>> Okay. We're now going to go into the customer panel, and we'd like to welcome Angelo Fausti, who's a software engineer at the Vera C. Rubin Observatory, and Caleb Maclachlan who's senior spacecraft operations software engineer at Loft Orbital. Guys, thanks for joining us. You don't want to miss folks this interview. Caleb, let's start with you. You work for an extremely cool company, you're launching satellites into space. Of course doing that is highly complex and not a cheap endeavor. Tell us about Loft Orbital and what you guys do to attack that problem. >> Yeah, absolutely. And thanks for having me here by the way. So Loft Orbital is a company that's a series B startup now, who, and our mission basically is to provide rapid access to space for all kinds of customers. 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, have a big software teams, and then eventually worry about, 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 deploying a VM in AWS or GCP is getting your programs, your mission deployed on orbit with access to different sensors, cameras, radios, stuff like that. So, that's kind of our mission and just to give a really brief example of the kind of customer that we can serve. There's a really cool company called Totum Labs, who is working on building IoT cons, an IoT constellation for, internet of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor IoT which means you have this little modem inside a container that container that you track from anywhere in the world as it's going across the ocean. So, and it's really little, and they've been able to stay a small startup that's focused on their product, which is the, 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 providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving a huge variety of customers with all kinds of different missions, and obviously generating a ton of data in space that we've got to handle. >> Yeah. So amazing Caleb, what you guys do. Now, I know you were lured to the skies very early in your career, but how did you kind of land in this business? >> Yeah, so, I guess just a little bit about me. For some people, they don't necessarily know what they want to do like earlier in their life. For me I was five years old and I knew I want to be in the space industry. So, I started in the Air Force, but have stayed in the space industry my whole career and been a part of, this is the fifth space startup that I've been a part of actually. So, I've kind of started out in satellites, spent some time in working in the launch industry on rockets, then, now I'm here back in satellites and honestly, this is the most exciting of the different space startups that I've been a part of. >> Super interesting. Okay. Angelo, let's talk about the Rubin Observatory. Vera C. Rubin, famous woman scientist, galaxy guru. Now you guys, the Observatory, you're up way up high, you get a good look at the Southern sky. And 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 got to be super excited, give us the update on the Observatory and your role. >> All right. So, yeah. Rubin is a state of the art observatory that is in construction on a remote mountain in Chile. And, with Rubin we'll conduct the large survey of space and time. We're going to observe the sky with eight meter optical telescope and take 1000 pictures every night with 2.2 Gigapixel camera. And we are going to do that for 10 years, which is the duration of the survey. >> Yeah, amazing project. Now, you earned 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 and astrophysics. So, this is something that you've been working on for the better part of your career, isn't it? >> Yeah, that's right, about 15 years. I studied physics in college. Then I got a PhD in astronomy. And, I worked for about five years in another project, the Dark Energy Survey before joining Rubin in 2015. >> Yeah, impressive. So it seems like both your organizations are looking at space from two different angles. One thing you guys both have in common of course is software, and you both use InfluxDB as part of your data infrastructure. How did you discover InfluxDB, get into it? How do you use the platform? Maybe Caleb you could start. >> Yeah, absolutely. So, the first company that I extensively used InfluxDB in, was a launch startup called Astra. And we were in the process of designing our first generation rocket there, and testing the engines, pumps, everything that goes into a rocket. And, when I joined the company our data story was not very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. And at first, there, you know, that's the way that a lot of engineers and scientists are used to working. And at first that was, like people weren't entirely sure that that was, that needed to change. But, it's, something, the nice thing about InfluxDB is that, it's so easy to deploy. So as, our software engineering team was able to get it deployed and, up and running very quickly and then quickly also backport all of the data that we collected this far into Influx. And, what was amazing to see and is kind of the super cool moment with Influx is, when we hooked that up to Grafana, Grafana as the visualization platform we used with Influx, 'cause it works really well with it. 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, 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, that you could add a column in a traditional RDBMS and do time series, but with the volume of data that you're talking about in the example that Caleb just gave, you have to have a purpose built time series database. Where did you first learn about InfluxDB? >> Yeah, correct. So, I work with the data management team, and my first project was the record metrics that measured the performance of our software, the software that we used to process the data. So I started implementing that in our relational database. 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 InfluxDB, and that was back in 2018. The, another use for InfluxDB that I'm also interested is the visits database. If you think about the observations, we are moving the telescope all the time and pointing to specific directions in the sky and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, we call a visit. So we want to record the metadata about those visits in InfluxDB. That time series is going to be 10 years long, with about 1000 points every night. It's actually not too much data compared to other problems. It's really just a different time scale. >> The telescope at the Rubin Observatory is like, pun intended, I guess the star of the show. And I believe I read that it's going to 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 Hubble's widest camera view, which is amazing. Like, that's like 40 moons in an image, amazingly fast as well. What else can you tell us about the telescope? >> This telescope it has to move really fast. And, it also has to carry 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, the telescope needs to be very compact and stiff. And one thing that's amazing about it's design is that, the telescope, this 300 tons structure, it sits on a tiny film of oil, which has the diameter of human hair. And that makes an, almost zero friction interface. In fact, a few people can move this enormous structure with only their hands. As you said, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So, each image has, in diameter the size of about seven full moons. And, with that, we can map the entire sky in only three days. And of course, during operations everything's controlled by software and it is automatic. There's a very complex piece of software called the Scheduler, which is responsible for moving the telescope, and the camera, which is recording 15 terabytes of data every night. >> And Angelo, all this data lands in InfluxDB, correct? And what are you doing with all that data? >> Yeah, actually not. So we use InfluxDB 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 you have some high frequency data that the system needs to keep up, and, we need to store this data and have it around for the lifetime of the project. >> Got it. Thank you. Okay, Caleb, let's bring you back in. Tell us more about the, you got these dishwasher size satellites, kind of using a multi-tenant model, I think it's genius. But tell us about the satellites themselves. >> Yeah, absolutely. So, we have in space some satellites already that as you said, are like dishwasher, mini fridge kind of size. And we're working on a bunch more that are a variety of sizes from shoebox to, I guess, a few times larger than what we have today. And it is, we do shoot to have effectively something like a multi-tenant model where we will buy a bus off the shelf. The bus is 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. 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, so we integrate that, we launch it, and those things because they're in lower Earth orbit, they're orbiting the earth every 90 minutes. That's, seven kilometers per second which is several times faster than a speeding bullet. So we have one of the unique challenges of operating spacecraft in lower Earth 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, where we get to talk to them through our ground sites, either in Antarctica or in the North pole region. >> Talk more about how you use InfluxDB 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 so slow and 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. So we migrated to InfluxDB to store our time series telemetry from the spacecraft. So, that's things like power level, voltage, currents, counts, whatever metadata we need to monitor about the spacecraft, we now store that in InfluxDB. And that has, now we can actually easily store the entire volume of data for the mission life so far without having to worry about the size bloating to an unmanageable amount, and we can also seamlessly query large chunks of data. Like if I need to see, you know, for example, as an operator, I might want to see how my battery state of charge is evolving over the course of the year, I can have, plot in an Influx that loads that in a fraction of a second for a year's worth of data because it does, intelligent, it can intelligently group the data by assigning time interval. So, it's been extremely powerful for us to access the data. And, as time has gone on, we've gradually migrated more and more of our operating data into Influx. >> Yeah. Let's talk a little bit about, 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 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 Astra where our engineer's feedback loop went from 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. But to give another practical example, 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 in near real time, about a second worth of latency is all that's acceptable for us to react to see what is coming down from the spacecraft. And building that pipeline is challenging from a software engineering standpoint. My primary language is Python which isn't necessarily that fast. So what we've done is started, and the goal of being data-driven is publish metrics on individual, 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. So we have kind of a production monitoring 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 focus our development efforts in terms of improving our software efficiency, just because we have that visibility into where the real problems are. And 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. But 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 from supporting a couple of satellites to supporting many, many satellites at once. >> Yeah, of course is how you reduced those dead ends. Maybe Angelo you could talk about what sort of data-driven means to you and your teams. >> I would say that, having real time visibility to the telemetry data and metrics is crucial for us. We need to make sure that the images that we collect with the telescope have good quality, and, that they are within the specifications to meet our science goals. And so if they are not, we want to know that as soon as possible and then start fixing problems. >> Caleb, what are your sort of event, you know, intervals like? >> So I would say that, as of today on the spacecraft, the event, the level of timing that we deal with probably tops out at about 20 Hertz, 20 measurements per second on things like our gyroscopes. But, the, I think 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 from when I worked at, on the rockets at Astra. There, our baseline data rate that we would ingest data during a test is 500 Hertz. So 500 samples per second, and in some cases we would actually need to ingest much higher rate data, even up to like 1.5 kilohertz, so extremely, extremely high precision data there where timing really matters a lot. And, 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, 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 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 Angelo 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 want to have that at, micro, or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at this exact moment, was that before or after this valve opened? That kind of visibility is critical in these kind of scientific applications, and absolutely game changing to be able to see that in near real time, and with, a really easy way for engineers to be able to visualize this data themselves without having to wait for us software engineers to go build it for them. >> Can the scientists do self-serve or do you have to design and build all the analytics and queries for your scientists? >> Well, I think that's absolutely, from my perspective that's absolutely one of the best things about Influx and what I've seen be game changing is that, generally I'd say anyone can learn to use Influx. And honestly, most of our users might not even know they're using Influx, because, the interface that we expose to them is Grafana, which is a generic graphing, open source graphing library that is very similar to Influx zone Chronograf. >> Sure. >> And what it does is, it provides this 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 the particular fields you want to 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 that Influx provides. >> Putting data in the hands of those who have the context, the domain experts is key. Angelo, is it the same situation for you, is it self-serve? >> Yeah, correct. As I mentioned before, we have the astronomers making their own dashboards because they know what exactly what they need to visualize. >> Yeah, I mean, it's all about using the right tool for the job. I think for us, when I joined the company we weren't using InfluxDB and 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 pretty exciting to see how the edge, is mountaintops, lower Earth orbits, I mean space is the ultimate edge, isn't it? I wonder if you could answer two questions to wrap here. You know, what comes next for you guys? And is there something that you're really excited about that you're working on? Caleb maybe you could go first and then Angelo you can bring us home. >> Basically what's next for Loft Orbital is more satellites, a greater push towards infrastructure, and really making, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, making that happen. It's extremely exciting, an 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 people are taking advantage of, and with companies like SpaceX and the, now rapidly lowering cost of launch it's just a really exciting place to be in. 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 improvements that we're working on to really scale up our control software to be best in class and make it capable of handling such a large workload, so. >> Are you guys hiring? >> We are absolutely hiring, so I would, we have positions all over the company, so, we need software engineers, we need people who do more aerospace specific stuff. So absolutely, I'd encourage anyone to check out the Loft Orbital website, if this is at all interesting. >> All right, Angelo, bring us home. >> Yeah. So what's next for us is really getting this telescope working and collecting data. And when that's happened is going to be just a deluge of data coming out of this camera and handling all that data is going to be really challenging. Yeah, I want to be here for that, I'm looking forward. Like for next year we have like an important milestone, which is our commissioning camera, which is a simplified version of the full camera, it's going to be on sky, and so yeah, most of the system has to be working by then. >> Nice. All right guys, with that we're going to end it. Thank you so much, really fascinating, and thanks to InfluxDB for making this possible, really groundbreaking stuff, enabling value creation at the Edge, 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 Vellante, and you're watching theCUBE, the leader in high tech enterprise coverage. >> Welcome. Telegraf is a popular open source data collection agent. Telegraf 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 Telegraf project has a very welcoming and active Open Source community. Learn how to get involved by visiting the Telegraf GitHub page. Whether you want to contribute code, improve documentation, participate in testing, or just show what you're doing with Telegraf. We'd love to hear what you're building. >> Thanks for watching Moving the World with InfluxDB, made possible by Influx Data. I hope you learned 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 want to 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 in the resources below. Remember, all these recordings are going to be available on demand of thecube.net and influxdata.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 Vellante for theCUBE, we'll see you soon. (upbeat music)

(upbeat music) >> We're back at VeeamON 2022. We're here at the Aria in Las Vegas Dave Vellante with Dave Nicholson. Bill Andrews is here. He's the president and CEO of ExaGrid, mass boy. Bill, thanks for coming on theCUBE. >> Thanks for having me. >> So I hear a lot about obviously data protection, cyber resiliency, what's the big picture trends that you're seeing when you talk to customers? >> Well, I think clearly we were talking just a few minutes ago, data's growing like crazy, right This morning, I think they said it was 28% growth a year, right? So data's doubling almost just a little less than every three years. And then you get the attacks on the data which was the keynote speech this morning as well, right. All about the ransomware attacks. So we've got more and more data, and that data is more and more under attack. So I think those are the two big themes. >> So ExaGrid as a company been around for a long time. You've kind of been the steady kind of Eddy, if you will. Tell us about ExaGrid, maybe share with us some of the differentiators that you share with customers. >> Sure, so specifically, let's say in the Veeam world you're backing up your data, and you really only have two choices. You can back that up to disc. So some primary storage disc from a Dell, or a Hewlett Packard, or an NetApp or somebody, or you're going to back it up to what's called an inline deduplication appliance maybe a Dell Data Domain or an HPE StoreOnce, right? So what ExaGrid does is we've taken the best of both those but not the challenges of both those and put 'em together. So with disc, you're going to get fast backups and fast restores, but because in backup you keep weekly's, monthly's, yearly retention, the cost of this becomes exorbitant. If you go to a deduplication appliance, and let's say the Dell or the HPs, the data comes in, has to be deduplicated, compare one backup to the next to reduce that storage, which lowers the cost. So fixes that problem, but the fact that they do it inline slows the backups down dramatically. All the data is deduplicated so the restores are slow, and then the backup window keeps growing as the data grows 'cause they're all scale up technologies. >> And the restores are slow 'cause you got to rehydrate. >> You got to rehydrate every time. So what we did is we said, you got to have both. So our appliances have a front end disc cache landing zone. So you're right directed to the disc., Nothing else happens to it, whatever speed the backup app could write at that's the speed we take it in at. And then we keep the most recent backups in that landing zone ready to go. So you want to boot a VM, it's not an hour like a deduplication appliance it's a minute or two. Secondly, we then deduplicate the data into a second tier which is a repository tier, but we have all the deduplicated data for the long term retention, which gets the cost down. And on top of that, we're scale out. Every appliance has networking processor memory end disc. So if you double, triple, quadruple the data you double, triple, quadruple everything. And if the backup window is six hours at 100 terabyte it's six hours at 200 terabyte, 500 terabyte, a petabyte it doesn't matter. >> 'Cause you scale out. >> Right, and then lastly, our repository tier is non-network facing. We're the only ones in the industry with this. So that under a ransomware attack, if you get hold of a rogue server or you hack the media server, get to the backup storage whether it's disc or deduplication appliance, you can wipe out all the backup data. So you have nothing to recover from. In our case, you wipe it out, our landing zone will be wiped out. We're no different than anything else that's network facing. However, the only thing that talks to our repository tier is our object code. And we've set up security policies as to how long before you want us to delete data, let's say 10 days. So if you have an attack on Monday that data doesn't get deleted till like a week from Thursday, let's say. So you can freeze the system at any time and do restores. And then we have immutable data objects and all the other stuff. But the culmination of a non-network facing tier and the fact that we do the delayed deletes makes us the only one in the industry that can actually truly recover. And that's accelerating our growth, of course. >> Wow, great description. So that disc cache layer is a memory, it's a flash? >> It's disc, it's spinning disc. >> Spinning disc, okay. >> Yeah, no different than any other disc. >> And then the tiered is what, less expensive spinning disc? >> No, it's still the same. It's all SaaS disc 'cause you want the quality, right? So it's all SaaS, and so we use Western Digital or Seagate drives just like everybody else. The difference is that we're not doing any deduplication coming in or out of that landing zone to have fast backups and fast restores. So think of it like this, you've got disc and you say, boy it's too expensive. What I really want to do then is put maybe a deduplication appliance behind it to lower the cost or reverse it. I've got a deduplication appliance, ugh, it's too slow for backups and restores. I really want to throw this in front of it to have fast backups first. Basically, that's what we did. >> So where does the cost savings, Bill come in though, on the tier? >> The cost savings comes in the fact that we got deduplication in that repository. So only the most recent backup >> Ah okay, so I get it. >> are the duplicated data. But let's say you had 40 copies of retention. You know, 10 weekly's, 36 monthly's, a few yearly. All of that's deduplicated >> Okay, so you're deduping the stuff that's not as current. >> Right. >> Okay. >> And only a handful of us deduplicate at the layer we do. In other words, deduplication could be anywhere from two to one, up to 50 to one. I mean it's all over the place depending on the algorithm. Now it's what everybody's algorithms do. Some backup apps do two to one, some do five to one, we do 20 to one as well as much as 50 to one depending on the data types. >> Yeah, so the workload is going to largely determine the combination >> The content type, right. with the algos, right? >> Yeah, the content type. >> So the part of the environment that's behind the illogical air gap, if you will, is deduped data. >> Yes. >> So in this case, is it fair to say that you're trading a positive economic value for a little bit longer restore from that environment? >> No, because if you think about backup 95% of the customers restores are from the most recent data. >> From the disc cache. >> 95% of the time 'cause you think about why do you need fast restores? Somebody deleted a file, somebody overwrote a file. They can't go work, they can't open a file. It's encrypted, it's corrupted. That's what IT people are trying to keep users productive. When do you go for longer-term retention data? It's an SEC audit. It's a HIPAA audit. It's a legal discovery, you don't need that data right away. You have days and weeks to get that ready for that legal discovery or that audit. So we found that boundary where you keep users productive by keeping the most recent data in the disc cache landing zone, but anything that's long term. And by the way, everyone else is long term, at that point. >> Yeah, so the economics are comparable to the dedupe upfront. Are they better, obviously get the performance advance? >> So we would be a lot looped. The thing we replaced the most believe it or not is disc, we're a lot less expensive than the disc. I was meeting with some Veeam folks this morning and we were up against Cisco 3260 disc at a children's hospital. And on our quote was $500,000. The disc was 1.4 million. Just to give you an example of the savings. On a Data Domain we're typically about half the price of a Data Domain. >> Really now? >> The reason why is their front end control are so expensive. They need the fastest trip on the planet 'cause they're trying to do inline deduplication. >> Yeah, so they're chasing >> They need the fastest memory >> on the planet. >> this chips all the time. They need SSD on data to move in and out of the hash table. In order to keep up with inline, they've got to throw so much compute at it that it drives their cost up. >> But now in the case of ransomware attack, are you saying that the landing zone is still available for recovery in some circumstances? Or are you expecting that that disc landing zone would be encrypted by the attacker? >> Those are two different things. One is deletion, one is encryption. So let's do the first scenario. >> I'm talking about malicious encryption. >> Yeah, absolutely. So the first scenario is the threat actor encrypts all your primary data. What's does he go for next? The backup data. 'Cause he knows that's your belt and suspend is to not pay the ransom. If it's disc he's going to go in and put delete commands at the disc, wipe out the disc. If it's a data domain or HPE StoreOnce, it's all going to be gone 'cause it's one tier. He's going to go after our landing zone, it's going to be gone too. It's going to wipe out our landing zone. Except behind that we have the most recent backup deduplicate in the repository as well as all the other backups. So what'll happen is they'll freeze the system 'cause we weren't going to delete anything in the repository for X days 'cause you set up a policy, and then you restore the most recent backup into the landing zone or we can restore it directly to your primary storage area, right? >> Because that tier is not network facing. >> That's right. >> It's fenced off essentially. >> People call us every day of the week saying, you saved me, you saved me again. People are coming up to me here, you saved me, you saved me. >> Tell us a story about that, I mean don't give me the names but how so. >> I'll actually do a funnier story, 'cause these are the ones that our vendors like to tell. 'Cause I'm self-serving as the CEO that's good of course, a little humor. >> It's your 15 minutes of job. >> That is my 15 minutes of fame. So we had one international company who had one ExaGrid at one location, 19 Data Domains at the other locations. Ransomware attack guess what? 19 Data Domains wiped out. The one ExaGrid, the only place they could restore. So now all 20 locations of course are ExaGrids, China, Russia, Mexico, Germany, US, et cetera. They rolled us out worldwide. So it's very common for that to occur. And think about why that is, everyone who's network facing you can get to the storage. You can say all the media servers are buttoned up, but I can find a rogue server and snake my way over the storage, I can. Now, we also of course support the Veeam Data Mover. So let's talk about that since we're at a Veeam conference. We were the first company to ever integrate the Veeam Data Mover. So we were the first actually ever integration with Veeam. And so that Veeam Data Mover is a protocol that goes from Veeam to the ExaGrid, and we run it on both ends. So that's a more secure protocol 'cause it's not an open format protocol like SaaS. So with running the Veeam Data Mover we get about 30% more performance, but you do have a more secure protocol layer. So if you don't get through Veeam but you get through the protocol, boom, we've got a stronger protocol. If you make it through that somehow, or you get to it from a rogue server somewhere else we still have the repository. So we have all these layers so that you can't get at it. >> So you guys have been at this for a while, I mean decade and a half plus. And you've raised a fair amount of money but in today's terms, not really. So you've just had really strong growth, sequential growth. I understand it, and double digit growth year on year. >> Yeah, about 25% a year right now >> 25%, what's your global strategy? >> So we have sales offices in about 30 countries already. So we have three sales teams in Brazil, and three in Germany, and three in the UK, and two in France, and a lot of individual countries, Chile, Argentina, Columbia, Mexico, South Africa, Saudi, Czech Republic, Poland, Dubai, Hong Kong, Australia, Singapore, et cetera. We've just added two sales territories in Japan. We're adding two in India. And we're installed in over 50 countries. So we've been international all along the way. The goal of the company is we're growing nicely. We have not raised money in almost 10 years. >> So you're self-funding. You're cash positive. >> We are cash positive and self-funded and people say, how have you done that for 10 years? >> You know what's interesting is I remember, Dave Scott, Dave Scott was the CEO of 3PAR, and he told me when he came into that job, he told the VCs, they wanted to give him 30 million. He said, I need 80 million. I think he might have raised closer to a hundred which is right around what you guys have raised. But like you said, you haven't raised it in a long time. And in today's terms, that's nothing, right? >> 100 is 500 in today's terms. >> Yeah, right, exactly. And so the thing that really hurt 3PAR, they were public companies so you could see all this stuff is they couldn't expand internationally. It was just too damn expensive to set up the channels, and somehow you guys have figured that out. >> 40% of our business comes out of international. We're growing faster internationally than we are domestically. >> What was the formula there, Bill, was that just slow and steady or? >> It's a great question. >> No, so what we did, we said let's build ExaGrid like a McDonald's franchise, nobody's ever done that before in high tech. So what does that mean? That means you have to have the same product worldwide. You have to have the same spares model worldwide. You have to have the same support model worldwide. So we early on built the installation. So we do 100% of our installs remotely. 100% of our support remotely, yet we're in large enterprises. Customers racks and stacks the appliances we get on with them. We do the entire install on 30 minutes to about three hours. And we've been developing that into the product since day one. So we can remotely install anywhere in the world. We keep spares depots all over the world. We can bring 'em up really quick. Our support model is we have in theater support people. So they're in Europe, they're in APAC, they're in the US, et cetera. And we assign customers to the support people. So they deal with the same support person all the time. So everything is scalable. So right now we're going to open up India. It's the same way we've opened up every other country. Once you've got the McDonald's formula we just stamp it all over the world. >> That's amazing. >> Same pricing, same product same model, same everything. >> So what was the inspiration for that? I mean, you've done this since day one, which is what like 15, 16 years ago. Or just you do engineering or? >> No, so our whole thought was, first of all you can't survive anymore in this world without being an international company. 'Cause if you're going to go after large companies they have offices all over the world. We have companies now that have 17, 18, 20, 30 locations. And there were in every country in the world, you can't go into this business without being able to ship anywhere in the world and support it for a single customer. You're not going into Singapore because of that. You're going to Singapore because some company in Germany has offices in the U.S, Mexico Singapore and Australia. You have to be international. It's a must now. So that was the initial thing is that, our goal is to become a billion dollar company. And we're on path to do that, right. >> You can see a billion. >> Well, I can absolutely see a billion. And we're bigger than everybody thinks. Everybody guesses our revenue always guesses low. So we're bigger than you think. The reason why we don't talk about it is we don't need to. >> That's the headline for our writers, ExaGrid is a billion dollar company and nobody's know about it. >> Million dollar company. >> On its way to a billion. >> That's right. >> You're not disclosing. (Bill laughing) But that's awesome. I mean, that's a great story. I mean, you kind of are a well kept secret, aren't you? >> Well, I dunno if it's a well kept secret. You know, smaller companies never have their awareness of big companies, right? The Dells of the world are a hundred billion. IBM is 70 billion, Cisco is 60 billion. Easy to have awareness, right? If you're under a billion, I got to give a funny story then I think we got to close out here. >> Oh go ahead please. >> So there's one funny story. So I was talking to the CIO of a super large Fortune 500 company. And I said to him, "Just so who do you use?" "I use IBM Db2, and I use, Cisco routers, and I use EMC primary storage, et cetera. And I use all these big." And I said, "Would you ever switch from Db2?" "Oh no, the switching costs would kill me. I could never go to Oracle." So I said to him, "Look would you ever use like a Pure Storage, right. A couple billion dollar company." He says, "Who?" >> Huh, interesting. >> I said to him, all right so skip that. I said, "VMware, would you ever think about going with Nutanix?" "Who?" Those are billion dollar plus companies. And he was saying who? >> Public companies. >> And he was saying who? That's not uncommon when I talk to CIOs. They see the big 30 and that's it. >> Oh, that's interesting. What about your partnership with Veeam? Tell us more about that. >> Yeah, so I would actually, and I'm going to be bold when I say this 'cause I think you can ask anybody here at the conference. We're probably closer first of all, to the Veeam sales force than any company there is. You talk to any Veeam sales rep, they work closer with ExaGrid than any other. Yeah, we are very tight in the field and have been for a long time. We're integrated with the Veeam Data Boomer. We're integrated with SOBR. We're integrated with all the integrations or with the product as well. We have a lot of joint customers. We actually do a lot of selling together, where we go in as Veeam ExaGrid 'cause it's a great end to end story. Especially when we're replacing, let's say a Dell Avamar to Dell Data Domain or a Dell Network with a Dell Data Domain, very commonly Veeam ExaGrid go in together on those types of sales. So we do a lot of co-selling together. We constantly train their systems engineers around the world, every given week we're training either inside sales teams, and we've trained their customer support teams in Columbus and Prague. So we're very tight with 'em we've been tight for over a decade. >> Is your head count public? Can you share that with us? >> So we're just over 300 employees. >> Really, wow. >> We have 70 open positions, so. >> Yeah, what are you looking for? Yeah, everything, right? >> We are looking for engineers. We are looking for customer support people. We're looking for marketing people. We're looking for inside sales people, field people. And we've been hiring, as of late, major account reps that just focus on the Fortune 500. So we've separated that out now. >> When you hire engineers, I mean I think I saw you were long time ago, DG, right? Is that true? >> Yeah, way back in the '80s. >> But systems guy. >> That's how old I am. >> Right, systems guy. I mean, I remember them well Eddie Castro and company. >> Tom West. >> EMV series. >> Tom West was the hero of course. >> The EMV 4000, the EMV 20,000, right? >> When were kids, "The Soul of a New Machine" was the inspirational book but anyway, >> Yeah Tracy Kidder, it was great. >> Are you looking for systems people, what kind of talent are you looking for in engineering? >> So it's a lot of Linux programming type stuff in the product 'cause we run on a Linux space. So it's a lot of Linux programs so its people in those storage. >> Yeah, cool, Bill, hey, thanks for coming on to theCUBE. Well learned a lot, great story. >> It's a pleasure. >> That was fun. >> Congratulations. >> Thanks. >> And good luck. >> All right, thank you. >> All right, and thank you for watching theCUBE's coverage of VeeamON 2022, Dave Vellante for Dave Nicholson. We'll be right back right after this short break, stay with us. (soft beat music)

>>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.

