>> Announcer: Live from San Jose, California, it's theCUBE covering innovating to fuel the next decade of big data. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick, here with theCUBE. We are at the Western Digital Headquarters in San Jose, California. It's the Al-Mady Campus. A historic campus. It's had a lot of great innovation, especially in hard drives for years and years and years. This event's called Innovating to Fuel the Next Data Big Data. And we're excited to have a big brain on. We like to get smart people who's been watching this story for a while and will give us a little bit of historical perspective. It's John Rydning. He is the Research Vice President for Hard Drives for IEC. John, Welcome. >> Thank you, Jeff. >> Absolutely. So, what is your take on today's announcement? >> I think it's our very meaningful announcement, especially when you consider that the previous BIGIT Technology announcement for the industry was Helium, about four or five years ago. But, really, the last big technology announcement prior to that was back in 2005, 2006, when the industry announced making this transition to what they called at that time, "Perpendicular Magnetic Recording." And when that was announced it was kind of a similar problem at that time in the industry that we have today, where the industry was just having a difficult time putting more data on each disc inside that drive. And, so they kind of hit this technology wall. And they announced Perpendicular Magnetic Recording and it really put them on a new S curve in terms of their ability to pack more data on each disc and just kind of put it in some perspective. So, after they announce Perpendicular Magnetic Recording, the capacity per disc increased about 30% a year for about five years. And then over, really, a ten year period, increased about an average of about 20% a year. And, so today's announcement is I see a lot of parallels to that. You know, back when Perpendicular Magnetic Recording was announced, really they build. They increased the capacity per platter was growing very slowly. That's where we are today. And with this announcement of MAMR Technology the direction that Western Digital's choosing really could put the industry on a new S curve and putting in terms of putting more capacity, storage capacity on each one of those discs. >> It's interesting. Always reminds me kind of back to the OS in Microsoft in Intel battles. Right? Intel would come out with a new chip and then Microsoft would make a bigger OS and they go back and back and forth and back and forth. >> John: Yeah, that's very >> And we're seeing that here, right? Cuz the demands for the data are growing exponentially. I think one of the numbers that was thrown out earlier today that the data thrown off by people and the data thrown off by machines is so exponentially larger than the data thrown off by business, which has been kind of the big driver of IT spin. And it's really changing. >> It's a huge fundamental shift. It really is >> They had to do something. Right? >> Yeah, the demand for a storage capacity by these large data centers is just phenomenal and yet at the same time, they don't want to just keep building new data center buildings. And putting more and more racks. They want to put more storage density in that footprint inside that building. So, that's what's really pushing the demand for these higher capacity storage devices. They want to really increase the storage capacity per cubic meter. >> Right, right. >> Inside these data centers. >> It's also just fascinating that our expectation is that they're going to somehow pull it off, right? Our expectation that Moore's laws continue, things are going to get better, faster, cheaper, and bigger. But, back in the back room, somebody's actually got to figure out how to do it. And as you said, we hit these kind of seminal moments where >> Yeah, that's right. >> You do get on a new S curve, and without that it does flatten out over time. >> You know, what's interesting though, Jeff, is really about the time that Perpendicular Magnetic Recording was announced way back in 2005, 2006, the industry was really, already at that time, talking about these thermal assist technologies like MAMR that Western Digital announced today. And it's always been a little bit of a question for those folks that are either in the industry or watching the industry, like IDC. And maybe even even more importantly for some of the HDD industry customers. They're kind of wondering, so what's really going to be the next technology race horse that takes us to that next capacity point? And it's always been a bit of a horse race between HAMR and MAMR. And there's been this lack of clarity or kind of a huge question mark hanging over the industry about which one is it going to be. And Western Digital certainly put a stake in the ground today that they see MAMR as that next technology for the future. >> (mumbles words) Just read a quote today (rushes through name) key alumni just took a new job. And he's got a pin tweet at the top of his thing. And he says, "The smart man looks for ways "To solve the problem. "Or looks at new solutions. "The wise man really spends his time studying the problem." >> I like that. >> And it's really interesting here cuz it seems kind of obvious there. Heat's never necessarily a good thing with electronics and data centers as you mentioned trying to get efficiency up. There's pressure as these things have become huge, energy consumption machines. That said, they're relatively efficient, based on other means that we've been doing they compute and the demand for this compute continues to increase, increase, increase, increase. >> Absolutely >> So, as you kind of look forward, is there anything kind of? Any gems in the numbers that maybe those of us at a layman level are kind of a first read are missing that we should really be paying attention that give us a little bit of a clue of what the feature looks like? >> Well, there's a couple of major trends going on. One is that, at least for the hard drive industry, if you kind of look back the last ten years or so, a pretty significant percentage of the revenue that they've generated a pretty good percentage of the petabytes that they ship have really gone into the PC market. And that's fundamentally shifting. And, so now it's really the data centers, so that by the time you get to 2020, 2021, about 60 plus percent of the petabytes that the industry's shipping is going into data centers, where if you look back a few years ago, 60% was going into PCs. That's a big, big change for the industry. And it's really that kind of change that's pushing the need for these higher capacity hard drives. >> Jeff: Right. >> So, that's, I think, one of the biggest shifts has taking place. >> Well, the other thing that's interesting in that comment because we know scale drives innovation better than anything and clearly Intel microprocessors rode the PC boom to get out scale to drive the innovation. And, so if you're saying, now, that the biggest scale is happening in the data center Then, that's a tremendous force for innovation in there versus Flash, which is really piggy-backing on the growth of these jobs, because that's where it's getting it's scale. So, when you look at kind of the Flash hard drive comparison, right? Obviously, Flash is the shiny new toy getting a lot of buzz over the last couple years. Western Digital has a play across the portfolio, but the announcement earlier today said, you're still going to have like this TenX cost differentiation. >> Yeah, that's right. >> Even through, I think it was 20, 25. I don't want to say what the numbers were. Over a long period of time. You see that kind of continuing DC&E kind of conflict between those two? Or is there a pretty clear stratification between what's going to go into Flash systems, or what's going to hard drives? >> That's a great question, now. So, even in the very large HyperScale data centers and we definitely see where Flash and hard disk drives are very complimentary. They're really addressing different challenges, different problems, and so I think one of the charts that we saw today at the briefing really is something that we agree with strongly at IDC. Today, maybe, about 7% or 8% of all of the combined HDD SSD petabyte shipped for enterprise are SSD petabytes. And then, that grows to maybe ten. >> What was it? Like 7% you said? >> 6% to 7%. >> 6% to 7% okay. Yeah, so we still have 92, 93%, 94% of all petabytes that again are HDD SSD petabytes for enterprise. Those are still HDD petabytes. And even when you get out to 2020, 2021, again, still bought 90%. We agree with what Western Digital talked about today. About 90% of the combined HDD SSD petabytes that are shipping for enterprise continue to be HDD. So, we do see the two technologies very complementary. Talked about SSD is kind of getting their scale on PCs and that's true. They really are going to quickly continue to become a bigger slice of the storage devices attached to new PCs. But, in the data center you really need that bulk storage capacity, low cost capacity. And that's where we see that the two SSDs and HDDs are going to live together for a long time. >> Yeah, and as we said the conflict barrier, complimentary nature of the two different applications are very different. You need the big data to build the models, to run the algorithms, to do stuff. But, at the same time, you need the fast data that's coming in. You need the real time analytics to make modifications to the algorithms and learn from the algorithms >> That's right, yeah. It's the two of those things together that are one plus one makes three type of solution. Exactly, and especially to address latency. Everybody wants their data fast. When you type something into Google, you want your response right away. And that's where SSDs really come into play, but when you do deep searches, you're looking through a lot of data that has been collected over years and a lot of that's probably sitting on hard disc drives. >> Yeah. The last piece of the puzzle, I just want to you to address before we sign off, That was an interesting point is that not just necessarily the technology story, but the ecosystem story. And I thought that was really kind of, I thought, the most interesting part of the MAMR announcement was that it fits in the same form factor, there's no change to OS, there's no kind of change in the ecosystem components in which you plug this in. >> Yeah, that's right. It's just you take out the smaller drive, the 10, or the 12, or whatever, or 14 I guess is coming up. And plug in. They showed a picture of a 40 terabyte drive. >> Right. >> You know, that's the other part of the story that maybe doesn't get as much play as it should. You're playing in an ecosystem. You can't just come up with this completely, kind of independent, radical, new thing, unless it'S so radical that people are willing to swap out their existing infrastructure. >> I completely agree. It's can be very difficult for the customer to figure out how to adopt some of these new technologies and actually, the hard disk drive industry has thrown a couple of technologies at their customers over the past five, six years, that have been a little challenging for them to adopt. So, one was when the industry went from a native 512 by sectors to 4K sectors. Seems like a pretty small change that you're making inside the drive, but it actually presented some big challenges for some of the enterprise customers. And even the single magnetic recording technologies. So, it has a way to get more data on the disc, and Western Digital certainly talked about that today. But, for the customer trying to plug and play that into a system and SMR technology actually created some real challenges for them to figure out how to adopt that. So, I agree that what was shown today about the MAMR technology is definitely a plug and play. >> Alright, we'll give you the last word as people are driving away today from the headquarters. They got a bumper sticker as to why this is so important. What's it say on the bumper sticker about MAMR? It says that we continue to get more capacity at a lower cost. >> (chuckles) Isn't that just always the goal? >> I agree. >> (chuckles) Alright, well thank you for stopping by and sharing your insight. Really appreciate it. >> Thanks, Jeff. >> Alright. Jeff Frick here at Western Digital. You're watching theCUBE! Thanks for watching. (futuristic beat)

>> Announcer: Live from San Jose, California, it's theCUBE, covering Innovating to Fuel the Next Decade of Big Data, brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at Western Digital at their global headquarters in San Jose, California, it's the Almaden campus. This campus has a long history of innovation, and we're excited to be here, and probably have the smartest person in the building, if not the county, area code and zip code. I love to embarrass here, Janet George, she is the Fellow and Chief Data Scientist for Western Digital. We saw you at Women in Data Science, you were just at Grace Hopper, you're everywhere and get to get a chance to sit down again. >> Thank you Jeff, I appreciate it very much. >> So as a data scientist, today's announcement about MAMR, how does that make you feel, why is this exciting, how is this going to make you be more successful in your job and more importantly, the areas in which you study? >> So today's announcement is actually a breakthrough announcement, both in the field of machine learning and AI, because we've been on this data journey, and we have been very selectively storing data on our storage devices, and the selection is actually coming from the preconstructed queries that we do with business data, and now we no longer have to preconstruct these queries. We can store the data at scale in raw form. We don't even have to worry about the format or the schema of the data. We can look at the schema dynamically as the data grows within the storage and within the applications. >> Right, cause there's been two things, right. Before data was bad 'cause it was expensive to store >> Yes. >> Now suddenly we want to store it 'cause we know data is good, but even then, it still can be expensive, but you know, we've got this concept of data lakes and data swamps and data all kind of oceans, pick your favorite metaphor, but we want the data 'cause we're not really sure what we're going to do with it, and I think what's interesting that you said earlier today, is it was schema on write, then we evolved to schema on read, which was all the rage at Hadoop Summit a couple years ago, but you're talking about the whole next generation, which is an evolving dynamic schema >> Exactly. >> Based whatever happens to drive that query at the time. >> Exactly, exactly. So as we go through this journey, we are now getting independent of schema, we are decoupled from schema, and what we are finding out is we can capture data at its raw form, and we can do the learning at the raw form without human interference, in terms of transformation of the data and assigning a schema to that data. We got to understand the fidelity of the data, but we can train at scale from that data. So with massive amounts of training, the models already know to train itself from raw data. So now we are only talking about incremental learning, as the train model goes out into the field in production, and actually performs, now we are talking about how does the model learn, and this is where fast data plays a very big role. >> So that's interesting, 'cause you talked about that also earlier in your part of the presentation, kind of the fast data versus big data, which kind of maps the flash versus hard drive, and the two are not, it's not either or, but it's really both, because within the storage of the big data, you build the base foundations of the models, and then you can adapt, learn and grow, change with the fast data, with the streaming data on the front end, >> Exactly >> It's a whole new world. >> Exactly, so the fast data actually helps us after the training phase, right, and these are evolving architectures. This is part of your journey. As you come through the big data journey you experience this. But for fast data, what we are seeing is, these architectures like Lambda and Kappa are evolving, and especially the Lambda architecture is very interesting, because it allows for batch processing of historical data, and then it allows for what we call a high latency layer or a speed layer, where this data can then be promoted up the stack for serving purposes. And then Kappa architecture's where the data is being streamed near real time, bounded and unbounded streams of data. So this is again very important when we build machine learning and AI applications, because evolution is happening on the fly, learning is happening on the fly. Also, if you think about the learning, we are mimicking more and more on how humans learn. We don't really learn with very large chunks of data all at once, right? That's important for initially model training and model learning, but on a regular basis, we are learning with small chunks of data that are streamed to us near real time. >> Right, learning on the Delta. >> Learning on the Delta. >> So what is the bound versus the unbound? Unpack that a little bit. What does that mean? >> So what is bounded is basically saying, hey we are going to get certain amounts of data, so you're sizing the data for example. Unbounded is infinite streams of data coming to you. And so if your architecture can absorb infinite streams of data, like for example, the sensors constantly transmitting data to you, right? At that point you're not worried about whether you can store that data, you're simply worried about the fidelity of that data. But bounded would be saying, I'm going to send the data in chunks. You could also do bounded where you basically say, I'm going to pre-process the data a little bit just to see if the data's healthy, or if there is signal in the data. You don't want to find that out later as you're training, right? You're trying to figure that out up front. >> But it's funny, everything is ultimately bounded, it just depends on how you define the unit of time, right, 'cause you take it down to infinite zero, everything is frozen. But I love the example of the autonomous cars. We were at the event with, just talking about navigation just for autonomous cars. Goldman Sachs says it's going to be a seven billion dollar industry, and the great example that you used of the two systems working well together, 'cause is it the car centers or is it the map? >> Janet: That's right. >> And he says, well you know, you want to use the map, and the data from the map as much as you can to set the stage for the car driving down the road to give it some level of intelligence, but if today we happen to be paving lane number two on 101, and there's cones, now it's the real time data that's going to train the system. But the two have to work together, and the two are not autonomous and really can't work independent of each other. >> Yes. >> Pretty interesting. >> It makes perfect sense, right. And why it makes perfect sense is because first the autonomous cars have to learn to drive. Then the autonomous cars have to become an experienced driver. And the experience cannot be learned. It comes on the road. So one of the things I was watching was how insurance companies were doing testing on these cars, and they had a human, a human driving a car, and then an autonomous car. And the autonomous car, with the sensors, were predicting the behavior, every permutation and combination of how a bicycle would react to that car. It was almost predicting what the human on the bicycle would do, like jump in front of the car, and it got it right 80% of the cases. But a human driving a car, we're not sure how the bicycle is going to perform. We don't have peripheral vision, and we can't predict how the bicycle is going to perform, so we get it wrong. Now, we can't transmit that knowledge. If I'm a driver and I just encountered a bicycle, I can't transmit that knowledge to you. But a driverless car can learn, it can predict the behavior of the bicycle, and then it can transfer that information to a fleet of cars. So it's very powerful in where the learning can scale. >> Such a big part of the autonomous vehicle story that most people don't understand, that not only is the car driving down the road, but it's constantly measuring and modeling everything that's happening around it, including bikes, including pedestrians, including everything else, and whether it gets in a crash or not, it's still gathering that data and building the model and advancing the models, and I think that's, you know, people just don't talk about that enough. I want follow up on another topic. So we were both at Grace Hopper last week, which is a phenomenal experience, if you haven't been, go. Ill just leave it at that. But Dr. Fei-Fei Li gave one of the keynotes, and she made a really deep statement at the end of her keynote, and we were both talking about it before we turned the cameras on, which is, there's no question that AI is going to change the world, and it's changing the world today. The real question is, who are the people that are going to build the algorithms that train the AI? So you sit in your position here, with the power, both in the data and the tools and the compute that are available today, and this brand new world of AI and ML. How do you think about that? How does that make you feel about the opportunity to define the systems that drive the cars, et cetera. >> I think not just the diversity in data, but the diversity in the representation of that data are equally powerful. We need both. Because we cannot tackle diverse data, diverse experiences with only a single representation. We need multiple representation to be able to tackle that data. And this is how we will overcome bias of every sort. So it's not the question of who is going to build the AI models, it is a question of who is going to build the models, but not the question of will the AI models be built, because the AI models are already being built, but some of the models have biases into it from any kind of lack of representation. Like who's building the model, right? So I think it's very important. I think we have a powerful moment in history to change that, to make real impact. >> Because the trick is we all have bias. You can't do anything about it. We grew up in the world in which we grew up, we saw what we saw, we went to our schools, we had our family relationships et cetera. So everyone is locked into who they are. That's not the problem. The problem is the acceptance of bring in some other, (chuckles) and the combination will provide better outcomes, it's a proven scientific fact. >> I very much agree with that. I also think that having the freedom, having the choice to hear another person's conditioning, another person's experiences is very powerful, because that enriches our own experiences. Even if we are constrained, even if we are like that storage that has been structured and processed, we know that there's this other storage, and we can figure out how to get the freedom between the two point of views, right? And we have the freedom to choose. So that's very, very powerful, just having that freedom. >> So as we get ready to turn the calendar on 2017, which is hard to imagine it's true, it is. You look to 2018, what are some of your personal and professional priorities, what are you looking forward to, what are you working on, what's top of mind for Janet George? >> So right now I'm thinking about genetic algorithms, genetic machine learning algorithms. This has been around for a while, but I'll tell you where the power of genetic algorithms is, especially when you're creating powerful new technology memory cell. So when you start out trying to create a new technology memory cell, you have materials, material deformations, you have process, you have hundred permutation combination, and the genetic algorithms, we can quickly assign a cause function, and we can kill all the survival of the fittest, all that won't fit we can kill, arriving to the fastest, quickest new technology node, and then from there, we can scale that in mass production. So we can use these survival of the fittest mechanisms that evolution has used for a long period of time. So this is biology inspired. And using a cause function we can figure out how to get the best of every process, every technology, all the coupling effects, all the master effects of introducing a program voltage on a particular cell, reducing the program voltage on a particular cell, resetting and setting, and the neighboring effects, we can pull all that together, so 600, 700 permutation combination that we've been struggling on and not trying to figure out how to quickly narrow down to that perfect cell, which is the new technology node that we can then scale out into tens of millions of vehicles, right? >> Right, you're going to have to >> Getting to that spot. >> You're going to have to get me on the whiteboard on that one, Janet. That is amazing. Smart lady. >> Thank you. >> Thanks for taking a few minutes out of your time. Always great to catch up, and it was terrific to see you at Grace Hopper as well. >> Thank you, I really appreciate it, I appreciate it very much. >> All right, Janet George, I'm Jeff Frick. You are watching theCUBE. We're at Western Digital headquarters at Innovating to Fuel the Next Generation of Big Data. Thanks for watching.

>> Announcer: Live from San Jose, California, it's theCUBE, covering Innovating to Fuel the Next Decade of Big Data, brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at Western Digital's headquarters in San Jose, California at the Almaden campus. Lot of innovation's been going on here, especially in storage for decades, and we're excited to be at this special press and analyst event that Western Digital put on today to announce some exciting new products. It's called Innovating to Fuel the Next Decade of Data. I'm super happy to have a long-time industry veteran, he just told me, 35 years, I don't know if I can tell (Mark laughs) that on air or not. He's Mark Grace, he's the Senior Vice President of Devices for Western Digital, Mar, great to have you on. >> Thanks Jeff, glad to be here. >> Absolutely, so you've seen this movie over and over and over, I mean that's one of the cool things about being in the Valley, is this relentless pace of innovation. So how does today's announcement stack up as you kind of look at this versus kind of where we've come from? >> Oh I think this is maybe one of the, as big as it comes, Jeff, to be honest. I think we've plotted a course now that I think was relatively uncertain for the hard drive industry and the data center, and plotted a course that I think we can speak clearly to the market, and clearly to customers about the value proposition for rotating magnetic storage for decades to come. >> Which is pretty interesting, 'cause, you know, rotating drives have been taking a hit over the last couple of years, right, flash has been kind of the sexy new kid on the block, so this is something new, >> Mark: It is. >> And a new S curve I think as John said. >> I agree, we're jumping onto a, we're extending the S curve, let's call it that. I think there's actually plenty of other S curve opportunities for us, but in this case, I think the industry, and I would say our customer base, we have been less than clear with those guys about how we see the future of rotating storage in the cloud and enterprise space, and I think today's announcement clarifies that and gives some confidence about architectural decisions relative to rotating storage going forward for a long time. >> Well I think it's pretty interesting, 'cause compared to the other technology that was highlighted, the other option, the HAMR versus the MAMR, this was a much more elegant, simpler way to add this new S curve into an existing ecosystem. >> You know, elegant's probably a good word for it, and it's always the best solution I would say. HAMR's been a push for many years. I can't remember the first time I heard about HAMR. It's still something we're going to continue to explore and invest in, but it has numerous hurdles compared to the simplicity and elegance, as you say, of MAMR, not the least of which is we're going to operate at normal ambient temperatures versus apply tremendous heat to try and energize the recording and the technologies. So any time you introduce extraordinary heat you face all kinds of ancillary engineering challenges, and this simplifies those challenges down to one critical innovation, which is the oscillator. >> Pretty interesting, 'cause it seems pretty obvious that heat's never a good thing. So it's curious that in the quest for this next S curve that the HAMR path was pursued for as long as it was, it sounds like, because it sounds like that's a pretty tough thing to overcome. >> Yeah, I think it initially presented perhaps the most longevity perhaps in early exploration days. I would say that HAMR has certainly received the most press as far as trying to assert it as the extending recording technology for enterprise HDDs. I would say we've invested for almost as long in MAMR, but we've been extremely quiet about it. This is kind of our nature. When we're ready to talk about something, you can kind of be sure we're ready to go with it, and ready to think about productization. So we're quite confident in what we're doing. >> But I'm curious from your perspective, having been in the business a long time, you know, we who are not directly building these magical machines, just now have come to expect that Moore's Law will contain, has zero to do with semiconductor physics anymore, it's really an attitude and this relentless pace of innovation that now is expected and taken for granted. You're on the other side, and have to face real physics and mechanical limitations of the media and the science and everything else. So is that something that gets you up every day >> Mark: Keeps me awake every night! >> Obviously keeps you awake at night and up every day. You've been doing it for 35 years, so there must be some appeal. >> Yeah. (laughs) >> But you know, it's a unique challenge, 'cause at the same time not only has it got to be better and faster and bigger, it's got to be cheaper, and it has been. So when you look at that, how does that kind of motivate you, the teams here, to deliver on that promise? >> Yeah, I mean in this case, we are a little bit defensive, in the sense of the flash expectations that you mentioned, and I think as we digest our news today, we'll be level setting a little bit more in a more balanced way the expectations for contribution from rotating magnetic storage and solid state storage to what I think is a more accurate picture of its future going forward in the enterprise and hyperscale space. To your point about just relentless innovation, a few of us were talking the other day in advance of this announcement that this MAMR adventure feels like the early days of PMR, perpendicular, the current recording technology. It feels like we understand a certain amount of distance ahead of us, and that's about this four-terabit per inch kind of distance, but it feels like the early days where we could only see so far but the road actually goes much further, and we're going to find more and more ways to extend this technology, and keep that order of magnitude cost advantage going from a hard drive standpoint versus flash. >> I wonder how this period compares to that, just to continue, in terms of on the demand side, 'cause you know, back in the day, the demand and the applications for these magical compute machines weren't near, I would presume, as pervasive as now, or am I missing the boat? 'Cause now clearly there is no shortage of demand for storage and compute. >> Yeah, depending on where you're coming from, you could take two different views of that. The engine that drove the scale of the hard drive industry to date has, a big piece of it in the long history of the hard drive industry has been the PC space. So you see that industry converting to flash and solid state storage more aggressively, and we embrace that, you know we're invested in flash and we have great products in that space, and we see that happening. The opportunity in the hyperscale and cloud space is we're only at the tip of the iceberg, and therefore I think, as we think about this generation, we think about it differently than those opportunities in terms of breadth of applications, PCs, and all that kind of create the foundation for the hard drive, but what we see here is the virtuous cycle of more storage, more economical storage begets more value proposition, more opportunities to integrate more data, more data collection, more storage. And that virtuous cycle seems to me that we're just getting started. So long live data, that's what we say. (both laugh) >> The other piece that I find interesting is before the PCs were the driver of scale relative to an enterprise data center, but with the hyperscale guys and the proliferation of cloud and actually the growth of PCs is slowing down dramatically, that it's kind of flipped the bit. Now the data centers themselves have the scale to drive >> Absolutely. >> the scale innovation that before was before was really limited to either a PC or a phone or some more consumer device. >> Absolutely the case. When you take that cross-section of hard drive applications, that's a hundred percent the case, and in fact, we look at the utilization as a vertically integrated company we look at our media facilities for the disks, we look at our wafer facilities for heads, and those facilities as we look forward are going to be as busy as busier than they've ever been. I mean the amount of data is relative to the density as well as disks and heads and how many you can employ. So we see this in terms of fundamental technology and component construction, manufacturing, busier than it's ever been. We'll make fewer units. I mean there will be fewer units as they become bigger and denser for this application space, but the fundamental consumption of magnetic recording technology and components is all-time records. >> Right. And you haven't even talked about the software-defined piece that's dragging the utilization of that data across multiple applications. >> And it's just one of these that come in to help everybody there too, yeah. >> Jeff: You got another 35 years more years in you? (both laugh) >> I hope so. >> All right. >> But that would be the edge of it, I think. >> All right, we're going to take Mark Grace here, only 35 more years, Lord knows what he'll be working on. Well Mark, thanks for taking a few minutes and answering your prospective >> No that's fine, thanks a lot. >> Absolutely, Mark Grace, I'm Jeff Frick, you're watching theCUBE from Western Digital headquarters in San Jose, California. Thanks for watching. >> Mark: All right.

