>>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.

>>Welcome back to VMO 2022. We're in the home stretch. Now, Dave ante for Dave Nicholson, and we're excited to have drew Schlissel on he's the director of product marketing at wasabi, and he is joined by Dustin Albertson, the manager of cloud and application alliances, product, product management at Veeam software. Dustin, did I get that right? You got it right. All right. You're gonna explain all those little titles in a moment. So wasabi is a company cool name, but you may not know much about them drew. What does wasabi do? >>We do cloud storage, plain and simple. It is the one thing we do extremely well. It's S3 compatible, and it covers a broad range of use cases, right? Primarily we work with Veeam on backup and recovery, and >>We're gonna get into that. But when we, what there's a lot of people do cloud storage, a lot of people do object store. What makes you wasabi unique >>Simplicity, predictability performance security, right? Predictability. Let's talk about price, right? That's the thing that gets people's attention, right? Oh, sure. Okay. You can look at it. One of two ways. It's either one fit the price of all the hyperscalers, significant difference there, or right. For fundamentally the same price. You get five times more storage, which makes a huge difference, especially in the backup space. When you want to have a lot of backups, right. Folks would prefer to have months of backups as opposed to days or weeks. Right? >>How do you, how do you do that? Because, because there's, you know, maybe >>It sounds like magic, doesn't >>It? Yeah. Yeah. I mean, you know, look at us, we've all been around the block quite a few times and we know that the bits and the bites and the bolts are all basically the same. What are you doing to get that level of? >>I can't tell you >>Secret's secret. It's secret. >>Look, it, it doesn't have to be that expensive. Okay. Now granted, there's some things obviously we do that are proprietary and different from, >>Well, like stealing electricity from your neighbor or something. I mean, what, >>You just run a cord over a >>Absolutely that's one way to cut down on price. But because we are so focused on just the storage, right. And our founders, you know, the gentleman who founded Carbonite, no a thing or two about storage. Sure. Right. We have a very highly optimized stack, very efficient. You know, you guys know what raw to usable story is. Right? You've gone through that TCO analysis before, and we're highly efficient in how we use the raw storage. And we pass that price on to our customers. Right. We believe that a low price cloud storage, right? One tier always hot, always available. It gives our customers the ability to spend their money in other places. Right. >>Well, and, and there's a price umbrella that the public cloud guys have is kind of a gift that they've given you. Hey, look at Amazon's operating profits last quarter. It was 35%. Those are like Oracle operating margins. Not that I, we don't know what your operating margins are, but I I've followed David friend's career for a long, long time. He's got good nose for business. But so Dustin, when you, when you hear drew talk about the ability to retain that much data, what does that mean for Veeam customers? >>So the primary thing for Veeam customers is the ease of use. I would say, you know, the, the performance and things like that are all nice, right? They're, they're important. But primarily what I see is people say how easy it is to use and how easy it is to price. Now, the objective, you know, the alternative is you go to another cloud provider and you say, well, how much will this cost me per month? You really have to underst yes, you really have to understand object storage, how Veeam works, how we're moving data, all the API calls, all of that to really kind of correlate out a guesstimate of what your price would be per month. You know, with LASA it's, it's a flat fee it's per terabyte. You know what it is gonna be? That's it? There's no API charges. There's no egres. So the customers really love that. Ease of use this become one of the most popular endpoints for object storage for our customers. >>Imagine this, right? You go to best buy and you buy a refrigerator and you bring it home and you stock it with all your favorite drinks and snacks. Okay. You on game day, you go and you open the fridge and you hear a sound Bing. And it's your phone and it's your credit card company telling you that you've been charged a door opening fee. Okay. And then you grab a beer out of that fridge, Bing, Bing, and you hear another ring and now you're getting a beer extraction fee. Okay. Now I want to be fair to, you know, all the sponsors here, but okay. With wasabi, you can open that door. You could stand there. You can air condition, the whole house. You can take a beer out and put a beer back or whatever your favorite beverage is. And you're not gonna hear that noise. Okay. Very straightforward. Like in, in geometry class, right? The slope of a line Y equals MX plus B B equals zero. Okay. Well, >>Whoa. Well, you had me at free beer. You didn't, >>You don't, but you understand why? >>Why would you, you don't need to go see >>To open your fridge and take out a beverage, take out a snack. Okay. That's the predictable part of wasabi. That's what's resonating so strongly with folks where everything else is in this world. Unpredictable. >>So ease, simplicity. Maybe the answer to that is, well, there's all this other stuff in the cloud. I can just, it's convenient for me. It's right there. So how do you address that convenience factor? All these other services, you know, that I can get streaming and machine learning and all that other great stuff. How do you address that? >>Sometimes all you need is storage. Okay. That no, it that's yet put, okay. That's beauty of wasabi. We're not trying to be everything to everyone. We're trying to be one thing executed very well for a, a specific set of users and use cases. >>I may be a little objective here, but I, I, you know, I've grown up with you guys, right? You, you, you were one of the first partners that I started working with and, and, you know, I've seen you kind of grow, but one of the things I think that you've done a real good job at is, is like you say, sticking to your, your lanes, you know, just going after use cases that just need data. Right. I don't need to get into the AI or the analytics or all of this. We just do this and do it well. And, and people have resonated with that. Right? Yeah. >>So big topic here of course is ransomware. Yeah. 3, 2 11, 0. What is that? What are the threes? The twos, the ones >>That's you, you gotta explain that one. Okay. >>So forever we had the 3, 2, 1 rule, right? Like three copies of data, two different, two different copies, two different media types. Yeah. One offsite. And then one is, is testing. And then zero now is, is validation. BA basically reuse that data. Make sure that you're testing it because if you're not, if you're following through two one, and you're not actually testing your data, is it really good? You don't know. You're just, you may have bad copies spread out all over the place. So one of the things where wasabi shines is is that they don't have these E risk charges. They don't have these API charges. So you can test that data. You can, after you send a backup up there, restore it somewhere else and validate that it works and then get rid of it. And it's still sitting up there in BAA. >>So you're not trying to balance your activities and your operational requirements with your, with your bill. Correct. You're not getting yelled at, by the, the controller at the end of the month. >>You're unconstrained. Yeah. Right. And I think also imutability comes into play. Correct. As well. >>Talk about >>That. Right. So, you know, we heard this morning in the keynote, right? That backup data sets are, you know, one of the main attack vectors, right. For cyber criminals. And it makes sense, right. They take down your primary systems and they control your backup systems. They've got you. You have no choice, but to pay that ransom. Okay. So mutability, that means that your backups are untouchable, your root user, your admins, the folks at wasabi, the folks at Veeam, nobody can alter that data period. End of story. Okay. That saves you from yourself that saves you from the hackers, right? I mean the most disturbing story I've read about cyber warfare right now is that people are getting bribe offers from these cyber gangs. And they're just, you know, for a couple of Bitcoin handing over the keys to the kingdom with imutability, you're actually safe from that scenario. >>So that's a service, correct? >>No, it's a feature. >>Okay. So can I turn it off? >>Yeah. You don't have to use it. >>No. Can I, after I've, after I've turned it on, can I turn it off? >>Oh, it's up to you. I mean, why don't you talk about >>That? Yeah. Yeah. So it's, it's an API. So if let's say you send some backups up there today and you set it for two weeks and you decide today. Oh, I made a mistake. I wanna turn it off. You can't turn it off. Yeah. >>Okay. So as long as you set that policy, it's, it's a big warning, right? You can't undo this. Correct. Okay. So even if I come, come to jump to the admin with a bunch of Bitcoin yep. He or she can't undo, right? >>Nope. That's right. And you can set it for two weeks, two months, two years. Right. You can use it to secure your backups. Yep. Right. You can also use that same feature in compliance situations. Right. Regulatory environments, where you've gotta retain customer data for, you know, 5, 7, 10 years. Right. By using that imutability feature, you guarantee the integrity of that data for whatever period you set. >>And it's a feature it's not a paid for service. Is that right? >>It is included as part of the service. >>Okay. So I don't >>Free beer and free meat. >>I think I'm correct that some, some competitors you're paying for that service. So if you turn it off, there's a, if you don't stop paying, there's a, there's a theory. They could turn it off on you. They will warn you. >>Sure. But >>That says to me that somebody could be tempted by a few Bitcoin. >>That's not a mutable. Well's >>Notable. I agree. Yeah. Yeah. Yeah. >>Well, and, and there is a charge to use it in other places because it's an API request. Right. It's an action. It's opening the fridge. >>It's like texting. Yes. Maybe a charge. >>Yeah. I remember. I remember those days. Was it 10 cents? A 10 cents a message or something Telegraph. >>Yeah. >>Yeah. >>Yeah. You still get those messages. Right? Text, text fees may apply. I'm like really? Okay. So tell me more about, so you got me. I'm sold. Okay. I've I've David friends got good job. Got cred, got credibility. Okay. But I have some other questions. Like where's my data. You guys running your own data centers. What's your global footprint. How do you deal with data sovereignty? All that stuff. >>So right now, oh boy. Now I'm on the spot. I wanna say 11 locations around the world. It's our gear. We're running it in concert with folks who are helping us host that system. Right. But we have complete control of course, over our systems. We're everywhere. Right? Just open, let's see. Toronto Frankfurt, Paris, London, Sydney just spun up in the last week. We've got Singapore coming online. I think in the next two weeks. Two >>In Japan. >>Yep. Two in Japan, multiple locations in the United States. So in terms of sovereignty, right, as long as folks are keeping it within, you know, their, their physical boundaries, not a problem. And if folks want to use, you know, other locations in other countries, great. We can support that as well. >>So you got momentum as a business. I mean, that's pretty clear. Yeah. Just from the discussions I've had with, with folks like David, and obviously you you're excited about this, where's it coming from? Is it really that, that price factor that's driving people to you? Is it Dustin said simplicity. I mean, where are you seeing the momentum geographies? Where is it? Where's the action. >>I I'll say, you know, from my point of view, it's, it's been a combination of all that, right? It, it's simple. It's easy to use a, like a user can, any user who's not cloud friendly, right. Can log in and create one. It's a simple portal to create a bucket and then start sending stuff off site. But also they've, they've kind of, they reminded me of a younger Veeam, like when they first started, because they went after the channel and they went and started these partner programs and, and MSP programs and things like that that have been really successful as far as one of the key markets is MSPs. Right? Because they, you know, want a cheap place to put this data. They don't wanna have to buy appliances. They don't wanna have to go to AWS and things like that. So this has been really appealing to >>Them. You know, it's interesting. So I have a, we have a partnership with a data company down in New York called enterprise technology research. We write a breaking analysis every week and we use a lot of their data. One of the things that popped up recently, maybe a year ago, OpenStack I'm like OpenStack. So we dug in like where's OpenStack and what it was was MSPs didn't want pay the VTax. Right. So they were rolling their own with, with open source and open stack. It was red hat services, blah, blah, blah. But it sounds like a similar dynamic, especially with the MSPs. >>I, so I think we've, I, I hate to use the, the metaphor, but I will. Right. There's a perfect storm happening, right. Especially in the last, what, two years. All right. The cloud has been gaining traction, but we've been around long enough to see the pendulum swinging. Right. Some folks went crazy for the cloud and then they got their bill and then they went crazy to get back out of the cloud. But now, you know, with distributed workforces, with the, you know, the, the constant attacks on their, their on-prem systems, right. The growth in cloud across the board has been phenomenal. I know you're a market watcher. Right. I know you guys are keeping close eyes. I saw your recent analysis on the cybersecurity firms. Right. It continues to grow. There's no question about it. We're we're on that wave. Right. And I think we've, you know, we're not, we're, we're, I don't know if it's the long board or the short little snappy board. Yes. We actually identify and, and, and went after the opportunity to partner with Veeam very early on, because it's the perfect work case work, work load. >>How long can you sustain that? And still resist the temptation to come out with some new shiny object to distract people? >>I >>Mean, what, what, what does that, what does that look like in terms of, as you look out in this laser focused yeah. Addressable market that you're going after now. >>So, you know, the best part about being here this week is having great conversations and, and talking to folks about what they're seeing in the marketplace and the different verticals. I don't think we've even scratched the surface of any of the verticals that we are working in today. Right. First and foremost, when it comes to backup and recovery, there's so much more opportunity with Veeam, right? Whether it's healthcare, manufacturing, logistics, analytics, backup of IML, you know, analysis, I think it's almost limitless, right. Data's growing what, 40, 80% year, over year, depending on who you ask. Right. Then the other things that we do, which maybe folks don't even know about, we have a burgeoning business in video surveillance, right. We're working with all the top partners in that sector. And the takeup is phenomenal because they are tweaking their technology to maintain a relatively small cash, right. OnPrem or in the central office. And then they're just kind of, you know, tearing that off to the cloud to have essentially a bottomless backup or archive of that footage. And they can do it at 4k. Here's the best part, right. When AK comes out, guess what, you know, that data set doubles in size. >>Right. But that's right in your zone. That's not stepping out that that's not stepping after that's that's classic leveraging. Good >>Answer. In other words. Yeah. Yeah. Thank you. >>I mean, if >>You're, if you're, if you're hitting singles and doubles all day long, right. Do you have to switch to be a power hitter and go for the fences and drop your batting average down, but hope that your slugging percentage goes up. I think you keep hitting singles doubles, you know, in triples, >>A lot of people on Sandhill road or, you know, at the bar at the Rosewood would disagree with you. Wow. And so I, I appreciate the discipline. >>Yeah. And it's true. And, and as we know, the industry is littered with a lot of those names that just didn't didn't make it >>Let's stay positive, you know? >>Okay. No he's saying yeah, no, no. A lot of guys at sand hill road would say, no, you gotta go for it. Yeah. You gotta, you gotta forget these singles. We want, >>Yeah. We need home runs gotta be >>Shiny. Well, I mean, look at Vema as a, as a, as an example right. Of a disciplined approach. Right. Exactly. To, to a space that they have steadily grown. I mean, congratulations. Right. You guys have been identified by IDC, right. Is essentially, you know, co number ones. And I expect that to be the number one in the market. Right. I think, you know, David friend clearly has provided excellent guidance, right. To steer the company that way. And I'm just really happy >>To be about that. Oh. And the Tam is data. Right. And you're, you're just another node on the data universe. Right. Which is, that's what you want. You want, you don't necessarily wanna move it around. Yeah. If you don't have to. >>It is interesting though. I mean, we, we are seeing more and more analysts identifying with Sabi as like the fourth player. Yeah. Which is pretty cool. Right. And I also heard it from some good sources this week that let's say one of the hyperscalers has, you know, started to yeah. Have conversations about us. Let's just >>Leave it. That's good. It means you're bothering people. Yeah. Said, all right, guys, we gotta go. Thanks so much for coming on the queue. Thank you. Great to have you. That was easy. Thank you. Appreciate it. Very welcome. All right. Keep it right there. We'll be back to wrap up day one from VMO in 2022, right back.

