 Okay, now we're joined by Brian Gilmore, Director of IoT and Emerging Technology 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 with Influx DB. What's the momentum? Where do you see this coming from? What's the value coming out of this? Well, I think we're sort of hitting a point where the technology 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 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, riding along with them as 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 2013, 14, even like five years ago, that convergence of physical and digital coming together, IP enabled edge. Edge has always been kind of hyped up, but why now, 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 twofold. I think that there was, I would think for some people, everybody was so focused on cloud over the last probably 10 years that they forgot about the compute that was available at the edge. And I think those, especially in the OT and on the factory floor who weren't able to take full advantage of cloud through their applications, 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 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 that's giving them the opportunity to build solutions that really deliver value to whatever it is they're trying to do, whether it's the outereaches of outer space or whether it's optimizing the factory floor. Yeah, I think one of the things you also mentioned, the genome too, big data's coming to the real world. And I think IoT has been kind of like this thing for OT in some use case, but now with the cloud, all companies have an edge strategy now. So 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 computers getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the microcontroller layer with 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 fantastic. Now you can take that, you can run that locally, you can do your local decision making, you can use Inflex 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. What's interesting is the open source success has been something that we've talked about a lot in theCUBE about how people are leveraging that. You guys have users in the enterprise, users at IoT market, but you got developers now 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 Inflex DB? What's the problem? 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, new services, new products, things like that. And especially on the consumer side of IoT, there's a lot of that, just those developers. But I think we got to pay attention to those enterprise developers as well, right? There are tons of people with the title of engineer in your regular enterprise organizations and they're there for systems integration. They're there for looking at what they would build versus what they would buy. And a lot of them come from a strong open source background and they know the communities, they know the top platforms in those spaces and they're excited to be able to adopt and use to optimize inside the business as compared to just building a brand new one. You know what's interesting too when Evan and I were talking about open source versus closed OT systems. So how do you support the backwards compatibility of older systems while maintaining 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 edges you'd like. It speaks dozens of different protocols in its own right, a couple of which MQTT, OPCUA are very, very 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 Capware and Highbyte 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, you know. And so by making those connections and building those ecosystems, we get the best of both worlds. 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 share with you? Can you share some anecdotal, kind of like, wow, that's the best thing I've ever used. It's really changed my business. Or this is a great tech that's been helped me in these other areas. What are some of the 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 has these tunnel-boring machines that go deep into the earth to like drill tunnels for, you know, cars 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, you have the customers who are way far beyond the monitoring use case where they're actually using the historical records and the time series database to 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 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 Influx TV to apply it and detect those anomalies, which are- I think that's going to be an area, personally I think that's a hot area because I think, if you look at AI right now, it's all about training the machine learning algorithms after the fact. So time series becomes hugely important. Because now you're thinking, okay, the data matters post-time. And then it gets updated, the new time. 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. Yeah, I mean, I think- You agree? Yeah, of course, yeah. The ability to sort of handle those pipelines of data smartly, intelligently, and then to be able to do all of the things you need to do with that data in stream before it hits your sort of central repository. 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 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 want to do it. You're routing it to different destinations. And it's not something that really has been in the realm of possibility until this point, yeah. And when Evan was on, it's great, he was a CEO, so he sees the big picture with customers. He kind of put the package together and said, hey, we got a system, we got customers, people wanted to leverage our product, what's your PO? 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 Inflex DB system architect. He 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. The edge is there. 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 generally like compartmentalized to a point of like, you know, a single asset or a single factory, a line, whatever. But what people do who want to pros, they want to be able to make the decisions there at the edge locally, 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 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 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, the physical plant, physical business with virtual data cloud, all coming together. What's the future for InfluxDB from a tech standpoint? Because 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. Yeah. Yeah. I mean, I think, you know, again, we get 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 going to be that the world is only going to 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 being generated at the edge. And then there's going to be data generated sort of at all points in between, like physical locations, as well as things that are very virtual. And I think, you know, we're building some technology right now that's going to allow 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 solutions that we'll be bringing to the customer sort of over the next little bit. 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 going to talk to two of them 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. And to be able to process data like that and to do so in a way, we got to build the fundamentals for that right now on the factory floor and in the mines and in the tunnels so that we'll be ready for that. 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 a new thinking is hyperscale has 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 and that's to separate out the data complexity from the app. So space, genome, driving cars, throws off massive data. It does. So is Tesla, is the car the same as the data layer? I mean, yeah, it's certainly a point of origin. I think the thing that we want to do is we want to let the developers work on the world changing problems, the things that they're trying to solve, whether it's energy or any of the other health or other challenges that these teams 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, 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 going to allow them to be more iterative, they're going to be able to do more testing and things like that. And ultimately, it'll accelerate the adoption and the creation of technology. You mentioned earlier in our talk about unification of data. How about APIs? Because developers love APIs in the cloud, unifying APIs, how do you 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 built on our public APIs. You know, and it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then 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 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 data field is going to have this kind of SRE role that DevOps had, Site Reliability Engines, which manage a bunch of servers. There's so much data out there now. Yeah. Yeah, it's like reining data for sure. And I think like that ability to, like one of the best jobs on the planet is going to 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, collections, storage work. That's data as code. I mean, data engineering is becoming a new discipline. It for sure. And the democratization is the benefit. Yeah. Everyone data science get easier. I mean, data science, they want to make it easy, right? Yeah. They want to do the analysis, right? Yeah. I mean, I think, you know, 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 going to 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 the complexities and IoT that you guys helped take away with APIs and assembly and all the system architectures that are changing. Edge is real, Cloud is real. Absolutely. Mainstream enterprises and you got developer attraction too, so congratulations. Yeah, it's great. Well, thank you very much. Any last word you want to share? Give it a deal. No, just, I mean, please, you know, if you're going to check out Influx TV, download it, try out the open source, contribute if you can, that's a huge thing. It's part of being the open source community. You know what, definitely 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 going to hear more about that in the next segment too. All right, thanks for coming on. Okay. Thanks. When we return, Dave Vellante will lead a panel on Edge and data in Influx DB. You're watching theCUBE, the leader in high tech enterprise coverage.