>>Hey, everyone, welcome back to the cubes on the ground. Coverage of Pedro Duty Summit 22. I'm your host, Lisa Martin. I'm very excited to be joined by Manjula Toleration, the S VP and chief customer officer at page duty. Welcome to the programme. >>Thank you, Lisa. It's great to have chatted with you this morning as well, >>isn't it? I have had the great fortune of watching her fireside chat. That Mandela did, um, with is the logic monitor that was >>she of logic. >>And I thought, She's got great energy. We're gonna have a great conversation. So let's talk about the customer experience these days. One of the things I think that's been very, very short supply in the pandemic is patience. I know it's been in short supply with me and, of course, in our consumer lives in our business lives. The customer experience, though, has been something that every company needs to really pin their businesses on. Because if it's not a good customer experience, that customer goes right to social media. They churn. They leave, but they take others down with them. Talk to me about how the customer experience fits into this year's summit. Especially for this, we have to be ready for everything in a digital world environment. >>I love this question, and I reason I love this question is I even look at my own behaviour. But before we get into that, let's talk about data. I'm just reading an article. Mackenzie did a survey. Did you know that from pre covid to today customer interactions that have moved to digital are from 41% to 65%? That's exponential. That's huge. And guess what? We've all got impatient. You become like our kids, and I think about myself as an individual. If I need tied right now to do my laundry, I need it right now. So if I go to Costco website to order it so that it can get delivered in the next hour, and even if there's a second glitch on it, I'll swap over to Amazon and I'll swap over to target. That's what's happening in real world, whether it be to see or it's b to B, and why is it important to the points we are making in terms of ready for anything in the world of digital everything. It's important because customers are impatient. It's a digital world. I don't walk into the store to do any interactions anymore. And the reality of all of this is it's grounded on trust. Customers have to trust you and the window of choice not only in the B two b, but a lot in the enterprise and the B to B world. It's about trust, right? And what does pager duty do? Pager duty is at the heart of this pager. Duty is at the heart of making every second matter, and every second is equal to money. Absolutely. And it's about customer experience. And it isn't about just the experience where of an employee who may not sleep at night because they got a disruption due to an incident which is also super important during the mass resignation. But it is also about the CEO agenda and the boardroom, because how our CEO s driving customer trust in order to keep customers and drive this new era of digital everything as digital transformation is occurring. Well, >>I know patriarchy was doing that. I had the chance to watch um, CEO Jennifer to, uh, fireside chat, her keynote, and then her fireside chat with the CEO of Doc, You Sign And you. The Storey was very bidirectional, very symbiotic in terms of the trust that he has in Houston and Austin has and Pedro duty. But talk to me as the chief customer officer. What is it that's unique about how patriarchy works with its customers? 21,000 plus now to build and maintain that trust, especially in such volatile times? >>You know what is really cool? I joined page duty a little less than two years ago. In the next few days, it'll be two years now. What do I find exciting as a chief customer officer and the go to market teams differentiation versus other customers? We had a born SAS company and what do we have access to form our customers? We have access to their operations data and that combination of our core values that is championing the customer and the data science that we have about how customers are using our data is a differentiation. That's the magic. So if you think about why pager duty is bringing this level of trust to the customers, it's because we know how many and let's take an example. Employee retention, mass resignation. We know which employee was called. How many times at night during an outage. Can we give that guidance to managers and leaders in order to drive that trust? Absolutely. And on the other hand, we are driving amazing return on investment at the executive levels for the customer experience that they are driving. So Peter Duty is becoming the trusted advisor all the way from practitioners, where we are improving their work life balance to the executive levels, >>improving work. Life balance is so critical. There was a stat that Sean Scott shared this morning that that was looking at the amount of work volume from 2020 compared to 2021 42% of people said, I am working more hours. I don't think I've ever heard anyone say, Can I work more? Please? No. That work life balance is critical, but also the ability to deliver that seamless digital customer experience that we all expect, Um, and and to get it right the first time is critical. But using that customer data as you're saying, empowering the organisations, not just the customer support folks or the SRS or the develops folks but all the way up to the C suite to ensure that their brand reputation is valuable, it's maintained, and that trust is really bidirectional. That's the secret sauce. >>You're absolutely right. You know, there's a different dimension to this as well. We think about how we're using customer data in order to achieve the results. We want three vectors here. Number one is we'll use customer data to really understand what is best in class on up time. What is the best in class to reduce noise during alert, what is best in best in class for customer service operations? And because we have customer data, we can benchmark we can benchmark. What industry? What's happening in the financial services industry? What's happening in the technology industry? What's happening in the retail industry. Our customers love that, so we will share with them. The customer success organisation, especially the customer success managers, will go in and meet with the customers and say This is where you stand in reference to your peers and customers love here about that. This is the differentiated value proposition, right? The second thing that our customer success managers do is share with the customers This is where you are in reference to your peers in your vertical other vertical. But let me tell you how you can improve your deployment, the performance of our technology and you're all operating model. As a result of the data we've got, >>there's the proactive nous. That's another differentiator of of what I was hearing today from pager duty. That you're enabling those CSM is to be proactive when so often many are reactive, and it's the customer that's found the problem first. >>Yes, I'll even talk more about the reactive to proactive. We build a methodology, and I'm sure Shaun Scott covered it as well, which is a maturity curve moving from reactive to proactive because so many of our customers are saying we are reacting when we have a disruption on our digital platform, but 30% of the times we are hearing from customers before we are hearing from ourselves. So how do we become proactive? And how does that data signs actually start showing the signs when a potential disruption could occur? And that is about moving reactive to overall proactive. I'd also like to add one more dimension to this, you know, when customers are doing really well. They're optimised on our platform. They don't want to hear from our post sales organisation all the time. They want a human touch when they need it. They want a digital touch when they need it. By using our data and our data science, we are becoming one of the best world class customer success organisations in the world and you ask why? The reason is because we are using data science in order to build and we have built the early warning system. The early warning system tells us how every single of our customers is doing in terms of both their growth as well as the risk that they may leave us. So if a customer is very healthy on a scale of 1 200 if we have a healthy customer, we will engage with them potentially just digitally and engage with them with our services are customer success team and our entire post sales organisation, when there is an optimisation and when they really need us. So data scientists being used not only in terms of giving customer the right information to grow them, but how we interact with them as well, >>that's brilliant. And there's so many organisations that I talked to across industries that cannot get that right. >>And >>so customers are being contacted too frequently. They may have said. I opted out, I don't want and then suddenly that that the first responders, the incident responders, is marketing. But that happens so frequently, you think. But there's an opportunity there. It's not rocket science, but it's about leveraging that data in an optimal, smart way. But you guys are light years ahead of a lot of other companies that haven't figured that >>out. No, we are leading edge and we are leading edge because we had a born SAS company and we've got effective operations data of the customer, and we have some of the best data scientists and the analysts within my organisation. Looking at this, engaging with the customer and only optimising the magic is data science and humans coming together to engage with customers and drive customer success for the customer and ultimately building their customer experience for their customers. >>Let's talk about some of the numbers Mandela, because they are really impressive. I was looking at some stats. You're paid your duties renewal rates are over 95%. Your growth is incredible, just coming off the biggest quarter ever, but also the gross annual benefit from customers. Talk to me about that alone. That can be up to $10 million. These read these tangible business outcomes that pager duty is delivering to customers are significant, >>and again, it's based on data science. This is not making you know what traditional companies do. Traditional companies will go to the customer and say, Tell me your business imperatives. Tell me your what are the business problems you're solving are because we have the data science. We have our oi arranging from 309 100% very impressive within a couple of months. We think about it if we are able to drive incidents that are very, very significant. And I know you've got the numbers in terms of growing our reducing the workload on very expensive engineering. Uh, individuals within the organisation from, I believe, 3200 and 25,000, and I know you have those numbers think about If 30% of your organisation focuses just on innovation and product development, worse is on an incident, and they work, life balance, the quality of life increases, the retention of the employees, and yet the company's only driving their growth. That is why our customers love us. That is why our renewal rates are greater than 95%. That's why a net retention scores are greater than 100 and 2020% over five quarters. And that is why we have more than 30% growth year over year, quarter over quarter. >>When I saw that stat Manville about you know, the number of incidents reduced, >>that >>translates to employee productivity and and looking at it in terms of FTE. From a quantity perspective, that's the first time I've seen a company and I interview a lot of companies actually put it in that perspective, and I thought, That is huge. That's how organisations should be talking about that rather than reducing feeds are going. We are victims of the great resignation is look at the impact that can be made here by using data science by using the right mix of human and automation together. It's that's the first time. So congratulations to you and Pedro duty for the first time I've seen that and I think everybody needs to be working to be able to explain it that way, especially the fact that we're still in a volatile environment. >>Absolutely. It's about customer experience, but it is just as much employee experience. There is so much that the industry is talking about. That's top of mind for board levels. That's top of mind from CEO S. How do I retain my employees and drive greater operational efficiency? And now, with the macro economic challenges that are occurring in terms of inflation and in and the cost to serve and increasing the profits are customers are making. Operational efficiency is becoming even more important so that the employees are focusing more on innovation rather than downtime or disruptions. And it's actually about growing the business rather than just running the business. And if we can optimise running the business growth is what our customers are looking >>for, right? I always think, and we're almost out of time here. But I always think the employee experience and the customer experience are like this, and they should be. But it's critical to optimise both. How do you when you talk to some of those big and our price customers. We have Doc Watson on the main stage this morning, but I was looking at the website and three that jumped out to me that I use peloton, salesforce and slack. How do you advise them? You have this wealthiest gold of information on customers. This is how you need to leverage it in the right way to grow your business. What are some of the top three things you recommend those customers do, for example, >>that so let me talk about a couple of customers as an example. There are some customers of ours in the retail business, or it is a telecommunication company that is trying to increase their, um, up time from 98.7% to 3 nines as an example, or a tech company that doesn't even know that they were down for six hours in one small part of their business. And we're trying to figure out how do we solve for that as customers are overall complaining. So for us as a organisation, the magic is again bringing data together employee engagement, and what we do is we use the data to engage with their customers to ultimately understand what is their business value proposition. If you don't do it in isolation, you do it in. What is the customer trying to achieve? Are they trying to achieve the best in class website? Are they trying to achieve increased operational efficiency? What are their metrics? What are their numbers? And we take our data, our people, to marry all of that together. And that's the magic. >>I love it. I wish we had more time. Angela. We are out of time but talking about the value of the customer experience, the impact that is possible to be made leveraging technologies like pager duty. It's It's revolutionising operations. It's revolutionising customers 21,000 plus one million plus users at a time. It's awesome. You have to come back so we can talk more because I can. No, we're just scratching the surface here. >>Yes, we are. This is a very, very exciting area right now, and it is a great opportunities for chief customer officers on really rallying the whole company on championing the customers because whether it's a product, our capabilities, it's really a major transformation happening in the in the industry, and we need to stay very close to it? >>Absolutely. Thank you so much for joining me today. It's been such a pleasure talking to you. I look forward to seeing you again. >>Real pleasure, Lisa, To get to know you. And the gun was she was awesome. >>Good. Thank you for Manjula. Televisa. I'm Lisa Martin. You're watching the cubes on the ground. Coverage of pager duty. Summit 22 from San Francisco. Thanks for watching. And bye for now. Mm mm. Mm mm.

>>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)

>>Hello, and welcome to the cubes. Special showcase with unstoppable domains. I'm John furrier, your host of the cube here in Palo Alto, California and Matt Gould, who is the founder and CEO of unstoppable domains. Matt, great to come on. Congratulations on the success of your company on stumbled domains. Thanks for kicking off this showcase. >>Thank you. Happy to be here. So >>Love, first of all, love the story you got going on here. Love the approach, very innovative, but you're also on the big web three wave, which we know where that leads into. Metaverse unlimited new ways. People are consuming information, content applications are being built differently. This is a major wave and it's happening. Some people are trying to squint through the hype versus reality, but you don't have to be a rocket science to realize that it's a cultural shift and a technical shift going on with web three. So this is kind of the what's happening in the market. So give us your take. What's your reaction? You're in the middle of it. You're on this wave. >>Yeah. Well, I would say it's a torrent of change and the get unleashed just over a decade ago with Bitcoin coming out and giving people the ability to have a digital items that they could actually own themselves online. And this is a new thing. And people coming, especially from my generation of millennials, they spend their time online in these digital spaces and they've wanted to be able to own these items. Do you see it from, you know, gaming and Fortnite and skins and Warcraft and all these other places, but this is really being enabled by this new crypto technology to just extend a whole lot more, uh, applications for money, which everyone's familiar with, uh, to, uh, NFT projects, uh, like boarding school. >>You know, I was listening to your podcast. You guys got a great pot. I think you're on a 117 episodes now and growing, you guys do a deep dive. So people watching check out the unstoppable podcast, but in the last podcast, man, you mentioned, you know, some of the older generations like me, I grew up with IP addresses and before the web, they called it information super highway. It wasn't even called the web yet. Um, but IP was, was generated by the United States department of commerce and R and D that became the internet. The internet became the web back then it was just get some webpages up and find what you're looking for. Right. Very analog compared to what's. Now, today, now you mentioned gaming, you mentioned, uh, how people are changing. Can you talk about your view of this cultural shift? And we've been talking about in the queue for many, many years now, but it's actually happening now where the expectation of the audience and the users and the people consuming and communicating and bonding and groups, whether it's gaming or communities are expecting new behaviors, new applications, and it's a forcing function. >>This shift is having now, what's your reaction to that? What's your explanation? >>Yeah, well, I think, uh, it just goes back to the shift of peoples, where are they spending their time? And if you look today, most people spend 50% plus of their time in front of a screen. And that's just a tremendous amount of effort. But if you look at how much, how much of assets are digital, it's like less than 1% of their portfolio would be some sort of digital asset, uh, compared to, you know, literally 50% of every day sitting in front of a screen and simultaneously what's happening is these new technologies are emerging around, uh, cryptocurrencies, blockchain systems, uh, ways for you to track the digital ownership of things, and then kind of bring that into, uh, your different applications. So one of the big things that's happening with web three is this concept of data portability, meaning that I can own something on one application. >>And I could potentially take that with me to several other applications across the internet. And so this is like the emerging digital property rights that are happening right now. As we transitioned from a model in web to where you're on a hosted service, like Facebook, it's a walled garden, they own and control everything. You are the product, you know, they're mining you for data and they're just selling ads, right? So to assist them where it's much more open, you can go into these worlds and experiences. You can take things with you, uh, and you can, you can leave with them. And most people are doing this with cryptocurrency. Maybe you earn an in-game currency, you can leave and take that to a different game and you can spend it somewhere else. Uh, so the user is now enabled to bring their data to the party. Whereas before now you couldn't really do that. And that data includes their money or that includes their digital items. And so I think that's the big shift that we're seeing and that changes a lot and how applications, uh, serve up to user. So it's going to change their user experiences. For instance, >>The flip, the script has flipped and you're right on. I agree with you. I think you guys are smart to see it. And I think everyone who's on this wave will see it. Let's get into that because this is happening. People are saying I'm done with being mined and being manipulated by the big Facebooks and the LinkedIns of the world who were using the user. Now, the contract was a free product and you gave it your data, but then it got too far. Now people want to be in charge of their data. They want to broker their data. They want to collect their digital exhaust, maybe collect some things in a game, or maybe do some commerce in an application or a marketplace. So these are the new use cases. How does the digital identity architecture work with unstoppable? How are you guys enabling that? Could you take us through the vision of where you guys came on this because it's unique in an NFT and kind of the domain name concept coming together? Can you explain? >>Yeah. So, uh, we think we approach the problem for if we're going to rebuild the way that people interact online, uh, what are kind of the first primitives that they're going to need in order to make that possible? And we thought that one of the things that you have on every network, like when you log on Twitter, you have a Twitter handle. When you log on, uh, you know, Instagram, you have an Instagram handle, it's your name, right? You have that name that's that's on those applications. And right now what happens is if users get kicked off the platform, they lose a hundred percent of their followers, right? And theirs. And they also, in some cases, they can't even directly contact their followers on some of these platforms. There's no way for them to retain this social network. So you have all these influencers who are, today's small businesses who build up these large, you know, profitable, small businesses online, uh, you know, being key opinion leaders to their demographic. >>Uh, and then they could be D platform, or they're unable to take this data and move to another platform. If that platform raised their fees, you've seen several platforms, increase their take rates. You have 10, 20, 30, 40%, and they're getting locked in and they're getting squeezed. Right. Uh, so we just said, you know what, the first thing you're going to want to own that this is going to be your piece of digital property. It's going to be your name across these applications. And if you look at every computer network in the history of computing networks, the end up with a naming system, and when we've looked back at DDA desk, which came out in the nineties, uh, it was just a way for people to find these webpages much easier, you know, instead of mapping these IP addresses. Uh, and then we said to ourselves, you know, uh, what's going to happen in the future is just like everyone has an email address that they use in their web two world in order to, uh, identify themselves as they log into all these applications. >>They're going to have an NFT domain in the web three world in order to authenticate and, and, uh, bring their data with them across these applications. So we saw a direct correlation there between DNS and what we're doing with NFT domain name systems. Um, and the bigger breakthrough here is at NMT domain systems or these NFT assets that live on a blockchain. They are owned by users to build on these open systems so that multiple applications could read data off of them. And that makes them portable. So we were looking for an infrastructure play like a picks and shovels play for the emerging web three metaverse. Uh, and we thought that names were just something that if we wanted a future to happen, where all 3.5 billion people, you know, with cell phones are sending crypto and digital assets back and forth, they're gonna need to have a name to make this a lot easier instead of, you know, these long IP addresses or a hex addresses in the case of Porto. >>So people have multiple wallets too. It's not like there's all kinds of wallet, variations, name, verification, you see link trees everywhere. You know, that's essentially just an app and it doesn't really do anything. I mean, so you're seeing people kind of trying to figure it out. I mean, you've got to get up, Angela got a LinkedIn handle. I mean, what do you do with it? >>Yeah. And, and then specific to crypto, there was a very hair on fire use case for people who buy their first Bitcoin. And for those in the audience who haven't done this yet, when you go in and you go into an app, you buy your first Bitcoin or Ethereum or whatever cryptocurrency. And then the first time you try to send it, there's this, there's this field where you want to send it. And it's this very long text address. And it looks like an IP address from the 1980s, right? And it's, it's like a bank number and no one's going to use that to send money back and forth to each other. And so just like domain names and the DNS system replace IP addresses in Ft domains, uh, on blockchain systems, replace hex addresses for sending and receiving, you know, cryptocurrency, Bitcoin, Ethereum, whatever. And that's its first use case is it really plugs in there. So when you want to send money to someone, you can just, instead of sending money to a large hex address that you have to copy and paste, you can have an error or you can send it to the wrong place. It's pretty scary. You could send it to John furrier dot, uh, NFT. And uh, so we thought that you're just not going to get global adoption without better UX, same thing. It worked with the.com domains. And this is the same thing for the coin and other >>Crypto. It's interesting to look at the web two or trend one to two web one went to two. It was all about user ease of use, right? And making things simpler. Clutter, you have more pages. You can't find things that was search that was Google since then. Has there actually been an advancement? Facebook certainly is not an advancement. They're hoarding all the data. So I think we're broken between that step of, you know, a free search to all the resources in the world, to which, by the way, they're mining a lot of data too, with the toolbar and Chrome. But now where's that web three crossover. So take us through your vision on digital identity on web to Google searching, Facebook's broken democracy is broken users. Aren't in charge to web three. >>Got it. Well, we can start at web one. So the way that I think about it is if you go to web one, it was very simple, just text web pages. So it was just a way for someone to like put up a billboard and here's a piece of information and here's some things that you could read about it. Right. Uh, and then what happened with web two was you started having applications being built that had backend infrastructure to provide services. So if you think about web two, these are all, you know, these are websites or web portals that have services attached to them, whether that's a social network service or search engine or whatever. And then as we moved to web three, the new thing that's happening here is the user is coming on to that experience. And they're able to connect in their wallet or their web three identity, uh, to that app and they can bring their data to the party. >>So it's kind of like web one, you just have a static web page whip, two, you have a static web page with a service, like a server back here. And then with three, the user can come in and bring their database with them, uh, in order to have much better app experiences. So how does that change things? Well, for one, that means that the, you want data to be portable across apps. So we've touched on gaming earlier and maybe if I have an end game item for one, a game that I'm playing for a certain company, I can take it across two or three different games. Uh, it also impacts money. Money is just digital information. So now I can connect to a bunch of different apps and I can just use cryptocurrency to make those payments across those things instead of having to use a credit card. >>Uh, but then another thing that happens is I can bring in from, you know, an unlimited amount of additional information about myself. When I plug in my wallet, uh, as an example, when I plug in to Google search, for instance, they could take a look at my wallet that I've connected and they could pull information about me that I enabled that I share with them. And this means that I'm going to get a much more personalized experience on these websites. And I'm also going to have much more control over my data. There's a lot of people out there right now who are worried about data privacy, especially in places like Europe. And one of the ways to solve that is simply to not store the data and instead have the user bring it with them. >>I always thought about this and I always debated it with David laundry. My cohost does top down governance, privacy laws outweigh the organic bottoms up innovation. So what you're getting at here is, Hey, if you can actually have that solved before it even starts, it was almost as if those services were built for the problem of web two. Yes, not three. Write your reaction to that. >>I think that is, uh, right on the money. And, uh, if you look at it as a security, like if I put my security researcher hat on, I think the biggest problem we have with security and privacy on the web today is that we have these large organizations that are collecting so much data on us and they just become these honeypots. And there have been huge, uh, breaches like Equifax, you know, a few years back is a big one and just all your credit card data got leaked, right? And all your, uh, credit information got leaked. And we just have this model where these big companies silo your data. They create a giant database, which is worth hundreds of millions of dollars, if not, billions, to be attacked. And then someone eventually is going to hack that in order to pull that information. Well, if instead, and you can look at this at web three. >>So for those of the audience who have used the web three application, one of these depths, um, you know, trade cryptocurrencies or something, you'll know that when you go there, you actually connect to your wall. So when you're working with these web, you connect, you, you know, you bring your information with you and you connect it. That means that the app has none of that storage, right? So these apps that people are using for crypto trading cryptocurrency on depths or whatever, they have no stored information. So if someone hacks one of these DFI exchanges, for instance, uh, there's nothing to steal. And that's because the only time the information is being accessed is when the users actively using the site. And so as someone who cares about security and privacy, I go, wow, that's a much better data model. And that give so much more control of user because the user just permissions access to the data only during the time period in which they're interacting with the application. Um, and so I think you're right. And like, we are very excited to be building these tools, right? Because I see, like, if you look at Europe, they basically pass GDPR. And then all the companies are going, we can't comply with that and they keep postponing it or like changing a little bit and trying to make it easier to comply with. But honestly we just need to switch the data models. So the companies aren't even taking the data and then they're gonna be in a much better spot. >>The GDPR is again, a nightmare. I think it's the wrong approach. Oh, I said it was screwed up because most companies don't even know where stuff is stored. Nevermind how they delete someone's entering a database. They don't even know what they're collecting. Some at some level it becomes so complicated. So right on the money are good. Good call out there. Question for you. Is this then? Okay. So do you decouple the wallet from the ID or are they together? Uh, and is it going to be a universal wallet? Do you guys see yourselves as universal domains? Take me through the thinking around how you're looking at the wallet and the actual identity of the user, which obviously is super important on the identity side while it, is that just universal or is that going to be coming together? >>Well, I think so. The way that we kind of think about it is that wallets