(upbeat music) >> Okay, we're now going to go into the customer panel. And we'd like to welcome Angelo Fausti, who's software engineer at the Vera C Rubin Observatory, and Caleb Maclachlan, who's senior spacecraft operations software engineer at Loft Orbital. Guys, thanks for joining us. You don't want to miss folks, this interview. Caleb, let's start with you. You work for an extremely cool company. You're launching satellites into space. Cause doing that is highly complex and not a cheap endeavor. Tell us about Loft Orbital and what you guys do to attack that problem? >> Yeah, absolutely. And thanks for having me here, by the way. So Loft Orbital is a company that's a series B startup now. And our mission basically is to provide rapid access to space for all kinds of customers. 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, have big software teams, and then eventually worry about 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 deploying a VM in AWS or GCP, as getting your programs, your mission deployed on orbit, with access to different sensors, cameras, radios, stuff like that. So that's kind of our mission. And just to give a really brief example of the kind of customer that we can serve. There's a really cool company called Totum labs, who is working on building an IoT constellation, for Internet of Things. Basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor IoT, which means you have this little modem inside a container. A container that you track from anywhere on the world as it's going across the ocean. So it's really little. And they've been able to stay small startup that's focused on their product, which is that super crazy, complicated, cool radio, while we handle the whole space segment for them, which just, before Loft was really impossible. So that's our mission is, providing space infrastructure as a service. We are kind of groundbreaking in this area, and we're serving a huge variety of customers with all kinds of different missions, and obviously, generating a ton of data in space that we've got to handle. >> Yeah, so amazing, Caleb, what you guys do. I know you were lured to the skies very early in your career, but how did you kind of land in this business? >> Yeah, so I guess just a little bit about me. For some people, they don't necessarily know what they want to do, early in their life. For me, I was five years old and I knew, I want to be in the space industry. So I started in the Air Force, but have stayed in the space industry my whole career and been a part of, this is the fifth space startup that I've been a part of, actually. So I've kind of started out in satellites, did spend some time in working in the launch industry on rockets. Now I'm here back in satellites. And honestly, this is the most exciting of the different space startups that I've been a part of. So, always been passionate about space and basically writing software for operating in space for basically extending how we write software into orbit. >> Super interesting. Okay, Angelo. Let's talk about the Rubin Observatory Vera C. Rubin, famous woman scientists, Galaxy guru, Now you guys, the observatory are up, way up high, you're going to get a good look at the southern sky. I know COVID slowed you guys down a bit. But no doubt you continue to code away on the software. I know you're getting close. You got to be super excited. Give us the update on the observatory and your role. >> All right. So yeah, Rubin is state of the art observatory that is in construction on a remote mountain in Chile. And with Rubin we'll conduct the large survey of space and time. We are going to observe the sky with eight meter optical telescope and take 1000 pictures every night with 3.2 gigapixel camera. And we're going to do that for 10 years, which is the duration of the survey. The goal is to produce an unprecedented data set. Which is going to be about .5 exabytes of image data. And from these images will detect and measure the properties of billions of astronomical objects. We are also building a science platform that's hosted on Google Cloud, so that the scientists and the public can explore this data to make discoveries. >> Yeah, amazing project. Now, you aren't a Doctor of Philosophy. So you probably spent some time thinking about what's out there. And then you went on to earn a PhD in astronomy and astrophysics. So this is something that you've been working on for the better part of your career, isn't it? >> Yeah, that's right. About 15 years. I studied physics in college, then I got a PhD in astronomy. And I worked for about five years in another project, the Dark Energy survey before joining Rubin in 2015. >> Yeah, impressive. So it seems like both your organizations are looking at space from two different angles. One thing you guys both have in common, of course, is software. And you both use InfluxDB as part of your data infrastructure. How did you discover InfluxDB, get into it? How do you use the platform? Maybe Caleb, you can start. >> Yeah, absolutely. So the first company that I extensively used InfluxDB in was a launch startup called Astra. And we were in the process of designing our first generation rocket there and testing the engines, pumps. Everything that goes into a rocket. And when I joined the company, our data story was not very mature. We were collecting a bunch of data in LabVIEW. And engineers were taking that over to MATLAB to process it. And at first, that's the way that a lot of engineers and scientists are used to working. And at first that was, like, people weren't entirely sure that, that needed to change. But it's something, the nice thing about InfluxDB is that, it's so easy to deploy. So our software engineering team was able to get it deployed and up and running very quickly and then quickly also backport all of the data that we've collected thus far into Influx. And what was amazing to see and it's kind of the super cool moment with Influx is, when we hooked that up to Grafana, Grafana, is the visualization platform we use with influx, because it works really well with it. 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 I saw them implementing crazy rocket equation type stuff in Influx and it just was totally game changing for how we tested. And things that previously it would be like run a test, then wait an hour for the engineers to crunch the data and then we run another test with some changed parameters or a changed startup sequence or something like that, became, by the time the test is over, the engineers know what the next step is, because they have this just like instant game changing access to data. So since that experience, basically everywhere I've gone, every company since then, I've been promoting InfluxDB and using it and spinning it up and quickly showing people how simple and easy it is. >> Yeah, thank you. So Angelo, I was explaining in my open that, you know you could add a column in a traditional RDBMS and do time series. But with the volume of data that you're talking about in the example that Caleb just gave, you have to have a purpose built time series database. Where did you first learn about InfluxDB? >> Yeah, correct. So I worked with the data management team and my first project was the record metrics that measure the performance of our software. The software that we use to process the data. So I started implementing that in our relational database. 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 InfluxDB, that was back in 2018. Then I got involved in another project. To record telemetry data from the telescope itself. It's very challenging because you have so many subsystems and sensors, producing data. And with that data, the goal is to look at the telescope harder in real time so we can make decisions and make sure that everything's doing the right thing. And another use for InfluxDB that I'm also interested, is the visits database. If you think about the observations, we are moving the telescope all the time and pointing to specific directions in the sky and taking pictures every 30 seconds. So that itself is a time series. And every point in the time series, we call that visit. So we want to record the metadata about those visits in InfluxDB. That time series is going to be 10 years long, with about 1000 points every night. It's actually not too much data compared to the other problems. It's really just the different time scale. So yeah, we have plans on continuing using InfluxDB and finding new applications in the project. >> Yeah and the speed with which you can actually get high quality images. Angelo, my understanding is, you use InfluxDB, as you said, you're monitoring the telescope hardware and the software. And just say, some of the scientific data as well. The telescope at the Rubin Observatory is like, no pun intended, I guess, the star of the show. And I believe, I read that it's going to 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 Hubble's widest camera view, which is amazing. That's like 40 moons in an image, and amazingly fast as well. What else can you tell us about the telescope? >> Yeah, so it's really a challenging project, from the point of view of engineering. This telescope, it has to move really fast. And it also has to carry the primary mirror, which is an eight meter piece of glass, it's very heavy. And it has to carry a camera, which is about the size of a small car. And this whole structure weighs about 300 pounds. For that to work, the telescope needs to be very compact and stiff. And one thing that's amazing about its design is that the telescope, this 300 tons structure, it sits on a tiny film of oil, which has the diameter of human hair, in that brings an almost zero friction interface. In fact, a few people can move this enormous structure with only their hands. As you said, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So each image has, in diameter, the size of about seven full moons. And with that we can map the entire sky in only three days. And of course, during operations, everything's controlled by software, and it's automatic. There's a very complex piece of software called the scheduler, which is responsible for moving the telescope and the camera. Which will record the 15 terabytes of data every night. >> And Angelo, all this data lands in InfluxDB, correct? And what are you doing with all that data? >> Yeah, actually not. So we're using InfluxDB to record engineering data and metadata about the observations, like telemetry events and the commands from the telescope. That's a much smaller data set compared to the images. But it is still challenging because you have some high frequency data that the system needs to keep up and we need to store this data and have it around for the lifetime of the project. >> Hm. So at the mountain, we keep the data for 30 days. So the observers, they use Influx and InfluxDB instance, running there to analyze the data. But we also replicate the data to another instance running at the US data facility, where we have more computational resources and so more people can look at the data without interfering with the observations. Yeah, I have to say that InfluxDB has been really instrumental for us, and especially at this phase of the project where we are testing and integrating the different pieces of hardware. And it's not just the database, right. It's the whole platform. So I like to give this example, when we are doing this kind of task, it's hard to know in advance which dashboards and visualizations you're going to need, right. So what you really need is a data exploration tool. And with tools like chronograph, for example, having the ability to query and create dashboards on the fly was really a game changer for us. So astronomers, they typically are not software engineers, but they are the ones that know better than anyone, what needs to be monitored. And so they use chronograph and they can create the dashboards and the visualizations that they need. >> Got it. Thank you. Okay, Caleb, let's bring you back in. Tell us more about, you got these dishwasher size satellites are kind of using a multi tenant model. I think it's genius. But tell us about the satellites themselves. >> Yeah, absolutely. So we have in space, some satellites already. That, as you said, are like dishwasher, mini fridge kind of size. And we're working on a bunch more that are a variety of sizes from shoe box to I guess, a few times larger than what we have today. And it is, we do shoot to have, effectively something like a multi tenant model where we will buy a bus off the shelf, the bus is, 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, it handles the altitude 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 so we integrate that, we launch it, and those things, because they're in low Earth orbit, they're orbiting the Earth every 90 minutes. That's seven kilometers per second, which is several times faster than a speeding bullet. So we've got, we have one of the unique challenges of operating spacecraft in lower Earth 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. Where we get to talk to them through our ground sites, either in Antarctica or in the North Pole region. So we'll see them for 10 minutes, and then we won't see them for the next 90 minutes as they zip around the Earth collecting data. So one of the challenges that exists for a company like ours is, that's a lot of, you have to be able to make real time decisions operationally, in those short windows that can sometimes be critical to the health and safety of the spacecraft. And it could be possible that we put ourselves into a low power state in the previous orbit or something potentially dangerous to the satellite can occur. And so as an operator, you need to very quickly process that data coming in. And not just the the live data, but also the massive amounts of data that were collected in, what we call the back orbit, which is the time that we couldn't see the spacecraft. >> We got it. So talk more about how you use InfluxDB to make sense of this data from all those tech that you're launching into space. >> Yeah, so 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 so slow, and the size of our data would balloon over the course of a couple of days to the point where we weren't able to even store all of the data that we were getting. So we migrated to InfluxDB to store our time series telemetry from the spacecraft. So that thing's like power level voltage, currents counts, whatever metadata we need to monitor about the spacecraft, we now store that in InfluxDB. 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 the size bloating to an unmanageable amount. And we can also seamlessly query large chunks of data, like if I need to see, for example, as an operator, I might want to see how my battery state of charge is evolving over the course of the year, I can have a plot in an Influx that loads that in a fraction of a second for a year's worth of data, because it does, you know, intelligent. I can intelligently group the data by citing time interval. So it's been extremely powerful for us to access the data. And as time has gone on, we've gradually migrated more and more of our operating data into Influx. So not only do we store the basic telemetry about the bus and our payload hub, but we're also storing data for our customers, that our customers are generating on board about things like you know, one example of a customer that's doing something pretty cool. They have a computer on our satellite, which they can reprogram themselves to do some AI enabled edge compute type capability in space. And so they're sending us some metrics about the status of their workloads, in addition to the basics, like the temperature of their payload, their computer or whatever else. And we're delivering that data to them through Influx in a Grafana dashboard that they can plot where they can see, not only has this pipeline succeeded or failed, but also where was the spacecraft when this occurred? What was the voltage being supplied to their payload? Whatever they need to see, it's all right there for them. Because we're aggregating all that data in InfluxDB. >> That's awesome. You're measuring everything. Let's talk a little bit about, we throw this term around a lot, 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 clearest example of when I saw this, be like totally game changing is, what I mentioned before it, at Astra, were our engineers feedback loop went from 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. But to give another practical example, as I said, we have a huge amount of data that comes down every orbit, and we need to be able to ingest all that data almost instantaneously and provide it to the operator in near real time. 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. Our primary language is Python, which isn't necessarily that fast. So what we've done is started, in the in the goal being data driven, is publish metrics on individual, 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. So we have kind of a production monitoring 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 focus our development efforts in terms of improving our software efficiency, just because we have that visibility into where the real problems are. At 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. But 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 scaled from supporting a couple of satellites to supporting many, many satellites at once. >> So you reduce those dead ends. Maybe Angela, you could talk about what sort of data driven means to you and your team? >> Yeah, I would say that having real time visibility, to the telemetry data and metrics is crucial for us. We need to make sure that the images that we collect, with the telescope have good quality and that they are within the specifications to meet our science goals. And so if they are not, we want to know that as soon as possible, and then start fixing problems. >> Yeah, so I mean, you think about these big science use cases, Angelo. They are extremely high precision, you have to have a lot of granularity, very tight tolerances. How does that play into your time series data strategy? >> Yeah, so one of the subsystems that produce the high volume and high rates is the structure that supports the telescope's primary mirror. So on that structure, we have hundreds of actuators that compensate the shape of the mirror for the formations. That's part of our active updated system. So that's really real time. And we have to record this high data rates, and we have requirements to handle data that are a few 100 hertz. So we can easily configure our database with milliseconds precision, that's for telemetry data. But for events, sometimes we have events that are very close to each other and then we need to configure database with higher precision. >> um hm For example, micro seconds. >> Yeah, so Caleb, what are your event intervals like? >> So I would say that, as of today on the spacecraft, the event, the level of timing that we deal with probably tops out at about 20 hertz, 20 measurements per second on things like our gyroscopes. But I think 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 you an example, from when I worked on the rockets at Astra. There, our baseline data rate that we would ingest data during a test is 500 hertz, so 500 samples per second. And in some cases, we would actually need to ingest much higher rate data. Even up to like 1.5 kilohertz. So extremely, extremely high precision data there, where timing really matters a lot. And, 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. Because there's times when we're looking at the results of firing, where you're zooming in. I've talked earlier about how on my current job, we often zoom out to 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 Angelo 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 want to have that at micro or even nanosecond precision, so that we know, okay, we saw a spike in chamber pressure at this exact moment, was that before or after this valve open? That kind of visibility is critical in these kinds of scientific applications and absolutely game changing, to be able to see that in near real time. And with a really easy way for engineers to be able to visualize this data themselves without having to wait for us software engineers to go build it for them. >> Can the scientists do self serve? Or do you have to design and build all the analytics and queries for scientists? >> I think that's absolutely from my perspective, that's absolutely one of the best things about Influx, and what I've seen be game changing is that, generally, I'd say anyone can learn to use Influx. And honestly, most of our users might not even know they're using Influx. Because the interface that we expose to them is Grafana, which is generic graphing, open source graphing library that is very similar to Influx zone chronograph. >> Sure. >> And what it does is, it provides this, almost, it's a very intuitive UI for building your query. So you choose a measurement, and it shows a drop down of available measurements, and then you choose the particular field you want to look at. And again, that's a drop down. So it's really easy for our users to discover it. 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 API's and functionality that Influx provides. So yes, absolutely, that's been the most powerful thing about it, is that it gets us out of the way, us software engineers, who may not know quite as much as the scientists and engineers that are closer to the interesting math. And they build these crazy dashboards that I'm just like, wow, I had no idea you could do that. I had no idea that, that is something that you would want to see. And absolutely, that's the most empowering piece. >> Yeah, putting data in the hands of those who have the context, the domain experts is key. Angelo is it the same situation for you? Is it self serve? >> Yeah, correct. As I mentioned before, we have the astronomers making their own dashboards, because they know exactly what they need to visualize. And I have an example just from last week. We had an engineer at the observatory that was building a dashboard to monitor the cooling system of the entire building. And he was familiar with InfluxQL, which was the primarily query language in version one of InfluxDB. And he had, that was really a challenge because he had all the data spread at multiple InfluxDB measurements. And he was like doing one query for each measurement and was not able to produce what he needed. And then, but that's the perfect use case for Flux, which is the new data scripting language that Influx data developed and introduced as the main language in version two. And so with Flux, he was able to combine data from multiple measurements and summarize this data in a nice table. So yeah, having more flexible and powerful language, also allows you to make better a visualization. >> So Angelo, where would you be without time series database, that technology generally, may be specifically InfluxDB, as one of the leading platforms. Would you be able to do this? >> Yeah, it's hard to imagine, doing what we are doing without InfluxDB. And I don't know, perhaps it would be just a matter of time to rediscover InfluxDB. >> Yeah. How about you Caleb? >> Yeah, I mean, it's all about using the right tool for the job. I think for us, when I joined the company, we weren't using InfluxDB and we were dealing with serious issues of the database growing to a 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. So time series database is, if you're dealing with large volumes of time series data, Time series database is the right tool for the job and Influx is a great one for it. So, yeah, it's absolutely required to use for this kind of data, there is not really any other option. >> Guys, this has been really informative. It's pretty exciting to see, how the edge is mountain tops, lower Earth orbits. Space is the ultimate edge. Isn't it. I wonder if you could two questions to wrap here. What comes next for you guys? And is there something that you're really excited about? That you're working on. Caleb, may be you could go first and than Angelo you could bring us home. >> Yeah absolutely, So basically, what's next for Loft Orbital is more, more satellites a greater push towards infrastructure and really making, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, 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 people are taking advantage of and with companies like SpaceX, now rapidly lowering cost of launch. It's just a really exciting place to be in. And we're launching more satellites. We're scaling up for some constellations and our ground system has to be improved to match. So there is a lot of improvements that we are working on to really scale up our control systems to be best in class and make it capable of handling such large workloads. So, yeah. What's next for us is just really 10X ing what we are doing. And that's extremely exciting. >> And anything else you are excited about? Maybe something personal? Maybe, you know, the titbit you want to share. Are you guys hiring? >> We're absolutely hiring. So, we've positions all over the company. So we need software engineers. We need people who do more aerospace specific stuff. So absolutely, I'd encourage anyone to check out the Loft Orbital website, if this is at all interesting. Personal wise, I don't have any interesting personal things that are data related. But my current hobby is sea kayaking, so I'm working on becoming a sea kayaking instructor. So if anyone likes to go sea kayaking out in the San Francisco Bay area, hopefully I'll see you out there. >> Love it. All right, Angelo, bring us home. >> Yeah. So what's next for us is, we're getting this telescope working and collecting data and when that's happened, it's going to be just a delish of data coming out of this camera. And handling all that data, is going to be a really challenging. Yeah, I wonder I might not be here for that I'm looking for it, like for next year we have an important milestone, which is our commissioning camera, which is a simplified version of the full camera, is going to be on sky and so most of the system has to be working by then. >> Any cool hobbies that you are working on or any side project? >> Yeah, actually, during the pandemic I started gardening. And I live here in Two Sun, Arizona. It gets really challenging during the summer because of the lack of water, right. And so, we have an automatic irrigation system at the farm and I'm trying to develop a small system to monitor the irrigation and make sure that our plants have enough water to survive. >> Nice. All right guys, with that we're going to end it. Thank you so much. Really fascinating and thanks to InfluxDB for making this possible. Really ground breaking stuff, enabling value at the edge, in the cloud and of course beyond, at the space. Really transformational work, that you guys are doing. So congratulations and I really appreciate the broader community. I can't wait to see what comes next from this entire eco system. Now in the moment, I'll be back to wrap up. This is Dave Vallante. And you are watching The cube, the leader in high tech enterprise coverage. (upbeat music)

(upbeat music) >> Hello, welcome to theCUBE's presentation of Women in Tech, Global Event, celebrating International Women's Day. I'm John Furrier, your host of theCUBE here in Palo Alto, California. We got two great guests. Cindy Polin, head of Solution Architects for Public Sector in Mexico for AWS. And Fernanda Spinardi, who's also the head of Solution Architects for Public Sector in Brazil, both with AWS. Thanks for coming, appreciate your time. >> Thanks for the invitation. >> Thank you, John. >> So we're celebrating International Women's Day this week, and this month, and pretty much every day, I think we're going to be doing a lot of good stuff. But today's a special day. And talking about people's careers, their roles, the gender gap, is a big theme this year. These are all the topics that are going on and being discussed. So, it's a been a lot of fun when learning a lot, I have to ask you guys with AWS, Cindy we'll start with you. How is AWS addressing the gender gap in its technical teams? Because solution architects, they're technical. And we need more women in there. How is AWS addressing the gender gap with its technical teams? >> Yes, for sure, thank you very much. Let me start with a quick note about what is the situation in Mexico. Let me go first into a report published by IMCO, and this is talking about this gender gaps in a STEM career. So let me tell you that three out of 10 professionals who choose careers related with the STEM, with the science technology, engineering and mathematics, are women. So, can you imagine this difference, It's really critical because for sure, we have few women. And in the moment that you try to reach people, to be part of the company, it's difficult. So it's important for AWS to be very very supportive in this initiative and also to be supporting diverse teams. So, that's why we are very supportive in bringing diverse talent in the company. >> There's a lot of focus on getting people early into the pipe lining. Is that some another big area? Did the study show anything there? >> Well, basically it's that we are studying to push harder, to bring more information to the ladies, to the women in general. And also to start developing the technical skills. Because it's really difficult and in the moment that you try to do this, it start like seeing these behaviors or stigmas about this is only for men, it's not for women. So we are trying to start breaking this point in general. >> Fernanda, we had a great chat about Latin America reinvent on theCUBE with your leader over there and, we were talking about the broader community and how you guys are partnering with external organizations and customers. How is Amazon Web Services, AWS, aiming to foster better balance and gender balance and technology partnerships in Latin America? >> Sure, so while the situation in Brazil is not different from the situation that Cindy was mentioning in Mexico right? Our research shows that women only represent around 37% of the workforce where in the country we have over 51-52% of women as part of our population. While we can take this from a gap perspective, also, we can take it from an opportunity perspective. There is such a huge unexplored workforce that we can bring to be part of AWS in the technology world, right? So for us on AWS and Amazon, it's part part of our day one culture. So we are still learning, right? And we are still trying, experimenting to see how we can bring more women to the tech world. One of the things that we are investing in Brazil and in Latin America, are the early in career talent programs. This is something that we have the opportunity to work with the students. And in LATAM, it's a little bit different from the US. We have the opportunity to work with them for one year sometimes for two years in a role while they work they are still in the university and we prepare that talent really early in their career and bring them to be part of Amazon. So yeah, I'm super excited with those programs, I can, talk more about it, but this is one of the initiatives that we are betting that will maybe be a game changer for us in the technology. >> Yeah, those are very interesting stats, 37% of the workers in country where women represent over half of the population. So definitely a lot of work to be done. I got to ask both of you. Amazon has a leadership principle that says that they want to strive to be the world's, or earth's best employer earth being, Earth Day and all that sustainability as well. Diversity, inclusion and equity is a big part of that mission more. And also Amazon's also known for high performing work environment. So, so having the best diversity and inclusion you know, is a, is a, as some say and many are saying is a force multiplier in performance. How is that going in your areas? Can you talk about how the culture that you're in, the countries that you're in and the Amazonian leadership principles tie together? Can you share your thoughts and experiences? >> Sure. I can, I can get started maybe with that one. So, although we have a new leadership principle from my perspective, we have we have always had leadership principles that foster diversity and, and inclusion, right. Pick up, earn trust as an example like it says, listen carefully, right. And speak candidly, this is for me it's the baseline for any, any inclusion conversation. Right. And also you have things like have backbone, disagree and commit. Like you are empowering people to actually have an opinion and bring back that opinion and be heard. Right. So it was already there. I think the thing now is that we have a very specific leadership principle so that there is no, no room for interpretation. Right. It's right there saying that there is a mission a mission to, to be the best employer. Right. And, and I'm, I'm very excited about it. >> John: Cindy, share your thoughts too. I like that comment because you know, Amazon culture's known for, you know, debate then align. Okay. And now you got that cultural factor. Now it's in the leadership principle. What's your reaction? >> Yes. And, and let me add a comment on that about Fernanda's point is that this LP is giving us like the empower to give this environment to prepare, to to give this space to the team and also to be more creative. And also to be more diverse is really important for us to have this space with a lot of empathy, with the in the space to have a lot of fun. And it's important to keep all the time in mind that are we doing the right thing for our employees? Are we are empowering them to be the best of, of the world? So, that is something that is critical for us and, and well that is something that we are right now working on it. >> Okay. So first of all I'm very impressed by both of you. You're inspiring. And I can also tell you that being a solution architect is not an easy job. But it's also in high demand. A lot of people want to, they need solution architects. It's one of the most coveted positions in the industry right now. So how do we get more women in that role? What ideas do you guys have besides being great role models, yourselves? How do we get more solution architects? Because it's super valuable and everyone wants to hire them. >> Fernanda, did you want to start? >> It's you guys. >> You touched a very important point, John. It's about having, having good examples. Like, I mean, it's about you seeing yourself in the role right? You, you believing that it's, it's possible. It's for everyone. If you have a spirit where you, you want to build things if you have this spirit of exploring new possibilities if you like to experiment, well, then you have all that we need in a solution architect, right? It's just then a matter of, you know, know learning technical, learning technology, technical stuff. But this is, this is about having fun on your journey as as a solution architect as well. >> And, and let me tell you something that we are also investing in trainings. Training is online for the for the women that they are, that has this interest that they want to learn more about the technology. They want to have a deeper knowledge about the technical stuff. So we are supporting these initiatives and that is something that they can do background and in their own pace. >> And this is an important role because they need the leadership as head of solution architects. It's a good thing. Is, is there any ways that you found that's a best practice for identifying or advice for people to know if they have what it takes or they have an affinity towards technology? Sometimes it's math. Because cloud is great levels it out. I mean, cloud is new, is more jobs open now that didn't exist years ago, couple years ago. So anyone can rise to the top. >> Yeah. I think that's the beauty of the cloud. There is so much space when we say technology I think this is such a, a broad word, right? It means so much, right. It can be someone that likes to develop code. It can be someone that likes to work with infrastructure. It can be someone that likes machine learning or databases or someone that is inspired about applications for the education world or to research genomes or cure cancer. So, yeah, I don't think that there is like any more like a specific profile. I think it's very open for everyone to explore what they love doing. And even from a technology perspective AWS is working to simplify access to the technology. If we take our services on machine learning. For instance, they are for people, for business people like you don't have to know much about algorithms, right. To use some of the AWS services. So I think we're experiencing the democratization of the technology, and with that more opportunity for people to join us. >> A lot of people are changing careers into cloud. So Cindy, I want to ask you guys also if you can share how the mentoring process works there. Is there mentoring? How does that work? Do you match people? Have you found a nice formula for providing some mentoring and some pathways as people come in? >> Yes, we have many ways but one is very important, is that we have user groups. That is a way that we have like a community with internal and external people, and we share advices, guidance, best practices for the people that is interested in this matter. So for one side as I already mentioned, we have training online that you can reach. We have a lot of free courses. Maybe you can start jumping into artificial intelligence. IUT whatever you want to, to, to want that given them. But in the other hand, we have this option to have this kind of support. We have AWS Girl Chile user groups. We have AWS women, Colombian user groups girls in Argentina, we have many of them. We have four hundreds of user communities. So, that is the way that we can keep in touch. >> Any other programs? I mean, Amazon Web Service and Amazon has very strong representation of women. There's a lot of pockets of women groups in all over the world. How does it come together? Because you also have customers in the user groups. You have partners in the partner network. You have technologists learning. So you have this ecosystem of people. It's not just AWS. How are you guys extending that gap into those