>>Time series data is any data that's stamped in time in some way that could be every second, every minute, every five minutes, every hour, every nanosecond, whatever it might be. And typically that data comes from sources in the physical world like devices or sensors, temperature, gauges, batteries, any device really, or things in the virtual world could be software, maybe it's software in the cloud or data and containers or microservices or virtual machines. So all of these items, whether in the physical or virtual world, they're generating a lot of time series data. Now time series data has been around for a long time, and there are many examples in our everyday lives. All you gotta do is punch up any stock, ticker and look at its price over time and graphical form. And that's a simple use case that anyone can relate to and you can build timestamps into a traditional relational database. >>You just add a column to capture time and as well, there are examples of log data being dumped into a data store that can be searched and captured and ingested and visualized. Now, the problem with the latter example that I just gave you is that you gotta hunt and Peck and search and extract what you're looking for. And the problem with the former is that traditional general purpose databases they're designed as sort of a Swiss army knife for any workload. And there are a lot of functions that get in the way and make them inefficient for time series analysis, especially at scale. Like when you think about O T and edge scale, where things are happening super fast, ingestion is coming from many different sources and analysis often needs to be done in real time or near real time. And that's where time series databases come in. >>They're purpose built and can much more efficiently support ingesting metrics at scale, and then comparing data points over time, time series databases can write and read at significantly higher speeds and deal with far more data than traditional database methods. And they're more cost effective instead of throwing processing power at the problem. For example, the underlying architecture and algorithms of time series databases can optimize queries and they can reclaim wasted storage space and reuse it. At scale time, series databases are simply a better fit for the job. Welcome to moving the world with influx DB made possible by influx data. My name is Dave Valante and I'll be your host today. Influx data is the company behind InfluxDB. The open source time series database InfluxDB is designed specifically to handle time series data. As I just explained, we have an exciting program for you today, and we're gonna showcase some really interesting use cases. >>First, we'll kick it off in our Palo Alto studios where my colleague, John furrier will interview Evan Kaplan. Who's the CEO of influx data after John and Evan set the table. John's gonna sit down with Brian Gilmore. He's the director of IOT and emerging tech at influx data. And they're gonna dig into where influx data is gaining traction and why adoption is occurring and, and why it's so robust. And they're gonna have tons of examples and double click into the technology. And then we bring it back here to our east coast studios, where I get to talk to two practitioners, doing amazing things in space with satellites and modern telescopes. These use cases will blow your mind. You don't want to miss it. So thanks for being here today. And with that, let's get started. Take it away. Palo Alto. >>Okay. Today we welcome Evan Kaplan, CEO of influx data, the company behind influx DB. Welcome Evan. Thanks for coming on. >>Hey John, thanks for having me >>Great segment here on the influx DB story. What is the story? Take us through the history. Why time series? What's the story >><laugh> so the history history is actually actually pretty interesting. Um, Paul dicks, my partner in this and our founder, um, super passionate about developers and developer experience. And, um, he had worked on wall street building a number of time series kind of platform trading platforms for trading stocks. And from his point of view, it was always what he would call a yak shave, which means you had to do a ton of work just to start doing work, which means you had to write a bunch of extrinsic routines. You had to write a bunch of application handling on existing relational databases in order to come up with something that was optimized for a trading platform or a time series platform. And he sort of, he just developed this real clear point of view is this is not how developers should work. And so in 2013, he went through why Combinator and he built something for, he made his first commit to open source in flu DB at the end of 2013. And, and he basically, you know, from my point of view, he invented modern time series, which is you start with a purpose-built time series platform to do these kind of workloads. And you get all the benefits of having something right outta the box. So a developer can be totally productive right away. >>And how many people in the company what's the history of employees and stuff? >>Yeah, I think we're, I, you know, I always forget the number, but it's something like 230 or 240 people now. Um, the company, I joined the company in 2016 and I love Paul's vision. And I just had a strong conviction about the relationship between time series and IOT. Cuz if you think about it, what sensors do is they speak time, series, pressure, temperature, volume, humidity, light, they're measuring they're instrumenting something over time. And so I thought that would be super relevant over long term and I've not regretted it. >>Oh no. And it's interesting at that time, go back in the history, you know, the role of databases, well, relational database is the one database to rule the world. And then as clouds started coming in, you starting to see more databases, proliferate types of databases and time series in particular is interesting. Cuz real time has become super valuable from an application standpoint, O T which speaks time series means something it's like time matters >>Time. >>Yeah. And sometimes data's not worth it after the time, sometimes it worth it. And then you get the data lake. So you have this whole new evolution. Is this the momentum? What's the momentum, I guess the question is what's the momentum behind >>You mean what's causing us to grow. So >>Yeah, the time series, why is time series >>And the >>Category momentum? What's the bottom line? >>Well, think about it. You think about it from a broad, broad sort of frame, which is where, what everybody's trying to do is build increasingly intelligent systems, whether it's a self-driving car or a robotic system that does what you want to do or a self-healing software system, everybody wants to build increasing intelligent systems. And so in order to build these increasing intelligent systems, you have to instrument the system well, and you have to instrument it over time, better and better. And so you need a tool, a fundamental tool to drive that instrumentation. And that's become clear to everybody that that instrumentation is all based on time. And so what happened, what happened, what happened what's gonna happen? And so you get to these applications like predictive maintenance or smarter systems. And increasingly you want to do that stuff, not just intelligently, but fast in real time. So millisecond response so that when you're driving a self-driving car and the system realizes that you're about to do something, essentially you wanna be able to act in something that looks like real time, all systems want to do that, want to be more intelligent and they want to be more real time. And so we just happen to, you know, we happen to show up at the right time in the evolution of a >>Market. It's interesting near real time. Isn't good enough when you need real time. >><laugh> yeah, it's not, it's not. And it's like, and it's like, everybody wants, even when you don't need it, ironically, you want it. It's like having the feature for, you know, you buy a new television, you want that one feature, even though you're not gonna use it, you decide that your buying criteria real time is a buying criteria >>For, so you, I mean, what you're saying then is near real time is getting closer to real time as possible, as fast as possible. Right. Okay. So talk about the aspect of data, cuz we're hearing a lot of conversations on the cube in particular around how people are implementing and actually getting better. So iterating on data, but you have to know when it happened to get, know how to fix it. So this is a big part of how we're seeing with people saying, Hey, you know, I wanna make my machine learning algorithms better after the fact I wanna learn from the data. Um, how does that, how do you see that evolving? Is that one of the use cases of sensors as people bring data in off the network, getting better with the data knowing when it happened? >>Well, for sure. So, so for sure, what you're saying is, is, is none of this is non-linear, it's all incremental. And so if you take something, you know, just as an easy example, if you take a self-driving car, what you're doing is you're instrumenting that car to understand where it can perform in the real world in real time. And if you do that, if you run the loop, which is I instrumented, I watch what happens, oh, that's wrong? Oh, I have to correct for that. I correct for that in the software. If you do that for a billion times, you get a self-driving car, but every system moves along that evolution. And so you get the dynamic of, you know, of constantly instrumenting watching the system behave and do it. And this and sets up driving car is one thing. But even in the human genome, if you look at some of our customers, you know, people like, you know, people doing solar arrays, people doing power walls, like all of these systems are getting smarter. >>Well, let's get into that. What are the top applications? What are you seeing for your, with in, with influx DB, the time series, what's the sweet spot for the application use case and some customers give some >>Examples. Yeah. So it's, it's pretty easy to understand on one side of the equation that's the physical side is sensors are sensors are getting cheap. Obviously we know that and they're getting the whole physical world is getting instrumented, your home, your car, the factory floor, your wrist, watch your healthcare, you name it. It's getting instrumented in the physical world. We're watching the physical world in real time. And so there are three or four sweet spots for us, but, but they're all on that side. They're all about IOT. So they're think about consumer IOT projects like Google's nest todo, um, particle sensors, um, even delivery engines like rapid who deliver the Instacart of south America, like anywhere there's a physical location do and that's on the consumer side. And then another exciting space is the industrial side factories are changing dramatically over time. Increasingly moving away from proprietary equipment to develop or driven systems that run operational because what, what has to get smarter when you're building, when you're building a factory is systems all have to get smarter. And then, um, lastly, a lot in the renewables sustainability. So a lot, you know, Tesla, lucid, motors, Cola, motors, um, you know, lots to do with electric cars, solar arrays, windmills, arrays, just anything that's gonna get instrumented that where that instrumentation becomes part of what the purpose >>Is. It's interesting. The convergence of physical and digital is happening with the data IOT. You mentioned, you know, you think of IOT, look at the use cases there, it was proprietary OT systems. Now becoming more IP enabled internet protocol and now edge compute, getting smaller, faster, cheaper AI going to the edge. Now you have all kinds of new capabilities that bring that real time and time series opportunity. Are you seeing IOT going to a new level? What was the, what's the IOT where's the IOT dots connecting to because you know, as these two cultures merge yeah. Operations, basically industrial factory car, they gotta get smarter, intelligent edge is a buzzword, but I mean, it has to be more intelligent. Where's the, where's the action in all this. So the >>Action, really, it really at the core, it's at the developer, right? Because you're looking at these things, it's very hard to get an off the shelf system to do the kinds of physical and software interaction. So the actions really happen at the developer. And so what you're seeing is a movement in the world that, that maybe you and I grew up in with it or OT moving increasingly that developer driven capability. And so all of these IOT systems they're bespoke, they don't come out of the box. And so the developer, the architect, the CTO, they define what's my business. What am I trying to do? Am I trying to sequence a human genome and figure out when these genes express theself or am I trying to figure out when the next heart rate monitor's gonna show up on my apple watch, right? What am I trying to do? What's the system I need to build. And so starting with the developers where all of the good stuff happens here, which is different than it used to be, right. Used to be you'd buy an application or a service or a SA thing for, but with this dynamic, with this integration of systems, it's all about bespoke. It's all about building >>Something. So let's get to the developer real quick, real highlight point here is the data. I mean, I could see a developer saying, okay, I need to have an application for the edge IOT edge or car. I mean, we're gonna have, I mean, Tesla's got applications of the car it's right there. I mean, yes, there's the modern application life cycle now. So take us through how this impacts the developer. Does it impact their C I C D pipeline? Is it cloud native? I mean, where does this all, where does this go to? >>Well, so first of all, you're talking about, there was an internal journey that we had to go through as a company, which, which I think is fascinating for anybody who's interested is we went from primarily a monolithic software that was open sourced to building a cloud native platform, which means we had to move from an agile development environment to a C I C D environment. So to a degree that you are moving your service, whether it's, you know, Tesla monitoring your car and updating your power walls, right. Or whether it's a solar company updating the arrays, right. To degree that that service is cloud. Then increasingly remove from an agile development to a C I C D environment, which you're shipping code to production every day. And so it's not just the developers, all the infrastructure to support the developers to run that service and that sort of stuff. I think that's also gonna happen in a big way >>When your customer base that you have now, and as you see, evolving with infl DB, is it that they're gonna be writing more of the application or relying more on others? I mean, obviously there's an open source component here. So when you bring in kind of old way, new way old way was I got a proprietary, a platform running all this O T stuff and I gotta write, here's an application. That's general purpose. Yeah. I have some flexibility, somewhat brittle, maybe not a lot of robustness to it, but it does its job >>A good way to think about this is versus a new way >>Is >>What so yeah, good way to think about this is what, what's the role of the developer slash architect CTO that chain within a large, within an enterprise or a company. And so, um, the way to think about it is I started my career in the aerospace industry <laugh> and so when you look at what Boeing does to assemble a plane, they build very, very few of the parts. Instead, what they do is they assemble, they buy the wings, they buy the engines, they assemble, actually, they don't buy the wings. It's the one thing they buy the, the material for the w they build the wings, cuz there's a lot of tech in the wings and they end up being assemblers smart assemblers of what ends up being a flying airplane, which is pretty big deal even now. And so what, what happens with software people is they have the ability to pull from, you know, the best of the open source world. So they would pull a time series capability from us. Then they would assemble that with, with potentially some ETL logic from somebody else, or they'd assemble it with, um, a Kafka interface to be able to stream the data in. And so they become very good integrators and assemblers, but they become masters of that bespoke application. And I think that's where it goes, cuz you're not writing native code for everything. >>So they're more flexible. They have faster time to market cuz they're assembling way faster and they get to still maintain their core competency. Okay. Their wings in this case, >>They become increasingly not just coders, but designers and developers. They become broadly builders is what we like to think of it. People who start and build stuff by the way, this is not different than the people just up the road Google have been doing for years or the tier one, Amazon building all their own. >>Well, I think one of the things that's interesting is is that this idea of a systems developing a system architecture, I mean systems, uh, uh, systems have consequences when you make changes. So when you have now cloud data center on premise and edge working together, how does that work across the system? You can't have a wing that doesn't work with the other wing kind of thing. >>That's exactly. But that's where the that's where the, you know, that that Boeing or that airplane building analogy comes in for us. We've really been thoughtful about that because IOT it's critical. So our open source edge has the same API as our cloud native stuff that has enterprise on pre edge. So our multiple products have the same API and they have a relationship with each other. They can talk with each other. So the builder builds it once. And so this is where, when you start thinking about the components that people have to use to build these services is that you wanna make sure, at least that base layer, that database layer, that those components talk to each other. >>So I'll have to ask you if I'm the customer. I put my customer hat on. Okay. Hey, I'm dealing with a lot. >>That mean you have a PO for <laugh> >>A big check. I blank check. If you can answer this question only if the tech, if, if you get the question right, I got all this important operation stuff. I got my factory, I got my self-driving cars. This isn't like trivial stuff. This is my business. How should I be thinking about time series? Because now I have to make these architectural decisions, as you mentioned, and it's gonna impact my application development. So huge decision point for your customers. What should I care about the most? So what's in it for me. Why is time series >>Important? Yeah, that's a great question. So chances are, if you've got a business that was, you know, 20 years old or 25 years old, you were already thinking about time series. You probably didn't call it that you built something on a Oracle or you built something on IBM's DB two, right. And you made it work within your system. Right? And so that's what you started building. So it's already out there. There are, you know, there are probably hundreds of millions of time series applications out there today. But as you start to think about this increasing need for real time, and you start to think about increasing intelligence, you think about optimizing those systems over time. I hate the word, but digital transformation. Then you start with time series. It's a foundational base layer for any system that you're gonna build. There's no system I can think of where time series, shouldn't be the foundational base layer. If you just wanna store your data and just leave it there and then maybe look it up every five years. That's fine. That's not time. Series time series is when you're building a smarter, more intelligent, more real time system. And the developers now know that. And so the more they play a role in building these systems, the more obvious it becomes. >>And since I have a PO for you and a big check, yeah. What is, what's the value to me as I, when I implement this, what's the end state, what's it look like when it's up and running? What's the value proposition for me. What's an >>So, so when it's up and running, you're able to handle the queries, the writing of the data, the down sampling of the data, they're transforming it in near real time. So that the other dependencies that a system that gets for adjusting a solar array or trading energy off of a power wall or some sort of human genome, those systems work better. So time series is foundational. It's not like it's, you know, it's not like it's doing every action that's above, but it's foundational to build a really compelling, intelligent system. I think that's what developers and archs are seeing now. >>Bottom line, final word. What's in it for the customer. What's what, what's your, um, what's your statement to the customer? What would you say to someone looking to do something in time series on edge? >>Yeah. So, so it's pretty clear to clear to us that if you're building, if you view yourself as being in the build business of building systems that you want 'em to be increasingly intelligent, self-healing autonomous. You want 'em to operate in real time that you start from time series. But I also wanna say what's in it for us influx what's in it for us is people are doing some amazing stuff. You know, I highlighted some of the energy stuff, some of the human genome, some of the healthcare it's hard not to be proud or feel like, wow. Yeah. Somehow I've been lucky. I've arrived at the right time, in the right place with the right people to be able to deliver on that. That's that's also exciting on our side of the equation. >>Yeah. It's critical infrastructure, critical, critical operations. >>Yeah. >>Yeah. Great stuff, Evan. Thanks for coming on. Appreciate this segment. All right. In a moment, Brian Gilmore director of IOT and emerging technology that influx day will join me. You're watching the cube leader in tech coverage. Thanks for watching >>Time series data from sensors systems and applications is a key source in driving automation and prediction in technologies around the world. But managing the massive amount of timestamp data generated these days is overwhelming, especially at scale. That's why influx data developed influx DB, a time series data platform that collects stores and analyzes data influx DB empowers developers to extract valuable insights and turn them into action by building transformative IOT analytics and cloud native applications, purpose built and optimized to handle the scale and velocity of timestamped data. InfluxDB puts the power in your hands with developer tools that make it easy to get started quickly with less code InfluxDB is more than a database. It's a robust developer platform with integrated tooling. That's written in the languages you love. So you can innovate faster, run in flex DB anywhere you want by choosing the provider and region that best fits your needs across AWS, Microsoft Azure and Google cloud flex DB is fast and automatically scalable. So you can spend time delivering value to customers, not managing clusters, take control of your time series data. So you can focus on the features and functionalities that give your applications a competitive edge. Get started for free with influx DB, visit influx data.com/cloud to learn more. >>Okay. Now we're joined by Brian Gilmore director of IOT and emerging technologies at influx data. Welcome to the show. >>Thank you, John. Great to be here. >>We just spent some time with Evan going through the company and the value proposition, um, with influx DV, what's the momentum, where do you see this coming from? What's the value coming out of this? >>Well, I think it, we're sort of hitting a point where the technology is, is like the adoption of it is becoming mainstream. We're seeing it in all sorts of organizations, everybody from like the most well funded sort of advanced big technology companies to the smaller academics, the startups and the managing of that sort of data that emits from that technology is time series and us being able to give them a, a platform, a tool that's super easy to use, easy to start. And then of course will grow with them is, is been key to us. Sort of, you know, riding along with them is they're successful. >>Evan was mentioning that time series has been on everyone's radar and that's in the OT business for years. Now, you go back since 20 13, 14, even like five years ago that convergence of physical and digital coming together, IP enabled edge. Yeah. Edge has always been kind of hyped up, but why now? Why, why is the edge so hot right now from an adoption standpoint? Is it because it's just evolution, the tech getting better? >>I think it's, it's, it's twofold. I think that, you know, there was, I would think for some people, everybody was so focused on cloud over the last probably 10 years. Mm-hmm <affirmative> that they forgot about the compute that was available at the edge. And I think, you know, those, especially in the OT and on the factory floor who weren't able to take Avan full advantage of cloud through their applications, you know, still needed to be able to leverage that compute at the edge. I think the big thing that we're seeing now, which is interesting is, is that there's like a hybrid nature to all of these applications where there's definitely some data that's generated on the edge. There's definitely done some data that's generated in the cloud. And it's the ability for a developer to sort of like tie those two systems together and work with that data in a very unified uniform way. Um, that's giving them the opportunity to build solutions that, you know, really deliver value to whatever it is they're trying to do, whether it's, you know, the, the out reaches of outer space or whether it's optimizing the factory floor. >>Yeah. I think, I think one of the things you also mentions genome too, dig big data is coming to the real world. And I think I, OT has been kind of like this thing for OT and, and in some use case, but now with the, with the cloud, all companies have an edge strategy now. So yeah, what's the secret sauce because now this is hot, hot product for the whole world and not just industrial, but all businesses. What's the secret sauce. >>Well, I mean, I think part of it is just that the technology is becoming more capable and that's especially on the hardware side, right? I mean, like technology compute is getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the micro controller layer with our, um, you know, our client libraries and then working hard to make our applications, especially the database as small as possible so that it can be located as close to sort of the point of origin of that data in the edge as possible is, is, is