>> theCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Hey everyone, guys and gals, good to see you. It's theCUBE live in Barcelona at MWC23. Lisa Martin here with Dave Vellante on day one of four days of wall to wall CUBE coverage. Dave, today is ecosystem day. We've had some great conversations about why the open ecosystem is so important and some of the key players in it. >> Well and I'm in search of disruptors, so I'm looking for, okay, who are the network operators that are going to actually lean into the future and drive it and challenge the existing incumbents. We'll talk about that today. >> And we're going to be talking about that next. We've got one of our alumni back with us. Satish Iyer is here, the Vice President of Emerging Services at Dell. Great to have you back on the program. >> Thank you. >> Richard Leitao is with us as well, the Vice President of National Development at DISH Network. Welcome. >> Pleasure to be here. >> So, lots of, this is day one, the theme is velocity. I feel like the day has gone by so quickly. But Dell and DISH have partnered together on a multi-year initiative to build your nationwide cloud-native 5G network that's going to cover a lot of the US. Talk a little bit about that partnership, we'll get both of your perspectives. Richard, we'll start with you. >> Sure. So thank you again for having me. So DISH had the opportunity of, of going through this experience, of innovating once more. For the ones that know DISH, DISH is a company that was founded in 1980 by an innovator, a disruptor. Of course, in the course of the next 40 years, we had the opportunities of even disrupting ourselves. We launched our first satellite TV service. We then launched the first streaming, video streaming platform, disrupting our own satellite business. And since 2008, we have been acquiring Spectrum and, you know, Spectrum, the most valuable asset of a wireless operator. We felt that this was the right opportunity, having 5G , having O-RAN, and we decided to go full in in a greenfield project building national network, 5G O-RAN cloud-based network, one of a kind network in in the US and, and most of all, using O-RAN, it's very important to us, what, what it can bring and it can bring to DISH but to the entire ecosystem of, of this sector in the US. >> Satish, talk a little bit about the partnership from Dell's perspective and some of the unique advantages that Dell is delivering to DISH. >> Oh absolutely. Again, like Richard was saying, I mean the telecom network is being desegregated as we speak. You know, companies like DISH and everybody else is looking at what are the best-in-class technologies we can bring to the table. I would like to say that, you know, the cloud is coming to the telco world, right? A lot of us have seen the tremendous transformation in the cloud world in the last few years. Now, you know, DISH is a big enterprise company. As you know, you know, we are pretty strong within the cloud space and enterprise space. So what we try to work with DISH is Dell, is to bring to DISH is, you know, that notion of cloud scale and the cloud ecosystem into telecom, right? By means best-in-class infrastructure products, best-in-class software products, to allow somebody like DISH to innovate and incre, you know, basically expand and build their O-RAN network. So it's absolutely important for us as we build and get into the telecom space to work with somebody like DISH who's also disrupting as a carrier in that space. >> So it's early days for Open RAN but you've decided, "okay, we're all in". >> Yeah. >> Right? So (chuckling) you burn the bridge, as they say, "go for it". (Lisa chuckles) So when you talk to most people, they say, "okay, it's, it's, it's, it's immature." It's got to be able to get to the levels of, of the, the the hardened stack reliability. But of course it brings the advantage of flexibility and speed. Are you optimizing for one or the other right now? How are you dealing with that balance? >> Well, it, it's, it's not mature in the sense that most of operators that think about it, they have a legacy network. And in order to go full in on the O-RAN side, they need to scrap a lot of things that they have and honestly, they don't want, and it doesn't make sense. So being a greenfield operator, give us that advantage. Give us the advantage and, and desegregation, it's all about chip sets, boxes and software and the chip sets part and what I like the most in desegregation is the time of innovation. The time that we can use new chip sets coming into the market, the size of the boxes that we are using. Obviously our footprint onsite is much smaller than traditional carriers or proprietary systems. So all of that Dell has been critical in supporting us. Supporting us having the best chip sets, having the smallest footprint and, you know, the software, the cycle of innovation is much faster than in proprietary systems. So ma-, it's maturing. I'm glad to say that probably two years ago here O-RAN was more like a, a pilot type of technology. It is not, we are live, we are live for more than 30 million customers in the US and, you know, the performance levels are very similar to traditional networks. >> So you don't just buy a nationwide cloud-native 5G network out of the box, you got to- >> No, you don't. >> You got to build it. So I'm curious as to what Dell's role is in that, in that build out. >> Right? >> How and how, I'm really curious how to, how you would grade Dell but we'll get there. >> Yeah, I mean, look, yes, you don't. So I think the, the, the first and foremost is again, as, as we, Dell, comes into the telco space, one of the things we have to look at is to understand what makes Dell better in the enterprise space, right? It is the best-in-class infrastructure. It is the software ties together. As you talk about desegregated networks, it's important to understand lot of these piece parts have to still be touched together, right? So I think the integration and integration aspects becomes really key which is really Dell is very good at. So one of the things we are working really closely with DISH Tech, you know Richard was alluding to, is bringing all, not just bringing all the software and hardware assets together, but how do you continuously innovate and keep fixing things faster, right? So in the old days, traditional ways, you have a software stack, it takes you 18 months, 20 months to actually get an upgrade done. Here we have continuously CI/CD pipelines where if you want to a change done within, within a week's or within a few days, where we can actually go and test and make sure these things work. So I think a lot of the best enterprise software practices, cloud practices, combined with whatever needs for telco, actually is what makes it very unique. >> I, I saw that this started out as an FCC compliance initiative that turned into a partnership, obviously a very successful one. Richard, talk about what DISH saw in Dell that really made it the right choice, knowing you have choices, you have options. >> You know, we saw the capability to execute, but we also saw the capability to innovate. From an execution level, at the end of the day, like we were talking, we started the project in the middle of COVID, and we had the first mandate to cover 20% of the US population by June, 2022. And now we have a second one, 70% of US population by June 2023. At the beginning of the project, it was all about availability of materials, logistics, how to distribute, how to transport material. So Dell has a world-class supply chain, we felt that working with Dell through all these challenges made things easier. So from an execution perspective, whenever you need to build a network and you, you are building thousands of sites, you need to have materials, you need to distribute them and you need to install them. Dell helped us across the board. Our expectations obviously will change. We have a network, we want to cooperate with Dell in many other areas. We want to, you know, leverage on Dell ability to reach the enterprise market, to have private 5G offers. So hopefully this collaboration will endure in time and, and, you know, will change and evolve in time. >> And it's a big bet. I mean, it's not like a single, it's not like a little transaction that you guys are doing. I feel like, you know Michael Dell and Eric Carlson had dinner and they said, "okay, we're going to, we're going to partner up and this is going to be a multi-decade partnership. You had to be transparent, "Hey, we're new at this, even though we're really good at enterprise tech and so you're going to, obviously if you take a chance on us, here's what we promise you." >> Absolutely. >> And vice versa, you guys had to say, "all right, hey, we're willing to roll the dice because we're trying to change the world." So what was that dynamic like? I mean, how did, I'm curious as to this has to be a lot of different levels, engineering, senior management, board level discussions. >> You know, we felt a huge buy-in from Dell on the Open RAN concept. >> Right. >> Yeah, okay. >> And, you know, edge computing and, and the ability to get us the best product and evolve the best product, Intel is is critical in all these offerings. Intel has a great relationship with Dell. Dell helped us. Dell sponsored the DISH program and some of these suppliers, So it was definitely good to have their support and the buy-in on the O-RAN concept. We felt it from day one and we felt secure on that. >> Yeah, I mean, I, to add to that, I mean, you know DISH was very instrumental in driving, dictating and executing to our roadmap, right? They're one of the key, I mean, since they are out there and they're really turning in a way, it's important that a customer who's actually at the out front of innovation, helps us drive our own roadmap. So to Richard's point, a lot of our product roadmaps, in terms of what have you built and all that, was based on what DISH thinks as going to be market-based requirements. They also helped us a lot in the integration aspects. Like I said, one of the things about open desegregation of these networks is there is a lot of integration because, you know, there is, it's not a one, one monolithic pipe smokestack anymore. You are picking up best-in-class pieces, bits and pieces and tying it together. And it's important to understand when you tie it together things will go wrong, right? So there is a lot of learnings from an integration standpoint. Supportability, deployment, one of the things Richard talked about was supply chain, you know. Other Dell's ability to, lot of these deployments, a lot of these configs in the factory, right, in the second part. So especially a lot of these partnerships started during COVID time and as you all know, you know what we went through two years ago. So we had to make sure that lot of these things are done in one place and a factory, and not done in the field because we couldn't do a lot of these things. So there's a lot of, lot of experimentation, lot of, lot, lot of innovation on that. >> So it's 2030, what's this look like? What's the vision if we can work backwards from there? Well, a, a great network coverage to the entire country, bringing new services to enterprises, to verticals, bringing value add to customers and, you know, technology cycles, they are lasting much less than they were. I cannot even say what will happen in three years. 2030, I mean, I know, I know somebody has a vision for 2030. That's another thing. (everyone laughs) >> A lot of it is "build it and they will come", right? >> Yeah. >> I mean it really is right? You put that network in place and then innovation happens on top. That's the best thing. >> Yeah. And look and and I think the biggest people think about Open RAN in terms of cost, which, you know, you, you have some things in cost that you appreciate in Open RAN. The footprint, the the possibility to diversify suppliers and and have more competition. But for me, Open RAN is about innovation and cycles of innovation. I used to work for Nokia, I used to work for Alcatel. I knew from the generation of an idea to an execution and having a feature delivered to a certain customer, it, it took months. We want innovation to take weeks. We are innovating at the speed, speed of the cloud. We are cooperating with new players, players on the cloud and, and we expect things to happen much faster than they traditionally happen on the telecom sector. >> Move fast and break things. >> Well, we also expect that speed- >> Break and fix. (everyone laughs) >> Yeah, thank you for that. >> But speaking of speed, your customers expect that, right? They expect the service to be up 24/7. They expect to be able to access whatever content they want, whenever they want from wherever they are. So comment, Richard, in our last few minutes here of, of how the, the Dell partnership is helping DISH to really deliver the excellent customer experience that your customers just expect that you're going to deliver. >> Well by setting up the system, number one, we are leveraging on a number of services. And I mentioned the supply chain, but in reality Dell made much more than that for our 20% milestone and is supporting our 70% milestone by installing, testing, verifying most of our data center equipment. We found that this offering from Dell was really addressing some of our needs because, you know, we, we believe they know a lot in this area and they, they can provide the best advice and the best speed to market in, in terms of having this equipment. Because we are working on a time clock, we need to have this done as soon as possible. You know for the future, I hope that they can help us in driving more services. I hope they can bring all the infrastructure that we need to offer to our customers. And, you know, we keep committed to O-RAN. O-RAN is really important. We are not compromising that. And I think the future is bright for both of us. >> Yeah, and Dell learns from the experience. >> Exactly. >> Absolutely. >> There's got to be a catalyst for expanding your roadmap and vision in telecom. >> Yeah, I mean, like you said, I mean, you asked a 2030 question and I think that, you know, know six, seven years from now I think people should look at what DISH and Dell and say they were the trailblazers of make, bringing Open RAN to the market and making 5G a reality. I mean, you talk about 5G, but every 5G is on a different stages. I do think that this combination, this partnership has the best chance to be the first ones to actually have a truly Open RAN network to be successful in commercial. >> Awesome guys. Trailblazers, Dell and DISH. Well, we look forward to watching this story unfold. Thank you- >> Thank you. >> for joining Dave and me on the program today talking about what you're doing together. We appreciate it. >> Thanks for having us. >> Our pleasure. >> Thank you, bye. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Barcelona at MWC23. We'll be back after a short break, so we'll see you soon.

(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)

>>Good evening, brilliant humans. My name is Savannah Peterson and very delighted to be streaming to you. Live from the Cube Studios here in Motor City, Michigan. I've got John Furrier on my left. John, this is our last interview of the day. Energy just seems to keep oozing. How >>You doing? Take two, Three days of coverage, the queue love segments. This one's great cuz we have a practitioner who's implementing all the hard core talks to be awesome. Can't wait to get into it. >>Yeah, I'm very excited for this one. If it's not very clear, we are a community focused community is a huge theme here at the show at Cape Con. And our next guests are actually a provider and a customer. Turning it over to you. Lisa and Jake, welcome to the show. >>Thank you so much for having us. >>It's great to be here. It is our pleasure. Lisa, you're with Cockroach. Just in case the audience isn't familiar, give us a quick little sound bite. >>We're a distributed sequel database. Highly scalable, reliable. The database you can't kill, right? We will survive the apocalypse. So very resilient. Our customers, mostly retail, FinTech game meet online gambling. They, they, they need that resiliency, they need that scalability. So the indestructible database is the elevator pitch >>And the success has been very well documented. Valuation obviously is a scorp guard, but huge customers. We were at the Escape 19. Just for the record, the first ever multi-cloud conference hasn't come back baby. Love it. It'll come back soon. >>Yeah, well we did a similar version of it just a month ago and I was, that was before Cockroach. I was a different company there talking a lot about multi-cloud. So, but I'm, I've been a car a couple of years now and I run community, I run developer relations. I'm still also a CNCF ambassador, so I lead community as well. I still run a really large user group in the San Francisco Bay area. So we've just >>Been in >>Community, take through the use case. Jake's story set us up. >>Well I would like Jake to take him through the use case and Cockroach is a part of it, but what they've built is amazing. And also Jake's history is amazing. So you can start Jake, >>Wherever you take >>Your Yeah, sure. I'm Jake, I'm CEO and co-founder of Offset. Oted is the commercial entity behind Spice Dvy and Spice Dvy is a permission service. Cool. So a permission service is something that lets developers and let's platform teams really unlock the full potential of their applications. So a lot of people get stuck on My R back isn't flexible enough. How do I do these fine grain things? How do I do these complex sharing workflows that my product manager thinks is so important? And so our service enables those platform teams and developers to do those kinds of things. >>What's your, what's your infrastructure? What's your setup look like? What, how are you guys looking like on the back end? >>Sure. Yeah. So we're obviously built on top of Kubernetes as well. One of the reasons that we're here. So we use Kubernetes, we use Kubernetes operators to orchestrate everything. And then we use, use Cockroach TV as our production data store, our production backend data store. >>So I'm curious, cause I love when these little matchmakers come together. You said you've now been presenting on a little bit of a road show, which is very exciting. Lisa, how are you and the team surfacing stories like Jakes, >>Well, I mean any, any place we can obviously all the social medias, all the blogs, How >>Are you finding it though? >>How, how did you Oh, like from our customers? Yeah, we have an open source version so people start to use us a long time before we even sometimes know about them. And then they'll come to us and they'll be like, I love Cockroach, and like, tell me about it. Like, tell me what you build and if it's interesting, you know, we'll we'll try to give it some light. And it's always interesting to me what people do with it because it's an interesting technology. I like what they've done with it. I mean the, the fact that it's globally distributed, right? That was like a really important thing to you. Totally. >>Yeah. We're also long term fans of Cockroach, so we actually all work together out of Workbench, which was a co-working space and investor in New York City. So yeah, we go way back. We knew the founders. I, I'm constantly saying like if I could have invested early in cockroach, that would've been the easiest check I could have ever signed. >>Yeah, that's awesome. And then we've been following that too and you guys are now using them, but folks that are out there looking to have the, the same challenges, what are the big challenges on selecting the database? I mean, as you know, the history of Cockroach and you're originating the story, folks out there might not know and they're also gonna choose a database. What's the, what's the big challenge that they can solve that that kind of comes together? What, what would you describe that? >>Sure. So we're, as I said, we're a permission service and per the data that you store in a permission service is incredibly sensitive. You need it to be around, right? You need it to be available. If the permission service goes down, almost everything else goes down because it's all calling into the permission service. Is this user allowed to do this? Are they allowed to do that? And if we can't answer those questions, then our customer is down, right? So when we're looking at a database, we're looking for reliability, we're looking for durability, disaster recovery, and then permission services are one of the only services that you usually don't shard geographically. So if you look at like AWS's iam, that's a global service, even though the individual things that they run are actually sharded by region. So we also needed a globally distributed database with all of those other properties. So that's what led us >>To, this is a huge topic. So man, we've been talking about all week the cloud is essentially distributed database at this point and it's distributed system. So distributed database is a hot topic, totally not really well reported. A lot of people talking about it, but how would you describe this distributed trend that's going on? What are the key reasons that they're driving it? What's making this more important than ever in your mind, in your opinion? >>I mean, for our use case, it was just a hard requirement, right? We had to be able to have this global service. But I think just for general use cases, a distributed database, distributed database has that like shared nothing architecture that allows you to kind of keep it running and horizontally scale it. And as your requirements and as your applications needs change, you can just keep adding on capacity and keep adding on reliability and availability. >>I'd love to get both of your opinion. You've been talking about the, the, the, the phases of customers, the advanced got Kubernetes going crazy distributed, super alpha geek. Then you got the, the people who are building now, then you got the lagers who are coming online. Where do you guys see the market now in terms of, I know the Alphas are all building all the great stuff and you guys had great success with all the top logos and they're all doing hardcore stuff. As the mainstream enterprise comes in, where's their psychology, what's on their mind? What's, you share any insight into your perspective on that? Because we're seeing a lot more of it folks becoming like real cloud players. >>Yeah, I feel like in mainstream enterprise hasn't been lagging as much as people think. You know, certainly there's been pockets in big enterprises that have been looking at this and as distributed sequel, it gives you that scalability that it's absolutely essential for big enterprises. But also it gives you the, the multi-region, you know, the, you have to be globally distributed. And for us, for enterprises, you know, you need your data near where the users are. I know this is hugely important to you as well. So you have to be able to have a multi-region functionality and that's one thing that distributed SQL lets you build and that what we built into our product. And I know that's one of the things you like too. >>Yeah, well we're a brand new product. I mean we only founded the company two years ago, but we're actually getting inbound interest from big enterprises because we solve the kinds of challenges that they have and whether, I mean, most of them already do have a cockroach footprint, but whether they did or didn't, once they need to bring in our product, they're going to be adopting cockroach transitively anyway. >>So, So you're built on top of Cockroach, right? And Spice dv, is that open source or? >>It >>Is, yep. Okay. And explain the role of open source and your business model. Can you take a minute to talk about the relevance of that? >>Yeah, open source is key. My background is, before this I was at Red Hat. Before that we were at CoreOS, so CoreOS acquisition and before that, >>One of the best acquisitions that ever happened for the value. That was a great, great team. Yeah, >>We, we, we had fun and before that we built Qua. So my co-founders and I, we built Quay, which is a, a first private docker registry. So CoreOS and, and all of those things are all open source or deeply open source. So it's just in our dna. We also see it as part of our go-to market motion. So if you are a database, a lot of people won't even consider what you're doing without being open source. Cuz they say, I don't want to take a, I don't want to, I don't want to end up in an Oracle situation >>Again. Yeah, Oracle meaning they go, you get you locked in, get you in a headlock, Increase prices. >>Yeah. Oh yeah, >>Can, can >>I got triggered. >>You need to talk about your PTSD there >>Or what. >>I mean we have 20,000 stars on GitHub because we've been open and transparent from the beginning. >>Yeah. And it >>Well, and both of your projects were started based on Google Papers, >>Right? >>That is true. Yep. And that's actually, so we're based off of the Google Zans of our paper. And as you know, Cockroach is based off of the Google Span paper and in the the Zanzibar paper, they have this globally distributed database that they're built on top of. And so when I said we're gonna go and we're gonna make a company around the Zabar paper, people would go, Well, what are you gonna do for Span? And I was like, Easy cockroach, they've got us covered. >>Yeah, I know the guys and my friends. Yeah. So the question is why didn't you get into the first round of Cockroach? She said don't answer that. >>The question he did answer though was one of those age old arguments in our community about pronunciation. We used to argue about Quay, I always called it Key of course. And the co-founder obviously knows how it's pronounced, you know, it's the et cd argument, it's the co cuddl versus the control versus coo, CTL Quay from the co-founder. That is end of argument. You heard it here first >>And we're keeping it going with Osted. So awesome. A lot of people will say Zeed or, you know, so we, we just like to have a little ambiguity >>In the, you gotta have some semantic arguments, arm wrestling here. I mean, it keeps, it keeps everyone entertained, especially on the over the weekend. What's, what's next? You got obviously Kubernetes in there. Can you explain the relationship between Kubernetes, how you're handling Spice dv? What, what does the Kubernetes piece fit in and where, where is that going to be going? >>Yeah, great question. Our flagship product right now is a dedicated, and in a dedicated, what we're doing is we're spinning up a single tenant Kubernetes cluster. We're installing all of our operator suite, and then we're installing the application and running it in a single tenant fashion for our customers in the same region, in the same data center where they're running their applications to minimize latency. Because of this, as an authorization service, latency gets passed on directly to the end user. So everybody's trying to squeeze the latency down as far as they can. And our strategy is to just run these single tenant stacks for people with the minimal latency that we can and give them a VPC dedicated link very similar to what Cockroach does in their dedicated >>Product. And the distributed architecture makes that possible because it's lighter way, it's not as heavy. Is that one of the reasons? >>Yep. And Kubernetes really gives us sort of like a, a level playing field where we can say, we're going going to take the provider, the cloud providers Kubernetes offering, normalize it, lay down our operators, and then use that as the base for delivering >>Our application. You know, Jake, you made me think of something I wanted to bring up with other guests, but now since you're here, you're an expert, I wanna bring that up, but talk about Super Cloud. We, we coined that term, but it's kind of multi-cloud, is that having workloads on multiple clouds is hard. I mean there are, they are, there are workloads on, on clouds, but the complexity of one clouds, let's take aws, they got availability zones, they got regions, you got now data issues in each one being global, not that easy on one cloud, nevermind all clouds. Can you share your thoughts on how you see that progression? Because when you start getting, as its distributed database, a lot of good things might come up that could fit into solving the complexity of global workloads. Could you share your thoughts on or scoping that problem space of, of geography? Yeah, because you mentioned latency, like that's huge. What are some of the other challenges that other people have with mobile? >>Yeah, absolutely. When you have a service like ours where the data is small, but very critical, you can get a vendor like Cockroach to step in and to fill that gap and to give you that globally distributed database that you can call into and retrieve the data. I think the trickier issues come up when you have larger data, you have huge binary blobs. So back when we were doing Quay, we wanted to be a global service as well, but we had, you know, terabytes, petabytes of data that we were like, how do we get this replicated everywhere and not go broke? Yeah. So I think those are kind of the interesting issues moving forward is what do you do with like those huge data lakes, the huge amount of data, but for the, the smaller bits, like the things that we can keep in a relational database. Yeah, we're, we're happy that that's quickly becoming a solved >>Problem. And by the way, that that data problem also is compounded when the architecture goes to the edge. >>Totally. >>I mean this is a big issue. >>Exactly. Yeah. Edge is something that we're thinking a lot about too. Yeah, we're lucky that right now the applications that are consuming us are in a data center already. But as they start to move to the edge, we're going to have to move to the edge with them. And it's a story that we're gonna have to figure out. >>All right, so you're a customer cockroach, what's the testimonial if I put you on the spot, say, hey, what's it like working with these guys? You know, what, what's the, what's the, you know, the founders, so you know, you give a good description, little biased, but we'll, we'll we'll hold you on it. >>Yeah. Working with Cockroach has been great. We've had a couple things that we've run into along the way and we've gotten great support from our account managers. They've brought in the right technical expertise when we need it. Cuz what we're doing with Cockroach is not you, you couldn't do it on Postgres, right? So it's not just a simple rip and replace for us, we're using all of the features of Cockroach, right? We're doing as of system time queries, we're doing global replication. We're, you know, we're, we're consuming it all. And so we do need help from them sometimes and they've been great. Yeah. >>And that's natural as they grow their service. I mean the world's changing. >>Well I think one of the important points that you mentioned with multi-cloud, we want you to have the choice. You know, you can run it in in clouds, you can run it hybrid, you can run it OnPrem, you can do whatever you want and it's just, it's one application that you can run in these different data centers. And so really it's up to you how do you want to build your infrastructure? >>And one of the things we've been talking about, the super cloud concept that we've been issue getting a lot of contrary, but, but people are leaning into it is that it's the refactoring and taking advantage of the services. Like what you mentioned about cockroach. People are doing that now on cloud going the lift and shift market kind of had it time now it's like hey, I can start taking advantage of these higher level services or capability of someone else's stack and refactoring it. So I think that's a dynamic that I'm seeing a lot more of. And it sounds like it's working out great in this situation. >>I just came from a talk and I asked them, you know, what don't you wanna put in the cloud and what don't you wanna run in Kubernetes or on containers and good Yeah. And the customers that I was on stage with, one of the guys made a joke and he said I would put my dog in a container room. I could, he was like in the category, which is his right, which he is in the category of like, I'll put everything in containers and these are, you know, including like mis critical apps, heritage apps, since they don't wanna see legacy anymore. Heritage apps, these are huge enterprises and they wanna put everything in the cloud. Everything >>You so want your dog that gets stuck on the airplane when it's on the tarmac. >>Oh >>God, that's, she was the, don't take that analogy. Literally don't think about that. Well that's, >>That's let's not containerize. >>There's always supply chain concern. >>It. So I mean going macro and especially given where we are cncf, it's all about open source. Do y'all think that open source builds a better future? >>Yeah and a better past. I mean this is, so much of this software is founded on open source. I, we wouldn't be here really. I've been in open source community for many, many years so I wouldn't say I'm biased. I would say this is how we build software. I came from like in a high school we're all like, oh let's build a really cool application. Oh you know what? I built this cuz I needed it, but maybe somebody else needs it too. And you put it out there and that is the ethos of Silicon Valley, right? That's where we grew up. So I've always had that mindset, you know, and social coding and why I have three people, right? Working on the same thing when one person you could share it's so inefficient. All of that. Yeah. So I think it's great that people work on what they're really good at. You know, we all, now you need some standardization, you need some kind of control around this whole thing. Sometimes some foundations to, you know, herd the cats. Yeah. But it's, it's great. Which is why I'm a c CF ambassador and I spend a lot of time, you know, in my free time talking about open source. Yeah, yeah. >>It's clear how passionate you are about it. Jake, >>This is my second company that we founded now and I don't think either of them could have existed without the base of open source, right? Like when you look at I have this cool idea for an app or a company and I want to go try it out, the last thing I want to do is go and negotiate with a vendor to get like the core data component. Yeah. To even be able to get to the >>Prototypes. NK too, by the way. Yeah. >>Hey >>Nk >>Or hire, you know, a bunch of PhDs to go and build that core component for me. So yeah, I mean nobody can argue that >>It truly is, I gotta say a best time if you're a developer right now, it's awesome to be a developer right now. It's only gonna get better. As we were riff from the last session about productivity, we believe that if you follow the digital transformation to its conclusion, developers and it aren't a department serving the business, they are the business. And that means they're running the show, which means that now their entire workflow is gonna change. It's gonna be have to be leveraging services partnering. So yeah, open source just fills that. So the more code coming up, it's just no doubt in our mind that that's go, that's happening and will accelerate. So yeah, >>You know, no one company is gonna be able to compete with a community. 50,000 users contributing versus you riding it yourself in your garage with >>Your dogs. Well it's people driven too. It's humans not container. It's humans working together. And here you'll see, I won't say horse training, that's a bad term, but like as projects start to get traction, hey, why don't we come together as, as the world starts to settle and the projects have traction, you start to see visibility into use cases, functionality. Some projects might not be, they have to kind of see more kind >>Of, not every feature is gonna be development. Oh. So I mean, you know, this is why you connect with truly brilliant people who can architect and distribute sequel database. Like who thought of that? It's amazing. It's as, as our friend >>You say, Well let me ask you a question before we wrap up, both by time, what is the secret of Kubernetes success? What made Kubernetes specifically successful? Was it timing? Was it the, the unambitious nature of it, the unification of it? Was it, what was the reason why is Kubernetes successful, right? And why nothing else? >>Well, you know what I'm gonna say? So I'm gonna let Dave >>First don't Jake, you go first. >>Oh boy. If we look at what was happening when Kubernetes first came out, it was, Mesosphere was kind of like the, the big player in the space. I think Kubernetes really, it had the backing from the right companies. It had the, you know, it had the credibility, it was sort of loosely based on Borg, but with the story of like, we've fixed everything that was broken in Borg. Yeah. And it's better now. Yeah. So I think it was just kind and, and obviously people were looking for a solution to this problem as they were going through their containerization journey. And I, yeah, I think it was just right >>Place, the timing consensus of hey, if we just let this happen, something good might come together for everybody. That's the way I felt. I >>Think it was right place, right time, right solution. And then it just kind of exploded when we were at Cores. Alex Povi, our ceo, he heard about Kubernetes and he was like, you know, we, we had a thing called Fleet D or we had a tool called Fleet. And he's like, Nope, we're all in on Kubernetes now. And that was an amazing Yeah, >>I remember that interview. >>I, amazing decision. >>Yeah, >>It's clear we can feel the shift. It's something that's come up a lot this week is is the commitment. Everybody's all in. People are ready for their transformation and Kubernetes is definitely gonna be the orchestrator that we're >>Leveraging. Yeah. And it's an amazing community. But it was, we got lucky that the, the foundational technology, I mean, you know, coming out of Google based on Go conferences, based on Go, it's no to coincidence that this sort of nature of, you know, pods horizontally, scalable, it's all fits together. I does make sense. Yeah. I mean, no offense to Python and some of the other technologies that were built in other languages, but Go is an awesome language. It's so, so innovative. Innovative things you could do with it. >>Awesome. Oh definitely. Jake, I'm very curious since we learned on the way and you are a Detroit native? >>I am. Yep. I grew up in the in Warren, which is just a suburb right outside of Detroit. >>So what does it mean to you as a Michigan born bloke to be here, see your entire community invade? >>It is, I grew up coming to the Detroit Auto Show in this very room >>That brought me to Detroit the first time. Love n a I a s. Been there with our friends at Ford just behind us. >>And it's just so interesting to me to see the accumulation, the accumulation of tech coming to Detroit cuz it's really not something that historically has been a huge presence. And I just love it. I love to see the activity out on the streets. I love to see all the restaurants and coffee shops full of people. Just, I might tear up. >>Well, I was wondering if it would give you a little bit of that hometown pride and also the joy of bringing your community together. I mean, this is merging your two probably most core communities. Yeah, >>Yeah. Your >>Youth and your, and your career. It doesn't get more personal than that really. Right. >>It's just been, it's been really exciting to see the energy. >>Well thanks for going on the queue. Thanks for sharing. Appreciate it. Thanks >>For having us. Yeah, thank you both so much. Lisa, you were a joy of ball of energy right when you walked up. Jake, what a compelling story. Really appreciate you sharing it with us. John, thanks for the banter and the fabulous questions. I'm >>Glad I could help out. >>Yeah, you do. A lot more than help out sweetheart. And to all of you watching the Cube today, thank you so much for joining us live from Detroit, the Cube Studios. My name is Savannah Peterson and we'll see you for our event wrap up next.