are where people have their financial interactions online. Right. And then identity is much more about, it's kind of like being your passport. So it's like your driver's license for the internet. So these are two kind of separate products we see longer term, uh, and they actually work together. So, you know, like if you have a domain name, it actually is easier to make deposits into your wallet because it's easier to remember to send money to, you know, method, rules dot crypto. And that way it's easier for me to receive payments or whatever. And then inside my wallet, I'm going to be doing defy trades or whatever. And doesn't really have an interaction with names necessarily in order to do those transactions. But then if I want to, uh, you know, sign into a website or something, I could connect that with my NFT domain. >>And I do think that these two things are kind of separate. I think there's, we're gonna still early. So figuring out exactly how the industry is gonna shake out over like a five to 10 year time horizon. And it may be a little bit more difficult and we could see some other emerging, uh, what you would consider like cornerstones of the crypto ecosystem. But I do think identity and reputation is one of those. Uh, and I also think that your financial applications of defy are going to be another. So those are the two areas where I see it. Um, and just to, you know, a note on this, when you have a wallet, it usually has multiple cryptocurrency address. So you're going to have like 50 cryptocurrency addresses in a wallet. Uh, you're going to want to have one domain name that links back to all those, because you're just not going to remember those 50 different addresses. So that's how I think that they collaborate. And we collaborate with several large wallets as well, uh, like blockchain.com, uh, and you know, another 30 plus of these, uh, to make it easier for sending out and receiving cryptocurrency. >>So the wallet, basically as a D app, the way you look at it, you integrate whatever you want, just integrate in. How do I log into decentralized applications with my NFT domain name? Because this becomes okay, I got to love the idea, love my identity. I'm in my own NFT. I mean, hell, this video is going to be an NFT. Soon. We get on board with the program here. Uh, but I do, I log into my app, I'm going to have a D app and I got my domain name. Do I have to submit, is there benchmarking, is there approval process? Is there API APIs and a SDK kind of thinking around it? How do you thinking about dealing with the apps? >>Yeah, so all of the above and what we're trying to, what we're trying to do here is build like an SSO solution. Uh, but that it's consumer based. So, uh, what we've done is adapted some SSL protocols that other people have used the standard ones, uh, in order to connect that back to an NFT domain in this case. And that way you keep the best of both worlds. So you can use these authorization protocols for data permissioning that are standard web to API APIs. Uh, but then the permissioning system is actually based on the user controlled in FTE. So they're assigning that with their private public key pair order to make those updates. Um, so that, that allows you to connect into both of these systems. Uh, we think that that's how technology typically impacts the world is it's not like you have something that just replaces something overnight. >>You have an integration of these technologies over time. Uh, and we really see these three components in MTU domains integrating nicely into regular apps. So as an example in the future, when you log in right now, you see Google or Facebook, or you can type in an email address, you can see not ensemble domains or NFT, uh, authorization, and you can SSO in with that, to that website. When you go to a website like an e-commerce website, you could share information about yourself because you've connected your wallet now. So you could say, yes, I am a unique individual. I do live in New York, uh, and I just bought a new house. Right. And then when you permission all that information about yourself to that application, you can serve up a new user experience for you. Um, and we think it's going to be very interesting for doing rewards and discounts, um, online for e-commerce specifically, uh, in the future, because that opens up a whole new market because they can ask you questions about yourself and you can deliver that information. >>Yeah. I really think that the gaming market has totally nailed the future use case, which is in game currency in game to engagement in game data. And now bringing that, so kind of a horizontally scalable, like surface areas is huge, right? So, you know, I think you're, that's huge success on the concept. The question I have to ask you is, um, you getting any pushback from ICANN, the international corporates have name and numbers. They got dot everything now.club, cause the clubhouse, they got dot, you know, party.live. I mean, so the real domain name people are over here, web too. You guys are coming out with the web three where's that connect for people who are not following along the web three trend. How do they, how do you rationalize the, the domain angle here? >>Yeah, well, uh, so I would say that NFTE domains or what domains on DNS were always meant to be 30 plus years ago and they just didn't have blockchain systems back in the nineties when they were building these things. So there's no way to make them for individuals. So what happened was for DNS, it actually ended up being the business. So if you look at DNS names, there's about 350 million registrations. They're basically all small business. And it's like, you know, 20 to 50 million small businesses, uh, who, uh, own the majority of these, uh, these.com or these regular DNS domain names. And that's their focus NFTE domains because all of a sudden you have the, uh, the Walton, if you have them in your wallet and your crypto wallet, they're actually for individuals. So that market, instead of being for small businesses is actually end-users. So, and instead of being for, you know, 20 to 50 million small businesses, we're talking about being useful for three to 4 billion people who have an internet connection. >>Uh, and so we actually think that the market size we're in a few domains and somewhere 50 to 100 X, the market size for traditional domain names. And then the use cases are going to be much more for, uh, individuals on a day-to-day basis. So it's like people are gonna want you on to use them for receiving cryptocurrency versus receiving dollars or payments or USCC point where they're going to want to use them as identifiers on social networks, where they're going to want to use them for SSO. Uh, and they're not gonna want to use them as much for things like websites, which is what web is. And if I'm being perfectly honest, if I'm looking out 10 years from now, I think that these traditional domain name systems are gonna want to work with and adopt this new NFC technology. Cause they're going to want to have these features for the domain next. So like in short, I think NMT domain names or domain names with superpowers, this is the next generation of, uh, naming systems and naming systems were always meant to be identity networks. >>Yeah. They hit a car, they hit a glass ceiling. I mean, they just can't, they're not built for that. Right. So I mean, and, and having people, having their own names is essentially what decentralization is all about. Cause what does a company, it's a collection of humans that aren't working in one place they're decentralized. So, and then you decentralize the identity and everything's can been changed so completely love it. I think you guys are onto something really huge here. Um, you pretty much laid out what's next for web three, but you guys are in this state of, of growth. You've seen people signing up for names. That's great. What are the, what are the, um, best practices? What are the steps are people taking? What's the common, uh, use case for folks we're putting this to work right now for you guys? Why do you see what's the progression? >>Yeah. So the, the thing that we want to solve for people most immediately is, uh, we want to make it easier for sending and receiving crypto payments. And I, and I know that sounds like a niche market, but there's over 200 million people right now who have some form of cryptocurrency, right? And 99.9% of them are still sending crypto using these really long hex addresses. And that market is growing at 60 to a hundred percent year over year. So, uh, first we need to get crypto into everybody's pocket and that's going to happen over the next three to five years. Let's call it if it doubles every year for the next five years, we'll be there. Uh, and then we want to make it easier for all those people to sit encrypted back and forth. And I, and I will admit I'm a big fan of these stable coins and these like, you know, I would say utility focused, uh, tokens that are coming out just to make it easier for, you know, transferring money from here to Turkey and back or whatever. >>Uh, and that's the really the first step freight FTE domain names. But what happens is when you have an NFTE domain and that's what you're using to receive payments, um, and then you realize, oh, I can also use this to log into my favorite apps. It starts building that identity piece. And so we're also building products and services to make it more like your identity. And we think that it's going to build up over time. So instead of like doing an identity network, top-down where you're like a government or a corporation say, oh, you have to have ID. Here's your password. You have to have it. We're going to do a bottoms up. We're going to give everyone on the planet, NFTE domain name, it's going to give them to the utility to make it easier to send, receive cryptocurrency. They're going to say, Hey, do you want to verify your Twitter profile? Yes. Okay, great. You test that back. Hey, you want to verify your Reddit? Yes. Instagram. Yes. Tik TOK. Yes. You want to verify your driver's license? Okay. Yeah, we can attach that back. Uh, and then what happens is you end up building up organically, uh, digital identifiers for people using these blockchain, uh, naming systems. And once they have that, they're gonna just, they're going to be able to share that information. Uh, and that's gonna lead to better experiences online for, uh, both commerce, but also just better user experiences. >>You know, every company when they web came along, first of all, everyone, poo-pooed the web ones. That was terrible, bad idea. Oh. And so unreliable. So slow, hard to find things. Web two, everyone bought a domain name for their company, but then as they added webpages, these permalinks became so long. The web page address fully qualified, you know, permalink string, they bought keywords. And then that's another layer on top. So you started to see that evolution in the web. Now it's kind of hit a ceiling here. Everyone gets their NFT. They, they started doing more things. Then it becomes much more of a use case where it's more usable, not just for one thing. Um, so we saw that movie before, so it's like a permalink permanent. Yeah. >>Yes. I mean, if we're lucky, it will be a decentralized bottoms up global identity, uh, that appreciates user privacy and allows people to opt in. And that's what we want to build. >>And the gas prices thing that's always coming. That's always an objection here that, I mean, blockchain is perfect for this because it's immutable, it's written on the chain. All good, totally secure. What about the efficiency? How do you see that evolving real quick? >>Well, so a couple of comments on efficiency. Uh, first of all, we picked domains as a first product to market because, you know, as you need to take a look and see if the technology is capable of handling what you're trying to do, uh, and for domain names, you're not updating that every day. Right? So like, if you look at traditional domain names, you only update it a couple of times per year. So, so the usage for that to set this up and configure it, you know, most people set up and configure it and then it'll have a few changes for years. First of all, the overall it's not like a game problem. Right, right, right. So, so that, that part's good. We picked a good place to start for going to market. And then the second piece is like, you're really just asking our computer, system's going to get more efficient over time. >>And if you know, the history of that has always been yes. Uh, and you know, I remember the nineties, I had a modem and it was, you know, whatever, 14 kilobits and then it was 28 and then 56, then 100. And now I have a hundred megabits up and down. Uh, and I look at blockchain systems and I don't know if anyone has a law for this yet, but throughput of blockchains is going up over time. And you know, there's, there's going to be continued improvements over this over the next decade. We need them. We're going to use all of it. Uh, and you just need to make sure you're planning a business makes sense for the current environment. Just as an example, if you had tried to launch Netflix for online streaming in 1990, you would have had a bad time because no one had bandwidth. So yeah. Some applications are going to wait to be a little bit later on in the cycle, but I actually think identity is perfectly fine to go ahead and get off the ground now. >>Yeah. The motivated parties for innovations here, I mean, a point cast failed miserably that was like the, they try to stream video over T1 lines, but back in the days, nothing. So again, we've seen those speeds double, triple on homes right now, Matt. Congratulations. Great stuff. Final tick, tock moment here. How would you summarize short in a short clip? The difference between digital identity in web two and web three, >>Uh, in, in web too, you don't get to own your own online presidents and in web three, you do get to own it. So I think if you were gonna simplify it really web three is about ownership and we're excited to give everyone on the planet a chance to own their name and choose when and where and how they want to share information about themselves. >>So now users are in charge. >>Exactly. >>They're not the product anymore. Going to be the product might as well monetize the product. And that's the data. Um, real quick thoughts just to close out the role of data in all this, your view. >>We haven't enabled users to own their data online since the beginning of the internet. And we're now starting to do that. It's going to have profound changes for how every application on the planet interacts with >>Awesome stuff, man, I take a minute to give a plug for the company. How many employees you got? What do you guys looking for for hiring, um, fundraising, give a quick, a quick commercial for what's going on, on unstoppable domains. Yeah. >>So if you haven't already check us out@ensembledomains.com, we're also on Twitter at unstoppable web, and we have a wonderful podcast as well that you should check out if you haven't already. And, uh, we are just crossed a hundred people. We've, we're growing, you know, three to five, a hundred percent year over year. Uh, we're basically hiring every position across the company right now. So if you're interested in getting into web three, even if you're coming from a traditional web two background, please reach out. Uh, we love teaching people about this new world and how you can be a part of it. >>And you're a virtual company. Do you have a little headquarters or is it all virtual? What's the situation there? >>Yeah, I actually just assumed we were a hundred percent remote and asynchronous and we're currently in five countries across the planet. Uh, mostly concentrated in the U S and EU areas, >>Rumor to maybe you can confirm or admit or deny this rumor. I heard a rumor that you have mandatory vacation policy. >>Uh, this is true. Uh, and that's because we are a team of people who like to get things done. And, but we also know that recovery is an important part of any organizations. So if you push too hard, uh, we want to remind people we're on a marathon, right? This is not a sprint. Uh, and so we want people to be with us term. Uh, we do think that this is a ten-year move. And so yeah. Do force people. We'll unplug you at the end of the year, if you have >>To ask me, so what's the consequence of, I don't think vacation. >>Yeah. We literally unplug it. You won't be able to get it. You won't be able to get into slack. Right. And that's a, that's how we regulate. >>Well, when people start having their avatars be their bot and you don't even know what you're unplugging at some point, that's where you guys come in with the NFD saying that that's not the real person. It's not the real human And FTS. Great innovation, great use case, Matt. Congratulations. Thanks for coming on and sharing the story to kick off this showcase with the cube. Thanks for sharing all that great insight. Appreciate it. >>John had a wonderful time. All right. Just the >>Cube unstoppable domains showcasing. We got great 10 great pieces of content we're dropping all today. Check them out. Stay with us for more coverage on John furrier with cube. Thanks for watching.

>>Welcome back to the cubes coverage of ADA reinvent 2021 here, the cube coverage. I'm Judd for a, your host we're on the ground with two sets on the floor, real event. Of course, it's hybrid. It's online as well. You can check it out there. All the on-demand replays are there. We're here with Sandy Carter, worldwide vice president, public sector partners and programs. And we've got Fred Swanick, her founder, and chief curator of the room. We're talking about getting the best talent programming and in the cloud, doing great things, innovation all happening, Sandy. Great to see you. Thanks for coming on the cube, but appreciate it. Thanks for halfway to see. Okay. So tell us about the room. What is the room what's going on? >>Um, well, I mentioned in the room is to help the world's most extraordinary do us to fulfill their potential. So, um, it's a community of exceptional talent that we are building throughout the world, um, and connecting this talent to each other and connecting them to the organizations that are looking for people who can really move the needle for those organizations. >>So what kind of results are you guys seeing right now? Give us some stats. >>Well, it's a, it's a relatively new concept. So we're about 5,000 members so far, um, from 77 different countries. Um, and this is, you know, we're talking about sort of the top two to 3% of talent in different fields. Um, and, um, as we go forward, you know, we're really looking, seeing this as an opportunity to curate, um, exceptional talent. Um, and it feels like software engineering, data science, UX, UI design, cloud computing, um, and, uh, it really helped to, um, identify diverse talent as well from pockets that have typically been untapped for technology. Okay. >>I want to ask you kind of, what's the, how you read the tea leaves. How do I spot the talent, but first talk about the relationship with Amazon. What's the program together? How you guys working together? It's a great mission. I mean, we need more people anyway, coding everywhere, globally. What's the AWS connection. >>So Fred and I met and, uh, he had this, I mean the brilliant concept of the room. And so, uh, obviously you need to run that on the cloud. And so he's got organizations he's working at connecting them through the room and kind of that piece that he was needing was the technology. So we stepped in to help him with the technology piece because he's got all the subject matter expertise to train 3 million Africans, um, coming up on tech, we also were able to provide him some of the classwork as well for the cloud computing models. So some of those certs and things that we want to get out into the marketplace as well, we're also helping Fred with that as well. So >>I mean, want to, just to add onto that, you know, one of the things that's unique about the room is that we're trying to really build a long-term relationship with talent. So imagine joining the room as a 20 year old and being part of it until you're 60. So you're going to have a lot of that. You collect on someone as they progress through different stages of their career and the ability for us to leverage that data, um, and continuously learn about someone's, you know, skills and values and use, um, predictive algorithms to be able to match them to the right opportunities at the right time of their lives. And this is where the machine learning comes in and the, you know, the data lake that we're building to build to really store this massive data that we're going to be building on the top talent to the world. >>You know, that's a really good point. It's a list that's like big trend in tech where it's, it's still it's over the life's life of the horizon of the person. And it's also blends community, exactly nurturing, identifying, and assisting. But at the same day, not just giving people the answer, they got to grow on their own, but some people grow differently. So again, progressions are nonlinear sometimes and creativity can come out of nowhere. Got it. Uh, which brings me up to my number one question, because this always was on my mind is how do you spot talent? What's the secret sauce? >>Well, there is no real secret source because every person is unique. So what we look for are people who have an extra dose of five things, courage, passion, resilience, imagination, and good values, right? And this is what we're looking for. And you will someone who is unusually driven to achieve great things. Um, so of course, you know, you look at it from a combination of their, their training, you know, what they, what they've learned, but also what they've actually done in the workplace and feedback that you get from previous employers and data that we collect through our own interactions with this person. Um, and so we screened them through, you know, with the town that we had, didn't fly, we take them through really rigorous selection process. So, um, it takes, uh, for example, people go through an online assessments and then they go through an in-person interview and then we'll take them through a one to three month bootcamp to really identify, you know, people who are exceptional and of course get data from different sources about the person as well. >>Sandy, how do you see this collaboration helping, uh, your other clients? I mean, obviously talent, cross pollinates, um, learnings, what's your, you see this level of >>It has, uh, you know, AWS grows, obviously we're going to need more talent, especially in Africa because we're growing so rapidly there and there's going to be so much talent available in Africa here in just a few short years. Most of the tech talent will be in Africa. I think that that's really essential, but also as looking after my partners, I had Fred today on the keynote explaining to all my partners around the world, 55,000 streaming folks, how they can also leverage the room to fill some of their roles as well. Because if you think about it, you know, we heard from Presidio there's 3 million open cyber security roles. Um, you know, we're training 20 of mine million cloud folks because we have a gap. We see a gap around the world. And part of my responsibility with partners is making sure that they can get access to the right skills. And we're counting on the room and what Fred has produced to produce some of those great skills. You have AI, AML and dev ops. Tell us some of the areas you haven't. >>You know, we're looking at, uh, business intelligence, data science, um, full-stack software engineering, cybersecurity, um, you know, IOT talent. So fields that, um, the world needs a lot more talented. And I think today, a lot of technology, um, talent is moving from one place to another and what we need is new supply. And so what the room is doing is not only a community of top 10, but we're actually producing and training a lot more new talent. And that was going to hopefully, uh, remove a key bottleneck that a lot of companies are facing today as they try to undergo the digital trends. >>Well, maybe you can add some hosts on there. We need some cube hosts, come on, always looking for more talent on the set. You could be there. >>Yeah. The other interesting thing, John, Fred and I on stage today, he was talking about how easy to the first narrative written for easy to was written by a gentleman out of South Africa. So think about that right. ECE to talent. And he was talking about Ian Musk is based, you know, south African, right? So think about all the great talent that exists. There. There you go. There you go. So how do you get access to that talent? And that's why we're so excited to partner with Fred. Not only is he wicked impressive when a time's most influential people, but his mission, his life purpose has really been to develop this great talent. And for us, that gets us really excited because we, yeah, >>I think there's plenty of opportunities to around new business models in the U S for instance, um, my friends started upstart, which they were betting on people almost like a stock market. You know, almost like currency will fund you and you pay us back. And there's all kinds of gamification techniques that you can start to weave into the system. Exactly. As you get the flywheel going, exactly, you can look at it holistically and say, Hey, how do we get more people in and harvest the value of knowledge? >>That's exactly. I mean, one of the elements of the technology platform that we developed to the Amazon with AWS is the room intelligence platform. And in there is something called legacy points. So every time you, as a member of the room, give someone else an opportunity. You invest in their venture, you hire them, you mentor them, you get points and you can leverage those points for some really cool experiences, right? So you want to game-ify um, this community that is, uh, you know, essentially crowdsourcing opportunities. And you're not only getting things from the room, but you're also giving to others to enable everyone to grow. >>Yeah, what's the coolest thing you've seen. And this is a great initiative. First of all, it's a great model. I think it's, this is the future. Cause I'm a big believer that communities groups, as we get into this hybrid world is going to open up the virtualization. What the virtual world has shown us is virtualization, which is a cloud technology when Amazon started with Zen, which is virtualization technology, but virtualization, conceptually is replicating things. So if you think hybrid world, you can blend the connect people together. So now you have this social construct, this connective tissue between relationships, and it's always evolving, you know, this and you've been involved in community from, from, from the early days when you have that social evolution, it's not software as a mechanism. It's a human thing. Exactly. It's organism, it evolves. And so if you can get the software to think like that and the group to drive the behavior, it's not community software. >>Exactly. I mean, we say that the room is not an online community. It's really an offline community powered by technology. So our vision is to actually have physical rooms in different cities around the world, whether it's talent gathers, but imagine showing up at a, at a room space and we've got the technology to know what your interests are. We know that you're working on a new venture and there's this, there's a venture capitalists in that area, investing that venture, we can connect you right then that space powered by the, >>And then you can have watch parties. For instance, there's an event going on in us. You can do some watch parties and time shifted and then re replicated online and create a localization, but yet have that connection in >>Present. Exactly, exactly. Exactly. So what are the >>Learnings, what's your big learning share with the audience? What you've learned, because this is really kind of on the front edge of the new kind of innovation we're seeing, being enabled with software. >>I mean, one thing we're learning is that, uh, talent is truly, uh, evenly distribute around the world, but what is not as opportunity. And so, um, there's some truly exceptional talent that is hidden and on tap today. And if we can, you know, and, and today with the COVID pandemic companies or around the world, a lot more open to hiring more talent. So there's a huge opportunity to access new talent from, from sources that haven't been tapped before. Well, but also learnings the power of blending, the online and offline world. So, um, you know, the room is, as I mentioned, brings people together, normally in line, but also offline. And so when you're able to meet talent and actually see someone's personality and get a sense of the culture fit the 360 degree for your foot, some of that, you can't just get on a LinkedIn. Yes. That I built it to make a decision, to hire someone who is much better. And finally, we're also learning about the importance of long-term relationships. One of my motives in the room is relationships not transactions where, um, you actually get to meet someone in an environment where they're not pretending in an interview and you get to really see who they are and build relationships with them before you need to hide them. And these are some really unique ways that we think we can redefine how talent finds opportunity in the 21st. So >>You can put a cube in every room, we pick >>You up because, >>And the cube, what we do here is that when people collaborate, whether they're doing an interview together, riffing and sharing content is creating knowledge, but that shared experience creates a bonding. So when you have that kind of mindset and this room concept where it's not just resume, get a job, see you later, it's learning, having peers and colleagues and people around you, and then seeing them in a journey, multiple laps around the track of humans >>And going through a career, not just a job. >>Yes, exactly. And then, and then celebrating the ups and downs in learning. It's not always roses, as you know, it's always pain before you accelerate. >>Exactly. And you never quite arrive at your destination. You're always growing, and this is where technology can really play. >>Okay. So super exciting. Where's this go next, Sandy. And next couple of minutes left in. >>So, um, one of the things that we've envisioned, so this is not done yet, but, um, Fred and I imagined like, what if you could have an Alexa set up and you could say, Hey, you know, Alexa, what should be my next job? Or how should I go train? Or I'm really interested in being on a Ted talk. What could I do having an Alexa skill might be a really cool thing to do. And with the great funding that Fred Scott and you should talk about the $400 million to that, he's already raised $400 million. I mean, there, I think the sky's the limit on platforms. Like >>That's a nice chunk of change. There it is. We've got some fat financing as they say, >>But, well, it's a big mission. So to request significant resources, >>Who's backing you guys. What's the, who's the, where's the money coming from? >>It's coming from, um, the MasterCard foundation. They, our biggest funder, um, as well as, um, some philanthropists, um, and essentially these are people who truly see the potential, uh, to unlock, um, opportunity for millions of people global >>For Glen, a global scale. The vision has global >>Executive starting in Africa, but truly global. Our vision is eventually to have a community of about 10 to 20 million of the most extraordinary doers in the world, in this community, and to connect them to opportunity >>Angela and diverse John. I mean, this is the other thing that gets me excited because innovation comes from diversity of thought and given the community, we'll have so many diverse individuals in it that are going to get trained and mentored to create something that is amazing for their career as well. That really gets me excited too, as well as Amazon website, >>Smart people, and yet identifying the fresh voices and the fresh minds that come with it, all that that comes together, >>The social capital that they need to really accelerate their impact. >>Then you read the room and then you get wherever you need. Thanks so much. Congratulations on your great mission. Love the room. Um, you need to be the in Cuban, every room, you gotta get those fresh voices out there. See any graduates on a great project, super exciting. And SageMaker, AI's all part of, it's all kind of, it's a cool wave. It's fun. Can I join? Can I play? I tell you I need a room. >>I think he's top talent. >>Thanks so much for coming. I really appreciate your insight. Great stuff here, bringing you all the action and knowledge and insight here at re-invent with the cube two sets on the floor. It's a hybrid event. We're in person in Las Vegas for a real event. I'm John ferry with the cube, the leader in global tech coverage. Thanks for watching.