fantastic. Now you can take that. You can run that locally. You can do your local decision making. You can use influx DB as sort of an input to automation control the autonomy that people are trying to drive at the edge. But when you link it up with everything that's in the cloud, that's when you get all of the sort of cloud scale capabilities of parallelized, AI and machine learning and all of that. >>So what's interesting is the open source success has been something that we've talked about a lot in the cube about how people are leveraging that you guys have users in the enterprise users that IOT market mm-hmm <affirmative>, but you got developers now. Yeah. Kind of together brought that up. How do you see that emerging? How do developers engage? What are some of the things you're seeing that developers are really getting into with InfluxDB >>What's? Yeah. Well, I mean, I think there are the developers who are building companies, right? And these are the startups and the folks that we love to work with who are building new, you know, new services, new products, things like that. And, you know, especially on the consumer side of IOT, there's a lot of that, just those developers. But I think we, you gotta pay attention to those enterprise developers as well, right? There are tons of people with the, the title of engineer in, in your regular enterprise organizations. And they're there for systems integration. They're there for, you know, looking at what they would build versus what they would buy. And a lot of them come from, you know, a strong, open source background and they, they know the communities, they know the top platforms in those spaces and, and, you know, they're excited to be able to adopt and use, you know, to optimize inside the business as compared to just building a brand new one. >>You know, it's interesting too, when Evan and I were talking about open source versus closed OT systems, mm-hmm <affirmative> so how do you support the backwards compatibility of older systems while maintaining open dozens of data formats out there? Bunch of standards, protocols, new things are emerging. Everyone wants to have a control plane. Everyone wants to leverage the value of data. How do you guys keep track of it all? What do you guys support? >>Yeah, well, I mean, I think either through direct connection, like we have a product called Telegraph, it's unbelievable. It's open source, it's an edge agent. You can run it as close to the edge as you'd like, it speaks dozens of different protocols in its own, right? A couple of which MQTT B, C U a are very, very, um, applicable to these T use cases. But then we also, because we are sort of not only open source, but open in terms of our ability to collect data, we have a lot of partners who have built really great integrations from their own middleware, into influx DB. These are companies like ke wear and high bite who are really experts in those downstream industrial protocols. I mean, that's a business, not everybody wants to be in. It requires some very specialized, very hard work and a lot of support, um, you know, and so by making those connections and building those ecosystems, we get the best of both worlds. The customers can use the platforms they need up to the point where they would be putting into our database. >>What's some of customer testimonies that they, that share with you. Can you share some anecdotal kind of like, wow, that's the best thing I've ever used. This really changed my business, or this is a great tech that's helped me in these other areas. What are some of the, um, soundbites you hear from customers when they're successful? >>Yeah. I mean, I think it ranges. You've got customers who are, you know, just finally being able to do the monitoring of assets, you know, sort of at the edge in the field, we have a customer who's who's has these tunnel boring machines that go deep into the earth to like drill tunnels for, for, you know, cars and, and, you know, trains and things like that. You know, they are just excited to be able to stick a database onto those tunnel, boring machines, send them into the depths of the earth and know that when they come out, all of that telemetry at a very high frequency has been like safely stored. And then it can just very quickly and instantly connect up to their, you know, centralized database. So like just having that visibility is brand new to them. And that's super important. On the other hand, we have customers who are way far beyond the monitoring use case, where they're actually using the historical records in the time series database to, um, like I think Evan mentioned like forecast things. So for predictive maintenance, being able to pull in the telemetry from the machines, but then also all of that external enrichment data, the metadata, the temperatures, the pressure is who is operating the machine, those types of things, and being able to easily integrate with platforms like Jupyter notebooks or, you know, all of those scientific computing and machine learning libraries to be able to build the models, train the models, and then they can send that information back down to InfluxDB to apply it and detect those anomalies, which >>Are, I think that's gonna be an, an area. I personally think that's a hot area because I think if you look at AI right now, yeah. It's all about training the machine learning albums after the fact. So time series becomes hugely important. Yeah. Cause now you're thinking, okay, the data matters post time. Yeah. First time. And then it gets updated the new time. Yeah. So it's like constant data cleansing data iteration, data programming. We're starting to see this new use case emerge in the data field. >>Yep. Yeah. I mean, I think you agree. Yeah, of course. Yeah. The, the ability to sort of handle those pipelines of data smartly, um, intelligently, and then to be able to do all of the things you need to do with that data in stream, um, before it hits your sort of central repository. And, and we make that really easy for customers like Telegraph, not only does it have sort of the inputs to connect up to all of those protocols and the ability to capture and connect up to the, to the partner data. But also it has a whole bunch of capabilities around being able to process that data, enrich it, reform at it, route it, do whatever you need. So at that point you're basically able to, you're playing your data in exactly the way you would wanna do it. You're routing it to different, you know, destinations and, and it's, it's, it's not something that really has been in the realm of possibility until this point. Yeah. Yeah. >>And when Evan was on it's great. He was a CEO. So he sees the big picture with customers. He was, he kinda put the package together that said, Hey, we got a system. We got customers, people are wanting to leverage our product. What's your PO they're sell. He's selling too as well. So you have that whole CEO perspective, but he brought up this notion that there's multiple personas involved in kind of the influx DB system architect. You got developers and users. Can you talk about that? Reality as customers start to commercialize and operationalize this from a commercial standpoint, you got a relationship to the cloud. Yep. The edge is there. Yep. The edge is getting super important, but cloud brings a lot of scale to the table. So what is the relationship to the cloud? Can you share your thoughts on edge and its relationship to the cloud? >>Yeah. I mean, I think edge, you know, edges, you can think of it really as like the local information, right? So it's, it's generally like compartmentalized to a point of like, you know, a single asset or a single factory align, whatever. Um, but what people do who wanna pro they wanna be able to make the decisions there at the edge locally, um, quickly minus the latency of sort of taking that large volume of data, shipping it to the cloud and doing something with it there. So we allow them to do exactly that. Then what they can do is they can actually downsample that data or they can, you know, detect like the really important metrics or the anomalies. And then they can ship that to a central database in the cloud where they can do all sorts of really interesting things with it. Like you can get that centralized view of all of your global assets. You can start to compare asset to asset, and then you can do those things like we talked about, whereas you can do predictive types of analytics or, you know, larger scale anomaly detections. >>So in this model you have a lot of commercial operations, industrial equipment. Yep. The physical plant, physical business with virtual data cloud all coming together. What's the future for InfluxDB from a tech standpoint. Cause you got open. Yep. There's an ecosystem there. Yep. You have customers who want operational reliability for sure. I mean, so you got organic <laugh> >>Yeah. Yeah. I mean, I think, you know, again, we got iPhones when everybody's waiting for flying cars. Right. So I don't know. We can like absolutely perfectly predict what's coming, but I think there are some givens and I think those givens are gonna be that the world is only gonna become more hybrid. Right. And then, you know, so we are going to have much more widely distributed, you know, situations where you have data being generated in the cloud, you have data gen being generated at the edge and then there's gonna be data generated sort sort of at all points in between like physical locations as well as things that are, that are very virtual. And I think, you know, we are, we're building some technology right now. That's going to allow, um, the concept of a database to be much more fluid and flexible, sort of more aligned with what a file would be like. >>And so being able to move data to the compute for analysis or move the compute to the data for analysis, those are the types of, of solutions that we'll be bringing to the customers sort of over the next little bit. Um, but I also think we have to start thinking about like what happens when the edge is actually off the planet. Right. I mean, we've got customers, you're gonna talk to two of them, uh, in the panel who are actually working with data that comes from like outside the earth, like, you know, either in low earth orbit or you know, all the way sort of on the other side of the universe. Yeah. And, and to be able to process data like that and to do so in a way it's it's we gotta, we gotta build the fundamentals for that right now on the factory floor and in the mines and in the tunnels. Um, so that we'll be ready for that one. >>I think you bring up a good point there because one of the things that's common in the industry right now, people are talking about, this is kind of new thinking is hyper scale's always been built up full stack developers, even the old OT world, Evan was pointing out that they built everything right. And the world's going to more assembly with core competency and IP and also property being the core of their apple. So faster assembly and building, but also integration. You got all this new stuff happening. Yeah. And that's to separate out the data complexity from the app. Yes. So space genome. Yep. Driving cars throws off massive data. >>It >>Does. So is Tesla, uh, is the car the same as the data layer? >>I mean the, yeah, it's, it's certainly a point of origin. I think the thing that we wanna do is we wanna let the developers work on the world, changing problems, the things that they're trying to solve, whether it's, you know, energy or, you know, any of the other health or, you know, other challenges that these teams are, are building against. And we'll worry about that time series data and the underlying data platform so that they don't have to. Right. I mean, I think you talked about it, uh, you know, for them just to be able to adopt the platform quickly, integrate it with their data sources and the other pieces of their applications. It's going to allow them to bring much faster time to market on these products. It's gonna allow them to be more iterative. They're gonna be able to do more sort of testing and things like that. And ultimately it will, it'll accelerate the adoption and the creation of >>Technology. You mentioned earlier in, in our talk about unification of data. Yeah. How about APIs? Cuz developers love APIs in the cloud unifying APIs. How do you view view that? >>Yeah, I mean, we are APIs, that's the product itself. Like everything, people like to think of it as sort of having this nice front end, but the front end is B built on our public APIs. Um, you know, and it, it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then, you know, sort of data extraction to bring it to other platforms or other applications, microservices, whatever it might be. So, I mean, it is a world of APIs right now and you know, we, we bring a very sort of useful set of them for managing the time series data. These guys are all challenged with. It's >>Interesting. You and I were talking before we came on camera about how, um, data is, feels gonna have this kind of SRE role that DevOps had site reliability engineers, which manages a bunch of servers. There's so much data out there now. Yeah. >>Yeah. It's like reigning data for sure. And I think like that ability to be like one of the best jobs on the planet is gonna be to be able to like, sort of be that data Wrangler to be able to understand like what the data sources are, what the data formats are, how to be able to efficiently move that data from point a to point B and you know, to process it correctly so that the end users of that data aren't doing any of that sort of hard upfront preparation collection storage's >>Work. Yeah. That's data as code. I mean, data engineering is it is becoming a new discipline for sure. And, and the democratization is the benefit. Yeah. To everyone, data science get easier. I mean data science, but they wanna make it easy. Right. <laugh> yeah. They wanna do the analysis, >>Right? Yeah. I mean, I think, you know, it, it's a really good point. I think like we try to give our users as many ways as there could be possible to get data in and get data out. We sort of think about it as meeting them where they are. Right. So like we build, we have the sort of client libraries that allow them to just port to us, you know, directly from the applications and the languages that they're writing, but then they can also pull it out. And at that point nobody's gonna know the users, the end consumers of that data, better than those people who are building those applications. And so they're building these user interfaces, which are making all of that data accessible for, you know, their end users inside their organization. >>Well, Brian, great segment, great insight. Thanks for sharing all, all the complexities and, and IOT that you guys helped take away with the APIs and, and assembly and, and all the system architectures that are changing edge is real cloud is real. Yeah, absolutely. Mainstream enterprises. And you got developer attraction too, so congratulations. >>Yeah. It's >>Great. Well, thank any, any last word you wanna share >>Deal with? No, just, I mean, please, you know, if you're, if you're gonna, if you're gonna check out influx TV, download it, try out the open source contribute if you can. That's a, that's a huge thing. It's part of being the open source community. Um, you know, but definitely just, just use it. I think when once people use it, they try it out. They'll understand very, >>Very quickly. So open source with developers, enterprise and edge coming together all together. You're gonna hear more about that in the next segment, too. Right. Thanks for coming on. Okay. Thanks. When we return, Dave LAN will lead a panel on edge and data influx DB. You're watching the cube, the leader in high tech enterprise coverage. >>Why the startup, we move really fast. We find that in flex DB can move as fast as us. It's just a great group, very collaborative, very interested in manufacturing. And we see a bright future in working with influence. My name is Aaron Seley. I'm the CTO at HBI. Highlight's one of the first companies to focus on manufacturing data and apply the concepts of data ops, treat that as an asset to deliver to the it system, to enable applications like overall equipment effectiveness that can help the factory produce better, smarter, faster time series data. And manufacturing's really important. If you take a piece of equipment, you have the temperature pressure at the moment that you can look at to kind of see the state of what's going on. So without that context and understanding you can't do what manufacturers ultimately want to do, which is predict the future. >>Influx DB represents kind of a new way to storm time series data with some more advanced technology and more importantly, more open technologies. The other thing that influx does really well is once the data's influx, it's very easy to get out, right? They have a modern rest API and other ways to access the data. That would be much more difficult to do integrations with classic historians highlight can serve to model data, aggregate data on the shop floor from a multitude of sources, whether that be P C U a servers, manufacturing execution systems, E R P et cetera, and then push that seamlessly into influx to then be able to run calculations. Manufacturing is changing this industrial 4.0, and what we're seeing is influx being part of that equation. Being used to store data off the unified name space, we recommend InfluxDB all the time to customers that are exploring a new way to share data manufacturing called the unified name space who have open questions around how do I share this new data that's coming through my UNS or my QTT broker? How do I store this and be able to query it over time? And we often point to influx as a solution for that is a great brand. It's a great group of people and it's a great technology. >>Okay. We're now going to go into the customer panel and we'd like to welcome Angelo Fasi. Who's a software engineer at the Vera C Ruben observatory in Caleb McLaughlin whose senior spacecraft operations software engineer at loft orbital guys. Thanks for joining us. You don't wanna miss folks this interview, Caleb, let's start with you. You work for an extremely cool company. You're launching satellites into space. I mean, there, of course doing that is, is highly complex and not a cheap endeavor. Tell us about loft Orbi and what you guys do to attack that problem. >>Yeah, absolutely. And, uh, thanks for having me here by the way. Uh, so loft orbital is a, uh, company. That's a series B startup now, uh, who and our mission basically is to provide, uh, rapid access to space for all kinds of customers. Uh, historically if you want to fly something in space, do something in space, it's extremely expensive. You need to book a launch, build a bus, hire a team to operate it, you know, have a big software teams, uh, and then eventually worry about, you know, a bunch like just a lot of very specialized engineering. And what we're trying to do is change that from a super specialized problem that has an extremely high barrier of access to a infrastructure problem. So that it's almost as simple as, you know, deploying a VM in, uh, AWS or GCP is getting your, uh, programs, your mission deployed on orbit, uh, with access to, you know, different sensors, uh, cameras, radios, stuff like that. >>So that's, that's kind of our mission. And just to give a really brief example of the kind of customer that we can serve. Uh, there's a really cool company called, uh, totem labs who is working on building, uh, IOT cons, an IOT constellation for in of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor T, which means you have this little modem inside a container container that you, that you track from anywhere in the world as it's going across the ocean. Um, so they're, it's really little and they've been able to stay a small startup that's focused on their product, which is the, uh, that super crazy complicated, cool radio while we handle the whole space segment for them, which just, you know, before loft was really impossible. So that's, our mission is, uh, providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving, you know, a huge variety of customers with all kinds of different missions, um, and obviously generating a ton of data in space, uh, that we've gotta handle. Yeah. >>So amazing Caleb, what you guys do, I, now I know you were lured to the skies very early in your career, but how did you kinda land on this business? >>Yeah, so, you know, I've, I guess just a little bit about me for some people, you know, they don't necessarily know what they wanna do like early in their life. For me, I was five years old and I knew, you know, I want to be in the space industry. So, you know, I started in the air force, but have, uh, stayed in the space industry, my whole career and been a part of, uh, this is the fifth space startup that I've been a part of actually. So, you know, I've, I've, uh, kind of started out in satellites, did spent some time in working in, uh, the launch industry on rockets. Then, uh, now I'm here back in satellites and you know, honestly, this is the most exciting of the difference based startups. That I've been a part of >>Super interesting. Okay. Angelo, let's, let's talk about the Ruben observatory, ver C Ruben, famous woman scientist, you know, galaxy guru. Now you guys the observatory, you're up way up high. You're gonna get a good look at the Southern sky. Now I know COVID slowed you guys down a bit, but no doubt. You continued to code away on the software. I know you're getting close. You gotta be super excited. Give us the update on, on the observatory and your role. >>All right. So yeah, Rubin is a state of the art observatory that, uh, is in construction on a remote mountain in Chile. And, um, with Rubin, we conduct the, uh, large survey of space and time we are going to observe the sky with, uh, eight meter optical telescope and take, uh, a thousand pictures every night with a 3.2 gig up peaks of camera. And we are going to do that for 10 years, which is the duration of the survey. >>Yeah. Amazing project. Now you, you were a doctor of philosophy, so you probably spent some time thinking about what's out there and then you went out to earn a PhD in astronomy, in astrophysics. So this is something that you've been working on for the better part of your career, isn't it? >>Yeah, that's that's right. Uh, about 15 years, um, I studied physics in college, then I, um, got a PhD in astronomy and, uh, I worked for about five years in another project. Um, the dark energy survey before joining rubing in 2015. >>Yeah. Impressive. So it seems like you both, you know, your organizations are looking at space from two different angles. One thing you guys both have in common of course is, is, is software. And you both use InfluxDB as part of your, your data infrastructure. How did you discover influx DB get into it? How do you use the platform? Maybe Caleb, you could start. >>Uh, yeah, absolutely. So the first company that I extensively used, uh, influx DBN was a launch startup called, uh, Astra. And we were in the process of, uh, designing our, you know, our first generation rocket there and testing the engines, pumps, everything that goes into a rocket. Uh, and when I joined the company, our data story was not, uh, very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. Um, and at first there, you know, that's the way that a lot of engineers and scientists are used to working. Um, and at first that was, uh, like people weren't entirely sure that that was a, um, that that needed to change, but it's something the nice thing about InfluxDB is that, you know, it's so easy to deploy. So as the, our software engineering team was able to get it deployed and, you know, up and running very quickly and then quickly also backport all of the data that we collected thus far into influx and what, uh, was amazing to see. >>And as kind of the, the super cool moment with influx is, um, when we hooked that up to Grafana Grafana as the visualization platform we used with influx, cuz it works really well with it. Uh, there was like this aha moment of our engineers who are used to this post process kind of method for dealing with their data where they could just almost instantly easily discover data that they hadn't been able to see before and take the manual processes that they would run after a test and just throw those all in influx and have live data as tests were coming. And, you know, I saw them implementing like crazy rocket equation type stuff in influx, and it just was totally game changing for how we tested. >>So Angelo, I was explaining in my open, you know, you could, you could add a column in a traditional RDBMS and do time series, but with the volume of data that you're talking about, and the example of the Caleb just gave you, I mean, you have to have a purpose built time series database, where did you first learn about influx DB? >>Yeah, correct. So I work with the data management team, uh, and my first project was the record metrics that measured the performance of our software, uh, the software that we used to process the data. So I started implementing that in a relational database. Um, but then I realized that in fact, I was dealing with time series data and I should really use a solution built for that. And then I started looking at time series databases and I found influx B. And that was, uh, back in 2018. The another use for influx DB that I'm also interested is the visits database. Um, if you think about the observations we are moving the telescope all the time in pointing to specific directions, uh, in the Skype and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, uh, we call a visit. So we want to record the metadata about those visits and flex to, uh, that time here is going to be 10 years long, um, with about, uh, 1000 points every night. It's actually not too much data compared to other, other problems. It's, uh, really just a different, uh, time scale. >>The telescope at the Ruben observatory is like pun intended, I guess the star of the show. And I, I believe I read that it's gonna be the first of the next gen telescopes to come online. It's got this massive field of view, like three orders of magnitude times the Hub's widest camera view, which is amazing, right? That's like 40 moons in, in an image amazingly fast as well. What else can you tell us about the telescope? >>Um, this telescope, it has to move really fast and it also has to carry, uh, the primary mirror, which is an eight meter piece of glass. It's very heavy and it has to carry a camera, which has about the size of a small car. And this whole structure weighs about 300 tons for that to work. Uh, the telescope needs to be, uh, very compact and stiff. Uh, and one thing that's amazing about it's design is that the telescope, um, is 300 tons structure. It sits on a tiny film of oil, which has the diameter of, uh, human hair. And that makes an almost zero