>>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.

>>Okay. And welcome back to the cubes live coverage here in San Francisco, California for 80 us summit 2022 Amazon web services summit 2020 New York city is coming up in the summer will be there. Check us out the cube.net. Our next guest here is Jim long. The CEO of dig also known as local. BTV a very interesting AWS customer doing some really progressive things around video and, uh, challenging the status quo in code cutting and all kinds of broadcast models. Jim, welcome to the cube. Great to see you. >>Thank you, John. Great to be here. Okay. >>So first of all, before we get into some of the disrupt option, take a minute to explain what is dig and local BTV. >>Uh, dig is all about, uh, providing, uh, edge video networking for broadcast television, basically modernizing local television and hopefully extending it to hyper local content like high schools and community government and community channels and things like that. So essentially free bringing, using the internet as an antenna to bring broadcast television to your phone, your laptop you're connected TVs. >>So if I understand it correctly, if I UN and I look at the, the materials of your site, you basically go into each market, Metro areas like New York Philly bay area, grab the tee signal out of the air. >>Yep. >>Local TV, and then open that up to everyone. Who's got, um, an >>Correct. And, uh, what, we've, where we're essentially building a hybrid network with AWS. Uh, I like to say we got all the smart and account stuff, you know, in the cloud at AWS. And we have all the dumb, fast stuff in the actual TV market. We have servers and transcoding there we work with, uh, of course, um, uh, AWS on that centrally as well. But basically that hybrid cloud allows us to be the fastest simplest and lowest cost way to get a local video. Any type could be an antenna or an IP stream to a local house. So we're, so are the local pickup and delivery people. We're not building a brand, we're not building content. We're delivering the local content to the local views. You >>Like the pipes. >>We are, we're essentially an infrastructure company. Um, we're right at that wonderful intersection of the, uh, the infrastructure and the content where I always like to play. >>I like, I love the store. I think the cost of that nature, how you're using Amazon, it's really impressive. Um, what are some of the cool things you're doing on AWS that you think's notable? >>Well, of course the, the standard issue stuff where you want to store all your data in the cloud. Right? So we, uh, and we use a quick site to, to get to that. And obviously we're using S3 and we're using media tailor, which we really like, which is cuz we first actual company on the planet. I believe that's inserting digital ads, impression based ads into local broadcast streams. So that's, that's fun because the advertisers, they like the fact that they could still do traditional TV buys and they could spice it up with digital impressions based, but ads on us. Yeah. And, and we're adding to it a real fun thing called clip it, which is user clipping. It's an app that's been running on AWS for years. It's had over half a million plays in social media. Yeah. We're combining those together and, and AWS makes it very simple to do that. >>Well, I've been using your app on my Firestick and uh, download local BTV on the app store. Um, I gotta say the calendar's awesome. And the performance is 10 times better than, than some of the other streaming apps because the other performance they crash all the time. The calendar's weird. So congratulations. Clearly you're running the cloud technology. I gotta ask you what's going on in the market? Netflix missed their earnings. The stock was down big time. Um, obviously competition what's up going on with Netflix? >>Well, what's, it's a big shift. >>What does it mean for the streaming market? >>Well, what it means is, is, is a consumer choice. It's really the golden age of consumer choice. Uh, originally back when I was a kid, it was all antenna TV. We didn't even have DBRS right. And then, uh, the cable companies and the satellite companies, the phone companies came in and took over and all of a sudden everyone started paying for TV for just linear TV. Right? And then the next thing, you know, streaming comes around, uh, Netflix shows up for, for VOD or, or SVOD, they call it cuz it's payt TV and uh, and the whole, uh, that ecosystem starts to melt down. And now you have a consumer choice market where you can pay, pay for VAD or pay for, for linear. And everyone does linear and everyone does VAD or you can use free TV. Now we correctly guessed that free TV was gonna have a huge comeback. You know, know what is it about free even obviously gen Z smarter than us boomers. They love free too. Uh, targeted advertising makes the ads less, uh, painful or less of a distraction. Uh, so we knew that free ad supported TV was gonna happen. Lots of stuff happened. And then, then the, uh, major media companies started doing their own subscription apps. Right? They're all cool. >>We like paramount plus >>Paramount plus Disney pluses, PN peacock, uh, time Warner's doing something. I mean, it's all cool, but you know, people only have so much of a big pocketbook. So what it's doing is pay TV has now become much more complicated, but also you, you know, you gotta trade off. So you saw it with Netflix, right? Yeah. Netflix is suffering from there's too much pay TV. So where are you gonna put your money on Comcast? On YouTube TV paramount plus Netflix. >>Yeah. I mean, I love the free thing. I gotta bring up something. I wanna get your reaction to a company called low cast went under, they got sued out of their deal. They were the free TV. Are you guys have issues like them? What's the cast most people don't know got was, was >>Doing same. So we started before low cast and we're uh, what we would call a permissions based system, legal system. The broadcast Mar industry, uh, is, uh, is the wild wild west. I mean, I like to say antenna TV is a direct to consumer. The antenna is a direct to consumer device and it's controlled by the channel. People it's not controlled by a platform like Comcast, right? It's not controlled by a stick. >>When you say channel, do you mean like CBS or >>Yeah, CBS or the local Korean religious cooking channel or, uh, Spanish channels or local independent to television, which is really a national treasure for us. The United States really should be making sure that local content, local channels, uh, do well local businesses, you know, with targeted advertising, Janes nail salon can, can now advertise just in San Jose and not the entire San Francisco TV market. Um, so you ha you have, have all that going on and we recognize, you know, that, that local content, but you have to have permission from the channel stuff. It's not easy because you got channels on stations. You have syndicators, it's hard to keep track of. And sometimes you, you, uh, you, you know, you have to shift things around, but, uh, low cast, uh, like another kind before it just went hog wild, illegal, trying to use a loophole, uh, didn't quite work out for 'em and, uh, >>You see, they have put out of business by the networks, the names, the big names. Yes. Content people, >>Correct. I mean the big, the big guys, but I mean, because they weren't following the rules, um, >>The rules, meaning license, the content, right. >>Well correct. Or yes, >>Basically they, they were stealing the content in the eyes of the, >>Well, there is, there is, it is a little of, a bit of a gray area between the FCC and the copyright laws that Congress made. So, um, there are people certainly out there that think there is a path there, low cast, didn't find it. We're not trying to find it. Uh, we just want to get all the free TV, uh, the bottom line. And you've seen fast channels explode recently, Pluto, uh, Samsung TV. >>And what does that all mean? >>Well, what it means is people love free TV and the best free TV out there is your local TV. So putting that on the internet and those comp, but the media companies, they have trouble with this new stuff. What's, >>What's your >>They're overthinking it. What's >>Some of this CBS, NBC, all these big guys. >>Well, those guys have a little less trouble than the people that actually, uh, they're affiliates, right? So there's 210 TV markets and the, uh, your major networks, you know, they have their own stations. And in a bit, you know, in about 39% of the population, which is about 15 to 20, is it >>Cultural or is a system system problem? >>No, it's a, it's a problem of all the, the media companies are just having trouble moving towards the new technology and, and they're, I think they're siloing it. >>So why not? You gonna let 'em die. Are you trying to do deals with em? >>Oh no, no, absolutely. For us, if we don't make money, unless stations make money, we want local TV to, to flourish. It is local TV is Neilson, just report yesterday, you know, uh, that, uh, local TV is growing. We're taking advantage of that. And I think the station groups are having a little trouble realizing that they have the original, fast channels before Pluto, before Tubi did it in movies. And, and, and what >>Are people understanding in the, in the industry? I know NA's coming up a show. Yeah, >>That's right. >>National associated of broadcasters. What's going on in that industry right now. And you're, if you get to put it down the top three problems that are opportunities to be solved, what would they be? >>Well, I think, you know, I think the, the, the, the last, the, the best one that's left is what we're doing. I have to say it, uh, I think it's worth billions. >>You free TV over the air free and stream >>O TV. Oh yeah. Over the air TV that also works with the internet, right. Public internet connected to public television stations so that everybody, including homeless people, et cetera, that, you know, they don't have a TV, they don't have an antenna, they can't afford comp. They got an >>IPhone though. >>They an iPhone. For sure. And, and so it's, it's, uh, it's a wonderful thing. It's, you know, our national broadcasting and I don't think the station groups or the major networks are taking advantage of it they're as much as they should. Yeah. And, and I don't think, you know, obviously NBC and CBS with their new apps, they're sort of done with that. They did mergers, they got, they got the virtual pay guys. I mean, YouTube TV off the ground, the only thing left is suck another shitload of good, uh, eyeballs and, and advertising. >>Well, I mean, yeah, I think that, that, and what you said earlier around subscription fatigue, I mean, nobody wants to have 20 subscriptions. >>Well, that brings up a whole new other war. That's going on that, thank goodness. We're not part of it's the platforms versus the cable companies. Right. Versus whatever. Right. Everyone's trying to be your open garden or your closed garden. They're trying to get your subscriptions in bundle self bundling it's. But I mean, it's wonderful for consumers, if you can navigate through it. Uh, we wanna, we think we'll have one of the gems in any of that everyone's want local TV. And so we'll supply that we're already doing that. We're supplying it to a couple companies, uh, free cast as a company, uh, app, a universal streaming, you know, manager, your all, all your, uh, streaming, a streaming aggregation, put your paid stuff in, put your free stuff in. They do that. And, and as, as does Roku try trying to do that fire TV, Xfinity's trying to do it. So it's all, it's a new war for the platform and hopefully we'll be on everyone. >>Well, you've been in this industry for a long time, you know, the streaming market, you know, the TV market. Um, so it's, it's good. I think it's a new battle, the shift's happening. Um, what should people know about dig local? BTV what are some of your goals for the next year or two? What are you trying to do? >>Well, what we're really trying to do is make sure that local, uh, local television thrives so that it can support wider communities. It could support hyper local content. So if you're, if you're, and we love the old paradigm and channel change, right? Forget, you know, every other app has all these boxes going by on different rows and stuff. And, and yeah, you can search and find stuff, but there's nothing like just changing channels, whether a commercial's on or, or you, you wanna see what else is on. You know, you're gonna go from local television and maybe all of a sudden, you'll see the local high school play over on another part of the, of the spectrum. And, and what we're trying to do is get those communities together. And the local high school people come over and find the local, you know, uh, Spanish, uh, Nova channel or something like that. >>So local is the new hot. >>It is. Absolutely. And by the way, it's where this high CPMs are gonna go. And the more targeted you get >>Ad revenue, >>I mean, that's for us is, is, is our number one, re we have a number of revenue streams, but targeted ads are really great for local, right? And, and so we're, we're gonna make an announce. We've >>Lost that we've lost that local, I've seen local things that local Palo Alto paper, for instance, just shut down this local sports high school coverage, our youth sports, because they don't budget, right? There's no TV community channels, like some Comcast throwaway channel. Um, we lost, we, we lo we're losing >>Local. No, I think that's a real national shame. And so I think if we can strengthen local television, I think it'll strengthen all local media. So we expect to help local radio and local newspapers. That's a bigger part of the vision. Uh, but I it's gonna happen. There's >>An education angle here too. >>There is an education angle because the bottom line is you can use linear television as a way to augment. Uh, we have a really exciting project going on in New York, uh, uh, with, uh, some of the housing, uh, projects, uh, in Harlem and, and, and the Bronx, uh, their I idea is to have the, the homework channel and they can, and literally when you have a, and both swiping and everything you can have, I mean, literally you can have a hundred schools that, that have things well, >>We know zoom schooling sucks. I mean, that didn't work. So I think you're gonna see a lot of augmentation, right. >>Amazon. >>I was just talking to some people here, AI training, machine learning, training, all here could be online in linear format. >>Yeah. And exactly. And then I think about the linear format is it's discovery television, and you can also, um, you know, you can also record it. Yeah. Right. If you see a program and you want to record it, you sit >>Record. So final minute we have left. I want to just get your thoughts on this one thing and, and ask your question. Are you looking for content? Are you, I outreach at the content providers who, >>Well, we're, we're PRI our primary mission is to get more channel local channels on which really means station groups and independence. We have a number, I mean, basically 50% of the channels in any market. When we move into it are like, this is a no-brainer. I want more eyeballs. We're Nielsen, uh, RA, uh, rated mean we support. And so we, >>How many markets are you in right now? >>We're in 21 now. And we hope to be in, uh, over 50 by the end of the year, covering more than half the United States. >>So, all right, Jim, thanks for coming on the queue. Really appreciate it. >>My pleasure. Good luck >>Recognition. Very disruptive disrupting media, um, combination of over the air TV, local with I internet. Obviously we love that with a cube. We want a cube channel anywhere possible. I'm John furry host of the queue here at AWS summit. Highing all the big trends and technologies in cloud and media back with more coverage after this short break,

(upbeat music) >> We're back. This is Dave Vellante of theCUBE, and we're here with Ray Wang, who just wrote a book reminiscent of the famous Tears for Fears song, Everybody Wants to Rule the World: Surviving and Thriving in a World of Digital Giants. Ray, great to see again, man. >> What's going on, man, how are you? >> Oh great, thanks for coming on. You know, it was crazy, been crazy, but it's good to see you face-to-face. >> Ray: This is, we're in the flesh, it's live, we're having conversations, and the information that we're getting is cut right. >> Dave: Yeah, so why did you write this book and how did you find the time? >> Hey, we're in the middle of pandemic. No, I wrote the book because what was happening was digital transformation efforts, they're starting to pop up, but companies weren't always succeeding. And something was happening with digital giants that was very different. They were winning in the marketplace. And never in the form of, if you think about extreme capitalism, if we think about capitalism in general, never in the history of capitalism have we seen growth of large companies. They get large, they fall apart, they don't have anything to build, they can't scale. Their organizations are in shambles. But what happened? If you look at 2017, the combined market cap of the FAANGs and Microsoft was 2 trillion. Today, it is almost 10.2 trillion. It's quintupled. That's never happened. And there's something behind that business model that they put into place that others have copied, from the Airbnbs to the Robloxes to what's going to happen with like a Starlink, and of course, the Robinhoods and you know, Robinhoods and Coinbases of the world. >> And the fundamental premise is all around data, right? Putting data at the core, if you don't do that, you're going to fly blind. >> It is and the secret behind that is the long-term platforms called data-driven digital networks. These platforms take the ability, large memberships, our large devices, they look at that effect. Then they look at figuring out how to actually win on data supremacy. And then of course, they monetize off that data. And that's really the secret behind that is you've got to build that capability and what they do really well is they dis-intermediate customer account control. They take the relationships, aggregate them together. So food delivery app companies are great example of that. You know, small businesses are out there that hundreds and thousands of customers. Today, what happens? Well, they've been aggregated. Millions of customers together into food delivery app. >> Well, I think, you know, this is really interesting what you're saying, because if you think about how we deal with Netflix, we don't call the Netflix sales department or the marketing department of the service, just one interface, the Netflix. So they've been able to put data at their core. Can incumbents do that? How can they do that? >> Incumbents can definitely do that. And it's really about figuring out how to automate that capture. What you really want to do is you start in the cloud, you bring the data together, and you start putting the three A's, analytics, automation, and AI are what you have to be able to put into place. And when you do do that, you now have the ability to go out and figure out how to create that flywheel effect inside those data-driven digital networks. These DDDNS are important. So in Netflix, what are they capturing? They're looking at sentiment, they're looking at context. Like why did you interact with, you know, one title versus another? Did you watch Ted Lasso? Did you switch out of Apple TV to Netflix? Well, I want to know why, right? Did you actually jump into another category? You switched into genres. After 10:00 p.m., what are you watching? Maybe something very different than what you're watching at 2:00 p.m.. How many members are in the home, right? All these questions are being answered and that's the business graph behind all this. >> How much of this is kind of related to the way organizations or companies are organized? In other words, you think about, historically, they would maybe put the process at the core or the, in a bottling plant, the manufacturing facility at the core and the data's all dispersed. Everybody talks about silos. So will AI be the answer to that? Will some new database, Snowflake? Is that the answer? What's the answer to sort of bringing that data together and how do you deal with the organizational inertia? >> Well, the trick to it is really to have a single plane to be able to access that data. I don't care where the data sits, whether it's on premise, whether it's in the cloud, whether it's in the edge, it makes no difference. That's really what you want to be able to do is bring that information together. But the glue is the context. What time was it? What's the weather outside? What location are you in? What's your heart rate? Are you smiling, right? All of those factors come into play. And what we're trying to do is take a user, right? So it could be a customer, a supplier, a partner, or an employee. And how do they interact with an order doc, an invoice, an incident, and then apply the context. And what we're doing is mining that context and information. Now, the more, back to your other point on self service and automation, the more you can actually collect those data points, the more you can capture that context, the more you're able to get to refine that information. >> Context, that's interesting, because if you think about our operational systems, we've contextualized most of them, whether it's sales, marketing, logistics, but we haven't really contextualized our data systems, our data architecture. It's generally run by a technical group. They don't necessarily have the line of business context. You see what HPE is doing today is trying to be inclusive of data on prem. I mentioned Snowflake, they're saying no way. Frank Slootman says we're not going on prem. So that's kind of interesting. So how do you see sort of context evolving with the actually the business line? Not only who has the context actually can, I hate to use the word, but I'm going to, own the data. >> You have to have a data to decisions pathway. That data decisions pathway is you start with all types of data, structured, unstructured, semi-structured, you align it to a business process as an issue, issue to resolution, order to cash, procure to pay, hire to retire. You bring that together, and then you start mining and figuring out what patterns exist. Once you have the patterns, you can then figure out the next best action. And when you get the next best action, you can compete on decisions. And that becomes a very important part. That decision piece, that's going to be automated. And when we think about that, you and I make a decision one per second, how long does it get out of management committee? Could be a week, two weeks, a quarter, a year. It takes forever to get anything out of management committee. But these new systems, if you think about machines, can make decisions a hundred times per second, a thousand times per second. And that's what we're competing against. That asymmetry is the decision velocity. How quickly you can make decisions will be a competitive weapon. >> Is there a dissonance between the fact that you just mentioned, speed, compressing, that sort of time to decision, and the flip side of that coin, quality, security, governance. How do you see squaring that circle? >> Well, that's really why we're going to have to make that, that's the automated, that's the AI piece. Just like we have all types of data, we got to spew up automated ontologies, we got to spit them up, we got to be using, we've got to put them back into play, and then we got to be able to take back into action. And so you want enterprise class capabilities. That's your data quality. That's your security. That's the data governance. That's the ability to actually take that data and understand time series, and actually make sure that the integrity of that data is there. >> What do you think about this sort of notion that increasingly, people are going to be building data products and services that can be monetized? And that's kind of goes back to context, the business lines kind of being responsible for their own data, not having to get permission to add another data source. Do you see that trend? Do you see that decentralization trend? Two-part question. And where do you see HPE fitting into that? >> I see, one, that that trend is definitely going to exist. I'll give you an example. I can actually destroy the top two television manufacturers in the world in less than five years. I could take them out of the business and I'll show you how to do it. So I'm going to make you an offer. $15 per month for the next five years. I'm going to give you a 72 inch, is it 74? 75 inch, 75 inch smart TV, 4k, big TV, right? And it comes with a warranty. And if anything breaks, I'm going to return it to you in 48 hours or less with a brand new one. I don't want your personal information. I'm only going to monitor performance data. I want to know the operations. I want to know which supplier lied to me, which components are working, what features you use. I don't need to know your personal viewing habits, okay? Would you take that deal? >> TV is a service, sure, of course I would. >> 15 bucks and I'm going to make you a better deal. For $25 a month, you get to make an upgrade anytime during that five-year period. What would happen to the two largest TV manufacturers if I did that? >> Yeah, they'd be disrupted. Now, you obviously have a pile of VC money that you're going to do that. Will you ever make money at that model? >> Well, here's why I'll get there and I'll explain. What's going to happen is I lock them out of the market for four to five years. I'm going to take 50 to 60% of the market. Yes, I got to raise $10 billion to figure out how to do that. But that's not really what happens at the end. I become a data company because I have warranty data. I'm going to buy a company that does, you know, insurance like in Asurion. I'm going to get break/fix data from like a Best Buy or a company like that. I'm going to get at safety data from an underwriter's lab. It's a competition for data. And suddenly, I know those habits better than anyone else. I'm going to go do other things more than the TV. I'm not done with the TV. I'm going to do your entire kitchen. For $100 a month, I'll do a mid range. For like $500 a month, I'm going to take your dish washer, your washer, your dryer, your refrigerator, your range. And I'll do like Miele, Gaggenau, right? If you want to go down Viking, Wolf, I'll do it for $450 a month for the next 10 years. By year five, I have better insurance information than the insurance companies from warranty. And I can even make that deal portable. You see where we're going? >> Yeah so each of those are, I see them as data products. So you've got your TV service products, you've got your kitchen products, you've got your maintenance, you know, data products. All those can be monetized. >> And I went from TV manufacturer to underwriter overnight. I'm competing on data, on insurance, and underwriting. And more importantly, here's the green initiative. Here's why someone would give me $10 billion to do it. I now control 50% of all power consumption in North America because I'm also going to do HVAC units, right? And I can actually engineer the green capabilities in there to actually do better power purchase consumption, better monitoring, and of course, smart capabilities in those, in those appliances. And that's how you actually build a model like that. And that's how you can win on a data model. Now, where does HPE fit into that? Their job is to bring that data together at the edge. They bring that together in the middle. Then they have the ability to manage that on a remote basis and actually deliver those services in the cloud so that someone else can consume it. >> All right, so if you, you're hitting on something that some people have have talked about, but it's, I don't think it's widely sort of discussed. And that is, historically, if you're in an industry, you're in that industry's vertical stack, the sales, the marketing, the manufacturing, the R&D. You become an expert in insurance or financial services or whatever, you know, automobile manufacturing or radio and television, et cetera. Obviously, you're seeing the big internet giants, those 10 trillion, you know, some of the market caps, they're using data to traverse industries. We've never seen this before. Amazon in content, you're seeing Apple in finance, others going into the healthcare. So they're technology companies that are able to traverse industries. Never seen this before, and it's because of data. >> And it's the collapsing value chains. Their data value chains are collapsing. Comms, media, entertainment, tech, same business. Whether you sell me a live stream TV, a book, a video game, or some enterprise software, it's the same data value stream on multi-sided networks. And once you understand that, you can see retail, right? Distribution, manufacturing collapsed in the same kind of way. >> So Silicon Valley broadly defined, if I can include, you know, Microsoft and Amazon in there, they seem to have a dual disruption agenda, right? One is on the technology front, disrupting, you know, the traditional enterprise business. The other is they're disrupting industries. How do you see that playing out? >> Well the problem is, they're never going to be able to get into new industries going forward because of the monopoly power that people believe they have, and that's what's going on, but they're going to invest in creating joint venture startups in other industries, as they power the tools to enable other industries to jump and leap frog from where they are. So healthcare, for example, we're going to have AI in monitoring in ways that we never seen before. You can see devices enter healthcare, but you see joint venture partnerships between a big hyperscaler and some of the healthcare providers. >> So HPE transforming into a cloud company as a service, do you see them getting into insurance as you just described in your little digital example? >> No, but I see them powering the folks that are in insurance, right? >> They're not going to compete with their customers maybe the way that Amazon did. >> No, that's actually why you would go to them as opposed to a hyperscale that might compete with you, right? So is Google going to get into the insurance business? Probably not. Would Amazon? Maybe. Is Tesla in the business? Yeah, they're definitely in insurance. >> Yeah, big time, right. So, okay. So tell me more about your book. How's it being received? What's the reaction? What's your next book? >> So the book is doing well. We're really excited. We did a 20 city book tour. We had chances to meet everybody across the board. Clients we couldn't see in a while, partners we didn't see in a while. And that was fun. The reaction is, if you read the book carefully, there are $3 trillion market cap opportunities, $1000 billion unicorns that can be built right there. >> Is, do you have a copy for me that's signed? (audience laughing) >> Ray: Sorry (coughs) I'm choking on my makeup. I can get one actually, do you want one? >> Dave: I do, I want, I want one. >> Can someone bring my book bag? I actually have one, I can sign it right here. >> Dave: Yeah, you know what? If we have a book, I'd love to hold it. >> Ray: Do you have any here as well? >> So it's obviously you know, Everybody Wants to Rule the World: Surviving and Thriving in a world of Digital Giants, available, you know, wherever you buy books. >> Yeah, so, oh, are we still going? >> Dave: Yeah, yeah, we're going. >> Okay. >> Dave: What's the next book? >> Next book? Well, it's about disrupting those digital giants and it's going to happen in the metaverse economy. If we think about where the metaverse is, not just the hardware platforms, not just the engines, not just what's going on with the platforms around defy decentralization and the content producers, we see those as four different parts today. What we're going to actually see is a whole comp, it's a confluence of events that's going to happen where we actually bring in the metaverse economy and the stuff that Neal Stephenson was writing about ages ago in Snow Crash is going to come out real. >> So, okay. So you're laying out a scenario that the big guys, the disruptors, could get disrupted. It sounds like crypto is possibly a force in that disruption. >> Ray: Decentralized currencies, crypto plays a role, but it's the value exchange mechanisms in an Algorand, in an Ether, right, in a Cardano, that actually enables that to happen because the value exchange in the smart contracts power that capability, and what we're actually seeing is the reinvention of the internet. So you think, see things like SIOM pop-up, which actually is creating the new set of the internet standards, and when those things come together, what we're actually going to move from is the seller is completely transparent, the buyer's completely anonymous and it's in a trust framework that actually allows you to do that. >> Well, you think about those protocols, the internet protocols that were invented whenever, 30 years ago, maybe more, TCP/IP, wow. I mean, okay. And they've been co-opted by the internet giants. It's the crypto guys, some of the guys you've mentioned that are actually innovating and putting, putting down new innovation really and have been well-funded to do so. >> I mean, I'll give you another example of how this could happen. About four years ago, five years ago, I wanted to buy Air Canada's mileage program, $400 million, 10 million users, 40 bucks a user. What do I want them in a mileage program? Well think about it. It's funded, a penny per mile. It's redeemed at 1.6 cents a mile. It's 2 cents if you buy magazines, 2 1/2 cents if you want, you know, electronics, jewelry, or sporting equipment. You don't lose money on these. CFOs hate them, they're just like (groans) liability on the books, but they mortgage the crap out of them in the middle of an ish problem and banks pay millions of dollars a year pour those mileage points. But I don't want it for the 10 million flyers in Canada. What I really want is the access to 762 million people in Star Alliance. What would happen if I turned that airline mileage program into cryptocurrency? One, I would be the world's largest cryptocurrency on day one. What would happen on day two? I'd be the world's largest ad network. Cookie apocalypse, go away. We don't need that anymore. And more importantly, on day three, what would I do? My ESG here? 2.2 billion people are unbanked in the world. All you need is a mobile device and a connection, now you have a currency without any government regulation around, you know, crayon banking, intermediaries, a whole bunch of people like taking cuts, loansharking, that all goes away. You suddenly have people that are now banked and you've unbanked, you've banked the unbanked. And that creates a whole very different environment. >> Not a lot of people thinking about how the big giants get disintermediated. Get the book, look into it, big ideas. Ray Wang, great to see you, man. >> Ray: Hey man, thanks a lot. >> Hey, thank you. All right and thank you for watching. Keep it right there for more great content from HPE's big GreenLake announcements. Be right back. (bright music)