(upbeat techno music) >> Announcer: Live from San Jose, California it's theCUBE, covering Innovating to Fuel the Next Decade of Big Data, brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick here at theCUBE. We're at the Western Digital Headquarters off Almaden down in San Jose, a really important place. Western Digital's been here for a while, their headquarters. A lot of innovation's been going on here forever. So we're excited to be here really for the next generation. The event's called Innovating to Fuel the Next Generation of big data, and we're joined by many time Cuber, Dave Tang. He is the SVP in corporate marketing from Western Digital. Dave, always great to see you. >> Yeah. Always great to be here, Jeff. Thanks. >> Absolutely. So you got to MC the announcement today. >> Yes. >> So for the people that weren't there, let's give them a quick overview on what the announcement was and then we can dive in a little deeper. >> Great, so what we were announcing was a major breakthrough in technology that's going to allow us to drive the increase in capacity in density to support big data for the next decade and beyond, right? So capacities and densities had been starting to level off in terms of hard drive technology capability. So what we announced was microwave-assisted magnetic recording technology that will allow us to keep growing that areal density up and reducing the cost per terabyte. >> You know, it's fascinating cause everyone loves to talk about Moore's Law and have these silly architectural debates, whether Moore's Law is alive or dead, but, as anyone who's lived here knows, Moore's Law is really an attitude much more it is than the specific physics of microprocessor density growth. And it's interesting to see. As we know the growth of data is growing in giant and the types of data, and not only regular big data, but now streaming data are bigger and bigger and bigger. I think you talked about stuff coming off of people and machines compared to business data is way bigger. >> Right. >> But you guys continue to push limits and break through, and even though we expect everything to be cheaper, faster, and better, you guys actually have to execute it-- >> Dave: Right. >> Back at the factory. >> Right, well it's interesting. There's this healthy tension, right, a push and pull in the environment. So you're right, it's not just Moore's Law that's enabling a technology push, but we have this virtuous cycle, right? We've realized what the value is of data and how to extract the possibilities and value of data, so that means that we want to store more of that data and access more of that data, which drives the need for innovation to be able to support all of that in a cost effective way. But then that triggers another wave of new applications, new ways to tap into the possibilities of data. So it just feeds on itself, and fortunately we have great technologists, great means of innovation, and a great attitude and spirit of innovation to help drive that. >> Yeah, so for people that want more, they can go to the press releases and get the data. We won't dive deep into the weeds here on the technology, but I thought you had Janet George speak, and she's chief data scientist. Phenomenal, phenomenal big brain. >> Dave: Yes. >> A smart lady. But she talked about, from her perspective we're still just barely even getting onto this data opportunity in terms of automation, and we see over and over at theCUBE events, innovation's really not that complicated. Give more people access to the data, give them more access to the tools, and let them try things easier and faster and feel quick, there's actually a ton of innovation that companies can unlock within their own four walls. But the data is such an important piece of it, and there's more and more and more of this. >> Dave: Right, right. >> What used to be digital exhaust now is, I think maybe you said, or maybe it was Dave, that there's a whole economy now built on data like we used to do with petroleum. I thought that was really insightful. >> Yeah, right. It's like a gold mine. So not only are the sources of data increasing, which is driving increased volume, but, as Janet was alluding to, we're starting to come up with the tools and the sophistication with machine learning and artificial intelligence to be able to put that data to new use as well as to find the pieces of data to interconnect, to drive these new capabilities and new insights. >> Yeah, but unlike petroleum it doesn't get used up. I mean that's the beauty of data. (laughing) >> Yeah, that's right. >> It's a digital process that can be used over and over and over again. >> And a self-renewing, renewing resource. And you're right, in that sense that it's being used over and over again that the longevity of that data, the use for life is growing exponentially along with the volume. >> Right, and Western Digital's in a unique position cause you have systems and you have big systems that could be used in data centers, but you also have the media that powers a whole bunch of other people's systems. So I thought one of the real important announcements today was, yes it's an interesting new breakthrough technology that uses energy assist to get more density on the drives, but it's done in such a way that the stuff's all backward compatible. It's plug and play. You've got production scheduled in a couple years I think with test out the customers-- >> Dave: That's right. >> Next year. So, you know, that is such an important piece beyond the technology. What's the commercial acceptance? What are the commercial barriers? And this sounds like a pretty interesting way to skin that cow. >> Right, often times the best answers aren't the most complex answers. They're the more elegant and simplistic answers. So it goes from the standpoint of a user being able to plug and play with older systems, older technologies. That's beautiful, and for us, to be able to, the ability to manufacture it in high volume reliably and cost effectively is equally as important. >> And you also talked, which I think was interesting, is kind of the relationship between hard drives and flash, because, obviously, flash is a, I want to say the sexy new toy, but it's not a sexy new toy anymore. >> Right. >> It's been around for a while, but, with that pressure on flash performance, you're still seeing the massive amounts of big data, which is growing faster than that. And there is a rule for the high density hard drives in that environment, and, based on the forecast you shared, which I'm presuming came from IDC or people that do numbers for a living, still a significant portion of a whole lot of data is not going to be on flash. >> Yeah, that's right. I think we have a tendency, especially in technology, to think either or, right? Something is going to take over from something else, but in this case it's definitely an and, right. And a lot of that is driven by this notion that there's fast data and big data, and, while our attention seems to shift over to maybe some fast data applications like autonomous vehicles and realtime applications, surveillance applications, there's still a need for big data because the algorithms that drive those realtime applications have to come from analysis of vast amounts of data. So big data is here to stay. It's not going away or shifting over. >> I think it's a really interesting kind of cross over, which Janet talked about too, where you need the algorithms to continue sharing the system that are feeding, continuing, and reacting to the real data, but then that just adds more vocabulary to their learning set so they can continue to evolve overtime. >> Yeah, what really helps us out in the market place is that because we have technologies and products across that full spectrum of flash and rotating magnetic recording, and we sell to customers who buy devices as well as platforms and systems, we see a lot of applications, a lot of uses of data, and we're able to then anticipate what those needs are going to be in the near future and in the distant future. >> Right, so we're getting towards the end of 2017, which I find hard to say, but as you look forward kind of to 2018 and this insatiable desire for more storage, cause this insatiable creation of more data, what are some of your priorities for 2018 and what are you kind of looking at as, like I said, I can't believe we're going to actually flip the calendar here-- >> Dave: Right, right. >> In just a few short months. (laughing) >> Well, I think for us, it's the realization that all these applications that are coming at us are more and more diverse, and their needs are very specialized. So it's not just the storage, although we're thought of as a storage company, it's not just about the storage of that data, but you have contrive complete environments to capture and preserve and access and transform that data, which means we have to go well beyond storage and think about how that data is accessed, technical interfaces to our memory products as well as storage products, and then where compute sits. Does it still sit in a centralized place or do you move compute to out closer to where the data sits. So, all this innovation and changing the way that we think about how we can mine that data is top of the mind for us for the next year and beyond. >> It's only job security for you, Dave. (laughing) >> Dave: Funny to think about. >> Alright. He's Dave Tang. Thanks for inviting us and again congratulations on the presentation. >> Always a pleasure. >> Alright, Dave Tang, I'm Jeff Frick. You're watching theCUBE from Western Digital headquarters in San Jose, California. Thanks for watching. (upbeat techno music)

>> Announcer: Live from San Jose, California, it's The Cube. Covering Innovating to Fuel the Next Decade of Big Data. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick here with The Cube. We're at the Western Digital headquarters in San Jose, the Great Oaks Campus, a really historic place in the history of Silicon Valley and computing. It's The Innovating to Fuel the Next Generation of Big Data event with Western Digital. We're really excited to be joined by our next guest, Mike Cordano. He's the president and chief operating officer of Western Digital. Mike, great to see you. >> Great to see you as well. Happy you guys could be here. It's an exciting day. >> Absolutely. First off, I think the whole merger thing is about done, right? That's got to feel good. >> Yeah, it's done, but there's legs to it, right? So we've combined these companies now, three of them, three large ones, so obviously Western Digital and Hitachi Global Storage, now we've added SanDisk into one Western Digital, so we're all together. Obviously more to do, as you expect in a large scale integration. There will be a year or two of bringing all those business processes and systems together, but I got to say, the teams are coming together great, showing up in our financial performance and our product execution, so things are really coming together. >> Yeah, not an easy task by any stretch of the imagination. >> No, not easy, but certainly a compliment to our team. I mean, we've got great people. You know, like anything, if you can harness the capabilities of your team, there's a lot you can accomplish, and it really is a compliment to the team. >> Excellent. Well, congratulations on that, and talking a bit about this event here today, you've even used "Big Data" in the title of the event, so you guys are obviously in a really unique place, Western Digital. You make systems, big systems. You also make the media that feeds a lot of other people's systems, but as the big data grows, the demand for data grows, it's got to live somewhere, so you're sitting right at the edge where this stuff's got to sit. >> Yeah, that's right, and it's central to our strategy, right? So if you think about it, there's three fundamental technologies that we think are just inherent in all of the evolution of compute and IT architecture. Obviously, there is compute, there is storage or memory, and then there's sort of movement, or interconnect. We obviously live in the storage or memory node, and we have a very broad set of capabilities, all the way from rotating magnetic media, which was our heritage, now including non-volatile memory and flash, and that's just foundational to everything that is going to come, and as you said, we're not going to stop there. It's not just a devices or component company, we're going to continue to innovate above that into platforms and systems, and why that becomes important to us, is there's a lot of technology innovation we can do that enhances the offering that we can bring to market when we control the entire technology stat. >> Right. Now, we've had some other guests on and people can get more information on the nitty-gritty details of the announcement today, the main announcement. Basically, in a nutshell, enabling you to get a lot more capacity in hard drives. But, I thought in your opening remarks this morning, there were some more high-level things I wanted to dig into with you, and specifically, you made an analogy of the data economy, and compared it to the petroleum economy. I've never... A lot of times, they talk about big data, but no one really talks about it, that I've heard, in those terms, because when you think about the petroleum economy, it's so much more than fuel and cars, and the second-order impacts, and the third-order impacts on society are tremendous, and you're basically saying, "We're going to "do this all over again, but now it's based on data." >> Yeah, that's right, and I think it puts it into a form that people can understand, right? I think it's well-proven what happened around petroleum, so the discovery of petroleum, and then the derivative industries, whether it be automobiles, whether it be plastics, you pick it, the entire economy revolved around, and, to some degree, still revolves around petroleum. The same thing will occur around data. You're seeing it with investments, you hear now things like machine learning, or artificial intelligence, that is all ways to transform and mine data to create value. >> Right. >> And we're going to see industries change rapidly. Autonomous cars, that's going to be enabled by data, and capabilities here, so pick your domain. There's going to be innovation across a lot of fronts, across a lot of traditional vertical industries, that is all going to be about data and driven by data. >> It's interesting what Janet, Doctor Janet George talked about too a little bit is the types of data, and the nozzles of the data is also evolving very quickly from data at rest to data in motion, to real-time analytics, to, like you said, the machine learning and the AI, which is based on modeling prior data, but then ingesting new data, and adjusting those models so even the types and the rate and the speed of the data is under dramatic change right now. >> Yeah, that's right, and I think one of the things that we're helping enable is you kind of get to this concept of what do you need to do to do what you describe? There has to be an infrastructure there that actually enables it. So, when you think about the scale of data we're dealing with, that's one thing that we're innovating around, then the issue is, how do you allow multiple applications to simultaneously access and update and transform that? Those are all problems that need to be solved in the infrastructure to enable things like AI, right? And so, where we come into play, is creating that infrastructure layer that actually makes that possible. The other thing I talked about briefly in the Q and A was, think about the problem of a future where the data set is just too large to actually move it in a substantive way to the compute. We actually have to invert that model over time architecturally, and bring the compute to the data, right? Because it becomes too complicated and too expensive to move from the storage layer up to compute and back, right? That is a complex operation. That's why those three pillars of technology are so important. >> And you've talked, and we're seeing in the Cloud right, because this continuing kind of atomization, atomic, not automatic, but making these more atomic. A smaller unit that the Cloud has really popularized, so you need a lot, you need a little, really, by having smaller bits and bytes, it makes that that much more easy. But another concept that you delved into a little was fast data versus big data, and clearly flash has been the bright, shiny object for the last couple years, and you guys play in that market as well, but it is two very different ways to think of the data, and I thought the other statistic that was shared is you know, the amount of data coming off of the machines and people dwarfs the business data, which has been the driver of IT spend for the last several decades. >> Yeah, no, that's right, and sort of that... You think about that, and the best analogy is a broader definition of IOT, right? Where you've got all of these censors, whether it be camera censors, because that's just a censor, creating an image or a video, or if it's more industrialized too, you've got all these sources of data, and they're going to proliferate at an exponential rate, and our ability to aggregate that in some sort of an organized way, and then act upon it, again, let's use the autonomous car as the example. You've got all these censors that are in constant motion. You've got to be able to aggregate the data, and make decisions on it at the edge, so that's not something... You can't deal with latency up to the Cloud and back, if it's an automobile, and it needs to make an instantaneous decision, so you've got to create that capability locally, and so when you think about the evolution of all this, it's really the integration of the Cloud, which, as Janet talked about, is the ability to tap into this historical or legacy data to help inform a decision, but then there's things happening out at the edge that are real time, and you have to have the capability to ingest the content, make a decision on it very quickly, and then act on it. >> Right. There's a great example. We went to the autonomous... Just navigation for the autonomous vehicles. It's own subset that I think Goldman-Sachs said it a seven billion dollar industry in the not-too-distant future, and the great example is this combination of the big data and the live data is, when they actually are working on the road. So you've got maps that tell you, and are updated, kind of what the road looks like, but on Tuesday, they were shifting the lane, and that particular lane now has cones in it, so the combination of the two is such a powerful thing. >> That's right. >> I want to dive into another topic we talked about, which is really architecting for the future. Unlike oil, data doesn't get consumed and is no longer available, right? It's a reusable asset, and you talked about classic stove-topping of data within an application center world where now you want that data available for multiple applications, so very different architecture to be able to use it across many fronts, some of which you don't even know yet. >> That's right. I think that's a key point. One of the things, when we talk to CEOs, or CIOs I should say, what they're realizing, to the extent you can enable a cost-effective mechanism for me to store and keep everything, I don't know how I'll derive value from it some time in the future, because as applications evolve, we're finding new insights into what can help drive decisions or innovation, or, to take it to health care, some sort of innovation that cures disease. That's one of the things that everybody wants to do. I want to build aggregate everything. If I could do that cost effectively enough, I'll find a way to get value out of it over time, and that's something where, when we're thinking about big data and what we talked about today, that's central to that idea, and enabling it. >> Right, and digital transformation, right, the hot buzz word, but we hear, time and time again, such a big piece of that is giving the democratization. Democratization of the data, so more people have access to it, democratization of the tools to manipulate that data, not just Mahogany Row super smart people, and then to have a culture that lets people actually try, experiment, fail fast, and there's a lot of innovation that would be unlocked right within your four walls, that probably are not being tapped into. >> Well, that's right, and that's something that innovation, and an innovation culture is something that we're working hard at, right? So if you think about Western Digital, you might think of us as, you know, legacy Western Digital as sort of a fast following, very operational-centric company. We're still good at those things, but over the last five years, we've really pushed this notion of innovation, and really sort of pressing in to becoming more influential in those feature architectures. That drives a culture that, if we think about the technical community, if we create the right sort of mix of opportunity, appetite for some risk, that allows the best creativity to come out of our technical... Innovating along these lines. >> Right, I'll give you the last word. I can't believe we're going to turn the calendar here on 2017, which is a little scary. As you look forward to 2018, what are some of your top priorities? What are you going to be working on as we come into the new calendar year? >> Yeah, so as we look into 2018 and beyond, we really want to drive this continued architectural shift. You'll see us be very active, and I think you talked about it, you'll see us getting increasingly active in this democratization. So we're going to have to figure out how we engage the broader open-source development world, whether it be hardware or software. We agree with that mantra, we will support that. Obviously we can do unique development, but with some hooks and keys that we can drive a broader ecosystem movement, so that's something that's central to us, and one last word would be, one of the things that Martin Fink has talked about which is really part of our plans as we go onto the new year, is really this inverting the model, where we want to continue to drive an architecture that brings compute to the storage and enables some things that just can't be done today. >> All right, well Mike Cordano, thanks for taking a few minutes, and congratulations on the terrific event. >> Thank you. Appreciate it. >> He's Mike Cordano, I'm Jeff Frick, you're watching The Cube, we're at Western Digital headquarters in San Jose, Great Oaks Campus, it's historic. Check it out. Thanks for watching.

>> Male voiceover: Live from San Jose California, it's the Cube, covering Innovating to Fuel the Next Decade of Big Data. Brought to you by Western Digital. >> Hey, welcome back everybody, Jeff Frick here with the Cube. We're at the Western Digital World Headquarters It's the Almaden Campus in San Jose. If you know anything about the tech world, you know there's a lot of innovation that's been happening on this campus for years and years and years. Big announcement today called Innovating to Fuel the Next Generation of Big Data. Lot of exciting announcements and here to join us to tell us all about it is Brendan Collins. He's the Vice President of Product Marketing Devices for Western Digital. Brendan, great to see you. >> Thank you, glad to be here. >> Absolutely so, really exciting announcement. You know, I've talked to Kim Stevenson at Intel, we had an interview talking about Moore's law. And one thing she really reinforced is that Moore's law is really more of an attitude than it is specifically physics, and whether you want to argue the physics is one thing, but the attitude for innovation, to continue to deliver a lot more for less, just continues, continues, and continues, and you guys announced a huge step in that direction today. >> Yeah, we have a challenge that storage is growing at a rate of about 40 percent per year. And budgets from the data centers are not growing, right? So the challenge is for us to develop new technologies that allow us to stay on the technology curve, and cut costs and do that efficiently. >> Then this is a big one, so let's jump in. So actually it was years ago I was actually at the event when you guys introduced the Helium drives, and that was a big deal there, and you've continued to kind of move that innovation but then you can see a plateau. And the density of this data, so you guys had to come up with something new. >> Yeah, what we've seen is that our PMR technology that we use currently is slowly running out of steam, right? So in order to come down the cost curve, we needed to boost areal density. And luckily we were able to come up with a new breakthrough in MAMR technology that will allow us to do that for the next decade. >> It's interesting in the talk, you talked about you guys could see this kind of coming and you actually put a lot of bets on the table, you didn't just bet on MAMR, you bet on HAMR, and you continued along a number of multiple tracks, and you've been at this for a while. What was kind of the innovation that finally gave you a breakthrough moment that got us to where we are today? >> Well, there were multiple technologies that we could have invested in, and we decided to continue on the two major ones which were HAMR and MAMR but we made a decision to invest in a process called, a head fabrication process called damascene that allowed us to extend the life of PMR for the last five to six years, and it's been in all the products we've been shipping since 2013. >> And you talked the areal density, so that's basically the amount of information we can put on the square inch of surface area And you've really, you attacked it on two vectors. One is how many tracks, just think of a record, how many tracks can you get on an album, in terms of the number of lines, and then how much density then you can have on each of those tracks. >> That's right, that's right. And you're now seeing major improvements on both of those factors. >> Well if you look at, we've had three enabling technologies in our products for the past three to four years, right. One is helium, one is micro actuation, and the other is the damascene process. Damascene and micro actuation actually push track density which enables higher capacity. But the newer technology that we're talking about, MAMR, addresses both factors. So we push the track density even tighter together, But we also boost the linear density at the same time, and we do that without adding cost. >> Right. The other thing you talked about, and I think it's a really important piece, right it's not only the technology breakthrough, but it's also how does that fit within the existing ecosystem of your customers, and obviously big giant data centers and big giant cloud providers, we actually have a show going on at a big cloud show right now, and this technology was innovative in that you've got a breakthrough on density, but not so crazy that you introduced a whole bunch of new factors into the ecosystem that would then have to be incorporated into all these systems, because you guys not only make your own systems, but you make the media that feeds a whole host of ecosystems, and that was a pretty important piece. >> If you look at some previous technologies we've introduced whether it be even 4K sectors in the industry, or shingled magnetic reporting, both of those require whole side modifications. Any time you have whole side modifications, it generally slows down the adoption, right? With HAMR, one of the challenges that we had was because of the concerns with thermals on the media, we needed a process called wear leveling, and that required whole software changes. In contrast, when we go to MAMR, everything is seamless, everything is transparent, and it's great. >> Right. I thought it was much simpler than that. I thought just heat is bad, HAMR is heat, and MAMR is microwave, and you know, heat and efficiency and data centers and all those, kind of again, system-level concerns; heat's never a good thing in electronics. >> Well, and in the case of MAMR versus HAMR, there's like an order of magnitude difference in the temperature on the disk, which is the key concern. >> And then of course as you mentioned in the key note, this is real, you've got sample units going on, correct me if I'm wrong, as early as next year >> That's right. >> you're hoping you'd be in scale production in 2020. Where some of these other competing technologies, there's really still no forecasted ship date on the horizon. >> Yeah, you can generate samples, you can build lower quantities of these HAMR drives, but you still have that big concern out there in front of you, how do I address the reliability, how do I address the complexity of all these new materials, and then if I got all of that to work, how do I do it commercially because of the cost additives. >> Right; so I just want to get your perspective before we let you go, you're busy, there's a high demand for your time, as you kind of think back and look at these increasing demands for storage, this increasing demand for computers, and I think one of the data points given is, you know, the data required for humans and machines and IOT is growing way way way way faster than business focused data which has been the driver of a lot of this stuff, if you just kind of sit back and take a look, you know, what are some of your thoughts because I'm sure not that long ago you could have never imagined that there would be the demand for the types of capacities that we're talking about now and we both know that when we sit down five years from now, ten years ago, you know, ten years from now, we're going to look back at today and think, you know, that was zero. >> Yeah, way back in the day there were just PCs and servers and there was traditional IT with rate, today with autonomous cars and IOT and AI and machine learning, it's just going to continue, so that exponential growth that you saw, there's no sign of that slowing down, which is good news for us. >> Yeah, good job security for you for sure. >> You bet! >> Alright Brendan, well, again, thanks for taking a few minutes to sit down and congratulations on the great event and the launch of these new products. >> Thank you, thank you. >> He's Brendan Collins, I'm Jeff Frick, you're watching the Cube from the Western Digital Headquarters in San Jose California. Thanks for watching.

(mellow music) >> Hello everyone, welcome to theCUBE's coverage of AWS Startup Showcase. This is the closing panel session on AI machine learning, the top startups generating generative AI on AWS. It's a great panel. This is going to be the experts talking about riding the wave in generative AI. We got Ankur Mehrotra, who's the director and general manager of AI and machine learning at AWS, and Clem Delangue, co-founder and CEO of Hugging Face, and Ori Goshen, who's the co-founder and CEO of AI21 Labs. Ori from Tel Aviv dialing in, and rest coming in here on theCUBE. Appreciate you coming on for this closing session for the Startup Showcase. >> Thanks for having us. >> Thank you for having us. >> Thank you. >> I'm super excited to have you all on. Hugging Face was recently in the news with the AWS relationship, so congratulations. Open source, open science, really driving the machine learning. And we got the AI21 Labs access to the LLMs, generating huge scale live applications, commercial applications, coming to the market, all powered by AWS. So everyone, congratulations on all your success, and thank you for headlining this panel. Let's get right into it. AWS is powering this wave here. We're seeing a lot of push here from applications. Ankur, set the table for us on the AI machine learning. It's not new, it's been goin' on for a while. Past three years have been significant advancements, but there's been a lot of work done in AI machine learning. Now it's released to the public. Everybody's super excited and now says, "Oh, the future's here!" It's kind of been going on for a while and baking. Now it's kind of coming out. What's your view here? Let's get it started. >> Yes, thank you. So, yeah, as you may be aware, Amazon has been in investing in machine learning research and development since quite some time now. And we've used machine learning to innovate and improve user experiences across different Amazon products, whether it's Alexa or Amazon.com. But we've also brought in our expertise to extend what we are doing in the space and add more generative AI technology to our AWS products and services, starting with CodeWhisperer, which is an AWS service that we announced a few months ago, which is, you can think of it as a coding companion as a service, which uses generative AI models underneath. And so this is a service that customers who have no machine learning expertise can just use. And we also are talking to customers, and we see a lot of excitement about generative AI, and customers who want to build these models themselves, who have the talent and the expertise and resources. For them, AWS has a number of different options and capabilities they can leverage, such as our custom silicon, such as Trainium and Inferentia, as well as distributed machine learning capabilities that we offer as part of SageMaker, which is an end-to-end machine learning development service. At the same time, many of our customers tell us that they're interested in not training and building these generative AI models from scratch, given they can be expensive and can require specialized talent and skills to build. And so for those customers, we are also making it super easy to bring in existing generative AI models into their machine learning development environment within SageMaker for them to use. So we recently announced our partnership with Hugging Face, where we are making it super easy for customers to bring in those models into their SageMaker development environment for fine tuning and deployment. And then we are also partnering with other proprietary model providers such as AI21 and others, where we making these generative AI models available within SageMaker for our customers to use. So our approach here is to really provide customers options and choices and help them accelerate their generative AI journey. >> Ankur, thank you for setting the table there. Clem and Ori, I want to get your take, because the riding the waves, the theme of this session, and to me being in California, I imagine the big surf, the big waves, the big talent out there. This is like alpha geeks, alpha coders, developers are really leaning into this. You're seeing massive uptake from the smartest people. Whether they're young or around, they're coming in with their kind of surfboards, (chuckles) if you will. These early adopters, they've been on this for a while; Now the waves are hitting. This is a big wave, everyone sees it. What are some of those early adopter devs doing? What are some of the use cases you're seeing right out of the gate? And what does this mean for the folks that are going to come in and get on this wave? Can you guys share your perspective on this? Because you're seeing the best talent now leaning into this. >> Yeah, absolutely. I mean, from Hugging Face vantage points, it's not even a a wave, it's a tidal wave, or maybe even the tide itself. Because actually what we are seeing is that AI and machine learning is not something that you add to your products. It's very much a new paradigm to do all technology. It's this idea that we had in the past 15, 20 years, one way to build software and to build technology, which was writing a million lines of code, very rule-based, and then you get your product. Now what we are seeing is that every single product, every single feature, every single company is starting to adopt AI to build the next generation of technology. And that works both to make the existing use cases better, if you think of search, if you think of social network, if you think of SaaS, but also it's creating completely new capabilities that weren't possible with the previous paradigm. Now AI can generate text, it can generate image, it can describe your image, it can do so many new things that weren't possible before. >> It's going to really make the developers really productive, right? I mean, you're seeing the developer uptake strong, right? >> Yes, we have over 15,000 companies using Hugging Face now, and it keeps accelerating. I really think that maybe in like three, five years, there's not going to be any company not using AI. It's going to be really kind of the default to build all technology. >> Ori, weigh in on this. APIs, the cloud. Now I'm a developer, I want to have live applications, I want the commercial applications on this. What's your take? Weigh in here. >> Yeah, first, I absolutely agree. I mean, we're in the midst of a technology shift here. I think not a lot of people realize how big this is going to be. Just the number of possibilities is endless, and I think hard to imagine. And I don't think it's just the use cases. I think we can think of it as two separate categories. We'll see companies and products enhancing their offerings with these new AI capabilities, but we'll also see new companies that are AI first, that kind of reimagine certain experiences. They build something that wasn't possible before. And that's why I think it's actually extremely exciting times. And maybe more philosophically, I think now these large language models and large transformer based models are helping us people to express our thoughts and kind of making the bridge from our thinking to a creative digital asset in a speed we've never imagined before. I can write something down and get a piece of text, or an image, or a code. So I'll start by saying it's hard to imagine all the possibilities right now, but it's certainly big. And if I had to bet, I would say it's probably at least as big as the mobile revolution we've seen in the last 20 years. >> Yeah, this is the biggest. I mean, it's been compared to the Enlightenment Age. I saw the Wall Street Journal had a recent story on this. We've been saying that this is probably going to be bigger than all inflection points combined in the tech industry, given what transformation is coming. I guess I want to ask you guys, on the early adopters, we've been hearing on these interviews and throughout the industry that there's already a set of big companies, a set of companies out there that have a lot of data and they're already there, they're kind of tinkering. Kind of reminds me of the old hyper scaler days where they were building their own scale, and they're eatin' glass, spittin' nails out, you know, they're hardcore. Then you got everybody else kind of saying board level, "Hey team, how do I leverage this?" How do you see those two things coming together? You got the fast followers coming in behind the early adopters. What's it like for the second wave coming in? What are those conversations for those developers like? >> I mean, I think for me, the important switch for companies is to change their mindset from being kind of like a traditional software company to being an AI or machine learning company. And that means investing, hiring machine learning engineers, machine learning scientists, infrastructure in members who are working on how to put these models in production, team members who are able to optimize models, specialized models, customized models for the company's specific use cases. So it's really changing this mindset of how you build technology and optimize your company building around that. Things are moving so fast that I think now it's kind of like too late for low hanging fruits or small, small adjustments. I think it's important to realize that if you want to be good at that, and if you really want to surf this wave, you need massive investments. If there are like some surfers listening with this analogy of the wave, right, when there are waves, it's not enough just to stand and make a little bit of adjustments. You need to position yourself aggressively, paddle like crazy, and that's how you get into the waves. So that's what companies, in my opinion, need to do right now. >> Ori, what's your take on the generative models out there? We hear a lot about foundation models. What's your experience running end-to-end applications for large foundation models? Any insights you can share with the app developers out there who are looking to get in? >> Yeah, I think first of all, it's start create an economy, where it probably doesn't make sense for every company to create their own foundation models. You can basically start by using an existing foundation model, either open source or a proprietary one, and start deploying it for your needs. And then comes the second round when you are starting the optimization process. You bootstrap, whether it's a demo, or a small feature, or introducing new capability within your product, and then start collecting data. That data, and particularly the human feedback data, helps you to constantly improve the model, so you create this data flywheel. And I think we're now entering an era where customers have a lot of different choice of how they want to start their generative AI endeavor. And it's a good thing that there's a variety of choices. And the really amazing thing here is that every industry, any company you speak with, it could be something very traditional like industrial or financial, medical, really any company. I think peoples now start to imagine what are the possibilities, and seriously think what's their strategy for adopting this generative AI technology. And I think in that sense, the foundation model actually enabled this to become scalable. So the barrier to entry became lower; Now the adoption could actually accelerate. >> There's a lot of integration aspects here in this new wave that's a little bit different. Before it was like very monolithic, hardcore, very brittle. A lot more integration, you see a lot more data coming together. I have to ask you guys, as developers come in and grow, I mean, when I went to college and you were a software engineer, I mean, I got a degree in computer science, and software engineering, that's all you did was code, (chuckles) you coded. Now, isn't it like everyone's a machine learning engineer at this point? Because that will be ultimately the science. So, (chuckles) you got open source, you got open software, you got the communities. Swami called you guys the GitHub of machine learning, Hugging Face is the GitHub of machine learning, mainly because that's where people are going to code. So this is essentially, machine learning is computer science. What's your reaction to that? >> Yes, my co-founder Julien at Hugging Face have been having this thing for quite a while now, for over three years, which was saying that actually software engineering as we know it today is a subset of machine learning, instead of the other way around. People would call us crazy a few years ago when we're seeing that. But now we are realizing that you can actually code with machine learning. So machine learning is generating code. And we are starting to see that every software engineer can leverage machine learning through open models, through APIs, through different technology stack. So yeah, it's not crazy anymore to think that maybe in a few years, there's going to be more people doing AI and machine learning. However you call it, right? Maybe you'll still call them software engineers, maybe you'll call them machine learning engineers. But there might be more of these people in a couple of years than there is software engineers today. >> I bring this up as more tongue in cheek as well, because Ankur, infrastructure's co is what made Cloud great, right? That's kind of the DevOps movement. But here the shift is so massive, there will be a game-changing philosophy around coding. Machine learning as code, you're starting to see CodeWhisperer, you guys have had coding companions for a while on AWS. So this is a paradigm shift. How is the cloud playing into this for you guys? Because to me, I've been riffing on some interviews where it's like, okay, you got the cloud going next level. This is an example of that, where there is a DevOps-like moment happening with machine learning, whether you call it coding or whatever. It's writing code on its own. Can you guys comment on what this means on top of the cloud? What comes out of the scale? What comes out of the benefit here? >> Absolutely, so- >> Well first- >> Oh, go ahead. >> Yeah, so I think as far as scale is concerned, I think customers are really relying on cloud to make sure that the applications that they build can scale along with the needs of their business. But there's another aspect to it, which is that until a few years ago, John, what we saw was that machine learning was a data scientist heavy activity. They were data scientists who were taking the data and training models. And then as machine learning found its way more and more into production and actual usage, we saw the MLOps become a thing, and MLOps engineers become more involved into the process. And then we now are seeing, as machine learning is being used to solve more business critical problems, we're seeing even legal and compliance teams get involved. We are seeing business stakeholders more engaged. So, more and more machine learning is becoming an activity that's not just performed by data scientists, but is performed by a team and a group of people with different skills. And for them, we as AWS are focused on providing the best tools and services for these different personas to be able to do their job and really complete that end-to-end machine learning story. So that's where, whether it's tools related to MLOps or even for folks who cannot code or don't know any machine learning. For example, we launched SageMaker Canvas as a tool last year, which is a UI-based tool which data analysts and business analysts can use to build machine learning models. So overall, the spectrum in terms of persona and who can get involved in the machine learning process is expanding, and the cloud is playing a big role in that process. >> Ori, Clem, can you guys weigh in too? 'Cause this is just another abstraction layer of scale. What's it mean for you guys as you look forward to your customers and the use cases that you're enabling? >> Yes, I think what's important is that the AI companies and providers and the cloud kind of work together. That's how you make a seamless experience and you actually reduce the barrier to entry for this technology. So that's what we've been super happy to do with AWS for the past few years. We actually announced not too long ago that we are doubling down on our partnership with AWS. We're excited to have many, many customers on our shared product, the Hugging Face deep learning container on SageMaker. And we are working really closely with the Inferentia team and the Trainium team to release some more exciting stuff in the coming weeks and coming months. So I think when you have an ecosystem and a system where the AWS and the AI providers, AI startups can work hand in hand, it's to the benefit of the customers and the companies, because it makes it orders of magnitude easier for them to adopt this new paradigm to build technology AI. >> Ori, this is a scale on reasoning too. The data's out there and making sense out of it, making it reason, getting comprehension, having it make decisions is next, isn't it? And you need scale for that. >> Yes. Just a comment about the infrastructure side. So I think really the purpose is to streamline and make these technologies much more accessible. And I think we'll see, I predict that we'll see in the next few years more and more tooling that make this technology much more simple to consume. And I think it plays a very important role. There's so many aspects, like the monitoring the models and their kind of outputs they produce, and kind of containing and running them in a production environment. There's so much there to build on, the infrastructure side will play a very significant role. >> All right, that's awesome stuff. I'd love to change gears a little bit and get a little philosophy here around AI and how it's going to transform, if you guys don't mind. There's been a lot of conversations around, on theCUBE here as well as in some industry areas, where it's like, okay, all the heavy lifting is automated away with machine learning and AI, the complexity, there's some efficiencies, it's horizontal and scalable across all industries. Ankur, good point there. Everyone's going to use it for something. And a lot of stuff gets brought to the table with large language models and other things. But the key ingredient will be proprietary data or human input, or some sort of AI whisperer kind of role, or prompt engineering, people are saying. So with that being said, some are saying it's automating intelligence. And that creativity will be unleashed from this. If the heavy lifting goes away and AI can fill the void, that shifts the value to the intellect or the input. And so that means data's got to come together, interact, fuse, and understand each other. This is kind of new. I mean, old school AI was, okay, got a big model, I provisioned it long time, very expensive. Now it's all free flowing. Can you guys comment on where you see this going with this freeform, data flowing everywhere, heavy lifting, and then specialization? >> Yeah, I think- >> Go ahead. >> Yeah, I think, so what we are seeing with these large language models or generative models is that they're really good at creating stuff. But I think it's also important to recognize their limitations. They're not as good at reasoning and logic. And I think now we're seeing great enthusiasm, I think, which is justified. And the next phase would be how to make these systems more reliable. How to inject more reasoning capabilities into these models, or augment with other mechanisms that actually perform more reasoning so we can achieve more reliable results. And we can count on these models to perform for critical tasks, whether it's medical tasks, legal tasks. We really want to kind of offload a lot of the intelligence to these systems. And then we'll have to get back, we'll have to make sure these are reliable, we'll have to make sure we get some sort of explainability that we can understand the process behind the generated results that we received. So I think this is kind of the next phase of systems that are based on these generated models. >> Clem, what's your view on this? Obviously you're at open community, open source has been around, it's been a great track record, proven model. I'm assuming creativity's going to come out of the woodwork, and if we can automate open source contribution, and relationships, and onboarding more developers, there's going to be unleashing of creativity. >> Yes, it's been so exciting on the open source front. We all know Bert, Bloom, GPT-J, T5, Stable Diffusion, that work up. The previous or the current generation of open source models that are on Hugging Face. It has been accelerating in the past few months. So I'm super excited about ControlNet right now that is really having a lot of impact, which is kind of like a way to control the generation of images. Super excited about Flan UL2, which is like a new model that has been recently released and is open source. So yeah, it's really fun to see the ecosystem coming together. Open source has been the basis for traditional software, with like open source programming languages, of course, but also all the great open source that we've gotten over the years. So we're happy to see that the same thing is happening for machine learning and AI, and hopefully can help a lot of companies reduce a little bit the barrier to entry. So yeah, it's going to be exciting to see how it evolves in the next few years in that respect. >> I think the developer productivity angle that's been talked about a lot in the industry will be accelerated significantly. I think security will be enhanced by this. I think in general, applications are going to transform at a radical rate, accelerated, incredible rate. So I think it's not a big wave, it's the water, right? I mean, (chuckles) it's the new thing. My final question for you guys, if you don't mind, I'd love to get each of you to answer the question I'm going to ask you, which is, a lot of conversations around data. Data infrastructure's obviously involved in this. And the common thread that I'm hearing is that every company that looks at this is asking themselves, if we don't rebuild our company, start thinking about rebuilding our business model around AI, we might be dinosaurs, we might be extinct. And it reminds me that scene in Moneyball when, at the end, it's like, if we're not building the model around your model, every company will be out of business. What's your advice to companies out there that are having those kind of moments where it's like, okay, this is real, this is next gen, this is happening. I better start thinking and putting into motion plans to refactor my business, 'cause it's happening, business transformation is happening on the cloud. This kind of puts an exclamation point on, with the AI, as a next step function. Big increase in value. So it's an opportunity for leaders. Ankur, we'll start with you. What's your advice for folks out there thinking about this? Do they put their toe in the water? Do they jump right into the deep end? What's your advice? >> Yeah, John, so we talk to a lot of customers, and customers are excited about what's happening in the space, but they often ask us like, "Hey, where do we start?" So we always advise our customers to do a lot of proof of concepts, understand where they can drive the biggest ROI. And then also leverage existing tools and services to move fast and scale, and try and not reinvent the wheel where it doesn't need to be. That's basically our advice to customers. >> Get it. Ori, what's your advice to folks who are scratching their head going, "I better jump in here. "How do I get started?" What's your advice? >> So I actually think that need to think about it really economically. Both on the opportunity side and the challenges. So there's a lot of opportunities for many companies to actually gain revenue upside by building these new generative features and capabilities. On the other hand, of course, this would probably affect the cogs, and incorporating these capabilities could probably affect the cogs. So I think we really need to think carefully about both of these sides, and also understand clearly if this is a project or an F word towards cost reduction, then the ROI is pretty clear, or revenue amplifier, where there's, again, a lot of different opportunities. So I think once you think about this in a structured way, I think, and map the different initiatives, then it's probably a good way to start and a good way to start thinking about these endeavors. >> Awesome. Clem, what's your take on this? What's your advice, folks out there? >> Yes, all of these are very good advice already. Something that you said before, John, that I disagreed a little bit, a lot of people are talking about the data mode and proprietary data. Actually, when you look at some of the organizations that have been building the best models, they don't have specialized or unique access to data. So I'm not sure that's so important today. I think what's important for companies, and it's been the same for the previous generation of technology, is their ability to build better technology faster than others. And in this new paradigm, that means being able to build machine learning faster than others, and better. So that's how, in my opinion, you should approach this. And kind of like how can you evolve your company, your teams, your products, so that you are able in the long run to build machine learning better and faster than your competitors. And if you manage to put yourself in that situation, then that's when you'll be able to differentiate yourself to really kind of be impactful and get results. That's really hard to do. It's something really different, because machine learning and AI is a different paradigm than traditional software. So this is going to be challenging, but I think if you manage to nail that, then the future is going to be very interesting for your company. >> That's a great point. Thanks for calling that out. I think this all reminds me of the cloud days early on. If you went to the cloud early, you took advantage of it when the pandemic hit. If you weren't native in the cloud, you got hamstrung by that, you were flatfooted. So just get in there. (laughs) Get in the cloud, get into AI, you're going to be good. Thanks for for calling that. Final parting comments, what's your most exciting thing going on right now for you guys? Ori, Clem, what's the most exciting thing on your plate right now that you'd like to share with folks? >> I mean, for me it's just the diversity of use cases and really creative ways of companies leveraging this technology. Every day I speak with about two, three customers, and I'm continuously being surprised by the creative ideas. And the future is really exciting of what can be achieved here. And also I'm amazed by the pace that things move in this industry. It's just, there's not at dull moment. So, definitely exciting times. >> Clem, what are you most excited about right now? >> For me, it's all the new open source models that have been released in the past few weeks, and that they'll keep being released in the next few weeks. I'm also super excited about more and more companies getting into this capability of chaining different models and different APIs. I think that's a very, very interesting development, because it creates new capabilities, new possibilities, new functionalities that weren't possible before. You can plug an API with an open source embedding model, with like a no-geo transcription model. So that's also very exciting. This capability of having more interoperable machine learning will also, I think, open a lot of interesting things in the future. >> Clem, congratulations on your success at Hugging Face. Please pass that on to your team. Ori, congratulations on your success, and continue to, just day one. I mean, it's just the beginning. It's not even scratching the service. Ankur, I'll give you the last word. What are you excited for at AWS? More cloud goodness coming here with AI. Give you the final word. >> Yeah, so as both Clem and Ori said, I think the research in the space is moving really, really fast, so we are excited about that. But we are also excited to see the speed at which enterprises and other AWS customers are applying machine learning to solve real business problems, and the kind of results they're seeing. So when they come back to us and tell us the kind of improvement in their business metrics and overall customer experience that they're driving and they're seeing real business results, that's what keeps us going and inspires us to continue inventing on their behalf. >> Gentlemen, thank you so much for this awesome high impact panel. Ankur, Clem, Ori, congratulations on all your success. We'll see you around. Thanks for coming on. Generative AI, riding the wave, it's a tidal wave, it's the water, it's all happening. All great stuff. This is season three, episode one of AWS Startup Showcase closing panel. This is the AI ML episode, the top startups building generative AI on AWS. I'm John Furrier, your host. Thanks for watching. (mellow music)