areas? >> Exactly. And those conversations are getting more and more constant with our customers, right? So we used to talk about technology, we used to talk about business problems, now we talk about diversity. We talk about improving representation and improving the sentiment of inclusion within our customers as well. And one of the things that I can bring, we have been working with a number of our customers in Brazil just to mention New Bank, one of our customers there in building programs. between AWS and the customer, where we train people, and we expose that people to the market, even if it's inside AWS, inside New Bank or any other partner in that ecosystem. So we are building talent not only for us, but for for the entire ecosystem to benefit from. >> Okay, so I have to ask you guys How did you guys get into the tech, Cindy? What was your way? Did it just jump at you? Did it grab you? Did you kind of discover it early? When did you kind of get into the tech? >> That's a good question. I was remembering this moment that when I was seven years old I just started like working with cars and also with that kind of companies, literally companies. And in that moment say, "I want to be part of this technology work." And after that in high school, I have the opportunity to touch a computer. In that moment I said, "This is the thing that I want to do in the rest of my life." >> Yeah. that's it right there. You got the diction, you taste it. Fernanda, what about you? What's your story? How did you get into it? What was the moment? Was there an exact moment or did it just surround you? >> Yeah, I think I was always curious about how things work. I was not thinking about a career in tech honestly. I was thinking about becoming a lawyer, but at some point in time just clicked, right? And I had actually to fight my way into the technical world literally because, I had this very important university close to my house, like maybe 15 minutes from my house. But at that point in time in Brazil, that particular institution was not accepting women. And believe me, it was not like a hundred years ago. Like it was.... (laughing) >> Yeah, you're young, it's just recently. >> Yeah, so I had to move out out of my hometown, back to the city, to Sao Paulo, which is our biggest city in Brazil to find a place for me on an university that would take women. So yeah, I had to fight my way into technology, but I am very proud of that I was able to. >> Yeah, you know what's great now is you have YouTube, you have all these resources, these videos are going to be going everywhere. We're going to put this out there. There's communities where people can learn and see people like themselves out in positions of leadership and technology. So more and more contents being out there. And I think hopefully no one will have to fight to get into tech. If they like it, they're in it. One of the leaders at AWS she said, "We're in a nerd native environment now, the young generation is natively technical." And, I believe that, I see that. I think that's going to be a really exciting trend and seeing leaders like yourselves out there is really wonderful, so thank you for spending the time with us here on theCUBE. Final question I'll ask you, what's next for you Cindy and Fernanda? What's next in your journey? >> Okay, I think the next for me is to keep pushing the women in Mexico to keep installing and also to start thinking into what is the next step in my career? Where should I go? So I think that is the point that I want to do. >> Cindy, what's next for you? >> I feel I'm just starting. (laughing) So much to do, so much to do. I mean, there is a big business for us to make happen in Brazil right now, and we are looking for talent. So, if the video's going to go on YouTube, I would like everybody there to know that yeah, we are looking for talents in Brazil with opportunities all over the world actually. And yeah, that's building, building and building. >> And there's some rig twitch channels by the way too on some developer programmings, tons of programming, it's all out there. Congratulations, and we're looking forward to following up with you both in the future to get an update and thank you for spending the time and sharing your your stories here on theCUBE I really appreciate, thank you. >> Thank you too. >> Thank you so much. >> Okay, theCUBE presentation of Women in Tech, Global Events celebrating International Women's Day. This is the beginning of more programming. We're going to see more episodes from theCUBE, I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of Women In Tech, International Women's Day 2022. I'm your host, Lisa Martin. I have two guests from AWS here with me. Carolina Pina joins us, the head of Enterprise Enablement for LATAM and Laura Alvarez Modernel is here as well, Public Sector Programs Manager at AWS. Ladies, it's great to have you on theCUBE. >> Nice to meet you. >> Thank you for having us. >> Carolina, let's start with you. Talk to me a little bit about your role, what it is that you're doing there. >> So my role in AWS is to actually create mechanisms of massive training to try to close the talent gap that we have in the region. And when I mentioned talent gap, I'm talking about obviously digital and cloud-computing skills. So that's, that's, in a nutshell what my role entails. >> Lisa: Got it. How long have you been in that role? Just curious. >> So I've been at AWS a little bit over, over two years. I was actually in the public sector team when I joined, leading the education vertical for Latin American Canada. And I recently joined the commercial sector now leading these massive training efforts for the region for LATAM. >> And Laura, you're in public sector. Talk to me a little bit about your role. >> Yes, I'm in public sector. I'm also based in Buenos Aires, Argentina. So yeah, I'm from Latin America, and I lead educational and community impact programs in the Southern cone of Latin America. I also lead diversity, equity and inclusion efforts and I'm part of the Women at Amazon global board. That's our affinity group to make sure we make efforts towards building a more equal world. And on a personal note I'm really passionate about the topic of gender equality because I truly think it affects us all as women and as Latins. So that's something that I'm always interested in collaborating with. >> Lisa: Excellent. Carolina back to you. If we think about from an enablement perspective how is AWS partnering with its customers and its partners to train and employ women particularly in technology? >> Oh, sure. Lisa, so it's not a surprise. We, like I mentioned, you know we have a big cloud skills, talent gap in the region. In fact, you know, 69% of companies have reported talent shortages and difficulty hiring. So, and this represents a 15 year high. So, many of these companies are actually, you know, our own commercial customers. So they approach us saying, you know, asking for for support training and developing their talent. So like I mentioned, in my role I create massive training efforts and initiatives. So we always take into consideration women, minorities, underrepresented community, and not just for the current talent, meaning like the people that are currently employed, but also to ensure that we are proactively implementing initiatives to develop a talent of younger you know, a younger generation and a talent. So we can, you know, to inspire them and, and ensure that they, that we're seeing them represented in companies like AWS, you know and our customers, and in our partners. And obviously we, when we sit down with customers to craft these massive trainings you know, leveraging their ecosystems and communities, we actually try to use all our AWS training and certification portfolio which includes, you know, in live in class with live in structures, in classroom trainings. We also have our AWS Skill Builder platform which is the platform that allows us to, you know to reach a broader audience because it has, you know over 500 free and on-demand classes. And we also have a lot of different other programs that touches in different audiences. You know, we have AWS re/Start for underrepresented, and underemployed minorities. We also have AWS Academy, which is the program that we have for higher education institutions. And we have AWS, you know, Educate which also touches, you know, cloud beginners. So in every single of these programs, we ensure that we are encompassing and really speaking to women and developing training and developing women. >> Lisa: That's a great focus there. Laura, talk to me about upskilling. I know AWS is very much about promoting from within. What are some of the things that it's doing to help women in Latin America develop those tech skills and upskill from where, maybe where they are now? >> Well, Lisa, I think that is super interesting because there's definitely a skills gap problem, right? We have all heard about. And what's funny is also that we have this huge opportunity in Latin America to train people and to help further develop the countries. And we have the companies that need the talent. So why is there still a gap, right? And I think that's because there's no magic solution to solving this problem. No, like epic Hollywood movie scene that it's going to show how we close the gap. And it takes stepping out of our comfort zone. And as Carolina mentioned, collaborating. So, we at AWS have a commitment to help 29 million people globally to grow their technical skills with free cloud-computing skills training by 2025. I know that sounds a lot through educational programs but we do have as Carolina mentioned, a Skill Builder you can go into the website for free, enter, choose your path, get trained. We have Academy that we implement with universities. Re/Start that is a program that's already available in Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Costa Rica. So there are a lot of opportunities, but you also mentioned something else that I would like to dive a bit deeper that is Latin American women. And yesterday we had the opportunity to record a panel about intersectionality with three amazing Latin women. And what we have to learn from that is that these are two minorities that intersect, right. We're talking about females that are minority. Latinas are minority. And in tech, that is also something that is even bigger minority. So there are more difficulties there and we need to make sure that we are meeting that talent that is there that is in Latin America, that exists. We know for sure we have unicorns in Latin America that are even AWS customers like Mercado Libre, and we have to meet them with the opportunities. And that's why we created a program that came from identifying how this problem evolves in Latin America, that there is a lack of confidence in women also that they don't feel prepared or equipped. There is a cultural component why we don't choose tech careers. And we partner with universities, more than 12 universities in Latin America with the International American Development Bank as well to create tech skills that's a free five weeks program in order to get students and get female in Latin America, into the tech world. And we also have them with mentorship. So I think that is an opportunity to truly collaborate because we as AWS are not going to solve these by ourselves, right? We need everyone pitching in on that. >> Lisa: Right. It's absolutely a team effort. You mentioned something important in terms of helping women, and especially minorities get out of their comfort zone. Carolina, I'm curious when you're talking with women and getting them into the program and sharing with them all of the enablement programs that you have, how do you help them be confident to get out of that comfort zone? That's a hard thing to do. >> Yeah, no, for sure. For sure, Lisa, well, I, you know, a lot of times actually I use myself as an example because, you know, I studied engineering and industrial systems engineering many years ago. And you know, a lot of my career has been in in higher education and innovation and startups. And as I mentioned in the intro I've been at AWS for a little bit over two years. So I, my career has not been in cloud and I recently joined the cloud. So I actually had to go through our own trainings and get our own certifications. So I, that's, you know a lot of times I actually, I use my own example, so people understand that you don't have to come from tech, you don't have to come, you can actually be a non-tech person and, and also see the the benefits of the cloud. And you don't have to only, you know, learn cloud if you're in the IT department or in an IT team. So sometimes, I also emphasize that the cloud and the future is absolutely the cloud. In fact, the world economic foreign, you know teaches us that cloud-computing is that the technology that's going to be mostly adopted by 2025. So that's why we need to ensure that every single person, women and others are really knowledgeable in the cloud. So that's why, you know, technical and untechnical. But I, you know, I use myself as an example for them to say, you know, you can actually do it. And obviously also I collaborate with Laura and a lot of the women at Amazon Latin America Group to also you know, ensure that we're doing webinars and panels. So we show them ourselves as role model like, Laura is an incredible role model for our community. And so it's also to to show examples of what the possibilities are. And that's what we do. >> Lisa: I love that you're sharing >> And can I make a note there also? >> Please, yes. >> To add to that. I think it also requires the companies and the, and the private sector to get out of their comfort zone, right? Because we are not going to find solutions doing what we are already doing. We truly need to go and get near these persons with a new message. Their interest is there in these programs we have reached more than 3,000 women already in Latin America with tech skills. So it's not that women are not interested. It's like, how do we reach them with a message that resounds with them, right? Like how we can explain the power of technology to transform the world and to actually improve their communities. I think there's something there also that we need to think further of. >> It's so important. You know, we say often when we're talking about women in tech, that she needs to see what she can be or if she can't see it, she can't be it. So having those role models and those mentors and sponsors is absolutely critical for women to get, I call it getting comfortably uncomfortable out of that comfort zone and recognizing there's so many opportunities. Carolina, to your point, you know, these days every company is a tech company, a data company whether you're talking about a car dealer, a grocery market. So your point about, you know, and obviously the future being cloud there's so much opportunity that that opens up, for everybody really, but that's an important thing for people to recognize how they can be a part of that get out of their comfort zone and try something that they maybe hadn't considered before. >> Yes. And, actually, Lisa I would love to share an example. So we have a group, O Boticário, which is one of our customers one of the, the lead retails in Brazil. And they've been a customer of AWS since 2013 when they realized that, you know the urgency and the importance of embracing state of the art technology, to your point, like, you know this is a retail company that understands that needs to be, you know embrace digital transformation, especially because, you know they get very busy during mother's days and other holidays during the year. So they realized that they, instead of outsourcing their IT requirements to technology experts they decided to actually start developing and bringing the talent, you know within itself, within, you know, technology in-house. So they decided to start training within. And that's when we, obviously we partnered with them to also create a very comprehensive training and certification plan that started with, you know a lot of the infrastructure and security teams but then it was actually then implemented in the rest of the company. So going back to the point like everybody really needs to know. And what we also love about O Boticário is they they really care about the diversion and inclusion aspect of this equation. And we actually collaborated with them as well through this program called Desenvolve with the Brazilian government. And Desenvolve means developing Portuguese and they this program really ensures that we are also closing that gender and that race gap and ensuring that they're actually, you know, developing talent in cloud for Brazil. So we, you know, obviously have been very successful with them and we will continue to do even more things with them particular for this topic. >> Lisa: I've always known how customer focused AWS is every time we get to go to re:Invent or some of the events but it's so nice to hear these the educational programs that you're doing with customers to help them improve DEI to help them enable their own women in their organizations to learn skills. I didn't realize that. I think that's fantastic very much a symbiotic part of AWS. If we think about the theme for this year's International Women's Day, Breaking The Bias I want to get both of your opinions and Laura we'll start with you, what that means to you, and where do you think we are in Latin America with breaking the bias? >> Well, I think breaking the bias is the first step to truly being who we are every day and being able to bring that to our work as well. I think we are in a learning curve of that. The companies are changing culturally, as Carolina mentioned we have customers that are aware of the importance of having women. And as we say at AWS not only because there is a good business reason because there is, because there are studies that show that we can increase the country's CPD, but also because it's important and it's the right thing to do. So in terms of breaking the bias I think we are learning and we have a long way to go. I talked a bit earlier about intersectionality and that is something that is also important to highlight, right? Because we are talking about females but we are also talking about another minorities. We're talking about underrepresented communities, Indigenous People, Latins. So when these overlap, we face even bigger challenges to get where we want to get, right? And to get to decision making places because technology is transforming the ways we take decisions, we live, and we need someone like us taking those decisions. So I think it's important at first to be aware and to see that you can get there and eventually to start the conversation going and to build the conversation, not to just leave it but to make sure we hear people and their input and what they're going through. >> Lisa: Yes. We definitely need to hear them. Carolina, what's your take on breaking the bias and where do you from your experience, where do you think we are with it? >> Yeah, no, I'm as passionate as Laura on this topic. And that's why we, you know we're collaborating in the Women at Amazon Latin America Chapter, because we're both very, I think breaking the bias starts with us and ourselves. And we are very proactive within AWS and externally. And I feel it's also, I mean, Lisa, what we've been doing is not only, obviously gathering you know, the troops and really making sure that, that we have very aggressive goals internally, but also bringing you know, bringing our male counterparts, and other, you know, other members of the other communities, because the change, we're not going to make it alone. Like the change where it is not women only talking to women is going to make the change. We actually need to make sure the male and other groups are represented. And the dialogue that they're that we're very conscious about that. And I feel like we're seeing more and more that the topic is becoming more of a priority not only within AWS and Amazon but we also see it because now that I meet with when I meet with customers around the region they really want to see how we can collaborate in these diversion and inclusion initiatives. So I think we are breaking the bias because now this topic is more top of mind. And then we are being more proactively addressing it and and training people and educating people. And I feel we're really in a pivoted point where the change that we've really been wanting to we will see in the next you know, few years which is very exciting. >> Lisa: Excellent, and we'll see that with the help of women like you guys. Thank you so much for joining me today, talking about what you're doing, how you're helping organizations across AWS's ecosystem, customers, partners, and helping, of course, folks from within you, right. It's a holistic effort, but we are on our way to breaking that bias and again, I thank you both for your insights. >> Thank you. >> Thank you, Lisa, for the opportunity. >> My pleasure. For Carolina Pina and Laura Alvarez Modernel, I'm Lisa Martin. You're watching theCUBE's coverage of Women in Tech, International Women's Day 2022. (upbeat music)

(upbeat music) >> Hello and welcome to today's session of the 2021 AWS Global Public Sector Partner Awards Program. This session's award is going to be profiling the Most Customer Obsessed Mission-based Win in the education domain. I'm your host, Donald Klein, with theCUBE. And today we are joined by Fernando Castillo. He's the Business Development Manager at CloudHesive, and then also Luis Muñoz, who's the Information Director at the Unibersidad de Los Lagos. >> Okay, everyone. Welcome to today's session. All right. Fernando, thanks for taking some time out and joining us today. Wanted to start with you and wanted to hear a little bit of background about CloudHesive. Obviously, you're a company that had won an award last year, but you're back on this year, again. Want you give us some a little bit of the story of CloudHesive, and what kind of services you provide? (speaking in foreign language) >> Translator: Thank you very much, Donald. Yes, CloudHesive is a managed consulting service provider in the cloud. We are AWS Partner and since 2014 we have been providing solutions focusing on security, trustability, and scalability in the cloud. Accompany companies to their main objective, which is reducing operational costs and increasing their productivity as they move forward in the adaption of cloud services. >> Very good. Okay. And then Luis, I'm going to turn to you now, want you talk to us a little bit about your role there at the Unibersidad de Los Lagos, and how you started this project? (speaking in foreign language) >> Translator: Good afternoon. I belong to the academic department of the engineering department at the University of Los Lagos and the director of the IT of this school. For several years, for about five years, we've been analyzing the deployment of these automation at universities of Chile. Since it's not a common item in the country, we've done several benchmarking worldwide, especially in Spain, Mexico, Columbia, and places where it's more developed. And eventually, we have to take some demos that allowed us to make some decisions. This topic was not going to be considered in 2020, but it happened because of a political situation, social political in Chile in 2019. So we have to move forward the process, but we had already made a global analysis and this was one of the reasons why we have to get closer to AWS Partners and this allowed us to move this process forward within the university. >> Okay. Very good. All right. Well then, what I'm going to do now is I'm going to come back to you, Fernando, and I want you to talk a little bit about the overall goal of what you were trying to help the university with. (speaking in foreign language) >> Translator: Well, within the main objectives we had in the project was to have a platform that would support a concurrent load of thousands of students, especially in University of Los Lagos. They had requested to have around 15,000 students and the main complication or the main challenge was to keep a virtual attendance, which is now known as learning management system, but also having the possibility of having video classes in two days, something similar to what we are doing today, but with 50 or up to 100 students. This was one of the main objectives of the project. >> Okay, understood. So the goal is here to deploy this platform and open source platform and make it available for about 15,000 students. Okay. Now coming back to you, Luis, there was a time constraint here, correct? You needed to get the system going very quickly. Maybe you could explain why you needed to accelerate this program so quickly. (speaking in foreign language) >> Translator: Well, literally, the pandemic conditions in the country started to be more evident and more severe since the first week of March in 2020. And so we have to make the decision, the double-sided decision of choosing an infrastructure that we could not buy at that time, given the emergency, logistic emergency of the pandemic at the server's room and to keep a stable platform for that number of users, student and professors of university. So we started conversations to make this scale up and move everything to the cloud. This was the first decision. So we decided to use Amazon and with CloudHesive, we were able to organize the academics charter in the same platform. So as to move no longer than three weeks so that we could give classes, online classes with the students while we were learning this new normal, which was virtual distance education. This was very difficult of every morning, afternoon, and evening of work, but this allowed us not to fall behind in the first semester of the educational needs of the students. With this modality, we have around 5% more students that we used to last year in 2020, in March 2020. And this allowed us to have a more visible structure for those who were questioning this new modality and we were applied to take this new modality in the end. >> Okay. So because of the pandemic, you had to accelerate the deployment of this learning management system very quickly. And you had to learn how to manage the system at the same time that you were deploying it. Okay. Understood. So a lot of challenges there. All right. So then maybe coming back to you, Fernando. Wanted you talk about your role and how CloudHesive helped with this sort of this very rapid deployment of this LMS system. (speaking in foreign language) >> Translator: Well, talking about the challenges and how we were able to get to the objective, within the plan, deployment and development have to accompany the University of Los Lagos not only with the use of the platform, but also how to change management. One of the biggest challenges was to do a security audit, the deployment of scalable infrastructures. And one of the main topics was, one of the main challenges for CloudHesive that we can now talk about and obtained objective was to do the tests from the point of view of scalability and security getting into 15,000 students, concurrent students, stimulating the workload of the university, keeping 99.5 availability of the platform. Going back to the challenges, it's not only the scalability and stability. Nowadays, the University of Los Lagos platform can continue to grow, as Luis mentioned, without the need to look for new resources. But with our implementation, deployment and development, we already have a scalable resource as they increase the number of professors and students to their university. >> Okay. Understood, understood. Now, maybe talk a little bit just to continue with that point. Maybe talk for a minute about how you leverage the AWS platform in order to be able to accelerate this project. What aspects of your partnership with AWS enabled you to deploy the system so quickly? (speaking in foreign language) >> Translator: Well, talking about that, we based on a referential architecture of AWS, which is an open source middle platform, and within these competencies and within things, they belong to the education. We also have the problems, the presence of (indistinct), which allows us to deploy new solution and new integrations. So this allowed us as the team to, within weeks, to develop new features that would allow us to deal with each of the requirements of the universities, specifically. So within the first week, the University of Los Lagos had the connectivity with the academic sector. On the second week, they had the infrastructure to support out two-way videos. And on the third week, they already had the platform completely deployed with all the security safeguards that we already have in all of our products and services. So having worked hand-in-hand with AWS allowed us to have success in time with this platform. >> Wow. So that's fantastic. You were able to deploy this entire system from the connection with the academics to the video infrastructure to actually getting all the security implementations in place. You were able to do that in a three week cycle, is that correct? >> Yeah, that's correct. >> Fantastic. Okay. So Luis, coming back to you then, so working with CloudHesive as a partner to help deploy the platform on AWS gave you fantastic speed and agility to get the system working. Maybe talk a little bit now about the challenges of getting students and educators to adapt the system, and what kind of successes you had? (speaking in foreign language) >> Translator: First of all, they have to, we need to need to know the geography, the landscape of the university. The geography is very varied. We have mountains and lakes and so forth, and connectivity concepts are very difficult in this area. In addition, University of Los Lagos has the characteristic of receiving students from very poor sectors within the region. So this means that more than 80% have a free education, as there are few universities that exist in the country. So one of the technological challenges was for these students to receive the mechanisms and technology to have the connectivity they needed. After that, we had a very big training plan with the deployment company, CloudHesive, with the permissions, and eventually together, we were able to go beyond students and professors. And I remember we had 50% students and professors logged in to the platform, and nowadays, we have 100% students and professors logged in having classes in the platform. But most importantly, nowadays, we have an analytical control because of an integration with CloudHesive, with certain tools that allow us to gather data in real time. And we can do a follow-up of the student that is closer actually from the previous situation when we didn't have this technology. If the student is not logged in, we can reach them directly or indirectly to know, what is happening with his meeting, which is the kind of support, academic, social or economic support that they need. Before, it was harder to get this. So we have a communion between technology and social services that we can provide as a university. And of course, the adaptability of CloudHesive in as much as most of the requirements that we needed. So as to have a good response, they've been very providing, they provided a very robust service in this terms. >> Fantastic. So you were able to reach 100% percent of your target audience very quickly. Is that correct? Great. >> Yes. >> And maybe just to kind of follow up one more. Just talk a little bit about the future of your program. Now that you've worked so hard to establish the system and to connect your students and your teachers and to optimize the system, what is your plan to use it going forward? Are you looking to expand it? What would you say are your goals? (speaking in foreign language) >> Translator: First of all, for better or for worse, this modality came here to stay. The pandemic may end, but it generated opportunities that nationwide, it moved forward at least seven or eight times faster, these kinds of possibilities. So it's hard to use or waste this opportunity with the face-to-face classes. The university nowadays, thanks to the platform and the work done by CloudHesive and AWS, the university won ministry projects from the Ministry of Education in the country, have a strengthening plans for other kinds of services that were not incorporated before, like the idea of virtual library, research work, academic development work, of training and cultural transformation as well. But eventually, they are happening in this virtually environments. And the university won this possibility through the ministry, bridging the gap between the academic sector and the students. And in order to elaborate a little bit more from the previous question, we did a survey last year and ended not long ago. And most professors said that 80%, more than 80% said that the virtual environment was considered as good or very good. So we have a very good assessment in order to participate in this project that were won by the university and they are nowadays being applied. So this generates development in the academic sector, in research, in library, in content creation, global communication, working together with other universities with work postgraduate courses and other universities without the need of getting out of home. So this is a very competitive advantage that we didn't have before. And since 2020, we were able to develop. >> Fantastic. Well, congratulations on a really well put together program. And I'm excited to hear that you've won an award in your country and that you're planning to expand the system more broadly. I think that's a fantastic success story. So maybe just to wrap this up here with you Fernando, why don't you talk a little bit about, so obviously, you guys were very critical in helping this system be deployed very quickly, but very securely at the same time. How do you see your role going forward in enabling these types of situations, this distance learning type formats? (speaking in foreign language) >> Translator: Well, just as Luis said, taking this project with the University of Los Lagos, this showed the importance of looking at technological advances and to improve the universities and research centers and how to focus on innovation and bringing the future education down. For us, the data generated in this virtual interactions are very valuable and having a clear perspective, so as to organize this data for, to make more effective decisions that allow us to act in real time. This is what we are focusing on right now. So as to keep, I mean, prove, and being able to provide new tools, the research centers and universities to operate quickly, safely, and cost effectively. >> Okay, fantastic. So really, the real lesson learned here is by working with a partner like yourself, you were able take an open source learning management system and then deploy it very quickly, manage it, and then secure it in a way that allowed the university then to do their work. So I think that's a really great end-to-end delivery story. So I think, maybe if you want to make one last comment, Fernando, about your role in any kind of future expansion for this type of work. (speaking in foreign language) >> Translator: Yes, of course. I would like to thank Amazon and University of Los Lagos, of course for giving us the chance to work together and develop this project successfully. And answering your question, I would like to say that this is a good incentive to build more robust solutions, as long as we have our focus on our clients, when working and as a final comment, I would just would like to thank you and hope to see you again with a new project. >> Okay, well, congratulations to you both on winning this award. And for CloudHesive, this is your second year in a row of winning a Public Sector Award. So with that, I'm going to sign off today and I'm going to thank you both for attending. Today, we've had Fernando Castillo, the Business Development Manager from CloudHesive and then Luis Muñoz, the Information Director at the Uniberisdad de Los Lagos, and thank you both for attending. This is Donald Klein for theCUBE, until next time. (bright music)