>>Um, >>Welcome to common volt connections. My name is Dave Volante, and we're going to dig into the changing security landscape and look specifically at ransomware and what steps organizations can take to better protect their data, their applications, and their people. As you know, cyber threats continue to escalate in the past 19 months, we've seen a major shift in CSO strategies, tactics and actions as a direct result of the trend toward remote work, greater use of the cloud and the increased sophistication of cyber criminals. In particular, we've seen a much more capable well-funded and motivated adversary than we've ever seen before. Stealthy techniques like living off the land island, hopping through the digital supply chain, self forming malware and escalations in ransomware attacks, necessitate vigilant responses. And we're super pleased today to be joined by Dave Martin. Who's a global chief security officer at ADP. Dave. Welcome. Good to see you. >>Thanks for having me today. It's >>Our pleasure. Okay. Let's get right into it as a great topic. I mean, ADP, we're talking about people's money. I mean, it doesn't get more personal and sensitive than that maybe healthcare, but money is right there on the priority list, but maybe you could start by telling us a bit about your role at the company, how you fit into the organization with your colleagues like the, you know, the CIO, the CDO. Maybe describe that a bit if you would. >>Yeah, absolutely. So we're somewhat unusual in both banks structure and we, one of the ways is aware a I have a very converged organization. So my responsibility extends from both the physical protection of kind of buildings, our associates, um, travel safety through fraud that we see in, uh, attempted in our products all the way through to I'm more traditional, a chief security officer, um, in the cyberspace. And, uh, the other thing that's a little bit unusual is rather than reporting into a technology organization. I actually report into our chief administrative officer. So my peers in that organization now, our legal compliance, uh, so we, it's, it's a great position to be in the organization and I've had various different reports during my career. And there's always a lot of debate in, uh, in, uh, with my kids about where's the best place for the report. And I think they always come back to, it's not really where you report it's about those relationships that you mentioned. So how do you actually collaborate and work with the chief data officer, the CIO, the head of product, the product organization, and how do you use that to create this kind of very dynamic Angela falls to defend against the threats we face today? >>Yeah. Now, so let's just want to clarify for the audience. So when you talk about that converged structure, oftentimes if I, if I understand what your point is that the network team might be responsible for some of the physical security or the network security, that's all under one roof in your organization, is that correct? >>So a lot of the controls and operations, something like firewalls is out in the CIO organization. Um, but the, the core responsibility and accountability, whether it's protecting the buildings, the data centers, the, uh, the data in our applications, the, uh, kind of the back office of all the services that we use to, to deliver value to our clients and kind of the same things that everyone has, the, uh, the ERP environments. Now, all of that, the protecting those environments rolls up to my team from an accountability and governance. >>Got it. So, I mean, as I was saying upfront, I mean, the, the acceleration, we all talk about that acceleration that compression, the force March to digital and that that's solar winds hack. It was like a Stuxnet Stuxnet moment to me. Cause it's signaled almost this new level of excellent escalation by cybercriminals and that had to send a shockwave through your community. I wonder if you could talk about at a high level, how did that impact the way that CSOs think about cyber attacks or, or did it >>Well, I think we're, we're very used to watching the outside world kind of adversaries don't stand to sell our businesses. Don't stand still, so we're constantly having to evolve. So it's just another call to action. How do we think about what we just saw and then how do we kind of realign the controls that we have and then how do we think about our program there, food that we need to address? >>Yeah. So we've seen, uh, when we talk to other CSOs, your colleagues, we, we, they tell us we've made a big sort of budget allocation toward end point security cloud identity, access management, uh, and, and obviously focus on a flatter network. And of course, ransomware, how have you shifted priorities as a result of sort of the last, you know, the pandemic 19 months? >>Yeah, definitely seeing that shift in kind of the necessity of working from home and kind of thinking by what tools that we need to get to our associates, um, to really make them successful. And then also keep our, uh, the integrity of our data and the availability of our services in that new model. And so we've made that shift in technology and controls, reinforced a lot of things that we already had. One thing thinking about the supply chain change that we saw out of SolarWinds is thinking about ransomware defense prior to that was very much around, uh, aligning the defenses within the perimeter of your network, a within the cloud environments. And I really thinking about where do I am inside that environment? Where do I exchange files from what connectivity do I have with partners and suppliers? What services do they provide, um, to support us as an enterprise and what's going to happen if they're not there at a minimum, but then what happens if they have a, some kind of a channel for that can actually drive some of this malware and spread into the network or via some of those file transfer, make sure we really sure shored up the controls in that area, but the, the response is actually part of that. >>How am I gonna react? When I hear from even applying, we're a very customer service focused company, we want to do whatever we can to help. And the instinct of one of our frontline associates, Hey, send, send me that Excel file. I'll take care of it. So now yet we still want to help that client through, but we want to think through a little bit more before we start sharing a, uh, an office file back and forth between two environments, one of which we know to be home, >>Right. That's interesting what you're saying about the change in just focus on the perimeter to the, the, the threats, you know, within, uh, without et cetera, because you don't even need a high school degree or, you know, gray diploma to be a ransomware attacker. These days, you could go on the dark or dark web, and if you're bad, bad person, you can hire ransomware as a service. If you have access to a server credentials, you know, you can do bad things and hopefully you'll end up in handcuffs, but, but that's a legitimate threat today, which is relatively new in the way in which people are escalating, whether it's, you know, crypto ransoms, et cetera, really do necessitate new thinking around or ransomware. So I wonder if you could talk a little bit more about, you know, the layered approach that you might take the air gapping, uh, be interested to understand where Convolt fits in to the, to the, to the portfolio, if you will. >>Sure. And really it's thinking about this in depth and you're not going to be able to, uh, to protect or recover everything. So really understanding, first of all, that, of what is most important to be able to maintain service, what data do you do you need to protect and have available armed with that? Now you can go through the rest of the nest cyber security framework and main things. You're doing the best for prevention, uh, for the detection and response in that area. And then kind of really, uh, interesting when we get to the recovery phase, both from a Convolt perspective and in many tanks where we really want to focus on prevention, but ultimately we'll likely to see a scenario. And even in some small part of our environment, whereas some kind of attack is effective and there, where we're back to that recovery step. >>And we don't want them to be the first time we're testing those backgrounds. We don't want to be the first time that we figured out that those backups have been on the network the whole time, and they can't be used for recovery. So partnering with everyone in the environment, it takes a village to defend against this kind of threat, getting everyone engaged the experts in each of these fields to make sure that we're thinking they understand that this threat and how real it is and what their role is going to be in setting up that protection and defense, and then calm that dark day that we all hope will never happen. What's the, when do you need them? When do you need them to be doing so that you can get back to a restoration and effective operation sooner possibly >>Yeah. Hope for the best plan for the worst. So it's a big part of that is education. Um, and of course the backup Corpus is an obvious target because everything's in there. Uh, but before we get into sort of the best practice around that, I wanted to ask you about your response, because one of the things that we've seen is that responses increasingly have to be stealthy, uh, so that you don't necessarily alert the, the attackers that you know, that they're inside. Is that sort of a new trend and how do you approach that? >>Yeah, I mean, it's always, it's always a balance depending on the type of data and the type of attack as to kind of heroine kind of violent and swept. And obviously you have to be to be able to protect the environment, protect the integrity of the data, and then also balance the games kind of tipping off the attacker, which could potentially make things worse. So always a conversation depending on the different threat type, um, you're going to have to go through. And it really helps to have some of those conversations up front to have tabletops, not just at a technical level to make sure that you're walking through the steps of a response to make it as seamless and quick and effective as possible, but also having that conversation with leadership team and even the board around the kind of decisions they're going to have to make and make sure that youth, that wherever possible use scenarios to, uh, to figure out what are some of those actions that are likely to be taken and also empower some teams. It's really important to be able to act autonomously and quickly you, uh, you don't want to be at 2:00 AM kind of looking for, uh, for the CEO or kind of the executive team to get them out there to make a decision. Some of these decisions need to be made very quickly and very effectively, and you can only do that with empowered upfront and sometimes even automated processes to do them. >>Dave, describe what you mean by tabletops. I presume you're talking to a top-down view versus sort of being in the weeds, but that's some color to that, please. Yeah, >>Yeah, definitely. It literally is kind of getting everyone around the table and at ADP, at least once per year, we actually get the full executive team together and challenge them with a scenario, making sure that they're working through the problem. They know what each of their roles are at the table. And I am lucky to have a fantastic leadership team. We're actually very practiced. We've done this often enough now that they really pull apart really hard problems and think about what that decision is going to mean to me. So come that dark day, if it ever does, then they're not kind of challenged by the never thought they don't they've understand the technical background of why being asked to make a decision to the limitations of what they're responsive to may be. >>So a lot of people in process goes into this, always the case, but let's talk a little bit about the tech. Eventually the backup Corpus is an obvious target before. What are some of the best tech practices in terms of protecting, whether it's that backup Corpus other data, uh, air gaps, maybe you could give us some guidance on that front. >>Sure. Hey, we're not going to be able to protect our things or focus on those favorite children is the, uh, the best advice up front to think about the, uh, the critical components that enabled me to bring things up easy, to go focus on that critical data and that most important half that everyone in the company understands, but all that cannot even start. If you don't have the foundation, the network's not up and running your authentication. So it's good to get a focus, some elements and practice that technical tabletop setting of what, how do you go through recovering an active directory forest bank to a known, trusted state because that's one of the foundations you're going to need to build. Anything else back off on the backup side is made sure that you don't use the same credentials that the, your backup administrators use everyday make. >>There's only the smallest number of people have access to be able to control the backpacks if at all possible and, uh, combo and many backup solutions in there and make sure they're using a second factor authentication to be able to get into those systems and also make sure that some of the backups that you have are kind of offline air gaps can be touched. Uh, and then also think about the duration, talk about the attack, being very smart and determined. They know how enterprises prepare and respond. So think about the, uh, how long you're retaining them, where you're retaining some of the backups, not just incremental is to be able to phone you restore a system, basically from ban that whole from backslide. >>And you're using Convolt software to manage some of this, this, this capability is that right? I'm sure you have a bevy of tooling, but yeah, >>We have a wide range of toning >>And somebody said, consultants said to me the day, you know, Dave, I'm thinking about advising my clients that their air gap process should be air gapped. In other words, they should have him as sort of a separate, you know, remote removed from the mainstream process, just for extra protection. And I was like, okay, that's kind of interesting, but at the same time then do they have the knowledge to get back to, you know, a low RPO state? What do you think about about that? >>So the challenges of any kind of recovery and control design is like making sure that you're make, not making things overly complex and introducing other issues. And also other exposures you're moving out of your normal control environment that you have a 24 by 7, 365 set of monitoring. The more creative you get and you prance are in danger of kind of having control erosion and visibility to that other state. Um, but it is really important to think about even at the communication level, um, is in this kind of attack, you may not be able to rely on email kind of teams, all the common services you have. So how are you actually going to communicate with this village? It's going to take, to recover, to be able to, uh, work through the process. So that's definitely an area that I would advocate for having offline capabilities to be able to have people react, gather, respond, plan, and control the recovery. Even though the, uh, the main enterprise may not be currently function. >>I wonder if I could pick your brain on another topic, which is, you know, zero trust prior to the pandemic. A lot of times people would roll their eyes. Like it's a buzzword, but it's kind of become a mandate where people are now talking about, you know, eliminating credentials to talking about converging identity, access management and governance and privilege access, access management. I mean, what are those, some of the sea changes you see around so-called zero trust. >>Yeah. I think kind of zero trust has become that kind of call to action buzzword. But these concepts that are embodied in zero trust journey are ones that have been around for forever least privilege. And it's how we think about that. You can't go buy a product that I like. I'm just implemented zero trust. How do you think strategically about way you take your starting point and then go on this journey to kind of increase the, uh, the various tools that start to limit improve the segmentation, not only from a network standpoint, from a service standpoint, from an identity standpoint and make sure you're embracing concepts like persona so that you start to break up the, uh, may not get to zero trust anytime soon, but you're able to get less and less trust in that model and to think about it in many different worlds. >>Think about your product access. If you're a service provider company, like we are as well as kind of the internal employee, uh, context. So there's many, um, elements, it's a complex journey. It's not something you're going to buy off the shelf and go implement. But it's one that you're going to have to, again, partner with those other stakeholders that you have because there's user experience and client experience components of this journey, some of which are actually quite positive. Uh, you mentioned penciled us as one of those components in the gym. Certainly something that actually has a better user experience and also can offer a, a better security and freedom from the traditional passwords that you've come to love to hate >>Dave. I know you're tight on time. I got two more questions for you. One is what is the CSOs number one challenge. >>Wow, that's a getting enough slate now. Um, and then he is just staying current with that business environment, that threat environment and the available tool sets and making sure that we're constantly working with those partners that we keep describing to chart that course to the future. So that we're, this is a race that doesn't have a finish line. The marathon gets a little bit longer every year and bringing my peers on and making them understand that it's easy to get fatigued and say, ah, don't worry. Tell me what I've done when we finished this initiative. It's just keeping everyone's energy up and focus on a very long then >>One a and that question, if I may, is, is many organizations lack the talent to be able to do that. You may not, you may, you may have a firmer, but the industry as a whole really lacks the skills and the talent, and really, that's why they're looking to automation. How acute do you see that talent shortage? >>It's definitely there. And I think it's important to realize that the, uh, back to that village concept, everybody has a play here. So what is a smaller, uh, available talent born in the, uh, the security industry is we've really got to be that call to action. We've got to explain why this is important. We've got to be the consultants that have lead brew. What changes are we going to need to make, to be successful? It's tempting to say, oh, they'll never do that. And they're like, we've got to do it ourselves. We will never be successful. And just being the security team that tries to do everything, it's bringing everyone along for the journey. And part of that is just going to be this constant socialization and education of what they need to do and why it's so important. And then you really will build a great partnership. >>My last question, I was kind of been keeping a list of Dave's best practice. I say, obviously, the layered approach you want to get to that NIST framework. There's a lot of education involved. You've got to partner with your colleagues that tabletops executive visibility. So everybody knows what their role is. Kind of the do your job. You've got to build zero trust. You can't just buy zero trust off the shelf. And, and, and, uh, so that is my kind of quick list. Am I missing anything? >>I think that's pretty good. And then I'm just in that partnership, you guys have it, this is a tiring, a hard thing to do and kind of just bringing everyone along or they, they, they can help you do so much, especially if you explained to them how it's going to make that product better. That was going to make that client experience better. How it's going to mean for the CIO, the internal associate experience about it, that this isn't just a Byron adding friction into a, an already challenging environment, >>You know, like frontline healthcare workers, the SecOps pros are heroes. Day-to-day, you don't necessarily hear a lot about the work they're doing, but, uh, but Dave, we really appreciate you coming on and sharing some of the best practices. And thank you for the great work that you guys are doing out there. And best of luck. Thanks for the exchange has been a pleasure. All right. And thank you for watching everybody. This is Dave Volante for the cube. Keep it right there.

>>mhm. What the special cube conversation here in Palo alto, I'm john Kerry host of the cube. We're here talking about kubernetes Cloud native and all things Cloud, cloud enterprise amir Sure VP of product and morgan Stanley is with me and we are great to have you on the cube. Thanks for coming on. I appreciate you taking the time, >>appreciate it, john good to be here. You >>know, cloud Native obviously super hot right now as the edges around the corner, you're seeing people looking at five G looking at amazon's wavelength outposts you've got as you got a lot of cloud companies really pushing distributed computing and I think one of the things that people really are getting into is okay, how do I take the cloud and re factor my business and then that's one business side then, the technical side. Okay, How do I do it? Like it's not that easy. Right. So it sounds, it sounds really easy to just go to move to the cloud. This is something that's been a big problem. So I know you guys in the center of all this uh and you've got, you know, microservices, kubernetes at the core of this, take a minute to introduce the company, what you guys do then I want to get into some specific questions. >>Mhm, of course. Well, bob Sani is a startup? Silicon Valley startup and what we do is automate system configuration that's typically worked at an engineer does and take lengthy and if done incorrectly at least to a lot of errors and cost overruns and the user experience problems. We completely automate that using an Ai and ml back end so that the engineering can focus on writing code and not worry about having to tune the little pieces working together. >>You know, I love the, I was talking to a V. C on our last uh startup showcase, cloud startup showcase and uh really prominent VC and he was talking about down stack up stack benefits and he says if you're going to be a down stack um, provider, you got to solve a problem. It has to be a big problem that people don't want to deal with. So, and you start getting into some of the systems configuration when you have automation at the center of this as a table stakes item problems are cropping up as new use cases are emerging. Can you talk about some of the problems that you guys see that you solve for developers and companies, >>of course. So they're basically, they're, the problem expresses itself in a number of domains. The first one is that he who pays the bills is separate from he who consumes the resources. It's the engineers that consume the resources and the incentives are to deliver code rapidly and deliver code that works well, but they don't really care about paying the bills. And then the CFO office sees the bills and there's a disparity between the two. The reason that creates a problem, a business problem is that the developers uh, will over provision stuff, uh to make sure that everything works and uh, they don't want to get caught in the middle of the night. You know, the bill comes due at the end of the month or into the quarter and then the CFO has smoke coming out of his ears because there's been clawed overruns. Then the reaction happens to all right, let's cut costs. And then, you know, there's an edict that comes down that says everything, reduce everything by 30%. So people go across and give a haircut to everything. So what happens next to systems out of balance? There's allocation resource misallocation and uh, systems start uh, suffering. So the customers become unhappy. And ironically, if you're not provisioned correctly, Not ironically, but maybe understandably, customers start suffering and that leads to a revenue problem down the line if you have too many problems unhappy. So you have to be very careful about how you cut costs and how you apportion resources. So both the revenue side is happy and it costs are happy because it all comes down to product experience and what the customers consume. You >>know, that's something that everyone who's done. Cloud development knows, you know, whose fault is it? You know, it's this fall. But now you can actually see the services you leave a switch open or, you know, I'm oversimplifying it. But, you know, you experiment services, you can the bills can just have massive, you know, overruns and then, and then you got to call the cloud company and you gotta call the engineers and say why did you do this? You got to get a refund or or the bad one. Bad apple could ruin it for everyone as you, as you highlighted over the bigger companies. So I have to ask you mean everyone lives this. How do companies have cost overruns? Is their patterns that you see that you guys wrote software 4-1, automate the obvious ones. Is there is there are certain things that you know always happen. Are there areas that have some indications? So why do, first of all, why do companies have cloud cost overruns? >>That's a great question. And let's start with a bit of history where we came from a pre cloud world, you built your own data centers, which means that you have an upfront Capex cost and you spend the money and you were forced to live within the needs that your data center provided. You really couldn't spend anymore. That provided kind of a predictable expenditure bottle it came in big chunks. But you know what, your budget was going to be four years from now, three years from now. And you built for that with the cloud computing, Your consumption is now on on demand basis and it's api enabled. So the developer can just ask for more resources. So without any kind of tools that tell the developer here is x amount of CPU or X amount of memory that you need for this particular service, that for it to deliver the right uh, performance that for the customer. The developers incentivized to basically give it a lot more than the application needs. Why? Because the developer doesn't want to pick up service tickets. He's incentivizing delivering functionality quickly and moving on to next project, not in optimizing costs. So that creates kind of uh an agency problem that the guy that actually controls how research are consumed is not incentivized to control the consumption of these resources. And we see that across the board in every company, engineers, engineering organization is a separate organization than the financial organization. So the control place is different. The consumption place and it breaks down the patterns are over provisions. And what we want to do is give engineers the tools to consume precisely the right amount of resources for the service level objectives that they have, given that you want a transaction rate of X and the literacy rate of Why here's how you configure your cloud infrastructure. So the application delivers according to the sls with the least possible resources consumed. >>So on this tool you guys have in the software you guys have, how how do you guys go to mark with that, you target the business buyer or the developer themselves and and how do you handle the developers say, I don't want anyone looking over my shoulder. I'm gonna go, I'm gonna have a blank check to do whatever it takes, um how do you guys roll that out because actually the business benefits are significant controlling the budget, I get that. Um how do you guys rolling this out? How do people engage with you? What's your strategy? >>Right. Are there, is the application owner, is the guy that owns the PML for the application? It tends to be a VP level or a senior director person that owns a SAAS platform and he or she is responsible for delivering good products to the market and delivering good financial results to the CFO So in that person of everything is rolled up, but that person will always favor the revenue site, which means consume more resources than you need in order to maximize customer happiness, therefore faster growth and uh they do that while sacrificing the cost side. So by giving the product owner the optimization tools autonomous of optimization tools that Sandy has, we allow him or her to deliver the right experience to the customer, with the right sufficient resources and address both the performance and the cost side of equation simultaneously, >>awesome. Can you talk about the impact c I C D s having in the cloud native computing on the optimization cycle? Um Obviously, you know, shifting left for security, we hear a lot of that, you're hearing a lot of more microservices being spun up, spun down automatically. Uh I'll see kubernetes clusters are going mainstream, you start to see a lot more dynamic uh activity if you if you in these new workflows, what is the impact of these new CSC D cloud? Native computing on the optimization cycle? >>C i c D is there to enable a fast delivery of software features basically. Uh So, you know, we have a combination of get get ups where you can just pull down repositories, libraries, open source projects from left and right. And using glue code, developers can deliver functionality really quick. In fact, microservices are there in service of that capability, deliver functionality quickly by being able to build functional blocks and then through a piece you put everything together. So ci cd is just accelerates the software delivery code. Between the time the boss says, give me an application until the application team plus the devops team plus SRE team puts it out in production. Now we can do this really quickly. The problem is though, nobody optimizes in the process. So when we deliver 1.0 in six months or less, we've done zero in terms of optimization and at one point, oh, becomes a way that we go through QA in many cases, unfortunately. And it also becomes a way that we go through the optimization. The customer screams that you eyes Laghi, you know, the throughput is really slow and we tinker and tinker and tinker and by the time it typically goes through a 12 month cycle of maturation, we get that system stability in the right performance with a I and machine learning that a person has enabled. We can deliver that, we can shrink that time out considerably. In fact, uh you know what we're going to announce in q khan is something that be called Kite storm is the ability to uh install our product and kubernetes environment in roughly 20 minutes and within two days you get the results. So before you have this optimization cycle that was going on for a very long time now that it's frank down and because of Ci Cd, you know, you don't have the luxury of waiting and the system itself can become part of the way of contributing system. The system being the uh ai ml service, that the presiding deliveries can be uh part and parcel of the Ci cd pipeline, that optimizes the code and gives you the right configuration and you get to go. So >>you guys are really getting down and injecting in some uh instrumentation for metadata around key areas. That right. Is that kind of how it's working? Are you getting in there with codes going to watch? Um how was it working under the hood? Can you just give me a quick example of, you know, how this would play out and what people might expect, how it would handle, >>of course. So what the way we optimize application performance is we have to have a metric against which we measure performance. That metric is an S L O service level, objective and in a kubernetes environment, we typically tap into Prometheus, which is the metrics gathering place metrics database for kubernetes workloads and we really focus on red metrics, the rate of transactions, the error rate and the for delay or latency. So we focus on these three metrics and what we have to do is inject a small container, it's an open source container into the application work space that we call that a container. Servo. Servo interacts with Prometheus to get the metrics and then it talks to our back end to tell the M L engine what's happening and then L engine and does this analysis and comes back with a new configuration which then servo implements in a canary instance. So the Canary instances where we run our experiments and we compare it against the main line, Which the application is doing after roughly 20 generations or so. The Bellingen Learns what part of the problem space to focus on in order to optimize to deliver optimal results. And then it very quickly comes to the right set of solutions to try and it tries those inside uh inside the canary instance and when it finds the optimal solution, it gives the recommendation back to the application team or alternatively, when you have enough trust in the tiny you can ought to promote it into mainline that >>gets the learning in there is a great example of some cloud native action. I want to get into some examples with your customer, but before we get there, I want to ask you, since I have you here, if you don't mind, what is cloud native mean these days, because you know, cloud native become kind of much cloud computing, um which essentially go move to the cloud, but as people start developing in the cloud where there's real new benefits, people talk about the word cloud native, could you take a quick minute to define? What is cloud Native, Does that even mean? What does cloud native mean? >>I'll try to give you my understanding government, we could get into a bit of philosophy. Uh Yeah, that's good. But basically cloud Native means it's, your application is built for the cloud and it takes advantages of the inherent benefits that a cloud environment can give you, which means that you can grow and shrink resources on the fly, if you built your application correctly, that you can scale up and scale down, you're a number of instances very quickly and uh, everything has taken advantage of a P I S so initially that was kind of done inside of the environment. Uh