(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

(bright upbeat music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCUBE. We've got a great conversation featuring Arthur AI. I'm your host. I'm excited to have Adam Wenchel who's the Co-Founder and CEO. Thanks for joining us today, appreciate it. >> Yeah, thanks for having me on, John, looking forward to the conversation. >> I got to say, it's been an exciting world in AI or artificial intelligence. Just an explosion of interest kind of in the mainstream with the language models, which people don't really get, but they're seeing the benefits of some of the hype around OpenAI. Which kind of wakes everyone up to, "Oh, I get it now." And then of course the pessimism comes in, all the skeptics are out there. But this breakthrough in generative AI field is just awesome, it's really a shift, it's a wave. We've been calling it probably the biggest inflection point, then the others combined of what this can do from a surge standpoint, applications. I mean, all aspects of what we used to know is the computing industry, software industry, hardware, is completely going to get turbo. So we're totally obviously bullish on this thing. So, this is really interesting. So my first question is, I got to ask you, what's you guys taking? 'Cause you've been doing this, you're in it, and now all of a sudden you're at the beach where the big waves are. What's the explosion of interest is there? What are you seeing right now? >> Yeah, I mean, it's amazing, so for starters, I've been in AI for over 20 years and just seeing this amount of excitement and the growth, and like you said, the inflection point we've hit in the last six months has just been amazing. And, you know, what we're seeing is like people are getting applications into production using LLMs. I mean, really all this excitement just started a few months ago, with ChatGPT and other breakthroughs and the amount of activity and the amount of new systems that we're seeing hitting production already so soon after that is just unlike anything we've ever seen. So it's pretty awesome. And, you know, these language models are just, they could be applied in so many different business contexts and that it's just the amount of value that's being created is again, like unprecedented compared to anything. >> Adam, you know, you've been in this for a while, so it's an interesting point you're bringing up, and this is a good point. I was talking with my friend John Markoff, former New York Times journalist and he was talking about, there's been a lot of work been done on ethics. So there's been, it's not like it's new. It's like been, there's a lot of stuff that's been baking over many, many years and, you know, decades. So now everyone wakes up in the season, so I think that is a key point I want to get into some of your observations. But before we get into it, I want you to explain for the folks watching, just so we can kind of get a definition on the record. What's an LLM, what's a foundational model and what's generative ai? Can you just quickly explain the three things there? >> Yeah, absolutely. So an LLM or a large language model, it's just a large, they would imply a large language model that's been trained on a huge amount of data typically pulled from the internet. And it's a general purpose language model that can be built on top for all sorts of different things, that includes traditional NLP tasks like document classification and sentiment understanding. But the thing that's gotten people really excited is it's used for generative tasks. So, you know, asking it to summarize documents or asking it to answer questions. And these aren't new techniques, they've been around for a while, but what's changed is just this new class of models that's based on new architectures. They're just so much more capable that they've gone from sort of science projects to something that's actually incredibly useful in the real world. And there's a number of companies that are making them accessible to everyone so that you can build on top of them. So that's the other big thing is, this kind of access to these models that can power generative tasks has been democratized in the last few months and it's just opening up all these new possibilities. And then the third one you mentioned foundation models is sort of a broader term for the category that includes LLMs, but it's not just language models that are included. So we've actually seen this for a while in the computer vision world. So people have been building on top of computer vision models, pre-trained computer vision models for a while for image classification, object detection, that's something we've had customers doing for three or four years already. And so, you know, like you said, there are antecedents to like, everything that's happened, it's not entirely new, but it does feel like a step change. >> Yeah, I did ask ChatGPT to give me a riveting introduction to you and it gave me an interesting read. If we have time, I'll read it. It's kind of, it's fun, you get a kick out of it. "Ladies and gentlemen, today we're a privileged "to have Adam Wenchel, Founder of Arthur who's going to talk "about the exciting world of artificial intelligence." And then it goes on with some really riveting sentences. So if we have time, I'll share that, it's kind of funny. It was good. >> Okay. >> So anyway, this is what people see and this is why I think it's exciting 'cause I think people are going to start refactoring what they do. And I've been saying this on theCUBE now for about a couple months is that, you know, there's a scene in "Moneyball" where Billy Beane sits down with the Red Sox owner and the Red Sox owner says, "If people aren't rebuilding their teams on your model, "they're going to be dinosaurs." And it reminds me of what's happening right now. And I think everyone that I talk to in the business sphere is looking at this and they're connecting the dots and just saying, if we don't rebuild our business with this new wave, they're going to be out of business because there's so much efficiency, there's so much automation, not like DevOps automation, but like the generative tasks that will free up the intellect of people. Like just the simple things like do an intro or do this for me, write some code, write a countermeasure to a hack. I mean, this is kind of what people are doing. And you mentioned computer vision, again, another huge field where 5G things are coming on, it's going to accelerate. What do you say to people when they kind of are leaning towards that, I need to rethink my business? >> Yeah, it's 100% accurate and what's been amazing to watch the last few months is the speed at which, and the urgency that companies like Microsoft and Google or others are actually racing to, to do that rethinking of their business. And you know, those teams, those companies which are large and haven't always been the fastest moving companies are working around the clock. And the pace at which they're rolling out LLMs across their suite of products is just phenomenal to watch. And it's not just the big, the large tech companies as well, I mean, we're seeing the number of startups, like we get, every week a couple of new startups get in touch with us for help with their LLMs and you know, there's just a huge amount of venture capital flowing into it right now because everyone realizes the opportunities for transforming like legal and healthcare and content creation in all these different areas is just wide open. And so there's a massive gold rush going on right now, which is amazing. >> And the cloud scale, obviously horizontal scalability of the cloud brings us to another level. We've been seeing data infrastructure since the Hadoop days where big data was coined. Now you're seeing this kind of take fruit, now you have vertical specialization where data shines, large language models all of a set up perfectly for kind of this piece. And you know, as you mentioned, you've been doing it for a long time. Let's take a step back and I want to get into how you started the company, what drove you to start it? Because you know, as an entrepreneur you're probably saw this opportunity before other people like, "Hey, this is finally it, it's here." Can you share the origination story of what you guys came up with, how you started it, what was the motivation and take us through that origination story. >> Yeah, absolutely. So as I mentioned, I've been doing AI for many years. I started my career at DARPA, but it wasn't really until 2015, 2016, my previous company was acquired by Capital One. Then I started working there and shortly after I joined, I was asked to start their AI team and scale it up. And for the first time I was actually doing it, had production models that we were working with, that was at scale, right? And so there was hundreds of millions of dollars of business revenue and certainly a big group of customers who were impacted by the way these models acted. And so it got me hyper-aware of these issues of when you get models into production, it, you know. So I think people who are earlier in the AI maturity look at that as a finish line, but it's really just the beginning and there's this constant drive to make them better, make sure they're not degrading, make sure you can explain what they're doing, if they're impacting people, making sure they're not biased. And so at that time, there really weren't any tools to exist to do this, there wasn't open source, there wasn't anything. And so after a few years there, I really started talking to other people in the industry and there was a really clear theme that this needed to be addressed. And so, I joined with my Co-Founder John Dickerson, who was on the faculty in University of Maryland and he'd been doing a lot of research in these areas. And so we ended up joining up together and starting Arthur. >> Awesome. Well, let's get into what you guys do. Can you explain the value proposition? What are people using you for now? Where's the action? What's the customers look like? What do prospects look like? Obviously you mentioned production, this has been the theme. It's not like people woke up one day and said, "Hey, I'm going to put stuff into production." This has kind of been happening. There's been companies that have been doing this at scale and then yet there's a whole follower model coming on mainstream enterprise and businesses. So there's kind of the early adopters are there now in production. What do you guys do? I mean, 'cause I think about just driving the car off the lot is not, you got to manage operations. I mean, that's a big thing. So what do you guys do? Talk about the value proposition and how you guys make money? >> Yeah, so what we do is, listen, when you go to validate ahead of deploying these models in production, starts at that point, right? So you want to make sure that if you're going to be upgrading a model, if you're going to replacing one that's currently in production, that you've proven that it's going to perform well, that it's going to be perform ethically and that you can explain what it's doing. And then when you launch it into production, traditionally data scientists would spend 25, 30% of their time just manually checking in on their model day-to-day babysitting as we call it, just to make sure that the data hasn't drifted, the model performance hasn't degraded, that a programmer did make a change in an upstream data system. You know, there's all sorts of reasons why the world changes and that can have a real adverse effect on these models. And so what we do is bring the same kind of automation that you have for other kinds of, let's say infrastructure monitoring, application monitoring, we bring that to your AI systems. And that way if there ever is an issue, it's not like weeks or months till you find it and you find it before it has an effect on your P&L and your balance sheet, which is too often before they had tools like Arthur, that was the way they were detected. >> You know, I was talking to Swami at Amazon who I've known for a long time for 13 years and been on theCUBE multiple times and you know, I watched Amazon try to pick up that sting with stage maker about six years ago and so much has happened since then. And he and I were talking about this wave, and I kind of brought up this analogy to how when cloud started, it was, Hey, I don't need a data center. 'Cause when I did my startup that time when Amazon, one of my startups at that time, my choice was put a box in the colo, get all the configuration before I could write over the line of code. So the cloud became the benefit for that and you can stand up stuff quickly and then it grew from there. Here it's kind of the same dynamic, you don't want to have to provision a large language model or do all this heavy lifting. So that seeing companies coming out there saying, you can get started faster, there's like a new way to get it going. So it's kind of like the same vibe of limiting that heavy lifting. >> Absolutely. >> How do you look at that because this seems to be a wave that's going to be coming in and how do you guys help companies who are going to move quickly and start developing? >> Yeah, so I think in the race to this kind of gold rush mentality, race to get these models into production, there's starting to see more sort of examples and evidence that there are a lot of risks that go along with it. Either your model says things, your system says things that are just wrong, you know, whether it's hallucination or just making things up, there's lots of examples. If you go on Twitter and the news, you can read about those, as well as sort of times when there could be toxic content coming out of things like that. And so there's a lot of risks there that you need to think about and be thoughtful about when you're deploying these systems. But you know, you need to balance that with the business imperative of getting these things into production and really transforming your business. And so that's where we help people, we say go ahead, put them in production, but just make sure you have the right guardrails in place so that you can do it in a smart way that's going to reflect well on you and your company. >> Let's frame the challenge for the companies now that you have, obviously there's the people who doing large scale production and then you have companies maybe like as small as us who have large linguistic databases or transcripts for example, right? So what are customers doing and why are they deploying AI right now? And is it a speed game, is it a cost game? Why have some companies been able to deploy AI at such faster rates than others? And what's a best practice to onboard new customers? >> Yeah, absolutely. So I mean, we're seeing across a bunch of different verticals, there are leaders who have really kind of started to solve this puzzle about getting AI models into production quickly and being able to iterate on them quickly. And I think those are the ones that realize that imperative that you mentioned earlier about how transformational this technology is. And you know, a lot of times, even like the CEOs or the boards are very personally kind of driving this sense of urgency around it. And so, you know, that creates a lot of movement, right? And so those companies have put in place really smart infrastructure and rails so that people can, data scientists aren't encumbered by having to like hunt down data, get access to it. They're not encumbered by having to stand up new platforms every time they want to deploy an AI system, but that stuff is already in place. There's a really nice ecosystem of products out there, including Arthur, that you can tap into. Compared to five or six years ago when I was building at a top 10 US bank, at that point you really had to build almost everything yourself and that's not the case now. And so it's really nice to have things like, you know, you mentioned AWS SageMaker and a whole host of other tools that can really accelerate things. >> What's your profile customer? Is it someone who already has a team or can people who are learning just dial into the service? What's the persona? What's the pitch, if you will, how do you align with that customer value proposition? Do people have to be built out with a team and in play or is it pre-production or can you start with people who are just getting going? >> Yeah, people do start using it pre-production for validation, but I think a lot of our customers do have a team going and they're starting to put, either close to putting something into production or about to, it's everything from large enterprises that have really sort of complicated, they have dozens of models running all over doing all sorts of use cases to tech startups that are very focused on a single problem, but that's like the lifeblood of the company and so they need to guarantee that it works well. And you know, we make it really easy to get started, especially if you're using one of the common model development platforms, you can just kind of turn key, get going and make sure that you have a nice feedback loop. So then when your models are out there, it's pointing out, areas where it's performing well, areas where it's performing less well, giving you that feedback so that you can make improvements, whether it's in training data or futurization work or algorithm selection. There's a number of, you know, depending on the symptoms, there's a number of things you can do to increase performance over time and we help guide people on that journey. >> So Adam, I have to ask, since you have such a great customer base and they're smart and they got teams and you're on the front end, I mean, early adopters is kind of an overused word, but they're killing it. They're putting stuff in the production's, not like it's a test, it's not like it's early. So as the next wave comes of fast followers, how do you see that coming online? What's your vision for that? How do you see companies that are like just waking up out of the frozen, you know, freeze of like old IT to like, okay, they got cloud, but they're not yet there. What do you see in the market? I see you're in the front end now with the top people really nailing AI and working hard. What's the- >> Yeah, I think a lot of these tools are becoming, or every year they get easier, more accessible, easier to use. And so, you know, even for that kind of like, as the market broadens, it takes less and less of a lift to put these systems in place. And the thing is, every business is unique, they have their own kind of data and so you can use these foundation models which have just been trained on generic data. They're a great starting point, a great accelerant, but then, in most cases you're either going to want to create a model or fine tune a model using data that's really kind of comes from your particular customers, the people you serve and so that it really reflects that and takes that into account. And so I do think that these, like the size of that market is expanding and its broadening as these tools just become easier to use and also the knowledge about how to build these systems becomes more widespread. >> Talk about your customer base you have now, what's the makeup, what size are they? Give a taste a little bit of a customer base you got there, what's they look like? I'll say Capital One, we know very well while you were at there, they were large scale, lot of data from fraud detection to all kinds of cool stuff. What do your customers now look like? >> Yeah, so we have a variety, but I would say one area we're really strong, we have several of the top 10 US banks, that's not surprising, that's a strength for us, but we also have Fortune 100 customers in healthcare, in manufacturing, in retail, in semiconductor and electronics. So what we find is like in any sort of these major verticals, there's typically, you know, one, two, three kind of companies that are really leading the charge and are the ones that, you know, in our opinion, those are the ones that for the next multiple decades are going to be the leaders, the ones that really kind of lead the charge on this AI transformation. And so we're very fortunate to be working with some of those. And then we have a number of startups as well who we love working with just because they're really pushing the boundaries technologically and so they provide great feedback and make sure that we're continuing to innovate and staying abreast of everything that's going on. >> You know, these early markups, even when the hyperscalers were coming online, they had to build everything themselves. That's the new, they're like the alphas out there building it. This is going to be a big wave again as that fast follower comes in. And so when you look at the scale, what advice would you give folks out there right now who want to tee it up and what's your secret sauce that will help them get there? >> Yeah, I think that the secret to teeing it up is just dive in and start like the, I think these are, there's not really a secret. I think it's amazing how accessible these are. I mean, there's all sorts of ways to access LLMs either via either API access or downloadable in some cases. And so, you know, go ahead and get started. And then our secret sauce really is the way that we provide that performance analysis of what's going on, right? So we can tell you in a very actionable way, like, hey, here's where your model is doing good things, here's where it's doing bad things. Here's something you want to take a look at, here's some potential remedies for it. We can help guide you through that. And that way when you're putting it out there, A, you're avoiding a lot of the common pitfalls that people see and B, you're able to really kind of make it better in a much faster way with that tight feedback loop. >> It's interesting, we've been kind of riffing on this supercloud idea because it was just different name than multicloud and you see apps like Snowflake built on top of AWS without even spending any CapEx, you just ride that cloud wave. This next AI, super AI wave is coming. I don't want to call AIOps because I think there's a different distinction. If you, MLOps and AIOps seem a little bit old, almost a few years back, how do you view that because everyone's is like, "Is this AIOps?" And like, "No, not kind of, but not really." How would you, you know, when someone says, just shoots off the hip, "Hey Adam, aren't you doing AIOps?" Do you say, yes we are, do you say, yes, but we do differently because it's doesn't seem like it's the same old AIOps. What's your- >> Yeah, it's a good question. AIOps has been a term that was co-opted for other things and MLOps also has people have used it for different meanings. So I like the term just AI infrastructure, I think it kind of like describes it really well and succinctly. >> But you guys are doing the ops. I mean that's the kind of ironic thing, it's like the next level, it's like NextGen ops, but it's not, you don't want to be put in that bucket. >> Yeah, no, it's very operationally focused platform that we have, I mean, it fires alerts, people can action off them. If you're familiar with like the way people run security operations centers or network operations centers, we do that for data science, right? So think of it as a DSOC, a Data Science Operations Center where all your models, you might have hundreds of models running across your organization, you may have five, but as problems are detected, alerts can be fired and you can actually work the case, make sure they're resolved, escalate them as necessary. And so there is a very strong operational aspect to it, you're right. >> You know, one of the things I think is interesting is, is that, if you don't mind commenting on it, is that the aspect of scale is huge and it feels like that was made up and now you have scale and production. What's your reaction to that when people say, how does scale impact this? >> Yeah, scale is huge for some of, you know, I think, I think look, the highest leverage business areas to apply these to, are generally going to be the ones at the biggest scale, right? And I think that's one of the advantages we have. Several of us come from enterprise backgrounds and we're used to doing things enterprise grade at scale and so, you know, we're seeing more and more companies, I think they started out deploying AI and sort of, you know, important but not necessarily like the crown jewel area of their business, but now they're deploying AI right in the heart of things and yeah, the scale that some of our companies are operating at is pretty impressive. >> John: Well, super exciting, great to have you on and congratulations. I got a final question for you, just random. What are you most excited about right now? Because I mean, you got to be pretty pumped right now with the way the world is going and again, I think this is just the beginning. What's your personal view? How do you feel right now? >> Yeah, the thing I'm really excited about for the next couple years now, you touched on it a little bit earlier, but is a sort of convergence of AI and AI systems with sort of turning into AI native businesses. And so, as you sort of do more, get good further along this transformation curve with AI, it turns out that like the better the performance of your AI systems, the better the performance of your business. Because these models are really starting to underpin all these key areas that cumulatively drive your P&L. And so one of the things that we work a lot with our customers is to do is just understand, you know, take these really esoteric data science notions and performance and tie them to all their business KPIs so that way you really are, it's kind of like the operating system for running your AI native business. And we're starting to see more and more companies get farther along that maturity curve and starting to think that way, which is really exciting. >> I love the AI native. I haven't heard any startup yet say AI first, although we kind of use the term, but I guarantee that's going to come in all the pitch decks, we're an AI first company, it's going to be great run. Adam, congratulations on your success to you and the team. Hey, if we do a few more interviews, we'll get the linguistics down. We can have bots just interact with you directly and ask you, have an interview directly. >> That sounds good, I'm going to go hang out on the beach, right? So, sounds good. >> Thanks for coming on, really appreciate the conversation. Super exciting, really important area and you guys doing great work. Thanks for coming on. >> Adam: Yeah, thanks John. >> Again, this is Cube Conversation. I'm John Furrier here in Palo Alto, AI going next gen. This is legit, this is going to a whole nother level that's going to open up huge opportunities for startups, that's going to use opportunities for investors and the value to the users and the experience will come in, in ways I think no one will ever see. So keep an eye out for more coverage on siliconangle.com and theCUBE.net, thanks for watching. (bright upbeat music)