(upbeat music) Developers have become the new king makers in the world of digital and cloud. The rise of containers and microservices has accelerated the transition to cloud native applications. A lot of people will talk about application architecture and the related paradigms and the benefits they bring for the process of writing and delivering new apps. But a major challenge continues to be, the how and the what when it comes to accessing, processing and getting insights from the massive amounts of data that we have to deal with in today's world. And with me are two experts from the data management world who will share with us how they think about the best techniques and practices based on what they see at large organizations who are working with data and developing so-called data-driven apps. Please welcome Maria Colgan and Gerald Venzl, two distinguish product managers from Oracle. Folks, welcome, thanks so much for coming on. >> Thanks for having us Dave. >> Thank you very much for having us. >> Okay, Maria let's start with you. So, we throw around this term data-driven, data-driven applications. What are we really talking about there? >> So data-driven applications are applications that work on a diverse set of data. So anything from spatial to sensor data, document data as well as your usual transaction processing data. And what they're going to do is they'll generate value from that data in very different ways to a traditional application. So for example, they may use machine learning, they are able to do product recommendations in the middle of a transaction. Or we could use graph to be able to identify an influencer within the community so we can target them with a specific promotion. It could also use spatial data to be able to help find the nearest stores to a particular customer. And because these apps are deployed on multiple platforms, everything from mobile devices as well as standard browsers, they need a data platform that's going to be both secure, reliable and scalable. >> Well, so when you think about how the workloads are shifting I mean, we're not talking about, you know it's not anymore a world of just your ERP or your HCM or your CRM, you know kind of the traditional operational systems. You really are seeing an explosion of these new data oriented apps. You're seeing, you know, modeling in the cloud, you are going to see more and more inferencing, inferencing at the edge. But Maria maybe you could talk a little bit about sort of the benefits that customers are seeing from developing these types of applications. I mean, why should people care about data-driven apps? >> Oh, for sure, there's massive benefits to them. I mean, probably the most obvious one for any business regardless of the industry, is that they not only allow you to understand what your customers are up to, but they allow you to be able to anticipate those customer's needs. So that helps businesses maintain that competitive edge and retain their customers. But it also helps them make data-driven decisions in real time based on actual data rather than on somebody's gut feeling or basing those decisions on historical data. So for example, you can do real-time price adjustments on products based on demand and so forth, that kind of thing. So it really changes the way people do business today. >> So Gerald, you think about the narrative in the industry everybody wants to be a platform player all your customers they are becoming software companies, they are becoming platform players. Everybody wants to be like, you know name a company that is huge trillion dollar market cap or whatever, and those are data-driven companies. And so it would seem to me that data-driven applications, there's nobody, no company really shouldn't be data-driven. Do you buy that? >> Yeah, absolutely. I mean, data-driven, and that naturally the whole industry is data-driven, right? It's like we all have information technologies about processing data and deriving information out of it. But when it comes to app development I think there is a big push to kind of like we have to do machine learning in our applications, we have to get insights from data. And when you actually look back a bit and take a step back, you see that there's of course many different kinds of applications out there as well that's not to be forgotten, right? So there is a usual front end user interfaces where really the application all it does is just entering some piece of information that's stored somewhere or perhaps a microservice that's not attached to a data to you at all but just receives or asks calls (indistinct). So I think it's not necessarily so important for every developer to kind of go on a bandwagon that they have to be data-driven. But I think it's equally important for those applications and those developers that build applications, that drive the business, that make business critical decisions as Maria mentioned before. Those guys should take really a close look into what data-driven apps means and what the data to you can actually give to them. Because what we see also happening a lot is that a lot of the things that are well known and out there just ready to use are being reimplemented in the applications. And for those applications, they essentially just ended up spending more time writing codes that will be already there and then have to maintain and debug the code as well rather than just going to market faster. >> Gerald can you talk to the prevailing approaches that developers take to build data-driven applications? What are the ones that you see? Let's dig into that a little bit more and maybe differentiate the different approaches and talk about that? >> Yeah, absolutely. I think right now the industry is like in two camps, it's like sort of a religious war going on that you'll see often happening with different architectures and so forth going on. So we have single purpose databases or data management technologies. Which are technologies that are as the name suggests build around a single purpose. So it's like, you know a typical example would be your ordinary key-value store. And a key-value store all it does is it allows you to store and retrieve a piece of data whatever that may be really, really fast but it doesn't really go beyond that. And then the other side of the house or the other camp would be multimodal databases, multimodal data management technologies. Those are technologies that allow you to store different types of data, different formats of data in the same technology in the same system alongside. And, you know, when you look at the geographics out there of what we have from technology, is pretty much any relational database or any database really has evolved into such a multimodal database. Whether that's MySQL that allows you to store or chase them alongside relational or even a MongoDB that allows you to do or gives you native graph support since (mumbles) and as well alongside the adjacent support. >> Well, it's clearly a trend in the industry. We've talked about this a lot in The Cube. We know where Oracle stands on this. I mean, you just mentioned MySQL but I mean, Oracle Databases you've been extending, you've mentioned JSON, we've got blockchain now in there you're infusing, you know ML and AI into the database, graph database capabilities, you know on and on and on. We talked a lot about we compared that to Amazon which is kind of the right tool, the right job approach. So maybe you could talk about, you know, your point of view, the benefits for developers of using that converged database if I can use that word approach being able to store multiple data formats? Why do you feel like that's a better approach? >> Yeah, I think on a high level it comes down to complexity. You are actually avoiding additional complexity, right? So not every use case that you have necessarily warrants to have yet another data management technology or yet the special build technology for managing that data, right? It's like many use cases that we see out there happily want to just store a piece of a chase and document, a piece of chase in a database and then perhaps retrieve it again afterwards so write some simple queries over it. And you really don't have to get a new database technology or a NoSQL database into the mix if you already have some to just fulfill that exact use case. You could just happily store that information as well in the database you already have. And what it really comes down to is the learning curve for developers, right? So it's like, as you use the same technology to store other types of data, you don't have to learn a new technology, you don't have to associate yourself with new and learn new drivers. You don't have to find new frameworks and you don't have to know how to necessarily operate or best model your data for that database. You can essentially just reuse your knowledge of the technology as well as the libraries and code you have already built in house perhaps in another application, perhaps, you know framework that you used against the same technology because it is still the same technology. So, kind of all comes down again to avoiding complexity rather than not fragmenting you know, the many different technologies we have. If you were to look at the different data formats that are out there today it's like, you know, you would end up with many different databases just to store them if you were to fully religiously follow the single purpose best built technology for every use case paradigm, right? And then you would just end up having to manage many different databases more than actually focusing on your app and getting value to your business or to your user. >> Okay, so I get that and I buy that by the way. I mean, especially if you're a larger organization and you've got all these projects going on but before we go back to Maria, Gerald, I want to just, I want to push on that a little bit. Because the counter to that argument would be in the analogy. And I wonder if you, I'd love for you to, you know knock this analogy off the blocks. The counter would be okay, Oracle is the Swiss Army knife and it's got, you know, all in one. But sometimes I need that specialized long screwdriver and I go into my toolbox and I grab that. It's better than the screwdriver in my Swiss Army knife. Why, are you the Swiss Army knife of databases? Or are you the all-in-one have that best of breed screwdriver for me? How do you think about that? >> Yeah, that's a fantastic question, right? And I think it's first of all, you have to separate between Oracle the company that has actually multiple data management technologies and databases out there as you said before, right? And Oracle Database. And I think Oracle Database is definitely a Swiss Army knife has many capabilities of since the last 40 years, you know that we've seen object support coming that's still in the Oracle Database today. We have seen XML coming, it's still in the Oracle Database, graph, spatial, et cetera. And so you have many different ways of managing your data and then on top of that going into the converge, not only do we allow you to store the different data model in there but we actually allow you also to, you apply all the security policies and so forth on top of it something Maria can talk more about the mission around converged database. I would also argue though that for some aspects, we do actually have to or add a screwdriver that you talked about as well. So especially in the relational world people get very quickly hung up on this idea that, oh, if you only do rows and columns, well, that's kind of what you put down on disk. And that was never true, it's the relational model is actually a logical model. What's probably being put down on disk is blocks that align themselves nice with block storage and always has been. So that allows you to actually model and process the data sort of differently. And one common example or one good example that we have that we introduced a couple of years ago was when, column and databases were very strong and you know, the competition came it's like, yeah, we have In-Memory column that stores now they're so much better. And we were like, well, orienting the data role-based or column-based really doesn't matter in the sense that we store them as blocks on disks. And so we introduced the in memory technology which gives you an In-Memory column, a representation of your data as well alongside your relational. So there is an example where you go like, well, actually you know, if you have this use case of the column or analytics all In-Memory, I would argue Oracle Database is also that screwdriver you want to go down to and gives you that capability. Because not only gives you representation in columnar, but also which many people then forget all the analytic power on top of SQL. It's one thing to store your data columnar, it's a completely different story to actually be able to run analytics on top of that and having all the built-in functionalities and stuff that you want to do with the data on top of it as you analyze it. >> You know, that's a great example, the kilometer 'cause I remember there was like a lot of hype around it. Oh, it's the Oracle killer, you know, at Vertica. Vertica is still around but, you know it never really hit escape velocity. But you know, good product, good company, whatever. Natezza, it kind of got buried inside of IBM. ParXL kind of became, you know, red shift with that deal so that kind of went away. Teradata bought a company, I forget which company it bought but. So that hype kind of disapated and now it's like, oh yeah, columnar. It's kind of like In-Memory, we've had a In-Memory databases ever since we've had databases you know, it's a kind of a feature not a sector. But anyway, Maria, let's come back to you. You've got a lot of customer experience. And you speak with a lot of companies, you know during your time at Oracle. What else are you seeing in terms of the benefits to this approach that might not be so intuitive and obvious right away? >> I think one of the biggest benefits to having a multimodel multiworkload or as we call it a converged database, is the fact that you can get greater data synergy from it. In other words, you can utilize all these different techniques and data models to get better value out of that data. So things like being able to do real-time machine learning, fraud detection inside a transaction or being able to do a product recommendation by accessing three different data models. So for example, if I'm trying to recommend a product for you Dave, I might use graph analytics to be able to figure out your community. Not just your friends, but other people on our system who look and behave just like you. Once I know that community then I can go over and see what products they bought by looking up our product catalog which may be stored as JSON. And then on top of that I can then see using the key-value what products inside that catalog those community members gave a five star rating to. So that way I can really pinpoint the right product for you. And I can do all of that in one transaction inside the database without having to transform that data into different models or God forbid, access different systems to be able to get all of that information. So it really simplifies how we can generate that value from the data. And of course, the other thing our customers love is when it comes to deploying data-driven apps, when you do it on a converged database it's much simpler because it is that standard data platform. So you're not having to manage multiple independent single purpose databases. You're not having to implement the security and the high availability policies, you know across a bunch of different diverse platforms. All of that can be done much simpler with a converged database 'cause the DBA team of course, is going to just use that standard set of tools to manage, monitor and secure those systems. >> Thank you for that. And you know, it's interesting, you talk about simplification and you are in Juan's organization so you've big focus on mission critical. And so one of the things that I think is often overlooked well, we talk about all the time is recovery. And if things are simpler, recovery is faster and easier. And so it's kind of the hallmark of Oracle is like the gold standard of the toughest apps, the most mission critical apps. But I wanted to get to the cloud Maria. So because everything is going to the cloud, right? Not all workloads are going to the cloud but everybody is talking about the cloud. Everybody has cloud first mentality and so yes, it's a hybrid world. But the natural next question is how do you think the cloud fits into this world of data-driven apps? >> I think just like any app that you're developing, the cloud helps to accelerate that development. And of course the deployment of these data-driven applications. 'Cause if you think about it, the developer is instantly able to provision a converged database that Oracle will automatically manage and look after for them. But what's great about doing something like that if you use like our autonomous database service is that it comes in different flavors. So you can get autonomous transaction processing, data warehousing or autonomous JSON so that the developer is going to get a database that's been optimized for their specific use case, whatever they are trying to solve. And it's also going to contain all of that great functionality and capabilities that we've been talking about. So what that really means to the developer though is as the project evolves and inevitably the business needs change a little, there's no need to panic when one of those changes comes in because your converged database or your autonomous database has all of those additional capabilities. So you can simply utilize those to able to address those evolving changes in the project. 'Cause let's face it, none of us normally know exactly what we need to build right at the very beginning. And on top of that they also kind of get a built-in buddy in the cloud, especially in the autonomous database. And that buddy comes in the form of built-in workload optimizations. So with the autonomous database we do things like automatic indexing where we're using machine learning to be that buddy for the developer. So what it'll do is it'll monitor the workload and see what kind of queries are being run on that system. And then it will actually determine if there are indexes that should be built to help improve the performance of that application. And not only does it bill those indexes but it verifies that they help improve the performance before publishing it to the application. So by the time the developer is finished with that app and it's ready to be deployed, it's actually also been optimized by the developers buddy, the Oracle autonomous database. So, you know, it's a really nice helping hand for developers when they're building any app especially data-driven apps. >> I like how you sort of gave us, you know the truth here is you don't always know where you're going when you're building an app. It's like it goes from you are trying to build it and they will come to start building it and we'll figure out where it's going to go. With Agile that's kind of how it works. But so I wonder, can you give some examples of maybe customers or maybe genericize them if you need to. Data-driven apps in the cloud where customers were able to drive more efficiency, where the cloud buddy allowed the customers to do more with less? >> No, we have tons of these but I'll try and keep it to just a couple. One that comes to mind straight away is retrace. These folks built a blockchain app in the Oracle Cloud that allows manufacturers to actually share the supply chain with the consumer. So the consumer can see exactly, who made their product? Using what raw materials? Where they were sourced from? How it was done? All of that is visible to the consumer. And in order to be able to share that they had to work on a very diverse set of data. So they had everything from JSON documents to images as well as your traditional transactions in there. And they store all of that information inside the Oracle autonomous database, they were able to build their app and deploy it on the cloud. And they were able to do all of that very, very quickly. So, you know, that ability to work on multiple different data types in a single database really helped them build that product and get it to market in a very short amount of time. Another customer that's doing something really, really interesting is MindSense. So these guys operate the largest mines in Canada, Chile, and Peru. But what they do is they put these x-ray devices on the massive mechanical shovels that are at the cove or at the mine face. And what that does is it senses the contents of the buckets inside these mining machines. And it's looking to see at that content, to see how it can optimize the processing of the ore inside in that bucket. So they're looking to minimize the amount of power and water that it's going to take to process that. And also of course, minimize the amount of waste that's going to come out of that project. So all of that sensor data is sent into an autonomous database where it's going to be processed by a whole host of different users. So everything from the mine engineers to the geo scientists, to even their own data scientists utilize that data to drive their business forward. And what I love about these guys is they're not happy with building just one app. MindSense actually use our built-in low core development environment, APEX that comes as part of the autonomous database and they actually produce applications constantly for different aspects of their business using that technology. And it's actually able to accelerate those new apps to the business. It takes them now just a couple of days or weeks to produce an app instead of months or years to build those new apps. >> Great, thank you for that Maria. Gerald, I'm going to push you again. So, I said upfront and talked about microservices and the cloud and containers and you know, anybody in the developer space follows that very closely. But some of the things that we've been talking about here people might look at that and say, well, they're kind of antithetical to microservices. This is our Oracles monolithic approach. But when you think about the benefits of microservices, people want freedom of choice, technology choice, seen as a big advantage of microservices and containers. How do you address such an argument? >> Yeah, that's an excellent question and I get that quite often. The microservices architecture in general as I said before had architectures, Linux distributions, et cetera. It's kind of always a bit of like there's an academic approach and there's a pragmatic approach. And when you look at the microservices the original definitions that came out at the early 2010s. They actually never said that each microservice has to have a database. And they also never said that if a microservice has a database, you have to use a different technology for each microservice. Just like they never said, you have to write a microservice in a different programming language, right? So where I'm going with this is like, yes you know, sometimes when you look at some vendors out there, some niche players, they push this message or they jump on this academic approach of like each microservice has the best tool at hand or I'd use a different database for your purpose, et cetera. Which almost often comes across like us. You know, we want to stay part of the conversation. Nothing stops a developer from, you know using a multimodal database for the microservice and just using that as a document store, right? Or just using that as a relational database. And, you know, sometimes I mean, it was actually something that happened that was really interesting yesterday I don't know whether you follow Dave or not. But Facebook had an outage yesterday, right? And Facebook is one of those companies that are seen as the Silicon Valley, you know know how to do microservices companies. And when you add through the outage, well, what happened, right? Some unfortunate logical error with configuration as a force that took a database cluster down. So, you know, there you have it where you go like, well, maybe not every microservice is actually in fact talking to its own database or its own special purpose database. I think there, you know, well, what we should, the industry should be focusing much more on this argument of which technology to use? What's the right tool for a job? Is more to ask themselves, what business problem actually are we trying to solve? And therefore what's the right approach and the right technology for this. And so therefore, just as I said before, you know multimodal databases they do have strong benefits. They have many built-in functionalities that are already there and they allow you to reduce this complexity of having to know many different technologies, right? And so it's not only to store different data models either you know, treat a multimodal database as a chasing documents store or a relational database but most databases are multimodal since 20 plus years. But it's also actually being able to perhaps if you store that data together, you can perhaps actually derive additional value for somebody else but perhaps not for your application. But like for example, if you were to use Oracle Database you can actually write queries on top of all of that data. It doesn't really matter for our query engine whether it's the data is format that then chase or the data is formatted in rows and columns you can just rather than query over it. And that's actually very powerful for those guys that have to, you know get the reporting done the end of the day, the end of the week. And for those guys that are the data scientists that they want to figure out, you know which product performed really well or can we tweak something here and there. When you look into that space you still see a huge divergence between the guys to put data in kind of the altarpiece style and guys that try to derive new insights. And there's still a lot of ETL going around and, you know we have big data technologies that some of them come and went and some of them came in that are still around like Apache Spark which is still like a SQL engine on top of any of your data kind of going back to the same concept. And so I will say that, you know, for developers when we look at microservices it's like, first of all, is the argument you were making because the vendor or the technology you want to use tells you this argument or, you know, you kind of want to have an argument to use a specific technology? Or is it really more because it is the best technology, to best use for this given use case for this given application that you have? And if so there's of course, also nothing wrong to use a single purpose technology either, right? >> Yeah, I mean, whenever I talk about Oracle I always come back to the most important applications, the mission critical. It's very difficult to architect databases with microservices and containers. You have to be really, really careful. And so and again, it comes back to what we were talking before about with Maria that the complexity and the recovery. But Gerald I want to stay with you for a minute. So there's other data management technologies popping out there. I mean, I've seen some people saying, okay just leave the data in an S3 bucket. We can query that, then we've got some magic sauce to do that. And so why are you optimistic about you know, traditional database technology going forward? >> I would say because of the history of databases. So one thing that once struck me when I came to Oracle and then got to meet great people like Juan Luis and Andy Mendelsohn who had been here for a long, long time. I come to realization that relational databases are around for about 45 years now. And, you know, I was like, I'm too young to have been around then, right? So I was like, what else was around 45 years? It's like just the tech stack that we have today. It's like, how does this look like? Well, Linux only came out in 93. Well, databases pre-date Linux a lot rather than as I started digging I saw a lot of technologies come and go, right? And you mentioned before like the technologies that data management systems that we had that came and went like the columnar databases or XML databases, object databases. And even before relational databases before Cot gave us the relational model there were apparently these networks stores network databases which to some extent look very similar to adjacent documents. There wasn't a harder storing data and a hierarchy to format. And, you know when you then start actually reading the Cot paper and diving a little bit more into the relation model, that's I think one important crux in there that most of the industry keeps forgetting or it hasn't been around to even know. And that is that when Cot created the relational model, he actually focused not so much on the application putting the data in, but on future users and applications still being able to making sense out of the data, right? And that's kind of like I said before we had those network models, we had XML databases you have adjacent documents stores. And the one thing that they all have along with it is like the application that puts the data in decides the structure of the data. And that's all well and good if you had an application of the developer writing an application. It can become really tricky when 10 years later you still want to look at that data and the application that the developer is no longer around then you go like, what does this all mean? Where is the structure defined? What is this attribute? What does it mean? How does it correlate to others? And the one thing that people tend to forget is that it's actually the data that's here to stay not someone who does the applications where it is. Ideally, every company wants to store every single byte of data that they have because there might be future value in it. Economically may not make sense that's now much more feasible than just years ago. But if you could, why wouldn't you want to store all your data, right? And sometimes you actually have to store the data for seven years or whatever because the laws require you to. And so coming back then and you know, like 10 years from now and looking at the data and going like making sense of that data can actually become a lot more difficult and a lot more challenging than having to first figure out and how we store this data for general use. And that kind of was what the relational model was all about. We decompose the data structures into tables and columns with relationships amongst each other so therefore between each other. So that therefore if somebody wants to, you know typical example would be well you store some purchases from your web store, right? There's a customer attribute in it. There's some credit card payment information in it, just some product information on what the customer bought. Well, in the relational model if you just want to figure out which products were sold on a given day or week, you just would query the payment and products table to get the sense out of it. You don't need to touch the customer and so forth. And with the hierarchical model you have to first sit down and understand how is the structure, what is the customer? Where is the payment? You know, does the document start with the payment or does it start with the customer? Where do I find this information? And then in the very early days those databases even struggled to then not having to scan all the documents to get the data out. So coming back to your question a bit, I apologize for going on here. But you know, it's like relational databases have been around for 45 years. I actually argue it's one of the most successful software technologies that we have out there when you look in the overall industry, right? 45 years is like, in IT terms it's like from a star being the ones who are going supernova. You have said it before that many technologies coming and went, right? And just want to add a more really interesting example by the way is Hadoop and HDFS, right? They kind of gave us this additional promise of like, you know, the 2010s like 2012, 2013 the hype of Hadoop and so forth and (mumbles) and HDFS. And people are just like, just put everything into HDFS and worry about the data later, right? And we can query it and map reduce it and whatever. And we had customers actually coming to us they were like, great we have half a petabyte of data on an HDFS cluster and we have no clue what's stored in there. How do we figure this out? What are we going to do now? Now you had a big data cleansing problem. And so I think that is why databases and also data modeling is something that will not go away anytime soon. And I think databases and database technologies are here for quite a while to stay. Because many of those are people they don't think about what's happening to the data five years from now. And many of the niche players also and also frankly even Amazon you know, following with this single purpose thing is like, just use the right tool for the job for your application, right? Just pull in the data there the way you wanted. And it's like, okay, so you use technologies all over the place and then five years from now you have your data fragmented everywhere in different formats and, you know inconsistencies, and, and, and. And those are usually when you come back to this data-driven business critical business decision applications the worst case scenario you can have, right? Because now you need an army of people to actually do data cleansing. And there's not a coincidence that data science has become very, very popular the last recent years as we kind of went on with this proliferation of different database or data management technologies some of those are not even database. But I think I leave it at that. >> It's an interesting talk track because you're right. I mean, no schema on right was alluring, but it definitely created some problems. It also created an entire, you know you referenced the hyper specialized roles and did the data cleansing component. I mean, maybe technology will eventually solve that problem but it hasn't up at least up tonight. Okay, last question, Maria maybe you could start off and Gerald if you want to chime in as well it'd be great. I mean, it's interesting to watch this industry when Oracle sort of won the top database mantle. I mean, I watched it, I saw it. It was, remember it was Informix and it was (indistinct) too and of course, Microsoft you got to give them credit with SQL server, but Oracle won the database wars. And then everything got kind of quiet for awhile database was sort of boring. And then it exploded, you know, all the, you know not only SQL and the key-value stores and the cloud databases and this is really a hot area now. And when we looked at Oracle we said, okay, Oracle it's all about Oracle Database, but we've seen the kind of resurgence in MySQL which everybody thought, you know once Oracle bought Sun they were going to kill MySQL. But now we see you investing in HeatWave, TimesTen, we talked about In-Memory databases before. So where do those fit in Maria in the grand scheme? How should we think about Oracle's database portfolio? >> So there's lots of places where you'd use those different things. 'Cause just like any other industry there are going to be new and boutique use cases that are going to benefit from a more specialized product or single purpose product. So good examples off the top of my head of the kind of systems that would benefit from that would be things like a stock exchange system or a telephone exchange system. Both of those are latency critical transaction processing applications where they need microsecond response times. And that's going to exceed perhaps what you might normally get or deploy with a converged database. And so Oracle's TimesTen database our In-Memory database is perfect for those kinds of applications. But there's also a host of MySQL applications out there today and you said it yourself there Dave, HeatWave is a great place to provision and deploy those kinds of applications because it's going to run 100 times faster than AWS (mumbles). So, you know, there really is a place in the market and in our customer's systems and the needs they have for all of these different members of our database family here at Oracle. >> Yeah, well, the internet is basically running in the lamp stack so I see MySQL going away. All right Gerald, will give you the final word, bring us home. >> Oh, thank you very much. Yeah, I mean, as Maria said, I think it comes back to what we discussed before. There is obviously still needs for special technologies or different technologies than a relational database or multimodal database. Oracle has actually many more databases that people may first think of. Not only the three that we have already mentioned but there's even SP so the Oracle's NoSQL database. And, you know, on a high level Oracle is a data management company, right? And we want to give our customers the best tools and the best technology to manage all of their data. Rather than therefore there has to be a need or there should be a part of the business that also focuses on this highly specialized systems and this highly specialized technologies that address those use cases. And I think it makes perfect sense. It's like, you know, when the customer comes to Oracle they're not only getting this, take this one product you know, and if you don't like it your problem but actually you have choice, right? And choice allows you to make a decision based on what's best for you and not necessarily best for the vendor you're talking to. >> Well guys, really appreciate your time today and your insights. Maria, Gerald, thanks so much for coming on The Cube. >> Thank you very much for having us. >> And thanks for watching this Cube conversation this is Dave Vellante and we'll see you next time. (upbeat music)