(cheery synth music) >> Hello, this is theCUBE. I'm John Furrier, your host. We're here for a KubeCon CloudNativeCon preview for the North America show in Los Angeles, here in person and a virtual event. Two of the co-chairs are with me again this year, Constance Caramanolis, principal engineer at Splunk, and of course, Stephen Augustus, head of Open Source at Cisco. Great to see you guys. Hey, thanks for coming on, virtually, for the preview. >> Great to be had! >> Constance: Thank you for having us. >> Stephen: Great to see you again John. (laughing) >> Constance: Yeah. >> So I love... well, KubeCon has gotten, It's my favorite event every year. This is where the DevOps actually, where the people are reading the tea leaves, connecting the dots, but also meeting up and doing what communities do best, which is set the agenda for the next, next generation that's happening in person. Last year, it was virtual. We had the European virtual KubeCon, CloudNativeCon. This year a mix. Give us a taste of updates that you want to share. Let's get, let's get into it. >> Sure. Uh, so I think, you know, um, I-I-I think uh, seeing this event in particular and uh, you know, one, we've got this, we've got this hopeful r-return to you know, some semblance of normalcy. I know that you know, over the last year and change, we've been uh, we've been kind of itching t-t-to see each other in person. And, and you know, and, and I-I think I say on a lot of uh, interviews that I, you know, one of my favorite parts of any conference is the, is the hallway track, right? It's really hard to, and, and we've- we've made, you know, we've made strides to replicate it, but there's- I don't think there's anything uh, you know, close t-to being in person, right? And, and getting to, to bounce i-ideas off of uh, your, your co-conspirators, (laughs) co-conspirators or compatriots. Um, so I'm- I'm really excited for that, um, I love the, I love the um, the mandates that we've put in place, uh, to make sure that people are uh, a little bit more safe. Um, and, you know, overall, like seeing uh- I-I think one of the things that gets me most excited is the, is the uh, the set of day zero events, right? Um, I-I think the, the increase in the uh, day zero events, we, we've got uh, Constance, what's the, what's the count at now? I'm, I'm looking over it and, and it's uh, it's, it's massive, right? You know, SupplyChainSecurityCon, Uh, the, you know, the Cloud Native for Eclipse Foundation, it's beyond, >> Too, hmm, too many to count right off the bat when I'm looking at it. >> Too many, too many to count! >> And it's also like, this is a reduced number because some people decide or some, not people, like projects, decide to do virtual uh, days or a non-conference outside of the normal KubeCon cycle because of... >> Yeah, well, let's get, let's get- >> that thing that should not be named. >> Let's get into some of the data. >> I want to jump into the trends. But just for the folks watching, this is a hybrid event, and- >> Yeah. >> There's going to be this day zero, which is the pre-programming. Which by the way, I think has evolved into a format that's just tremendous. You got the pregame, pre-event action. Very dynamic, very ad-hoc, ephemeral in the, in the, in the, in the, in the people getting together and making things happen. Then you got the structured event. It's uh, the 11th to the 12th on the pre-programming, day zero stuff, which you talked about, and then the 13th to the 15th, the main conference. It's in-person and virtual, so it's going to be a hybrid event, which should be dynamic because you have an in-person dynamic where it's a scarce resource of the face-to-face, working and trying to create synchronicity with the asynchronous environment on virtuals. So it should be an action packed and a must-watch event. So I'm personally excited, we'll be there in person. But I got to ask you guys, the co-chairs, how are you guys handling this? How are the papers coming, what's the call for talks? How are you structuring things? Can you just give a quick overview of what's, what's happening on the talks? >> Uh, talks, uh, I feel like it went really well this round. >> Um, really like, wide variety. I know it's pretty vague, but there's a wide variety of topics, uh, things that are getting I think, I feel like more popularity, like security is getting more popular. Uh, business value, one thing that I'm really passionate about, is getting a lot more traction. Uh, student track 101 is also, as always, I guess, as ever since it's been, since inception has been popular, um, it's definitely getting to the point where we're actually, well not to the point, but maybe it's just being more highlighted that a lot of the, like, like, some of the like great content from the day zeros are also showing up in KubeCon and then like, vice versa and they're kind of everywhere. Uh, Yeah, the talks I think was really- >> John: The sessions, the sessions are always driving it. Stephen I'm like from a, from a, from a maturisation standpoint, you have the, the, the people developing and then you got the f... the things are getting hardened. Can you talk about the trends around, what's kind of hardening out from a project basis on these sessions and what's forming relative to the trend line this year. >> Yeah. So, you know, so to Constance's point, I think that we're, we're starting to see some diversity in, or continued diversity and kind of the personas that are coming into the conference, right? So whether you're talking about that continuing 101 track or, the student track, which, you know, a lot of people have, have kind of jumped in and seeing that as an opportunity to, to, to not only start becoming part of the community, but also to immediately contribute to content. And then you've got that For me? It's, it's security, all day, right? I think, you know, I think that, you know, there's not a week, there's not a week that passes that I don't have a chat with someone around what's happening in security lately. And I think you'll see that highlighted in in all of the keynotes that we have planned there are, there's not one, not two, but three uh, keynotes around software supply chain security, and some of the different things that you have to consider as we're kind of walking into the space of you know, protecting, protecting your, your build pipeline, protecting your production artifacts, so that's something that really, you know, that goes to that, you know, that goes to my work on that, you know, in Kubernetes for SIG release, release engineering, that's, you know, something that we, we know that there are countless downstream consumers, right? So, some, you know, some that we may not have even had contact with yet from the upstream perspective, right? So it's, it's paramount for us to make sure that, you know, everything that we're pushing out to the community and to the wider world is safe to consume. So, so security is definitely top of mind for me. I would say for, you know, lots of things around you know, continue, continuing to talk about uh, GitOps observability. And I think, and I think that, you know, each of these, what's, you know, what's fun about um, each of these, uh, the, each of these topics, each of these areas is that they're all interconnected, right? So more and more you're seeing, you're seeing, oh, well, you know, the, you know, the Tekton folks are, you know, are talking to the Flux folks. And, and they're talking to the, the folks who are working on uh, Sigstore and Rekor and, and, and all of these fun tools about how to integrate into, you know, how to integrate into those respective areas. Um, so it's, it's, it's really a time of um, collaboration underscored by um, you know, protecting, protecting the community and the, and the end users. >> John: Yeah. We're seeing a lot of ah, um, you know, the security discussions. I mean, how far can you shift left before it becomes like standard, right? So like, you know, we're seeing that being built in. I got to ask you guys also on the trend of DevOps there's been a lot of conversations around Cloud Native, around obsolete management and in terms of ability, but data, the role of data has been different approaches on how people are leveraging machine learning and AI, can you, did that come up a lot in, in some of the, the discussions and the analysis? Because everyone's slapping machine learning on things these days, and there's a little bit of that going on, but it seems to be data and machine learning and horizontal scale, classic DevOps, things are happening. What's your reaction to, to some of those things that are happening? Can you guys, is there anything happening there? >> I feel like this year wasn't that big of a machine learning year in terms of submissions. >> Yes. >> I'm certain you agree with that, but it wasn't, as I think, like, security took a lot and, and, like, and this might also just be like, thinking about it holistically now, like security was, had such amazing submissions that it probably took a little bit of the spotlight off of when we were looking at the machine learning ones. Um... >> John: So security... >> Also I'm biased, so I think >> John: So security dominated more than, than everyone else did. >> Yeah. I think, you know, I think for this year, security is, security is dominating. I, you know, I think we even talked about this in the last uh, chat we had, um, the, you know, kind of from the AI side, I think you're, we're, we're running, there have been discussions around the, uh, you know, bias in, in AI models and um, you know, how we work through that, um, I'm not sure that we have any content for that this time around, but I think it, yeah, but I think, you know, as we start to talk about like how we collect data, you know, are, are we collecting the right types of data, how we serve it, especially as a, those relate to like collecting data at the edge, right? Like, how do we, how do we, how, how do we even deploy applications at the edge? We, we have a lot of potential solutions for that. But when you combine that with, well, how do we, how do we scrape information from the things that we're deploying from the edge, right? Or, or, or some, some of the things you'll see in the, in the program. >> Constance and Stephen, talk about the community vibe right now, because you know, that's the biggest part of this conference is seeing how the people come together, but it's also the vibe sets the tone. What's, what's the current vibe in the community that you're seeing and what do we expect this year at KubeCon, CloudNativeCon? >> Yeah, I'm going to say, I imagine the community's tired and it's been a long few, two years. It feels like 10 years, it feels like forever. And a lot of the in-person aspect that used to be like social validation, we just get like is lacking, so, but that being said, there's still been amazing, like collaboration from like the open, from like the Observability and Open Telemetry part. Like, I am seeing so many projects within the tag Observability collaborate together and making that a focus. And so even though we are tired, it's still, we're still doing good work. And we're still making a point of trying to keep that community tight even though it's much harder on Zoom and right, you know, it's going to try and do the awkward, like Zoom handshake. It just doesn't do the same thing there. But to Stephen's keynote, can't remember how long ago it is, about like resiliency. We are pretty resilient. And we're also, I think we're all learning to work at a slower pace because maybe we were working too fast beforehand. And I think that, I think that's a really good takeaway from all of this. So I think it's going to, for as safe as it can be to have some variation, it's probably going to just be like, it's going to be a big party because we're going to finally get to see each other after a long time then. >> John: Yeah. >> I hope we get to do that in a safe way. >> Stephen, you bring it in, Steve, you go. Oh, Steve, you always got the energy certainly on camera, but in person as well. >> (laughs) >> This in-person dynamic this year is huge. >> Yeah, we, >> Wh-what do you think is going to happen? What, give us your take. >> Yeah, so I mean, I, you know, I would echo Constance in saying that, you know, we're, we're, we're all tired, we're all very tired at this point. Um, but I, you know, but, they, they, the conference tagline for, for North America is, uh, is 'Resilience Realized', right? I think that, you know, throughout this, this year, um, the, the contributors, maintainers of, of all of these, you know, CNCF projects have made incredible strides uh, to empower the communities to, to, uh, to be together, to be family, to, to work better together, um, in spite of, you know, in spite of uh, location, location uh, boundaries, in spite of, you know, uh, uh, health concerns, like we've, we've really made the effort to um, to show up for each other. Um, so I think that, you know, what we'll see in the conference and, and, you know, one of my favorite tracks personally um, is the, the community track, um, so lots of, lots of content around, you know, a-around community building, around uh, I think more of the, the meta of, of maintaining communities, right? So the, you know, the, the, the, the code of conduct committee, as well as uh, steering committee uh, for Kubernetes got together um, last conference to, to talk about the values and principles of the community, right? And, and I think that, you know, that, that needs to continue to be highlighted, um, you know, some of the conversations that we've had around um, how you maintain groups, you know, how do you maintain groups, especially as um, especially as a, the, the, the size of the group grows, right? Once you escape that kind of like Dunbar's number uh, area, like it gets harder and harder to s have the s the same bandwidth conversations that you would in a smaller group, right? So making sure that we're continuing to, to have valuable conversations, but also be inclusive while we're doing that is, um, is something that will continue to be highlighted over the next year and change really. >> Well. I'm really impressed by what you guys do. And I know we're all tired getting, and we want to get back and, hats off to pulling it together and creating a great program because your, your group and your community is a social construct. It's, it's, we're all social animals. And this whole COVID virtual, now hybrid really is going to, going to show in real world as all playing out, and we're going to see how it evolves, and evolution is part of social communities. And I think that the progress has been made and, you know, and with the team and you guys putting together this great event. So my hat's off to you guys, thanks for, for doing that. Appreciate, great stuff. >> Thank you, thank you. >> Now, final question, um, what do you expect? Given, I mean, this is a social organization, um, things evolve, we're social organisms. We're going to be face to face. We're going to have virtual. We're going to have great talks, security obviously is prime time, Mainstream Enterprise Adoption in Kubernetes and Cloud Native. This is crunch time, so what do you guys expect for this event? Share your thoughts. >> Yeah, I-I think there's going to be lots of um, lots of fun, uh, I think uh more social conversations, less structured. Um, you know, i-if you have, if you haven't had the opportunity to kind of hang out on CNCF Slack, while one of these events are happening, we, we've spun up something of like a hallway track. Um, so, so people are hanging out, they're giving their takes during the um, you know, you know, in between uh talks, there, there was also a, you know, kind of after conference uh, hangout for, for the hallway track that we did. Um, so w we definitely want to continue some of that stuff. Um, as you know, between the last few conferences we've launched uh, Cloud Native TV um, and lots of great producers uh, and, and, and content over there. So you'll see, you'll see, kind of, us start to break the wall between um, that virtual content that we've created uh, across the last few months, as well as, you know, th s seeing that turn physical, right? Um, so how do we, you know, how, how do we, how do we manage that and h-how do we make that seamless for people who may be maybe participating virtually as opposed to physically, right. That there's going to be a bit of um, there, there's an aspect of like, you're, you're almost running two conferences, right. Simultaneously. So. >> It's a total experiment in the real world, but it's, it's all important. It's super important. Constance, your thoughts on, on the event, what people are expecting to see and surprises that might emerge, what do you, what's your thoughts? >> Um, I, well actually, see while you were saying something, I had an idea that I think we can make it more connected, So I just wrote it down, um, uh, I, I have some silly ideas when it comes to the conference stuff, which is why Stephen's laughing, although you can't see it. >> (both men laughing) >> Um, my, I, like, I'm, I'm trying to go in with no expectations, mostly because I'm so excited. I don't want to be disappointed um, and I don't want to miss out. I think, I actually think that probably a lot of the discussions are just going to be like, hi, like, it's so nice to actually meet you and just talk about random things. Maybe not as much technology discussions as maybe there would be at a normal, I like, ah, I don't want to say normal, right? Because we are in a new normal, like what KubeCon was several years ago. Um, I think that I do. I think that it would be probably a little painful, this hybrid part, since we don't know what to expect. I think there's going to be so many things that we're going to look back and be like, face palm and be like, oh, we should've thought about these things. So for anyone who's attending virtually, apologies in advance, and please give us feedback. There's so many things I know we're going to have to improve, we just, we don't know them yet. So please be patient with us and know that we wish that you could be there in person with us too. >> Um, uh, I don't know. >> Well, that's the thing, that's the thing. >> I'm just going to go in there with an open mind. Well that's the thing, it's, it's new, it's all new, virtual. So it's, it's, we're learning together. That's, I think, people put too much pressure. I think people like expecting, you know, some magic to happen, but it's all evolving. And I think the magic is the event. And I think, I think it's going to work out great. And by the way, there's no downside it's, you know, learn. >> Exactly! >> So, yeah. So, you know, so one of the things that I um, I, I have this spiel that I give to um, the release team, the Kubernetes release team, every time we start a new cycle, right? Um, you've got a set of returning contributors. You've got a set of uh, net new contributors, right? And um, and, and moving into the release team, you're kind of like thrown right into the fire of Kubernetes, right? So it's, it's, it's one of those things. I, I, I come in and, and, and, essentially say, um, be curious, question everything. Um, this is like, it's a, it's, it's very much like a human experience, right? And I think that, you know uh, to, to Constance's point, we're all here to, to learn and grow, make this a better experience for everyone. Um, so bring yourself, like bring yourself to the conference, right? I think it's, you know, in, in terms of offering feedback, we have, you know, feedback forms for every one of the, you know, every one of the, the talks that you attend, um, you can feel free to reach out to Constance, and myself and, and Jasmine, um, if you have feedback that you want to give personally, you know, there, there are, there are ways to get in touch with us. There are ways to make the event better. And I think that every time we, we uh, we incorporate, like, we incorporate a lot of this feedback into the next conference. So every time um, you provide some piece of information for us, that gives us an opportunity to make it better, right? So this conference is built, uh, this conference is built by the community, right? The, you know, it's not just a, you know, it's not a, you know, it's not a body just uh making, making decisions kind of off the cuff, it's, we are taking your ideas and we're trying to turn them into a program, right? So it's, it's the maintainers, it's the end users. It's the students, it's people who have never used Kubernetes in their lives, or never used Cloud Native technology in their lives. It's folks who are coming from the, you know, the, the corporate IT kind of classic uh, background, and, and just trying to understand how to be effective in this, in this new world for them. Um so it's like, it takes all kinds and we, we don't get it done without your feedback. So please, um, as you're coming to the conference, whether it's in-person or virtually, like, bring yourselves, be curious, ask questions, um, provide that feedback. And then um, and I think, you know, from the, you know, th-the kind of from the uh, the, yes, we need to be human, but we also need to um recognize some of the, the requirements, uh, that, that are, that we have going into this conference. So reminder that, you know, all of, all of the events are under, you know, under a code of conduct, please make sure to familiarize yourself with uh, code of conduct. I think that um, you know, I-I think that coming back into a physical space for a lot of people, the um, the, some of the social skills can, can erode over time. So please not just bring yourself, bring your best self. And, you know, be sure to review all of the policies around health and, and safety as we go into this. >> Constance, Stephen, that's great stuff. Love talking with you guys. Constance, you want to add something? Go ahead. >> I want to add one thing, also be gentle with yourself and like, be really kind to yourself and others, because this is going to be really overwhelming. I haven't been around more than 10 people at once in almost two years. And so, just remember to be kind as well, always be curious and question everything. >> Yeah. That's great stuff. Great reminder. This is what it's all about, face-to-face. Face-to-face, presence, being together, but also having the openness and the community around you. A lot of mentoring, you guys have a great community for people coming in that are new and there's great mentors, people are open and cool, great community. Thanks for coming on for this special preview for KubeCon CloudNativeCon, thank you so much. >> Thanks for having us. >> Thank you. >> Okay, this is theCUBE's coverage of Kubecon CloudNative, and we've been every year of KubeCon. It's been in fantastic growth. Going the next level again in person, a lot of security, real time adoption should be uh, should be great, virtual and in-person. I'm John Furrier, thanks for watching. (cheery synth music)