friction interface. In fact, a few people can move these enormous structure with only their hands. Uh, as you said, uh, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So each image has, uh, in diameter the size of about seven full moons. And, uh, with that, we can map the entire sky in only, uh, three days. And of course doing operations everything's, uh, controlled by software and it is automatic. Um there's a very complex piece of software, uh, called the scheduler, which is responsible for moving the telescope, um, and the camera, which is, uh, recording 15 terabytes of data every night. >>Hmm. And, and, and Angela, all this data lands in influx DB. Correct. And what are you doing with, with all that data? >>Yeah, actually not. Um, so we are using flex DB to record engineering data and metadata about the observations like telemetry events and commands from the telescope. That's a much smaller data set compared to the images, but it is still challenging because, uh, you, you have some high frequency data, uh, that the system needs to keep up and we need to, to start this data and have it around for the lifetime of the price. Mm, >>Got it. Thank you. Okay, Caleb, let's bring you back in and can tell us more about the, you got these dishwasher size satellites. You're kind of using a multi-tenant model. I think it's genius, but, but tell us about the satellites themselves. >>Yeah, absolutely. So, uh, we have in space, some satellites already that as you said, are like dishwasher, mini fridge kind of size. Um, and we're working on a bunch more that are, you know, a variety of sizes from shoebox to, I guess, a few times larger than what we have today. Uh, and it is, we do shoot to have effectively something like a multi-tenant model where, uh, we will buy a bus off the shelf. The bus is, uh, what you can kind of think of as the core piece of the satellite, almost like a motherboard or something where it's providing the power. It has the solar panels, it has some radios attached to it. Uh, it handles the attitude control, basically steers the spacecraft in orbit. And then we build also in house, what we call our payload hub, which is, has all, any customer payloads attached and our own kind of edge processing sort of capabilities built into it. >>And, uh, so we integrate that. We launch it, uh, and those things, because they're in lower orbit, they're orbiting the earth every 90 minutes. That's, you know, seven kilometers per second, which is several times faster than a speeding bullet. So we've got, we have, uh, one of the unique challenges of operating spacecraft and lower orbit is that generally you can't talk to them all the time. So we're managing these things through very brief windows of time, uh, where we get to talk to them through our ground sites, either in Antarctica or, you know, in the north pole region. >>Talk more about how you use influx DB to make sense of this data through all this tech that you're launching into space. >>We basically previously we started off when I joined the company, storing all of that as Angelo did in a regular relational database. And we found that it was, uh, so slow in the size of our data would balloon over the course of a couple days to the point where we weren't able to even store all of the data that we were getting. Uh, so we migrated to influx DB to store our time series telemetry from the spacecraft. So, you know, that's things like, uh, power level voltage, um, currents counts, whatever, whatever metadata we need to monitor about the spacecraft. We now store that in, uh, in influx DB. Uh, and that has, you know, now we can actually easily store the entire volume of data for the mission life so far without having to worry about, you know, the size bloating to an unmanageable amount. >>And we can also seamlessly query, uh, large chunks of data. Like if I need to see, you know, for example, as an operator, I might wanna see how my, uh, battery state of charge is evolving over the course of the year. I can have a plot and an influx that loads that in a fraction of a second for a year's worth of data, because it does, you know, intelligent, um, I can intelligently group the data by, uh, sliding time interval. Uh, so, you know, it's been extremely powerful for us to access the data and, you know, as time has gone on, we've gradually migrated more and more of our operating data into influx. >>You know, let's, let's talk a little bit, uh, uh, but we throw this term around a lot of, you know, data driven, a lot of companies say, oh, yes, we're data driven, but you guys really are. I mean, you' got data at the core, Caleb, what does that, what does that mean to you? >>Yeah, so, you know, I think the, and the clearest example of when I saw this be like totally game changing is what I mentioned before at Astro where our engineer's feedback loop went from, you know, a lot of kind of slow researching, digging into the data to like an instant instantaneous, almost seeing the data, making decisions based on it immediately, rather than having to wait for some processing. And that's something that I've also seen echoed in my current role. Um, but to give another practical example, uh, as I said, we have a huge amount of data that comes down every orbit, and we need to be able to ingest all of that data almost instantaneously and provide it to the operator. And near real time, you know, about a second worth of latency is all that's acceptable for us to react to, to see what is coming down from the spacecraft and building that pipeline is challenging from a software engineering standpoint. >>Um, our primary language is Python, which isn't necessarily that fast. So what we've done is started, you know, in the, in the goal of being data driven is publish metrics on individual, uh, how individual pieces of our data processing pipeline are performing into influx as well. And we do that in production as well as in dev. Uh, so we have kind of a production monitoring, uh, flow. And what that has done is allow us to make intelligent decisions on our software development roadmap, where it makes the most sense for us to, uh, focus our development efforts in terms of improving our software efficiency. Uh, just because we have that visibility into where the real problems are. Um, it's sometimes we've found ourselves before we started doing this kind of chasing rabbits that weren't necessarily the real root cause of issues that we were seeing. Uh, but now, now that we're being a bit more data driven, there we are being much more effective in where we're spending our resources and our time, which is especially critical to us as we scale to, from supporting a couple satellites, to supporting many, many satellites at >>Once. Yeah. Coach. So you reduced those dead ends, maybe Angela, you could talk about what, what sort of data driven means to, to you and your teams? >>I would say that, um, having, uh, real time visibility, uh, to the telemetry data and, and metrics is, is, is crucial for us. We, we need, we need to make sure that the image that we collect with the telescope, uh, have good quality and, um, that they are within the specifications, uh, to meet our science goals. And so if they are not, uh, we want to know that as soon as possible and then, uh, start fixing problems. >>Caleb, what are your sort of event, you know, intervals like? >>So I would say that, you know, as of today on the spacecraft, the event, the, the level of timing that we deal with probably tops out at about, uh, 20 Hertz, 20 measurements per second on, uh, things like our, uh, gyroscopes, but the, you know, I think the, the core point here of the ability to have high precision data is extremely important for these kinds of scientific applications. And I'll give an example, uh, from when I worked at, on the rocket at Astra there, our baseline data rate that we would ingest data during a test is, uh, 500 Hertz. So 500 samples per second. And in some cases we would actually, uh, need to ingest much higher rate data, even up to like 1.5 kilohertz. So, uh, extremely, extremely high precision, uh, data there where timing really matters a lot. And, uh, you know, I can, one of the really powerful things about influx is the fact that it can handle this. >>That's one of the reasons we chose it, uh, because there's times when we're looking at the results of a firing where you're zooming in, you know, I talked earlier about how on my current job, we often zoom out to look, look at a year's worth of data. You're zooming in to where your screen is preoccupied by a tiny fraction of a second. And you need to see same thing as Angela just said, not just the actual telemetry, which is coming in at a high rate, but the events that are coming out of our controllers. So that can be something like, Hey, I opened this valve at exactly this time and that goes, we wanna have that at, you know, micro or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at, you know, at this exact moment, was that before or after this valve open, those kind of, uh, that kind of visibility is critical in these kind of scientific, uh, applications and absolutely game changing to be able to see that in, uh, near real time and, uh, with a really easy way for engineers to be able to visualize this data themselves without having to wait for, uh, software engineers to go build it for them. >>Can the scientists do self-serve or are you, do you have to design and build all the analytics and, and queries for your >>Scientists? Well, I think that's, that's absolutely from, from my perspective, that's absolutely one of the best things about influx and what I've seen be game changing is that, uh, generally I'd say anyone can learn to use influx. Um, and honestly, most of our users might not even know they're using influx, um, because what this, the interface that we expose to them is Grafana, which is, um, a generic graphing, uh, open source graphing library that is very similar to influx own chronograph. Sure. And what it does is, uh, let it provides this, uh, almost it's a very intuitive UI for building your queries. So you choose a measurement and it shows a dropdown of available measurements. And then you choose a particular, the particular field you wanna look at. And again, that's a dropdown, so it's really easy for our users to discover. And there's kind of point and click options for doing math aggregations. You can even do like perfect kind of predictions all within Grafana, the Grafana user interface, which is really just a wrapper around the APIs and functionality of the influx provides putting >>Data in the hands of those, you know, who have the context of domain experts is, is key. Angela, is it the same situation for you? Is it self serve? >>Yeah, correct. Uh, as I mentioned before, um, we have the astronomers making their own dashboards because they know what exactly what they, they need to, to visualize. Yeah. I mean, it's all about using the right tool for the job. I think, uh, for us, when I joined the company, we weren't using influx DB and we, we were dealing with serious issues of the database growing to an incredible size extremely quickly, and being unable to like even querying short periods of data was taking on the order of seconds, which is just not possible for operations >>Guys. This has been really formative it's, it's pretty exciting to see how the edge is mountaintops, lower orbits to be space is the ultimate edge. Isn't it. I wonder if you could answer two questions to, to wrap here, you know, what comes next for you guys? Uh, and is there something that you're really excited about that, that you're working on Caleb, maybe you could go first and an Angela, you can bring us home. >>Uh, basically what's next for loft. Orbital is more, more satellites, a greater push towards infrastructure and really making, you know, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, uh, making that happen, it's extremely exciting and extremely exciting time to be in this company and to be in this industry as a whole, because there are so many interesting applications out there. So many cool ways of leveraging space that, uh, people are taking advantage of. And with, uh, companies like SpaceX and the now rapidly lowering cost, cost of launch, it's just a really exciting place to be. And we're launching more satellites. We are scaling up for some constellations and our ground system has to be improved to match. So there's a lot of, uh, improvements that we're working on to really scale up our control software, to be best in class and, uh, make it capable of handling such a large workload. So >>You guys hiring >><laugh>, we are absolutely hiring. So, uh, I would in we're we need, we have PE positions all over the company. So, uh, we need software engineers. We need people who do more aerospace, specific stuff. So, uh, absolutely. I'd encourage anyone to check out the loft orbital website, if there's, if this is at all interesting. >>All right. Angela, bring us home. >>Yeah. So what's next for us is really, uh, getting this, um, telescope working and collecting data. And when that's happen is going to be just, um, the Lu of data coming out of this camera and handling all, uh, that data is going to be really challenging. Uh, yeah. I wanna wanna be here for that. <laugh> I'm looking forward, uh, like for next year we have like an important milestone, which is our, um, commissioning camera, which is a simplified version of the, of the full camera it's going to be on sky. And so yeah, most of the system has to be working by them. >>Nice. All right, guys, you know, with that, we're gonna end it. Thank you so much, really fascinating, and thanks to influx DB for making this possible, really groundbreaking stuff, enabling value creation at the edge, you know, in the cloud and of course, beyond at the space. So really transformational work that you guys are doing. So congratulations and really appreciate the broader community. I can't wait to see what comes next from having this entire ecosystem. Now, in a moment, I'll be back to wrap up. This is Dave ante, and you're watching the cube, the leader in high tech enterprise coverage. >>Welcome Telegraph is a popular open source data collection. Agent Telegraph collects data from hundreds of systems like IOT sensors, cloud deployments, and enterprise applications. It's used by everyone from individual developers and hobbyists to large corporate teams. The Telegraph project has a very welcoming and active open source community. Learn how to get involved by visiting the Telegraph GitHub page, whether you want to contribute code, improve documentation, participate in testing, or just show what you're doing with Telegraph. We'd love to hear what you're building. >>Thanks for watching. Moving the world with influx DB made possible by influx data. I hope you learn some things and are inspired to look deeper into where time series databases might fit into your environment. If you're dealing with large and or fast data volumes, and you wanna scale cost effectively with the highest performance and you're analyzing metrics and data over time times, series databases just might be a great fit for you. Try InfluxDB out. You can start with a free cloud account by clicking on the link and the resources below. Remember all these recordings are gonna be available on demand of the cube.net and influx data.com. So check those out and poke around influx data. They are the folks behind InfluxDB and one of the leaders in the space, we hope you enjoyed the program. This is Dave Valante for the cube. We'll see you soon.

>>Welcome to this cube conversation. I'm John fury host of the cube here in Palo Alto, California. We're talking open source. We're talking about the data center. We're talking about cloud scale, bringing that software benefits all to the table, all around the network, the network operating system, and more gotta go a guest here, sir. Rob Capor director of product management, Dell networking, sir. Rob. Great to see you. Thanks for coming on. >>Thank you, John. Good to be here. Thanks for inviting me. >>You know, we were talking before we came on camera around how the networking business is changing, why hardware matters, why software's more important. And in all of this shift, that's happening in the transition to fully distributed computing, which Matt, you got the edge, you got the data center, you got the cloud all coming together. One of the common threads in all of this is open source. Okay. Open source software, next generations coming. You're seeing more and more new cool things in open source, but also in parallel with the enterprise. This is a huge kind of flash point to the next gen open source enterprise convergence with, with open source software and the communities and all and all that, all that good stuff. And you're in the middle of it. What's driving this Hmm. Source and the data center. We're seeing the levels of support like we've never seen before and specifically at the network level. >>Awesome. Yeah. So, well, to set the context, let's start by looking at the story of comput solution, right? Uh, in the nineties, the comput infrastructure was vertically integrated. Uh, there were multiple vendors each offering their own operating system, usually a version of hearings, uh, on, on the proprietary hardwares. If I wanted to run a Solas operating system, I had to run that over a Spoor and the applications were written, especially for, for that architecture. So, so this represented multiple challenges back then the customer were locked in growth and innovation developers had to recreate applications for, for different architectures and, and the interoperability was extremely difficult, but with the advent of X 86 architecture and standardized operating systems like like windows and Linux, the stack got disaggregated, which allowed for flexibility, innovation, affordability and finding expand engine. We see a similar trend happening on the networking side now where the traditional networking solutions, uh, are not flexible. >>The switch, the network operating system, the APIs are all provided by the same vendor. So if a feature is, is needed, the user has to either wait for the vendor to deliver it, or is forced to replace then time for structure. But with the of open networking and opensource networking based solutions, we see an evolution that has paved the way for the customers to unlock their data center technologies and innovate. The modern data center is, is no longer centered around protocol sax. It's about agility, flexibility, innovation, network automation, and simplicity. It's about how to make operations more agile, agile, more effective, and, and, you know, bake it into an overall infrastructure today. A large element of, uh, of, of business action behind open networking is that companies are moving towards application centricity and, and a true realization of as a service model. Right? So, so that is where Sonic comes into the picture, right? >>But it's large and diverse community around, around modular containers, architecture born in Microsoft as your environment, Sonic is, is built for automation, telemetry and scale. And the flexibility of this architecture allows you for, you know, in terms of running to applications by providing that high level of redundancy. So, so basically know Sonic kind of check marks to all the requirements of the modern data center from open flexible architectures to cloud economics. And if you have to follow a comput evolution analogy, we believe that, you know, switches is the server now in Sonic is the Linux for networking. >>It's like the Ker of networking. I mean, we'd be and reporting. We've had all the cube conversations where Sonic's been mentioned and people have been saying things like it's taking the networking world by storm. Um, and all, all that with open source kind of ties it and scales it together. Can you take a minute to explain a little bit about what it is? What is Sonic, what does it stand for? Why is it important? What does it do? What's the benefit to the customers? What are they, what what's going on around Sonic take a minute to explain what is Sonic. >>Absolutely. Yeah. So is Sonic stands for software for open talking in the cloud. It's a brain of Microsoft in, in 2016, they announced their contribution of Sonic to the open source community and, and through the networking technology to revolutionary set forward with the yet level of this aggregation by breaking the monolithic nos into multiple containers components. And, and through the use of ization, Sonic provides the, the network managers, the plug and place sensibility, the ability to run third party proprietary or open source application containers and, and perform those in-service updates with zero down time. Sonic is, is primarily designed across four main per principles. First one is the notion of control where, uh, Sonic is an open software organizations are deploying it, working on it. The network managers can decide what features they want to ship on a switch, so that there's less potential for bug and, and tailored for more of the use cases, right? >>Sonic was designed for extensibility for, uh, the developers to come and add new cable, roll those out rapidly on, on a platform. Uh it's it was designed for agility. The ability to take changes, roll those out rapidly, whether it's a bug fix or a new feature coming out, uh, which is significant. And finally Sonic was designed around this notion of open collaboration with such a diverse community around. We have Silicon vendors to ODM providers. It contribute is the more people work on it better and more like the software it becomes. Yeah. I mean, it has evolved considerably and, and since it's inception, it's, it's, uh, the growth is, is nurtured by an increasing set of users, uh, a vibrant open source community. Uh, and then there's a long, uh, trail of, of, you know, falling from, from the non hyperscalers where they like the value propers, you know, technology. And I want to adapt it for their environment. >>Yeah. And of course we love Silicon here at Silicon angle on the cube. Uh, but this is the whole new thing. Silicon advances is still software hardware matters. Dave LAN is doing a big thing called on why hardware matters with our team hardware and software together with open source really is coming back smaller, faster, cheaper. It's really good. So I want to ask you about Sonic, what types of customers mm-hmm <affirmative> would be looking to implement this, is this more of a, a reset in the data center? Is it a cloud scale team? Is it tributed computing? What's the new look of the customer who are implementing the like so, so, >>Well, uh, you know, it has evolved considerably since it's <inaudible> right. It was born into a hyperscale environment and we see a big end happening where, uh, you know, there's a wider appeal that is across non hyperscalers who want to emulate the best practices of the hyperscalers. They, but they want to do it on their own dumps. They want, uh, uh, a feature solution that is tailored for enterprise use cases. And, and, you know, looking at this whole contains architecture, Sonic kinda fits the build well where, you know, providing a Linux, no, that can be managed by the same set of automation management tools. Uh, and you know, these are the same teams, you know, uh, that have, you know, been acclimated world on website. Now with this all tool consolidation and consistent operations across the data center infrastructure, we, we see that Sonic brings a lot of value, uh, to these distributed application use cases, these modern data center environments, where you, you know, you have, you know, customers looking for cloud economics, multi vendor ecosystem, open and flexible architectures. And in fact, you know, uh, you know, we are told by the industry analyst that there's a strong possibility that, you know, during the next three to six years, Sonic is going to become analog as to Linux. Uh, now allowing the enterprises to, to sanitize on this. No, and, and, and, you know, they also predict that, uh, you know, 40% of the organizations that have, uh, you large data centers or 200 plus switches will deploy Sonic in production. And the market is going to be approximately 2.5 billion by, by 25. >>You know, we've Al we've always been riffing about the network layers, always the last area to kind of get the innovation, because it's so important. I mean, right. If you look at the advances of cloud and cloud scale, obviously Amazon did great work and what starts with networking lay what they did kind of with the, in the cloud, but even in the enterprise, it's so locked down, it's so important. Um, and things like policy, these are the concepts that have been moving up the stack. We see that, but also software's moving down the stack, right? So this notion of a network operating system kind of now is in play at the data center level, not just on the server, you're talking about like packets and observability monitoring, you know, more and more and more data coming in. So with data surging, tsunami of data, new, um, agile architectures changing in real time dynamic policy, this is what's happening. What's the role of Dell in all this? You guys got the hardware, um, you got the servers now it's open source, it's got community. What is Dell bringing to the table? What's your role in this development and the evolution of Sonic and, and what do you guys bringing to the table? >>Absolutely. So, so we are now, uh, enterprise Sonic distribution by Dell technologies, a commercial offering for Sonic in June last year. And our vision has been primarily to bridge the cap between hyperscale networking and enterprise networking. Right here we are, we are combining the stents and value proposition of Sonic and Dell technologies where the customers get an innovative, scalable, open source NA, which is hardened supported and backed by industry leader and open networking that has been, that has been our primary play into this where enterprise Sonic by Dell, we, we CU the customers, you know, get support and deployment services. Uh, we work with the customers in building out a roadmap that is, you know, a predictable software and hardware roadmap for them. Uh, we, we provide extended and validated use cases where, uh, you know, they can average, you know, Sonic for their, you know, specific environments, whether it's a cloud environment or the enterprise environment, uh, we've created a partner ecosystem where, uh, you know, with, with certain organizations that allow you to leverage the inherent automation, telemetric capabilities in the NAS, uh, to enhance the usability of the software, we have, uh, created an intuitive CLI framework called manage framework to allow you to better consume Sonic for your environment. >>We offer support for open conflict models and then also answerable playbooks for, for network automation. So, so it's been a journey, uh, you know, we are making the solution ready for enterprise consumption is a, a big fan falling that is happening from the non hyperscaler