(upbeat music) >> It'll be five, four, three and then silent two, one, and then you guys just follow my lead. We're just making some last minute adjustments. Like I said, we're down two hands today. So, you good Alex? Okay, are you guys ready? >> I'm ready. >> Ready. >> I got to get get one note here. >> So I noticed Maria you stopped anyway, so I have time. >> Just so they know Dave and the Boston Studio, are they both kind of concurrently be on film even when they're not speaking or will only the speaker be on film for like if Gerald's drawing while Maria is talking about-- >> Sorry but then I missed one part of my onboarding spiel. There should be, if you go into gallery there should be a label. There should be something labeled Boston live switch feed. If you pin that gallery view you'll see what our program currently being recorded is. So any time you don't see yourself on that feed is an excellent time to take a drink of water, scratch your nose, check your notes. Do whatever you got to do off screen. >> Can you give us a three shot, Alex? >> Yes, there it is. >> And then go to me, just give me a one-shot to Dave. So when I'm here you guys can take a drink or whatever >> That makes sense? >> Yeah. >> Excellent, I will get my recordings restarted and we'll open up when Dave's ready. >> All right, you guys ready? >> Ready. >> All right Steve, you go on mute. >> Okay, on me in 5, 4, 3. Developers have become the new king makers in the world of digital and cloud. The rise of containers and microservices has accelerated the transition to cloud native applications. A lot of people will talk about application architecture and the related paradigms and the benefits they bring for the process of writing and delivering new apps. But a major challenge continues to be, the how and the what when it comes to accessing, processing and getting insights from the massive amounts of data that we have to deal with in today's world. And with me are two experts from the data management world who will share with us how they think about the best techniques and practices based on what they see at large organizations who are working with data and developing so-called data-driven apps. Please welcome Maria Colgan and Gerald Venzl, two distinguish product managers from Oracle. Folks, welcome, thanks so much for coming on. >> Thanks for having us Dave. >> Thank you very much for having us. >> Okay, Maria let's start with you. So, we throw around this term data-driven, data-driven applications. What are we really talking about there? >> So data-driven applications are applications that work on a diverse set of data. So anything from spatial to sensor data, document data as well as your usual transaction processing data. And what they're going to do is they'll generate value from that data in very different ways to a traditional application. So for example, they may use machine learning, they are able to do product recommendations in the middle of a transaction. Or we could use graph to be able to identify an influencer within the community so we can target them with a specific promotion. It could also use spatial data to be able to help find the nearest stores to a particular customer. And because these apps are deployed on multiple platforms, everything from mobile devices as well as standard browsers, they need a data platform that's going to be both secure, reliable and scalable. >> Well, so when you think about how the workloads are shifting I mean, we're not talking about, you know it's not anymore a world of just your ERP or your HCM or your CRM, you know kind of the traditional operational systems. You really are seeing an explosion of these new data oriented apps. You're seeing, you know, modeling in the cloud, you are going to see more and more inferencing, inferencing at the edge. But Maria maybe you could talk a little bit about sort of the benefits that customers are seeing from developing these types of applications. I mean, why should people care about data-driven apps? >> Oh, for sure, there's massive benefits to them. I mean, probably the most obvious one for any business regardless of the industry, is that they not only allow you to understand what your customers are up to, but they allow you to be able to anticipate those customer's needs. So that helps businesses maintain that competitive edge and retain their customers. But it also helps them make data-driven decisions in real time based on actual data rather than on somebody's gut feeling or basing those decisions on historical data. So for example, you can do real-time price adjustments on products based on demand and so forth, that kind of thing. So it really changes the way people do business today. >> So Gerald, you think about the narrative in the industry everybody wants to be a platform player all your customers they are becoming software companies, they are becoming platform players. Everybody wants to be like, you know name a company that is huge trillion dollar market cap or whatever, and those are data-driven companies. And so it would seem to me that data-driven applications, there's nobody, no company really shouldn't be data-driven. Do you buy that? >> Yeah, absolutely. I mean, data-driven, and that naturally the whole industry is data-driven, right? It's like we all have information technologies about processing data and deriving information out of it. But when it comes to app development I think there is a big push to kind of like we have to do machine learning in our applications, we have to get insights from data. And when you actually look back a bit and take a step back, you see that there's of course many different kinds of applications out there as well that's not to be forgotten, right? So there is a usual front end user interfaces where really the application all it does is just entering some piece of information that's stored somewhere or perhaps a microservice that's not attached to a data to you at all but just receives or asks calls (indistinct). So I think it's not necessarily so important for every developer to kind of go on a bandwagon that they have to be data-driven. But I think it's equally important for those applications and those developers that build applications, that drive the business, that make business critical decisions as Maria mentioned before. Those guys should take really a close look into what data-driven apps means and what the data to you can actually give to them. Because what we see also happening a lot is that a lot of the things that are well known and out there just ready to use are being reimplemented in the applications. And for those applications, they essentially just ended up spending more time writing codes that will be already there and then have to maintain and debug the code as well rather than just going to market faster. >> Gerald can you talk to the prevailing approaches that developers take to build data-driven applications? What are the ones that you see? Let's dig into that a little bit more and maybe differentiate the different approaches and talk about that? >> Yeah, absolutely. I think right now the industry is like in two camps, it's like sort of a religious war going on that you'll see often happening with different architectures and so forth going on. So we have single purpose databases or data management technologies. Which are technologies that are as the name suggests build around a single purpose. So it's like, you know a typical example would be your ordinary key-value store. And a key-value store all it does is it allows you to store and retrieve a piece of data whatever that may be really, really fast but it doesn't really go beyond that. And then the other side of the house or the other camp would be multimodal databases, multimodal data management technologies. Those are technologies that allow you to store different types of data, different formats of data in the same technology in the same system alongside. And, you know, when you look at the geographics out there of what we have from technology, is pretty much any relational database or any database really has evolved into such a multimodal database. Whether that's MySQL that allows you to store or chase them alongside relational or even a MongoDB that allows you to do or gives you native graph support since (mumbles) and as well alongside the adjacent support. >> Well, it's clearly a trend in the industry. We've talked about this a lot in The Cube. We know where Oracle stands on this. I mean, you just mentioned MySQL but I mean, Oracle Databases you've been extending, you've mentioned JSON, we've got blockchain now in there you're infusing, you know ML and AI into the database, graph database capabilities, you know on and on and on. We talked a lot about we compared that to Amazon which is kind of the right tool, the right job approach. So maybe you could talk about, you know, your point of view, the benefits for developers of using that converged database if I can use that word approach being able to store multiple data formats? Why do you feel like that's a better approach? >> Yeah, I think on a high level it comes down to complexity. You are actually avoiding additional complexity, right? So not every use case that you have necessarily warrants to have yet another data management technology or yet the special build technology for managing that data, right? It's like many use cases that we see out there happily want to just store a piece of a chase and document, a piece of chase in a database and then perhaps retrieve it again afterwards so write some simple queries over it. And you really don't have to get a new database technology or a NoSQL database into the mix if you already have some to just fulfill that exact use case. You could just happily store that information as well in the database you already have. And what it really comes down to is the learning curve for developers, right? So it's like, as you use the same technology to store other types of data, you don't have to learn a new technology, you don't have to associate yourself with new and learn new drivers. You don't have to find new frameworks and you don't have to know how to necessarily operate or best model your data for that database. You can essentially just reuse your knowledge of the technology as well as the libraries and code you have already built in house perhaps in another application, perhaps, you know framework that you used against the same technology because it is still the same technology. So, kind of all comes down again to avoiding complexity rather than not fragmenting you know, the many different technologies we have. If you were to look at the different data formats that are out there today it's like, you know, you would end up with many different databases just to store them if you were to fully religiously follow the single purpose best built technology for every use case paradigm, right? And then you would just end up having to manage many different databases more than actually focusing on your app and getting value to your business or to your user. >> Okay, so I get that and I buy that by the way. I mean, especially if you're a larger organization and you've got all these projects going on but before we go back to Maria, Gerald, I want to just, I want to push on that a little bit. Because the counter to that argument would be in the analogy. And I wonder if you, I'd love for you to, you know knock this analogy off the blocks. The counter would be okay, Oracle is the Swiss Army knife and it's got, you know, all in one. But sometimes I need that specialized long screwdriver and I go into my toolbox and I grab that. It's better than the screwdriver in my Swiss Army knife. Why, are you the Swiss Army knife of databases? Or are you the all-in-one have that best of breed screwdriver for me? How do you think about that? >> Yeah, that's a fantastic question, right? And I think it's first of all, you have to separate between Oracle the company that has actually multiple data management technologies and databases out there as you said before, right? And Oracle Database. And I think Oracle Database is definitely a Swiss Army knife has many capabilities of since the last 40 years, you know that we've seen object support coming that's still in the Oracle Database today. We have seen XML coming, it's still in the Oracle Database, graph, spatial, et cetera. And so you have many different ways of managing your data and then on top of that going into the converge, not only do we allow you to store the different data model in there but we actually allow you also to, you apply all the security policies and so forth on top of it something Maria can talk more about the mission around converged database. I would also argue though that for some aspects, we do actually have to or add a screwdriver that you talked about as well. So especially in the relational world people get very quickly hung up on this idea that, oh, if you only do rows and columns, well, that's kind of what you put down on disk. And that was never true, it's the relational model is actually a logical model. What's probably being put down on disk is blocks that align themselves nice with block storage and always has been. So that allows you to actually model and process the data sort of differently. And one common example or one good example that we have that we introduced a couple of years ago was when, column and databases were very strong and you know, the competition came it's like, yeah, we have In-Memory column that stores now they're so much better. And we were like, well, orienting the data role-based or column-based really doesn't matter in the sense that we store them as blocks on disks. And so we introduced the in memory technology which gives you an In-Memory column, a representation of your data as well alongside your relational. So there is an example where you go like, well, actually you know, if you have this use case of the column or analytics all In-Memory, I would argue Oracle Database is also that screwdriver you want to go down to and gives you that capability. Because not only gives you representation in columnar, but also which many people then forget all the analytic power on top of SQL. It's one thing to store your data columnar, it's a completely different story to actually be able to run analytics on top of that and having all the built-in functionalities and stuff that you want to do with the data on top of it as you analyze it. >> You know, that's a great example, the kilometer 'cause I remember there was like a lot of hype around it. Oh, it's the Oracle killer, you know, at Vertica. Vertica is still around but, you know it never really hit escape velocity. But you know, good product, good company, whatever. Natezza, it kind of got buried inside of IBM. ParXL kind of became, you know, red shift with that deal so that kind of went away. Teradata bought a company, I forget which company it bought but. So that hype kind of disapated and now it's like, oh yeah, columnar. It's kind of like In-Memory, we've had a In-Memory databases ever since we've had databases you know, it's a kind of a feature not a sector. But anyway, Maria, let's come back to you. You've got a lot of customer experience. And you speak with a lot of companies, you know during your time at Oracle. What else are you seeing in terms of the benefits to this approach that might not be so intuitive and obvious right away? >> I think one of the biggest benefits to having a multimodel multiworkload or as we call it a converged database, is the fact that you can get greater data synergy from it. In other words, you can utilize all these different techniques and data models to get better value out of that data. So things like being able to do real-time machine learning, fraud detection inside a transaction or being able to do a product recommendation by accessing three different data models. So for example, if I'm trying to recommend a product for you Dave, I might use graph analytics to be able to figure out your community. Not just your friends, but other people on our system who look and behave just like you. Once I know that community then I can go over and see what products they bought by looking up our product catalog which may be stored as JSON. And then on top of that I can then see using the key-value what products inside that catalog those community members gave a five star rating to. So that way I can really pinpoint the right product for you. And I can do all of that in one transaction inside the database without having to transform that data into different models or God forbid, access different systems to be able to get all of that information. So it really simplifies how we can generate that value from the data. And of course, the other thing our customers love is when it comes to deploying data-driven apps, when you do it on a converged database it's much simpler because it is that standard data platform. So you're not having to manage multiple independent single purpose databases. You're not having to implement the security and the high availability policies, you know across a bunch of different diverse platforms. All of that can be done much simpler with a converged database 'cause the DBA team of course, is going to just use that standard set of tools to manage, monitor and secure those systems. >> Thank you for that. And you know, it's interesting, you talk about simplification and you are in Juan's organization so you've big focus on mission critical. And so one of the things that I think is often overlooked well, we talk about all the time is recovery. And if things are simpler, recovery is faster and easier. And so it's kind of the hallmark of Oracle is like the gold standard of the toughest apps, the most mission critical apps. But I wanted to get to the cloud Maria. So because everything is going to the cloud, right? Not all workloads are going to the cloud but everybody is talking about the cloud. Everybody has cloud first mentality and so yes, it's a hybrid world. But the natural next question is how do you think the cloud fits into this world of data-driven apps? >> I think just like any app that you're developing, the cloud helps to accelerate that development. And of course the deployment of these data-driven applications. 'Cause if you think about it, the developer is instantly able to provision a converged database that Oracle will automatically manage and look after for them. But what's great about doing something like that if you use like our autonomous database service is that it comes in different flavors. So you can get autonomous transaction processing, data warehousing or autonomous JSON so that the developer is going to get a database that's been optimized for their specific use case, whatever they are trying to solve. And it's also going to contain all of that great functionality and capabilities that we've been talking about. So what that really means to the developer though is as the project evolves and inevitably the business needs change a little, there's no need to panic when one of those changes comes in because your converged database or your autonomous database has all of those additional capabilities. So you can simply utilize those to able to address those evolving changes in the project. 'Cause let's face it, none of us normally know exactly what we need to build right at the very beginning. And on top of that they also kind of get a built-in buddy in the cloud, especially in the autonomous database. And that buddy comes in the form of built-in workload optimizations. So with the autonomous database we do things like automatic indexing where we're using machine learning to be that buddy for the developer. So what it'll do is it'll monitor the workload and see what kind of queries are being run on that system. And then it will actually determine if there are indexes that should be built to help improve the performance of that application. And not only does it bill those indexes but it verifies that they help improve the performance before publishing it to the application. So by the time the developer is finished with that app and it's ready to be deployed, it's actually also been optimized by the developers buddy, the Oracle autonomous database. So, you know, it's a really nice helping hand for developers when they're building any app especially data-driven apps. >> I like how you sort of gave us, you know the truth here is you don't always know where you're going when you're building an app. It's like it goes from you are trying to build it and they will come to start building it and we'll figure out where it's going to go. With Agile that's kind of how it works. But so I wonder, can you give some examples of maybe customers or maybe genericize them if you need to. Data-driven apps in the cloud where customers were able to drive more efficiency, where the cloud buddy allowed the customers to do more with less? >> No, we have tons of these but I'll try and keep it to just a couple. One that comes to mind straight away is retrace. These folks built a blockchain app in the Oracle Cloud that allows manufacturers to actually share the supply chain with the consumer. So the consumer can see exactly, who made their product? Using what raw materials? Where they were sourced from? How it was done? All of that is visible to the consumer. And in order to be able to share that they had to work on a very diverse set of data. So they had everything from JSON documents to images as well as your traditional transactions in there. And they store all of that information inside the Oracle autonomous database, they were able to build their app and deploy it on the cloud. And they were able to do all of that very, very quickly. So, you know, that ability to work on multiple different data types in a single database really helped them build that product and get it to market in a very short amount of time. Another customer that's doing something really, really interesting is MindSense. So these guys operate the largest mines in Canada, Chile, and Peru. But what they do is they put these x-ray devices on the massive mechanical shovels that are at the cove or at the mine face. And what that does is it senses the contents of the buckets inside these mining machines. And it's looking to see at that content, to see how it can optimize the processing of the ore inside in that bucket. So they're looking to minimize the amount of power and water that it's going to take to process that. And also of course, minimize the amount of waste that's going to come out of that project. So all of that sensor data is sent into an autonomous database where it's going to be processed by a whole host of different users. So everything from the mine engineers to the geo scientists, to even their own data scientists utilize that data to drive their business forward. And what I love about these guys is they're not happy with building just one app. MindSense actually use our built-in low core development environment, APEX that comes as part of the autonomous database and they actually produce applications constantly for different aspects of their business using that technology. And it's actually able to accelerate those new apps to the business. It takes them now just a couple of days or weeks to produce an app instead of months or years to build those new apps. >> Great, thank you for that Maria. Gerald, I'm going to push you again. So, I said upfront and talked about microservices and the cloud and containers and you know, anybody in the developer space follows that very closely. But some of the things that we've been talking about here people might look at that and say, well, they're kind of antithetical to microservices. This is our Oracles monolithic approach. But when you think about the benefits of microservices, people want freedom of choice, technology choice, seen as a big advantage of microservices and containers. How do you address such an argument? >> Yeah, that's an excellent question and I get that quite often. The microservices architecture in general as I said before had architectures, Linux distributions, et cetera. It's kind of always a bit of like there's an academic approach and there's a pragmatic approach. And when you look at the microservices the original definitions that came out at the early 2010s. They actually never said that each microservice has to have a database. And they also never said that if a microservice has a database, you have to use a different technology for each microservice. Just like they never said, you have to write a microservice in a different programming language, right? So where I'm going with this is like, yes you know, sometimes when you look at some vendors out there, some niche players, they push this message or they jump on this academic approach of like each microservice has the best tool at hand or I'd use a different database for your purpose, et cetera. Which almost often comes across like us. You know, we want to stay part of the conversation. Nothing stops a developer from, you know using a multimodal database for the microservice and just using that as a document store, right? Or just using that as a relational database. And, you know, sometimes I mean, it was actually something that happened that was really interesting yesterday I don't know whether you follow Dave or not. But Facebook had an outage yesterday, right? And Facebook is one of those companies that are seen as the Silicon Valley, you know know how to do microservices companies. And when you add through the outage, well, what happened, right? Some unfortunate logical error with configuration as a force that took a database cluster down. So, you know, there you have it where you go like, well, maybe not every microservice is actually in fact talking to its own database or its own special purpose database. I think there, you know, well, what we should, the industry should be focusing much more on this argument of which technology to use? What's the right tool for a job? Is more to ask themselves, what business problem actually are we trying to solve? And therefore what's the right approach and the right technology for this. And so therefore, just as I said before, you know multimodal databases they do have strong benefits. They have many built-in functionalities that are already there and they allow you to reduce this complexity of having to know many different technologies, right? And so it's not only to store different data models either you know, treat a multimodal database as a chasing documents store or a relational database but most databases are multimodal since 20 plus years. But it's also actually being able to perhaps if you store that data together, you can perhaps actually derive additional value for somebody else but perhaps not for your application. But like for example, if you were to use Oracle Database you can actually write queries on top of all of that data. It doesn't really matter for our query engine whether it's the data is format that then chase or the data is formatted in rows and columns you can just rather than query over it. And that's actually very powerful for those guys that have to, you know get the reporting done the end of the day, the end of the week. And for those guys that are the data scientists that they want to figure out, you know which product performed really well or can we tweak something here and there. When you look into that space you still see a huge divergence between the guys to put data in kind of the altarpiece style and guys that try to derive new insights. And there's still a lot of ETL going around and, you know we have big data technologies that some of them come and went and some of them came in that are still around like Apache Spark which is still like a SQL engine on top of any of your data kind of going back to the same concept. And so I will say that, you know, for developers when we look at microservices it's like, first of all, is the argument you were making because the vendor or the technology you want to use tells you this argument or, you know, you kind of want to have an argument to use a specific technology? Or is it really more because it is the best technology, to best use for this given use case for this given application that you have? And if so there's of course, also nothing wrong to use a single purpose technology either, right? >> Yeah, I mean, whenever I talk about Oracle I always come back to the most important applications, the mission critical. It's very difficult to architect databases with microservices and containers. You have to be really, really careful. And so and again, it comes back to what we were talking before about with Maria that the complexity and the recovery. But Gerald I want to stay with you for a minute. So there's other data management technologies popping out there. I mean, I've seen some people saying, okay just leave the data in an S3 bucket. We can query that, then we've got some magic sauce to do that. And so why are you optimistic about you know, traditional database technology going forward? >> I would say because of the history of databases. So one thing that once struck me when I came to Oracle and then got to meet great people like Juan Luis and Andy Mendelsohn who had been here for a long, long time. I come to realization that relational databases are around for about 45 years now. And, you know, I was like, I'm too young to have been around then, right? So I was like, what else was around 45 years? It's like just the tech stack that we have today. It's like, how does this look like? Well, Linux only came out in 93. Well, databases pre-date Linux a lot rather than as I started digging I saw a lot of technologies come and go, right? And you mentioned before like the technologies that data management systems that we had that came and went like the columnar databases or XML databases, object databases. And even before relational databases before Cot gave us the relational model there were apparently these networks stores network databases which to some extent look very similar to adjacent documents. There wasn't a harder storing data and a hierarchy to format. And, you know when you then start actually reading the Cot paper and diving a little bit more into the relation model, that's I think one important crux in there that most of the industry keeps forgetting or it hasn't been around to even know. And that is that when Cot created the relational model, he actually focused not so much on the application putting the data in, but on future users and applications still being able to making sense out of the data, right? And that's kind of like I said before we had those network models, we had XML databases you have adjacent documents stores. And the one thing that they all have along with it is like the application that puts the data in decides the structure of the data. And that's all well and good if you had an application of the developer writing an application. It can become really tricky when 10 years later you still want to look at that data and the application that the developer is no longer around then you go like, what does this all mean? Where is the structure defined? What is this attribute? What does it mean? How does it correlate to others? And the one thing that people tend to forget is that it's actually the data that's here to stay not someone who does the applications where it is. Ideally, every company wants to store every single byte of data that they have because there might be future value in it. Economically may not make sense that's now much more feasible than just years ago. But if you could, why wouldn't you want to store all your data, right? And sometimes you actually have to store the data for seven years or whatever because the laws require you to. And so coming back then and you know, like 10 years from now and looking at the data and going like making sense of that data can actually become a lot more difficult and a lot more challenging than having to first figure out and how we store this data for general use. And that kind of was what the relational model was all about. We decompose the data structures into tables and columns with relationships amongst each other so therefore between each other. So that therefore if somebody wants to, you know typical example would be well you store some purchases from your web store, right? There's a customer attribute in it. There's some credit card payment information in it, just some product information on what the customer bought. Well, in the relational model if you just want to figure out which products were sold on a given day or week, you just would query the payment and products table to get the sense out of it. You don't need to touch the customer and so forth. And with the hierarchical model you have to first sit down and understand how is the structure, what is the customer? Where is the payment? You know, does the document start with the payment or does it start with the customer? Where do I find this information? And then in the very early days those databases even struggled to then not having to scan all the documents to get the data out. So coming back to your question a bit, I apologize for going on here. But you know, it's like relational databases have been around for 45 years. I actually argue it's one of the most successful software technologies that we have out there when you look in the overall industry, right? 45 years is like, in IT terms it's like from a star being the ones who are going supernova. You have said it before that many technologies coming and went, right? And just want to add a more really interesting example by the way is Hadoop and HDFS, right? They kind of gave us this additional promise of like, you know, the 2010s like 2012, 2013 the hype of Hadoop and so forth and (mumbles) and HDFS. And people are just like, just put everything into HDFS and worry about the data later, right? And we can query it and map reduce it and whatever. And we had customers actually coming to us they were like, great we have half a petabyte of data on an HDFS cluster and we have no clue what's stored in there. How do we figure this out? What are we going to do now? Now you had a big data cleansing problem. And so I think that is why databases and also data modeling is something that will not go away anytime soon. And I think databases and database technologies are here for quite a while to stay. Because many of those are people they don't think about what's happening to the data five years from now. And many of the niche players also and also frankly even Amazon you know, following with this single purpose thing is like, just use the right tool for the job for your application, right? Just pull in the data there the way you wanted. And it's like, okay, so you use technologies all over the place and then five years from now you have your data fragmented everywhere in different formats and, you know inconsistencies, and, and, and. And those are usually when you come back to this data-driven business critical business decision applications the worst case scenario you can have, right? Because now you need an army of people to actually do data cleansing. And there's not a coincidence that data science has become very, very popular the last recent years as we kind of went on with this proliferation of different database or data management technologies some of those are not even database. But I think I leave it at that. >> It's an interesting talk track because you're right. I mean, no schema on right was alluring, but it definitely created some problems. It also created an entire, you know you referenced the hyper specialized roles and did the data cleansing component. I mean, maybe technology will eventually solve that problem but it hasn't up at least up tonight. Okay, last question, Maria maybe you could start off and Gerald if you want to chime in as well it'd be great. I mean, it's interesting to watch this industry when Oracle sort of won the top database mantle. I mean, I watched it, I saw it. It was, remember it was Informix and it was (indistinct) too and of course, Microsoft you got to give them credit with SQL server, but Oracle won the database wars. And then everything got kind of quiet for awhile database was sort of boring. And then it exploded, you know, all the, you know not only SQL and the key-value stores and the cloud databases and this is really a hot area now. And when we looked at Oracle we said, okay, Oracle it's all about Oracle Database, but we've seen the kind of resurgence in MySQL which everybody thought, you know once Oracle bought Sun they were going to kill MySQL. But now we see you investing in HeatWave, TimesTen, we talked about In-Memory databases before. So where do those fit in Maria in the grand scheme? How should we think about Oracle's database portfolio? >> So there's lots of places where you'd use those different things. 'Cause just like any other industry there are going to be new and boutique use cases that are going to benefit from a more specialized product or single purpose product. So good examples off the top of my head of the kind of systems that would benefit from that would be things like a stock exchange system or a telephone exchange system. Both of those are latency critical transaction processing applications where they need microsecond response times. And that's going to exceed perhaps what you might normally get or deploy with a converged database. And so Oracle's TimesTen database our In-Memory database is perfect for those kinds of applications. But there's also a host of MySQL applications out there today and you said it yourself there Dave, HeatWave is a great place to provision and deploy those kinds of applications because it's going to run 100 times faster than AWS (mumbles). So, you know, there really is a place in the market and in our customer's systems and the needs they have for all of these different members of our database family here at Oracle. >> Yeah, well, the internet is basically running in the lamp stack so I see MySQL going away. All right Gerald, will give you the final word, bring us home. >> Oh, thank you very much. Yeah, I mean, as Maria said, I think it comes back to what we discussed before. There is obviously still needs for special technologies or different technologies than a relational database or multimodal database. Oracle has actually many more databases that people may first think of. Not only the three that we have already mentioned but there's even SP so the Oracle's NoSQL database. And, you know, on a high level Oracle is a data management company, right? And we want to give our customers the best tools and the best technology to manage all of their data. Rather than therefore there has to be a need or there should be a part of the business that also focuses on this highly specialized systems and this highly specialized technologies that address those use cases. And I think it makes perfect sense. It's like, you know, when the customer comes to Oracle they're not only getting this, take this one product you know, and if you don't like it your problem but actually you have choice, right? And choice allows you to make a decision based on what's best for you and not necessarily best for the vendor you're talking to. >> Well guys, really appreciate your time today and your insights. Maria, Gerald, thanks so much for coming on The Cube. >> Thank you very much for having us. >> And thanks for watching this Cube conversation this is Dave Vellante and we'll see you next time. (upbeat music)