(upbeat music) >> Hello, everyone, welcome to theCUBE. I'm John Furrier, the host of theCUBE in Palo Alto, California. Also it's SiliconANGLE News. Got two great guests here to talk about AI, the impact of the future of the internet, the applications, the people. Amr Awadallah, the founder and CEO, Ed Alban is the CEO of Vectara, a new startup that emerged out of the original Cloudera, I would say, 'cause Amr's known, famous for the Cloudera founding, which was really the beginning of the big data movement. And now as AI goes mainstream, there's so much to talk about, so much to go on. And plus the new company is one of the, now what I call the wave, this next big wave, I call it the fifth wave in the industry. You know, you had PCs, you had the internet, you had mobile. This generative AI thing is real. And you're starting to see startups come out in droves. Amr obviously was founder of Cloudera, Big Data, and now Vectara. And Ed Albanese, you guys have a new company. Welcome to the show. >> Thank you. It's great to be here. >> So great to see you. Now the story is theCUBE started in the Cloudera office. Thanks to you, and your friendly entrepreneurship views that you have. We got to know each other over the years. But Cloudera had Hadoop, which was the beginning of what I call the big data wave, which then became what we now call data lakes, data oceans, and data infrastructure that's developed from that. It's almost interesting to look back 12 plus years, and see that what AI is doing now, right now, is opening up the eyes to the mainstream, and the application's almost mind blowing. You know, Sati Natel called it the Mosaic Moment, didn't say Netscape, he built Netscape (laughing) but called it the Mosaic Moment. You're seeing companies in startups, kind of the alpha geeks running here, because this is the new frontier, and there's real meat on the bone, in terms of like things to do. Why? Why is this happening now? What's is the confluence of the forces happening, that are making this happen? >> Yeah, I mean if you go back to the Cloudera days, with big data, and so on, that was more about data processing. Like how can we process data, so we can extract numbers from it, and do reporting, and maybe take some actions, like this is a fraud transaction, or this is not. And in the meanwhile, many of the researchers working in the neural network, and deep neural network space, were trying to focus on data understanding, like how can I understand the data, and learn from it, so I can take actual actions, based on the data directly, just like a human does. And we were only good at doing that at the level of somebody who was five years old, or seven years old, all the way until about 2013. And starting in 2013, which is only 10 years ago, a number of key innovations started taking place, and each one added on. It was no major innovation that just took place. It was a couple of really incremental ones, but they added on top of each other, in a very exponentially additive way, that led to, by the end of 2019, we now have models, deep neural network models, that can read and understand human text just like we do. Right? And they can reason about it, and argue with you, and explain it to you. And I think that's what is unlocking this whole new wave of innovation that we're seeing right now. So data understanding would be the essence of it. >> So it's not a Big Bang kind of theory, it's been evolving over time, and I think that the tipping point has been the advancements and other things. I mean look at cloud computing, and look how fast it just crept up on AWS. I mean AWS you back three, five years ago, I was talking to Swami yesterday, and their big news about AI, expanding the Hugging Face's relationship with AWS. And just three, five years ago, there wasn't a model training models out there. But as compute comes out, and you got more horsepower,, these large language models, these foundational models, they're flexible, they're not monolithic silos, they're interacting. There's a whole new, almost fusion of data happening. Do you see that? I mean is that part of this? >> Of course, of course. I mean this wave is building on all the previous waves. We wouldn't be at this point if we did not have hardware that can scale, in a very efficient way. We wouldn't be at this point, if we don't have data that we're collecting about everything we do, that we're able to process in this way. So this, this movement, this motion, this phase we're in, absolutely builds on the shoulders of all the previous phases. For some of the observers from the outside, when they see chatGPT for the first time, for them was like, "Oh my god, this just happened overnight." Like it didn't happen overnight. (laughing) GPT itself, like GPT3, which is what chatGPT is based on, was released a year ahead of chatGPT, and many of us were seeing the power it can provide, and what it can do. I don't know if Ed agrees with that. >> Yeah, Ed? >> I do. Although I would acknowledge that the possibilities now, because of what we've hit from a maturity standpoint, have just opened up in an incredible way, that just wasn't tenable even three years ago. And that's what makes it, it's true that it developed incrementally, in the same way that, you know, the possibilities of a mobile handheld device, you know, in 2006 were there, but when the iPhone came out, the possibilities just exploded. And that's the moment we're in. >> Well, I've had many conversations over the past couple months around this area with chatGPT. John Markoff told me the other day, that he calls it, "The five dollar toy," because it's not that big of a deal, in context to what AI's doing behind the scenes, and all the work that's done on ethics, that's happened over the years, but it has woken up the mainstream, so everyone immediately jumps to ethics. "Does it work? "It's not factual," And everyone who's inside the industry is like, "This is amazing." 'Cause you have two schools of thought there. One's like, people that think this is now the beginning of next gen, this is now we're here, this ain't your grandfather's chatbot, okay?" With NLP, it's got reasoning, it's got other things. >> I'm in that camp for sure. >> Yeah. Well I mean, everyone who knows what's going on is in that camp. And as the naysayers start to get through this, and they go, "Wow, it's not just plagiarizing homework, "it's helping me be better. "Like it could rewrite my memo, "bring the lead to the top." It's so the format of the user interface is interesting, but it's still a data-driven app. >> Absolutely. >> So where does it go from here? 'Cause I'm not even calling this the first ending. This is like pregame, in my opinion. What do you guys see this going, in terms of scratching the surface to what happens next? >> I mean, I'll start with, I just don't see how an application is going to look the same in the next three years. Who's going to want to input data manually, in a form field? Who is going to want, or expect, to have to put in some text in a search box, and then read through 15 different possibilities, and try to figure out which one of them actually most closely resembles the question they asked? You know, I don't see that happening. Who's going to start with an absolute blank sheet of paper, and expect no help? That is not how an application will work in the next three years, and it's going to fundamentally change how people interact and spend time with opening any element on their mobile phone, or on their computer, to get something done. >> Yes. I agree with that. Like every single application, over the next five years, will be rewritten, to fit within this model. So imagine an HR application, I don't want to name companies, but imagine an HR application, and you go into application and you clicking on buttons, because you want to take two weeks of vacation, and menus, and clicking here and there, reasons and managers, versus just telling the system, "I'm taking two weeks of vacation, going to Las Vegas," book it, done. >> Yeah. >> And the system just does it for you. If you weren't completing in your input, in your description, for what you want, then the system asks you back, "Did you mean this? "Did you mean that? "Were you trying to also do this as well?" >> Yeah. >> "What was the reason?" And that will fit it for you, and just do it for you. So I think the user interface that we have with apps, is going to change to be very similar to the user interface that we have with each other. And that's why all these apps will need to evolve. >> I know we don't have a lot of time, 'cause you guys are very busy, but I want to definitely have multiple segments with you guys, on this topic, because there's so much to talk about. There's a lot of parallels going on here. I was talking again with Swami who runs all the AI database at AWS, and I asked him, I go, "This feels a lot like the original AWS. "You don't have to provision a data center." A lot of this heavy lifting on the back end, is these large language models, with these foundational models. So the bottleneck in the past, was the energy, and cost to actually do it. Now you're seeing it being stood up faster. So there's definitely going to be a tsunami of apps. I would see that clearly. What is it? We don't know yet. But also people who are going to leverage the fact that I can get started building value. So I see a startup boom coming, and I see an application tsunami of refactoring things. >> Yes. >> So the replatforming is already kind of happening. >> Yes, >> OpenAI, chatGPT, whatever. So that's going to be a developer environment. I mean if Amazon turns this into an API, or a Microsoft, what you guys are doing. >> We're turning it into API as well. That's part of what we're doing as well, yes. >> This is why this is exciting. Amr, you've lived the big data dream, and and we used to talk, if you didn't have a big data problem, if you weren't full of data, you weren't really getting it. Now people have all the data, and they got to stand this up. >> Yeah. >> So the analogy is again, the mobile, I like the mobile movement, and using mobile as an analogy, most companies were not building for a mobile environment, right? They were just building for the web, and legacy way of doing apps. And as soon as the user expectations shifted, that my expectation now, I need to be able to do my job on this small screen, on the mobile device with a touchscreen. Everybody had to invest in re-architecting, and re-implementing every single app, to fit within that model, and that model of interaction. And we are seeing the exact same thing happen now. And one of the core things we're focused on at Vectara, is how to simplify that for organizations, because a lot of them are overwhelmed by large language models, and ML. >> They don't have the staff. >> Yeah, yeah, yeah. They're understaffed, they don't have the skills. >> But they got developers, they've got DevOps, right? >> Yes. >> So they have the DevSecOps going on. >> Exactly, yes. >> So our goal is to simplify it enough for them that they can start leveraging this technology effectively, within their applications. >> Ed, you're the COO of the company, obviously a startup. You guys are growing. You got great backup, and good team. You've also done a lot of business development, and technical business development in this area. If you look at the landscape right now, and I agree the apps are coming, every company I talk to, that has that jet chatGPT of, you know, epiphany, "Oh my God, look how cool this is. "Like magic." Like okay, it's code, settle down. >> Mm hmm. >> But everyone I talk to is using it in a very horizontal way. I talk to a very senior person, very tech alpha geek, very senior person in the industry, technically. they're using it for log data, they're using it for configuration of routers. And in other areas, they're using it for, every vertical has a use case. So this is horizontally scalable from a use case standpoint. When you hear horizontally scalable, first thing I chose in my mind is cloud, right? >> Mm hmm. >> So cloud, and scalability that way. And the data is very specialized. So now you have this vertical specialization, horizontally scalable, everyone will be refactoring. What do you see, and what are you seeing from customers, that you talk to, and prospects? >> Yeah, I mean put yourself in the shoes of an application developer, who is actually trying to make their application a bit more like magic. And to have that soon-to-be, honestly, expected experience. They've got to think about things like performance, and how efficiently that they can actually execute a query, or a question. They've got to think about cost. Generative isn't cheap, like the inference of it. And so you've got to be thoughtful about how and when you take advantage of it, you can't use it as a, you know, everything looks like a nail, and I've got a hammer, and I'm going to hit everything with it, because that will be wasteful. Developers also need to think about how they're going to take advantage of, but not lose their own data. So there has to be some controls around what they feed into the large language model, if anything. Like, should they fine tune a large language model with their own data? Can they keep it logically separated, but still take advantage of the powers of a large language model? And they've also got to take advantage, and be aware of the fact that when data is generated, that it is a different class of data. It might not fully be their own. >> Yeah. >> And it may not even be fully verified. And so when the logical cycle starts, of someone making a request, the relationship between that request, and the output, those things have to be stored safely, logically, and identified as such. >> Yeah. >> And taken advantage of in an ongoing fashion. So these are mega problems, each one of them independently, that, you know, you can think of it as middleware companies need to take advantage of, and think about, to help the next wave of application development be logical, sensible, and effective. It's not just calling some raw API on the cloud, like openAI, and then just, you know, you get your answer and you're done, because that is a very brute force approach. >> Well also I will point, first of all, I agree with your statement about the apps experience, that's going to be expected, form filling. Great point. The interesting about chatGPT. >> Sorry, it's not just form filling, it's any action you would like to take. >> Yeah. >> Instead of clicking, and dragging, and dropping, and doing it on a menu, or on a touch screen, you just say it, and it's and it happens perfectly. >> Yeah. It's a different interface. And that's why I love that UIUX experiences, that's the people falling out of their chair moment with chatGPT, right? But a lot of the things with chatGPT, if you feed it right, it works great. If you feed it wrong and it goes off the rails, it goes off the rails big. >> Yes, yes. >> So the the Bing catastrophes. >> Yeah. >> And that's an example of garbage in, garbage out, classic old school kind of comp-side phrase that we all use. >> Yep. >> Yes. >> This is about data in injection, right? It reminds me the old SQL days, if you had to, if you can sling some SQL, you were a magician, you know, to get the right answer, it's pretty much there. So you got to feed the AI. >> You do, Some people call this, the early word to describe this as prompt engineering. You know, old school, you know, search, or, you know, engagement with data would be, I'm going to, I have a question or I have a query. New school is, I have, I have to issue it a prompt, because I'm trying to get, you know, an action or a reaction, from the system. And the active engineering, there are a lot of different ways you could do it, all the way from, you know, raw, just I'm going to send you whatever I'm thinking. >> Yeah. >> And you get the unintended outcomes, to more constrained, where I'm going to just use my own data, and I'm going to constrain the initial inputs, the data I already know that's first party, and I trust, to, you know, hyper constrain, where the application is actually, it's looking for certain elements to respond to. >> It's interesting Amr, this is why I love this, because one we are in the media, we're recording this video now, we'll stream it. But we got all your linguistics, we're talking. >> Yes. >> This is data. >> Yep. >> So the data quality becomes now the new intellectual property, because, if you have that prompt source data, it makes data or content, in our case, the original content, intellectual property. >> Absolutely. >> Because that's the value. And that's where you see chatGPT fall down, is because they're trying to scroll the web, and people think it's search. It's not necessarily search, it's giving you something that you wanted. It is a lot of that, I remember in Cloudera, you said, "Ask the right questions." Remember that phrase you guys had, that slogan? >> Mm hmm. And that's prompt engineering. So that's exactly, that's the reinvention of "Ask the right question," is prompt engineering is, if you don't give these models the question in the right way, and very few people know how to frame it in the right way with the right context, then you will get garbage out. Right? That is the garbage in, garbage out. But if you specify the question correctly, and you provide with it the metadata that constrain what that question is going to be acted upon or answered upon, then you'll get much better answers. And that's exactly what we solved Vectara. >> Okay. So before we get into the last couple minutes we have left, I want to make sure we get a plug in for the opportunity, and the profile of Vectara, your new company. Can you guys both share with me what you think the current situation is? So for the folks who are now having those moments of, "Ah, AI's bullshit," or, "It's not real, it's a lot of stuff," from, "Oh my god, this is magic," to, "Okay, this is the future." >> Yes. >> What would you say to that person, if you're at a cocktail party, or in the elevator say, "Calm down, this is the first inning." How do you explain the dynamics going on right now, to someone who's either in the industry, but not in the ropes? How would you explain like, what this wave's about? How would you describe it, and how would you prepare them for how to change their life around this? >> Yeah, so I'll go first and then I'll let Ed go. Efficiency, efficiency is the description. So we figured that a way to be a lot more efficient, a way where you can write a lot more emails, create way more content, create way more presentations. Developers can develop 10 times faster than they normally would. And that is very similar to what happened during the Industrial Revolution. I always like to look at examples from the past, to read what will happen now, and what will happen in the future. So during the Industrial Revolution, it was about efficiency with our hands, right? So I had to make a piece of cloth, like this piece of cloth for this shirt I'm wearing. Our ancestors, they had to spend month taking the cotton, making it into threads, taking the threads, making them into pieces of cloth, and then cutting it. And now a machine makes it just like that, right? And the ancestors now turned from the people that do the thing, to manage the machines that do the thing. And I think the same thing is going to happen now, is our efficiency will be multiplied extremely, as human beings, and we'll be able to do a lot more. And many of us will be able to do things they couldn't do before. So another great example I always like to use is the example of Google Maps, and GPS. Very few of us knew how to drive a car from one location to another, and read a map, and get there correctly. But once that efficiency of an AI, by the way, behind these things is very, very complex AI, that figures out how to do that for us. All of us now became amazing navigators that can go from any point to any point. So that's kind of how I look at the future. >> And that's a great real example of impact. Ed, your take on how you would talk to a friend, or colleague, or anyone who asks like, "How do I make sense of the current situation? "Is it real? "What's in it for me, and what do I do?" I mean every company's rethinking their business right now, around this. What would you say to them? >> You know, I usually like to show, rather than describe. And so, you know, the other day I just got access, I've been using an application for a long time, called Notion, and it's super popular. There's like 30 or 40 million users. And the new version of Notion came out, which has AI embedded within it. And it's AI that allows you primarily to create. So if you could break down the world of AI into find and create, for a minute, just kind of logically separate those two things, find is certainly going to be massively impacted in our experiences as consumers on, you know, Google and Bing, and I can't believe I just said the word Bing in the same sentence as Google, but that's what's happening now (all laughing), because it's a good example of change. >> Yes. >> But also inside the business. But on the crate side, you know, Notion is a wiki product, where you try to, you know, note down things that you are thinking about, or you want to share and memorialize. But sometimes you do need help to get it down fast. And just in the first day of using this new product, like my experience has really fundamentally changed. And I think that anybody who would, you know, anybody say for example, that is using an existing app, I would show them, open up the app. Now imagine the possibility of getting a starting point right off the bat, in five seconds of, instead of having to whole cloth draft this thing, imagine getting a starting point then you can modify and edit, or just dispose of and retry again. And that's the potential for me. I can't imagine a scenario where, in a few years from now, I'm going to be satisfied if I don't have a little bit of help, in the same way that I don't manually spell check every email that I send. I automatically spell check it. I love when I'm getting type ahead support inside of Google, or anything. Doesn't mean I always take it, or when texting. >> That's efficiency too. I mean the cloud was about developers getting stuff up quick. >> Exactly. >> All that heavy lifting is there for you, so you don't have to do it. >> Right? >> And you get to the value faster. >> Exactly. I mean, if history taught us one thing, it's, you have to always embrace efficiency, and if you don't fast enough, you will fall behind. Again, looking at the industrial revolution, the companies that embraced the industrial revolution, they became the leaders in the world, and the ones who did not, they all like. >> Well the AI thing that we got to watch out for, is watching how it goes off the rails. If it doesn't have the right prompt engineering, or data architecture, infrastructure. >> Yes. >> It's a big part. So this comes back down to your startup, real quick, I know we got a couple minutes left. Talk about the company, the motivation, and we'll do a deeper dive on on the company. But what's the motivation? What are you targeting for the market, business model? The tech, let's go. >> Actually, I would like Ed to go first. Go ahead. >> Sure, I mean, we're a developer-first, API-first platform. So the product is oriented around allowing developers who may not be superstars, in being able to either leverage, or choose, or select their own large language models for appropriate use cases. But they that want to be able to instantly add the power of large language models into their application set. We started with search, because we think it's going to be one of the first places that people try to take advantage of large language models, to help find information within an application context. And we've built our own large language models, focused on making it very efficient, and elegant, to find information more quickly. So what a developer can do is, within minutes, go up, register for an account, and get access to a set of APIs, that allow them to send data, to be converted into a format that's easy to understand for large language models, vectors. And then secondarily, they can issue queries, ask questions. And they can ask them very, the questions that can be asked, are very natural language questions. So we're talking about long form sentences, you know, drill down types of questions, and they can get answers that either come back in depending upon the form factor of the user interface, in list form, or summarized form, where summarized equals the opportunity to kind of see a condensed, singular answer. >> All right. I have a. >> Oh okay, go ahead, you go. >> I was just going to say, I'm going to be a customer for you, because I want, my dream was to have a hologram of theCUBE host, me and Dave, and have questions be generated in the metaverse. So you know. (all laughing) >> There'll be no longer any guests here. They'll all be talking to you guys. >> Give a couple bullets, I'll spit out 10 good questions. Publish a story. This brings the automation, I'm sorry to interrupt you. >> No, no. No, no, I was just going to follow on on the same. So another way to look at exactly what Ed described is, we want to offer you chatGPT for your own data, right? So imagine taking all of the recordings of all of the interviews you have done, and having all of the content of that being ingested by a system, where you can now have a conversation with your own data and say, "Oh, last time when I met Amr, "which video games did we talk about? "Which movie or book did we use as an analogy "for how we should be embracing data science, "and big data, which is moneyball," I know you use moneyball all the time. And you start having that conversation. So, now the data doesn't become a passive asset that you just have in your organization. No. It's an active participant that's sitting with you, on the table, helping you make decisions. >> One of my favorite things to do with customers, is to go to their site or application, and show them me using it. So for example, one of the customers I talked to was one of the biggest property management companies in the world, that lets people go and rent homes, and houses, and things like that. And you know, I went and I showed them me searching through reviews, looking for information, and trying different words, and trying to find out like, you know, is this place quiet? Is it comfortable? And then I put all the same data into our platform, and I showed them the world of difference you can have when you start asking that question wholeheartedly, and getting real information that doesn't have anything to do with the words you asked, but is really focused on the meaning. You know, when I asked like, "Is it quiet?" You know, answers would come back like, "The wind whispered through the trees peacefully," and you know, it's like nothing to do with quiet in the literal word sense, but in the meaning sense, everything to do with it. And that that was magical even for them, to see that. >> Well you guys are the front end of this big wave. Congratulations on the startup, Amr. I know you guys got great pedigree in big data, and you've got a great team, and congratulations. Vectara is the name of the company, check 'em out. Again, the startup boom is coming. This will be one of the major waves, generative AI is here. I think we'll look back, and it will be pointed out as a major inflection point in the industry. >> Absolutely. >> There's not a lot of hype behind that. People are are seeing it, experts are. So it's going to be fun, thanks for watching. >> Thanks John. (soft music)

