's kind of what we're kind of rethinking real time you see that happen. >>Yeah. I think it's pretty spot on because when you look at what businesses are doing today, they're selecting software that enables them to be more self-reliant the reason why we have been growing as much among business analysts as we have is we deliver self-reliance software and in some way, uh, that's what tablet does. And so the, the winners in this space are going to be the ones that will really help users get to results faster for self-reliance. And that's, that's really what companies like Altrix Stanford today. >>So I want to ask you a follow up on that CMOs CIO discussion. Um, so given that, that, that CMOs are spending a lot more where's the, who owns the data, is that, is we, we talk, well, I don't know if I asked you this before, but do you see the role of a chief data officer emerging? And is that individual, is that individual part of the marketing organization? Is it part of it? Is it a separate parallel role? What are you, >>One of the things I will tell you is that as I've seen chief analytics and chief data officers emerge, and that is a real category entitled real deal of folks that have real responsibilities in the organization, the one place that's not is in it, which is interesting to see, right? Because oftentimes those individuals are reporting straight to the CEO, uh, or they have very close access to line of business owners, general managers, or the heads of marketing, the heads of sales. So I seeing that shift where wherever that chief data officer is, whether that's reporting to CEOs or line of business managers or general managers of, of, you know, large strategic business units, it's not in the information office, it's not in the CEO's, uh, purview anymore. And that, uh, is kind of telling for how people are thinking about their data, right? Data is becoming much more of an asset and a weapon for how companies grow and build their scale less. So about something that we just have to deal with. >>Yeah. And it's clearly emerging that role in certain industry sectors, you know, clearly financial services, government and healthcare, but slowly, but we have been saying that, >>Yeah, it's going to cross the board. Right. And one of the reasons why I wrote the article at the end of last year, I literally titled it. Uh, analytics is eating the world, is this exact idea, right? Because, uh, you have this, this notion that you no longer are locked down with data and infrastructure kind of holding you back, right? This is now much more in the hands of people who are responsible for making better decisions inside their organizations, using data to drive those decisions. And it doesn't matter the size and shape of the data that it's coming in. >>Yeah. Data is like the F the food that just spilled all over it spilled out from the truck and analytics is on the Pac-Man eating out. Sorry. >>Okay. Final question in this segment is, um, summarize big data SV for us this year, from your perspective, knowing what's going on now, what's the big game changer. What should the folks know who are watching and should take note of which they pay attention to? What's the big story here at this moment. >>There's definite swim lanes that are being created as you can see. I mean, and, and now that the bigger distribution providers, particularly on the Hadoop side of the world have started to call out what they all stand for. Right. You can tell that map are, is definitely about creating a fast, slightly proprietary Hadoop distro for enterprise. You can tell that the folks at cloud era are focusing themselves on enterprise scale and really building out that hub for enterprise scale. And you can tell Horton works is basically embedding, enabling an open source for anyone to be able to take advantage of. And certainly, you know, the previous announcements and some of the recent ones give you an indicator of that. So I see the sense swimlanes forming in that layer. And now what is going to happen is that focus and attention is going to move away from how that layer has evolved into what I would think of as advanced analytics, being able to do the visual analysis and blending of information. That's where the next, uh, you know, battle war turf is going to be in particularly, uh, the strata space. So we're, we're really looking forward to that because it basically puts us in a great position as a company and a market leader in particularly advanced analytics to really serve customers in how this new battleground is emerging. >>Well, we really appreciate you taking the time. You're an awesome guest on the queue biopsy. You know, you have a company that you're running and a great team, and you come and share your great knowledge with our fans and an audience. Appreciate it. Uh, what's next for you this year in the company with some of your goals, let's just share that. >>Yeah. We have a few things that are, we mentioned a person inspired coming up in June. There's a big product release. Most of our product team is actually here and we have a release coming up at the beginning of Q2, which is Altryx nine oh. So that has quite a bit involved in it, including expansion of connectivity, uh, being able to go and introduce a fair degree of modeling capability so that the AR based modeling that we do scales out very well with revolution and Cloudera in mind, as well as being able to package into play analytic apps very quickly from those data analysts in mind. So it's, uh, it's a release. That's been almost a year in the works, and we're very much looking forward to a big launch at the beginning of Q2. >>George, thanks so much. You got inspire coming out. A lot of great success as a growing market, valuations are high, and the good news is this is just the beginning, call it mid innings in the industry, but in the customers, I call the top of the first lot of build-out real deployment, real budgets, real deal, big data. It's going to collide with cloud again, and I'm going to start a load, get a lot of innovation all happening right here. Big data SV all the big data Silicon valley coverage here at the cube. I'm Jennifer with Dave Alonzo. We'll be right back with our next guest. After the short break.