AWS Ec two is a perfect example of that. Kubernetes shifted cloud native to container its workload because it allows for rapid, more, rapid deployment and even enables or it takes advantage of a more rapid development cycle as we look forward. Cloud Native is more likely to be a surplus environment where you write functions and the backend systems of the cloud service provider, just give you that capability and you don't have to worry about maintaining and managing a fleet of any sort, whether it's VMS or containers, that's where it's gonna go. Currently we are to contain our space >>so as you start getting into the service molly good land, which we've been playing with, loves that as you get into that, that's going to accelerate more data. So I gotta ask you as you get into more of this this month, I will say monitoring or observe ability, how we want to look at it. You gotta get at the data. This becomes a critical part of solving a lot of problems and also making sure the machine learning is learning the right thing. How do you view that you guys over there? Because I think everyone is like getting that cloud native and it's not hard sell to say that's all good, but we can go back, you know, the expression ships created ships and then you have shipwrecks, you know, there's always a double edged sword here. So what's the downside? If you don't get the data right? >>Uh well, so the for us, the problem is not too much data, it's lack of data. So if you don't get data right is you don't have enough data. And the places where optimization cannot be automated is where the transaction rates are slow, where you don't have enough fruit. But coming into the application and it really becomes difficult to optimize that application with any kind of speed. You have to be able to profile the application long enough to know what moves its needle and in order for you to hit the S. L. O. Targets. So it's not too much data, it's not enough data. That seems to be the problem. And there are a lot of applications that are expensive to run but have a low throughput. And I would uh in all cases actually in every customer environment that have been in, where that's been the case if the application is just over provision, if you have a low throughput environment and it's costing too much, don't use ml to solve it. That's a wrong application of the technology. Just take a sledgehammer and back your resources by 50%, see what happens. And if that thing breaks back it again, until you find the baggage point. >>Exactly for you over prison, you bang it back down again. It's like the old school now with the cloud. Take me through some examples when you guys had some success, obviously you guys are in the right area right now, you're seeing a lot of people looking at this area to do that in some cases like changing the whole data center and respect of their business. But as you get it with customers with the app side, what some successes can you share some of the use cases, what you guys are being successful, your customers can get some examples. >>Yeah. So well known financial software for midsize businesses that that does accounting. It's uh there are customer during a large fleet and this product has been around for a while. It's not a container ice product. This product runs on VMS. Angela is a large component of that. So the problem for this particular vendor has been that they run on heterogeneous fleet that the application has been a along around for a very long time. And as new instance types on AWS have come in, developers have used those. So the fleet itself is quite heterogeneous and depending on the time of the day and what kind of reports are being run by organisations, they, the mix of resources that the applications need are different. So uh when we started analyzing the stack, we started we started looking at three different tiers, we looked at the database level, we looked at the job of mid tier and we looked at the web front end. And uh one of the things that became counterproductive is that m L. Discovered that using for the mid tier using larger instances but fear of a lot for better performance and lower cost and uh typically your gut feel is to go with smaller instances and more of a larger fleet if you would. But in this case, what the ML produced was completely counter intuitive And the net result for the customer was 78% cost reduction while agency went down by 10%. So think about it that you're, the response time is less, uh 10% less but your costs are down almost 80% 78% in this case. And the other are the fact that happened in the job of mitt here is that we improve garbage collection significantly and because whenever garbage collection happens on a JV M it takes a pause and that from a customer perspective it reflects as downtime because the machines are not responding so by tuning garbage collection Andrzej VMS across this very large fleet we were able to recover over 5000 minutes and month across the entire fleet. So uh, these are some substantial savings and this is what the right application of machine learning on a large fleet can do for assess business. >>And so talk about this fleet dynamic, You mentioned several lists. How do you see the future evolving for you guys? Where are you skating to where the puck is? As the expression goes? Um obviously with server list is going to have essentially unlimited fleets potentially That's gonna put a lot of power in the hands of developers. Okay. And people building experiences, What's the next five years look like for you guys? >>So I'm looking at the product from a product perspective, the service market depends on the mercy of the cloud service provider and typically the algorithms that they use. Uh basically they keep very few instances warm for you until you're the rate of api calls goes up and they start they start uh start turning on VMS are containers for you and then the system becomes more responsive over time. One place that we can optimize the service environment is give predictability of what the cyclicality of load is. So we can pre provision those instances and warm up the engine before the loads come into the system always stays responsive. You may have noticed that some of your apps on your phone that when you start them up, they may have a start up like a minute or two. Especially if it's a it's a terror gap. What's happening in those cases that you're starting an api calls goes in containers being started up for you to start up that instance, not enough of our warm to give you that rapid response. And that can lead to customer churn. So by by analyzing what the load on the overall load of the system is and pre provision the system. We can prevent the downtime uh prevent the lag to start up black on the downside. Which when you know when the usage goes down, it doesn't make sense to keep that many instances up. So we can talk to the back in infrastructure and the commission of those VMS in order to make to prevent cost creeps basically. So that's one place that we're thinking about extending our technology. >>So it's like, it's like the classic example where people say, oh during black monday everyone searches to do e commerce. You guys are thinking about it on A level that's a user centric kind of use case where you look at the application and be smart about what the expectation is on any given situation and then flex the resources on that. Is that right? That by getting right? So if it's your example, the app is a good one. If I wanted to load fast, that's the expectation. It better load fast. >>Yes, that's exactly but more romantic. So I use valentine's day and flowers my example. But you know, it doesn't have to be annual cycles. It can be daily cycles or hourly cycles. And all those patterns are learning about by an Ml back in. >>Alright, so I gotta ask you love the, this, this this new concept because most people think auto scaling right? Because that's a server concept. Can auto scale or database. Okay. On a scale up, you're getting down to the point where, okay, we'll keep the engines warm, getting more detailed. How do you explain this versus a concept like auto scaling. Is it the same as a cousins? >>They're they're basically the way they're expressed, it's the same technology but their way there expressed is different. So uh in a cooper native environment, the H. B A is your auto scaler basically in response to the need, response more instances and you get more containers going on. What happens as services? Less environment is you're unaware of the underpinnings that do that scale up for you. But there is an auto Scaler in place that does that scale up for you. So the question becomes that we're in a stack from a customer's perspective, are you talking about if you imagine your instances we're dealing with the H. B. A. If you're managing at the functional level we have to have api calls on the service provider's infrastructure to pre warm up the engine before the load comes. >>I love I love this under the hood is kind of love new dynamics kind of the same wine, new bottle but still computer science, still coding, still cool and relevant to make these experiences great. Thanks for coming on this cube conversation. I really appreciate it. Take a minute to put a plug in for the company. What are you guys doing in terms of status funding scale employees, what are you looking for? And if someone's watching this and there should be a customer of you guys, what what's, what's, what's going on in their world? What tells them that they need to be calling you? >>Yeah, so we're serious. Dave we've had the privilege of uh, our we've been privileged by having a very good success with large enterprises. Uh, if you go to our website, you'll see the logos of who we have, we will be at Q khan and there were going to be actively targeting the mid market or smaller kubernetes instances, as I mentioned, it's gonna take about 20 minutes to get started and we'll show the results in two hours. And our goal is for our customers to deliver the best user experience in terms of performance, reliability. Uh, so that they, they delight their customers in return and they do so without breaking the bank. So deliver excellent products, do it at the most efficient way possible, deliver a good financial results for your stakeholders. This is what we do. So we encourage anybody who is running a SAS company to come and take a look at us because we think we can help them and we can accelerate there. The growth at the lower cost >>and the last thing people need is have someone coming breathing down their necks saying, hey, we're getting overcharged. Why are you guys screwing up when they're not? They're trying to make a great experience. And I think this is kind of where people really want to do push the envelope and not have to go back and revisit the cost overruns, which if it's actually a good sign if you get some cost overruns here and there because you're experimenting. But again, you don't want to get out of control. >>You don't want to be a visual like the U. S. Debt. >>Exactly. I'm here. Thank you for coming on. Great. We'll see a coupe con. The key will be there in person is a hybrid event. So uh, coupon is gonna be awesome and thanks for coming on the key. Appreciate it. >>John is a pleasure. Thank you for having me on. >>Okay. I'm john fryer with acute here in Palo alto California remote interview with upsetting hot startup series. I'm sure they're gonna do well in the right spot in the market. Really well poisoned cloud Native. Thanks for watching. Yeah.

(gentle upbeat music) >> People hate to be sold, but they love to buy. We become what we think about, think, and grow rich. If you want to gather honey, don't kick over the beehive. The world is replete with time-tested advice and motivational ideas for aspiring salespeople, Dale Carnegie, Napoleon Hill, Norman Vincent Peale, Earl Nightingale, and many others have all published classics with guidance that when followed closely, almost always leads to success. More modern personalities have emerged in the internet era, like Tony Robbins, and Gary Vaynerchuk, and Angela Duckworth. But for the most part, they've continued to rely on book publishing, seminars, and high value consulting to peddle their insights and inspire action. Welcome to this video exclusive on theCUBE. This is Dave Vellante, and I'm pleased to welcome back Professor Mark Roberge, who is one of the Managing Directors at Stage 2 Capital, and Paul Fifield, who's the CEO and Co-Founder of Sales Impact Academy. Gentlemen, welcome. Great to see you. >> You too Dave and thanks. >> All right, let's get right into it. Paul, you guys are announcing today a $4 million financing round. It comprises $3 million in a seed round led by Stage 2 and a million dollar in debt financing. So, first of all, congratulations. Paul, why did you start Sales Impact Academy? >> Cool, well, I think my background is sort of two times CRO, so I've built two reasonably successful companies. Built a hundred plus person teams. And so I've got kind of this firsthand experience of having to learn literally everything on the job whilst delivering these very kind of rapid, like achieving these very rapid growth targets. And so when I came out of those two journeys, I literally just started doing some voluntary teaching in and around London where I now live. I spend a bunch of time over in New York, and literally started this because I wanted to sort of kind of give back, but just really wanted to start helping people who were just really, really struggling in high pressure environments. And that's both leadership from sense of revenue leadership people, right down to sort of frontline SDRs. And I think as I started just doing this voluntary teaching, I kind of realized that actually the sort of global education system has done is a massive, massive disservice, right? I actually call it the greatest educational travesty of the last 50 years, where higher education has entirely overlooked sales as a profession. And the knock-on consequences of that have been absolutely disastrous for our profession. Partly that the profession is seen as a bit sort of embarrassing to be a part of. You kind of like go get a sales job if you can't get a degree. But more than that, the core fundamental within revenue teams and within sales people is now completely lacking 'cause there's no structured formal kind of like learning out there. So that's really the problem we're trying to solve on the kind of like the skill side. >> Great. Okay. And mark, always good to have you on, and I got to ask you. So even though, I know this is the wheelhouse for you and your partners, and of course, you've got a deep bench of LPs, but lay out the investment thesis here. What's the core problem that you saw and how are you looking at the market? >> Yeah, sure, Dave. So this one was a special one for me. We've spoken in the past. I mean, just personally I've always had a similar passion to Paul that it's amazing how important sales execution is to all companies, nevermind just the startup ecosystem. And I've always personally been motivated by anything that can help the startup ecosystem increase their success. Part of why I teach at Harvard and try to change some of the stuff that Paul's talking about, which is like, it's amazing how little education is done around sales. But in this particular one, not only personally was I excited about, but from a fun perspective, we've got to look at the economic outcomes. And we've been thinking a lot about the sales tech stack. It's evolved a ton in the last couple of decades. We've gone from the late '90s where every sales VP was just, they had a thing called the CRM that none of their reps even used, right? And we've come so far in 20 years, we've got all these amazing tools that help us cold call, that help us send emails efficiently and automatically and track everything, but nothing's really happened on the education side. And that's really the enormous gap that we've seen is, these organizations being much more proactive around adopting technology that can prove sales execution, but nothing on the education side. And the other piece that we saw is, it's almost like all these companies are reinventing the wheel of looking in the upcoming year, having a dozen sales people to hire, and trying to put together a sales enablement program within their organization to teach salespeople sales 101. Like how to find a champion, how to develop a budget, how to develop sense of urgency. And what Paul and team can do in the first phase of essay, is can sort of centralize that, so that all of these organizations can benefit from the best content and the best instructors for their team. >> So Paul, exactly, thank you, mark. Exactly what do you guys do? What do you sell? I'm curious, is this sort of, I'm thinking in my head, is this E-learning, is it really part of the sales stack? Maybe you could help us understand that better. >> Well, I think this problem of having to upscale teams has been around like forever. And kind of going back to the kind of education problem, it's what's wild is that we would never accept this of our lawyers, our accountants, or HR professionals. Imagine like someone in your finance team arriving on day one and they're searching YouTube to try and work out how to like put a balance sheet together. So it's a chronic, chronic problem. And so the way that we're addressing this, and I think the problem is well understood, but there's always been a terrible market, sort of product market fit for how the problem gets solved. So as mark was saying, typically it's in-house revenue leaders who themselves have got massive gaps in their knowledge, hack together some internal learning that is just pretty poor, 'cause it's not really their skillset. The other alternative is bringing in really expensive consultants, but they're consultants with a very single worldview and the complexity of a modern revenue organization is very, very high these days. And so one consultant is not going to really kind of like cover every topic you need. And then there's the kind of like fairly old fashioned sales training companies that just come in, one big hit, super expensive and then sort of leave again. So the sort of product market fit to solve, has always been a bit pretty bad. So what we've done is we've created a subscription model. We've essentially productized skills development. The way that we've done that is we teach live instruction. So one of the big challenges Andreessen Horowitz put a post out around this so quite recently, one of the big problems of online learning is that this kind of huge repository of online learning, which puts all the onus on the learner to have the discipline to go through these courses and consume them in an on-demand way is actually they're pretty ineffective. We see sort of completion rates of like 7 to 8%. So we've always gone from a live instruction model. So the sort of ingredients are the absolute very best people in the world in their very specific skill teaching live classes just two hours per week. So we're not overwhelming the learners who are already in work, and they have targets, and they've got a lot of pressure. And we have courses that last maybe four to like 12 hours over two to sort of six to seven weeks. So highly practical live instruction. We have 70, 80, sometimes even 90% completion rates of the sort of live class experience, and then teams then rapidly put that best practice into practice and see amazing results in things like top of funnel, or conversion, or retention. >> So live is compulsory and I presume on-demand? If you want to refresh you have an on demand option? >> Yeah, everything's recorded, so you can kind of catch up on a class if you've missed it, But that live instruction is powerful because it's kind of in your calendar, right? So you show up. But the really powerful thing, actually, is that entire teams within companies can actually learn at exactly the same pace. So we teach it eight o'clock Pacific, 11 o'clock Eastern, >> 4: 00 PM in the UK, and 5:00 PM Europe. So your entire European and North American teams can literally learn in the same class with a world-class expert, like a Mark, or like a Kevin Dorsey, or like Greg Holmes from Zoom. And you're learning from these incredible people. Class finishes, teams can come back together, talk about this incredible best practice they've just learned, and then immediately put it into practice. And that's where we're seeing these incredible, kind of almost instant impact on performance at real scale. >> So, Mark, in thinking about your investment, you must've been thinking about, okay, how do we scale this thing? You've got an instructor component, you've got this live piece. How are you thinking about that at scale? >> Yeah, there's a lot of different business model options there. And I actually think multiple of them are achievable in the longer term. That's something we've been working with Paul quite a bit, is like, they're all quite compelling. So just trying to think about which two to start with. But I think you've seen a lot of this in education models today. Is a mixture of on-demand with prerecorded. And so I think that will be the starting point. And I think from a scalability standpoint, we were also, we don't always try to do this with our investments, but clearly our LP base or limited partner base was going to be a key ingredient to at least the first cycle of this business. You know, our VC firm's backed by over 250 CRO CMOs heads of customer success, all of which are prospective instructors, prospective content developers, and prospective customers. So that was a little nicety around the scale and investment thesis for this one. >> And what's in it for them? I mean, they get paid. Obviously, you have a stake in the game, but what's in it for the instructors. They get paid on a sort of a per course basis? How does that model work? >> Yeah, we have a development fee for each kind of hour of teaching that gets created So we've mapped out a pretty significant curriculum. And we have about 250 hours of life teaching now already written. We actually think it's going to be about 3000 hours of learning before you get even close to a complete curriculum for every aspect of a revenue organization from revenue operations, to customer success, to marketing, to sales, to leadership, and management. But we have a development fee per class, and we have a teaching fee as well. >> Yeah, so, I mean, I think you guys, it's really an underserved market, and then when you think about it, most organizations, they just don't invest in training. And so, I mean, I would think you'd want to take it, I don't know what the right number is, 5, 10% of your sales budget and actually put it on this and the return would be enormous. How do you guys think about the market size? Like I said before, is it E-learning, is it part of the CRM stack? How do you size this market? >> Well, I think for us it's service to people. A highly skilled sales rep with an email address, a phone and a spreadsheet would do really well, okay? You don't need this world-class tech stack to do well in sales. You need the skills to be able to do the job. But the reverse, that's not true, right? An unskilled person with a world-class tech stack won't do well. And so fundamentally, the skill level of your team is the number one most important thing to get right to be successful in revenue. But as I said before, the product market for it to solve that problem, has been pretty terrible. So we see ourselves 100%. And so if you're looking at like a com, you look at Gong, who we've just signed as a customer, which is fantastic. Gong has a technology that helps salespeople do better through call recording. You have Outreach, who is also a customer. They have technologies that help SDRs be more efficient in outreach. And now you have Sales Impact Academy, and we help with skills development of your team, of the entirety of your revenue function. So we absolutely see ourselves as a key part of that stack. In terms of the TAM, 60 million people in sales are on, according to LinkedIn. You're probably talking 150 million people in go to market to include all of the different roles. 50% of the world's companies are B2B. The TAM is huge. But what blows my mind, and this kind of goes back to this why the global education system has overlooked this because essentially if half the world's companies are B2B, that's probably a proxy for the half of the world's GDP, Half of the world's economic growth is relying on the revenue function of half the world's companies, and they don't really know what they're doing, (laughs) which is absolutely staggering. And if we can solve that in a meaningfully meaningful way at massive scale, then the impact should be absolutely enormous. >> So, Mark, no lack of TAM. I know that you guys at Stage 2, you're also very much focused on the metrics. You have a fundamental philosophy that your product market fit and retention should come before hyper growth. So what were the metrics that enticed you to make this investment? >> Yeah, it's a good question, Dave, 'cause that's where we always look first, which I think is a little different than most early stage investors. There's a big, I guess, meme, triple, triple, double, double that's popular in Silicon Valley these days, which refers to triple your revenue in year one, triple your revenue in year two, double in year three, and four, and five. And that type of a hyper growth is critical, but it's often jumped too quickly in our opinion. That there's a premature victory called on product market fit, which kills a larger percentage of businesses than is necessary. And so with all our investments, we look very heavily first at user engagement, any early indicators of user retention. And the numbers were just off the charts for SIA in terms of the customers, in terms of the NPS scores that they were getting on their sessions, in terms of the completion rate on their courses, in terms of the customers that started with a couple of seats and expanded to more seats once they got a taste of the program. So that's where we look first as a strong foundation to build a scalable business, and it was off the charts positive for SIA. >> So how about the competition? If I Google sales training software, I'll get like dozens of companies. Lessonly, and MindTickle, or Brainshark will come up, that's not really a fit. So how do you think about the competition? How are you different? >> Yeah, well, one thing we try and avoid is any reference to sales training, 'cause that really sort of speaks to this very old kind of fashioned way of doing this. And I actually think that from a pure pedagogy perspective, so from a pure learning design perspective, the old fashioned way of doing sales training was pull a whole team off site, usually in a really terrible hotel with no windows for a day or two. And that's it, that's your learning experience. And that's not how human beings learn, right? So just even if the content was fantastic, the learning experience was so terrible, it was just very kind of ineffective. So we sort of avoid kind of like sales training, The likes of MindTickle, we're actually talking to them at the moment about a partnership there. They're a platform play, and we're certainly building a platform, but we're very much about the live instruction and creating the biggest curriculum and the broadest curriculum on the internet, in the world, basically, for revenue teams. So the competition is kind of interesting 'cause there is not really a direct subscription-based live like learning offering out there. There's some similar ish companies. I honestly think at the moment it's kind of status quo. We're genuinely creating a new category of in-work learning for revenue teams. And so we're in this kind of semi and sort of evangelical sort of phase. So really, status quo is one of the biggest sort of competitors. But if you think about some of those old, old fashioned sort of Miller Heimans, and then perhaps even like Sandlers, there's an analogy perhaps here, which is kind of interesting, which is a little bit like Siebel and Salesforce in the sort of late '90s, where in Siebel you have this kind of old way of doing things. It was a little bit ineffective. It was really expensive. Not accessible to a huge space of the market. And Salesforce came along and said, "Hey, we're going to create this cool thing. It's going to be through the browser, it's going to be accessible to everyone, and it's going to be really, really effective." And so there's some really kind of interesting parallels almost between like Siebel and Salesforce and what we're doing to completely kind of upend the sort of the old fashioned way of delivering sort of sales training, if you like. >> And your target customer profile is, you're selling to teams, right? B2B teams, right? It's not for individuals. Is that correct, Paul? >> Currently. Yeah, yeah. So currently we've got a big foothold in series A to series B. So broadly speaking out, our target market currently is really fast growth technology companies. That's the sector that we're really focusing on. We've got a very good strong foothold in series A series B companies. We've now won some much larger later stage companies. We've actually even won a couple of corporates, I can't say names yet, but names that are very, very, very familiar and we're incredibly excited by them, which could end up being thousand plus seat deals 'cause we do this on a per seat basis. But yeah, very much at the moment it's fast growth tech companies, and we're sort of moving up the chain towards enterprise. >> And how do you deal with the sort of maturity curve, if you will, of your students? You've got some that are brand new, just fresh out of school. You've got others that are more seasoned. What do you do, pop them into different points of the curriculum? How do you handle it? >> Yeah we have, I'll say we have about 30 courses right now. We have about another 15 in development where post this fundraise, we want to be able to get to around about 20 courses that we're developing every quarter and getting out to market. So we're literally, we've sort of identified about 20 to 25 key roles across everything within revenue. That's, let's say revenue ops, customer success, account management, sales, engineering, all these different kinds of roles. And we are literally plotting the sort of skills development for these individuals over multiple, multiple years. And I think what we've never ceases to amaze me is actually the breadth of learning in revenue is absolutely enormous. And what kind of just makes you laugh is, this is all of this knowledge that we're now creating it's what companies just hope that their teams somehow acquire through osmosis, through blogs, through events. And it's just kind of crazy that there is... It's absolutely insane that we don't already exist, basically. >> And if I understand it correctly, just from looking at your website, you've got the entry level package. I think it's up to 15 seats, and then you scale up from there, correct? Is it sort of as a seat-based license model? >> Yeah, it's a seat-based model, as Mark mentioned. In some cases we sell, let's say 20 or $30,000 deal out the gate and that's most of the team. That will be maybe a series A, series B deal, but then we've got these land and expand models that are working tremendously well. We have seven, eight customers in Q1 that have doubled their spend Q2. That's the impact that they're seeing. And our net revenue retention number for Q2 is looking like it's going to be 177% to think exceeds companies like Snowflakes. Well, our underlying retention metrics, because people are seeing this incredible impact on teams and performance, is really, really strong. >> That's a nice metric compare with Snowflake (Paul laughs) It's all right. (Dave and Paul laugh) >> So, Mark, this is a larger investment for Stage 2 You guys have been growing and sort of upping your game. And maybe talk about that a little bit. >> Yeah, we're in the middle of Fund II right now. So, Fund I was in 2018. We were doing smaller checks. It was our first time out of the gate. The mission has really taken of, our LP base has really taken off. And so this deal looks a lot like more like our second fund. We'll actually make an announcement in a few weeks now that we've closed that out. But it's a much larger fund and our first investments should be in that 2 to $3 million range. >> Hey, Paul, what are you going to do with the money? What are the use of funds? >> Put it on black, (chuckles) we're going to like- (Dave laughs) >> Saratoga is open. (laughs) (Mark laughs) >> We're going to, look, the curriculum development for us is absolutely everything, but we're also going to be investing in building our own technology platform as well. And there are some other really important aspects to the kind of overall offering. We're looking at building an assessment tool so we can actually kind of like start to assess skills across teams. We certify every course has an exam, so we want to get more robust around the certification as well, because we're hoping that our certification becomes the global standard in understanding for the first time in the industry what individual competencies and skills people have, which will be huge. So we have a broad range of things that we want to start initiating now. But I just wanted to quickly say Stage 2 has been nothing short of incredible in every kind of which way. Of course, this investment, the fit is kind of insane, but the LPs have been extraordinary in helping. We've got a huge number of them are now customers very quickly. Mark and the team are helping enormously on our own kind of like go to market and metrics. I've been doing this for 20 years. I've raised over 100 million myself in venture capital. I've never known a venture capital firm with such value add like ever, or even heard of other people getting the kind of value add that we're getting. So I just wanted to a quick shout out for Stage 2. >> Quite a testimony of you guys. Definitely Stage 2 punches above its weight. Guys, we'll leave it there. Thanks so much for coming on. Good luck and we'll be watching. Appreciate your time. >> Thanks, Dave. >> Thank you very much. >> All right, thank you everybody for watching this Cube conversation. This is Dave Vellante, and we'll see you next time.