>>From around the globe. It's the cube covering Fortinet security summit brought to you by Fortinet. >>Welcome back to Napa Lisa Martin here at the 40, that championship security summit. I'm pleased to welcome the CEO of links us who joins me next. Harry do Hurst, Harry, welcome to the program. Great to you're here we are in an in-person event. One, which is fantastic. Two we're outdoors, three we're in Napa. >>What's not to love. >>There's nothing, nothing not to love. So you had a session this morning. Talk to me about some of the things that you shared with attendees. >>So the session was, was talking about hybrid work and really the how to make that successful. And, you know, we, as a business have really focused making it, not just work for companies, but for companies to thrive and to really embrace, um, the hybrid work and, and, and extract the Mo the most benefit from it. So we, we spoke about the challenges that, that, that, uh, that has, and some of the solutions to, uh, to solving those challenges. >>Tell me about some of the solutions I'm very familiar with as someone who has been working from home for 18 months, some of the challenges I know, understand it too, from an enterprise security perspective, but what are some of the solutions that links us CS? >>So the solutions are fall into kind of three main categories. The first is of course having the best and latest wireless technologies. So that's wifi six wifi, um, it's of course, needs to be coupled with having a good pipe into your home, or all leveraging 5g and other wireless technologies to have, have great connectivity, then having mesh networking to enable it to be wall-to-wall coverage, seamless roaming between, between all the devices to mean that your, your network infrastructure within the home is very robust. Th th the second kind of pillar of, of, of solution is, is around. Now, you can bring enterprise grade security into the home. Typically it would sit in server cupboards in, in, in, in offices and now, um, with, with us and fortunate, we've created a product which brings that enterprise grade technology for the first time into the, into the home. So it managers no longer have to, um, compromise when it comes to security and they can apply the same policies that they would be doing in an office of 10,000 people to 10,000 offices that are in individual's homes. And, and that's a kind of a first, first world first, I would say, but, um, is going to be critical. And again, it, it, it's about moving from it's good enough to let's make it amazing. Um, and let's not compromise on something as critical as security and safety. >>Absolutely. We know we've spoken a lot with 40 net today and over the last year and a half about the massive changes to the threat landscape, the expansion of it, especially with this pivot, when suddenly there were all of these devices, personal devices on home networks, corporate devices on home networks, it's really changed, not just the threat landscape, but also what enterprises need to do. You guys, you mentioned this new announcement came out yesterday, the Linx has homework solution powered by Fordanet talk to us about that, the Genesis of it, and what we're enterprises can actually get access to this. >>Sure. So, so yeah, this is a product that really it's been a meeting of minds. You know, lynxes, lynxes are a leader and have been a leader since the very beginning of wireless. And, and we are, you know, a leader today. Um, Fortnite of course, we're a leader in enterprise security. So the two combined providing the best in class, uh, home internet experience coupled with, um, the, the security, which can be managed by the business. So when as a, as a, as an end user, as a, as a, as an employee, when I plug in this equipment, it automatically phones home to, to, to, to link LyncSys. And then in turn to force net, we know that it's Harriet LyncSys, that that has been been plugged in. It will spin up a network for me, personally, and my family to use in the home. So the, the benefit to the, to the, to the consumer is that there's a fantastic wifi, six mesh solution throughout their home, which is most likely a significant upgrade on their Verizon equipment or whatever it might be. Um, and it's been spins up a corporate network and that corporate network for all intensive purposes is, is imitating exactly like if you were sitting at your desk in the office, in the corporate office. So it becomes an extension of the corporate network. Um, and as I say, it sits behind, behind the FortiGate. >>Talk to me about the Genesis of the solution. Was it the pandemic, because lynxes has seen the challenges from the consumer centric point of view. Talk to me about really kind of the catalyst for these two powerhouses coming together. >>So it was actually something that we were working on three pandemic and fortunate work. We're, we're, we're also looking at how to support the remote work because remote work is not like totally new, this, this pandemic has rapidly accelerated it, but, um, there was already a market and growing, this has just accelerated it. So both businesses independently of one another, where we're kind of toying with it. So when, when we then kind of came together, it was, it was a no brainer. And there was a kind of light bulb moment. And, and we, we realized that the combined solution with the two businesses and bringing together the expertise from both was really, would be how, how we would succeed. >>Do you see any in the last, I know it was just announced yesterday, but any, any industries in particular that you think are really like low-hanging fruit for this type of technology? >>I mean, I think finance is of course, um, you know, there's the high stakes poker in, in that industry. So, um, same goes for healthcare, um, and, and, and even education. So ones that where security is paramount of, and of course security is paramount everywhere, but those ones in particular, given the nature of, of the, those industries. So, so we really expect to see banking, finance, healthcare, uh, pharma, as, as key verticals that we would, uh, we would expect to be successful. >>Okay, excellent. Well, one of the challenges with the ransomware increases, the 40 net threat landscape report showed it's nearly up 11% in the last 12 months. Of course, we have that rapid pivot to work from home 18 months ago, and ransomware and phishing and, and techniques and social engineering getting so much more sophisticated and personalized. Now you've got someone working from home who probably has a million distractions, kids, spouses, et cetera. So easy to click on a link that for most of it looks very legitimate. So having a solution like this in place is really critical for >>Absolutely. And, and I think, you know, until those vulnerabilities are sealed, you know, the attacks will continue. And this solution is part of the, the, the soul for that. Because as soon as, as soon as these, these holes in the bucket of a tape shut, um, you know, the, the appetite to, to invest time in, in attacks, we'll, we'll, we'll fade, >>Hopefully that's the direction that we need to see it going, right. Not up until the right down. Talk to me about, so you mentioned from the it perspective, I'm looking for the benefits for an enterprise, it organization, centralized visibility, they can see in terms of productivity. I imagine it's much better for the end user, but give me that kind of it business perspective, how does this help them come together? >>So for all intents and purposes, the it manager will see within their, their fortunate, uh, interface, these devices, these links devices in people's homes, just in the same way that they would see 40 gates in their office in New York or their office in Pittsburgh. So, um, you know, it really is this, there were 15,000 people in five offices. There's now 15,000 people in 15,000 offices, and, but they can push and manage an and, and push those security, um, policies seamlessly down to all 15,000. They can categorize them. They can, they can do fall intensive purposes. Those, those employees are sitting in the, in one of their facilities. And, and that's really the, the bar that I believe companies should be holding themselves to because, um, it, it provides security for the company. It provides security for the employee, and of course, then by them being able to connect efficiently and secure securely and with great speed and no interruption, that's good for productivity, which is good for the company's profitability. >>Absolutely. It's all interconnected. And this is tuned for video conferencing. Is that >>Yes. So, so we've actually partnered with, um, both zoom and teams, Microsoft teams to, um, we've done an integration with them whereby we're able to identify and optimize that traffic within the network. So, so that adds an added benefit to, to users of those services. And we'll, we'll, we'll be rolling out further, um, partnerships with other key, um, utilities that enable that to optimization to, to, to help it be streamlined. >>So prioritize zoom and teams for the parents kick the kids >>Off. I mean, we've all experienced. The apple TV gets fired up, zoom goes down or, or fought for fortnight, uh, gaming sessions cause you know, havoc within the home. So it it's that application prioritization and optimization that, that I think will also really benefit, um, companies and the employees. The, the frustration is immense. >>I agree I've experienced some of that, but what you're really doing is providing a very secure lifeline that the enterprise needs, the employee needs. It, it's all tied together, productive employees, that our customer experience that our products and services it's, it's really these days, especially considering we don't know how much longer this is going to persist. We expect that there will be some amount of hybrid that will probably be permanent, but that's a lifeline. >>Yes, no, absolutely. I think to your point around the permanence of this, you know, of course we're not all going to be hermits and leave live at home forever, but that, you know, I think this has opened both companies and individuals eyes to what's possible. And I think if you implement these, these types of measures, then you you're setting it up for success. And, and, um, you know, I believe that the solution that we've launched is, is a part of the, the, the piece of the puzzle. >>Maybe the acceleration of it had a bit of a silver lining from what we've all experienced in the last 18 months. Yes. Yes. Talk to me about some of the comments and the feedback that you got from your session this morning. I'm sure people are very excited to hear about what you're doing. >>Yeah. I mean, since, since the announcement came out yesterday, there's been, there's been certainly a lot of interests in appetite. Um, and yeah, we're super excited about the reception it's received. Um, I think that a lot of people that are like, oh, wow, of course, why, why wouldn't this exist already? Um, and, and when you look at it like that, it kind of is obvious, but it, you know, no one expected of course the pandemic and therefore the, no one was ready for it and it's taken us a year or so to, to get a product that's, that's, that's viable and ready and going to be going to be really, really, um, a great utility for companies, but there really was nothing else out there. >>It is surprising in a sense, but then you're right. No one was prepared for the pandemic. We didn't see it coming. And we didn't think that this was a situation that we were going to have to prepare for, let alone live for as long as, as TBD, long as we have. >>Yeah, no, absolutely. That's um, I think it caught everyone by surprise. I think maybe if, if it had happened several years later than the hybrid work movement had started, it was in its infancy. It got very, very quickly ramped up to adulthood. >>I definitely >>Did. So, uh, so great news, very exciting. What you guys are doing with 49. I'm sure that there's going to be great customer feedback. We'll be excited to watch what happens as it gets deployed and rolled out and see how this really transforms the enterprise experience, the employee experience. And I imagine this is a great differentiator for links us business. No. Um, I think it's, it's a really exciting next chapter of, of our, of our history. You know, we've been around for 30 plus years and, um, I think this is, this is a real step change in, in, in where we're focused and I'm super excited about the future. >>I like that change in the future. Well, here we are in beautiful Napa. You said you're not a golfer, but your wife has, >>My wife is golfing. I I'm going to be keeping very many fingers crossed tomorrow during the program for this, for the safety of the spectators. >>That's awesome that she's in the program and here you are settled with all these meetings and all those >>Things. >>Exactly. Well, Harry, it's been a pleasure talking to you. Thank you for joining me on the program, explaining the links as homework solution powered by 49 and all the great things that are going to come from that. Thank you for Harry. Do Hurst. I'm Lisa Martin. You're watching the cube and Napa at the 40 minute security championship.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Okay, >>we're now going to explore the vision of the future of cloud computing From the perspective of one of the leaders in the field, J G >>Share >>a pure off is the vice president of As Your Data ai and Edge at Microsoft G. Welcome to the Cuban cloud. Thanks so much for participating. >>Well, thank you, Dave, and it's a real pleasure to be here with you. And I just wanna welcome the audience as well. >>Well, jg judging from your title, we have a lot of ground to cover, and our audience is definitely interested in all the topics that are implied there. So let's get right into it. You know, we've said many times in the Cube that the new innovation cocktail comprises machine intelligence or a I applied to troves of data. With the scale of the cloud. It's it's no longer, you know, we're driven by Moore's law. It's really those three factors, and those ingredients are gonna power the next wave of value creation and the economy. So, first, do you buy into that premise? >>Yes, absolutely. we do buy into it. And I think, you know, one of the reasons why we put Data Analytics and Ai together is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from a I as being on that continues off, having started off with really things like analytics and proceeding toe. You know, machine learning and the use of data. Interesting breaks. Yes. >>I'd like to get some more thoughts around a data and how you see the future data and the role of cloud and maybe how >>Microsoft, you >>know, strategy fits in there. I mean, you, your portfolio, you got you got sequel server, Azure, Azure sequel. You got arc, which is kinda azure everywhere for people that aren't familiar with that. You've got synapse. Which course that's all the integration a data warehouse, and get things ready for B I and consumption by the business and and the whole data pipeline and a lot of other services as your data bricks you got You got cosmos in their, uh, Blockchain. You've got open source services like Post Dress and my sequel. So lots of choices there. And I'm wondering, you know, how do you think about the future of Of of Cloud data platforms? It looks like your strategies, right tool for the right job? Is that fair? >>It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today is that customer was the Sikh really a comprehensive proposition? And when I say a comprehensive proposition, it is sometimes not just about saying that. Hey, listen way No, you're a sequel server company. We absolutely trust that you have the best Azure sequel database in the cloud, but tell us more. We've got data that's sitting in her group systems. We've got data that's sitting in Post Press in things like mongo DB, right? So that open source proposition today and data and data management and database management has become front and center, so are really sort of push. There is when it comes to migration management, modernization of data to present the broadest possible choice to our customers so we can meet them where they are. However, when it comes to analytics. One of the things they asked for is give us a lot more convergence use. You know it, really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like synapse Fitzer, where in just land any kind of data in the leg and then use any compute engine on top of it to drive insights from it. So, fundamentally, you know, it is that flexibility that we really sort of focus on to meet our customers where they are and really not pushing our dogma and our beliefs on it. But to meet our customers according to the way they have deployed stuff like this. >>So that's great. I want to stick on this for a minute because, you know, I know when when I have guests on like yourself, do you never want to talk about you know, the competition? But that's all we ever talk about. That's all your customers ever talk about, because because the counter to that right tool for the right job and that I would say, is really kind of Amazon's approach is is that you got the single unified data platform, the mega database that does it all. And that's kind of Oracle's approach. It sounds like you wanna have your cake and eat it, too, so you you got the right tool for the right job approach. But you've got an integration layer that allows you to have that converge database. I wonder if you could add color to that and you confirm or deny what I just said. >>No, that's a That's a very fair observation, but I I say there's a nuance in what I sort of describe when it comes to data management. When it comes to APS, we have them customers with the broadest choice. Even in that, even in that perspective, we also offer convergence. So, case in point, when you think about Cosmos TV under that one sort of service, you get multiple engines, but with the same properties, right global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native AB toe, adopt cosmos Davey and adopted in a way that it's and choose an engine that is most flexible. Tow them, however you know when it comes to say, you know, writing a sequel server, for example from organizing it you know you want. Sometimes you just want to lift and shift it into things. Like I asked In other cases, you want to completely rewrite it, so you need to have the flexibility of choice there that is presented by a legacy off What's its on premises? When it moved into things like analytics, we absolutely believe in convergence, right? So we don't believe that look, you need to have a relation of data warehouse that is separate from a loop system that is separate from, say, a B I system. That is just, you know, it's a bolt on for us. We love the proposition off, really building things that are so integrated that once you land data, once you prep it inside the lake, you can use it for analytics. You can use it for being. You can use it for machine learning. So I think you know, are sort of differentiated. Approach speaks for itself there. Well, >>that's that's interesting, because essentially, again, you're not saying it's an either or, and you're seeing a lot of that in the marketplace. You got some companies say no, it's the Data Lake and others saying No, no put in the data warehouse and that causes confusion and complexity around the data pipeline and a lot of calls. And I'd love to get your thoughts on this. Ah, lot of customers struggled to get value out of data and and specifically data product builders of frustrated that it takes too long to go from. You know, this idea of Hey, I have an idea for a data service and it could drive monetization, but to get there, you gotta go through this complex data lifecycle on pipeline and beg people to add new data sources. And do you do you feel like we have to rethink the way that we approach data architectures? >>Look, I think we do in the cloud, and I think what's happening today and I think the place where I see the most amount of rethink the most amount of push from our customers to really rethink is the area of analytics in a I. It's almost as if what worked in the past will not work going forward. Right? So when you think about analytics on in the Enterprise today, you have relational systems, you have produced systems. You've got data marts. You've got data warehouses. You've got enterprise data warehouses. You know, those large honking databases that you use, uh, to close your books with right? But when you start to modernize it, what deep you are saying is that we don't want to simply take all of that complexity that we've built over say, you know, 34 decades and simply migrated on mass exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see shape or form inside the lake. Once you landed inside the lake, you can essentially use a synapse studio toe. Prep it in the way that you like, use any compute engine of your choice and and operate on this data in any way that you see fit. So, case in point, if you want to hydrate relation all data warehouse, you can do so if you want to do ad hoc analytics using something like spark. You can do so if you want to invoke power. Bi I on that data or b i on that data you can do so if you want to bring in a machine learning model on this breath data you can do so, so inherently. So when customers buy into this proposition, what it solves for them and what it gives them is complete simplicity, right? One way to land the data, multiple ways to use it. And it's all eso. >>Should we think of synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way toe? Think about it. >>Yeah, you can think of it that way. It abstracts away, Dave a couple of things. It takes away the type of data, you know, sort of the complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data and fundamentally puts it in a place where it can be now consumed by any sort of entity inside the actual proposition. And by that token, even data breaks. You know, you can, in fact, use data breaks in in sort off an integrated way with a synapse, Right, >>Well, so that leads me to this notion of and then wonder if you buy into it s Oh, my inference is that a data warehouse or a data lake >>could >>just be a node in inside of a global data >>mesh on. >>Then it's synapses sort of managing, uh, that technology on top. Do you buy into that that global data mesh concept >>we do. And we actually do see our customers using synapse and the value proposition that it brings together in that way. Now it's not where they start. Often times when a customer comes and says, Look, I've got an enterprise data warehouse, I want to migrate it or I have a group system. I want to migrate it. But from there, the evolution is absolutely interesting to see. I give you an example. You know, one of the customers that we're very proud off his FedEx And what FedEx is doing is it's completely reimagining its's logistics system that basically the system that delivers What is it? The three million packages a day on in doing so in this covert times, with the view of basically delivering our covert vaccines. One of the ways they're doing it is basically using synapse. Synapse is essentially that analytic hub where they can get complete view into their logistic processes. Way things are moving, understand things like delays and really put all that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, you know, is one of my favorite, uh, we see once customers buy into it, they essentially can do other things with it. So an example of this is, uh is really my favorite story is Peace Parks Initiative. It is the premier Air White Rhino Conservancy in the world. They essentially are using data that has landed in azure images in particular. So, basically, you know, use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue so that this part with about 200 rangers can scramble surgically versus having to read range across the last area that they cover. So What do you see here is you know, the importance is really getting your data in order. Landed consistently. Whatever the kind of data ideas build the right pipelines and then the possibilities of transformation are just endless. >>Yeah, that's very nice how you worked in some of the customer examples. I appreciate that. I wanna ask you, though, that that some people might say that putting in that layer while it clearly adds simplification and e think a great thing that they're begins over time to be be a gap, if you will, between the ability of that layer to integrate all the primitives and all the peace parts on that, that you lose some of that fine grain control and it slows you down. What would you say to that? >>Look, I think that's what we excel at, and that's what we completely sort of buy into on. It's our job to basically provide that level off integration that granularity in the way that so it's an art, absolutely admit it's an art. There are areas where people create simplicity and not a lot of you know, sort of knobs and dials and things like that. But there are areas where customers want flexibility, right? So I think just to give you an example of both of them in landing the data inconsistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. Just 100 to do it. When it comes to computing and reducing this data analyzing this data, they want flexibility. This is one of the reasons why we say, Hey, listen, you want to use data breaks? If you're you're buying into that proposition and you're absolutely happy with them, you can plug plug it into it. You want to use B I and no, essentially do a small data mart. You can use B I If you say that. Look, I've landed in the lake. I really only want to use em melt, bringing your animal models and party on. So that's where the flexibility comes in. So that's sort of really sort of think about it. Well, >>I like the strategy because, you know, my one of our guest, Jim Octagon, e E. I think one of the foremost thinkers on this notion of off the data mesh and her premises that that that data builders, data product and service builders air frustrated because the the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system, you could get products to market much, much, much faster. So but that seems to be your philosophy. But I'm gonna jump ahead to do my ecosystem question. You've mentioned data breaks a couple of times. There's another partner that you have, which is snowflake. They're kind of trying to build out their own, uh, data cloud, if you will, on global mesh in and the one hand, their partner. On the other hand, there are competitors. How do you sort of balance and square that circle? >>Look, when I see snowflake, I actually see a partner. You know that when we essentially you know, we are. When you think about as you know, this is where I sort of step back and look at Azure as a whole and in azure as a whole. Companies like snowflakes are vital in our ecosystem, right? I mean, there are places we compete, but you know, effectively by helping them build the best snowflake service on Asia. We essentially are able toe, you know, differentiate and offer a differentiated value proposition compared to, say, a Google or on AWS. In fact, that's being our approach with data breaks as well, where you know they are effectively on multiple club, and our opportunity with data breaks is toe essentially integrate them in a way where we offer the best experience. The best integrations on Azure Barna That's always been a focus. >>That's hard to argue with. Strategy. Our data with our data partner eat er, shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I wanna come back thio ai a little bit. It's obviously one of the fastest growing areas in our in our survey data. As I said, clearly, Microsoft is a leader in this space. What's your what's your vision of the future of machine intelligence and how Microsoft will will participate in that opportunity? >>Yeah, so fundamentally, you know, we've built on decades of research around, you know, around, you know, essentially, you know, vision, speech and language that's being the three core building blocks and for the for a really focused period of time we focused on essentially ensuring human parody. So if you ever wondered what the keys to the kingdom are it, czar, it's the most we built in ensuring that the research posture that we've taken there, what we then done is essentially a couple of things we focused on, essentially looking at the spectrum. That is a I both from saying that, Hollis and you know it's gotta work for data. Analysts were looking toe basically use machine learning techniques, toe developers who are essentially, you know, coding and building a machine learning models from scratch. So for that select proposition manifesto us, as you know, really a. I focused on all skill levels. The other court thing we've done is that we've also said, Look, it will only work as long as people trust their data and they can trust their AI models. So there's a tremendous body of work and research we do in things like responsibility. So if you ask me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum off a I, and you can sort of come together for any skill level, and we keep that responsibly. I proposition. Absolutely strong now against that canvas, Dave. I'll also tell you that you know, as edge devices get way more capable, right where they can input on the edge, see a camera or a mike or something like that, you will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality. But the core really is all skill levels and that responsible denia. >>Yeah, So that that brings me to this notion of wanna bring an edge and and hybrid cloud Understand how you're thinking about hybrid cloud multi cloud. Obviously one of your competitors, Amazon won't even say the word multi cloud you guys have, Ah, you know, different approach there. But what's the strategy with regard? Toe, toe hybrid. You know, Do you see the cloud you bringing azure to the edge? Maybe you could talk about that and talk about how you're different from the competition. >>Yeah, I think in the edge from Annette, you know, I live in I'll be the first one to say that the word nge itself is conflated. Okay, It's, uh but I will tell you, just focusing on hybrid. This is one of the places where you know I would say the 2020 if I would have looked back from a corporate perspective. In particular, it has Bean the most informative because we absolutely saw customers digitizing moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became really from a cloud computing perspective and an example of this is we understood it's not all or nothing. So sometimes customers want azure consistency in their data centers. This is where things like Azure stack comes in. Sometimes they basically come to us and say, We want the flexibility of adopting flexible pattern, you know, platforms like, say, containers orchestra, Cuban Pettis, so that we can essentially deployed wherever you want. And so when we design things like art, it was built for that flexibility in mind. So here is the beauty of what's something like our can do for you. If you have a kubernetes endpoint anywhere we can deploy and as your service onto it, that is the promise, which means if for some reason, the customer says that. Hey, I've got this kubernetes endpoint in AWS and I love as your sequel. You will be able to run as your sequel inside AWS. There's nothing that stops you from doing it so inherently you remember. Our first principle is always to meet our customers where they are. So from that perspective, multi cloud is here to stay. You know, we're never going to be the people that says, I'm sorry, we will never see a But it is a reality for our customers. >>So I wonder if we could close. Thank you for that by looking, looking back and then and then ahead. And I wanna e wanna put forth. Maybe it's, Ah criticism, but maybe not. Maybe it's an art of Microsoft, but But first you know, you get Microsoft an incredible job of transitioning. It's business as your nominee president Azzawi said. Our data shows that so two part question First, Microsoft got there by investing in the cloud, really changing its mind set, I think, in leveraging its huge software state and customer base to put Azure at the center of its strategy, and many have said me included that you got there by creating products that air Good enough. You know, we do a 1.0, it's not that great. And the two Dato, and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expanding market. >>How >>do you respond to that? Is that is that a fair comment? Ume or than good enough? I wonder if you could share your >>thoughts, gave you? You hurt my feelings with that question. I don't hate me, g getting >>it out there. >>So there was. First of all, thank you for asking me that. You know, I am absolutely the biggest cheerleader. You'll find a Microsoft. I absolutely believe you know that I represent the work off almost 9000 engineers and we wake up every day worrying about our customer and worrying about the customer condition and toe. Absolutely. Make sure we deliver the best in the first time that we do. So when you take the platter off products we've delivered in nausea, be it as your sequel, be it as your cosmos TV synapse as your data breaks, which we did in partnership with data breaks, a za machine learning and recently when we prevail, we sort off, you know, sort of offered the world's first comprehensive data government solution in azure purview. I would humbly submit to you that we're leading the way and we're essentially showing how the future off data ai and the actual work in the cloud. >>I'd be disappointed if you if you had If you didn't, if you capitulated in any way J g So so thank you for that. And the kind of last question is, is looking forward and how you're thinking about the future of cloud last decade. A lot about your cloud migration simplifying infrastructure management, deployment SAS if eyeing my enterprise, lot of simplification and cost savings. And, of course, the redeployment of resource is toward digital transformation. Other other other valuable activities. How >>do >>you think this coming decade will will be defined? Will it be sort of more of the same? Or is there Is there something else out there? >>I think I think that the coming decade will be one where customers start one law outside value out of this. You know what happened in the last decade when people leave the foundation and people essentially looked at the world and said, Look, we've got to make the move, you know, the largely hybrid, but we're going to start making steps to basically digitize and modernize our platforms. I would tell you that with the amount of data that people are moving to the cloud just as an example, you're going to see use of analytics ai for business outcomes explode. You're also going to see a huge sort of focus on things like governance. You know, people need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all other privacy and compliance regulations out there. Essentially, they're complying this posture. So I think the unlocking of outcomes versus simply Hey, I've saved money Second, really putting this comprehensive sort off, you know, governance, regime in place. And then, finally, security and trust. It's going to be more paramount than ever before. Yeah, >>nobody's gonna use the data if they don't trust it. I'm glad you brought up your security. It's It's a topic that hits number one on the CEO list. J G. Great conversation. Obviously the strategy is working, and thanks so much for participating in Cuba on cloud. >>Thank you. Thank you, David. I appreciate it and thank you to. Everybody was tuning in today. >>All right? And keep it right there. I'll be back with our next guest right after this short break.