>> Narrator: From around the globe, it's the CUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome back to IBM Think 2021. My name is Dave Vellante. And I'm pleased to welcome onto the CUBE, our next guest, Michael Perera, who is the general manager for IBM Technology Support Services. Hello Michael, good to see you. >> Hi Dave, how are you? Thanks for having me. >> Yeah, my pleasure. Look, everybody wants to talk about transformation. And we're going to talk about how to do that while at the same time running your business. So Michael, talk about some of the challenges that businesses are facing today, they got to keep the lights on, they got to deal with remote workers, they got to continue to bring new products and services, they're dealing with cloud migration, they got new security that has to worry about endpoint and identifying their own workers in a different way. Budget serves are depressed are numbers, you know, our numbers between four minus four minus 5% last year, we're seeing a big uptick this year. But you guys TSS right in the middle of all that, what are you seeing? >> Yeah, so we're kind of in the boiler room, so to speak, supporting our clients across the board, hardware, software, and everything else, and ultimately ensuring that our clients keep the lights on, while they also transform as we go forward. You know, for me, the last year has really just accelerated with the pandemic, all of the challenges. And it really brought shining light on those challenges that you just mentioned, that all of our clients are trying to deal with, you know, not just how do they keep the lights on? But how do they transform at the same time, this world of hybrid cloud? And what do they choose to keep versus what do they move? How do they integrate those things together? How do they carve out budget, as well as time in order to make all those things happen, which those are generally conflicting forces of the universe. And then, you know, on top of all that, you take COVID, and the pandemic and the shift from many of our clients 100% face to face to 100% remote, almost overnight, from 80% face to face, 20% digital sales model to the reverse almost overnight. Our retail clients, many of which in May had transaction numbers far exceeding Cyber Monday and Black Friday, not something that they plan for, but they need to be able to adapt to it. And for it, while minimizing everything that they've known historically, right, which is counting on lower volumes at certain points in time of the month, or of the year. And all of that adds up to just a tremendous number of challenges for the infrastructure of our clients. We've jumped in, you know, arm and arm with them, being able to answer things like how do we help their teams who no longer have physical access to a site, be able to go and fix things when vendors are not allowed. So leveraging technology, like augmented reality, as an example, gaining visibility into those environments to avoid outages ahead of time based on these huge peaks that they hadn't expected or seen before. And then also bringing up brand new digital services, and what does that mean to the broader infrastructure and how they extend it and expand it in a way that is constrained physically and from an access perspective. So definitely an exciting time to say the least. And it's we've been weaving and bobbing and dodging and sprinting with our clients along the way. >> Well, let's talk about (murmurs), 'cause you had this tight budget climate that we both talked about. And it had basic infrastructure, you had to buy laptops, you know, secure the endpoints, maybe spin up some VDI and do some things that I hadn't planned on, and maybe, you know, HQ, maybe there's pent up demand there. I'd be interested in your thoughts, and maybe it's been sort of, you know, neglected over the past 12-14 months. And then I've got this, you know, we talked about digital transformation, pre pandemic. And, you know, there was some movement, of course, but there was also a lot of complacency. And then he had this forced march to digital, and it wasn't planned at all, it wasn't planned for, it wasn't strategic, it was just like, go. So what do you tell clients who are facing those budget pressures, they still got to get stuff done. And they really need to rethink or think through their cloud and digital transformation strategy. What's that conversation like? >> Well, the first part is we can help and we can help very clearly by saving them 30% on average on their IT spend in terms of maintenance. So we've done in conjunction with Forrester, we've done a study of almost 300 of our clients over the last year and 30% is the number that they have spent. And that's 30% opex, straight to the bottom line or straight to reinvest directly back into their business. So it's companies like McKesson, who's a health care services provider, who's been swamped, distributing COVID vaccines across the US and enabling them to scale on IBM Power and storage along with Cisco Networking, software, including Linux, what they do around hard drive retentions, as they're swapping things in and out and expanding in order to meet regulatory requirements. It's Vodafone in New Zealand, adding 3000 network devices due to increased traffic from COVID, where we could save them 20% right off the bat as part of our overall umbrella maintenance agreement and being the single point of contact for them. It's Banco Santader in Chile, who have their own custom branch infrastructure and giving them anywhere between the two to 24 hour response time, depending on the location, the ones that are in the Andes takes a little bit more time to get there sometime by helicopter versus road, but nonetheless, you know, providing that kind of support. So those are the types of things that, you know, we've been seeing and how we've been helping our clients, they take that money reinvest it back in, but also, they start to work better and smarter as they go. So, you know, we've also introduced a cloud based support insights platform, which has helped clients like Maple Leaf Foods in Canada give them access and visibility into what is their network look like? What are the devices that they've got? Where do they have security vulnerabilities and in identifying hardware and software bugs. So giving them the ability to work smarter, so that they can also not just save on opex and the money that they're paying somebody else for maintenance, but also so that they can put their resources to work more efficiently and as a result, be able to go spend more time on other things? >> So I want to double click on that. So you know, this gain sharing idea. Does IT get any of that? Or does it all go back to the CFO? In other words, you know, can they reinvest that in in technology? Or is it part of that? What are you seeing there is that pie in the sky thinking the CIO is going to be able to take that game share? >> No, I don't think it's pie in the sky at all. CIOs, in my experience, have a budget, right, and they're responsible and have control of that budget. So if they can clear headroom from that existing budget, an opex of which maintenance is a big piece of that then, you know, generally, that's their money, so to speak, to go spend on other places and redirect that investment so that as you're reporting to the CFO, then that numbers ultimately still tie back to whatever their budget is. >> So where are they spending those dollars? I mean, are there any patterns that you're discerning in terms of how they're applying them? I mean, people always say, we're going to shift it to more strategic areas. What specifically does that mean? >> Well, so you know, we're seeing a number of places which are not, you know, unique, to say the least when you look at security, as one example, if you look at move to public cloud, for certain workloads, as another enterprise agility is a third, resiliency is another. So those tend to be the top areas that we're seeing clients prioritizing, and in taking those savings that they get from working with us and then applying them other places from a technology perspective. But then you also have the workforce aspect, and where are they investing and work play safety is one training skills being another and then ultimately, employee engagement and satisfaction is the third. >> Now this might be a little bit out of your swim lane, but because you're in the boiler room, I'm going to ask I mean, when we talked about organizations, you know, shifting the focus of their teams to these more strategic initiatives to really try to get differentiation and build moats that a lot of times, there's skills gaps, so how are clients dealing with that challenge? >> Also, there's a couple of things that we're participating and co-creating with our clients on. So one of them is you're right there based on that skills gap. Training is one aspect. But you can also leverage technology in order to fill some of those skills gaps around technology, somewhat ironic. So open source as an example, and looking at what open source packages are compatible or not compatible. And people who have not necessarily spent a lot of time in open source may spend a ton of time trying to debug something which is just a matter of a mismatch on packages from different open source runtimes as an example, so that's one where we've got assets that we've developed that holds a full library of those interoperability between open source packages. Vulnerabilities is another one where, if you're highly skilled, you know where to go to find those vulnerabilities, you understand how to assess them, you understand which ones are important or which ones are not important. But if you're not, then having something that you can go use as a quick guide is can be very valuable. And again, another asset that that we've developed, and it's enabled clients to move very quickly and bring brand new applications to market. So as an example, National Telecom in Thailand who have developed an application for specifically for the COVID pandemic, based on open source in order to attract COVID testing and vaccine status for tourists, and essential personnel, all built on open source, given the critical nature of it, they needed it supported in a way that they could get immediate responses and fixes, not something that they have the skills to do on their own. So we ended up partnering them in order to do just that. >> Okay, so the training piece, you're teaching them to fish, and then you're automating the catch where possible. So let's talk about getting a lot of talk about cloud, public cloud, OnPrem, cross cloud, edge. I'm interested in hearing more about the integration challenges, the more this universe grows, the more complex it gets across all these locations. How are you helping clients address these integration challenges? >> Yeah, so, you know, I think that the ultimate promise of cloud was, oh, you just put it all in the cloud. And poof, everything magically happens. But the reality is, only 20% of the workloads are sitting in the cloud, which means 80% of them are sitting somewhere else. And the vast majority of those workloads need to interact together. And you can ask yourself, so why is it only 20%? And there's a litany of reasons why ranging from security to integration with data sources, regulatory requirements, which is why we IBM released the financial services, public cloud in order to deal with that for our clients and with ISVs. End to end visibility and scalability. So how do I know where the bottlenecks are? How do I know where the problem point was, and an end to end application that's built of microservices that are running all over the place, architectural flexibility and complexity across multiple vendors. So if I've got all of these moving parts from all of these different OEMs, or sources, how do I actually get support and know which part is broken? And who to call and when to call? And then, you know, ultimately, it boils down to skills, which we talked about before and time and money. So, again, you know, for us this is about taking the holistic approach, a heterogeneous approach, a hybrid approach, if you will, and being able to provide our clients with the end to end support for that hybrid environment. >> Alright, last question, big question. But we're not much time but, you know, the, we call it the new abnormal, look, bring out your telescope. We're not going back. Where are we going? What do you see? >> Well, so I agree 100%, that we're not going back. And the pandemic has certainly done nothing to change that perspective. In fact, it's just accelerated it from my point of view. And it's true in the adoption, and more acceptance, really, of digital everything compared to where it was. We see it today all the time with clients who may have been hesitant in remote support as an example. But now they're embracing it with arms wide open, areas where they would have asked for us to provide technical personnel to come in and fix something. Now, because of access to data centers or unlimited access to data centers, we're supporting them remotely leveraging augmented reality, and they're using their own people, we ship the parts, they use your own people, we walk them through it. And in doing all that, we've actually seen our industry leading Net Promoter Score go up, which is somewhat counterintuitive, because historically, without a pandemic, you would have thought, if we would have tried to push that type of technology on clients who are not really ready for it or accepting, our Net Promoter Scores would have gone the other direction. But you know, in practice, they're already outpacing industry by 20 points, and they've actually been going up significantly over the last few years time. So for us, this is about embracing digital, it's about embracing the hybrid cloud and hybrid environments. It's about partnering with our clients in order to give them what they need and when they need it and be flexible and agile along the way to help them scale so definitely an exciting time no doubt of where we are as well as where we're going. >> Love the story, Michael, I miss bread and butter. You know, maybe you guys don't get a lot of the headlines, I guess unless something goes wrong but so you don't get a lot of headlines. That's good news. But congratulations by the way on the NPS. That's awesome. And thanks for coming on the CUBE. >> Great, thanks for having me Dave. >> You're welcome, and thank you for watching everybody. Keep it right there for more great content from IBM Think 2021. This is Dave Vellante for the CUBE. 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(bright music) >> Narrator: From around the globe, it's the CUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome back to IBM Think 2021. My name is Dave Vellante. And I'm pleased to welcome onto the CUBE, our next guest, Michael Perera, who is the general manager for IBM Technology Support Services. Hello Michael, good to see you. >> Hi Dave, how are you? Thanks for having me. >> Yeah, my pleasure. Look, everybody wants to talk about transformation. And we're going to talk about how to do that while at the same time running your business. So Michael, talk about some of the challenges that businesses are facing today, they got to keep the lights on, they got to deal with remote workers, they got to continue to bring new products and services, they're dealing with cloud migration, they got new security that has to worry about endpoint and identifying their own workers in a different way. Budget serves are depressed are numbers, you know, our numbers between four minus four minus 5% last year, we're seeing a big uptick this year. But you guys TSS right in the middle of all that, what are you seeing? >> Yeah, so we're kind of in the boiler room, so to speak, supporting our clients across the board, hardware, software, and everything else, and ultimately ensuring that our clients keep the lights on, while they also transform as we go forward. You know, for me, the last year has really just accelerated with the pandemic, all of the challenges. And it really brought shining light on those challenges that you just mentioned, that all of our clients are trying to deal with, you know, not just how do they keep the lights on? But how do they transform at the same time, this world of hybrid cloud? And what do they choose to keep versus what do they move? How do they integrate those things together? How do they carve out budget, as well as time in order to make all those things happen, which those are generally conflicting forces of the universe. And then, you know, on top of all that, you take COVID, and the pandemic and the shift from many of our clients 100% face to face to 100% remote, almost overnight, from 80% face to face, 20% digital sales model to the reverse almost overnight. Our retail clients, many of which in May had transaction numbers far exceeding Cyber Monday and Black Friday, not something that they plan for, but they need to be able to adapt to it. And for it, while minimizing everything that they've known historically, right, which is counting on lower volumes at certain points in time of the month, or of the year. And all of that adds up to just a tremendous number of challenges for the infrastructure of our clients. We've jumped in, you know, arm and arm with them, being able to answer things like how do we help their teams who no longer have physical access to a site, be able to go and fix things when vendors are not allowed. So leveraging technology, like augmented reality, as an example, gaining visibility into those environments to avoid outages ahead of time based on these huge peaks that they hadn't expected or seen before. And then also bringing up brand new digital services, and what does that mean to the broader infrastructure and how they extend it and expand it in a way that is constrained physically and from an access perspective. So definitely an exciting time to say the least. And it's we've been weaving and bobbing and dodging and sprinting with our clients along the way. >> Well, let's talk about (murmurs), 'cause you had this tight budget climate that we both talked about. And it had basic infrastructure, you had to buy laptops, you know, secure the endpoints, maybe spin up some VDI and do some things that I hadn't planned on, and maybe, you know, HQ, maybe there's pent up demand there. I'd be interested in your thoughts, and maybe it's been sort of, you know, neglected over the past 12-14 months. And then I've got this, you know, we talked about digital transformation, pre pandemic. And, you know, there was some movement, of course, but there was also a lot of complacency. And then he had this forced march to digital, and it wasn't planned at all, it wasn't planned for, it wasn't strategic, it was just like, go. So what do you tell clients who are facing those budget pressures, they still got to get stuff done. And they really need to rethink or think through their cloud and digital transformation strategy. What's that conversation like? >> Well, the first part is we can help and we can help very clearly by saving them 30% on average on their IT spend in terms of maintenance. So we've done in conjunction with Forrester, we've done a study of almost 300 of our clients over the last year and 30% is the number that they have spent. And that's 30% opex, straight to the bottom line or straight to reinvest directly back into their business. So it's companies like McKesson, who's a health care services provider, who's been swamped, distributing COVID vaccines across the US and enabling them to scale on IBM Power and storage along with Cisco Networking, software, including Linux, what they do around hard drive retentions, as they're swapping things in and out and expanding in order to meet regulatory requirements. It's Vodafone in New Zealand, adding 3000 network devices due to increased traffic from COVID, where we could save them 20% right off the bat as part of our overall umbrella maintenance agreement and being the single point of contact for them. It's Banco Santader in Chile, who have their own custom branch infrastructure and giving them anywhere between the two to 24 hour response time, depending on the location, the ones that are in the Andes takes a little bit more time to get there sometime by helicopter versus road, but nonetheless, you know, providing that kind of support. So those are the types of things that, you know, we've been seeing and how we've been helping our clients, they take that money reinvest it back in, but also, they start to work better and smarter as they go. So, you know, we've also introduced a cloud based support insights platform, which has helped clients like Maple Leaf Foods in Canada give them access and visibility into what is their network look like? What are the devices that they've got? Where do they have security vulnerabilities and in identifying hardware and software bugs. So giving them the ability to work smarter, so that they can also not just save on opex and the money that they're paying somebody else for maintenance, but also so that they can put their resources to work more efficiently and as a result, be able to go spend more time on other things? >> So I want to double click on that. So you know, this gain sharing idea. Does IT get any of that? Or does it all go back to the CFO? In other words, you know, can they reinvest that in in technology? Or is it part of that? What are you seeing there is that pie in the sky thinking the CIO is going to be able to take that game share? >> No, I don't think it's pie in the sky at all. CIOs, in my experience, have a budget, right, and they're responsible and have control of that budget. So if they can clear headroom from that existing budget, an opex of which maintenance is a big piece of that then, you know, generally, that's their money, so to speak, to go spend on other places and redirect that investment so that as you're reporting to the CFO, then that numbers ultimately still tie back to whatever their budget is. >> So where are they spending those dollars? I mean, are there any patterns that you're discerning in terms of how they're applying them? I mean, people always say, we're going to shift it to more strategic areas. What specifically does that mean? >> Well, so you know, we're seeing a number of places which are not, you know, unique, to say the least when you look at security, as one example, if you look at move to public cloud, for certain workloads, as another enterprise agility is a third, resiliency is another. So those tend to be the top areas that we're seeing clients prioritizing, and in taking those savings that they get from working with us and then applying them other places from a technology perspective. But then you also have the workforce aspect, and where are they investing and work play safety is one training skills being another and then ultimately, employee engagement and satisfaction is the third. >> Now this might be a little bit out of your swim lane, but because you're in the boiler room, I'm going to ask I mean, when we talked about organizations, you know, shifting the focus of their teams to these more strategic initiatives to really try to get differentiation and build moats that a lot of times, there's skills gaps, so how are clients dealing with that challenge? >> Also, there's a couple of things that we're participating and co-creating with our clients on. So one of them is you're right there based on that skills gap. Training is one aspect. But you can also leverage technology in order to fill some of those skills gaps around technology, somewhat ironic. So open source as an example, and looking at what open source packages are compatible or not compatible. And people who have not necessarily spent a lot of time in open source may spend a ton of time trying to debug something which is just a matter of a mismatch on packages from different open source runtimes as an example, so that's one where we've got assets that we've developed that holds a full library of those interoperability between open source packages. Vulnerabilities is another one where, if you're highly skilled, you know where to go to find those vulnerabilities, you understand how to assess them, you understand which ones are important or which ones are not important. But if you're not, then having something that you can go use as a quick guide is can be very valuable. And again, another asset that that we've developed, and it's enabled clients to move very quickly and bring brand new applications to market. So as an example, National Telecom in Thailand who have developed an application for specifically for the COVID pandemic, based on open source in order to attract COVID testing and vaccine status for tourists, and essential personnel, all built on open source, given the critical nature of it, they needed it supported in a way that they could get immediate responses and fixes, not something that they have the skills to do on their own. So we ended up partnering them in order to do just that. >> Okay, so the training piece, you're teaching them to fish, and then you're automating the catch where possible. So let's talk about getting a lot of talk about cloud, public cloud, OnPrem, cross cloud, edge. I'm interested in hearing more about the integration challenges, the more this universe grows, the more complex it gets across all these locations. How are you helping clients address these integration challenges? >> Yeah, so, you know, I think that the ultimate promise of cloud was, oh, you just put it all in the cloud. And poof, everything magically happens. But the reality is, only 20% of the workloads are sitting in the cloud, which means 80% of them are sitting somewhere else. And the vast majority of those workloads need to interact together. And you can ask yourself, so why is it only 20%? And there's a litany of reasons why ranging from security to integration with data sources, regulatory requirements, which is why we IBM released the financial services, public cloud in order to deal with that for our clients and with ISVs. End to end visibility and scalability. So how do I know where the bottlenecks are? How do I know where the problem point was, and an end to end application that's built of microservices that are running all over the place, architectural flexibility and complexity across multiple vendors. So if I've got all of these moving parts from all of these different OEMs, or sources, how do I actually get support and know which part is broken? And who to call and when to call? And then, you know, ultimately, it boils down to skills, which we talked about before and time and money. So, again, you know, for us this is about taking the holistic approach, a heterogeneous approach, a hybrid approach, if you will, and being able to provide our clients with the end to end support for that hybrid environment. >> Alright, last question, big question. But we're not much time but, you know, the, we call it the new abnormal, look, bring out your telescope. We're not going back. Where are we going? What do you see? >> Well, so I agree 100%, that we're not going back. And the pandemic has certainly done nothing to change that perspective. In fact, it's just accelerated it from my point of view. And it's true in the adoption, and more acceptance, really, of digital everything compared to where it was. We see it today all the time with clients who may have been hesitant in remote support as an example. But now they're embracing it with arms wide open, areas where they would have asked for us to provide technical personnel to come in and fix something. Now, because of access to data centers or unlimited access to data centers, we're supporting them remotely leveraging augmented reality, and they're using their own people, we ship the parts, they use your own people, we walk them through it. And in doing all that, we've actually seen our industry leading Net Promoter Score go up, which is somewhat counterintuitive, because historically, without a pandemic, you would have thought, if we would have tried to push that type of technology on clients who are not really ready for it or accepting, our Net Promoter Scores would have gone the other direction. But you know, in practice, they're already outpacing industry by 20 points, and they've actually been going up significantly over the last few years time. So for us, this is about embracing digital, it's about embracing the hybrid cloud and hybrid environments. It's about partnering with our clients in order to give them what they need and when they need it and be flexible and agile along the way to help them scale so definitely an exciting time no doubt of where we are as well as where we're going. >> Love the story, Michael, I miss bread and butter. You know, maybe you guys don't get a lot of the headlines, I guess unless something goes wrong but so you don't get a lot of headlines. That's good news. But congratulations by the way on the NPS. That's awesome. And thanks for coming on the CUBE. >> Great, thanks for having me Dave. >> You're welcome, and thank you for watching everybody. Keep it right there for more great content from IBM Think 2021. This is Dave Vellante for the CUBE. 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>> Announcer: From around the globe, it's theCUBE with digital coverage of Dell Technologies world, digital experience, brought to you by Dell Technologies. >> Hi, I'm Stu Miniman and this is theCUBE coverage of Dell Technologies world digital experience. Happy to welcome to the program. First we have a first time guest Akanksha Mehrotra, she's the Vice President of Marketing with Dell Technologies. Joining us one of our CUBE alumni, Caitlin Gordon, she's the Vice President of Product Marketing, also with Dell Technologies. Caitlin, welcome back, Akanksha welcome to the program. >> Thank you Stu, happy to be here. >> Alright, so one of the big models we've been talking about for the last few years is a change in how customers acquire things, big thing we've talked about, for many years, this shift from CAPEX to OPEX. How cloud is impacting everything Jeff Clarke in the keynote was talking about, it's the Dell Technologies on demand, DTOD, I guess is the, four letter acronym we use Akansha help us understand a little bit from your standpoint, what is it? Why is it important to your customers? >> Yeah, so Stu, as soon as you as you heard, as part of the keynote, from from Jeff and others earlier today, we've been working really hard to bring the benefits of on demand IT to our customers, in private cloud, public cloud and edge. And certainly this year, especially, we've seen a lot of interest in this, COVID have catalyzed customer interest in flexible consumption in as a service. As we talk with our customers and partners, we hear this almost daily, it's required a level of agility that candidly traditional CAPEX based models simply haven't been able to provide, I mean, imagine taking your workforce remote over the weekend, and the stress that puts on your infrastructure. And so I think that's kind of forced IT to consider some of these alternatives. Another factor has also been, companies have been wanting to preserve capital, right, and avoid large cash outlays and having this type of flexibility and being able to pay for infrastructure, as you're using it, it gives them a way to do that. So I mean, those are some of the customer drivers that we've seen. Last year at Dell Tech Summit, around the this time last year, actually, in November timeframe, we introduced Dell Technologies on demand as our umbrella program for a flexible consumption and as a service solutions. And really what it what it seeks to do is make it easier for customers to get the simplicity and flexibility of cloud, along with the performance and security of on-premises infrastructure. So it's giving them a range of consumption models that include both payment option as well as services that they can apply on any one of the products in our portfolio from end user devices to core data center infrastructure to hybrid cloud solutions. And we've announced that last year, one of the things that you heard about today, and that we're announcing over this event is that we're continually looking to make it easier and simpler for our customers with various turnkey offerings and simpler offerings for them, given the interest that we've seen. >> Yeah, I want to key off of, you mentioned the impact of COVID-19. And for your customers, it's something we've definitely seen that the promise of cloud always has been to be highly flexible, we can scale up, we can scale down. We know that some services out there aren't always as flexible as we might hope. There's certain SaaS solutions, where you're signing up for a multi year offering and even for the cloud, I might lock in some savings by buying something in bulk. So help us understand, what are the benefits that your customer sees, the savings that they get and is this truly cloud flexible, which means I can burst up and scale as I need. And I can it reached the point, oh, hey, I need half the capacity for the next six months. Can I do that? >> Yeah, absolutely. So, Stu we actually commissioned IBC to talk to a few of our customers. So let me maybe share some of the benefits that they saw in broad terms, and then I can maybe share a specific example of what a particular customer saw. So we had IDC talk to several of the customers using Dell Technologies on demand models, various GIOS, and various sort of sizes. And what they found was that on average, they saw about a 23%, lower cost of storage operations per year, which is great, right? Lower cost of operations is always great. IT is always looking for those efficiencies, especially, in the current environment, but that's not all. I think that's just sort of part of the story. What they also shared with us is that, these types of models were able to help them become much more agile in how they work and change how they work. And what they found was that they saw 54% fewer incidents of downtime and they were 92% faster in their ability to deploy storage capacity, because they had that capacity in their data center available ready for that spike when their business saw it. ` So those are just some of the broad examples of what our customers have seen. Another specific example that I would would share with you is a large multinational institution, financial services company, we've been working with them for years to service their, enterprise scale, private cloud. And then more recently, they had us also, manage their storage as a service managed utility. And they've seen phenomenal results, they've been able to get 50% more compute power at 8%, lower cost, and 90% faster or reduce time and provisioning data. It's all about the yes, it's about the cost savings but really, it's about the agility that the business gets, right. And as you started out, right, with COVID, they really needed that agility and that flexibility and having these models available, ready to spike, ready to go down, right, have been able to provide that. >> Yeah, I think another thing we've seen is, people rush to cloud because it promised that agility, and we've had those conversations before is, there's a reality of what that means, which it might not be the resiliency you're looking for, it also might not actually be as simple as he thought it might be. And we're seeing some of that come back on-prem, whether you need resiliency or performance or security, or you don't want to be really locked into a specific public cloud but you still want to have that agility in the benefits of really running your data center in a service oriented model. And that trend has been picking up over the past couple years. And as we've already said a couple times today, we've seen that accelerate, but also, we starting to see more customers ask for it. It's not just the big and more strategic and the aggressive customers that are looking for this more and more customers are kind of seeing that this is the end game and that's kind of leads into where we're going, which is, how do we make this more accessible to others? >> Well, Caitlin, you're using one of one of my punch lines that I've used for a number of years now if remember, when we thought that cloud was inexpensive and easy to use, it's not. And if we look at what customers are doing, it's a hybrid model. They're deploying in multiple environments, we're seeing the public cloud look more like the enterprise, the enterprise look more like, the public cloud. So these offerings have, OPEX flexibility and the like, make a whole lot of sense. So you've said that, you've seen a lot of growth, especially this year, any metrics you can give us on, adoption, love the one customer example, in the financial space, anything else to kind of paint the picture as to, how prevalent this is becoming. >> Yeah, maybe I'll get started. So, we've seen nearly 50%, year over year growth in the customer base or our most recent quarter, and it's growing, we've seen over 500% increase, year on year in signed contracts, customer demand in these types of models has caused us to expand our offerings to into countries like Brazil, Chile, Colombia, India, and China. I mean, we already offered about 50 plus countries and along with our partner, network and even more, so, I mean, those are just some of the data points around business traction. In the models that we have another proof point that I could point you to is that, in April, we include, we announced a payment flexibility program, which gave our customers a number of promotions and options to extend this flexibility into, across our portfolio and into other parts of our businesses. And just recently, about a month ago, we extended that, and we've seen really good traction in that as well. So I think overall, like you said there's aspects about public cloud that customers really like, and they tell us, hey, I want to be able to pay as I go, I want to be able to extend and contract the infrastructure as I'm using it. I want a simple management experience. But then as Caitlin said, they realize that Oh, but I don't want to, pay for the refactoring and then the egress and the ingress charges and some of my workloads are better off on premises for performance, locality, security, compliance reasons, right. And therein lies the promise of as a service for on-prem infrastructure, 'cause really, I keep looking for the best of both worlds. And this gives you that right you can use the consumption models to grow and shrink as you needed, you can us the payment models to only pay for what you're using and along with our partner network, you can have in the location that you want so you can sort of have your cake and eat it too. >> Yeah, and I would just add on to that is that more and more of the conversation is both about how can I consume that more as a service and pay for just what I'm using? But also, how can I spend less time maybe zero time and energy actually managing that infrastructure? And how can I then allocate the time energy resources into running my business and investing in more strategic things? So becomes both an important financial conversation but even more so a conversation about how IT can empower the business, which really just changes what we're able to do for customers. So it's an exciting kind of transition to see this really evolve into really not talking about products anymore, and helping our customers have all their business. >> Well, Caitlin, that's a really interesting point, I want you to talk to us a little bit about the Dell Tech storage as a service, how does that fit, we were just talking about don't want to talk about products, we want to talk about really moving to that full OPEX model so help connect the dots for us. >> Yeah, so we're really excited about this, this will be coming in the first half of next year, as you probably heard earlier today. And what we're doing here is we've really taken what we already have had in market. And we've really upped that to the next level, we've accelerated the simplicity of what we offer here. And think of the experience is all starting in a single console, where you just pick up four things, what's the type of storage you want, what's the performance you want, how much and for how long, that's it. And then now we're counting the time from then to when it's in your data center in days, not months, not weeks, but in days and we're able to get you up and going. And it's your data center of your choice, whether that's on-prem in your own data center, or at a colo facility, we bring that equipment in, we get that deployed, we manage it for you, you operate it, and you simply pay for what you use. So you're really in a quick time to value you're in a very simple model and you're not really responsible for managing infrastructure that's really on us. And that moves you into being in a true OPEX model and it also enables you to accelerate what you're able to leverage that whether it's Blob Storage, file storage, you can get up and running quickly and let us worry about how to manage the infrastructure and we give you the ability to operate what you need to. >> Caitlin, maybe if you could give us a little bit of color as to what happens behind the scenes to make that work. As it sounds wonderful, you've had the program around for a year, these aren't trivial things that you're talking about all the logistics, the management the the gear, and making sure that the physical and the power and everything is all set. So help us understand the engineering, the development, and what this means from kind of a services and go to market that make a solution like this work. >> Yeah, and a lot of ways we're having to change our entire business to help our customers change there's, it goes from top to bottom, and you'll get to hear a lot more about it when we're actually available next year. But when you think about it, we have a lot of the DNA, we have a lot of the experience, we have the technology, but we almost have to completely flip the script on ourselves of how we deliver it, who our customer is, what our then end user customer needs from us, and what the role of things like our global services organization is what the role of our global sales organization is and how do we accelerate providing outcomes to our customers and get the rest out of their way. And the fact that I haven't mentioned a product name, but by the way, we actually have industry leading products and pretty much every category. So of course, on the back end, all of this is going to be powered by our industry leading storage solutions, like power store will be in your data center but at the same time, we will actually have worked to really masked that you don't even need to know that nor do you need to really operate much beyond what you need to really run your business. And that's really it's been an interesting work for us to just flip how we think about everything and you'll hear a whole lot more about it next year as we really bring this out into market but it's been really fun and a big learning for everyone. >> Excellent well yeah, something something power is underneath there well Caitlin. All right why don't you both give us the final takeaway for the Dell Tech on demand account. Start with you in just give us the final takeaway. >> Yeah, so I think look, I back to kind of what we were talking about, we've actually been offering these types of solutions to our customers for a really long time. Through Dell financial services, we've been offering payment flexibility for over 23 years, over 15 years and manage utility. So the customer example that I gave you is a customer who's running storage as a service and has been for many years, I think, building on that experience, listening to our customers feedback over that time period and over, of course, this past year, we're looking to apply all of that, to make it even more simpler for them to consume our infrastructure in the near future. And so, storage as a service is going to be a really exciting proof point of that, the momentum stats and some of the other things that I shared with you today and that you're going to hear about over the next couple of days or another proof point of it. But we're excited about this, and looking forward to continuing the dialogue with our customers with our partners and (mumbles) >> Then I would I'll kind of play off of one of your words there which is is all about simplicity for us is how do we take what we've been able to do for a lot of our customers accelerate that and simplify it to a point where we can offer that for all of our customers. And we're really looking to accelerate this first with storage and then get all of our offerings really into this model, because it's really about getting our customers out of managing infrastructure and give them the time, energy, resources to manage their business and simplicity is paramount to making sure that happens. >> Caitlin and Akanksha, thank you so much for giving us the updates. Congratulations to all the progress and definitely looking forward to hearing more beginning of next year. Thanks for joining. >> Thank you Stu. >> Thank you Stu >> All right, I'm Stu Miniman this is Dell Technology world digital experience. I'm Stu Miniman. And thank you as always for watching theCUBE (upbeat music)