awards. And, uh, we've made significant in, in, in the community as well. Yeah. 1 million lines plus of code what fixes and, and helping with the documentation. So we are at the forefront of, of so journey. >>So you're saying that you, you're saying Dell for the folks watching you guys are putting the work in you're investing in opensource. >>Yeah, yeah, absolutely. I mean, we, we, we are, uh, you know, extending open source to the bottom market, you know, making it enterprise ready, uh, with, with feature enhancements and building a partner ecosystem. Uh, you know, we, we ensure that, you know, it advanced through extensive internal testing and validation for the customers. And then, uh, in order to allow the customers to absorb this new technology in house, uh, you know, we, we provide virtual to MOS. We have, you know, hands on labs for, for customers and channel partners. We, we also help them with, with a lot of documentation and reference architecture so that, you know, it's a knowledge repository across the board that can be leveraged for the modern use cases. So, yeah, so that's been a, it's been a journey with the customers and it's always in evolution where we, you know, get better of it with extended use cases and, and more capabilities on the portfolio. >>You know, I always, I always talk with Michael Dell at the Dell tech world every year. And sometimes we text back and forth. Uh, we kind of grew up together in the industry about the same age. Um, and we joke about the Dell early days of Dell, how supply chain was really part of their advantage. Um, and this is getting a little bit of a throwback, but you look back back then it was a of systems architecture. You have suppliers, you have chips, you have boards, you build PCs, you build servers. And the DNA of Dell, Dell technologies has always been around the system and with open source and tributed computing cloud data center edge, it's a system. And we're hearing words like supply chain in software, right? So when you start to think about Sonic and network operating systems and that kind of, those kinds of systems, when you modernize it, it's still gotta enable things to enable value. So what's the enabling value that Sonic has for the modern era here and comput as new kinds of supply chains emerge, new kinds of partnerships have to evolve. And the environment under the covers is changing too. You got cloud native, you got growth of containers. I think DACA was telling us that the container market there is pushing 20 million developers. I mean, massive cloud native activity and open source growth. This is a system. >>No, absolutely. I mean, uh, you know, the modern world has changed so much from, from, you know, the proprietary infrastructure and stacks. Now, uh, we Dell, you know, becoming, uh, uh, you know, more software focused now because that's a real value, uh, that you bring to the customers. Now, it's all about application centricity. Nobody is talking about out, you know, protocol stacks, or, you know, they, they want simplicity. They want ease of network management. And how do you expose all these capabilities? It's, it's with software, right? Sonic being open software. There's so much happening, uh, in, in the community around it. You know, we provide not bond interfaces that, you know, customers can hook up into their applications and get better at monitoring, get better at you managing that entire CIC CD pipeline in the infrastructure. So I think, you know, soft is, is a core in the heart of, you know, the modern data center infrastructure today. And, you know, we've been, uh, you know, uh, uh, at the forefront of this journey with, with Sonic and, uh, you know, bringing the real choice and flexibility for the customers. >>It's certainly an exciting time if you're in the data center, you're in, in architecture, cloud architecture, urine in data engineering, a new growing field, not just data science data is code. We did a big special on that recently in the cube, but also just overall scale. And so this, these are all new factors in C CXOs are dealing with obviously securities playing a big part of it and the role of data, uh, and also application developers all in the partner ecosystem becomes a really important part of, so I have to ask you, can you expand a little bit more on your comment earlier about the partner ecosystem and the importance of plays mm-hmm, <affirmative> in providing a best in class service because you're relying on others in open source, but you're commercializing Sonic with others. So there's a, there's a ecosystem play here. What's, what's talk more about that and, and the importance of it, >>Right, right. Yes, sir. As I mentioned earlier, right, the modern data center is no longer centered on protocol stack, right? So it's about agility, flexibility, choice, uh, network automation, simplicity, and based on these needs, we've built up, uh, portfolio with, with plethora of options, for, uh, you know, integrations into open source tool chains and, and also building enterprise partnerships for, uh, with, with technologies that matter to the customers. Right? So, uh, the ecosystem partners, uh, are, are, you know, apps are Juniper. Um, Okta, there are crews that offer solutions at simplify network management and monitoring of, of massive complex networks and leverage the, the inherent automation telemetry capabilities in Sonic. It comes to the open source tools. Uh, you know, these, these are tools that, you know, the broader, the, the tier two cloud of this point is the large enterprises also want, you know, based on how they're moving towards an open source based ecosystem. We have, you know, created ible modules for network automation. We have integrated into open source marketing tools like Telegraph or far and Promeus, and then we continue to, you know, scaling and expanding on easy integrations and ecosystem partners, uh, to bring the choice, flexibility, uh, to the customers where, uh, you know, they can leverage inherent software capabilities and leverage it to their application business needs. >>Rob, great to have you on the cube Sergeant Kabar, director of product management, Dell tech, Dell networking, Dell technologies, um, networking really important area. That's where the innovation is. It matters the most latency. You can't change the, the laws of physics, but you can certainly change architectures. This is kind of the new normal going on. Find final point final comment. What can people expect to see around Sonic and where this goes? What, what happens next? How do you see this evolving? >>Well, there's a, uh, you know, I think we start off a journey to an exciting, you know, evolution on a networking happening with Sonic so much. This, this technology has to offer with, you know, a lot of technical value prop and microservices, container architecture with such a diverse community around it. There's, uh, a lot of feature additions, extended use cases that are coming up with Sonic. And we, we, we actively engage in the community with lot of feature enhancements and help also helping stay the community in, in a direction that, you know, uh, brings Sonic to the wider market. So, you know, I think this is, this is great, you know, start to a fantastic journey here. And, uh, we look forward to the exciting things that are coming on the so journey. >>Awesome. Thanks for coming on. Great cube culture. We'll follow up more. I wanna track this Dell networking networking's important software operating systems. It's a system approach distributed computings back modernizing here with Dell technologies. Thanks for coming on. Appreciate it. >>Awesome. Thank you, John. >>I'm John furry with the cube here at Palo Alto, California. Thanks for watching.

>>Okay. Now we're joined by Brian Gilmore, director of IOT and emerging technologies at influx data. Welcome to the show. >>Thank you, John. Great to be >>Here. We just spent some time with Evan going through the company and the value proposition, um, with influx DB, what's the momentum. What do see this coming from? What's the value coming out of this? >>Well, I think it, we're sort of hitting a point where the technology is, is like the adoption of it is becoming mainstream. We're seeing it in all sorts of organizations, everybody from like the most well funded sort of advanced big technology companies to the smaller academics, the startups and the managing of that sort, sort of data that emits from that technology is time series and us being able to give them a, a platform, a tool that's super easy to use, easy to start. And then of course we'll grow with them is, has been key to us, sort of, you know, riding along with them is they're successful. >>Evan was mentioning that time series has been on everyone's radar and that's in the OT business for years. Now, you go back 20 13, 14, even like five years ago that convergence of physical and digital coming together, IP enabled edge. Yeah. Edge has always been kind of hyped up, but why now? Why, why is the edge so hot right now from an adoption standpoint? Is it because it's just evolution, the tech getting better? >>I think it's, it's, it's twofold. I think that, you know, there was, I would think for some people, everybody was so focused on cloud over the last probably 10 years. Mm-hmm <affirmative> that they forgot about the compute that was available at the edge. And I think, you know, those, especially in the OT and on the factory floor who weren't able to take advantage full advantage of cloud through their applications, you know, still needed to be able to leverage that compute at the edge. I think the big thing that we're seeing now, which is trusting is, is that there's like a hybrid nature to all of these applications where there is definitely some data that's generated on the edge. There's definitely done some data that's generated in the cloud. And it's the ability for a developer to sort of like tie those two systems together and work with that data in a very unified uniform way. Um, that's giving them the opportunity to build solutions that, you know, really deliver value to whatever it is they're trying to do, whether it's, you know, the, the outer reaches of outer space or whether it's optimizing the factory floor. >>Yeah. I think, I think one of the things you also mentioned genome too, dig big data is coming to the real world. And I think I, I O T has been kind of like this thing for OT and, and some use case, but now with the, with the cloud, all companies have an edge strategy now. So yeah, what's the secret sauce because now this is hot, hot product for the whole world and not just industrial, but all businesses. What's the secret sauce. >>Well, I mean, I think part of it is just that the technology is becoming more capable and that's especially on the hardware side, right? I mean, like technology compute is getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the micro controller layer with our, um, you know, our client libraries and then working hard to make our applications, especially the database as small as possible so that it can be located as close to sort of the point of origin of that data in the edge as possible is, is, is fantastic. Now you can take that. You can run that locally. You can do your local decision making. You can use influx DB as sort of an input to automation control the autonomy that people are trying to drive at the edge, but when you link it up with everything that's in the cloud, that's when you get all of the sort of cloud scale capabilities of parallel eyes, AI, and machine learning and all of that. So >>What's interesting is the open source success has been something that we've talked about a lot in the cube about how people are leveraging that you guys have users in the enterprise users at I O T market mm-hmm <affirmative>, but you got developers now. Yeah. Kind of together brought that up. How do you see that emerging? How do developers engage? What are some of, as you're seeing that developers are really getting into with influx DB what's >>Yeah. Well, I mean, I think there are the developers who are building companies, right? I mean, these are the startups and the folks that we love to work with who are building new, you know, new services, new products, things like that. And, you know, especially on the consumer side of, I T there's a lot of that, just those developers, but I think we, you gotta pay attention to those enterprise develop as well, right? There are tons of people with the, the title of engineer in, in your regular enterprise organizations. And they're there for a systems integration. They're there for, you know, looking at what they would build versus what they would buy. And a lot of them come from, you know, a strong, open source background and they, they know the communities, they know the top platforms in those spaces and, and, you know, they're excited to be able to adopt and use, you know, to optimize inside the business as compared to just building a brand new one. >>You know, it's interesting too, when Evan and I were talking about open source versus closed OT systems, mm-hmm <affirmative> so how do you support the backwards compatibility of older systems while maintaining opens dozens of data formats out there? A bunch of standards, protocols, new things are emerging, and everyone wants to have a control plane. Everyone wants to leverage the value of data. How do you guys keep track of it all? What do you guys support? >>Yeah, well, I mean, I think either through direct connection, like we have a product called Telegraph, it's unbelievable. It's open source, it's an edge agent. You can run it as close to the edge as you'd like, it speaks dozens of different protocols and its own, right. A couple of which M Q T T UA are very, very, um, applicable to these IOT use cases. But then we also, because we are sort of not only open source, but open in terms of our ability to collect data, we have a lot of partners who have built really great integrations from their own middleware, into influx DB. These are companies like cap wire and high by who are really experts in those downstream industrial protocols. I mean, that's a business, not everybody wants to be in. It requires some very specialized, very hard work and a lot of support, um, you know, and so by making those connections and building those ecosystems, we get the best of both worlds. The customers can use the platforms they need up to the point where they would be putting into our database. >>What's some of the customer testimonies that they, that share with you. Can you share some anecdotal, all kind of like, wow, that's the best thing I've ever used. That's really changed my business. Or this is a great tech that didn't helped me in these other areas. What are some of the, um, sound bites you hear from customers when they're successful? >>Yeah. I mean, I think it ranges. You've got customers who are, you know, just finally being able to do the monitoring of assets, you know, sort of at the edge in the field, we have a customer who's who has these tunnel boring machines that go deep into the earth to like drill tunnels for, for, you know, cars and, and, you know, trains and things like that. You know, they are just excited to be able to stick a database onto those tunnel, boring machines, send them in to the depths of the earth and know that when they come out, all of that telemetry at a very high frequency has been like safely stored. And then it can just very quickly and instantly connect up to their, you know, centralized database. So like just having that visibility is brand new to them. And that's super important. On the other hand, you have customers who are way far beyond the monitoring use case. >>We're, they're actually using the historical records in the time series database to, um, like I think Evan mentioned like forecast things. So for predictive maintenance, being able to pull in the telemetry from the machines, but then also all of that external enrichment data, the metadata, the temperatures, the pressures who was operating the machine, those types of things, and being able to of easily integrate with platforms like Jupyter notebooks. Yeah. Or, you know, all of those scientific computing and machine learning libraries to be able to build the models, train the models, and then they can send that information back down to influx TV to apply it and detect those anomalies, which >>Are, I think that's gonna be an, an area. I personally think that's a hot area because I think if you look at AI right now yeah. It's all about two training, the machine learning albums after the fact. So time series becomes hugely important. Yeah. Cause now you're thinking, okay, the data matters post time. Yeah. For sure. And then it gets updated the new time. Yeah. So it's like constant data cleansing data iteration, data programming. We're starting to see this new use case emerge in the data feed. Yep. >>Yeah. I mean, I think >>You >>Agree. Yeah, of course. Yeah. The, the ability to sort of handle those pipelines of data smartly, um, intelligently, and then to be able to do all of the things you need to do with that data in stream, um, before it hits your sort of central repository. And, and we make that really easy for customers like Telegraph, not only does it have sort of the inputs to connect up to all of those protocols and the ability to capture and connect up to the, to the partner data. But also it has a whole bunch of capabilities around being able to process that data, enrich it, reformat it, route it, do whatever you need. So at that point you're basically able to, you're playing your data in exactly the way you would wanna do it. You're routing it to D and you know, destinations and, and it's, it's, it's not something that really has been in the realm of possibility until this point. Yeah. >>Yeah. And when Evan was on it's great. He was a CEO. So he sees the big picture with customers. He was, he kind of put the package together that said, Hey, we got a system. We got customers, people are wanting to leverage our product. What's your PO they're sell, he's selling too as well. So you have that whole C your perspective, but he brought up this notion that there's multiple personas involved in kind of the influx DB system architect. You got developers and users. Can you talk about that? Reality as customers start to commercialize and operationalize this from a commercial standpoint, you got a relationship to the cloud. Yep. The edge is there. Yep. The edge is getting super important, but cloud brings a lot of scale to the table. So what is the relationship to the cloud? Can you share your thoughts on edge and its relationship to the cloud? Yeah. >>I mean, I think edge, you know, edge is you can think of it really as like the local information, right? So it's, it's generally like compartmentalized to a point of like, you know, a single asset or a single factory align, whatever. Um, but what people do who wanna pro they wanna be able to make the decisions there at the edge locally, um, quickly minus the latency of sort of taking that large volume of data, shipping it to the cloud and doing something with it there. So we allow, allow them to do exactly that. Then what they can do is they can actually down sample that data or they can, you know, detect like the really important metrics or the anomalies. And then they can ship that to a central database in the cloud where they can do all sorts of really interesting things with it. Like you can get that centralized view of all of your global assets. You can start to compare asset to asset, and then you can do as things like we talked about, whereas you can do predictive types of analytics or, you know, larger scale anomaly >>Detections. So in this model you have a lot of commercial operations, industrial equipment. Yep. The physical plant, physical business with virtual data cloud all coming together. What's the future for influx DB from a tech standpoint. Cause you got open. Yep. There's an ecosystem there. Yep. You have customers who want operational reliability for sure. I mean, so you got organic <laugh> >>Yeah. Yeah. I mean, I think, you know, again, we got iPhones when everybody's waiting for flying cars. Right. So I don't know. We can like absolutely perfectly predict what's coming, but I think there are some givens and I think those givens are gonna be that the world is only gonna become more hybrid. Right. And then, you know, so we are going to have much more widely distributed, you know, situations where you have data being generated in the cloud, you have data gen being generated at the edge and then there's gonna be data generated sort sort of at all points in between like physical locations as well as things that are, that are very virtual. And I think, you know, we are, we're building some technology right now. That's going to allow, um, the concept of a database to be much more fluid and flexible, sort of more aligned with what a file would be like. >>And so being able to move data to the compute for analysis or move the compute to the data for analysis, those are the types of, of solution is that we'll be bringing to the customers sort of over the next little bit. Um, but I also think we have to start thinking about like what happens when the edge is actually off the planet, right. I mean, we've got customers, you're gonna talk to two of them, uh, in the panel who are actually working with data that comes from like outside the earth. Like, you know, either in low earth orbit or, you know, all the, you sort of on the other side of the universe and, and to be able to process data like that and to do so in a way it's it's we gotta, we gotta build the fundamentals for that right now on the factory floor and in the mines and in the tunnels. Um, so that we'll be ready for that >>One. I think you bring up a good point there because one of the things that's common in the industry right now, people are talking about, this is kind of new thinking is hyper scale's always been built up full stack developers, even the old OT world that Evan was pointing out, that they built everything. Right. And the world's going into more assembly with core competency and IP and also property being the core of their apple. So faster assembly and building <affirmative>, but also integration. You got all this new stuff happening. Yeah. And that's to separate out the data complexity from the app. Yes. So space genome. Yep. Driving cars throws off massive data. >>It does. >>So is Tesla and there is the car the same as the data layer. >>I mean, yeah. It's, it's certainly a point of origin. I think the thing that we wanna do is we wanna let the developers work on the world, changing problems, the things that they're trying to solve, whether it's, you know, energy or, you know, any of the other health or, you know, other challenges that these teams are, are building against. And we'll worry about that time series data in the underlying data platforms so that they don't have to. Right. I mean, I think you talked about it, uh, you know, for them just to be able to adopt the platform quickly, integrate it with their data sources and the other pieces of their applications. It's going to allow them to bring much faster time to market on these products. It's gonna allow them to be more iterative. They're gonna be able to do more sort of testing and things like that. And ultimately will it'll accelerate the adoption and the creation of >>Technology. You mentioned earlier in, in our talk about unification of data. Yeah. How about APIs? Cuz developers love APIs in the cloud unifying APIs. How do you view view that? >>Yeah, I mean, we are APIs, that's the product itself. Like everything people like to think of it is sort of having this nice front end, but the front end is B built on our public APIs. Um, you know, and it, it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then, you know, sort of data extraction to bring it to other platforms or other applications, microservices, whatever it might be. So, I mean, it is a world of APIs right now and you know, we, we bring a very sort of useful set of them for managing the time series data. These guys are all challenged with. >>It's interesting. You and I were talking before we came on camera about how, um, data feels gonna have this kind of SRE role that DevOps had site reliability engineers, which managed a bunch of there's so much data out there now. Yeah. >>Yeah. It's like raining data for sure. And I think like that ability to like one of the best jobs on the planet is gonna be to be able to like, sort of be that data Wrangler, to be able to understand like what the data sources are, what the data formats are, how to be able to efficiently move that data from point a to point B and you know, to process it correctly so that the end users of that data aren't doing any of that sort of hard upfront preparation collection, storage work >>That's data as code. I mean, data engineering. It is, it is becoming a new discipline it for sure. And, and the democratization is the benefit. Yeah. To everyone, data science get easier. I mean, data science, but they wanna make it easy. Right. <laugh> yeah. They wanna do the analysis, right? >>Yeah. I mean, I think, you know, it's, it's a really good point. I think like we try to give our users as many ways as there could be possible to get data in and get data out. We sort of think about it as meeting them where they are. Right. So like we build, we have the sort of client libraries that allow them to just port to us, you know, directly from the applications and the languages that they're writing, but then they can also pull it out. And at that point nobody's gonna know the users, the end consumers of that data, better than those people who are building those applications. And so they're building these users and interfaces, which are making all of that data accessible for, you know, their end users inside their organization. >>Well, Brian, great segment, great insight. Thanks for sharing all, all the complexities and, and IOT that you guys help take away with APIs and, and assembly and, and all the system architectures that are changing edge is real cloud is real, absolutely mainstream enterprises. New got developer attraction too. So congratulations. >>Yeah. It's >>Great. Well, thank you. Any, any last word you wanna share >>Deal with? No, just, I mean, please, you know, if you're, if you're gonna, if you're gonna check out influx TV, download it, try out the open source contribute if you can. That's a, that's a huge thing. It's part of being the open source community. Um, you know, but definitely just, just use it. I think once people use it, they try it out. They'll understand very, very >>Quickly awesome open source with developers, enterprise and edge coming together >>All together all together. You're gonna hear more about that in the next segment, too. >>Thanks for coming on. Okay. Thanks. When we return, Dave Lon will lead a panel on edge and data influx DB. You're watching the cube, the leader and high tech enterprise coverage.