>> Announcer: From around the globe, it's theCUBE, with digital coverage of AWS re:Invent 2020, sponsored by Intel, AWS and our community partners. >> Hey is Keith Townsend a CTO Advisor on the Twitter and we have yet another CUBE alum for this, AWS re:Invent 2020 virtual coverage. AWS re:Invent 2020 unlike any other, I think it's safe to say unlike any other virtual event, AWS, nearly 60, 70,000 people in person, every conference, there's hundreds of thousands of people tuning in to watch the coverage, and we're talking to builders. No exception to that is our friends at Densify, co founder and CTO of Densify Andrew Hillier, welcome back to the show. >> Thanks, Keith, it's great to be with you again. >> So we're recording this right before it gets cold in Toronto. I hope you're enjoying some of this, breaking the cold weather? >> Yeah, no, we're getting the same whether you are right now it's fantastic. We're ready for the worst, I think in the shorter days, but we'll get through it. >> So for those of you that haven't watched any of the past episodes of theCUBE in which Andrew has appeared. Andrew can you recap, Densify, what do you guys do? >> Well, we're analytics where you can think of us as very advanced cost analytics for cloud and containers. And when I say advanced, what I mean is, there's a number of different aspects of cost, there's understanding your bill, there's how to purchase. And we do those, but we also focus heavily on the resources that you're buying, and try to change that behavior. So it's basically, boils down to a business value of saving a ton of money, but by actually changing what you're using in the cloud, as well as providing visibility. So it's, again, a form of cost optimization, but combined with resource optimization. >> So cost of resource optimization, we understand this stuff on-premises, we understand network, compute, storage, heating, cooling, etc. All of that is abstracted from us in the public cloud, what are the drivers for cost in the public cloud? >> Well, I think you directly or indirectly pay for all of those things. The funny thing about it is that it happens in a very different way. And I think everybody's aware, of course, on-demand, and be able to get resources when you need them. But the flip side of on-demand, the not so good size, is it causes what we call micro-purchasing. So when you're buying stuff, if you go and turn on a, like an Amazon Cloud instance, you're paying for that instance, you're paying Rogers and storage as well. And, implicitly for some networking, a few dollars at a time. And that really kind of creates a new situation and scale because all of a sudden now what was a control purchase on-prem, becomes a bunch of possibly junior people buying things in a very granular way, that adds up to a huge amount of money. So the very thing that makes cloud powerful, the on-demand aspects, the elasticity, also causes a very different form of purchasing behavior, which I think is one of the causes of the cost problem. >> So we're about 10, 12 years into this cloud movement, where public cloud has really become mainstream inside of traditional enterprises. What are some of the common themes you've seen when it comes to good cloud management, the cost management, hygiene across organizations? >> Yeah, and hygiene is a great word for that. I think it's evolved, you're right it's been around this is nothing new. I mean, we've probably been going to cloud expos for over a decade now. But it's kind of coming waves as far as the business problem, I think the initial problem was more around, I don't understand this bill. 'Cause to your point, all those things that you purchase on-prem, you're still purchasing in some way, and a bunch of other services. And it all shows up in this really complicated bill. And so you're trying to figure out, well, who in my organization owes what. And so that was a very early driver years ago, we saw a lot of focus on slicing and dicing the bill, as we like to call it. And then that led to well, now I know where my costs are going, can I purchase a little more intelligently. And so that was the next step. And that was an interesting step because what the problem is, the people that care about cost can't always change what's being used, but they can buy discounts and coupons, and RIs and Savings Plans. So we saw that there was a, then start to be focused on, I'm going to come up with ways of buying it, where I can get a bit of a discount. And it's like having a phone bill where I can't stop people making long distance calls, but I can get on a better phone plan. And that, kind of the second wave, and what we're seeing is the next big wave now is that, okay, I've done that, now I actually should just change what I'm actually using because, there's a lot of inefficiency in there. I've got a handle on those other problems, I need to actually, hopefully make people not buy giant instances all the time, for example. >> So let's talk about that feedback loop, understand what's driving the cost, the people that's consuming that, those services and need to understand those costs. How does Densify breach that gap? >> Well, again, we have aspects of our product that lineup with basically all three of those business problems I mentioned. So there's a there's a cloud cost intelligence module that basically lets you look at the bill any different ways by different tags. Look for anomalies, we find that very important, you say, well, this something unusual happened in my bill. So there's aspect that just focuses on kind of accountability of what's happening in the cost world. And then now, one of the strengths of our product is that when we do our analytics, we look at a whole lot of things at once. So we look at, the instances and their utilization, and what the catalog is, and the RIs and Savings Plans, and everything all together. So if you want to purchase more intelligently, that can be very complicated. So we see a lot of customers that say, well, I do want to buy savings plans, but man, it's difficult to figure out exactly what to do. So we like to think of ourselves as kind of a, it's almost like a, an analytics engine that's got an equation with a lot of terms in. It's got a lot of detail of what we're taking into account when we tell you what you should be doing. And that helps you by more intelligently, it also helps you consume more intelligently, 'cause they're all interrelated. I don't want to change an instance I'm using if there's no RI on it, that would take you backwards. I don't want to buy RIs for instances that I shouldn't be using, that takes you backwards. So it's all interconnected. And we feel that looking at everything at once is the path to getting the right answer. And having the right answer is the path to having people actually make a change. >> So when I interviewed you a few years ago, we talked about very high level containers, and how containers is changing the way that we can consume Cloud Services, containers introduced this concept of oversubscription, and the public cloud. We couldn't really oversubscribe and for large instance, back then. But we can now with containers, how are containers in general complicating cloud costing? >> So it's interesting because they do allow overcommit but not in the same way that a virtual environment does. So in a virtual environment, if I say I need two CPUs for job X, I need two CPUs for job Y, I can put them both on a machine that has two CPUs, and there will be over committed. So over committed in a virtual environment, it is a very well established operation. It lets you get past people asking for too much effectively. Containers don't quite do that in the same way, when they refer to overcommit, they refer to the fact that you can ask for one CPU, but you can use up to four, and that difference is if you overcommit. But the fact that I'm asking for one CPU is actually a pretty big problem. So let me give an example. If I look into my laptop here, and I've got Outlook and Word and all these things on it, and I had to tell you how many millicores I had to give each one, or with Zoom, let's see I'm running Zoom. Now, well, I want Zoom to work well, I want to give it $4,000 millicores, I want to give it four CPUs, because it uses that when it needs it. But my PowerPoint, I also want to give 4000 or $2,000 millicores. So I add all these things up of what I need based on the actual more granular requirements. And it might add up to four laptops. But containers don't overcommit the same way, if I asked for those requests by using containers, I actually will use for laptops. So it's those request values that are the trick, if I say I need a CPU, I get a CPU, it's not the same as a virtual CPU would be in a virtual environment. So we see that as the cause of a lot of the problem and that people quite rationally say I need these resources for these containers. But because containers are much more granular, I'm asking for a lot of individual resource, that when you add them up, it's a ton of resources. So almost every container running, we see that they're very low utilization, because everybody, rightfully so asked for individual resources for each container, but they are the wrong resources, or in aggregate, it's not creating the behavior you wanted. So we find containers are a bit, people think they're going to magically cause problems to go away. But in fact, what happens is, when you start running a lot of them, you end up just with a ton of cost. And people are just starting to get to that point now. >> Yeah, I can see how that could easily be the case inside of a virtual environment. I can easily save my VM needs four CPUs, four VCPUs. And I can do that across 100 applications. And that really doesn't cost me a lot in the private data center, tools like VMware, DRS, and all of that kind of fix that for me on the back-end is magical. In the public cloud, if I ask for four CPUs, I get four CPUs, and I'm going to pay for four CPUs, even if I don't utilize it, there's no auto-balancing. So how does Densify help actually solve that problem? >> Well, so they, there's multiple aspects for that problem, ones of the thing was that people don't necessarily ask for the right thing in the first place, that's one of the biggest ones. So, I give the example of, I need to give Zoom 4,000 millicores, that's probably not true at all, if I analyze what it's doing, maybe for a second it uses that, but for the most of the time, it's not using nearly those resources. So the first step is to analyze the container behavior patterns, and say, well, those numbers should be different. And so for example, the one thing we do with that is, we say, well if a developer is using terraform templates to stand up containers, we can say, instead of putting the number 1000, in that, a thousand millercores, or 400 millicores in your template, just put a variable and that references our analytics, just let the analytics figure what that number should be. And so it's a very elegant solution to say, the machine learning will actually figure out what resources that container needs, 'cause humans are not very good at it, especially when there's 10s of thousands of containers. So that's kind of the, one of the big things is to optimize the container of requests. And then once you've done that the nodes that you're running on can be optimized, because now they start to look different. Maybe you don't have, you don't need as much memory or as much CPU. So it's all again, it's all interrelated, but it's a methodical step that's based on analytics. And, people, they're too busy to figure this out, that they can't figure it out for thousands of things. Again, if I asked you don't get your laptop, on your laptop, how many miillicores do you need to get PowerPoint? You don't know. But in containers, you have to know. So we're saying let the machine figure out. >> Yes kind of like when you're asked how many miillicores do you need to give Zoom answer's yes. >> Yeah exactly. >> (laughs) So at the end of the day, you need some way to quantify that. So you guys are doing the two things. One, you're quantifying, you're measuring how much this application typically take. And then when I go to provision it, we're using a tool like terraform. Though then instead of me answering the question, the answer is go ask Densify, and Densify will tell you, and then I'll optimize my environment. So I get both ends of that equation, if I'm kind of summarizing it correctly. >> Absolutely. And that last part is extremely important because, in a legacy environment, like in a virtual environment, I can call an API and change the size of VM, and it will stay that way. And so that's a viable automation strategy for those types of environments. In the cloud, or when you're using terraform, or in containers, they will go right back to what's in the terraform template, that's one of the powerful things about terraform is that it always matches what's in the code. So I can't go and change the cloud, it'll just go back to whatever is in the terraform template next time, it's provision. So we have to go upstream, you have to actually do it at the source, when you're provisioning applications, the actual resource specifications should be coming through at that point, you can't, you don't want to change them after the fact, you can update the terraform and redeploy with a new value, that that's the way to do automation in a container environment, it doesn't, you can't do it, like you did in a VMware environment, because it won't stick, it just gets undone the next time the DevOps pipeline triggers. So it's both a, it's a big opportunity for a kind of a whole new generation of automation, doing it, we call it CICDCO. It's, Continuous Integration, Continuous Delivery, Continuous Optimization. It's just part of the, of the fabric of the way you deploy Ops, and it's a much more elegant way to do it. >> So you hit two trigger words, or a few trigger terms, one, DevOps, two, I'm saying DevOps, CICD, and Continuous Operations. What is the typical profile of a Densify customer? >> Well, usually, they're a mix of a bunch of different technologies. So I don't want to make it sound like you have to be a DevOps shop to benefit from this, most of our customers have some DevOps teams, they also have a lot of legacy workloads, they have virtual environments, they have cloud environments. So don't necessarily have 100%, of all of these things. But usually, it's a mix of things where, there might be some newer born in the cloud as being deployed, and this whole CICDCO concept really makes sense for them, they might just have another few thousand cloud instances that they stood up, not as a part of a DevOps pipeline, but just to run apps or maybe even migrated from on-prem. So it's a pretty big mix, we see almost every company has a mix, unless you just started a company yesterday, you're going to have a mix of some EC2 services that are kind of standalone and static, maybe some skill groups running, or containers running skill groups. And there's a generally a mix of these things. So the things I'm describing do not require DevOps, the notion of optimizing the cloud instances, by changing the marching orders when they're provisioned not after the fact, that that applies to any anybody using the cloud. And our customers tend to be a mix, some again very new, new school processes and born in the cloud. And some more legacy applications that are running that look a little more like on-prem environment would, where they're not turning on and off dynamically, they're just running transactional workloads. >> So let's talk about the kind of industries, because you you hit on a key point, we kind of associate a certain type of company with born in the cloud, et cetera. What type of organizations or industries are we seeing Densify deployed in. >> So we don't really have a specific market vertical that we focus on, we have a wide variety. So we find we have a lot of customers in financial services, banks, insurance companies. And I think that's because those are very large, complicated environments, where analytics really pay dividends, if you have a lot of business services, that are doing different things, and different criticality levels. The things I'm describing are very important. But we also have logistics companies, software companies. So again, complexity plays a part, I think elasticity plays a part in the organization that wants to be able to make use of the cloud in a smart way where they're more elastic, and obviously drive costs down. So again, we have customers across all different types of industries, manufacturing, pharmaceutical. So it's a broad range, we have partners as well that use our like IBM, that use our product, and their customers. So there's no one type of company that we focus on, certainly. But we do see, again, environments that are complicated or mission critical, or that they really want to run in a more of elastic way, those tend to be very good customers for us. >> Well, CUBE alum Andrew Hillier, thank you for joining us on theCUBE coverage of AWS re:Invent 2020 virtual. Say goodbye to a couple hundred thousand of your closest friends. >> Okay, and thanks for having me. >> That concludes our interview with Densify. We really appreciate the folks that Densify, having us again to have this conversation around workload analytics and management. To find out more of, well or find out just more great CUBE coverage, visit us on the web SiliconANGLE TV. Talk to you next episode of theCUBE. (upbeat music)

>>on the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Hello and welcome back to the Cubes Live coverage Cube live here in Palo Alto, California, with the Virtual Cube this year because we can't be there in person. I'm your host, John Fairy year. We're kicking off Day two of the three weeks of reinvent a lot of great leadership sessions to review, obviously still buzzing from the Andy Jassy three. Our keynote, which had so many storylines, is really hard to impact. We're gonna dig that into into into that today with Jerry Chan, who has been a Cube alumni since the beginning of our AWS coverage. Going back to 2013, Jerry was wandering the hallways as a um, in between. You were in between vm ware and V C. And then we saw you there. You've been on the Cube every year at reinvent with us. So special commentary from you. Thanks for coming on. >>Hey, John, Thanks for having me and a belated happy birthday as well. If everyone out there John's birthday was yesterday. So and hardest. Howard's working man in technology he spent his entire birthday doing live coverage of Amazon re events. Happy birthday, buddy. >>Well, I love my work. I love doing this. And reinvent is the biggest event of the year because it really is. It's become a bellwether and eso super excited to have you on. We've had great conversations by looking back at our conversations over the Thanksgiving weekend. Jerry, the stuff we were talking about it was very proposed that Jassy is leaning in with this whole messaging around change and horizontal scalability. He didn't really say that, but he was saying you could disrupt in these industries and still use machine learning. This was some of the early conversations we were having on the Cube. Now fast forward, more mainstream than ever before. So big, big part of the theme there. >>Yeah, it z you Amazon reinvent Amazon evolution to your point, right, because it's both reinventing what countries are using with the cloud. But also what Amazon's done is is they're evolving year after year with their services. So they start a simple infrastructure, you know, s three and e c. Two. And now they're building basically a lot of what Andy said you actually deconstructed crm? Ah, lot of stuff they're doing around the call centers, almost going after Salesforce with kind of a deconstructed CRM services, which is super interesting. But the day you know, Amazon announces all those technologies, not to mention the AI stuff, the seminar stuff you have slack and inquired by Salesforce for $27.7 billion. So ah, lot of stuff going on in the cloud world these days, and it's funny part of it, >>you know, it really is interesting. You look up the slack acquisition by, um, by Salesforce. It's interesting, you know, That kind of takes slack out of the play here. I mean, they were doing really well again. Message board service turns into, um, or collaboration software. They hit the mainstream. They have great revenue. Is that going to really change the landscape of the industry for Salesforce? They've got to acquire it. It opens the door up from, or innovation. And it's funny you mention the contact Center because I was pressing Jassy on my exclusive one on one with him. Like they said, Andy, my my daughter and my sons, they don't use the phone. They're not gonna call. What's this? Is it a call center deal? And he goes, No, it's the It's about the contact. So think about that notion of the contact. It's not about the call center. It's the point of contact. Okay, Linked in is with Microsoft. You got slack and Salesforce Contact driven collaboration. Interesting kind of play for Microsoft to use voice and their data. What's your take on that? >>I think it's, um you know, I have this framework. As you know, I talked my friend systems of engagement over systems intelligence and systems record. Right? And so you could argue voice email slack because we're all different systems of engagement, and they sit on top of system of record like CRM customer support ticketing HR. Something like that. Now what sells first did by buying slack is they now own a system engagement, right? Not on Lee is slack. A system engagement for CRM, but also system engagement for E. R. P Service. Now is how you interact with a bunch of applications. And so if you think about sales for strategy in the space, compete against Marcus Soft or serves now or other large AARP's now they own slack of system engagement, that super powerful way to actually compete against rival SAS companies. Because if you own the layer engagement layer, you can now just intermediate what's in the background. Likewise, the context center its own voice. Email, chat messaging, right? You can just inter mediate this stuff in the back, and so they're trying to own the system engagement. And then, likewise, Facebook just bought that company customer a week ago for a billion dollars, which also Omni Channel support because it is chat messaging voice. It's again the system engagement between End User, which could be a customer or could be employees. >>You know, this really gonna make Cit's enterprise has been so much fun over the past 10 years, I gotta say, in the past five, you know, it's been even more fun, has become or the new fun area, you know, And the impact to enterprise has been interesting because and we're talking about just engaging system of record. This is now the new challenge for the enterprise. So I wanna get your thoughts, Jerry, because how you see the Sea, X O's and CSOs and the architects out there trying to reinvent the enterprise. Jassy saying Look and find the truth. Be on the right side of history here. Certainly he's got himself service interest there, but there is a true band eight with Cove it and with digital acceleration for the enterprise to change. Um, given all these new opportunities Thio, revolutionize or disrupt or radically improve, what's the C. C X's do? What's your take on? How do you see that? >>It's increasingly messy for the CXS, and I don't I don't envy them, right? Because back in the day they kind of controlled all the I t spend and kind of they had a standard of what technologies they use in the company. And then along came Amazon in cloud all of sudden, like your developers and Dio Hey, let me swipe my credit card and I'm gonna access to a bunch of a P I s around computing stories. Likewise. Now they could swipe the credit card and you strike for billing, right? There's a whole bunch of services now, so it becomes incumbent upon CSOs. They need Thio new set of management tools, right? So not only just like, um, security tools they need, they need also observe ability, tools, understanding what services are being used by the customers, when and how. And I would say the following John like CSOs is both a challenge for them. But I think if I was a C X, so I'll be pretty excited because now I have a bunch of other weapons and other bunch of services I could offer. My end users, my developers, my employees, my customers and, you know it's exciting for them is not only could they do different things, but they also changed how their business being done. And so I think both interact with their end users. Be a chat like slack or be a phone like a contact center or instagram for your for your for your kids. It's actually a new challenge if I were sick. So it's it's time to build again, you know, I think Cove it has said it is time to build again. You can build >>to kind of take that phrase from the movie Shawshank Redemption. Get busy building or get busy dying. Kinda rephrase it there. And that's kind of the theme I'm seeing here because covert kind of forced people saying, Look, this things like work at home. Who would have thought 100% people would be working at home? Who would have thought that now the workloads gonna change differently? So it's an opportunity to deconstruct or distant intermediate these services. And I think, you know, in all the trends that I've seen over my career, it's been those inflection points where breaking the monolith or breaking the proprietary piece of it has always been an opportunity for for entrepreneur. So you know, and and for companies, whether you're CEO or startup by decomposing and you can come in and create value E I think to me, snowflake going public on the back of Amazon. Basically, this is interesting. I mean, so you don't have to be. You could kill one feature and nail it and go big. >>I think we talked to the past like it's Amazon or Google or Microsoft Gonna win. Everything is winner take all winner take most, and you could argue that it's hard to find oxygen as a start up in a broad platform play. But we think Snowflake and other companies have done and comes like mongo DB, for example, elastic have shown that if you can pick a service or a problem space and either developed like I p. That's super deep or own developer audience. You can actually fight the big guys. The Big Three cloud vendors be Amazon, Google or or market soft in different markets. And I think if you're a startup founder, you should not be afraid of competing with the big cloud vendors because there there are success patterns and how you can win and you know and create a lot of value. So I have found Investor. I'm super excited by that because, you know, I don't think you're gonna find a company takedown Amazon completely because they're just the scale and the network effects is too large. But you can create a lot of value and build Valuable comes like snowflake in and around the Amazon. Google Microsoft Ecosystem. >>Yeah, I want to get your thoughts. You have one portfolio we've covered rock rock set, which does a lot of sequel. Um, one of your investments. Interesting part of the Kino yesterday was Andy Jassy kind of going after Microsoft saying Windows sequel server um, they're targeting that with this new, uh, tool, but, you know, sucks in the database of it is called the Babel Fish for Aurora for post Chris sequel. Um, well, how was your take on that? I mean, obviously Microsoft big. Their enterprise sales tactics are looking like more like Oracle, which he was kind of hinting at and commenting on. But sequel is Lingua Franca for data >>correct. I think we went to, like, kind of a no sequel phase, which was kind of a trendy thing for a while and that no sequel still around, not only sequel like mongo DB Document TV. Kind of that interface still holds true, but your point. The world speaks sequel. All your applications be sequel, right? So if you want backwards, compatibility to your applications speaks equal. If you want your tire installed base of employees that no sequel, we gotta speak sequel. So, Rock said, when the first public conversations about what they're building was on on the key with you and Me and vent hat, the founder. And what Rock said is doing their building real time. Snowflake Thio, Lack of better term. It's a real time sequel database in the cloud that's super elastic, just like Snowflake is. But unlike snowflake, which is a data warehouse mostly for dashboards and analytics. Rock set is like millisecond queries for real time applications, and so think of them is the evolution of where cloud databases air going is not only elastic like snowflake in the cloud like Snowflake. We're talking 10 15 millisecond queries versus one or two second queries, and I think what any Jassy did and Amazon with bowel officials say, Hey, Sequels, Legal frank of the cloud. There's a large installed base of sequel server developers out there and applications, and we're gonna use Babel fish to kind of move those applications from on premise the cloud or from old workload to the new workloads. And, I think, the name of the game. For for cloud vendors across the board, big and small startups thio Google markets, often Amazon is how do you reduce friction like, How do you reduce friction to try a new service to get your data in the cloud to move your data from one place to the next? And so you know, Amazon is trying to reduce friction by using Babel fish, and I think it is a great move by them. >>Yeah, by the way. Not only is it for Aurora Post Chris equal, they're also open sourcing it. So that's gonna be something that is gonna be interesting to play out. Because once they open source it essentially, that's an escape valve for locking. I mean, if you're a Microsoft customer, I mean, it ultimately is. Could be that Gateway drug. It's like it is ultimately like, Hey, if you don't like the licensing, come here. Now there's gonna be some questions on the translations. Um, Vince, um, scuttlebutt about that. But we'll see it's open source. We'll see what goes on. Um great stuff on on rocks that great. Great. Start up next. Next, uh, talk track I wanna get with you is You know, over the years, you know, we've talked about your history. We're gonna vm Where, uh, now being a venture capitalist. Successful, wanted Greylock. You've seen the waves, and I would call it the two ways pre cloud Early days of cloud. And now, with co vid, we're kind of in the, you know, not just born in the cloud Total cloud scale cloud operations. This is kind of what jazz he was going after. E think I tweeted Cloud is eating the world and on premise and the edges. What it's hungry for. It kind of goof on mark injuries since quote a software eating the world. This is where it's going. So it's a whole another chapter coming. You saw the pre cloud you saw Cloud. Now we've got basically global I t everything else >>It's cloud only I would say, You know, we saw pre cloud right the VM ware days and before that he called like, you know, data centers. I would say Amazon lawns of what, 6 4007, the Web services. So the past 14 15 years have been what I've been calling cloud transition, right? And so you had cos technologies that were either doing on migration from on premise and cloud or hybrid on premise off premise. And now you're seeing a generation of technologies and companies. Their cloud only John to your point. And so you could argue that this 15 year transitions were like, you know, Thio use a bad metaphor like amphibians. You're half in the water, half on land, you know, And like, you know, you're not You're not purely cloud. You're not purely on premise, but you can do both ways, and that's great. That's great, because that's a that's a dominant architecture today. But come just like rock set and snowflake, your cloud only right? They're born in the cloud, they're built on the cloud And now we're seeing a generation Startups and technology companies that are cloud only. And so, you know, unlike you have this transitionary evolution of like amphibians, land and sea. Now we have ah, no mammals, whatever that are Onley in the cloud Onley on land. And because of that, you can take advantage of a whole different set of constraints that are their cloud. Only that could build different services that you can't have going backwards. And so I think for 2021 forward, we're going to see a bunch of companies or cloud only, and they're gonna look very, very different than the previous set of companies the past 15 years. And as an investor, as you covering as analysts, is gonna be super interesting to see the difference. And if anything, the cloud only companies will accelerate the move of I t spending the move of mawr developers to the cloud because the cloud only technologies are gonna be so much more