>>covering the space and cybersecurity symposium 2020 hosted by Cal poly. Hold on. Welcome to this special presentation with Cal poly hosting the space and cybersecurity symposium, 2020 virtual, um, John for your host with the cube and Silicon angle here in our Palo Alto studios with our remote guests, we couldn't be there in person, but we're going to be here remotely. Got a great session and a panel for one hour topic preparing students for the jobs of today and tomorrow, but a great lineup. Bill Britain, Lieutenant Colonel from the us air force, retired vice president for information technology and CIO and the director of the California cyber security Institute for Cal poly bill. Thanks for joining us, dr. Amy Fisher, who's the Dean of the college of engineering at Cal poly and trunk fam professor and researcher at the U S air force Academy. Folks, thanks for joining me today. >>Our pleasure got a great, great panel. This is one of my favorite topics preparing students for the next generation, the jobs for today and tomorrow. We've got an hour. I'd love you guys to start with an opening statement, to kick things off a bill. We'll start with you. Well, I'm really pleased to be, to start on this. Um, as the director for the cybersecurity Institute and the CIO at Cal poly, it's really a fun, exciting job because as a Polytechnic technology, as such a forefront in what we're doing, and we've had a, a wonderful opportunity being 40 miles from Vandenberg air force base to really look at the nexus of space and cyber security. And if you add into that, uh, both commercial government and civil space and cybersecurity, this is an expanding wide open time for cyber and space. In that role that we have with the cyber security Institute, we partner with elements of the state and the university. >>And we try to really add value above our academic level, which is some of the highest in the nation and to really merge down and go a little lower and start younger. So we actually are running the week prior to this showing a cybersecurity competition for high schools or middle schools in the state of California, that competition this year is based on a scenario around hacking of a commercial satellite and the forensics of the payload that was hacked and the networks associated with it. This is going to be done using products like Wireshark autopsy and other tools that will give those high school students. What we hope is a huge desire to follow up and go into cyber and cyber space and space and follow that career path. And either come to Cal poly or some other institution that's going to let them really expand their horizons in cybersecurity and space for the future >>Of our nation. >>Bill, thanks for that intro, by the way, it's gonna give you props for an amazing team and job you guys are doing at Cal poly, that Dex hub and the efforts you guys are having with your challenge. Congratulations on that great work. Thank you >>Star team. It's absolutely amazing. You find that much talent in one location. And I think Amy is going to tell you she's got the same amount of talent in her staff. So it's, it's a great place to be. >>Amy flasher. You guys have a great organization down there, amazing curriculum, grazing people, great community, your opening statement. >>Hello everybody. It's really great to be a part of this panel on behalf of the Cal poly college of engineering here at Cal poly, we really take preparing students for the jobs of today and tomorrow completely seriously. And we claim that our students really graduate. So they're ready day one for their first real job, but that means that in getting them to that point, we have to help them get valuable and meaningful job experience before they graduate, but through our curriculum and through multiple internship or summer research opportunities. So we focus our curriculum on what we call a learn by doing philosophy. And this means that we have a combination of practical experience and learn by doing both in and out of the classroom. And we find that to be really critical for preparing students for the workforce here at Cal poly, we have more than 6,000 engineering students. >>We're one of the largest undergraduate engineering schools in the country. Um, and us news ranks us the eighth best undergraduate engineering program in the, in the country and the top ranked state school. We're really, really proud that we offer this impactful hands on engineering education that really exceeds that of virtually all private universities while reaching a wider audience of students. We offer 14 degree programs and really we're talking today about cyber and space. And I think most of those degree programs can really make an impact in the space and cybersecurity economy. And this includes not only things like Aero and cyber directly, but also electrical engineering, mechanical engineering, computer engineering, materials, engineering, even manufacturing, civil and biomedical engineering. As there's a lot of infrastructure needs that go into supporting launch capabilities. Our aerospace program graduates hundreds of aerospace engineers, and most of them are working right here in California. >>I'm with many of our corporate partners, including Northrop Grumman, Lockheed, Boeing, Raytheon space, X, Virgin, galactic JPL, and so many other places where we have Cal poly engineer's impacting the space economy. Our cybersecurity focus is found mainly in our computer science and software engineering programs. And it's really a rapidly growing interest among our students. Computer science is our most popular major and industry interest and partnerships are integrated into our curriculum. And we do that oftentimes through support from industry. So we have partnerships with Northrop Grumman for professorship and a cyber lab and from PG and E for critical infrastructure, cybersecurity lab, and professorship. And we think that industry partnerships like these are really critical to preparing students for the future as the field's evolving so quickly and making sure we adapt our facilities and our curriculum to stay in line with what we're seeing in industry is incredibly important. >>In our aerospace program, we have an educational partnership with the air force research labs. That's allowing us to install new high performance computing capabilities and a space environments lab. That's going to enhance our satellite design capabilities. And if we talk about satellite design, Cal poly is the founding home of the cube sat program, which pioneered small satellite capabilities. And we remain the worldwide leader in maintaining the cube set standard. And our student program has launched more cube sets than any other program. So here again, we have this learn by doing experience every year for dozens of aerospace, electrical, computer science, mechanical engineering students, and other student activities that we think are just as important include ethical hacking through our white hat club, Cal poly space systems, which does really, really big rocket launches and our support program for women in both of these fields like wish, which is women in software and hardware. >>Now, you know, really trying to bring in a wide variety of people into these fields is incredibly important and outreach and support to those demographics. Traditionally underrepresented in these fields is going to be really critical to future success. So by drawing on the lived experiences by people with different types of backgrounds, while we develop the type of culture and environment where all of us can get to the best solution. So in terms of bringing people into the field, we see that research shows, we need to reach kids when they're in late elementary and middle schools to really overcome that cultural bias that works against diversity in our fields. And you heard bill talking about the cyber cybersec, the California cybersecurity institutes a year late cyber challenge. There's a lot of other people who are working to bring in a wider variety of, uh, of people into the field, like girl Scouts, which has introduced dozens of new badges over the past few years, including a whole cybersecurity series of badges and a concert with Palo Alto networks. So we have our work cut out for us, but we know what we need to do. And if we're really committed to prep properly preparing the workforce for today and tomorrow, I think our future is going to be bright. I'm looking forward to our discussion today. >>Yeah, you got a flashy for great, great comment, opening statement and congratulations. You got the right formula down there, the right mindset, and you got a lot of talent and community as well. Thank thank you for that opening statement. Next step from Colorado Springs, trunk fam, who's a professor and researcher. The us air force Academy is doing a lot of research around the areas that are most important for the intersection of space and technology trunk. >>Good afternoon, first electric and Cal poli for the opportunity. And today I want to go briefly about cyber security in S application. Whenever we talk about cyber security, the impression is got yes, a new phew that is really highly complex involving a lot of technical area. But in reality, in my personal opinion, it is in be complex because involve many disciplines. The first thing we think about is computer engineering and computer networking, but it's also involving communication sociology, law practice. And this practice of cyber security goes in on the info computer expert, but it's also info everybody else who has a computing device that is connected to the internet. And this participation is obviously every body in today's environment. When we think about the internet, we know that is a good source of information, but come with the convenience of information that we can access. >>We are constantly faced in being from the internet. Some of them, we might be aware of some of them we might not be aware of. For example, when we search on the internet, a lot of time, our browser will be saved and gotten this site is not trusted. So we will be more careful. What about the sites that we trusted? We know getting those salad chicken sites, but they're not a hundred percent good at proof. What happened? It was all side, uh, attack by hacker. And then they will be a silent source that we might not be aware of. So in the reality, we need to be more practicing the, um, cyber security from our SIBO point of view and not from a technical point of view. When we talk about space application, we should know that all the hardware, a computer based tool by computer system and therefore the hardware and the software must go through some certification process so that they can be record that air with the flight. >>What the, when we know that in the certification process is focusing on the functionality of the hardware and software, but one aspect that is explicitly and implicitly required is the security of those components. And we know that those components have to be connected with the ground control station and be communication is through the air, through the layby or signal. So anybody who has access to those communication regular signal will be able to control the space system that we put up there. And we certainly do not want our system to be hijacked by a third party. >>I'm not going to aspect of cybersecurity is we try to design the space system in a very strong manner. So it's almost impossible to hack in, but what about some August week system that might be connected to so strong system? For example, the spare system will be connected to the ground control station and on the ground control station, we have the human controller in those people have cell phone. They are allowed to use cell phones for communication, but at the same time, they are connected to the internet, to the cell phone and their cell phone might be connected to the computer that control the flight software and hardware. So what I want to say is that we try to build strong system and we protected them, but there will be some weaker system that we could not intended, but exists to be connected to our strong system. And those are the points that hacker will be trying to attack. If we know how to control the access to those points, we will be having a much better system for the space system. And when we see the cybersecurity that is requiring the participation everywhere, it's important to Merck that there is a source of opportunity for students to engage the workforce. To concede the obviously student in engineering can focus their knowledge and expertise to provide technological solution, to protect the system that we view. But we also >>Have students in business who can focus to write a business plan to reach the market. We also have student in law who can focus policy governing the cyber security. And we also have student in education who can focus the expert. She should be saying how to teach cyber security practice and students can focus the effort to implement security measures and it implies job opportunity. >>Thank you trunk for those great comments, great technology opportunities, but interesting as well as the theme that we're seeing across the entire symposium and in the virtual hallways that we're hearing conversations and you pointed out some of them, dr. Fleischer did as well. And bill, you mentioned it. It's not one thing. It's not just technology, it's different skills. And, um, Amy, you mentioned that computer science is the hottest degree, but you have the hottest aerospace program in the world. I mean, so all of this is kind of balancing it's interdisciplinary. It's a structural change before we get into some of the, um, how they prepare the students. Can you guys talk about some of the structural changes that are modern now in preparing, um, in these opportunities because societal impact is a law potentially impact it's, it's how we educate there's no cross-discipline skillsets. It's not just get the degree, see out in the field bill, you want to start. >>Well, what's really fun about this job is, is that in the air force, uh, I worked in the space and missile business and what we saw was a heavy reliance on checklist format, security procedures, analog systems, and what we're seeing now in our world, both in the government and the commercial side, uh, is a move to a digital environment. And the digital environment is a very quick and adaptive environment. And it's going to require a digital understanding. Matter of fact, um, the, uh, under secretary of the air force for acquisition, uh, rev recently referenced the need to understand the digital environment and how that's affecting acquisition. So as, as both Amy, um, and trunk said, even business students are now in the >>Cybersecurity business. And, and so, again, what we're seeing is, is the change. Now, another phenomenon that we're seeing in the space world is there's just so much data. Uh, one of the ways that we addressed that in the past was to look at high performance computing. It was a lot stricter control over how that worked, but now what we're seeing these adaptation of cloud cloud technologies in space support, space, data, command, and control. Uh, and so what we see is a modern space engineer who asked to understand digital, has to understand cloud and has to understand the context of all those with a cyber environment. That's really changing the forefront of what is a space engineer, what is a digital engineer and what does a future engineer, both commercial or government? So I think the opportunity for all of these things is really good, particularly for a Polytechnic air force Academy and others that are focusing on a more, uh, widened experiential level of cloud and engineering and other capabilities. >>And I'll tell you the part that as the CIO, I have to remind everybody, all this stuff works for the it stuff. So you've got to understand how your it infrastructures are tied and working together. Um, as we noted earlier, one of the things is, is that these are all relays from point the point, and that architecture is part of your cybersecurity architecture. So again, every component has now become a cyber aware cyber knowledgeable, and in what we'd like to call as a cyber cognizant citizen, where they have to understand the context, patients chip software, that the Fleischer talk about your perspective, because you mentioned some of the things that computer science. Remember when I'm in the eighties, when I got my computer science degree, they call the software engineers, and then you became software developers. And then, so again, engineering is the theme. If you're engineering a system, there's now software involved, um, and there's also business engineering business models. So talk about some of your comments was, you mentioned, computer science is hot. You got the aerospace, you've got these multidisciplines you got definitely diversity as well. It brings more perspectives in as well. Your thoughts on these structural interdisciplinary things. >>I think this is, this is really key to making sure that students are prepared to work in the workforce is looking at the, the blurring between fields no longer are you just a computer scientist, no longer are you just an aerospace engineer? You really have to have an expertise where you can work with people across disciplines. All of these, all of these fields are just working with each other in ways we haven't seen before. And bill brought up data, you know, data science is something that's cross cutting across all of our fields. So we want engineers that have the disciplinary expertise so that they can go deep into these fields, but we want them to be able to communicate with each and to be able to communicate across disciplines and to be able to work in teams that are across disciplines. You can no longer just work with other computer scientists or just work with other aerospace engineers. >>There's no part of engineering that is siloed anymore. So that's how we're changing. You have to be able to work across those, those disciplines. And as you, as Tron pointed out, you know, ethics has to come into this. So you can no longer try to fully separate what we would traditionally have called the, the liberal arts and say, well, that's over there in general education. No ethics is an important part of what we're doing and how we integrate that into our curriculum. So it was communication. So is working on public policy and seeing where all of these different aspects tied together to make the impact that we want to have in the world. So it, you no longer can work solo in these fields. >>Great point. And bill also mentioned the cloud. One thing about the cloud that showed us as horizontal scalability has created a lot of value and certainly data is now horizontal Trung. You mentioned some of the things about cryptography for the kids out there. I mean, you can look at the pathway for career. You can do a lot of tech and, but you don't have to go deep. Sometimes you can go, you can go as deep as you want, but there's so much more there. Um, what technology do you see, how it's going to help students in your opinion? >>Well, I'm a professor in computer science, so I'd like to talk out a little bit about computer programming. Now we, uh, working in complex project. So most of the time we design a system from scratch. We view it from different components and the components that we have either we get it from or some time we get it from the internet in the open source environment, it's fun to get the source code and then work to our own application. So now when we are looking at a Logie, when we talk about encryption, for example, we can easily get the source code from the internet. And the question is, is safe to use those source code. And my, my, my question is maybe not. So I always encourage my students to learn how to write source score distribution, where that I learned a long time ago before I allow them to use the open source environment. And one of the things that they have to be careful, especially with encryption is be quote that might be hidden in the, in the source, get the download here, some of the source. >>So open source, it's a wonderful place to be, but it's also that we have to be aware of >>Great point before we get into some of the common one quick thing for each of you like to get your comments on, you know, the there's been a big movement on growth mindset, which has been a great, I'm a big believer in having a growth mindset and learning and all that good stuff. But now that when you talk about some of these things that we're mentioning about systems, there's, there's an, there's a new trend around a systems mindset, because if everything's now a system distributed systems, now you have space in cyber security, you have to understand the consequences of changes. And you mentioned some of that Trung in changes in the source code. Could you guys share your quick opinions on the, the idea of systems thinking, is that a mindset that people should be looking at? Because it used to be just one thing, Oh, you're a systems guy or galley. There you go. You're done. Now. It seems to be in social media and data. Everything seems to be systems. What's your take dr. Fleischer, we'll start with you. >>Uh, I'd say it's a, it's another way of looking at, um, not being just so deep in your discipline. You have to understand what the impact of the decisions that you're making have on a much broader, uh, system. And so I think it's important for all of our students to get some exposure to that systems level thinking and looking at the greater impact of the decision that they're making. Now, the issue is where do you set the systems boundary, right? And you can set the systems boundary very close in and concentrate on an aspect of a design, or you can continually move that system boundary out and see, where do you hit the intersections of engineering and science along with ethics and public policy and the greater society. And I think that's where some of the interesting work is going to be. And I think at least exposing students and letting them know that they're going to have to make some of these considerations as they move throughout their career is going to be vital as we move into the future. Bill. What's your thoughts? >>Um, I absolutely agree with Amy and I think there's a context here that reverse engineering, um, and forensics analysis and forensics engineering are becoming more critical than ever, uh, the ability to look at what you have designed in a system and then tear it apart and look at it for gaps and holes and problem sets, or when you're given some software that's already been pre developed, checking it to make sure it is, is really going to do what it says it's going to do. That forensics ability becomes more and more a skillset that also you need the verbal skills to explain what it is you're doing and what you found. So the communication side, the systems analysis, >>The forensics analysis side, >>These are all things that are part of that system >>Approach that I think you could spend hours on. And we still haven't really done great job on it. So it's a, it's. One of my fortes is the really the whole analysis side of forensics and it reverse engineering >>Try and real quick systems thinking. >>Well, I'd like to share with you my experience. When I worked in the space patient program at NASA, we had two different approaches. One is a down approach where we design it from the system general point of view, where we put components to complex system. But at the same time, we have the bottom up approach where we have Ken Chile who spent time and effort the individual component. And they have to be expert in those Chinese component. That might be general component the gallery. And in the space station program, we bring together the welcome up engineer, who designed everything in detail in the system manager who manage the system design from the top down. And we meet in the middle and took the idea with compromise a lot of differences. Then we can leave a display station that we are operating to be okay, >>Great insight. And that's the whole teamwork collaboration that, that was mentioning. Thanks so much for that insight. I wanted to get that out there because I know myself as a, as a parent, I'm always trying to think about what's best for my kids in their friends, as they grow up into the workforce. I know educators and leaders in industry would love to know some of the best practices around some of the structural changes. So thanks for that insight, but this topics about students and helping them prepare. Uh, so we heard, you know, be, be multiple discipline, broaden your horizons, think like systems top down, bottom up, work together as a team and follow the data. So I got to ask you guys, there's a huge amount of job openings in cybersecurity. It's well documented and certainly at the intersection of space and cyber, it's only gonna get bigger, right? You're going to see more and more demand for new types of jobs. How do we get high school and college students interested in security as a career at the flagship? We'll start with you in this one. >>I would say really one of the best ways to get students interested in the career is to show them the impact that it's going to have. There's definitely always going to be students who are going to want to do the technology for the technology sake, but that will limit you to a narrow set of students. And by showing that the greater impact that these types of careers are going to have on the types of problems that you're going to be able to solve and the impact you're going to be able to have on the world, around you, that's the word that we really need to get out. And a wide variety of students really respond to these messages. So I think it's really kind of reaching out at the, uh, the elementary, the middle school level, and really kind of getting this idea that you can make a big difference, a big positive difference in the field with some of these careers is going to be really critical. >>Real question, follow up. What do you think is the best entry point? You mentioned middle squad in here, elementary school. This comes, there's a lot of discussions around pipelining and we're going to get into women in tech and under-represented matters later, but you know, is it too early or what's the, what's your feeling on this? >>My feeling is the earlier we can normalize it the better the, uh, if you can normalize an interest in, in computers and technology and building an elementary school, that's absolutely critical. But the dropoff point that we're seeing is between what I would call like late elementary and early middle school. Um, and just kind of as an anecdote, I, for years ran an outreach program for girl Scouts in grades four and five and grade six, seven, and eight. And we had a hundred slots in each program. And every year the program would sell out for girls in grades four and five, and every year we'd have spots remaining in grades six, seven, and eight. And that's literally where the drop-off is occurring between that late elementary and that middle school range. So that's the area that we need to target to make sure we keep those young women involved and interested as we move forward. >>Bill, how are we going to get these kids interested in security? You mentioned a few programs you got. Yeah. I mean, who wants to, who wouldn't want to be a white hat hacker? I mean, yeah, that sounds exciting. Yeah. Great questions. Let's start with some basic principles though. Is let me ask you a question, John, a name for me, one white hat, good person hacker. The name who works in the space industry and is an exemplar for students to look up to, um, you, um, Oh man. I'm hearing really. I can't, I can't, I can't, I can't imagine because the answer we normally get is the cricket sound. So we don't have individuals we've identified in those areas for them to look up to. I was going to be snarky and say, most white hackers won't even use their real name, but, um, there's a, there's an aura around their anonymity here. >>So, so again, the real question is, is how do we get them engaged and keep them engaged? And that's what Amy was pointing out too. Exactly the engagement and sticking with it. So one of the things that we're trying to do through our competition on the state level and other elements is providing connections. We call them ambassadors. These are people in the business who can contact the students that are in the game or in that, uh, challenge environment and let them interact and let them talk about what they do and what they're doing in life would give them a challenging game format. Um, a lot of computer based training, um, capture the flag stuff is great, but if you can make it hands on, if you can make it a learn by doing experiment, if you can make it am personally involved and see the benefit as a result of doing that challenge and then talk to the people who do that on a daily basis, that's how you get them involved. >>The second part is as part of what we're doing is, is we're involving partnership companies in the development of the teams. So this year's competition that we're running has 82 teams from across the state of California, uh, of those 82 teams at six students team, middle school, high school, and many of those have company partners. And these are practitioners in cybersecurity who are working with those students to participate. It's it's that adult connectivity, it's that visualization. Um, so at the competition this year, um, we have the founder of Def con red flag is a participant to talk to the students. We have Vince surf as who is of course, very well known for something called the internet to participate. It's really getting the students to understand who's in this. Who can I look up to and how do I stay engaged with them? >>There's definitely a celebrity aspect of it. I will agree. I mean, the influencer aspect here with knowledge is key. Can you talk about, um, these ambassadors and, and, and how far along are you on that program? First of all, the challenge stuff is anything gamification wise. We've seen that with hackathons is just really works well. Grades, bonding, people who create together kinda get sticky and get very high community aspect to it. Talking about this ambassador thing. What does that industry is that academic >>Absolutely partners that we've identified? Um, some of which, and I won't hit all of them. So I'm sure I'll short changes, but, uh, Palo Alto, Cisco, um, Splunk, um, many of the companies in California and what we've done is identified, uh, schools, uh, to participate in the challenge that may not have a strong STEM program or have any cyber program. And the idea of the company is they look for their employees who are in those school districts to partner with the schools to help provide outreach. It could be as simple as a couple hours a week, or it's a team support captain or it's providing computers and other devices to use. Uh, and so again, it's really about a constant connectivity and, uh, trying to help where some schools may not have the staff or support units in an area to really provide them what they need for connectivity. What that does gives us an opportunity to not just focus on it once a year, but throughout the year. So for the competition, all the teams that are participating have been receiving, um, training and educational opportunities in the game of education side, since they signed up to participate. So there's a website, there's learning materials, there's materials provided by certain vendor companies like Wireshark and others. So it's a continuum of opportunity for the, >>You know, I've seen just the re randomly, just going to random thought, you know, robotics clubs are moving den closer into that middle school area, in fact Fleischer. And certainly in high schools, it's almost like a varsity sport. E-sports is another one. My son just combined made the JV at the college Dean, you know, it's big and it's up and serious. Right. And, um, it's fun. This is the aspect of fun. It's hands on. This is part of the culture down there you learn by doing, is there like a group? Is it like, um, is it like a club? I mean, how do you guys organize these bottoms up organically interest topics? >>So, so here in the college of engineering, uh, when we talk about learning by doing, we have learned by doing both in the classroom and out of the classroom. And if we look at the, these types of, out of the classroom activities, we have over 80 clubs working on all different aspects of many of these are bottom up. The students have decided what they want to work on and have organized themselves around that. And then they get the leadership opportunities. The more experienced students train in the less experienced students. And it continues to build from