>>Welcome to this cube conversation. I'm John fury host of the cube here in Palo Alto, California. We're talking open source. We're talking about the data center. We're talking about cloud scale, bringing that software benefits all to the table, all around the network, the network operating system, and more gotta go a guest here, sir. Rob Capor director of product management, Dell networking, sir. Rob. Great to see you. Thanks for coming on. >>Thank you, John. Good to be here. Thanks for inviting me. >>You know, we were talking before we came on camera around how the networking business is changing, why hardware matters, why software's more important. And in all of this shift, that's happening in the transition to fully distributed computing, which Matt, you got the edge, you got the data center, you got the cloud all coming together. One of the common threads in all of this is open source. Okay. Open source software, next generations coming. You're seeing more and more new cool things in open source, but also in parallel with the enterprise. This is a huge kind of flash point to the next gen open source enterprise convergence with, with open source software and the communities and all and all that, all that good stuff. And you're in the middle of it. What's driving this Hmm. Source and the data center. We're seeing the levels of support like we've never seen before and specifically at the network level. >>Awesome. Yeah. So, well, to set the context, let's start by looking at the story of comput solution, right? Uh, in the nineties, the comput infrastructure was vertically integrated. Uh, there were multiple vendors each offering their own operating system, usually a version of hearings, uh, on, on the proprietary hardwares. If I wanted to run a Solas operating system, I had to run that over a Spoor and the applications were written, especially for, for that architecture. So, so this represented multiple challenges back then the customer were locked in growth and innovation developers had to recreate applications for, for different architectures and, and the interoperability was extremely difficult, but with the advent of X 86 architecture and standardized operating systems like like windows and Linux, the stack got disaggregated, which allowed for flexibility, innovation, affordability and finding expand engine. We see a similar trend happening on the networking side now where the traditional networking solutions, uh, are not flexible. >>The switch, the network operating system, the APIs are all provided by the same vendor. So if a feature is, is needed, the user has to either wait for the vendor to deliver it, or is forced to replace then time for structure. But with the of open networking and opensource networking based solutions, we see an evolution that has paved the way for the customers to unlock their data center technologies and innovate. The modern data center is, is no longer centered around protocol sax. It's about agility, flexibility, innovation, network automation, and simplicity. It's about how to make operations more agile, agile, more effective, and, and, you know, bake it into an overall infrastructure today. A large element of, uh, of, of business action behind open networking is that companies are moving towards application centricity and, and a true realization of as a service model. Right? So, so that is where Sonic comes into the picture, right? >>But it's large and diverse community around, around modular containers, architecture born in Microsoft as your environment, Sonic is, is built for automation, telemetry and scale. And the flexibility of this architecture allows you for, you know, in terms of running to applications by providing that high level of redundancy. So, so basically know Sonic kind of check marks to all the requirements of the modern data center from open flexible architectures to cloud economics. And if you have to follow a comput evolution analogy, we believe that, you know, switches is the server now in Sonic is the Linux for networking. >>It's like the Ker of networking. I mean, we'd be and reporting. We've had all the cube conversations where Sonic's been mentioned and people have been saying things like it's taking the networking world by storm. Um, and all, all that with open source kind of ties it and scales it together. Can you take a minute to explain a little bit about what it is? What is Sonic, what does it stand for? Why is it important? What does it do? What's the benefit to the customers? What are they, what what's going on around Sonic take a minute to explain what is Sonic. >>Absolutely. Yeah. So is Sonic stands for software for open talking in the cloud. It's a brain of Microsoft in, in 2016, they announced their contribution of Sonic to the open source community and, and through the networking technology to revolutionary set forward with the yet level of this aggregation by breaking the monolithic nos into multiple containers components. And, and through the use of ization, Sonic provides the, the network managers, the plug and place sensibility, the ability to run third party proprietary or open source application containers and, and perform those in-service updates with zero down time. Sonic is, is primarily designed across four main per principles. First one is the notion of control where, uh, Sonic is an open software organizations are deploying it, working on it. The network managers can decide what features they want to ship on a switch, so that there's less potential for bug and, and tailored for more of the use cases, right? >>Sonic was designed for extensibility for, uh, the developers to come and add new cable, roll those out rapidly on, on a platform. Uh it's it was designed for agility. The ability to take changes, roll those out rapidly, whether it's a bug fix or a new feature coming out, uh, which is significant. And finally Sonic was designed around this notion of open collaboration with such a diverse community around, we have Silicon vendors to ODM providers. It contribute is the more people work on it, the better and more like the software it becomes. Yeah. And, and it has >>Go ahead, continue. >>Yeah. I mean, it has evolved considerably and, and since it's inception, it's, uh, the growth is, is nurtured by an increasing set of users, uh, a vibrant open source community. Uh, and then there's a long, uh, trail of, of, of, you know, falling from, from the non-hyper killers where they like the value propers of technology and they want to adapt it for their environment. >>Yeah. And of course we love Silicon here at Silicon angle in the cube. Uh, but this is the whole new thing. Silicon advances is still software hardware matters. Dave LAN is doing a big thing called on why hardware matters with our team hardware and software together with open source really is coming back smaller, faster, cheaper. It's really good. So I want to ask you about Sonic, what types of customers mm-hmm, <affirmative> what we looking to implement this, is this more of a, a reset in the data centers? Is it a cloud scale team? Is it distributing computing? What's the new look of the customer who are implementing the like so, so, >>Well, uh, you know, it has evolved considerably since it's ion, right. It was born into a hyperscale environment and we see a big 10 happening where, uh, you know, there's a wider appeal that is across non hyperscalers who want to emulate the best practices of the hyperscalers. They, but they want to do it on their own terms. They want a feature solution that is tailored for enterprise use cases. And, and, you know, looking at this whole contain architecture, Sonic kinda fits the build well where, you know, providing a Linux, no, that can be managed by the, the same set of automation management tools. Uh, and, and, you know, these are the same teams, you know, uh, that have, you know, been acclimated to the world on the server side. Now with this all tool consolidation and consistent operations across the data center infrastructure, we, we see that Sonic brings a lot of value, uh, to these distributed application use cases, these modern data center environments, where you, you know, you have, you know, customers looking for cloud economic, multi vendor ecosystem open and flexible architectures. And in fact, you know, uh, you know, we are told by the industry analyst that there's a strong possibility that, you know, during the next three to six years, Sonic is going to become analog as to Linux, uh, now allowing the enterprises to, to sanitize on this. No, and, and, and, you know, they also predict that, uh, you know, 40% of the organizations that have, you know, large data centers or 200 plus switches will deploy Sonic in production. And the market is going to be approximately 2.5 billion by, by 2025. >>You know, we've, we've always been riffing about the network layers, always the last area to kind of get the innovation because it's so important. I mean, right. If you look at the advances of cloud and cloud scale, obviously Amazon did great work, Amazon what starts with networking lay, what they did kind of with in the cloud, but even in the enterprise, it's so locked down, it's so important. Um, and things like policy, these are concepts that have been moving up the stack. We see that, but also software's moving down the stack, right? So this notion of a network operating system kind of out is in play at the data center level, not just on the server, you're talking about like packets and observability monitoring, you know, more and more and more data coming in. So with data surging, tsunami of data, new, um, agile architectures changing in real time dynamic policy, this is what's happening. What's the role of the Dell and all this, you guys got the hardware, um, you got the servers now it's open source, it's got community. What is Dell bringing to the table? What's your role in this development and the evolution of Sonic and, and what are you guys bringing to the table? >>Absolutely. So, so we are now, uh, enterprise Sonic distribution by Dell technologies, a commercial offering for Sonic in June last year. And our, our vision has been primarily to bridge the gap between hyperscale networking and enterprise networking. Right here we are, we are combining the strengths and value proposition of Sonic and Dell technologies where the customers get an innovative, scalable opensource NA, which is hard and supported and backed by industry leader in open networking. That has been, that has been our primary play into this where enterprise Sonic by Dell, we, we cus the customers, you know, get support and deployment services. Uh, we work with the customers in building out a roadmap that is, you know, predictable, soft, and hardware roadmap for them. Uh, we, we provide at extended and validated use cases where, uh, you know, they can leverage, you know, Sonic for their, you know, specific environments, whether it's a cloud environment or the enterprise environment, uh, we've created a partner ecosystem where, uh, you know, with, with certain organizations that allow you to leverage the inherent automation, telemetry capabilities in the NAS, uh, to enhance the usability of the software, we have, uh, created an intuitive CLI framework called management framework to allow you to better consume Sonic for your employment. >>We offer support for open conflict models and then also answerable playbooks for, for network automation. So, so it's been a journey, uh, you know, we are making the solution ready for enterprise consumption is a, a big fan falling that is happening the non hyperscale awards. And, uh, we made significant contributions in, in, in the community as well. Yeah. 1 million lines plus of court, what fixes and, and helping with the documentation. So we are at the forefront of, of so journey. >>So you're saying that you, you're saying Dell for the folks watching you guys are putting the work in you're investing in opensource. >>Yeah, absolutely. I mean, we, we, we are, uh, you know, extending open source to the bottom market, you know, making it enterprise ready, uh, with, with feature enhancements and building a partner ecosystem. Uh, you know, we, we ensure that, you know, it advanced through extensive internal testing and validation for the customers. And then, uh, in order to allow the customers to, of this new technology in house, uh, you know, we, we provide virtual demos. We have, you know, hands on labs for, for customers and channel partners. We, we also help them with, with a lot of documentation and reference architecture so that, you know, it's a knowledge repository across the board that can be leveraged for the modern use cases. So, yeah, so that's been a, it's been a journey with the customers, and it's always in evolution where we, you know, get better with, with extended use cases and, and more capabilities on the portfolio. >>You know, I always, I always talk with Michael Dell at the Dell tech world every year. And sometimes we text back and forth. Uh, we kind of grew up together in the industry about the same age. Um, and we joke about the Dell early days of Dell house supply chain was really part of their advantage. And this is getting a little bit of a throwback, but look back back then it was a systems architecture. You have suppliers, you have chips, you have boards, you build PCs, you build servers. And the DNA of Dell, Dell technologies has always been around this system. And with open source and tributed computing cloud data center edge, it's a system. And we're hearing words like supply chain in software, right? So when you start to think about Sonic and network operating systems and that kind of, those kinds of systems, when you modernize it, it's still gotta enable things to enable value. So what's the enabling value that Sonic has for the modern era here in computing as new kinds of supply chains emerge, new kinds of partnerships have to evolve. And the environment under the covers is changing too. You got cloud native, you got growth of containers. I think DACA was telling us that the container market there is pushing 20 million developers. I mean, massive cloud native activity and openside growth. This is a system. >>No, absolutely. I mean, uh, you know, the modern world has changed so much from, from, you know, the proprietary infrastructure and stacks. Now, uh, we tell, you know, becoming, uh, uh, you know, more software focused now because that's a real value, uh, that you bring to the customer is now it's all about application centricity. Nobody is talking about, you know, protocol stacks that, you know, they, they want simplicity. They want ease of network management. And how do you expose all these capabilities? It's it's software, right? Sonic being open software, there's so much happening, uh, in, in the community around it. We know we provide not bond interfaces that, you know, customers can hook up into their app applications and get better at monitoring, get better at, you know, managing that entire C I CD pipeline in the infrastructure. So I think, you know, soft is, is a core in the heart of, you know, the modern data center infrastructures today. And, you know, we've been, uh, you know, uh, uh, at the forefront of this journey with, with Sonic and, uh, you know, bringing the real choice and flexibility for the >>Customers. It's certainly an exciting time if you're in the data center, you're in, in architecture, cloud architecture, you're in data engineering, a new growing field, not just data science data is code. We did a big special on that recently in the cube, but also just overall scale. And so this, these are all new factors in C CXOs are dealing with obviously securities playing a big part of, and the role of data and also application developers all in play. The partner ecosystem becomes a really important part of, so I have to ask you, can you expand a little bit more on your comment earlier about the partner ecosystem and the importance of ways in providing a best in class service, because you're relying on others in open source, but you're commercializing Sonic with others. So there's a, the ecosystem play here. What's, what's talk more about that and, and the importance of it, >>Right, right. Yes, sir. As I mentioned earlier, right, the modern data center is no longer centered around protocol Sachs. It it's about agility, flexibility, choice, uh, network automation, simplicity. And based on these needs, we built up portfolio with, with plethora of options for, uh, you know, into open source tool chains and, and also building enterprise partnerships for, uh, with, with technologies that matter to the customers. Right? So, uh, the ecosystem partners, uh, are, are, you know, abstract, Juniper, um, Okta, and are crew that offer solutions at simplify network management and monitoring of, of massive complex networks and leverage the, the inherent automation telemetry capabilities in Sonic. It comes to the open source tools. Uh, you know, these, these are tools that, you know, the product, the, the tier two cloud at this point is the large enterprises also want based on how they're moving towards an open source based ecosystem. So we have, you know, created ible modules for network automation. We have integrated into opensource modeling tools like Telegraph or FA and pros. And then we are continue to, you know, scaling and expanding on these integrations and ecosystem partners, uh, to bring that choice, flexibility, uh, to the customers where, uh, you know, they can leverage the, the inherent software capabilities and leverage it to their application business needs. >>Rob, great to have you on the cube Sergeant Kalo, director of product management, Dell tech, Dell networking, Dell technologies, um, networking really important area. That's where the innovation is. It matters the most latency. You can't change their, the laws of physics, but you can certainly change architectures. This is kind of the new normal going on final point final comment. What can people expect to see around Sonic and where this goes? What, what happens next? How do you see this evolving? >>Well, there's a, uh, you know, I think we start of our journey to an exciting, you know, evolution on and networking happening with Sonic. There's so much this, this has to offer with, you know, a lot of technical value prop around microservices, container architecture with such a diverse community around it. There's, uh, a lot of feature addition, extended use cases that are coming up with Sonic. And we, we, we actively engage in the community with lot of feature enhancements and help also helping stay the com community in, in a direction that, you know, uh, bring Sonic to the wider market. So, uh, you know, I think this is, this is great, you know, start to a fantastic journey here. And, uh, we look forward to the exciting things that are coming on the Sonic journey. >>Awesome. Thanks for coming on. Great cube culture. We'll follow up more. I wanna track this Dell networking, networking it's important software operating systems. It's a system approach distributed computings back modernizing here with Dell technologies. Thanks for coming on. Appreciate it. >>Awesome. Thank you, John. >>I'm John furry with the cube here in Palo Alto, California. Thanks for watching.