>>from around the globe. It's the Cube presenting Cuban cloud brought >>to you by silicon angle. Okay, we're back. Here. Live Cuban Cloud. And this is Dave. Want with my co host, John Ferrier Were all remote. We're getting into the analyst power half hour. Really pleased to have Maribel Lopez here. She's the principal and founder of Lopez Research and Zias Caraballo, who is the principal and founder of ZK research. Guys, great to see you. Let's get into it. How you doing? >>Great. How you been? Good, >>thanks. Really good. John's hanging in there quarantining and, uh, all healthy, So I hope you guys are too. Hey, Mary, But let's start with you. You know, here we are on 2021 you know, just exited one of the strangest years, if not the strangest year of our lives. But looking back in the past decade of cloud and we're looking forward. How do you see that? Where do we come from? Where we at and where we going >>When we obviously started with the whole let's build a public cloud and everything was about public cloud. Uh, then we went thio the notion of private cloud than we had hybrid cloud and multi cloud. So we've done a lot of different clouds right now. And I think where we are today is that there's a healthy recognition on the cloud computing providers that you need to give it to the customers the way they want it, not the way you've decided to build it. So how do you meet them where they are so that they can have a cloud like experience wherever they want their data to be? >>Yes and yes, you've, you know, observed, This is well, in the early days of cloud, you heard a lot of rhetoric. It was private cloud And and then now we're, you know, hearing a lot of multi cloud and so forth. But initially, a lot of the traditional vendors kind of pooh poohed it. They called us analysts. We said we were all cloud crazy, but they seem to have got their religion. >>Well, everything. Everyone's got a definition of cloud, but I actually think we are right in the midst of another transformation of clouds Miracle talked about. We went from, you know, private clouds, which is really hosting the public cloud to multi cloud hybrid cloud. And if you look at the last post that put on Silicon Angle, which was talking about five acquisition of Volterra, I actually think we're in the midst of the transition to what's called distributed Club, where if you look at modernized cloud apps today, they're actually made up of services from different clouds on also distributed edge locations. And that's gonna have a pretty profound impact on the way we build out, because those distributed edges be a telco edge, cellular vagina. Th whatever the services that lived there are much more ephemeral in nature, right? So the way we secure the way we connect changes quite a bit. But I think that the great thing about Cloud is we've seen several several evolutionary changes. So what the definition is and we're going through that now, which is which is pretty cool to think about, right? It's not a static thing. Um, it's, uh, you know, it's a it's an ongoing transition. But I think, uh, you know, we're moving into this distributed Cloudera, which to me is a lot more complex than what we're dealing with in the Palace. >>I'm actually pretty excited about that because I think that this move toe edge and the distribution that you've talked about, it's like we now have processing everywhere. We've got it on devices, we've got it in, cars were moving, the data centers closer and closer to where the action's happening. And I think that's gonna be a huge trend for 2021. Is that distributed that you were talking about a lot of edge discussion? You >>know what? The >>reason we're doing This, too, is we want. It's not just we're moving the data closer to the user, right? And some. If you think you brought up the autonomous vehicle right in the car being an edge, you think of the data that generates right? There's some things such as the decision to stop or not right that should be done in car. I don't wanna transport that data all the way back to Google him back to decide whether I want to stop. You could also use the same data determine whether drivers driving safely for insurance purposes, right? So the same data give me located at the edge or in a centralized cloud for different purposes, and I think that's what you know, kind of cool about this is we're being able to use our data and much different ways. Now. >>You know, it's interesting is it's so complex. It's mind blowing because this is distributed computing. Everyone kind of agrees this is where it is. But if you think about the complexity and I want to get your guys reaction to this because you know some of the like side fringe trend discussions are data sovereignty, misinformation as a vulnerability. Okay, you get the chips now you got gravitas on with Amazon in front. Apple's got their own chips. Intel is gonna do a whole new direction. So you've got tons of computer. And then you mentioned the ephemeral nature. How do you manage those? What's the observe ability look like? They're what's the trust equation? So all these things kind of play into it. It sounds almost mind blowing, just even thinking about it. But how do you guys, this analyst tryto understand where someone's either blowing bullshit or kind of like has the real deal? Because all those things come into play? I mean, you could have a misinformation campaign targeting the car. Let's say Hey, you know that that data is needs to be. This is this is misinformation who's a >>in a lot of ways, this creates almost unprecedented opportunity now for for starts and for companies to transform right. The fundamental tenet of my research has always been share shifts happen when markets transition and we're in the middle of the big one. If the computer resource is we're using, John and the application resource will be using or ephemeral nature than all the things that surrounded the way we secured the way we connect. Those also have to be equal, equally agile, right, So you can't have, you know, you think of a micro services based application being secured with traditional firewalls, right? Just the amount of, or even virtual the way that the length of time it takes to spend those things up is way too long. So in many ways, this distributed cloud change changes everything in I T. And that that includes all of the services in the the infrastructure that we used to secure and connect. And that's a that is a profound change, and you mentioned the observe ability. You're right. That's another thing that the traditional observe ability tools are based on static maps and things and, you know, traditional up, down and we don't. Things go up and down so quickly now that that that those don't make any sense. So I think we are going to see quite a rise in different types of management tools and the way they look at things to be much more. I suppose you know Angela also So we can measure things that currently aren't measurable. >>So you're talking about the entire stack. Really? Changing is really what you're inferring anyway from your commentary. And that would include the programming model as well, wouldn't it? >>Absolutely. Yeah. You know, the thing that is really interesting about where we have been versus where we're going is we spent a lot of time talking about virtual izing hardware and moving that around. And what does that look like? And that, and creating that is more of a software paradigm. And the thing we're talking about now is what is cloud is an operating model look like? What is the manageability of that? What is the security of that? What? You know, we've talked a lot about containers and moving into a different you know, Dev suck ups and all those different trends that we've been talking about, like now we're doing them. So we've only got into the first crank of that. And I think every technology vendor we talked to now has to address how are they going to do a highly distributed management and security landscape? Like, what are they gonna layer on top of that? Because it's not just about Oh, I've taken Iraq of something server storage, compute and virtualized it. I now have to create a new operating model around it. In a way, we're almost redoing what the OS I stack looks like and what the software and solutions are for that. >>So >>it was really Hold on, hold on, hold on their lengthened. Because that side stack that came up earlier today, Mayor. But we're talking about Yeah, we were riffing on the OSC model, but back in the day and we were comparing the S n a definite the, you know, the proprietary protocol stacks that they were out there and someone >>said Amazon's S N a. Is that recall? E think that's what you said? >>No, no. Someone in the chest. That's a comment like Amazon's proprietary meaning, their scale. And I said, Oh, that means there s n a But if you think about it, that's kind of almost that can hang. Hang together. If the kubernetes is like a new connective tissue, is that the TCP pipe moment? Because I think Os I kind of was standardizing at the lower end of the stack Ethernet token ring. You know, the data link layer physical layer and that when you got to the TCP layer and really magic happened right to me, that's when Cisco's happened and everything started happening then and then. It kind of stopped because the application is kinda maintain their peace there. A little history there, but like that's kind of happening now. If you think about it and then you put me a factor in the edge, it just kind of really explodes it. So who's gonna write that software? E >>think you know, Dave, your your dad doesn't change what you build ups. It's already changed in the consumer world, you look atyou, no uber and Waze and things like that. Those absolute already highly decomposed applications that make a P I calls and DNS calls from dozens of different resource is already right. We just haven't really brought that into the enterprise space. There's a number, you know, what kind of you know knew were born in the cloud companies that have that have done that. But they're they're very few and far between today. And John, your point about the connectivity. We do need to think about connectivity at the network layer. Still, obviously, But now we're creating that standardization that standardized connectivity all the way a player seven. So you look at a lot of the, you know, one of the big things that was a PDP. I calls right, you know, from different cloud services. And so we do need to standardize in every layer and then stitch that together. So that does make It does make things a lot more complicated. Now I'm not saying Don't do it because you can do a whole lot more with absolute than you could ever do before. It's just that we kind of cranked up the level of complexity here, and flowered isn't just a single thing anymore, right? That's that. That's what we're talking about here It's a collection of edges and private clouds and public clouds. They all have to be stitched together at every layer in orderto work. >>So I was I was talking a few CEOs earlier in the day. We had we had them on, I was asking them. Okay, So how do you How do you approach this complexity? Do you build that abstraction layer? Do you rely on someone like Microsoft to build that abstraction layer? Doesn't appear that Amazon's gonna do it, you know? Where does that come from? Or is it or is it dozens of abstraction layers? And one of the CEO said, Look, it's on us. We have to figure out, you know, we get this a p I economy, but But you guys were talking about a mawr complicated environment, uh, moving so so fast. Eso if if my enterprise looks like my my iPhone APs. Yes, maybe it's simpler on an individual at basis, but its app creep and my application portfolio grows. Maybe they talk to each other a little bit better. But that level of complexity is something that that that users are gonna have to deal >>with what you thought. So I think quite what Zs was trying to get it and correct me if I'm wrong. Zia's right. We've got to the part where we've broken down what was a traditional application, right? And now we've gotten into a P. I calls, and we have to think about different things. Like we have to think about how we secure those a p I s right. That becomes a new criteria that we're looking at. How do we manage them? How do they have a life cycle? So what was the life cycle of, say, an application is now the life cycle of components and so that's a That's a pretty complex thing. So it's not so much that you're getting app creep, but you're definitely rethinking how you want to design your applications and services and some of those you're gonna do yourself and a lot of them are going to say it's too complicated. I'm just going to go to some kind of SAS cloud offering for that and let it go. But I think that many of the larger companies I speak to are looking for a larger company to help them build some kind of framework to migrate from what they've used with them to what they need tohave going forward. >>Yeah, I think. Where the complexities. John, You asked who who creates the normalization layer? You know, obviously, if you look to the cloud providers A W s does a great job of stitching together all things AWS and Microsoft does a great job of stitching together all things Microsoft right in saying with Google. >>But >>then they don't. But if if I want to do some Microsoft to Amazon or Google Toe Microsoft, you know, connectivity, they don't help so much of that. And that's where the third party vendors that you know aviatrix on the network side will tear of the security side of companies like that. Even Cisco's been doing a lot of work with those companies, and so what we what we don't really have And we probably won't for a while if somebody is gonna stitch everything together at every >>you >>know, at every layer. So Andi and I do think we do get after it. Maribel, I think if you look at the world of consumer APS, we moved to a lot more kind of purpose built almost throwaway apps. They serve a purpose or to use them for a while. Then you stop using them. And in the enterprise space, we really haven't kind of converted to them modeling on the mobile side. But I think that's coming. Well, >>I think with micro APS, right, that that was kind of the issue with micro APS. It's like, Oh, I'm not gonna build a full scale out that's gonna take too long. I'm just gonna create this little workflow, and we're gonna have, like, 200 work flows on someone's phone. And I think we did that. And not everybody did it, though, to your point. So I do think that some people that are a little late to the game might end up in in that app creep. But, hey, listen, this is a fabulous opportunity that just, you know, throw a lot of stuff out and do it differently. What What? I think what I hear people struggling with ah lot is be to get it to work. It typically is something that is more vertically integrated. So are you buying all into a Microsoft all you're buying all into an Amazon and people are starting to get a little fear about doing the full scale buy into any specific platform yet. In absence of that, they can't get anything to work. >>Yeah, So I think again what? What I'm hearing from from practitioners, I'm gonna put a micro serve. And I think I think, uh, Mirabelle, this is what you're implying. I'm gonna put a micro services layer. Oh, my, my. If I can't get rid of them, If I can't get rid of my oracle, you know, workloads. I'm gonna connect them to my modernize them with a layer, and I'm gonna impart build that. I'm gonna, you know, partner to get that done. But that seems to be a a critical path forward. If I don't take that step, gonna be stuck in the path in the past and not be able to move forward. >>Yeah, absolutely. I mean, you do have to bridge to the past. You you aren't gonna throw everything out right away. That's just you can't. You can't drive the bus and take the wheels off that the same time. Maybe one wheel, but not all four of them at the same time. So I think that this this concept of what are the technologies and services that you use to make sure you can keep operational, but that you're not just putting on Lee new workloads into the cloud or new workloads as decomposed APS that you're really starting to think about. What do I want to keep in whatever I want to get rid of many of the companies you speak Thio. They have thousands of applications. So are they going to do this for thousands of applications? Are they gonna take this as an opportunity to streamline? Yeah, >>well, a lot of legacy never goes away, right? And I was how companies make this transition is gonna be interesting because there's no there's no really the fact away I was I was talking to this one company. This is New York Bank, and they've broken their I t division down into modern I t and legacy I t. And so modern. Everything is cloud first. And so imagine me, the CEO of Legacy i e 02 miracles. But what they're doing, if they're driving the old bus >>and >>then they're building a new bus and parallel and eventually, you know, slowly they take seats out of the old bus and they take, you know, the seat and and they eventually start stripping away things. That old bus, >>But >>that old bus is going to keep running for a long time. And so stitching the those different worlds together is where a lot of especially big organizations that really can't commit to everything in the cloud are gonna struggle. But it is a It is a whole new world. And like I said, I think it creates so much opportunity for people. You know, e >>whole bus thing reminds me that movie speed when they drive around 55 miles an hour, just put it out to the airport and just blew up E >>got But you know, we all we all say that things were going to go away. But to Zia's point, you know, nothing goes away. We're still in 2021 talking about mainframes just as an aside, right? So I think we're going to continue tohave some legacy in the network. But the But the issue is ah, lot will change around that, and they're gonna be some people. They're gonna make a lot of money selling little startups that Just do one specific piece of that. You know, we just automation of X. Oh, >>yeah, that's a great vertical thing. This is the This is the distributed network argument, right? If you have a note in the network and you could put a containerized environment around it with some micro services um, connective tissue glue layer, if you will software abstract away some integration points, it's a note on the network. So if in mainframe or whatever, it's just I mean makes the argument right, it's not core. You're not building a platform around the mainframe, but if it's punching out, I bank jobs from IBM kicks or something, you know, whatever, Right? So >>And if those were those workloads probably aren't gonna move anywhere, right, they're not. Is there a point in putting those in the cloud? You could say Just leave them where they are. Put a connection to the past Bridge. >>Remember that bank when you talk about bank guy we interviewed in the off the record after the Cube interviews like, Yeah, I'm still running the mainframe, so I never get rid of. I love it. Run our kicks job. I would never think about moving that thing. >>There was a large, large non US bank who said I buy. I buy the next IBM mainframe sight unseen. Andi, he's got no choice. They just write the check. >>But milliseconds is like millions of dollars of millisecond for him on his back, >>so those aren't going anywhere. But then, but then, but they're not growing right. It's just static. >>No, no, that markets not growing its's, in fact. But you could make a lot of money and monetizing the legacy, right? So there are vendors that will do that. But I do think if you look at the well, we've already seen a pretty big transition here. If you look at the growth in a company like twilio, right, that it obviates the need for a company to rack and stack your own phone system to be able to do, um, you know, calling from mobile lapse or even messaging. Now you just do a P. I calls. Um, you know, it allows in a lot of ways that this new world we live in democratizes development, and so any you know, two people in the garage can start up a company and have a service up and running another time at all, and that creates competitiveness. You know much more competitiveness than we've ever had before, which is good for the entire industry. And, you know, because that keeps the bigger companies on their toes and they're always looking over their shoulder. You know what, the banks you're looking at? The venues and companies like that Brian figure out a way to monetize. So I think what we're, you know well, that old stuff never going away. The new stuff is where the competitive screen competitiveness screen. >>It's interesting. Um IDs Avery. Earlier today, I was talking about no code in loco development, how it's different from the old four g l days where we didn't actually expand the base of developers. Now we are to your point is really is democratizing and, >>well, everybody's a developer. It could be a developer, right? A lot of these tools were written in a way that line of business people create their own APs to point and click interface is, and so the barrier. It reminds me of when, when I started my career, I was a I. I used to code and HTML build websites and then went to five years. People using drag and drop interface is right, so that that kind of job went away because it became so easy to dio. >>Yeah, >>sorry. A >>data e was going to say, I think we're getting to the part. We're just starting to talk about data, right? So, you know, when you think of twilio, that's like a service. It's connecting you to specific data. When you think of Snowflake, you know, there's been all these kinds of companies that have crept up into the landscape to feel like a very specific void. And so now the Now the question is, if it's really all about the data, they're going to be new companies that get built that are just focusing on different aspects of how that data secured, how that data is transferred, how that data. You know what happens to that data, because and and does that shift the balance of power about it being out of like, Oh, I've created these data centers with large recommend stack ums that are virtualized thio. A whole other set of you know this is a big software play. It's all about software. >>Well, we just heard from Jim Octagon e You guys talking earlier about just distributed system. She basically laid down that look. Our data architectures air flawed there monolithic. And data by its very nature is distributed so that she's putting forth the whole new paradigm around distributed decentralized data models, >>which Howie shoe is just talking about. Who's gonna build the visual studio for data, right? So programmatic. Kind of thinking around data >>I didn't >>gathering. We didn't touch on because >>I do think there's >>an opportunity for that for, you know, data governance and data ownership and data transport. But it's also the analytics of it. Most companies don't have the in house, um, you know, data scientists to build on a I algorithms. Right. So you're gonna start seeing, you know, cos pop up to do very specific types of data. I don't know if you saw this morning, um, you know, uniforms bought this company that does, you know, video emotion detection so they could tell on the video whether somebody's paying attention, Not right. And so that's something that it would be eso hard for a company to build that in house. But I think what you're going to see is a rise in these, you know, these types of companies that help with specific types of analytics. And then you drop you pull those in his resource is into your application. And so it's not only the storage and the governance of the data, but also the analytics and the analytics. Frankly, there were a lot of the, uh, differentiation for companies is gonna come from. I know Maribel has written a lot on a I, as have I, and I think that's one of the more exciting areas to look at this year. >>I actually want to rip off your point because I think it's really important because where we left off in 2020 was yes, there was hybrid cloud, but we just started to see the era of the vertical eyes cloud the cloud for something you know, the cloud for finance, the cloud for health care, the telco and edge cloud, right? So when you start doing that, it becomes much more about what is the specialized stream that we're looking at. So what's a specialized analytic stream? What's a specialized security stack stream? Right? So until now, like everything was just trying to get to what I would call horizontal parody where you took the things you had before you replicated them in a new world with, like, some different software, but it was still kind of the same. And now we're saying, OK, let's try Thio. Let's try to move out of everything, just being a generic sort of cloud set of services and being more total cloud services. >>That is the evolution of everything technology, the first movement. Everything doing technology is we try and make the old thing the new thing look like the old thing, right? First PCs was a mainframe emulator. We took our virtual servers and we made them look like physical service, then eventually figure out, Oh, there's a whole bunch of other stuff that I could do then I couldn't do before. And that's the part we're trying to hop into now. Right? Is like, Oh, now that I've gone cloud native, what can I do that I couldn't do before? Right? So we're just we're sort of hitting that inflection point. That's when you're really going to see the growth takeoff. But for whatever reason, and i t. All we ever do is we're trying to replicate the old until we figure out the old didn't really work, and we should do something new. >>Well, let me throw something old and controversial. Controversial old but old old trope out there. Consumerism ation of I t. I mean, if you think about what year was first year you heard that term, was it 15 years ago? 20 years ago. When did that first >>podcast? Yeah, so that was a long time ago >>way. So if you think about it like, it kind of is happening. And what does it mean, right? Come. What does What does that actually mean in today's world Doesn't exist. >>Well, you heard you heard. Like Fred Luddy, whose founder of service now saying that was his dream to bring consumer like experiences to the enterprise will. Well, it didn't really happen. I mean, service not pretty. Pretty complicated compared toa what? We know what we do here, but so it's It's evolving. >>Yeah, I think there's also the enterprise ation of consumer technology that John the companies, you know, you look a zoom. They came to market with a highly consumer facing product, realized it didn't have the security tools, you know, to really be corporate great. And then they had to go invest a bunch of money in that. So, you know, I think that waken swing the pendulum all the way over to the consumer side, but that that kind of failed us, right? So now we're trying to bring it back to center a little bit where we blend the two together. >>Cloud kind of brings that I never looked at that way. That's interesting and surprising of consumer. Yeah, that's >>alright, guys. Hey, we gotta wrap Zs, Maribel. Always a pleasure having you guys on great great insights from the half hour flies by. Thanks so much. We appreciate it. >>Thank >>you guys. >>Alright, keep it right there. Mortgage rate content coming from the Cuban Cloud Day Volonte with John Ferrier and a whole lineup still to come Keep right there.