year after year after year with them even doing aspects of strategic planning from year to year for some of these competitions. So, yeah, it's an absolutely great experience. And we don't define for them how their learned by doing experiences should be, we want them to define it. And I think the really cool thing about that is they have the ownership and they have the interest and they can come up with new clubs year after year to see which direction they want to take it. And, you know, we will help support those clubs as old clubs fade out and new clubs come in >>Trunk real quick. Before we go on the next, uh, talk track, what, what do you recommend for, um, middle school, high school or even elementary? Um, a little bit of coding Minecraft. I mean, what, how do you get them hooked on the fun and the dopamine of, uh, technology and cybersecurity? What's your, what's your take on that? >>On, on this aspect, I like to share with you my experience as a junior high and high school student in Texas, the university of Texas in Austin organized a competition for every high school in Texas. If we phew from poetry to mathematics, to science, computer engineering, but it's not about with university of Texas. The university of Texas is on the serving SSN for the final competition that we divide the competition to be strict and then regional, and then spit at each level, we have local university and colleges volunteering to host it competition and make it fun. >>Also students with private enterprises to raise funding for scholarship. So students who see the competition they get exposed to so they can see different option. They also get a scholarship when they attend university in college. So I've seen the combination in competition aspect would be a good thing to be >>Got the engagement, the aspiration scholarship, you know, and you mentioned a volunteer. I think one of the things I'll observe is you guys are kind of hitting this as community. I mean, the story of Steve jobs and was, was building the Mac, they call it bill Hewlett up in Palo Alto. It was in the phone book and they scoured some parts from them. That's community. This is kind of what you're getting at. So this is kind of the formula we're seeing. So the next question I really want to get into is the women in technology, STEM, underrepresented minorities, how do we get them on cybersecurity career path? Is there a best practices there, bill, we'll start with you? >>Well, I think it's really interesting. First thing I want to add is if I could have just a clarification, what's really cool that the competition that we have and we're running, it's run by student from Cal poly. Uh, so, you know, Amy referenced the clubs and other activities. So many of the, uh, organizers and developers of the competition that we're running are the students, but not just from engineering. So we actually have theater and liberal arts majors and technology for liberal arts majors who are part of the competition. And we use their areas of expertise, set design, and other things, uh, visualization of virtualization. Those are all part of how we then teach and educate cyber in our game effication and other areas. So they're all involved in their learning as well. So we have our students teaching other students. So we're really excited about that. And I think that's part of what leads to a mentoring aspect of what we're providing, where our students are mentoring the other students. And I think it's also something that's really important in the game. Um, the first year we held the game, we had several all girl teams and it was really interesting because a, they, they didn't really know if they could compete. I mean, this is their, their reference point. We don't know if they did better than anybody. I mean, they, they knocked the ball out >>Of the park. The second part then is building that confidence level that they can going back and telling their cohorts that, Hey, it's not this thing you can't do. It's something real that you can compete and win. And so again, it's building that comradery, that spirit, that knowledge that they can succeed. And I think that goes a long way and an Amy's programs and the reach out and the reach out that Cal poly does to schools to develop. Uh, I think that's what it really is going to take. It. It is going to take that village approach to really increase diversity and inclusivity for the community. >>That's the flusher. I'd love to get your thoughts. You mentioned, um, your, your outreach program and the dropoff, some of those data, uh, you're deeply involved in this. You're passionate about it. What's your thoughts on this career path opportunity for STEM? >>Yeah, I think STEM is an incredible career path opportunity for so many people. There's so many interesting problems that we can solve, particularly in cyber and in space systems. And I think we have to meet the kids where they are and kind of show them, you know, what the exciting part is about it, right. But, you know, bill was, was alluding to this. And when he was talking about, you know, trying to name somebody that you can can point to. And I think having those visible people where you can see yourself in that is, is absolutely critical and those mentors and that mentorship program. So we use a lot of our students going out into California, middle schools and elementary schools. And you want to see somebody that's like you, somebody that came from your background and was able to do this. So a lot of times we have students from our national society of black engineers or a society of Hispanic professional engineers or our society of women engineers. >>We have over a thousand members, a thousand student members in our society of women engineers who were doing these outreach programs. But like I also said, it's hitting them at the lower levels too. And girl Scouts is actually distinguishing themselves as one of the leading STEM advocates in the country. And like I said, they developed all these cybersecurity badges, starting in kindergarten. There's a cybersecurity badge for kindergarten and first graders. And it goes all the way up through late high school, the same thing with space systems. And they did the space systems in partnership with NASA. They did the cybersecurity and partnership with Palo Alto networks. And what you do is you want to build these, these skills that the girls are developing. And like bill said, work in and girl led teams where they can do it. And if they're doing it from kindergarten on, it just becomes normal. And they never think, well, this is not for me. And they see the older girls who are doing it and they see a very clear path leading them into these careers. >>Yeah. It's interesting. You used the word normalization earlier. That's exactly what it is. It's life, you get life skills and a new kind of badge. Why wouldn't learn how to be a white, white hat hacker, or have fun or learn new skills just in, in the, in the grind of your fun day. Super exciting. Okay. Trung your thoughts on this. I mean, you have a diverse diversity. It brings perspective to the table in cybersecurity because you have to think like the other, the adversary, you got to be the white headed hippie, a white hat, unless you know how black hat thinks. So there's a lot of needs here for more, more, more points of view. How are we going to get people trained on this from under represented minorities and women? What's your thoughts? >>Well, as a member of, I took a professional society of directed pool in the electronic engineer. You have the, uh, we participate in the engineering week. We'll be ploy our members to local junior high school and high school to talk about our project, to promote the discovery of engineering. But at the same time, we also participate in the science fair that we scaled up flex. As the squad organizing our engineer will be mentoring students, number one, to help them with the part check, but number two, to help us identify talents so that we can recruit them further into the field of STEM. One of the participation that week was the competition of the, what they call future CV. We're still going, we'll be doing a CT on a computer simulation. And in recent year we promote ops smart CV where CT will be connected the individual houses to be added in through the internet. >>And we want to bring awareness of cybersecurity into competition. So we deploy engineer to supervise the people, the students who participate in the competition, we bring awareness, not in the technical be challenged level, but in what we've called the compound level. So speargun will be able to know what is, why to provide cyber security for the smart city that they are building. And at the same time, we were able to identify talent, especially talent in the minority and in the room. And so that we can recruit them more actively. And we also raise money for scholarship. We believe that scholarship is the best way to get students to continue education in Epic college level. So with scholarship, it's very easy to recruit them, to give you and then push them to go further into the cyber security Eylea. >>Yeah. I mean, you know, I see a lot of the parents like, Oh, my kid's going to go join the soccer team, >>Private lessons, and maybe look at a scholarship >>Someday. Well, they only do have scholarships anyway. I mean, this is if they spent that time doing other things, it's just, again, this is a new lifestyle, like the girl Scouts. And this is where I want to get into this whole silo breaking down because Amy, you brought this up and bill, you were talking about as well, you've got multiple stakeholders here with this event. You got, you know, public, you got private and you've got educators. It's the intersection of all of them. It's again, that those, if those silos break down the confluence of those three stakeholders have to work together. So let's, let's talk about that. Educators. You guys are educating young minds, you're interfacing with private institutions and now the public. What about educators? What can they do to make cyber better? Cause there's no real manual. I mean, it's not like this court is a body of work of how to educate cybersecurity is maybe it's more recent, it's cutting edge, best practices, but still it's an, it's an evolving playbook. What's your thoughts for educators, bill? We'll start with you. >>Well, I don't really, I'm going to turn it off. >>I would say, I would say as, as educators, it's really important for us to stay on top of how the field is evolving, right? So what we want to do is we want to promote these tight connections between educators and our faculty and, um, applied research in industry and with industry partnerships. And I think that's how we're going to make sure that we're educating students in the best way. And you're talking about that inner, that confluence of the three different areas. And I think you have to keep those communication lines open to make sure that the information on where the field is going and what we need to concentrate on is flowing down into our educational process. And that, that works in both ways that, you know, we can talk as educators and we can be telling industry what we're working on and what are types of skills our students have and working with them to get the opportunities for our students to work in industry and develop those skills along the way as well. >>And I think it's just all part of this is really looking at, at what's going to be happening and how do we get people talking to each other and the same thing with looking at public policy and bringing that into our education and into these real hands on experiences. And that's how you really cement this type of knowledge with students, not by not by talking to them and not by showing them, but letting them do it. It's this learn by doing and building the resiliency that it takes when you learn by doing. And sometimes you learn by failing, but you just up and you keep going. >>And these are important skills that you develop along the way >>You mentioned, um, um, sharing too. That's the key collaborating and sharing knowledge. It's an open, open world and everyone's collaborating feel private public partnerships. I mean, there's a real private companies. You mentioned Palo Alto networks and others. There's a real intersection there there's, they're motivated. They could, the scholarship opportunities, trunk points to that. What is the public private educator view there? How do companies get involved? What's the benefit for them? >>Well, that's what a lot of the universities are doing is to bring in as part of either their cyber centers or institutes, people who are really focused on developing and furthering those public private partnerships. That's really what my role is in all these things is to take us to a different level in those areas, uh, not to take away from the academic side, but to add additional opportunities for both sides. Remember in a public private partnership, all entities have to have some gain in the process. Now, what I think is really interesting is the timing on particularly this subject space and cyber security. This has been an absolute banner year for space. The Stanhope of space force, the launch of commercial partnership, leaving commercial platforms, delivering astronauts to the space station, recovering them and bringing back the ability of a commercial satellite platform to be launched a commercial platforms that not only launch, but return back to where they're launched from. >>These are things that are stirring the hearts of the American citizens, the kids, again, they're getting interested, they're seeing this and getting enthused. So we have to seize upon that and we have to find a way to connect that public private partnerships is the answer for that. It's not one segment that can handle it all. It's all of them combined together. If you look at space, space is going to be about commercial. It's going to be about civil moving from one side of the earth, to the other via space. And it's about government. And what's really cool for us. All those things are in our backyard. Yeah. That's where that public private comes together. The government's involved, the private sector is involved. The educators are involved and we're all looking at the same things and trying to figure out like this forum, what works best to go to the future. >>You know, if people are bored and they want to look for an exciting challenge, he couldn't have laid it out any clearer. It's the most exciting discipline. It hits everything. I mean, we just talk about space. GPS is everything we do is well tested. Do with satellites. >>I have to tell you a story on that, right? We have a very unique GPS story right in our backyard. So our sheriff is the son of the father of GPS for the air force. So you can't get better than that when it comes to being connected to all those platforms. So we, we really want to say, you know, this is so exciting for all of us because >>It gives everybody a job for a long time. >>You know, the kids that don't think tick toxic, exciting, wait til they see what's going on here with you guys, this program, trunk final word on this from the public side, you're at the air force. You're doing research. Are you guys opening it up? Are you integrating into the private and educational sectors? How do you see that formula playing out? And what's the best practice for students and preparing them? >>I think it's the same in athlete university CP in the engineering program will require our students to be final project before graduation. And in this kind of project, we send them out to work in the private industry. The private company got sponsor. Then they get the benefit of having an intern working for them and they get the benefit of reviewing the students as the prospective employee in the future. So it's good for the student to gain practical experience working in this program. Some, some kind of, we call that a core program, some kind, we call that a capstone program and the company will accept the students on a trial PRCS, giving them some assignment and then pay them a little bit of money. So it's good for the student to earn some extra money, to have some experience that they can put on their resume when they apply for the final of the job. >>So the collaboration between university and private sector is really important. We, when I joined a faculty, normally they already exist that connection. It came from. Normally it came from the Dean of engineering who would whine and dine with companies. We work relationship and sign up women, but it's approach to do a good performance so that we can be credibility to continue the relationship with those company and the students that we selected to send to those company. We have to make sure that they will represent the university. Well, they will go a good job and they will make a good impression. >>Thank you very much for great insight, trunk, bill, Amy, amazing topic. I'd like to end this session with each of you to make a statement on the importance of cybersecurity to space. We'll go Trung bill and Amy Truong, the importance of cybersecurity space statement. >>We know that it's affecting components that we are using and we are connecting to. And normally we use them for personal purpose. But when we connect to the important system that the government public company put into space, so it's really important to practice cyber security and a lot of time, it's very easy to know concept. We have to be careful, but in reality, we tend to forget to partnership the way we forget how to ride safely. And with driving a car, we have a program called defensive driving that requires every two or three years to get. We can get discount. >>We are providing the cyber security practice, not to tell people about the technology, but to remind them not practicing cybersecurity. And it's a requirement for every one of us, bill, the importance of cyber security to space. It's not just about young people. It's about all of us as we grow and we change as I referenced it, you know, we're changing from an analog world to a digital world. Those of us who have been in the business and have hair that looks like mine. We need to be just as cognizant about cybersecurity practice as the young people, we need to understand how it affects our lives and particularly in space, because we're going to be talking about people, moving people to space, moving payloads, data, transfer all of those things. And so there's a whole workforce that needs to be retrained or upskilled in cyber that's out there. So the opportunity is ever expensive for all of us, Amy, the importance of cybersecurity space, >>Uh, and the, the emphasis of cybersecurity is space. Just simply, can't be over emphasized. There are so many aspects that are going to have to be considered as systems get ever more complex. And as we pointed out, we're putting people's lives at stake here. This is incredibly, incredibly complicated and incredibly impactful, and actually really exciting the opportunities that are here for students and the workforce of the future to really make an enormous impact on the world around us. And I hope we're able to get that message out to students, to children >>Today. But these are my really interesting fields that you need to consider. >>Thank you very much. I'm John foray with the cube and the importance of cybersecurity and space is the future of the world's all going to happen in and around space with technology, people and society. Thank you to Cal poly. And thank you for watching the Cypress of computer security and space symposium 2020.

>> from Seattle Washington It's the Q covering AWS Imagine brought to you by Amazon Web service is >> Hey, welcome back there, buddy Geoffrey here with the Cube. We're in downtown Seattle Day Ws Imagine Edie, you event. It's their education event and every education Everything from K through 12. The higher education community College Retraining after service is a really great show. It's a second year. We're happy to be here. We've got somebody has come all the way from Spain to talk about his very special company. It's Ignasi. Nuclear is he is >> the CEO of click dot edu. Yeah, nice. You see? Welcome. >> Thank you are way really pleased to be with you. >> Great. So tell us, kind of what is clicky? Do you What? What is kind of your core value? >> It's ah, platform that makes all the things that the school needs seeing atleast in Spain. So it's a miss system also on elements also the communication with the family that Petra is Ah Wei Tau financial the school and also a lot of things that they are related on >> right? And you've been around for a while. So when did the company started? How was kind of some basic numbers on how many customers do you have? Could you operate in a lot of countries? A lot of schools? >> The as we have schools working with us already in all of Spain, Also in Chile, Colombia, Arneson, UK. On also in a little country in Europe that is called Andorra. So we're really happy because you have more than 1,000,000 off users working with us. >> 1,000,000. Congratulations. And is it mainly do you specialize between, say, K through 12 or higher education? Or we're kind of all over the place? >> Yes, we're focusing K 12 schools. So the one off the important parts are the communication with parents on dhe to follow all the things that the student. That's >> right. So you guys have a very special thing that you're announcing here at the show is really focusing on Alexa for K through 12 which nobody else is doing. That's really something unique that you guys, How did you get in that? What did you see in voice communication and Alexa that you couldn't do in the platform before that? You really saw the opportunity? >> Yes. All the people say is that >> the future or the present Now is the voice on all we will communicate by boys in the future over Internet. You see a lot off young guys doing all the things my boys know, right? Texting, etcetera. So we thought that it could be a nice idea that the communication between parents and also for a students to the school and be on in the other way, could be could be by boys. So we imagine how to do >> it on. We did it. It's really knew. >> When did you start it? When did you start that project? >> This project we began three months ago, >> three months ago. So, >> yeah, it's really, really knew the boy's idea, right? It was in >> a show that I have seen. Ah ah, law. A lot of people were talking about that, but there were, at least in Spain, in the Spanish. Nothing about so with it, we can be the first. So >> we leave. That's >> great. So before we turn the >> cameras on, we're talking about some of the issues that you have in one of the ones is integration to all these systems because, you know, I have kids. I might have multiple kids in a couple different grades. You have kids and a fine looking for access on their homework or their test scores. You know he's got integrate with all those different back ends to keep things private. But you're kind of in a good spot because your system is the one that's on the back end, right? Yeah, so that worked pretty well. And then the other piece, he talked about his two way voice. I don't think a lot of people think in voice communication, yet it's still more of an ask and get a reply asking and get a reply. But you guys are actually pushing notice vacations from the school, out to the families using voice. How's that working out? You know what are some of the use cases? Yeah, >> it's like it's like the parent can ask Toe elixir, for example, What's a home or for tomorrow for one of your son or daughter on DA on The Echo tell you about that. So it's really impressive, because in that moment the system goes to the school system to get that information on our system. Yeah, on Alexa translating voice So it's It's It's funny >> I just think it's funny that I get e mails from all my digital assistants telling me, suggesting things that I should ask them because it's really not native yet as as an interface to work with these machines. But, well, he's mentioned that the young people voices much more natural. So I wonder if there's been some surprises or some things you didn't expect in terms of people comfort level with voice as a way to communicate with me. >> Say, I think it's, ah most natural way also for us that we are not not if but off course. So we communicate better by boys and writing or texting. So, so off course. It's the future because it's another away. So the use off that systems goes up because off that. So I think it's the most the most thing that for for causes more surprising, >> right? And so will you guys supply the Alexa? It's for people's homes. Or is it something they can tap into their existing Alexa Yeah, >> uh, usually, ah, the case for using that is in your home or else on your phone so you can install licks on your phone and you can ask them. I'll see if the UK fun ankle, >> but handle it. But how do I look? How do I hook my existing echo? Yeah, yes, I bought into the school system. >> Yes, because sometimes some universities are They pulled their A coin. I don't know in the university, or but you can use your echo that you are using it for other things. Listen, music me Listen, missing music or whatever >> and you >> can use the >> same. Yeah, you can. You >> only have to, like, download an >> app for >> your phone. There >> is more less is the same us Alexa to >> install, click in the Web or a skill that it's cow. It's called right, and then you >> have it. So what's next? What's on the road? Map on the voice specifically, Where do you see this kind of evolving over the next little while? >> Yes, our our next goal in the parties that they can use the teachers in the school. The boy systems also so for doing what they do every day in ah Maur writing or whatever, we can do it by voice. For example, interview with the parents, a transcript or, for example, to say that somebody hasn't come to the school or toe tell to the Transportacion that something is company. These kind of things is what we are. Imagine it's in our next things that we will do it with voice. >> It'll be Lexa in the classroom, hoping, thinking, Yeah, right. What about privacy? I would imagine knows funny. In the early days of Cloud, security was a was was not good of the show stopper. People were concerned about 10 years later. Now security is a strength of cloud, right? It's probably more secure than most people's data centers or disgruntled employees. I would imagine privacy and security. This is probably pretty top of mind in the school district as well as a lot of personal information. Are they comfortable? Do they kind of get the security of cloud and cloud infrastructure, or is that still sticking point? >> You know that in Europe there are really strict low of our protection off that right, so we are really concerned about that. So we are talking with the school's what kindof systems. They will be comfortable because you want to use it, so we'll have to find >> the clue to do that. But It's really >> important, I think, all over the world, but in the stage or in Europe who are really concerned about that. So we'll see how to find it. But we can create a private skill, right? Yes, because there are birds shown off, Alexa, that is for business. So you can create your provide things on. You don't have to be for that. Somebody's listening. You >> right? All right. So the last last question here at the conference and you come last year? >> No. So what do >> you know? Just your impressions of the conference Has it nice to be with a bunch of like minded, you know, kind of forward thinking educators because because education doesn't always get the best reputation being kind of forward looking. But here you're surrounded. So I just wonder you could share some of your thoughts of the of the event so far. Yeah, >> I think this guy no five ins give you more motivation on you. Increase your you're way t to see that there are a lot of people that is pushing to innovate and do the things different. So really, really interesting to goto some machine learning. Ah, suppose is shown about California. What? They are doing that right? So I'm really interested. >> Good. Get all right. Look Nazi. Thanks for taking a few minutes. And, uh, congratulations on that project. That's really crazy. Thank >> you for your interest in. >> All right, >> Jeff, you're watching the Cube. Where it aws Imagine in downtown Seattle. Thanks for watching. We'll see you next time.

>> live from Washington, D. C. It's the Cube covering a ws public sector summit brought to you by Amazon Web services. >> Welcome back, everyone to the Cubes Live coverage of a ws Public sector summit here in Washington D. C. Our nation's capital. I'm your host, Rebecca Knight co hosting alongside John Farrier wear welcoming Back to the Cuba, Cuba and esteemed Cube veteran Teresa Carlson, vice president Worldwide public Sector A W s. >> Thank you really appreciate always being on the key, But I appreciate you being here and our public sector. Sandy, >> Thank you for having us. So give up. Give us the numbers. How many people are in this room? How many people are here? >> Well, we have now today. Well, for this time that we're here, there's probably about 13,000 people here will expect a couple of 1,000 more. I think by the time it's all said Dan, we'll have about 15,000 at the conference. Of course, you had my keynote today with whole Benson sessions. They're all packed, and tomorrow you'll have Andy, jazzy herewith made ing a fireside chat at 11 o'clock on Wednesday, so I think that room will be overflowing with Andy Kelly as well, Because everybody loves him >> and Andy just coming back from a conference for the Silicon Valley elites on the west coast, where he put a big plug in for public sector, which is awesome. Yes. Now there you guys are kicking some serious butt. Congratulations. >> Thank you. Yeah. Thank you. >> I mean, what's it like for you? You're the leader. You're the chief of the public sector business. You've grown it. It's now cruising altitude that seem so cruising. >> Yeah, it. Well, first of all, this Nana, this would've been possible without Andy Jassy actually kind of believing and the mission of public sector when he hired me in 2010. And you're right, John. We started. You've hurt, covered the story. We started with two people in 2010 at the end of 2010. And now we have thousands of people around the world and, you know, over 35 countries, customers and 100 72 2 countries. And the business is growing at more than 41% every year date of yes, and we're $31,000,000,000. Business with public sector ban important component in that business. So for s here today. It is very meaningful. And the reason it is so meaningful. It is about our customers. And this is This is a testament to that. Our customers left what a TBS provides. And in the public sector business, it is a game changer to their mission way >> We're talking on our insure this morning. Rebecca and I around this new generation of workers, and that's almost like a revolution of red tape. Why's it in the way you gotta do better ways to be management cloud health care you named the vertical isn't a capacity to disrupt, create value. So you have this kind of shift happening. But you guys are also technology leaders. So when when you see things like space, >> Yeah, these were kind >> of tell signs that the CIA adopting the d o d. Look at the big contracts are coming in. People are working it hard. These air tell signs that the growth Israel >> Yeah, grab reaction to that gross Israel and I and I like to talk to my leaders about while we've had phenomenal growth, and that's fantastic. Way really are only getting started because now, in 2018 I really saw our customers doing unbelievable work leads very hard mission. Critical work was that they were meeting from it from it's kind of old environment, moving it on day to be asked, migrating and totally optimizing it. Now what's changing within the intelligence community and D o d is that you know, in 2013 when the icy made this decision made, it started changing even enterprise views of moving to the cloud from a security perspective. But you have that shift has happened. Now you see d o d moving for Jet I, which will be announced hopefully in July or August. Hope hopefully scene. But even without Jed, I. D o. D is making massive mate to cloud. I mean, and by the way, there no blockers now, like a year ago when we talked here, there were still some blockers for them. Today, really pretty much every blocker has been remade so that they can move a lot faster. So even outside of Jed, I we see our d o. D customers moving. You heard Kenny Bow and our debt today on stage, Who's the CEO of the special access program? Talk about what they're doing and why Cloud became an important element of their mission. And I could tell you, Kenny works on some very challenging and difficult mission programs for D. O. D. So that these air kind examples. On the flip side, I met with some CIA's yesterday from the state and local government. Now that has been a super surprising market for me where I'm seeing them. Actually, 2018 was a true change of year for them. Massive workloads in the state Medicaid systems that are moving off of legacy systems on a TVs, justice and public safety systems moving off on TBS. So that's where you're seeing moves. But you know what they shared with me yesterday, and my theme, as you saw today, was removing barriers. But they talked about acquisition barrier still, that states still don't know how to buy cloud, and they were asking for help. Can you help kind of educate and work with their acquisition officials? So it's nice when they're asking us for help in areas that they see their own walkers. >> So what accounts for the fact that these blockers air sort of disappearing as you set up on the main stage this morning? cloud is the new normal, right? Everyone is really adopting this cloud first approach. And what accounts for the fact that these challenges ey're sort of slowly dissipating? Well, there, you know, some of >> the blockers had been very legacy, and I'd like to tell you already that kind of old guard helped create a lot of these models. And most of these models, as an example of acquisition, were created so that governments had to pay at friend. So these models were like, pay me a lot of many a friend and then let's hope I will use them all that technology. So now we come along and say, Actually, no, you don't need to pay us anything up front. You could try it and pay as you use it and then scale that and they're like, Wait, wait a minute. We don't know how to do that model. So part of these things have been created because of all systems that what's changing those systems is that you can't you again if you can't change gravity, and we're at the point where it is the new normal, and you cannot change gravity, and they're seeing security. If you think about security is the number one reason they're moving to the cloud. Once you start having security issues, they on their own start removing blockers because they're like we've got it made faster because we wanted our secure. >> I know you've got a lot of things going on. You got customer visits. Your time's very tight. Appreciate you coming on. But I got to get and I want to talk about check for good programs you launched what happened at the breakfast of the stories. We could go for an hour on that, but I really want to dig into this ground station thing. And one of the coolest thing I saw reinvent when it kind of got launch. This is literally it reminds me the old Christopher Columbus days is the world flat is flat. We'll know the world is round. You have space? Yeah, space and data. It's gonna change the coyote edge to be the world. Right? So this is a game changer. I see this game changer way had your GM on earlier. Brett, what's what's going on with ground? So how is that going to help? Because it's almost provisioning back haul. It's gonna help. Certainly. Rural area st >> Yeah, way ahead of Earth and Space Day yesterday. So we kicked off with that with two amazing speakers. And the reason ground station is so important. By the way, it was a customer of ours in the US intelligence community that told us about six years ago we needed to create this. So you know where I said 95% of our services or customer driven? It was a customer that said, Why doesn't a TVs have a ground station and we really listen to them? Work backwards? And then we launch a ground station. I became general availability in May, and that is really about creating a ubiquitous environment for everyone, for space, for the space and satellite communications. So you can downlink an uplink data. But then the element of utilizing the cloud the process and analyze that data in real time and be ableto have that wherever you are is really I mean, it truly is going to be an opportunity for best commercial enterprises and public sector customers. And you know, John, right now, the pipeline that we have seen already for ground station, even I'm surprised at how Many of our customers and partners are so interested with acid ate a >> government thing about, like traffic lights, bio sensors Now back hauling all that into a global, >> you know, many different way. And now start. If he saw the announced with the Cloud Innovation Center at Cal Poly, we're gonna be doing some research with them on space communications and programs around ground station. Chile is another location You've heard me talk about that has missed tell escapes in the world. And we're gonna be working in Chile doing some work on ground station there in the Middle East. So this is, by the way, global. While the Qena it kind of came. Tosto, >> go to Cal Poly together way. We're gonna go to Chile. >> Chile next. Yeah, chili is great. So you could get two best locations with me. I would love that line here. Next. Exactly 11. Yes. >> Thank you so much for >> back. And make sure we get all those other days. >> Yes, because next time I've got to tell you that tape for good. There's too much not to talk about. So we have to convene again. >> Come to your office in the next couple months of summer. I'll make a trip down. We'll come to >> thank you all for being here. Thank you so much. Thank you. >> Thanks so much, Theresa. I'm Rebecca Knight for John Furrier. Stay tuned. You are watching the Cube.