(energetic music) >> Narrator: Live from San Juan, Puerto Rico, it's theCUBE, covering CoinAgenda. Brought to you by SiliconANGLE. >> Hello and welcome to our exclusive Puerto Rico coverage of CoinAgenda, I'm John Furrier with theCUBE. We're here covering all the action at Restart, we've got a ton of events, all the thoughts leaders, influencers, decision makers, you name it, in the industry, pioneers making it happen. My next guest is Toni Lane, who's the founder of CoinGraph. She's a true influencer with a lot of impact in this market. Welcome to theCUBE. >> Thank you for having me. >> We're so glad to have you on. Like the little joke at the beginning about being an influencer, you actually are an influencer. You've done such great work in the industry, well regarded in the community. You have publication and you do a lot of great content. Thanks for coming on. >> Oh, for sure, yeah, thanks for having me. >> So being the influencer, what does that mean these days? Because we were just talking before the camera on, we came on camera, influence changes. You can't be an influencer all the time. You can be super or expert at something, but your expertise could change, you move to a new topic, learn something. And there's a lot of people in the digital marketing world saying I'm an influencer. It's kind of half baked, and really, I mean, it's not about the followers, your thoughts? >> Well, I mean, most of those followers are purchased. So there's a big difference between being an influencer and having actual influence. Because if you're, you know, if you have a million followers on Twitter, that's nice. How much engagement do you have? And that's actually what you look for, it's like when you look at someone's, whether it's, you know, social media, their digital presence, it's not about followers, it's all about engagement. You know, I don't even have that many, like I don't spend a lot of time doing that, at least I haven't so far, it's something I'm getting more into. But I have people that are really engaged, and so I look at people that have 15 million followers and I'm like, you have just as many likes on your things as I do, right. Because these people aren't real people. And it's less about, having influence in general is in many ways about having authenticity. And so influence is your ability to get something done. Being an influencer is your ability to hold someone's attention for a fragment of time. But being an influencer is not the same as having influence. >> And this community here, certainly, with decentralization here, you get the decentralized applications coming up blockchain, you got ICOs booming. It's all about the network effect, if you look at network effect, that is a new concept that ad technology does not know because you can't cookie a network connection. The only way to measure someone's true network is through malware today, and that's not good, no one does that. Well, they do, they're-- >> Toni: Unfortunately, yeah. >> But you can't you do it at business price, not sustainable. So the point is, it's not about how many followers you have. It could be that one follower, maybe 200 or 2,000, that opens up more. This is the network effect. This is what this community is all about, so I want to get your thoughts on this community's vibe. A lot of mission-driven, impact-oriented, merged with tech. So you have a fusion of technology, artistry, craftsmanship and mission-driven societal change in one melting pot. This is your wheelhouse. Share your thoughts on this. >> Well, so all of the different digital currencies have different value systems and they attract a different breed. And there are different incentives for each of these based on how the technology is designed, each protocol, right? So if you look at Bitcoin, in Bitcoin, the incentives are, you know, mining is done by computers, so your only incentive is like having social influence? And this is, I think, why we've seen a lot of kind of I would call it a scarcity mentality in terms of the way, why we see even more trolls in Bitcoin is because social influence is a huge way that success is measured, because as a developer, you can't have, you can't achieve a level of status any other way as a developer or as an influencer in Bitcoin, because the Bitcoin network is so far removed from that. And that's actually a perverse incentive in and of itself, and not only that, but early days in Bitcoin, there were major organizations who would hire people to man 100 Reddit and Twitter accounts and go into the Bitcoin community and actually fragment the public opinion using a technique grassroots psychological insurgency. So buying Reddit accounts that had been active for the last 10 years and going through and, you know, essentially just stabbing at people and creating, even having conversations with themselves to empower the voice of trolls. And what happens is you start bringing out what we call, actually, what the former Assad called, after Henry Kissinger, there was a big move that happened in the Middle East, where Kissinger realized the Middle East was becoming too powerful, and he saw it as a threat to American democracy. And so Kissinger organized a deal that fragmented the Middle East. And Assad said to Kissinger that his actions would be, he played Assad, basically. And Assad said to Kissinger, "Your actions "will bring up demons hidden underneath "the surface of the Arab world." And that strategy is actually something used in the Bitcoin community to leverage the incentives that are created, which is why we have seen previously so much, even from our industry leaders, so much fragmentation and so much tension. But the network is the most secure and the least corruptible, hands down, fundamentally. It's real cryptography. >> But let's talk about that, I love this conversation, because with networks, you have the concept of self-heal, and this gets nerdy on the packets, how packets move, at that level, self-healing networks has been a paradigm that's been proven. So that's out there, that's got to go to a societal level. The other one is the incentive system, if you have an immune system, if you will, in a network, this is a cultural thing. So actions, the Reddit's obvious, right. Weaponizing content has been well-documented, it's coming now mainstream, people are getting it that this outcome was actually manufactured by bad behavior. Now, I argue that there's an exact opposite effect. You can actually weaponize for good, 'cause everything has a polar opposite. So what is your view on that, because this is something that we've been teasing out for the first time. How do you weaponize content for good, (mumbles) not the right word, but look for the opposite value? >> Right, yeah, I mean, it is in so many ways, right. So I think it's about, there's a professor at Stanford whose name is BJ Fogg, and he's a behavioral researcher and he talks about essentially, you know, he writes a lot about habits. But something that's even more interesting about his understanding of propaganda is I studied a lot of Edward Bernays, he's responsible, he created the theory of propaganda, right. And he's the nephew of Sigmund Freud, he's responsible for essentially every consumptive theory in like leading up to the last century, he's actually, I would say he's responsible for the state of advertising and the economy today, almost really single handedly. And what's fascinating about this theory is that you can use propaganda to get women to smoke by unearthing what it is unconsciously in men that makes them not want to smoke. You can also use propaganda to get people to invest in health and wellness. You can also use propaganda to get people to stop their bad habits. So it's understanding that a technique works in a cognitive capacity in a way that affects a large amount of people. And it's really about the intention behind why a person who has influence, as we were saying, is leveraging that relationship. So I would say it's more about-- >> So we have to reimagine influence. Because the signalings that are igniting the cognitive brain can be tweaked. So that's what you're getting at here, right, so that's what we have to do. >> And it's an illusion from almost every angle. It's even the idea that, in the United States, the level of influence the president has and who's running, you know, and who, yeah, and who's at the wheel, right. So it's, we live in a world that is built on manufactured consent, and manufactured consent is enabled through thinkers like Bernays and through what I call the illusion of things like our former construct of even American democracy. That these things we've imagined to be so, the foundation and the structure for the way that we live. All of those things have become so far removed from their theory that they're no longer serving the principles under which they were founded, and that disconnect is actually a huge, it's a gap, it's an inertia gap for exploitation or it's an inertia gap for growth, and usually what happens is you have the exploitation first. Someone says oh, here's a big gap of information asymmetry, so I'm going to exploit the information asymmetry. And then once people start realizing that that information asymmetry is being exploited, you experience a huge inversion of that and you have enough kind of, you have enough inertia behind that slingshot to launch it into something totally different. >> Yeah, this is a great concept, I interviewed the founder of the Halcyon HAL in Washington, DC, and she's an amazing woman. And she had a great conscious about this, and what she postulated was, bubbles that burst, exploitation's always, we've seen it in all trends. The underbelly, 'cause it's motivated, no dogma. They don't care about structural incentives, they just want to make cash. But she had an interesting theory, she was talking about you can let the air out of the bubble with community and data. So all the societal entrepreneurship activities now that are mission-driven, now getting back to mission-driven is interesting. There might be a way to actually avoid the pop. Because, depending upon what the backlash might be on the exploitation side, as we saw in the dotcom bubble, you can actually let the air out a little bit through things like data. I mean, how do you see, in your mind, just thinking out loud, how do you see that playing out, because we have community now. We have access to open data. Blockchain is all about immutability. It's all about power to the user's data. This is a mega trend. Your thoughts? >> So interdependence is huge in the blockchain community, and that's actually to touch back on the incentives in Bitcoin, I think that that's actually one of Bitcoin's, it's not that it's a wrong or a right, it just is, right, like sidechains will be launched eventually, but the idea that Ethereum created something that was adaptable and empowered people to be creative, and yet they're creating incentives for her people to launch products that are, I believe, 'causing, in some ways, could cause some serious harm to the ecosystem once the air is let out of that bubble. >> John: The data. >> The data, so data, yes yes yes. >> How do you let the air out of the bubble, because the pop will be massively implosion, it'll leave a crater. >> So data is a non-scarce resource. This is actually how I describe blockchain to people. And this is actually, I think, one of the, the challenge, if you want to look at it from the perspective of challenge, and then I'll talk about for the benefit, just between Bitcoin and Ethereum, there are obviously other blockchains, EOS is like coming out super soon, Holochain. There are tons, Steem has actually its own infrastructure, tons of other blockchains to speak about. But just to take these two main blockchains, which are not competitors. In Bitcoin, you have, it's really cryptography. Cryptography is not about, you know, like let's do some rapid prototyping, cryptography is about let's like put a lot of thought into this thing and have mathematical certainty that this is not exploitatable. And Ethereum is just kind of like, well, let's build a framework and then let people play as much as they can. And so there are challenges and benefits to both of those models, the challenge of Ethereum being that you've let all of this capital into the industry which is not actually, 46% of ICOs have already failed. Already failed. And then if you look at Bitcoin-- >> And a person with your industry (mumbles) at 1,200, so it's a 50% discount. >> Oh yeah, oh yeah. And then if you do the same thing and you're looking at the Bitcoin blockchain, we've seen that the capacity for innovation, Bitcoin could have done what, they could've been the first to market for what Ethereum is doing. And they chose a different route, and I think there are some pros and cons to both of those things, but I think there is an intentionality behind why the world played out in the way that it did. And I think it's the right strategy for both products. So the way I describe applications using blockchain technology to people and what I call the future of an infinite economy is that, if you think about why are Facebook and Google these multi billion dollars companies, it's really simple. It's because what do they own, right? The data, the data. And they're some of the last companies that are still stewarding these things in a way that is taking vast amount of aggregated ownership over an asset that people are generating every day that's extremely valuable to companies in the private sector. So the way that I describe blockchain is that, if we being to own our self-sovereign identity, then when we're owning our data, that's the foundation for universal basic income. If we take a non-scarce resource like data that's being generated every day, not just from us, right, but the data in the health of the ocean, right. The stewardship of the ocean, the health of the fish, actually saying okay, fish are thriving in this area, and so there's a healthy ecosystem, and so this coin is trading higher because we're stewarding this area of the ocean so we don't overfish. The quality of the air so that, when we're actually de-polluting the air collectively, everyone around us is creating and generating data to say we're making the air better. The air, actually, the health of our bodies, of our Earth, of our minds, of our planet, of even the health of our innovation. Right, what are the incentives behind our innovation, those are all forms of data. And that's a non-scarce resource, so if we take all of these different applications and make many different blockchains. Which I fundamentally believe that there's a powerful theory in having blockchains that are economically scarce, because I believe you're going to empower more diverse spectrums and also have a level of difficulty in creating the coin. You're going to have more innovation. And so-- >> Well, this is a key area. I mean, this is super important. Well, I mean, you step back for a second, you zoom out, you say okay, we have data, data's super valuable, if you take it to the individual's levels, which has not been, quite frankly, the individual's been exploited. Facebooks of the world, these siloed platforms, have been using the data for advertising. That's just what everyone knows, but there's other examples. The point is, when you put the data in the hands of the users, combine that with cloud computing and the Internet of Things when you can have an edge of the network high powered computer, the use cases have never been pushed before. The envelope that we're pushing now has never been in this configuration. You could never have a decentralized network, immutable, storing users' data, you've never had the ability to write the kind of software you can today, you've never had cloud computing, you've never had compute at the edge, which is where the users live, they are the edge. You have the ability where the user's role can enable a new kind of collective intelligence. This is like mind blowing. So I mean, just how would you explain that to a common person? I mean, 'cause this is the challenge, 'cause collective intelligence has been well documented in data science. User generated content is kind of the beginning of what we see in user wearables. But if you can control the data streaming into the network, with all the self-healing and all the geek stuff we're talking about, it's going to change structural things. How do you explain that to a normal person? >> You don't, you don't, right. So you show them. Because I can sit here all day and I can talk to you about, you know, I could talk to you about all of these things, but at the end of the day, with normal people, it's not something you want to explain. You want to show them, because with my, actually, my grandma gets Bitcoin. My grandma hit me up in like 2012 and she was like, "Do you know what that Bitcoin thing is?" I'm like, "Mimi," I'm like, "How do you know "what Bitcoin is, Mimi?" And she's just like, "I don't know, I read." You know, I was like, "This is, so what are you reading? "Like, are you hanging out on like libertarian forums, "like what's up?" And so-- >> What's going on in the club there, I mean, are they playing-- >> Yeah, but she is a really unique lady. So I would say that, for most people, they are not going to, when you explain things to people-- >> What would you show them, I mean, what's an example? >> The way that, so when I was, so I got into Bitcoin in 2011, and the way that I would explain Bitcoin to people is I would just send it to them. I would be like, "Here's Bitcoin, like take this Bitcoin, "here's some Bitcoin for you." And that was, people got it, because they were like, I have five dollars now in my hands that was not there. And this person just sent it to me. And for some people even still, you know, to be honest, even then, I remember how much energy it took for me to do that. Everywhere I go, I'd be like, in cabs, I'd be checking out grocery stores and I would try, I would essentially pitch Bitcoin to every person that I met. >> John: You were evangelizing a lot of it. >> It took so much energy though, and even after that, there was a period-- >> It was hard for people to receive it, they would have to do what at that time? Think about what the process was back then. >> Oh yeah. There were very few people who, even after doing that, really got it. But you know what happened. This is so much perspective for me, I remember doing that in 2013 and I remember, in 2018, actually, I think it was the end of 2017. I went to a gas station, it's the only gas station in San Francisco with a Bitcoin ATM. And I was like, I need to get some cash and I'm running on Bitcoin. >> John: You guys want a mountain view now. >> Yeah, yeah. And so I go in and these guys, I'm like frustrated, I'm like oh, the ATM is like the worst user experience ever, I'm like (groans). That's literally, I'm like, it's just, it was like eyes rolling in the back of my head, like just so frustrated because I'm a super privacy freak. And so it was just a super complex process, but the guys that, the guy's (mumbles) he looks at me and he goes, "Yo." And I was like, "What's up, man?" And he goes, "Are you trying to buy some Bitcoin?" I was like, "I'm trying to sell some Bitcoin right now." (John laughs) >> You're dispensing it, they're like yeah. >> Yeah, he's like, "Oh, word." And he's like, "How much are you trying to sell?" And I'm like, "I don't know, like 2K." And so he goes, "Aight." And he's like, "Let me hit up my friends," he literally calls three of his friends who come down and they just like, they're like, "Do you want to sell more?" They're like, all they just peer to peer. It's like we bypass the ATM and it was actually a peer to peer exchange. And I didn't have to explain anything. You know what made people get it? You showed them the money, you showed them the money. And sometimes people don't, you can explain these concepts that are world-changing, super high level or whatever. People are not actually going to get it until it's useful to them. And that's why a user interface is so important. Like, if you even look at the Internet. Who made the money on the Internet, right, it was the people who understood how to own the user interface. >> I had a conversation with Fred Kruger from WorkCoin, he's been around the block for a long time, great guy. We were riffing on the old days. But we talked about the killer app for the mini computer and the mainframe, the mini computer and then the PC, it was email, for 20 years, the killer app was email. We were like, what's the killer app for blockchain? It's money, the killer app is money. And it's going to be 50 year killer app. Now, the marketplace is certainly maybe tier two killer app, but the killer app is money. >> For sure, that's amazing. >> That's the killer app. Okay, so we're talking about money, let's talk about wallets and whatnot. There's a lot of people that I know personally that had been, wallets had been hacked. Double authentication (mumbles) news articles on this, but even early on, you got to protect yourself. It's something that you're an advocate of, I know recently, you've been sharing some stuff on Telegram. Share your thoughts on newbies coming in, be careful. Your wallet can be hacked, and you got to take care of yourself online. Is there a best practice, can you share some color commentary on when you get into the system, when you get Bitcoin or crypto, what are some of the best practices? >> It's not even, I think you need to remember a key principle of cryptography when you're dealing with digital currency, which was like don't really trust anything unless you call someone, you have like first hand verification from a person that you trust. Because these things are, I mean, I've had, literally last week, I had seven friends contact me, actually more than that once I posted about it, and they were like, "Is this you?" And I was like what, like people would literally just go online, they would scrape my Facebook photo, they'd go on Telegram and they would make, my name is @ToniLaneC, T-O-N-I-L-A-N-E-C, and so is my Twitter, and people would scrape my photos from my Twitter or my Telegram or my Facebook and they would create fake accounts. And they would start messaging people and say "Hey, like "what's up, how are you, that's cool, great, awesome. "So like, I need like 20 BTC for a loan. "Can you help me?" And all my friends were like, "I was just talking to you, is this you?" And I'm like no. And so I think that there's, the other thing you have to, it's not just security in terms of, and this is actually a problem Blockchain has to solve, right. It's not just security in terms of protecting your wallet and, you know, getting like a Ledger or a Trezor and making sure that you're keeping things like in cold storage, that you're going, there are so many, keeping your money in a hard wallet, not keeping your private keys on your computer, like keeping everything, storing your passwords in multiple places that you know are safe. Both handwritten, like in lock boxes, putting it in your safe deposit box or, you know, there are so many different ways that we can get into like the complexities of protecting yourself and security. Not using centralized cell networks is one of the big ways that I do this. Because if you are using two factor-- >> John: What's a centralized cell network? >> AT&T, Verizon, T-Mobile. Because you are putting yourself in a situation where, if you're using a centralized system, those centralized systems