>>Yeah. >>Welcome back for our last session of the day how to deliver career making business outcomes with Search and AI. So we're very lucky to be hearing from Canada. Canadian Tire, one of Canada's largest and most successful retailers, have been powered 4.5 1000 employees to maximize the value of data with self service insights. So today we're joining us. We have Yarrow Baturin, who is the manager of Merch analytics and planning to support at Canadian Tire and then also Andrea Frisk, who is the engagement manager manager for thoughts. What s O U R Andrea? Thanks so much for being here. And with >>that, >>I'll pass the mic to you guys. >>Thank you for having us. Um, already, I I think I'll start with an introduction off who I am, what I do. A Canadian entire on what Canadian pair is all about. So, as a manager of Merch analytics at Canadian Tire, I support merchant organization with reporting tools, and then be I platform to enable decision making on a day to day basis. What is? Canadian Tire's Canadian tire is one of the largest retailers in Canada. Um, serving Canadians with a number of lines of business spanning automotive fixing, living, playing and SNG departments. We have a number of banners, including sport check Marks Party City Phl that covers more than 1700 locations. So as an organization, we've got vast variety of different data, whether it's product or loyalty. Now, as the time goes on, the number of asks the number off data points. The complexity of the analysis has been increasing on banned traditional tools. Analytical tools such as Excel Microsoft Access do find job but start hitting their limitations. So we started on the journey of exploring what other B I platforms would be suitable for our needs. And the criteria that we thought about as we started on that journey is to make sure that we enable customization as well as the McCarthy ization of data. What does that mean? That means we wanted to ensure that each one of the end users have ability to create their own versions off the report while having consistency from the data standpoint, we also wanted Thio ensure that they're able to create there at hawks search queries and draw insights based on the desired business needs. As each one of our lines of business as each one of our departments is quite unique in their nature. And this is where thoughts about comes into play. Um, you checked off all the boxes? Um, as current customers, as potential customers, you will discover that this is the tool that allows that at hawks search ability within a matter of seconds and ability to visualize the information and create those curated pin boards for each one of the business units, depending on what the needs are. And now where? I guess well, Andrea will talk a little bit more about how we gained adoption, but the usage was like and how we, uh, implemented the tool successfully in the organization. >>Okay, so I actually used to work for Canadian tire on DSO. During that time, I helped Thio build training and engaging users to sort of really kick start our use cases. Andi, the ongoing process of adopting thought spot through Canadian Tire s 01 of the sort of reasons that we moved into using thought spot was there was a need Thio evolve, um, in order to see the wealth of data that we had coming in. So the existing reporting again. And this is this sort of standard thoughts bought fix is, um, it brings the data toe. Everyone on git makes it more accessible, so you get more out of your data. So we want to provide users with the ability to customize what they could see and personalized three information so that they could get their specific business requirements out of the data rather than relying on the weekly monthly quarterly reporting. That was all usually fairly generic eso without the ability to deep dive in. So this gave the users the agility thio optimize their campaigns, optimize product murder, urgency where products are or where there's maybe supply chain gaps. Andi just really bring this out for trillions of rose to become accessible. Thio the Canadian tire. That's what user base think. That's the slide. >>That's the slight, Um So as Andrea talked about the business use of the particular tool, let's talk a little bit about how we set it up and a wonderful journey of how it's evolved. So we first implemented 5.3 version of that spot on the Falcon server on we've been adding horsepower to it over time. Now mhm. What I want to stress is the importance off the very first, Data said. That goes into the tool toe. Actually engage the users and to gain the adoption and to make sure there is no argument whether the tool is accurate or not. So what we've started with is a key p I marked layer with all the major metrics that we have and all the available permutations and combinations off the dimensions, whether it's a calendar dimension, proud of dimension or, let's say, customer attribute now, as we started with that data set, we wanted to make sure that we're we have the ability to add and the dimensions right. So now, as we're implementing the tool, we're starting to add in more dimension tables to satisfy the needs off our clients if you want to call it that way as they want to evolve their analytics. So we started adding in some of the store attributes we started adding in some of the product attributes on when I refer to a product attributes, let's say, uh, it involves costs and involves prices involved in some of the strategic internal pieces that we're thinking about now as the comprehensive mark contains right now, in our instance, close to five billion records. This is where it becomes the one source of truth for people declaring information against right so as they go in, we also wanted to make sure when they Corey thought spot there, we're really Onley. According one source of data. One source of truth. It became apparent over time, obviously, that more metrics are needed. They might not be all set up in that particular mark. And that's when we went on the journey off implementing some of the new worksheets or some of the new data sets particularly focused on the four looking pieces. And uh, that's where it becomes important to say This is how you gain the interest and keep the interests of the public right. So you're not just implementing a number off data sets all at once and then letting the users be you're implementing pieces and stages. You're keeping the interest thio, the tool relevant. You're keeping, um, the needs of the public in mind. Now, as you can imagine on the Falcon server piece, um, adding in the horsepower capacity might become challenging the mawr. Billions of Rosie erratic eso were actually in the middle of transitioning our environment to azure in snowflake so that we can connect it. Thio embrace capability of thoughts cloud. And that's where I'm looking forward to that in 2021 I truly believe this will enable us Thio increase the speed off adoption Increase the speed of getting insights out of the tool and scale with regards Thio new data sets that we're thinking about implementing as we're continuing our thoughts about journey >>Okay, so how we drove adoption Thio 4500 plus users eso When we first started Thio approach our use case with the merchants within Canadian Tire We had meetings with these users with who are used place is gonna be with and sort of found out. What are they searching for, Where they typically looking at what existing reports are available for them. Andi kind of sought out to like, What are those things where you're pulling this on your own or someone else's pulling this data because it's not accessible yet And we really use that as our foundation to determine one what data we needed to initially bring into the system but also to sort of create those launchpad pin boards that had the base information that the users we're gonna need so that we could twofold, make it easy for them, toe adopt into the tool and also quickly start Thio, deactivate or discontinue those reports. And just like these air now only available in thought spot because with the sort of formatting within thought spot around dates, it's really easy to make this year's report last year report etcetera. Just have everything roll over every month or a recorder s. So that was kind of some of the pre work foundation when we originally did it. But really, it's been a lot of training, a lot of training. So we conducted ah, lot of in person training, obviously pre co vid eso. We've started to train the group that we targeted, which was the merchants and all of the like, surrounding support groups. Eso we had planners going in and training as well, so that everyone who was really closely connected to the merchants I had an idea of what thoughts about what was and how to use it and where the reports were, and so we just sort of rolled it out that way, and then it started to fly like wildfire. Eso the merchants start to engage with supply chain to have conversations, or the merchants were engaging with the vendors to sort of have negotiations about pricing. And they're creating these reports and getting the access to the information so quickly, and they're sharing it out that we had other groups just coming to us asking, How do I get into thoughts about how can I get in on DSO on top of those groups, we also sought out other heavy analytics groups such a supply chain where we felt like they could have the same benefits if they on boarded into thought spot with their data as well on Ben. Just continuing to evolve the training roll out. Um, you know, we continued to engage with the users, >>so >>we had a newsletter briefly Thio, sort of just keep informing users of the new data coming in or when we actually upgraded our system. So the here are the new features that you'll start seeing. We did virtual trainings and maintaining an F A Q document with the incoming questions from the users, and then eventually evolved into a self guided learning so that users that were coming to a group, or maybe we've already done a full rollout could come in and have the opportunity to learn how to use thought spot, have examples that were relevant to the business and really get started. Eso then each use case sort of after our initial started to build into a formula of the things that we needed to have. So you need to understand it. Having SMEs ready and having the database Onda worksheets built out sort of became the step by step path to drive adoption. Um, from an implementation timeline, I think they're saying, Took about two months and about half of that waas Kenny entire figuring out how figuring out our security, how to get the data in on, Do we need the time to set up the environment and get on Falcon? So then, after that initial two months, then each use case that we come through. Generally, we've got users trained and SMEs set up within about 2 to 3 weeks after the data is ingested. It's not obviously, once snowflakes set up on the data starts to get into that and the data feeds in, then you're really just looking at the 2 to 3 weeks because the data is easily connected in, >>um, no. All right, let's talk about some of the use cases. So we started with what data we've implemented. Andrea touched upon what Use a training look like what the back curate that piece wants. Now let's talk a little bit about use cases and how we actually leverage thoughts bought together the insights. So the very first one is ultimately the benefit of the tool to the entire organization. Israel Time insights. To reiterate what Andrea said, we first implemented the tool with our buyers. They're the nucleus of any retail organization as they work with everybody within the company and as the buyer's eyes, Their responsibility to ensure both the procurement and the sales channel, um, stays afloat at the end of the day, right? So they need information on a regular basis. They needed fast. They needed timely, and they needed in a fashion that they choose to digest it. It right? Not every business is the same. Not every individual is the same. They consume digest, analyze information differently. And that's what that's what allows you to dio whether it's the search, whether it's a customized onboard, please now supply chain unexpected things. As Andrea mentioned Irish work a lot of supply chain. What is the goal of supply chain to receive product and to be able to ship that product to the stores Now, as our organization has been growing and is doing extremely well, we've actually published Q three results recently. Um, the aspect off prioritization at D C level becomes very important, And what drives some of that prioritization is the analysis around what the upcoming sales would be for specific products for specific categories. And that's where again thoughts. But is one of the tools that we've utilized recently to set our prioritization logic from both inbound and outbound us. It's right because it gives you most recent results. It gives you most granular results, depending on the business problem that you're trying to tackle. Now let's chat a little bit about covert 19 response, because this one is an extremely interesting case as a pandemic hit back in March. Um, as you can imagine, the everyday life a Canadian entire became as business unusual is our executives referred to it under business unusual. This speed and the intensity of the insights and the analytics has grown exponentially. And the speed and the intensity of the insights is driven by the fact that we were trying Thio ensure that we have the right selection of products for our Canadian customers because that's ultimately bread and butter off all of the retailers is the customers, right? So thoughts bought allowed us to have early trends off both sales and inventory patterns, where, whether we were stalking out of some of the products in specific stories of provinces, whether we saw some of the upload off different lines of business, depending on the region, ality right as pandemic hit, for example, um, gym's closed restaurants closed. So as Canadian pack carries a wide variety of different lines of business, we actually offer a wide selection of exercise equipment and accessories, cycling products as well as the kitchen appliances and kitchen accessories pieces. Right? So all of those items started growing exponentially and in certain areas more than others. And this is where thoughts about comes into play. A typical analysis on what the region ality of the sales has been over the last couple of days, which is lifetime and pandemic terms, um, could have taken days weeks for analysts to ultimately cobbled together an Excel spreadsheet. Meanwhile, it can take a couple of seconds for 12 Korean tosspot set up a PIN board that can be shared through a wide variety of individuals rather than fording that one Excel spreadsheet that gets manipulated every single time. And then you don't get the right inside. So from again merch supply chain covert response aspect of things. That spot has been one of those blessings and one of those amazing tools to utilize and improve the speed off insights, improved the speed of analytics and improve the speed of decision making that's ultimately impacting, then consumer at the store level. So Andrea talked about 4500 users that we have that number of school. But what I owe the recently like to focus on, uh, Andrew and I laughing because I think the last time we've spoken at a larger forum with the fastball community, I think we had only 500 users. That was in the beginning >>of the year in in February, we were aiming to have like 1000 >>exactly. So mission accomplished. So we've got 4500 employees now. Everybody asked me, Yeah, that's a big number, but how many times do people actually log in on a weekly or daily basis? I'm or interested in that statistic? So lately, um, we've had more than 400 users on the weekly basis. What's what's been cool lately is, uh, the exponential growth off ad hoc ways. So throughout October, we've reached a 75,000 ad hoc ways in our system and about 13,000 PIN board views. So why is that's that's significant? We started off, I would say, in January of 2020 when Andrea refers to it, I think we started off with about 40 45,000 ad hoc worries a month. So again, that was cool. But at the end of the day, we were able to thio double that amount as more people migrate to act hawk searches from PIN board views, and that's that's a tremendous phenomena, because that's what that's about is all about. So I touched upon a little bit about exercise and cycling. So these are our quarterly results for Q two, um, that have showed tremendous growth that we did not plan for, that we were able to achieve with, ultimately the individuals who work throughout the organization, whether it's the merch organization or whether it's the supply chain side of the business. But coming together and utilizing a B I platform by tools such a hot spot, we can see triple digit growth results. Eso What's next for us users at Hawks searches? That's fantastic. I would still like to get to more than 1200 people on the weekly basis. The cool number to me is if all of our lifetime users were you were getting into the tool on a weekly basis. That would be cool. And what's proven to be true is ultimately the only way to achieve it is to keep surprising and delighting them and your surprising and delighting them with the functionality of the tool. With more of the relevant content and ultimately data adding in more data, um, is again possible through ET else, and it's possible through pulling that information manually. But it's expensive, expensive not from the sense of monetary value, but it's expensive from the size time, all of those aspects of things So what I'm looking forward to is migrating our platform to azure in snowflake and being able thio scale our insights accordingly. Toe adding more data to Adam or incites more, uh, more individual worksheets and data sets for people to Korea against helps the each one of the individuals learn. Get some of the insights. Helps my team in particular be, well, more well versed in the data that we have existing throughout the organization. Um, and then now Andrea, in touch upon how we scale it further and and how each one of the individuals can become better with this wonderful >>Yeah, soas used a zero mentioned theater hawk searches going up. It's sort of it's a little internal victory because our starting platform had really been thio build the pin boards to replicate what the users were already expecting. So that was sort of how we easily got people in. And then we just cut off the tap Thio, whatever the previous report waas. So it gave them away. Thio get into the tool and understand the information. So now that they're using ad hoc really means they understand the tool. Um, then they they have the data literacy Thio access the information and use it how they need. So that's it's a really cool piece. Um, that worked on for Canadian tire. A very report oriented and heavy organization. So it was a good starting platforms. So seeing those ad hoc searches go up is great. Um, one of the ways that we sort of scaled out of our initial group and I kind of mentioned this earlier I sort of stepped on my own toes here. Um is that once it was a proven success with the merchants and it started to spread through word of mouth and we sought out the analyst teams. Um, we really just kept sort of driving the insights, finding the data and learning more about the pieces of the business. As you would like to think he knows everything about everything. He only knows what he knows. Eso You have to continue to cultivate the internal champions. Um Thio really keep growing the adoption eso find this means that air excited about the possibility of using thought spot and what they can do with it. You need to find those people because they're the ones who are going to be excited to have this rapid access to the information and also to just be able to quickly spend less time telling a user had access it in thought spot. Then they would running the report because euro mentioned we basically hit a curiosity tax, right? You you didn't want to search for things or you didn't want to ask questions of the data because it was so conversed. Um, it was took too much time to get the data. And if you didn't know exactly what you were looking for, it was worse. So, you know, you wouldn't run a query and be like, Oh, that's interesting. Let me let me now run another query of all that information to get more data. Just not. It's not time effective or resource effective. Actually, at the point, eso scaling the adoption is really cultivating those people who are really into it as well. Um, from a personal development perspective, sort of as a user, I mean, one who doesn't like being smartest person in the room on bought spot sort of provides that possibility. Andi, it makes it easier for you to get recognized for delivering results on Dahlia ble insights and sort of driving the business forward. So you know, B b that all star be the Trailblazer with all the answers, and then you can just sort of find out what really like helping the organization realized the power of thought spot on, baby. Make it into a career. >>Amazing. I love love that you've joined us, Andrea. Such a such an amazing create trajectory. No bias that all of my s o heaps of great information there. Thank you both. So much for sharing your story on driving such amazing adoption and the impact that you've been able to make a T organization through. That we've got a couple of minutes remaining. So just enough time for questions. Eso Andrea. Our first questions for you from your experience. What is one thing you would recommend to new thoughts about users? >>Um, yeah, I would say Be curious and creative. Um, there's one phrase that we used a lot in training, which was just mess around in the tool. Um, it's sort of became a catchphrase. It is really true. Just just try and use it. You can't break. It s Oh, just just play around. Try it you're only limitation of what you're gonna find is your own creativity. Um, and the last thing I would say is don't get trapped by trying to replicate things. Is that exactly as they were? B, this is how we've always done it. Isin necessarily The the best move on day isn't necessarily gonna find new insights. Right. So the change forces you thio look at things from a different perspective on defined. Find new value in the data. >>Yeah, absolutely. Sage advice there. Andan another one here for Yaro. So I guess our theme for beyond this year is analytics meets Cloud Open for everyone. So, in your experience, what does What does that mean for you? >>Wonderful question. Yeah. Listen, Angela Okay, so to me, in short, uh, means scale and it means turning Yes. Sorry. No, into a yes. Uh, no, I'm gonna elaborate. Is interest is laughing at me a little bit. That's right. >>I can talk >>Fancy Two. Okay, So scale from the scale perspective Cloud a zai touched upon Throw our conversation on our presentation cloud enables your ability Thio store have more data, have access to more data without necessarily employing a number off PTL developers and going toe a number of security aspect of things in different data sources now turning a no into a yes. What does that mean with more data with more scalability? Um, the analytics possibilities become infinite throughout my career at Canadian Tire. Other organizations, if you don't necessarily have access thio data or you do not have the necessary granularity, you always tell individuals No, it's not possible. I'm not able to deliver that result. And quite often that becomes the norm, saying no becomes the norm. And I think what we're all striving towards here on this call Aziz part the conference is turning that no one say yes on then making a yes a new, uh, standard a new form. Um, as we have more access to the data, more access to the insights. So that would be my answer. >>Love it. Amazing. Well, that kind of brings in into this session. So thank you, everyone for joining us today on did wrap up this dream. Don't miss the upcoming product roadmap eso We'll be sticking around to speak thio some of the speakers you heard earlier today and I'll make the experts round table, and you can absolutely continue the conversation with this life. Q. On Q and A So you've got an opportunity here to ask questions that maybe keep you up at night. Perhaps, but yet stay tuned for the meat. The experts secrets to scaling analytics adoption after the product roadmap session. Thanks everyone. And thank you again for joining us. Guys. Appreciate it. >>Thank you. Thanks. Thanks.

>>Yeah, yeah, >>Hello. We're back with Today's the last session in the creating engaging analytics experiences for all track breaking down data silos. A conversation with Snowflake on Western Union Earlier today, we did a few deep dives into the thought spot product with sessions on thoughts about one. Thoughts were everywhere on spot. Take you to close out this track. We're joined by industry leading experts Christian Kleinerman s VP of product at Snowflake and Tom Matzzie, Pharaoh, chief data officer at Western Union, for a thought provoking conversation on data transformation on how to avoid the pitfalls of traditional analytics. They'll be discussing in key challenges faced by organizations, why user engagement matters and looking towards the future of the industry. No Joining Thomas and Christian in conversation is Angela Cooper, vice president of customer success at Thought spot. Thank you all for being here today. We're so excited for what is what this conversation has in store. Handing it over now to Christian to kick things off. >>Hi. So, a few years ago, when when someone asked about Snowflake, the most common answer, it was like, what is snowflake and what do you do? Hopefully in the last couple off months, things have changed and and here I am showing a couple of momentum data points on, uh, where we have accomplished here it Snowflake. So we we have received Ah, a lot of attention and buzz. Recently, we were listed in the New York Stock Exchange And we even though we still think of ourselves as a small start up company, we have crossed the 2000 employees mark. More important, we count with 3 3000 plus amazing customers. And something that we obsess about is the a satisfaction of our customers. We really are working hard. The laboring technology that having a platform for better decisions, better analytics and then the promoters course off 71 depicted here is a testament of that. And last, but certainly not least about snowflake. It's very important that we know that we succeed with our partners. We know that we don't go to market by ourselves. We actually have Ah, fantastic set of partners and of course, thoughts. But it is one of our most important partners. >>Good morning. Good afternoon. Eso Amman Thomas affair on the chief kid officer here at Western Union. It's gonna be a background of a Western union and what we, uh, what we do and how we service our customers. So today we are in over 200 countries and territories worldwide. We have a 550,000 retail Asian network to service all of our customers, uh, needs from what he transfer and picking up in a depositing cash. We also have our digital transformation underway, where we now have educate abilities up and running and over 35 countries with paled options to accounts in over 120 countries. We think about our overall business and how support are over our customers and our services. It really has transformed over the past 12 months with Cove it and it's part of that We have to be able to really accelerate our transformation on a digital front to help to enable in the super those customers going forward. Eso as part of that, You know, a big, big help in a big supporter of that transformation has been snowflake and has been thought spot as part of that transformation. If you go the next to the next slide are our current, uh B I in our illegal tools right to date, uh, have been very useful up until the last one or two years. As data explodes and as as our customer needs transform and as our solutions and our time to act in our time to react in the overall market becomes faster and faster, we need to be able to basically look across our entire company, our entire organization and cross functionally to visit to leverage data leverage our insights to really basically pivot our overall business and our overall model to support our customers and our and to enable those services and products going forward. So as part of that, snowflakes been a huge part of that journey, right, allowing us to consolidate over our 30 plus data stores across the company on able to really leverage that overall data and insights to drive, uh, quick reaction right with the pivot, our business offered to enable new services and improve customer experiences going forward and then being able to use a snowflake and then being put the applications on top of that like thought spot, which allows, uh, users that are both technical and nontechnical to the go in and just, um, ask the question as if the searching on Google or Yahoo or being they can just ask any question they want and then get the results back in real time, made that business call and then really go forward through these is this larger ecosystem as a whole. It's really enabled us to really transform our business and supporter customers going forward. >>Wonderful. Thank you, Tom. Thank you, Christian, for the overview of both snowflake and Western Union. Both have big presence in Denver, which is where Tom and I are tonight. Um, I'm here. I'm the vice president of customer success for Thought spot, and I wanted to ask both of you some questions about the industry and specific things that you're facing within Western Union. So first I was hoping Christian that you could talk to me a little bit about Snowflake has thousands of customers at this point, servicing essentially located data sets. But what are you seeing? Has been the top challenges that businesses air facing and how it snowflake uniquely positioned to help. Yeah, >>so certainly the think the challenges air made. I would say that the macro challenge above everything is how to turn data into a competitive differentiator, their study after study that says companies that embrace data and insights and analytics they are outperforming their competitors. So that would be my macro challenge. Once you go into the next level, maybe I can think of three elements. The first one Tom already perfectly teed up the topic of of silence and the reality For most organizations, data is fragmented across different database systems. Even filed systems in some instances transactional databases, analytical data bases and what customers expect is to have, ah, unified experience like I am dealing with company extra company. Why? And I really don't care if behind the scenes there's 10 different teams or 100 different systems. I just want a unified experience. And the Congress is true. The opportunity to deliver personalized custom experiences is reliant on a single view of the day. The other topic that comes to mind this is the one of data governance, Um, as data becomes more important than a reorganization, understanding the constraints and security and privacy also become critical to not only advanced data capability but do it doing so responsibly and within the norms off regulation and the last one which is something court to tow our vision. We are pioneering the concept of the data cloud and the challenge that that we're addressing there is the problem around access to data, right. You can no longer as an organization think of making decisions just on your own data. But there's lots of data collaboration, data enrichment. Maybe I wanna put my data in context. And that's what we're trying to simplify and democratize access and simplify connecting to the data that improves decisions on all three fronts. Obviously, we're obsessed. That's no bling on on tearing down the silos on delivering a solution that is very focused on data governance. And for sure, the data cloud simplifies access to data. >>Wonderful. Now, I know we we really focused on those data silos is a business challenge. But Tom, going through your digital transformation journey are there specific challenges that you faced with Western Union That thought spot and snowflake have helped you overcome? >>Yeah. So? So first off fully agree what Christian just said, right? Those are absolutely, you know, problems that we faced. And we've had overcome, um, service, any company right being able to the transforming to modernize the cloud. Um, for us, one of the biggest things is being able to not just access our information, but have it in a way that it can be consumed, right? Have it in a way that it could be understood, right? Have it in a way that we can then drive business business decision points and and be able to use that information to either fix a problem that we see or better service our customers or offer a product that we're seeing right now is a miss in the marketplace to service in a underserved community or underserved, um, customer base. Also, from our standpoint, being able toe look, um um, uh and predict in forecast what's going to happen and be able to use that information and use our insights to then be proactive and thio in either, You know, be thoughtful about how do we shift our focus, or how do we then change our strategy to take advantage of that for that forecast in that position that we're seeing into the future? >>Wonderful. I've heard from many customers you could not have predicted what was going to happen to our businesses in the year 2020 with the traditional models and especially with what did you say? 30 plus different data silos. Being able to do that type of prediction across those systems must have been very, very difficult. You also mentioned going through a digital transformation at Western Union. So can you talk to me, Tom? A little bit about kind of present day? And why? Why is it important to enable your frontline knowledge workers with the right data at the right time with the right technology? >>Yeah, so? So you're spot on, by the way. But, uh, no one predicted that that we would have a pandemic that would literally consume the entire globe right And change how consumers, um uh, use and buy services and products, or how economies would either shut down or at the reopening shut down again. And then how different interests to be impacted by this? Right. So, uh, what we learned and what we were able to pivot was being able to do exactly what you just said, right. Being able to understand what's happening the date of the right time, right then being able to with the right technology with the right capabilities, understand? what's happening. I understand. Then what should our pivot be? And how should we then go focus on that pivot to go into go and transform? I think it's e. It's more than just just the front lines. Also, our executives. It's also are back office operations, right, because as you think through this, right as customers were having issues right, go into retail locations that were closed. It end of Q one Earlier, Q two. We obviously had a a large surplus right of phone calls coming into our call centers, asking for help, asking for How can we transact better? Where can we go? Right? How do we handle the operationally? Right? As we had a massive surge onto our digital platform where we were, we had 100% increase year over year in Q one and Q two. How do we make sure that our platform the technology can scale right and still provide the right S L A's and and and and the right, um uh, support to our internal customers as well as our extra customers in the future? Eso so really interesting, though, you know, on on on the front line side, our sales staff, right? And even our front line associates with our agent locations A to retail side, you know, for us, is really around. How do we best support them? So how do we partner with them to understand? You know, when a certain certain governments or certain, uh, regions were going toe lock down, how do we support them to keep them open, right. How do we make them a essential service going forward? How do we enable them? Right, the Wright systems or technology to do things a bit differently than they have in the past to adopt right with the changing times. But, you know, I'll tell you the amount of transformation in the basement we've done this year, I think you know, has a massive and actually on Lee, you know, created a larger wave for us to actually ride into the future as we can, to base to innovate, you know, in partnership with both thought spot and with the snowflake into the future. >>Absolutely. I've seen many, many a industry analyst reports talking about how companies now in 2020 have accelerated that digital transformation movement because of current day. In current time, Christian What are you seeing with the rest of the industry and other global companies about enabling data across the globe at the right time? >>Yeah, so I can't agree more with with with with what? Tom said. And he gave some very, um, compelling and very riel use cases where the timeliness of data and and and and and at the right time concept make a big difference. Right? They aske part of our data marketplace with snowflake with deliver, for example, um, up to date low ladies information on, uh, covert 19 data sets where we're infection spiking. And what were the trends? And the use case was very, very riel. Every single company was trying to make sense of the numbers. Uh, all machine learning models were sort of like, out of whack, because no trends and no patterns may make sense anymore. And it was They need to be able to join my data and my activity with this health data set and make decisions at the right time. Imagine if if the cycle to makes all these decisions waas Ah, monthlong. You would never catch up, right? And he speaks to tow a concept that it that is, um, dear, it wasa snowflake and is the lifetime value data right? The notion of ableto act on a piece of data on an event at the right time and obviously with the slow laden see it's possible, makes a big difference. And and there is no end of example. Stomach gives her all again very compelling ones. Um, there's many others, but if you're running a marketing campaign and would you want to know five minutes later that it's not working out, you're burning your daughters? Or would you want to know the next day? Or if someone is going to give you you have a subscription based business and you're going toe, for example, have a model that predicts the turn of your customer? How useful is if you find out Hey, your customer is gonna turn, but you found out two months later. Once probably you are really toe action and change the outcome. Eyes different and and and this order to manage that I'm talking about days or months are not uncommon. Many organizations today, and that's where the topic of right technology matters. Um, I love asking questions about Do you know, an organization and customers. Do you run data, transformations and ingests at two and three in the morning? And the most common answer is yes. And then you start asking why. And usually the answer is some flavor off technology made me do it and a big part of what we're trying to do, like what we're pioneering is. How about ingesting data, transforming data enriching data when the business needs it at the right time with the right timeliness? Not when the technology had cycles. So they were Scipio available, so the importance can't be overstated. There is value in in in analyzing understanding data on time, and we provide technology and platform to any of this. >>That's such a good point. Christian. We ended up on Lee doing processes and loading in the middle of the night because that's what the technology at that time would allow. You couldn't have the concurrency. You couldn't have, um, data happening all at the same time. And so wonderful point that stuff like enables. I think another piece that's interesting that you guys a hit on is that it's important to have the same user experiencing user interface at the right time. And so what I found talking to customers. And Tom what? You and I have discussed this. When you have 30 different data sets and you have a interface that's different, you have a legacy reports system. Maybe you have excel on top of another. You have thought spot on one. You have your dashboard of choice on another, those different sources in different ways. To view that data, it can all be so disjointed. And the combination of thought spot with snowflake and all the data in one place with a centralized, unified user experience just helps users take advantage off the insights that they need right at that right moment. So kind of finishing up for our last question for today I'm interested to hear about Christian will go back to you quickly about what do you see from snowflakes? Perspective is ahead. Future facing for data and analytics. >>One of the topics you just alluded toe Angela, which is the fact that many data sets are gonna be part of the processes by which we make decisions and that that's where were the experience with thoughts but a single unified search experience for a single unified. Um automatic insects, which is what's para que does That is the future, right? I I don't think that x many years from now on, and I think that that X is a small number. Organizations are going to say I had some business activity. I collected some data. I did some analysis and I have conclusions because it always has to be okay, put it in context or look at industry trends and look at other activity that can help him make more sense about my data. The example of tracking they covert are breaking is ah, timely one. But you can always say go on, put it in context with, I don't know, maybe the GDP of the country or the adoption of a platform and things like that. So I think that's ah big trend on having multiple data sets. Contributing towards better decisions towards better product experience is for better services. And, of course, Snowflake is trying to do its part, is doing its part with vision and simplify answers today and the answer on hot spot simplifying blending the interface so that would be super useful. The other big piece, of course, is, um, Predictive Analytics people Talk machine Learning and AI, which is a little bit to buzz worthy. But it is true that we have the technology to drive predictions and and do a better job of understanding behaviors off what's supposed to happen based on understanding the best and the last one. If if if I'm allowed one. Exco What's ahead for data industry, which sounds obvious, but But we're not all the way. There is both cloud the adoption and moving to the cloud as well as the topic of multi Cloud. Increasingly, I think we we finally shifted conversations from Should I go to the cloud or not? Now it's How fast do I do it? And increasingly what we hear is I may want to take the best of the different clouds and how doe I go in and and and embrace a multi cloud reality without having to learn 100 plus different services and nuances of services on on every car and this work technologies like snowflake and thoughts about that can can support a different multiple deployment are being well received by different customs, nerve fault, >>Tom industry trends, or one thing I know. Western Union is really leading in the digital transformation and in your space, What's next for Western Union? >>Yeah, so just add on Requip Thio Christian before I dive into a Western Union use case just to your point. Christian, I really see a convergence happening between how people today work or or manage their personal life, where the applications, the user experiences and the responses are at your fingertips. Easy to use don't need to learn different tools. It's just all there, right, whether you're an android user or an apple user rights, although your fingertips I ask you the same innovation and transmission happening now on the work side, where I see to your point right a convergence happening where not just that the technology teams but even the business teams. They wanna have that same feature, that same functionality, where all their insights their entire way to interact with the business with the business teams with their data with their systems with their products for their services are at their fingertips right where they can go and they can make a change on an iPad or an iPhone and instant effect. They can go change a rule. They could go and modify Uh uh, an algorithm. They can go and look at expanding their product base, and it's just there. It's instant now. This would take time, right? Because this is going to be a transformational journey right across many different industries, but it's part of that. I really see that type of instant gratification, uh, satisfaction, that type of being able to instantly get those insights. Be able thio to really, you know, do what you do on your personal life in your work life every single day. That trend is absolutely it's actually happening. And it's kind of like tag team that into what we're doing at Western Union is exactly that we are actually transforming how our business teams, uh, in our technology teams are able to interact with our customers, interact with our products, interact with our services, interact with our data and our systems instantly. Right? Perfect example that it's that spot where they could go on typing any question they want. And they instigate an answer like that that that was unheard of a year ago, at least for our business. Right being able to to to go and put in in a new rule and and have it flow through the rules engine and have an instant customer impact that's coming right. Being able to instantly change or configure a new product or service with new fee structure and launch in 15 minutes. That's coming, right? All these new transformations about how do we actually better, uh, leverage our capabilities, our products and our services to meet those customer demands instantly. That's where I see the industry going the next couple of years. >>Wonderful. Um, excited to have both of you on the panel this afternoon. So thank you so much for joining us, Christian and Tom as just a quick wrap up. I, you know, learned quite a bit about industry trends and the problems facing companies today. And from the macro view with snowflake and thousands of customers and thought spots, customers and Western Union. The underlying theme is data unity, right? No more fragmented silos, no more fragmented user experiences, but truly bringing everything together in a governed safe way for users. Toe have trust in the data to have trust in what to answer and what insight is being put in front of them. And all of this pulled together so that businesses can make those better decisions more informed and more personalized. Consumer like experiences for your customers in modern technology stacks. So again, thank you both today for joining us, and we look forward to many more conversations in the future. Thank you >>for having me very happy to be here. >>Thank you so much. >>Thanks. >>Thank you, Angela. And thank you, Tom and Christian for sharing your stories. It was really interesting to hear how the events of this year have prompted Western Union to accelerate their digital transformation with snowflake and thought spot and just reflecting on alot sessions in this track, I love seeing how we're making the search experience even easier and even more consumer like in that first session and then moving on to the second session with our customer Hayes. It was really impressive to see how quickly they'd embedded thought spot into their own MD audit product. And then, of course, we heard about Spot Ike, which is making it easier for everybody to get to the Y faster with automated insights. So I'm afraid that wraps up the sessions in this track. We've come to an end, But remember to join us for the exciting product roadmap session coming right up. And then after that, put your questions to the speakers that you've heard in Track two in I'll meet the Experts Roundtable, creating engaging analytics experiences for all. Now all that remains is for me to say thank you for joining us. We really appreciate you taking the time. I hope it's been interesting and valuable. And if it has, we'd love to pick up with you for a 1 to 1 conversation Bye for now.