compelling than than the amphibians, if you will. >>Yeah, insisting to see your point. And you saw the news announcement had a ton of news, a ton of stage making right calls, kind of the democratization layer. We'll look at some of the insights that Amazon's getting just as the monster that they are in terms of size. The scope of what? Their observation spaces. They're seeing all these workloads. They have the Dev Ops guru. They launched that Dev Ops Guru thing I found interesting. They got data acquisition, right? So when you think about these new the new data paradigm with cloud on Lee, it opens up new things. Um, new patterns. Um, S o. I think I think to me. I think that's to me. I see where this notion of agility moves to a whole nother level, where it's it's not just moving fast, it's new capabilities. So how do you How do you see that happening? Because this is where I think the new generation is gonna come in and be like servers. Lambs. I like you guys actually provisioned E c. Two instances before I was servers on data centers. Now you got ec2. What? Lambda. So you're starting to see smaller compute? Um, new learnings, All these historical data insights feeding into the development process and to the application. >>I think it's interesting. So I think if you really want to take the next evolution, how do you make the cloud programmable for everybody? Right. And I think you mentioned stage maker machine learning data scientists, the sage maker user. The data scientists, for example, does not on provisioned containers and, you know, kodama files and understand communities, right? Like just like the developed today. Don't wanna rack servers like Oh, my God, Jerry, you had Iraq servers and data center and install VM ware. The generation beyond us doesn't want to think about the underlying infrastructure. You wanna think about it? How do you just program my app and program? The cloud writ large. And so I think where you can see going forward is two things. One people who call themselves developers. That definition has expanded the past 10, 15 years. It's on Lee growing, so everyone is gonna be developed right now from your white collar knowledge worker to your hard core infrastructure developer. But the populist developers expanding especially around machine learning and kind of the sage maker audience, for sure. And then what's gonna happen is, ah, law. This audience doesn't want to care about the stuff you just mentioned, John in terms of the online plumbing. So what Amazon Google on Azure will do is make that stuff easy, right? Or a starved could make it easy. And I think that the move towards land and services that moved specifically that don't think about the underlying plumbing. We're gonna make it easy for you. Just program your app and then either a startup, well, abstract away, all the all the underlying, um, infrastructure bits or the big three cloud vendors to say, you know, all this stuff would do in a serverless fashion. So I think serverless as, ah paradigm and have, quite frankly, a battlefront for the Big Three clouds and for startups is probably one in the front lines of the next generation. Whoever owns this kind of program will cloud model programming the Internet program. The cloud will be maybe the next platform the next 10 or 15 years. I still have two up for grabs. >>Yeah, I think that is so insightful. I think that's worth calling out. I think that's gonna be a multi year, um, effort. I mean, look at just how containers now, with ks anywhere and you've got the container Service of control plane built in, you got, you know, real time analytics coming in from rock set. And Amazon. You have pinned Pandora Panorama appliance that does machine learning and computer vision with sensors. I mean, this is just a whole new level of purpose built stuff software powered software operated. So you have this notion of Dev ops going to hand in the glove software and operations? Kind of. How do you operate this stuff? So I think the whole new next question was Okay, this is all great. But Amazon's always had this problem. It's just so hard. Like there's so much good stuff. Like, who do you hired operate it? It is not yet programmable. This has been a big problem for them. Your thoughts on that, >>um e think that the data illusion around Dev ops etcetera is the solution. So also that you're gonna have information from Amazon from startups. They're gonna automate a bunch of the operations. And so, you know, I'm involved to come to Kronos Fear that we talked about the past team kind of uber the Bilson called m three. That's basically next generation data dog. Next generation of visibility platform. They're gonna collect all the data from the applications. And once they have their your data, they're gonna know how to operate and automate scaling up, scaling down and the basic remediation for you. So you're going to see a bunch of tools, take the information from running your application infrastructure and automate exactly how to scale and manager your app. And so AI and machine learning where large John is gonna be, say, make a lot of plumbing go away or maybe not completely, but lets you scale better. So you, as a single system admin are used. A single SRE site reliability engineer can scale and manage a bigger application, and it's all gonna be around automation and and to your point, you said earlier, if you have the data, that's a powerful situations. Once have the data can build models on it and can start building solutions on the data. And so I think What happens is when Bill this program of cloud for for your, you know, broad development population automating all this stuff becomes important. So that's why I say service or this, You know, automation of infrastructure is the next battleground for the cloud because whoever does that for you is gonna be your virtualized back and virtualized data center virtualized SRE. And if whoever owns that, it's gonna be a very, very strategic position. >>Yeah, it's great stuff. This is back to the theme of this notion of virtualization is now gone beyond server virtualization. It's, you know, media virtualization with the Cube. My big joke here with the Q virtual. But it's to your point. It's everything can now be replicated in software and scale the cloud scale. So it's super big opportunity for entrepreneurs and companies. Thio, pivot and differentiate. Uh, the question I have for you next is on that thread Huge edge discussion going on, right. So, you know, I think I said it two years ago or three years ago. The data center is just a edges just a big fat edge. Jassy kind of said that in his keynote Hey, looks at that is just a Nedum point with his from his standpoint. But you have data center. You have re alleges you've got five G with wavelength. This local zone concept, which is, you know, Amazon in these metro areas reminds me the old wireless point of presence kind of vibe. And then you've got just purpose built devices like cameras and factory. So huge industrial innovation, robotics, meet software. I mean, whole huge edge development exploding, Which what's your view of this? And how do you look at that from? Is an investor in industry, >>I think edges both the opportunity for start ups and companies as well as a threat to Amazon, right to the reason why they have outposts and all the stuff the edges if you think about, you know, decentralizing your application and moving into the eggs from my wearable to my home to my car to my my city block edges access Super interesting. And so a couple things. One companies like Cloudflare Fastly company I'm involved with called Kato Networks that does. SAS is secure access service edge write their names and the edges In the category definition sassy is about How do you like get compute to the edge securely for your developers, for your customers, for your workers, for end users and what you know comes like Cloudflare and Kate have done is they built out a network of pops across the world, their their own infrastructure So they're not dependent upon. You know, the big cloud providers, the telco providers, you know, they're partnering with Big Cloud, their parting with the telcos. But they have their own kind of system, our own kind of platform to get to the edge. And so companies like Kato Networks in Cloud Player that have, ah, presence on the edge and their own infrastructure more or less, I think, are gonna be in a strategic position. And so Kate was seen benefits in the past year of Of of Cove it and locked down because more remote access more developers, Um, I think edge is gonna be a super great area development going forward. I think if you're Amazon, you're pushing to the edge aggressively without post. I think you're a developer startup. You know, creating your own infrastructure and riding this edge wave could be a great way to build a moat against a big cloud guy. So I'm super excited. You think edge in this whole idea of your own infrastructure. Like what Kato has done, it is gonna be super useful going forward. And you're going to see more and more companies. Um, spend the money to try to copy kind of, ah, Cloudflare Kato presence around the world. Because once you own your own kind of, um, infrastructure instead of pops and you're less depend upon them a cloud provider, you're you're in a good position because there's the Amazon outage last week and I think like twilio and a bunch of services went down for for a few hours. If you own your own set of pops, your independent that it is actually really, really secure >>if you and if they go down to the it's on you. But that was the kinesis outage that they had, uh, they before Thanksgiving. Um, yeah, that that's a problem. So on this on. So I guess the question for you on that is that Is it better to partner with Amazon or try to get a position on the edge? Have them either by you or computer, create value or coexist? How do you see that that strategy move. Do you coexist? Do you play with them? >>E think you have to co exist? I think that the partner coexist, right? I think like all things you compete with Amazon. Amazon is so broad that will be part of Amazon and you're gonna compete with and that's that's fair game, you know, like so Snowflake competes against red shift, but they also part of Amazon's. They're running Amazon. So I think if you're a startup trying to find the edge, you have to coexist in Amazon because they're so big. Big cloud, right, The Big three cloud Amazon, Google, Azure. They're not going anywhere. So if you're a startup founder, you definitely coexist. Leverage the good things of cloud. But then you gotta invest in your own edge. Both both figure early what? Your edge and literally the edge. Right. And I think you know you complement your edge presence be it the home, the car, the city block, the zip code with, you know, using Amazon strategically because Amazon is gonna help you get two different countries, different regions. You know you can't build a company without touching Amazon in some form of fashion these days. But if you're a star found or doing strategically, how use Amazon and picking how you differentiate is gonna be key. And if the differentiation might be small, John. But it could be super valuable, right? So maybe only 10 or 15%. But that could be ah Holton of value that you're building on top of it. >>Yeah, and there's a little bit of growth hack to with Amazon if you you know how it works. If you compete directly against the core building blocks like a C two has three, you're gonna get killed, right? They're gonna kill you if the the white space is interest. In the old days in Microsoft, you had a white space. They give it to you or they would roll you over and level you out. Amazon. If you're a customer and you're in a white space and do better than them, they're cool with that. They're like, basically like, Hey, if you could innovate on behalf of the customer, they let you do that as long as you have a big bill. Yeah. Snowflakes paying a lot of money to Amazon. Sure, but they also are doing a good job. So again, Amazon has been very clear on that. If you do a better job than us for, the customer will do it. But if they want Amazon Red Shift, they want Amazon Onley. They can choose that eso kind of the playbook. >>I think it is absolutely right, John is it sets from any jassy and that the Amazon culture of the customer comes first, right? And so whatever is best for the customer that's like their their mission statement. So whatever they do, they do for the customer. And if you build value for the customer and you're on top of Amazon, they'll be happy. You might compete with some Amazon services, which, no, the GM of that business may not be happy, but overall. Net Net. Amazon's getting a share of those dollars that you're that you're charging the customer getting a share of the value you're creating. They're happy, right? Because you know what? The line rising tide floats all the boats. So the Mork cloud usage is gonna only benefit the Big Three cloud providers Amazon, particularly because they're the biggest of the three. But more and more dollars go the cloud. If you're helping move more. Absolute cloud helping build more solutions in the cloud. Amazon is gonna be happy because they know that regardless of what you're doing, you will get a fraction of those dollars. Now, the key for a startup founder and what I'm looking for is how do we get mawr than you know? A sliver of the dollars. How to get a bigger slice of the pie, if you will. So I think edge and surveillance or two areas I'm thinking about because I think there are two areas where you can actually invest, own some I p owned some surface area and capture more of the value, um, to use a startup founder and, you know, are built last t to Amazon. >>Yeah. Great. Great thesis. Jerry has always been great. You've been with the Cube since the beginning on our first reinvented 2013. Um, and so we're now on our eighth year. Great to see your success. Great investment. You make your world class investor to great firm Greylock. Um great to have you on from your perspective. Final take on this year. What's your view of Jackie's keynote? Just in general, What's the vibe. What's the quick, um, soundbite >>from you? First, I'm so impressed and you can do you feel like a three Archy? No more or less by himself. Right then, that is, that is, um, that's a one man show, and I'm All of that is I don't think I could pull that off. Number one. Number two It's, um, the ability to for for Amazon to execute at so many different levels of stack from semiconductors. Right there, there there ai chips to high level services around healthcare solutions and legit solutions. It's amazing. So I would say both. I'm impressed by Amazon's ability. Thio go so broad up and down the stack. But also, I think the theme from From From Andy Jassy is like It's just acceleration. It's, you know now that we will have things unique to the cloud, and that could be just a I chips unique to the cloud or the services that are cloud only you're going to see a tipping point. We saw acceleration in the past 15 years, John. He called like this cloud transition. But you know, I think you know, we're talking about 2021 beyond you'll see a tipping point where now you can only get certain things in the cloud. Right? And that could be the underlying inference. Instances are training instances, the Amazons giving. So all of a sudden you as a founder or developer, says, Look, I guess so much more in the cloud there's there's no reason for me to do this hybrid thing. You know, Khyber is not gonna go away on Prem is not going away. But for sure. We're going to see, uh, increasing celebration off cloud only services. Um, our edge only services or things. They're only on functions that serve like serverless. That'll be defined the next 10 years of compute. And so that for you and I was gonna be a space and watch >>Jerry Chen always pleasure. Great insight. Great to have you on the Cube again. Great to see you. Thanks for coming on. >>Congrats to you guys in the Cube. Seven years growing. It's amazing to see all the content put on. So you think it isn't? Just Last point is you see the growth of the curve growth curves of the cloud. I'd be curious Johnson, The growth curve of the cube content You know, I would say you guys are also going exponential as well. So super impressed with what you guys have dealt. Congratulations. >>Thank you so much. Cute. Virtual. We've been virtualized. Virtualization is coming here, or Cubans were not in person this year because of the pandemic. But we'll be hybrid soon as events come back. I'm John for a year. Host for AWS reinvent coverage with the Cube. Thanks for watching. Stay tuned for more coverage all day. Next three weeks. Stay with us from around the globe. It's the Cube with digital coverage of aws reinvent 2020 sponsored by Intel >>and AWS. Welcome back here to our coverage here on the Cube of AWS.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome back to the cubes Coverage of AWS reinvent 2020 The digital edition. I'm Lisa Martin, and I'm welcoming back one of our Cube alumni. Paul Saville joins me the S VP of product management and services from Lumen Technologies. Paul, welcome back to the Cube. >>Thank you, Lisa. It's great to be here. >>Last time I got to go to an event was aws reinvent 2019. You were there, but when you were there, you were with centurylink Centurylink. Lumen, What's the correlation? >>Yeah, well, thanks for asking that question. Yes. So we did Rand rebrand our company to loom in technologies. And there's a reason for that because, really, a few years ago, centurylink was largely a consumer telecom business. It's roughly half of its business was in the consumer space, delivering home broadband services, voice services. The other half of the business was around enterprise services and telecom services. But now our company has grown, and we've become much more than that. Now the consumer side of our business is much smaller it's. It's less than 25% of our business overall, and we brought in many more capabilities and technologies. And so we really felt like we were at a point where we and talking to our customers and doing brand analysis around the world because we're now a global, uh, company that has operations in over 100 countries around the world. Um, we felt like we needed to change that branding to represent who we are as terms of that, that large enterprise services company that does a lot more than just telecom services. And so that's why we came up with the name of Lumen Technologies. And as I said, the consumer side, the business still has a centurylink brand. But now the Enterprise Services piece of our company is called Lumen. >>So as that's transpired during this very dynamic time, just give me a little bit of perspective from your customers. How are they embracing this reading? Because we know rebrand is far more than simply rebranding product names and things like that. >>Yes, yeah, I think our customers we're really embracing it. Well, I mean, we've got great feedback from them on the new naming approach and our customers love the name. And but they also more than just the name they love, the idea of, of what we're doing and how we're positioning, how we're transforming our company to really represent what we do as being a company that delivers a platform for managing and distributing digital applications and digital assets across the world. And as you as this audience really knows, uh, enterprises values arm or and MAWR being being determined by their digital assets, whether that is content or whether it's applications. Or it could be, um, processes and things that the intellectual property that that companies own. And when we thought about our company and what it was that we really do for our customers, it really boils down to that is that customers trust us to move their their most valuable digital assets around the world to place them where they need to be when they need to be secured them in place and remove them when they don't need them there anymore. >>And that trust is absolutely critical. I want to get your perspective on something I noticed on Lumens website saying powering progress and the promise of the fourth Industrial Revolution. First of all, what is the promise of the fourth Industrial Revolution? And how is Lumen positioned to deliver progress on it? >>Yeah, So the fourth Industrial Revolution. Some of the audience may not understand what we mean by that when there's really been been. Up to now, there have been three industrial or industrial revolutions. The last one was the advent of the Internet and electron ICS And, you know, looming in its history plays a big role in the third Industrial Revolution because of the build out of the global Internet. You know, we operate one of the largest public Internet networks in the world, and but now we see that technology is pacing. Is taking a ramp up in the next phase of leveraging technologies like artificial intelligence and machine learning i O. T technologies technologies that that require applications and data that need to be distributed in a much more wide basis because computers happening everywhere in the fourth Industrial Revolution. And when we say that we're enabling that and we're enabling the promise of that, we're looking at what we do as having a platform that enables enterprise customers to create capabilities that leverage Fourth Industrial Revolution Technologies and distribute those around the world on a dynamic basis in a real time basis, in in in the fashion of How Cloud has evolved over the last few years. >>So how are you guys working together with AWS to enable customers to be able to leverage that technology that power the ability to get data that they need all across the globe as quickly as possible? >>Yes, so we worked with AWS and a number of ways in that front. You know, of course, AWS makes some great products that are based in the cloud. And they do all these technologies that are speaking about in terms of artificial intelligence and machine learning and video analytics or things and tools that AWS is built to be run out of their out of their cloud services. But Lemon works with AWS in that distribution aspect of it, and taking those assets and those applications and making them operate on a much widely distributed basis and dropping them on customer premise locations at the deep edge in into different markets wherever it makes the most sense for customers, from a performance and economic standpoint to be running those, uh, those next generation types of applications. And so we work with in combination with a W s to build those solutions into end for customers. Lumen has a professional services I t services organization also, that helps customers put together complex solutions involving Internet of things. So we, for instance, we just deployed a factory environment that has a million square foot factory with high level of automation that's run using these types of analytics tools where we're we're putting together the integration on the factory floor back to, uh, the cloud a cloud like aws. >>So in the last, you know, nine months of the world being in such a different place with businesses overnight suddenly having to dio almost 100% remote operations, how does the technology that you just talked about? How does that facilitate a business to keep up and running to not just be able to survive and continue to pivot as they need to during this time, but also to be able to really become the drivers of tomorrow? >>Yes, you know, and from our position is having, you know, over 100,000 enterprise customers and operating in regions over the world are perspective. We've really been able to see how our customers have survived and thrived and those who have not thrived so well through this whole cove it pandemic. And, you know, one of the keys for the companies that have really kind of excelled during this time has been there how far along they were in the adoption curve of cloud technologies and things like the Fourth Industrial Revolution types of technologies. Because those companies were able to dynamically scale up re shift, their resource is they were able to act remotely and control things remotely without having to have humans on premise on site engaging. Um, you know, some of the factory things that we've seen some of the work from home situations that we've seen those companies that were not operating with the kind of flexibility and scale that the cloud environment and the the four ir environment enables have really have really struggled, while the others have really been able to step up on bond, even outperform in many ways from where they were before. >>Yeah, we've been talking for months on the Cube about this acceleration of digital transformation that this pandemic has really forced and seen those companies to your point. Those that were already poised to be agile to adopted are in a much better position. One of the companies I was talking to you recently has Webcams all over the globe, and they're providing, um, you could get it throughout your Apple TV or I think, in Amazon Fire Stick where you can have these virtual experiences going into what's going on in Paris right now, of course, helping us live vicariously since we can't travel. But that's the whole proliferation of the edge and the amount of data that's being generated and process at the edge to the cloud to the core and getting that quickly to the consumer, whether it's a business or an actual consumer, what are you guys doing to help your business is your customers leverage the edge in a in an efficient way so that this accelerated pace that we're living in is actually able to help them. Dr Value. >>Yeah, we we have seen a really uptick in terms of edge opportunities since the Kobe pandemic hit and s so I can give you a great example of one that we that we recently just publicly announced its with a interesting situation with a company called Cyber Reef. Cyber Reef Builds has security technology that they help protect school systems and kids that are now being educated at home instead of in the public schools. Physically, they're they're they're at home, and those kids need protection from the Internet because they're on the Internet all day now. And Cyber Reef provides security tools for the public school systems to help protect those Children and what they're doing and making sure that there focused on school and not, you know, getting. They're having bad actors reached them through the public Internet. They're doing that That is an edge application because they needed to place their security software control tools very close to the edge deep into these markets, with good connection into public Internet and close proximity to the eyeballs of these, uh, these schoolchildren that around in the area, and so they have deployed across the country across our footprint, their their their platform, basically on on our platform to support those deployments toe help our Children as they get educated, >>so important. And if you think about a year ago when we were all in Vegas for reinvent 2019, we wouldn't even have thought we would need something of that scale. I'm here we are with this massive need and companies like Lumet and A W s being able to enable that. Talk to me a little bit about though what you guys are doing with a W s outpost is that part of what you just talked about? >>It wasn't for that example that I just gave, but we are working a lot with AWS outpost. And so we have we see aws outpost, a za key part of our total edged portfolio of solutions that we that we deliver. We have been, uh, investing a lot in our data centers across the world, because looming has hundreds of data centers that are deeply distributed into all of these markets around the world and working with aided without the ws on certifying those locations as outpost deployment, uh, locations. We have also used that I T services organization that that can provide consultation and I t management services for our enterprise customers. Thio. We've been certifying them on outpost configurations. So we've been training our I T professionals on, uh, the AWS solution and on the outpost solution in getting those certification credentials so that we can bring joint products to market with AWS that involved outposts as part of the solution and build in the end capabilities that combine our our services and capabilities with AWS and outpost for for combined solution. >>And can that combined solution to help your customers your joint customers get faster access to their data? Because we know data volume is only going up and up and up, and businesses need to be able to gain insights in real time. Is this the technology that could help get faster insights or access data faster? >>Absolutely. You know, that's and that's one of the key value propositions of ah, a solution like an outpost. Is that because you can drop them pretty much anywhere in the world that you that you need to put compute close to the point of digital interaction? Then, uh, it makes an ideal solution for customers that, uh, that want to work in that AWS environment and also leverage all of the other tools that eight of us can bring to bear from the cloud, uh, platform that that they that they offer but yeah, the place and compute close to that. That point of digital interaction is what it's all about, and it isn't just driven by performance, and performance is a really key part of it because they wanna have that fast interaction at the edge. But there are other things there, too. I mean, sometimes there are economics that play out for many companies that just make it make more sense to act on on compute or storage that it sits, sits more centrally, too many notes that could be aggregated in a market to that one essential location. We're running across use cases where customers, uh, they want to keep that data local because of governance issues or because of privacy issues or because of some kind of a regulatory requirement that they've got that they don't. They need to know exactly where that that data resides at all times, and it needs to be localized in a certain market or country. And eso they're the types of reasons why they would want to use an outpost to really there's there numerous. >>So last question. When you're talking with customers, I imagine the conversations quite different the last nine months or so. Maybe even the level of which you're having these conversations has gone up to the C suite or maybe even to the board. What do you what's your advice to businesses in any industry that really need to move forward quickly, transform to be able to start harnessing the power that four er can deliver but are just not sure where to start. >>Yeah, so, you know, we're just my advice is that they're gonna have to embrace the future embrace that, you know, embrace change. We're Look, we we have never been in a period of time where the pace of change has been assed fast as it is now, and it's not going to slow down. And so you do have to embrace that. But when you But if you're sitting there struggling, I appreciate the dilemma that they're in because, like, Well, where do I start? What do I what do I try? The thing is that that you can you you should pick a project that you can manage and deploy it. But when you deploy it and test it, make sure that you've got really measurable results. that you have really clear KP eyes of what you're trying to achieve and what you know. Are you out for financial goals or you out for performance improvement? Are you out for I t. Greater I t agility. Build the measures around that, Then test the technology that you want to try because we find that some companies approach it and they're kind of like doing it as a science experiment. And then they go, Wow, this was This was cool. It was a good science experiment, but it didn't, but it didn't wind up. They didn't capture the the actual benefit of it. And so then they don't They can't go in and prove it in anymore. And it's kind of like it sets them back because they didn't take that extra preparation >>and businesses in any industry. Nobody has. Has the time Thio face a setback because there's gonna be somebody right behind you in the rear view mirror who's gonna be smaller, agile, more nimble to take advantage. Paul. Great advice for businesses in every industry, and thank you for talking to us about what Lumen Technologies is what you guys are doing with a W s to help customers really embrace the capabilities of the Fourth Industrial Revolution. We appreciate your time. >>All right. Thank you. And thank you to the Cuba. It's good to see you all again. >>Good to see you too. Glad you're safe. And hopefully next time we'll get to see you in person soon For Paul Saville. I'm Lisa Martin. You're watching the cubes coverage of aws reinvent 2020? Yeah.