are really easily exploitable. I know because my mom, when I was a kid one time, she put a password on my account so I couldn't buy games. I was not happy about it, it was my money that I was using, it was my money I was using to buy games, she was like, "You should just spend your money on better things." And so I remember going in when I was a kid, and I was like, this is my money, I totally want to buy this upgrade on this game. And so I went in and I essentially figured out how to hack into my own phone to be able to use my own money to buy the games that I wanted to buy-- >> Highly motivated learning opportunity there (laughs). >> But I realized that, in the same way we were talking about things that can be used for good can be used for bad, in the same way that someone can do something like that, you can also say, well, I'm in a call and say that I'm this person and take their phone and then get their two factor auth. So I don't use centralized cell networks, I don't use cell networks at all. >> John: What do you use? >> So, I mean, I have different kinds of like strategies or different things that mostly-- >> You might not want to say it here, okay, all right. >> Yeah yeah yeah, they're different, I'm happy to talk about those privately. The way that I've kind of handled that situation, and then the other thing that I would say is like, we really need hardcore reputation systems in our industry and for the world. And not social reputation systems like what is happening in China right now, where you can have someone leave you, like let's say I get into an Uber and I'm 30 seconds late. I can end up in a situation where I'm like not able to be admitted into a hospital or I'm not able to take a public train. Because someone rates me lower on this reputation system, I think that's a huge human rights issue. >> John: Yeah, that's a huge problem. >> And so not reputation systems like this, but reputations like the one we're working on at CULTU.RE that are really based more on the idea of restoration and humanization, rather than continued social exploitation to create some kind of collective norm, I think that kind of model is, it's not only a-- >> Well, the network should reject that by-- >> Toni: Exactly, exactly. >> All right, so let's talk about digital nations, we have China, so there's some bad behavior going on there. I mean, some will argue that there's really no R&D over there, and now they're trying to export the R&D that they stole into other countries, again, that's my personal rant. But the innovation there is clear, we chat and other things are happening. They finally turned the corner where they're driving a lot of, you know, mainly because of the mobile. But there's other nations out there that are kind of left behind. The UK just signed this week with Coinbase a pretty instrumental landmark licensing deal, which is a signal, 'cause I know Estonia, Armenia, you name every country wants to, Bahrain's got, you know, Dubai envy. So I mean, every country wants to be the crypto country. Every country wants to be the smart cities digital nation. I know this is something that you liked, and you and I were talking about 'cause we both are interested in. Your reaction, your thoughts on where that's going, I see, it's a good sign. What are the thresholds there, what are some of the keys things that they need to do to be a real digital nation? >> Well, I think it's less about digital nations in terms of like a nation is a series of borders, and more about first nations that we are, this is what we work on at CULTU.RE, that we are actually a nation of people and a lot of those nations have overlap and we should be able to participate in many different nations who have many different economies that are all really cooperating interdependently to create the best possible life for all human good, rather than just saying like I care about me and mine, because that strategy, the way government works now, it's a closed network with low trust that is extremely inefficient in management of resources. And the only way you can really-- >> That's the opposite, by the way, of what this movement's about. >> Yeah, exactly. And the only way you can have influence in government is to go in government and to work through government. All right. So it's the idea that, look at how much food we waste in the United States. If we took the food we wasted in the United States and repurposed it, we could literally cure world hunger. That is how bad it has gotten, right. And there are people starving in the US. There are people on food stamps in the US. >> Well, I mean, every institution, education, healthcare, you name it, it's all, you know, FUBAR, big time. >> Yeah, but we're throwing away tons of lettuce and all of this different kinds of produce because it like looks funky. Like this peach looks a little too much like a bottom. So we're like not able to sell it. >> Or lettuce got a little brown on it, throw the whole thing away. >> Yes, exactly, exactly, and that waste is unacceptable. So what we need to move toward is a model of open networks of governance where we have peer to peer distribution of finance and of resources in a way that allows people to aggregate around the marketplaces that are actually benefiting the way that they believe the world should work. So it's about creating a collective strategy of collective non-violence and eliminating harm, so obviously, you know, having a society that has enough proper incentives so that people are well off and that people are provided for, and I think blockchain will-- >> I noticed you're wearing a United Nations pin. >> Woo-hoo, yeah. And blockchain, I think, will also create this. >> John: I have one too. >> Let's up top. (slap) Yeah, I think blockchain will also help create universal basic income, but in addition to that, it's the idea that, if I'm living next door, I'll give two examples. So one is about the legality of the way that we contribute to the society. So let's say I have a next door neighbor. And let's say that this next door neighbor and I feel literally, we totally get along on everything, there's just one issue we feel we're like, I totally disagree with this, I totally disagree. And that issue is the use of, and I hope this isn't controversial to say, but anyway. So the use of medical marijuana, right. And it shouldn't be, because we can have two different opinions and the world can still work and that's the point. >> Well, in California, it's now legal to own marijuana. >> Yeah, for sure, it's legal here as well. So it's the idea that, if I, so let's say I'm a woman who, you know, I have someone in my life who was injured by a driver who was driving under the influence of marijuana. And so that's all I know about marijuana because I don't really do drugs, I've never been around drugs. So when I hear that word, I immediately think about the person in my life who was harmed because of, yes, and so immediately triggered, and I'm like, I don't want to support anything, I don't want to support anything to do with marijuana, I think marijuana is like the Devil's lettuce. And I have no interest in supporting marijuana. She never has to support marijuana, she doesn't have to. But her next door neighbor is a veteran with Parkinson's disease, her, me, whatever, is a veteran with Parkinson's disease, okay. And the only way that this man can move is, he's literally shaking, but when he smokes medical marijuana, he's actually able to, you watch and literally 30-45 minutes, he's upright, he looks like a normal healthy man. And so he says, "I believe that every, "after I fought in this, I believe every person "should have access to medical marijuana, "because this is the only way I'm able "to even operate my life." >> The different context. >> And I'm so, yes, exactly. And so what culture is really about is about understanding each other's context, that's even how reputation works. It's contextual awareness that provides greater understanding of who we are as individuals and the way we work together to make society work. So maybe they can mutually agree that he is not going to smoke while he's driving and he can pay to support everyone to have access who needs access to medical marijuana. >> Or he could finance Uber rides for them. You know, or whatever, I mean, these are mechanisms. >> Yes, yes, but it's the, yes, exactly, exactly. It's the idea that we are all, we're coming together to share context is a way that's not aggressive and not accusatory, so two people can believe two totally different things and still develop enough mutual respect to live together peacefully in a society. >> You know, the other too I'm riffing on that is that now KYC is a concept (mumbles) kicked around here, know your customer. I've been riffing on the notion of KYC for know your neighbor. And what we're seeing in these communities, even the analog world, people don't know who their neighbors are. Like, they don't actually even like care about them. >> Toni: For sure. >> You know, maybe I grew up in, you know, a different culture where, you know, everyone played freely, the parents were on the porch having their cocktail or socializing and watching the kids from the porch play on the lawn. Now I call that Snapchat, right. So I can see my kids Snapchat, so I'm not involved, but I have peripheral view. >> Toni: For sure. >> But we took care of each other. That doesn't happen much anymore, and I think one of the things that's interesting in some of these community dynamics that's been successful is this empathy about respect. They kind of get to know people in a non-judgmental way. And I think that is something that you see in some of these fragmented communities, where it's just like, if they just did things a little bit different. Do you agree, I see you're shaking your head, your thoughts on this? This super interesting social science thing that's, now you can measure it with digital or you can measure that kind of-- >> We can incentivize it. We can incentivize it. And that's the difference, measurement is one thing. Incentive is a behavior changer. Incentive is a behavior changer. And that is what we actually have to do in the way we think about the foundation of these systems, is it's not incentivizing competitive marketplaces that are like my way of thinking about this is right and your way of thinking about this thing is wrong, and like ah, it's not about that. At the end of the day like, I think we forget or misquote so much of, so many of the great thinkers of the last generation, like if you think about Darwin. What does everyone know about Darwin, right, it's like survival of the fittest. It's not what Darwin said, okay. It's misquoted and it's used, it's like one of those things where people who want to exploit-- >> It's a meme, basically. >> Yeah, people who want to exploit someone else's knowledge for their own ends will use that to, in some way, uplift the kind of like strategy of, you know, incentives of the time. What Darwin actually said was that human beings with the highest capacity for sympathy, qualities we now identify as altruism, compassion, empathy, reciprocity, will be the most likely to survive during hardship. Fundamentally, I mean, look at the state of the world today. It doesn't look good, it's like, you look at the way people interact with each other, it's like a virus that's attacking itself in an ecosystem that is our planet Earth, and we need to be, you know what is the antibody, our own sense of consideration for our fellow man. That is the antibody to violence. And so we can incentivize this, and we're going to have to because we're going to, AI, automation, these will fundamentally transform the way we think about jobs in a way that will liberate us like we've never known before. And once given the freedom, I think that we'll see the world start to change. >> Toni, I really appreciate you spending the time in this thought leadership conversation, riffing back and forth. Feels great and it's a great productive conversation. I got to ask you, how did you get there? I mean, who are you? I mean, you're amazing. Like, how did you get here, you obviously, Coin Telegraph's one of the projects you're running, great content. You're wearing the UN pin, I'm aligning with that. Got a great perspective. What's your story? Where did you come from originally, I mean... How did you get here? >> I think, you know, I don't know. I'm really connected to Saturn, I don't know where my home planet is. >> Which spaceship did you come in on? No, I mean, seriously, what's your background? How did you weave into this? 'Cause you have a holistic view on things, it's impressive. But you also can get down and dirty on the tech, and you have a good, strong network. Did you kind of back into this by accident on purpose, or was it something that you studied? What's the evolution that you have? >> Yeah, you know. I studied performance art and I was an artist all of my life. And I had a really big existential crisis, because I realized, as I was looking around, that technology was replacing every form. I remember the first time I watched an AI generate, this was maybe in like, I don't remember how, this was a long time ago, but I was essentially watching, before like the deep dream stuff, maybe like 2009 or 10. And I remember watching computers generate art. And I just was like, I was like mic drop, I was like anything that could ever be created can and will be created by computers, because these are, you are looking at this data, you can scan every art piece in the world and create an amalgamation of this in a way that extends so far beyond team and capacity that the form that we have used to express artistic integrity, all forms will, in some way, become obsolete as a form of creative expression. And I had this huge existential crisis as a performer, realizing that the value of my work was essentially, like, how long would the value of my work live on if no one is, I am not alive to continue singing the song. You don't remember the people who played Carmen, you remember Bizet who wrote the opera, you remember Carmen the character, but the life of the performer is like that of a butterfly. It's like you emerge from the cocoon, you fly around the world beautifully for a very short amount of time. And then you just, you know, stardust again. And so I had this huge existential moment, and it was a really big awakening call. It was as though the gravity of the universe came into the entire dimension of my being and said these, what you have learned has given you a skill, but this is not your path. So I went okay, I just need some time to like process that and so, 'cause this is my entire life, it's the only thing I ever imagined I would ever do. And so I ended up spending three months in silence meditating. And people are like whoa, like how did you do that? And I don't think people, I don't know, not that people don't understand, but I'm not certain that a lot of people have the level of this kind of existential moment that I experienced. And I couldn't have done anything else, I really just needed to take that time to process that I was actually reformulating every construct at the foundation of my own reality. And that was going to take, that's not something you just do overnight, right, like some people can do it more fluidly, but this was a real shift, a conscious shift. And so I asked myself three questions in that meditation, it was what is my purpose, what is the paradigm shift and where is my love. And so I just meditated on these three questions and started to, I don't know how deeply you've studied lucid dreaming or out of body experiences, but that's another, a conversation we can get into in another time, that was my area of study during that period. And so I ended up leaving the three months in silence and I just kind of, I started following my intuition. So I would just, essentially, sometimes I'd walk into a library and I would just shut my eyes and I would just walk around and I would touch books. And I would just feel what they felt like to me, like the density of their knowledge. And I would just feel something that I felt called to, and I would just pull it out of the shelf and just read it. And I don't know how to explain it-- >> (mumbles) Energy, basically-- >> I was guided, I was guided to this. This was in 2011. And so what I started getting into was propaganda theory, the dissolution of Aristotelian politics as an idea of citizen and state when we're really all consumers in a Keynesian economy structured by Edward Bernays, the inventor of propaganda, who essentially based our entire attitude of economic health on, you know, a dissolving human well being. Like, the evolution of our economic well being and our human well being were fundamentally at odds, and not only was that system non-sustainable, but it was a complete illusion. At every touch, point and turn, that the systems we lived in were illusions. And so is all of the world, right, like this whole world is an illusion, but these illusions in particular have some serious implications in terms of people who don't have the capacity, or not the capacity, everyone has the capacity, but who have not explored that deeply, right, who haven't gone that deep with themselves. >> And one of those books was like a tech book or was like-- >> It was just multiple, no, it was multiple books. And it's not that I would even read all of the books all of the way through. Sometimes I would just pick up a book and I would just open it to a certain page and I would read like a passage or a couple pages, and I'd just feel like that's all I need to read out of that book. It's, you just tune into it. >> When was your first trade on Bitcoin, first buy, 2011? >> You want to know something nuts? People always, people are like, "When did you first buy Bitcoin?" I was not, I didn't. So after I started, once you know, all this knowledge came to me, I just started talking about it, I was like, I've been given some wisdom, I just have to share it. So I started going out into the world and finding podiums and sharing. And that was when someone put a USB full of Bitcoin into my hands. I very rarely, I don't necessarily buy, I've just been gifted a lot. >> Good gifts. >> Toni: They've been great gifts, yeah. >> And then when did you start Coin Telegraph, when did that come online? >> So that was in 2013. I joined, the property had been operable for I think like three or four months. And some guys called me and they said, "We're just really impressed with you "and we want to work with you." And I said, "Well, that's nice," I was like, "But you don't have a business, right?" And they were like, "What do you mean?" And I was like, "Well, you have a blog, right?" And so I went in and I said, essentially like, here's, to scale the property, I was like, "Here's a plan for the next three years. "If we really want to get this property to where "it needs to be." I'm like, "Here are the programs that we need "to institute, here's like this entire, "countries we can be operable in "and then other acquisitions of other properties." I essentially went in and said like, "Here's the business model and the plan at scale," and they were just like, I think they were a little like, the first call that we had, I think they were just like, "We just called you to," it was a bold move, like, "We just called you to offer you something, "and you countered our offer by saying "we don't have a business?" It was one of those things, but they-- >> Well, it was the labor of love for them, right, I mean-- >> Well, for all of us, yeah, for all of us. >> When all you do is you're blogging, you're just sharing. And then you start thinking about, you know, how to grow, and you got to nurture it, you need cash. >> Yes, and so I essentially came in and then started, I was both editor in chief and CEO and co-founder of the property who helped bring in a lot of the network, build the reputation for the brand, create a scaling strategy. A lot of mergers and acquisitions, a lot of franchises and-- >> How many properties did you buy roughly, handful, six, less than six? >> So I would also say that-- >> Little blogs and kind of (mumbles) them together, bring people together, was that the thinking? >> Yeah, you know, what's interesting is media from all shapes and sizes, 15 to 20 offices in 25 different countries. I always say this when I talk about this, a very important lesson that I learned. How do you manage a team of 40 anarchists? You don't, you don't, that's the answer, you don't, you don't even like, you're like oh. I remember when I was like, "We're a team!" And someone was like, "No, we're not, "I don't believe in teams, I work for myself "and I don't need," I was like oh, wow. I was like oh-- >> John: The power of we, no. >> I was just like, all right, but it was a good learning experience, because I was like well, this is the way, these are your needs. So if that's your, I was like, well, let's embrace that, let's embrace the idea-- >> But that's the culture, you can't change it. >> And let's create the economy around that, let's actually do direct incentive for it, if you think that you're, if you want to be in this on your own, then let's say okay, we're going to make this fully free market economics and we're going to have a matter of consensus on whether or not someone who's exploiting the system, you write an article, you send it out, the number of views and shares that it gets from accounts that are, you know, proven verified, that is how much you get out of the bounty that's created from our ad sales, and if the community comes together in a consensus and says that someone wrote an article that was basically exploiting the system, like beer, guns, tits and weed plus Bitcoin and then they just shared it with everyone, then obviously, they would be weighted differently because the community would reach consensus so-- >> Change the incentive system. >> We just, I started, yeah, I started redesigning, essentially, once I had that moment, I was like okay, I was like, well, we really got to change the incentives here then because the incentives are not going to work like that. If that's the, if there's a consensus that that is the way you guys want to do things, then I got to change things around that. All right, cool, and so yeah, it was a really interesting awesome learning experience from like, you know, a team of like, maybe like 20 to 40 into, probably took it up 40, and then with all of the other, you know, companies and franchises, to about 435 people. And then just took the revenue from, yeah, just took, it was like skating revenue and then rocketing revenue. So that was really my role in the growth of the business and we're all, you know, it's amazing to see how these kind of blockchain holacracies work, you know, at a micro scale and at a macro scale. And what it really takes to build a movement, right. And then, in some ways, I guess it'd either become or create a meme. >> Well, I really appreciate the movement you've been supporting, we're here to bring theCUBE to the movement, our second show, third show we've been doing. And getting a lot more this year, as the ecosystem is coming together, the norms are forming, they're storming, they're forming, it's great stuff. You've been a great thought leader, and thanks for sharing the awesome range of topics here for theCUBE. >> For sure. >> Toni Lane here inside theCUBE, I'm John Furrier. Thanks for watching our exclusive Puerto Rico coverage of CoinAgenda, we'll be right back. (energetic music)