>>the >>globe. It's the Cube with digital coverage of AWS >>reinvent 2020 >>sponsored by Intel, AWS and our community partners. Everyone welcome back to the cubes. Virtual coverage of AWS reinvent 2020 Virtual I'm John for your host. Got a great segment here with two analyst day Volonte and Zia's Carvell who's head principles of zk research dot com. Guys. Great to see you A W s Kino. Thanks for >>coming on. Let's be back in the cube. >>Welcome back. Great to see you guys. Wanna get your thoughts? Um, it's mainly you because we talked with the enterprise a lot. You are leading analyst. You cover a broad range from networking all the way up to the C suite for enterprise buyers and and technology trends. Um, Andy Jassy laid down, in my opinion, what was directionally his next 20 mile stare. The next conquest for Amazon. And that is global. I t spend they locked in the infrastructures of service pass kicking ass. There. Check check. Hello, Enterprise. Different ballgame. What's your thoughts? >>Yeah, they have so much in different areas, obviously. You know, they have dominated cloud instances right there. Mawr compute storage memory. You know insists that anybody but you can see him, um, spreading their wings now, right? I think one of the more interesting announcements was actually what they're doing with Amazon connect. That's their contact center platform. And this is something that I think, Even last year, a lot of people weren't really even sure if they'd be in a long primary in the pocket. People about this market, they were asking, If you really think Amazon's in this, there's something they're experimenting. But we're here to stay. And I think one of the interesting things that they bring to market is, you know, almost unprecedented scale with their cloud platform as well as all the machine learning algorithms. And I think if if you believe that machine learning artificial intelligence is changing, I t. Forever and that's everything from the infrastructure to the network through the applications, then they have an inherent advantage because they have all those machine learning albums built into this stuff that they dio and so they can constantly look at these different markets and disruptive, disruptive, disrupt and take more and more sharing that and that's what they've done. E think that's you know, the context and announcements were great example that they're not doing the telephony things, and, you know, they're kind of bare table stakes. They do that pretty well, but they've just unloaded a whole bunch of ai based features that >>Dave, what's your take on this context center? Because it's not just call centers. I mean, there was a whole industry around call center, unified communications. That whole world. This is about the contact. It's about the person. This is not just a nuanced thing like telephony or, you know, PBX is in the old days. Remember those days? Things is not about the call. It's about the contact. This is what Jazzy saying. >>I think that way had Diana or on early. And I said, I like the fact that their AWS specifically is going after these solutions because several years ago it was just sort of. Here's a bunch of tools. Go figure it out. I think the contact center is I mean, everybody can relate Thio the pains of going through getting rerouted, having to restate all your credentials, not knowing who you are. And so between machine learning, Alexa, Natural language processing, better work flows. I mean there's this huge opportunity toe reinvent the whole call center contact center. So, uh, yeah, I think you called it John. It's a no brainer for a W s toe Really disrupt that >>business. Well, it also puts him in a position. You know, news is breaking on the day of and yet his keynote here at reinvent that, uh, you got Salesforce spying slack for 27 close toe, $28 billion. That's a 55% premium over when they announced it. And that's like a 30 x or 50 x on on revenue. Massive number to confess the message board software. I mean, so So. So. If Amazon can come in and get the context center model, which is not just voice, it's chat, it's machine learning. It's bots. And the innovation to create a step function kind of brings it back into the that integration of user network compute. You know, I just think that it feels very edgy in the sense of edge computing, because if I'm a person, I'm mobile. If I'm a person at work or at home, so there's a whole redefinition Zs, what's your take on this edge? Play from Amazon in context toe the enterprise software landscape. That seems to be, you know, focus on buying companies like Salesforce. >>Well, I think edges really the next big foray for computing. If one of the things and you ask we talked about this, you know, was that the compute, the unit of Compute, has gotten smaller and smaller, Right? We went from data centers to servers to virtual machines, the virtual machines and clouds. Now we're talking about containers and containers on edges, and this requires, um if you if you believe in the world of distributed computing where we're gonna have mawr containers running in MAWR, places on MAWR edges, right. The value proposition where companies is now they can move their data closer to the customer. They could move data closer to the user. And so, if I'm a retailer and I'm trying to understand what a customer is doing, I could do that in store. If I'm Tesla and I'm trying to understand what the drivers doing, I could do that in car, right? If I'm a cellular provider, I could do it by cellular edge. So the edge, I think, is where a lot of the innovation is going to be at Amazon has the luxury of this massive global network. You know, they just announced the number another a number of other local nodes, including Boston and a few other places. So they've got the footprint in place. And this this is what makes Amazon's are difficult to compete with, right? They built this massive network and this all these, no doubt for their e commerce business. And now they're leveraging that deliver I t services. You can't just go build this from the ground up the variety, right? You have to be able to monetize it another way. And they've been doing that with the commerce for a long time. And so it makes them. It makes it very, very difficult for them to capture Google could with Daniel forget about the item. Oh, yeah, so good. Microsoft. Possibly. But they I think that the more distributed compute becomes the more favors Amazon, >>I would add to that if I could, John, I mean, look good. Look at the prevailing way in which many of the infrastructure the old guard is Andy. Jesse calls them. Companies have pursued the edge they've essentially taking, taking x 86 boxes and, you know, maybe made him rugged and throwing them over the fence to the edge. And that really is not gonna play the edges. Now there's not one edge. I mean, there's a very highly specific use cases and factories and windmills. And maybe maybe it's small retail organizations, and whatever it is that those are gonna be really unique situations. And I think the idea of putting a programmable infrastructure at the edge is gonna win. I also think that the edge architecture is gonna be different. It's going to require much more efficient processing to do a I Influencing a lot of the data is gonna be, uh, stay at the edge. A lot of it's not gonna be persisted. Some of it's gonna come back to the cloud. But I think most of it is actually gonna gonna either not be persisted or stay at the edge and be affected in real time. When you think of autonomous vehicles so totally different programming model, >>well, I think that's the point of what I was saying earlier Zeus was talking about Is that it's It's the edges is just different. I mean, you got purpose built stuff. I mean, they were talking by the way they have snowball. So they have, ah, hard edge device. And they got out outpost now in multiple flavors and sizes. But they also were talking about computer vision and machine learning. We're going together for that. The panoramic appliance. I think it was where there's all these different cases to your point, Dave, where it's just different. At the edge, you have the zones for five G. I mean, if you go to a five g tower, that's essentially an edge. Just there's equipment up to this. Radios is transceivers and other back haul equipment. So when you look at the totality of what it is, the diversity, I think that's why this whole idea of Lambda and Containers is interesting. Toe Zia's. When you were saying about the compute sizes being small, because if you could put compute at the edge on small pieces to match the form factor that becomes interesting. I think that's what this Lambda container announcement I found interesting because I see that playing directly into that your reaction to >>that. It actually, um, makes it. If not done correctly, it could make I t much more complex because, um, containers air interesting because they're not like virtual machines. First live in perpetuity. Containers you They're very ephemeral, right? You spin them up to 30 seconds, you spin them up for a couple of minutes that you deprecate them. So at any given point in time, you could have thousands of containers, a handful of containers, millions of containers, Right? But it necessitates a common management. Uh huh. Underlay that could be used to visualize where these containers are, what's running on them. And that's what AWS provides. You know, all the stuff they're doing Lambda and Eks and things like that that lends itself to that. So a customer can then go and almost create a container architecture that spans all their cloud's edges, even on Prem. Now, uh, when Amazon has but still be able to manage it and simplify it, I think somebody's trying to do it themselves. They're gonna find that the complexity almost becomes untenable. Unless you have a Nike organization the size of Amazon companies don't. So we're >>gonna here, we're gonna hear from Deepak singing in a few sessions. He did the eks anywhere. That's essentially kubernetes service on the data center. But look at what they did with eks anywhere and then CCS, which has a common control plane to your point, that's compelling. And so, you know, if you're a developer or you're an enterprise, you might not have If you want to go with this. I t world. We talked about earlier zeros before you came on on our last segment. Most I t is not that built out in terms of capabilities. So learning new stuff is hard, so operating Amazon might be foreign to most I t shops. This is a challenge. Did you agree with that? Or or how do you see that? >>Um, well, a lot of Amazon used, obviously just the interviews and numbers of fucked that right. Um, but I think the concept of in a world where you have that common operating layer that spans it's no longer geographically limited to a data center or to a server. You know, it's it's now distributed across your entire multi cloud or distributed cloud environment. And so one of the important things right people remember is the world is becoming more dynamic and or distributed, and your I t strategy has to follow that. If you're doing things that are counted that you're not only standing still, you're actually going backwards. And so what Amazon is doing is they're allowing companies to be is dynamic distributors. They need to be to be able to maintain that that common operating layer that actually makes it management, because without it, you just you wind up in a situation. Like I said, that's incredible. A lot of people facing that today. And that's why that's why there's this big divergence, right? This five native cos they're going fast and legacy companies that can. >>Guys, I want to spend the next 10 minutes we have getting into more of the business side from this keynote because because I know your research on digital transmission first. I know you know the networking side up and down the stack and all that good stuff, but you've been doing a lot of research around the digital transformation with the cloud. Dave, you just put out a great great breaking and else think your 55th, um, episode on digital transformation with the cloud. It's very clear that Jackie is basically preaching, saying, Hey, Clay Christensen is former professor who passed away. He brought up this whole innovator's dilemma kind of theme and saying, Hey, if you don't get the reality that you're in, you better wake up and smell the coffee. It's a wake up call. That's what he's basically saying That's my take away. This is really this business management lesson. Leadership thinking is super important, and I know we've We've talked about people process, technology. Uh, let's Covad eyes this real quick. Bottom line. What is the playbook? Do you agree with jazz? His point of view here? Um, he's pretty being hardcore. He's like, literally saying adapter die in his own way. What, you guys thoughts on this? This is a true forcing function. This cove, In reality, >>I mean I mean, if you talk about the business transformation, digital transformation, business transformation, you know, what does that mean? I, like, said earlier that the last 10 years about I t transformation, I think the next 10 is gonna be about business transformation, organizational industry transformation, and I think what that means is the entire operational stack is gonna get digitized. So your sales you're marketing your your customer support your logistics. You know you're gonna have one interface to the customer as opposed toe, you know, fragmented stovepipe siloed. You know, data sets all over the place, and that is a major change. And I think that's ultimately what a W. S is trying to affect with its model and has obviously big challenges in doing so. But But that, to me, is what digital transformation is ultimately all about. And I think you're going to see it unfold very rapidly over the next several >>years. What's your reaction? What's your view on on the on Jackie? >>And he talked about his eight steps toe reinvention. Um and e think what digital transformation to me is the willingness to re invent disruptive own business even in the face that it might look horrible for your business, right? But understanding he is there something that I think is true. And a lot of, um, business leaders don't fully by this that if something is good for your customer, they're going to do it, and you can either make it happen, or you gonna watch it happen and then have the market taken away from me because there's a lot of cases you look at how slow you know, A lot of the banks, you know, operated until you know, the a lot of these, uh, cloud native, uh, money exchange systems came around the cape. Alan Ben more and things like that, right? Even retailers Amazon completely disrupted that model. You could say that Amazon killed, you know, Toys R us, but 20 rescue Toys R Us E. And I think there's got to be this hard willingness to look at your business model and be willing to disrupt yourself. And what Kobe did, John, I think, is a taught us a lesson that you have to be prepared for anything because nobody saw this coming. And sure you can. And a lot of companies thrived out of this, and a lot of one's gone away, but that the ability to be agile has never been more important. But you're only is Angela's. Ike lets you be, and that's what that's what. The W. Is going to sell us the ability to do anything you want with your business. But the staff, you have to have the business because they're willing to do that. >>You know, that's a great point. That's so smart. It's crime that's worth calling out. And we were talking before we came on live about our business with the Cube. There's no virtual, there's no floor anymore. So we had to go virtual if we weren't in the cloud. If we weren't doing R and D and tinkering with some software and having our studio, we'd be out of business. Dave. Everyone knows it. Now Get the Cube virtual. We have some software were position, and this kind of speaks directly to what Andy Jassy said. He said. Quote. If you're not in the process of figuring out as a company, how you're going to reinvent your customer experience in your product and reinvent who you are, you are starting to unwind. You may not realize it, but you are. What he's saying is you better wake up and smell the coffee and I want to get your guys reacted. You, particularly you around your experience and research. I've noticed that some customers that had cloud going on did well with co vid and said ones that didn't are still struggling not to catch up. So you're kind of intense. You got some companies that were that were on the wave, Maybe kind of figuring it out, that we're in good position and some that were flat footed and are desperate. Um, seems to be a trend. Do you agree with that? And what's your view on this idea of being ready? What does that even mean to be? Have readiness or >>take, you don't get the data points that Andy threw up there, right? That 50% of the companies that were the global fortune $500.2000 or are no longer here, Right? That Zatz Pretty shocking statistic. And that does come, uh, you know, from the willingness to disrupt your business. And if you got you're right. The companies that had a good, solid class raging in place, we're able to adapt their business very quickly. You could you look at retailers. Some had a very strong online presence. They had online customer service set up those companies didn't find other ones, were really forced to try and figure out how to let people in the store had a mimic. You know, the in store experience, you know, through from, uh, you know, support interface or whatever. Those are the ones that really struggling. So you're right. I think companies that were on the offensive plug to Dover companies that were fully in the cloud really accelerated their business and ones that didn't buy into it. I think they're struggling to survive in a lot of They're gone. >>Yeah, and all that. John, When Jesus was talking about his view of digital transformation, I was just writing down some of the examples to your point. The folks that were sort of had were cloud ready, covert ready, if you will. And those that weren't But think about think about automobiles. You know, there's testily even a manufacturer of automobiles or they software company. Personal health has completely changed over the last nine months with remote. You know, uh, telehealth automated manufacturing. You think about digital cash, e commerce and retail is completely, you know, accelerated. Obviously toe online. Think about kids in college and kids in high school and remote learning farming. You know, we've done a great job in terms of mono crops and actually creating a lot of food. But now I think the next 10 years is gonna be how do we get more nutritious food to people and so virtually every industry is ripe for disruption, and the cloud is the underpinning of that disruption. >>Alright, guys, got a few more minutes left. I want to get your thoughts quickly on the keynote. What it means for the customers that we're watching again. This is not a sales and marketing conference as they talk about. But if you're sitting in the audience, you guys, we're watching and we're virtual um Did it hit home with you? If you're a customer, what did he what? Give us Give the grades. Where do you Where do you hit a home run? Where he missed. Did he leave anything out? What's your take Zia's? We'll start with you. >>Um, I thought it was actually really good Keynote. I thought you did a good job of making the case for AWS. They talked about the open. They have more instances than anybody. So you could do almost any kind of compute in their cloud. I think one of the important lessons variety to is the importance. You can't just do everything. The software right? Hardware Still important silicon still important that, and to meet the needs of very special he needs from things like machine learning and AI. Amazon's actually spending their own silicon very much like Athens doing with their computers. And so if you are going to be a customer service focused company, you need to think of the I T. Stack and everything from the silicon, the hardware through the software, and build that integrated experience to Amazon's giving a tools to do that Now E. Do I would like to see Amazon be a little more, um, a supposed the cloud competitive friendly. The one thing I hear from customers all the time is they love the Amazon tools. They love the optimization capabilities, but you know, if they are adopting some kind of multi cloud strategy, the Amazon tools don't work in Azure and the capital don't work in Amazon. The same with Google, and it would be well within the best interests of those three companies. They find a way to get together and allow their common framework to work across clouds. Amazon's already got a lead that they could do that, and I don't think it's gonna be, but that that is something I think that's still missing from this world is they make it very difficult for customers to move the multi cloud. >>Well, some would say some people are saying, saying that the number one in the cloud I mean, got cloud wars Bob Evans over there saying Microsoft is dominating number one position over everybody else, multiple quarters in a row Now he's looking at revenue and granted. You got a lot of propping up there you got. You know, Windows server and sequel. You got a bunch of professional services, But clearly the I as in past side of the market, Microsoft is, like, way behind um So, yeah, they've got the numbers little legacy in their Microsoft should, and they got a little base. If I'm Amazon, I'm not. I'm worried about Microsoft more than anybody. I think you know, I looking at the Civil War between the Seattle forces. I mean, this is really Microsoft's gotta greatest all base, and they could flip that license deals and >>the cloud is good enough. I mean, it's myself doing very, very well with its classic Microsoft. You know >>they your point. Microsoft is the king of good enough, right? They put out features. They market heavily to the I t pro on. They put out licensing packages, so you're almost foolish to not at least fry their products. And then they do roll it out. So it's good enough and then you live with it for a while. But ultimately, whenever people use Microsoft, they do have an alternative under in there for a very special case. But e don't wanna >>the king of good enough. That's a great line. I love that. I'm gonna use that. But this Babel fish thing for Aurora that is a huge dagger. Potentially, it's an escape valve for customers. They wanna leave Microsoft. But clearly, if Microsoft you're gonna get penalized by running your license on Amazon. >>If our CEO our i t c t, I'd say, Okay, I definitely want to do business with with Amazon. That's what I heard today from Jassy, and I would want to hedge my bets either with Microsoft, especially if I'm a Microsoft shop or with Google's from analytics heavy unquestionably. I'd want to hedge my bets and have some kind of 70 30 80 20 mix. >>Look, if you're Andy Jassy and he's told me my interview, do it directly. I asked this question. He was very forthright. He doesn't hide from the fact that, uh, customers have multiple clouds, but they have a primary and secondary, but they're not gonna have, like, five or six major clouds. Yeah, it's hard to get these teams trained at to begin with. So there's a hedge. There's a supplier leverage. I get that. He's totally gets that. But if you're Amazon, you're gonna have your annual conference. You really don't wanna be in the business of talking about the other guys cloud, you say hybrid, right? It's on my show. You know, like you're competing. This is there's definitely competition between Microsoft and A W s. So you gotta respect that. But yeah, of course. There's multiple clouds called hybrid eks everywhere. Uh, container service. I mean, >>especially global, right? Different cloud providers of different strengths in different regions. You know, Microsoft, very strong in the Gulf. AWS isn't you know. So if you're a global company, um, you know, then you almost by default, have to go multi cloud multiple cloud vendors because of geographic differences. Obviously, China, with its own set of cloud providers. So, you know, smaller midsize businesses could get away with one, but As soon as you become global, you have to use more. >>Well, I'm a big fan of distributed computing. I loved the large scale concept of distribute computing. You got regions. Now you've got local zones. You got I O t edge. You got cloud going on Prem Edge. It's really an edge game at this point. Greater now distributed hyper Put hyper next to anything hyper cloud on your sounds better Piper >>Cube. And the opportunities the cloud providers and Amazon, you know, certainly is leading. This is the ability to take this complex, hyper distributed world and use their management tools toe create a normalized operating simplify What would be an overly complex world about it? >>Okay, we got a break. Just quick plug. There's a big salesforce event coming up on December 10th. Check it out on the Amazon site that that plug in you watching the cube stay tuned for more coverage after this break