(upbeat music) >> Hi everyone. Welcome back to this Cube conversation here in Palo Alto, California. I'm John Furrier, host of "theCUBE." Got a great conversation around Cloud Native, Cloud Native Journey, how enterprises are looking at Cloud Native and putting it all together. And it comes down to operations, developer productivity, and security. It's the hottest topic in technology. We got Chris Jones here in the studio, director of Product Management for Platform9. Chris, thanks for coming in. >> Hey, thanks. >> So when we always chat about, when we're at KubeCon. KubeConEU is coming up and in a few, in a few months, the number one conversation is developer productivity. And the developers are driving all the standards. It's interesting to see how they just throw everything out there and whatever gets adopted ends up becoming the standard, not the old school way of kind of getting stuff done. So that's cool. Security Kubernetes and Containers are all kind of now that next level. So you're starting to see the early adopters moving to the mainstream. Enterprises, a variety of different approaches. You guys are at the center of this. We've had a couple conversations with your CEO and your tech team over there. What are you seeing? You're building the products. What's the core product focus right now for Platform9? What are you guys aiming for? >> The core is that blend of enabling your infrastructure and PlatformOps or DevOps teams to be able to go fast and run in a stable environment, but at the same time enable developers. We don't want people going back to what I've been calling Shadow IT 2.0. It's, hey, I've been told to do something. I kicked off this Container initiative. I need to run my software somewhere. I'm just going to go figure it out. We want to keep those people productive. At the same time we want to enable velocity for our operations teams, be it PlatformOps or DevOps. >> Take us through in your mind and how you see the industry rolling out this Cloud Native journey. Where do you see customers out there? Because DevOps have been around, DevSecOps is rocking, you're seeing AI, hot trend now. Developers are still in charge. Is there a change to the infrastructure of how developers get their coding done and the infrastructure, setting up the DevOps is key, but when you add the Cloud Native journey for an enterprise, what changes? What is the, what is the, I guess what is the Cloud Native journey for an enterprise these days? >> The Cloud Native journey or the change? When- >> Let's start with the, let's start with what they want to do. What's the goal and then how does that happen? >> I think the goal is that promise land. Increased resiliency, better scalability, and overall reduced costs. I've gone from physical to virtual that gave me a higher level of density, packing of resources. I'm moving to Containers. I'm removing that OS layer again. I'm getting a better density again, but all of a sudden I'm running Kubernetes. What does that, what does that fundamentally do to my operations? Does it magically give me scalability and resiliency? Or do I need to change what I'm running and how it's running so it fits that infrastructure? And that's the reality, is you can't just take a Container and drop it into Kubernetes and say, hey, I'm now Cloud Native. I've got reduced cost, or I've got better resiliency. There's things that your engineering teams need to do to make sure that application is a Cloud Native. And then there's what I think is one of the largest shifts of virtual machines to containers. When I was in the world of application performance monitoring, we would see customers saying, well, my engineering team have this Java app, and they said it needs a VM with 12 gig of RAM and eight cores, and that's what we gave it. But it's running slow. I'm working with the application team and you can see it's running slow. And they're like, well, it's got all of its resources. One of those nice features of virtualization is over provisioning. So the infrastructure team would say, well, we gave it, we gave it all a RAM it needed. And what's wrong with that being over provisioned? It's like, well, Java expects that RAM to be there. Now all of a sudden, when you move to the world of containers, what we've got is that's not a set resource limit, really is like it used to be in a VM, right? When you set it for a container, your application teams really need to be paying attention to your resource limits and constraints within the world of Kubernetes. So instead of just being able to say, hey, I'm throwing over the fence and now it's just going to run on a VM, and that VMs got everything it needs. It's now really running on more, much more of a shared infrastructure where limits and constraints are going to impact the neighbors. They are going to impact who's making that decision around resourcing. Because that Kubernetes concept of over provisioning and the virtualization concept of over provisioning are not the same. So when I look at this problem, it's like, well, what changed? Well, I'll do my scale tests as an application developer and tester, and I'd see what resources it needs. I asked for that in the VM, that sets the high watermark, job's done. Well, Kubernetes, it's no longer a VM, it's a Kubernetes manifest. And well, who owns that? Who's writing it? Who's setting those limits? To me, that should be the application team. But then when it goes into operations world, they're like, well, that's now us. Can we change those? So it's that amalgamation of the two that is saying, I'm a developer. I used to pay attention, but now I need to pay attention. And an infrastructure person saying, I used to just give 'em what they wanted, but now I really need to know what they've wanted, because it's going to potentially have a catastrophic impact on what I'm running. >> So what's the impact for the developer? Because, infrastructure's code is what everybody wants. The developer just wants to get the code going and they got to pay attention to all these things, or don't they? Is that where you guys come in? How do you guys see the problem? Actually scope the problem that you guys solve? 'Cause I think you're getting at I think the core issue here, which is, I've got Kubernetes, I've got containers, I've got developer productivity that I want to focus on. What's the problem that you guys solve? >> Platform operation teams that are adopting Cloud Native in their environment, they've got that steep learning curve of Kubernetes plus this fundamental change of how an app runs. What we're doing is taking away the burden of needing to operate and run Kubernetes and giving them the choice of the flexibility of infrastructure and location. Be that an air gap environment like a, let's say a telco provider that needs to run a containerized network function and containerized workloads for 5G. That's one thing that we can deploy and achieve in a completely inaccessible environment all the way through to Platform9 running traditionally as SaaS, as we were born, that's remotely managing and controlling your Kubernetes environments on-premise AWS. That hybrid cloud experience that could be also Bare Metal, but it's our platform running your environments with our support there, 24 by seven, that's proactively reaching out. So it's removing a lot of that burden and the complications that come along with operating the environment and standing it up, which means all of a sudden your DevOps and platform operations teams can go and work with your engineers and application developers and say, hey, let's get, let's focus on the stuff that, that we need to be focused on, which is running our business and providing a service to our customers. Not figuring out how to upgrade a Kubernetes cluster, add new nodes, and configure all of the low level. >> I mean there are, that's operations that just needs to work. And sounds like as they get into the Cloud Native kind of ops, there's a lot of stuff that kind of goes wrong. Or you go, oops, what do we buy into? Because the CIOs, let's go, let's go Cloud Native. We want to, we got to get set up for the future. We're going to be Cloud Native, not just lift and shift and we're going to actually build it out right. Okay, that sounds good. And when we have to actually get done. >> Chris: Yeah. >> You got to spin things up and stand up the infrastructure. What specifically use case do you guys see that emerges for Platform9 when people call you up and you go talk to customers and prospects? What's the one thing or use case or cases that you guys see that you guys solve the best? >> So I think one of the, one of the, I guess new use cases that are coming up now, everyone's talking about economic pressures. I think the, the tap blows open, just get it done. CIO is saying let's modernize, let's use the cloud. Now all of a sudden they're recognizing, well wait, we're spending a lot of money now. We've opened that tap all the way, what do we do? So now they're looking at ways to control that spend. So we're seeing that as a big emerging trend. What we're also sort of seeing is people looking at their data centers and saying, well, I've got this huge legacy environment that's running a hypervisor. It's running VMs. Can we still actually do what we need to do? Can we modernize? Can we start this Cloud Native journey without leaving our data centers, our co-locations? Or if I do want to reduce costs, is that that thing that says maybe I'm repatriating or doing a reverse migration? Do I have to go back to my data center or are there other alternatives? And we're seeing that trend a lot. And our roadmap and what we have in the product today was specifically built to handle those, those occurrences. So we brought in KubeVirt in terms of virtualization. We have a long legacy doing OpenStack and private clouds. And we've worked with a lot of those users and customers that we have and asked the questions, what's important? And today, when we look at the world of Cloud Native, you can run virtualization within Kubernetes. So you can, instead of running two separate platforms, you can have one. So all of a sudden, if you're looking to modernize, you can start on that new infrastructure stack that can run anywhere, Kubernetes, and you can start bringing VMs over there as you are containerizing at the same time. So now you can keep your application operations in one environment. And this also helps if you're trying to reduce costs. If you really are saying, we put that Dev environment in AWS, we've got a huge amount of velocity out of it now, can we do that elsewhere? Is there a co-location we can go to? Is there a provider that we can go to where we can run that infrastructure or run the Kubernetes, but not have to run the infrastructure? >> It's going to be interesting too, when you see the Edge come online, you start, we've got Mobile World Congress coming up, KubeCon events we're going to be at, the conversation is not just about public cloud. And you guys obviously solve a lot of do-it-yourself implementation hassles that emerge when people try to kind of stand up their own environment. And we hear from developers consistency between code, managing new updates, making sure everything is all solid so they can go fast. That's the goal. And that, and then people can get standardized on that. But as you get public cloud and do it yourself, kind of brings up like, okay, there's some gaps there as the architecture changes to be more distributed computing, Edge, on-premises cloud, it's cloud operations. So that's cool for DevOps and Cloud Native. How do you guys differentiate from say, some the public cloud opportunities and the folks who are doing it themselves? How do you guys fit in that world and what's the pitch or what's the story? >> The fit that we look at is that third alternative. Let's get your team focused on what's high value to your business and let us deliver that public cloud experience on your infrastructure or in the public cloud, which gives you that ability to still be flexible if you want to make choices to run consistently for your developers in two different locations. So as I touched on earlier, instead of saying go figure out Kubernetes, how do you upgrade a hundred worker nodes in place upgrade. We've solved that problem. That's what we do every single day of the week. Don't go and try to figure out how to upgrade a cluster and then upgrade all of the, what I call Kubernetes friends, your core DNSs, your metrics server, your Kubernetes dashboard. These are all things that we package, we test, we version. So when you click upgrade, we've already handled that entire process. So it's saying don't have your team focused on that lower level piece of work. Get them focused on what is important, which is your business services. >> Yeah, the infrastructure and getting that stood up. I mean, I think the thing that's interesting, if you look at the market right now, you mentioned cost savings and recovery, obviously kind of a recession. I mean, people are tightening their belts for sure. I don't think the digital transformation and Cloud Native spend is going to plummet. It's going to probably be on hold and be squeezed a little bit. But to your point, people are refactoring looking at how to get the best out of what they got. It's not just open the tap of spend the cash like it used to be. Yeah, a couple months, even a couple years ago. So okay, I get that. But then you look at the what's coming, AI. You're seeing all the new data infrastructure that's coming. The containers, Kubernetes stuff, got to get stood up pretty quickly and it's got to be reliable. So to your point, the teams need to get done with this and move on to the next thing. >> Chris: Yeah, yeah, yeah. >> 'Cause there's more coming. I mean, there's a lot coming for the apps that are building in Data Native, AI-Native, Cloud Native. So it seems that this Kubernetes thing needs to get solved. Is that kind of what you guys are focused on right now? >> So, I mean to use a customer, we have a customer that's in AI/ML and they run their platform at customer sites and that's hardware bound. You can't run AI machine learning on anything anywhere. Well, with Platform9 they can. So we're enabling them to deliver services into their customers that's running their AI/ML platform in their customer's data centers anywhere in the world on hardware that is purpose-built for running that workload. They're not Kubernetes experts. That's what we are. We're bringing them that ability to focus on what's important and just delivering their business services whilst they're enabling our team. And our 24 by seven proactive management are always on assurance to keep that up and running for them. So when something goes bump at the night at 2:00am, our guys get woken up. They're the ones that are reaching out to the customer saying, your environments have a problem, we're taking these actions to fix it. Obviously sometimes, especially if it is running on Bare Metal, there's things you can't do remotely. So you might need someone to go and do that. But even when that happens, you're not by yourself. You're not sitting there like I did when I worked for a bank in one of my first jobs, three o'clock in the morning saying, wow, our end of day processing is stuck. Who else am I waking up? Right? >> Exactly, yeah. Got to get that cash going. But this is a great use case. I want to get to the customer. What do some of the successful customers say to you for the folks watching that aren't yet a customer of Platform9, what are some of the accolades and comments or anecdotes that you guys hear from customers that you have? >> It just works, which I think is probably one of the best ones you can get. Customers coming back and being able to show to their business that they've delivered growth, like business growth and productivity growth and keeping their organization size the same. So we started on our containerization journey. We went to Kubernetes. We've deployed all these new workloads and our operations team is still six people. We're doing way more with growth less, and I think that's also talking to the strength that we're bringing, 'cause we're, we're augmenting that team. They're spending less time on the really low level stuff and automating a lot of the growth activity that's involved. So when it comes to being able to grow their business, they can just focus on that, not- >> Well you guys do the heavy lifting, keep on top of the Kubernetes, make sure that all the versions are all done. Everything's stable and consistent so they can go on and do the build out and provide their services. That seems to be what you guys are best at. >> Correct, correct. >> And so what's on the roadmap? You have the product, direct product management, you get the keys to the kingdom. What is, what is the focus? What's your focus right now? Obviously Kubernetes is growing up, Containers. We've been hearing a lot at the last KubeCon about the security containers is getting better. You've seen verification, a lot more standards around some things. What are you focused on right now for at a product over there? >> Edge is a really big focus for us. And I think in Edge you can look at it in two ways. The mantra that I drive is Edge must be remote. If you can't do something remotely at the Edge, you are using a human being, that's not Edge. Our Edge management capabilities and being in the market for over two years are a hundred percent remote. You want to stand up a store, you just ship the server in there, it gets racked, the rest of it's remote. Imagine a store manager in, I don't know, KFC, just plugging in the server, putting in the ethernet cable, pressing the power button. The rest of all that provisioning for that Cloud Native stack, Kubernetes, KubeVirt for virtualization is done remotely. So we're continuing to focus on that. The next piece that is related to that is allowing people to run Platform9 SaaS in their data centers. So we do ag app today and we've had a really strong focus on telecommunications and the containerized network functions that come along with that. So this next piece is saying, we're bringing what we run as SaaS into your data center, so then you can run it. 'Cause there are many people out there that are saying, we want these capabilities and we want everything that the Platform9 control plane brings and simplifies. But unfortunately, regulatory compliance reasons means that we can't leverage SaaS. So they might be using a cloud, but they're saying that's still our infrastructure. We're still closed that network down, or they're still on-prem. So they're two big priorities for us this year. And that on-premise experiences is paramount, even to the point that we will be delivering a way that when you run an on-premise, you can still say, wait a second, well I can send outbound alerts to Platform9. So their support team can still be proactively helping me as much as they could, even though I'm running Platform9s control plane. So it's sort of giving that blend of two experiences. They're big, they're big priorities. And the third pillar is all around virtualization. It's saying if you have economic pressures, then I think it's important to look at what you're spending today and realistically say, can that be reduced? And I think hypervisors and virtualization is something that should be looked at, because if you can actually reduce that spend, you can bring in some modernization at the same time. Let's take some of those nos that exist that are two years into their five year hardware life cycle. Let's turn that into a Cloud Native environment, which is enabling your modernization in place. It's giving your engineers and application developers the new toys, the new experiences, and then you can start running some of those virtualized workloads with KubeVirt, there. So you're reducing cost and you're modernizing at the same time with your existing infrastructure. >> You know Chris, the topic of this content series that we're doing with you guys is finding the right path, trusting the right path to Cloud Native. What does that mean? I mean, if you had to kind of summarize that phrase, trusting the right path to Cloud Native, what does that mean? It mean in terms of architecture, is it deployment? Is it operations? What's the underlying main theme of that quote? What's the, what's? How would you talk to a customer and say, what does that mean if someone said, "Hey, what does that right path mean?" >> I think the right path means focusing on what you should be focusing on. I know I've said it a hundred times, but if your entire operations team is trying to figure out the nuts and bolts of Kubernetes and getting three months into a journey and discovering, ah, I need Metrics Server to make something function. I want to use Horizontal Pod Autoscaler or Vertical Pod Autoscaler and I need this other thing, now I need to manage that. That's not the right path. That's literally learning what other people have been learning for the last five, seven years that have been focused on Kubernetes solely. So the why- >> There's been a lot of grind. People have been grinding it out. I mean, that's what you're talking about here. They've been standing up the, when Kubernetes started, it was all the promise. >> Chris: Yep. >> And essentially manually kind of getting in in the weeds and configuring it. Now it's matured up. They want stability. >> Chris: Yeah. >> Not everyone can get down and dirty with Kubernetes. It's not something that people want to generally do unless you're totally into it, right? Like I mean, I mean ops teams, I mean, yeah. You know what I mean? It's not like it's heavy lifting. Yeah, it's important. Just got to get it going. >> Yeah, I mean if you're deploying with Platform9, your Ops teams can tinker to their hearts content. We're completely compliant upstream Kubernetes. You can go and change an API server flag, let's go and mess with the scheduler, because we want to. You can still do that, but don't, don't have your team investing in all this time to figure it out. It's been figured out. >> John: Got it. >> Get them focused on enabling velocity for your business. >> So it's not build, but run. >> Chris: Correct? >> Or run Kubernetes, not necessarily figure out how to kind of get it all, consume it out. >> You know we've talked to a lot of customers out there that are saying, "I want to be able to deliver a service to my users." Our response is, "Cool, let us run it. You consume it, therefore deliver it." And we're solving that in one hit versus figuring out how to first run it, then operate it, then turn that into a consumable service. >> So the alternative Platform9 is what? They got to do it themselves or use the Cloud or what's the, what's the alternative for the customer for not using Platform9? Hiring more people to kind of work on it? What's the? >> People, building that kind of PaaS experience? Something that I've been very passionate about for the past year is looking at that world of sort of GitOps and what that means. And if you go out there and you sort of start asking the question what's happening? Just generally with Kubernetes as well and GitOps in that scope, then you'll hear some people saying, well, I'm making it PaaS, because Kubernetes is too complicated for my developers and we need to give them something. There's some great material out there from the likes of Intuit and Adobe where for two big contributors to Argo and the Argo projects, they almost have, well they do have, different experiences. One is saying, we went down the PaaS route and it failed. The other one is saying, well we've built a really stable PaaS and it's working. What are they trying to do? They're trying to deliver an outcome to make it easy to use and consume Kubernetes. So you could go out there and say, hey, I'm going to build a Kubernetes cluster. Sounds like Argo CD is a great way to expose that to my developers so they can use Kubernetes without having to use Kubernetes and start automating things. That is an approach, but you're going to be going completely open source and you're going to have to bring in all the individual components, or you could just lay that, lay it down, and consume it as a service and not have to- >> And mentioned to it. They were the ones who kind of brought that into the open. >> They did. Inuit is the primary contributor to the Argo set of products. >> How has that been received in the market? I mean, they had the event at the Computer History Museum last fall. What's the momentum there? What's the big takeaway from that project? >> Growth. To me, growth. I mean go and track the stars on that one. It's just, it's growth. It's unlocking machine learning. Argo workflows can do more than just make things happen. Argo CD I think the approach they're taking is, hey let's make this simple to use, which I think can be lost. And I think credit where credit's due, they're really pushing to bring in a lot of capabilities to make it easier to work with applications and microservices on Kubernetes. It's not just that, hey, here's a GitOps tool. It can take something from a Git repo and deploy it and maybe prioritize it and help you scale your operations from that perspective. It's taking a step back and saying, well how did we get to production in the first place? And what can be done down there to help as well? I think it's growth expansion of features. They had a huge release just come out in, I think it was 2.6, that brought in things that as a product manager that I don't often look at like really deep technical things and say wow, that's powerful. But they have, they've got some great features in that release that really do solve real problems. >> And as the product, as the product person, who's the target buyer for you? Who's the customer? Who's making that? And you got decision maker, influencer, and recommender. Take us through the customer persona for you guys. >> So that Platform Ops, DevOps space, right, the people that need to be delivering Containers as a service out to their organization. But then it's also important to say, well who else are our primary users? And that's developers, engineers, right? They shouldn't have to say, oh well I have access to a Kubernetes cluster. Do I have to use kubectl or do I need to go find some other tool? No, they can just log to Platform9. It's integrated with your enterprise id. >> They're the end customer at the end of the day, they're the user. >> Yeah, yeah. They can log in. And they can see the clusters you've given them access to as a Platform Ops Administrator. >> So job well done for you guys. And your mind is the developers are moving 'em fast, coding and happy. >> Chris: Yeah, yeah. >> And and from a customer standpoint, you reduce the maintenance cost, because you keep the Ops smoother, so you got efficiency and maintenance costs kind of reduced or is that kind of the benefits? >> Yeah, yep, yeah. And at two o'clock in the morning when things go inevitably wrong, they're not there by themselves, and we're proactively working with them. >> And that's the uptime issue. >> That is the uptime issue. And Cloud doesn't solve that, right? Everyone experienced that Clouds can go down, entire regions can go offline. That's happened to all Cloud providers. And what do you do then? Kubernetes isn't your recovery plan. It's part of it, right, but it's that piece. >> You know Chris, to wrap up this interview, I will say that "theCUBE" is 12 years old now. We've been to OpenStack early days. We had you guys on when we were covering OpenStack and now Cloud has just been booming. You got AI around the corner, AI Ops, now you got all this new data infrastructure, it's just amazing Cloud growth, Cloud Native, Security Native, Cloud Native, Data Native, AI Native. It's going to be all, this is the new app environment, but there's also existing infrastructure. So going back to OpenStack, rolling our own cloud, building your own cloud, building infrastructure cloud, in a cloud way, is what the pioneers have done. I mean this is what we're at. Now we're at this scale next level, abstracted away and make it operational. It seems to be the key focus. We look at CNCF at KubeCon and what they're doing with the cloud SecurityCon, it's all about operations. >> Chris: Yep, right. >> Ops and you know, that's going to sound counterintuitive 'cause it's a developer open source environment, but you're starting to see that Ops focus in a good way. >> Chris: Yeah, yeah, yeah. >> Infrastructure as code way. >> Chris: Yep. >> What's your reaction to that? How would you summarize where we are in the industry relative to, am I getting, am I getting it right there? Is that the right view? What am I missing? What's the current state of the next level, NextGen infrastructure? >> It's a good question. When I think back to sort of late 2019, I sort of had this aha moment as I saw what really truly is delivering infrastructure as code happening at Platform9. There's an open source project Ironic, which is now also available within Kubernetes that is Metal Kubed that automates Bare Metal as code, which means you can go from an empty server, lay down your operating system, lay down Kubernetes, and you've just done everything delivered to your customer as code with a Cloud Native platform. That to me was sort of the biggest realization that I had as I was moving into this industry was, wait, it's there. This can be done. And the evolution of tooling and operations is getting to the point where that can be achieved and it's focused on by a number of different open source projects. Not just Ironic and and Metal Kubed, but that's a huge win. That is truly getting your infrastructure. >> John: That's an inflection point, really. >> Yeah. >> If you think about it, 'cause that's one of the problems. We had with the Bare Metal piece was the automation and also making it Cloud Ops, cloud operations. >> Right, yeah. I mean, one of the things that I think Ironic did really well was saying let's just treat that piece of Bare Metal like a Cloud VM or an instance. If you got a problem with it, just give the person using it or whatever's using it, a new one and reimage it. Just tell it to reimage itself and it'll just (snaps fingers) go. You can do self-service with it. In Platform9, if you log in to our SaaS Ironic, you can go and say, I want that physical server to myself, because I've got a giant workload, or let's turn it into a Kubernetes cluster. That whole thing is automated. To me that's infrastructure as code. I think one of the other important things that's happening at the same time is we're seeing GitOps, we're seeing things like Terraform. I think it's important for organizations to look at what they have and ask, am I using tools that are fit for tomorrow or am I using tools that are yesterday's tools to solve tomorrow's problems? And when especially it comes to modernizing infrastructure as code, I think that's a big piece to look at. >> Do you see Terraform as old or new? >> I see Terraform as old. It's a fantastic tool, capable of many great things and it can work with basically every single provider out there on the planet. It is able to do things. Is it best fit to run in a GitOps methodology? I don't think it is quite at that point. In fact, if you went and looked at Flux, Flux has ways that make Terraform GitOps compliant, which is absolutely fantastic. It's using two tools, the best of breeds, which is solving that tomorrow problem with tomorrow solutions. >> Is the new solutions old versus new. I like this old way, new way. I mean, Terraform is not that old and it's been around for about eight years or so, whatever. But HashiCorp is doing a great job with that. I mean, so okay with Terraform, what's the new address? Is it more complex environments? Because Terraform made sense when you had basic DevOps, but now it sounds like there's a whole another level of complexity. >> I got to say. >> New tools. >> That kind of amalgamation of that application into infrastructure. Now my app team is paying way more attention to that manifest file, which is what GitOps is trying to solve. Let's templatize things. Let's version control our manifest, be it helm, customize, or just a straight up Kubernetes manifest file, plain and boring. Let's get that version controlled. Let's make sure that we know what is there, why it was changed. Let's get some auditability and things like that. And then let's get that deployment all automated. So that's predicated on the cluster existing. Well why can't we do the same thing with the cluster, the inception problem. So even if you're in public cloud, the question is like, well what's calling that API to call that thing to happen? Where is that file living? How well can I manage that in a large team? Oh my God, something just changed. Who changed it? Where is that file? And I think that's one of big, the big pieces to be sold. >> Yeah, and you talk about Edge too and on-premises. I think one of the things I'm observing and certainly when DevOps was rocking and rolling and infrastructures code was like the real push, it was pretty much the public cloud, right? >> Chris: Yep. >> And you did Cloud Native and you had stuff on-premises. Yeah you did some lifting and shifting in the cloud, but the cool stuff was going in the public cloud and you ran DevOps. Okay, now you got on-premise cloud operation and Edge. Is that the new DevOps? I mean 'cause what you're kind of getting at with old new, old new Terraform example is an interesting point, because you're pointing out potentially that that was good DevOps back in the day or it still is. >> Chris: It is, I was going to say. >> But depending on how you define what DevOps is. So if you say, I got the new DevOps with public on-premise and Edge, that's just not all public cloud, that's essentially distributed Cloud Native. >> Correct. Is that the new DevOps in your mind or is that? How would you, or is that oversimplifying it? >> Or is that that term where everyone's saying Platform Ops, right? Has it shifted? >> Well you bring up a good point about Terraform. I mean Terraform is well proven. People love it. It's got great use cases and now there seems to be new things happening. We call things like super cloud emerging, which is multicloud and abstraction layers. So you're starting to see stuff being abstracted away for the benefits of moving to the next level, so teams don't get stuck doing the same old thing. They can move on. Like what you guys are doing with Platform9 is providing a service so that teams don't have to do it. >> Correct, yeah. >> That makes a lot of sense, So you just, now it's running and then they move on to the next thing. >> Chris: Yeah, right. >> So what is that next thing? >> I think Edge is a big part of that next thing. The propensity for someone to put up with a delay, I think it's gone. For some reason, we've all become fairly short-tempered, Short fused. You know, I click the button, it should happen now, type people. And for better or worse, hopefully it gets better and we all become a bit more patient. But how do I get more effective and efficient at delivering that to that really demanding- >> I think you bring up a great point. I mean, it's not just people are getting short-tempered. I think it's more of applications are being deployed faster, security is more exposed if they don't see things quicker. You got data now infrastructure scaling up massively. So, there's a double-edged swords to scale. >> Chris: Yeah, yeah. I mean, maintenance, downtime, uptime, security. So yeah, I think there's a tension around, and one hand enthusiasm around pushing a lot of code and new apps. But is the confidence truly there? It's interesting one little, (snaps finger) supply chain software, look at Container Security for instance. >> Yeah, yeah. It's big. I mean it was codified. >> Do you agree that people, that's kind of an issue right now. >> Yeah, and it was, I mean even the supply chain has been codified by the US federal government saying there's things we need to improve. We don't want to see software being a point of vulnerability, and software includes that whole process of getting it to a running point. >> It's funny you mentioned remote and one of the thing things that you're passionate about, certainly Edge has to be remote. You don't want to roll a truck or labor at the Edge. But I was doing a conversation with, at Rebars last year about space. It's hard to do brake fix on space. It's hard to do a, to roll a someone to configure satellite, right? Right? >> Chris: Yeah. >> So Kubernetes is in space. We're seeing a lot of Cloud Native stuff in apps, in space, so just an example. This highlights the fact that it's got to be automated. Is there a machine learning AI angle with all this ChatGPT talk going on? You see all the AI going the next level. Some pretty cool stuff and it's only, I know it's the beginning, but I've heard people using some of the new machine learning, large language models, large foundational models in areas I've never heard of. Machine learning and data centers, machine learning and configuration management, a lot of different ways. How do you see as the product person, you incorporating the AI piece into the products for Platform9? >> I think that's a lot about looking at the telemetry and the information that we get back and to use one of those like old idle terms, that continuous improvement loop to feed it back in. And I think that's really where machine learning to start with comes into effect. As we run across all these customers, our system that helps at two o'clock in the morning has that telemetry, it's got that data. We can see what's changing and what's happening. So it's writing the right algorithms, creating the right machine learning to- >> So training will work for you guys. You have enough data and the telemetry to do get that training data. >> Yeah, obviously there's a lot of investment required to get there, but that is something that ultimately that could be achieved with what we see in operating people's environments. >> Great. Chris, great to have you here in the studio. Going wide ranging conversation on Kubernetes and Platform9. I guess my final question would be how do you look at the next five years out there? Because you got to run the product management, you got to have that 20 mile steer, you got to look at the customers, you got to look at what's going on in the engineering and you got to kind of have that arc. This is the right path kind of view. What's the five year arc look like for you guys? How do you see this playing out? 'Cause KubeCon is coming up and we're you seeing Kubernetes kind of break away with security? They had, they didn't call it KubeCon Security, they call it CloudNativeSecurityCon, they just had in Seattle inaugural events seemed to go well. So security is kind of breaking out and you got Kubernetes. It's getting bigger. Certainly not going away, but what's your five year arc of of how Platform9 and Kubernetes and Ops evolve? >> It's to stay on that theme, it's focusing on what is most important to our users and getting them to a point where they can just consume it, so they're not having to operate it. So it's finding those big items and bringing that into our platform. It's something that's consumable, that's just taken care of, that's tested with each release. So it's simplifying operations more and more. We've always said freedom in cloud computing. Well we started on, we started on OpenStack and made that simple. Stable, easy, you just have it, it works. We're doing that with Kubernetes. We're expanding out that user, right, we're saying bring your developers in, they can download their Kube conflict. They can see those Containers that are running there. They can access the events, the log files. They can log in and build a VM using KubeVirt. They're self servicing. So it's alleviating pressures off of the Ops team, removing the help desk systems that people still seem to rely on. So it's like what comes into that field that is the next biggest issue? Is it things like CI/CD? Is it simplifying GitOps? Is it bringing in security capabilities to talk to that? Or is that a piece that is a best of breed? Is there a reason that it's been spun out to its own conference? Is this something that deserves a focus that should be a specialized capability instead of tooling and vendors that we work with, that we partner with, that could be brought in as a service. I think it's looking at those trends and making sure that what we bring in has the biggest impact to our users. >> That's awesome. Thanks for coming in. I'll give you the last word. Put a plug in for Platform9 for the people who are watching. What should they know about Platform9 that they might not know about it or what should? When should they call you guys and when should they engage? Take a take a minute to give the plug. >> The plug. I think it's, if your operations team is focused on building Kubernetes, stop. That shouldn't be the cloud. That shouldn't be in the Edge, that shouldn't be at the data center. They should be consuming it. If your engineering teams are all trying different ways and doing different things to use and consume Cloud Native services and Kubernetes, they shouldn't be. You want consistency. That's how you get economies of scale. Provide them with a simple platform that's integrated with all of your enterprise identity where they can just start consuming instead of having to solve these problems themselves. It's those, it's those two personas, right? Where the problems manifest. What are my operations teams doing, and are they delivering to my company or are they building infrastructure again? And are my engineers sprinting or crawling? 'Cause if they're not sprinting, you should be asked the question, do I have the right Cloud Native tooling in my environment and how can I get them back? >> I think it's developer productivity, uptime, security are the tell signs. You get that done. That's the goal of what you guys are doing, your mission. >> Chris: Yep. >> Great to have you on, Chris. Thanks for coming on. Appreciate it. >> Chris: Thanks very much. 0 Okay, this is "theCUBE" here, finding the right path to Cloud Native. I'm John Furrier, host of "theCUBE." Thanks for watching. (upbeat music)