(upbeat music) >> Announcer: From around the globe. It's theCUBE, with digital coverage of AWS reinvent 2020, sponsored by Intel, AWS and our community partners. >> Welcome to theCUBE's coverage of AWS reinvent 2020, the virtual edition. I'm Lisa Martin. I have a couple of guests joining me next to talk about AWS and Druva. From Druva, Chris White is here, the chief revenue officer. Hey Chris, nice to have you on the program. >> Excellent, thanks Lisa. Excited to be here. >> And from AWS Sabina Joseph joins us. She is the general manager of the Americas technology partners. Sabina, welcome. >> Thank you, Lisa. >> So looking forward to talking to you guys unfortunately, we can't be together in a very loud space in Las Vegas, so this will have to do but I'm excited to be able to talk to you guys today. So Chris, we're going to start with you, Druva and AWS have a longstanding partnership. Talk to us about that and some of the evolution that's going on there. >> Absolutely, yeah. we certainly have, we had a great long-term partnership. I'm excited to talk to everybody about it today and be here with Sabina and you Lisa as well. So, we actually re architect our entire environment on AWS, 100% on AWS back in 2013. That enables us to not only innovate back in 2013, but continue to innovate today and in the future, right. It gives us flexibility on a 100% platform to bring that to our customers, to our partners, and to the market out there, right? In doing so, we're delivering on data protection, disaster recovery, e-discovery, and ransomware protection, right? All of that's being leveraged on the AWS platform as I said, and that allows uniqueness from a standpoint of resiliency, protection, flexibility, and really future-proofing the environment, not only today, but in the future. And over this time AWS has been an outstanding partner for Druva. >> Excellent Chris, thank you. Sabina, you lead the America's technology partners as we mentioned, Druva is an AWS advanced technology partner. Talk to us from through AWS lens on the Druva AWS partnership and from your perspective as well. >> Sure, Lisa. So I've had the privilege of working with Druva since 2014 and it has been an amazing journey over the last six and a half years. You know, overall, when we work with partners on technical solutions, we have to talk in a better architect, their solution for AWS, but also take their feedback on our features and capabilities that our mutual customers want to see. So for example, Druva has actually provided feedback to AWS on performance, usability, enhancements, security, posture and suggestions on additional features and functionality that we could have on AWS snowball edge, AWS dynamoDB and other services in fact. And in the same way, we provide feedback to Druva, we provide recommendations and it really is a unique process of exposing our partners to AWS best practices. When customers use Druva, they are benefiting from the AWS recommended best practices for data durability, security and compliance. And our engineering teams work very closely together. We collaborate, we have regular meetings, and that really sets the foundation for a very strong solution for our mutual customers. >> So it sounds very symbiotic. And as you talked about that engineering collaboration and the collaboration across all levels. So now let's talk about some of the things that you're helping customers to do as we are all navigating a very different environment this year. Chris, talk to us about how Druva is helping customers navigate some of those big challenges you talked about ransomware for example, this massive pivot to remote workforce. Chris (mumbles) got going on there. >> Yeah, absolutely. So the, one of the things that we've seen consistently, right, it's been customers are looking for simplicity. Customers are looking for cost-effective solutions, and then you couple that with the ability to do that all on a single platform, that's what the combination of Druva and AWS does together, right? And as you mentioned, Lisa, you've got work from home. That's increased right with the unfortunate events going across the globe over the last almost 12 months now, nine months now. Increased ransomware that threats, right? The bad actors tend to take advantage of these situations unfortunately, and you've got to be working with partners like AWS like Druva, coming together, to build that barrier against the bad actors out there. So, right. We've got double layer of protection based on the partnership with AWS. And then if you look at the rising concerns around governance, right? The complexity of government, if you look at Japan adding some increased complexity to governance, you look at what's going on across, but across the globe across the pond with GDPR, number of different areas around compliance and governance that allows us to better report upon that. We built the right solution to support the migration of these customers. And everything I just talked about is just accelerated the need for folks to migrate to the cloud, migrate to AWS, migrate to leveraging, through the solutions. And there's no better time to partner with Druva and AWS, just because of that. >> Something we're all talking about. And every key segment we're doing, this acceleration of digital transformation and customers really having to make quick decisions and pivot their businesses over and over again to get from survival to thriving mode. Sabina talk to us about how Druva and AWS align on key customer use cases especially in these turbulent times. >> Yeah, so, for us as you said Lisa, right. When we start working with partners, we really focus on making sure that we are aligned on those customer use cases. And from the very first discussions, we want to ensure that feedback mechanisms are in place to help us understand and improve the services and the solutions. Chris has, he mentioned migrations, right? And we have customers who are migrating their applications to AWS and really want to move the data into the cloud. And you know what? This is not a simple problem because there's large amounts of data. And the customer has limited bandwidth Druva of course as they have always been, is an early adopter of AWS snowball edge and has worked closely with us to provide a solution where customers can just order a snowball edge directly from AWS. It gets shipped to them, they turn it on, they connect it to the network, and just start backing up their data to the snowball edge. And then once they are done, they can just pack it up, ship it back. And then all of this data gets loaded into the Druva solution on AWS. And then you also, those customers who are running applications locally on AWS Outposts, Druva was once again, an early adopter. In fact, last reinvent, they actually tested out AWS Outposts and they were one of the first launch partners. Once again, further expanding the data protection options they provide to our mutual customers. >> Well, as that landscape changes so dramatically it's imperative that customers have data center workloads, AWS workloads, cloud workloads, endpoints, protected especially as people scattered, right, in the last few months. And also, as we talked about the ransomware rise, Chris, I saw on Druva's website, one ransomware attack every 11 seconds. And so, now you've got to be able to help customers recover and have that resiliency, right. Cause it's not about, are we going to get hit? It's a matter of when, how does Druva help facilitate that resiliency? >> Yeah, now that's a great point Lisa. and as you look at our joint customer base, we've got thousands of joint customers together and we continue to see positive business impact because of that. And it's to your point, it's not if it's when you get hit and it's ultimately you've got to be prepared to recover in order to do that. And based on the security levels that we jointly have, based on our architecture and also the benefits of the architecture within AWS, we've got a double layer of defense up there that most companies just can't offer today. So, if we look at that from an example standpoint, right, transitioning offer specific use case of ransomware but really look at a cast media companies, right? One of the largest media companies out there across the globe, 400 radio stations, 800 TV stations, over a hundred thousand podcasts, over 4,000 or 5,000 streams happening on an annual basis, very active and candidly very public, which freaks the target. They really came to us for three key things, right? And they looked for reduced complexity, really reducing their workload internally from a backup and recovery standpoint, really to simplify that backup environment. And they started with Druva, really focused on the end points. How do we protect and manage the end points from a data protection standpoint, ultimately, the cost savings that they saw, the efficiency they saw, they ended up moving on and doing key workloads, right? So data protection, data center workloads that they were backing up and protecting. This all came from a great partnership and relationship from AWS as well. And as we continued to simplify that environment, it allowed them to expand their partnership with AWS. So not only was it a win for the customer, we helped solve those business problems for them. Ultimately, they got a (mumbles) benefit from both Druva and AWS and that partnership. So, we continue to see that partnership accelerate and evolve to go really look at the entire platform and where we can help them, in addition to AWS services that they're offering. >> And that was... It sounds like them going to cloud data production, was that an acceleration of their cloud strategy that they then had to accelerate even further during the last nine months, Chris? >> Yeah, well, the good news for cast is that at least from a backup and recovery standpoint, they've been ahead of the curve, right? They were one of those customers that was proactive, in driving on their cloud journey, and proactive and driving beyond the work from home. It did change the dynamics on how they work and how they act from a work from home standpoint, but they were already set up. So then they didn't really skip a beat as they continue to drive that. But overall, to your point, Lisa, we've seen an increase and acceleration and companies really moving towards the cloud, right. Which is why that migration strategy, joint migration strategy, that Sabina talked about is so important because it really has accelerated. And in some companies, this has become the safety net for them, in some ways their DR Strategy, to shift to the cloud, that maybe they weren't looking to do until maybe 2022 or 2023, it's all been accelerated. >> Everything's, but we have like whiplash on the acceleration going on. >> Sabina, talk to us about some of those joint successes through AWS's lens, a couple of customers, you're going to talk about the University of Manchester, and the Queensland Brain Institute, dig into those for us. >> Yeah, absolutely. So, I thank Chris sharing those stories there. So the two that kind of come into my mind is a University of Manchester. They have nearly 7,000 academic staff and researchers and they're, part of their digital transformation strategy was adopting VMware cloud on AWS. And the University actually chose Druva, to back up 160 plus virtual machine images, because Druva provided a simple and secure cloud-based backup solution. And in fact, saved them 50% of their data protection costs. Another one is Queensland Brain Institute, which has over 400 researchers who really worked on brain diseases and really finding therapeutic solutions for these brain diseases. As you can imagine, this research generates terabytes critical data that they not only needed protected, but they also wanted to collaborate and get access to this data continuously. They chose Druva and now using Druva solution, they can back up over 1200 plus research papers, residing on their devices, providing global and also reliable access 24 by seven. And I do want to mention, Lisa, right? The pandemic has changed all of humanity as we know it, right? Until we can all find a solution to this. And we've also together had to work to adjust what can we do to work effectively together? We've actually together with Druva shifted all of our day-to-day activities, 200% virtual. And we, but despite all of that, we've maintained regular cadence for our review business and technical roadmap updates and other regular activities. And if I may mention this, right, last month we AWS actually launched the digital workplace competency, clearly enabling customers to find specialized solutions around remote work and secure remote work and Druva, even though we are all in this virtual environment today, Druva was one of the launch partners for this competency. And it was a great fit given the solution that they have to enable the remote work environments securely, and also providing an end-to-end digital workplace in the cloud. >> That's absolutely critical because that's been one of the biggest challenges I think that we've all been through as well as, you know trying to go, do I live at work or do I work from home? I'm not sure some of the days, but being able to have that continuity and you know, your customers being able to access their data at 24 by seven, as you said, because there's no point in mapping up your data, if you can't recover it but being able to allow the continuation of the relationship that you have. I want to move on now to some of the announcements. Chris, you mentioned actually Sabina you did, when you were talking about the University of Manchester, the VMware ready certification Chris, Druva just announced a couple of things there. Talk to us about that. >> Thank you. Yeah, Lisa you're right. There's been a ton of great announcements over the past several months and throughout this entire fiscal year. To be in this touch base on a couple of them around the AWS digital workplace, we absolutely have certification on AWS around VMware cloud, both on AWS and Dell EMC, through AWS. In addition to continuing to drive innovation because of this unique partnership around powerful security encryption and overall security benefits across the board. So that includes AWS gov cloud. That includes HIPAA compliance, includes FedRAMP, as well as SOC two type two, certifications as well and protection there. So we're going to continue to drive that innovation. We just recently announced as well that we now have data protection for Kubernetes, 100% cloud offering, right? One of the most active and growing workloads around data, around orchestration platform, right? So, doing that with AWS, some of my opening comments back when we built this 100% AWS, that allows us to continue to innovate and be nimble and meet the needs of customers. So whether that be VMware workloads NAS workloads, new workloads, like Kubernetes we're always going to be well positioned to address those, not only over time, but on the front end. And as these emerging technologies come out the nimbleness of our joint partnership just continues to be demonstrated there. >> And Sabina, I know that AWS has a working backwards approach. Talk to me about how you use that to accomplish all of the things that Chris and you both described over the last six, seven plus years. >> Yes, so the working backwards process we use it internally when we build our own services, but we also worked through it with our partners, right? It's about putting the customers first, aligning on those use cases. And it all goes back to our Amazon leadership principle on customer obsession, focusing on the customer experience, making sure that we have mechanisms in place, to have feedback from the customers and operate that into our services solutions and also with our partners. Well, one of the nice things about Druva since I've been working with them since 2014 is their focus on customer obsession. Through this process, we've developed great relationship, Druva, together with our service team, building solutions that deliver value by providing a full Saas service for customers, who want to protect their data, not only in AWS, but also in a hybrid architecture model on premises. And this is really critical to us cause our customers want us to work with Druva, to solve the pain points, creating a completely maybe a new customer experience, right. That makes them happy. And ultimately what we have found together with Druva, is I think Chris would agree with this, is that when we focus on our mutual customers, it leads to a very longterm successful partnership as we have today with Druva. >> It sounds like you talked about that feedback loop in the beginning from customers, but it sounds like that's really intertwined the entire relationship. And certainly from what you guys described in terms of the evolution, the customer successes, and all of the things that have been announced recently, a lot of stuff going on. So we'll let you guys get back to work. We appreciate your time, Chris. Thank you for joining me today. For Chris white and Sabina Joseph, I'm Lisa Martin and you're watching theCUBE. (soft music fades)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah. Welcome to the cubes. Virtual coverage of 80 was reinvent 2020. I'm John for your host. We are the Cube virtual not there in person, but we're doing remote, as is a W s. Although there there on stage live. And we're here with Dave Brown, Vice President of the Sea, to compute. Great to see you again. Great keynote last night, kicking off everything for the opening night. Great stuff. >>Yeah, well, John, it's always good to be on the Cuban. Thanks for having me back. >>You know, you're in the hot seat these days in the sense of there's so much going on. I mean, Andy, that could do a three week announcement. Keynote. It was like in three hours of nonstop you take a break to go The bathroom. You missed two announcements, right? So, so much going on. You opened up reinvent 2020 with your announcement ec2 of mac instances. And there was a ton of compute. And the theme was really you know, reinventing and reimagining compute both. I want to get into that. But let's start with the hard news. Tell me about the Mac instances. Um, you had a great use case there, That kind of illustrated in your talk. But where is this coming from? It's obviously Mac developers are big, but is this market something that you guys saw from customers or was a necessity? Take us through the thinking around the Mac instance. Easy to for Mac instances, um are going for >>absolutely absolutely So I mean me personally Matthews, a longtime Matthews that we've often thought about. Could we ever bring Mac OS to AWS? Right This thing we've spoken about on and off for many, many years and, you know, it was about a year and a half about two years ago. You know, we're always hearing new use cases from customers, and that's kind of what we're doing. So we're saying what a customer is trying to do that we don't support today, and how would we support them in that? And we started a year from customers that they have been able to successfully migrate all of the AWS workloads to AWS. So most of the server workloads to AWS and then they've got this Mac bold workload that they just weren't able to bring to us. We just didn't support Max into. It was a great example who I had on stage with me last night where you know, they over the last couple of years have been moving Ah, lot of their workloads to AWS. And and then they had these Mac money sitting around that they had to manage themselves. And so we said, could we actually do this? And so that was the one thing the customer ask. And the other thing that we realized was with the nitro system in the work that we've been doing there over the last, you know, six years, seven years since 2012, Really? And just where we are from the From nitro system point of view, we were able to wrap a Mac money without making any changes to it with nitro cards plugging a FireWire to the thunderbolt port and and and actually control that device. And so it means that you get the best of Apple hardware, which is what Apple's all about is the hard way that they make and the way that their software works with it. together with the nitric system and the cards around that inte integrating with the rest of AWS. So we're giving you, you know, high speed secure networking. We're giving you great access to elastic block store Was just integrates natively into the magma Nias? Well, a So we realized that the technology was there, the customer asked, was there and then obviously went to Apple and worked with them very closely to make it happen. And so that's kind of how it all came together. And I was incredibly excited to announce it last night. And the feedback today has just been amazing. A lot of excitement. >>Yeah, take me through the use case because, you know, obviously there's two trends going on. There's custom chips and server list kind of thing happening where you guys, I mean, really doing a good job of the eye as layer, innovating there and then platform as a service. All that software on top. I totally get that. You could see that happening. Chips custom ships to Intel, A, M, D. And others. Now you got Mac hardware. Where's the innovation use case because one would start would say, Hey, why don't you care about whether it's Mac hardware or not. Because I'm server lists. I should be programming the infrastructure actually be getting compute generically. Where does the Mac tying come in? Because that's the first question I was thinking of was, I'm a Mac user. I love Mac, but I'm also got some windows actually going on now. And ultimately, do I really care if it's compute? What's your reaction to that? Yeah, >>absolutely. I mean, if you look at Apple's ecosystem today, right, they have millions of applications in the APP store. They have 28 million developers worldwide, actually building those applications just incredible. And many of those applications, all these millions in the In the APP store itself, there's many more applications that are both by enterprises and companies, right? We have an application that we use internally at Amazon is available on my phone. That's not in the APP store, and you know, many companies are doing that and to build applications for the ecosystem, they have to be built on Mac hardware. And that's just how Apple works, right? So if you wanna build for iPad or iPhone or even Apple TV and Apple watch, you have to build those applications on a Mac. And so what we see companies doing is, you know, the old develop a meme off. Well, it works on my computer, right when you build something, you don't wanna be bullied on your local laptop for production. So they typically have a fleet of machines that they either under somebody's desk or in a data center somewhere that they use for for building these Mac applications. And so it's not possible to build a Mac application on anything other than a Mac itself. And we when we looked at it, we really didn't feel that virtualization made sense, right? Apple? I mean, they have some some virtualization that they're able to do within Mac OS itself. But if you think about how do we solve the customer use case, it's really bringing apple hardware too easy to to solve the problem and giving customers that exactly same exact same experience that they have on prep. And if you look into it like that, models just worked right. We gave them better access. Uh, you know, they've been using that data which you normally say, Hey, don't don't run production workloads on a beta. But you know, I found out if I interview with the BPS at Intuit critique that they've actually moved 80% of their production pulled wear clothes too easy to already to run on the Mac instances. And so that, and that's in the space of two months. And so, just as seamless ability to move because it's the same hardware is kind of what we were going >>after. Great, thanks for sharing that and say, one thing I wanna point out is Mac does have their own chips as well. They're going custom chips. Amazon's going custom chips. And I think I think you nailed what I was trying to understand, which is this developer community for Mac. And there's some things that are purpose built for Mac devices. So on Mac ecosystem, get the marketplace as well as you know, that that was the hardware PCs and devices, and they're only doing more and more. So this brings me to the i o t. Um, piece of it, because Apple does make devices that people wear and I watch is, um, iPhones. I mean, they're not computers anymore. They're everything. So this kind of brings up the edge conversation. So whether it's an iPhone or a five G in a Metro or I'm a stadium watching a football game and there's some sensor camera vision industrial thing there, this is the new normal. This is where you guys are kind of eating, eating up the software side that that business, because there's new capabilities here. Can you explain how compute he's, particularly C two gets to the edges because no one wants to move data around. They wanna move, compute, not data, because data is expensive and it's and it's fat. So we we talked about that we keep on years ago, but you gotta move. Compute. So how does that work Take us through your vision? >>Absolutely. And this is This is a massively growing area for us. I mean, you mentioned Apple's new M one silicon Apple silicon that they just launched a swell, and we're super excited about Apple's been doing there. We've been doing the same thing with our grab. It's on two processor and really saving customers. An incredible amount on price performance. Tried customers moving and getting 40% improvement and price performance just by moving to grab it on too. It's just incredible. Um, in terms of the edge, you know, we started this journey. We started this journey quite some time ago and bringing, you know, Lambda functions to cloudwatch and things like that. How do we bring compute to the edge? We took a look at five G, which I think it's gonna feel a lot of this right if if we look at our cell phones today was actually just talking to the Apple team yesterday with the iPhone, only came out, you know, 13 years ago. It's kind of amazing to think just how much progress we've had and what four g did for the device that's in our pocket in terms of, you know, just how much we rely on that today and what we get. Well, five g is just a step function in both in terms of latency, but also in terms of throughput. And so, you know, one of the projects we announced last year with Verizon and we now Andy announced this morning we're also gonna be rolling out with Katy D I and SK Telecom and Vodafone next year. Um is a project always like that brings aws compute to the edge of the telco network. And so with Verizon, we now have eight locations around the U. S. Where we have AWS compute capacity. And what I mean by that is literally C five instances uh, G four GPU instances for customers that want to do influence and graphics processing on the edge. And that's embedded into the five G network on DSO customers. You know, we've got a number of customers that are doing a lot of interesting things with five G in the sports area, where they have five G cameras that are, you know, submitted directly to wavelength. We no longer need to drive a truck to a stadium to record a game. You just have five G cameras, um, to, you know, automated factories where they doing robotics in factories and yet really low latency. And they don't want the computer, the factory they wanted in five G and so just exciting area for us. That's growing really, really quickly. Thea Other thing we did is obviously with local zones. We launched our first local zones in L a X last year, Los Angeles on that's being used by the movie industry, so you know right now is a lot of exciting up and running off the covert and shut down for a period of time and filming the next release of all of our favorite episodes and across all of these various streaming platforms. And a lot of that work is actually the post production is being done on on AWS on G four instances within the Los Angeles region. So, you know, very low agency for colorization animation, special effects, all that sort of things happening there. What we heard from a lot of customers was they loved outposts as well, which is our offering to put a server into a data center. And you heard from riot games in Andy's Keynote, where they actually bought a number of outposts and put them all over the U. S. And also other places of the world to really lower the Leighton see for their latest game. And so what Andy also just announced is the availability off three additional local zones. So Atlanta, Miami and Houston Sorry, Boston Miami in Houston available today, and then additional 12 available local zones next year, and what that does is that sort of spreads AWS capacity compute capacity at the edge in all of our major metropolitan hubs all of their capacities on the AWS backbone as well, but brings customers that low latency connectivity that they're looking for. Gaming developers were, you know, every every millisecond counts in terms of gameplay on so super excited to be going after that use case, which I think, you know, it's difficult to tell what the next 10 years is gonna be like. But I think Layton's he's gonna have a big part to play in the types of applications we see on our phones going forward. >>Great stuff, final question for you as we wrap up, obviously with virtualization with virtualization. But you know, the cove it is. And he pointed out, People are gonna change, is gonna be winners and losers. He kind of clearly pointed out, But the people who do lean into the cloud who have been on the cloud or taking advantage of the tail