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to you by Dell Technologies. Hello, everyone. And welcome back to the cubes wall to wall coverage of Dell Technologies World, The digital experience 2020. The Virtual Cube is coming at you. I'm Dave Volonte. And with me or two Great guest, my colleague and longtime business friend Howard Elias. He's the chief customer officer and president of services and digital Adele. And also joining me is Sri Raj, aka Sri Can't him Nene, who is the managing director of digital insights at Cargo, which is one of the world's largest privately held companies in the top maker and distributor of agricultural products and the things that we eat every day. Gentlemen, thanks so much for your time and coming on the Cube. Great to see you. >>Great to see you, Dave. And three. Great to see you again as well. >>Good to be with you both. So >>I wanna Howard, I wanna talk about start by talking about digital transformation. I'm gonna make it laugh. So I was talking to a customer every day or the other day, and we all talk about, you know, digital transformation. And I said, What's digital transformation to you? He said, Dave, my S a P system was 15 years old and I have to upgrade. It was like, Okay, there's eso There's a spectrum, as you know, but what do you seeing as digital transformation? What does that mean to your customers? >>Well, what we're seeing is a glimpse of the future. And first of all, Dave, Great to be with you again, uh, free and all of you out there hope everybody's safe. And, well, thanks for joining us, Adele Technologies World today. But digital transformation from our customers perspectives the technology enablement of experiences with customers, partners and employees, a swells automating processes to deliver value to the all key stakeholders. And we've just seen a glimpse of the future. Customers are accelerating their adoption of technology. We see this through necessity, right when everybody had to pivot from or toe work from home, especially those professional workers and for the most part, whether companies plan forward or not, we all embraced and learned new ways of being productive remotely, and that was all enabled by technology. But we've seen it in every walk of life. It's really an acceleration of trends that were already underway, whether it was the remote experience for professional employees, whether it's e commerce experience, whether it's telemedicine, distance learning. All of these things have been available for a while, but we've seen them be embraced and accelerated tremendously due to what we've seen over the last six months in all industries. And free will talk about what's happening specifically in the agricultural industry, and what we've seen is customers that have made investments over the years have been ableto move even faster in their specific industries. We've just on a survey of about 4600 customers around the world, and 80% have accelerated their investments in digital technology to improve the experience of their employees of their customers and of their partners. >>Yes, so So thank you for that, Howard. Three. I mean, a lot of people might think of cargo. There's physical business, but it's anything but. I mean, you've got such a huge data component to your business, but I wonder what you would add. I mean, we're maybe talk a little bit. I mean, it's such amazingly, you know, rich and deep company. But maybe talk about your digital transformation journey and at least in your sphere of the world where you're at. >>Yeah, thanks, David. You know, Howard's absolutely right. What? What Cove it has done is just accelerated the need for technology on farm and with our customers. And and certainly in the last few months, we've seen that accelerate tremendously, right? A t end of the day. Agriculture has been a technology first, um, industry for for hundreds of years, and and so we're seeing that take fold in the form of digital adoption, the use of analytics, the use of really unique sensor technologies like cameras and computer vision. Um, sound I liken it to the senses that we all have every day that we used to make decisions. Well, we're now seeing that adopted with our with our customers. And so it's a really interesting time, and I think an opportunity for for the industry to really move forward. >>I mean, in terms of the three in terms of the pandemic, you know, we we talked to a lot of customers. Howard just mentioned a survey. You certainly saw the pivot in tow work from home you know, increase in laptop momentum. And in Dell's business, we saw that you're seeing identity access, management, cloud security and point security. Even even VD I These were big tail winds early on. What did the pandemic due to your business and just in terms of your your priorities did you have to obviously shift to those things to support work from home? What happened to your digital transformation was was anything put on hold and is restarting. Can you just Yeah, I don't know what you could tell us about that, but anything you could describe and add some color to that narrative would be really helpful to our audience. >>Certainly. Yeah. You know, I think overnight we had, ah, workforce that went from being in the office toe working from home and and that just accelerated the need for for collaboration tools. Things like like teams and and Skype and Zoom have just taken off right? But also technologies that allow for virtual engagement, like white boarding and brainstorming sessions that we used to do in the office with customers and suppliers. We're now having to do in a virtual setting. So so that has just transformed how we do business on the customer. And, you know, technologies like computer vision and and sound really transform the need to to leverage labor differently. Right? One of the biggest challenges that the cove it has has placed is how labor interacts with animals and and with food production. And we've just seen a significant adoption of technology to help alleviate some of those stresses. >>Now you guys probably have seen the tongue in cheek cartoons, the covert wrecking ball, you know, the guys in the audience or the building saying digital transformation. Not on my watch in the cove, it comes in. I've often joked, uh, I guess we have to have a sense of humor in these times, but But if it ain't broke, don't fix it. We'll cove. It kind of broke everything. And Howard, when you think about digital transformation, yes, was going on before co vid. But But there are a lot of industries that hadn't been disrupted. I think about health care. I think about financial services. I think about defense. I mean, the list goes on unlike publishing, for instance, which got totally disrupted by the Internet. But now it seems like If you're not a digital business, you're out of business. Eso Are you seeing like virtually every industry adopting digital? Or are you seeing any trends that are different by industry? What are you seeing out there >>were absolutely seeing every company in every industry adopted in their own way, thinking through their business models. I mean, even think about what's happened in your local town. How technology is able enabled restaurants to dio, you know, uh, take out and delivery through digital tools, your local dry cleaner, your your local butcher and your baker. I mean, everybody's having toe be creative and reinvent. It's not just the, you know, large professional industrial financial services companies who are also reinventing. But I go back to what I said before what we're seeing. These trends were already underway. They've just been put into hyperspeed what folks were thinking about doing in two or three years we're doing into two or three months. The pivot toe work from home worldwide happened in two or three weeks, and it's not the crisis we planned for, but we're always preparing for the future. The groundwork was laid, and now it's just been accelerated. We're seeing it everywhere, including inside Adele. You know, I think about all the processes and the way we serve our employees, our customers and partners we've accelerated were adopting the product model within our own Del digital organization, for example, that's been accelerated. The move to multi cloud on having a cloud operating model no matter where the infrastructure has been accelerated. And you know, everything we've talked about on the client experience. Security models, networking model software, defined models, every every industry, every company has had to embrace this >>so sorry. I mean, I'm fascinated by your business. I mean again, I think a lot of people think of it as a real physical business. But there's so much data. You're the head of digital insights, which is You've got data running through your your entire operations. There's other things. There's there's double take words I see in your your background like aqua culture. So So how are you re imagining the future of your industry? >>That's Ah, that's a fascinating question, Dave. You know, think, Imagine this. You could listen to a shrimp eat and then turn that into unique insights about the feeding patterns on behaviors of shrimp, right? Who would have imagined 10 years ago that we would have technology that enabled us to do things like that? Right? And so, from aquaculture thio the dairy industry to, you know, grain origination. We're leveraging digital and data to really help our customers and producers make better, more informed decisions where in in the past it was really experience that allowed them toe be good farmers and and good stewards of our planet. Now we're using technology, so it's really an opportunity toe harness, the power of digital for our industry. >>Well, you know, and it's critical because we have people to feed and actually it's working. I mean, the yields that air coming out of the industry or are amazing. I know there's a lot of discussion now, but hey, you know, we're actually getting a lot of food to people. And now there's a discussion around nutrition that's that's front and center, and I presume technology and data fit in there as well. Three. I wonder if you could comment. >>Yeah, you know, by 2050 day there will be nearly 10 billion people on this planet. And to feed that growing population, we're gonna need 70% more protein on DSO. As you think about the impacts that that that growing population has on the planet. There's also, you know, nutrition. But think about sustainability. How do we how do we grow this food and get it from the place that it's produced to the place where it's consumed in a way that's a resource efficient and effective? So there's nutrition in just the middle class in Asia, you know, having a higher propensity to spend and dealing with that challenge on one end of the spectrum and then on the other end of the spectrum, being ableto really deal with with sustainability. >>I would have watched your career over the decades, and you've had so many roles, and I always used to joke with you. They give you the hardest problems if you want. If you want to get stuff done, you give it to the busiest guy. It was always Howard, you know, help us with with our own transformations. Help us do the integrations, whether it was m and a or the course, the largest in just >>industry I love a good challenge is you know, >>I do know and so I want to get. Get the update on Dell's own transformation. I've been talking to a number of your executives this week, and it looks like you know, you guys air, drinking your own champagne, dog food and whatever you wanna call it. But but bring us up to date on what you guys are doing internally. >>We are, and we're no different than any of our customers. And having Thio focus on our digital transformation agenda, I mentioned earlier the adoption of our product model, you know, moving from a project based Dell Digital and I T Organization to one that's a product model. So these are balanced teams with a product manager, a designer and developers working closely with the business and the function in an agile manner and the C I. C. D pipeline manner. And all of this again has been accelerated. We have our own del digital cloud, which is our hybrid cloud that we leverage internally. We're software defining everything, and it's really paying dividends because what we've seen literally in the last 6 to 8 months is higher levels of security, higher levels of availability, higher levels of resiliency. We've been able to handle all of the increased transactions on our e commerce engines, all at higher quality and lower costs. Now we the groundwork for this with Jen Felch in the team over the last couple of years, but again, by necessity, had to accelerate. And we've done that. And we're even moving faster now on data pipelines and really understanding all of our key processes and understanding the work flows and the data flows, working with machine learning and artificial intelligence again, exactly the way Cargill and other of our customers are doing in their businesses. I know you're talking or have talked to Doug Schmidt. You know, we've digitized and automated thousands of processes and our services organization Theobald bility on a remote basis to service our customers were we've invented new and innovative ways the service our customers remotely versus going on site, not just in break fix, deployment, remote change, management, manage services, consulting. It's just, you know, great to see all this wonderful innovation come together serving our customers. >>Thank you for that, Howard. And you, you said something that triggered me in a good way. Data pipelines. I use that term a lot. And three I wonder if you could talk about this because you're You guys have been around since the 18 hundreds, I think the largest privately held company in United States, I think, right, and probably close to one of the largest in the world. And so >>you >>got a lot of data and a lot of different places. So a huge challenge for you is okay. How do you manage those data pipelines? Those data, the data lifecycle, And I would think the company the size of cargo to the extent that you can reduce the end to end time it takes to go from raw data to insights E. That's gonna be telephone numbers for for your business and your bottom line that you can then reinvest and get back to customers, etcetera and be competitive. I wonder if you could talk about >>you >>know, that whole concept of the data pipeline And how are you using data and and some of the challenges of compressing that end to end cycle time and Leighton >>see, to >>get to insights >>that day. You know, Carlos, 155 year old company and and at our core were a supply chain company. Right? Um, you know, taking food from where it's produced, getting it through the manufacturing process, toe customers. And so at the end of the day, I I joked that not only are we have physical supply chain company, but we're also a data supply chain company. So the data value chain right is really about taking all the different inputs in data that we have in turning that into unique insights. And I don't think there's ah company on the planet in the food space that has the ability to connect those dots in the way that we dio. And so our ability to create unique, actionable insights for our customers is going to be really powerful, especially in the in the coming years. >>So talk about let's talk about Dell a little bit. I always ask, uh, technology leaders how your vendors doing for you? How did they help you through the pandemic? How would you grade del uh, in terms of its support through the pandemic? >>Dell has been absolutely fantastic, right? I mean, I think it is really need to have partners like Dell helping us achieve our mission for our customers. And I know they feel that way about us as their customers. So it's really wonderful. Toe have the type of collaboration and partnership that we do. >>Alright, Howard, Same question for you. How would you grade Del Onda? How you guys have done through the through the pandemic with regard to supporting your customers. I mean, you're you're never one toe overhype, uh, in my experience with you. But give us the your take. >>Why would grade del by what our customers say? And we do it both through direct conversations as well as the data and telemetry we get and the data and telemetry we have in terms of our NPS r R C sat scores or service level objectives that were delivering all have remained in profile. The team has really risen to the occasion. Been super creative, passionate, full of grit. We heard Alison and Angela talk about that the Dell Technologies world this morning, and our team is embodied that spirit and that great to be able to deliver. But in the conversations we're having with customers three and his peers, uh, you know, look, it's it's been a challenging time, but as you know, Dell has always focused on delivering value for the long term. We're not in it for the short term, and that has served us well. That philosophy Theobald active. We have with working with customers, eyes always about what's in the best interests of our customers in the long term. Because if we do that, it will ultimately be in the best interest of Dell. >>Well, it's It's been amazing to just watch. I mean, it's just ironic that we got hit with this at the beginning of this decade. It's gonna It's obviously gonna define. You know what we do going forward. I think we've all talked about it. It's funny. Everybody in our business and the technology business. We've become covert experts in some way, shape or form overnight. But we've talked a lot about the the things that we see as as permanent, and I think that >>you >>know you clearly the your two companies are examples of agility leaning into technology. And, as you said, Howard here for the long term, 155 years old, I think story said so well, here's to another 155 years. Gentlemen, thanks so much for coming to Cuba. Awesome guests. >>Thanks. Day. Appreciate it. >>Thank you for watching everybody. Our continuing coverage of Dell Technologies World 2020. You're watching the Cube?

>> Narrator: From theCUBE Studios in Palo Alto in Boston, connecting you with thought leaders all around the world, this is a CUBE Conversation. >> Hi, I'm Stu Miniman and this is a CUBE Conversation, I'm coming to from our Boston area studio. Happy to welcome to the program, Angela Medina. She is the director of product marketing at ThousandEyes. Thank you so much for joining us. >> Thanks for having me Stu. >> All right, so Angelique, we get to dig into some research, a new report, it's set up to be annual, the 2020 Internet Performance Report. Of course, internet, like everything else in 2020, things are a little bit different. So help us understand a little bit the purpose of this report and what led to its inaugural incarnation. >> Yeah, absolutely. So it's really interesting. So about the March period, when suddenly there were shutdowns in the US and other regions, and a lot of the workforce was working from home, we started to get a lot of inbounds from our customers and other interested parties about how the internet was holding up. So there was systemic network degradation, are the operators able to handle all of this traffic and these pretty significant traffic shifts. So in responding to this, we decided to put out or make public our outage detection capabilities. So we put out an outage hub and this was back in May, and it basically shows ongoing and recent disruptions, so that's overlaid on a map and you can see where outages are taking place and which networks they're taking place in. So, that's been out for a few months, but we wanted to look at not just outages in real time, but historically as well. So, in looking at the last few months from January through the end of June, that's a really interesting time capsule because we can look at how networks perform and behave, not just under normal conditions, so maybe January and February, but also highly abnormal conditions. So it's a very interesting way to understand how the different types of network operators perform, given what we're enduring right now. >> Yeah, It's fascinating to me if you've watched the networking industry Angelique, normally these kinds of changes in networking are things that we measure in years if not a decade. I remember it felt like it was at least 10 years we talked about the mega trend of North-South to going East-West, how virtualization was changing everything. And of course in 2020, it's all of a sudden, all right, everybody work from home, everybody's home internet is going to be stressed. So help us understand a little bit how much of that is a blip that we saw over a very short period of time and what the resulting output is. >> Yeah, it was really interesting because beginning in... I mean, there are always a certain level of disruptions. Disruptions are just a part of operating a network. Occasionally you're going to have a little bit of downtime, stuff breaks, but in March we saw a pretty dramatic spike, particularly in North America and Asia pack. The level of disruptions, the duration of the disruptions and the scope of them. So more infrastructure impacted was pretty significant. So it was something like a 66% increase globally. And this did start to go down in subsequent months, so we're at a point where it's not quite back to January, February baseline, but it's pretty close. So we definitely saw an increase, but it seems to have stabilized as in a lot of areas, traffic has plateaued or normalized. >> And when you talk about the internet, of course, the internet is made up of lots of devices and lots of companies. What particularly is interesting there, if you look at some of the internet service providers out there, if you're look at companies that are doing remote contact centers, are you able to see a heat map or some of the areas that might have been under more stresses and strain? >> Yeah, absolutely. So we're looking at it not only from a geographic standpoint, but we're also looking at different network types. So we're defining the internet fairly broadly, not to just be connectivity as a service providers like your broadband providers or your transit providers, but also cloud networks networks. Cloud networks these days are really an extension of the internet. So we're also looking at their performance and how they held up as well as the networks of really key services like CDN providers, content delivery network providers, as well as DNS. So the collection of these networks is really what is the foundation of what most people think of as the internet. >> Yeah, it's the thing that we've been saying for a number of years is if traditionally you were somebody that managed the network, it used to be something that you would touch. And now, of course, most network operators, you are responsible for a lot of things that you don't necessarily touch. I'll give the disclaimer of course, anybody watching, Cisco has made the announcement to acquire ThousandEyes of course, the gorilla in the networking world. We look forward to talking about that more once the deal is completely closed. So Angelique, how are the cloud providers doing? How are end customers reliance on all of these various services? How are things holding up? >> Yeah, I mean, you bring up a really good point about the fact that a lot of enterprises have dramatically transformed or are in the process of transforming themselves where they're now so dependent on external services like cloud providers, like provider networks, more internet service providers, as opposed to managed services. So overall, there hasn't been any kind of systemic breakage across all of these providers, but really the devil is in the details. Oftentimes you might have issues where there might be of an increase in disruptions, or you can't quite pinpoint where the source of an issue is. So really being able to see into these external services and understand how they're performing and have that visibility so you can communicate is really important if you're going to be successful operating in this new IT reality. But in terms of how the different providers perform, how providers are vastly more reliable than internet service providers, probably for a number of reasons having to do with how they've built their networks. They're software defined, they're not as dependent on the underlying infrastructure. So they have much newer networks too, less technical debt, for sure. >> Anything specific when you look at the data over time, are we through the biggest shift in what's happening in more of the ripples now, or have things settled out a little bit, I guess, since some of the initial shocks? >> Yeah, so it varies by provider and region. If just think about the United States or North America. So in North America, we definitely have seen that the number of disruptions have come down over the last couple of months. And we're at a point we're really only about 20% off from what we were seeing in January and February, but cloud provider disruptions haven't quite returned to earlier levels. So they're still on the upswing. So it will be interesting to see where that goes, if that continues, or if that eventually starts to plateau and then decline. But they're going in different directions in North America, disruptions are up, but ISP disruptions are down. >> All right, maybe, could you explain a little bit, what does it mean by an outage? Actually, I pull up right now, the internet outage map which you have on your website and there's these scary, glowing red circles, but you and I are connecting from across the country, obviously using the internet to be able to do videos. So just because there's red glowing lights doesn't mean that we don't have internet access. >> Right, right. I mean, so just in terms of what an outage is, an outage, as we define it, is where there is a hundred percent packet loss within a provider's networks, so traffic is effectively terminating at an interface within the infrastructure of a provider. And so when that happens, we'll flag it. And this is based by the way, on billions of network probes that are sent over the internet using our platform each day. That's how we effectively derive these measurements. And when we see these disruptions, again, we'll flag them and to your point, yes, big, glowing outages on a map, but you know, you're right, not all of them were necessarily going to be disruptive to users for a number of reasons. One of the earlier points you brought up is that the internet is made up of thousands of independently operated networks. It's not like a utility, so you may go through a provider that's having an issue when you may not and that can change dynamically depending on where you're connecting to and what service you're trying to reach. It gets very complicated. >> Is that so? Yeah, so I'm curious one of the biggest challenges out there is that companies have to rapidly make changes. Whether it is adopting cloud services or getting ready remote call centers or the like, are there anything that they can take from the survey data or these maps as to how should I plan things? How should I make these changes? What can practitioners learn from this? >> So I think it's important to understand how do operators, what are their habits and practices, depending on where you're located and we've seen regional differences. So for example, in the US, with internet service providers, they tend to have disruptions that are taking place outside of traditional business hours. So less disruptive, more likely to be due to a maintenance window, change that they're making, versus other regions where many more of these disruptions are taking place at times that might impact a business. So understanding how different providers vary in terms of their practices, gives you an opportunity to have that conversation with providers to hold them accountable and to work collaboratively with them so that you understand when are they going to be making changes. If there are increases in traffic, maybe you have some resiliency measures in place because you know that the operators might be a little bit stressed responding to these increases in either traffic or changing traffic patterns. >> All right, are there any other key takeaways that people should take from this new report? >> Yeah, I mean, I think, one of the key things is that not every outage is created equal. Not all providers are created equal. I mean, they really do vary. Whether it's the fact that the cloud providers have significantly fewer disruptions within their network. Some countries that we've seen have not really been impacted in terms of traffic increases while others have. It really can depend. And so the only way for you to know how your provider is performing or how the key services that you rely on are performing is to have visibility because these days, very often, you don't directly own or manage it. So the only way to ensure that you're getting performance that you need, is to have insight. >> All right, in addition to the report that's coming out, you've got a weekly series I believe that sharing data along with one of our other CUBE Alarms Archana, tell us a little bit about what you're doing there and how that differs. >> Yeah, so we do a weekly podcast. It's just about 15 minutes. It's just to check in to look at what's happened the previous week. So we put this out every Monday and we're looking at whether there have been outages, any interesting news that's taken place, and we'll often go and deep dive on disruptions that have happened. So last week, you probably heard about the CloudFlare outage. It was a pretty big deal. I was getting lots of folks telling me, "Hey, the Internet's down." And it was really just CloudFlare and their DNS service that wasn't available. So we go under the hood and dissect what happened and how it unfolded, and we can show a lot of interesting visualizations around that. >> All right, one last thing, going back to the report, obviously you gather data, you look to be this yearly report, anything along gathering that data, surprises that you've found along this, or putting together the report, are there certain things that longitudinally you might look to do in future studies? >> Yeah, so I do think that maybe it wasn't as surprising to a lot of people, but it was surprising to us given that looking at the same amount of data or same amount of infrastructure that cloud providers were just so dramatically experienced fewer outages. ISP is like 10 times the number of outages as cloud providers. I think going forward, it would be interesting to incorporate more insight into LastMile connectivity, 'cause we're really focusing on backbone networks, really anything other than LastMiles. So, in subsequent reports, we'll fold in some additional insight into LastMile performance as well. >> Excellent. All right, Angelique, I'll let you have the final word, final takeaways you want people to have from this internet performance report. >> You know, I think what you should take away is that if you're able to see how providers are performing, you really can influence how they operate and have a more productive experience working with them. And because these days they're really foundational to most enterprises business, so it's really important to understand the differences between the cloud providers, as well as differences between internet service providers and how that works across different regions. >> All right, well, Angelique, thank you so much for sharing the results of this. Definitely look forward to digging into the data and hearing more from your weekly activities. >> Thanks for having me. >> All right, thank you so much for joining, I'm Stu Miniman and thank you for watching theCUBE. (bright music)