(gentle synth music) (music ends) >> Welcome back to Supercloud 2, an open industry collaboration between technologists, consultants, analysts, and of course practitioners to help shape the future of cloud. At this event, one of the key areas we're exploring is the intersection of cloud and data. And how building value on top of hyperscale clouds and across clouds is evolving, a concept of course we call "Supercloud". And we're pleased to welcome our friends from Enterprise Technology research, Erik Bradley and Darren Brabham. Guys, thanks for joining us, great to see you. we love to bring the data into these conversations. >> Thank you for having us, Dave, I appreciate it. >> Yeah, thanks. >> You bet. And so, let me do the setup on what is Supercloud. It's a concept that we've floated, Before re:Invent 2021, based on the idea that cloud infrastructure is becoming ubiquitous, incredibly powerful, but there's a lack of standards across the big three clouds. That creates friction. So we defined over the period of time, you know, better part of a year, a set of essential elements, deployment models for so-called supercloud, which create this common experience for specific cloud services that, of course, again, span multiple clouds and even on-premise data. So Erik, with that as background, I wonder if you could add your general thoughts on the term supercloud, maybe play proxy for the CIO community, 'cause you do these round tables, you talk to these guys all the time, you gather a lot of amazing information from senior IT DMs that compliment your survey. So what are your thoughts on the term and the concept? >> Yeah, sure. I'll even go back to last year when you and I did our predictions panel, right? And we threw it out there. And to your point, you know, there's some haters. Anytime you throw out a new term, "Is it marketing buzz? Is it worth it? Why are you even doing it?" But you know, from my own perspective, and then also speaking to the IT DMs that we interview on a regular basis, this is just a natural evolution. It's something that's inevitable in enterprise tech, right? The internet was not built for what it has become. It was never intended to be the underlying infrastructure of our daily lives and work. The cloud also was not built to be what it's become. But where we're at now is, we have to figure out what the cloud is and what it needs to be to be scalable, resilient, secure, and have the governance wrapped around it. And to me that's what supercloud is. It's a way to define operantly, what the next generation, the continued iteration and evolution of the cloud and what its needs to be. And that's what the supercloud means to me. And what depends, if you want to call it metacloud, supercloud, it doesn't matter. The point is that we're trying to define the next layer, the next future of work, which is inevitable in enterprise tech. Now, from the IT DM perspective, I have two interesting call outs. One is from basically a senior developer IT architecture and DevSecOps who says he uses the term all the time. And the reason he uses the term, is that because multi-cloud has a stigma attached to it, when he is talking to his business executives. (David chuckles) the stigma is because it's complex and it's expensive. So he switched to supercloud to better explain to his business executives and his CFO and his CIO what he's trying to do. And we can get into more later about what it means to him. But the inverse of that, of course, is a good CSO friend of mine for a very large enterprise says the concern with Supercloud is the reduction of complexity. And I'll explain, he believes anything that takes the requirement of specific expertise out of the equation, even a little bit, as a CSO worries him. So as you said, David, always two sides to the coin, but I do believe supercloud is a relevant term, and it is necessary because the cloud is continuing to be defined. >> You know, that's really interesting too, 'cause you know, Darren, we use Snowflake a lot as an example, sort of early supercloud, and you think from a security standpoint, we've always pushed Amazon and, "Are you ever going to kind of abstract the complexity away from all these primitives?" and their position has always been, "Look, if we produce these primitives, and offer these primitives, we we can move as the market moves. When you abstract, then it becomes harder to peel the layers." But Darren, from a data standpoint, like I say, we use Snowflake a lot. I think of like Tim Burners-Lee when Web 2.0 came out, he said, "Well this is what the internet was always supposed to be." So in a way, you know, supercloud is maybe what multi-cloud was supposed to be. But I mean, you think about data sharing, Darren, across clouds, it's always been a challenge. Snowflake always, you know, obviously trying to solve that problem, as are others. But what are your thoughts on the concept? >> Yeah, I think the concept fits, right? It is reflective of, it's a paradigm shift, right? Things, as a pendulum have swung back and forth between needing to piece together a bunch of different tools that have specific unique use cases and they're best in breed in what they do. And then focusing on the duct tape that holds 'em all together and all the engineering complexity and skill, it shifted from that end of the pendulum all the way back to, "Let's streamline this, let's simplify it. Maybe we have budget crunches and we need to consolidate tools or eliminate tools." And so then you kind of see this back and forth over time. And with data and analytics for instance, a lot of organizations were trying to bring the data closer to the business. That's where we saw self-service analytics coming in. And tools like Snowflake, what they did was they helped point to different databases, they helped unify data, and organize it in a single place that was, you know, in a sense neutral, away from a single cloud vendor or a single database, and allowed the business to kind of be more flexible in how it brought stuff together and provided it out to the business units. So Snowflake was an example of one of those times where we pulled back from the granular, multiple points of the spear, back to a simple way to do things. And I think Snowflake has continued to kind of keep that mantle to a degree, and we see other tools trying to do that, but that's all it is. It's a paradigm shift back to this kind of meta abstraction layer that kind of simplifies what is the reality, that you need a complex multi-use case, multi-region way of doing business. And it sort of reflects the reality of that. >> And you know, to me it's a spectrum. As part of Supercloud 2, we're talking to a number of of practitioners, Ionis Pharmaceuticals, US West, we got Walmart. And it's a spectrum, right? In some cases the practitioner's saying, "You know, the way I solve multi-cloud complexity is mono-cloud, I just do one cloud." (laughs) Others like Walmart are saying, "Hey, you know, we actually are building an abstraction layer ourselves, take advantage of it." So my general question to both of you is, is this a concept, is the lack of standards across clouds, you know, really a problem, you know, or is supercloud a solution looking for a problem? Or do you hear from practitioners that "No, this is really an issue, we have to bring together a set of standards to sort of unify our cloud estates." >> Allow me to answer that at a higher level, and then we're going to hand it over to Dr. Brabham because he is a little bit more detailed on the realtime streaming analytics use cases, which I think is where we're going to get to. But to answer that question, it really depends on the size and the complexity of your business. At the very large enterprise, Dave, Yes, a hundred percent. This needs to happen. There is complexity, there is not only complexity in the compute and actually deploying the applications, but the governance and the security around them. But for lower end or, you know, business use cases, and for smaller businesses, it's a little less necessary. You certainly don't need to have all of these. Some of the things that come into mind from the interviews that Darren and I have done are, you know, financial services, if you're doing real-time trading, anything that has real-time data metrics involved in your transactions, is going to be necessary. And another use case that we hear about is in online travel agencies. So I think it is very relevant, the complexity does need to be solved, and I'll allow Darren to explain a little bit more about how that's used from an analytics perspective. >> Yeah, go for it. >> Yeah, exactly. I mean, I think any modern, you know, multinational company that's going to have a footprint in the US and Europe, in China, or works in different areas like manufacturing, where you're probably going to have on-prem instances that will stay on-prem forever, for various performance reasons. You have these complicated governance and security and regulatory issues. So inherently, I think, large multinational companies and or companies that are in certain areas like finance or in, you know, online e-commerce, or things that need real-time data, they inherently are going to have a very complex environment that's going to need to be managed in some kind of cleaner way. You know, they're looking for one door to open, one pane of glass to look at, one thing to do to manage these multi points. And, streaming's a good example of that. I mean, not every organization has a real-time streaming use case, and may not ever, but a lot of organizations do, a lot of industries do. And so there's this need to use, you know, they want to use open-source tools, they want to use Apache Kafka for instance. They want to use different megacloud vendors offerings, like Google Pub/Sub or you know, Amazon Kinesis Firehose. They have all these different pieces they want to use for different use cases at different stages of maturity or proof of concept, you name it. They're going to have to have this complexity. And I think that's why we're seeing this need, to have sort of this supercloud concept, to juggle all this, to wrangle all of it. 'Cause the reality is, it's complex and you have to simplify it somehow. >> Great, thanks you guys. All right, let's bring up the graphic, and take a look. Anybody who follows the breaking analysis, which is co-branded with ETR Cube Insights powered by ETR, knows we like to bring data to the table. ETR does amazing survey work every quarter, 1200 plus 1500 practitioners that that answer a number of questions. The vertical axis here is net score, which is ETR's proprietary methodology, which is a measure of spending momentum, spending velocity. And the horizontal axis here is overlap, but it's the presence pervasiveness, and the dataset, the ends, that table insert on the bottom right shows you how the dots are plotted, the net score and then the ends in the survey. And what we've done is we've plotted a bunch of the so-called supercloud suspects, let's start in the upper right, the cloud platforms. Without these hyperscale clouds, you can't have a supercloud. And as always, Azure and AWS, up and to the right, it's amazing we're talking about, you know, 80 plus billion dollar company in AWS. Azure's business is, if you just look at the IaaS is in the 50 billion range, I mean it's just amazing to me the net scores here. Anything above 40% we consider highly elevated. And you got Azure and you got Snowflake, Databricks, HashiCorp, we'll get to them. And you got AWS, you know, right up there at that size, it's quite amazing. With really big ends as well, you know, 700 plus ends in the survey. So, you know, kind of half the survey actually has these platforms. So my question to you guys is, what are you seeing in terms of cloud adoption within the big three cloud players? I wonder if you could could comment, maybe Erik, you could start. >> Yeah, sure. Now we're talking data, now I'm happy. So yeah, we'll get into some of it. Right now, the January, 2023 TSIS is approaching 1500 survey respondents. One caveat, it's not closed yet, it will close on Friday, but with an end that big we are over statistically significant. We also recently did a cloud survey, and there's a couple of key points on that I want to get into before we get into individual vendors. What we're seeing here, is that annual spend on cloud infrastructure is expected to grow at almost a 70% CAGR over the next three years. The percentage of those workloads for cloud infrastructure are expected to grow over 70% as three years as well. And as you mentioned, Azure and AWS are still dominant. However, we're seeing some share shift spreading around a little bit. Now to get into the individual vendors you mentioned about, yes, Azure is still number one, AWS is number two. What we're seeing, which is incredibly interesting, CloudFlare is number three. It's actually beating GCP. That's the first time we've seen it. What I do want to state, is this is on net score only, which is our measure of spending intentions. When you talk about actual pervasion in the enterprise, it's not even close. But from a spending velocity intention point of view, CloudFlare is now number three above GCP, and even Salesforce is creeping up to be at GCPs level. So what we're seeing here, is a continued domination by Azure and AWS, but some of these other players that maybe might fit into your moniker. And I definitely want to talk about CloudFlare more in a bit, but I'm going to stop there. But what we're seeing is some of these other players that fit into your Supercloud moniker, are starting to creep up, Dave. >> Yeah, I just want to clarify. So as you also know, we track IaaS and PaaS revenue and we try to extract, so AWS reports in its quarterly earnings, you know, they're just IaaS and PaaS, they don't have a SaaS play, a little bit maybe, whereas Microsoft and Google include their applications and so we extract those out and if you do that, AWS is bigger, but in the surveys, you know, customers, they see cloud, SaaS to them as cloud. So that's one of the reasons why you see, you know, Microsoft as larger in pervasion. If you bring up that survey again, Alex, the survey results, you see them further to the right and they have higher spending momentum, which is consistent with what you see in the earnings calls. Now, interesting about CloudFlare because the CEO of CloudFlare actually, and CloudFlare itself uses the term supercloud basically saying, "Hey, we're building a new type of internet." So what are your thoughts? Do you have additional information on CloudFlare, Erik that you want to share? I mean, you've seen them pop up. I mean this is a really interesting company that is pretty forward thinking and vocal about how it's disrupting the industry. >> Sure, we've been tracking 'em for a long time, and even from the disruption of just a traditional CDN where they took down Akamai and what they're doing. But for me, the definition of a true supercloud provider can't just be one instance. You have to have multiple. So it's not just the cloud, it's networking aspect on top of it, it's also security. And to me, CloudFlare is the only one that has all of it. That they actually have the ability to offer all of those things. Whereas you look at some of the other names, they're still piggybacking on the infrastructure or platform as a service of the hyperscalers. CloudFlare does not need to, they actually have the cloud, the networking, and the security all themselves. So to me that lends credibility to their own internal usage of that moniker Supercloud. And also, again, just what we're seeing right here that their net score is now creeping above AGCP really does state it. And then just one real last thing, one of the other things we do in our surveys is we track adoption and replacement reasoning. And when you look at Cloudflare's adoption rate, which is extremely high, it's based on technical capabilities, the breadth of their feature set, it's also based on what we call the ability to avoid stack alignment. So those are again, really supporting reasons that makes CloudFlare a top candidate for your moniker of supercloud. >> And they've also announced an object store (chuckles) and a database. So, you know, that's going to be, it takes a while as you well know, to get database adoption going, but you know, they're ambitious and going for it. All right, let's bring the chart back up, and I want to focus Darren in on the ecosystem now, and really, we've identified Snowflake and Databricks, it's always fun to talk about those guys, and there are a number of other, you know, data platforms out there, but we use those too as really proxies for leaders. We got a bunch of the backup guys, the data protection folks, Rubric, Cohesity, and Veeam. They're sort of in a cluster, although Rubric, you know, ahead of those guys in terms of spending momentum. And then VMware, Tanzu and Red Hat as sort of the cross cloud platform. But I want to focus, Darren, on the data piece of it. We're seeing a lot of activity around data sharing, governed data sharing. Databricks is using Delta Sharing as their sort of place, Snowflakes is sort of this walled garden like the app store. What are your thoughts on, you know, in the context of Supercloud, cross cloud capabilities for the data platforms? >> Yeah, good question. You know, I think Databricks is an interesting player because they sort of have made some interesting moves, with their Data Lakehouse technology. So they're trying to kind of complicate, or not complicate, they're trying to take away the complications of, you know, the downsides of data warehousing and data lakes, and trying to find that middle ground, where you have the benefits of a managed, governed, you know, data warehouse environment, but you have sort of the lower cost, you know, capability of a data lake. And so, you know, Databricks has become really attractive, especially by data scientists, right? We've been tracking them in the AI machine learning sector for quite some time here at ETR, attractive for a data scientist because it looks and acts like a lake, but can have some managed capabilities like a warehouse. So it's kind of the best of both worlds. So in some ways I think you've seen sort of a data science driver for the adoption of Databricks that has now become a little bit more mainstream across the business. Snowflake, maybe the other direction, you know, it's a cloud data warehouse that you know, is starting to expand its capabilities and add on new things like Streamlit is a good example in the analytics space, with apps. So you see these tools starting to branch and creep out a bit, but they offer that sort of neutrality, right? We heard one IT decision maker we recently interviewed that referred to Snowflake and Databricks as the quote unquote Switzerland of what they do. And so there's this desirability from an organization to find these tools that can solve the complex multi-headed use-case of data and analytics, which every business unit needs in different ways. And figure out a way to do that, an elegant way that's governed and centrally managed, that federated kind of best of both worlds that you get by bringing the data close to the business while having a central governed instance. So these tools are incredibly powerful and I think there's only going to be room for growth, for those two especially. I think they're going to expand and do different things and maybe, you know, join forces with others and a lot of the power of what they do well is trying to define these connections and find these partnerships with other vendors, and try to be seen as the nice add-on to your existing environment that plays nicely with everyone. So I think that's where those two tools are going, but they certainly fit this sort of label of, you know, trying to be that supercloud neutral, you know, layer that unites everything. >> Yeah, and if you bring the graphic back up, please, there's obviously big data plays in each of the cloud platforms, you know, Microsoft, big database player, AWS is, you know, 11, 12, 15, data stores. And of course, you know, BigQuery and other, you know, data platforms within Google. But you know, I'm not sure the big cloud guys are going to go hard after so-called supercloud, cross-cloud services. Although, we see Oracle getting in bed with Microsoft and Azure, with a database service that is cross-cloud, certainly Google with Anthos and you know, you never say never with with AWS. I guess what I would say guys, and I'll I'll leave you with this is that, you know, just like all players today are cloud players, I feel like anybody in the business or most companies are going to be so-called supercloud players. In other words, they're going to have a cross-cloud strategy, they're going to try to build connections if they're coming from on-prem like a Dell or an HPE, you know, or Pure or you know, many of these other companies, Cohesity is another one. They're going to try to connect to their on-premise states, of course, and create a consistent experience. It's natural that they're going to have sort of some consistency across clouds. You know, the big question is, what's that spectrum look like? I think on the one hand you're going to have some, you know, maybe some rudimentary, you know, instances of supercloud or maybe they just run on the individual clouds versus where Snowflake and others and even beyond that are trying to go with a single global instance, basically building out what I would think of as their own cloud, and importantly their own ecosystem. I'll give you guys the last thought. Maybe you could each give us, you know, closing thoughts. Maybe Darren, you could start and Erik, you could bring us home on just this entire topic, the future of cloud and data. >> Yeah, I mean I think, you know, two points to make on that is, this question of these, I guess what we'll call legacy on-prem players. These, mega vendors that have been around a long time, have big on-prem footprints and a lot of people have them for that reason. I think it's foolish to assume that a company, especially a large, mature, multinational company that's been around a long time, it's foolish to think that they can just uproot and leave on-premises entirely full scale. There will almost always be an on-prem footprint from any company that was not, you know, natively born in the cloud after 2010, right? I just don't think that's reasonable anytime soon. I think there's some industries that need on-prem, things like, you know, industrial manufacturing and so on. So I don't think on-prem is going away, and I think vendors that are going to, you know, go very cloud forward, very big on the cloud, if they neglect having at least decent connectors to on-prem legacy vendors, they're going to miss out. So I think that's something that these players need to keep in mind is that they continue to reach back to some of these players that have big footprints on-prem, and make sure that those integrations are seamless and work well, or else their customers will always have a multi-cloud or hybrid experience. And then I think a second point here about the future is, you know, we talk about the three big, you know, cloud providers, the Google, Microsoft, AWS as sort of the opposite of, or different from this new supercloud paradigm that's emerging. But I want to kind of point out that, they will always try to make a play to become that and I think, you know, we'll certainly see someone like Microsoft trying to expand their licensing and expand how they play in order to become that super cloud provider for folks. So also don't want to downplay them. I think you're going to see those three big players continue to move, and take over what players like CloudFlare are doing and try to, you know, cut them off before they get too big. So, keep an eye on them as well. >> Great points, I mean, I think you're right, the first point, if you're Dell, HPE, Cisco, IBM, your strategy should be to make your on-premise state as cloud-like as possible and you know, make those differences as minimal as possible. And you know, if you're a customer, then the business case is going to be low for you to move off of that. And I think you're right. I think the cloud guys, if this is a real problem, the cloud guys are going to play in there, and they're going to make some money at it. Erik, bring us home please. >> Yeah, I'm going to revert back to our data and this on the macro side. So to kind of support this concept of a supercloud right now, you know Dave, you and I know, we check overall spending and what we're seeing right now is total year spent is expected to only be 4.6%. We ended 2022 at 5% even though it began at almost eight and a half. So this is clearly declining and in that environment, we're seeing the top two strategies to reduce spend are actually vendor consolidation with 36% of our respondents saying they're actively seeking a way to reduce their number of vendors, and consolidate into one. That's obviously supporting a supercloud type of play. Number two is reducing excess cloud resources. So when I look at both of those combined, with a drop in the overall spending reduction, I think you're on the right thread here, Dave. You know, the overall macro view that we're seeing in the data supports this happening. And if I can real quick, couple of names we did not touch on that I do think deserve to be in this conversation, one is HashiCorp. HashiCorp is the number one player in our infrastructure sector, with a 56% net score. It does multiple things within infrastructure and it is completely agnostic to your environment. And if we're also speaking about something that's just a singular feature, we would look at Rubric for data, backup, storage, recovery. They're not going to offer you your full cloud or your networking of course, but if you are looking for your backup, recovery, and storage Rubric, also number one in that sector with a 53% net score. Two other names that deserve to be in this conversation as we watch it move and evolve. >> Great, thank you for bringing that up. Yeah, we had both of those guys in the chart and I failed to focus in on HashiCorp. And clearly a Supercloud enabler. All right guys, we got to go. Thank you so much for joining us, appreciate it. Let's keep this conversation going. >> Always enjoy talking to you Dave, thanks. >> Yeah, thanks for having us. >> All right, keep it right there for more content from Supercloud 2. This is Dave Valente for John Ferg and the entire Cube team. We'll be right back. (gentle synth music) (music fades)

(upbeat electronic music) >> Okay, welcome back to Supercloud 2, live here in Palo Alto. I'm John Furrier, with Dave Vellante. Again, all day wall-to-wall coverage, just had a great interview with Walmart, we've got a Next interview coming up, you're going to hear from Bob Muglia and Tristan Handy, two experts, both experienced entrepreneurs, executives in technology. We're here to break down what just happened with Walmart, and what's coming up with George Gilbert, former colleague, Wikibon analyst, Gartner Analyst, and now independent investor and expert. George, great to see you, I know you're following this space. Like you read about it, remember the first days when Dataverse came out, we were talking about them coming out of Berkeley? >> Dave: Snowflake. >> John: Snowflake. >> Dave: Snowflake In the early days. >> We, collectively, have been chronicling the data movement since 2010, you were part of our team, now you've got your nose to the grindstone, you're seeing the next wave. What's this all about? Walmart building their own super cloud, we got Bob Muglia talking about how these next wave of apps are coming. What are the super apps? What's the super cloud to you? >> Well, this key's off Dave's really interesting questions to Walmart, which was like, how are they building their supercloud? 'Cause it makes a concrete example. But what was most interesting about his description of the Walmart WCMP, I forgot what it stood for. >> Dave: Walmart Cloud Native Platform. >> Walmart, okay. He was describing where the logic could run in these stateless containers, and maybe eventually serverless functions. But that's just it, and that's the paradigm of microservices, where the logic is in this stateless thing, where you can shoot it, or it fails, and you can spin up another one, and you've lost nothing. >> That was their triplet model. >> Yeah, in fact, and that was what they were trying to move to, where these things move fluidly between data centers. >> But there's a but, right? Which is they're all stateless apps in the cloud. >> George: Yeah. >> And all their stateful apps are on-prem and VMs. >> Or the stateful part of the apps are in VMs. >> Okay. >> And so if they really want to lift their super cloud layer off of this different provider's infrastructure, they're going to need a much more advanced software platform that manages data. And that goes to the -- >> Muglia and Handy, that you and I did, that's coming up next. So the big takeaway there, George, was, I'll set it up and you can chime in, a new breed of data apps is emerging, and this highly decentralized infrastructure. And Tristan Handy of DBT Labs has a sort of a solution to begin the journey today, Muglia is working on something that's way out there, describe what you learned from it. >> Okay. So to talk about what the new data apps are, and then the platform to run them, I go back to the using what will probably be seen as one of the first data app examples, was Uber, where you're describing entities in the real world, riders, drivers, routes, city, like a city plan, these are all defined by data. And the data is described in a structure called a knowledge graph, for lack of a, no one's come up with a better term. But that means the tough, the stuff that Jack built, which was all stateless and sits above cloud vendors' infrastructure, it needs an entirely different type of software that's much, much harder to build. And the way Bob described it is, you're going to need an entirely new data management infrastructure to handle this. But where, you know, we had this really colorful interview where it was like Rock 'Em Sock 'Em, but they weren't really that much in opposition to each other, because Tristan is going to define this layer, starting with like business intelligence metrics, where you're defining things like bookings, billings, and revenue, in business terms, not in SQL terms -- >> Well, business terms, if I can interrupt, he said the one thing we haven't figured out how to APIify is KPIs that sit inside of a data warehouse, and that's essentially what he's doing. >> George: That's what he's doing, yes. >> Right. And so then you can now expose those APIs, those KPIs, that sit inside of a data warehouse, or a data lake, a data store, whatever, through APIs. >> George: And the difference -- >> So what does that do for you? >> Okay, so all of a sudden, instead of working at technical data terms, where you're dealing with tables and columns and rows, you're dealing instead with business entities, using the Uber example of drivers, riders, routes, you know, ETA prices. But you can define, DBT will be able to define those progressively in richer terms, today they're just doing things like bookings, billings, and revenue. But Bob's point was, today, the data warehouse that actually runs that stuff, whereas DBT defines it, the data warehouse that runs it, you can't do it with relational technology >> Dave: Relational totality, cashing architecture. >> SQL, you can't -- >> SQL caching architectures in memory, you can't do it, you've got to rethink down to the way the data lake is laid out on the disk or cache. Which by the way, Thomas Hazel, who's speaking later, he's the chief scientist and founder at Chaos Search, he says, "I've actually done this," basically leave it in an S3 bucket, and I'm going to query it, you know, with no caching. >> All right, so what I hear you saying then, tell me if I got this right, there are some some things that are inadequate in today's world, that's not compatible with the Supercloud wave. >> Yeah. >> Specifically how you're using storage, and data, and stateful. >> Yes. >> And then the software that makes it run, is that what you're saying? >> George: Yeah. >> There's one other thing you mentioned to me, it's like, when you're using a CRM system, a human is inputting data. >> George: Nothing happens till the human does something. >> Right, nothing happens until that data entry occurs. What you're talking about is a world that self forms, polling data from the transaction system, or the ERP system, and then builds a plan without human intervention. >> Yeah. Something in the real world happens, where the user says, "I want a ride." And then the software goes out and says, "Okay, we got to match a driver to the rider, we got to calculate how long it takes to get there, how long to deliver 'em." That's not driven by a form, other than the first person hitting a button and saying, "I want a ride." All the other stuff happens autonomously, driven by data and analytics. >> But my question was different, Dave, so I want to get specific, because this is where the startups are going to come in, this is the disruption. Snowflake is a data warehouse that's in the cloud, they call it a data cloud, they refactored it, they did it differently, the success, we all know it looks like. These areas where it's inadequate for the future are areas that'll probably be either disrupted, or refactored. What is that? >> That's what Muglia's contention is, that the DBT can start adding that layer where you define these business entities, they're like mini digital twins, you can define them, but the data warehouse isn't strong enough to actually manage and run them. And Muglia is behind a company that is rethinking the database, really in a fundamental way that hasn't been done in 40 or 50 years. It's the first, in his contention, the first real rethink of database technology in a fundamental way since the rise of the relational database 50 years ago. >> And I think you admit it's a real Hail Mary, I mean it's quite a long shot right? >> George: Yes. >> Huge potential. >> But they're pretty far along. >> Well, we've been talking on theCUBE for 12 years, and what, 10 years going to AWS Reinvent, Dave, that no one database will rule the world, Amazon kind of showed that with them. What's different, is it databases are changing, or you can have multiple databases, or? >> It's a good question. And the reason we've had multiple different types of databases, each one specialized for a different type of workload, but actually what Muglia is behind is a new engine that would essentially, you'll never get rid of the data warehouse, or the equivalent engine in like a Databricks datalake house, but it's a new engine that manages the thing that describes all the data and holds it together, and that's the new application platform. >> George, we have one minute left, I want to get real quick thought, you're an investor, and we know your history, and the folks watching, George's got a deep pedigree in investment data, and we can testify against that. If you're going to invest in a company right now, if you're a customer, I got to make a bet, what does success look like for me, what do I want walking through my door, and what do I want to send out? What companies do I want to look at? What's the kind of of vendor do I want to evaluate? Which ones do I want to send home? >> Well, the first thing a customer really has to do when they're thinking about next gen applications, all the people have told you guys, "we got to get our data in order," getting that data in order means building an integrated view of all your data landscape, which is data coming out of all your applications. It starts with the data model, so, today, you basically extract data from all your operational systems, put it in this one giant, central place, like a warehouse or lake house, but eventually you want this, whether you call it a fabric or a mesh, it's all the data that describes how everything hangs together as in one big knowledge graph. There's different ways to implement that. And that's the most critical thing, 'cause that describes your Uber landscape, your Uber platform. >> That's going to power the digital transformation, which will power the business transformation, which powers the business model, which allows the builders to build -- >> Yes. >> Coders to code. That's Supercloud application. >> Yeah. >> George, great stuff. Next interview you're going to see right here is Bob Muglia and Tristan Handy, they're going to unpack this new wave. Great segment, really worth unpacking and reading between the lines with George, and Dave Vellante, and those two great guests. And then we'll come back here for the studio for more of the live coverage of Supercloud 2. Thanks for watching. (upbeat electronic music)

(upbeat electronic music) >> Hello everyone, welcome to this CUBE conversation here from the studios in the CUBE in Palo Alto, California. I'm John Furrier, your host. We're featuring a startup, Astronomer. Astronomer.io is the URL, check it out. And we're going to have a great conversation around one of the most important topics hitting the industry, and that is the future of machine learning and AI, and the data that powers it underneath it. There's a lot of things that need to get done, and we're excited to have some of the co-founders of Astronomer here. Viraj Parekh, who is co-founder of Astronomer, and Paola Peraza Calderon, another co-founder, both with Astronomer. Thanks for coming on. First of all, how many co-founders do you guys have? >> You know, I think the answer's around six or seven. I forget the exact, but there's really been a lot of people around the table who've worked very hard to get this company to the point that it's at. We have long ways to go, right? But there's been a lot of people involved that have been absolutely necessary for the path we've been on so far. >> Thanks for that, Viraj, appreciate that. The first question I want to get out on the table, and then we'll get into some of the details, is take a minute to explain what you guys are doing. How did you guys get here? Obviously, multiple co-founders, sounds like a great project. The timing couldn't have been better. ChatGPT has essentially done so much public relations for the AI industry to kind of highlight this shift that's happening. It's real, we've been chronicalizing, take a minute to explain what you guys do. >> Yeah, sure, we can get started. So, yeah, when Viraj and I joined Astronomer in 2017, we really wanted to build a business around data, and we were using an open source project called Apache Airflow that we were just using sort of as customers ourselves. And over time, we realized that there was actually a market for companies who use Apache Airflow, which is a data pipeline management tool, which we'll get into, and that running Airflow is actually quite challenging, and that there's a big opportunity for us to create a set of commercial products and an opportunity to grow that open source community and actually build a company around that. So the crux of what we do is help companies run data pipelines with Apache Airflow. And certainly we've grown in our ambitions beyond that, but that's sort of the crux of what we do for folks. >> You know, data orchestration, data management has always been a big item in the old classic data infrastructure. But with AI, you're seeing a lot more emphasis on scale, tuning, training. Data orchestration is the center of the value proposition, when you're looking at coordinating resources, it's one of the most important things. Can you guys explain what data orchestration entails? What does it mean? Take us through the definition of what data orchestration entails. >> Yeah, for sure. I can take this one, and Viraj, feel free to jump in. So if you google data orchestration, here's what you're going to get. You're going to get something that says, "Data orchestration is the automated process" "for organizing silo data from numerous" "data storage points, standardizing it," "and making it accessible and prepared for data analysis." And you say, "Okay, but what does that actually mean," right, and so let's give sort of an an example. So let's say you're a business and you have sort of the following basic asks of your data team, right? Okay, give me a dashboard in Sigma, for example, for the number of customers or monthly active users, and then make sure that that gets updated on an hourly basis. And then number two, a consistent list of active customers that I have in HubSpot so that I can send them a monthly product newsletter, right? Two very basic asks for all sorts of companies and organizations. And when that data team, which has data engineers, data scientists, ML engineers, data analysts get that request, they're looking at an ecosystem of data sources that can help them get there, right? And that includes application databases, for example, that actually have in product user behavior and third party APIs from tools that the company uses that also has different attributes and qualities of those customers or users. And that data team needs to use tools like Fivetran to ingest data, a data warehouse, like Snowflake or Databricks to actually store that data and do analysis on top of it, a tool like DBT to do transformations and make sure that data is standardized in the way that it needs to be, a tool like Hightouch for reverse ETL. I mean, we could go on and on. There's so many partners of ours in this industry that are doing really, really exciting and critical things for those data movements. And the whole point here is that data teams have this plethora of tooling that they use to both ingest the right data and come up with the right interfaces to transform and interact with that data. And data orchestration, in our view, is really the heartbeat of all of those processes, right? And tangibly the unit of data orchestration is a data pipeline, a set of tasks or jobs that each do something with data over time and eventually run that on a schedule to make sure that those things are happening continuously as time moves on and the company advances. And so, for us, we're building a business around Apache Airflow, which is a workflow management tool that allows you to author, run, and monitor data pipelines. And so when we talk about data orchestration, we talk about sort of two things. One is that crux of data pipelines that, like I said, connect that large ecosystem of data tooling in your company. But number two, it's not just that data pipeline that needs to run every day, right? And Viraj will probably touch on this as we talk more about Astronomer and our value prop on top of Airflow. But then it's all the things that you need to actually run data and production and make sure that it's trustworthy, right? So it's actually not just that you're running things on a schedule, but it's also things like CICD tooling, secure secrets management, user permissions, monitoring, data lineage, documentation, things that enable other personas in your data team to actually use those tools. So long-winded way of saying that it's the heartbeat, we think, of of the data ecosystem, and certainly goes beyond scheduling, but again, data pipelines are really at the center of it. >> One of the things that jumped out, Viraj, if you can get into this, I'd like to hear more about how you guys look at all those little tools that are out. You mentioned a variety of things. You look at the data infrastructure, it's not just one stack. You've got an analytic stack, you've got a realtime stack, you've got a data lake stack, you got an AI stack potentially. I mean you have these stacks now emerging in the data world that are fundamental, that were once served by either a full package, old school software, and then a bunch of point solution. You mentioned Fivetran there, I would say in the analytics stack. Then you got S3, they're on the data lake stack. So all these things are kind of munged together. >> Yeah. >> How do you guys fit into that world? You make it easier, or like, what's the deal? >> Great question, right? And you know, I think that one of the biggest things we've found in working with customers over the last however many years is that if a data team is using a bunch of tools to get what they need done, and the number of tools they're using is growing exponentially and they're kind of roping things together here and there, that's actually a sign of a productive team, not a bad thing, right? It's because that team is moving fast. They have needs that are very specific to them, and they're trying to make something that's exactly tailored to their business. So a lot of times what we find is that customers have some sort of base layer, right? That's kind of like, it might be they're running most of the things in AWS, right? And then on top of that, they'll be using some of the things AWS offers, things like SageMaker, Redshift, whatever, but they also might need things that their cloud can't provide. Something like Fivetran, or Hightouch, those are other tools. And where data orchestration really shines, and something that we've had the pleasure of helping our customers build, is how do you take all those requirements, all those different tools and whip them together into something that fulfills a business need? So that somebody can read a dashboard and trust the number that it says, or somebody can make sure that the right emails go out to their customers. And Airflow serves as this amazing kind of glue between that data stack, right? It's to make it so that for any use case, be it ELT pipelines, or machine learning, or whatever, you need different things to do them, and Airflow helps tie them together in a way that's really specific for a individual business' needs. >> Take a step back and share the journey of what you guys went through as a company startup. So you mentioned Apache, open source. I was just having an interview with a VC, we were talking about foundational models. You got a lot of proprietary and open source development going on. It's almost the iPhone/Android moment in this whole generative space and foundational side. This is kind of important, the open source piece of it. Can you share how you guys started? And I can imagine your customers probably have their hair on fire and are probably building stuff on their own. Are you guys helping them? Take us through, 'cause you guys are on the front end of a big, big wave, and that is to make sense of the chaos, rain it in. Take us through your journey and why this is important. >> Yeah, Paola, I can take a crack at this, then I'll kind of hand it over to you to fill in whatever I miss in details. But you know, like Paola is saying, the heart of our company is open source, because we started using Airflow as an end user and started to say like, "Hey wait a second," "more and more people need this." Airflow, for background, started at Airbnb, and they were actually using that as a foundation for their whole data stack. Kind of how they made it so that they could give you recommendations, and predictions, and all of the processes that needed orchestrated. Airbnb created Airflow, gave it away to the public, and then fast forward a couple years and we're building a company around it, and we're really excited about that. >> That's a beautiful thing. That's exactly why open source is so great. >> Yeah, yeah. And for us, it's really been about watching the community and our customers take these problems, find a solution to those problems, standardize those solutions, and then building on top of that, right? So we're reaching to a point where a lot of our earlier customers who started to just using Airflow to get the base of their BI stack down and their reporting in their ELP infrastructure, they've solved that problem and now they're moving on to things like doing machine learning with their data, because now that they've built that foundation, all the connective tissue for their data arriving on time and being orchestrated correctly is happening, they can build a layer on top of that. And it's just been really, really exciting kind of watching what customers do once they're empowered to pick all the tools that they need, tie them together in the way they need to, and really deliver real value to their business. >> Can you share some of the use cases of these customers? Because I think that's where you're starting to see the innovation. What are some of the companies that you're working with, what are they doing? >> Viraj, I'll let you take that one too. (group laughs) >> So you know, a lot of it is... It goes across the gamut, right? Because it doesn't matter what you are, what you're doing with data, it needs to be orchestrated. So there's a lot of customers using us for their ETL and ELT reporting, right? Just getting data from other disparate sources into one place and then building on top of that. Be it building dashboards, answering questions for the business, building other data products and so on and so forth. From there, these use cases evolve a lot. You do see folks doing things like fraud detection, because Airflow's orchestrating how transactions go, transactions get analyzed. They do things like analyzing marketing spend to see where your highest ROI is. And then you kind of can't not talk about all of the machine learning that goes on, right? Where customers are taking data about their own customers, kind of analyze and aggregating that at scale, and trying to automate decision making processes. So it goes from your most basic, what we call data plumbing, right? Just to make sure data's moving as needed, all the ways to your more exciting expansive use cases around automated decision making and machine learning. >> And I'd say, I mean, I'd say that's one of the things that I think gets me most excited about our future, is how critical Airflow is to all of those processes, and I think when you know a tool is valuable is when something goes wrong and one of those critical processes doesn't work. And we know that our system is so mission critical to answering basic questions about your business and the growth of your company for so many organizations that we work with. So it's, I think, one of the things that gets Viraj and I and the rest of our company up every single morning is knowing how important the work that we do for all of those use cases across industries, across company sizes, and it's really quite energizing. >> It was such a big focus this year at AWS re:Invent, the role of data. And I think one of the things that's exciting about the open AI and all the movement towards large language models is that you can integrate data into these models from outside. So you're starting to see the integration easier to deal with. Still a lot of plumbing issues. So a lot of things happening. So I have to ask you guys, what is the state of the data orchestration area? Is it ready for disruption? Has it already been disrupted? Would you categorize it as a new first inning kind of opportunity, or what's the state of the data orchestration area right now? Both technically and from a business model standpoint. How would you guys describe that state of the market? >> Yeah, I mean, I think in a lot of ways, in some ways I think we're category creating. Schedulers have been around for a long time. I released a data presentation sort of on the evolution of going from something like Kron, which I think was built in like the 1970s out of Carnegie Mellon. And that's a long time ago, that's 50 years ago. So sort of like the basic need to schedule and do something with your data on a schedule is not a new concept. But to our point earlier, I think everything that you need around your ecosystem, first of all, the number of data tools and developer tooling that has come out industry has 5X'd over the last 10 years. And so obviously as that ecosystem grows, and grows, and grows, and grows, the need for orchestration only increases. And I think, as Astronomer, I think we... And we work with so many different types of companies, companies that have been around for 50 years, and companies that got started not even 12 months ago. And so I think for us it's trying to, in a ways, category create and adjust sort of what we sell and the value that we can provide for companies all across that journey. There are folks who are just getting started with orchestration, and then there's folks who have such advanced use case, 'cause they're hitting sort of a ceiling and only want to go up from there. And so I think we, as a company, care about both ends of that spectrum, and certainly want to build and continue building products for companies of all sorts, regardless of where they are on the maturity curve of data orchestration. >> That's a really good point, Paola. And I think the other thing to really take into account is it's the companies themselves, but also individuals who have to do their jobs. If you rewind the clock like 5 or 10 years ago, data engineers would be the ones responsible for orchestrating data through their org. But when we look at our customers today, it's not just data engineers anymore. There's data analysts who sit a lot closer to the business, and the data scientists who want to automate things around their models. So this idea that orchestration is this new category is right on the money. And what we're finding is the need for it is spreading to all parts of the data team, naturally where Airflow's emerged as an open source standard and we're hoping to take things to the next level. >> That's awesome. We've been up saying that the data market's kind of like the SRE with servers, right? You're going to need one person to deal with a lot of data, and that's data engineering, and then you're got to have the practitioners, the democratization. Clearly that's coming in what you're seeing. So I have to ask, how do you guys fit in from a value proposition standpoint? What's the pitch that you have to customers, or is it more inbound coming into you guys? Are you guys doing a lot of outreach, customer engagements? I'm sure they're getting a lot of great requirements from customers. What's the current value proposition? How do you guys engage? >> Yeah, I mean, there's so many... Sorry, Viraj, you can jump in. So there's so many companies using Airflow, right? So the baseline is that the open source project that is Airflow that came out of Airbnb, over five years ago at this point, has grown exponentially in users and continues to grow. And so the folks that we sell to primarily are folks who are already committed to using Apache Airflow, need data orchestration in their organization, and just want to do it better, want to do it more efficiently, want to do it without managing that infrastructure. And so our baseline proposition is for those organizations. Now to Viraj's point, obviously I think our ambitions go beyond that, both in terms of the personas that we addressed and going beyond that data engineer, but really it's to start at the baseline, as we continue to grow our our company, it's really making sure that we're adding value to folks using Airflow and help them do so in a better way, in a larger way, in a more efficient way, and that's really the crux of who we sell to. And so to answer your question on, we get a lot of inbound because they're... >> You have a built in audience. (laughs) >> The world that use it. Those are the folks who we talk to and come to our website and chat with us and get value from our content. I mean, the power of the opensource community is really just so, so big, and I think that's also one of the things that makes this job fun. >> And you guys are in a great position. Viraj, you can comment a little, get your reaction. There's been a big successful business model to starting a company around these big projects for a lot of reasons. One is open source is continuing to be great, but there's also supply chain challenges in there. There's also we want to continue more innovation and more code and keeping it free and and flowing. And then there's the commercialization of productizing it, operationalizing it. This is a huge new dynamic, I mean, in the past 5 or so years, 10 years, it's been happening all on CNCF from other areas like Apache, Linux Foundation, they're all implementing this. This is a huge opportunity for entrepreneurs to do this. >> Yeah, yeah. Open source is always going to be core to what we do, because we wouldn't exist without the open source community around us. They are huge in numbers. Oftentimes they're nameless people who are working on making something better in a way that everybody benefits from it. But open source is really hard, especially if you're a company whose core competency is running a business, right? Maybe you're running an e-commerce business, or maybe you're running, I don't know, some sort of like, any sort of business, especially if you're a company running a business, you don't really want to spend your time figuring out how to run open source software. You just want to use it, you want to use the best of it, you want to use the community around it, you want to be able to google something and get answers for it, you want the benefits of open source. You don't have the time or the resources to invest in becoming an expert in open source, right? And I think that dynamic is really what's given companies like us an ability to kind of form businesses around that in the sense that we'll make it so people get the best of both worlds. You'll get this vast open ecosystem that you can build on top of, that you can benefit from, that you can learn from. But you won't have to spend your time doing undifferentiated heavy lifting. You can do things that are just specific to your business. >> It's always been great to see that business model evolve. We used a debate 10 years ago, can there be another Red Hat? And we said, not really the same, but there'll be a lot of little ones that'll grow up to be big soon. Great stuff. Final question, can you guys share the history of the company? The milestones of Astromer's journey in data orchestration? >> Yeah, we could. So yeah, I mean, I think, so Viraj and I have obviously been at Astronomer along with our other founding team and leadership folks for over five years now. And it's been such an incredible journey of learning, of hiring really amazing people, solving, again, mission critical problems for so many types of organizations. We've had some funding that has allowed us to invest in the team that we have and in the software that we have, and that's been really phenomenal. And so that investment, I think, keeps us confident, even despite these sort of macroeconomic conditions that we're finding ourselves in. And so honestly, the milestones for us are focusing on our product, focusing on our customers over the next year, focusing on that market for us that we know can get valuable out of what we do, and making developers' lives better, and growing the open source community and making sure that everything that we're doing makes it easier for folks to get started, to contribute to the project and to feel a part of the community that we're cultivating here. >> You guys raised a little bit of money. How much have you guys raised? >> Don't know what the total is, but it's in the ballpark over $200 million. It feels good to... >> A little bit of capital. Got a little bit of cap to work with there. Great success. I know as a Series C Financing, you guys have been down. So you're up and running, what's next? What are you guys looking to do? What's the big horizon look like for you from a vision standpoint, more hiring, more product, what is some of the key things you're looking at doing? >> Yeah, it's really a little of all of the above, right? Kind of one of the best and worst things about working at earlier stage startups is there's always so much to do and you often have to just kind of figure out a way to get everything done. But really investing our product over the next, at least over the course of our company lifetime. And there's a lot of ways we want to make it more accessible to users, easier to get started with, easier to use, kind of on all areas there. And really, we really want to do more for the community, right, like I was saying, we wouldn't be anything without the large open source community around us. And we want to figure out ways to give back more in more creative ways, in more code driven ways, in more kind of events and everything else that we can keep those folks galvanized and just keep them happy using Airflow. >> Paola, any final words as we close out? >> No, I mean, I'm super excited. I think we'll keep growing the team this year. We've got a couple of offices in the the US, which we're excited about, and a fully global team that will only continue to grow. So Viraj and I are both here in New York, and we're excited to be engaging with our coworkers in person finally, after years of not doing so. We've got a bustling office in San Francisco as well. So growing those teams and continuing to hire all over the world, and really focusing on our product and the open source community is where our heads are at this year. So, excited. >> Congratulations. 200 million in funding, plus. Good runway, put that money in the bank, squirrel it away. It's a good time to kind of get some good interest on it, but still grow. Congratulations on all the work you guys do. We appreciate you and the open source community does, and good luck with the venture, continue to be successful, and we'll see you at the Startup Showcase. >> Thank you. >> Yeah, thanks so much, John. Appreciate it. >> Okay, that's the CUBE Conversation featuring astronomer.io, that's the website. Astronomer is doing well. Multiple rounds of funding, over 200 million in funding. Open source continues to lead the way in innovation. Great business model, good solution for the next gen cloud scale data operations, data stacks that are emerging. I'm John Furrier, your host, thanks for watching. (soft upbeat music)