winds of cove in because of the capabilities there are two bills air higher, and you should be happy for that. But they're also gonna have more demand for you to say, Hey, I need more services. So How do you speak to those people who are leaning in who are leveraging, more, compute? What should they be looking at? What kinds of services should be connecting into compute? How should they be thinking about the future of compute so that they can take advantage of those capabilities? The lower costs, higher performance? What things are complementary for these customers as they come in, not toe dip in the water kind of things against really driving. And what do they need? >>Yeah, absolutely. And this has been a big focus on us. You know, things has bean, as I cover in my keynote, which leadership session that I'm doing tomorrow Wednesday. You know, a lot of this year has been helping customers through covert and what covert is meant for their business. Whether that is cost savings for many of them or whether it's just demand, you know that they've never experienced are expected before. I mean, we've been incredibly hard at work in servicing those customers, right? I actually catch up with Scott Sikora. In my keynote. He leads our capacity team. We talked through what it meant and how we actually provided the capacity that our customers needed during Colbert Times. But for a customer moving to us, the first thing is obviously we wanna find ways to make them very successful in the cloud, but more importantly, lower price performance for them. So what we wanted to do is give them the best possible performance that's available at the lowest possible cost. And if you look at a number of the announcements that Andy made today, you know whether it's our latest graviton processor where you can, you know, when you move to arm. I think customers often overestimate how much work it will be to move to arm. And when I talked to them after they have moved, that's ahead. Wasn't actually that much work. We actually got it up and running relatively quickly. So what's simpler than people expect? But that's an opportunity to save 40% on price performance. You know these new newer workloads like our graphics. We just launched a new G four a D, which is an AMG based GPU solution, the first time we have had an AMG GPU on the EEC too. And that's also looking to say, if you know upwards of 40% price performance of other GPU offering so just incredibly exciting for graphics, work, clothes and then in the machine learning space. Like I think, if you know, machine learning is just become the new normal, like everybody is doing it. And you know, just three years ago, everybody was thinking about whether they should do it. How would how they would use it Now that it's a lot of companies are doing it. It's really How do you How do I use it more? And that comes down to again saving costs. And so what we know with without Inferential Chip and then the new Habbaniya chip we just announced it with with the work with Intel that we're doing and then a new trainee, um, ship for training, training. We're really working to lower the cost of machine learning. And so, like we've seen many customers like Alexa was a great use case the other day. Being able to lower the cost of inference for Alexa by 35% again just helps customers, you know, move to the cloud. But I mean, just generally, you know, we're trying to support customers everywhere where there were, you know, if there are many customers are in their own data centers looking to move to AWS. You know, we have great models that can support them with our existing compute. A new savings plan offering we announced last year just great for saving costs on getting the price down So a lot. You can look at it. You know, I could go on forever. Really. It >>Certainly it's certainly is MAWR. We'll we'll do a deeper dive follow up after reinvent, but it is a wake up call. As I wrote in my post, um, for a cloud on Finally, I've been saying this for years. Horizontal scalability is a disruption on the infrastructure side, but you've got vertical specialization with data to create great modern apse of machine learning. And I actually playing out in full display here is Andy said, um, net right now. So all this benefits and all these opportunities to disrupt horizontally and then leverage the data all tied together, all coming together. You're clear. Leading the team. Great Brown, vice president of E C. Two in charge of the team that's driving the future. Compute. Thanks for coming on The Cube Cube Live coverage. Thanks. >>Thanks for having me. >>Okay. I'm John for the Q back for more live coverage after this short break

>>Okay, >>listen, we're gearing up for the start of the snowflake Data Cloud Summit, and we wanna go back to the early roots of Snowflake. We've got some of the founding engineers here. Abdul Monir, Ashish Motive, Allah and Alison Lee There three individuals that were at snowflake in the early years and participated in many of the technical decisions that led to the platform and is making snowflake famous today. Folks, great to see you. Thanks so much for taking some time out of your busy schedules. Hey, it's gotta be really gratifying. Thio, See this platform that you've built, you know, taking off and changing businesses. So I'm sure it was always smooth sailing. Right? There were. There were no debates. Wherever. >>I've never seen an engineer get into the bed. >>Alright, So seriously so take us back to the early days. You guys, you know, choose whoever wants to start. But what was it like early on? We're talking 2013 here, right? >>When I think back to the early days of Snowflake, I just think of all of us sitting in one room at the time. You know, we just had an office that was one room with, you know, 12 or 13 engineers sitting there clacking away on our keyboards, uh, working really hard, turning out code, uh, punctuated by you know, somebody asking a question about Hey, what should we do about this, or what should we do about that? And then everyone kind of looking up from their keyboards and getting into discussions and debates about the work that we're doing. >>So so Abdul it was just kind of heads down headphones on, just coating or e think there was >>a lot of talking and followed by a lot of typing. Andi, I think there were periods of time where where you know, anyone could just walk in into the office and probably out of the office and all the here is probably people, uh, typing away at their keyboards. And one of my member vivid, most vivid memories is actually I used to sit right across from Alison, and there's these huge to two huge monitor monitors between us and I would just here typing away in our keyboard, and sometimes I was thinking and and and, uh and all that type and got me nervous because it seemed like Alison knew exactly what what, what she needed to do, and I was just still thinking about it. >>So she she was just like bliss for for you as a developer engineer was it was a stressful time. What was the mood? So when you don't have >>a whole lot of customers, there's a lot of bliss. But at the same time, there was a lot of pressure on us to make sure that we build the product. There was a time line ahead of us. We knew we had to build this in a certain time frame. Um, so one thing I'll add to what Alison and Abdulle said is we did a lot of white boarding as well. There are a lot of discussions, and those discussions were a lot of fun. They actually cemented what we wanted to build. They made sure everyone was in tune, and and there we have it. >>Yes, so I mean, it is a really exciting time doing any start up. But when you know when you have to make decisions and development, invariably you come to a fork in the road. So I'm curious as to what some of those forks might have been. How you guys decided You know which fork to take. Was there a Yoda in the room that served as the Jedi master? I mean, how are those decisions made? Maybe you could talk about that a little bit. >>Yeah, that's an interesting question. And I think one of a Zai think back. One of the memories that that sticks out in my mind is is this, uh, epic meeting and one of our conference rooms called Northstar. Many of our conference rooms are named after ski resorts because the founders, they're really into skiing. And that's why that's where the snowflake name comes from. So there was this epic meeting and I'm not even sure exactly what topic we were discussing. I think it was It was the sign up flow and and there were a few different options on the table and and and one of the options that that people were gravitating Teoh, one of the founders, didn't like it and and on, and they said a few times that there's this makes no sense. There's no other system in the world that does it this way, and and I think one of the other founders said, uh, that's exactly why we should do it this way. And or at least seriously, consider this option. So I think there was always this, um, this this, uh, this tendency and and and this impulse that that we needed to think big and think differently and and not see the world the way it is but the way we wanted it to be and then work our way backwards and try to make it happen. >>Alison, Any fork in the road moments that you remember. >>Well, I'm just thinking back to a really early meeting with sheesh! And and a few of our founders where we're debating something probably not super exciting to a lot of people outside of hardcore database people, which was how to represent our our column metadata. Andi, I think it's funny that you that you mentioned Yoda because we often make jokes about one of our founders. Teary Bond refer to him as Yoda because he hasn't its tendency to say very concise things that kind of make you scratch your head and say, Wow, why didn't I think of that? Or you know, what exactly does that mean? I never thought about it that way. So I think when I think of the Yoda in the room, it was definitely Terry, >>uh, excuse you. Anything you can add to this, this conversation >>I'll agree with Alison on the you're a comment for short. Another big fork in the road, I recall, was when we changed. What are meta store where we store our own internal metadata? We used >>to use >>a tool called my sequel and we changed it. Thio another database called Foundation TV. I think that was a big game changer for us. And, you know, it was a tough decision. It took us a long time. For the longest time, we even had our own little branch. It was called Foundation DB, and everybody was developing on that branch. It's a little embarrassing, but, you know, those are the kind of decisions that have altered altered the shape of snowflake. >>Yeah. I mean, these air, really, you know, down in the weeds, hardcore stuff that a lot of people that might not be exposed to What would you say was the least obvious technical decision that you had to make it the time. And I wanna ask you about the most obvious to. But what was the what was the one that was so out of the box? I mean, you kind of maybe mentioned it a little bit before, but what if we could double click on that? >>Well, I think one of the core decisions in our architectures the separation of compute and storage on Do you know that is really court architecture. And there's so many features that we have today, um, for instance, data sharing zero copy cloning that that we couldn't have without that architecture. Er, um and I think it was both not obvious. And when we told people about it in the early days, there was definitely skepticism about being able to make that work on being able Thio have that architecture and still get great performance. >>Anything? Yeah, anything that was, like, clearly obvious, that is, Maybe that maybe that was the least and the most that that separation from computing story because it allowed you toe actually take advantage of cloud native. But But was there an obvious one that, you know, it's sort of dogma that you, you know, philosophically lived behind. You know, to this day, >>I think one really obvious thing, um is the sort of no tuning, no knobs, ease of use story behind snowflake. Andi and I say it's really obvious because everybody wants their system to be easy to use. But then I would say there are tons of decisions behind that, that it's not always obvious three implications of of such a choice, right, and really sticking to that. And I think that that's really like a core principle behind Snowflake that that led to a lot of non obvious decisions as a result of sticking to that principle. So, yeah, I >>think to add to that now, now you've gotten us thinking I think another really interesting one was was really, um, should we start from scratch or or should we use something that already exists and and build on top of that? And I think that was one of these, um, almost philosophical kind of stances that we took that that a lot of the systems that were out there were the way they were because because they weren't built for the for the platforms that they were running on, and the big thing that we were targeting was the cloud. And so one of the big stances we took was that we were gonna build it from scratch, and we weren't gonna borrow a single line of code from many other database out there. And this was something that really shocked a lot of people and and many times that this was pretty crazy and it waas. But this is how you build great products. >>That's awesome. All right. She should give you the last word. We got, like, just like 30 seconds left to bring us home >>Your till date. Actually, one of those said shocks people when you talk to them and they say, Wow, you're not You're not really using any other database and you build this entirely yourself. The number of people who actually can build a database from scratch are fairly limited. The group is fairly small, and so it was really a humongous task. And as you mentioned, you know, it really changed the direction off how we design the database. What we what does the database really mean? Tow us right the way Snowflake has built a database. It's really a number of organs that come together and form the body and That's also a concept that's novel to the database industry. >>Guys, congratulations. You must be so proud. And, uh, there's gonna be awesome watching the next next decade, so thank you so much for sharing your stories. >>Thanks, dude. >>Thank you.

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. >>Welcome to the Cubes Coverage of Dell Technologies World 2020. The digital coverage Find Lisa Martin And then we started to be talking with one of Dell Technologies customers. Earth Camp. Joining Me is built sharp, the senior VP of product development and strategy from Earth Camp Phil, Welcome to the Cube. >>Thank you so much. >>So talk to me a little bit. About what Earth Cam does this very interesting Web can technology? You guys have tens of thousands of cameras and sensors all over the globe give her audience and understanding of what you guys are all about. >>Sure thing. The world's leading provider of Webcam technologies and mentioned content services were leaders and live streaming time lapse imaging primary focus in the vertical construction. So a lot of these, the most ambitious, largest construction projects around the world, you see, these amazing time lapse movies were capturing all of that imagery. You know, basically, around the clock of these cameras are are sending all of that image content to us when we're generating these time lapse movies from it. >>You guys, you're headquartered in New Jersey and I was commenting before we went live about your great background. So you're actually getting to be on site today? >>Yes, Yes, that's where lives from our headquarters in Upper Saddle River, New Jersey. >>Excellent. So in terms of the types of information that you're capturing. So I was looking at the website and see from a construction perspective or some of the big projects you guys have done the Hudson Yards, the Panama Canal expansion, the 9 11 Museum. But you talked about one of the biggest focus is that you have is in the construction industry in terms of what type of data you're capturing from all of these thousands of edge devices give us a little bit of insight into how much data you're capturing high per day, how it gets from the edge, presumably back to your court data center for editing. >>Sure, and it's not just construction were also in travel, hospitality, tourism, security, architectural engineering, basically, any any industry that that need high resolution visualization of their their projects or their their performance or of their, you know, product flow. So it's it's high resolution documentation is basically our business. There are billions of files in the isil on system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that air up to 30 giga pixel, sometimes typically around 1 to 2 giga pixel. But that composite imagery Eyes represents millions of images per per month coming into the storage system and then being, uh, stitched together to those those composites >>the millions of images coming in every month. You mentioned Isil on talk to me a little bit about before you were working with Delhi, EMC and Power Scale. How are you managing this massive volume of data? >>Sure we had. We've used a number of other enterprise storage systems. It was really nothing was as easy to manage Azazel on really is there was there was a lot of a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, you to manage that, uh, and and it's interesting with the amount of data that we handle. This is being billions of relatively small files there there, you know, half a megabyte to a couple of megabytes each. It's an interesting data profile, which, which isil on really is well suited for. >>So if we think about some of the massive changes that we've all been through the last in 2020 what are some of the changes that that Earth Kemp has seen with respect to the needs for organizations? Or you mentioned other industries, like travel hospitality? Since none of us could get to these great travel destinations, Have you seen a big drive up in the demand and the need to process data more data faster? >>Yeah, that's an injury interesting point with with the Pandemic. Obviously we had to pivot and move a lot of people toe working from home, which we were able to do pretty quickly. But there's also an interesting opportunity that arose from this, where so many of our customers and other people also have to do the same. And there is an increased demand for our our technology so people can remotely collaborate. They can. They can work at a distance. They can stay at home and see what's going on in these projects sites. So we really so kind of an uptick in the in the need for our products and services. And we've also created Cem basically virtual travel applications. We have an application on the Amazon Fire TV, which is the number one app in the travel platform of people can kind of virtually travel when they can't really get out there. So it's, uh, we've been doing kind of giving back Thio to people that are having having some issues with being able to travel around. We've done the fireworks of the Washington Mall around the Statue of Liberty for the July 4th, and this year will be Webcasting and New Year's in Times Square for our 25th year, actually. So again, helping people travel virtually and be, uh, maintain can be collectivity with with each other and with their projects, >>which is so essential during these times, where for the last 67 months everyone is trying to get a sense of community, and most of us just have the Internet. So I also heard you guys were available on Apple TV, someone to fire that up later and maybe virtually travel. Um, but tell me a little bit about how working in conjunction with Delta Technologies and Power Cell How is that enabled you to manage this massive volume change you've experienced this year? Because, as you said, it's also about facilitating collaboration, which is largely online these days. >>Yeah, I mean, the the great things they're working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we worked with in the past we've always found ourselves kind of second guessing. Obviously, resolutions are increasing. The camera performance is increasing. Streaming video is everything is is constantly getting bigger and better, faster. Maurits And we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at the second guess we're at with it With with this, this did L infrastructure. That's been it's been fantastic. We don't really have to think about that as much. We just continue innovating everything scales as we needed to dio. It's it's much easier to work with, >>so you've got power scale at your core data center in New Jersey. Tell me a little bit about how data gets from thes tens of thousands of devices at the edge, back to your editors for editing and how power scale facilitates faster editing, for example. >>Basically, you imagine every one of these cameras on It's not just camera. We have mobile applications. We have fixed position of robotic cameras. There's all these different data acquisition systems were integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the Internet, so these are all endpoints in our network. Eso that's that's constantly being ingested into our network and say WTO. I salon the big the big thing that's really been a timesaver Working with the video editors is, instead of having to take that content, move it into an editing environment where we have we have a whole team of award winning video editors. Creating these time lapse is we don't need to keep moving that around. We're working natively on Iselin clusters. They're doing their editing, their subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all it happened right there on that live environment on the retention. Is there if we have to go back later on all of our customers, data is really kept within that 11 area. It's consolidated, its secure. >>I was looking at the Del Tech website. There's a case study that you guys did earth campaign with Deltek saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I could imagine what the volumes changing so much now but on Li not only is huge for your business, but to the demands that your customers have as well, depending on where there's demands are coming from >>absolutely and and just being able to do that a lot faster and be more nimble allows us to scale. We've added actually against speaking on this pandemic, we've actually added person who we've been hiring people. A lot of those people are working remotely, as as we've stated before on it's just with the increase in business. We have to continue to keep building on that on this storage environments been been great. >>Tell me about what you guys really kind of think about with respect to power scale in terms of data management, not storage management and what that difference means to your business. >>Well, again, I mean number number one was was really eliminating the amount of resource is amount of time we have to spend managing it. We've almost eliminated any downtime of any of any kind. We have greater storage density, were able to have better visualization on how our data is being used, how it's being access so as thes as thes things, a revolving. We really have good visibility on how the how the storage system is being used in both our production and our and also in our backup environments. It's really, really easy for us Thio to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >>And you mentioned hiring folks during the pandemic, which is fantastic but also being able to do things much in a much more streamlined way with respect to managing all of this data. But I am curious in terms of of innovation and new product development. What have you been able to achieve because you've got more resource is presumably to focus on being more innovative rather than managing storage >>well again? It's were always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 Giga pixel. You know, people are talking about megapixel images were stitching hundreds of these together. We've we're just really changing the way imagery is used, uh, both in the time lapse and also just in archival process. Ah, lot of these things we've done with the interior. You know, we have this virtual reality product where you can you can walk through and see in the 3 60 bubble. We're taking that imagery, and we're combining it with with these been models who are actually taking the three D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress and different things that are happening on the site. Look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people, time and money on these construction sites. We've also introduced a I machine learning applications into directly into the workflow in this in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure, it really is seamless and working with YSL on now. >>Imagine, by being able to infuse AI and machine learning, you're able to get insight faster to be ableto either respond faster to those construction customers, for example, or alert them. If perhaps something isn't going according to plan. >>A lot of it's about schedule. It's about saving money about saving time and again, with not as many people traveling to the sites, they really just have have constant visualization of what's going on. Day to day, we're detecting things like different types of construction equipment and things that are happening on the side. We're partnering with people that are doing safety analytics and things of that nature. So these these are all things that are very important to construction sites. >>What are some of the things as we are rounding out the calendar year 2020? What are some of the things that you're excited about going forward in 2021? That Earth cam is going to be able to get into and to deliver >>it, just MAWR and more people really, finally seeing the value. I mean, I've been doing this for 20 years, and it's just it's it's It's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with things they can do with this imagery. That's what we're all about that's really exciting to us in a very challenging environment right now is that people are are recognizing the need for this technology and really starting to put it on a lot more projects. >>Well, it's You can kind of consider an essential service, whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget on schedule, as you said, Or maybe even just the essential nous of helping folks from any country in the world connect with a favorite favorite travel location or sending the right to help. From an emotional perspective, I think the essential nous of what you guys are delivering is probably even more impactful now, don't you think? >>Absolutely and again about connecting people and when they're at home. And recently we we webcast the president's speech from the Flight 93 9 11 observation from the memorial. There was something where the only the immediate families were allowed to travel there. We webcast that so people could see that around the world we have documented again some of the biggest construction projects out there. The new rate years greater stadium was one of the recent ones, uh, is delivering this kind of flagship content. Wall Street Journal is to use some of our content recently to really show the things that have happened during the pandemic in Times Square's. We have these cameras around the world. So again, it's really bringing awareness of letting people virtually travel and share and really remain connected during this this challenging time on and again, we're seeing a really increase demand in the traffic in those areas as well. >>I can imagine some of these things that you're doing that you're achieving now are going to become permanent, not necessarily artifacts of Cove in 19 as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value off this type of video to be able to reach consumers that they probably could never reach before. >>Yeah, I think the whole nature of business and communication and travel on everything is really going to be changed from this point forward. It's really people are looking at things very, very differently and again, seeing the technology really can help with so many different areas that, uh, that it's just it's gonna be a different kind of landscape out there we feel on that's really, you know, continuing to be seen on the uptick in our business and how many people are adopting this technology. We're developing a lot more. Partnerships with other companies were expanding into new industries on again. You know, we're confident that the current platform is going to keep up with us and help us, you know, really scale and evolved as thes needs air growing. >>It sounds to me like you have the foundation with Dell Technologies with power scale to be able to facilitate the massive growth that you're saying and the skill in the future like you've got that foundation. You're ready to go? >>Yeah, we've been We've been We've been using the system for five years already. We've already added capacity. We can add capacity on the fly, Really haven't hit any limits. And what we can do, It's It's almost infinitely scalable, highly redundant. Gives everyone a real sense of security on our side. And, you know, we could just keep innovating, which is what we do without hitting any any technological limits with with our partnership. >>Excellent. Well, Bill, I'm gonna let you get back to innovating for Earth camp. It's been a pleasure talking to you. Thank you so much for your time today. >>Thank you so much. It's been a pleasure >>for Bill Sharp and Lisa Martin. You're watching the cubes. Digital coverage of Dell Technologies World 2020. Thanks for watching. Yeah,