(bright music) >> Hello everyone. Welcome to theCUBE's coverage of AWS re:Invent 22. I'm John Furrier, host of theCUBE. Got some great coverage here talking about software supply chain and sustainability in the cloud. We've got a great conversation. Gunnar Hellekson, vice president and general manager at Red Hat Enterprise Linux and Business Unit of Red Hat. Thanks for coming on. And Adnan Ijaz, director of product management of commercial software services, AWS. Gentlemen, thanks for joining me today. >> It's a pleasure. (Adnan speaks indistinctly) >> You know, the hottest topic coming out of Cloud Native developer communities is slide chain software sustainability. This is a huge issue. As open source continues to power away and fund and grow this next generation modern development environment, you know, supply chain, you know, sustainability is a huge discussion because you got to check things out, what's in the code. Okay, open source is great, but now we got to commercialize it. This is the topic, Gunnar, let's get in with you. What are you seeing here and what's some of the things that you're seeing around the sustainability piece of it? Because, you know, containers, Kubernetes, we're seeing that that run time really dominate this new abstraction layer, cloud scale. What's your thoughts? >> Yeah, so I, it's interesting that the, you know, so Red Hat's been doing this for 20 years, right? Making open source safe to consume in the enterprise. And there was a time when in order to do that you needed to have a long term life cycle and you needed to be very good at remediating security vulnerabilities. And that was kind of, that was the bar that you had to climb over. Nowadays with the number of vulnerabilities coming through, what people are most worried about is, kind of, the providence of the software and making sure that it has been vetted and it's been safe, and that things that you get from your vendor should be more secure than things that you've just downloaded off of GitHub, for example. Right? And that's a place where Red Hat's very comfortable living, right? Because we've been doing it for 20 years. I think there's another aspect to this supply chain question as well, especially with the pandemic. You know, we've got these supply chains have been jammed up. The actual physical supply chains have been jammed up. And the two of these issues actually come together, right? Because as we go through the pandemic, we've got these digital transformation efforts, which are in large part, people creating software in order to manage better their physical supply chain problems. And so as part of that digital transformation, you have another supply chain problem, which is the software supply chain problem, right? And so these two things kind of merge on these as people are trying to improve the performance of transportation systems, logistics, et cetera. Ultimately, it all boils down to, both supply chain problems actually boil down to a software problem. It's very interesting. >> Well, that is interesting. I want to just follow up on that real quick if you don't mind. Because if you think about the convergence of the software and physical world, you know, that's, you know, IOT and also hybridcloud kind of plays into that at scale, this opens up more surface area for attacks, especially when you're under a lot of pressure. This is where, you know, you have a service area on the physical side and you have constraints there. And obviously the pandemic causes problems. But now you've got the software side. How are you guys handling that? Can you just share a little bit more of how you guys looking at that with Red Hat? What's the customer challenge? Obviously, you know, skills gaps is one, but, like, that's a convergence at the same time more security problems. >> Yeah, yeah, that's right. And certainly the volume of, if we just look at security vulnerabilities themselves, just the volume of security vulnerabilities has gone up considerably as more people begin using the software. And as the software becomes more important to, kind of, critical infrastructure. More eyeballs around it and so we're uncovering more problems, which is kind of, that's okay, that's how the world works. And so certainly the number of remediations required every year has gone up. But also the customer expectations, as I mentioned before, the customer expectations have changed, right? People want to be able to show to their auditors and to their regulators that no, in fact, I can show the providence of the software that I'm using. I didn't just download something random off the internet. I actually have like, you know, adults paying attention to how the software gets put together. And it's still, honestly, it's still very early days. I think as an industry, I think we're very good at managing, identifying remediating vulnerabilities in the aggregate. We're pretty good at that. I think things are less clear when we talk about, kind of, the management of that supply chain, proving the providence, and creating a resilient supply chain for software. We have lots of tools, but we don't really have lots of shared expectations. And so it's going to be interesting over the next few years, I think we're going to have more rules are going to come out. I see NIST has already published some of them. And as these new rules come out, the whole industry is going to have to kind of pull together and really rally around some of this shared understanding so we can all have shared expectations and we can all speak the same language when we're talking about this problem. >> That's awesome. Adnan, Amazon web service is obviously the largest cloud platform out there. You know, the pandemic, even post pandemic, some of these supply chain issues, whether it's physical or software, you're also an outlet for that. So if someone can't buy hardware or something physical, they can always get to the cloud. You guys have great network compute and whatnot and you got thousands of ISVs across the globe. How are you helping customers with this supply chain problem? Because whether it's, you know, I need to get in my networking gears and delay, I'm going to go to the cloud and get help there. Or whether it's knowing the workloads and what's going on inside them with respect to open source. 'Cause you've got open source, which is kind of an external forcing function. You've got AWS and you got, you know, physical compute stores, networking, et cetera. How are you guys helping customers with the supply chain challenge, which could be an opportunity? >> Yeah, thanks John. I think there are multiple layers to that. At the most basic level, we are helping customers by abstracting away all these data center constructs that they would have to worry about if they were running their own data centers. They would have to figure out how the networking gear, you talk about, you know, having the right compute, right physical hardware. So by moving to the cloud, at least they're delegating that problem to AWS and letting us manage and making sure that we have an instance available for them whenever they want it. And if they want to scale it, the capacity is there for them to use. Now then, so we kind of give them space to work on the second part of the problem, which is building their own supply chain solutions. And we work with all kinds of customers here at AWS from all different industry segments, automotive, retail, manufacturing. And you know, you see the complexity of the supply chain with all those moving pieces, like hundreds and thousands of moving pieces, it's very daunting. And then on the other hand, customers need more better services. So you need to move fast. So you need to build your agility in the supply chain itself. And that is where, you know, Red Hat and AWS come together. Where we can enable customers to build their supply chain solutions on platforms like Red Hat Enterprise Linux RHEL or Red Hat OpenShift on AWS, we call it ROSA. And the benefit there is that you can actually use the services that are relevant for the supply chain solutions like Amazon managed blockchain, you know, SageMaker. So you can actually build predictive analytics, you can improve forecasting, you can make sure that you have solutions that help you identify where you can cut costs. And so those are some of the ways we're helping customers, you know, figure out how they actually want to deal with the supply chain challenges that we're running into in today's world. >> Yeah, and you know, you mentioned sustainability outside of software sustainability, you know, as people move to the cloud, we've reported on SiliconANGLE here in theCUBE, that it's better to have the sustainability with the cloud because then the data centers aren't using all that energy too. So there's also all kinds of sustainability advantages. Gunnar, because this is kind of how your relationship with Amazon's expanded. You mentioned ROSA, which is Red Hat, you know, on OpenShift, on AWS. This is interesting because one of the biggest discussions is skills gap, but we were also talking about the fact that the humans are a huge part of the talent value. In other words, the humans still need to be involved. And having that relationship with managed services and Red Hat, this piece becomes one of those things that's not talked about much, which is the talent is increasing in value, the humans, and now you got managed services on the cloud. So we'll look at scale and human interaction. Can you share, you know, how you guys are working together on this piece? 'Cause this is interesting, 'cause this kind of brings up the relationship of that operator or developer. >> Yeah, yeah. So I think there's, so I think about this in a few dimensions. First is that it's difficult to find a customer who is not talking about automation at some level right now. And obviously you can automate the processes and the physical infrastructure that you already have, that's using tools like Ansible, right? But I think that combining it with the elasticity of a solution like AWS, so you combine the automation with kind of elastic and converting a lot of the capital expenses into operating expenses, that's a great way actually to save labor, right? So instead of like racking hard drives, you can have somebody do something a little more like, you know, more valuable work, right? And so, okay, but that gives you a platform. And then what do you do with that platform? You know, if you've got your systems automated and you've got this kind of elastic infrastructure underneath you, what you do on top of it is really interesting. So a great example of this is the collaboration that we had with running the RHEL workstation on AWS. So you might think, like, well why would anybody want to run a workstation on a cloud? That doesn't make a whole lot of sense. Unless you consider how complex it is to set up, if you have, the use case here is like industrial workstations, right? So it's animators, people doing computational fluid dynamics, things like this. So these are industries that are extremely data heavy. Workstations have very large hardware requirements, often with accelerated GPUs and things like this. That is an extremely expensive thing to install on-premise anywhere. And if the pandemic taught us anything, it's if you have a bunch of very expensive talent and they all have to work from home, it is very difficult to go provide them with, you know, several tens of thousands of dollars worth of workstation equipment. And so combine the RHEL workstation with the AWS infrastructure and now all that workstation computational infrastructure is available on demand and available right next to the considerable amount of data that they're analyzing or animating or working on. So it's a really interesting, it was actually, this is an idea that was actually born with the pandemic. >> Yeah. >> And it's kind of a combination of everything that we're talking about, right? It's the supply chain challenges of the customer, it's the lack of talent, making sure that people are being put to their best and highest use. And it's also having this kind of elastic, I think, OpEx heavy infrastructure as opposed to a CapEx heavy infrastructure. >> That's a great example. I think that illustrates to me what I love about cloud right now is that you can put stuff in the cloud and then flex what you need, when you need it, in the cloud rather than either ingress or egress of data. You just get more versatility around the workload needs, whether it's more compute or more storage or other high level services. This is kind of where this next gen cloud is going. This is where customers want to go once their workloads are up and running. How do you simplify all this and how do you guys look at this from a joint customer perspective? Because that example I think will be something that all companies will be working on, which is put it in the cloud and flex to whatever the workload needs and put it closer to the compute. I want to put it there. If I want to leverage more storage and networking, well, I'll do that too. It's not one thing, it's got to flex around. How are you guys simplifying this? >> Yeah, I think, so, I'll give my point of view and then I'm very curious to hear what Adnan has to say about it. But I think about it in a few dimensions, right? So there is a technically, like, any solution that Adnan's team and my team want to put together needs to be kind of technically coherent, right? Things need to work well together. But that's not even most of the job. Most of the job is actually ensuring an operational consistency and operational simplicity, so that everything is, the day-to-day operations of these things kind of work well together. And then also, all the way to things like support and even acquisition, right? Making sure that all the contracts work together, right? It's a really... So when Adnan and I think about places of working together, it's very rare that we're just looking at a technical collaboration. It's actually a holistic collaboration across support, acquisition, as well as all the engineering that we have to do. >> Adnan, your view on how you're simplifying it with Red Hat for your joint customers making collaborations? >> Yeah, Gunnar covered it well. I think the benefit here is that Red Hat has been the leading Linux distribution provider. So they have a lot of experience. AWS has been the leading cloud provider. So we have both our own points of view, our own learning from our respective set of customers. So the way we try to simplify and bring these things together is working closely. In fact, I sometimes joke internally that if you see Gunnar and my team talking to each other on a call, you cannot really tell who belongs to which team. Because we're always figuring out, okay, how do we simplify discount experience? How do we simplify programs? How do we simplify go to market? How do we simplify the product pieces? So it's really bringing our learning and share our perspective to the table and then really figure out how do we actually help customers make progress. ROSA that we talked about is a great example of that, you know, together we figured out, hey, there is a need for customers to have this capability in AWS and we went out and built it. So those are just some of the examples in how both teams are working together to simplify the experience, make it complete, make it more coherent. >> Great, that's awesome. Next question is really around how you help organizations with the sustainability piece, how to support them simplifying it. But first, before we get into that, what is the core problem around this sustainability discussion we're talking about here, supply chain sustainability, what is the core challenge? Can you both share your thoughts on what that problem is and what the solution looks like and then we can get into advice? >> Yeah. Well from my point of view, it's, I think, you know, one of the lessons of the last three years is every organization is kind of taking a careful look at how resilient it is, or I should say, every organization learned exactly how resilient it was, right? And that comes from both the physical challenges and the logistics challenges that everyone had, the talent challenges you mentioned earlier. And of course the software challenges, you know, as everyone kind of embarks on this digital transformation journey that we've all been talking about. And I think, so I really frame it as resilience, right? And resilience at bottom is really about ensuring that you have options and that you have choices. The more choices you have, the more options you have, the more resilient you and your organization is going to be. And so I know that's how I approach the market. I'm pretty sure that's how Adnan is approaching the market, is ensuring that we are providing as many options as possible to customers so that they can assemble the right pieces to create a solution that works for their particular set of challenges or their unique set of challenges and unique context. Adnan, does that sound about right to you? >> Yeah, I think you covered it well. I can speak to another aspect of sustainability, which is becoming increasingly top of mind for our customers. Like, how do they build products and services and solutions and whether it's supply chain or anything else which is sustainable, which is for the long term good of the planet. And I think that is where we have also been very intentional and focused in how we design our data center, how we actually build our cooling system so that those are energy efficient. You know, we are on track to power all our operations with renewable energy by 2025, which is five years ahead of our initial commitment. And perhaps the most obvious example of all of this is our work with ARM processors, Graviton3, where, you know, we are building our own chip to make sure that we are designing energy efficiency into the process. And you know, the ARM Graviton3 processor chips, they are about 60% more energy efficient compared to some of the CD6 comparable. So all those things that also we are working on in making sure that whatever our customers build on our platform is long term sustainable. So that's another dimension of how we are working that into our platform. >> That's awesome. This is a great conversation. You know, the supply chain is on both sides, physical and software. You're starting to see them come together in great conversations. And certainly moving workloads to the cloud and running them more efficiently will help on the sustainability side, in my opinion. Of course, you guys talked about that and we've covered it. But now you start getting into how to refactor, and this is a big conversation we've been having lately is as you not just lift and shift, but replatform it and refactor, customers are seeing great advantages on this. So I have to ask you guys, how are you helping customers and organizations support sustainability and simplify the complex environment that has a lot of potential integrations? Obviously API's help of course, but that's the kind of baseline. What's the advice that you give customers? 'Cause you know, it can look complex and it becomes complex, but there's an answer here. What's your thoughts? >> Yeah, I think, so whenever I get questions like this from customers, the first thing I guide them to is, we talked earlier about this notion of consistency and how important that is. One way to solve the problem is to create an entirely new operational model, an entirely new acquisition model, and an entirely new stack of technologies in order to be more sustainable. That is probably not in the cards for most folks. What they want to do is have their existing estate and they're trying to introduce sustainability into the work that they are already doing. They don't need to build another silo in order to create sustainability, right? And so there has to be some common threads, there has to be some common platforms across the existing estate and your more sustainable estate, right? And so things like Red Hat Enterprise Linux, which can provide this kind of common, not just a technical substrate, but a common operational substrate on which you can build these solutions. If you have a common platform on which you are building solutions, whether it's RHEL or whether it's OpenShift or any of our other platforms, that creates options for you underneath. So that in some cases maybe you need to run things on-premises, some things you need to run in the cloud, but you don't have to profoundly change how you work when you're moving from one place to another. >> Adnan, what's your thoughts on the simplification? >> Yeah, I mean, when you talk about replatforming and refactoring, it is a daunting undertaking, you know, especially in today's fast paced world. But the good news is you don't have to do it by yourself. Customers don't have to do it on their own. You know, together AWS and Red Hat, we have our rich partner ecosystem, you know, AWS has over 100,000 partners that can help you take that journey, the transformation journey. And within AWS and working with our partners like Red Hat, we make sure that we have- In my mind, there are really three big pillars that you have to have to make sure that customers can successfully re-platform, refactor their applications to the modern cloud architecture. You need to have the rich set of services and tools that meet their different scenarios, different use cases. Because no one size fits all. You have to have the right programs because sometimes customers need those incentives, they need those, you know, that help in the first step. And last but not least, they need training. So all of that, we try to cover that as we work with our customers, work with our partners. And that is where, you know, together we try to help customers take that step, which is a challenging step to take. >> Yeah, you know, it's great to talk to you guys, both leaders in your field. Obviously Red Hats, I remember the days back when I was provisioning and loading OSs on hardware with CDs, if you remember those days, Gunnar. But now with the high level services, if you look at this year's reinvent, and this is kind of my final question for the segment is, that we'll get your reaction to, last year we talked about higher level service. I sat down with Adam Saleski, we talked about that. If you look at what's happened this year, you're starting to see people talk about their environment as their cloud. So Amazon has the gift of the CapEx, all that investment and people can operate on top of it. They're calling that environment their cloud. Okay? For the first time we're seeing this new dynamic where it's like they have a cloud, but Amazon's the CapEx, they're operating. So, you're starting to see the operational visibility, Gunnar, around how to operate this environment. And it's not hybrid, this, that, it's just, it's cloud. This is kind of an inflection point. Do you guys agree with that or have a reaction to that statement? Because I think this is, kind of, the next gen supercloud-like capability. We're going, we're building the cloud. It's now an environment. It's not talking about private cloud, this cloud, it's all cloud. What's your reaction? >> Yeah, I think, well, I think it's very natural. I mean, we use words like hybridcloud, multicloud, I guess supercloud is what the kids are saying now, right? It's all describing the same phenomena, right? Which is being able to take advantage of lots of different infrastructure options, but still having something that creates some commonality among them so that you can manage them effectively, right? So that you can have, kind of, uniform compliance across your estate. So that you can have, kind of, you can make the best use of your talent across the estate. I mean this is, it's a very natural thing. >> John: They're calling it cloud, the estate is the cloud. >> Yeah. So yeah, so fine, if it means that we no longer have to argue about what's multicloud and what's hybridcloud, I think that's great. Let's just call it cloud. >> Adnan, what's your reaction, 'cause this is kind of the next gen benefits of higher level services combined with amazing, you know, compute and resource at the infrastructure level. What's your view on that? >> Yeah, I think the construct of a unified environment makes sense for customers who have all these use cases which require, like for instance, if you are doing some edge computing and you're running WS outpost or you know, wavelength and these things. So, and it is fair for customer to think that, hey, this is one environment, same set of tooling that they want to build that works across all their different environments. That is why we work with partners like Red Hat so that customers who are running Red Hat Enterprise Linux on-premises and who are running in AWS get the same level of support, get the same level of security features, all of that. So from that sense, it actually makes sense for us to build these capabilities in a way that customers don't have to worry about, okay, now I'm actually in the AWS data center versus I'm running outpost on-premises. It is all one. They just use the same set of CLI, command line APIs and all of that. So in that sense it actually helps customers have that unification so that consistency of experience helps their workforce and be more productive versus figuring out, okay, what do I do, which tool I use where? >> Adnan, you just nailed it. This is about supply chain sustainability, moving the workloads into a cloud environment. You mentioned wavelength, this conversation's going to continue. We haven't even talked about the edge yet. This is something that's going to be all about operating these workloads at scale and all with the cloud services. So thanks for sharing that and we'll pick up that edge piece later. But for re:Invent right now, this is really the key conversation. How to make the sustained supply chain work in a complex environment, making it simpler. And so thanks you for sharing your insights here on theCUBE. >> Thanks, thanks for having us. >> Okay, this is theCUBE's coverage of AWS re:Invent 22. I'm John Furrier, your host. Thanks for watching. (bright music)

>>In the early part of this century, we're talking about the 2005 to 2007 timeframe. There was a lot of talk about so-called green it. And at that time there was some organizational friction. Like for example, the line was that the CIO never saw the power bill, so he or she didn't care, or that the facilities folks, they rarely talked to the IT department. So it was kind of that split brain. And, and then the oh 7 0 8 financial crisis really created an inflection point in a couple of ways. First, it caused organizations to kind of pump the brakes on it spending, and then they took their eye off the sustainability ball. And the second big trend, of course, was the cloud model, you know, kind of became a benchmark for it. Simplicity and automation and efficiency, the ability to dial down and dial up capacity as needed. >>And the third was by the end of the first decade of the, the two thousands, the technology of virtualization was really hitting its best stride. And then you had innovations like flash storage, which largely eliminated the need for these massive farms of spinning mechanical devices that sucked up a lot of power. And so really these technologies began their march to mainstream adoption. And as we progressed through the 2020s, the effect of climate change really come into focus as a critical component of esg. Environmental, social, and governance. Shareholders have come to demand metrics around sustainability. Employees are often choosing employers based on their ESG posture. And most importantly, companies are finding that savings on power cooling and footprint, it has a bottom line impact on the income statement. Now you add to that the energy challenges around the world, particularly facing Europe right now, the effects of global inflation and even more advanced technologies like machine intelligence. >>And you've got a perfect storm where technology can really provide some relief to organizations. Hello and welcome to the Path to Sustainable It Made Possible by Pure Storage and Collaboration with the Cube. My name is Dave Valante and I'm one of the host of the program, along with my colleague Lisa Martin. Now, today we're gonna hear from three leaders on the sustainability topic. First up, Lisa will talk to Nicole Johnson. She's the head of Social Impact and Sustainability at Pure Storage. Nicole will talk about the results from a study of around a thousand sustainability leaders worldwide, and she'll share some metrics from that study. And then next, Lisa will speak to AJ Singh. He's the Chief Product Officer at Pure Storage. We've had had him on the cube before, and not only will he share some useful stats in the market, I'll also talk about some of the technology innovations that customers can tap to address their energy consumption, not the least of which is ai, which is is entering every aspect of our lives, including how we deal with energy consumption. And then we'll bring it back to our Boston studio and go north of Italy with Mattia Ballero of Elec Informatica, a services provider with deep expertise on the topic of sustainability. We hope you enjoyed the program today. Thanks for watching. Let's get started >>At Pure Storage, the opportunity for change and our commitment to a sustainable future are a direct reflection of the way we've always operated and the values we live by every day. We are making significant and immediate impact worldwide through our environmental sustainability efforts. The milestones of change can be seen everywhere in everything we do. Pure's Evergreen Storage architecture delivers two key environmental benefits to customers, the reduction of wasted energy and the reduction of e-waste. Additionally, Pure's implemented a series of product packaging redesigns, promoting recycled and reuse in order to reduce waste that will not only benefit our customers, but also the environment. Pure is committed to doing what is right and leading the way with innovation. That has always been the pure difference, making a difference by enabling our customers to drive out energy usage and their data storage systems by up to 80%. Today, more than 97% of pure arrays purchased six years ago are still in service. And tomorrow our goal for the future is to reduce Scope three. Emissions Pure is committing to further reducing our sold products emissions by 66% per petabyte by 2030. All of this means what we said at the beginning, change that is simple and that is what it has always been about. Pure has a vision for the future today, tomorrow, forever. >>Hi everyone, welcome to this special event, pure Storage, the Path to Sustainable it. I'm your host, Lisa Martin. Very pleased to be joined by Nicole Johnson, the head of Social Impact and Sustainability at Pure Storage. Nicole, welcome to the Cube. Thanks >>For having me, Lisa. >>Sustainability is such an important topic to talk about and I understand that Pure just announced a report today about sustainability. What can you tell me what nuggets are in this report? >>Well, actually quite a few really interesting nuggets, at least for us. And I, I think probably for you and your viewers as well. So we actually commissioned about a thousand sustainability leaders across the globe to understand, you know, what are their sustainability goals, what are they working on, and what are the impacts of buying decisions, particularly around infrastructure when it comes to sustainable goals. I think one of the things that was really interesting for us was the fact that around the world we did not see a significant variation in terms of sustainability being a top priority. You've, I'm sure you've heard about the energy crisis that's happening across Europe. And so, you know, there was some thought that perhaps that might play into AMEA being a larger, you know, having sustainability goals that were more significant. But we actually did not find that we found sustainability to be really important no matter where the respondents were located. >>So very interesting at Pure sustainability is really at the heart of what we do and has been since our founding. It's interesting because we set out to make storage really simple, but it turns out really simple is also really sustainable. And the products and services that we bring to our customers have really powerful outcomes when it comes to decreasing their, their own carbon footprints. And so, you know, we often hear from customers that we've actually really helped them to significantly improve their storage performance, but also allow them to save on space power and cooling costs and, and their footprint. So really significant findings. One example of that is a company called Cengage, which is a global education technology company. They recently shared with us that they have actually been able to reduce their overall storage footprint by 80% while doubling to tripling the performance of their storage systems. So it's really critical for, for companies who are thinking about their sustainability goals, to consider the dynamic between their sustainability program and their IT teams who are making these buying decisions, >>Right? Those two teams need to be really inextricably linked these days. You talked about the fact that there was really consistency across the regions in terms of sustainability being of high priority for organizations. You had a great customer story that you shared that showed significant impact can be made there by bringing the sustainability both together with it. But I'm wondering why are we seeing that so much of the vendor selection process still isn't revolving around sustainability or it's overlooked? What are some of the things that you received despite so many people saying sustainability, huge priority? >>Well, in this survey, the most commonly cited challenge was really around the fact that there was a lack of management buy-in. 40% of respondents told us this was the top roadblock. So getting, I think getting that out of the way. And then we also just heard that sustainability teams were not brought into tech purchasing processes until after it's already rolling, right? So they're not even looped in. And that being said, you know, we know that it has been identified as one of the key departments to supporting a company sustainability goals. So we, we really want to ensure that these two teams are talking more to each other. When we look even closer at the data from the respondents, we see some really positive correlations. We see that 65% of respondents reported that they're on track to meet their sustainability goals. And the IT of those 65%, it is significantly engaged with reporting data for those sustainability initiatives. We saw that, that for those who did report, the sustainability is a top priority for vendor selection. They were twice as likely to be on track with their goals and their sustainability directors said that they were getting involved at the beginning of the tech purchasing program. Our process, I'm sorry, rather than towards the end. And so, you know, we know that to curb the impact of climate crisis, we really need to embrace sustainability from a cross-functional viewpoint. >>Definitely has to be cross-functional. So, so strong correlations there in the report that organizations that had closer alignment between the sustainability folks and the IT folks were farther along in their sustainability program development, execution, et cetera, those co was correlations, were they a surprise? >>Not entirely. You know, when we look at some of the statistics that come from the, you know, places like the World Economic Forum, they say that digitization generated 4% of greenhouse gas emissions in 2020. So, and that, you know, that's now almost three years ago, digital data only accelerates, and by 2025, we expect that number could be almost double. And so we know that that communication and that correlation is gonna be really important because data centers are taking up such a huge footprint of when companies are looking at their emissions. And it's, I mean, quite frankly, a really interesting opportunity for it to be a trailblazer in the sustainability journey. And, you know, perhaps people that are in IT haven't thought about how they can make an impact in this area, but there really is some incredible ways to help us work on cutting carbon emissions, both from your company's perspective and from the world's perspective, right? >>Like we are, we're all doing this because it's something that we know we have to do to drive down climate change. So I think when you, when you think about how to be a trailblazer, how to do things differently, how to differentiate your own department, it's a really interesting connection that IT and sustainability work together. I would also say, you know, I'll just note that of the respondents to the survey we were discussing, we do over half of those respondents expect to see closer alignment between the organization's IT and sustainability teams as they move forward. >>And that's really a, a tip a hat to those organizations embracing cultural change. That's always hard to do, but for those two, for sustainability in IT to come together as part of really the overall ethos of an organization, that's huge. And it's great to see the data demonstrating that, that those, that alignment, that close alignment is really on its way to helping organizations across industries make a big impact. I wanna dig in a little bit to here's ESG goals. What can you share with us about >>That? Absolutely. So as I mentioned peers kind of at the beginning of our formal ESG journey, but really has been working on the, on the sustainability front for a long time. I would, it's funny as we're, as we're doing a lot of this work and, and kind of building our own profile around this, we're coming back to some of the things that we have done in the past that consumers weren't necessarily interested in then but are now because the world has changed, becoming more and more invested in. So that's exciting. So we did a baseline scope one, two, and three analysis and discovered, interestingly enough that 70% of our emissions comes from use of sold products. So our customers work running our products in their data centers. So we know that we, we've made some ambitious goals around our Scope one and two emissions, which is our own office, our utilities, you know, those, they only account for 6% of our emissions. So we know that to really address the issue of climate change, we need to work on the use of sold products. So we've also made a, a really ambitious commitment to decrease our carbon emissions by 66% per bed per petabyte by 2030 in our product. So decreasing our own carbon footprint, but also affecting our customers as well. And we've also committed to a science-based target initiative and our road mapping how to achieve the ambitious goals set out in the Paris agreement. >>That's fantastic. It sounds like you really dialed in on where is the biggest opportunity for us as Pure Storage to make the biggest impact across our organization, across our customers organizations. There lofty goals that pure set, but knowing what I know about Pure, you guys are probably well on track to, to accomplish those goals in record time, >>I hope So. >>Talk a little bit about advice that you would give to viewers who might be at the very beginning of their sustainability journey and really wondering what are the core elements besides it, sustainability, team alignment that I need to bring into this program to make it actually successful? >>Yeah, so I think, you know, understanding that you don't have to pick between really powerful technology and sustainable technology. There are opportunities to get both and not just in storage right in, in your entire IT portfolio. We know that, you know, we're in a place in the world where we have to look at things from the bigger picture. We have to solve new challenges and we have to approach business a little bit differently. So adopting solutions and services that are environmentally efficient can actually help to scale and deliver more effective and efficient IT solutions over time. So I think that that's something that we need to, to really remind ourselves, right? We have to go about business a little bit differently and that's okay. We also know that data centers utilize an incredible amount of, of energy and, and carbon. And so everything that we can do to drive that down is going to address the sustainability goals for us individually as well as, again, drive down that climate change. So we, we need to get out of the mindset that data centers are, are about reliability or cost, et cetera, and really think about efficiency and carbon footprint when you're making those business decisions. I'll also say that, you know, the earlier that we can get sustainability teams into the conversation, the more impactful your business decisions are going to be and helping you to guide sustainable decision making. >>So shifting sustainability and IT left almost together really shows that the correlation between those folks getting together in the beginning with intention, the report shows and the successes that peers had demonstrate that that's very impactful for organizations to actually be able to implement even the cultural change that's needed for sustainability programs to be successful. My last question for you goes back to that report. You mentioned in there that the data show a lot of organizations are hampered by management buy-in, where sustainability is concerned. How can pure help its customers navigate around those barriers so that they get that management buy-in and they understand that the value in it for >>Them? Yeah, so I mean, I think that for me, my advice is always to speak to hearts and minds, right? And help the management to understand, first of all, the impact right on climate change. So I think that's the kind of hearts piece on the mind piece. I think it's addressing the sustainability goals that these companies have set for themselves and helping management understand how to, you know, how their IT buying decisions can actually really help them to reach these goals. We also, you know, we always run kind of TCOs for customers to understand what is the actual cost of, of the equipment. And so, you know, especially if you're in a, in a location in which energy costs are rising, I mean, I think we're seeing that around the world right now with inflation. Better understanding your energy costs can really help your management to understand the, again, the bigger picture and what that total cost is gonna be. Often we see, you know, that maybe the I the person who's buying the IT equipment isn't the same person who's purchasing, who's paying the, the electricity bills, right? And so sometimes even those two teams aren't talking. And there's a great opportunity there, I think, to just to just, you know, look at it from a more high level lens to better understand what total cost of ownership is. >>That's a great point. Great advice. Nicole, thank you so much for joining me on the program today, talking about the new report that on sustainability that Pure put out some really compelling nuggets in there, but really also some great successes that you've already achieved internally on your own ESG goals and what you're helping customers to achieve in terms of driving down their carbon footprint and emissions. We so appreciate your insights and your thoughts. >>Thank you, Lisa. It's been great speaking with you. >>AJ Singh joins me, the Chief Product Officer at Peer Storage. Aj, it's great to have you back on the program. >>Great to be back on, Lisa, good morning. >>Good morning. And sustainability is such an important topic to talk about. So we're gonna really unpack what PEER is doing, we're gonna get your viewpoints on what you're seeing and you're gonna leave the audience with some recommendations on how they can get started on their ESG journey. First question, we've been hearing a lot from pure AJ about the role that technology plays in organizations achieving sustainability goals. What's been the biggest environmental impact associated with, with customers achieving that given the massive volumes of data that keep being generated? >>Absolutely, Lisa, you can imagine that the data is only growing and exploding and, and, and, and there's a good reason for it. You know, data is the new currency. Some people call it the new oil. And the opportunity to go process this data gain insights is really helping customers drive an edge in the digital transformation. It's gonna make a difference between them being on the leaderboard a decade from now when the digital transformation kind of pans out versus, you know, being kind of somebody that, you know, quite missed the boat. So data is super critical and and obviously as part of that we see all these big benefits, but it has to be stored and, and, and that means it's gonna consume a lot of resources and, and the, and therefore data center usage has only accelerated, right? You can imagine the amount of data being generated, you know, recent study pointed to roughly by twenty twenty five, a hundred and seventy five zetabytes, which where each zettabyte is a billion terabytes. So just think of that size and scale of data. That's huge. And, and they also say that, you know, pretty soon, today, in fact in the developed world, every person is having an interaction with the data center literally every 18 seconds. So whether it's on Facebook or Twitter or you know, your email, people are constantly interacting with data. So you can imagine this data is only exploding. It has to be stored and it consumes a lot of energy. In fact, >>It, oh, go ahead. Sorry. >>No, I was saying in fact, you know, there's some studies have shown that data center usage literally consumes one to 2% of global energy consumption. So if there's one place we could really help climate change and, and all those aspects, if you can kind of really, you know, tamp down the data center, energy consumption, sorry, you were saying, >>I was just gonna say, it's, it's an incredibly important topic and the, the, the stats on data that you provided and also I, I like how you talked about, you know, every 18 seconds we're interacting with a data center, whether we know it or not, we think about the long term implications, the fact that data is growing massively. As you shared with the stats that you mentioned. If we think about though the responsibility that companies have, every company in today's world needs to be a data company, right? And we consumers expect it. We expect that you are gonna deliver these relevant, personalized experiences whether we're doing a transaction in our personal lives or in business. But what is the, what requirements do technology companies have to really start billing down their carbon footprints? >>No, absolutely. If you can think about it, just to kind of finish up the data story a little bit, the explosion is to the point where, in fact, if you just recently was in the news that Ireland went up and said, sorry, we can't have any more data centers here. We just don't have the power to supply them. That was big in the news and you know, all the hyperscale that was crashing the head. I know they've come around that and figured out a way around it, but it's getting there. Some, some organizations and and areas jurisdictions are saying pretty much no data center the law, you know, we're, we just can't do it. And so as you said, so companies like Pure, I mean, our view is that it has an opportunity here to really do our bit for climate change and be able to, you know, drive a sustainable environment. >>And, and at Pure we believe that, you know, today's data success really ultimately hinges on energy efficiency, you know, so to to really be energy efficient means you are gonna be successful long term with data. Because if you think of classic data infrastructures, the legacy infrastructures, you know, we've got disk infrastructures, hybrid infrastructures, flash infrastructures, low end systems, medium end systems, high end systems. So a lot of silos, you know, a lot of inefficiency across the silos. Cause the data doesn't get used across that. In fact, you know, today a lot of data centers are not really built with kind of the efficiency and environmental mindset. So there's a big opportunity there. >>So aj, talk to me about some of the steps that Pure is implementing as its chief product officer. Would love to get your your thoughts, what steps is it implementing to help Pures customers become more sustainable? >>No, absolutely. So essentially we are all inherently motivated, like pure and, and, and, and everybody else to solve problems for customers and really forward the status quo, right? You know, innovation, you know, that's what we are all about. And while we are doing that, the challenge is to how do you make technology and the data we feed into it faster, smarter, scalable obviously, but more importantly sustainable. And you can do all of that, but if you miss the sustainability bit, you're kind of missing the boat. And I also feel from an ethical perspective, that's really important for us. Not only you do all the other things, but also kind of make it sustainable. In fact, today 80% of the companies, the companies are realizing this, 80% today are in fact report out on sustainability, which is great. In fact, 80% of leadership at companies, you know, CEOs and senior executives say they've been impacted by some climate change event, you know, where it's a fire in the place they had to evacuate or floods or storms or hurricanes, you, you name it, right? >>So mitigating the carbon impact can in fact today be a competitive advantage for companies because that's where the puck is going and everybody's, you know, it's skating, wanting to skate towards the, and it's good, it's good business too to be sustainable and, and, and meet these, you know, customer requirements. In fact, the the recent survey that we released today is saying that more and more organizations are kickstarting, their sustainability initiatives and many take are aiming to make a significant progress against that over the next decade. So that's, that's really, you know, part of the big, the really, so our view is that that IT infrastructure, you know, can really make a big push towards greener it and not just kind of greenwash it, but actually, you know, you know, make things more greener and, and, and really take the, the lead in, in esg. And so it's important that organizations can reach alignment with their IT teams and challenge their IT teams to continue to lead, you know, for the organization, the sustainability aspects. >>I'm curious, aj, when you're in customer conversations, are you seeing that it's really the C-suite plus it coming together and, and how does peer help facilitate that? To your point, it needs to be able to deliver this, but it's, it's a board level objective these days. >>Absolutely. We're seeing increasingly, especially in Europe with the, you know, the war in Ukraine and the energy crisis that, you know, that's, that's, you know, unleashed. We definitely see it's becoming a bigger and bigger board level objective for, for a lot of companies. And we definitely see customers in starting to do that. So, so in particular, I do want to touch briefly on what steps we are taking as a company, you know, to to to make it sustainable. And obviously customers are doing all the things we talked about and, and we're also helping them become smarter with data. But the key difference is, you know, we have a big focus on efficiency, which is really optimizing performance per wat with unmatched storage density. So you can reduce the footprint and dramatically lower the power required. And and how efficient is that? You know, compared to other old flash systems, we tend to be one fifth, we tend to take one fifth the power compared to other flash systems and substantially lower compared to spinning this. >>So you can imagine, you know, cutting your, if data center consumption is a 2% of global consumption, roughly 40% of that tends to be storage cause of all the spinning disc. So you add about, you know, 0.8% to global consumption and if you can cut that by four fifths, you know, you can already start to make an impact. So, so we feel we can do that. And also we're quite a bit more denser, 10 times more denser. So imagine one fifth the power, one 10th the density, but then we take it a step further because okay, you've got the storage system in the data center, but what about the end of life aspect? What about the waste and reclamation? So we also have something called non-disruptive upgrades. We, using our AI technology in pure one, we can start to sense when a particular part is going to fail and just before it goes to failure, we actually replace it in a non-disruptive fashion. So customer's data is not impacted and then we recycle that so you get a full end to end life cycle, you know, from all the way from the time you deploy much lower power, much lower density, but then also at the back end, you know, reduction in e-waste and those kind of things. >>That's a great point you, that you bring up in terms of the reclamation process. It sounds like Pure does that on its own, the customer doesn't have to be involved in that. >>That's right. And we do that, it's a part of our evergreen, you know, service that we offer. A lot of customers sign up for that service and in fact they don't even, we tell them, Hey, you know, that part's about to go, we're gonna come in, we're gonna swap it out and, and then we actually recycle that part, >>The power of ai. Love that. What are some of the, the things that companies can do if they're, if they're early in this journey on sustainability, what are some of the specific steps companies can take to get started and maybe accelerate that journey as it's becoming climate change and things are becoming just more and more of a, of a daily topic on the news? >>No, absolutely. There's a lot of things companies can do. In fact, the four four item that we're gonna highlight, the first one is, you know, they can just start by doing a materiality assessment and a materiality assessment essentially engages all the stakeholders to find out which specific issues are important for the business, right? So you identify your key priorities that intersect with what the stakeholders want, you know, your different groups from sales, customers, partners, you know, different departments in the organization. And for example, for us, when we conducted our materiality assessment, for us, our product we felt was the biggest area of focus that could contribute a lot towards, you know, making an impact in, in, in from a sustainability standpoint. That's number one. I think number two companies can also think about taking an Azure service approach. The beauty of the Azure service approach is that you are buying a, your customer, they're buying outcomes with SLAs and, and when you are starting to buy outcomes with SLAs, you can start small and then grow as you consume more. >>So that way you don't have systems sitting idle waiting for you to consume more, right? And that's the beauty of the as service approach. And so for example, for us, you know, we have something called Evergreen one, which is our as service offer, where essentially customers are able to only use and have systems turned onto as much as they're consuming. So, so that reduces the waste associated with underutilized systems, right? That's number two. Number three is also you can optimize your supply chains end to end, right? Basically by making sure you're moving, recycling, packaging and eliminating waste in that thing so you can recycle it back to your suppliers. And you can also choose a sustainable supplier network that following sort of good practices, you know, you know, across the globe and such supply chains that are responsive and diverse can really help you. Also, the big business benefit benefited. >>You can also handle surges and demand, for example, for us during the pandemic with this global supply chain shortages, you know, whereas most of our competitors, you know, lead times went to 40, 50 weeks, our lead times went from three to six weeks cuz you know, we had this sustainable, you know, supply chain. And so all of these things, you know, the three things important, but the fourth thing I say more cultural and, and the cultural thing is how do you actually begin to have sustainability become a core part of your ethos at the company, you know, across all the departments, you know, and we've at Pure, definitely it's big for us, you know, you know, around sustainability starting with a product design, but all of the areas as well, if you follow those four items, they'll do the great place to start. >>That's great advice, great recommendations. You talk about the, the, the supply chain, sustainable supply chain optimization. We've been having a lot of conversations with businesses and vendors alike about that and how important it is. You bring up a great point too on supplier diversity, if we could have a whole conversation on that. Yes. But I'm also glad that you brought up culture that's huge to, for organizations to adopt an ESG strategy and really drive sustainability in their business. It has to become, to your point, part of their ethos. Yes. It's challenging. Cultural change management is challenging. Although I think with climate change and the things that are so public, it's, it's more on, on the top mindset folks. But it's a great point that the organization really as a whole needs to embrace the sustainability mindset so that it as a, as an organization lives and breathes that. Yes. And last question for you is advice. So you, you outlined the Four Steps organizations can take. I look how you made that quite simple. What advice would you give organizations who are on that journey to adopting those, those actions, as you said, as they look to really build and deploy and execute an ESG strategy? >>No, absolutely. And so obviously, you know, the advice is gonna come from, you know, a company like Pure, you know, our background kind of being a supplier of products. And so, you know, our advice is for companies that have products, usually they tend to be the biggest generator, the products that you sell to your, your customers, especially if they've got hardware components in it. But, you know, the biggest generator of e-waste and, and and, and, and, and kind of from a sustainability standpoint. So it's really important to have an intentional design approach towards your products with sustainability in mind. So it's not something that's, that you can handle at the very back end. You design it front in the product and so that sustainable design becomes very intentional. So for us, for example, doing these non-disruptive upgrades had to be designed up front so that, you know, a, you know, one of our repair person could go into a customer shop and be able to pull out a card and put in a new card without any change in the customer system. >>That non-receptive approach, it has to be designed into the hardware software systems to be able to pull that on. And that intentional design enables you to recover pieces just when they're about to fail and then putting them through a recovery, you know, waste recovery process. So that, that's kind of the one thing I would say that philosophy, again, it comes down to if that is, you know, seeping into the culture, into your core ethos, you will start to do, you know, you know, that type of work. So, so I mean it's important thing, you know, look, this year, you know, with the spike in energy prices, you know, you know, gas prices going up, it's super important that all of us, you know, do our bit in there and start to drive products that are fundamentally sustainable, not just at the initial, you know, install point, but from an end to end full life cycle standpoint. >>Absolutely. And I love that you brought up intention that is everything that peers doing is with, with such thought and intention and really for organizations and any industry to become more sustainable, to develop an ESG strategy. To your point, it all needs to start with intention. And of course that that cultural adoption, aj, it's been so great to have you on the program talking about what PEER is doing to help organizations really navigate that path to sustainable it. We appreciate your insights on your time. >>Thank you, Lisa. Pleasure being on board >>At Pure Storage. The opportunity for change and our commitment to a sustainable future are a direct reflection of the way we've always operated and the values we live by every day. We are making significant and immediate impact worldwide through our environmental sustainability efforts. The milestones of change can be seen everywhere in everything we do. Pures Evergreen storage architecture delivers two key environmental benefits to customers, the reduction of wasted energy and the reduction of e-waste. Additionally, pures implemented a series of product packaging redesigns, promoting recycle and reuse in order to reduce waste that will not only benefit our customers, but also the environment. Pure is committed to doing what is right and leading the way with innovation. That has always been the pure difference, making a difference by enabling our customers to drive out energy usage and their data storage systems by up to 80% today, more than 97% of Pure Array purchased six years ago are still in service. And tomorrow our goal for the future is to reduce Scope three emissions Pure is committing to further reducing our sold products emissions by 66% per petabyte by 2030. All of this means what we said at the beginning, change that is simple and that is what it has always been about. Pure has a vision for the future today, tomorrow, forever. >>We're back talking about the path to sustainable it and now we're gonna get the perspective from Mattia Valerio, who is with Elec Informatica and IT services firm and the beautiful Lombardi region of Italy north of Milano. Mattia, welcome to the Cube. Thanks so much for coming on. >>Thank you very much, Dave. Thank you. >>All right, before we jump in, tell us a little bit more about Elec Informatica. What's your focus, talk about your unique value add to customers. >>Yeah, so basically Alma Informatica is middle company from the north part of Italy and is managed service provider in the IT area. Okay. So the, the main focus area of Al Meca is reach digital transformation innovation to our clients with focus on infrastructure services, workplace services, and also cybersecurity services. Okay. And we try to follow the path of our clients to the digital transformation and the innovation through technology and sustainability. >>Yeah. Obviously very hot topics right now. Sustainability, environmental impact, they're growing areas of focus among leaders across all industries. A particularly acute right now in, in Europe with the, you know, the energy challenges you've talked about things like sustainable business. What does that mean? What does that term Yeah. You know, speak to and, and what can others learn from it? >>Yeah. At at, at our approach to sustainability is grounded in science and, and values and also in customer territory, but also employee centered. I mean, we conduct regular assessments to understand the most significant environment and social issues for our business with, with the goal of prioritizing what we do for a sustainability future. Our service delivery methodology, employee care relationship with the local supplier and local area and institution are a major factor for us to, to build a such a responsibility strategy. Specifically during the past year, we have been particularly focused on define sustainability governance in the company based on stakeholder engagement, defining material issues, establishing quantitative indicators to monitor and setting medium to long-term goals. >>Okay, so you have a lot of data. You can go into a customer, you can do an assessment, you can set a baseline, and then you have other data by which you can compare that and, and understand what's achievable. So what's your vision for sustainable business? You know, that strategy, you know, how has it affected your business in terms of the evolution? Cuz this wasn't, hasn't always been as hot a topic as it is today. And and is it a competitive advantage for you? >>Yeah, yeah. For, for, for all intense and proposed sustainability is a competitive advantage for elec. I mean, it's so, because at the time of profound transformation in the work, in the world of work, CSR issues make a company more attractive when searching for new talent to enter in the workforce of our company. In addition, efforts to ensure people's proper work life balance are a strong retention factor. And regarding our business proposition, ELEX attempts is to meet high standard of sustainability and reliability. Our green data center, you said is a prime example of this approach as at the same time, is there a conditioning activity that is done to give a second life to technology devices that come from back from rental? I mean, our customer inquiries with respect to sustainability are increasingly frequent and in depth and which is why we monitor our performance and invest in certification such as EcoVadis or ISO 14,001. Okay, >>Got it. So in a previous life I actually did some work with, with, with power companies and there were two big factors in it that affected the power consumption. Obviously virtualization was a big one, if you could consolidate servers, you know, that was huge. But the other was the advent of flash storage and that was, we used to actually go in with the, the engineers and the power company put in alligator clips to measure of, of, of an all flash array versus, you know, the spinning disc and it was a big impact. So you, I wanna talk about your, your experience with Pure Storage. You use Flash Array and the Evergreen architecture. Can you talk about what your experience there, why did you make that decision to select Pure Storage? How does that help you meet sustainability and operational requirements? Do those benefits scale as your customers grow? What's your experience been? >>Yeah, it was basically an easy and easy answer to our, to our business needs. Okay. Because you said before that in Elec we, we manage a lot of data, okay? And in the past we, we, we see it, we see that the constraints of managing so many, many data was very, very difficult to manage in terms of power consumption or simply for the, the space of storing the data. And when, when Pure came to us and share our products, their vision to the data management journey for Element Informatica, it was very easy to choose pure why with values and numbers. We, we create a business case and we said that we, we see that our power consumption usage was much less, more than 90% of previous technology that we used in the past. Okay. And so of course you have to manage a grade oil deploy of flash technology storage, but it was a good target. >>So we have tried to monitoring the adoption of flash technology and monitor monitoring also the power consumption and the efficiency that the pure technology bring to our, to our IT systems and of course the IT systems of our clients. And so this is one, the first part, the first good part of our trip with, with Pure. And after that we approach also the sustainability in long term of choosing pure technology storage. You mentioned the Evergreen models of Pure, and of course this was, again, challenge for us because it allows, it allow us to extend the life cycle management of our data centers, but also the, IT allows us to improve the facility of the facilities of using technology from our technical side. Okay. So we are much more efficient than in the past with the choose of Pure storage technologies. Okay. Of course, this easy users, easy usage mode, let me say it, allow us to bring this value to our, to all our clients that put their data in our data centers. >>So you talked about how you've seen a 90% improvement relative to previous technologies. I always, I haven't put you in the spot. Yeah, because I, I, I was on Pure's website and I saw in their ESG report some com, you know, it was a comparison with a generic competitor presuming that competitor was not, you know, a 2010 spinning disc system. But, but, so I'm curious as to the results that you're seeing with Pure in terms of footprint and power usage. You, you're referencing some of that. We heard some metrics from Nicole and AJ earlier in the program. Do you think, again, I'm gonna put you in the spot, do you think that Pure's architecture and the way they've applied, whether it's machine intelligence or the Evergreen model, et cetera, is more competitive than other platforms that you've seen? >>Yeah, of course. Is more competitor improve competitive because basically it allows to service provider to do much more efficient value proposition and offer services that are more, that brings more values to, to the customers. Okay. So the customer is always at the center of a proposition of a service provider and trying to adopt the methodology and also the, the value that pure as inside by design in the technology is, is for us very, very, very important and very, very strategic because, because with like a glass, we can, our self transfer try to transfer the values of pure, pure technologies to our service provider client. >>Okay. Matta, let's wrap and talk about sort of near term 2023 and then longer term it looks like sustainability is a topic that's here to stay. Unlike when we were putting alligator clips on storage arrays, trying to help customers get rebates that just didn't have legs. It was too complicated. Now it's a, a topic that everybody's measuring. What's next for elec in its sustainability journey? What advice would you might have? Sustainability leaders that wanna make a meaningful impact on the environment, but also on the bottom line. >>Okay, so sustainability is fortunately a widely spread concept. And our role in, in this great game is to define a strategy, align with the common and fundamentals goals for the future of planet and capable of expressing our inclination and the, and the particularities and accessibility goals in the near future. I, I say, I can say that are will be basically free one define sustainability plan. Okay? It's fundamentals to define a sustainability plan. Then it's very important to monitor the its emissions and we will calculate our carbon footprint. Okay? And least button list produces certifiable and comprehensive sustainability report with respect to the demands of customers, suppliers, and also partners. Okay. So I can say that this three target will be our direction in the, in the future. Okay. >>Yeah. So I mean, pretty straightforward. Make a plan. You gotta monitor and measure, you can't improve what you can't measure. So you gonna set a baseline, you're gonna report on that. Yep. You're gonna analyze the data and you're gonna make continuous improvement. >>Yep. >>Matea, thanks so much for joining us today in sharing your perspectives from the, the northern part of Italy. Really appreciate it. >>Yeah, thank you for having aboard. Thank you very >>Much. It was really our pleasure. Okay, in a moment, I'm gonna be back to wrap up the program and share some resources that could be valuable in your sustainability journey. Keep it right there. >>Sustainability is becoming increasingly important and is hitting more RFPs than ever before as a critical decision point for customers. Environmental benefits are not the only impetus. Rather bottom line cost savings are proving that sustainability actually means better business. You can make a strong business case around sustainability and you should, many more organizations are setting mid and long-term goals for sustainability and putting forth published metrics for shareholders and customers. Whereas early green IT initiatives at the beginning of this century, were met with skepticism and somewhat disappointing results. Today, vendor r and d is driving innovation in system design, semiconductor advancements, automation in machine intelligence that's really beginning to show tangible results. Thankfully. Now remember, all these videos are available on demand@thecube.net. So check them out at your convenience and don't forget to go to silicon angle.com for all the enterprise tech news of the day. You also want to check out pure storage.com. >>There are a ton of resources there. As an aside, pure is the only company I can recall to allow you to access resources like a Gartner Magic Quadrant without forcing you to fill out a lead gen form. So thank you for that. Pure storage, I love that. There's no squeeze page on that. No friction. It's kind of on brand there for pure well done. But to the topic today, sustainability, there's some really good information on the site around esg, Pure's Environmental, social and Governance mission. So there's more in there than just sustainability. You'll see some transparent statistics on things like gender and ethnic diversity, and of course you'll see that Pure has some work to do there. But kudos for publishing those stats transparently and setting goals so we can track your progress. And there's plenty on the sustainability topic as well, including some competitive benchmarks, which are interesting to look at and may give you some other things to think about. We hope you've enjoyed the path to Sustainable it made possible by Pure Storage produced with the Cube, your leader in enterprise and emerging tech, tech coverage.